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: 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: Cancers

Article Title: Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer

doi: 10.3390/cancers13194923

Figure Lengend Snippet: scCMS of individual cells. ( a ) Heatmap plot of normalized enrichment scores of the 27 marker gene-sets across all single tumor cells of 10 CRC samples from Lee et al. cohort. ( b ) UMAP plot of different tumor-tissue cell lineages. ( c ) UMAP plot of the four scCMS distributions and mixed transcriptomic states of the single tumor cells. ( d ) Proportions of scCMS groups in different cell lineages from Lee et al. scRNASeq dataset. ( e ) The UMAP plot of the epithelial-cell subtypes. ( f ) scCMS distributions within the epithelial-cell cluster. ( g ) Proportions of scCMSs in the populations of enterocytes and goblet- and transit-amplifying (TA) cells.

Article Snippet: The processed single-cell RNA sequencing (scRNAseq) profiles and corresponding metadata of ten CRC tissue samples obtained from five CRC patients (border and core regions of tumor tissue of each patient) of Commissie Medische Ethiek UZ KU Leuven/Onderzoek, Belgium, were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database with the accession code GSE144735 [ ].

Techniques: Marker

Journal: Cancers

Article Title: Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer

doi: 10.3390/cancers13194923

Figure Lengend Snippet: scCMSs from two CRC with mixed subtype from bulk transcriptome. UMAP plots of different single-cell types and scCMS predictions in ( a , b ) Patient #1 and ( c , d ) Patient #2 scRNAseq profiles, respectively. UMAP plots of single tumor cells were plotted using the normalized enrichment scores of the 27 marker gene sets obtained from ssGSEA. ( e ) Distributions of scCMSs in immune-, epithelial-, and stromal cell populations in patient #1 and patient #2.

Article Snippet: The processed single-cell RNA sequencing (scRNAseq) profiles and corresponding metadata of ten CRC tissue samples obtained from five CRC patients (border and core regions of tumor tissue of each patient) of Commissie Medische Ethiek UZ KU Leuven/Onderzoek, Belgium, were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database with the accession code GSE144735 [ ].

Techniques: Marker

Journal: PLoS Computational Biology

Article Title: Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons

doi: 10.1371/journal.pcbi.1010071

Figure Lengend Snippet: (A) Bidimensional representation of parameter values transformed using UMAP: each dot represents one individual, and closed lines indicate the convex hulls associated with all the individuals obtained with a given morphology (color-coded accordingly to both the convex hull and the points contained in it). (B) UMAP projection and clustering of CA3 excitatory neurons based on scRNAseq data. Using the Leiden clustering algorithm with a resolution of 0.65 delineated the primary division in the CA3 principal neuron population. Note that CA3 principal cells are primarily composed of a larger population of cells (cluster 1, black) and a second minority population (cluster 2, red). (C) Violin plots of the distributions of maximal conductance values for four different classes of ion channels (potassium, calcium, sodium and hyperpolarization-activated) for the model cells included in the analysis, normalized over the range of allowed variability of each parameter as reported in (black and red indicate thorny and a-thorny cells, respectively). Dashed lines indicate the median of the population, while the upper and lower dotted lines represent the 25th and 75th percentile of the distributions. Most parameter distributions were significantly different between the two cell-types (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001). For the remaining parameters see . (D) Expression levels for cells belonging to cluster 1 (black) or cluster 2 (red) for analogous classes of ion channel genes as shown in (C). Note that expression levels for most Na channel genes were not significantly different while Ca and K channel genes were significantly differentially expressed between the two clusters (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001).

Article Snippet: We utilized scRNAseq data for 314 cells from the CA3 region of the hippocampus from the Allen institute for Brain Science (2018 Allen Institute for Brain Science.

Techniques: Transformation Assay, Expressing

Journal: Journal of Cell Science

Article Title: Revisiting the significance of keratin expression in complex epithelia

doi: 10.1242/jcs.260594

Figure Lengend Snippet: Abundant and pairwise expression of keratin genes in human trunk skin. (A) Cryosection of healthy human trunk skin, stained with hematoxylin-eosin (archival material obtained with institutional IRB approval). The status of keratinocytes and their corresponding keratin expression are indicated on the right. Scale bar: 20 μm. (B) Heatmap of keratin gene expression in the 15 Seurat clusters of keratinocytes in healthy human trunk skin, analyzed by scRNAseq (Basal1-6, Mitotic1-3 and Spinous1-6). The genes shown at the top (MALAT1, EEF1A1 and B2M) represent genes that are highly expressed across all clusters. (C–E) Correlations between specific type I and type II keratin genes across the entire array of epidermal keratinocytes (n=24,979 cell). Red lines represent linear correlations, and r2 values are reported. (C) KRT5 versus KRT14. (D) KRT1 versus KRT10. (E) KRT5 versus KRT15. In E, the magenta line conveys the linear correlation for the Basal4 cluster, specifically. Data set published by Cheng et al. (2018) and reanalyzed using a standard Seurat pipeline (see Materials and Methods section). Data set publicly available at EGAS00001002927.

