Structured Review

Illumina Inc scl seq
Association of 5hmCs with DNA motifs. a Logos of de novo motifs and transcription factor binding motifs retrieved by the SeqPos motif tool from Cistrome ( http://cistrome.dfci.harvard.edu ) in 100 bp sequences centered on <t>SCL-exo</t> id CpGs included in hMeDIP peaks. As a control, motif search was run on 100 bp sequences centered on randomly selected CpGs. For each logo, the associated p value and z-score are indicated. b , c CpG hydroxymethylation prefers RCGY to YCGR motifs. Average profiles of RCGY ( b ) and YCGR ( c ) motif densities (R = A or G, and Y = C or T) around 27,031 SCL-exo id CpGs with at least 30× coverage
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1) Product Images from "Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets"

Article Title: Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets

Journal: Genome Biology

doi: 10.1186/s13059-016-0919-y

Association of 5hmCs with DNA motifs. a Logos of de novo motifs and transcription factor binding motifs retrieved by the SeqPos motif tool from Cistrome ( http://cistrome.dfci.harvard.edu ) in 100 bp sequences centered on SCL-exo id CpGs included in hMeDIP peaks. As a control, motif search was run on 100 bp sequences centered on randomly selected CpGs. For each logo, the associated p value and z-score are indicated. b , c CpG hydroxymethylation prefers RCGY to YCGR motifs. Average profiles of RCGY ( b ) and YCGR ( c ) motif densities (R = A or G, and Y = C or T) around 27,031 SCL-exo id CpGs with at least 30× coverage
Figure Legend Snippet: Association of 5hmCs with DNA motifs. a Logos of de novo motifs and transcription factor binding motifs retrieved by the SeqPos motif tool from Cistrome ( http://cistrome.dfci.harvard.edu ) in 100 bp sequences centered on SCL-exo id CpGs included in hMeDIP peaks. As a control, motif search was run on 100 bp sequences centered on randomly selected CpGs. For each logo, the associated p value and z-score are indicated. b , c CpG hydroxymethylation prefers RCGY to YCGR motifs. Average profiles of RCGY ( b ) and YCGR ( c ) motif densities (R = A or G, and Y = C or T) around 27,031 SCL-exo id CpGs with at least 30× coverage

Techniques Used: Binding Assay

SCL-exo id CpGs show low conservation among vertebrates. a Average PhastCons score around SCL-exo id CpGs found within a Meis1 ChIP-seq peak either in a 5000 bp window ( left panel ) or in a close-up view of 200 bp ( right panel ). b Screenshot of UCSC genome browser showing the conservation between species of sequences including a SCL-exo id CpG ( boxed in red ). c SCL-exo id CpGs were sorted into three groups: High conservation (PhastCons score between 0.75 and 1), Intermediate conservation (PhastCons score between 0.075 and 0.75), and No conservation (PhastCons score between 0 and 0.075). Box plots illustrate the distribution of the PhastCons score for the three groups of id CpGs. d Best ranked motif for each of the three groups of id CpGs sorted according to their conservation, as determined by the SeqPos motif tool from Cistrome
Figure Legend Snippet: SCL-exo id CpGs show low conservation among vertebrates. a Average PhastCons score around SCL-exo id CpGs found within a Meis1 ChIP-seq peak either in a 5000 bp window ( left panel ) or in a close-up view of 200 bp ( right panel ). b Screenshot of UCSC genome browser showing the conservation between species of sequences including a SCL-exo id CpG ( boxed in red ). c SCL-exo id CpGs were sorted into three groups: High conservation (PhastCons score between 0.75 and 1), Intermediate conservation (PhastCons score between 0.075 and 0.75), and No conservation (PhastCons score between 0 and 0.075). Box plots illustrate the distribution of the PhastCons score for the three groups of id CpGs. d Best ranked motif for each of the three groups of id CpGs sorted according to their conservation, as determined by the SeqPos motif tool from Cistrome

Techniques Used: Chromatin Immunoprecipitation

SCL-exo of a 5hmC-containing DNA standard. a Schematic representation of the SCL-exo procedure. Note that, for the sake of clarity, only single-stranded DNA is shown. b Sequence of the forward strand of a 224-bp hydroxymethylated DNA standard obtained by PCR amplification of mm8 chr3:93,697,590-93,697,813, using 5hmdCTP instead of dCTP. Sequences corresponding to the primers are underlined and do not contain 5hmCs. All other cytosines were hydroxymethylated. Positions of the three first 5hmCs of the forward strand and of the four first 5hmCs of the reverse strand have been numbered 1, 2, 3 and 4, 5, 6, 7, respectively. c Cytosine density along read length (10,000 reads for each strand). d Number of reads covering each position along the DNA standard for both forward and reverse strands. Numbering on the graph indicates the 5hmCs identified 1, 2, 3 and 4, 5, 6,7 in ( b ). e Coverage of each C of the DNA standard found within 10 bases from the start of all reads. f Close-up view of the signal shown within the blue box in ( e ) and associated to the first 60 bases of the forward strand. The sequence is shown below and 5hmCs have been marked by asterisks
Figure Legend Snippet: SCL-exo of a 5hmC-containing DNA standard. a Schematic representation of the SCL-exo procedure. Note that, for the sake of clarity, only single-stranded DNA is shown. b Sequence of the forward strand of a 224-bp hydroxymethylated DNA standard obtained by PCR amplification of mm8 chr3:93,697,590-93,697,813, using 5hmdCTP instead of dCTP. Sequences corresponding to the primers are underlined and do not contain 5hmCs. All other cytosines were hydroxymethylated. Positions of the three first 5hmCs of the forward strand and of the four first 5hmCs of the reverse strand have been numbered 1, 2, 3 and 4, 5, 6, 7, respectively. c Cytosine density along read length (10,000 reads for each strand). d Number of reads covering each position along the DNA standard for both forward and reverse strands. Numbering on the graph indicates the 5hmCs identified 1, 2, 3 and 4, 5, 6,7 in ( b ). e Coverage of each C of the DNA standard found within 10 bases from the start of all reads. f Close-up view of the signal shown within the blue box in ( e ) and associated to the first 60 bases of the forward strand. The sequence is shown below and 5hmCs have been marked by asterisks

Techniques Used: Sequencing, Polymerase Chain Reaction, Amplification

Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)
Figure Legend Snippet: Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)

Techniques Used: Derivative Assay, Genome Wide

5hmCpG mapping by SCL-exo in mouse ES cells. a Venn diagrams indicating the percentage of overlapping id CpGs between two technical replicates of SCL-exo ( left diagram ) and two technical replicates of TAB-seq ( right diagram ) in E14 mESCs. SCL-exo id CpGs were selected for having a coverage ≥ 20× and TAB-seq id CpGs for being at least 20 % hydroxymethylated. b Genome-wide correlation coefficient value for two technical replicates of SCL-exo ( left panel ) and TAB-seq ( right panel ). Signals were compared for id CpGs with between 20× and 60× coverage in SCL-exo and with between 15 % and 40 % of hydroxymethylation in TAB-seq. c Graph representing the percentage of overlapping CpGs between either SCL-exo and TAB-seq (hydroxymethylation ≥ 20 %) or SCL-exo and Aba-seq (coverage ≥ 20×, 2,320,973 CpGs), as a function of the coverage of SCL-exo id CpGs. For each SCL-exo coverage value a similar number of CpGs were randomly picked among the 21,342,492 CpGs of the mm9 genome, and submitted to the same analysis. d Venn diagrams indicating the percentage of id CpGs from either SCL-exo (called from the consensus.wig file with a threshold ( th ) of 40×, left diagram ) or TAB-seq (hyroxymethylation ≥ 20 %, right diagram ), overlapping with hMeDIP-seq peaks (called with a threshold of 20×). e Functional annotation of the SCL-exo and TAB-seq identified 5hmCpGs. Annotation was done with GREAT ( http://bejerano.stanford.edu/great/public/html/ ) and binomial raw p values are given in brackets for each item
Figure Legend Snippet: 5hmCpG mapping by SCL-exo in mouse ES cells. a Venn diagrams indicating the percentage of overlapping id CpGs between two technical replicates of SCL-exo ( left diagram ) and two technical replicates of TAB-seq ( right diagram ) in E14 mESCs. SCL-exo id CpGs were selected for having a coverage ≥ 20× and TAB-seq id CpGs for being at least 20 % hydroxymethylated. b Genome-wide correlation coefficient value for two technical replicates of SCL-exo ( left panel ) and TAB-seq ( right panel ). Signals were compared for id CpGs with between 20× and 60× coverage in SCL-exo and with between 15 % and 40 % of hydroxymethylation in TAB-seq. c Graph representing the percentage of overlapping CpGs between either SCL-exo and TAB-seq (hydroxymethylation ≥ 20 %) or SCL-exo and Aba-seq (coverage ≥ 20×, 2,320,973 CpGs), as a function of the coverage of SCL-exo id CpGs. For each SCL-exo coverage value a similar number of CpGs were randomly picked among the 21,342,492 CpGs of the mm9 genome, and submitted to the same analysis. d Venn diagrams indicating the percentage of id CpGs from either SCL-exo (called from the consensus.wig file with a threshold ( th ) of 40×, left diagram ) or TAB-seq (hyroxymethylation ≥ 20 %, right diagram ), overlapping with hMeDIP-seq peaks (called with a threshold of 20×). e Functional annotation of the SCL-exo and TAB-seq identified 5hmCpGs. Annotation was done with GREAT ( http://bejerano.stanford.edu/great/public/html/ ) and binomial raw p values are given in brackets for each item

Techniques Used: Genome Wide, Functional Assay

5hmC detection by SCL-exo in genomic DNA from NPLCs does not depend on CpG density. a Average TET1 ChIP-seq signal in 4000 bp regions around SCL-exo id CpGs sorted according to their CpG density (number of CpGs per 300 bp). b Mean SCL-exo signal at id CpGs sorted according to their surrounding CpG density. c Scatter plot of the SCL-exo signal at id CpGs as a function of CpG density (r: Pearson’s correlation coefficient). d Mean SCL-exo signal at SCL-exo id CpG as a function of CpGs density. e Mean hMeDIP-seq signal at id CpGs sorted according to their surrounding CpG density. f Inclusion of SCL-exo id CpGs in hMeDIP peaks as a function of CpG density
Figure Legend Snippet: 5hmC detection by SCL-exo in genomic DNA from NPLCs does not depend on CpG density. a Average TET1 ChIP-seq signal in 4000 bp regions around SCL-exo id CpGs sorted according to their CpG density (number of CpGs per 300 bp). b Mean SCL-exo signal at id CpGs sorted according to their surrounding CpG density. c Scatter plot of the SCL-exo signal at id CpGs as a function of CpG density (r: Pearson’s correlation coefficient). d Mean SCL-exo signal at SCL-exo id CpG as a function of CpGs density. e Mean hMeDIP-seq signal at id CpGs sorted according to their surrounding CpG density. f Inclusion of SCL-exo id CpGs in hMeDIP peaks as a function of CpG density

Techniques Used: Chromatin Immunoprecipitation

2) Product Images from "Tumor Necrosis Factor dynamically regulates the mRNA stabilome in rheumatoid arthritis fibroblast-like synoviocytes"

Article Title: Tumor Necrosis Factor dynamically regulates the mRNA stabilome in rheumatoid arthritis fibroblast-like synoviocytes

Journal: PLoS ONE

doi: 10.1371/journal.pone.0179762

Genome-wide evaluation of mRNA stability states of expressed genes in RA FLS. (a-c), Gene tracks showing sequencing reads from RNA sequencing mapped to CCL20 (a), JUN (b) and IRF1 (c) genes. The sequencing reads after TNF stimulation for 1 hour without (blue) or with Act D (orange) are shown. (d), Stacked bar graphs illustrating the mRNA stability states of genes expressed in unstimulated (Control) and TNF-stimulated FLS (1, 3, 24 and 72 hours of TNF stimulation). The mRNA stability status was calculated as the ratio of expression levels at the TNF+Act D condition divided to the expression levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The expressed genes were classified into five groups with distinct stability states and the size of each group is represented as % of total number of expressed genes for each condition.
Figure Legend Snippet: Genome-wide evaluation of mRNA stability states of expressed genes in RA FLS. (a-c), Gene tracks showing sequencing reads from RNA sequencing mapped to CCL20 (a), JUN (b) and IRF1 (c) genes. The sequencing reads after TNF stimulation for 1 hour without (blue) or with Act D (orange) are shown. (d), Stacked bar graphs illustrating the mRNA stability states of genes expressed in unstimulated (Control) and TNF-stimulated FLS (1, 3, 24 and 72 hours of TNF stimulation). The mRNA stability status was calculated as the ratio of expression levels at the TNF+Act D condition divided to the expression levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The expressed genes were classified into five groups with distinct stability states and the size of each group is represented as % of total number of expressed genes for each condition.

Techniques Used: Genome Wide, Sequencing, RNA Sequencing Assay, Activated Clotting Time Assay, Expressing

Scatterplots comparing the expression levels to the mRNA stability states of the expressed genes in RA FLS. Two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1, 3, 24, or 72 hours. Subsequently, actinomycin D (Act D, 10μg/ml) was added for 3 hours to block active transcription and gene expression was measured by RNA sequencing. RPKM values were generated using CuffDiff2. The mRNA stability status was calculated genome-wide as the ratio of RPKM levels at the TNF+Act D condition divided to the RPKM levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The genes expressed at 1 (a), 3 (b), 24 (c), and 72 (d) hours of TNF stimulation were plotted based on their expression levels and the mRNA stability states. Shades of blue represent the region of unstable genes, and shades of red represent the zone of stable genes.
Figure Legend Snippet: Scatterplots comparing the expression levels to the mRNA stability states of the expressed genes in RA FLS. Two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1, 3, 24, or 72 hours. Subsequently, actinomycin D (Act D, 10μg/ml) was added for 3 hours to block active transcription and gene expression was measured by RNA sequencing. RPKM values were generated using CuffDiff2. The mRNA stability status was calculated genome-wide as the ratio of RPKM levels at the TNF+Act D condition divided to the RPKM levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The genes expressed at 1 (a), 3 (b), 24 (c), and 72 (d) hours of TNF stimulation were plotted based on their expression levels and the mRNA stability states. Shades of blue represent the region of unstable genes, and shades of red represent the zone of stable genes.

Techniques Used: Expressing, Derivative Assay, Activated Clotting Time Assay, Blocking Assay, RNA Sequencing Assay, Generated, Genome Wide

Association of expression kinetics with mRNA stability states of TNF-inducible genes in RA FLS. For (a-b), two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1-72h. Subsequently, Act D (10 μg/ml) was added for 3h and gene expression was measured by RNA sequencing. 386 genes were identified as highly induced (≥5-fold) by TNF at any time point and were clustered into 6 clusters with distinct kinetics of peak expression. (a), Heatmap illustrating the expression kinetics of the 6 clusters (red represents the maximum and blue the minimum expression level across the lane). (b), Stacked bar graphs illustrating the stability states of genes for Cluster 1, Clusters 2 3, Cluster 4, and Clusters 5 6. For (c-f), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 1–72 hours. Primers specific for the eighth intronic region of MMP3 and for the first intronic region of CCL5 were designed to capture primary transcripts (PT) of MMP3 and CCL5 . qPCR was used to measure the levels of PT and total mRNA of MMP3 (c-d) and CCL5 (e-f). Cumulative results from six independent experiments are shown. Values were normalized relative to mRNA for GAPDH and are presented as mean ±SEM.
Figure Legend Snippet: Association of expression kinetics with mRNA stability states of TNF-inducible genes in RA FLS. For (a-b), two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1-72h. Subsequently, Act D (10 μg/ml) was added for 3h and gene expression was measured by RNA sequencing. 386 genes were identified as highly induced (≥5-fold) by TNF at any time point and were clustered into 6 clusters with distinct kinetics of peak expression. (a), Heatmap illustrating the expression kinetics of the 6 clusters (red represents the maximum and blue the minimum expression level across the lane). (b), Stacked bar graphs illustrating the stability states of genes for Cluster 1, Clusters 2 3, Cluster 4, and Clusters 5 6. For (c-f), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 1–72 hours. Primers specific for the eighth intronic region of MMP3 and for the first intronic region of CCL5 were designed to capture primary transcripts (PT) of MMP3 and CCL5 . qPCR was used to measure the levels of PT and total mRNA of MMP3 (c-d) and CCL5 (e-f). Cumulative results from six independent experiments are shown. Values were normalized relative to mRNA for GAPDH and are presented as mean ±SEM.

