ercc Search Results


  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 99
    Thermo Fisher ercc
    Assessment of required sequencing depth, technical and biological variation, dynamic range and reproducibility of single cell <t>RNA-seq</t> data of 80 single distal lung epithelial cells at E18.5 (a) Saturation analysis reveals the sequencing depth required for the detection of most genes expressed by single cells. To detect most expressed genes, single cell RNA-seq libraries have to be sequenced only to a depth of about 1 million reads, whereas libraries of bulk samples have to be sequenced deeper. The number of genes detected in the ensemble of all single cells (synthetic bulk) is comparable to the number of genes detected in the true bulk experiment. Each point on the saturation curve was generated by randomly selecting a number of raw reads from each sample library (bulk: 200 cell bulk library, single cell: single cell RNA-seq libraries of 80 lung epithelial cells, single cell ensemble: bioinformatically pooled single cell libraries) and then using the same alignment pipeline to call genes with mean FPKM > 1. Each point represents four replicate sub-samplings, error bars represent standard errors. (b) Technical noise and biological variation in single cell RNA-seq data. Relationship between mean expression level and coefficient of variation for 10,946 genes in single embryonic lung epithelial cells. Several genes exhibit strong biological variation (blue), as they exhibit higher variability than the average noise at a given average gene expression. Housekeeping genes are shown in yellow. (c) Average detected transcript levels (mean FPKM, log 2 ) for 92 <t>ERCC</t> RNA spike-ins as a function of provided number of molecules per lysis reaction for each of the three independent single cell RNA-seq experiments performed at E18.5. Linear regression fits through data points are shown. The length of each ERCC RNA spike-in transcript is encoded in the size and color of the data points. No particular bias towards the detection of shorter versus longer transcripts is observed. The method shows single transcript sensitivity as well as a dynamic range of approximately 6 orders of magnitude, in agreement with a previous study evaluating microfluidic single cell RNA-seq 7 . (d,e) Correlation between (d) transcript levels of a 200-cell population and median transcript levels of single cells of the same pool of embryonic lungs, and (e) transcript levels of two single AT2 cells. r , Pearson correlation coefficients. (f,g) Correlation between (f) transcript levels of all genes detected in the single lung and the pooled lung experiment and between (g) transcript levels of all genes detected in the two independent experiments on pooled embryonic lungs. Pearson correlation coefficients r are given.
    Ercc, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 99/100, based on 274 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ercc/product/Thermo Fisher
    Average 99 stars, based on 274 article reviews
    Price from $9.99 to $1999.99
    ercc - by Bioz Stars, 2020-08
    99/100 stars
      Buy from Supplier

    88
    Thermo Fisher ercc spike in rna
    Single cell and low input <t>RNA</t> sequencing of bone marrow MKs a. Human bone marrow MK phenotype. Left panel: Cytocentrifugation of enriched MK population stained with Roberts stain, 40x objective; Middle panel: Immunofluorescent staining of enriched megakaryocyte population with DAPI DNA stain (blue) and CD41 surface stain (green); Right panel: Fluorescence activated cell sorting for primary megakaryocytes from whole human bone marrow. Ploidy plot shown detecting levels of Hoechst 33342 staining showing typical ploidy distribution for human bone marrow MKs (cells shown are CD41a+, CD42a+); b. Single MK cell filtering. Performed using a 5-round training scheme of random forest models 37 trained cDNA from 20 cell MK pools that were isolated using an identical sorting and sequencing protocol. High and low quality cells shown by mitochondrial mapping (ChrM), <t>ERCC</t> ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 1106 cells, 282 were taken forward for further analysis based on high quality; c. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000); d. Ordering differentiation trajectories between HSC cell clusters using Monocle 2 with the addition of the MK 20-100 cell pools. HSC clusters and MKs are differentiated by color.
    Ercc Spike In Rna, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 88/100, based on 51 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ercc spike in rna/product/Thermo Fisher
    Average 88 stars, based on 51 article reviews
    Price from $9.99 to $1999.99
    ercc spike in rna - by Bioz Stars, 2020-08
    88/100 stars
      Buy from Supplier

    92
    Thermo Fisher ercc mix1
    Single cell and low input <t>RNA</t> sequencing of bone marrow MKs a. Human bone marrow MK phenotype. Left panel: Cytocentrifugation of enriched MK population stained with Roberts stain, 40x objective; Middle panel: Immunofluorescent staining of enriched megakaryocyte population with DAPI DNA stain (blue) and CD41 surface stain (green); Right panel: Fluorescence activated cell sorting for primary megakaryocytes from whole human bone marrow. Ploidy plot shown detecting levels of Hoechst 33342 staining showing typical ploidy distribution for human bone marrow MKs (cells shown are CD41a+, CD42a+); b. Single MK cell filtering. Performed using a 5-round training scheme of random forest models 37 trained cDNA from 20 cell MK pools that were isolated using an identical sorting and sequencing protocol. High and low quality cells shown by mitochondrial mapping (ChrM), <t>ERCC</t> ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 1106 cells, 282 were taken forward for further analysis based on high quality; c. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000); d. Ordering differentiation trajectories between HSC cell clusters using Monocle 2 with the addition of the MK 20-100 cell pools. HSC clusters and MKs are differentiated by color.
    Ercc Mix1, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 92/100, based on 18 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ercc mix1/product/Thermo Fisher
    Average 92 stars, based on 18 article reviews
    Price from $9.99 to $1999.99
    ercc mix1 - by Bioz Stars, 2020-08
    92/100 stars
      Buy from Supplier

    91
    Thermo Fisher ercc spike in mix1
    Single cell and low input <t>RNA</t> sequencing of bone marrow MKs a. Human bone marrow MK phenotype. Left panel: Cytocentrifugation of enriched MK population stained with Roberts stain, 40x objective; Middle panel: Immunofluorescent staining of enriched megakaryocyte population with DAPI DNA stain (blue) and CD41 surface stain (green); Right panel: Fluorescence activated cell sorting for primary megakaryocytes from whole human bone marrow. Ploidy plot shown detecting levels of Hoechst 33342 staining showing typical ploidy distribution for human bone marrow MKs (cells shown are CD41a+, CD42a+); b. Single MK cell filtering. Performed using a 5-round training scheme of random forest models 37 trained cDNA from 20 cell MK pools that were isolated using an identical sorting and sequencing protocol. High and low quality cells shown by mitochondrial mapping (ChrM), <t>ERCC</t> ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 1106 cells, 282 were taken forward for further analysis based on high quality; c. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000); d. Ordering differentiation trajectories between HSC cell clusters using Monocle 2 with the addition of the MK 20-100 cell pools. HSC clusters and MKs are differentiated by color.
    Ercc Spike In Mix1, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 91/100, based on 15 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ercc spike in mix1/product/Thermo Fisher
    Average 91 stars, based on 15 article reviews
    Price from $9.99 to $1999.99
    ercc spike in mix1 - by Bioz Stars, 2020-08
    91/100 stars
      Buy from Supplier

