Structured Review

10X Genomics pbmcs
Custom single-cell-RNA sequencing maps <t>JAK1</t> allele distribution, transcriptomic impact and expression patterns. (A) tSNE plots and cell type assignments from single cell RNA sequencing of patient <t>PBMCs</t> with an inDrop platform adapted to target the mutant JAK1 transcript. n=4763 cells. (B) tSNE plots representing the subset of cells with sufficient JAK1 counts to be assigned putative JAK1 genotypes (based on transcript sequences). Cells in which any mutant transcript was detected above empirically-determined thresholds were assigned ‘S703I JAK1’ (purple) while cells with only WT transcript detected were assigned ‘WT JAK1’ (orange). (C) Doughnut charts quantifying allele distribution in cells meeting genotyping criteria (cell count in the center), as in panel B. (D) Expression of the interferon stimulated gene IFI44L, a statistically significant differentially expressed gene in the comparison of WT JAK1 and S703I JAK1 genotyped cells. (E) Gene set scores for IFNα signaling in CD14+ monocytes (F) Number of unique transcripts detected per cell for the WT or S703I JAK1 allele (left) or a control variant GNLY rs12845 (right). Bubble size indicates number of cells. Color coding indicates cells containing: S703I JAK1 (purple), WT JAK1 without S703I JAK1 (orange), WT JAK1 with S703I JAK1 detected below threshold (yellow), or insufficient transcripts counts (gray). (G) Transcript genotyping of JAK1 rs2230587 from healthy control PBMCs (n=96) by single cell qPCR with allele-specific probes. Histogram represents relative frequency of cells expressing binned allele ratios as quantified by oligonucleotide standards. (I) Single cell qPCR transcript genotyping of control gene NACA (rs4902).
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1) Product Images from "Complex Autoinflammatory Syndrome Unveils Fundamental Principles of JAK1 Transcriptional and Biochemical Function"

Article Title: Complex Autoinflammatory Syndrome Unveils Fundamental Principles of JAK1 Transcriptional and Biochemical Function

Journal: bioRxiv

doi: 10.1101/807669

Custom single-cell-RNA sequencing maps JAK1 allele distribution, transcriptomic impact and expression patterns. (A) tSNE plots and cell type assignments from single cell RNA sequencing of patient PBMCs with an inDrop platform adapted to target the mutant JAK1 transcript. n=4763 cells. (B) tSNE plots representing the subset of cells with sufficient JAK1 counts to be assigned putative JAK1 genotypes (based on transcript sequences). Cells in which any mutant transcript was detected above empirically-determined thresholds were assigned ‘S703I JAK1’ (purple) while cells with only WT transcript detected were assigned ‘WT JAK1’ (orange). (C) Doughnut charts quantifying allele distribution in cells meeting genotyping criteria (cell count in the center), as in panel B. (D) Expression of the interferon stimulated gene IFI44L, a statistically significant differentially expressed gene in the comparison of WT JAK1 and S703I JAK1 genotyped cells. (E) Gene set scores for IFNα signaling in CD14+ monocytes (F) Number of unique transcripts detected per cell for the WT or S703I JAK1 allele (left) or a control variant GNLY rs12845 (right). Bubble size indicates number of cells. Color coding indicates cells containing: S703I JAK1 (purple), WT JAK1 without S703I JAK1 (orange), WT JAK1 with S703I JAK1 detected below threshold (yellow), or insufficient transcripts counts (gray). (G) Transcript genotyping of JAK1 rs2230587 from healthy control PBMCs (n=96) by single cell qPCR with allele-specific probes. Histogram represents relative frequency of cells expressing binned allele ratios as quantified by oligonucleotide standards. (I) Single cell qPCR transcript genotyping of control gene NACA (rs4902).
Figure Legend Snippet: Custom single-cell-RNA sequencing maps JAK1 allele distribution, transcriptomic impact and expression patterns. (A) tSNE plots and cell type assignments from single cell RNA sequencing of patient PBMCs with an inDrop platform adapted to target the mutant JAK1 transcript. n=4763 cells. (B) tSNE plots representing the subset of cells with sufficient JAK1 counts to be assigned putative JAK1 genotypes (based on transcript sequences). Cells in which any mutant transcript was detected above empirically-determined thresholds were assigned ‘S703I JAK1’ (purple) while cells with only WT transcript detected were assigned ‘WT JAK1’ (orange). (C) Doughnut charts quantifying allele distribution in cells meeting genotyping criteria (cell count in the center), as in panel B. (D) Expression of the interferon stimulated gene IFI44L, a statistically significant differentially expressed gene in the comparison of WT JAK1 and S703I JAK1 genotyped cells. (E) Gene set scores for IFNα signaling in CD14+ monocytes (F) Number of unique transcripts detected per cell for the WT or S703I JAK1 allele (left) or a control variant GNLY rs12845 (right). Bubble size indicates number of cells. Color coding indicates cells containing: S703I JAK1 (purple), WT JAK1 without S703I JAK1 (orange), WT JAK1 with S703I JAK1 detected below threshold (yellow), or insufficient transcripts counts (gray). (G) Transcript genotyping of JAK1 rs2230587 from healthy control PBMCs (n=96) by single cell qPCR with allele-specific probes. Histogram represents relative frequency of cells expressing binned allele ratios as quantified by oligonucleotide standards. (I) Single cell qPCR transcript genotyping of control gene NACA (rs4902).