Techniques Used: Expressing, Derivative Assay, Activated Clotting Time Assay, RNA Sequencing Assay, Real-time Polymerase Chain Reaction

Genome-wide identification of transcripts stabilized by TNF in RA FLS. Two biologic replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1 or 72h. Subsequently, Act D was added for 3h and gene expression was measured by RNA sequencing. The degree of TNF-induced mRNA stabilization was calculated as the log 2 difference of TNF+Act D/TNF ratio between 1 and 72h of TNF stimulation and the adjusted p values of TNF-induced stabilization were calculated by RiboDiff. (a), Scatter-plot of the genes displaying TNF-induced mRNA stabilization comparing the degree of mRNA stabilization (y axis) to the adjusted p values of the stabilizing effect of TNF (x-axis). (b), The top 40 genes displaying the highest TNF-induced mRNA stabilization ranked by the degree of stabilization. (c), Enriched biological processes identified by GSEA/MSigDB pathway analysis of the top 10% of the genes (n = 593) displaying the highest degree of TNF-induced mRNA stabilization.
Figure Legend Snippet: Genome-wide identification of transcripts stabilized by TNF in RA FLS. Two biologic replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1 or 72h. Subsequently, Act D was added for 3h and gene expression was measured by RNA sequencing. The degree of TNF-induced mRNA stabilization was calculated as the log 2 difference of TNF+Act D/TNF ratio between 1 and 72h of TNF stimulation and the adjusted p values of TNF-induced stabilization were calculated by RiboDiff. (a), Scatter-plot of the genes displaying TNF-induced mRNA stabilization comparing the degree of mRNA stabilization (y axis) to the adjusted p values of the stabilizing effect of TNF (x-axis). (b), The top 40 genes displaying the highest TNF-induced mRNA stabilization ranked by the degree of stabilization. (c), Enriched biological processes identified by GSEA/MSigDB pathway analysis of the top 10% of the genes (n = 593) displaying the highest degree of TNF-induced mRNA stabilization.

Techniques Used: Genome Wide, Derivative Assay, Activated Clotting Time Assay, Expressing, RNA Sequencing Assay

TNF induces expression of mRNA-stabilizing pathways and mRNA stabilization is MAPK-dependent. (a), RNA sequencing was performed in 2 biological replicates (derived from two different RA patients) of TNF-stimulated RA FLS and Panther-Gene Ontology was used to evaluate their enrichment for the biological process “Regulation of RNA stability” (GO:0043487 or GO:0043488). F.E = fold enrichment and ns = not significant. (b-h), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 72h and then Act D (10 μg/ml) was added for 20 mins to block active transcription. Subsequently, the cells were treated for 4h with SB202190 (p38 inhibitor) alone or in various combinations with U0126 (MEK inhibitor) and SP600125 (JNK inhibitor). qPCR was used to measure the mRNA levels of CCL5 (b), IL-6 (c), IL-8 (d), CXCL3 (e), CCL2 (f), PTGS2 (g), and CXCL1 (h). Cumulative results from 4 independent experiments are shown. Values were normalized relative to GAPDH mRNA and presented as mean ±SEM. The mRNA expression at the TNF+Act D condition was set to 100 and the mRNA expression at all the other conditions was calculated as % of the TNF+Act D condition. P values were calculated by one-way ANOVA and Tukey post-test analysis (* = p
Figure Legend Snippet: TNF induces expression of mRNA-stabilizing pathways and mRNA stabilization is MAPK-dependent. (a), RNA sequencing was performed in 2 biological replicates (derived from two different RA patients) of TNF-stimulated RA FLS and Panther-Gene Ontology was used to evaluate their enrichment for the biological process “Regulation of RNA stability” (GO:0043487 or GO:0043488). F.E = fold enrichment and ns = not significant. (b-h), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 72h and then Act D (10 μg/ml) was added for 20 mins to block active transcription. Subsequently, the cells were treated for 4h with SB202190 (p38 inhibitor) alone or in various combinations with U0126 (MEK inhibitor) and SP600125 (JNK inhibitor). qPCR was used to measure the mRNA levels of CCL5 (b), IL-6 (c), IL-8 (d), CXCL3 (e), CCL2 (f), PTGS2 (g), and CXCL1 (h). Cumulative results from 4 independent experiments are shown. Values were normalized relative to GAPDH mRNA and presented as mean ±SEM. The mRNA expression at the TNF+Act D condition was set to 100 and the mRNA expression at all the other conditions was calculated as % of the TNF+Act D condition. P values were calculated by one-way ANOVA and Tukey post-test analysis (* = p

Techniques Used: Expressing, RNA Sequencing Assay, Derivative Assay, Activated Clotting Time Assay, Blocking Assay, Real-time Polymerase Chain Reaction

3) Product Images from "A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb"

Article Title: A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb

Journal: Scientific Reports

doi: 10.1038/srep18864

Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .
Figure Legend Snippet: Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .

Techniques Used: RNA Sequencing Assay

4) Product Images from "Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets"

Article Title: Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets

Journal: Genome Biology

doi: 10.1186/s13059-016-0919-y

Association of 5hmCs with DNA motifs. a Logos of de novo motifs and transcription factor binding motifs retrieved by the SeqPos motif tool from Cistrome ( http://cistrome.dfci.harvard.edu ) in 100 bp sequences centered on SCL-exo id CpGs included in hMeDIP peaks. As a control, motif search was run on 100 bp sequences centered on randomly selected CpGs. For each logo, the associated p value and z-score are indicated. b , c CpG hydroxymethylation prefers RCGY to YCGR motifs. Average profiles of RCGY ( b ) and YCGR ( c ) motif densities (R = A or G, and Y = C or T) around 27,031 SCL-exo id CpGs with at least 30× coverage
Figure Legend Snippet: Association of 5hmCs with DNA motifs. a Logos of de novo motifs and transcription factor binding motifs retrieved by the SeqPos motif tool from Cistrome ( http://cistrome.dfci.harvard.edu ) in 100 bp sequences centered on SCL-exo id CpGs included in hMeDIP peaks. As a control, motif search was run on 100 bp sequences centered on randomly selected CpGs. For each logo, the associated p value and z-score are indicated. b , c CpG hydroxymethylation prefers RCGY to YCGR motifs. Average profiles of RCGY ( b ) and YCGR ( c ) motif densities (R = A or G, and Y = C or T) around 27,031 SCL-exo id CpGs with at least 30× coverage

Techniques Used: Binding Assay

SCL-exo id CpGs show low conservation among vertebrates. a Average PhastCons score around SCL-exo id CpGs found within a Meis1 ChIP-seq peak either in a 5000 bp window ( left panel ) or in a close-up view of 200 bp ( right panel ). b Screenshot of UCSC genome browser showing the conservation between species of sequences including a SCL-exo id CpG ( boxed in red ). c SCL-exo id CpGs were sorted into three groups: High conservation (PhastCons score between 0.75 and 1), Intermediate conservation (PhastCons score between 0.075 and 0.75), and No conservation (PhastCons score between 0 and 0.075). Box plots illustrate the distribution of the PhastCons score for the three groups of id CpGs. d Best ranked motif for each of the three groups of id CpGs sorted according to their conservation, as determined by the SeqPos motif tool from Cistrome
Figure Legend Snippet: SCL-exo id CpGs show low conservation among vertebrates. a Average PhastCons score around SCL-exo id CpGs found within a Meis1 ChIP-seq peak either in a 5000 bp window ( left panel ) or in a close-up view of 200 bp ( right panel ). b Screenshot of UCSC genome browser showing the conservation between species of sequences including a SCL-exo id CpG ( boxed in red ). c SCL-exo id CpGs were sorted into three groups: High conservation (PhastCons score between 0.75 and 1), Intermediate conservation (PhastCons score between 0.075 and 0.75), and No conservation (PhastCons score between 0 and 0.075). Box plots illustrate the distribution of the PhastCons score for the three groups of id CpGs. d Best ranked motif for each of the three groups of id CpGs sorted according to their conservation, as determined by the SeqPos motif tool from Cistrome

Techniques Used: Chromatin Immunoprecipitation

SCL-exo of a 5hmC-containing DNA standard. a Schematic representation of the SCL-exo procedure. Note that, for the sake of clarity, only single-stranded DNA is shown. b Sequence of the forward strand of a 224-bp hydroxymethylated DNA standard obtained by PCR amplification of mm8 chr3:93,697,590-93,697,813, using 5hmdCTP instead of dCTP. Sequences corresponding to the primers are underlined and do not contain 5hmCs. All other cytosines were hydroxymethylated. Positions of the three first 5hmCs of the forward strand and of the four first 5hmCs of the reverse strand have been numbered 1, 2, 3 and 4, 5, 6, 7, respectively. c Cytosine density along read length (10,000 reads for each strand). d Number of reads covering each position along the DNA standard for both forward and reverse strands. Numbering on the graph indicates the 5hmCs identified 1, 2, 3 and 4, 5, 6,7 in ( b ). e Coverage of each C of the DNA standard found within 10 bases from the start of all reads. f Close-up view of the signal shown within the blue box in ( e ) and associated to the first 60 bases of the forward strand. The sequence is shown below and 5hmCs have been marked by asterisks
Figure Legend Snippet: SCL-exo of a 5hmC-containing DNA standard. a Schematic representation of the SCL-exo procedure. Note that, for the sake of clarity, only single-stranded DNA is shown. b Sequence of the forward strand of a 224-bp hydroxymethylated DNA standard obtained by PCR amplification of mm8 chr3:93,697,590-93,697,813, using 5hmdCTP instead of dCTP. Sequences corresponding to the primers are underlined and do not contain 5hmCs. All other cytosines were hydroxymethylated. Positions of the three first 5hmCs of the forward strand and of the four first 5hmCs of the reverse strand have been numbered 1, 2, 3 and 4, 5, 6, 7, respectively. c Cytosine density along read length (10,000 reads for each strand). d Number of reads covering each position along the DNA standard for both forward and reverse strands. Numbering on the graph indicates the 5hmCs identified 1, 2, 3 and 4, 5, 6,7 in ( b ). e Coverage of each C of the DNA standard found within 10 bases from the start of all reads. f Close-up view of the signal shown within the blue box in ( e ) and associated to the first 60 bases of the forward strand. The sequence is shown below and 5hmCs have been marked by asterisks

Techniques Used: Sequencing, Polymerase Chain Reaction, Amplification

Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)
Figure Legend Snippet: Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)

Techniques Used: Derivative Assay, Genome Wide

5hmCpG mapping by SCL-exo in mouse ES cells. a Venn diagrams indicating the percentage of overlapping id CpGs between two technical replicates of SCL-exo ( left diagram ) and two technical replicates of TAB-seq ( right diagram ) in E14 mESCs. SCL-exo id CpGs were selected for having a coverage ≥ 20× and TAB-seq id CpGs for being at least 20 % hydroxymethylated. b Genome-wide correlation coefficient value for two technical replicates of SCL-exo ( left panel ) and TAB-seq ( right panel ). Signals were compared for id CpGs with between 20× and 60× coverage in SCL-exo and with between 15 % and 40 % of hydroxymethylation in TAB-seq. c Graph representing the percentage of overlapping CpGs between either SCL-exo and TAB-seq (hydroxymethylation ≥ 20 %) or SCL-exo and Aba-seq (coverage ≥ 20×, 2,320,973 CpGs), as a function of the coverage of SCL-exo id CpGs. For each SCL-exo coverage value a similar number of CpGs were randomly picked among the 21,342,492 CpGs of the mm9 genome, and submitted to the same analysis. d Venn diagrams indicating the percentage of id CpGs from either SCL-exo (called from the consensus.wig file with a threshold ( th ) of 40×, left diagram ) or TAB-seq (hyroxymethylation ≥ 20 %, right diagram ), overlapping with hMeDIP-seq peaks (called with a threshold of 20×). e Functional annotation of the SCL-exo and TAB-seq identified 5hmCpGs. Annotation was done with GREAT ( http://bejerano.stanford.edu/great/public/html/ ) and binomial raw p values are given in brackets for each item
Figure Legend Snippet: 5hmCpG mapping by SCL-exo in mouse ES cells. a Venn diagrams indicating the percentage of overlapping id CpGs between two technical replicates of SCL-exo ( left diagram ) and two technical replicates of TAB-seq ( right diagram ) in E14 mESCs. SCL-exo id CpGs were selected for having a coverage ≥ 20× and TAB-seq id CpGs for being at least 20 % hydroxymethylated. b Genome-wide correlation coefficient value for two technical replicates of SCL-exo ( left panel ) and TAB-seq ( right panel ). Signals were compared for id CpGs with between 20× and 60× coverage in SCL-exo and with between 15 % and 40 % of hydroxymethylation in TAB-seq. c Graph representing the percentage of overlapping CpGs between either SCL-exo and TAB-seq (hydroxymethylation ≥ 20 %) or SCL-exo and Aba-seq (coverage ≥ 20×, 2,320,973 CpGs), as a function of the coverage of SCL-exo id CpGs. For each SCL-exo coverage value a similar number of CpGs were randomly picked among the 21,342,492 CpGs of the mm9 genome, and submitted to the same analysis. d Venn diagrams indicating the percentage of id CpGs from either SCL-exo (called from the consensus.wig file with a threshold ( th ) of 40×, left diagram ) or TAB-seq (hyroxymethylation ≥ 20 %, right diagram ), overlapping with hMeDIP-seq peaks (called with a threshold of 20×). e Functional annotation of the SCL-exo and TAB-seq identified 5hmCpGs. Annotation was done with GREAT ( http://bejerano.stanford.edu/great/public/html/ ) and binomial raw p values are given in brackets for each item

Techniques Used: Genome Wide, Functional Assay

5hmC detection by SCL-exo in genomic DNA from NPLCs does not depend on CpG density. a Average TET1 ChIP-seq signal in 4000 bp regions around SCL-exo id CpGs sorted according to their CpG density (number of CpGs per 300 bp). b Mean SCL-exo signal at id CpGs sorted according to their surrounding CpG density. c Scatter plot of the SCL-exo signal at id CpGs as a function of CpG density (r: Pearson’s correlation coefficient). d Mean SCL-exo signal at SCL-exo id CpG as a function of CpGs density. e Mean hMeDIP-seq signal at id CpGs sorted according to their surrounding CpG density. f Inclusion of SCL-exo id CpGs in hMeDIP peaks as a function of CpG density
Figure Legend Snippet: 5hmC detection by SCL-exo in genomic DNA from NPLCs does not depend on CpG density. a Average TET1 ChIP-seq signal in 4000 bp regions around SCL-exo id CpGs sorted according to their CpG density (number of CpGs per 300 bp). b Mean SCL-exo signal at id CpGs sorted according to their surrounding CpG density. c Scatter plot of the SCL-exo signal at id CpGs as a function of CpG density (r: Pearson’s correlation coefficient). d Mean SCL-exo signal at SCL-exo id CpG as a function of CpGs density. e Mean hMeDIP-seq signal at id CpGs sorted according to their surrounding CpG density. f Inclusion of SCL-exo id CpGs in hMeDIP peaks as a function of CpG density