    97
    Thermo Fisher ercc exfold rna spike in
    TempO-Seq assay sensitivity. (A) URR <t>RNA</t> was diluted in 10-fold steps with input of 100 ng down to 0.1 pg total RNA, plus no-input, in triplicate. Error bars indicate 1 standard deviation. Each color indicates a different gene, selected across the dynamic range. (B) MDA MB 231 cell lysates were diluted in 10-fold steps, for a range of 4,000 down to 0.004 cells in the assay. Genes were selected as for (A). (C) Mix 2 of the synthetic reference <t>ERCC</t> <t>ExFold</t> RNA Mixtures was diluted in 10-fold steps from 1x10 -3 down to 1x10 -6 of the supplied stock in URR as carrier, then assayed using a detector oligo pool specific for the ERCC RNAs. Average reads per sample ranged from 3.6K for the 1x10 -6 dilution to 340K for the 1x10 -3 dilution. Results from the 1 x 10 −5 dilution are shown. (D) MDA MB 231 cells were diluted in 10-fold increments into a constant background of MCF7 cells (blue bars), or MCF-7 cells were diluted into a constant background of MDA MB 231 cells (green bars), then lysed and assayed for cell-specific transcripts. Of the 13 and 14 genes monitored, respectively, the fraction that were significantly above background is shown for each cell dilution. Read depth ranged from 3.6M/sample for 100%, 299K for 0.1%, and down to 64K for 0.00001% for both titrations.
    Ercc Exfold Rna Spike In, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 97/100, based on 46 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ercc exfold rna spike in/product/Thermo Fisher
    Average 97 stars, based on 46 article reviews
    Price from $9.99 to $1999.99
    ercc exfold rna spike in - by Bioz Stars, 2020-08
    97/100 stars
      Buy from Supplier

    Image Search Results


    Assessment of required sequencing depth, technical and biological variation, dynamic range and reproducibility of single cell RNA-seq data of 80 single distal lung epithelial cells at E18.5 (a) Saturation analysis reveals the sequencing depth required for the detection of most genes expressed by single cells. To detect most expressed genes, single cell RNA-seq libraries have to be sequenced only to a depth of about 1 million reads, whereas libraries of bulk samples have to be sequenced deeper. The number of genes detected in the ensemble of all single cells (synthetic bulk) is comparable to the number of genes detected in the true bulk experiment. Each point on the saturation curve was generated by randomly selecting a number of raw reads from each sample library (bulk: 200 cell bulk library, single cell: single cell RNA-seq libraries of 80 lung epithelial cells, single cell ensemble: bioinformatically pooled single cell libraries) and then using the same alignment pipeline to call genes with mean FPKM > 1. Each point represents four replicate sub-samplings, error bars represent standard errors. (b) Technical noise and biological variation in single cell RNA-seq data. Relationship between mean expression level and coefficient of variation for 10,946 genes in single embryonic lung epithelial cells. Several genes exhibit strong biological variation (blue), as they exhibit higher variability than the average noise at a given average gene expression. Housekeeping genes are shown in yellow. (c) Average detected transcript levels (mean FPKM, log 2 ) for 92 ERCC RNA spike-ins as a function of provided number of molecules per lysis reaction for each of the three independent single cell RNA-seq experiments performed at E18.5. Linear regression fits through data points are shown. The length of each ERCC RNA spike-in transcript is encoded in the size and color of the data points. No particular bias towards the detection of shorter versus longer transcripts is observed. The method shows single transcript sensitivity as well as a dynamic range of approximately 6 orders of magnitude, in agreement with a previous study evaluating microfluidic single cell RNA-seq 7 . (d,e) Correlation between (d) transcript levels of a 200-cell population and median transcript levels of single cells of the same pool of embryonic lungs, and (e) transcript levels of two single AT2 cells. r , Pearson correlation coefficients. (f,g) Correlation between (f) transcript levels of all genes detected in the single lung and the pooled lung experiment and between (g) transcript levels of all genes detected in the two independent experiments on pooled embryonic lungs. Pearson correlation coefficients r are given.

    Journal: Nature

    Article Title: Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq

    doi: 10.1038/nature13173

    Figure Lengend Snippet: Assessment of required sequencing depth, technical and biological variation, dynamic range and reproducibility of single cell RNA-seq data of 80 single distal lung epithelial cells at E18.5 (a) Saturation analysis reveals the sequencing depth required for the detection of most genes expressed by single cells. To detect most expressed genes, single cell RNA-seq libraries have to be sequenced only to a depth of about 1 million reads, whereas libraries of bulk samples have to be sequenced deeper. The number of genes detected in the ensemble of all single cells (synthetic bulk) is comparable to the number of genes detected in the true bulk experiment. Each point on the saturation curve was generated by randomly selecting a number of raw reads from each sample library (bulk: 200 cell bulk library, single cell: single cell RNA-seq libraries of 80 lung epithelial cells, single cell ensemble: bioinformatically pooled single cell libraries) and then using the same alignment pipeline to call genes with mean FPKM > 1. Each point represents four replicate sub-samplings, error bars represent standard errors. (b) Technical noise and biological variation in single cell RNA-seq data. Relationship between mean expression level and coefficient of variation for 10,946 genes in single embryonic lung epithelial cells. Several genes exhibit strong biological variation (blue), as they exhibit higher variability than the average noise at a given average gene expression. Housekeeping genes are shown in yellow. (c) Average detected transcript levels (mean FPKM, log 2 ) for 92 ERCC RNA spike-ins as a function of provided number of molecules per lysis reaction for each of the three independent single cell RNA-seq experiments performed at E18.5. Linear regression fits through data points are shown. The length of each ERCC RNA spike-in transcript is encoded in the size and color of the data points. No particular bias towards the detection of shorter versus longer transcripts is observed. The method shows single transcript sensitivity as well as a dynamic range of approximately 6 orders of magnitude, in agreement with a previous study evaluating microfluidic single cell RNA-seq 7 . (d,e) Correlation between (d) transcript levels of a 200-cell population and median transcript levels of single cells of the same pool of embryonic lungs, and (e) transcript levels of two single AT2 cells. r , Pearson correlation coefficients. (f,g) Correlation between (f) transcript levels of all genes detected in the single lung and the pooled lung experiment and between (g) transcript levels of all genes detected in the two independent experiments on pooled embryonic lungs. Pearson correlation coefficients r are given.

    Article Snippet: ERCC (External RNA Controls Consortium) RNA spike-in Mix (Ambion, Life Technologies) was added to the lysis reaction and processed in parallel to cellular mRNA.

