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Illumina Inc str locus amplification
<t>MiSeq‐based</t> short tandem repeat ( <t>STR</t> ) genotyping of wild chimpanzees. (a) Schematic representation of singleplex STR amplification and MiSeq sequencing of chimpanzee fecal DNA . (b) Schematic representation of the CHIIMP analysis pipeline with decision tree and downstream data reports
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1) Product Images from "CHIIMP: An automated high‐throughput microsatellite genotyping platform reveals greater allelic diversity in wild chimpanzees, et al. CHIIMP: An automated high‐throughput microsatellite genotyping platform reveals greater allelic diversity in wild chimpanzees"

Article Title: CHIIMP: An automated high‐throughput microsatellite genotyping platform reveals greater allelic diversity in wild chimpanzees, et al. CHIIMP: An automated high‐throughput microsatellite genotyping platform reveals greater allelic diversity in wild chimpanzees

Journal: Ecology and Evolution

doi: 10.1002/ece3.4302

MiSeq‐based short tandem repeat ( STR ) genotyping of wild chimpanzees. (a) Schematic representation of singleplex STR amplification and MiSeq sequencing of chimpanzee fecal DNA . (b) Schematic representation of the CHIIMP analysis pipeline with decision tree and downstream data reports
Figure Legend Snippet: MiSeq‐based short tandem repeat ( STR ) genotyping of wild chimpanzees. (a) Schematic representation of singleplex STR amplification and MiSeq sequencing of chimpanzee fecal DNA . (b) Schematic representation of the CHIIMP analysis pipeline with decision tree and downstream data reports

Techniques Used: Amplification, Sequencing

2) Product Images from "Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli"

Article Title: Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli

Journal: Nature Communications

doi: 10.1038/s41467-018-06143-y

Collateral effects in gyrA mutants with decreased susceptibility to ciprofloxacin. Relative changes in antimicrobial susceptibilities, CS (blue) and CR (red), were determined by comparing average IC 90 values of nine first-step mutants to their respective wild-type strain. Antimicrobials are ordered by antimicrobial class, as in Supplementary Fig. 2
Figure Legend Snippet: Collateral effects in gyrA mutants with decreased susceptibility to ciprofloxacin. Relative changes in antimicrobial susceptibilities, CS (blue) and CR (red), were determined by comparing average IC 90 values of nine first-step mutants to their respective wild-type strain. Antimicrobials are ordered by antimicrobial class, as in Supplementary Fig. 2

Techniques Used:

Presentations of the potential effects and implications of CS and CR. a Sequential drug administration informed by CS (blue) could potentially narrow or shift the mutant selection window (MSW) downwards in concentration space whereas ( b ) CR (red) results in a widened or shifted upwards mutant selection window for secondary antimicrobials. This would affect the probability of acquiring second-step mutations leading to high-level resistance. Consequently, CS-informed secondary therapies could reduce selection and thus propagation of first-step mutants resulting in a reduced opportunity for second-step mutations to occur. Dots represent bacteria resistant to a primary antibiotic (gray), spontaneous mutants with reduced susceptibility to a secondary drug (pink), or those with high-level resistance to the secondary drug (dark red). Note that these are hypothetical schematics and in many cases the maximum concentration achieved ( C max ) may be below the MPC. c Arrows indicate conserved collateral responses, where CS (blue) and CR (red) are depicted. The collateral responses in this study are mainly predicted by efflux-related mutations in the ciprofloxacin-resistant mutants. These data suggest potential secondary treatment options that may reduce the rate of resistance evolution ( a , b ) following initial treatment failure. d Green arrows indicate putative temporal administration of four antimicrobials used for the treatment of urinary-tract infections, as informed by the collateral networks in ( c )
Figure Legend Snippet: Presentations of the potential effects and implications of CS and CR. a Sequential drug administration informed by CS (blue) could potentially narrow or shift the mutant selection window (MSW) downwards in concentration space whereas ( b ) CR (red) results in a widened or shifted upwards mutant selection window for secondary antimicrobials. This would affect the probability of acquiring second-step mutations leading to high-level resistance. Consequently, CS-informed secondary therapies could reduce selection and thus propagation of first-step mutants resulting in a reduced opportunity for second-step mutations to occur. Dots represent bacteria resistant to a primary antibiotic (gray), spontaneous mutants with reduced susceptibility to a secondary drug (pink), or those with high-level resistance to the secondary drug (dark red). Note that these are hypothetical schematics and in many cases the maximum concentration achieved ( C max ) may be below the MPC. c Arrows indicate conserved collateral responses, where CS (blue) and CR (red) are depicted. The collateral responses in this study are mainly predicted by efflux-related mutations in the ciprofloxacin-resistant mutants. These data suggest potential secondary treatment options that may reduce the rate of resistance evolution ( a , b ) following initial treatment failure. d Green arrows indicate putative temporal administration of four antimicrobials used for the treatment of urinary-tract infections, as informed by the collateral networks in ( c )

Techniques Used: Mutagenesis, Selection, Concentration Assay

Results of multivariate statistical modeling. Graphical representations of two redundancy analyses (RDA, triplot) results relating various parameters to the observed changes in IC 90 between resistant mutants and respective wild-type strains for ( a , b ) 16 antimicrobials tested and ( c , d ) a subset of these antimicrobials, excluding ciprofloxacin, mecillinam, nitrofurantoin, trimethoprim, and trimethoprim-sulfamethoxazole. Each RDA is broken down into two plots; ( a , c ) where weighted averages of resistant mutants are plotted as colored symbols (color indicates resistance group, shape the assigned efflux group, and symbol size proportional to relative fitness, see Supplementary Fig. 4 ). In ( b , d ) antimicrobial drug names indicate the tip of vectors that pass through the origin in the direction of increasing IC 90 fold change or CR (direction of steepest ascent). Vectors can be used to interpret the change in IC 90 for the antimicrobials shown. For both statistical models, the first and second RDA axes shown display the majority of explained variation in IC 90 changes. Large gray symbols show centroids (average effect) for all resistant mutants within a given efflux group (shape). The vector tip of relative fitness (brown) is also shown. a The majority of explained variation is driven by primary resistances, where ciprofloxacin (pink)-resistant and mecillinam (green)-resistant mutants cluster distinctly from the other resistance groups, which showed higher relative fitness. b Resistant mutants possessing MdtK mutations alone (diamond) or together with AcrAB-TolC mutations (circle) are likely to show CR to chloramphenicol, ceftazidime, temocillin, and azithromycin, but sensitivity to gentamicin, fosfomycin, and trimethoprim. Whereas those without efflux mutations (triangle) are more likely to display low-level CS or no change to most antimicrobials tested. The analysis of the subset RDA ( c , d ) shows patterns consistent with the full model, but with less clustering of mutants by resistance group ( c ). The combination of AcrAB-TolC and MdtK efflux mutations displayed the greatest fitness costs, while mutants lacking efflux-related mutations were the most fit ( d ). RDA significance was assessed by permutation tests (1000 permutations), where p ≤ 0.05 was considered significant. For more comprehensive multivariate models see Supplementary Fig. 5 – 6
Figure Legend Snippet: Results of multivariate statistical modeling. Graphical representations of two redundancy analyses (RDA, triplot) results relating various parameters to the observed changes in IC 90 between resistant mutants and respective wild-type strains for ( a , b ) 16 antimicrobials tested and ( c , d ) a subset of these antimicrobials, excluding ciprofloxacin, mecillinam, nitrofurantoin, trimethoprim, and trimethoprim-sulfamethoxazole. Each RDA is broken down into two plots; ( a , c ) where weighted averages of resistant mutants are plotted as colored symbols (color indicates resistance group, shape the assigned efflux group, and symbol size proportional to relative fitness, see Supplementary Fig. 4 ). In ( b , d ) antimicrobial drug names indicate the tip of vectors that pass through the origin in the direction of increasing IC 90 fold change or CR (direction of steepest ascent). Vectors can be used to interpret the change in IC 90 for the antimicrobials shown. For both statistical models, the first and second RDA axes shown display the majority of explained variation in IC 90 changes. Large gray symbols show centroids (average effect) for all resistant mutants within a given efflux group (shape). The vector tip of relative fitness (brown) is also shown. a The majority of explained variation is driven by primary resistances, where ciprofloxacin (pink)-resistant and mecillinam (green)-resistant mutants cluster distinctly from the other resistance groups, which showed higher relative fitness. b Resistant mutants possessing MdtK mutations alone (diamond) or together with AcrAB-TolC mutations (circle) are likely to show CR to chloramphenicol, ceftazidime, temocillin, and azithromycin, but sensitivity to gentamicin, fosfomycin, and trimethoprim. Whereas those without efflux mutations (triangle) are more likely to display low-level CS or no change to most antimicrobials tested. The analysis of the subset RDA ( c , d ) shows patterns consistent with the full model, but with less clustering of mutants by resistance group ( c ). The combination of AcrAB-TolC and MdtK efflux mutations displayed the greatest fitness costs, while mutants lacking efflux-related mutations were the most fit ( d ). RDA significance was assessed by permutation tests (1000 permutations), where p ≤ 0.05 was considered significant. For more comprehensive multivariate models see Supplementary Fig. 5 – 6

Techniques Used: Plasmid Preparation

3) Product Images from "3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling"

Article Title: 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Journal: BMC Genomics

doi: 10.1186/s12864-020-6478-3

A schematic representation of the 3’Pool-seq protocol. The use of anchored oligo-dT primers with standard indexed TruSeq i7 adapter overhangs for first strand synthesis allows immediate pooling of multiple samples after reverse transcription. Within a pool, each sample can be uniquely identified by the TruSeq i7 index. Once pooled, purification, PCR, and Nextera tagmentation reagents are used to generate cDNA fragments. A second PCR step using standard TruSeq i7 and indexed Nextera i5 adapters allows selective amplification of only 3′-end cDNA fragments and barcoding of each sample pool with a standard Nextera i5 index. The final product is a dual-indexed hybrid Nextera/TruSeq 3′-library where the i5 Nextera index serves as the pool index, and the i7 TruSeq index serves as the sample index within a pool. Multiple indexed library pools can be further quantified and combined in equal proportions into a superpool for sequencing
Figure Legend Snippet: A schematic representation of the 3’Pool-seq protocol. The use of anchored oligo-dT primers with standard indexed TruSeq i7 adapter overhangs for first strand synthesis allows immediate pooling of multiple samples after reverse transcription. Within a pool, each sample can be uniquely identified by the TruSeq i7 index. Once pooled, purification, PCR, and Nextera tagmentation reagents are used to generate cDNA fragments. A second PCR step using standard TruSeq i7 and indexed Nextera i5 adapters allows selective amplification of only 3′-end cDNA fragments and barcoding of each sample pool with a standard Nextera i5 index. The final product is a dual-indexed hybrid Nextera/TruSeq 3′-library where the i5 Nextera index serves as the pool index, and the i7 TruSeq index serves as the sample index within a pool. Multiple indexed library pools can be further quantified and combined in equal proportions into a superpool for sequencing

Techniques Used: Purification, Polymerase Chain Reaction, Amplification, Sequencing

4) Product Images from "3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling"

Article Title: 3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling

Journal: BMC Genomics

doi: 10.1186/s12864-020-6478-3

A schematic representation of the 3’Pool-seq protocol. The use of anchored oligo-dT primers with standard indexed TruSeq i7 adapter overhangs for first strand synthesis allows immediate pooling of multiple samples after reverse transcription. Within a pool, each sample can be uniquely identified by the TruSeq i7 index. Once pooled, purification, PCR, and Nextera tagmentation reagents are used to generate cDNA fragments. A second PCR step using standard TruSeq i7 and indexed Nextera i5 adapters allows selective amplification of only 3′-end cDNA fragments and barcoding of each sample pool with a standard Nextera i5 index. The final product is a dual-indexed hybrid Nextera/TruSeq 3′-library where the i5 Nextera index serves as the pool index, and the i7 TruSeq index serves as the sample index within a pool. Multiple indexed library pools can be further quantified and combined in equal proportions into a superpool for sequencing
Figure Legend Snippet: A schematic representation of the 3’Pool-seq protocol. The use of anchored oligo-dT primers with standard indexed TruSeq i7 adapter overhangs for first strand synthesis allows immediate pooling of multiple samples after reverse transcription. Within a pool, each sample can be uniquely identified by the TruSeq i7 index. Once pooled, purification, PCR, and Nextera tagmentation reagents are used to generate cDNA fragments. A second PCR step using standard TruSeq i7 and indexed Nextera i5 adapters allows selective amplification of only 3′-end cDNA fragments and barcoding of each sample pool with a standard Nextera i5 index. The final product is a dual-indexed hybrid Nextera/TruSeq 3′-library where the i5 Nextera index serves as the pool index, and the i7 TruSeq index serves as the sample index within a pool. Multiple indexed library pools can be further quantified and combined in equal proportions into a superpool for sequencing

Techniques Used: Purification, Polymerase Chain Reaction, Amplification, Sequencing

5) Product Images from "MicroRNA and mRNA interactions coordinate the immune response in non-lethal heat stressed Litopenaeus vannamei against AHPND-causing Vibrio parahaemolyticus"

Article Title: MicroRNA and mRNA interactions coordinate the immune response in non-lethal heat stressed Litopenaeus vannamei against AHPND-causing Vibrio parahaemolyticus

Journal: Scientific Reports

doi: 10.1038/s41598-019-57409-4

Validation of RNA-Seq using RT-qPCR. Eight representative genes (relish, lipoprotein receptor, dynamin, importin7, juvenile hormone epoxide hydroxylase 1; JHEH-1, DNAJ5, prophenoloxidase 1; PO1, and prophenoloxidase 2; PO2) were evaluated for their expression in hemocytes of shrimp under the NLHS and NH conditions in response to VP AHPND infection and are referred to as NLHS-VP and NH-VP, respectively. Total RNA from hemocytes of NLHS-VP and NH-VP L. vannamei at 0, 6, and 24 hpi was used for cDNA synthesis. The relative expression levels of eight genes were determined by RT-qPCR and normalized against EF-1α, the internal reference. The relative expression ratio was calculated using the 2 −ΔΔCT method. The experiments were completed using triplicates. The expression level was calculated relative to that of the normal shrimp under the NH condition at 0 h after the VP AHPND challenge. The bar graphs are the data from RT-qPCR presented as means ± standard deviations and the triangles (▲) are data from the RNA-Seq. Asterisks indicate significant difference ( P
Figure Legend Snippet: Validation of RNA-Seq using RT-qPCR. Eight representative genes (relish, lipoprotein receptor, dynamin, importin7, juvenile hormone epoxide hydroxylase 1; JHEH-1, DNAJ5, prophenoloxidase 1; PO1, and prophenoloxidase 2; PO2) were evaluated for their expression in hemocytes of shrimp under the NLHS and NH conditions in response to VP AHPND infection and are referred to as NLHS-VP and NH-VP, respectively. Total RNA from hemocytes of NLHS-VP and NH-VP L. vannamei at 0, 6, and 24 hpi was used for cDNA synthesis. The relative expression levels of eight genes were determined by RT-qPCR and normalized against EF-1α, the internal reference. The relative expression ratio was calculated using the 2 −ΔΔCT method. The experiments were completed using triplicates. The expression level was calculated relative to that of the normal shrimp under the NH condition at 0 h after the VP AHPND challenge. The bar graphs are the data from RT-qPCR presented as means ± standard deviations and the triangles (▲) are data from the RNA-Seq. Asterisks indicate significant difference ( P

Techniques Used: RNA Sequencing Assay, Quantitative RT-PCR, Expressing, Infection

Relative expression analysis of miRNAs in response to VP AHPND infection following the NLHS and NH treatments in L. vannamei hemocyte. Total small RNAs from hemocyte of VP AHPND -infected L. vannamei under NH- and NLHS-treated conditions which are NH-VP and NLHS-VP, respectively, were used as templates for specific stem-loop first strand cDNA synthesis. Relative expression levels of 10 miRNAs (lva-miR-7170-5p, lva-miR-2169-3p, lva-miR-184, lva-miR-92b-5p, lva-miR-317, lva-miR-4901, lva-miR-92a-3p, lva-miR-61, lva-miR-2898, and lva-miR-6090) were determined by RT-qPCR and normalized against U6, the internal reference, at 0, 6, and 24 hpi. The bar graphs are data from RT-qPCR presented as means ± standard deviations and triangles (▲) are data from the small RNA-Seq. The results were derived from triplicate experiments. Asterisks indicate significant differences ( P
Figure Legend Snippet: Relative expression analysis of miRNAs in response to VP AHPND infection following the NLHS and NH treatments in L. vannamei hemocyte. Total small RNAs from hemocyte of VP AHPND -infected L. vannamei under NH- and NLHS-treated conditions which are NH-VP and NLHS-VP, respectively, were used as templates for specific stem-loop first strand cDNA synthesis. Relative expression levels of 10 miRNAs (lva-miR-7170-5p, lva-miR-2169-3p, lva-miR-184, lva-miR-92b-5p, lva-miR-317, lva-miR-4901, lva-miR-92a-3p, lva-miR-61, lva-miR-2898, and lva-miR-6090) were determined by RT-qPCR and normalized against U6, the internal reference, at 0, 6, and 24 hpi. The bar graphs are data from RT-qPCR presented as means ± standard deviations and triangles (▲) are data from the small RNA-Seq. The results were derived from triplicate experiments. Asterisks indicate significant differences ( P

Techniques Used: Expressing, Infection, Quantitative RT-PCR, RNA Sequencing Assay, Derivative Assay

6) Product Images from "Copy Number Heterogeneity of JC Virus Standards"

Article Title: Copy Number Heterogeneity of JC Virus Standards

Journal: Journal of Clinical Microbiology

doi: 10.1128/JCM.02337-16

Next-generation sequencing of JC virus standards reveals deletions and copy number heterogeneity. (A) Six different JC virus materials were deep sequenced, and five standards were tested by qPCR and ddPCR. Gene organization of each JC virus is shown in green, with regulatory regions depicted in orange. Of note, the 9.4-kb pMAD1 plasmid inserted the backbone at the location of the probe used in the T antigen qPCR and ddPCR assay and cannot be quantitated at that locus. (B) Coverage plot of six different standards of JC virus mapped to the JC virus NCBI reference genome (GenBank accession number NC_001699 ). The y axis is normalized such that 1 designates the average coverage across the viral genome to highlight relative differences in coverage. Three of the six standards include a large deletion in the T antigen region that constitutes a greater than 4-fold difference in copy number relative to the structural genes. Reductions in coverage in the regulatory repeat region are due both to small deletions and sequence divergence relative to JC virus reference genome. Primers for the Focus PCR analyte-specific reagent targeting the VP2/3 region are shown on the JC virus genome in red, while the pep primers targeting the T antigen region are shown in blue. (C, D) Confirmation of the copy number differences seen by sequencing was performed with qPCR (C) and ddPCR (D) using PCR primers against the VP2/3 gene (red) and T-ag gene (blue). Ten-fold dilutions of each of the standards depicted were quantitated, and the discrepancy in cycle threshold ( C T ) and absolute quantitation (D) between the VP2/3 and T-ag assays are depicted (green) for each standard. Δ C T , change in C T .
Figure Legend Snippet: Next-generation sequencing of JC virus standards reveals deletions and copy number heterogeneity. (A) Six different JC virus materials were deep sequenced, and five standards were tested by qPCR and ddPCR. Gene organization of each JC virus is shown in green, with regulatory regions depicted in orange. Of note, the 9.4-kb pMAD1 plasmid inserted the backbone at the location of the probe used in the T antigen qPCR and ddPCR assay and cannot be quantitated at that locus. (B) Coverage plot of six different standards of JC virus mapped to the JC virus NCBI reference genome (GenBank accession number NC_001699 ). The y axis is normalized such that 1 designates the average coverage across the viral genome to highlight relative differences in coverage. Three of the six standards include a large deletion in the T antigen region that constitutes a greater than 4-fold difference in copy number relative to the structural genes. Reductions in coverage in the regulatory repeat region are due both to small deletions and sequence divergence relative to JC virus reference genome. Primers for the Focus PCR analyte-specific reagent targeting the VP2/3 region are shown on the JC virus genome in red, while the pep primers targeting the T antigen region are shown in blue. (C, D) Confirmation of the copy number differences seen by sequencing was performed with qPCR (C) and ddPCR (D) using PCR primers against the VP2/3 gene (red) and T-ag gene (blue). Ten-fold dilutions of each of the standards depicted were quantitated, and the discrepancy in cycle threshold ( C T ) and absolute quantitation (D) between the VP2/3 and T-ag assays are depicted (green) for each standard. Δ C T , change in C T .