Techniques Used: Chromatin Immunoprecipitation

5) Product Images from "A Diverse Range of Novel RNA Viruses in Geographically Distinct Honey Bee Populations"

Article Title: A Diverse Range of Novel RNA Viruses in Geographically Distinct Honey Bee Populations

Journal: Journal of Virology

doi: 10.1128/JVI.00158-17

Small RNA analysis of ARV-1 and ARV-2 in Varroa . Left panels show ARV-1, and right panels show ARV-2. (A and B) Size distributions (15 to 37 nt) and 5′-nucleotide compositions of small RNAs in mite 1 (M1) arising from ARV-1 (A) and ARV-2 (B). Bars plotted above the x axis represent reads that map to the positive strand, and those plotted below represent those that map to the negative strand. Bars are colored according to the proportions of reads starting with A, C, G, and T. (C and D) Mapping of 23- to 25-nt-long viRNAs to the genomes of ARV-1 (C) and ARV-2 (D). The cartoon shows the domains of the viral genomes as shown in Fig. 2 . (E and F) Size distributions and 5′-nucleotide compositions of the sense small RNAs from panels A and B, respectively, normalized to the number of sense reads present. Mapping of the 19- to 24-nt sense reads to the viral genomes is also shown. (G and H) Phasing scores over 8 phasing cycles for each position within a 24-nt phasing window for ARV-1 (G) and ARV-2 (H). The top and bottom graphs show the phasing scores for the sense and antisense reads, respectively. This analysis was performed by using the 24-nt-long reads only. (I and J) Observed 5′ nucleotides (Obs) compared with those expected (Exp) from the base compositions of the viral genomes of ARV-1 (I) and ARV-2 (J). Sense (S) and antisense (AS) reads were compared by using a chi-squared test. *, P value of
Figure Legend Snippet: Small RNA analysis of ARV-1 and ARV-2 in Varroa . Left panels show ARV-1, and right panels show ARV-2. (A and B) Size distributions (15 to 37 nt) and 5′-nucleotide compositions of small RNAs in mite 1 (M1) arising from ARV-1 (A) and ARV-2 (B). Bars plotted above the x axis represent reads that map to the positive strand, and those plotted below represent those that map to the negative strand. Bars are colored according to the proportions of reads starting with A, C, G, and T. (C and D) Mapping of 23- to 25-nt-long viRNAs to the genomes of ARV-1 (C) and ARV-2 (D). The cartoon shows the domains of the viral genomes as shown in Fig. 2 . (E and F) Size distributions and 5′-nucleotide compositions of the sense small RNAs from panels A and B, respectively, normalized to the number of sense reads present. Mapping of the 19- to 24-nt sense reads to the viral genomes is also shown. (G and H) Phasing scores over 8 phasing cycles for each position within a 24-nt phasing window for ARV-1 (G) and ARV-2 (H). The top and bottom graphs show the phasing scores for the sense and antisense reads, respectively. This analysis was performed by using the 24-nt-long reads only. (I and J) Observed 5′ nucleotides (Obs) compared with those expected (Exp) from the base compositions of the viral genomes of ARV-1 (I) and ARV-2 (J). Sense (S) and antisense (AS) reads were compared by using a chi-squared test. *, P value of

Techniques Used:

6) Product Images from "An analysis of exome sequencing for diagnostic testing of the genes associated with muscle disease and spastic paraplegia"

Article Title: An analysis of exome sequencing for diagnostic testing of the genes associated with muscle disease and spastic paraplegia

Journal: Human mutation

doi: 10.1002/humu.22032

Box-and-whisker plots of the fold coverage in each exome for the nucleotides in the UCSC-annotated exonic sequences of 64 muscle disease (MD) genes (A, B,C) and 24 hereditary spastic paraplegia (SPG) genes (D,E,F). The box represents the interquartile range (IQR): first quartile, median (midline), and third quartile; whiskers represent the smallest and largest observations within 1.5 times the IQR; circles represent the outliers.(A and D) The fold coverage of ES for 78 samples captured using the 38 Mb Agilent exon enrichment kit (E-38). (B and E) The fold coverage of ES for 36 samples captured using the 50 Mb Agilent exon enrichment kit (E-50). (C and F) The fold coverage of ES for 11 samples captured using the 62 Mb TruSeq exon enrichment kit (E-62).
Figure Legend Snippet: Box-and-whisker plots of the fold coverage in each exome for the nucleotides in the UCSC-annotated exonic sequences of 64 muscle disease (MD) genes (A, B,C) and 24 hereditary spastic paraplegia (SPG) genes (D,E,F). The box represents the interquartile range (IQR): first quartile, median (midline), and third quartile; whiskers represent the smallest and largest observations within 1.5 times the IQR; circles represent the outliers.(A and D) The fold coverage of ES for 78 samples captured using the 38 Mb Agilent exon enrichment kit (E-38). (B and E) The fold coverage of ES for 36 samples captured using the 50 Mb Agilent exon enrichment kit (E-50). (C and F) The fold coverage of ES for 11 samples captured using the 62 Mb TruSeq exon enrichment kit (E-62).

Techniques Used: Whisker Assay

7) Product Images from "A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb"

Article Title: A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb

Journal: Scientific Reports

doi: 10.1038/srep18864

Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .
Figure Legend Snippet: Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .

Techniques Used: RNA Sequencing Assay

8) Product Images from "A Diverse Range of Novel RNA Viruses in Geographically Distinct Honey Bee Populations"

Article Title: A Diverse Range of Novel RNA Viruses in Geographically Distinct Honey Bee Populations

Journal: Journal of Virology

doi: 10.1128/JVI.00158-17

Small RNA analysis of ARV-1 and ARV-2 in Varroa . Left panels show ARV-1, and right panels show ARV-2. (A and B) Size distributions (15 to 37 nt) and 5′-nucleotide compositions of small RNAs in mite 1 (M1) arising from ARV-1 (A) and ARV-2 (B). Bars plotted above the x axis represent reads that map to the positive strand, and those plotted below represent those that map to the negative strand. Bars are colored according to the proportions of reads starting with A, C, G, and T. (C and D) Mapping of 23- to 25-nt-long viRNAs to the genomes of ARV-1 (C) and ARV-2 (D). The cartoon shows the domains of the viral genomes as shown in Fig. 2 . (E and F) Size distributions and 5′-nucleotide compositions of the sense small RNAs from panels A and B, respectively, normalized to the number of sense reads present. Mapping of the 19- to 24-nt sense reads to the viral genomes is also shown. (G and H) Phasing scores over 8 phasing cycles for each position within a 24-nt phasing window for ARV-1 (G) and ARV-2 (H). The top and bottom graphs show the phasing scores for the sense and antisense reads, respectively. This analysis was performed by using the 24-nt-long reads only. (I and J) Observed 5′ nucleotides (Obs) compared with those expected (Exp) from the base compositions of the viral genomes of ARV-1 (I) and ARV-2 (J). Sense (S) and antisense (AS) reads were compared by using a chi-squared test. *, P value of
Figure Legend Snippet: Small RNA analysis of ARV-1 and ARV-2 in Varroa . Left panels show ARV-1, and right panels show ARV-2. (A and B) Size distributions (15 to 37 nt) and 5′-nucleotide compositions of small RNAs in mite 1 (M1) arising from ARV-1 (A) and ARV-2 (B). Bars plotted above the x axis represent reads that map to the positive strand, and those plotted below represent those that map to the negative strand. Bars are colored according to the proportions of reads starting with A, C, G, and T. (C and D) Mapping of 23- to 25-nt-long viRNAs to the genomes of ARV-1 (C) and ARV-2 (D). The cartoon shows the domains of the viral genomes as shown in Fig. 2 . (E and F) Size distributions and 5′-nucleotide compositions of the sense small RNAs from panels A and B, respectively, normalized to the number of sense reads present. Mapping of the 19- to 24-nt sense reads to the viral genomes is also shown. (G and H) Phasing scores over 8 phasing cycles for each position within a 24-nt phasing window for ARV-1 (G) and ARV-2 (H). The top and bottom graphs show the phasing scores for the sense and antisense reads, respectively. This analysis was performed by using the 24-nt-long reads only. (I and J) Observed 5′ nucleotides (Obs) compared with those expected (Exp) from the base compositions of the viral genomes of ARV-1 (I) and ARV-2 (J). Sense (S) and antisense (AS) reads were compared by using a chi-squared test. *, P value of

Techniques Used:

9) Product Images from "Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte"

Article Title: Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte

Journal: Nature

doi: 10.1038/nature24454

Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.
Figure Legend Snippet: Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.

Techniques Used: RNA Sequencing Assay, Isolation, Mouse Assay, Purification, Magnetic Cell Separation, Transduction, Staining, Labeling, FACS, Chromatin Immunoprecipitation, In Situ, Amplification, Concentration Assay, Lysis, Expressing, Sequencing

10) Product Images from "A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum)"

Article Title: A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum)

Journal: BMC Genomics

doi: 10.1186/s12864-019-6236-6

The putative regulation network of non-coding RNAs involved in QS gene in nicotine pathway. a Normalized expression levels of QS , circQS and nta-miR6024 based on the RNA sequencing data; b Alignment of nta-miR6024 and its target RNAs. Base pairing between miRNA and its targets is shown, in which a vertical line means a Watson-Crick pair, two dots represent a G-U pair, and 0 means a mismatch; c qRT-PCR analyses of QS , circQS and nta-miR6024 in roots from the topping-treated and control plants
Figure Legend Snippet: The putative regulation network of non-coding RNAs involved in QS gene in nicotine pathway. a Normalized expression levels of QS , circQS and nta-miR6024 based on the RNA sequencing data; b Alignment of nta-miR6024 and its target RNAs. Base pairing between miRNA and its targets is shown, in which a vertical line means a Watson-Crick pair, two dots represent a G-U pair, and 0 means a mismatch; c qRT-PCR analyses of QS , circQS and nta-miR6024 in roots from the topping-treated and control plants

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

The potential interaction of RNA molecules involved in the nicotine biosynthesis pathway in tobacco root. The differential expressed circRNAs which generated from parental genes involved in nicotine biosynthesis pathway were presented. The differential expressed miRNAs which target to genes or circRNAs were also shown in the figure. The nicotine biosynthesis pathway diagram drew based on previous studies in tobacco. The up-regulated genes/non-coding RNAs after topping treatment were presented in red, while down-regulated ones were in green
Figure Legend Snippet: The potential interaction of RNA molecules involved in the nicotine biosynthesis pathway in tobacco root. The differential expressed circRNAs which generated from parental genes involved in nicotine biosynthesis pathway were presented. The differential expressed miRNAs which target to genes or circRNAs were also shown in the figure. The nicotine biosynthesis pathway diagram drew based on previous studies in tobacco. The up-regulated genes/non-coding RNAs after topping treatment were presented in red, while down-regulated ones were in green

Techniques Used: Generated

Normalized expression levels of 4 types of topping responsive RNA molecules and verified by qRT-PCR. a ) Normalized expression level of 27 nicotine biosynthesis and metabolism related mRNAs based on RNAseq data and partial RT-qPCR results. b Normalized expression level of 7 topping response miRNAs on RNAseq data and partial RT-qPCR results. c 24 circRNAs generated from nicotine biosynthesis and metabolism related genes on RNAseq data and partial RT-qPCR results. d Normalized expression level of 20 topping response lncRNAs based on RNAseq data and partial RT-qPCR results. Data are means ± SD ( n = 3) * P
Figure Legend Snippet: Normalized expression levels of 4 types of topping responsive RNA molecules and verified by qRT-PCR. a ) Normalized expression level of 27 nicotine biosynthesis and metabolism related mRNAs based on RNAseq data and partial RT-qPCR results. b Normalized expression level of 7 topping response miRNAs on RNAseq data and partial RT-qPCR results. c 24 circRNAs generated from nicotine biosynthesis and metabolism related genes on RNAseq data and partial RT-qPCR results. d Normalized expression level of 20 topping response lncRNAs based on RNAseq data and partial RT-qPCR results. Data are means ± SD ( n = 3) * P

Techniques Used: Expressing, Quantitative RT-PCR, Generated

11) Product Images from "KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function"

Article Title: KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function

Journal: Oncogene

doi: 10.1038/s41388-018-0273-5

ERα is reprogrammed in shKMT2C-R cells. a Venn diagram showing overlap between shKMT2C-R (in CSS, red) and shRenilla (in full serum, purple) ERα binding sites. b . c , e , g IGV browser views for ERα in shRenilla and shKMT2C-R cells. Sites of increased ERα binding outlined in orange. For CMYC ]. d , f , h qChIP analysis. IgG used as negative control. Values correspond to mean percentage of input ± s.e.m. of triplicate qPCR reactions of a single replicate. Data correspond to one representative assay from a total of two independent assays
Figure Legend Snippet: ERα is reprogrammed in shKMT2C-R cells. a Venn diagram showing overlap between shKMT2C-R (in CSS, red) and shRenilla (in full serum, purple) ERα binding sites. b . c , e , g IGV browser views for ERα in shRenilla and shKMT2C-R cells. Sites of increased ERα binding outlined in orange. For CMYC ]. d , f , h qChIP analysis. IgG used as negative control. Values correspond to mean percentage of input ± s.e.m. of triplicate qPCR reactions of a single replicate. Data correspond to one representative assay from a total of two independent assays

Techniques Used: Binding Assay, Negative Control, Real-time Polymerase Chain Reaction

KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val
Figure Legend Snippet: KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val

Techniques Used: Cell Culture

KMT2C loss suppresses ERα target gene expression. a Supervised analysis of the 7938 differentially expressed genes between shRenilla and shKMT2C (shKMT2C#1 and #2 combined) MCF7 cells. All cells are cultured in full serum containing media. b GSEA showing 3857 genes downregulated in shKMT2C cells are enriched among genes downregulated following 5-day estrogen deprivation of shRenilla cells (≥3-fold). c GSEA of 3857 genes downregulated in shKMT2C as compared to the Hallmark Estrogen Response Early Geneset (Broad Institute). d GSEA of 3857 genes downregulated in shKMT2C as compared the Hallmark Estrogen Response Late Geneset. e mRNA levels; values correspond to the mean of three replicates ± s.e.m. E2, estradiol. f mRNA levels; values correspond to the mean of three replicates ± s.e.m.; two-tailed Student’s t -test with a desired FDR = 1% was used to determine statistical significance; ** P
Figure Legend Snippet: KMT2C loss suppresses ERα target gene expression. a Supervised analysis of the 7938 differentially expressed genes between shRenilla and shKMT2C (shKMT2C#1 and #2 combined) MCF7 cells. All cells are cultured in full serum containing media. b GSEA showing 3857 genes downregulated in shKMT2C cells are enriched among genes downregulated following 5-day estrogen deprivation of shRenilla cells (≥3-fold). c GSEA of 3857 genes downregulated in shKMT2C as compared to the Hallmark Estrogen Response Early Geneset (Broad Institute). d GSEA of 3857 genes downregulated in shKMT2C as compared the Hallmark Estrogen Response Late Geneset. e mRNA levels; values correspond to the mean of three replicates ± s.e.m. E2, estradiol. f mRNA levels; values correspond to the mean of three replicates ± s.e.m.; two-tailed Student’s t -test with a desired FDR = 1% was used to determine statistical significance; ** P