Techniques Used: RNA Sequencing Assay, Expressing, Mutagenesis, Cell Counting, Variant Assay, Real-time Polymerase Chain Reaction

2) Product Images from "Complex Autoinflammatory Syndrome Unveils Fundamental Principles of JAK1 Kinase Transcriptional and Biochemical Function"

Article Title: Complex Autoinflammatory Syndrome Unveils Fundamental Principles of JAK1 Kinase Transcriptional and Biochemical Function

Journal: Immunity

doi: 10.1016/j.immuni.2020.07.006

Custom scRNA-Seq Maps JAK1 Allele Distribution, Transcriptomic Impact, and Expression Patterns (A) tSNE plots and cell-type assignments from scRNA-seq of patient PBMCs, with an inDrops platform adapted to target the mutant JAK1 transcript. n = 4,763 cells. (B) tSNE plots representing the subset of cells with sufficient JAK1 counts to be assigned putative JAK1 genotypes (based on transcript sequences). Cells in which any mutant transcript was detected above empirically determined thresholds ( > 5 JAK1 transcripts) were assigned “S703I JAK1” (purple), while cells with only WT transcript detected were assigned “WT JAK1” (orange). (C) Doughnut charts quantifying allele distribution in cells meeting genotyping criteria (cell count in the center), as in (B). (D) Expression of the ISG IFI44L , a statistically significant differentially expressed gene in the comparison of WT JAK1 and S703I JAK1 genotyped cells. (E) Gene set scores for IFN-α signaling in CD14 + monocytes. (F) Number of unique transcripts detected per cell for the WT or S703I JAK1 allele (left) or a control variant GNLY rs12845 (right). The bubble size indicates the number of cells. The color coding indicates cells containing S703I JAK1 (purple), WT JAK1 without S703I JAK1 (orange), WT JAK1 with S703I JAK1 detected below threshold (yellow), or insufficient transcripts counts (gray). (G) Transcript genotyping of JAK1 rs2230587 from healthy control PBMCs (n = 96) by single-cell qPCR with allele-specific probes. The histogram represents the relative frequency of cells expressing binned allele ratios as quantified by oligonucleotide standards. (I) Single-cell qPCR transcript genotyping of control gene NACA (rs4902). See also Figure S5 .
Figure Legend Snippet: Custom scRNA-Seq Maps JAK1 Allele Distribution, Transcriptomic Impact, and Expression Patterns (A) tSNE plots and cell-type assignments from scRNA-seq of patient PBMCs, with an inDrops platform adapted to target the mutant JAK1 transcript. n = 4,763 cells. (B) tSNE plots representing the subset of cells with sufficient JAK1 counts to be assigned putative JAK1 genotypes (based on transcript sequences). Cells in which any mutant transcript was detected above empirically determined thresholds ( > 5 JAK1 transcripts) were assigned “S703I JAK1” (purple), while cells with only WT transcript detected were assigned “WT JAK1” (orange). (C) Doughnut charts quantifying allele distribution in cells meeting genotyping criteria (cell count in the center), as in (B). (D) Expression of the ISG IFI44L , a statistically significant differentially expressed gene in the comparison of WT JAK1 and S703I JAK1 genotyped cells. (E) Gene set scores for IFN-α signaling in CD14 + monocytes. (F) Number of unique transcripts detected per cell for the WT or S703I JAK1 allele (left) or a control variant GNLY rs12845 (right). The bubble size indicates the number of cells. The color coding indicates cells containing S703I JAK1 (purple), WT JAK1 without S703I JAK1 (orange), WT JAK1 with S703I JAK1 detected below threshold (yellow), or insufficient transcripts counts (gray). (G) Transcript genotyping of JAK1 rs2230587 from healthy control PBMCs (n = 96) by single-cell qPCR with allele-specific probes. The histogram represents the relative frequency of cells expressing binned allele ratios as quantified by oligonucleotide standards. (I) Single-cell qPCR transcript genotyping of control gene NACA (rs4902). See also Figure S5 .

Techniques Used: Expressing, Mutagenesis, Cell Counting, Variant Assay, Real-time Polymerase Chain Reaction

3) Product Images from "Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications"

Article Title: Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Journal: Genome Biology