Techniques Used: Next-Generation Sequencing, Real-time Polymerase Chain Reaction, Plasmid Preparation, Sequencing, Polymerase Chain Reaction, Quantitation Assay

Sanger confirmation of junction reads from next-generation sequencing data. (A) Gel electrophoresis of PCR products amplified with primers between nucleotides 2416 and 4543 based on the JC virus reference genome in NCBI (GenBank accession number NC_001699 ). NTC, no template control. (B) Expected PCR amplicons in control materials used in this study based on nucleotide distance. The JC virus plasmid pMAD1 contains a 4-kb backbone insert within this PCR amplicon. (C) The PCR amplicon of 340 bp recovered from the WHO standard demonstrates one of the large deletions in the T antigen region that was first identified by next-generation sequencing data. (D) The PCR amplicon of 2,100 bp demonstrates no deletion in the T antigen region in a JC virus from a clinical urine specimen. (E) The PCR amplicon of 700 bp demonstrates a complex rearrangement in the Exact v2 standard and JCV ATCC 1397 strain that was first identified by next-generation sequencing data. (F) Junctional reads with more than 5% allele frequency from the deep sequencing of the WHO JC virus standard are depicted.
Figure Legend Snippet: Sanger confirmation of junction reads from next-generation sequencing data. (A) Gel electrophoresis of PCR products amplified with primers between nucleotides 2416 and 4543 based on the JC virus reference genome in NCBI (GenBank accession number NC_001699 ). NTC, no template control. (B) Expected PCR amplicons in control materials used in this study based on nucleotide distance. The JC virus plasmid pMAD1 contains a 4-kb backbone insert within this PCR amplicon. (C) The PCR amplicon of 340 bp recovered from the WHO standard demonstrates one of the large deletions in the T antigen region that was first identified by next-generation sequencing data. (D) The PCR amplicon of 2,100 bp demonstrates no deletion in the T antigen region in a JC virus from a clinical urine specimen. (E) The PCR amplicon of 700 bp demonstrates a complex rearrangement in the Exact v2 standard and JCV ATCC 1397 strain that was first identified by next-generation sequencing data. (F) Junctional reads with more than 5% allele frequency from the deep sequencing of the WHO JC virus standard are depicted.

Techniques Used: Next-Generation Sequencing, Nucleic Acid Electrophoresis, Polymerase Chain Reaction, Amplification, Plasmid Preparation, Sequencing

7) Product Images from "Optimization and validation of sample preparation for metagenomic sequencing of viruses in clinical samples"

Article Title: Optimization and validation of sample preparation for metagenomic sequencing of viruses in clinical samples

Journal: Microbiome

doi: 10.1186/s40168-017-0317-z

Optimized workflow for metagenomic virus sequencing. A workflow for metagenomic virus sequencing for diagnostic use was developed. Sample pre-processing included low-speed centrifugation, 0.45-μm filtration, storage at −80 °C, and DNase and RNase digestion. Random reverse transcription with an 8N primer including an anchor sequence, second strand synthesis, and anchor PCR amplification was performed separately for an RNA and DNA workflow. The two workflows were pooled in equal concentration for library preparation with NexteraXT
Figure Legend Snippet: Optimized workflow for metagenomic virus sequencing. A workflow for metagenomic virus sequencing for diagnostic use was developed. Sample pre-processing included low-speed centrifugation, 0.45-μm filtration, storage at −80 °C, and DNase and RNase digestion. Random reverse transcription with an 8N primer including an anchor sequence, second strand synthesis, and anchor PCR amplification was performed separately for an RNA and DNA workflow. The two workflows were pooled in equal concentration for library preparation with NexteraXT

Techniques Used: Sequencing, Diagnostic Assay, Centrifugation, Filtration, Polymerase Chain Reaction, Amplification, Concentration Assay

Separate workflows for RNA and DNA yielded higher sequencing reads for DNA viruses. Plasma samples were spiked with four different viruses (adenovirus, HHV-4, influenzavirus, poliovirus) and processed and sequenced with the combined and the new separate workflow. In the separate workflow, random amplification products were pooled before NexteraXT library preparation in equal concentrations. The experiment was performed in triplicates. a Distribution of sequencing reads into the different taxonomic categories viral, human, bacterial, and unknown origin. b Number of reads ( upper panels ) and fraction of all quality passing reads ( lower panels ) obtained for each individual virus
Figure Legend Snippet: Separate workflows for RNA and DNA yielded higher sequencing reads for DNA viruses. Plasma samples were spiked with four different viruses (adenovirus, HHV-4, influenzavirus, poliovirus) and processed and sequenced with the combined and the new separate workflow. In the separate workflow, random amplification products were pooled before NexteraXT library preparation in equal concentrations. The experiment was performed in triplicates. a Distribution of sequencing reads into the different taxonomic categories viral, human, bacterial, and unknown origin. b Number of reads ( upper panels ) and fraction of all quality passing reads ( lower panels ) obtained for each individual virus

Techniques Used: Sequencing, Amplification

8) Product Images from "The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency"

Article Title: The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency

Journal: Nature

doi: 10.1038/nature25784

Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.
Figure Legend Snippet: Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.

Techniques Used: Functional Assay, Real-time Polymerase Chain Reaction, Cell Culture, Expressing, Negative Control, shRNA, Western Blot, Methylation, Immunoprecipitation, Dot Blot, Immunofluorescence, Staining, Flow Cytometry, Cytometry, Fluorescence, Negative Staining

9) Product Images from "A Comprehensive Functional Portrait of Two Heat Shock Factor-Type Transcriptional Regulators Involved in Candida albicans Morphogenesis and Virulence"

Article Title: A Comprehensive Functional Portrait of Two Heat Shock Factor-Type Transcriptional Regulators Involved in Candida albicans Morphogenesis and Virulence

Journal: PLoS Pathogens

doi: 10.1371/journal.ppat.1003519

Efg1p binds to the promoter of many Sfl1p and Sfl2p targets and co-immunoprecipitates with Sfl1p and Sfl2p, in vivo . ( A ) ChIP-PCR assay of selected Sfl1p and Sfl2p target promoters. Strains SFL1-TAP (CEC1922), SFL2-TAP (CEC1918) and EFG1-HA (HLCEEFG1) were grown in SC medium at 30°C (30°C) or in Lee's medium at 37°C (37°C) together with the SC5314 control strain (Control) during 4 h before being subjected to chromatin immunoprecipitation (Anti-TAP, Anti-HA) followed by PCR using primers specific to the indicated promoter regions. The URA3 and YAK1 genes were used as negative controls for ChIP enrichment. ( B ) Co-Immunoprecipitation of Efg1p with Sfl1p and Sfl2p. Strains coexpressing SFL1-TAP and EFG1-HA (Lanes 2 and 3) or SFL2-TAP and EFG1-HA (Lanes 7 and 8) or controls (Lanes 1 and 6, EFG1-HA only; lanes 4 and 9, SFL1-TAP only; lanes 5 and 10, SFL2-TAP only) were cultivated in SC medium at 30°C or in Lee's medium at 37°C before crosslinking with formaldehyde. Total extracts were incubated with Dynal PanMouse IgG beads directed against TAP epitope tag prior to washing and Western blotting using anti-TAP (IP Anti-TAP, 10% of the beads/total extracts mixture) and anti-HA (Co-IP Anti-HA) antibodies. A portion of the total cell extracts (∼2%) was included to verify the presence of the Efg1p-HA fusion (Total extracts Anti-HA).
Figure Legend Snippet: Efg1p binds to the promoter of many Sfl1p and Sfl2p targets and co-immunoprecipitates with Sfl1p and Sfl2p, in vivo . ( A ) ChIP-PCR assay of selected Sfl1p and Sfl2p target promoters. Strains SFL1-TAP (CEC1922), SFL2-TAP (CEC1918) and EFG1-HA (HLCEEFG1) were grown in SC medium at 30°C (30°C) or in Lee's medium at 37°C (37°C) together with the SC5314 control strain (Control) during 4 h before being subjected to chromatin immunoprecipitation (Anti-TAP, Anti-HA) followed by PCR using primers specific to the indicated promoter regions. The URA3 and YAK1 genes were used as negative controls for ChIP enrichment. ( B ) Co-Immunoprecipitation of Efg1p with Sfl1p and Sfl2p. Strains coexpressing SFL1-TAP and EFG1-HA (Lanes 2 and 3) or SFL2-TAP and EFG1-HA (Lanes 7 and 8) or controls (Lanes 1 and 6, EFG1-HA only; lanes 4 and 9, SFL1-TAP only; lanes 5 and 10, SFL2-TAP only) were cultivated in SC medium at 30°C or in Lee's medium at 37°C before crosslinking with formaldehyde. Total extracts were incubated with Dynal PanMouse IgG beads directed against TAP epitope tag prior to washing and Western blotting using anti-TAP (IP Anti-TAP, 10% of the beads/total extracts mixture) and anti-HA (Co-IP Anti-HA) antibodies. A portion of the total cell extracts (∼2%) was included to verify the presence of the Efg1p-HA fusion (Total extracts Anti-HA).

Techniques Used: In Vivo, Chromatin Immunoprecipitation, Polymerase Chain Reaction, Immunoprecipitation, Incubation, Western Blot, Co-Immunoprecipitation Assay

Sfl1p and Sfl2p transcriptional modules. Venn diagrams of the overlap between the genes that are modulated in ( A ) SFL1 or SFL2 transcriptomics (light red circles, upregulated; light green circles, downregulated; gene expression fold-change cut-off ≥1.5; P-value cut-off ≤0.05) and commonly bound by Sfl1p and Sfl2p (light blue circle) or ( B ) SFL2 transcriptomics (light red circle, upregulated; light green circle, downregulated; gene expression fold-change cut-off ≥1.5; P-value cut-off ≤0.05) and specifically bound by Sfl2p (light grey circle). Numbers in the Venn diagrams indicate the number of genes. Circled numbers indicate the number of genes that are ( A ) both modulated in SFL1 or SFL2 transcriptomics data and commonly bound by Sfl1p and Sfl2p or ( B ) both modulated in SFL2 transcriptomics data and specifically bound by Sfl2p. The name of these genes (or their orf19 nomenclature) and the functional categories to which they belong are shown in the linked boxes. *, DCK1 is required for hyphal formation; orf19.3475 is a hyphal induced gene.
Figure Legend Snippet: Sfl1p and Sfl2p transcriptional modules. Venn diagrams of the overlap between the genes that are modulated in ( A ) SFL1 or SFL2 transcriptomics (light red circles, upregulated; light green circles, downregulated; gene expression fold-change cut-off ≥1.5; P-value cut-off ≤0.05) and commonly bound by Sfl1p and Sfl2p (light blue circle) or ( B ) SFL2 transcriptomics (light red circle, upregulated; light green circle, downregulated; gene expression fold-change cut-off ≥1.5; P-value cut-off ≤0.05) and specifically bound by Sfl2p (light grey circle). Numbers in the Venn diagrams indicate the number of genes. Circled numbers indicate the number of genes that are ( A ) both modulated in SFL1 or SFL2 transcriptomics data and commonly bound by Sfl1p and Sfl2p or ( B ) both modulated in SFL2 transcriptomics data and specifically bound by Sfl2p. The name of these genes (or their orf19 nomenclature) and the functional categories to which they belong are shown in the linked boxes. *, DCK1 is required for hyphal formation; orf19.3475 is a hyphal induced gene.

Techniques Used: Expressing, Functional Assay

Sfl1p and Sfl2p binding locations overlap with those of Ndt80p and Efg1p. ( A, B and C ) Motif discovery analyses of Sfl1p and Sfl2p binding data. Motif logos of conserved sequences in ( A ) Sfl1p- and ( B ) Sfl2p-enriched DNA fragments as well as in ( C ) fragments overlapping with binding regions that are specific to Sfl2p. DNA sequences encompassing ±250 bp around peak summits in Sfl1p or Sfl2p binding data were used as input for motif discovery using two independent motif discovery algorithms, the RSA-tools (RSAT) peak-motifs ( http://rsat.ulb.ac.be/rsat/ , [55] ) and SCOPE (genie.dartmouth.edu/scope/, [56] ) (See Materials and Methods for details). High scoring motifs from either SCOPE or RSAT algorithms are shown. These include the Ndt80p and Efg1p binding motifs, suggesting a functional interaction between Sfl1p, Sfl2p, Ndt80p and Efg1p. The distribution of motif occurrences in the input sequences are shown at the right of each motif panel. Plotted are the number of occurrences of each motif ( y -axis, motif occurrence) at a given position relative to peak center (distance to peak center in base pairs, x -axis). ( D ) Overlap of Ndt80p and Efg1p binding with Sfl1p and Sfl1p occupancies at selected locations from the C. albicans genome (selected genome interval shown above each panel). Genome-wide location data from Sellam et al. (Ndt80p, from 59-bp tiling array data, one of the two replicates of the study is shown [57] ) and Lassak et al. (Efg1p, from 50–75-mer tiling array data for Efg1p binding in cells grown under yeast form and during hyphal induction [51] , one of the three replicates in each condition is shown) are used to compare Ndt80p and Efg1p binding profiles to those of Sfl1p and Sfl2p (read counts in 10 bp windows from wiggle files of Sfl1p and Sfl2p binding data were used).
Figure Legend Snippet: Sfl1p and Sfl2p binding locations overlap with those of Ndt80p and Efg1p. ( A, B and C ) Motif discovery analyses of Sfl1p and Sfl2p binding data. Motif logos of conserved sequences in ( A ) Sfl1p- and ( B ) Sfl2p-enriched DNA fragments as well as in ( C ) fragments overlapping with binding regions that are specific to Sfl2p. DNA sequences encompassing ±250 bp around peak summits in Sfl1p or Sfl2p binding data were used as input for motif discovery using two independent motif discovery algorithms, the RSA-tools (RSAT) peak-motifs ( http://rsat.ulb.ac.be/rsat/ , [55] ) and SCOPE (genie.dartmouth.edu/scope/, [56] ) (See Materials and Methods for details). High scoring motifs from either SCOPE or RSAT algorithms are shown. These include the Ndt80p and Efg1p binding motifs, suggesting a functional interaction between Sfl1p, Sfl2p, Ndt80p and Efg1p. The distribution of motif occurrences in the input sequences are shown at the right of each motif panel. Plotted are the number of occurrences of each motif ( y -axis, motif occurrence) at a given position relative to peak center (distance to peak center in base pairs, x -axis). ( D ) Overlap of Ndt80p and Efg1p binding with Sfl1p and Sfl1p occupancies at selected locations from the C. albicans genome (selected genome interval shown above each panel). Genome-wide location data from Sellam et al. (Ndt80p, from 59-bp tiling array data, one of the two replicates of the study is shown [57] ) and Lassak et al. (Efg1p, from 50–75-mer tiling array data for Efg1p binding in cells grown under yeast form and during hyphal induction [51] , one of the three replicates in each condition is shown) are used to compare Ndt80p and Efg1p binding profiles to those of Sfl1p and Sfl2p (read counts in 10 bp windows from wiggle files of Sfl1p and Sfl2p binding data were used).

Techniques Used: Binding Assay, Functional Assay, Genome Wide

Model of Sfl1p and Sfl2p regulatory network. Sfl2p (red oval), which induces hyphal growth in response to temperature increase or upon overexpression (red dashed arrow), and Sfl1p (orange oval) bind directly, together with Efg1p and Ndt80p depending on growth conditions (green and white ovals, respectively; dashed lines indicate hypothetical physical and/or functional interaction), to the promoter of common (blue boxes) target genes encoding major transcriptional activators ( UME6 , TEC1 and BRG1 ) or repressors ( NRG1 , RFG1 , SSN6 ) of hyphal growth as well as to the promoter of genes associated with yeast-form growth ( RME1 , RHD1 and YWP1 ) and modulate the expression of many of them (for simplicity, only modulatory direct interactions are shown i.e. both binding at and transcriptional modulation of a given target; arrowed lines indicate direct upregulation whereas blunt lines indicate direct downregulation). On the other hand, Sfl2p directly upregulates the expression of specific targets (grey boxes), including a high proportion of hyphal-specific genes (HSGs), while exerting a direct negative regulation on the expression of yeast-form associated genes ( PIR1 and RHD3 ). Sfl1p and Sfl2p also exert a direct negative regulation on the expression of each other. The execution of Sfl1p or Sfl2p transcriptional control inputs allows to regulate the commitment (dashed line; blunt, inhibition; arrowed, activation) of C. albicans to form hyphae or yeast-form cells.
Figure Legend Snippet: Model of Sfl1p and Sfl2p regulatory network. Sfl2p (red oval), which induces hyphal growth in response to temperature increase or upon overexpression (red dashed arrow), and Sfl1p (orange oval) bind directly, together with Efg1p and Ndt80p depending on growth conditions (green and white ovals, respectively; dashed lines indicate hypothetical physical and/or functional interaction), to the promoter of common (blue boxes) target genes encoding major transcriptional activators ( UME6 , TEC1 and BRG1 ) or repressors ( NRG1 , RFG1 , SSN6 ) of hyphal growth as well as to the promoter of genes associated with yeast-form growth ( RME1 , RHD1 and YWP1 ) and modulate the expression of many of them (for simplicity, only modulatory direct interactions are shown i.e. both binding at and transcriptional modulation of a given target; arrowed lines indicate direct upregulation whereas blunt lines indicate direct downregulation). On the other hand, Sfl2p directly upregulates the expression of specific targets (grey boxes), including a high proportion of hyphal-specific genes (HSGs), while exerting a direct negative regulation on the expression of yeast-form associated genes ( PIR1 and RHD3 ). Sfl1p and Sfl2p also exert a direct negative regulation on the expression of each other. The execution of Sfl1p or Sfl2p transcriptional control inputs allows to regulate the commitment (dashed line; blunt, inhibition; arrowed, activation) of C. albicans to form hyphae or yeast-form cells.

Techniques Used: Over Expression, Functional Assay, Expressing, Binding Assay, Inhibition, Activation Assay

Strategy for tagging Sfl1p and Sfl2p with a triple hemagglutinin (3×HA) epitope tag and characterization of the tagged strains. ( A ) Schematic representation of the SFL1-HA 3 or SFL2-HA 3 tagging cassette allowing expression of the Sfl1p-HA 3 or Sfl2p-HA 3 fusion proteins following a Stu I digestion ( Stu I) and integration at the RPS1 locus ( RPS1 , black rectangles) [42] . A triple HA tag (dark grey box) was inserted in frame with the SFL1 or SFL2 coding sequences ( SFL1 or SFL2 ; black arrowed rectangle) in plasmid pCaEXP [42] . The tagged alleles are placed under the control of the MET3 promoter ( MET3 p; ligh grey rectangle), which is induced in the absence of methionine and cysteine, and are followed by the C. albicans URA3 marker (open rectangle). ( B ) Western blot analysis of homozygous sfl1 or sfl2 mutants ( sfl1 Δ/ sfl1 Δ or sfl2 Δ/ sfl2 Δ) expressing HA 3 -tagged versions of the SFL1 or SFL2 genes, respectively ( SFL1-HA 3 or SFL2- HA 3 ) together with the corresponding empty vector controls (Vector). The SGY243 strain expressing the CAP1-HA 3 ( CAP1-HA 3 ) or carrying the empty vector (Vector) were used as a positive control [43] . Strains were grown overnight in SD medium (P MET3 -inducing conditions) and total protein extracts were prepared then subjected to SDS-PAGE. Western blotting was performed using an anti-HA antibody. Positions of the molecular mass standards are indicated on the left (kDa). Immunopositive signals from the Sfl1p-HA 3 and Sfl2p-HA 3 fusions are indicated with black arrows ( C ) Phenotypic analysis of the strains expressing the HA 3 -tagged SFL1 or SFL2 alleles. Strain SC5314 (control) together with the homozygous sfl1 or sfl2 mutants expressing the SFL1-HA 3 or SFL2-HA 3 alleles ( SFL1-HA 3 , SFL2-HA 3 ), respectively, or carrying the empty vector (Vector) were grown overnight in YPD at 30°C then transferred to Lee's medium lacking methionine and cysteine and allowed to grow during 4 h at 37°C before being examined microscopically (40× magnification).
Figure Legend Snippet: Strategy for tagging Sfl1p and Sfl2p with a triple hemagglutinin (3×HA) epitope tag and characterization of the tagged strains. ( A ) Schematic representation of the SFL1-HA 3 or SFL2-HA 3 tagging cassette allowing expression of the Sfl1p-HA 3 or Sfl2p-HA 3 fusion proteins following a Stu I digestion ( Stu I) and integration at the RPS1 locus ( RPS1 , black rectangles) [42] . A triple HA tag (dark grey box) was inserted in frame with the SFL1 or SFL2 coding sequences ( SFL1 or SFL2 ; black arrowed rectangle) in plasmid pCaEXP [42] . The tagged alleles are placed under the control of the MET3 promoter ( MET3 p; ligh grey rectangle), which is induced in the absence of methionine and cysteine, and are followed by the C. albicans URA3 marker (open rectangle). ( B ) Western blot analysis of homozygous sfl1 or sfl2 mutants ( sfl1 Δ/ sfl1 Δ or sfl2 Δ/ sfl2 Δ) expressing HA 3 -tagged versions of the SFL1 or SFL2 genes, respectively ( SFL1-HA 3 or SFL2- HA 3 ) together with the corresponding empty vector controls (Vector). The SGY243 strain expressing the CAP1-HA 3 ( CAP1-HA 3 ) or carrying the empty vector (Vector) were used as a positive control [43] . Strains were grown overnight in SD medium (P MET3 -inducing conditions) and total protein extracts were prepared then subjected to SDS-PAGE. Western blotting was performed using an anti-HA antibody. Positions of the molecular mass standards are indicated on the left (kDa). Immunopositive signals from the Sfl1p-HA 3 and Sfl2p-HA 3 fusions are indicated with black arrows ( C ) Phenotypic analysis of the strains expressing the HA 3 -tagged SFL1 or SFL2 alleles. Strain SC5314 (control) together with the homozygous sfl1 or sfl2 mutants expressing the SFL1-HA 3 or SFL2-HA 3 alleles ( SFL1-HA 3 , SFL2-HA 3 ), respectively, or carrying the empty vector (Vector) were grown overnight in YPD at 30°C then transferred to Lee's medium lacking methionine and cysteine and allowed to grow during 4 h at 37°C before being examined microscopically (40× magnification).