Techniques Used: Expressing, Cell Culture, Two Tailed Test

12) Product Images from "KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function"

Article Title: KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function

Journal: Oncogene

doi: 10.1038/s41388-018-0273-5

ERα is reprogrammed in shKMT2C-R cells. a Venn diagram showing overlap between shKMT2C-R (in CSS, red) and shRenilla (in full serum, purple) ERα binding sites. b HOMER motif analysis at the 10,512 novel ERα loci in shKMT2C-R. Full list in Fig. S17 . c , e , g IGV browser views for ERα in shRenilla and shKMT2C-R cells. Sites of increased ERα binding outlined in orange. For CMYC , orange outline encompasses a previously defined AP-1 dependent ERα site [ 34 ]. d , f , h qChIP analysis. IgG used as negative control. Values correspond to mean percentage of input ± s.e.m. of triplicate qPCR reactions of a single replicate. Data correspond to one representative assay from a total of two independent assays
Figure Legend Snippet: ERα is reprogrammed in shKMT2C-R cells. a Venn diagram showing overlap between shKMT2C-R (in CSS, red) and shRenilla (in full serum, purple) ERα binding sites. b HOMER motif analysis at the 10,512 novel ERα loci in shKMT2C-R. Full list in Fig. S17 . c , e , g IGV browser views for ERα in shRenilla and shKMT2C-R cells. Sites of increased ERα binding outlined in orange. For CMYC , orange outline encompasses a previously defined AP-1 dependent ERα site [ 34 ]. d , f , h qChIP analysis. IgG used as negative control. Values correspond to mean percentage of input ± s.e.m. of triplicate qPCR reactions of a single replicate. Data correspond to one representative assay from a total of two independent assays

Techniques Used: Binding Assay, Negative Control, Real-time Polymerase Chain Reaction

KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val
Figure Legend Snippet: KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val

Techniques Used: Cell Culture

KMT2C loss suppresses ERα target gene expression. a Supervised analysis of the 7938 differentially expressed genes between shRenilla and shKMT2C (shKMT2C#1 and #2 combined) MCF7 cells. All cells are cultured in full serum containing media. b GSEA showing 3857 genes downregulated in shKMT2C cells are enriched among genes downregulated following 5-day estrogen deprivation of shRenilla cells (≥3-fold). c GSEA of 3857 genes downregulated in shKMT2C as compared to the Hallmark Estrogen Response Early Geneset (Broad Institute). d GSEA of 3857 genes downregulated in shKMT2C as compared the Hallmark Estrogen Response Late Geneset. e mRNA levels; values correspond to the mean of three replicates ± s.e.m. E2, estradiol. f mRNA levels; values correspond to the mean of three replicates ± s.e.m.; two-tailed Student’s t -test with a desired FDR = 1% was used to determine statistical significance; ** P
Figure Legend Snippet: KMT2C loss suppresses ERα target gene expression. a Supervised analysis of the 7938 differentially expressed genes between shRenilla and shKMT2C (shKMT2C#1 and #2 combined) MCF7 cells. All cells are cultured in full serum containing media. b GSEA showing 3857 genes downregulated in shKMT2C cells are enriched among genes downregulated following 5-day estrogen deprivation of shRenilla cells (≥3-fold). c GSEA of 3857 genes downregulated in shKMT2C as compared to the Hallmark Estrogen Response Early Geneset (Broad Institute). d GSEA of 3857 genes downregulated in shKMT2C as compared the Hallmark Estrogen Response Late Geneset. e mRNA levels; values correspond to the mean of three replicates ± s.e.m. E2, estradiol. f mRNA levels; values correspond to the mean of three replicates ± s.e.m.; two-tailed Student’s t -test with a desired FDR = 1% was used to determine statistical significance; ** P

Techniques Used: Expressing, Cell Culture, Two Tailed Test

13) Product Images from "Ancient individuals from the North American Northwest Coast reveal 10,000 years of regional genetic continuity"

Article Title: Ancient individuals from the North American Northwest Coast reveal 10,000 years of regional genetic continuity

Journal: Proceedings of the National Academy of Sciences of the United States of America

doi: 10.1073/pnas.1620410114

Scenarios tested by the D statistic involving the spread of mitochondrial haplogroups in the Northwest Coast. A supports Anzick-1 being basal to both Shuká K áa and 939, which all share the D4h3a haplogroup. B supports Anzick-1 being basal
Figure Legend Snippet: Scenarios tested by the D statistic involving the spread of mitochondrial haplogroups in the Northwest Coast. A supports Anzick-1 being basal to both Shuká K áa and 939, which all share the D4h3a haplogroup. B supports Anzick-1 being basal

Techniques Used:

Genetic affinity of Shuká K áa and the other Northwest Coast prehistoric humans to global and regional indigenous populations. ( A ) Heat map represents the outgroup ƒ 3 statistics estimating the amount of shared genetic drift between
Figure Legend Snippet: Genetic affinity of Shuká K áa and the other Northwest Coast prehistoric humans to global and regional indigenous populations. ( A ) Heat map represents the outgroup ƒ 3 statistics estimating the amount of shared genetic drift between

Techniques Used:

Hypothetical scenarios for the regional peopling of the Northwest Coast. ( A ) Scenario tested by the D statistic where Anzick-1 is basal to both Shuká K áa and South America, which is rejected, indicating a closer affinity to South America.
Figure Legend Snippet: Hypothetical scenarios for the regional peopling of the Northwest Coast. ( A ) Scenario tested by the D statistic where Anzick-1 is basal to both Shuká K áa and South America, which is rejected, indicating a closer affinity to South America.

Techniques Used:

Principal components (PC) analysis (PC2 and PC3). Shuká K ), with Native American
Figure Legend Snippet: Principal components (PC) analysis (PC2 and PC3). Shuká K ), with Native American

Techniques Used:

Simulated Z -score distributions for different scenarios relating Shuká K áa to Northwest Coast and South American lineages. ( A ) Two scenarios relating Shuká K áa to Northwest Coast and South American linages. Scenario 1 places
Figure Legend Snippet: Simulated Z -score distributions for different scenarios relating Shuká K áa to Northwest Coast and South American lineages. ( A ) Two scenarios relating Shuká K áa to Northwest Coast and South American linages. Scenario 1 places

Techniques Used:

Shuká K áa in relation to other Native American groups. Schematic showing Shuká K áa placed on the branch leading to North Americans, which is supported by simulation-based D statistics.
Figure Legend Snippet: Shuká K áa in relation to other Native American groups. Schematic showing Shuká K áa placed on the branch leading to North Americans, which is supported by simulation-based D statistics.

Techniques Used:

ML tree with Shuká K áa and one migration. Samples and parameters are the same as in . Adding the migration event causes Shuká K áa to fall into the Pacific Northwest clade (Athabascan and Tsimshian), with an edge
Figure Legend Snippet: ML tree with Shuká K áa and one migration. Samples and parameters are the same as in . Adding the migration event causes Shuká K áa to fall into the Pacific Northwest clade (Athabascan and Tsimshian), with an edge

Techniques Used: Migration

Cluster analysis generated by ADMIXTURE. Set includes indigenous populations from the Americas, Siberia, and the Arctic, Greenland and the Anzick-1, Kennewick, Saqqaq, Shuká K áa, 939, 302, and 443 samples. The number of displayed clusters
Figure Legend Snippet: Cluster analysis generated by ADMIXTURE. Set includes indigenous populations from the Americas, Siberia, and the Arctic, Greenland and the Anzick-1, Kennewick, Saqqaq, Shuká K áa, 939, 302, and 443 samples. The number of displayed clusters

Techniques Used: Generated

14) Product Images from "Integrated analysis identifying long non-coding RNAs (lncRNAs) for competing endogenous RNAs (ceRNAs) network-regulated palatal shelf fusion in the development of mouse cleft palate"

Article Title: Integrated analysis identifying long non-coding RNAs (lncRNAs) for competing endogenous RNAs (ceRNAs) network-regulated palatal shelf fusion in the development of mouse cleft palate

Journal: Annals of Translational Medicine

doi: 10.21037/atm.2019.11.93

Histomorphology observations of palate shelf tissues at E14.5 between ATRA-treated vs. control mice. (A,B) The ATRA-treated palatal shelves remained separated without fusion; (C,D) at the same time, the palatal control shelves had contacted each other to complete initial palatal fusion. ATRA, all-trans retinoic acid; E14.5, embryonic gestation day 14.5; PS, palatal shelf; T, tongue; NS, nasal septum; H E, hematoxylin and eosin. Magnification in (B,D): ×10. (from Shu X, Cheng L, Dong Z, et al . Identification of circular RNA-associated competing endogenous RNA network in the development of cleft palate. J Cell Biochem 2019;120:16062-74).
Figure Legend Snippet: Histomorphology observations of palate shelf tissues at E14.5 between ATRA-treated vs. control mice. (A,B) The ATRA-treated palatal shelves remained separated without fusion; (C,D) at the same time, the palatal control shelves had contacted each other to complete initial palatal fusion. ATRA, all-trans retinoic acid; E14.5, embryonic gestation day 14.5; PS, palatal shelf; T, tongue; NS, nasal septum; H E, hematoxylin and eosin. Magnification in (B,D): ×10. (from Shu X, Cheng L, Dong Z, et al . Identification of circular RNA-associated competing endogenous RNA network in the development of cleft palate. J Cell Biochem 2019;120:16062-74).

Techniques Used: Mouse Assay

15) Product Images from "Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets"

Article Title: Single-CpG resolution mapping of 5-hydroxymethylcytosine by chemical labeling and exonuclease digestion identifies evolutionarily unconserved CpGs as TET targets

Journal: Genome Biology

doi: 10.1186/s13059-016-0919-y

Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)
Figure Legend Snippet: Validation of the SCL-exo strategy for single-CpG resolution mapping of 5hmC in genomic DNA from P19 cell-derived NPLCs. a Average CpG density along read length from SCL-seq (375,104 reads from chr7) and from SCL-exo (899,652 reads from chr7). b IGB visualization of hMeDIP and SCL-exo signals from three technical replicates in the 3’ region of the Centg2 gene. c Genome-wide correlation coefficient value (Pearson’s coefficient, r) for.wig files corresponding to two technical replicates of SCL-exo identified (id) CpGs. d Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-exo. e Venn diagram indicating the percentage of SCL-exo id CpGs (called from the consensus.wig file with a coverage threshold of 20×) included in hMeDIP-seq peaks (called with a threshold ( th ) of 12×). f Genome-wide correlation coefficient value for one replicate of hMeDIP and one replicate of SCL-seq. g Genome-wide correlation coefficient value for one replicate of SCL-exo and one replicate of SCL-seq. h Correlation between SCL-exo signal at id CpGs (number of reads) and their percentage of hydroxymethylation determined with the EpiMark kit for 27 selected CCGG sites (r: Pearson’s correlation coefficient)

Techniques Used: Derivative Assay, Genome Wide

16) Product Images from "Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte"

Article Title: Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte

Journal: Nature

doi: 10.1038/nature24454

Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.
Figure Legend Snippet: Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.

Techniques Used: RNA Sequencing Assay, Isolation, Mouse Assay, Purification, Magnetic Cell Separation, Transduction, Staining, Labeling, FACS, Chromatin Immunoprecipitation, In Situ, Amplification, Concentration Assay, Lysis, Expressing, Sequencing

17) Product Images from "Transcriptome analysis illuminates the nature of the intracellular interaction in a vertebrate-algal symbiosis"

Article Title: Transcriptome analysis illuminates the nature of the intracellular interaction in a vertebrate-algal symbiosis

Journal: eLife

doi: 10.7554/eLife.22054

GC and transcript length bias in SMARTer-cDNA synthesis-Nextera-XT libraries compared to TrueSeq libraries. Red lines indicate the GC content or transcript length biases in reads obtained from SMARTer-cDNA synthesis-Nextera-XT libraries. Blue lines indicate the GC content or transcript length biases in reads obtained from TrueSeq libraries. ( a ) GC content and length are plotted against ‘QRfit’ which is a measure of fit by quantile regression to the models in Hansen et al. (2012) . This metric approximates bias in the sequence dataset by comparing read counts to expected models based on quantiles in the distribution of the GC content of the transcripts. The opposing trends in the two sets of lines shows that GC content bias between the two different libraries is vastly different. The reads obtained from SMARTer-cDNA synthesis-Nextera-XT libraries will tend to have more counts for low GC content transcripts, while the reads obtained from TrueSeq libraries will tend to have more counts for high GC content transcripts, systemically. ( b ) There is also some moderate transcript length bias differences between the two library prep methods visualized as the separation between the groups of red and blue lines. The methods implemented by the conditional quantile normalization (cqn) package in R handles both types of bias to make the gene count data from both library preparation methods comparable. DOI: http://dx.doi.org/10.7554/eLife.22054.026
Figure Legend Snippet: GC and transcript length bias in SMARTer-cDNA synthesis-Nextera-XT libraries compared to TrueSeq libraries. Red lines indicate the GC content or transcript length biases in reads obtained from SMARTer-cDNA synthesis-Nextera-XT libraries. Blue lines indicate the GC content or transcript length biases in reads obtained from TrueSeq libraries. ( a ) GC content and length are plotted against ‘QRfit’ which is a measure of fit by quantile regression to the models in Hansen et al. (2012) . This metric approximates bias in the sequence dataset by comparing read counts to expected models based on quantiles in the distribution of the GC content of the transcripts. The opposing trends in the two sets of lines shows that GC content bias between the two different libraries is vastly different. The reads obtained from SMARTer-cDNA synthesis-Nextera-XT libraries will tend to have more counts for low GC content transcripts, while the reads obtained from TrueSeq libraries will tend to have more counts for high GC content transcripts, systemically. ( b ) There is also some moderate transcript length bias differences between the two library prep methods visualized as the separation between the groups of red and blue lines. The methods implemented by the conditional quantile normalization (cqn) package in R handles both types of bias to make the gene count data from both library preparation methods comparable. DOI: http://dx.doi.org/10.7554/eLife.22054.026

Techniques Used: Sequencing

High GC content algal genes were not detected by the combination of SMARTer cDNA synthesis and Nextera-XT library preparation. ( a ) The GC content distribution of algal transcripts generated using TrueSeq library preparation of total RNA, sequenced on the MySeq platform with approximately 30 million 75 bp paired end reads. 79% of eukaryote BUSCOs were detected in this assembly. The median GC content (green dashed line) is 62%. ( b ) The GC content distribution from ( a ), split by library preparation method. Red bars represent algal transcripts found in transcriptomes generated by both library preparation methods (SMARTer-Netxtera-XT and TruSeq). Blue bars represent transcripts found only in the transcriptome assembly from the TrueSeq library preparation method, that are absent from the transcriptome generated using the SMARTer cDNA synthesis-Nextera-XT library preparation method. There is an apparent bias against high GC content algal transcripts in library prepared using the SMARTer cDNA synthesis-Nextera-XT protocol (Kolgomorov-Smirnov test, p
Figure Legend Snippet: High GC content algal genes were not detected by the combination of SMARTer cDNA synthesis and Nextera-XT library preparation. ( a ) The GC content distribution of algal transcripts generated using TrueSeq library preparation of total RNA, sequenced on the MySeq platform with approximately 30 million 75 bp paired end reads. 79% of eukaryote BUSCOs were detected in this assembly. The median GC content (green dashed line) is 62%. ( b ) The GC content distribution from ( a ), split by library preparation method. Red bars represent algal transcripts found in transcriptomes generated by both library preparation methods (SMARTer-Netxtera-XT and TruSeq). Blue bars represent transcripts found only in the transcriptome assembly from the TrueSeq library preparation method, that are absent from the transcriptome generated using the SMARTer cDNA synthesis-Nextera-XT library preparation method. There is an apparent bias against high GC content algal transcripts in library prepared using the SMARTer cDNA synthesis-Nextera-XT protocol (Kolgomorov-Smirnov test, p