doi: 10.1186/s13059-018-1406-4

Biologically meaningful DE results for the 10x Genomics PBMC dataset. a Scatterplot of the first two t-SNE dimensions obtained from the first ten principal components. Cells are color-coded by clusters found using the SEURAT graph-based clustering method on the first ten principal components. Pseudo-color images on the right display normalized enrichment scores after gene set enrichment analysis for cell types related to CD4+ T cells (see “ Methods ”), for clustering based on b the first ten principal components and c W from ZINB-WaVE with K =20. For dimensionality reduction, ZINB-WaVE was fitted with X = 1 n , V = 1 J , K =20 for W (based on the Akaike information criterion), common dispersion, and ε =10 12 . To compute the weights for differential expression analysis, ZINB-WaVE was fitted with intercept and cell-type covariate in X , V = 1 J , K =0 for W , common dispersion, and ε =10 12 . Normalized enrichment scores for more cell types are shown in Additional file 1 : Figure S17. PCA principal component analysis
Figure Legend Snippet: Biologically meaningful DE results for the 10x Genomics PBMC dataset. a Scatterplot of the first two t-SNE dimensions obtained from the first ten principal components. Cells are color-coded by clusters found using the SEURAT graph-based clustering method on the first ten principal components. Pseudo-color images on the right display normalized enrichment scores after gene set enrichment analysis for cell types related to CD4+ T cells (see “ Methods ”), for clustering based on b the first ten principal components and c W from ZINB-WaVE with K =20. For dimensionality reduction, ZINB-WaVE was fitted with X = 1 n , V = 1 J , K =20 for W (based on the Akaike information criterion), common dispersion, and ε =10 12 . To compute the weights for differential expression analysis, ZINB-WaVE was fitted with intercept and cell-type covariate in X , V = 1 J , K =0 for W , common dispersion, and ε =10 12 . Normalized enrichment scores for more cell types are shown in Additional file 1 : Figure S17. PCA principal component analysis

Techniques Used: Expressing

Comparison of differential expression methods on simulated scRNA-seq datasets. Differential expression methods are compared based on FDP-TPR curves for data simulated from a 10x Genomics PBMC single-cell RNA-seq dataset ( n =1200). Zoomed versions of the FDP-TPR curves are shown here and full curves are in Additional file 1 : Figure S12. Circles represent working points on a nominal 5% FDR level and are filled if the empirical FDR (i.e., FDP) is below the nominal FDR. 10x Genomics sequencing typically involves high-throughput and massive multiplexing, resulting in very shallow sequencing depths and thus, low counts, making it extremely difficult to identify excess zeros. Unweighted and ZINB-WaVE-weighted EDGER are tied for best performance, followed by ZINB-WaVE-weighted DESEQ2 . In general, bulk RNA-seq methods perform well in this simulation, probably because the extremely high zero abundance in combination with low counts can be reasonably accommodated by the negative binomial distribution. The behavior in the lower half of the curve for NODES is due to a smooth increase in true positives with an identical number of false positives over a range of low FDR cut-offs. FDP false discovery proportion, FDR false discovery rate, PBMC peripheral blood mononuclear cell, TPR true positive rate
Figure Legend Snippet: Comparison of differential expression methods on simulated scRNA-seq datasets. Differential expression methods are compared based on FDP-TPR curves for data simulated from a 10x Genomics PBMC single-cell RNA-seq dataset ( n =1200). Zoomed versions of the FDP-TPR curves are shown here and full curves are in Additional file 1 : Figure S12. Circles represent working points on a nominal 5% FDR level and are filled if the empirical FDR (i.e., FDP) is below the nominal FDR. 10x Genomics sequencing typically involves high-throughput and massive multiplexing, resulting in very shallow sequencing depths and thus, low counts, making it extremely difficult to identify excess zeros. Unweighted and ZINB-WaVE-weighted EDGER are tied for best performance, followed by ZINB-WaVE-weighted DESEQ2 . In general, bulk RNA-seq methods perform well in this simulation, probably because the extremely high zero abundance in combination with low counts can be reasonably accommodated by the negative binomial distribution. The behavior in the lower half of the curve for NODES is due to a smooth increase in true positives with an identical number of false positives over a range of low FDR cut-offs. FDP false discovery proportion, FDR false discovery rate, PBMC peripheral blood mononuclear cell, TPR true positive rate

Techniques Used: Expressing, RNA Sequencing Assay, Sequencing, High Throughput Screening Assay, Multiplexing

4) Product Images from "IL-34 Actions on FOXP3+ Tregs and CD14+ Monocytes Control Human Graft Rejection"

Article Title: IL-34 Actions on FOXP3+ Tregs and CD14+ Monocytes Control Human Graft Rejection

