Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the pbmc_4k dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Marker, Expressing

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: for estimating the coefficient of variation (CV), the plug-in estimates become more inflated as the sequencing depth becomes shallower (from right to left along the x axis), whereas the EB estimates are consistent. 3-std confidence intervals are provided for this panel. Middle: both brain_1k and brain_1.3m are from the mouse brain, and hence each gene should have a similar CV value between the two datasets. This is indeed the case for the EB estimator (right), which is adaptive to different sequencing depths. However, as brain_1k is twice deeper than brain_1.3m, the plug-in estimates are biased that most points are above the 45-degree line (red). Bottom: distribution recovery for the gene GZMA from a dataset that is subsampled to be five times shallower (left). The EB estimator provides a reasonable estimation for both the zero proportion and the tail shape, resulting in a small total variation error (right). b Feature selection and PCA. The task is to first select features (genes) based on CV, and then perform PCA on the selected features. The results on the full data (pbmc_4k) and a subsampled (three times shallower) are compared. EB estimates are more consistent between the full data and the subsampled data for both the CV ranks (top) and the PCA plots (bottom).

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Selection

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: the EB-estimated Pearson correlation for some marker genes in pbmc_4k are visualized, ordered by different cell populations (top). The clear block-diagonal structure implies that the EB estimator is capable of capturing the gene functional groups. As a comparison, the plug-in estimator also recovers those modules but with a weaker contrast (bottom left panel, plug-in with 100%). Bottom: a subsample experiment further shows that the EB estimator can recover the module with 5% of the data. For the plug-in estimator, the first block (T cells) is blurred with 25% of the data, and the entire structure vanishes with 10% of the data. b Gene network based on the EB-estimated Pearson correlation using the pbmc_4k dataset. Most gene modules correspond to important cell types or functions, including T cells, B cells, NK-cells, myeloid-derived cells, megakaryocytes/platelets, ribosomal protein genes, and mitochondrially encoded protein-coding genes. c Left: the estimated Pearson correlations between all genes and LCK (1st panel) and CD3D (2nd panel), two known T-cell markers. There are three modes for the EB-estimated values, where the positive mode, the zero mode, and the negative mode correspond to genes in the same module, different modules, and irrelevant genes, respectively. The plug-in estimated values are nonetheless much closer to zero even for the truly correlated ones, indicating an artificial shrinkage of the estimated values. Right: two instances where the EB estimates are significantly different from the plug-in estimates. The axes represent read counts, and the color codes the number of cells. Both gene pairs are biologically validated (see Gene network analysis in Methods). See also Supplementary Figs. – for more examples.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Marker, Blocking Assay, Functional Assay, Derivative Assay

Journal: Cancer research

Article Title: Microenvironment-driven dynamic chromatin changes in glioblastoma recapitulate early neural development at single-cell resolution

doi: 10.1158/0008-5472.CAN-22-2872

Figure Lengend Snippet: A. Schematic overview of model generation. B. Representative GFP-expressing organoids for each patient; scale bar, 400 μm; n = 6 organoids per cell line. C. GFP sorting by flow cytometry for GLICO samples submitted for scATAC-seq (GLICO-320, GLICO-810, GLICO-1206) and multiome (GLICO-607, GLICO-728). D. Uniform manifold approximation and projection (UMAP) of six samples (GSC-320, GSC-810, GSC-1206) colored by Patient (left) and Culture Condition (right). E. UMAP of cells colored by projected cell state from matched scRNA-seq samples (5). F. Pie charts of predicted cell states for each scATAC-seq sample grown in GLICO. G. Principal component analysis of normalized ATAC counts of TCGA-GBM cohort (n=9) aggregated over a peak atlas defined from the TCGA cohort (28). Patients with EGFR or NF1 mutations were labeled as “differentiated-like” and patients with CDK4 or H3.3 mutations are labeled as “stem-like” (3,5,26). H. Bulk ATAC-seq tracks and scATAC-seq tracks aggregated by cell state at marker chromatin accessibility peaks.

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies FRA1 (D80B4) Rabbit mAb Cell Signaling Cat #5281 Anti-β-Actin antibody SIGMA A2228 CD44 coupled to APC Miltenyi Biotec #130-113-338 Plasmids pLIX-FOSL1 This study - Primers qPCR: FOSL1_F ( 26 ) CGAAGGCCTTGTGAACAGA qPCR: FOSL1_R ( 26 ) GTTCCTTCCTCCGGTTCCT qPCR:DLX5_F ( 10 ) GTCTTCAGCTACCGATTCTGAC qPCR:DLX5_R ( 10 ) CTTTGCCATAGGAAGCCGAG Chemicals, Peptides, and Recombinant Proteins ROCK inhibitor (Y-27632 2HCl) Selleck Chemicals Cat# S1049 Matrigel Corning #354277 Critical Commercial Assays Papain Dissociation System Worthington Biochemical LK003150 Experimental Models: Cell Lines Patient-Derived Glioma Stem Cells: GSC-320, GSC-607, GSC-728, GSC-810, GSC-1206 National Institutes of Health and Weill Cornell Medicine/NYU Presbyterian Hospital N/A NIH-registered human H1 (WA01) embryonic stem cells WiCell Research Institute Cat# WA01; RRID: CVCL_9771 NIH-registered human H9 (WA09) embryonic stem cells WiCell Research Institute Cat# WA09; RRID: CVCL_9773 Critical commercial assays ATAC-seq NextGEM kit 10X Genomics PN: 1000175 Single Cell Multiome ATAC + Gene Expression kit 10X Genomics PN: 1000285 Deposited data scATAC-seq and multiome data This study PRJNA819057 Software and algorithms Genome assembly https://www.ncbi.nlm.nih.gov/grc/human hg38 / GRCh38 CellRanger 10X Genomics CellRanger v6.0.1 CellRanger-ATAC 10X Genomics CellRanger-ATAC v1.1.0 CellRanger-ARC 10X Genomics CellRanger-ARC v2.0.0 ArchR ( 16 ) ArchR v1.0.1 scanpy ( 23 ) scanpy v1.7.2 MACS2 ( 17 ) macs2 v2.1.2 DESeq2 ( 19 ) DESeq2 v_1.26.0 Palantir ( 24 ) Palantir v0.2.6 Fiji NIH https://imagej.net/software/fiji/ GraphPad Prism 8 GraphPad RRID: SCR_000306 Open in a separate window RESOURCES TABLE.