Techniques Used: Expressing, Plasmid Preparation, Marker, Western Blot, Positive Control, SDS Page

Binding of Sfl1p-HA 3 and Sfl2p-HA 3 to selected target promoters. Strains sfl1 -CaEXP- SFL1-HA 3 (Sfl1p-HA 3 ) and sfl2 -CaEXP- SFL2-HA 3 (Sfl2p-HA 3 ) together with their respective untagged control strains (Vector) were grown under the same conditions as those for the ChIP-Seq experiment prior to ChIP followed by PCR to detect specific Sfl1p and Sfl2p binding enrichment at selected target promoters (See Materials and Methods for details). PCR was performed using primers corresponding to the promoter region of the indicated genes. The URA3 and YAK1 genes were used as a negative control for ChIP enrichment. Primer efficiency (shown on the right panel) was tested by the ability of the corresponding primers to quantify 10-fold serially diluted whole cell extract DNA (WCE, ChIP input samples, dilution factors are indicated at the top of the right panel).
Figure Legend Snippet: Binding of Sfl1p-HA 3 and Sfl2p-HA 3 to selected target promoters. Strains sfl1 -CaEXP- SFL1-HA 3 (Sfl1p-HA 3 ) and sfl2 -CaEXP- SFL2-HA 3 (Sfl2p-HA 3 ) together with their respective untagged control strains (Vector) were grown under the same conditions as those for the ChIP-Seq experiment prior to ChIP followed by PCR to detect specific Sfl1p and Sfl2p binding enrichment at selected target promoters (See Materials and Methods for details). PCR was performed using primers corresponding to the promoter region of the indicated genes. The URA3 and YAK1 genes were used as a negative control for ChIP enrichment. Primer efficiency (shown on the right panel) was tested by the ability of the corresponding primers to quantify 10-fold serially diluted whole cell extract DNA (WCE, ChIP input samples, dilution factors are indicated at the top of the right panel).

Techniques Used: Binding Assay, Plasmid Preparation, Chromatin Immunoprecipitation, Polymerase Chain Reaction, Negative Control

Genome-wide location of Candida albicans Sfl1p and Sfl2p, in vivo , at a single-nucleotide resolution. ( A ) Venn diagram of the overlap between Sfl1p and Sfl2p binding targets. All 113 Sfl1p targets are also bound by Sfl2p, while 75 target promoters are Sfl2p-specific. The total number of Sfl1p or Sfl2p target promoters are indicated between parentheses. Target promoters include those that are clearly associated with given ORFs as well as those that are shared by two ORFs in opposite orientations. ( B ) A single-nucleotide resolution of Sfl1p and Sfl2p binding at selected C. albicans genomic regions in vivo . Plotted are read-count signal intensities of HA 3 -tagged SFL1 - ( sfl1 -CaEXP- SFL1-HA 3 ) or SFL2 - ( sfl2 -CaEXP- SFL2-HA 3 ) coimmunoprecipitated DNA and the corresponding empty-vector control signals ( sfl1 -CaEXP, sfl2 -CaEXP, respectively) from merged BAM files of two independent biological replicates. Some read-count signals extend beyond the maximum graduation (not shown) that ranges between 0–500 reads for Sfl1 data ( sfl1 -CaEXP and sfl1 -CaEXP- SFL1-HA 3 ) and 0–1000 reads for Sfl2 data ( sfl2 -CaEXP and sfl2 -CaEXP- SFL2-HA 3 ). The position of each signal in selected C. albicans genomic regions from assembly 21 is shown on the x -axis. The location of each selected region from the corresponding chromosome (Chr) is indicated at the top of each panel (limits are shown between parentheses in base pairs). The orientation of each ORF is depicted by the arrowed black rectangle. ( C ) Enrichment scores of the Gene Ontology (GO) terms to which are assigned Sfl1p and Sfl2p common (shaded area) or Sfl2p-specific (unshaded area) binding targets. GO term enrichment scores are calculated as the negative value of the log 10 -transformed P -value. The number of genes of each category is shown at the right of each horizontal bar.
Figure Legend Snippet: Genome-wide location of Candida albicans Sfl1p and Sfl2p, in vivo , at a single-nucleotide resolution. ( A ) Venn diagram of the overlap between Sfl1p and Sfl2p binding targets. All 113 Sfl1p targets are also bound by Sfl2p, while 75 target promoters are Sfl2p-specific. The total number of Sfl1p or Sfl2p target promoters are indicated between parentheses. Target promoters include those that are clearly associated with given ORFs as well as those that are shared by two ORFs in opposite orientations. ( B ) A single-nucleotide resolution of Sfl1p and Sfl2p binding at selected C. albicans genomic regions in vivo . Plotted are read-count signal intensities of HA 3 -tagged SFL1 - ( sfl1 -CaEXP- SFL1-HA 3 ) or SFL2 - ( sfl2 -CaEXP- SFL2-HA 3 ) coimmunoprecipitated DNA and the corresponding empty-vector control signals ( sfl1 -CaEXP, sfl2 -CaEXP, respectively) from merged BAM files of two independent biological replicates. Some read-count signals extend beyond the maximum graduation (not shown) that ranges between 0–500 reads for Sfl1 data ( sfl1 -CaEXP and sfl1 -CaEXP- SFL1-HA 3 ) and 0–1000 reads for Sfl2 data ( sfl2 -CaEXP and sfl2 -CaEXP- SFL2-HA 3 ). The position of each signal in selected C. albicans genomic regions from assembly 21 is shown on the x -axis. The location of each selected region from the corresponding chromosome (Chr) is indicated at the top of each panel (limits are shown between parentheses in base pairs). The orientation of each ORF is depicted by the arrowed black rectangle. ( C ) Enrichment scores of the Gene Ontology (GO) terms to which are assigned Sfl1p and Sfl2p common (shaded area) or Sfl2p-specific (unshaded area) binding targets. GO term enrichment scores are calculated as the negative value of the log 10 -transformed P -value. The number of genes of each category is shown at the right of each horizontal bar.

Techniques Used: Genome Wide, In Vivo, Binding Assay, Plasmid Preparation, Transformation Assay

Sfl1p and Sfl2p transcriptomics. ( A ) GeneSpring expression profile plots of each of the three biological replicates from the sfl1 -CaEXP- SFL1-HA 3 versus sfl1 -CaEXP ( sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP) and the sfl2 -CaEXP- SFL2-HA 3 versus sfl2 -CaEXP ( sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP) transcriptomics data. The log 2 -transformed relative expression level of each gene from averaged signal intensities of two nonoverlapping gene-specific microarray probes (See Materials and Methods for details), is shown on the y -axis and the corresponding biological replicate sample for each condition (1, 2 and 3) is shown on the x -axis. The profile plot is coloured according to the ratio observed for replicate 1 in the sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP condition. ( B ) Heat maps of the 30 highest log 2 -transformed relative gene expression levels in the sfl1 -CaEXP- SFL1-HA 3 versus sfl1 -CaEXP ( sfl1 -CaEXP- SFL1-HA 3 vs sfl1 -CaEXP, left panels, UP and DWN) and the sfl2 -CaEXP- SFL2-HA 3 versus sfl2 -CaEXP ( sfl2 -CaEXP- SFL2-HA 3 vs sfl2 -CaEXP, right panels, UP and DWN) transcriptomics data (combination of the 3 biological replicates in each condition). The most upregulated (UP, descending signal intensity) or downregulated (DWN, ascending signal intensity) genes in sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP (left panels, SFL1 column) or sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP ( SFL2 , right panels) transcriptomics data and their matching probe intensities from the sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP condition (left panels, SFL2 column) or the sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP (right panels, SFL1 column), respectively, are indicated with their corresponding name or orf19 nomenclature. Heat maps were constructed using Genesis version 1.7.6 [83] .
Figure Legend Snippet: Sfl1p and Sfl2p transcriptomics. ( A ) GeneSpring expression profile plots of each of the three biological replicates from the sfl1 -CaEXP- SFL1-HA 3 versus sfl1 -CaEXP ( sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP) and the sfl2 -CaEXP- SFL2-HA 3 versus sfl2 -CaEXP ( sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP) transcriptomics data. The log 2 -transformed relative expression level of each gene from averaged signal intensities of two nonoverlapping gene-specific microarray probes (See Materials and Methods for details), is shown on the y -axis and the corresponding biological replicate sample for each condition (1, 2 and 3) is shown on the x -axis. The profile plot is coloured according to the ratio observed for replicate 1 in the sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP condition. ( B ) Heat maps of the 30 highest log 2 -transformed relative gene expression levels in the sfl1 -CaEXP- SFL1-HA 3 versus sfl1 -CaEXP ( sfl1 -CaEXP- SFL1-HA 3 vs sfl1 -CaEXP, left panels, UP and DWN) and the sfl2 -CaEXP- SFL2-HA 3 versus sfl2 -CaEXP ( sfl2 -CaEXP- SFL2-HA 3 vs sfl2 -CaEXP, right panels, UP and DWN) transcriptomics data (combination of the 3 biological replicates in each condition). The most upregulated (UP, descending signal intensity) or downregulated (DWN, ascending signal intensity) genes in sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP (left panels, SFL1 column) or sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP ( SFL2 , right panels) transcriptomics data and their matching probe intensities from the sfl2 -CaEXP- SFL2-HA 3 vs. sfl2 -CaEXP condition (left panels, SFL2 column) or the sfl1 -CaEXP- SFL1-HA 3 vs. sfl1 -CaEXP (right panels, SFL1 column), respectively, are indicated with their corresponding name or orf19 nomenclature. Heat maps were constructed using Genesis version 1.7.6 [83] .

Techniques Used: Expressing, Transformation Assay, Microarray, Construct

10) Product Images from "Zika Virus Infects Early- and Midgestation Human Maternal Decidual Tissues, Inducing Distinct Innate Tissue Responses in the Maternal-Fetal Interface"

Article Title: Zika Virus Infects Early- and Midgestation Human Maternal Decidual Tissues, Inducing Distinct Innate Tissue Responses in the Maternal-Fetal Interface

Journal: Journal of Virology

doi: 10.1128/JVI.01905-16

Decidual and chorionic villus tissue innate responses to ZIKV and HCMV infection. Decidual and chorionic villus organ cultures were mock infected or infected in parallel with ZIKV or HCMV (5 × 10 4 PFU/well). At day 2 postinfection, RNA was extracted and subjected to transcriptome analysis. To account for the potential donors' tissue-to-tissue variability, decidual and chorionic villus tissues from the same donors were infected in parallel with ZIKV and HCMV. Two pools (each representing a mixture of 5 independent donor tissues) for each experimental condition were used, together representing 10 tissues from different individuals. (A) Number of overlapping and uniquely differentially expressed genes under the different indicated conditions. The numbers in parentheses represent the total number of significantly differentially expressed genes under each of the indicated experimental conditions. The number in each cell represents the number of significantly differentially expressed genes in the corresponding category. (B) Number of genes upregulated or downregulated under each indicated condition shown in relation to the magnitude of the fold change from mock-infected tissue. (C and D) Molecular functions enriched under each condition. Ingenuity pathway analysis was used to summarize the molecular functions most significantly changed in infected decidual (C) and placental villus (D) tissues.
Figure Legend Snippet: Decidual and chorionic villus tissue innate responses to ZIKV and HCMV infection. Decidual and chorionic villus organ cultures were mock infected or infected in parallel with ZIKV or HCMV (5 × 10 4 PFU/well). At day 2 postinfection, RNA was extracted and subjected to transcriptome analysis. To account for the potential donors' tissue-to-tissue variability, decidual and chorionic villus tissues from the same donors were infected in parallel with ZIKV and HCMV. Two pools (each representing a mixture of 5 independent donor tissues) for each experimental condition were used, together representing 10 tissues from different individuals. (A) Number of overlapping and uniquely differentially expressed genes under the different indicated conditions. The numbers in parentheses represent the total number of significantly differentially expressed genes under each of the indicated experimental conditions. The number in each cell represents the number of significantly differentially expressed genes in the corresponding category. (B) Number of genes upregulated or downregulated under each indicated condition shown in relation to the magnitude of the fold change from mock-infected tissue. (C and D) Molecular functions enriched under each condition. Ingenuity pathway analysis was used to summarize the molecular functions most significantly changed in infected decidual (C) and placental villus (D) tissues.

Techniques Used: Infection

11) Product Images from "Comprehensive profiling of retroviral integration sites using target enrichment methods from historical koala samples without an assembled reference genome"

Article Title: Comprehensive profiling of retroviral integration sites using target enrichment methods from historical koala samples without an assembled reference genome

Journal: PeerJ

doi: 10.7717/peerj.1847

Bioinformatic pipeline for identification of KoRV integration sites. The pipeline was run separately for each data set obtained by three different techniques. For the key steps, the number of sequences retained is indicated in parentheses for each technique in this order from left to right: PEC, SPEX and hybridization capture. After processing NGS reads, KoRV integration sites were identified in a two-step analysis of KoRV LTR ends, next to the host DNA flanking KoRV. The first round of selection targeted the A region of the LTR end and its output, was used for subsequent identification of the B region. The LTR ends of all sequences were trimmed off, and only sequences longer than four bp were considered. Using a sequence clustering approach, unique vs. shared integration sites were sorted into clusters. The consensus of each non-singleton cluster was computed using a multiple sequence alignment. These consensus sequences and singleton sequences were queried against wallaby genomic scaffolds and koala Illumina Hiseq reads to determine whether they represented KoRV flanking sequences. At the same time extension products into the KoRV genome were identified.
Figure Legend Snippet: Bioinformatic pipeline for identification of KoRV integration sites. The pipeline was run separately for each data set obtained by three different techniques. For the key steps, the number of sequences retained is indicated in parentheses for each technique in this order from left to right: PEC, SPEX and hybridization capture. After processing NGS reads, KoRV integration sites were identified in a two-step analysis of KoRV LTR ends, next to the host DNA flanking KoRV. The first round of selection targeted the A region of the LTR end and its output, was used for subsequent identification of the B region. The LTR ends of all sequences were trimmed off, and only sequences longer than four bp were considered. Using a sequence clustering approach, unique vs. shared integration sites were sorted into clusters. The consensus of each non-singleton cluster was computed using a multiple sequence alignment. These consensus sequences and singleton sequences were queried against wallaby genomic scaffolds and koala Illumina Hiseq reads to determine whether they represented KoRV flanking sequences. At the same time extension products into the KoRV genome were identified.

Techniques Used: Hybridization, Next-Generation Sequencing, Selection, Sequencing

12) Product Images from "Not All Sequence Tags Are Created Equal: Designing and Validating Sequence Identification Tags Robust to Indels"

Article Title: Not All Sequence Tags Are Created Equal: Designing and Validating Sequence Identification Tags Robust to Indels

Journal: PLoS ONE

doi: 10.1371/journal.pone.0042543

Pairwise edit distance between 25 tags of five nucleotides in length and edit distance three designed using EDITTAG.
Figure Legend Snippet: Pairwise edit distance between 25 tags of five nucleotides in length and edit distance three designed using EDITTAG.

Techniques Used:

Number of HiSeq reads returned for libraries prepared using Illumina TruSeq adapters versus libraries prepared using adapters integrating edit metric sequence tags designed using EDITTAG.
Figure Legend Snippet: Number of HiSeq reads returned for libraries prepared using Illumina TruSeq adapters versus libraries prepared using adapters integrating edit metric sequence tags designed using EDITTAG.

Techniques Used: Sequencing

13) Product Images from "Mitochondrial DNA Hypomethylation Is a Biomarker Associated with Induced Senescence in Human Fetal Heart Mesenchymal Stem Cells"

Article Title: Mitochondrial DNA Hypomethylation Is a Biomarker Associated with Induced Senescence in Human Fetal Heart Mesenchymal Stem Cells

Journal: Stem Cells International

doi: 10.1155/2017/1764549

Characterization of human fetal heart-derived mesenchymal stem cells (HMSCs). (a) The profile of stem cell markers in cultured HMSCs. Immunophenotypes of MSCs were determined by flow cytometry using labeled antibodies specific for the indicated human surface antigens. (b) Differentiation potential of HMSCs. Cells were stained by Alizarin Red for calcium deposits during osteogenic differentiation. Adipogenic differentiation was detected by Oil Red O staining (200x). (c) The “molecular memory of cardiac origin” of HMSCs. Left panel: schematic diagram of the published cardiac stem cell pathways [ 4 – 8 ]. Right panel: expression of pathway genes in HMSCs. Total RNAs were isolated from HMSCs for RNA-Seq using a HiSeq4000 (Illumina). Colors represent from high (red) to low (blue) expression based on normalized FPKM values for each gene.
Figure Legend Snippet: Characterization of human fetal heart-derived mesenchymal stem cells (HMSCs). (a) The profile of stem cell markers in cultured HMSCs. Immunophenotypes of MSCs were determined by flow cytometry using labeled antibodies specific for the indicated human surface antigens. (b) Differentiation potential of HMSCs. Cells were stained by Alizarin Red for calcium deposits during osteogenic differentiation. Adipogenic differentiation was detected by Oil Red O staining (200x). (c) The “molecular memory of cardiac origin” of HMSCs. Left panel: schematic diagram of the published cardiac stem cell pathways [ 4 – 8 ]. Right panel: expression of pathway genes in HMSCs. Total RNAs were isolated from HMSCs for RNA-Seq using a HiSeq4000 (Illumina). Colors represent from high (red) to low (blue) expression based on normalized FPKM values for each gene.

Techniques Used: Derivative Assay, Cell Culture, Flow Cytometry, Cytometry, Labeling, Staining, Expressing, Isolation, RNA Sequencing Assay

14) Product Images from "Toxoplasma gondii-Induced Long-Term Changes in the Upper Intestinal Microflora during the Chronic Stage of Infection"

Article Title: Toxoplasma gondii-Induced Long-Term Changes in the Upper Intestinal Microflora during the Chronic Stage of Infection

Journal: Scientifica

doi: 10.1155/2018/2308619

Individual differences at the species level in chronic T. gondii -infected and healthy mice of the first cohort. CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 5 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 mpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile small intestinal microbiome. Sequences were analyzed using QIIME pipeline focusing on the v3-v4 region. The graph shows (a) the log-transformed average of highly variable species in infected and control groups and (b) the percentage of highly variable species of the upper intestinal microflora in each mouse. Highly variable species only appeared in the infected mice and the vast majority of them belonged to the Firmicutes phyla.
Figure Legend Snippet: Individual differences at the species level in chronic T. gondii -infected and healthy mice of the first cohort. CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 5 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 mpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile small intestinal microbiome. Sequences were analyzed using QIIME pipeline focusing on the v3-v4 region. The graph shows (a) the log-transformed average of highly variable species in infected and control groups and (b) the percentage of highly variable species of the upper intestinal microflora in each mouse. Highly variable species only appeared in the infected mice and the vast majority of them belonged to the Firmicutes phyla.

Techniques Used: Infection, Mouse Assay, Sequencing, Transformation Assay

Individual differences at species level in acute T. gondii -infected and healthy mice of the first cohort. 5 CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 5 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 dpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile small intestinal microbiome. Sequences were analyzed using QIIME pipeline focusing on the v3-v4 region. The graph shows (a) the log-transformed ratios of averaged standard deviation of the relative abundance of highly variable species in infected and control mice and (b) the percentage of highly variable species of the upper intestinal microflora in each mouse. Lactobacillus, Proteus, and Bacteroidetes species are increasingly unstable in the acute infected mice, while Bacillaceae species appear unstable in the uninfected mice.
Figure Legend Snippet: Individual differences at species level in acute T. gondii -infected and healthy mice of the first cohort. 5 CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 5 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 dpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile small intestinal microbiome. Sequences were analyzed using QIIME pipeline focusing on the v3-v4 region. The graph shows (a) the log-transformed ratios of averaged standard deviation of the relative abundance of highly variable species in infected and control mice and (b) the percentage of highly variable species of the upper intestinal microflora in each mouse. Lactobacillus, Proteus, and Bacteroidetes species are increasingly unstable in the acute infected mice, while Bacillaceae species appear unstable in the uninfected mice.