    Techniques: Sequencing, RNA Sequencing Assay, Generated, Expressing, Lysis

    The number of unique genes and the total number of transcripts expressed by a single cell strongly correlates with its differentiation state (a) Saturation analysis of single cell RNA-seq data of lung epithelial cells at different embryonic and adult time points (E14.5, E18.5, adult AT2) reveals that the number of unique genes expressed by single lung epithelial cells decreases with progressing differentiation state. Distal lung epithelial cells at E14.5 express over 6000 genes, whereas cells at E18.5 express approximately 3000 and mature AT2 cells only around 2000 genes. Each point on the saturation curve was generated by randomly selecting a number of raw reads from each sample library and then using the same alignment pipeline to call genes with mean FPKM > 1. Each point represents four replicate sub-samplings. Error bars represent standard errors. All libraries were sequenced to a depth of at least 2 million reads. (b) Single cell RNA-seq reveals that the total number of transcripts expressed by single cells decreases with increasing differentiation state of the cell. Number of transcripts per cell were calculated from the FPKM values of all genes in each cell using the correlation between number of transcripts of exogenous spike-in mRNA sequences and their respective measured mean FPKM values (exemplary calibration curves are shown in Extended Data Figure 3c for three replicates at E18.5). Area normalized density distributions are shown for embryonic cells at E14.5 (45 cells), E16.5 (27 cells), E18.5 (80 cells) and for 46 Sftpc + adult AT2 cells. The number of transcripts is highest in lung epithelial progenitor cells at E16.5 and E14.5 and decreases in cells at E18.5 and even further in mature AT2 cells. Note that single cell RNA-seq libraries for E14.5, E18.5 and adult AT2 cells were sequenced to a depth of 2-6 million reads, whereas the libraries for cells at E16.5 were sequenced to a lower depth of 100,000-550,000 reads. (c) Calibration of Ct values measured by single cell qPCR to number of molecules. Average detected transcript levels (log2Ex = Ct LoD – Ct, Ct LoD = 22) for 6 ERCC RNA spike-ins as a function of provided number of molecules per lysis reaction for each of three independent single cell qPCR experiments performed on embryonic (E16.5, 2 replicates, red and green) and adult mouse lung (adult AT2, 1 replicate, blue). Linear regression fits through data points and corresponding equations are shown and were used to convert C T values measured by qPCR into numbers of transcripts. (d) Single cell qPCR confirms the presence of a higher number of transcripts in lung epithelial progenitor cells as compared to fully differentiated alveolar epithelial cells. The median number of transcripts per cell as detected by single cell RNA-seq (y-axis) and by single cell multiplexed qPCR of 90 genes (x-axis) is shown for distal lung epithelial cells at E16.5 (qPCR: 33 cells, RNA-seq: 27 cells) and mature AT2 cells (qPCR: 48 cells, RNA-seq: 46 cells).

    Journal: Nature

    Article Title: Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq

    doi: 10.1038/nature13173

    Figure Lengend Snippet: The number of unique genes and the total number of transcripts expressed by a single cell strongly correlates with its differentiation state (a) Saturation analysis of single cell RNA-seq data of lung epithelial cells at different embryonic and adult time points (E14.5, E18.5, adult AT2) reveals that the number of unique genes expressed by single lung epithelial cells decreases with progressing differentiation state. Distal lung epithelial cells at E14.5 express over 6000 genes, whereas cells at E18.5 express approximately 3000 and mature AT2 cells only around 2000 genes. Each point on the saturation curve was generated by randomly selecting a number of raw reads from each sample library and then using the same alignment pipeline to call genes with mean FPKM > 1. Each point represents four replicate sub-samplings. Error bars represent standard errors. All libraries were sequenced to a depth of at least 2 million reads. (b) Single cell RNA-seq reveals that the total number of transcripts expressed by single cells decreases with increasing differentiation state of the cell. Number of transcripts per cell were calculated from the FPKM values of all genes in each cell using the correlation between number of transcripts of exogenous spike-in mRNA sequences and their respective measured mean FPKM values (exemplary calibration curves are shown in Extended Data Figure 3c for three replicates at E18.5). Area normalized density distributions are shown for embryonic cells at E14.5 (45 cells), E16.5 (27 cells), E18.5 (80 cells) and for 46 Sftpc + adult AT2 cells. The number of transcripts is highest in lung epithelial progenitor cells at E16.5 and E14.5 and decreases in cells at E18.5 and even further in mature AT2 cells. Note that single cell RNA-seq libraries for E14.5, E18.5 and adult AT2 cells were sequenced to a depth of 2-6 million reads, whereas the libraries for cells at E16.5 were sequenced to a lower depth of 100,000-550,000 reads. (c) Calibration of Ct values measured by single cell qPCR to number of molecules. Average detected transcript levels (log2Ex = Ct LoD – Ct, Ct LoD = 22) for 6 ERCC RNA spike-ins as a function of provided number of molecules per lysis reaction for each of three independent single cell qPCR experiments performed on embryonic (E16.5, 2 replicates, red and green) and adult mouse lung (adult AT2, 1 replicate, blue). Linear regression fits through data points and corresponding equations are shown and were used to convert C T values measured by qPCR into numbers of transcripts. (d) Single cell qPCR confirms the presence of a higher number of transcripts in lung epithelial progenitor cells as compared to fully differentiated alveolar epithelial cells. The median number of transcripts per cell as detected by single cell RNA-seq (y-axis) and by single cell multiplexed qPCR of 90 genes (x-axis) is shown for distal lung epithelial cells at E16.5 (qPCR: 33 cells, RNA-seq: 27 cells) and mature AT2 cells (qPCR: 48 cells, RNA-seq: 46 cells).

    Article Snippet: ERCC (External RNA Controls Consortium) RNA spike-in Mix (Ambion, Life Technologies) was added to the lysis reaction and processed in parallel to cellular mRNA.

    Techniques: RNA Sequencing Assay, Generated, Real-time Polymerase Chain Reaction, Lysis

    Impact of normalization on differential expression analysis. ( a ) For SEQC dataset, difference between RNA-seq and qRT-PCR estimates of Sample A/Sample B log-fold-changes, i.e., bias in RNA-seq when viewing qRT-PCR as gold standard. All RUV versions lead to unbiased log-fold-change estimates; CL based on ERCC spike-ins leads to severe bias. ( b ) For SEQC dataset, receiver operating characteristic (ROC) curves using a set of 370 positive and 86 negative qRT-PCR controls as gold standard. RUVg (based on either empirical or spike-in controls) and UQ normalization perform slightly better than no normalization. UQ based on spike-ins performs similarly to no normalization and CL based on spike-ins performs the worst. ( c ) For Zebrafish dataset, distribution of edgeR p -values for tests of DE between treated and control samples. UQ and CL normalization based on spike-ins lead to distributions far from the expected uniform. ( d ) For Zebrafish dataset, heatmap of expression measures for the 61 genes found DE between control (Ctl) and treated (Trt) samples after UQ but not after RUVg normalization. Clustering of samples is driven by outlying Library 11. ( e ) Heatmap of expression measures for the 475 genes found DE after RUVg but not after UQ normalization. Samples cluster as expected by treatment.