Techniques: Expressing, Flow Cytometry, Labeling, Marker

Journal: Cancer research

Article Title: Microenvironment-driven dynamic chromatin changes in glioblastoma recapitulate early neural development at single-cell resolution

doi: 10.1158/0008-5472.CAN-22-2872

Figure Lengend Snippet: A. Force-directed layout colored by pseudotime (left) and entropy (right). B. Boxplots showing distribution of pseudotime (left) and entropy (right) grouped by cell state assignment, **** P < 0.0001 NPC vs. rest (one-way ANOVA test). C. Heatmaps showing Z-score normalized motif accessibility and gene expression of TFs with correlated accessibility and expression, ordered by multiome GLICO-728 chromatin-based pseudotime. D. Visualization of early TF gene regulation in GLICO-728 pseudotime. Edges are drawn when a TF’s motif accessibility (blue node) is highly correlated to TF expression (text node). E. Motif dynamics of cells in non-mesenchymal (GLICO-320, GLICO-810, GLICO-607) and mesenchymal (GLICO-728, GLICO-1206) samples. ChromVAR motif deviation z-scores of representative motifs are plotted over pseudotime sorted cells, colored by patient and cell state assignment. F. Single sample GSEA (ssGSEA) deconvolution of bulk ATAC-seq TCGA-GBM and TCGA-LGG cohorts of GLICO-majority clusters.

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies FRA1 (D80B4) Rabbit mAb Cell Signaling Cat #5281 Anti-β-Actin antibody SIGMA A2228 CD44 coupled to APC Miltenyi Biotec #130-113-338 Plasmids pLIX-FOSL1 This study - Primers qPCR: FOSL1_F ( 26 ) CGAAGGCCTTGTGAACAGA qPCR: FOSL1_R ( 26 ) GTTCCTTCCTCCGGTTCCT qPCR:DLX5_F ( 10 ) GTCTTCAGCTACCGATTCTGAC qPCR:DLX5_R ( 10 ) CTTTGCCATAGGAAGCCGAG Chemicals, Peptides, and Recombinant Proteins ROCK inhibitor (Y-27632 2HCl) Selleck Chemicals Cat# S1049 Matrigel Corning #354277 Critical Commercial Assays Papain Dissociation System Worthington Biochemical LK003150 Experimental Models: Cell Lines Patient-Derived Glioma Stem Cells: GSC-320, GSC-607, GSC-728, GSC-810, GSC-1206 National Institutes of Health and Weill Cornell Medicine/NYU Presbyterian Hospital N/A NIH-registered human H1 (WA01) embryonic stem cells WiCell Research Institute Cat# WA01; RRID: CVCL_9771 NIH-registered human H9 (WA09) embryonic stem cells WiCell Research Institute Cat# WA09; RRID: CVCL_9773 Critical commercial assays ATAC-seq NextGEM kit 10X Genomics PN: 1000175 Single Cell Multiome ATAC + Gene Expression kit 10X Genomics PN: 1000285 Deposited data scATAC-seq and multiome data This study PRJNA819057 Software and algorithms Genome assembly https://www.ncbi.nlm.nih.gov/grc/human hg38 / GRCh38 CellRanger 10X Genomics CellRanger v6.0.1 CellRanger-ATAC 10X Genomics CellRanger-ATAC v1.1.0 CellRanger-ARC 10X Genomics CellRanger-ARC v2.0.0 ArchR ( 16 ) ArchR v1.0.1 scanpy ( 23 ) scanpy v1.7.2 MACS2 ( 17 ) macs2 v2.1.2 DESeq2 ( 19 ) DESeq2 v_1.26.0 Palantir ( 24 ) Palantir v0.2.6 Fiji NIH https://imagej.net/software/fiji/ GraphPad Prism 8 GraphPad RRID: SCR_000306 Open in a separate window RESOURCES TABLE.

Techniques: Expressing

Journal: Cancer research

Article Title: Microenvironment-driven dynamic chromatin changes in glioblastoma recapitulate early neural development at single-cell resolution

doi: 10.1158/0008-5472.CAN-22-2872

Figure Lengend Snippet: A. Heatmap of radial glia marker gene expression z-scores for cells assigned to scATAC-seq derived clusters from Figure 2A; markers from (22). B. Pie charts of cell state breakdown (top) and of culture condition breakdown (bottom) of radial glia cluster C10. C. Immunofluorescence staining of PAX6 and CD44 in GSC-1206 from the 2-D and GLICO models (scale bar, 10 μm). Arrows indicate GSCs PAX6+. D. Differential accessibility between each aggregated scATAC-seq cluster: radial glia (C10), NPC (C7), and OPC (C8) vs. all other clusters. Points in color represent differentially accessible peaks (P adj. < 0.05). E. Aggregated scATAC-seq tracks across scATAC-seq clusters for PAX6 and PRDM16 loci. F. Force-directed layout of five epigenomes, including two additional multiome patients (GLICO-607, GLICO-728), colored by Patient, Cell State, PRDM16, and PAX6 locus accessibility. G. Top 50 genes differentially expressed in RG cluster vs. rest in two multiome patients (Wilcoxon test). Known RG marker genes are bolded (22). H. Top results from gene set enrichment analysis (GSEA) of RG signature in Figure 3G. (Top: NES=3.03, FDR < 0.001; Bottom: NES=2.35, FDR = 0.005) I. Bar plot of cells in primary patient tumors colored by maximum score cell state of AC, MES, NPC, OPC, or RG signature, patients from (5). J. Scatterplot of RG signature expression score versus PTPRZ1 expression. Highest correlated gene shown (Pearson’s R=0.81).

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies FRA1 (D80B4) Rabbit mAb Cell Signaling Cat #5281 Anti-β-Actin antibody SIGMA A2228 CD44 coupled to APC Miltenyi Biotec #130-113-338 Plasmids pLIX-FOSL1 This study - Primers qPCR: FOSL1_F ( 26 ) CGAAGGCCTTGTGAACAGA qPCR: FOSL1_R ( 26 ) GTTCCTTCCTCCGGTTCCT qPCR:DLX5_F ( 10 ) GTCTTCAGCTACCGATTCTGAC qPCR:DLX5_R ( 10 ) CTTTGCCATAGGAAGCCGAG Chemicals, Peptides, and Recombinant Proteins ROCK inhibitor (Y-27632 2HCl) Selleck Chemicals Cat# S1049 Matrigel Corning #354277 Critical Commercial Assays Papain Dissociation System Worthington Biochemical LK003150 Experimental Models: Cell Lines Patient-Derived Glioma Stem Cells: GSC-320, GSC-607, GSC-728, GSC-810, GSC-1206 National Institutes of Health and Weill Cornell Medicine/NYU Presbyterian Hospital N/A NIH-registered human H1 (WA01) embryonic stem cells WiCell Research Institute Cat# WA01; RRID: CVCL_9771 NIH-registered human H9 (WA09) embryonic stem cells WiCell Research Institute Cat# WA09; RRID: CVCL_9773 Critical commercial assays ATAC-seq NextGEM kit 10X Genomics PN: 1000175 Single Cell Multiome ATAC + Gene Expression kit 10X Genomics PN: 1000285 Deposited data scATAC-seq and multiome data This study PRJNA819057 Software and algorithms Genome assembly https://www.ncbi.nlm.nih.gov/grc/human hg38 / GRCh38 CellRanger 10X Genomics CellRanger v6.0.1 CellRanger-ATAC 10X Genomics CellRanger-ATAC v1.1.0 CellRanger-ARC 10X Genomics CellRanger-ARC v2.0.0 ArchR ( 16 ) ArchR v1.0.1 scanpy ( 23 ) scanpy v1.7.2 MACS2 ( 17 ) macs2 v2.1.2 DESeq2 ( 19 ) DESeq2 v_1.26.0 Palantir ( 24 ) Palantir v0.2.6 Fiji NIH https://imagej.net/software/fiji/ GraphPad Prism 8 GraphPad RRID: SCR_000306 Open in a separate window RESOURCES TABLE.

Techniques: Marker, Expressing, Derivative Assay, Immunofluorescence, Staining

Journal: Cancer research

Article Title: Microenvironment-driven dynamic chromatin changes in glioblastoma recapitulate early neural development at single-cell resolution

doi: 10.1158/0008-5472.CAN-22-2872

Figure Lengend Snippet: RESOURCES TABLE

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies FRA1 (D80B4) Rabbit mAb Cell Signaling Cat #5281 Anti-β-Actin antibody SIGMA A2228 CD44 coupled to APC Miltenyi Biotec #130-113-338 Plasmids pLIX-FOSL1 This study - Primers qPCR: FOSL1_F ( 26 ) CGAAGGCCTTGTGAACAGA qPCR: FOSL1_R ( 26 ) GTTCCTTCCTCCGGTTCCT qPCR:DLX5_F ( 10 ) GTCTTCAGCTACCGATTCTGAC qPCR:DLX5_R ( 10 ) CTTTGCCATAGGAAGCCGAG Chemicals, Peptides, and Recombinant Proteins ROCK inhibitor (Y-27632 2HCl) Selleck Chemicals Cat# S1049 Matrigel Corning #354277 Critical Commercial Assays Papain Dissociation System Worthington Biochemical LK003150 Experimental Models: Cell Lines Patient-Derived Glioma Stem Cells: GSC-320, GSC-607, GSC-728, GSC-810, GSC-1206 National Institutes of Health and Weill Cornell Medicine/NYU Presbyterian Hospital N/A NIH-registered human H1 (WA01) embryonic stem cells WiCell Research Institute Cat# WA01; RRID: CVCL_9771 NIH-registered human H9 (WA09) embryonic stem cells WiCell Research Institute Cat# WA09; RRID: CVCL_9773 Critical commercial assays ATAC-seq NextGEM kit 10X Genomics PN: 1000175 Single Cell Multiome ATAC + Gene Expression kit 10X Genomics PN: 1000285 Deposited data scATAC-seq and multiome data This study PRJNA819057 Software and algorithms Genome assembly https://www.ncbi.nlm.nih.gov/grc/human hg38 / GRCh38 CellRanger 10X Genomics CellRanger v6.0.1 CellRanger-ATAC 10X Genomics CellRanger-ATAC v1.1.0 CellRanger-ARC 10X Genomics CellRanger-ARC v2.0.0 ArchR ( 16 ) ArchR v1.0.1 scanpy ( 23 ) scanpy v1.7.2 MACS2 ( 17 ) macs2 v2.1.2 DESeq2 ( 19 ) DESeq2 v_1.26.0 Palantir ( 24 ) Palantir v0.2.6 Fiji NIH https://imagej.net/software/fiji/ GraphPad Prism 8 GraphPad RRID: SCR_000306 Open in a separate window RESOURCES TABLE.

Techniques: Recombinant, Expressing, Software

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: pMHCII-NP-induced TR1-like cells are transcriptionally homogeneous but oligoclonal and coexist with a Tet + TFH-like subpopulation that contains identical clonotypes. A t-SNE plot of Smartseq2-based scRNAseq data for sorted Tet + and Tet − cells from NOD mice treated with BDC2.5 mi or InsB 9-23 /IA g7 -NPs (from n = 5 mice for Tet + cells and 15 mice for Tet − cells; aliquots of the sorted cells were also used for other experiments). The data are from 4 experiments. B Seurat clustering analysis of the Tet + pools from A showed the presence of two clusters for each pMHCII type. C Two-dimensional plot of the average log2FC values for the differentially expressed genes (adjusted P value < 0.05) between pMHCII-NP-induced Tet + TR1-like, Tet + TFH-like and Tet − cell types pooled from samples from BDC2.5/IA g7 -NP-treated NOD mice ( n = 5 mice for Tet + cells and 15 mice for Tet − cells) and samples from Fla 462-472 /IA b -NP-treated C57BL/6 mice ( n = 6 mice for Tet + cells and 5 mice for Tet − cells). The data were obtained by SmartSeq2 scRNAseq in 2 experiments. The X-axis shows Tet + TFH-like vs. Tconv cells, and the Y-axis shows Tet + TR1-like vs. Tconv cells. The dot color represents the cell subset specificity of differential gene expression. Only the genes with the greatest differential expression are labeled. D , E Distribution of unique TCR sequences in cells in the Tet + pools arising in response to treatment with two different pMHCII-NP types (BDC2.5mi- or InsB 9-23 /IA g7 -NPs) in NOD mice. The histogram shows the distribution of the different TCRαβ clonotypes identified vs. the number of cells (clones) expressing each TCRαβ pair. The data are from 1 ( D ) and 3 ( E ) experiments. F tSNE plot from ( B ) showing the cluster locations for cells with TCRαβ pairs expressed by more than one cell (in black). The data correspond to BDC2.5mi- or InsB 9-23 /IA g7 -NP-treated mice from 4 experiments. G Venn diagram from F showing the distribution of repeated TCRαβ pairs in clusters #1 (TFH-like) vs. #2 (TR1-like). Most (34/46) of the clonotypes found in the TFH-like cluster (#1) were also found in the TR1-like cluster (#2) (34/69)

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Gene Expression, Quantitative Proteomics, Labeling, Clone Assay, Expressing

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: Identification of the tetramer + TFH-like and TR1-like subclusters via mass cytometry and 10x Genomics scRNAseq. A tSNE plot for Tet − CXCR5 − PD-1 − (Tconv), Tet − PD-1 hi CXCR5 hi (TFH) and BDC2.5 mi/IA g7 Tet + splenic CD4 + T cells stained with the 32-marker CyTOF panel listed in Supplementary Table . The data correspond to n = 4 samples each from 2 experiments. B t-SNE plot of 10x Genomics-based scRNAseq data for sorted Tet + and Tet – cells from NOD mice treated with BDC2.5 mi or InsB 9-23 /IA g7 -NPs (from n = 5 mice for Tet + cells and 15 mice for Tet − cells; aliquots of the sorted cells were also used for other experiments). The data are from 4 experiments. C Volcano plots showing the Tet + vs. Tconv comparison (from C ), with representative TR1-associated and non-TR1-associated genes identified. Red, upregulated genes; blue, downregulated genes. D K-means clustering of the cells from ( B ). E Venn diagram comparing differentially expressed genes between bulk TFH and Tconv cells from KLH-immunized mice and between Tet + TFH-like and Tet – Tconv cells from pMHCII-NP-treated mice. F Representative feature plots for genes enriched in the Tconv, Tet + TFH and Tet + TR1 subclusters or shared between the latter two. The P values in ( B ) were calculated by the Mann‒Whitney U test

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Mass Cytometry, Staining, Marker, Comparison

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: BLIMP1-dependent TFH-to-TR1 cell reprogramming. A tSNE plots of 10x Genomics scRNAseq data for sorted BDC2.5 mi/IA g7 tetramer + (Tet + ) cells from BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre ( n = 3) and NOD. Prdm1 loxP/loxP ( n = 3) mice vs. NOD. Cd4-Cre.Prdm1 loxP/loxP ( n = 2) mice from 1 experiment. Cell subsets were identified via k-means clustering and prediction using the 10x Genomics scRNAseq data from Fig. as a reference. B Fraction of total cells corresponding to each Tet + subcluster. C Two-dimensional plot of the average log2FC values for the differentially expressed genes in the terminally differentiated TR1 vs. TFH (y-axis) vs. TR1-like vs. TFH (x-axis) comparisons in Prdm1 -competent mice. D Trajectory analysis results on UMAP plots generated with 10x Genomics scRNAseq data from BDC2.5 mi/IA g7 tetramer (Tet + ) cells isolated from BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre and NOD. Prdm1 loxP/loxP mice. Left, cell cluster identity; right, pseudotime analysis. E Relative expression levels of representative TFH- and TR1-specific genes vs. pseudotime. F Top, tSNE plot for Tet – CXCR5 – PD-1 − (Tconv) and BDC2.5 mi/IA g7 Tet + splenic CD4 + T cells (Tet + ) from BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre and NOD. Prdm1 loxP/loxP mice stained with the 32-marker CyTOF panel listed in Supplementary Table . Bottom, average % of cells ±S.E.M. values in clusters #1 and #2. The data correspond to 2 mice/strain from 1 experiment. G Percentages of BDC2.5 mi/IA g7 Tet + CD4 + T cells in various lymphoid organs from BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre . Tbx21 loxP/loxP vs. NOD. Cd4-Cre mice. The data correspond to 3 mice/strain from 3 experiments. H Average percentages of PD-1 hi CXCR5 hi (TFH) cells within the Tet + and Tet – subsets of the mice in ( G ). I Cytokine secretion profiles of splenic Tet + CD4 + T cells from the mice in ( G ) upon stimulation with anti-CD3/anti-CD28 mAb-coated beads. J Left: tSNE plots of 10x Genomics scRNAseq data for sorted BDC2.5 mi/IA g7 tetramer (Tet + ) cells from BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre.Tbx21 loxP/loxP mice ( n = 3) from 1 experiment. Right: Fraction of total cells corresponding to each Tet + subcluster in BDC2.5 mi/IA g7 -NP-treated NOD. Cd4-Cre.Tbx21 loxP/loxP vs. control (NOD. Cd4-Cre and NOD. Prdm1 loxP/loxP ) mice. The data in ( G – I ) correspond to the mean ± SEM values. The P values in ( F ) and ( G – I ) were calculated via two-way ANOVA and the Mann‒Whitney U test, respectively

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Generated, Isolation, Expressing, Staining, Marker, Control

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: BCL6 and BLIMP1 dependency of anti-CD3-induced TR1-like cells. A K-means clustering of splenic IL-10 + (eGFP + ) cells isolated from anti-CD3 mAb-treated NOD. Il10-eGFP ( Bcl6 +/+ ) mice, based on 10x Genomics scRNAseq data. The data correspond to 2 mice from 1 experiment. B Feature plots for representative TR1 cell markers in the tSNE plots shown in ( A ). C Heatmap comparing clusters #0, #1 and #2 from ( A ) to the BDC2.5 mi/IA g7 -NP-induced Tet + TFH and Tet + TR1-like subclusters from Fig. . The dendrogram was generated using the BuildClusterTree function of Seurat. D , tSNE plots of 10x Genomics scRNAseq for splenic IL-10 + (eGFP + ) cells isolated from anti-CD3 mAb-treated NOD. Il10-eGFP ( Bcl6 +/+ ) and NOD. Il10-eGFP . Cd4-Cre.Bcl6 loxP/loxP mice. The data correspond to 2 mice/strain. E Percentages of cells in clusters #0, #1 and #2 in the mice from ( D ). F Average percentages of IL-10 + CD4 + T cells in anti-CD3 mAb-treated NOD. Cd4-Cre . Bcl6 loxP/loxP and NOD. Cd4-Cre.Prdm1 loxP/loxP vs. NOD. Cd4-Cre mice, as determined using the mass cytometry marker panel from Supplementary Table (with the anti-CD49b-PE antibody instead of pMHCII tetramer-PE). The data correspond to 3 (NOD. Cd4-Cre . Bcl6 loxP/loxP ), 2 (NOD. Cd4-Cre.Prdm1 loxP/loxP ) and 3 (NOD. Cd4-Cre ) mice/strain from 3 experiments. G Average percentages of CD49b + LAG-3 + cells in the splenic IL-10 + FOXP3 − CD4 + T-cell pools of anti-CD3 mAb-treated NOD. Cd4-Cre . Bcl6 loxP/loxP and NOD. Cd4-Cre.Prdm1 loxP/loxP vs. NOD. Cd4-Cre mice as determined by mass cytometry. Top left, gating strategy; bottom, heatmap comparing the expression levels of the various markers; top right, average % values ± S.E.M. values. The data correspond to 3 (NOD. Cd4-Cre.Bcl6 loxP / loxP ), 2 (NOD. Cd4-Cre.Prdm1 loxP/loxP ) and 3 (NOD. Cd4-Cre ) mice/strain from 3 experiments. The data in ( F ) and ( G ) correspond to the mean ± SEM values. The P value in ( E ) was calculated using the absolute number of cells in each cluster via contingency table analysis (chi-square test). The P values in ( F ) and ( G ) were calculated via one-way ANOVA

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Isolation, Generated, Mass Cytometry, Marker, Expressing

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: Single-cell multiomic analysis of pMHCII-NP-induced TFH-TR1 cells vs. KLH-induced TFH cells. A tSNE plot of weighted nearest neighbor-integrated scRNAseq and scATACseq data from BDC2.5 mi/IA g7 Tet + , KLH-induced TFH and TH0 cells. The colors represent the different K-means and their predicted identity. BDC2.5 mi/IA g7 Tet + subclustered into TFH, TR1-like and TR1 cells, while KLH-induced cells subclustered into TFH.1, TFH.2 and TFH.3 cells. BDC2.5 mi/IA g7 Tet + TFH and KLH-induced TFH.1 cells clustered together. The data are from 4 (BDC2.5 mi/IA g7 Tet + ) and 8 mice (KLH-induced TFH and TH0) from 2 experiments. B Two-dimensional plot of the average log2FC values for the differentially expressed genes (adjusted P value <0.05) among KLH-DNP-induced TFH cell subtypes (TFH.1, TFH.2 and TFH.3) from ( A ). The data were obtained from 10X Genomics scRNAseq for CD4 + CD44 hi PD-1 hi CXCR5 hi T cells sorted from KLH-DNP-immunized NOD mice ( n = 5). The X-axis shows the TFH.2 vs. TFH.1 comparison; the Y-axis, the TFH.3 vs. TFH.1 comparison. The dot color represents the subset specificity of differential gene expression. C Heatmap and dendrogram showing the average relative gene expression levels in BDC2.5 mi/IA g7 Tet + TFH cells and the three clusters within the KLH-induced TFH cell pool (TFH.1, TFH.2 and TFH.3). D Single-cell resolution heatmap showing the relative expression levels of TFH-related genes in BDC2.5 mi/IA g7 Tet + TFH cells and the three clusters within the KLH-induced TFH pool (TFH.1, TFH.2 and TFH.3)

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Comparison, Gene Expression, Expressing

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: pMHCII-NP-induced formation of TR1-like and terminally differentiated TR1 cells from PD-1 hi CXCR5 hi precursors. A Cartoon showing the experimental approach used to track the development of TR1 cells from PD-1 hi CXCR5 hi precursors. Briefly, we transferred FACS-sorted PD-1 hi CXCR5 hi CD4 + T cells from the spleens of female NOD mice treated with 5 doses of BDC2.5 mi/I-A g7 -NPs (1.5 × 10 5 ) into female NOD. Scid hosts and treated the hosts with 10 additional doses of pMHCII-NPs. B UMAP-based feature plots for representative TFH-associated gene transcripts (and Foxp3 ) corresponding to the PD-1 hi CXCR5 hi CD4 + T cells used for transfer. C FACS profile of BDC2.5 mi/I-A g7 tetramer + CD4 + cells arising in PD-1 hi CXCR5 hi cell-transfused NOD. Scid hosts upon BDC2.5 mi/I-A g7 -NP treatment (10 doses over 5 weeks). The data correspond to cells pooled from 2 hosts from 1 experiment. D UMAP plots showing the Tet + TFH, TR1-like and TR1 subsets within the BDC2.5 mi/I-A g7 tetramer + CD4 + cell pool from NOD. Scid hosts ( C ), after sorting and scRNAseq analysis. E Feature plots for representative TFH and TR1-associated gene transcripts in the UMAP dimensionality reduction analysis from ( D ). F FACS profiles for BDC2.5 mi/I-A g7 tetramer + CD4 + cells arising in PD-1 hi CXCR5 hi cell-transfused NOD. Scid hosts upon treatment with BDC2.5 mi/I-A g7 -NP (10 doses over 5 weeks) and the anti-CD25 mAb or rat IgG (10 doses of 500 μg i.p). The data correspond to cells pooled from 2 hosts for each treatment group from 1 experiment. The value shown corresponds to the percentage of tetramer + cells within the CD4 + gate. G scRNAseq profiles for the tetramer + cells from ( F ). Left, UMAP plots; right, relative percentages of TFH, TR1-like and TR1 cells within the tetramer + cell pool. H FACS profiles for BDC2.5 mi/I-A g7 tetramer + CD4 + cells arising in NOD. Scid hosts transfused with PD-1 hi CXCR5 hi cells from NOD. Cd4-Cre or NOD. Cd4-Cre.Prdm1 loxP/loxP mice upon treatment with BDC2.5 mi/I-A g7 -NPs (10 doses over 5 weeks). The data correspond to cells pooled from 2 hosts for each treatment group from 1 experiment. The value shown corresponds to the percentage of tetramer + cells within the CD4 + gate. I scRNAseq profiles for the tetramer + cells from ( H ). Left, UMAP plots; right, relative percentages of TFH, TR1-like and TR1 cells within the tetramer + cell pool

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques:

Journal: Cellular and Molecular Immunology

Article Title: A T follicular helper cell origin for T regulatory type 1 cells

doi: 10.1038/s41423-023-00989-z

Figure Lengend Snippet: Functional properties of TFH and TR1 cells arising in response to pMHCII-NP therapy. A Top, Average percentages of tetramer + CD4 + T cells within the islet-associated CD4 + T-cell pool of BDC2.5 mi/IA g7 -NP- vs. control NP-treated NOD mice. Bottom, representative tetramer staining profiles. The data correspond to n = 3 samples per treatment type from 5 to 10 mice each from one experiment. B Left: UMAP scRNAseq plots for the islet-associated tetramer + CD4 + T cells from ( A ). Right: Relative distribution of TFH, TR1-like and TR1 subpools within the islet- and spleen-associated tetramer + cell pools of BDC2.5 mi/IA g7 -NP-treated NOD mice. C Percentages of splenic pMOG 38-49 /I-A b Tet + cells in B6, B6. Tbx21-Cre . Il10 loxP/loxP , B6.Cd4-Cre . Prdm1 loxP/loxP and B6. Tbx21-Cre . Prdm1 loxP/loxP mice (both males and females) upon treatment with pMOG 38-49 /I-A b -NPs ( n = 33 (B6), 17 (B6. Tbx21-Cre . Il10 loxP/loxP ), 6 ( B6.Cd4-Cre . Prdm1 loxP/loxP ) and 6 (B6. Tbx21-Cre . Prdm1 loxP/loxP )) or Cys-NPs (control; n = 23, 17, 3 and 5, respectively) (10 doses over 5 weeks, starting when the EAE score was >1.5/5). The data are from 6, 4, 1 and 1 experiments, respectively. D Top left: normalized EAE scores in B6 vs. Cre-negative B6. Il10 loxP/loxP mice upon pMOG 38-49 /I-A b -NP or Cys-NP treatment ( n = 12 (B6) and 13 (Cre-negative B6. Il10 loxP/loxP ), respectively, from 2 experiments). Top right: normalized EAE scores in B6 vs. B6. Tbx21-Cre . Il10 loxP/loxP mice upon treatment with pMOG 38-49 /I-A b -NPs ( n = 40 and 22, respectively) or Cys-NPs (control; n = 28 and 19, respectively; from 6 and 4 experiments, respectively). Bottom left: normalized EAE scores in B6 vs. B6.Cd4-Cre . Prdm1 loxP/loxP mice upon treatment with pMOG 38-49 /I-A b -NPs (n = 40 and 6, respectively) or Cys-NPs (control; n = 28 and 4; from 6 and 1 experiments, respectively). Bottom right: normalized EAE scores in B6 vs. B6. Tbx21-Cre . Prdm1 loxP/loxP mice upon treatment with pMOG 38-49 /I-A b -NPs ( n = 40 and 6, respectively) or Cys-NPs (control; n = 28 and 6; from 6 and 1 experiments, respectively). E Percentages of GL7 + IgG + cells in cultures of purified Tet + PD-1 hi CXCR5 hi CD4 + T cells from BDC2.5 mi/I-A g7 -NP-treated NOD mice (10 doses over 5 weeks) (3 × 10 4 cells/well) with BDC2.5 mi peptide-pulsed or nonpulsed B cells (5 × 10 4 cells/condition/well) isolated from the draining lymph nodes of KLH-DNP-immunized NOD mice in the presence of an anti-CD3 (2 μg/mL) and/or anti-IgM (5 μg/mL) antibody for 6 days. The data correspond to two samples per condition from one experiment. F Cartoon showing the experimental approach used to measure the ability of pMHCII-NP-expanded TFH-like cells to promote GC B-cell formation and antibody production. Briefly, we first treated 10 NOD. Cd4-Cre . Prdm1 loxP/loxP mice with 10 doses of Cys-NPs or BDC2.5 mi/I-A g7 -NPs ( n = 5 mice each). The total splenic CD4 + T cells from each donor were then transferred into NOD. Scid hosts (1.6 × 10 7 /host), and the hosts were treated with 5 additional doses of Cys-NPs or BDC2.5 mi/I-A g7 -NPs. At this point, all the hosts were transfused with BDC2.5 mi peptide-pulsed splenic B cells isolated from NOD mice immunized with KLH-DNP (10 7 cells/host). Seven days after B-cell transfer, the hosts were killed, their spleens were analyzed for the presence of Tet + CD4 + T cells, GC B cells (GL7 + sIgG + ) and non-GC B cells (GL7 − sIgG + or GL7 − sIgG − ) within the B220 + cell pool , and their serum was analyzed for the presence of anti-DNP antibodies. G Left: Representative BDC2.5/I-A g7 tetramer and CD4 staining profiles from the pMHCII-NP- and Cys-NP-treated NOD. Scid hosts from ( F ). Right: Average percentages of BDC2.5/I-A g7 Tet + CD4 + T cells in the hosts from ( F ). The data correspond to n = 5 female mice per group from one experiment. H Left: Representative GL7 and IgG staining profiles for splenic B cells from the NOD. Scid hosts from ( F ). Right: percentages of GL7 + sIgG + , GL7 − sIgG + and GL7 − sIgG − cells among the splenic B cells of the NOD. Scid hosts from ( F ). The data correspond to n = 5 female mice per group from one experiment. I Serum anti-DNP antibody levels in the NOD. Scid hosts from ( D ). The data correspond to n = 5 female mice per group from one experiment. The data in ( C – E ) and ( G – I ) correspond to the mean ± SEM values. The P values in ( C , G – I ) were calculated via the Mann‒Whitney U test. The P values in ( D ) were calculated via two-way ANOVA

Article Snippet: Differentially expressed genes (|FC | > 2 and FDR < 0.05) were obtained from 10X Genomics-based scRNAseq data corresponding to TFH- and TR1-like subclusters within sorted Tet + cells from NOD mice treated with BDC2.5 mi/IA g7 -NPs.

Techniques: Functional Assay, Control, Staining, Purification, Isolation

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Technologies used for the study. The parents and the heifer were sequenced with Oxford Nanopore Technologies on GridION and PromethION, Chromium 10X and the Hi-C method on MiSeq, HiSeq or NovaSeq 6000. The heifer was additionally sequenced with Illumina 2 × 250 bp on NovaSeq 6000 and PacBio Sequel II (CLR and CCS (i.e HiFi reads) mode). For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques: Hi-C

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Summary of heifer polished assemblies. Only ONT contig assembly was polished as CCS and 10X are low error rate reads. As CLR assembly is better than ONT assembly, we can expect at least similar result after polishing step. For details about pipeline used in this study, refer to Fig. 2 .

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques:

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Summary of heifer produced chromosomes assemblies. As CLR assembly is better than ONT assembly, we can expect at least similar result after polishing and final steps. For details about pipeline used in this study, refer to Fig. 2 .

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques: Produced

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Summary of heifer produced contigs assemblies. For details about pipeline used in this study, refer to Fig. 2 . *BUSCO analysis was performed on polished contigs, **Inspector Quality Value is calculated on reference alignment andreads alignment.

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques: Produced

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Details of the 5 pipelines used to produce our assemblies. a-Long reads assemblies from Oxford Nanopore Technologies and Pacific Biosciences followed by polishing step for erroneous assemblies and scaffolding step. b-10X Chromium assembly and scaffolding with Supernova. c-Phased assembly with HiFi and parental illumina reads. d-Phased assembly with HiFi and and Hi-C data.

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques: Scaffolding, Hi-C

Journal: Scientific Data

Article Title: A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

doi: 10.1038/s41597-023-02249-1

Figure Lengend Snippet: Summary of data used in this study. Characteristics of these data for the Charolais trio are described here, and in more detail in this manuscript. For information about pipeline used in this study, refer to Fig. 2 .

Article Snippet: For the Trio approach, parent reads (2 × 150bp) from 10X Genomics Chromium datas were used.

Techniques: Sequencing

Journal: Cell

Article Title: Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing

doi: 10.1016/j.cell.2018.05.036

Figure Lengend Snippet: (a) Landscape of rearrangements and sequencing metrics across the 10XG WGS mCRPC cohort. Structural variant classification defined in STAR Methods. I.A., investigational agent.

Article Snippet: 10X Genomics WGS data will be available via dbGAP controlled access ( https://www.ncbi.nlm.nih.gov/gap ) under study accession phs001577.v1.p1.

Techniques: Sequencing, Variant Assay

Journal: Cell

Article Title: Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing

doi: 10.1016/j.cell.2018.05.036

Figure Lengend Snippet: (a) TDP, CDK12 alteration, and ETS-rearrangement status in 10XG WGS mCRPC cohort.

Article Snippet: 10X Genomics WGS data will be available via dbGAP controlled access ( https://www.ncbi.nlm.nih.gov/gap ) under study accession phs001577.v1.p1.

Techniques:

Journal: Cell

Article Title: Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing

doi: 10.1016/j.cell.2018.05.036

Figure Lengend Snippet: (a) CIRCOS plot for a representative TDP sample profiled by 10XG WGS. Red arcs, tandem duplications.

Article Snippet: 10X Genomics WGS data will be available via dbGAP controlled access ( https://www.ncbi.nlm.nih.gov/gap ) under study accession phs001577.v1.p1.

Techniques:

Journal: Cell

Article Title: Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing

doi: 10.1016/j.cell.2018.05.036

Figure Lengend Snippet: (a) Purity-adjusted copy number status at the AR gene and enhancer loci in three paired 10XG WGS tumor biopsy samples taken from patients prior to and after progression on enzalutamide.

Article Snippet: 10X Genomics WGS data will be available via dbGAP controlled access ( https://www.ncbi.nlm.nih.gov/gap ) under study accession phs001577.v1.p1.

Techniques:

Journal: Cell

Article Title: Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing

doi: 10.1016/j.cell.2018.05.036

Figure Lengend Snippet: (a) Median of normalized molecule coverage near the AR gene and enhancer in the 10XG WGS mCRPC cohort; bins containing the enhancer overlaps with a DHS in LNCaP cells. Bin size, 100 kb.

Article Snippet: 10X Genomics WGS data will be available via dbGAP controlled access ( https://www.ncbi.nlm.nih.gov/gap ) under study accession phs001577.v1.p1.

Techniques:

Journal: Nature immunology

Article Title: ThPOK is a critical multifaceted regulator of myeloid lineage development

doi: 10.1038/s41590-023-01549-3

Figure Lengend Snippet: a-b) UMAP plots of (a) wild- type (left) and (b) ThPOK−/− (right) C-GMP myeloid progenitors (Lin-, Kit+, Sca1-, CD34hi, CD16/32+/−, gated as in Extended data Fig.5b). Shown cell populations were defined based on reference alignment to a prior curated murine cKit+ CITE-Seq dataset (Extended data Fig.5a). Overlaid arrows indicate predicted RNA velocities derived from spliced versus unspliced scRNA-Seq reads. Areas with the greatest predicted observed trajectory differences are denoted by purple or red circles. c) Number of differentially expressed genes (DEGs) that are up- or downregulated in ThPOK−/− versus wild-type cells by cellHarmony analysis for indicated cell populations. d) cellHarmony organized heatmap of dynamically regulated DEGs in ThPOK−/− and wt for the most frequency detected cell populations (n=1,685 genes, fold > 1.1 and empirical Bayes moderated t-test p<0.05, FDR corrected). Yellow = upregulated gene, blue = downregulated gene. Genes noted in the text are called out to the right of the heatmap. e) Relative statistical enrichment (GO-Elite Z-score) of ThPOK−/− versus wild-type up-regulated genes against all prior defined hematopoietic differentiation markers17, indicates altered differentiation programs in ThPOK−/− mice, for lineage priming (top), lineage specification (middle) and neutrophil commitment (bottom). f) Gene Ontology enrichment analysis (GO-Elite) of down- (right) and up-regulated genes in ThPOK−/− versus wt MDP cells, with example terms highlighted.

Article Snippet: After projecting labels from the 10x Genomics cKit data onto the Fluidigm transcriptome of WT cells ( – ), we observed a progressive increase in alternative splicing in proNeu-1 through preNeu-1 stages.

Techniques: Derivative Assay

Journal: Nature immunology

Article Title: ThPOK is a critical multifaceted regulator of myeloid lineage development

doi: 10.1038/s41590-023-01549-3

Figure Lengend Snippet: a) FACS analysis of cKit, Sca1, CD16/32 and CD34 expression by gated total lineage negative (Lin-) or Lin- cKit+ Sca1- BM cells, as indicated. CMP, GMP, MEP and CD34lo gates are shown in right panels. b) Plots showing frequency of Lin-Sca-cKit+ cells in total BM and of CMP, GMP, MEP and CD34lo subsets in gated cKit+ Sca1- Lin- BM cells from ThPOK-deficient or WT mice (same gates as panel a) (n = 5 biologically independent animals per genotype). Error bars represent standard deviations (centre refers to mean). Significant differences between ThPOK−/− and WT mice were determined by two sided unpaired T test with Welch’s correction, and indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). c) FACS comparison of CD11b, CD115, Ly6c, and Ly6g expression by gated cGMP (conventional GMP) (grey histograms) and atypical c-Kit+ CD34lo CD16hi Lin- (green histograms) BM subsets, from ThPOK-deficient mice. d) FACS analysis of CD11b, Ly6g, Vcam1, CD115, Ly6c, CD16/32, cKit and CD34 expression by indicated gated BM subsets. Gates for preNeu1-3 (preN1-3), proNeu1/2 and other precursor populations are shown. e) Plots showing frequency (left panels) or cell number (right panels) of indicated gated precursor populations in ThPOK-deficient or WT mice (same mice as panel d). Data are presented as mean values +/− SEM. Significant differences between ThPOK−/− and WT mice were determined by two sided unpaired T test, and indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). n = 5 biologically independent animals.

Article Snippet: After projecting labels from the 10x Genomics cKit data onto the Fluidigm transcriptome of WT cells ( – ), we observed a progressive increase in alternative splicing in proNeu-1 through preNeu-1 stages.

Techniques: Expressing, Comparison

Journal: Nature immunology

Article Title: ThPOK is a critical multifaceted regulator of myeloid lineage development

doi: 10.1038/s41590-023-01549-3

Figure Lengend Snippet: a) Bar graphs showing absolute cell numbers for CMP, GMP, MEP and CD34lo subsets among gated cKit+ Sca1- Lin- BM cells from ThPOK-deficient or WT mice (same mice as Fig. 2 A, ​,B)B) (n = 5 independent animals per genotype). Error bars represent SEM. Significant differences between ThPOK−/− and WT mice were determined by two-sided unpaired T test with Welch’s correction, and indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). b) FACS analysis of CD135 and CD115 expression by gated CMPs from WT and ThPOK−/− mice (left panels). Bar graph at right shows % of indicated gated subsets (n = 5 independent animals per genotype). c) FACS analysis of CD11b, CD115, Ly6c, and Ly6g expression by gated cGMP (conventional GMP) (grey histograms) and c-Kit+ CD34lo CD16hi Lin- (green histograms) BM subsets from WT mice. d) FACS analysis of CD16/32 and CD34 expression by gated Lin- c-Kit+ Sca1- BM cells from ThPOPK+/− mice. CMP, GMP, MEP and CD34lo subsets are marked. e) CFU assay of FACS-sorted Lin- BM cells, from indicated WT or ThPOK-deficient mice. Error bars represent SEM. Significant differences between ThPOK−/− and WT mice were determined by two-sided multiple multiple unpaired T with Welch’s correction, and indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). Note that ThPOK−/− Lin- progenitors exhibited a substantial (** p < 0.01) increase in CFU-GM myeloid colony production even after secondary plating. f) FACS analysis of CD11b, Thy1, CD41 and Ter119 lineage marker expression by WT and ThPOK-deficient cells after secondary CFU assay (same experiment as in panel d).

Article Snippet: After projecting labels from the 10x Genomics cKit data onto the Fluidigm transcriptome of WT cells ( – ), we observed a progressive increase in alternative splicing in proNeu-1 through preNeu-1 stages.

Techniques: Expressing, Colony-forming Unit Assay, Marker

Journal: Nature immunology

Article Title: ThPOK is a critical multifaceted regulator of myeloid lineage development

doi: 10.1038/s41590-023-01549-3

Figure Lengend Snippet: a) UMAP plot of curated cKit+ progenitor scRNA-Seq cell populations that serves as the reference for cellHarmony alignment analyses in these studies (see Methods), b) Flow cytometry selection of c-Kit+ BM C-GMP progenitors (gated cells denoted in blue). c) Unsupervised clustering UMAP plot of ~26,000 combined C-GMP CITE-Seq captured mRNA profiles (WT and ThPOK−/−) following analysis with the software ICGS2. Indicated cell-population labels are those automatically assigned by ICGS2 (AltAnalyze BioMarker database). d) UMAP plot of all WT and ThPOK−/− cells from panel c aligned to the reference described in panel a. e) UMAP plot from panel d showing the expression levels of ThPOK mRNA in the wild-type CITE-Seq populations. f) Cell-population percentage of distinct BM progenitor populations detected by CITE-Seq from WT and ThPOK−/− mice. g–h) Heatmap of relative normalized (TotalVI) CITE-Seq antibody derived-tag (ADT) intensities for cellHarmony cKit+ aligned cell populations. Panels (g) and (h) displays cells from WT and ThPOK−/− BM progenitors, respectively. i–p) Gene Ontology enrichment analyses from the software GO-Elite, for each of the indicated cell population differential expression analysis comparisons (all ThPOK−/− vs. WT).

Article Snippet: After projecting labels from the 10x Genomics cKit data onto the Fluidigm transcriptome of WT cells ( – ), we observed a progressive increase in alternative splicing in proNeu-1 through preNeu-1 stages.

Techniques: Flow Cytometry, Selection, Software, Biomarker Discovery, Expressing, Derivative Assay, Quantitative Proteomics

Journal: Nature immunology

Article Title: ThPOK is a critical multifaceted regulator of myeloid lineage development

doi: 10.1038/s41590-023-01549-3

Figure Lengend Snippet: (a–b) UMAP plot of single-cell Fluidigm RNA-Seq analysis of prior profiled (a) wild-type hematopoietic progenitors and (b) Ly6c-ThPOK−/− GMPs aligned to cKit+ CITE-Seq. Consistent alignment of cell populations in the UMAP space indicates a lack of apparent batch effects. c) Gene Ontology enrichment analysis of ThPOK−/− dependent alternative splicing events in proNeu-1 cells, for splicing events with the opposite pattern of exon inclusion in proNeu-1 versus MultiLin (discordant = more immature splicing) or (d) with the same pattern (concordant = promoting neutrophil specification associated splicing). (e‒f) Validation of deregulated gene expression in ThPOK−/− versus WT progenitors for important transcripts as observed in CITE seq data: e) qRTPCR analyses of indicated flow-sorted progenitors (pooled from 4 mice/genotype) for monocyte/DC genes (Irf8, Zeb2, Runx1), granulocyte lineage genes (Cebpe, Pde4d, Cd63 and Nkg7) and RNA binding protein genes (Ddx3x and Srsf5). The experiment was repeated 3 times independently with similar results. f) Expression ratio of Hmga1 alternate transcript versus reference transcript in indicated progenitor populations (pooled from 4 mice/genotype). g) Representative confocal micrographs of EZH2 protein (top), DAPI (middle), and H3K27Me3 (bottom) staining in indicated subsets. Each experiment was reproduced twice and significant differences between ThPOK−/− and WT mice were determined by two-sided unpaired T test with Welch’s correction, and indicated by asterisks (* p < 0.05; ** p < 0.01; *** p < 0.001). h) Heat maps showing relative mRNA expression of indicated genes in WT progenitors (left panel), or in ThPOK−/− versus WT progenitor subsets (right panel) for genes that are relatively up-regulated in any ThPOK−/− progenitor population and that are known Ezh2 targets in WT immune cells by ChIP-seq (GSE181873). Note that in WT mice most of these genes show marked stage-specific regulation.

Article Snippet: After projecting labels from the 10x Genomics cKit data onto the Fluidigm transcriptome of WT cells ( – ), we observed a progressive increase in alternative splicing in proNeu-1 through preNeu-1 stages.

Techniques: RNA Sequencing, Alternative Splicing, Biomarker Discovery, Gene Expression, RNA Binding Assay, Expressing, Staining, ChIP-sequencing

Journal: Molecular psychiatry

Article Title: Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

doi: 10.1038/s41380-021-01201-2

Figure Lengend Snippet: Resources and tools for Omics studies.

Article Snippet: , Epigenomics , 3DIV , 3D-genome Interaction Viewer and database for Hi-C and pcHi-c data , http://kobic.kr/3div.

Techniques: Expressing, Sequencing, Microarray, Imaging, Ligand Binding Assay, In Situ Hybridization, DNA Methylation Assay, Mass Spectrometry, Membrane, Genome Wide, Functional Assay, Labeling