Journal: Frontiers in Immunology

doi: 10.3389/fimmu.2020.01496

CSF-1R and PTPζ expression is restricted to monocytes and FOXP3 + Tregs. PBMCs were analyzed for CSF-1R expression at single cell transcriptional (A,B) and proteomic levels (C–F) . (A) Top: UMAP visualization of a public dataset of resting Human PBMC single cell RNA-seq from one healthy volunteer for which subsets of monocytes, T cells, B cells, and NK cells were identified by antibody staining. Bottom: CSF-1R expression in total PBMCs. One point represents one cell. Relative expression level is scaled from gray to dark blue. (B) Monocyte subsets were further subdivided based on RNA (RNAseq, bottom left) and protein expressions (CITEseq, bottom right) of CD14 (left) and FCGR3A (CD16) (right) summarized in the UMAP visualization (upper left), and subsets were analyzed for CSF-1R RNA expression (upper middle and right). One point represents one cell. Relative expression level is scaled from gray to dark blue (RNA expression) or from gray to dark green (protein expression). Upper Right: Violin plot representing the expression level of mRNA for CSF-1R in CD14 ++ CD16 − monocytes (red), in CD16 ++ CD14 dim monocytes (pink), and in CD14 ++ CD16 + monocytes (purple). (C) Representative gating strategy for FACS analysis of CSF-1R, PTPζ, and CD138 expression in living (DAPI − ) non-NK cells (CD56 − NKp46 − ) CD14 ++/dim CD16 ++/+/− cell subsets from PBMCs. Representative from three individuals. (D) Frequency (left) of CSF-1R, PTPζ, and CD138 expressing cells and expression level (MFI) of CSF-1R and PTPζ (right) in CD14 ++/dim CD16 ++/+/− cell subsets. n = 3 individuals. (E) Frequency of CSF-1R + , PTPζ + , and CD138 + monocytes in total PBMCs. n = 3 individuals. (F) Frequency of CSF-1R expressing cells in stimulated (black) or not (white) FOXP3 +/− CD4 + or CD8 + T cells. n = 5 individuals. Mann Whitney tests, * p
Figure Legend Snippet: CSF-1R and PTPζ expression is restricted to monocytes and FOXP3 + Tregs. PBMCs were analyzed for CSF-1R expression at single cell transcriptional (A,B) and proteomic levels (C–F) . (A) Top: UMAP visualization of a public dataset of resting Human PBMC single cell RNA-seq from one healthy volunteer for which subsets of monocytes, T cells, B cells, and NK cells were identified by antibody staining. Bottom: CSF-1R expression in total PBMCs. One point represents one cell. Relative expression level is scaled from gray to dark blue. (B) Monocyte subsets were further subdivided based on RNA (RNAseq, bottom left) and protein expressions (CITEseq, bottom right) of CD14 (left) and FCGR3A (CD16) (right) summarized in the UMAP visualization (upper left), and subsets were analyzed for CSF-1R RNA expression (upper middle and right). One point represents one cell. Relative expression level is scaled from gray to dark blue (RNA expression) or from gray to dark green (protein expression). Upper Right: Violin plot representing the expression level of mRNA for CSF-1R in CD14 ++ CD16 − monocytes (red), in CD16 ++ CD14 dim monocytes (pink), and in CD14 ++ CD16 + monocytes (purple). (C) Representative gating strategy for FACS analysis of CSF-1R, PTPζ, and CD138 expression in living (DAPI − ) non-NK cells (CD56 − NKp46 − ) CD14 ++/dim CD16 ++/+/− cell subsets from PBMCs. Representative from three individuals. (D) Frequency (left) of CSF-1R, PTPζ, and CD138 expressing cells and expression level (MFI) of CSF-1R and PTPζ (right) in CD14 ++/dim CD16 ++/+/− cell subsets. n = 3 individuals. (E) Frequency of CSF-1R + , PTPζ + , and CD138 + monocytes in total PBMCs. n = 3 individuals. (F) Frequency of CSF-1R expressing cells in stimulated (black) or not (white) FOXP3 +/− CD4 + or CD8 + T cells. n = 5 individuals. Mann Whitney tests, * p

Techniques Used: Expressing, RNA Sequencing Assay, Staining, RNA Expression, FACS, MANN-WHITNEY

5) Product Images from "Design and power analysis for multi-sample single cell genomics experiments"

Article Title: Design and power analysis for multi-sample single cell genomics experiments

Journal: bioRxiv

doi: 10.1101/2020.04.01.019851

Optimal parameters for varying budgets and 10X Genomics data. The figure shows the maximal reachable detection power (y-axis, first column) for a given experimental budget (x-axis) and the corresponding optimal parameter combinations for that budget (y-axis, second till fourth column). The colored lines indicate different effect sizes and gene expression rank distributions. Subplots A-B visualize different simulated effect sizes and rank distributions (simulation names see text) for DEG studies (A) and eQTL studies (B) with models fitted on 10X PBMC data. Subplots C-D visualize effect sizes and rank distributions observed in cell type sorted bulk RNA-seq DEG studies (C) and eQTL studies (D) with model fits analogously to A-B.
Figure Legend Snippet: Optimal parameters for varying budgets and 10X Genomics data. The figure shows the maximal reachable detection power (y-axis, first column) for a given experimental budget (x-axis) and the corresponding optimal parameter combinations for that budget (y-axis, second till fourth column). The colored lines indicate different effect sizes and gene expression rank distributions. Subplots A-B visualize different simulated effect sizes and rank distributions (simulation names see text) for DEG studies (A) and eQTL studies (B) with models fitted on 10X PBMC data. Subplots C-D visualize effect sizes and rank distributions observed in cell type sorted bulk RNA-seq DEG studies (C) and eQTL studies (D) with model fits analogously to A-B.