    Journal: Nature biotechnology

    Article Title: Normalization of RNA-seq data using factor analysis of control genes or samples

    doi: 10.1038/nbt.2931

    Figure Lengend Snippet: Impact of normalization on differential expression analysis. ( a ) For SEQC dataset, difference between RNA-seq and qRT-PCR estimates of Sample A/Sample B log-fold-changes, i.e., bias in RNA-seq when viewing qRT-PCR as gold standard. All RUV versions lead to unbiased log-fold-change estimates; CL based on ERCC spike-ins leads to severe bias. ( b ) For SEQC dataset, receiver operating characteristic (ROC) curves using a set of 370 positive and 86 negative qRT-PCR controls as gold standard. RUVg (based on either empirical or spike-in controls) and UQ normalization perform slightly better than no normalization. UQ based on spike-ins performs similarly to no normalization and CL based on spike-ins performs the worst. ( c ) For Zebrafish dataset, distribution of edgeR p -values for tests of DE between treated and control samples. UQ and CL normalization based on spike-ins lead to distributions far from the expected uniform. ( d ) For Zebrafish dataset, heatmap of expression measures for the 61 genes found DE between control (Ctl) and treated (Trt) samples after UQ but not after RUVg normalization. Clustering of samples is driven by outlying Library 11. ( e ) Heatmap of expression measures for the 475 genes found DE after RUVg but not after UQ normalization. Samples cluster as expected by treatment.

    Article Snippet: Ambion commercializes two ERCC spike-in mixes, ERCC ExFold RNA Spike-in Control Mix 1 and Mix 2.

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

    Nanopore DRS with cap-dependent ligation of 5′ adapter RNA. ( A ) Histogram showing the distribution of 5′ adapter RNA length in the nanopore raw current signal, as inferred from alignment of the mRNA sequence to the signal using nanopolish eventalign. The median signal length was 1441 points and 96% of adapter signals were 3000 points or less. ( B ) Out-of-bag receiver operator characteristic curve showing the performance of the trained convolutional neural network at detecting 5′ adapter RNA using 3000 points of signal. The curve was generated using five-fold cross validation. ( C ) Out-of-bag precision recall curve showing the performance of trained neural network, generated using five-fold cross validation. ( D ) Alternative transcription start sites were identified using nanopore DRS with cap-dependent ligation of a 5′ end adapter at the AT1G17050 and AT5G18650 genes. Orange, 5’ coverage for capped nanoPARE reads; blue track, nanopore DRS coverage with cap-dependent ligation of 5′ adapter RNA; blue, isoforms detected by nanopore DRS with cap-dependent ligation of 5′ adapter RNA; black, Araport11 annotation. ( E ) Reads mapping to ERCC RNA spike-ins lack approximately 11 nt of sequence at the 5′ end. Histogram showing the distance to the 5′ end for ERCC RNA spike-in reads (each spike-in is shown in a different colour; only those with > 1000 supporting reads are shown). ( F ) Reads mapping to in vitro transcribed mGFP lack approximately 11 nt of sequence at the 5′ end. Histogram showing the distance to the 5′ end for in vitro transcribed mGFP. ( G ) Araport11 annotation overestimates the length of 5′ UTRs. The cumulative distribution function shows the distance to the nearest TSS identified from full-length transcripts cloned as part of the RIKEN RAFL project (blue) and Araport11 annotation (orange). ( H ) Nanopore DRS detects miR170/miR171 cleavage products of Hairy Meristem 1 (HAM1, AT2G45160) transcripts. Orange, 5’ coverage from capped nanoPARE reads; purple, 5’ coverage from uncapped nanoPARE reads; blue, nanopore DRS 5’ coverage; grey, miRNA target site alignment is shown; black, Araport11 annotation. microRNA cleavage site predictions supported by enrichment of nanopore 5’ ends – Figure 3—figure supplement 1H .

    Journal: eLife

    Article Title: Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification

    doi: 10.7554/eLife.49658

    Figure Lengend Snippet: Nanopore DRS with cap-dependent ligation of 5′ adapter RNA. ( A ) Histogram showing the distribution of 5′ adapter RNA length in the nanopore raw current signal, as inferred from alignment of the mRNA sequence to the signal using nanopolish eventalign. The median signal length was 1441 points and 96% of adapter signals were 3000 points or less. ( B ) Out-of-bag receiver operator characteristic curve showing the performance of the trained convolutional neural network at detecting 5′ adapter RNA using 3000 points of signal. The curve was generated using five-fold cross validation. ( C ) Out-of-bag precision recall curve showing the performance of trained neural network, generated using five-fold cross validation. ( D ) Alternative transcription start sites were identified using nanopore DRS with cap-dependent ligation of a 5′ end adapter at the AT1G17050 and AT5G18650 genes. Orange, 5’ coverage for capped nanoPARE reads; blue track, nanopore DRS coverage with cap-dependent ligation of 5′ adapter RNA; blue, isoforms detected by nanopore DRS with cap-dependent ligation of 5′ adapter RNA; black, Araport11 annotation. ( E ) Reads mapping to ERCC RNA spike-ins lack approximately 11 nt of sequence at the 5′ end. Histogram showing the distance to the 5′ end for ERCC RNA spike-in reads (each spike-in is shown in a different colour; only those with > 1000 supporting reads are shown). ( F ) Reads mapping to in vitro transcribed mGFP lack approximately 11 nt of sequence at the 5′ end. Histogram showing the distance to the 5′ end for in vitro transcribed mGFP. ( G ) Araport11 annotation overestimates the length of 5′ UTRs. The cumulative distribution function shows the distance to the nearest TSS identified from full-length transcripts cloned as part of the RIKEN RAFL project (blue) and Araport11 annotation (orange). ( H ) Nanopore DRS detects miR170/miR171 cleavage products of Hairy Meristem 1 (HAM1, AT2G45160) transcripts. Orange, 5’ coverage from capped nanoPARE reads; purple, 5’ coverage from uncapped nanoPARE reads; blue, nanopore DRS 5’ coverage; grey, miRNA target site alignment is shown; black, Araport11 annotation. microRNA cleavage site predictions supported by enrichment of nanopore 5’ ends – Figure 3—figure supplement 1H .

    Article Snippet: ERCC RNA Spike-In mixes (Thermo Fisher Scientific) ( ; ) were included in each of the libraries using concentrations advised by the manufacturer.

    Techniques: Ligation, Sequencing, Generated, In Vitro, Clone Assay

    Properties of nanopore DRS sequencing data. ( A ) Nanopore DRS identified a 12.8 kb transcript generated from the AT1G67120 gene that includes 58 exons. Blue, nanopore DRS isoform; black, Araport11 annotation. ( B ) Synthetic ERCC RNA spike-in mixes are detected in a quantitative manner. Absolute concentrations of spike-ins are plotted against counts per million (CPM) reads in log 10 scale. Blue, ERCC RNA spike-in mix 1; orange, ERCC RNA spike-in mix 2. ( C ) Overview of the sequencing and alignment characteristics of nanopore DRS data for ERCC RNA spike-ins. Left, distribution of the length fraction of each sequenced read that aligns to the ERCC RNA spike-in reference; centre, distribution of fraction of identity that matches between the sequence of the read and the ERCC RNA spike-in reference for the aligned portion of each read; right, distributions of the occurrence of insertions (black), substitutions (orange) and deletions (blue) as a proportion of the number of aligned bases in each read. ( D ) Substitution preference for each nucleotide (left to right: adenine [A], uracil [U], guanine [G], cytosine [C]). When substituted, G is replaced with A in more than 63% of its substitutions, while C is replaced by U in 73%. Conversely, U is rarely replaced with G (12%) and A is rarely substituted with C (16%). ( E ) Nucleotide representation within the ERCC RNA Spike-In reference sequences (black dots) compared with nucleotide representation within four categories from the nanopore DRS reads. Identity matches between the sequence of the read and the ERCC RNA spike-in reference (green crosses), insertions (blue pentagons), deletions (yellow stars) and substitutions (purple diamonds).G is under-represented and U is over-represented for all three categories of error (insertion, deletion and substitution) relative to the reference nucleotide distribution. C is over-represented in deletions and substitutions. A is over-represented in insertions and deletions and under-represented in substitutions. ( F ) Signals originating from the RH3 transcripts are susceptible to systematic over-splitting around exons 7–9 (highlighted using a purple dashed box), resulting in reads with apparently novel 5′ or 3′ positions. This appears only to occur at high frequency in datasets collected after May 2018 ( Supplementary file 1 ) and may result from an update to the MinKNOW software. ( G ) PIN7 antisense RNAs detected using nanopore DRS. Blue, Col-0 PIN7 sense Illumina RNAseq coverage and nanopore PIN7 sense read alignments; orange, Col-0 PIN7 antisense Illumina RNAseq coverage and nanopore PIN7 antisense read alignments; green, hen2–two mutant PIN7 sense Illumina RNAseq coverage; purple, hen2–two mutant PIN7 antisense Illumina RNAseq coverage; black, PIN7 sense RNA isoforms found in Araport11 annotation; grey, PIN7 antisense differentially expressed regions detected with DERfinder.

    Journal: eLife

    Article Title: Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification

    doi: 10.7554/eLife.49658

    Figure Lengend Snippet: Properties of nanopore DRS sequencing data. ( A ) Nanopore DRS identified a 12.8 kb transcript generated from the AT1G67120 gene that includes 58 exons. Blue, nanopore DRS isoform; black, Araport11 annotation. ( B ) Synthetic ERCC RNA spike-in mixes are detected in a quantitative manner. Absolute concentrations of spike-ins are plotted against counts per million (CPM) reads in log 10 scale. Blue, ERCC RNA spike-in mix 1; orange, ERCC RNA spike-in mix 2. ( C ) Overview of the sequencing and alignment characteristics of nanopore DRS data for ERCC RNA spike-ins. Left, distribution of the length fraction of each sequenced read that aligns to the ERCC RNA spike-in reference; centre, distribution of fraction of identity that matches between the sequence of the read and the ERCC RNA spike-in reference for the aligned portion of each read; right, distributions of the occurrence of insertions (black), substitutions (orange) and deletions (blue) as a proportion of the number of aligned bases in each read. ( D ) Substitution preference for each nucleotide (left to right: adenine [A], uracil [U], guanine [G], cytosine [C]). When substituted, G is replaced with A in more than 63% of its substitutions, while C is replaced by U in 73%. Conversely, U is rarely replaced with G (12%) and A is rarely substituted with C (16%). ( E ) Nucleotide representation within the ERCC RNA Spike-In reference sequences (black dots) compared with nucleotide representation within four categories from the nanopore DRS reads. Identity matches between the sequence of the read and the ERCC RNA spike-in reference (green crosses), insertions (blue pentagons), deletions (yellow stars) and substitutions (purple diamonds).G is under-represented and U is over-represented for all three categories of error (insertion, deletion and substitution) relative to the reference nucleotide distribution. C is over-represented in deletions and substitutions. A is over-represented in insertions and deletions and under-represented in substitutions. ( F ) Signals originating from the RH3 transcripts are susceptible to systematic over-splitting around exons 7–9 (highlighted using a purple dashed box), resulting in reads with apparently novel 5′ or 3′ positions. This appears only to occur at high frequency in datasets collected after May 2018 ( Supplementary file 1 ) and may result from an update to the MinKNOW software. ( G ) PIN7 antisense RNAs detected using nanopore DRS. Blue, Col-0 PIN7 sense Illumina RNAseq coverage and nanopore PIN7 sense read alignments; orange, Col-0 PIN7 antisense Illumina RNAseq coverage and nanopore PIN7 antisense read alignments; green, hen2–two mutant PIN7 sense Illumina RNAseq coverage; purple, hen2–two mutant PIN7 antisense Illumina RNAseq coverage; black, PIN7 sense RNA isoforms found in Araport11 annotation; grey, PIN7 antisense differentially expressed regions detected with DERfinder.

    Article Snippet: ERCC RNA Spike-In mixes (Thermo Fisher Scientific) ( ; ) were included in each of the libraries using concentrations advised by the manufacturer.

    Techniques: Sequencing, Generated, Software, Mutagenesis

    3′ end processing is revealed by nanopore DRS. ( A ) Poly(A) tail length estimates for ERCC spike-in controls. Boxplots showing distribution of poly(A) length estimates for ERCC spike-in controls with more than 100 mapped reads for which tail length could be successfully estimated. Expected poly(A) tail lengths are shown as orange points. ( B ) The RNA poly(A) tail length negatively correlates with the gene expression level. Expression in log 2 scale of counts per million (CPM) obtained from nanopore DRS data is plotted against the median poly(A) tail length. ρ, Spearman’s correlation coefficient; black line, locally weighted scatterplot smoothing (LOWESS) regression fit. ( C ) Nanopore DRS identified known 3′ polyadenylation sites in RNAs transcribed from the IBM1 (AT3G07610) locus. Blue track, nanopore DRS coverage; blue, isoforms detected by nanopore DRS; black, Araport11 annotation; PAS, proximal polyadenylation site. ( D ) Nanopore DRS identified novel 3’ polyadenylation sites in RNAs transcribed from the PTM (AT5G35210) locus. Black (on the top), Helicos DRS 3’ coverage; blue track, nanopore DRS coverage; blue, isoforms detected by nanopore DRS; black (on the bottom), Araport11 annotation; green, five annotated transmembrane domain regions from Uniprot entry F4JYC8 (PTM_ARATH) that mapped to exons; pPAS, proximal polyadenylation site; dPAS, distal polyadenylation sites. Poly(A) tail length estimations generated from ERCC spike-in reads – Figure 2—figure supplement 1A . Per gene poly(A) tail length estimate distributions generated from Col-0 reads - Figure 2—figure supplement 1B .

    Journal: eLife

    Article Title: Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification

    doi: 10.7554/eLife.49658

    Figure Lengend Snippet: 3′ end processing is revealed by nanopore DRS. ( A ) Poly(A) tail length estimates for ERCC spike-in controls. Boxplots showing distribution of poly(A) length estimates for ERCC spike-in controls with more than 100 mapped reads for which tail length could be successfully estimated. Expected poly(A) tail lengths are shown as orange points. ( B ) The RNA poly(A) tail length negatively correlates with the gene expression level. Expression in log 2 scale of counts per million (CPM) obtained from nanopore DRS data is plotted against the median poly(A) tail length. ρ, Spearman’s correlation coefficient; black line, locally weighted scatterplot smoothing (LOWESS) regression fit. ( C ) Nanopore DRS identified known 3′ polyadenylation sites in RNAs transcribed from the IBM1 (AT3G07610) locus. Blue track, nanopore DRS coverage; blue, isoforms detected by nanopore DRS; black, Araport11 annotation; PAS, proximal polyadenylation site. ( D ) Nanopore DRS identified novel 3’ polyadenylation sites in RNAs transcribed from the PTM (AT5G35210) locus. Black (on the top), Helicos DRS 3’ coverage; blue track, nanopore DRS coverage; blue, isoforms detected by nanopore DRS; black (on the bottom), Araport11 annotation; green, five annotated transmembrane domain regions from Uniprot entry F4JYC8 (PTM_ARATH) that mapped to exons; pPAS, proximal polyadenylation site; dPAS, distal polyadenylation sites. Poly(A) tail length estimations generated from ERCC spike-in reads – Figure 2—figure supplement 1A . Per gene poly(A) tail length estimate distributions generated from Col-0 reads - Figure 2—figure supplement 1B .

    Article Snippet: ERCC RNA Spike-In mixes (Thermo Fisher Scientific) ( ; ) were included in each of the libraries using concentrations advised by the manufacturer.

    Techniques: Expressing, Generated

    Triplication of a Down Syndrome Critical Region or HMGN1 Overexpression Alone Results in Increased RNA per Transcript per Cell (A) Log 2 fold change per transcript from RNA-sequencing of progenitor B cell colonies from Ts1Rhr compared to wild-type (top) and from HMGN1-OE transgenic versus wild-type (bottom) bone marrow. n = 3 biological replicates per genotype. Plots on the left are median read count normalized between samples (“Relative”), and plots on right are ERCC spike-in per cell-normalized (“Absolute”). Distribution compared to the null hypothesis of no difference between genotypes using single sample t test. (B) The data from (A) are plotted with each dot representing a single gene’s expression quantitated in the indicated genotypes. The dotted line represents the unity line of no difference between genotypes. The contour lines and legend represent high (green) to low (blue) relative bin density. Red and blue numbers represent the number of genes that increase or decrease, respectively, in Ts1Rhr or HMGN1 versus wild-type (fold-change > 1.5, p

    Journal: Cell reports

    Article Title: Trisomy of a Down Syndrome Critical Region Globally Amplifies Transcription via HMGN1 Overexpression

    doi: 10.1016/j.celrep.2018.10.061

    Figure Lengend Snippet: Triplication of a Down Syndrome Critical Region or HMGN1 Overexpression Alone Results in Increased RNA per Transcript per Cell (A) Log 2 fold change per transcript from RNA-sequencing of progenitor B cell colonies from Ts1Rhr compared to wild-type (top) and from HMGN1-OE transgenic versus wild-type (bottom) bone marrow. n = 3 biological replicates per genotype. Plots on the left are median read count normalized between samples (“Relative”), and plots on right are ERCC spike-in per cell-normalized (“Absolute”). Distribution compared to the null hypothesis of no difference between genotypes using single sample t test. (B) The data from (A) are plotted with each dot representing a single gene’s expression quantitated in the indicated genotypes. The dotted line represents the unity line of no difference between genotypes. The contour lines and legend represent high (green) to low (blue) relative bin density. Red and blue numbers represent the number of genes that increase or decrease, respectively, in Ts1Rhr or HMGN1 versus wild-type (fold-change > 1.5, p

    Article Snippet: For per cell normalization, 1 μl of Mix #1 ERCC exogenous spike-in RNA (Ambion, 4456740, diluted 1:1000) was added to each RNA sample.

    Techniques: Over Expression, RNA Sequencing Assay, Transgenic Assay, Expressing

    Non-poly(A) transcripts are also affected in Exosc10 cKO oocytes detected by RiboMinus RNA-seq. ( A ) Total RNA abundance indicated by RNA reads normalized to ERCC reads. ( B ) Further normalization of A by the mean value of the GV stage within each genotype. ( C ) PCA of all libraries. Each dot represents one library, color-coded by genotype and stage. ( D ) MA-plot of transcript changes from GV to MII in control oocytes. The increased and decreased transcripts are labeled by red and blue, respectively (both have P -adjust

    Journal: Nucleic Acids Research

    Article Title: EXOSC10 sculpts the transcriptome during the growth-to-maturation transition in mouse oocytes

    doi: 10.1093/nar/gkaa249

    Figure Lengend Snippet: Non-poly(A) transcripts are also affected in Exosc10 cKO oocytes detected by RiboMinus RNA-seq. ( A ) Total RNA abundance indicated by RNA reads normalized to ERCC reads. ( B ) Further normalization of A by the mean value of the GV stage within each genotype. ( C ) PCA of all libraries. Each dot represents one library, color-coded by genotype and stage. ( D ) MA-plot of transcript changes from GV to MII in control oocytes. The increased and decreased transcripts are labeled by red and blue, respectively (both have P -adjust

    Article Snippet: Briefly, 1 μl of 105-fold diluted ERCC RNA spike-in mix (Thermo Fisher Scientific, 4456740) was added to each oocyte lysis sample.

    Techniques: RNA Sequencing Assay, Labeling

    Exosc10 cKO oocytes exhibit dysregulated transcriptomes during oocyte maturation. ( A ) Schematic illustrating the pipeline of single oocyte RNA-seq. After individual oocyte lysis, oligo-dT beads captured poly(A) RNAs for library construction and sequencing. Genotypes were determined from genomic DNA. ( B ) Total RNA levels were normalized for each library by an ERCC RNA spike-in mix. ( C ) Further normalization of total RNA level in B by the mean value of the GV stage within each genotype. ( D ) Heatmap of all libraries, each row represents one gene and each column represents one library. Genes are ranked from highest to lowest expression level, and every 100th gene from the top half were selected to represent the transcriptome. The transcription level was color-coded from high to low. ( E ) PCA of the 64 libraries. Each dot represents one library, color-coded by genotype and stage. ( F ) Log 2 fold change of Exosc10 and Gapdh in cKO versus control oocytes. The bars and lines are log 2 fold change and standard error of the mean from DESeq2 analyses. **** P -adjust

    Journal: Nucleic Acids Research

    Article Title: EXOSC10 sculpts the transcriptome during the growth-to-maturation transition in mouse oocytes

    doi: 10.1093/nar/gkaa249

    Figure Lengend Snippet: Exosc10 cKO oocytes exhibit dysregulated transcriptomes during oocyte maturation. ( A ) Schematic illustrating the pipeline of single oocyte RNA-seq. After individual oocyte lysis, oligo-dT beads captured poly(A) RNAs for library construction and sequencing. Genotypes were determined from genomic DNA. ( B ) Total RNA levels were normalized for each library by an ERCC RNA spike-in mix. ( C ) Further normalization of total RNA level in B by the mean value of the GV stage within each genotype. ( D ) Heatmap of all libraries, each row represents one gene and each column represents one library. Genes are ranked from highest to lowest expression level, and every 100th gene from the top half were selected to represent the transcriptome. The transcription level was color-coded from high to low. ( E ) PCA of the 64 libraries. Each dot represents one library, color-coded by genotype and stage. ( F ) Log 2 fold change of Exosc10 and Gapdh in cKO versus control oocytes. The bars and lines are log 2 fold change and standard error of the mean from DESeq2 analyses. **** P -adjust

    Article Snippet: Briefly, 1 μl of 105-fold diluted ERCC RNA spike-in mix (Thermo Fisher Scientific, 4456740) was added to each oocyte lysis sample.

    Techniques: RNA Sequencing Assay, Lysis, Sequencing, Expressing

    Design of the ribosomal depletion study: Schematic of the sample processing is shown. A single sample of UHR RNA with SIRV spike-ins was kept intact or heat degraded followed by addition of the ERCC spike-in. The two samples were then distributed to the participating sites where they were run as technical duplicates for each kit. All graphics were either produced by the authors or are public domain images that are no longer under copyright

    Journal: BMC Genomics

    Article Title: Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

    doi: 10.1186/s12864-018-4585-1

    Figure Lengend Snippet: Design of the ribosomal depletion study: Schematic of the sample processing is shown. A single sample of UHR RNA with SIRV spike-ins was kept intact or heat degraded followed by addition of the ERCC spike-in. The two samples were then distributed to the participating sites where they were run as technical duplicates for each kit. All graphics were either produced by the authors or are public domain images that are no longer under copyright

    Article Snippet: The sample was split into two aliquots, one of which was then heated at 94° C on an Eppendorf™Thermomixer for 1 h and 27 min. 1 μl of ERCC RNA Spike-In Mix 1 (ThermoFisher Scientific) was added to both the intact and degraded samples before running on a Bioanalyzer 2100 RNA Nano chip (Agilent) (Additional file : Figure S1).

    Techniques: Produced

    Single cell and low input RNA sequencing of bone marrow MKs a. Human bone marrow MK phenotype. Left panel: Cytocentrifugation of enriched MK population stained with Roberts stain, 40x objective; Middle panel: Immunofluorescent staining of enriched megakaryocyte population with DAPI DNA stain (blue) and CD41 surface stain (green); Right panel: Fluorescence activated cell sorting for primary megakaryocytes from whole human bone marrow. Ploidy plot shown detecting levels of Hoechst 33342 staining showing typical ploidy distribution for human bone marrow MKs (cells shown are CD41a+, CD42a+); b. Single MK cell filtering. Performed using a 5-round training scheme of random forest models 37 trained cDNA from 20 cell MK pools that were isolated using an identical sorting and sequencing protocol. High and low quality cells shown by mitochondrial mapping (ChrM), ERCC ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 1106 cells, 282 were taken forward for further analysis based on high quality; c. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000); d. Ordering differentiation trajectories between HSC cell clusters using Monocle 2 with the addition of the MK 20-100 cell pools. HSC clusters and MKs are differentiated by color.

    Journal: bioRxiv

    Article Title: Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

    doi: 10.1101/2020.02.20.957936

    Figure Lengend Snippet: Single cell and low input RNA sequencing of bone marrow MKs a. Human bone marrow MK phenotype. Left panel: Cytocentrifugation of enriched MK population stained with Roberts stain, 40x objective; Middle panel: Immunofluorescent staining of enriched megakaryocyte population with DAPI DNA stain (blue) and CD41 surface stain (green); Right panel: Fluorescence activated cell sorting for primary megakaryocytes from whole human bone marrow. Ploidy plot shown detecting levels of Hoechst 33342 staining showing typical ploidy distribution for human bone marrow MKs (cells shown are CD41a+, CD42a+); b. Single MK cell filtering. Performed using a 5-round training scheme of random forest models 37 trained cDNA from 20 cell MK pools that were isolated using an identical sorting and sequencing protocol. High and low quality cells shown by mitochondrial mapping (ChrM), ERCC ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 1106 cells, 282 were taken forward for further analysis based on high quality; c. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000); d. Ordering differentiation trajectories between HSC cell clusters using Monocle 2 with the addition of the MK 20-100 cell pools. HSC clusters and MKs are differentiated by color.

    Article Snippet: ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

    Techniques: RNA Sequencing Assay, Staining, Fluorescence, FACS, Isolation, Sequencing, Expressing

    Single cell RNA sequencing of ex vivo human bone marrow HSCs a. Experimental design. Sternal bone marrow was harvested from 5 individuals undergoing sternotomy for heart valve replacement. It was stained with antibodies for surface markers that define the human HSC population and sorted as single cells using index FACS into cell lysis buffer. Single cell RNA sequencing libraries were then prepared, sequenced on a HiSeq 4000 instrument and analysis was performed as described in Methods; b. Cell filtering. Performed using a SVM machine learning method (modified from a previously described model 37 ) trained on cDNA from HSCs selected by expression of the housekeeping gene GAPDH. High and low quality cells shown by mitochondrial mapping (ChrM), ERCC ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 884 cells, 119 were taken forward for further analysis based on high quality; c. Filtered cells are larger. Flow cytometric plot FSC/SSC showing cells filtered as high quality by the SVM machine learning method. Green denotes high quality, red denotes low quality; d. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000).

    Journal: bioRxiv

    Article Title: Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

    doi: 10.1101/2020.02.20.957936

    Figure Lengend Snippet: Single cell RNA sequencing of ex vivo human bone marrow HSCs a. Experimental design. Sternal bone marrow was harvested from 5 individuals undergoing sternotomy for heart valve replacement. It was stained with antibodies for surface markers that define the human HSC population and sorted as single cells using index FACS into cell lysis buffer. Single cell RNA sequencing libraries were then prepared, sequenced on a HiSeq 4000 instrument and analysis was performed as described in Methods; b. Cell filtering. Performed using a SVM machine learning method (modified from a previously described model 37 ) trained on cDNA from HSCs selected by expression of the housekeeping gene GAPDH. High and low quality cells shown by mitochondrial mapping (ChrM), ERCC ratio (ERCC), exonic ratio (Exon), number of called genes (Genes). Green denotes high quality, red denotes low quality. Out of 884 cells, 119 were taken forward for further analysis based on high quality; c. Filtered cells are larger. Flow cytometric plot FSC/SSC showing cells filtered as high quality by the SVM machine learning method. Green denotes high quality, red denotes low quality; d. Gene filtering. Variance of normalized log-expression values for each gene in the HSC dataset, plotted against the mean log-expression. Variance estimates for ERCC spike-in transcripts and curve fit are highlighted in red. Blue dots represent highly varying genes (n=2000).

    Article Snippet: ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

    Techniques: RNA Sequencing Assay, Ex Vivo, Staining, FACS, Lysis, Modification, Expressing

    TempO-Seq assay sensitivity. (A) URR RNA was diluted in 10-fold steps with input of 100 ng down to 0.1 pg total RNA, plus no-input, in triplicate. Error bars indicate 1 standard deviation. Each color indicates a different gene, selected across the dynamic range. (B) MDA MB 231 cell lysates were diluted in 10-fold steps, for a range of 4,000 down to 0.004 cells in the assay. Genes were selected as for (A). (C) Mix 2 of the synthetic reference ERCC ExFold RNA Mixtures was diluted in 10-fold steps from 1x10 -3 down to 1x10 -6 of the supplied stock in URR as carrier, then assayed using a detector oligo pool specific for the ERCC RNAs. Average reads per sample ranged from 3.6K for the 1x10 -6 dilution to 340K for the 1x10 -3 dilution. Results from the 1 x 10 −5 dilution are shown. (D) MDA MB 231 cells were diluted in 10-fold increments into a constant background of MCF7 cells (blue bars), or MCF-7 cells were diluted into a constant background of MDA MB 231 cells (green bars), then lysed and assayed for cell-specific transcripts. Of the 13 and 14 genes monitored, respectively, the fraction that were significantly above background is shown for each cell dilution. Read depth ranged from 3.6M/sample for 100%, 299K for 0.1%, and down to 64K for 0.00001% for both titrations.

    Journal: PLoS ONE

    Article Title: A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling

    doi: 10.1371/journal.pone.0178302

    Figure Lengend Snippet: TempO-Seq assay sensitivity. (A) URR RNA was diluted in 10-fold steps with input of 100 ng down to 0.1 pg total RNA, plus no-input, in triplicate. Error bars indicate 1 standard deviation. Each color indicates a different gene, selected across the dynamic range. (B) MDA MB 231 cell lysates were diluted in 10-fold steps, for a range of 4,000 down to 0.004 cells in the assay. Genes were selected as for (A). (C) Mix 2 of the synthetic reference ERCC ExFold RNA Mixtures was diluted in 10-fold steps from 1x10 -3 down to 1x10 -6 of the supplied stock in URR as carrier, then assayed using a detector oligo pool specific for the ERCC RNAs. Average reads per sample ranged from 3.6K for the 1x10 -6 dilution to 340K for the 1x10 -3 dilution. Results from the 1 x 10 −5 dilution are shown. (D) MDA MB 231 cells were diluted in 10-fold increments into a constant background of MCF7 cells (blue bars), or MCF-7 cells were diluted into a constant background of MDA MB 231 cells (green bars), then lysed and assayed for cell-specific transcripts. Of the 13 and 14 genes monitored, respectively, the fraction that were significantly above background is shown for each cell dilution. Read depth ranged from 3.6M/sample for 100%, 299K for 0.1%, and down to 64K for 0.00001% for both titrations.

    Article Snippet: Synthetic RNAs for testing absolute sensitivity and fold change were the ERCC ExFold RNA Spike-In Mixes, obtained from Thermo Fisher Scientific (cat # 4456739).

    Techniques: Standard Deviation, Multiple Displacement Amplification

    Cross-platform comparison of TempO-Seq and RNA-Seq. (A) Log2 fold changes between MCF-7 and MDA MB 231 RNAs as measured by TempO-Seq (~4 M reads/sample) are compared to those measured by RNA-Seq (~15 M reads/sample) for the 6,500 genes that had over 20 read counts in both cell types on each platform. R 2 = 0.91. (B) ERCC Mixes 1 and 2 were diluted 1:1,000 and spiked into 100 ng URR, then assayed with a dedicated DO pool. Fold differences in sequencing reads are compared to the fold differences in concentration between the two mixes. Read depth was 340K reads/sample.

    Journal: PLoS ONE

    Article Title: A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling

    doi: 10.1371/journal.pone.0178302

    Figure Lengend Snippet: Cross-platform comparison of TempO-Seq and RNA-Seq. (A) Log2 fold changes between MCF-7 and MDA MB 231 RNAs as measured by TempO-Seq (~4 M reads/sample) are compared to those measured by RNA-Seq (~15 M reads/sample) for the 6,500 genes that had over 20 read counts in both cell types on each platform. R 2 = 0.91. (B) ERCC Mixes 1 and 2 were diluted 1:1,000 and spiked into 100 ng URR, then assayed with a dedicated DO pool. Fold differences in sequencing reads are compared to the fold differences in concentration between the two mixes. Read depth was 340K reads/sample.

    Article Snippet: Synthetic RNAs for testing absolute sensitivity and fold change were the ERCC ExFold RNA Spike-In Mixes, obtained from Thermo Fisher Scientific (cat # 4456739).

    Techniques: RNA Sequencing Assay, Multiple Displacement Amplification, Sequencing, Concentration Assay

    Technical validation of Smart-3SEQ with reference RNAs. ( A ) Standard curve from ERCC transcripts, r . ( B ) Comparison with TaqMan qPCR quantification of human transcripts, r . ( C ) Alignability of Smart-3SEQ reads from human reference RNA dilutions and no-template controls.

    Journal: Genome Research

    Article Title: Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ

    doi: 10.1101/gr.234807.118

    Figure Lengend Snippet: Technical validation of Smart-3SEQ with reference RNAs. ( A ) Standard curve from ERCC transcripts, r . ( B ) Comparison with TaqMan qPCR quantification of human transcripts, r . ( C ) Alignability of Smart-3SEQ reads from human reference RNA dilutions and no-template controls.

    Article Snippet: ERCC standards and Human Brain Reference RNA were purchased from Thermo Fisher Scientific (4456739 and AM6050, respectively).

    Techniques: Real-time Polymerase Chain Reaction