Techniques Used: Generated

18) Product Images from "Polycomb enables primitive endoderm lineage priming in embryonic stem cells"

Article Title: Polycomb enables primitive endoderm lineage priming in embryonic stem cells

Journal: eLife

doi: 10.7554/eLife.14926

Invariant H3K4me3 levels at Epi- and PrEn-primed genes. ( A ) Example H3K4me3 ChIP-seq profiles (log2(IP/Input)) for Actb and Hoxd (control), Aldh1b1 (upregulated in HV - S + ) and Zmiz1 (upregulated in HV + S + ). Profiles were generated using the UCSC genome browser with coordinates given for mm9. ( B ) H3K4me3 ratios (log 2 (IP HV-S+/ Input HV-S+ )-log 2 (IP HV+S+ /Input HV+S+ )) between the HV - S + and HV + S + populations across a composite gene with normalized length representing genes upregulated in HV - S + (green) and HV + S + (red). The bars at the top represent mean read depth of both populations across the locus. ( C ) Boxplots showing the ratio of H3K4me3 ChIP-seq signal between HV - S + and HV + S + ESC populations for TSS (left; ± 500 bp) and gene body (right) for all genes (grey), genes upregulated in HV - S + (green) and genes upregulated in HV + S + (red). ( D ) Heatmaps and summary plots of H3K4me3 (log 2 (IP/Input)) around the TSS (± 2.5 kb) for differentially expressed gene sets in both the HV - S + and HV + S + ESC populations. DOI: http://dx.doi.org/10.7554/eLife.14926.005
Figure Legend Snippet: Invariant H3K4me3 levels at Epi- and PrEn-primed genes. ( A ) Example H3K4me3 ChIP-seq profiles (log2(IP/Input)) for Actb and Hoxd (control), Aldh1b1 (upregulated in HV - S + ) and Zmiz1 (upregulated in HV + S + ). Profiles were generated using the UCSC genome browser with coordinates given for mm9. ( B ) H3K4me3 ratios (log 2 (IP HV-S+/ Input HV-S+ )-log 2 (IP HV+S+ /Input HV+S+ )) between the HV - S + and HV + S + populations across a composite gene with normalized length representing genes upregulated in HV - S + (green) and HV + S + (red). The bars at the top represent mean read depth of both populations across the locus. ( C ) Boxplots showing the ratio of H3K4me3 ChIP-seq signal between HV - S + and HV + S + ESC populations for TSS (left; ± 500 bp) and gene body (right) for all genes (grey), genes upregulated in HV - S + (green) and genes upregulated in HV + S + (red). ( D ) Heatmaps and summary plots of H3K4me3 (log 2 (IP/Input)) around the TSS (± 2.5 kb) for differentially expressed gene sets in both the HV - S + and HV + S + ESC populations. DOI: http://dx.doi.org/10.7554/eLife.14926.005

Techniques Used: Chromatin Immunoprecipitation, Generated

19) Product Images from "Differential regulation of lymphopoiesis and leukemogenesis by individual zinc fingers of Ikaros"

Article Title: Differential regulation of lymphopoiesis and leukemogenesis by individual zinc fingers of Ikaros

Journal: Nature immunology

doi: 10.1038/ni.2707

Deregulation of distinct sets of genes in Ikzf1 ΔF1/ΔF1 and Ikzf1 ΔF4/ΔF4 DP thymocytes. ( a ) RNA-Seq was performed with FACS-sorted DP thymocyte mRNA from 4-week-old wild-type and mutant mice in duplicate. A Venn diagram shows the number of genes that exhibited increased or decreased mRNA levels of at least 3-fold ( P ≤0.001) in one of the mutant strains. Genes with RPKM≥4 in at least one of the six samples were included in this analysis. ( b ) A Venn diagram shows the number of genes from the RNA-Seq experiment described above that exhibited increased or decreased mRNA levels of at least 10-fold ( P ≤0.001) in one of the mutant strains. ( c ) Gene Ontology analysis of genes upregulated more than 3-fold ( P ≤0.001) in Ikzf1 ΔF4/ΔF4 DP thymocytes. ( d ) The distribution of RPKMs (mRNA levels) is shown for all annotated genes (left) and all genes that were upregulated by at least 3-fold ( P ≤0.001) in Ikzf1 ΔF1/ΔF1 (middle) or Ikzf1 ΔF1/ΔF1 (right) mutant mice.
Figure Legend Snippet: Deregulation of distinct sets of genes in Ikzf1 ΔF1/ΔF1 and Ikzf1 ΔF4/ΔF4 DP thymocytes. ( a ) RNA-Seq was performed with FACS-sorted DP thymocyte mRNA from 4-week-old wild-type and mutant mice in duplicate. A Venn diagram shows the number of genes that exhibited increased or decreased mRNA levels of at least 3-fold ( P ≤0.001) in one of the mutant strains. Genes with RPKM≥4 in at least one of the six samples were included in this analysis. ( b ) A Venn diagram shows the number of genes from the RNA-Seq experiment described above that exhibited increased or decreased mRNA levels of at least 10-fold ( P ≤0.001) in one of the mutant strains. ( c ) Gene Ontology analysis of genes upregulated more than 3-fold ( P ≤0.001) in Ikzf1 ΔF4/ΔF4 DP thymocytes. ( d ) The distribution of RPKMs (mRNA levels) is shown for all annotated genes (left) and all genes that were upregulated by at least 3-fold ( P ≤0.001) in Ikzf1 ΔF1/ΔF1 (middle) or Ikzf1 ΔF1/ΔF1 (right) mutant mice.

Techniques Used: RNA Sequencing Assay, FACS, Mutagenesis, Mouse Assay

Selective synergy between BCR-ABL and the Ikzf1 ΔF4/Δ F4 mutation in vitro and in vivo . ( a ) In vitro growth curves from one representative experiment (of three) are shown for bone marrow cells from wild-type, Ikzf1 ΔF1/ΔF1 , and Ikzf1 ΔF4/ΔF4 mice transduced with a BCR-ABL-expressing retrovirus and grown under B-ALL culture conditions. ( b ) Kaplan-Meier survival curves are shown for irradiated recipient mice transplanted with 10 6 BCR-ABL-transduced bone marrow cells from wild-type (n=15), Ikzf1 ΔF1/ΔF1 (n-8), and Ikzf1 ΔF4/ΔF4 (n=14) mice. ( c ) RNA-Seq was performed with mRNA from sorted pro B cells (pro-B) and pre-BI+large pre-BII cells (pre-BI-II L ), as well as from BCR-ABL-transformed wild-type, Ikzf1 ΔF1/ΔF1 , and Ikzf1 ΔF4/ΔF4 cells harvested at day 21 (d21) or day 28 (d28) in culture. Genes whose mRNA levels differed by 3-fold or more between any two samples among the 12 samples analyzed were clustered by k-means clustering. ( d ) Genes that were selectively upregulated or downregulated in BCR-ABL-transformed Ikzf1 ΔF4/ΔF4 cells were identified by k-means clustering of the data sets from the six BCR-ABL-transformed cultures. Only the two clusters containing genes that were selectively upregulated or downregulated in the Ikzf1 ΔF4/ΔF4 samples are shown. Expression data from pro B cells (pro-B) and pre-BI+large pre-BII cells (pre-BI-II L ) were aligned after the cluster analysis was completed. ( e ) One representative flow cytometry experiment out of three or more is shown for the in vitro cultures described in panel a . Cells are gated on YFP + cells. ( f ) Reduced Il2ra (CD25) and increased Kit mRNA levels are shown from the RNA-seq data (n=2).
Figure Legend Snippet: Selective synergy between BCR-ABL and the Ikzf1 ΔF4/Δ F4 mutation in vitro and in vivo . ( a ) In vitro growth curves from one representative experiment (of three) are shown for bone marrow cells from wild-type, Ikzf1 ΔF1/ΔF1 , and Ikzf1 ΔF4/ΔF4 mice transduced with a BCR-ABL-expressing retrovirus and grown under B-ALL culture conditions. ( b ) Kaplan-Meier survival curves are shown for irradiated recipient mice transplanted with 10 6 BCR-ABL-transduced bone marrow cells from wild-type (n=15), Ikzf1 ΔF1/ΔF1 (n-8), and Ikzf1 ΔF4/ΔF4 (n=14) mice. ( c ) RNA-Seq was performed with mRNA from sorted pro B cells (pro-B) and pre-BI+large pre-BII cells (pre-BI-II L ), as well as from BCR-ABL-transformed wild-type, Ikzf1 ΔF1/ΔF1 , and Ikzf1 ΔF4/ΔF4 cells harvested at day 21 (d21) or day 28 (d28) in culture. Genes whose mRNA levels differed by 3-fold or more between any two samples among the 12 samples analyzed were clustered by k-means clustering. ( d ) Genes that were selectively upregulated or downregulated in BCR-ABL-transformed Ikzf1 ΔF4/ΔF4 cells were identified by k-means clustering of the data sets from the six BCR-ABL-transformed cultures. Only the two clusters containing genes that were selectively upregulated or downregulated in the Ikzf1 ΔF4/ΔF4 samples are shown. Expression data from pro B cells (pro-B) and pre-BI+large pre-BII cells (pre-BI-II L ) were aligned after the cluster analysis was completed. ( e ) One representative flow cytometry experiment out of three or more is shown for the in vitro cultures described in panel a . Cells are gated on YFP + cells. ( f ) Reduced Il2ra (CD25) and increased Kit mRNA levels are shown from the RNA-seq data (n=2).

Techniques Used: Mutagenesis, In Vitro, In Vivo, Mouse Assay, Transduction, Expressing, Irradiation, RNA Sequencing Assay, Transformation Assay, Flow Cytometry, Cytometry

20) Product Images from "Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques"

Article Title: Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques

Journal: Frontiers in Genetics

doi: 10.3389/fgene.2019.00150

Comparison of coverage between poly-A RNA-seq, SFL, and 3′DGE. (A) Boxplots of distribution of library size for each platform. (B) Cumulative distribution of reads assigned to individual genes per sample. The x -axis indicates the quantile for each gene in terms of ranking by relative expression. The y -axis shows the cumulative proportion of total counted reads assigned to these genes, i.e., the running sum of reads divided by the total number of reads across all genes. (C) The top 3 boxplots show the percentage of reads aligned (i) , uniquely aligned (ii) , and counted (iii) relative to the total library size for each platform. The bottom boxplot (iv) shows the proportion of genes with counts > 1, for protein-coding genes annotated across all 3 platforms (18,488). For (ii) , “Reads Uniquely Aligned” is not shown for 3′DGE because “Reads Uniquely Aligned” and “Reads Counted” are the same values as a result of the data pre-processing protocol, specific to 3′DGE (see section “Materials and Methods”). Counts values for these percentages are given in Supplementary Figure S1A . (D) Analysis of the principal component error of subsampled counted library sizes for full coverage poly-A RNA-seq, SFL, and 3′DGE for principal component 1. Results for principal component 2–5 is shown in Supplementary Figure S1D . Initial subsamples of Poly-A RNA-seq and 3′DGE to the SFL library size are also given as dotted lines.
Figure Legend Snippet: Comparison of coverage between poly-A RNA-seq, SFL, and 3′DGE. (A) Boxplots of distribution of library size for each platform. (B) Cumulative distribution of reads assigned to individual genes per sample. The x -axis indicates the quantile for each gene in terms of ranking by relative expression. The y -axis shows the cumulative proportion of total counted reads assigned to these genes, i.e., the running sum of reads divided by the total number of reads across all genes. (C) The top 3 boxplots show the percentage of reads aligned (i) , uniquely aligned (ii) , and counted (iii) relative to the total library size for each platform. The bottom boxplot (iv) shows the proportion of genes with counts > 1, for protein-coding genes annotated across all 3 platforms (18,488). For (ii) , “Reads Uniquely Aligned” is not shown for 3′DGE because “Reads Uniquely Aligned” and “Reads Counted” are the same values as a result of the data pre-processing protocol, specific to 3′DGE (see section “Materials and Methods”). Counts values for these percentages are given in Supplementary Figure S1A . (D) Analysis of the principal component error of subsampled counted library sizes for full coverage poly-A RNA-seq, SFL, and 3′DGE for principal component 1. Results for principal component 2–5 is shown in Supplementary Figure S1D . Initial subsamples of Poly-A RNA-seq and 3′DGE to the SFL library size are also given as dotted lines.

Techniques Used: RNA Sequencing Assay, Expressing

Comparison of gene-set enrichment of gene mutation signatures across SFL and 3′DGE. (A) Comparison of the gene set enrichment results between SFL and 3′DGE with respect to the “DMSO-treated; genotypic perturbation vs. control” differential signatures. Points indicate gene set enrichment against concordant signatures, e.g., PIK3CA mutation and CNA gene sets against the “PIK3CA vs. HcRed” differential signatures. Shown are the transformed FDR Q -values from permutation-based testing by pre-ranked GSEA. | –Log10(FDR Q -Values)| corresponding to the FDR = 0.05 significance thresholds are shown as vertical and horizontal gray lines for the y - and x -axes, respectively. The names of the gene sets whose enrichment meets this threshold in either of the two platforms are shown and their points are filled in. Colors and shape of points denote direction and source of the gene set, respectively. Additional results for CSC, NNK, and BaP stratified genotypic perturbation signatures, as well as comparisons between full coverage RNA-seq and either SFL and 3′DGE are shown in Supplementary Figure S9 . (B) Discovery rates for genotypic perturbations across full coverage poly-A RNA-seq, SFL, and 3′DGE, for chemically untreated (full coverage RNA-seq) and DMSO treated (SFL and 3′DGE) samples. Results demonstrate full counted library size, as well as subsampled libraries. (C) Correlation between transformed FDR Q -values from gene set enrichment at different subsamples of each platform and the results from the full counted library size. Shown are the results from genotypic perturbations from untreated (full coverage RNA-seq)/DMSO treated (SFL and 3′DGE), CSC, and NNK chemically treated samples.
Figure Legend Snippet: Comparison of gene-set enrichment of gene mutation signatures across SFL and 3′DGE. (A) Comparison of the gene set enrichment results between SFL and 3′DGE with respect to the “DMSO-treated; genotypic perturbation vs. control” differential signatures. Points indicate gene set enrichment against concordant signatures, e.g., PIK3CA mutation and CNA gene sets against the “PIK3CA vs. HcRed” differential signatures. Shown are the transformed FDR Q -values from permutation-based testing by pre-ranked GSEA. | –Log10(FDR Q -Values)| corresponding to the FDR = 0.05 significance thresholds are shown as vertical and horizontal gray lines for the y - and x -axes, respectively. The names of the gene sets whose enrichment meets this threshold in either of the two platforms are shown and their points are filled in. Colors and shape of points denote direction and source of the gene set, respectively. Additional results for CSC, NNK, and BaP stratified genotypic perturbation signatures, as well as comparisons between full coverage RNA-seq and either SFL and 3′DGE are shown in Supplementary Figure S9 . (B) Discovery rates for genotypic perturbations across full coverage poly-A RNA-seq, SFL, and 3′DGE, for chemically untreated (full coverage RNA-seq) and DMSO treated (SFL and 3′DGE) samples. Results demonstrate full counted library size, as well as subsampled libraries. (C) Correlation between transformed FDR Q -values from gene set enrichment at different subsamples of each platform and the results from the full counted library size. Shown are the results from genotypic perturbations from untreated (full coverage RNA-seq)/DMSO treated (SFL and 3′DGE), CSC, and NNK chemically treated samples.