Techniques Used: Expressing, RNA Sequencing Assay

6) Product Images from "Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories"

Article Title: Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Journal: bioRxiv

doi: 10.1101/2020.07.27.224063

Integrating multi-omic profiles across immune cell types resolves an orchestrated gene module that correlates with disease severity A. Illustration of integrating data from different immune cell types from all samples, followed by reduction in single a dimensional representation (gene module 1 (M1)) using surprisal analysis. B. Distribution of individual PBMC data sets along M1 for healthy donors (green), non-ICU (yellow), and ICU (red) patients. C. Spearman correlations of M1 with disease severity, as measured by the WHO ordinal scale), and with principal component (PC)1 of the plasma proteomic data and PC1 of the plasma metabolomics data (in Figure 1C and 1D ). Regression lines are indicated in black, with 95% confidence area in shaded gray. Spearman Correlation coefficient and associated p-value shown. D. Correlations between M1 with clinical data (top panel: 1-dimensional map) and plasma proteomic and metabolomics data (bottom left and right panels, respectively). For the clinical data, the square size corresponds to absolute value of the correlation coefficient. Blue indicates positive correlation and red indicate negative correlation (*p
Figure Legend Snippet: Integrating multi-omic profiles across immune cell types resolves an orchestrated gene module that correlates with disease severity A. Illustration of integrating data from different immune cell types from all samples, followed by reduction in single a dimensional representation (gene module 1 (M1)) using surprisal analysis. B. Distribution of individual PBMC data sets along M1 for healthy donors (green), non-ICU (yellow), and ICU (red) patients. C. Spearman correlations of M1 with disease severity, as measured by the WHO ordinal scale), and with principal component (PC)1 of the plasma proteomic data and PC1 of the plasma metabolomics data (in Figure 1C and 1D ). Regression lines are indicated in black, with 95% confidence area in shaded gray. Spearman Correlation coefficient and associated p-value shown. D. Correlations between M1 with clinical data (top panel: 1-dimensional map) and plasma proteomic and metabolomics data (bottom left and right panels, respectively). For the clinical data, the square size corresponds to absolute value of the correlation coefficient. Blue indicates positive correlation and red indicate negative correlation (*p

Techniques Used:

Overview of the multi-omic characterization of immune responses in COVID-19 patients. A. Overview of the Swedish/ISB INCOV study of COVID-19 patients. The bar graph represents the disease severity, using the WHO ordinal score, of (hospitalized) patients studied through detailed analyses of PBMCs and blood plasma, collected near the time of diagnosis (T1), and approximately 1 week later (T2). PBMCs were characterized using 10x genomics and Isoplexis single cell methods. The 10X analysis simultaneously profiled the whole transcriptome, 192 surface proteins, and T cell and B cell receptor sequences. Isoplexis analyzes for a 32-plex secretome at single cell resolution from selected immune cell phenotypes. Plasma was analyzes using O-link technology to quantify the levels of 454 proteins, and Metabolon technology to assay for the levels of 847 metabolites. These data were integrated with clinical data from electronic health record for detailed characterization of the patients. B. Levels of selected clinical parameters comparing non-ICU (yellow) and ICU (red) COVID-19 samples. Ranges from healthy donors are below the orange and above the blue-dashed lines. Sample numbers analyzed were non-ICU (n=34) and ICU (n=16). C. Plasma protein analysis. Left panel: PCA analysis of 454 plasma proteins measured from 85 blood draws. Each dot is a single patient/healthy sample, with a color that corresponds to disease severity (see key). Middle panel: Forest plots depicting the odds ratios obtained from logistic regression analysis between cytokines and WHO ordinal scale. Grey shading indicates plot areas with odds ratios
Figure Legend Snippet: Overview of the multi-omic characterization of immune responses in COVID-19 patients. A. Overview of the Swedish/ISB INCOV study of COVID-19 patients. The bar graph represents the disease severity, using the WHO ordinal score, of (hospitalized) patients studied through detailed analyses of PBMCs and blood plasma, collected near the time of diagnosis (T1), and approximately 1 week later (T2). PBMCs were characterized using 10x genomics and Isoplexis single cell methods. The 10X analysis simultaneously profiled the whole transcriptome, 192 surface proteins, and T cell and B cell receptor sequences. Isoplexis analyzes for a 32-plex secretome at single cell resolution from selected immune cell phenotypes. Plasma was analyzes using O-link technology to quantify the levels of 454 proteins, and Metabolon technology to assay for the levels of 847 metabolites. These data were integrated with clinical data from electronic health record for detailed characterization of the patients. B. Levels of selected clinical parameters comparing non-ICU (yellow) and ICU (red) COVID-19 samples. Ranges from healthy donors are below the orange and above the blue-dashed lines. Sample numbers analyzed were non-ICU (n=34) and ICU (n=16). C. Plasma protein analysis. Left panel: PCA analysis of 454 plasma proteins measured from 85 blood draws. Each dot is a single patient/healthy sample, with a color that corresponds to disease severity (see key). Middle panel: Forest plots depicting the odds ratios obtained from logistic regression analysis between cytokines and WHO ordinal scale. Grey shading indicates plot areas with odds ratios

Techniques Used:

7) Product Images from "Massively parallel digital transcriptional profiling of single cells"

Article Title: Massively parallel digital transcriptional profiling of single cells