Techniques Used: Infection, Mouse Assay, Sequencing, Transformation Assay, Standard Deviation

Individual differences at phylum level in chronically T. gondii infected and healthy mice of the second cohort. 45 CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 17 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 mpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile the small intestinal microbiome. Sequences were analyzed using QIIME 2 pipeline focusing on the v3-v4 region. The graph shows enrichment of Bacteroidetes phylum in chronically infected mice.
Figure Legend Snippet: Individual differences at phylum level in chronically T. gondii infected and healthy mice of the second cohort. 45 CD1 mice were infected IP with 500 T. gondii tachyzoites (GT1) in PBS, along with 17 control CD1 mice by the IP route with PBS only. The group was sacrificed at 5 mpi. Whole 16S rDNA libraries were sequenced using the MiSeq Illumina sequencing platform to profile the small intestinal microbiome. Sequences were analyzed using QIIME 2 pipeline focusing on the v3-v4 region. The graph shows enrichment of Bacteroidetes phylum in chronically infected mice.

Techniques Used: Infection, Mouse Assay, Sequencing

15) Product Images from "Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing"

Article Title: Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing

Journal: Microbiome

doi: 10.1186/s40168-017-0361-8

NMDS ordination of resistome composition at the class level, for a the entire resistome (i.e., all gene accessions identified) and b the low-abundance resistome (i.e., the additional gene accessions identified via enrichment). Each mark is one sample, mark color indicates sample type ( blue beef, orange poultry, red swine, gray WWTP), and mark shape indicates library prep assay ( circle Resistome-UMI, diamond Resistome, asterisk Metagenome-UMI, triangle , Metagenome). In both a and b , ordination by sample type was significant with a large effect size ( R = 0.73 and P
Figure Legend Snippet: NMDS ordination of resistome composition at the class level, for a the entire resistome (i.e., all gene accessions identified) and b the low-abundance resistome (i.e., the additional gene accessions identified via enrichment). Each mark is one sample, mark color indicates sample type ( blue beef, orange poultry, red swine, gray WWTP), and mark shape indicates library prep assay ( circle Resistome-UMI, diamond Resistome, asterisk Metagenome-UMI, triangle , Metagenome). In both a and b , ordination by sample type was significant with a large effect size ( R = 0.73 and P

Techniques Used:

One hundred percent stacked graphs of resistome/virulome composition, by sample type and by library preparation assay ( R-UMI Resistome-UMI, R Resistome, M-UMI Metagenome-UMI, M Metagenome, E composition in portion of the resistome identified only through enrichment). Proportional abundances were calculated by dividing the number of de-duplicated hits to each class by the total number of de-duplicated hits. Classes are shown individually if they contained at least 10% relative abundance in one of the assays within each sample type; all other classes were grouped into the “Other” category
Figure Legend Snippet: One hundred percent stacked graphs of resistome/virulome composition, by sample type and by library preparation assay ( R-UMI Resistome-UMI, R Resistome, M-UMI Metagenome-UMI, M Metagenome, E composition in portion of the resistome identified only through enrichment). Proportional abundances were calculated by dividing the number of de-duplicated hits to each class by the total number of de-duplicated hits. Classes are shown individually if they contained at least 10% relative abundance in one of the assays within each sample type; all other classes were grouped into the “Other” category

Techniques Used:

16) Product Images from "Genes adapt to outsmart gene targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation"

Article Title: Genes adapt to outsmart gene targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation

Journal: bioRxiv

doi: 10.1101/2020.04.22.041087

Targeted KO-first targeting strategy in Rhbdf1 (A3) generates novel transcripts and N-terminally truncated functional proteins a. Schematic of the strategy used by Li et al. for generation of Rhbdf1 −/− homozygous mutant mice; the Rhbdf1 KO-first allele was crossed to Flp recombinase mice to remove the FRT-flanked “lacZ reporter and a neomycin resistance (neo) gene” to generate conditional-ready mice, which were later crossed with cre transgenic mice to excise the floxed gene segment (exons 4-11), generating Rhbdf1 −/− homozygous mutant mice (hereafter referred as viable2 mice, Rhbdf1 v2/v2 mice). b. Whole-exome sequencing of spleen tissue from Rhbdf1 v2/v2 mice showing loss of exons 4 through 11 in Rhbdf1 v2/v2 mutant mice. c. RT-PCR on spleens from Rhbdf1 +/+ and Rhbdf1 v2/v2 mutant mice using primers to amplify exons 6 through 8, exons 7 through 10, and exons 16 and 17. Exons 4-11 are deleted in Rhbdf1 v2/v2 mutant mice; hence no amplicons were generated using either exon 6 forward and exon 8 reverse, or exon 7 forward and exon 10 reverse, primers. However, exon 16 forward and exon 17 reverse primers generated a 211-bp product. d. RNA-Seq analysis of spleens from Rhbdf1 v2/v2 mutant mice indicating loss of exons 4 through 11; however, there is strong evidence for mutant mRNA, as indicated by the presence of the rest of the transcript, which encodes exons 12 through 18 and is not degraded by the nonsense-mediated decay mechanism. e. Schematic representation of exons and introns in the Rhbdf1 v2/v2 mutant allele. 5’ RACE using a gene-specific exon 16-17 fusion primer (GSP) was used to obtain 5’ ends of the Rhbdf1 v2/v2 mutant mRNA. We identified several novel mutant mRNAs with different translation initiation sites that could potentially generate N-terminally truncated RHBDF1 mutant proteins. See supplemental figures for variant protein and 5’ UTR sequences. Alternative exons are indicated as red boxes; predicted translation initiation sites are indicated by “START,” and termination codons are indicated by “STOP.” f. C-terminal Myc-DDK-tagged Rhbdf1 v2/v2 variant protein 1 (lanes 1, 2) or variant protein 2 (lanes 3,4), or empty vector (lanes 5, 6) were transiently expressed in 293T cells, and cell lysates were analyzed using western blotting with FLAG-specific antibody. After visualization of blots with a G:Box chemiluminescent imaging system, blots were washed, blocked in 5% nonfat dry milk, and re-probed with anti-actin antibody. g. Rescue of phenotype in Rhbdf1 −/− MEFs. Rhbdf1 +/+ (top) and Rhbdf1 −/− (bottom) MEFs were transiently transfected with 2 μg of either variant 1 or variant 2 vectors, or an empty vector, using Lipofectamine LTX. 48 h post-transfection, cells were stimulated overnight with either DMSO or 100 nM PMA, and cell-culture supernatants were analyzed using a mouse AREG ELISA kit. Data represent mean ± S.D; *p
Figure Legend Snippet: Targeted KO-first targeting strategy in Rhbdf1 (A3) generates novel transcripts and N-terminally truncated functional proteins a. Schematic of the strategy used by Li et al. for generation of Rhbdf1 −/− homozygous mutant mice; the Rhbdf1 KO-first allele was crossed to Flp recombinase mice to remove the FRT-flanked “lacZ reporter and a neomycin resistance (neo) gene” to generate conditional-ready mice, which were later crossed with cre transgenic mice to excise the floxed gene segment (exons 4-11), generating Rhbdf1 −/− homozygous mutant mice (hereafter referred as viable2 mice, Rhbdf1 v2/v2 mice). b. Whole-exome sequencing of spleen tissue from Rhbdf1 v2/v2 mice showing loss of exons 4 through 11 in Rhbdf1 v2/v2 mutant mice. c. RT-PCR on spleens from Rhbdf1 +/+ and Rhbdf1 v2/v2 mutant mice using primers to amplify exons 6 through 8, exons 7 through 10, and exons 16 and 17. Exons 4-11 are deleted in Rhbdf1 v2/v2 mutant mice; hence no amplicons were generated using either exon 6 forward and exon 8 reverse, or exon 7 forward and exon 10 reverse, primers. However, exon 16 forward and exon 17 reverse primers generated a 211-bp product. d. RNA-Seq analysis of spleens from Rhbdf1 v2/v2 mutant mice indicating loss of exons 4 through 11; however, there is strong evidence for mutant mRNA, as indicated by the presence of the rest of the transcript, which encodes exons 12 through 18 and is not degraded by the nonsense-mediated decay mechanism. e. Schematic representation of exons and introns in the Rhbdf1 v2/v2 mutant allele. 5’ RACE using a gene-specific exon 16-17 fusion primer (GSP) was used to obtain 5’ ends of the Rhbdf1 v2/v2 mutant mRNA. We identified several novel mutant mRNAs with different translation initiation sites that could potentially generate N-terminally truncated RHBDF1 mutant proteins. See supplemental figures for variant protein and 5’ UTR sequences. Alternative exons are indicated as red boxes; predicted translation initiation sites are indicated by “START,” and termination codons are indicated by “STOP.” f. C-terminal Myc-DDK-tagged Rhbdf1 v2/v2 variant protein 1 (lanes 1, 2) or variant protein 2 (lanes 3,4), or empty vector (lanes 5, 6) were transiently expressed in 293T cells, and cell lysates were analyzed using western blotting with FLAG-specific antibody. After visualization of blots with a G:Box chemiluminescent imaging system, blots were washed, blocked in 5% nonfat dry milk, and re-probed with anti-actin antibody. g. Rescue of phenotype in Rhbdf1 −/− MEFs. Rhbdf1 +/+ (top) and Rhbdf1 −/− (bottom) MEFs were transiently transfected with 2 μg of either variant 1 or variant 2 vectors, or an empty vector, using Lipofectamine LTX. 48 h post-transfection, cells were stimulated overnight with either DMSO or 100 nM PMA, and cell-culture supernatants were analyzed using a mouse AREG ELISA kit. Data represent mean ± S.D; *p

Techniques Used: Functional Assay, Mutagenesis, Mouse Assay, Transgenic Assay, Sequencing, Reverse Transcription Polymerase Chain Reaction, Generated, RNA Sequencing Assay, Variant Assay, Plasmid Preparation, Western Blot, Imaging, Transfection, Cell Culture, Enzyme-linked Immunosorbent Assay

17) Product Images from "Cell-Specific Transcriptional Responses to Heat Shock in the Mouse Utricle Epithelium"

Article Title: Cell-Specific Transcriptional Responses to Heat Shock in the Mouse Utricle Epithelium

Journal: Frontiers in Cellular Neuroscience

doi: 10.3389/fncel.2020.00123

Identification of previously-known and newly-validated cell-type-specific transcripts. (A) Gfi1-Cre RiboTag IPs are enriched for canonical HC markers, and GLAST-CreER RiboTag IPs are enriched for SC markers. Scatterplot of Log 2 FC values vs. normalized transcript abundance from the comparison of the Gfi1-Cre IP to the GLAST-CreER IP in the control (no heat shock) condition. Some known HC and SC markers are labeled (yellow) as well as transcripts selected for validation (green). (B–L) Immunohistochemical staining for the four targets selected in (A) . Rbm24 (green, C ) and Calb1 (green, F ) staining is observed in HCs. Myo7a (white) was used as a known HC marker (B,E) with merged images (D,G) . Rbp1 (green, I ) and Tspan8 (green, L ) are observed in SCs. Myo7a (white) was used as a known HC marker (H,K) with merged images (J,M) . Images are 900 μm 2 composites from confocal images taken at 63× magnification. Scale bar (M) represents 10 μm and applies to all panels.
Figure Legend Snippet: Identification of previously-known and newly-validated cell-type-specific transcripts. (A) Gfi1-Cre RiboTag IPs are enriched for canonical HC markers, and GLAST-CreER RiboTag IPs are enriched for SC markers. Scatterplot of Log 2 FC values vs. normalized transcript abundance from the comparison of the Gfi1-Cre IP to the GLAST-CreER IP in the control (no heat shock) condition. Some known HC and SC markers are labeled (yellow) as well as transcripts selected for validation (green). (B–L) Immunohistochemical staining for the four targets selected in (A) . Rbm24 (green, C ) and Calb1 (green, F ) staining is observed in HCs. Myo7a (white) was used as a known HC marker (B,E) with merged images (D,G) . Rbp1 (green, I ) and Tspan8 (green, L ) are observed in SCs. Myo7a (white) was used as a known HC marker (H,K) with merged images (J,M) . Images are 900 μm 2 composites from confocal images taken at 63× magnification. Scale bar (M) represents 10 μm and applies to all panels.

Techniques Used: Labeling, Immunohistochemistry, Staining, Marker

Hair cell (HC) and supporting cell (SC)-specific recombination using the Cre drivers selected for the study. (A–E) Gfi1-Cre results in recombination in HCs. (A–D) Representative maximum intensity projections from a Gfi1-Cre x Rosa26-tdTomato mouse utricle showing tdTomato expression (A) . Myo7a (C) was used as a hair cell marker to count HCs, and Hoechst staining (B) was used to count SC nuclei. Composite image (D) shows localization of the tdTomato signal primarily in HCs. (E) 96.5% of HCs and 1.1% of SCs are tdTomato+ in utricles from Gfi1-Cre;Rosa26-tdTomato mice. (F–I) GLAST-CreER results in recombination in SCs. Representative maximum intensity projections from a tamoxifen-injected GLAST-CreER;Rosa26-tdTomato mouse showing tdTomato expression (F) , Myo7a (hair cell marker) staining (H) , Hoechst (G) , and a composite (I) immunostaining. Localization in SCs is observed in the composite image. (J) Quantification of tdTomato expression in cells in both vehicle-injected and tamoxifen-injected mice showing that tamoxifen results in tdTomato induction in SCs with little induction in HCs. Scale bars (I) represent 50 μm (large panel) and 10 μm (small panel) and apply to all panels of the same respective size.
Figure Legend Snippet: Hair cell (HC) and supporting cell (SC)-specific recombination using the Cre drivers selected for the study. (A–E) Gfi1-Cre results in recombination in HCs. (A–D) Representative maximum intensity projections from a Gfi1-Cre x Rosa26-tdTomato mouse utricle showing tdTomato expression (A) . Myo7a (C) was used as a hair cell marker to count HCs, and Hoechst staining (B) was used to count SC nuclei. Composite image (D) shows localization of the tdTomato signal primarily in HCs. (E) 96.5% of HCs and 1.1% of SCs are tdTomato+ in utricles from Gfi1-Cre;Rosa26-tdTomato mice. (F–I) GLAST-CreER results in recombination in SCs. Representative maximum intensity projections from a tamoxifen-injected GLAST-CreER;Rosa26-tdTomato mouse showing tdTomato expression (F) , Myo7a (hair cell marker) staining (H) , Hoechst (G) , and a composite (I) immunostaining. Localization in SCs is observed in the composite image. (J) Quantification of tdTomato expression in cells in both vehicle-injected and tamoxifen-injected mice showing that tamoxifen results in tdTomato induction in SCs with little induction in HCs. Scale bars (I) represent 50 μm (large panel) and 10 μm (small panel) and apply to all panels of the same respective size.

Techniques Used: Expressing, Marker, Staining, Mouse Assay, Injection, Immunostaining

Principal component analysis (PCA) of RiboTag IP samples. (A) PCA of whole-tissue IP samples from RiboTag Gfi1-Cre (red) and GLAST-CreER (blue) IPs. PC1 represents 54% of the total variance in the experimental data, and PC2 represents 19.87% of the total variance. Ellipses represent 95% confidence ellipses around each group of samples, and the color of each ellipse corresponds to the treatment type (heat shock in orange or control in gray). (B) Tables show the 10 genes with the highest loadings in either direction for the two PCs plotted in (A) . (C) PCA of IP samples from RiboTag Gfi1-Cre whole-tissue (purple) and stroma-free (red) IPs. PC1 represents 62.71% of the total variance in the experimental data, and PC2 represents 25.81% of the total variance. Ellipses represent 95% confidence ellipses around each group of at least three samples, and the color of each ellipse corresponds to the treatment type (heat shock in orange or control in gray). (D) Tables show the 10 genes with the highest loadings in either direction for the two PCs plotted in (C) .
Figure Legend Snippet: Principal component analysis (PCA) of RiboTag IP samples. (A) PCA of whole-tissue IP samples from RiboTag Gfi1-Cre (red) and GLAST-CreER (blue) IPs. PC1 represents 54% of the total variance in the experimental data, and PC2 represents 19.87% of the total variance. Ellipses represent 95% confidence ellipses around each group of samples, and the color of each ellipse corresponds to the treatment type (heat shock in orange or control in gray). (B) Tables show the 10 genes with the highest loadings in either direction for the two PCs plotted in (A) . (C) PCA of IP samples from RiboTag Gfi1-Cre whole-tissue (purple) and stroma-free (red) IPs. PC1 represents 62.71% of the total variance in the experimental data, and PC2 represents 25.81% of the total variance. Ellipses represent 95% confidence ellipses around each group of at least three samples, and the color of each ellipse corresponds to the treatment type (heat shock in orange or control in gray). (D) Tables show the 10 genes with the highest loadings in either direction for the two PCs plotted in (C) .

Techniques Used:

Isolation of cell type-specific transcripts from both HCs and tissue macrophages in the Gfi1-Cre RiboTag model. (A) Identification of macrophage-specific transcripts was achieved by comparing differentially expressed genes (DEGs) from whole utricle Gfi1-Cre RiboTag IPs (top) to those from utricles in which the stroma (containing resident macrophages) had been removed from the sensory epithelium, yielding a stroma-free isolated sensory epithelium (bottom). Shown are transcripts from HCs (red), SCs (blue), stroma (beige), and macrophages (purple). (B) Comparison of whole utricle Gfi1-Cre IP to input DEGs revealed enrichment for both hair cell and macrophage markers. Scatterplot shows Log 2 FC values vs. normalized transcript abundance from DEG comparison of the whole tissue Gfi1-Cre IP to the input. Markers of HC type ( Myo3a, Atoh1, Calb1, Ocm, Pvalb, Gfi1, Calb2, Bdnf, Otof ) are shown in red and labeled along with markers of tissue macrophage cell type shown in purple [ Itgam (CD11b), Ptprc (CD45), Cx3cr1 , Adgre1 (F4/80)]. (C) Comparison of whole tissue (sensory epithelium plus stroma, purple) to isolated sensory epithelium (without stroma, red)DEGs revealed macrophage markers. Scatterplot shows Log 2 FC values vs. normalized transcript abundance from DEG comparison of the whole tissue Gfi1-Cre IP to the isolated sensory epithelium Gfi1-Cre. Markers of HC type ( Otof , Cdh23 ) are labeled along with markers of tissue macrophage cell type [ Itgam (CD11b), Ptprc (CD45), Cx3cr1 , Adgre1 (F4/80)].
Figure Legend Snippet: Isolation of cell type-specific transcripts from both HCs and tissue macrophages in the Gfi1-Cre RiboTag model. (A) Identification of macrophage-specific transcripts was achieved by comparing differentially expressed genes (DEGs) from whole utricle Gfi1-Cre RiboTag IPs (top) to those from utricles in which the stroma (containing resident macrophages) had been removed from the sensory epithelium, yielding a stroma-free isolated sensory epithelium (bottom). Shown are transcripts from HCs (red), SCs (blue), stroma (beige), and macrophages (purple). (B) Comparison of whole utricle Gfi1-Cre IP to input DEGs revealed enrichment for both hair cell and macrophage markers. Scatterplot shows Log 2 FC values vs. normalized transcript abundance from DEG comparison of the whole tissue Gfi1-Cre IP to the input. Markers of HC type ( Myo3a, Atoh1, Calb1, Ocm, Pvalb, Gfi1, Calb2, Bdnf, Otof ) are shown in red and labeled along with markers of tissue macrophage cell type shown in purple [ Itgam (CD11b), Ptprc (CD45), Cx3cr1 , Adgre1 (F4/80)]. (C) Comparison of whole tissue (sensory epithelium plus stroma, purple) to isolated sensory epithelium (without stroma, red)DEGs revealed macrophage markers. Scatterplot shows Log 2 FC values vs. normalized transcript abundance from DEG comparison of the whole tissue Gfi1-Cre IP to the isolated sensory epithelium Gfi1-Cre. Markers of HC type ( Otof , Cdh23 ) are labeled along with markers of tissue macrophage cell type [ Itgam (CD11b), Ptprc (CD45), Cx3cr1 , Adgre1 (F4/80)].

Techniques Used: Isolation, Labeling

HCs and SCs demonstrate differential transcriptional responses to heat shock. (A) Hair cell response to heat shock. Scatterplot shows Log 2 FC values vs. normalized transcript abundance for the heat shock Gfi1-Cre IP group vs. the control Gfi1-Cre IP group. DEGs identified in both HCs and SCs are shown in orange, and DEGs identified uniquely in HCs are shown in red. Enriched HSPs are labeled and colored accordingly. (B) Supporting cell response to heat shock. Scatterplot shows Log 2 FC values vs. normalized transcript abundance for the heat shock GLAST-CreER IP group vs. the control GLAST-CreER IP group. DEGs identified in HCs and SCs are shown in orange, and DEGs identified uniquely in SCs are shown in red. Enriched heat shock proteins (HSPs) are labeled and colored accordingly. (C) Heat shock responses in HCs vs. SCs. Venn diagram illustrates the HSP transcripts significantly enriched in HCs and SCs after heat shock. Four DEGs were common between the two cell types and are members of the HSP90, HSP10, and HSP40 families. One DEG ( Cct8 ) was upregulated only in HCs and is a member of the chaperonin/CCT family. Three DEGs were upregulated only in SCs ( Cryab , Hspa1l, Hspb1 ) and are members of the HSP20, HSP70, and HSP27 families.
Figure Legend Snippet: HCs and SCs demonstrate differential transcriptional responses to heat shock. (A) Hair cell response to heat shock. Scatterplot shows Log 2 FC values vs. normalized transcript abundance for the heat shock Gfi1-Cre IP group vs. the control Gfi1-Cre IP group. DEGs identified in both HCs and SCs are shown in orange, and DEGs identified uniquely in HCs are shown in red. Enriched HSPs are labeled and colored accordingly. (B) Supporting cell response to heat shock. Scatterplot shows Log 2 FC values vs. normalized transcript abundance for the heat shock GLAST-CreER IP group vs. the control GLAST-CreER IP group. DEGs identified in HCs and SCs are shown in orange, and DEGs identified uniquely in SCs are shown in red. Enriched heat shock proteins (HSPs) are labeled and colored accordingly. (C) Heat shock responses in HCs vs. SCs. Venn diagram illustrates the HSP transcripts significantly enriched in HCs and SCs after heat shock. Four DEGs were common between the two cell types and are members of the HSP90, HSP10, and HSP40 families. One DEG ( Cct8 ) was upregulated only in HCs and is a member of the chaperonin/CCT family. Three DEGs were upregulated only in SCs ( Cryab , Hspa1l, Hspb1 ) and are members of the HSP20, HSP70, and HSP27 families.

Techniques Used: Labeling

18) Product Images from "The Reference Transcriptome of the Adult Female Biting Midge (Culicoides sonorensis) and Differential Gene Expression Profiling during Teneral, Blood, and Sucrose Feeding Conditions"

Article Title: The Reference Transcriptome of the Adult Female Biting Midge (Culicoides sonorensis) and Differential Gene Expression Profiling during Teneral, Blood, and Sucrose Feeding Conditions

Journal: PLoS ONE

doi: 10.1371/journal.pone.0098123

GO classification of the adult female midge unigene set. Classification and functional distrubuion of the 19,041 unigenes of the C. sonorensis transcriptome according to the 3 major classifications of the Gene Ontology: Biological Process, Molecular Function, and Cellular Component.
Figure Legend Snippet: GO classification of the adult female midge unigene set. Classification and functional distrubuion of the 19,041 unigenes of the C. sonorensis transcriptome according to the 3 major classifications of the Gene Ontology: Biological Process, Molecular Function, and Cellular Component.

Techniques Used: Functional Assay

Transcriptome profiles and temporal expression analysis (early or late) within diet (sucrose or blood) in female Culicoides sonorensis . Comparison of the genes that were unique and shared between: A. teneral, early sucrose meal (2, 6, 12 h pooled; ES), and late sucrose meal (36 h; LS), or B. teneral, early blood meal (2, 6, 12 h pooled; EB), and late blood meal (36 h; LB). Right (boxes): The number of genes with differential expression profiles (P≤0.01), and the percent of shared unigene (in parentheses).
Figure Legend Snippet: Transcriptome profiles and temporal expression analysis (early or late) within diet (sucrose or blood) in female Culicoides sonorensis . Comparison of the genes that were unique and shared between: A. teneral, early sucrose meal (2, 6, 12 h pooled; ES), and late sucrose meal (36 h; LS), or B. teneral, early blood meal (2, 6, 12 h pooled; EB), and late blood meal (36 h; LB). Right (boxes): The number of genes with differential expression profiles (P≤0.01), and the percent of shared unigene (in parentheses).

Techniques Used: Expressing

Global response to blood and sucrose meals in female Culicoides sonorensis . Global transcript upregulation (blue) and downregulation (red) in response to a blood meal (A; early and late transcriptomes combined) or sucrose meal (B; early and late transcriptomes combined). Note difference in vertical scale between A and B.
Figure Legend Snippet: Global response to blood and sucrose meals in female Culicoides sonorensis . Global transcript upregulation (blue) and downregulation (red) in response to a blood meal (A; early and late transcriptomes combined) or sucrose meal (B; early and late transcriptomes combined). Note difference in vertical scale between A and B.

Techniques Used:

Transcriptome profiles and temporal expression analysis (early or late) across diet (sucrose or blood) in female Culicoides sonorensis . Comparison of the genes that were unique and shared between: A. teneral and early blood (EB) and sucrose (ES) meals (both 2, 6, 12 h post ingestion, pooled), or B. teneral and late blood (LB) and sucrose (LS) meals (both 36 h). Right (boxes): The number of genes with differential expression profiles (P≤0.01), and the percent of shared unigene (in parentheses).
Figure Legend Snippet: Transcriptome profiles and temporal expression analysis (early or late) across diet (sucrose or blood) in female Culicoides sonorensis . Comparison of the genes that were unique and shared between: A. teneral and early blood (EB) and sucrose (ES) meals (both 2, 6, 12 h post ingestion, pooled), or B. teneral and late blood (LB) and sucrose (LS) meals (both 36 h). Right (boxes): The number of genes with differential expression profiles (P≤0.01), and the percent of shared unigene (in parentheses).

Techniques Used: Expressing

19) Product Images from "Monitoring the immune response to vaccination with an inactivated vaccine associated to bovine neonatal pancytopenia by deep sequencing transcriptome analysis in cattle"

Article Title: Monitoring the immune response to vaccination with an inactivated vaccine associated to bovine neonatal pancytopenia by deep sequencing transcriptome analysis in cattle

Journal: Veterinary Research

doi: 10.1186/1297-9716-44-93

Expression of the novel locus XLOC_032517 prior and after vaccination. A : Number of transcripts for locus XLOC_032517 before and after vaccination in 12 cows determined by RNA seq. cpm: counts per million reads. B : Fold change at transcript level for locus XLOC_032517 after vaccination in 12 cows determined by RNAseq and single-locus quantitative RT-PCR.
Figure Legend Snippet: Expression of the novel locus XLOC_032517 prior and after vaccination. A : Number of transcripts for locus XLOC_032517 before and after vaccination in 12 cows determined by RNA seq. cpm: counts per million reads. B : Fold change at transcript level for locus XLOC_032517 after vaccination in 12 cows determined by RNAseq and single-locus quantitative RT-PCR.

Techniques Used: Expressing, RNA Sequencing Assay, Quantitative RT-PCR

20) Product Images from "Comparison of Next-Generation Sequencing Technologies for Comprehensive Assessment of Full-Length Hepatitis C Viral Genomes"

Article Title: Comparison of Next-Generation Sequencing Technologies for Comprehensive Assessment of Full-Length Hepatitis C Viral Genomes

Journal: Journal of Clinical Microbiology

doi: 10.1128/JCM.00330-16

Assessment of viral diversity: sequence differences between the global consensus and majority sequences generated by each NGS method, and the association of HCV viral load with diversity. (A and B) Distribution of the numbers of nucleotide and amino acid differences, respectively ( y axis, log scale) between the global consensus sequence and the individual majority-rule sequences generated by each NGS method ( x axis). Sequences phylogenetically unrelated to the global consensus (shaded green in Fig. 4 ) or where there was no global consensus (shaded red in Fig. 4 ) have been excluded from this analysis. Gray bars represent median values for the distribution. (C) Nonsynonymous/synonymous ratio of substitutions between each assembled sequence and the corresponding global consensus sequence. More-divergent sequences showing ≥5 differences (Diffs) from the global consensus are plotted with gray filled circles. (D) Distribution of nucleotide and amino acid differences between directly sequenced amplicons derived from the NS3 (positions 3288 to 5727) and NS5B region (positions 7407 to 9366) of 12 samples from the evaluation panel with corresponding regions from the global consensus obtained by NGS methods.
Figure Legend Snippet: Assessment of viral diversity: sequence differences between the global consensus and majority sequences generated by each NGS method, and the association of HCV viral load with diversity. (A and B) Distribution of the numbers of nucleotide and amino acid differences, respectively ( y axis, log scale) between the global consensus sequence and the individual majority-rule sequences generated by each NGS method ( x axis). Sequences phylogenetically unrelated to the global consensus (shaded green in Fig. 4 ) or where there was no global consensus (shaded red in Fig. 4 ) have been excluded from this analysis. Gray bars represent median values for the distribution. (C) Nonsynonymous/synonymous ratio of substitutions between each assembled sequence and the corresponding global consensus sequence. More-divergent sequences showing ≥5 differences (Diffs) from the global consensus are plotted with gray filled circles. (D) Distribution of nucleotide and amino acid differences between directly sequenced amplicons derived from the NS3 (positions 3288 to 5727) and NS5B region (positions 7407 to 9366) of 12 samples from the evaluation panel with corresponding regions from the global consensus obtained by NGS methods.

Techniques Used: Sequencing, Generated, Next-Generation Sequencing, Derivative Assay

21) Product Images from "Multiplex Target Enrichment Using DNA Indexing for Ultra-High Throughput SNP Detection"

Article Title: Multiplex Target Enrichment Using DNA Indexing for Ultra-High Throughput SNP Detection

Journal: DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes

doi: 10.1093/dnares/dsq029

Experimental Design. Genomic DNA from nine HapMap samples was chosen for the study (three trio families). DNA from one of the samples (NA11881) was prepared twice (with and without an indexed adapter), target enriched and sequenced separately as single samples (non-indexed sample and one indexed sample in Step 1 and enriched libraries 1 and 2 in Step 2). One trio family (NA11881, NA11882 and NA10859; all indexed) was pooled after the Illumina genomic DNA sample prep and enriched together using one SureSelect enrichment reaction to produce the enriched library 3 sample. Indexed DNA from all nine samples was also pooled after the Illumina genomic DNA sample prep and enriched together using one SureSelect enrichment reaction to produce the enriched library 4 sample. Note: enriched libraries 3 and 4 were also sequenced using 80 bp reads to generate additional data for validation of the method for SNP detection.
Figure Legend Snippet: Experimental Design. Genomic DNA from nine HapMap samples was chosen for the study (three trio families). DNA from one of the samples (NA11881) was prepared twice (with and without an indexed adapter), target enriched and sequenced separately as single samples (non-indexed sample and one indexed sample in Step 1 and enriched libraries 1 and 2 in Step 2). One trio family (NA11881, NA11882 and NA10859; all indexed) was pooled after the Illumina genomic DNA sample prep and enriched together using one SureSelect enrichment reaction to produce the enriched library 3 sample. Indexed DNA from all nine samples was also pooled after the Illumina genomic DNA sample prep and enriched together using one SureSelect enrichment reaction to produce the enriched library 4 sample. Note: enriched libraries 3 and 4 were also sequenced using 80 bp reads to generate additional data for validation of the method for SNP detection.

Techniques Used: Sample Prep

22) Product Images from "Effect of miglitol on the suppression of nonalcoholic steatohepatitis development and improvement of the gut environment in a rodent model"

Article Title: Effect of miglitol on the suppression of nonalcoholic steatohepatitis development and improvement of the gut environment in a rodent model

Journal: Journal of Gastroenterology

doi: 10.1007/s00535-017-1331-4

Gut microbiota changes in response to miglitol treatments. Mice were divided into three groups and fed a normal chow diet ( NCD ), a high-fat high-sucrose diet ( HFHSD ), or HFHSD containing 0.04% miglitol ( HFHSD+M ) for 12 weeks and then killed. a Estimates of bacterial diversity as assessed by the Shannon index ( n = 7 per group). b Principal coordinates analysis ( PCoA ) plots based on the weighted Unifrac distance matrices showing the clustering of global microbiota ( n = 7 per group). c Relative abundance of phyla in fecal samples between groups of mice ( n = 7 per group). d Relative abundance of families in fecal samples between groups of mice ( n = 7 per group). Data are presented as the mean ± standard error of the mean. PC principal coordinate, rRNA ribosomal RNA, asterisk statistical significance ( p
Figure Legend Snippet: Gut microbiota changes in response to miglitol treatments. Mice were divided into three groups and fed a normal chow diet ( NCD ), a high-fat high-sucrose diet ( HFHSD ), or HFHSD containing 0.04% miglitol ( HFHSD+M ) for 12 weeks and then killed. a Estimates of bacterial diversity as assessed by the Shannon index ( n = 7 per group). b Principal coordinates analysis ( PCoA ) plots based on the weighted Unifrac distance matrices showing the clustering of global microbiota ( n = 7 per group). c Relative abundance of phyla in fecal samples between groups of mice ( n = 7 per group). d Relative abundance of families in fecal samples between groups of mice ( n = 7 per group). Data are presented as the mean ± standard error of the mean. PC principal coordinate, rRNA ribosomal RNA, asterisk statistical significance ( p

Techniques Used: Mouse Assay

23) Product Images from "Simultaneous digital quantification and fluorescence-based size characterization of massively parallel sequencing libraries"

Article Title: Simultaneous digital quantification and fluorescence-based size characterization of massively parallel sequencing libraries

Journal: BioTechniques

doi: 10.2144/000114063

ddPCR amplification of 10 size standards designed for use with the QuantiSize assay
Figure Legend Snippet: ddPCR amplification of 10 size standards designed for use with the QuantiSize assay

Techniques Used: Amplification

Cluster density and number of sequencing reads ± SEM across multiple sequencing runs performed using QuantiSize
Figure Legend Snippet: Cluster density and number of sequencing reads ± SEM across multiple sequencing runs performed using QuantiSize

Techniques Used: Sequencing

24) Product Images from "T cell fate following Salmonella infection is determined by a STING-IRF1 signaling axis in mice"

Article Title: T cell fate following Salmonella infection is determined by a STING-IRF1 signaling axis in mice

Journal: Communications Biology

doi: 10.1038/s42003-019-0701-2

IRF1 is activated upon interaction with STING. a Differential gene expression analysis of IRFs in wild-type and Tmem173 −/− BM-DCs after stimulation by c-di-GMP (50 μg/ml) for 18 h. b Real-time PCR analysis of Irf1 , Irf3 , and Irf7 mRNA expressions in BM-DCs after stimulation with c-di-GMP for 4 and 18 h. Results are presented relative to normalized expression of the 18S ribosomal RNA. n = 4. *** p
Figure Legend Snippet: IRF1 is activated upon interaction with STING. a Differential gene expression analysis of IRFs in wild-type and Tmem173 −/− BM-DCs after stimulation by c-di-GMP (50 μg/ml) for 18 h. b Real-time PCR analysis of Irf1 , Irf3 , and Irf7 mRNA expressions in BM-DCs after stimulation with c-di-GMP for 4 and 18 h. Results are presented relative to normalized expression of the 18S ribosomal RNA. n = 4. *** p

Techniques Used: Expressing, Real-time Polymerase Chain Reaction

25) Product Images from "The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency"

Article Title: The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency

Journal: Nature

doi: 10.1038/nature25784

Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.
Figure Legend Snippet: Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.

Techniques Used: Functional Assay, Real-time Polymerase Chain Reaction, Cell Culture, Expressing, Negative Control, shRNA, Western Blot, Methylation, Immunoprecipitation, Dot Blot, Immunofluorescence, Staining, Flow Cytometry, Cytometry, Fluorescence, Negative Staining

26) Product Images from "Host adaption to the bacteriophage carrier state of Campylobacter jejuni"

Article Title: Host adaption to the bacteriophage carrier state of Campylobacter jejuni

Journal: Research in Microbiology

doi: 10.1016/j.resmic.2015.05.003

C. jejuni PT14 genes differentially expressed in the phage carrier state ordered by functional category . Individual genes that were found to be greater than 2-fold differentially expressed in PT14CP8CS (A) or PT14CP30ACS (B) compared to non-infected bacteria are represented using Circos diagrams [29] with each gene represented and colour-coded according to functional class ( http://www.nature.com/nature/journal/v403/n6770/suppinfo/403665a0.html ) as follows: 1A) degradation; 1B) energy metabolism; 1C) central intermediary metabolism; 1D) amino acid biosynthesis; 1E) polyamine synthesis; 1F) purines, pyrimidines, nucleosides and nucleotides; 1G) biosynthesis of cofactors, prosthetic groups and carriers; 2) broad regulatory functions; 3A) synthesis and modification of macromolecules; 3B) degradation of macromolecules; 3C) cell envelope; 4A) transport/binding proteins; 4B) chaperones; chaperonins, heat shock; 4C) cell division; 4D) chemotaxis and mobility; 4E) protein and peptide secretion; 4G) detoxification; 4I) pathogenicity; 5A) IS elements; 5D) drug/analogue sensitivity; 5G) antibiotic resistance; 5H) conserved hypothetical proteins; 5I) unknown; 6A) miscellaneous; RNA) tRNA and rRNA. Only genes that are differentially expressed in both PT14CP8CS and PT14CP30ACS are labelled by locus-tag on the outer circle in red type for increased expression or black type for decreased expression. Histograms in the central rings represent the fold-change with increased expression in the carrier state compared to PT14 represented by red bars and reduced expression represented by black bars. Scale indicated by grey circles with values greater than 10-fold truncated, so bars touching the inner or outer rings represent a fold change ≥ -10 or 10 respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Figure Legend Snippet: C. jejuni PT14 genes differentially expressed in the phage carrier state ordered by functional category . Individual genes that were found to be greater than 2-fold differentially expressed in PT14CP8CS (A) or PT14CP30ACS (B) compared to non-infected bacteria are represented using Circos diagrams [29] with each gene represented and colour-coded according to functional class ( http://www.nature.com/nature/journal/v403/n6770/suppinfo/403665a0.html ) as follows: 1A) degradation; 1B) energy metabolism; 1C) central intermediary metabolism; 1D) amino acid biosynthesis; 1E) polyamine synthesis; 1F) purines, pyrimidines, nucleosides and nucleotides; 1G) biosynthesis of cofactors, prosthetic groups and carriers; 2) broad regulatory functions; 3A) synthesis and modification of macromolecules; 3B) degradation of macromolecules; 3C) cell envelope; 4A) transport/binding proteins; 4B) chaperones; chaperonins, heat shock; 4C) cell division; 4D) chemotaxis and mobility; 4E) protein and peptide secretion; 4G) detoxification; 4I) pathogenicity; 5A) IS elements; 5D) drug/analogue sensitivity; 5G) antibiotic resistance; 5H) conserved hypothetical proteins; 5I) unknown; 6A) miscellaneous; RNA) tRNA and rRNA. Only genes that are differentially expressed in both PT14CP8CS and PT14CP30ACS are labelled by locus-tag on the outer circle in red type for increased expression or black type for decreased expression. Histograms in the central rings represent the fold-change with increased expression in the carrier state compared to PT14 represented by red bars and reduced expression represented by black bars. Scale indicated by grey circles with values greater than 10-fold truncated, so bars touching the inner or outer rings represent a fold change ≥ -10 or 10 respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Techniques Used: Functional Assay, Infection, Modification, Binding Assay, Chemotaxis Assay, Expressing

27) Product Images from "Rapid and Inexpensive Whole-Genome Genotyping-by-Sequencing for Crossover Localization and Fine-Scale Genetic Mapping"

Article Title: Rapid and Inexpensive Whole-Genome Genotyping-by-Sequencing for Crossover Localization and Fine-Scale Genetic Mapping

Journal: G3: Genes|Genomes|Genetics

doi: 10.1534/g3.114.016501

Library preparation workflow and sequencing coverage results. (A) Comparison of our protocol for the rapid production of paired-end libraries for whole-genome sequencing with Illumina TruSeq Nano protocol. “A” indicates size selection step, “B” indicates quantification/normalization step, and “C” indicates pooling step. * indicates an optional step. (B) Coverage distribution for reads generated from a single DNA library prepared for the A. thaliana accession Ws-2 using our high-throughput method and mapped against the Col-0 TAIR10 reference genome (including repetitive alignments). The average coverage in 5-kb bins is shown and the maximum coverage value has been capped to exclude the top 0.1% of average counts for each chromosome in order to compare all chromosomes at the same scale. Red circles indicate bins in which the average coverage was less than 1x. The genome-wide average depth of coverage was 25.8x. (C) The average representation of reads assigned to a specific index sequence over four separate multiplexed pools.
Figure Legend Snippet: Library preparation workflow and sequencing coverage results. (A) Comparison of our protocol for the rapid production of paired-end libraries for whole-genome sequencing with Illumina TruSeq Nano protocol. “A” indicates size selection step, “B” indicates quantification/normalization step, and “C” indicates pooling step. * indicates an optional step. (B) Coverage distribution for reads generated from a single DNA library prepared for the A. thaliana accession Ws-2 using our high-throughput method and mapped against the Col-0 TAIR10 reference genome (including repetitive alignments). The average coverage in 5-kb bins is shown and the maximum coverage value has been capped to exclude the top 0.1% of average counts for each chromosome in order to compare all chromosomes at the same scale. Red circles indicate bins in which the average coverage was less than 1x. The genome-wide average depth of coverage was 25.8x. (C) The average representation of reads assigned to a specific index sequence over four separate multiplexed pools.

Techniques Used: Sequencing, Selection, Generated, High Throughput Screening Assay, Genome Wide

28) Product Images from "Peptidomic discovery of short open reading frame-encoded peptides in human cells"

Article Title: Peptidomic discovery of short open reading frame-encoded peptides in human cells

Journal: Nature chemical biology

doi: 10.1038/nchembio.1120

Expression of SEPs ( a ) Transient transfection of HEK293T cells with constructs containing a cDNA sequence corresponding to the full-length RefSeq mRNA (i.e., including the 5′- and 3′-UTRs). We appended a C-terminal FLAG-tag on the SEP coding sequence that could be detected by immunofluorescence. In these images the nuclei are stained with DAPI (blue) and the SEPs are detected with anti-FLAG antibody (green). ASNSD1-SEP and FRAT2-SEP sORFs in the 5′-UTR (uORFs) but FRAT2-SEP starts with a non-AUG codon. DEDD2-SEP (CDS) and H2AFx-SEP (3′-UTR) were not translated from the RefSeq RNAs, which is consistent with a scanning model of eukaryotic translation. ( b ) DEDD2-SEP was subcloned and expressed in HeLa cells to examine is expression and localization by immunofluorescence. Co-staining with MitoTracker (red) indicated that the DEDD2-SEP localizes to the mitochondria (overlay). (Note: RNA maps are not to scale. See Supplementary Fig. 12 for lengths of the RNAs and sORFs.)
Figure Legend Snippet: Expression of SEPs ( a ) Transient transfection of HEK293T cells with constructs containing a cDNA sequence corresponding to the full-length RefSeq mRNA (i.e., including the 5′- and 3′-UTRs). We appended a C-terminal FLAG-tag on the SEP coding sequence that could be detected by immunofluorescence. In these images the nuclei are stained with DAPI (blue) and the SEPs are detected with anti-FLAG antibody (green). ASNSD1-SEP and FRAT2-SEP sORFs in the 5′-UTR (uORFs) but FRAT2-SEP starts with a non-AUG codon. DEDD2-SEP (CDS) and H2AFx-SEP (3′-UTR) were not translated from the RefSeq RNAs, which is consistent with a scanning model of eukaryotic translation. ( b ) DEDD2-SEP was subcloned and expressed in HeLa cells to examine is expression and localization by immunofluorescence. Co-staining with MitoTracker (red) indicated that the DEDD2-SEP localizes to the mitochondria (overlay). (Note: RNA maps are not to scale. See Supplementary Fig. 12 for lengths of the RNAs and sORFs.)

Techniques Used: Expressing, Transfection, Construct, Sequencing, FLAG-tag, Immunofluorescence, Staining

29) Product Images from "MicroRNA and mRNA interactions coordinate the immune response in non-lethal heat stressed Litopenaeus vannamei against AHPND-causing Vibrio parahaemolyticus"

Article Title: MicroRNA and mRNA interactions coordinate the immune response in non-lethal heat stressed Litopenaeus vannamei against AHPND-causing Vibrio parahaemolyticus

Journal: Scientific Reports

doi: 10.1038/s41598-019-57409-4

Validation of RNA-Seq using RT-qPCR. Eight representative genes (relish, lipoprotein receptor, dynamin, importin7, juvenile hormone epoxide hydroxylase 1; JHEH-1, DNAJ5, prophenoloxidase 1; PO1, and prophenoloxidase 2; PO2) were evaluated for their expression in hemocytes of shrimp under the NLHS and NH conditions in response to VP AHPND infection and are referred to as NLHS-VP and NH-VP, respectively. Total RNA from hemocytes of NLHS-VP and NH-VP L. vannamei at 0, 6, and 24 hpi was used for cDNA synthesis. The relative expression levels of eight genes were determined by RT-qPCR and normalized against EF-1α, the internal reference. The relative expression ratio was calculated using the 2 −ΔΔCT method. The experiments were completed using triplicates. The expression level was calculated relative to that of the normal shrimp under the NH condition at 0 h after the VP AHPND challenge. The bar graphs are the data from RT-qPCR presented as means ± standard deviations and the triangles (▲) are data from the RNA-Seq. Asterisks indicate significant difference ( P
Figure Legend Snippet: Validation of RNA-Seq using RT-qPCR. Eight representative genes (relish, lipoprotein receptor, dynamin, importin7, juvenile hormone epoxide hydroxylase 1; JHEH-1, DNAJ5, prophenoloxidase 1; PO1, and prophenoloxidase 2; PO2) were evaluated for their expression in hemocytes of shrimp under the NLHS and NH conditions in response to VP AHPND infection and are referred to as NLHS-VP and NH-VP, respectively. Total RNA from hemocytes of NLHS-VP and NH-VP L. vannamei at 0, 6, and 24 hpi was used for cDNA synthesis. The relative expression levels of eight genes were determined by RT-qPCR and normalized against EF-1α, the internal reference. The relative expression ratio was calculated using the 2 −ΔΔCT method. The experiments were completed using triplicates. The expression level was calculated relative to that of the normal shrimp under the NH condition at 0 h after the VP AHPND challenge. The bar graphs are the data from RT-qPCR presented as means ± standard deviations and the triangles (▲) are data from the RNA-Seq. Asterisks indicate significant difference ( P

Techniques Used: RNA Sequencing Assay, Quantitative RT-PCR, Expressing, Infection

Length distribution, abundance and composition of small RNA libraries of VP AHPND -infected and NLHS-treated L. vannamei hemocytes. ( A ) Length distribution and abundance of small RNAs from hemocytes of NLHS-treated L. vannamei infected with VP AHPND at 0 (0 mir-NLHS-VP), 6 (6 mir-NLHS-VP), and 24 (24 mir-NLHS-VP) hpi. ( B ) Composition of RNAs in each small RNA-Seq library.
Figure Legend Snippet: Length distribution, abundance and composition of small RNA libraries of VP AHPND -infected and NLHS-treated L. vannamei hemocytes. ( A ) Length distribution and abundance of small RNAs from hemocytes of NLHS-treated L. vannamei infected with VP AHPND at 0 (0 mir-NLHS-VP), 6 (6 mir-NLHS-VP), and 24 (24 mir-NLHS-VP) hpi. ( B ) Composition of RNAs in each small RNA-Seq library.

Techniques Used: Infection, RNA Sequencing Assay

Relative expression analysis of miRNAs in response to VP AHPND infection following the NLHS and NH treatments in L. vannamei hemocyte. Total small RNAs from hemocyte of VP AHPND -infected L. vannamei under NH- and NLHS-treated conditions which are NH-VP and NLHS-VP, respectively, were used as templates for specific stem-loop first strand cDNA synthesis. Relative expression levels of 10 miRNAs (lva-miR-7170-5p, lva-miR-2169-3p, lva-miR-184, lva-miR-92b-5p, lva-miR-317, lva-miR-4901, lva-miR-92a-3p, lva-miR-61, lva-miR-2898, and lva-miR-6090) were determined by RT-qPCR and normalized against U6, the internal reference, at 0, 6, and 24 hpi. The bar graphs are data from RT-qPCR presented as means ± standard deviations and triangles (▲) are data from the small RNA-Seq. The results were derived from triplicate experiments. Asterisks indicate significant differences ( P
Figure Legend Snippet: Relative expression analysis of miRNAs in response to VP AHPND infection following the NLHS and NH treatments in L. vannamei hemocyte. Total small RNAs from hemocyte of VP AHPND -infected L. vannamei under NH- and NLHS-treated conditions which are NH-VP and NLHS-VP, respectively, were used as templates for specific stem-loop first strand cDNA synthesis. Relative expression levels of 10 miRNAs (lva-miR-7170-5p, lva-miR-2169-3p, lva-miR-184, lva-miR-92b-5p, lva-miR-317, lva-miR-4901, lva-miR-92a-3p, lva-miR-61, lva-miR-2898, and lva-miR-6090) were determined by RT-qPCR and normalized against U6, the internal reference, at 0, 6, and 24 hpi. The bar graphs are data from RT-qPCR presented as means ± standard deviations and triangles (▲) are data from the small RNA-Seq. The results were derived from triplicate experiments. Asterisks indicate significant differences ( P

Techniques Used: Expressing, Infection, Quantitative RT-PCR, RNA Sequencing Assay, Derivative Assay

Network analysis for miRNA/mRNA interaction. ( A ) The Venn diagrams represent number of unique and common L. vannamei mRNAs and miRNAs that are expressed in response to VP AHPND infection and following the NLHS and NH treatments. The differentially expressed genes (DEGs) and miRNAs (DEMs) were identified based on RNA-Seq data and small RNA-Seq data, respectively. The number of DEGs or DEMs identified through the normalization of genes or miRNAs expressed by L. vannamei hemocytes infected with VP AHPND (NLHS-VP) at 6 and 24 hpi (6 NLHS-VP and 24 NLHS-VP) to that of 0 hpi (0 NLHS-VP) is shown in the red circle. The blue circle represents the number of DEGs or DEMs derived from VP AHPND -infected L. vannamei hemocyte controls (NH-VP) at 6 and 24 hpi (6 NH-VP and 24 NH-VP) with that of 0 hpi (0 NH-VP). ( B ) The miRNA/mRNA negative correlation network based on the predicted miRNA target function. The upper panel is the miRNA/mRNA network of up-regulated miRNAs (blue circle) and down-regulated genes (yellow circle), whereas the lower panel is the miRNA/mRNA network of down-regulated miRNAs (yellow circle) and up-regulated genes (blue circle). ( C ) The miRNA/mRNA negative correlation network of selected unique isotigs. The upper panel is the miRNA/mRNA network of up-regulated miRNAs (blue circle) and down-regulated genes (yellow circle), whereas the lower panel is the miRNA/mRNA network of down-regulated miRNAs (yellow circle) and up-regulated genes (blue circle). The circular nodes represent mRNAs and miRNAs. The degree of connectivity, which represents the number of genes regulated by a given miRNA, is indicated by the size of the node.
Figure Legend Snippet: Network analysis for miRNA/mRNA interaction. ( A ) The Venn diagrams represent number of unique and common L. vannamei mRNAs and miRNAs that are expressed in response to VP AHPND infection and following the NLHS and NH treatments. The differentially expressed genes (DEGs) and miRNAs (DEMs) were identified based on RNA-Seq data and small RNA-Seq data, respectively. The number of DEGs or DEMs identified through the normalization of genes or miRNAs expressed by L. vannamei hemocytes infected with VP AHPND (NLHS-VP) at 6 and 24 hpi (6 NLHS-VP and 24 NLHS-VP) to that of 0 hpi (0 NLHS-VP) is shown in the red circle. The blue circle represents the number of DEGs or DEMs derived from VP AHPND -infected L. vannamei hemocyte controls (NH-VP) at 6 and 24 hpi (6 NH-VP and 24 NH-VP) with that of 0 hpi (0 NH-VP). ( B ) The miRNA/mRNA negative correlation network based on the predicted miRNA target function. The upper panel is the miRNA/mRNA network of up-regulated miRNAs (blue circle) and down-regulated genes (yellow circle), whereas the lower panel is the miRNA/mRNA network of down-regulated miRNAs (yellow circle) and up-regulated genes (blue circle). ( C ) The miRNA/mRNA negative correlation network of selected unique isotigs. The upper panel is the miRNA/mRNA network of up-regulated miRNAs (blue circle) and down-regulated genes (yellow circle), whereas the lower panel is the miRNA/mRNA network of down-regulated miRNAs (yellow circle) and up-regulated genes (blue circle). The circular nodes represent mRNAs and miRNAs. The degree of connectivity, which represents the number of genes regulated by a given miRNA, is indicated by the size of the node.

Techniques Used: Infection, RNA Sequencing Assay, Derivative Assay

30) Product Images from "Sequence conservation of mitochondrial (mt)DNA during expansion of clonal mammary epithelial populations suggests a common mtDNA template in CzechII mice"

Article Title: Sequence conservation of mitochondrial (mt)DNA during expansion of clonal mammary epithelial populations suggests a common mtDNA template in CzechII mice

Journal: Oncotarget

doi: 10.18632/oncotarget.27429

CzechII mammary CZN5 tumor 1 mtDNA SNP variant calling via Next Generation sequencing. Next Generation sequencing was performed on CZN5 tumor 1 that arose from a CzechII CZN5 hyperplasia. SNP variant calling was performed to analyze common somatic SNPs that were conserved across CZN5 hyperplasia and tumor outgrowth, in comparison to CzechII lactating mammary gland control.
Figure Legend Snippet: CzechII mammary CZN5 tumor 1 mtDNA SNP variant calling via Next Generation sequencing. Next Generation sequencing was performed on CZN5 tumor 1 that arose from a CzechII CZN5 hyperplasia. SNP variant calling was performed to analyze common somatic SNPs that were conserved across CZN5 hyperplasia and tumor outgrowth, in comparison to CzechII lactating mammary gland control.

Techniques Used: Variant Assay, Next-Generation Sequencing

CzechII mammary R12 tumor mtDNA SNP variant calling via Next Generation sequencing. Next generation sequencing was performed on R12 mammary tumor and the tumors from 7 serially transplanted CzechII mammary R12 tumor fragments, SNP variant calling was performed to analyze common somatic SNPs that were conserved across R12 tumor fragments in comparison to CzechII Lactating mammary gland, negative control and CzechII Primary R12, positive control. Samples ran in duplicates.
Figure Legend Snippet: CzechII mammary R12 tumor mtDNA SNP variant calling via Next Generation sequencing. Next generation sequencing was performed on R12 mammary tumor and the tumors from 7 serially transplanted CzechII mammary R12 tumor fragments, SNP variant calling was performed to analyze common somatic SNPs that were conserved across R12 tumor fragments in comparison to CzechII Lactating mammary gland, negative control and CzechII Primary R12, positive control. Samples ran in duplicates.

Techniques Used: Variant Assay, Next-Generation Sequencing, Negative Control, Positive Control

CzechII mammary R12 metastatic tumor mtDNA SNP variant calling via Next Generation sequencing. Next generation sequencing was performed on R12 mammary tumor and 5 CzechII mammary R12 serially transplanted metastatic tumor fragments from R12 Tumor, SNP variant calling was performed to analyze common somatic SNPs that were conserved across R12 metastatic tumor fragments in comparison to CzechII Lactating mammary gland, negative control and CzechII Primary R12, positive control.
Figure Legend Snippet: CzechII mammary R12 metastatic tumor mtDNA SNP variant calling via Next Generation sequencing. Next generation sequencing was performed on R12 mammary tumor and 5 CzechII mammary R12 serially transplanted metastatic tumor fragments from R12 Tumor, SNP variant calling was performed to analyze common somatic SNPs that were conserved across R12 metastatic tumor fragments in comparison to CzechII Lactating mammary gland, negative control and CzechII Primary R12, positive control.

Techniques Used: Variant Assay, Next-Generation Sequencing, Negative Control, Positive Control

CzechII mammary CZN5 tumor 2 mtDNA SNP variant calling via Next Generation sequencing. Next Generation sequencing was performed on CZN5 tumor 2 that arose from a CzechII CZN5 hyperplasia. SNP variant calling was performed to analyze common somatic SNPs that were conserved across CZN5 hyperplasia and tumor outgrowth, in comparison to CzechII lactating mammary gland control.
Figure Legend Snippet: CzechII mammary CZN5 tumor 2 mtDNA SNP variant calling via Next Generation sequencing. Next Generation sequencing was performed on CZN5 tumor 2 that arose from a CzechII CZN5 hyperplasia. SNP variant calling was performed to analyze common somatic SNPs that were conserved across CZN5 hyperplasia and tumor outgrowth, in comparison to CzechII lactating mammary gland control.

Techniques Used: Variant Assay, Next-Generation Sequencing

31) Product Images from "Global DNA methylation synergistically regulates the nuclear and mitochondrial genomes in glioblastoma cells"

Article Title: Global DNA methylation synergistically regulates the nuclear and mitochondrial genomes in glioblastoma cells

Journal: Nucleic Acids Research

doi: 10.1093/nar/gky339

Levels of DNA demethylation in the mitochondrial genome and expression levels of the mtDNA encoded genes. ( A ) Detection of nDNA contamination after mtDNA purification. Copy number for nDNA (β-globin) relative to the copy number for mtDNA was plotted. Multiple t-tests were performed between the purified mtDNA samples (gray bars) and corresponding total DNA samples (black bars) isolated from GBM cells. Bars represent the mean ± SEM ( n = 3). ( B ) Agarose gel showing long-PCR products from purified mtDNA samples to demonstrate the presence of mtDNA methylation. Long PCR using two pairs of primers (primer set 2) spanning across the mitochondrial genome was performed on: (i) total DNA isolated from non-treated GBM cells (Total DNA); (ii) the positive control - MeDIP performed on the long PCR products previously generated with primer set 1 and treated with the M.SssI enzyme (Pos-longPCR-5mC); (iii) purified whole mtDNA having undergone MeDIP - (mtDNA-5mC); (iv) the negative control - MeDIP performed on the long PCR products previously generated with primer set 1 (Neg-longPCR-5mC); (v) the non-antibody control for MeDIP – MeDIP was performed on the purified mtDNA without using the 5mC antibody (mtDNA-NAC); and (vi) a non-template control (H 2 O) for PCR (NTC). Fragment sizes are indicated. ( C ) Normalized levels of DNA methylation within regions of the mitochondrial genome determined on purified mtDNA samples that had undergone MeDIP with sonication. The normalized levels of DNA methylation were determined by normalizing the MeDIP results (5mC/Input) against the positive and negative controls that were used to represent 100% and 0% of DNA methylation, respectively. Statistical significance was determined between the 5Aza and VitC treated cohorts and the GBM cohort and between ND5 and all the other genes by two-way ANOVA ( n = 3). ( D ) Significant differential expression of the mitochondrial and chromosomal genes encoding the ETC subunits identified in the treated and non-treated cohorts using the Fluidigm array. Bars represent the mean of the relative quantification levels normalized to the GBM cohort ( n = 6). *, **, ***, **** indicate P values of
Figure Legend Snippet: Levels of DNA demethylation in the mitochondrial genome and expression levels of the mtDNA encoded genes. ( A ) Detection of nDNA contamination after mtDNA purification. Copy number for nDNA (β-globin) relative to the copy number for mtDNA was plotted. Multiple t-tests were performed between the purified mtDNA samples (gray bars) and corresponding total DNA samples (black bars) isolated from GBM cells. Bars represent the mean ± SEM ( n = 3). ( B ) Agarose gel showing long-PCR products from purified mtDNA samples to demonstrate the presence of mtDNA methylation. Long PCR using two pairs of primers (primer set 2) spanning across the mitochondrial genome was performed on: (i) total DNA isolated from non-treated GBM cells (Total DNA); (ii) the positive control - MeDIP performed on the long PCR products previously generated with primer set 1 and treated with the M.SssI enzyme (Pos-longPCR-5mC); (iii) purified whole mtDNA having undergone MeDIP - (mtDNA-5mC); (iv) the negative control - MeDIP performed on the long PCR products previously generated with primer set 1 (Neg-longPCR-5mC); (v) the non-antibody control for MeDIP – MeDIP was performed on the purified mtDNA without using the 5mC antibody (mtDNA-NAC); and (vi) a non-template control (H 2 O) for PCR (NTC). Fragment sizes are indicated. ( C ) Normalized levels of DNA methylation within regions of the mitochondrial genome determined on purified mtDNA samples that had undergone MeDIP with sonication. The normalized levels of DNA methylation were determined by normalizing the MeDIP results (5mC/Input) against the positive and negative controls that were used to represent 100% and 0% of DNA methylation, respectively. Statistical significance was determined between the 5Aza and VitC treated cohorts and the GBM cohort and between ND5 and all the other genes by two-way ANOVA ( n = 3). ( D ) Significant differential expression of the mitochondrial and chromosomal genes encoding the ETC subunits identified in the treated and non-treated cohorts using the Fluidigm array. Bars represent the mean of the relative quantification levels normalized to the GBM cohort ( n = 6). *, **, ***, **** indicate P values of

Techniques Used: Expressing, Purification, Isolation, Agarose Gel Electrophoresis, Polymerase Chain Reaction, Methylation, Positive Control, Methylated DNA Immunoprecipitation, Generated, Negative Control, DNA Methylation Assay, Sonication

32) Product Images from "STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods"

Article Title: STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods

Journal: Scientific Reports

doi: 10.1038/s41598-017-17525-5

Experimental design. Cells from the Loucy cell line were preserved for 24 hours in Cell-Free DNA BCT reagent. Samples consisting of 1- or 3-cells were isolated from this fixed cell suspension for each WGA method. Ampli1, DOPlify, PicoPLEX and REPLI-g were used for amplification, followed by Illumina PCR-Free library preparation and next generation sequencing. In parallel, STR-PCR and capillary electrophoresis was performed on all samples, including a bulk sample from the cell line.
Figure Legend Snippet: Experimental design. Cells from the Loucy cell line were preserved for 24 hours in Cell-Free DNA BCT reagent. Samples consisting of 1- or 3-cells were isolated from this fixed cell suspension for each WGA method. Ampli1, DOPlify, PicoPLEX and REPLI-g were used for amplification, followed by Illumina PCR-Free library preparation and next generation sequencing. In parallel, STR-PCR and capillary electrophoresis was performed on all samples, including a bulk sample from the cell line.