Article Snippet: The authors are very grateful to Dr Raymond Cho (UCSF, USA) and Dr Joseph Powell (University of Queensland, Australia) for making their scRNAseq data sets publicly available, and to members of the Coulombe laboratory for support.

Techniques: Expressing, Staining, Gene Expression

Journal: Journal of Cell Science

Article Title: Revisiting the significance of keratin expression in complex epithelia

doi: 10.1242/jcs.260594

Figure Lengend Snippet: scRNAseq data identifies a KRT10high/KRT14high hybrid keratinocyte population. (A) Correlation of KRT10 and KRT14 in human trunk epidermis (n=24,979 cells). Quadrants were defined by abundancy of cells expressing high KRT14 (>4.5) or high KRT10 (>4.0). (B) Histogram reporting on the composite G2/M score across the same cell population. Dashed line represents the threshold for G2/M cells (>0.75, ∼3% of cells). (C) Mapping of G2/M cells with a high (>0.75) G2/M score (pink) on the KRT10-KRT14 scatter plot shown in A. (D) Histogram reporting on the composite ‘Basal Score’ across the population of epidermal keratinocytes. Blue and red dashed lines partition the cells into low (less than 1.0 or LT1.0), high (greater than 1.75 or GT1.75) and ‘Between’ basal score groups. (E) Violin plots of KRT14 and KRT10 expression in cells with LT1.0, ‘Between’, and GT1.75 basal scores. Dashed lines are boundaries between the ‘LT1.0’, ‘Between’ and ‘GT1.75’ subgroups. (F–G). Mapping of cells with a (F) ‘between’ and a (G) GT1.75 basal score on the KRT10-KRT14 scatter plot shown in A. (H) Heatmap representation of expression levels for genes associated with basal and spinous differentiation state in Seurat clusters and custom scores. Data set published by Cheng et al. (2018) and reanalyzed using a standard Seurat pipeline (see Materials and Methods section). Data set publicly available at EGAS00001002927.

Article Snippet: The authors are very grateful to Dr Raymond Cho (UCSF, USA) and Dr Joseph Powell (University of Queensland, Australia) for making their scRNAseq data sets publicly available, and to members of the Coulombe laboratory for support.

Techniques: Expressing

Journal: Journal of Cell Science

Article Title: Revisiting the significance of keratin expression in complex epithelia

doi: 10.1242/jcs.260594

Figure Lengend Snippet: Keratin expression in mouse ear skin. (A) Heatmap of keratin gene expression in the six Seurat clusters of keratinocytes (n=4761 cells) in healthy mouse ear skin, analyzed by scRNAseq (Basal1-2; Mitotic and Spinous1-3). Eef1a1 provides a reference for a highly expressed gene across all clusters. (B,C) Correlations between specific type I and type II keratin genes across the entire array of epidermal keratinocytes in mouse ear skin (n=4979 cells). Red lines represent linear correlations, and r2 values are reported. (B) Krt5 versus Krt14. (C) Krt2 versus Krt10. (D) Correlation of Krt10 and Krt14 in mouse ear epidermis. Quadrants were defined by abundancy of cells expressing high Krt14 (>5.0) or high Krt10 (>5.0). (E) Histogram reporting on the composite G2/M score across the same cell population. Dashed line represent threshold for G2/M cells (>0.6, ∼5.5% of cells). (F) Mapping of G2/M cells with a high (>0.6) G2/M score (pink) on the Krt10-Krt14 scatter plot shown in D. (G) Mapping of the basal score for individual cells on the original UMAP from the mouse ear skin data set (see Fig. S3). (H) Violin plot representation of basal score in cells that express low and/or high combinations of Krt14 and Krt10 (see D). The red lines represent the median and dashed lines the quartile positions. ****P<0.0001 (adjusted) as tested by one-way ANOVA with Tukey's multiple comparisons test. Data set published by Lukowski et al. (2018) and reanalyzed using a standard Seurat pipeline (see Materials and Methods section). Data set publicly available at E-MTAB-6429.

Article Snippet: The authors are very grateful to Dr Raymond Cho (UCSF, USA) and Dr Joseph Powell (University of Queensland, Australia) for making their scRNAseq data sets publicly available, and to members of the Coulombe laboratory for support.

Techniques: Expressing, Gene Expression