Journal: Nature Communications

doi: 10.1038/ncomms14049

Distinct populations can be detected in fresh 68k PBMCs. ( a ) Distribution of number of genes (left) and UMI counts (right) detected per 68k PBMCs. ( b ) tSNE projection of 68k PBMCs, where each cell is grouped into one of the 10 clusters (distinguished by their colours). Cluster number is indicated, with the percentage of cells in each cluster noted within parentheses. ( c ) Normalized expression (centred) of the top variable genes (rows) from each of 10 clusters (columns) is shown in a heatmap. Numbers at the top indicate cluster number in ( b ), with connecting lines indicating the hierarchical relationship between clusters. Representative markers from each cluster are shown on the right, and an inferred cluster assignment is shown on the left. ( d – i ) tSNE projection of 68k PBMCs, with each cell coloured based on their normalized expression of CD3D , CD8A , NKG7 , FCER1A , CD16 and S100A8 . UMI normalization was performed by first dividing UMI counts by the total UMI counts in each cell, followed by multiplication with the median of the total UMI counts across cells. Then, we took the natural log of the UMI counts. Finally, each gene was normalized such that the mean signal for each gene is 0, and standard deviation is 1. ( j ) tSNE projection of 68k PBMCs, with each cell coloured based on their correlation-based assignment to a purified subpopulation of PBMCs. Subclusters within T cells are marked by dashed polygons. NK, natural killer cells; reg T, regulatory T cells.
Figure Legend Snippet: Distinct populations can be detected in fresh 68k PBMCs. ( a ) Distribution of number of genes (left) and UMI counts (right) detected per 68k PBMCs. ( b ) tSNE projection of 68k PBMCs, where each cell is grouped into one of the 10 clusters (distinguished by their colours). Cluster number is indicated, with the percentage of cells in each cluster noted within parentheses. ( c ) Normalized expression (centred) of the top variable genes (rows) from each of 10 clusters (columns) is shown in a heatmap. Numbers at the top indicate cluster number in ( b ), with connecting lines indicating the hierarchical relationship between clusters. Representative markers from each cluster are shown on the right, and an inferred cluster assignment is shown on the left. ( d – i ) tSNE projection of 68k PBMCs, with each cell coloured based on their normalized expression of CD3D , CD8A , NKG7 , FCER1A , CD16 and S100A8 . UMI normalization was performed by first dividing UMI counts by the total UMI counts in each cell, followed by multiplication with the median of the total UMI counts across cells. Then, we took the natural log of the UMI counts. Finally, each gene was normalized such that the mean signal for each gene is 0, and standard deviation is 1. ( j ) tSNE projection of 68k PBMCs, with each cell coloured based on their correlation-based assignment to a purified subpopulation of PBMCs. Subclusters within T cells are marked by dashed polygons. NK, natural killer cells; reg T, regulatory T cells.

Techniques Used: Expressing, Standard Deviation, Purification

8) Product Images from "Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN"

Article Title: Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

Journal: bioRxiv

doi: 10.1101/770388

Galapagos’ resolution is limited by t-SNE’s ability to separate cells belonging to related cell types. a Identification of T cell subtype identities using protein expression measurements from the PBMC CITE-Seq dataset. b Original t-SNE result from Figure 1b , overlaid with the T cell subtype identities established in ( a ). c t-SNE result obtained after selecting only the cells identified as T cells in Figure 1c , overlaid with the same cell type identities as in ( b ).
Figure Legend Snippet: Galapagos’ resolution is limited by t-SNE’s ability to separate cells belonging to related cell types. a Identification of T cell subtype identities using protein expression measurements from the PBMC CITE-Seq dataset. b Original t-SNE result from Figure 1b , overlaid with the T cell subtype identities established in ( a ). c t-SNE result obtained after selecting only the cells identified as T cells in Figure 1c , overlaid with the same cell type identities as in ( b ).

Techniques Used: Expressing

Examination of Galapagos clustering accuracy on simulated PBMC data. a Design of the simulation study. b Application of Galapagos to the ground truth. Shown are clustering results as well as manual cell type annotations. c Heatmap showing the relative expression levels of key marker genes, as in Figure 2b . d Comparison of the total amount of variance present in the simulated datasets. e Galapagos t-SNE visualizations of the three simulated datasets, overlaid with the true cell type assignments shown in ( b ).
Figure Legend Snippet: Examination of Galapagos clustering accuracy on simulated PBMC data. a Design of the simulation study. b Application of Galapagos to the ground truth. Shown are clustering results as well as manual cell type annotations. c Heatmap showing the relative expression levels of key marker genes, as in Figure 2b . d Comparison of the total amount of variance present in the simulated datasets. e Galapagos t-SNE visualizations of the three simulated datasets, overlaid with the true cell type assignments shown in ( b ).

Techniques Used: Expressing, Marker

Galapagos workflow and application to human peripheral blood mononuclear cells (PBMCs). a Flow chart showing each step of the workflow and the associated parameters (if any). b t-SNE visualization (left) and DBSCAN clustering results (right) for a 10x Genomics PBMC dataset. Numbers indicate four main cell islands, and lower case letters indicate “sub-islands” in close proximity of each other. The workflow was run with default PCA and t-SNE parameters, and fine-tuned DBSCAN parameters (see main text). c Heatmap showing the mean normalized expression level of key marker genes in each of the clusters. For each gene, the expression levels are expressed in percent, relative to the maximum value across all clusters.
Figure Legend Snippet: Galapagos workflow and application to human peripheral blood mononuclear cells (PBMCs). a Flow chart showing each step of the workflow and the associated parameters (if any). b t-SNE visualization (left) and DBSCAN clustering results (right) for a 10x Genomics PBMC dataset. Numbers indicate four main cell islands, and lower case letters indicate “sub-islands” in close proximity of each other. The workflow was run with default PCA and t-SNE parameters, and fine-tuned DBSCAN parameters (see main text). c Heatmap showing the mean normalized expression level of key marker genes in each of the clusters. For each gene, the expression levels are expressed in percent, relative to the maximum value across all clusters.