Techniques Used: Isolation, Whole Genome Amplification, Amplification, Polymerase Chain Reaction, Next-Generation Sequencing, Electrophoresis

33) Product Images from "Genes adapt to outsmart gene-targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation"

Article Title: Genes adapt to outsmart gene-targeting strategies in mutant mouse strains by skipping exons to reinitiate transcription and translation

Journal: Genome Biology

doi: 10.1186/s13059-020-02086-0

Targeted KO-first targeting strategy in Rhbdf1 (A3) generates novel transcripts and N-terminally truncated functional proteins. a Schematic of the strategy used by Li et al. for generation of Rhbdf1 −/− homozygous mutant mice; the Rhbdf1 KO-first allele was crossed to Cre transgenic mice to excise the floxed gene segment (exons 4–11), generating Rhbdf1 −/− homozygous mutant mice (hereafter referred as viable2 mice, Rhbdf1 v2/v2 mice). b Whole-exome sequencing of spleen tissue from Rhbdf1 v2/v2 mice showing loss of exons 4 through 11 in Rhbdf1 v2/v2 mutant mice. c RT-PCR on spleens from Rhbdf1 +/+ and Rhbdf1 v2/v2 mutant mice using primers to amplify exons 6 through 8, exons 7 through 10, and exons 16 and 17. Exons 4–11 are deleted in Rhbdf1 v2/v2 mutant mice; hence, no amplicons were generated using either exon 6 forward and exon 8 reverse, or exon 7 forward and exon 10 reverse, primers. However, exon 16 forward and exon 17 reverse primers generated a 211-bp product. d RNA-Seq analysis of spleens from Rhbdf1 v2/v2 mutant mice indicating loss of exons 4 through 11; however, there is strong evidence for mutant mRNA, as indicated by the presence of the rest of the transcript, which encodes exons 12 through 18 and is not degraded by the nonsense-mediated decay mechanism. e Schematic representation of exons and introns in the Rhbdf1 v2/v2 mutant allele. 5′ RACE using a gene-specific exon 16–17 fusion primer (GSP) was used to obtain 5′ ends of the Rhbdf1 v2/v2 mutant mRNA. We identified several novel mutant mRNAs with different translation initiation sites that could potentially generate N-terminally truncated RHBDF1 mutant proteins. See supplemental figures for variant protein and 5′ UTR sequences. Alternative exons are indicated as red boxes; predicted translation initiation sites are indicated by “START,” and termination codons are indicated by “STOP.” f C-terminal Myc-DDK-tagged Rhbdf1 v2/v2 variant protein 1 (lanes 1, 2) or variant protein 2 (lanes 3,4), or empty vector (lanes 5, 6) were transiently expressed in 293T cells, and cell lysates were analyzed using western blotting with FLAG-specific antibody. After visualization of blots with a G:Box chemiluminescent imaging system, blots were washed, blocked in 5% nonfat dry milk, and re-probed with anti-actin antibody. g Rescue of phenotype in Rhbdf1 −/− MEFs. Rhbdf1 +/+ (top) and Rhbdf1 −/− (bottom) MEFs were transiently transfected with 2 μg of either variant 1 or variant 2 vectors, or an empty vector, using Lipofectamine LTX. Forty-eight hours post-transfection, cells were stimulated overnight with either DMSO or 100 nM PMA, and cell culture supernatants were analyzed using a mouse AREG ELISA kit. Data represent mean ± S.D.; * p
Figure Legend Snippet: Targeted KO-first targeting strategy in Rhbdf1 (A3) generates novel transcripts and N-terminally truncated functional proteins. a Schematic of the strategy used by Li et al. for generation of Rhbdf1 −/− homozygous mutant mice; the Rhbdf1 KO-first allele was crossed to Cre transgenic mice to excise the floxed gene segment (exons 4–11), generating Rhbdf1 −/− homozygous mutant mice (hereafter referred as viable2 mice, Rhbdf1 v2/v2 mice). b Whole-exome sequencing of spleen tissue from Rhbdf1 v2/v2 mice showing loss of exons 4 through 11 in Rhbdf1 v2/v2 mutant mice. c RT-PCR on spleens from Rhbdf1 +/+ and Rhbdf1 v2/v2 mutant mice using primers to amplify exons 6 through 8, exons 7 through 10, and exons 16 and 17. Exons 4–11 are deleted in Rhbdf1 v2/v2 mutant mice; hence, no amplicons were generated using either exon 6 forward and exon 8 reverse, or exon 7 forward and exon 10 reverse, primers. However, exon 16 forward and exon 17 reverse primers generated a 211-bp product. d RNA-Seq analysis of spleens from Rhbdf1 v2/v2 mutant mice indicating loss of exons 4 through 11; however, there is strong evidence for mutant mRNA, as indicated by the presence of the rest of the transcript, which encodes exons 12 through 18 and is not degraded by the nonsense-mediated decay mechanism. e Schematic representation of exons and introns in the Rhbdf1 v2/v2 mutant allele. 5′ RACE using a gene-specific exon 16–17 fusion primer (GSP) was used to obtain 5′ ends of the Rhbdf1 v2/v2 mutant mRNA. We identified several novel mutant mRNAs with different translation initiation sites that could potentially generate N-terminally truncated RHBDF1 mutant proteins. See supplemental figures for variant protein and 5′ UTR sequences. Alternative exons are indicated as red boxes; predicted translation initiation sites are indicated by “START,” and termination codons are indicated by “STOP.” f C-terminal Myc-DDK-tagged Rhbdf1 v2/v2 variant protein 1 (lanes 1, 2) or variant protein 2 (lanes 3,4), or empty vector (lanes 5, 6) were transiently expressed in 293T cells, and cell lysates were analyzed using western blotting with FLAG-specific antibody. After visualization of blots with a G:Box chemiluminescent imaging system, blots were washed, blocked in 5% nonfat dry milk, and re-probed with anti-actin antibody. g Rescue of phenotype in Rhbdf1 −/− MEFs. Rhbdf1 +/+ (top) and Rhbdf1 −/− (bottom) MEFs were transiently transfected with 2 μg of either variant 1 or variant 2 vectors, or an empty vector, using Lipofectamine LTX. Forty-eight hours post-transfection, cells were stimulated overnight with either DMSO or 100 nM PMA, and cell culture supernatants were analyzed using a mouse AREG ELISA kit. Data represent mean ± S.D.; * p

Techniques Used: Functional Assay, Mutagenesis, Mouse Assay, Transgenic Assay, Sequencing, Reverse Transcription Polymerase Chain Reaction, Generated, RNA Sequencing Assay, Variant Assay, Plasmid Preparation, Western Blot, Imaging, Transfection, Cell Culture, Enzyme-linked Immunosorbent Assay

34) Product Images from "An efficient ORF selection system for DNA fragment libraries based on split beta-lactamase complementation"

Article Title: An efficient ORF selection system for DNA fragment libraries based on split beta-lactamase complementation

Journal: PLoS ONE

doi: 10.1371/journal.pone.0235853

Analysis of ORF selection efficiency by high-throughput sequencing of MTBLIB42 before and after ORF selection. MTBLIB42C01 and MTBLIB42C02 libraries were sequenced using MiSeq Nano v2 chemistry for 2 x 250 cycles. A total of 5,66,496 (MTBLIB42C01) and 5,38,956 (MTBLIB42C02) merged reads aligned to 30 M . tuberculosis genes, and the data was further analyzed using in-house developed pipeline, ORFSELECT. (A) MTBLIB42C01, (B) MTBLIB42C02. However, the number of in-frame clones in the unselected library was higher (42.3%) as compared to the theoretically possible number (5.6%; 1 in 18 is in-frame). To further investigate this difference, we generated overlapping 30 gene fragment libraries of 100 bp, 200 bp and 300 bp fragment size with 1 bp incremental shift in silico and determined the number of theoretically possible in-frame clones. The number of in-frame clones for theoretical 30 gene fragment libraries of size 100 bp, 200 bp and 300 bp was found to 58.3%, 40.9%, and 31.9%, respectively, which corroborated with our experimental findings.
Figure Legend Snippet: Analysis of ORF selection efficiency by high-throughput sequencing of MTBLIB42 before and after ORF selection. MTBLIB42C01 and MTBLIB42C02 libraries were sequenced using MiSeq Nano v2 chemistry for 2 x 250 cycles. A total of 5,66,496 (MTBLIB42C01) and 5,38,956 (MTBLIB42C02) merged reads aligned to 30 M . tuberculosis genes, and the data was further analyzed using in-house developed pipeline, ORFSELECT. (A) MTBLIB42C01, (B) MTBLIB42C02. However, the number of in-frame clones in the unselected library was higher (42.3%) as compared to the theoretically possible number (5.6%; 1 in 18 is in-frame). To further investigate this difference, we generated overlapping 30 gene fragment libraries of 100 bp, 200 bp and 300 bp fragment size with 1 bp incremental shift in silico and determined the number of theoretically possible in-frame clones. The number of in-frame clones for theoretical 30 gene fragment libraries of size 100 bp, 200 bp and 300 bp was found to 58.3%, 40.9%, and 31.9%, respectively, which corroborated with our experimental findings.

Techniques Used: Selection, Next-Generation Sequencing, Clone Assay, Generated, In Silico

Expression and solubility analysis of 12 randomly selected clones obtained after transfer of MTBLIB42C02 in pVMH10D-BAP001 expression vector. Twelve randomly selected clones were subjected to auto-induction at 18°C and solubility was assayed using PopCulture assay. The total cell (T) and soluble (S) fractions were analyzed using SDS-PAGE.
Figure Legend Snippet: Expression and solubility analysis of 12 randomly selected clones obtained after transfer of MTBLIB42C02 in pVMH10D-BAP001 expression vector. Twelve randomly selected clones were subjected to auto-induction at 18°C and solubility was assayed using PopCulture assay. The total cell (T) and soluble (S) fractions were analyzed using SDS-PAGE.

Techniques Used: Expressing, Solubility, Clone Assay, Plasmid Preparation, SDS Page

35) Product Images from "The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency"

Article Title: The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency

Journal: Nature

doi: 10.1038/nature25784

Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.
Figure Legend Snippet: Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.

Techniques Used: Functional Assay, Real-time Polymerase Chain Reaction, Cell Culture, Expressing, Negative Control, shRNA, Western Blot, Methylation, Immunoprecipitation, Dot Blot, Immunofluorescence, Staining, Flow Cytometry, Cytometry, Fluorescence, Negative Staining

Monitoring the changes in m6A deposition rapidly induced by Activin/Nodal inhibition. ( a-b ) m6A methylated RNA immunoprecipitation (MeRIP) qPCR results from purified mRNA, total cellular RNA, or cellular RNA species separated following nuclear/cytoplasmic subcellular fractionation. hESCs were cultured in pluripotency-maintaining conditions containing Activin, or subjected to Activin/Nodal inhibition for 2h with SB-431542 (SB). IgG MeRIP experiments were performed as negative controls. The mean is indicated, n=2 technical replicates. Differences between Activin and SB-treated cells were observed only in the nuclear-enriched fraction. Therefore, the nuclear-enriched MeRIP protocol (NeMeRIP) was used for subsequent experiments (refer to the Supplementary Discussion ). Results are representative of two independent experiments. ( c ) Overlap with the indicated genomic features of m6A peaks identified by NeMeRIP-seq using two different bioinformatics pipelines in which peak calling was performed using MetDiff or MACS2. For each pipeline, the analyses were performed on the union of peaks identified from data obtained in hESCs cultured in presence of Activin or subjected to Activin/Nodal inhibition for 2h with SB (n=3 cultures). Note that the sum of the percentages within each graph does not add to 100% because some m6A peaks overlap several feature types. MetDiff is an exome peak caller, and accordingly 100% of peaks map to exons. MACS2 identifies peaks throughout the genome. ( d ) Venn diagrams showing the overlap of peaks identified by the two pipelines. Only MetDiff peaks that were also identified MACS2 were considered for subsequent analyses focused on m6A peaks on exons. ( e ) Top sequence motifs identified de novo on all m6A exon peaks, or on such peaks that showed significant downregulation following Activin/Nodal inhibition (Activin/Nodal-sensitive m6A peaks; Supplementary Table 2 ). The position of the methylated adenosine is indicated by a box. ( f ) Coverage profiles for all m6A exon peaks across the length of different genomic features. Each feature type is expressed as 100 bins of equal length with 5’ to 3’ directionality. ( g-h ) Overlap of m6A exon peaks to transcription start sites (TSS) or transcription end sites (TES). In g, the analysis was performed for all m6A peaks. In h, only Activin/Nodal-sensitive peaks were considered. ( i ) On the left, Activin/Nodal-sensitive m6A exon peaks were evaluated for direct overlap with SMAD2/3 binding sites measured by ChIP-seq 30 . n=482 peaks; FDR=0.41 (non-significant at 95% confidence interval, N.S.) as calculated by the permutation test implemented by the GAT python package. On the right, overlap was calculated after the same features were mapped to their corresponding transcripts or genes, respectively. A significant overlap was observed for the transcript-gene overlap. n=372 genes; hypergeometric test p-value (p) of 2.88E-18, significant at 95% confidence interval. ( j ) m6A NeMeRIP-seq results for selected transcripts (n=3 cultures; replicates combined for visualization). Coverage tracks represent read-enrichments normalized by million mapped reads and size of the library. Blue: sequencing results of m6A NeMeRIP. Orange: sequencing results of pre-NeMeRIP input RNA (negative control). GENCODE gene annotations are shown (red: protein coding exons; white: untranslated exons; note that all potential exons are shown and overlaid). The location of SMAD2/3 ChIP-seq binding sites is also reported. Compared to the other genes shown, the m6A levels on SOX2 were unaffected by Activin/Nodal inhibition, showing specificity of action. OCT4/POU5F1 is reported as negative control since it is known not to have any m6A site 23 , as confirmed by the lack of m6A enrichment compared to the input.
Figure Legend Snippet: Monitoring the changes in m6A deposition rapidly induced by Activin/Nodal inhibition. ( a-b ) m6A methylated RNA immunoprecipitation (MeRIP) qPCR results from purified mRNA, total cellular RNA, or cellular RNA species separated following nuclear/cytoplasmic subcellular fractionation. hESCs were cultured in pluripotency-maintaining conditions containing Activin, or subjected to Activin/Nodal inhibition for 2h with SB-431542 (SB). IgG MeRIP experiments were performed as negative controls. The mean is indicated, n=2 technical replicates. Differences between Activin and SB-treated cells were observed only in the nuclear-enriched fraction. Therefore, the nuclear-enriched MeRIP protocol (NeMeRIP) was used for subsequent experiments (refer to the Supplementary Discussion ). Results are representative of two independent experiments. ( c ) Overlap with the indicated genomic features of m6A peaks identified by NeMeRIP-seq using two different bioinformatics pipelines in which peak calling was performed using MetDiff or MACS2. For each pipeline, the analyses were performed on the union of peaks identified from data obtained in hESCs cultured in presence of Activin or subjected to Activin/Nodal inhibition for 2h with SB (n=3 cultures). Note that the sum of the percentages within each graph does not add to 100% because some m6A peaks overlap several feature types. MetDiff is an exome peak caller, and accordingly 100% of peaks map to exons. MACS2 identifies peaks throughout the genome. ( d ) Venn diagrams showing the overlap of peaks identified by the two pipelines. Only MetDiff peaks that were also identified MACS2 were considered for subsequent analyses focused on m6A peaks on exons. ( e ) Top sequence motifs identified de novo on all m6A exon peaks, or on such peaks that showed significant downregulation following Activin/Nodal inhibition (Activin/Nodal-sensitive m6A peaks; Supplementary Table 2 ). The position of the methylated adenosine is indicated by a box. ( f ) Coverage profiles for all m6A exon peaks across the length of different genomic features. Each feature type is expressed as 100 bins of equal length with 5’ to 3’ directionality. ( g-h ) Overlap of m6A exon peaks to transcription start sites (TSS) or transcription end sites (TES). In g, the analysis was performed for all m6A peaks. In h, only Activin/Nodal-sensitive peaks were considered. ( i ) On the left, Activin/Nodal-sensitive m6A exon peaks were evaluated for direct overlap with SMAD2/3 binding sites measured by ChIP-seq 30 . n=482 peaks; FDR=0.41 (non-significant at 95% confidence interval, N.S.) as calculated by the permutation test implemented by the GAT python package. On the right, overlap was calculated after the same features were mapped to their corresponding transcripts or genes, respectively. A significant overlap was observed for the transcript-gene overlap. n=372 genes; hypergeometric test p-value (p) of 2.88E-18, significant at 95% confidence interval. ( j ) m6A NeMeRIP-seq results for selected transcripts (n=3 cultures; replicates combined for visualization). Coverage tracks represent read-enrichments normalized by million mapped reads and size of the library. Blue: sequencing results of m6A NeMeRIP. Orange: sequencing results of pre-NeMeRIP input RNA (negative control). GENCODE gene annotations are shown (red: protein coding exons; white: untranslated exons; note that all potential exons are shown and overlaid). The location of SMAD2/3 ChIP-seq binding sites is also reported. Compared to the other genes shown, the m6A levels on SOX2 were unaffected by Activin/Nodal inhibition, showing specificity of action. OCT4/POU5F1 is reported as negative control since it is known not to have any m6A site 23 , as confirmed by the lack of m6A enrichment compared to the input.

Techniques Used: Inhibition, Methylation, Immunoprecipitation, Real-time Polymerase Chain Reaction, Purification, Fractionation, Cell Culture, Sequencing, Binding Assay, Chromatin Immunoprecipitation, Negative Control

36) Product Images from "Medium throughput bisulfite sequencing for accurate detection of 5-methylcytosine and 5-hydroxymethylcytosine"

Article Title: Medium throughput bisulfite sequencing for accurate detection of 5-methylcytosine and 5-hydroxymethylcytosine

Journal: BMC Genomics

doi: 10.1186/s12864-017-3489-9

Targeted BS-Seq performs to the same standard as Illumina’s NEXTERA XT kit. Three genomic regions located in the human OPRK1 gene were amplified by quantitative PCR and then used for library preparation with either (i) the commercially available, gold standard NEXTERA XT kit (Illumina) or (ii) our targeted BS-Seq approach. a In targeted BS-Seq, successive rounds of PCR are performed to amplify BS-DNA and add universal and reusable primers (CS1 and CS2), followed by adapters to the MiSeq flow cell (P5 and P7 + index). Gel electrophoresis was performed on Agilent’s Tape Station and shows the expected increases in size of PCR products for 2 replicates after each round of PCR. b–d Both NEXTERA XT and targeted BS-Seq yielded very high coverages throughout the amplicons of interest, with the exception of 4 CpG sites in NEXTERA XT results ( c, d ). Note that while the difference in coverage between the 2 methods only reflects the amount of material loaded onto the sequencer, the targeted BS-Seq method allows for a more even distribution of coverage along the amplicons and lower variability in coverage across biological samples. Values represent mean ± S.E.M. e NEXTERA XT (y-axis) and targeted BS-Seq (x-axis) measured very similar DNA modification levels at all CpGs interogated ( r 2 = 0.9947, p
Figure Legend Snippet: Targeted BS-Seq performs to the same standard as Illumina’s NEXTERA XT kit. Three genomic regions located in the human OPRK1 gene were amplified by quantitative PCR and then used for library preparation with either (i) the commercially available, gold standard NEXTERA XT kit (Illumina) or (ii) our targeted BS-Seq approach. a In targeted BS-Seq, successive rounds of PCR are performed to amplify BS-DNA and add universal and reusable primers (CS1 and CS2), followed by adapters to the MiSeq flow cell (P5 and P7 + index). Gel electrophoresis was performed on Agilent’s Tape Station and shows the expected increases in size of PCR products for 2 replicates after each round of PCR. b–d Both NEXTERA XT and targeted BS-Seq yielded very high coverages throughout the amplicons of interest, with the exception of 4 CpG sites in NEXTERA XT results ( c, d ). Note that while the difference in coverage between the 2 methods only reflects the amount of material loaded onto the sequencer, the targeted BS-Seq method allows for a more even distribution of coverage along the amplicons and lower variability in coverage across biological samples. Values represent mean ± S.E.M. e NEXTERA XT (y-axis) and targeted BS-Seq (x-axis) measured very similar DNA modification levels at all CpGs interogated ( r 2 = 0.9947, p

Techniques Used: Amplification, Real-time Polymerase Chain Reaction, Polymerase Chain Reaction, Flow Cytometry, Nucleic Acid Electrophoresis, Modification

37) Product Images from "The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency"

Article Title: The SMAD2/3 interactome reveals that TGFβ controls m6A mRNA methylation in pluripotency

Journal: Nature

doi: 10.1038/nature25784

Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.
Figure Legend Snippet: Generation and functional characterization of inducible knockdown hPSCs for the subunits of the m6A methyltransferase complex. ( a ) qPCR validation of tetracycline-inducible knockdown (iKD) hESCs cultured in presence of tetracycline (TET) for 5 days to drive gene knockdown. Two distinct shRNAs (sh) and multiple clonal sublines (cl) were tested for each gene. Expression is shown as normalized on the average level in hESCs carrying a negative control scrambled (SCR) shRNA. For each gene, sh1 cl1 was chosen for further analyses. The mean is indicated, n=2 cultures. ( b ) Western blot validation of selected iKD hESCs for the indicated genes. TUB4A4 (α-tubulin): loading control. Results are representative of three independent experiments. ( c ) m6A methylated RNA immunoprecipitation (MeRIP)-qPCR in iKD hESCs cultured for 10 days in absence (CTR) or presence of tetracycline (TET). m6A abundance is reported relative to control conditions in the same hESC line. The mean is indicated, n=2 technical replicates. Results are representative of two independent experiments. ( d ) m6A dot blot in WTAP or SCR iKD hESCs treated as described in panel c. Decreasing amounts of mRNA were spotted to facilitate semi-quantitative comparisons, as indicated. Results are representative of two independent experiments. ( e ) Immunofluorescence for the pluripotency markers NANOG and OCT4 in iKD hESCs cultured for three passages (15 days) in absence (CTR) or presence of tetracycline (TET). DAPI shows nuclear staining. Scale bars: 100μm. Results are representative of two independent experiments. ( f ) Flow cytometry quantifications for NANOG in cells treated as described for panel e. The percentage and median fluorescence intensity (MFI) of NANOG positive cells (NANOG+) are reported. The gates used for the analysis are shown, and were determined based on a secondary antibody only negative staining (NEG). Results are representative of two independent experiments.

Techniques Used: Functional Assay, Real-time Polymerase Chain Reaction, Cell Culture, Expressing, Negative Control, shRNA, Western Blot, Methylation, Immunoprecipitation, Dot Blot, Immunofluorescence, Staining, Flow Cytometry, Cytometry, Fluorescence, Negative Staining

Monitoring the changes in m6A deposition rapidly induced by Activin/Nodal inhibition. ( a-b ) m6A methylated RNA immunoprecipitation (MeRIP) qPCR results from purified mRNA, total cellular RNA, or cellular RNA species separated following nuclear/cytoplasmic subcellular fractionation. hESCs were cultured in pluripotency-maintaining conditions containing Activin, or subjected to Activin/Nodal inhibition for 2h with SB-431542 (SB). IgG MeRIP experiments were performed as negative controls. The mean is indicated, n=2 technical replicates. Differences between Activin and SB-treated cells were observed only in the nuclear-enriched fraction. Therefore, the nuclear-enriched MeRIP protocol (NeMeRIP) was used for subsequent experiments (refer to the Supplementary Discussion ). Results are representative of two independent experiments. ( c ) Overlap with the indicated genomic features of m6A peaks identified by NeMeRIP-seq using two different bioinformatics pipelines in which peak calling was performed using MetDiff or MACS2. For each pipeline, the analyses were performed on the union of peaks identified from data obtained in hESCs cultured in presence of Activin or subjected to Activin/Nodal inhibition for 2h with SB (n=3 cultures). Note that the sum of the percentages within each graph does not add to 100% because some m6A peaks overlap several feature types. MetDiff is an exome peak caller, and accordingly 100% of peaks map to exons. MACS2 identifies peaks throughout the genome. ( d ) Venn diagrams showing the overlap of peaks identified by the two pipelines. Only MetDiff peaks that were also identified MACS2 were considered for subsequent analyses focused on m6A peaks on exons. ( e ) Top sequence motifs identified de novo on all m6A exon peaks, or on such peaks that showed significant downregulation following Activin/Nodal inhibition (Activin/Nodal-sensitive m6A peaks; Supplementary Table 2 ). The position of the methylated adenosine is indicated by a box. ( f ) Coverage profiles for all m6A exon peaks across the length of different genomic features. Each feature type is expressed as 100 bins of equal length with 5’ to 3’ directionality. ( g-h ) Overlap of m6A exon peaks to transcription start sites (TSS) or transcription end sites (TES). In g, the analysis was performed for all m6A peaks. In h, only Activin/Nodal-sensitive peaks were considered. ( i ) On the left, Activin/Nodal-sensitive m6A exon peaks were evaluated for direct overlap with SMAD2/3 binding sites measured by ChIP-seq 30 . n=482 peaks; FDR=0.41 (non-significant at 95% confidence interval, N.S.) as calculated by the permutation test implemented by the GAT python package. On the right, overlap was calculated after the same features were mapped to their corresponding transcripts or genes, respectively. A significant overlap was observed for the transcript-gene overlap. n=372 genes; hypergeometric test p-value (p) of 2.88E-18, significant at 95% confidence interval. ( j ) m6A NeMeRIP-seq results for selected transcripts (n=3 cultures; replicates combined for visualization). Coverage tracks represent read-enrichments normalized by million mapped reads and size of the library. Blue: sequencing results of m6A NeMeRIP. Orange: sequencing results of pre-NeMeRIP input RNA (negative control). GENCODE gene annotations are shown (red: protein coding exons; white: untranslated exons; note that all potential exons are shown and overlaid). The location of SMAD2/3 ChIP-seq binding sites is also reported. Compared to the other genes shown, the m6A levels on SOX2 were unaffected by Activin/Nodal inhibition, showing specificity of action. OCT4/POU5F1 is reported as negative control since it is known not to have any m6A site 23 , as confirmed by the lack of m6A enrichment compared to the input.
Figure Legend Snippet: Monitoring the changes in m6A deposition rapidly induced by Activin/Nodal inhibition. ( a-b ) m6A methylated RNA immunoprecipitation (MeRIP) qPCR results from purified mRNA, total cellular RNA, or cellular RNA species separated following nuclear/cytoplasmic subcellular fractionation. hESCs were cultured in pluripotency-maintaining conditions containing Activin, or subjected to Activin/Nodal inhibition for 2h with SB-431542 (SB). IgG MeRIP experiments were performed as negative controls. The mean is indicated, n=2 technical replicates. Differences between Activin and SB-treated cells were observed only in the nuclear-enriched fraction. Therefore, the nuclear-enriched MeRIP protocol (NeMeRIP) was used for subsequent experiments (refer to the Supplementary Discussion ). Results are representative of two independent experiments. ( c ) Overlap with the indicated genomic features of m6A peaks identified by NeMeRIP-seq using two different bioinformatics pipelines in which peak calling was performed using MetDiff or MACS2. For each pipeline, the analyses were performed on the union of peaks identified from data obtained in hESCs cultured in presence of Activin or subjected to Activin/Nodal inhibition for 2h with SB (n=3 cultures). Note that the sum of the percentages within each graph does not add to 100% because some m6A peaks overlap several feature types. MetDiff is an exome peak caller, and accordingly 100% of peaks map to exons. MACS2 identifies peaks throughout the genome. ( d ) Venn diagrams showing the overlap of peaks identified by the two pipelines. Only MetDiff peaks that were also identified MACS2 were considered for subsequent analyses focused on m6A peaks on exons. ( e ) Top sequence motifs identified de novo on all m6A exon peaks, or on such peaks that showed significant downregulation following Activin/Nodal inhibition (Activin/Nodal-sensitive m6A peaks; Supplementary Table 2 ). The position of the methylated adenosine is indicated by a box. ( f ) Coverage profiles for all m6A exon peaks across the length of different genomic features. Each feature type is expressed as 100 bins of equal length with 5’ to 3’ directionality. ( g-h ) Overlap of m6A exon peaks to transcription start sites (TSS) or transcription end sites (TES). In g, the analysis was performed for all m6A peaks. In h, only Activin/Nodal-sensitive peaks were considered. ( i ) On the left, Activin/Nodal-sensitive m6A exon peaks were evaluated for direct overlap with SMAD2/3 binding sites measured by ChIP-seq 30 . n=482 peaks; FDR=0.41 (non-significant at 95% confidence interval, N.S.) as calculated by the permutation test implemented by the GAT python package. On the right, overlap was calculated after the same features were mapped to their corresponding transcripts or genes, respectively. A significant overlap was observed for the transcript-gene overlap. n=372 genes; hypergeometric test p-value (p) of 2.88E-18, significant at 95% confidence interval. ( j ) m6A NeMeRIP-seq results for selected transcripts (n=3 cultures; replicates combined for visualization). Coverage tracks represent read-enrichments normalized by million mapped reads and size of the library. Blue: sequencing results of m6A NeMeRIP. Orange: sequencing results of pre-NeMeRIP input RNA (negative control). GENCODE gene annotations are shown (red: protein coding exons; white: untranslated exons; note that all potential exons are shown and overlaid). The location of SMAD2/3 ChIP-seq binding sites is also reported. Compared to the other genes shown, the m6A levels on SOX2 were unaffected by Activin/Nodal inhibition, showing specificity of action. OCT4/POU5F1 is reported as negative control since it is known not to have any m6A site 23 , as confirmed by the lack of m6A enrichment compared to the input.

Techniques Used: Inhibition, Methylation, Immunoprecipitation, Real-time Polymerase Chain Reaction, Purification, Fractionation, Cell Culture, Sequencing, Binding Assay, Chromatin Immunoprecipitation, Negative Control

38) Product Images from "Comprehensive profiling of retroviral integration sites using target enrichment methods from historical koala samples without an assembled reference genome"

Article Title: Comprehensive profiling of retroviral integration sites using target enrichment methods from historical koala samples without an assembled reference genome

Journal: PeerJ

doi: 10.7717/peerj.1847

Bioinformatic pipeline for identification of KoRV integration sites. The pipeline was run separately for each data set obtained by three different techniques. For the key steps, the number of sequences retained is indicated in parentheses for each technique in this order from left to right: PEC, SPEX and hybridization capture. After processing NGS reads, KoRV integration sites were identified in a two-step analysis of KoRV LTR ends, next to the host DNA flanking KoRV. The first round of selection targeted the A region of the LTR end and its output, was used for subsequent identification of the B region. The LTR ends of all sequences were trimmed off, and only sequences longer than four bp were considered. Using a sequence clustering approach, unique vs. shared integration sites were sorted into clusters. The consensus of each non-singleton cluster was computed using a multiple sequence alignment. These consensus sequences and singleton sequences were queried against wallaby genomic scaffolds and koala Illumina Hiseq reads to determine whether they represented KoRV flanking sequences. At the same time extension products into the KoRV genome were identified.
Figure Legend Snippet: Bioinformatic pipeline for identification of KoRV integration sites. The pipeline was run separately for each data set obtained by three different techniques. For the key steps, the number of sequences retained is indicated in parentheses for each technique in this order from left to right: PEC, SPEX and hybridization capture. After processing NGS reads, KoRV integration sites were identified in a two-step analysis of KoRV LTR ends, next to the host DNA flanking KoRV. The first round of selection targeted the A region of the LTR end and its output, was used for subsequent identification of the B region. The LTR ends of all sequences were trimmed off, and only sequences longer than four bp were considered. Using a sequence clustering approach, unique vs. shared integration sites were sorted into clusters. The consensus of each non-singleton cluster was computed using a multiple sequence alignment. These consensus sequences and singleton sequences were queried against wallaby genomic scaffolds and koala Illumina Hiseq reads to determine whether they represented KoRV flanking sequences. At the same time extension products into the KoRV genome were identified.

Techniques Used: Hybridization, Next-Generation Sequencing, Selection, Sequencing

39) Product Images from "Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach"

Article Title: Diagnosis of Bacterial Bloodstream Infections: A 16S Metagenomics Approach

Journal: PLoS Neglected Tropical Diseases

doi: 10.1371/journal.pntd.0004470

Numbers of patients classified as confirmed bacterial bloodstream infection (bBSI) based on (A) blood culture alone and (B) combined data from blood culture and 16S metagenomics.
Figure Legend Snippet: Numbers of patients classified as confirmed bacterial bloodstream infection (bBSI) based on (A) blood culture alone and (B) combined data from blood culture and 16S metagenomics.

Techniques Used: Infection

40) Product Images from "Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis"

Article Title: Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Journal: Biology Direct

doi: 10.1186/s13062-018-0204-y

Post power-law correction, Pareto distributions and scatterplots of spike-in background and dilution datasets. a and b give the Pareto distribution plots of the scaled background counts from the spike-in background and NUGC3 dilution dataset respectively, after the power-law correction was applied. Both plots are segmented into the highest-count to lowest-count regions based on an order of magnitude per segment ( see vertical dotted lines across horizontal axis ). Both plots display a power-law distribution with a more uniform slope throughout all count segments. In fact, the power-law correction estimates how the true underlying distribution should have been without aliasing. Meanwhile, c and d show that the corrected count values exhibit less heteroskedasticity across all count segments and variation among the replicates with the increase in slope values after the power-law correction. Finally, the minimum count value of each replicate has increased such that the uncorrected count values previously ( See Fig. 2C and D ) in the low and lowest-count segment have now been moved into the mid-count segment
Figure Legend Snippet: Post power-law correction, Pareto distributions and scatterplots of spike-in background and dilution datasets. a and b give the Pareto distribution plots of the scaled background counts from the spike-in background and NUGC3 dilution dataset respectively, after the power-law correction was applied. Both plots are segmented into the highest-count to lowest-count regions based on an order of magnitude per segment ( see vertical dotted lines across horizontal axis ). Both plots display a power-law distribution with a more uniform slope throughout all count segments. In fact, the power-law correction estimates how the true underlying distribution should have been without aliasing. Meanwhile, c and d show that the corrected count values exhibit less heteroskedasticity across all count segments and variation among the replicates with the increase in slope values after the power-law correction. Finally, the minimum count value of each replicate has increased such that the uncorrected count values previously ( See Fig. 2C and D ) in the low and lowest-count segment have now been moved into the mid-count segment

Techniques Used:

MA-plots of dilution data set before and after power-law correction. Fig. 6 shows the MA-plots ( i.,e., average counts versus fold-changes ) of the dilution dataset before ( left-column ) and after ( right-column ) the power-law correction. In particular, Figs a , b , c and d shows the MA-plot analysis for 4 mapping ( Bowtie1, Bowtie2(global), Novoalign and BWA ) algorithms while the permutation of the 6 normalization algorithms ( DESeq, Relative Log Expression (RLE), Trimmed Mean of M-values (TMM), UpperQuartile (UQ), Count Per Million (CPM) and Quantile normalization ) are arranged in a row-wise manner. For the power-law correction, the optimum PPS setting was evaluated to be 55 ( See Additional file 6 : Fig. S5A). In each MA-plot, the positive and noise signal are shown in red and blue respectively. The noise model ( y = mx ) is shown in dotted lines; Ideally, the slope value is 0 for no bias. The signal and noise residuals with respect to the noise model give the fold-change variation along the average count axis ( or x-axis ). Overall, it is apparent that the heteroskedasticity ( see left-column ) of the uncorrected AGS and NUGC3 count values has propagated down to the level of comparative analysis regardless of any combination of mapping and normalization methods. However when power-law correction is applied, heteroskedasticity was dramatically minimized ( see right-column )
Figure Legend Snippet: MA-plots of dilution data set before and after power-law correction. Fig. 6 shows the MA-plots ( i.,e., average counts versus fold-changes ) of the dilution dataset before ( left-column ) and after ( right-column ) the power-law correction. In particular, Figs a , b , c and d shows the MA-plot analysis for 4 mapping ( Bowtie1, Bowtie2(global), Novoalign and BWA ) algorithms while the permutation of the 6 normalization algorithms ( DESeq, Relative Log Expression (RLE), Trimmed Mean of M-values (TMM), UpperQuartile (UQ), Count Per Million (CPM) and Quantile normalization ) are arranged in a row-wise manner. For the power-law correction, the optimum PPS setting was evaluated to be 55 ( See Additional file 6 : Fig. S5A). In each MA-plot, the positive and noise signal are shown in red and blue respectively. The noise model ( y = mx ) is shown in dotted lines; Ideally, the slope value is 0 for no bias. The signal and noise residuals with respect to the noise model give the fold-change variation along the average count axis ( or x-axis ). Overall, it is apparent that the heteroskedasticity ( see left-column ) of the uncorrected AGS and NUGC3 count values has propagated down to the level of comparative analysis regardless of any combination of mapping and normalization methods. However when power-law correction is applied, heteroskedasticity was dramatically minimized ( see right-column )

Techniques Used: Expressing

Pareto distributions and scatterplots of spike-in background and dilution datasets. a and b give the Pareto distribution plots of the scaled background counts from the spike-in background and NUGC3 dilution dataset respectively. Both plots are segmented into the highest-count to lowest-count regions based on an order of magnitude per segment ( see vertical dotted lines across horizontal axis ). Generally, Zipf’s law (i.e. , slope of − 1 ) holds well for the highest-count segments. c and d give the scatterplots of the highest sequencing depth replicate against the rest for the spike-in background and NUGC3 dilution dataset respectively. Both plots exhibit the hallmark of the Pareto’s mathematical moments where a change in variance is perpetuated by a change in the power-law exponent. The noise that plagued the low and lowest-count segments, serves to highlight the instability of the replicated count values when the corresponding power-law mathematical moments stem not only from low exponent values but of non-comparable magnitude as well
Figure Legend Snippet: Pareto distributions and scatterplots of spike-in background and dilution datasets. a and b give the Pareto distribution plots of the scaled background counts from the spike-in background and NUGC3 dilution dataset respectively. Both plots are segmented into the highest-count to lowest-count regions based on an order of magnitude per segment ( see vertical dotted lines across horizontal axis ). Generally, Zipf’s law (i.e. , slope of − 1 ) holds well for the highest-count segments. c and d give the scatterplots of the highest sequencing depth replicate against the rest for the spike-in background and NUGC3 dilution dataset respectively. Both plots exhibit the hallmark of the Pareto’s mathematical moments where a change in variance is perpetuated by a change in the power-law exponent. The noise that plagued the low and lowest-count segments, serves to highlight the instability of the replicated count values when the corresponding power-law mathematical moments stem not only from low exponent values but of non-comparable magnitude as well

Techniques Used: Sequencing

Post power-law correction, rank-frequency plots of NUGC3 dilution and spike-in background datasets. a , b , c , d and e show the rank-frequency plots for the 1.5p pair, 3p pair, 6p pair, single 12p replicate and the 11 UHR replicates against the best estimate of the original signals ( marked in black ) after the power-law correction. The observed alias noise ( marked in blue ) and the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), are also shown. In each subplot, the sampling frequency f s and the mean square error ( MSE is defined as the residual error between the observed and theoretical alias noise ) are given as well. The overall low MSE values of between 6.00e-4 to 1.87e-3 indicates a good fit between the theoretical model and the observed alias. Generally speaking, the corrected datasets shows a general absence of undersampling. For all plots, the observed alias noise ( marked in blue ), as well as the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), shows very slight aliasing in all cases given their new sampling frequencies of 1720, 1311, 1783, 3315 and 1920 respectively
Figure Legend Snippet: Post power-law correction, rank-frequency plots of NUGC3 dilution and spike-in background datasets. a , b , c , d and e show the rank-frequency plots for the 1.5p pair, 3p pair, 6p pair, single 12p replicate and the 11 UHR replicates against the best estimate of the original signals ( marked in black ) after the power-law correction. The observed alias noise ( marked in blue ) and the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), are also shown. In each subplot, the sampling frequency f s and the mean square error ( MSE is defined as the residual error between the observed and theoretical alias noise ) are given as well. The overall low MSE values of between 6.00e-4 to 1.87e-3 indicates a good fit between the theoretical model and the observed alias. Generally speaking, the corrected datasets shows a general absence of undersampling. For all plots, the observed alias noise ( marked in blue ), as well as the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), shows very slight aliasing in all cases given their new sampling frequencies of 1720, 1311, 1783, 3315 and 1920 respectively

Techniques Used: Sampling

Rank-frequency plots of NUGC3 dilution and spike-in background datasets. a , b , c , d and e show the rank-frequency plots for the 1.5p pair, 3p pair, 6p pair, single 12p replicate and the 11 UHR replicates against the best estimate of the original signals ( marked in black ). Meanwhile, the observed alias noise ( marked in blue ) and the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), are also shown. In each subplot, the sampling frequency f s and the mean square error ( MSE is defined as the residual error between the observed and theoretical alias noise ) are given as well. Overall, the low MSE values of between 5.67e-4 to 3.58e-3 indicates a good fit between the theoretical alias noise model and the observed alias datapoints. For the NUGC3 dilution set, the 1.5p, 3p, 6p replicates have failed to satisfy the Nyquist sampling criterion of f max
Figure Legend Snippet: Rank-frequency plots of NUGC3 dilution and spike-in background datasets. a , b , c , d and e show the rank-frequency plots for the 1.5p pair, 3p pair, 6p pair, single 12p replicate and the 11 UHR replicates against the best estimate of the original signals ( marked in black ). Meanwhile, the observed alias noise ( marked in blue ) and the theoretical alias noise S o ( f s − f ) − α ( marked in magenta ), are also shown. In each subplot, the sampling frequency f s and the mean square error ( MSE is defined as the residual error between the observed and theoretical alias noise ) are given as well. Overall, the low MSE values of between 5.67e-4 to 3.58e-3 indicates a good fit between the theoretical alias noise model and the observed alias datapoints. For the NUGC3 dilution set, the 1.5p, 3p, 6p replicates have failed to satisfy the Nyquist sampling criterion of f max

Techniques Used: Sampling

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