Techniques Used: Mutagenesis, Transformation Assay, RNA Sequencing Assay

21) Product Images from "A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum)"

Article Title: A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum)

Journal: BMC Genomics

doi: 10.1186/s12864-019-6236-6

Functional annotations of protein coding parental genes generating differentially expressed circRNAs. a Gene ontology (GO) classification of parental genes of circRNAs; b The top 20 enriched KEGG pathways of circRNA-producing parental genes
Figure Legend Snippet: Functional annotations of protein coding parental genes generating differentially expressed circRNAs. a Gene ontology (GO) classification of parental genes of circRNAs; b The top 20 enriched KEGG pathways of circRNA-producing parental genes

Techniques Used: Functional Assay

22) Product Images from "A putative Vibrio cholerae two-component system controls a conserved periplasmic protein in response to the antimicrobial peptide polymyxin B"

Article Title: A putative Vibrio cholerae two-component system controls a conserved periplasmic protein in response to the antimicrobial peptide polymyxin B

Journal: PLoS ONE

doi: 10.1371/journal.pone.0186199

Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq
Figure Legend Snippet: Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq

Techniques Used: RNA Sequencing Assay

Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq
Figure Legend Snippet: Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq

Techniques Used: RNA Sequencing Assay

23) Product Images from "A putative Vibrio cholerae two-component system controls a conserved periplasmic protein in response to the antimicrobial peptide polymyxin B"

Article Title: A putative Vibrio cholerae two-component system controls a conserved periplasmic protein in response to the antimicrobial peptide polymyxin B

Journal: PLoS ONE

doi: 10.1371/journal.pone.0186199

Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq
Figure Legend Snippet: Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq

Techniques Used: RNA Sequencing Assay

Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq
Figure Legend Snippet: Profiling the transcriptome of V . cholerae in response to polymyxin B exposure using Illumina-based RNA-Seq

Techniques Used: RNA Sequencing Assay

24) Product Images from "An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer"

Article Title: An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer

Journal: eLife

doi: 10.7554/eLife.37184

Identification of splicing targets regulated by QKI and RBFOX1. ( A ) Quantification of the different types of alternative splicing events regulated by QKI or RBFOX1 overexpression as determined using rMATS. Exclusion or inclusion are relative to control cells overexpressing EGFP. ( B ) RT-PCR validation of individual splicing events regulated by QKI or RBFOX1. The cDNA from cells expressing the indicated ORFs were subjected to PCR amplification using primers flanking the alternative exon. The ratios of the intensity of the upper (inclusion) and lower (exclusion) PCR product bands were quantified and the relative intensity of the upper band is indicated. Below are shown RNA-sequencing based quantification of the % inclusion of the alternative exon. n = 3 (EGFP, HcRed and RBFOX1) or n = 2 (QKI and SNAI1).
Figure Legend Snippet: Identification of splicing targets regulated by QKI and RBFOX1. ( A ) Quantification of the different types of alternative splicing events regulated by QKI or RBFOX1 overexpression as determined using rMATS. Exclusion or inclusion are relative to control cells overexpressing EGFP. ( B ) RT-PCR validation of individual splicing events regulated by QKI or RBFOX1. The cDNA from cells expressing the indicated ORFs were subjected to PCR amplification using primers flanking the alternative exon. The ratios of the intensity of the upper (inclusion) and lower (exclusion) PCR product bands were quantified and the relative intensity of the upper band is indicated. Below are shown RNA-sequencing based quantification of the % inclusion of the alternative exon. n = 3 (EGFP, HcRed and RBFOX1) or n = 2 (QKI and SNAI1).

Techniques Used: Over Expression, Reverse Transcription Polymerase Chain Reaction, Expressing, Polymerase Chain Reaction, Amplification, RNA Sequencing Assay

25) Product Images from "KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function"

Article Title: KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function

Journal: Oncogene

doi: 10.1038/s41388-018-0273-5

KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val
Figure Legend Snippet: KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val

Techniques Used: Cell Culture

26) Product Images from "A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb"

Article Title: A transcriptome-wide, organ-specific regulatory map of Dendrobium officinale, an important traditional Chinese orchid herb

Journal: Scientific Reports

doi: 10.1038/srep18864

Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .
Figure Legend Snippet: Tandemly distributed small RNAs (sRNAs) identified on the highly structured microRNA (miRNA) precursor candidates in Dendrobium officinale . ( A ) The transcript comp124801_c0_seq1 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially delineated by a pink box). In addition to generating miRNAs (dof-miR340, dof-miR341, dof-miR1002 and dof-miR1004) and miRNA*s (dof-miR1002* and dof-miR1004*), the long-stem region potentially encodes three pairs of tandemly distributed sRNAs (124801_sRNA1 and 124801_sRNA6, 124801_sRNA2 and 124801_sRNA5, and 124801_sRNA3 and 124801_sRNA4). Each pair possesses 2-nt 3’ overhangs. Five degradome signatures (124801_degr1 to 124801_degr5) were detected at the ends of certain tandemly distributed sRNAs. And, 124801_degr3 also appeared at the 5’ ends of dof-miR-340 and dof-miR-341, and 124801_degr4 and 124801_degr5 are present at the 3’ ends of dof-miR-1004. The accumulation levels (normalized in RPM, reads per million; please refer to Materials and Methods for RPM calculation) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. Their accumulation levels in the stems of Dendrobium officinale were highlighted in pink background color. ( B ) The transcript comp168357_c1_seq6 assembled by RNA-seq reads could form an internal hairpin structure with a long-stem region (partially included in a pink box). Within this region, three pairs of sRNAs (including 168357_sRNA2 and 168357_sRNA6, 168357_sRNA3 and 168357_sRNA5, and the dof-miR-1023/dof-miR-1023* duplex) along with two unpaired sRNAs (168357_sRNA1 and 168357_sRNA4) were identified to be distributed tandemly. Each pair possesses 2-nt 3’ overhangs. Eleven degradome signatures (168357_degr1 to 168357_degr11) were detected at the ends of certain tandemly distributed sRNAs. The accumulation levels (in RPM) of the degradome signatures, the miRNAs, the miRNA*s and the tandemly distributed sRNAs are shown in the diagrams on the right of the panel. The secondary structures of the two transcripts were predicted by using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ) 22 .

Techniques Used: RNA Sequencing Assay

27) Product Images from "Integrated analysis of high-throughput sequencing data shows abscisic acid-responsive genes and miRNAs in strawberry receptacle fruit ripening"

Article Title: Integrated analysis of high-throughput sequencing data shows abscisic acid-responsive genes and miRNAs in strawberry receptacle fruit ripening

Journal: Horticulture Research

doi: 10.1038/s41438-018-0100-8

Degradome sequencing validated that three known miRNAs and one novel miRNA degraded their corresponding target genes. The secondary structure, expression of target gene, correlation rate between miRNA and target gene, and degradome T-plot of miR164c, miR172b, miR396c and Fa_novel6
Figure Legend Snippet: Degradome sequencing validated that three known miRNAs and one novel miRNA degraded their corresponding target genes. The secondary structure, expression of target gene, correlation rate between miRNA and target gene, and degradome T-plot of miR164c, miR172b, miR396c and Fa_novel6

Techniques Used: Sequencing, Expressing

28) Product Images from "An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer"

Article Title: An alternative splicing switch in FLNB promotes the mesenchymal cell state in human breast cancer

Journal: eLife

doi: 10.7554/eLife.37184

RNA-Seq and eCLIP-Seq analysis of HME cells expressing QKI and RBFOX1. ( A ) (Upper) Venn diagrams illustrating the overlap in AS events regulated by QKI and RBFOX1 for each AS event type, including alternative 5’ splice sites (A5SS), alternative 3’ splice sites (A3SS), mutually exclusive exons (MXE) and retained introns (RI) (related to Figure 4A ). The significance of overlapping events was determined by Fisher’s exact test and p values are shown. (Lower) Scatter plot of the change in ‘Percentage Spliced In’ (PSI) for the corresponding alternative splicing events shared between QKI and RBFOX1. ΔPSI values are for each ORF relative to EGFP (related to Figure 4G ). ( B ) Schematic of the eCLIP-seq experiment. Briefly, protein-RNA interactions were stabilized with UV crosslinking followed by immunoprecipitation. The RNA was then ligated to a 3’ end adapter and reverse-transcribed. An additional adapter was ligated to the 3’ end of the cDNA and the cDNA was amplified by PCR for Illumina sequencing. The crosslinked sites (‘RT stops’) correspond to the 5’ end of sequenced reads and allow the identification of specific protein-RNA interaction sites. ( C ) Interaction between QKI and RBFOX1 as determined by immunoprecipitation of endogenous QKI protein followed by immunoblotting for QKI, RBFOX1 or vinculin. The cell lysate was pretreated with the indicated concentration of RNase at room temperature for 20 min (left). Cell lysate was pretreated with 50 ng/ml of RNase before the immunoprecipitation of endogenous QKI proteins (right).
Figure Legend Snippet: RNA-Seq and eCLIP-Seq analysis of HME cells expressing QKI and RBFOX1. ( A ) (Upper) Venn diagrams illustrating the overlap in AS events regulated by QKI and RBFOX1 for each AS event type, including alternative 5’ splice sites (A5SS), alternative 3’ splice sites (A3SS), mutually exclusive exons (MXE) and retained introns (RI) (related to Figure 4A ). The significance of overlapping events was determined by Fisher’s exact test and p values are shown. (Lower) Scatter plot of the change in ‘Percentage Spliced In’ (PSI) for the corresponding alternative splicing events shared between QKI and RBFOX1. ΔPSI values are for each ORF relative to EGFP (related to Figure 4G ). ( B ) Schematic of the eCLIP-seq experiment. Briefly, protein-RNA interactions were stabilized with UV crosslinking followed by immunoprecipitation. The RNA was then ligated to a 3’ end adapter and reverse-transcribed. An additional adapter was ligated to the 3’ end of the cDNA and the cDNA was amplified by PCR for Illumina sequencing. The crosslinked sites (‘RT stops’) correspond to the 5’ end of sequenced reads and allow the identification of specific protein-RNA interaction sites. ( C ) Interaction between QKI and RBFOX1 as determined by immunoprecipitation of endogenous QKI protein followed by immunoblotting for QKI, RBFOX1 or vinculin. The cell lysate was pretreated with the indicated concentration of RNase at room temperature for 20 min (left). Cell lysate was pretreated with 50 ng/ml of RNase before the immunoprecipitation of endogenous QKI proteins (right).

Techniques Used: RNA Sequencing Assay, Expressing, Immunoprecipitation, Amplification, Polymerase Chain Reaction, Sequencing, Concentration Assay

29) Product Images from "Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte"

Article Title: Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte

Journal: Nature

doi: 10.1038/nature24454

Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.
Figure Legend Snippet: Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.

Techniques Used: RNA Sequencing Assay, Isolation, Mouse Assay, Purification, Magnetic Cell Separation, Transduction, Staining, Labeling, FACS, Chromatin Immunoprecipitation, In Situ, Amplification, Concentration Assay, Lysis, Expressing, Sequencing

30) Product Images from "Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte"

Article Title: Single Cell Transcriptomics Reconstructs Fate Conversion from Fibroblast to Cardiomyocyte

Journal: Nature

doi: 10.1038/nature24454

Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.
Figure Legend Snippet: Experimental design, analysis pipeline, quality control and normalization for single-cell RNA-seq ( a ) Experimental workflow. Hearts were isolated from P1.5 neonatal mice and cells were dissociated by enzymatic digestion. Thy1+ cells were then purified by MACS and plated overnight. The adherent cells (CF) were then transduced with retroviruses encoding the reprogramming factors M, G, and T or DsRed, or left untransduced (Mock). On day 3 post transduction, cells were trypsinized and live/dead stained. Additionally, for some experiments designed to examine the relative mouse RNA abundance in cells receiving different treatment, Mock/M+G+T cells were labeled with a green cell tracker CFSE, FACS-sorted for live cells, and then mixed at a designated ratio with FACS-sorted live DsRed+ cells from a parallel DsRed transduction. The single cell suspension was loaded onto a medium size chip (10-17 μm) and single cells were captured on a Fluidigm C1 machine. Brightfield and for some experiments, fluorescent images, were taken for all capture sites. Individual cDNA libraries for each cell were prepared in situ by RT with pre-amplification after adding RNA spike-in. Brightfield and/or fluorescent images for each capture site were examined and libraries from nests with 0 or multiple cells were excluded from downstream analysis. Illumina libraries were then prepared for each cell, pooled, quality-checked and sequenced on Hiseq 2500. ( b ) Design of the seven independent single-cell RNA-seq experiments including treatment, RNA spike-ins, and Fluidigm chips used. ( c ) Data analysis pipeline. Barcodes were trimmed off from RNA-seq raw reads and the quality of these reads was confirmed with fastqc. High quality reads were mapped to the mm10 genome with Tophat2 and counted with Htseq-count. Outliers were removed as described in (d). The raw counts were normalized first to technical and biological size factors within each experiment using DEseq and then to expt size factors calculated based on relative mouse mRNA abundance in cells receiving different treatments (h). Residual batch effects between experiments receiving the same treatment were removed using ComBat. Cell grouping and modeling were then performed using the normalized gene counts with PCA, HC, SLICER and more. The most important three quality control steps were labeled in red in (a) and (c). The above strict quality control criteria ensured that only high-quality and biologically meaningful data from healthy single cells were analyzed. ( d ) For each of the seven single cell experiments, percentage of reads mapped to spike-in in each cell was plotted against percentage of reads mapped to mouse genome (left panel) or mouse mRNA (right panel) in that cell. Cells outside of the red circles were outliers. ( e ) For each of the five single cell experiments that contained ERCC spike-in, average count numbers of each ERCC spike-in was plotted against their concentration in the lysis mix A (see Fluidigm’s protocol for details). Linear regression coefficients (R value) and their corresponding p values (two-sided, α=0.05) are shown. The results showed a dynamic range (~10 5 ) of ERCC concentration that covers the full spectrum of mouse gene expression levels. The high R values indicate strong correlation between hypothetical molecular concentrations and measured gene counts in our experiments. ( f ) Squared coefficients of variation (CV 2 ) were plotted against average expression of ERCC spike-ins (left) or mouse genes (right) for experiments containing ERCC spikein. ( g ) DsRed counts in expt E3 and E4-E7 plotted against Mef2c counts and/or total mouse mRNA counts after normalization to technical and biological size factors within each experiment (Methods). Cells in in the four experiments were classified as DsRed-transduced (E3R, E5R, E6R, E7R), M+G+T-transduced (E5M, E6M), or untransduced cells (E3U, E7U) based on these plots. ( h-i ) Normalization to experiment size factors to account for technical contributions to expt-to-expt variations such as varied capture efficiency while retain biological variations such as differences in total mRNA abundance in cells receiving different treatments. ( h ) Median total mouse mRNA counts were calculated for each treatment in each experiment and average mRNA counts were compared between different treatments in one experiment (E3, E4-E7) with two-sided student’s t test (α=0.05). Experiment size factors were calculated based on the ratio of median mRNA counts between different treatments. After normalization to the expt size factors, the median mRNA count equals to 1,000,000 for uninfected and DsRed-transduced cells and 616136 for M+G+T-transduction. ( i ) PCA of two biological replicates E5 and E6 that have different sequencing depth/cell due to different capture efficiencies before (left) and after (right) normalization to experiment size factors. Top 400 PCA genes were used.

Techniques Used: RNA Sequencing Assay, Isolation, Mouse Assay, Purification, Magnetic Cell Separation, Transduction, Staining, Labeling, FACS, Chromatin Immunoprecipitation, In Situ, Amplification, Concentration Assay, Lysis, Expressing, Sequencing

31) Product Images from "Polycomb enables primitive endoderm lineage priming in embryonic stem cells"

Article Title: Polycomb enables primitive endoderm lineage priming in embryonic stem cells

Journal: eLife

doi: 10.7554/eLife.14926

Dynamic changes in gene-body H3K27me3 levels contrasts with a specific enrichment of H3K27me3 at the promoters of PrEn-primed genes. ( A ) Schematic representation of the stratification used to bin H3K27me3 signal with respect to genes (upper panel). Boxplots showing the log2 ratios of H3K27me3 ChIP-seq signal between the two primed ESC populations for genes with or without an H3K27me3 ‘peak’ at their TSS (middle and lower panels respectively; presented as in Figure 1G ). Refseq genes were scored as H3K27me3 +ve if a ‘peak’ was found within 100 bp of the annotated TSS. Two-sample permutation tests (oneway test) were used to compare the H3K27me3 ChIP-seq signal at priming vs all genes. Significant p values are indicated* and their values shown above the plot. ( B ) H3K27me3 ratios (log 2 (IP HV-S+ /Input HV-S+ )-log 2 (IPHV+S+/Input HV+S+ )) between the HV - S + and HV + S + populations presented as for Figure 1F . Barplots are presented for priming genes sub-divided into those with or without an associated ‘peak’ of H3K27me3 at their TSS (upper and lower panels respectively; association scored as for ( A ). ( C ) MA plot comparing nascent RNA levels (measured by GRO-seq) between HV - S + and HV + S + cell populations. Genes found to show a consistent differential RNA signal in both steady-state and GRO-seq datasets are highlighted in green and red (upregulated in HV - S + and HV + S + respectively; numbers of genes are indicated within the plot). ( D ) TSS (± 2.5 kb) heatmaps and summary plots depicting H3K27me3 signal (log 2 (IP/Input)) for genes with differential steady-state and nascent RNA levels (selection based on panel C) between the HV - S + and HV + S + ESC populations. DOI: http://dx.doi.org/10.7554/eLife.14926.006
Figure Legend Snippet: Dynamic changes in gene-body H3K27me3 levels contrasts with a specific enrichment of H3K27me3 at the promoters of PrEn-primed genes. ( A ) Schematic representation of the stratification used to bin H3K27me3 signal with respect to genes (upper panel). Boxplots showing the log2 ratios of H3K27me3 ChIP-seq signal between the two primed ESC populations for genes with or without an H3K27me3 ‘peak’ at their TSS (middle and lower panels respectively; presented as in Figure 1G ). Refseq genes were scored as H3K27me3 +ve if a ‘peak’ was found within 100 bp of the annotated TSS. Two-sample permutation tests (oneway test) were used to compare the H3K27me3 ChIP-seq signal at priming vs all genes. Significant p values are indicated* and their values shown above the plot. ( B ) H3K27me3 ratios (log 2 (IP HV-S+ /Input HV-S+ )-log 2 (IPHV+S+/Input HV+S+ )) between the HV - S + and HV + S + populations presented as for Figure 1F . Barplots are presented for priming genes sub-divided into those with or without an associated ‘peak’ of H3K27me3 at their TSS (upper and lower panels respectively; association scored as for ( A ). ( C ) MA plot comparing nascent RNA levels (measured by GRO-seq) between HV - S + and HV + S + cell populations. Genes found to show a consistent differential RNA signal in both steady-state and GRO-seq datasets are highlighted in green and red (upregulated in HV - S + and HV + S + respectively; numbers of genes are indicated within the plot). ( D ) TSS (± 2.5 kb) heatmaps and summary plots depicting H3K27me3 signal (log 2 (IP/Input)) for genes with differential steady-state and nascent RNA levels (selection based on panel C) between the HV - S + and HV + S + ESC populations. DOI: http://dx.doi.org/10.7554/eLife.14926.006

Techniques Used: Chromatin Immunoprecipitation, Selection

More pronounced changes in H3K27me3 levels are observed in differentiating cells. ( A ) Venn diagrams showing the overlap of genes with elevated expression in Rex + and HV - S + ESC populations (left) and elevated expression in Rex - and HV + S + (right) ESC populations. Numbers of overlapping genes are given for each comparison with significant over/under representation determined using a Fisher’s test. ( B ) Boxplots showing the ratio of H3K27me3 ChIP-seq signal between Rex + and Rex - ESC populations for TSS (left) and gene body (right) for all genes (grey), genes upregulated in Rex + (green), and genes upregulated in Rex - (red). Two-sample permutation tests (oneway test) were used to compare the H3K27me3 ChIP-seq signal between all vs. differentially expressed genes. P values of
Figure Legend Snippet: More pronounced changes in H3K27me3 levels are observed in differentiating cells. ( A ) Venn diagrams showing the overlap of genes with elevated expression in Rex + and HV - S + ESC populations (left) and elevated expression in Rex - and HV + S + (right) ESC populations. Numbers of overlapping genes are given for each comparison with significant over/under representation determined using a Fisher’s test. ( B ) Boxplots showing the ratio of H3K27me3 ChIP-seq signal between Rex + and Rex - ESC populations for TSS (left) and gene body (right) for all genes (grey), genes upregulated in Rex + (green), and genes upregulated in Rex - (red). Two-sample permutation tests (oneway test) were used to compare the H3K27me3 ChIP-seq signal between all vs. differentially expressed genes. P values of

Techniques Used: Expressing, Chromatin Immunoprecipitation

Eed deficient ESCs cannot maintain Hhex priming in self-renewal. ( A ) Western blot for EED, H3K27me3 and Tubulin showing Eed mutation in HV5.1 Eed clone 6 induced by CRISPR. A homozygous mutant clone is shown alongside several transfected clones that were either WT or heterozygous. The absence of Eed protein in the mutant clone is apparent and consistent with the observed loss of H3K27me3, indicating that Eed has been knocked out. ( B ) Flow cytometry of HV5.1 parental and Eed -/- HV5.1 c6 cells stained with either SSEA1-alexa647 or PECAM-APC. Eed -/- HV5.1 cells did not proliferate well in serum + LIF and required the addition of the Gsk3 inhibitor Chiron to support efficient expansion. After three passages, Chiron could be removed without a clear effect on proliferation. Under these conditions, Eed -/- HV5.1 cells exhibited a loss of Hhex heterogeneity in standard culture, but there was a marked stimulation of Hhex expression to levels not normally observed with the HV reporter, when they were cultured in Chiron. SSEA1 and PECAM profiles of Eed -/- HV5.1 c6 cells in either serum + LIF (2 passages after Chiron removal) or serum + LIF + Chiron (2 passages) are shown. ( C ) Eed -/- HV5.1 c6 cells in either serum + LIF or serum + LIF + Chiron can be shifted between media states and re-establish heterogeneity or loss thereof, based on the culture media. P3, P5 and P6 to the left of the histograms are passage numbers in the indicated media. DOI: http://dx.doi.org/10.7554/eLife.14926.011
Figure Legend Snippet: Eed deficient ESCs cannot maintain Hhex priming in self-renewal. ( A ) Western blot for EED, H3K27me3 and Tubulin showing Eed mutation in HV5.1 Eed clone 6 induced by CRISPR. A homozygous mutant clone is shown alongside several transfected clones that were either WT or heterozygous. The absence of Eed protein in the mutant clone is apparent and consistent with the observed loss of H3K27me3, indicating that Eed has been knocked out. ( B ) Flow cytometry of HV5.1 parental and Eed -/- HV5.1 c6 cells stained with either SSEA1-alexa647 or PECAM-APC. Eed -/- HV5.1 cells did not proliferate well in serum + LIF and required the addition of the Gsk3 inhibitor Chiron to support efficient expansion. After three passages, Chiron could be removed without a clear effect on proliferation. Under these conditions, Eed -/- HV5.1 cells exhibited a loss of Hhex heterogeneity in standard culture, but there was a marked stimulation of Hhex expression to levels not normally observed with the HV reporter, when they were cultured in Chiron. SSEA1 and PECAM profiles of Eed -/- HV5.1 c6 cells in either serum + LIF (2 passages after Chiron removal) or serum + LIF + Chiron (2 passages) are shown. ( C ) Eed -/- HV5.1 c6 cells in either serum + LIF or serum + LIF + Chiron can be shifted between media states and re-establish heterogeneity or loss thereof, based on the culture media. P3, P5 and P6 to the left of the histograms are passage numbers in the indicated media. DOI: http://dx.doi.org/10.7554/eLife.14926.011

Techniques Used: Western Blot, Mutagenesis, CRISPR, Transfection, Clone Assay, Flow Cytometry, Cytometry, Staining, Expressing, Cell Culture

Differential gene-body H3K27me3 and the re-equilibration dynamics of Hhex defined lineage priming. ( A ) FACS density plots showing the primed cell populations isolated from the HVHC and HFHCV reporter cell lines ( Hhex expression monitored by venus and cherry expression respectively; see materials and methods for details) and the pluripotency cell surface marker SSEA1. To monitor the rate at which these populations return to equilibrium, Epi- and PrEn-primed cells were isolated from each reporter line (upper panels) and re-cultured either individually or in combination for a total of 72 h (bottom panel). In both cases a lineage reporter, either H2b -mCherry (HVHC) or H2b -Venus (HFHCV), expressed at an order of magnitude higher levels than the Hhex reporter allows the identification of specific cell lines, regardless of Hhex expression (e.g. compare the Cherry expression in the HVHC cells on the top line, to those derived from the HFHCV, second line). This enables the identification of originating cell/population from which Hhex is expressed in mixing experiments. ( B ) Summary plot showing the population averaged Hhex expression when the Epi- and PrEn-primed cells (squares and triangles respectively) are re-cultured either alone or in combination (solid and dashed coloured lines respectively). Hhex expression values for each population represent the mean population signal relative to the initial unsorted mean value. ( C ) Summary plot showing the arithmetic mean Hex - vs. Hex + ratio plotted against time in culture post-sorting. The standard deviation for this data is highlighted in grey. ( D ) FACS density plots showing the Hhex and SSEA1 profiles of Epi- and PrEn primed populations (green and red gates respectively) isolated from HV5.1 reported ESCs. Shown are the primed populations at the point of sorting (upper panel) and following 30 h of re-culture (lower panel). H3K27me3 ChIP was performed on the total SSEA1 positive gate (S+; black box) from the re-cultured populations (see E and F ). ( E ) Example H3K27me3 ChIP-seq profiles (log 2 (IP/Input)) for Actb and Hoxd (Control Loci) for the 30 h re-cultured populations shown in ( D ). Coordinates are for the mm9 mouse genome assembly. ( F ) Schematic representation of the stratification used to bin H3K27me3 signal with respect to genes (upper panel) and boxplots showing the ratio of H3K27me3 ChIP-seq signal between the two re-cultured primed ESC populations from formaldehyde cross-linked ChIP. The presented data was generated from cells re-cultured from the sorts presented in Figure 2B . Two-sample permutation tests (oneway test) identified no significant differential H3K27me3 levels between the re-cultured populations. DOI: http://dx.doi.org/10.7554/eLife.14926.008
Figure Legend Snippet: Differential gene-body H3K27me3 and the re-equilibration dynamics of Hhex defined lineage priming. ( A ) FACS density plots showing the primed cell populations isolated from the HVHC and HFHCV reporter cell lines ( Hhex expression monitored by venus and cherry expression respectively; see materials and methods for details) and the pluripotency cell surface marker SSEA1. To monitor the rate at which these populations return to equilibrium, Epi- and PrEn-primed cells were isolated from each reporter line (upper panels) and re-cultured either individually or in combination for a total of 72 h (bottom panel). In both cases a lineage reporter, either H2b -mCherry (HVHC) or H2b -Venus (HFHCV), expressed at an order of magnitude higher levels than the Hhex reporter allows the identification of specific cell lines, regardless of Hhex expression (e.g. compare the Cherry expression in the HVHC cells on the top line, to those derived from the HFHCV, second line). This enables the identification of originating cell/population from which Hhex is expressed in mixing experiments. ( B ) Summary plot showing the population averaged Hhex expression when the Epi- and PrEn-primed cells (squares and triangles respectively) are re-cultured either alone or in combination (solid and dashed coloured lines respectively). Hhex expression values for each population represent the mean population signal relative to the initial unsorted mean value. ( C ) Summary plot showing the arithmetic mean Hex - vs. Hex + ratio plotted against time in culture post-sorting. The standard deviation for this data is highlighted in grey. ( D ) FACS density plots showing the Hhex and SSEA1 profiles of Epi- and PrEn primed populations (green and red gates respectively) isolated from HV5.1 reported ESCs. Shown are the primed populations at the point of sorting (upper panel) and following 30 h of re-culture (lower panel). H3K27me3 ChIP was performed on the total SSEA1 positive gate (S+; black box) from the re-cultured populations (see E and F ). ( E ) Example H3K27me3 ChIP-seq profiles (log 2 (IP/Input)) for Actb and Hoxd (Control Loci) for the 30 h re-cultured populations shown in ( D ). Coordinates are for the mm9 mouse genome assembly. ( F ) Schematic representation of the stratification used to bin H3K27me3 signal with respect to genes (upper panel) and boxplots showing the ratio of H3K27me3 ChIP-seq signal between the two re-cultured primed ESC populations from formaldehyde cross-linked ChIP. The presented data was generated from cells re-cultured from the sorts presented in Figure 2B . Two-sample permutation tests (oneway test) identified no significant differential H3K27me3 levels between the re-cultured populations. DOI: http://dx.doi.org/10.7554/eLife.14926.008

Techniques Used: FACS, Isolation, Expressing, Marker, Cell Culture, Derivative Assay, Standard Deviation, Chromatin Immunoprecipitation, Generated

Reduction and redistribution of H3K27me3 in ESCs treated with EPZ and 2i, respectively. ( A ) Barplots showing quantitative H3K27me3 ChIP-PCR from ESCs treated for 24h with either DMSO or EPZ (black and grey bars respectively). ChIP was performed in 3 independent Hhex reporter ESC lines (HFHC10.4, HFHC12.3 and HV9.3) and anti IgG ChIP was performed for clone HV9.3 as a negative control. Error bars represent the standard deviations for duplicated PCR reactions. ( B ) Schematic representation of the segmentation used to stratify the genomic distribution of H3K27me3 signal (left panel) and a barplot showing the fraction of the genome covered in each category (right panel). The genome is categorized into: CGI TSS (black; ± 5 kb), non-CGI TSS (dark grey; ± 5 kb), intragenic (grey) and intergenic (light grey). The percentage of the genome and the number of intervals representing each category are shown. ( C ) Barplot depicting the distribution of H3K27me3 ChIP-seq signal across each category as a percentage of total mappable reads for three independent datasets. Percentages for each category are tabulated below the plot. ( D ) Boxplots depicting the normalised read depths per genomic interval for each genomic fraction. The significance of increased/decreased signal was determined using a Wilcoxon rank-sum test and significant values are indicated (0.05 > p≥0.01* and p
Figure Legend Snippet: Reduction and redistribution of H3K27me3 in ESCs treated with EPZ and 2i, respectively. ( A ) Barplots showing quantitative H3K27me3 ChIP-PCR from ESCs treated for 24h with either DMSO or EPZ (black and grey bars respectively). ChIP was performed in 3 independent Hhex reporter ESC lines (HFHC10.4, HFHC12.3 and HV9.3) and anti IgG ChIP was performed for clone HV9.3 as a negative control. Error bars represent the standard deviations for duplicated PCR reactions. ( B ) Schematic representation of the segmentation used to stratify the genomic distribution of H3K27me3 signal (left panel) and a barplot showing the fraction of the genome covered in each category (right panel). The genome is categorized into: CGI TSS (black; ± 5 kb), non-CGI TSS (dark grey; ± 5 kb), intragenic (grey) and intergenic (light grey). The percentage of the genome and the number of intervals representing each category are shown. ( C ) Barplot depicting the distribution of H3K27me3 ChIP-seq signal across each category as a percentage of total mappable reads for three independent datasets. Percentages for each category are tabulated below the plot. ( D ) Boxplots depicting the normalised read depths per genomic interval for each genomic fraction. The significance of increased/decreased signal was determined using a Wilcoxon rank-sum test and significant values are indicated (0.05 > p≥0.01* and p

Techniques Used: Chromatin Immunoprecipitation, Polymerase Chain Reaction, Negative Control

32) Product Images from "Tumor Necrosis Factor dynamically regulates the mRNA stabilome in rheumatoid arthritis fibroblast-like synoviocytes"

Article Title: Tumor Necrosis Factor dynamically regulates the mRNA stabilome in rheumatoid arthritis fibroblast-like synoviocytes

Journal: PLoS ONE

doi: 10.1371/journal.pone.0179762

Genome-wide evaluation of mRNA stability states of expressed genes in RA FLS. (a-c), Gene tracks showing sequencing reads from RNA sequencing mapped to CCL20 (a), JUN (b) and IRF1 (c) genes. The sequencing reads after TNF stimulation for 1 hour without (blue) or with Act D (orange) are shown. (d), Stacked bar graphs illustrating the mRNA stability states of genes expressed in unstimulated (Control) and TNF-stimulated FLS (1, 3, 24 and 72 hours of TNF stimulation). The mRNA stability status was calculated as the ratio of expression levels at the TNF+Act D condition divided to the expression levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The expressed genes were classified into five groups with distinct stability states and the size of each group is represented as % of total number of expressed genes for each condition.
Figure Legend Snippet: Genome-wide evaluation of mRNA stability states of expressed genes in RA FLS. (a-c), Gene tracks showing sequencing reads from RNA sequencing mapped to CCL20 (a), JUN (b) and IRF1 (c) genes. The sequencing reads after TNF stimulation for 1 hour without (blue) or with Act D (orange) are shown. (d), Stacked bar graphs illustrating the mRNA stability states of genes expressed in unstimulated (Control) and TNF-stimulated FLS (1, 3, 24 and 72 hours of TNF stimulation). The mRNA stability status was calculated as the ratio of expression levels at the TNF+Act D condition divided to the expression levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The expressed genes were classified into five groups with distinct stability states and the size of each group is represented as % of total number of expressed genes for each condition.

Techniques Used: Genome Wide, Sequencing, RNA Sequencing Assay, Activated Clotting Time Assay, Expressing

Scatterplots comparing the expression levels to the mRNA stability states of the expressed genes in RA FLS. Two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1, 3, 24, or 72 hours. Subsequently, actinomycin D (Act D, 10μg/ml) was added for 3 hours to block active transcription and gene expression was measured by RNA sequencing. RPKM values were generated using CuffDiff2. The mRNA stability status was calculated genome-wide as the ratio of RPKM levels at the TNF+Act D condition divided to the RPKM levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The genes expressed at 1 (a), 3 (b), 24 (c), and 72 (d) hours of TNF stimulation were plotted based on their expression levels and the mRNA stability states. Shades of blue represent the region of unstable genes, and shades of red represent the zone of stable genes.
Figure Legend Snippet: Scatterplots comparing the expression levels to the mRNA stability states of the expressed genes in RA FLS. Two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1, 3, 24, or 72 hours. Subsequently, actinomycin D (Act D, 10μg/ml) was added for 3 hours to block active transcription and gene expression was measured by RNA sequencing. RPKM values were generated using CuffDiff2. The mRNA stability status was calculated genome-wide as the ratio of RPKM levels at the TNF+Act D condition divided to the RPKM levels at the TNF condition. This ratio ranges from 0 to 1 and classifies genes to a spectrum from very unstable to very stable transcripts. The genes expressed at 1 (a), 3 (b), 24 (c), and 72 (d) hours of TNF stimulation were plotted based on their expression levels and the mRNA stability states. Shades of blue represent the region of unstable genes, and shades of red represent the zone of stable genes.

Techniques Used: Expressing, Derivative Assay, Activated Clotting Time Assay, Blocking Assay, RNA Sequencing Assay, Generated, Genome Wide

Association of expression kinetics with mRNA stability states of TNF-inducible genes in RA FLS. For (a-b), two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1-72h. Subsequently, Act D (10 μg/ml) was added for 3h and gene expression was measured by RNA sequencing. 386 genes were identified as highly induced (≥5-fold) by TNF at any time point and were clustered into 6 clusters with distinct kinetics of peak expression. (a), Heatmap illustrating the expression kinetics of the 6 clusters (red represents the maximum and blue the minimum expression level across the lane). (b), Stacked bar graphs illustrating the stability states of genes for Cluster 1, Clusters 2 3, Cluster 4, and Clusters 5 6. For (c-f), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 1–72 hours. Primers specific for the eighth intronic region of MMP3 and for the first intronic region of CCL5 were designed to capture primary transcripts (PT) of MMP3 and CCL5 . qPCR was used to measure the levels of PT and total mRNA of MMP3 (c-d) and CCL5 (e-f). Cumulative results from six independent experiments are shown. Values were normalized relative to mRNA for GAPDH and are presented as mean ±SEM.
Figure Legend Snippet: Association of expression kinetics with mRNA stability states of TNF-inducible genes in RA FLS. For (a-b), two biological replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1-72h. Subsequently, Act D (10 μg/ml) was added for 3h and gene expression was measured by RNA sequencing. 386 genes were identified as highly induced (≥5-fold) by TNF at any time point and were clustered into 6 clusters with distinct kinetics of peak expression. (a), Heatmap illustrating the expression kinetics of the 6 clusters (red represents the maximum and blue the minimum expression level across the lane). (b), Stacked bar graphs illustrating the stability states of genes for Cluster 1, Clusters 2 3, Cluster 4, and Clusters 5 6. For (c-f), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 1–72 hours. Primers specific for the eighth intronic region of MMP3 and for the first intronic region of CCL5 were designed to capture primary transcripts (PT) of MMP3 and CCL5 . qPCR was used to measure the levels of PT and total mRNA of MMP3 (c-d) and CCL5 (e-f). Cumulative results from six independent experiments are shown. Values were normalized relative to mRNA for GAPDH and are presented as mean ±SEM.

Techniques Used: Expressing, Derivative Assay, Activated Clotting Time Assay, RNA Sequencing Assay, Real-time Polymerase Chain Reaction

Genome-wide identification of transcripts stabilized by TNF in RA FLS. Two biologic replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1 or 72h. Subsequently, Act D was added for 3h and gene expression was measured by RNA sequencing. The degree of TNF-induced mRNA stabilization was calculated as the log 2 difference of TNF+Act D/TNF ratio between 1 and 72h of TNF stimulation and the adjusted p values of TNF-induced stabilization were calculated by RiboDiff. (a), Scatter-plot of the genes displaying TNF-induced mRNA stabilization comparing the degree of mRNA stabilization (y axis) to the adjusted p values of the stabilizing effect of TNF (x-axis). (b), The top 40 genes displaying the highest TNF-induced mRNA stabilization ranked by the degree of stabilization. (c), Enriched biological processes identified by GSEA/MSigDB pathway analysis of the top 10% of the genes (n = 593) displaying the highest degree of TNF-induced mRNA stabilization.
Figure Legend Snippet: Genome-wide identification of transcripts stabilized by TNF in RA FLS. Two biologic replicates of RA FLS (derived from two different RA patients) were exposed to a single dose of TNF (10 ng/ml) for 1 or 72h. Subsequently, Act D was added for 3h and gene expression was measured by RNA sequencing. The degree of TNF-induced mRNA stabilization was calculated as the log 2 difference of TNF+Act D/TNF ratio between 1 and 72h of TNF stimulation and the adjusted p values of TNF-induced stabilization were calculated by RiboDiff. (a), Scatter-plot of the genes displaying TNF-induced mRNA stabilization comparing the degree of mRNA stabilization (y axis) to the adjusted p values of the stabilizing effect of TNF (x-axis). (b), The top 40 genes displaying the highest TNF-induced mRNA stabilization ranked by the degree of stabilization. (c), Enriched biological processes identified by GSEA/MSigDB pathway analysis of the top 10% of the genes (n = 593) displaying the highest degree of TNF-induced mRNA stabilization.

Techniques Used: Genome Wide, Derivative Assay, Activated Clotting Time Assay, Expressing, RNA Sequencing Assay

TNF induces expression of mRNA-stabilizing pathways and mRNA stabilization is MAPK-dependent. (a), RNA sequencing was performed in 2 biological replicates (derived from two different RA patients) of TNF-stimulated RA FLS and Panther-Gene Ontology was used to evaluate their enrichment for the biological process “Regulation of RNA stability” (GO:0043487 or GO:0043488). F.E = fold enrichment and ns = not significant. (b-h), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 72h and then Act D (10 μg/ml) was added for 20 mins to block active transcription. Subsequently, the cells were treated for 4h with SB202190 (p38 inhibitor) alone or in various combinations with U0126 (MEK inhibitor) and SP600125 (JNK inhibitor). qPCR was used to measure the mRNA levels of CCL5 (b), IL-6 (c), IL-8 (d), CXCL3 (e), CCL2 (f), PTGS2 (g), and CXCL1 (h). Cumulative results from 4 independent experiments are shown. Values were normalized relative to GAPDH mRNA and presented as mean ±SEM. The mRNA expression at the TNF+Act D condition was set to 100 and the mRNA expression at all the other conditions was calculated as % of the TNF+Act D condition. P values were calculated by one-way ANOVA and Tukey post-test analysis (* = p
Figure Legend Snippet: TNF induces expression of mRNA-stabilizing pathways and mRNA stabilization is MAPK-dependent. (a), RNA sequencing was performed in 2 biological replicates (derived from two different RA patients) of TNF-stimulated RA FLS and Panther-Gene Ontology was used to evaluate their enrichment for the biological process “Regulation of RNA stability” (GO:0043487 or GO:0043488). F.E = fold enrichment and ns = not significant. (b-h), RA FLS were exposed to a single dose of TNF (10 ng/ml) for 72h and then Act D (10 μg/ml) was added for 20 mins to block active transcription. Subsequently, the cells were treated for 4h with SB202190 (p38 inhibitor) alone or in various combinations with U0126 (MEK inhibitor) and SP600125 (JNK inhibitor). qPCR was used to measure the mRNA levels of CCL5 (b), IL-6 (c), IL-8 (d), CXCL3 (e), CCL2 (f), PTGS2 (g), and CXCL1 (h). Cumulative results from 4 independent experiments are shown. Values were normalized relative to GAPDH mRNA and presented as mean ±SEM. The mRNA expression at the TNF+Act D condition was set to 100 and the mRNA expression at all the other conditions was calculated as % of the TNF+Act D condition. P values were calculated by one-way ANOVA and Tukey post-test analysis (* = p

Techniques Used: Expressing, RNA Sequencing Assay, Derivative Assay, Activated Clotting Time Assay, Blocking Assay, Real-time Polymerase Chain Reaction

33) Product Images from "Whole Genome Expression Differences in Human Left and Right Atria Ascertained by RNA-Sequencing"

Article Title: Whole Genome Expression Differences in Human Left and Right Atria Ascertained by RNA-Sequencing

Journal: Circulation. Cardiovascular genetics

doi: 10.1161/CIRCGENETICS.111.961631

Size-distribution of the small-RNA reads in one representative sample. The mode read length post adapter trimming occurs at 22bp which is expected from reads generated from miRNAs. The majority of sequences that did not read into the Illumina adapters
Figure Legend Snippet: Size-distribution of the small-RNA reads in one representative sample. The mode read length post adapter trimming occurs at 22bp which is expected from reads generated from miRNAs. The majority of sequences that did not read into the Illumina adapters

Techniques Used: Generated

34) Product Images from "KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function"

Article Title: KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function

Journal: Oncogene

doi: 10.1038/s41388-018-0273-5

KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val
Figure Legend Snippet: KMT2C loss results in site-specific loss of H3K4me1 and H3K27ac at ERα enhancers. a Immunoblot; β-actin used for loading control. b Normalized heatmaps for H3K4me1 occupancy in shRenilla, shKMT2C#1 and #2 cells among the 869 differential sites. Heatmaps centered at the peak summit. All cells are cultured in full serum containing media c GSEA of 3857 genes significantly downregulated in MCF7 shKMT2C cells ( p -val

Techniques Used: Cell Culture

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Software:

Article Title: A combination of targeted enrichment methodologies for whole-exome sequencing reveals novel pathogenic mutations
Article Snippet: .. Exonic annotations from NCBI GRCh37.p10, ftp://ftp.ncbi.nih.gov/genomes/ MapView/Homo_sapiens/sequence/current/initial_release/seq_gene.md.gz ; NimbleGen SeqCap EZ Human Exome Library annotation, http://www.nimblegen.com/products/seqcap/ez/index.html ; Illumina TruSeq Exome Enrichment Kit annotation, https://support.illumina.com/sequencing/sequencing_kits/truseq_exome_enrichment_kit/downloads.ilmn ; bedtools software, http://bedtools.readthedocs.org ; NCBI SRA (obtained public WES data), http://www.ncbi.nlm.nih.gov/sra/ ; HaloPlex probe design software (SureDesign), https://earray.chem.agilent.com/suredesign/ ; BLAT software, http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64.v287/blat ; cutadapt software, https://code.google.com/p/cutadapt/ ; SAMtools software, http://samtools.sourceforge.net ; GATK software, http://www.broadinstitute.org/gatk/ ; dbSNP, http://www.ncbi.nlm.nih.gov/SNP/ ; 1000 Genomes Project, http://www.1000genomes.org ; ESP6500, http://evs.gs.washington.edu/ EVS/ ; HGVD, http://http://www.genome.med.kyoto-u.ac.jp/SnpDB/ ;HGMD, http://www.hgmd.org ; ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ ; Information of ASPM protein domain in InterPro (v.47), http://www.ebi.ac.uk/interpro/protein/Q8IZT6 ; MyDomains tool for graphic depiction of protein domains, http://prosite.expasy.org/mydomains/ . ..

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