Techniques Used: Expressing, Marker

Robustness of Galapagos t-SNE results to initialization points. Shown are t-SNE results for the same PBMC dataset as in Figure 1 , with three different initialization points (seed values). Overlaid onto t-SNE plot are the cluster assignments shown from Figure 1b (right). While different initialization points result in different geometries and spatial arrangements of the clusters, the cluster assignments are almost perfectly “coherent” across analyses.
Figure Legend Snippet: Robustness of Galapagos t-SNE results to initialization points. Shown are t-SNE results for the same PBMC dataset as in Figure 1 , with three different initialization points (seed values). Overlaid onto t-SNE plot are the cluster assignments shown from Figure 1b (right). While different initialization points result in different geometries and spatial arrangements of the clusters, the cluster assignments are almost perfectly “coherent” across analyses.

Techniques Used:

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Staining:

Article Title: Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets
Article Snippet: .. The stained PBMC were analyzed for CITE-seq using a 10XGenomics 3’ chemistry V3 platform, sequenced, pre-processed, and dimensionality reduction of the transcriptome datasets was performed with UMAP. ..

Functional Assay:

Article Title: Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories
Article Snippet: .. COVID-19 patients display varying clinical profiles, plasma proteomic and metabolomic profiles as well as circulating immune cell populations To comprehensively characterize the peripheral immune response in severe COVID-19, we analyzed peripheral blood mononuclear cells (PBMCs) and plasma from 35 healthy and 50 COVID-19 patient samples (about 5000 cells per sample) ( and S1A ) To capture the phenotypes and functional properties of PBMCs, we applied 10X Genomics’ droplet-based single-cell multi-omic technology ( ) to simultaneously measure whole transcriptome (over 25000 genes), the abundance of 192 surface proteins and T and B cell receptor (TCR and BCR) sequences from each single cell. .. Further, we utilized the Isoplexis assay ( ; ) to assess the levels of 32 secreted chemokines and cytokines from viable, single CD4+ , CD8+ T cells and monocytes.

other:

Article Title: Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Article Snippet: 10x Genomics PBMC dataset We analyzed a dataset of PBMCs that is freely available from 10x Genomics ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k ) [ ].

Article Title: Fast analysis of scATAC-seq data using a predefined set of genomic regions
Article Snippet: Single cell ATAC-seq data Single cell ATAC-seq data for PBMC were downloaded from the 10x Genomics public datasets ( https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_pbmc_10k) and include sequences for 10k PBMC from a healthy donor.

Expressing:

Article Title: IL-34 Actions on FOXP3+ Tregs and CD14+ Monocytes Control Human Graft Rejection
Article Snippet: .. To get a better overview of IL-34 action on the immune system, we analyzed the expression of its reported receptors CSF-1R (also called CD115), CD138 (also called SDC1), and PTPζ (also called PTPRZ1) on whole PBMCs using a public single cell RNAseq dataset ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.2/5k_pbmc_v3_nextgem ). .. We observed that CSF-1R single cell mRNA expression was restricted to monocytes and not significantly expressed by resting T, B and NK cells ( ).

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  • 84
    10X Genomics peripheral blood mononuclear cells pbmc10k
    totalVI posterior predictive and denoised Spearman correlations versus raw correlations in <t>PBMC10k.</t>
    Peripheral Blood Mononuclear Cells Pbmc10k, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 84/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/peripheral blood mononuclear cells pbmc10k/product/10X Genomics
    Average 84 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    peripheral blood mononuclear cells pbmc10k - by Bioz Stars, 2020-09
    84/100 stars
      Buy from Supplier

    84
    10X Genomics human pbmc cite seq dataset
    The performance of BREM-SC with the public human <t>PBMC</t> <t>CITE-Seq</t> dataset (from 10X Genomics). The UMAP projection of cells are colored by the approximate ground truth ( A ) and BREM-SC clustering results ( B ).
    Human Pbmc Cite Seq Dataset, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 84/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human pbmc cite seq dataset/product/10X Genomics
    Average 84 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    human pbmc cite seq dataset - by Bioz Stars, 2020-09
    84/100 stars
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    93
    10X Genomics human peripheral blood mononuclear cells pbmc scrna seq data
    Identification of cell types using <t>scRNA-seq</t> data from 10X Genomics Chromium system. (A) tSNE clustering of single cells in <t>PBMC.</t> (B) Alignment of clusters to known immune cell populations. (C) tSNE clustering of combined cluster 9 and 10 which was inferred as monocytes and DC. (D) Superimposed correlation-inferred cell type on the tSNE representation of combined cluster 9 and 10. (E) Superimposed CIBERSORT-based cell type classification on the tSNE representation of combined cluster 9 and 10.
    Human Peripheral Blood Mononuclear Cells Pbmc Scrna Seq Data, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 93/100, based on 3 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human peripheral blood mononuclear cells pbmc scrna seq data/product/10X Genomics
    Average 93 stars, based on 3 article reviews
    Price from $9.99 to $1999.99
    human peripheral blood mononuclear cells pbmc scrna seq data - by Bioz Stars, 2020-09
    93/100 stars
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    Image Search Results


    totalVI posterior predictive and denoised Spearman correlations versus raw correlations in PBMC10k.

    Journal: bioRxiv

    Article Title: A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

    doi: 10.1101/791947

    Figure Lengend Snippet: totalVI posterior predictive and denoised Spearman correlations versus raw correlations in PBMC10k.

    Article Snippet: For each of these tasks, we use two datasets: (1) 7,225 peripheral blood mononuclear cells (PBMC10k) from 10X Genomics [ ] and (2) 8,412 cells from a MALT tumor (MALT) [ ].

    Techniques:

    Investigation of totalVI background prediction for CD16 protein counts in PBMC10k.

    Journal: bioRxiv

    Article Title: A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

    doi: 10.1101/791947

    Figure Lengend Snippet: Investigation of totalVI background prediction for CD16 protein counts in PBMC10k.

    Article Snippet: For each of these tasks, we use two datasets: (1) 7,225 peripheral blood mononuclear cells (PBMC10k) from 10X Genomics [ ] and (2) 8,412 cells from a MALT tumor (MALT) [ ].

    Techniques:

    CD4 Protein disentanglement in PBMC10k. (Left) Distribution of log counts for CD4 protein. (Right) projected on UMAP.

    Journal: bioRxiv

    Article Title: A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

    doi: 10.1101/791947

    Figure Lengend Snippet: CD4 Protein disentanglement in PBMC10k. (Left) Distribution of log counts for CD4 protein. (Right) projected on UMAP.

    Article Snippet: For each of these tasks, we use two datasets: (1) 7,225 peripheral blood mononuclear cells (PBMC10k) from 10X Genomics [ ] and (2) 8,412 cells from a MALT tumor (MALT) [ ].

    Techniques:

    The performance of BREM-SC with the public human PBMC CITE-Seq dataset (from 10X Genomics). The UMAP projection of cells are colored by the approximate ground truth ( A ) and BREM-SC clustering results ( B ).

    Journal: Nucleic Acids Research

    Article Title: BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data

    doi: 10.1093/nar/gkaa314

    Figure Lengend Snippet: The performance of BREM-SC with the public human PBMC CITE-Seq dataset (from 10X Genomics). The UMAP projection of cells are colored by the approximate ground truth ( A ) and BREM-SC clustering results ( B ).

    Article Snippet: Public human peripheral blood mononuclear cells (PBMC) CITE-Seq dataset To assess the performance of BREM-SC, we used a published human PBMC CITE-Seq dataset downloaded from 10X Genomics website ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_protein_v3).

    Techniques:

    The performance of BREM-SC for in-house human PBMC CITE-Seq dataset. The UMAP projection of cells are colored by the ground truth ( A ) and BREM-SC clustering results ( B ).

    Journal: Nucleic Acids Research

    Article Title: BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data

    doi: 10.1093/nar/gkaa314

    Figure Lengend Snippet: The performance of BREM-SC for in-house human PBMC CITE-Seq dataset. The UMAP projection of cells are colored by the ground truth ( A ) and BREM-SC clustering results ( B ).

    Article Snippet: Public human peripheral blood mononuclear cells (PBMC) CITE-Seq dataset To assess the performance of BREM-SC, we used a published human PBMC CITE-Seq dataset downloaded from 10X Genomics website ( https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_protein_v3).

    Techniques:

    Identification of cell types using scRNA-seq data from 10X Genomics Chromium system. (A) tSNE clustering of single cells in PBMC. (B) Alignment of clusters to known immune cell populations. (C) tSNE clustering of combined cluster 9 and 10 which was inferred as monocytes and DC. (D) Superimposed correlation-inferred cell type on the tSNE representation of combined cluster 9 and 10. (E) Superimposed CIBERSORT-based cell type classification on the tSNE representation of combined cluster 9 and 10.

    Journal: Frontiers in Immunology

    Article Title: A Single-Cell Sequencing Guide for Immunologists

    doi: 10.3389/fimmu.2018.02425

    Figure Lengend Snippet: Identification of cell types using scRNA-seq data from 10X Genomics Chromium system. (A) tSNE clustering of single cells in PBMC. (B) Alignment of clusters to known immune cell populations. (C) tSNE clustering of combined cluster 9 and 10 which was inferred as monocytes and DC. (D) Superimposed correlation-inferred cell type on the tSNE representation of combined cluster 9 and 10. (E) Superimposed CIBERSORT-based cell type classification on the tSNE representation of combined cluster 9 and 10.

    Article Snippet: Here, we explore the use of the method, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) , in an unbiased cell type identification of single-cell transcriptomes, where we analyzed human peripheral blood mononuclear cells (PBMC) scRNA-seq data from two different studies that utilized either the 10X Genomics Chromium ( ) or SMART-seq2 ( ) platforms.

    Techniques: