Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Single-cell RNA-sequencing reveals distinct cell states and intertumoral heterogeneity in human RMS. a. Schematic of experimental design. UMAP renderings of representative fusion-negative (FN) RMS from patient-derived xenograft MAST111 (top) and primary patient 20696 (bottom). Non-tumor cells were removed from primary patient sample analysis using Cellassign and tumor cells verified for expression of RMS subtype-specific gene signatures and diagnostic marker expression (middle panel, bottom). Tumor cells were assigned to UMAP clusters and combined based on shared gene expression similarities (right). b. RMS cell state signatures queried against the Molecular Signatures Database v7.4. Top enriched molecular signatures were generated from analysis of all PDX samples (n=10, including MAST85 run in replicate) and are shown with False Discovery Rate (FDR) q-values noted. c. Representative heatmap showing single cells (x-axis) and genes enriched for specific transcriptional gene modules (y-axis) for FN-MAST111 PDX. d. Quantification of cell states within individual tumors. Frozen patient tumors denoted by asterisks. Fusion negative (FN, top) and fusion-positive (FP, bottom). PAX3:FOXO1 (P3F) and PAX7:FOXO1 (P7F). The black boxes indicate samples obtained from the same patient. MAST85-r1 and r2 are replicates of the same PDX tumor. Number of cells analyzed noted for each tumor within image panels.

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: RNA Sequencing, Derivative Assay, Expressing, Diagnostic Assay, Marker, Gene Expression, Generated

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Subtype-specific RMS core signatures are expressed at specific muscle development stages. a,b. Dot plot renderings showing the expression of ten representative genes that comprise the fusion-negative or fusion-positive core signature across all PDXs and their identified cell states (a) and across normal muscle cells stratified by age (b). c. UMAP rendering of scRNA sequencing data from embryonic, fetal, and adult skeletal muscle showing expression of representative subtype-specific core signature genes. Week or year of life is noted (Wk and Yr, respectively).

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Expressing, Sequencing

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Gene clusters identified by scRNA sequencing of RMS PDXs and expression of similar numbers of detected genes per cell across cell states. a. UMAP renderings of all PDXs, with exception of MAST111, MAST139, MAST85-r2, MSK72117, that were shown in Fig. 1a and Fig. 2d. b. Representative examples of FN-RMS (left) and FP-RMS (right). UMAP showing genes detected per cell (left). Violin plots showing genes detected within each cell for a given RMS subpopulation (right). c. All PDX models assessed by violin plots denoting the number of detected genes per cell across RMS subpopulations.

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Sequencing, Expressing

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Immunofluorescence antibody staining reveals intermingling of cell states in PDX tumors. a,c. Immunofluorescence staining within the central tumor mass (a) or at the invasive edge (c). Dashed lines indicate clustered cell populations. Arrows denote rare cells detected by IF staining. Scale bar= 50μm. b,d. Heterogeneity identified by single-cell RNA sequencing (left, b) and compared with immunofluorescence staining of the central tumor mass (b) or at the invasive edge (d, right). Color coding denotes that immunofluorescence was detected in tumor cells within the sections analyzed. Not detected (ND). Not applicable (NA). Evenly distributed through tumor (ED) or clustered (C) based on immunofluorescence staining. e-f. Quantitation of cell state percentages assessed by scRNA-sequencing or immunofluorescence. Error bar equals S.E.M. (n=4 image felids analyzed per condition, range 207-643 cells/field).

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Immunofluorescence, Staining, RNA Sequencing, Quantitation Assay, Sequencing

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Cell state heterogeneity in primary patient samples, PDX models, single cell engrafted tumors, and RD cells grown in mouse xenografts. a-c. 3D renderings of gene expression for muscle (x-axis), proliferation (z-axis), mesenchymal-like (y-axis) gene modules identified in RMS samples (a, FN-PDXs; b, FP-PDXs, c, primary patient samples). Individual cells are noted by dots and color coded based on cell assignments shown in Figure 1d. Not detected (ND) denotes lack of a given cell state both in the initial UMAP cell cluster annotations and in 3D gene expression space. d. Combined UMAP visualization for all parental and single cell derived PDX models. e-f. Single cell RNA sequencing of RD xenograft. Heatmap showing single cells (x-axis) and genes enriched for specific transcription modules (y-axis, e). Cells are arranged by UMAP clusters, combined based on expression similarity, and then assigned a specific cell state as noted. f. UMAP rendering of xenografted RD cells following single cell sequencing (left) and quantification of cell state composition of all 2,619 RD cells profiled (right). Similar cell states are observed in RD cells raised in 2D cell culture (See Figure 3).

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Gene Expression, Derivative Assay, RNA Sequencing, Expressing, Sequencing, Cell Culture

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Lineage And RNA RecoverY (LARRY) barcoding of human FN-RMS RD cells reveals that the mesenchymal-enriched cell subfraction is capable of driving tumor growth following culture in low serum, stress conditions. a. Schematic of experimental design. b. UMAP rendering and quantitation of cell states within the LARRY barcoded library (n=9,367 cells). c. Quantification of RMS cells within the library that share the same LARRY barcode and juxtaposed with cell state assigned by gene expression from scRNA sequencing. Cells that divided over the two days of culture adopted largely symmetric cell fates. Dashed yellow highlighting denotes a common and inferred oscillating cell state found in ground and proliferative RMS cells. d. Venn diagram showing shared barcodes found in the LARRY library and after growth under various conditions. e. UMAP renderings and quantitation of cell states following growth under various conditions. f. Analysis of parental cell contribution to overall tumor growth and subsequent generation of daughter cells following cell culture including high serum (top), low serum (middle), and low serum followed by replating into high serum (bottom). g. Quantification of cell lineage and fate decisions under varied growth conditions. Arrow direction and size indicates the probability of a parent cell dividing to produce a daughter cell with the specified cell fate.

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Quantitation Assay, Gene Expression, Sequencing, Cell Culture

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Mesenchymal-enriched FN-RMS TPCs share transcriptional and functional similarities with the bi-potent, skeletal muscle mesenchyme stem cell (SkM. Mesen). a. tSNE visualization of single cell RNA sequencing from human muscle cells (n=508 cells in 6-7 wk, n=2,345 cells in 9 wk, and n=554 cells in 12-14 wk normal muscle samples). Muscle cell states (top panels) and compared with combined gene expression for RMS cell state signatures including proliferation (Prolif.), differentiated muscle (Muscle), and Mesenchymal-like (Mes). Cells states annotated by dotted lines represent significant gene expression similarity by GSEA analysis (FDR<0.25, NES>1.5, p value<0.001). b. Representative examples of gene set enrichment analysis (GSEA) that assessed rhabdomyosarcoma cell state signature expression within the normal muscle cell subpopulations. *** denotes False discovery rate (FDR)<0.25, NES>1.5, p value<0.001. Not significant (ns). Week of life (Wk). c. UMAP visualizations showing cellular states (left) and gene expression for Osteoglycin (OGN), Matrix Gla protein (MGP), and CD90 that label mesenchymal-enriched RMS cells (right). MAST139, n=6,515 cells and MSK74711, n=2,105 cells. d. Quantitative real-time PCR validation of OGN and MGP in FACS isolated mesenchymal-enriched RMS cells from PDX MAST139 and MSK74711. Datum points show expression from three independently engrafted tumors. * p= 0.03, ** p=0.006, **** p<0.0001. e. Osteogenic differentiation assay using MAST139 cells. Representative images of MAST139 stained with Alizarin Red S after 18 days of growth in osteogenic differentiation medium (left) and quantification (right). (n=3 replicates obtained from a single tumor), *** p<0.001. f. Quantification of Alizarin Red S staining following culture of FACS isolated RD and 381T cells in osteogenic differentiation medium, n=3 replicates obtained from independent sorting of RD and 381T cells, RD Mesen+ vs. Mesen−, ** p=0.008, Mesen+ vs. Muscle−, ** p=0.008, *** p<0.001, **** p<0.0001. Mean±SEM., Statistical analysis used two-sided Student’s t-test (d,f).

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Functional Assay, RNA Sequencing, Gene Expression, Expressing, Real-time Polymerase Chain Reaction, Biomarker Discovery, Isolation, Differentiation Assay, Staining

Journal: Nature cancer

Article Title: Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

doi: 10.1038/s43018-022-00414-w

Figure Lengend Snippet: Rhabdomyosarcoma subtypes share common gene expression patterns and are arrested at distinct stages of fetal and embryonic muscle development. a. Subtype-specific core signatures were generated from pseudo-bulk analysis of single-cell RNA-sequencing data (n=4 FP-RMS PDXs and n=6 FN-RMS PDXs). b. Dot plot renderings showing representative subtype-specific gene expression across cell states in representative FP (left, MAST95) and FN (right, MAST39) RMS. Dot size indicates the percentage of cells in each subpopulation that express the gene and shading denotes the average expression across cells. c. Venn diagram comparing the FP- and FN-core signatures with PAX3 binding genes identified by Berkeley et al.35, left. LISA analysis showing the top predicted transcription factor binding sites (TF) that regulate the FP- or FN- core genes (right). p-values noted using Fisher Exact Test. d. UMAP rendering of scRNA sequencing of embryonic (n=5 samples), fetal (n=4 samples), and adult skeletal muscle (n=4 samples), each denoted by dotted lines. n=3,251 total cells analyzed. Transitory cells are noted by arrow. Week or year of life is noted (Wk and Yr, respectively). e. Expression of combined subtype-specific core signatures (left) and representative genes (right) expressed in normal muscle development.

Article Snippet: Single cell suspensions were also used for 3D sphere colony assays, q-RT-PCR experiments and/or 10X Genomics single cell sequencing.

Techniques: Gene Expression, Generated, RNA Sequencing, Expressing, Binding Assay, Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Standard droplet-based scRNA-seq, where cells are loaded at a low concentration (limiting dilution) to avoid cell doublets, and most droplets do not receive a cell. b) scifi-RNA-seq, which uses preindexing and droplet overloading to boost the throughput of droplet-based scRNA-seq. c) Detailed method design of scifi-RNA-seq. d) Representative images of droplets containing between one and ten nuclei, showing the overloading of a standard microfluidic droplet generator (10x Genomics Chromium). e) Droplet overloading boosts the percentage of droplets filled with nuclei from 16.4% (obtained for the maximum loading concentration of the standard Chromium protocol) to 95.5% (obtained for 100-fold overloading using 1.53 million nuclei per channel). f) Droplet overloading causes the average number of nuclei per droplet to increase in a controlled fashion while maintaining the desired Poisson-like loading distribution. g) Expected collision rate as a function of the cell/nuclei loading concentration per channel for standard droplet-based scRNA-seq and for scifi-RNA-seq with different numbers of round1 barcodes. h) Due to the high number of microfluidic (round2) barcodes, scifi-RNA-seq exceeds the barcoding capability of three-round combinatorial indexing protocols.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: Concentration Assay, RNA Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a-b) Representative microscopy images of droplets (top rows) and histograms showing the number of nuclei per droplet (bottom rows) at different loading concentrations (15,300, 191,000, 383,000, 765,000, and 1,530,000 nuclei per channel) for the Chromium scATAC v.1.0 chip (panel a) and for the scATAC v.1.1 Next GEM chip (panel b). To obtain these images, lysis reagents were omitted from the cell loading experiment, and a total of 3,265 (scATAC v.1.0) or 4,509 (scATAC v.1.1 Next GEM) droplets were manually counted. Moreover, the number of beads per droplet (rightmost image and diagram) was visualized and counted based on a loading experiment in which the nuclei suspension was substituted by 1x Nuclei Buffer, while Reducing Agent B was omitted. c) Despite substantial droplet overloading, stable droplet emulsions were obtained for all tested conditions. d) Box plots showing the droplet diameters for the Chromium scATAC v.1.0 and scATAC v.1.1 Next GEM microfluidic chips at different loading concentrations. For each setup, 100 droplets were evaluated. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range. e) Histogram showing droplet diameters (as in panel d) pooled across different loading concentrations (500 droplets per platform).

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: Microscopy, Lysis, Suspension

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Droplet overloading boosts the percentage of droplets filled with nuclei for the scATAC v.1.1 Next GEM microfluidic chip. b) Droplet overloading on the scATAC v.1.1 Next GEM chip increases the average number of nuclei per droplet in a controlled fashion, while maintaining the desired Poisson-like loading distribution. c) Expected collision rates on the Next GEM chip as a function of the loaded number of cells or nuclei per channel for standard droplet-based scRNA-seq and for scifi-RNA-seq with different numbers of round1 barcodes. The cell/nuclei fill rate was modeled as a zero-inflated Poisson distribution. d-f) Modeling of the microfluidic device loading using alternative distributions (Negative Binomial, Poisson, Zero Inflated Negative Binomial, Zero Inflated Poisson). The number of loaded nuclei is plotted against the number of nuclei per droplet on a linear scale (panel d), logarithmic scale (panel e), and as point estimates (panel f). g) Statistical properties of the distribution of nuclei per droplet across experiments. The relationship between mean and variance that is expected for a Poisson distribution is indicated by gray lines. h) Computational modeling of droplet loading as a zero-inflated Poisson function. i) Posterior probability distributions of lambda and psi sampled using a Markov Chain Monte Carlo (MCMC) analysis. j) Independent estimation of the cell doublet rates using Monte Carlo simulations. Error bars in panels d, e, h, and j indicate three standard deviations around the mean.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Schematic outline of scifi-RNA-seq including detailed oligonucleotide sequences. The reverse transcription is performed inside permeabilized cells or nuclei on a 96-well or 384-well plate, introducing well-specific round1 barcodes into the whole transcriptome. Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium). The round2 barcodes are introduced by thermocycling ligation with a complementary bridge oligo and thermostable ligase. The droplet emulsion is then broken, and a second defined end is introduced into the library via template switching. cDNA is enriched and tagmented with a custom i7-only transposome. Finally, the library is PCR-enriched, with the option to introduce an additional sample index. The read structure for next-generation sequencing on the Illumina No-vaSeq 6000 and NextSeq 500 platforms is shown. b) Nuclei recovery after pre-indexing of the whole transcriptome by reverse transcription. scifi-RNA-seq achieves high recovery rates for both cell lines and primary material. c) Nuclei with pre-indexed transcriptome, prior to microfluidic device loading, visualized under a microscope in a counting chamber. The selected image (representative of two replicate samples) shows nuclei derived from human primary T cells. d) Typical size distribution of enriched cDNA obtained with scifi-RNA-seq. e) Typical size distribution of final scifi-RNA-seq libraries ready for next-generation sequencing. f) Distribution of DNA bases along scifi-RNA-seq sequencing reads, showing the characteristic sequence patterns of the UMI, round1 barcode, sample barcode, round2 barcode, and transcript. g) Heatmap showing sequencing quality (Qscore) for each sequencing cycle.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Reverse Transcription, Emulsion, Ligation, Introduce, Next-Generation Sequencing, Microscopy, Derivative Assay, Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) Performance metrics for scifi-RNA-seq experiments using a mixture of human Jurkat cells and mouse 3T3 cells, starting from whole cells permeabilized by methanol, freshly isolated nuclei, and nuclei fixed with 1% or 4% formaldehyde (cryopreserved, re-hydrated, and permeabilized). The following plots are shown: (1) ranked barcodes plotted against reads, unique molecular identifiers (UMIs), and detected genes, distinguishing singlecell transcriptomes from background noise; (2) reads plotted against UMIs; (3) reads plotted against the number of detected genes; (4) reads plotted against the fraction of unique reads; (5) species mixing plot showing the number of UMIs per cell aligning to the mouse genome (x-axis) versus the human genome (y-axis). To facilitate comparisons between the different types of input material, the axes of the performance plots use the same scale across conditions. b) In a species mixing experiment with pre-indexed nuclei from human (Jurkat) and mouse (3T3) cells run at the maximum loading concentration of the standard Chromium protocol (15,300 nuclei per channel), the microfluidic round2 barcode (left plot) is sufficient to resolve single cells. Nevertheless, the combination of round1 and round2 barcodes still improves the separation (right plot). c) Coverage along human and mouse transcripts from 200 bp upstream of the transcription start site (TSS) to 200 bp downstream of the transcription end site (TES), shown for whole cells permeabilized by methanol, freshly isolated nuclei, and nuclei fixed with 1% or 4% formaldehyde (cryopreserved, re-hydrated, and permeabilized). Freshly isolated nuclei show the strongest 3’ enrichment. d) Box plots summarizing sequence alignment metrics across the different types of input material: Total reads sequenced, percent uniquely mapped reads, percent multi-mappers, percent alignments to exons plus introns, percent alignments to exons, and percent spliced reads. Freshly isolated nuclei showed the best performance for these alignment metrics. The box plots summarize a total of 2,299 whole cells; 2,000 fresh nuclei; 2,051 nuclei fixed with 1% formaldehyde and 1,896 nuclei fixed with 4% formaldehyde. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Isolation, Concentration Assay, Sequencing

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) ’Knee plot’ showing the number of UMIs (y-axis) per barcode ranked by frequency (x-axis) for scifi-RNA-seq on the Chromium scATAC v.1.0 chip versus the scATAC v.1.1 Next GEM chip. The characteristic inflection points are indicated, which separate cells/nuclei (left, colored lines) from background noise (right, grey lines). b) Reads per cell plotted against UMIs per cell to assess the level of sequencing saturation for the two microfluidic chips. c) Reads per cell plotted against the unique read fraction per cell to assess PCR duplication and library complexity for the two microfluidic chips. d) Alignments to the human genome versus alignments to the mouse genome in the species mixing experiment to assess the frequency of cell doublets for the two microfluidic chips. e) Alignment metrics for the two microfluidic chips. f) ‘Knee plot’ for the comparison of two reverse transcriptase enzymes (Maxima H Minus versus Superscript IV) in the reverse transcription step of scifi-RNA-seq (the template switching was performed with Maxima H Minus reverse transcriptase in both cases). g) Reads per cell plotted against UMIs per cell to assess the level of sequencing saturation for the two reverse transcriptases. h) Reads per cell plotted against the unique read fraction per cell to assess PCR duplication and library complexity for the two reverse transcriptases. i) Alignment metrics for the two reverse transcriptases.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Sequencing, Comparison, Reverse Transcription

Journal: Nature methods

Article Title: Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

doi: 10.1038/s41592-021-01153-z

Figure Lengend Snippet: a) ‘Knee plot’ showing the number of UMIs (y-axis) per barcode ranked by frequency (x-axis) for scifi-RNA-seq experiments loading different numbers of nuclei. The characteristic inflection points are indicated, which separate nuclei (left, colored lines) from background noise (right, gray lines). To facilitate the comparison between samples, UMIs are normalized to percent of maximum. b) Distribution of the number of nuclei (round1 barcode) per droplet (round2 barcode) when loading different numbers of nuclei. The mean number of nuclei per droplet and nuclei loading concentration per channel are indicated. c) Species-mixing plots showing, for each droplet, the number of reads aligned to the mouse genome (x axis) and human genome (y axis). Transcriptomes were demultiplexed on the basis of the microfluidic round2 barcode alone (left) or on the basis of the combination of round1 and round2 barcodes (right). Dashed lines indicate the expected 1:1 ratio. d) UMIs per cell and fraction of unique readsplotted against the number of nuclei contained in the respective droplet. Box plots depict the interquartile range with marked median and whiskers extending to 1.5 times the interquartile range. The number of droplets that each box plot summarizes is shown on top. e) ‘Knee plot’ for the comparison of scifi-RNA-seq and Chromium profilingusing intact cells, nuclei,or methanol-fixed cells with a standardized loading concentration of 7,500 cells or nuclei per microfluidic channel. f) Dimensionality reduction (UMAP) and clustering (Leiden algorithm)forthe four cell lines. Spurious clusters of doublet cells (gray) are common for Chromium but absent for scifi-RNA-seq. g) Recovery rates for the four cell lines across technologies and cell preparation methods. h) Heatmap showing pairwise correlations and hierarchical clustering for the gene expression profiles across cell lines, cell preparation methods and profiling technologies. i) Dimensionality reduction for aggregated (pseudo-bulk) sample profiles in a large-scale scifi-RNA-seq experiment. j) Dimensionality reduction for 151,788 single-cell transcriptomes, colored by round1 barcodes corresponding to cell lines (left), UMIs per cell (top right) and marker gene expression (bottom right). k) Heatmap showing unfiltered, randomly sampled scifi-RNA-seq profiles for the 100 most specific genes per cell line. l) Gene set enrichment analysis of differentially expressed genes relative to the ARCHS4 database. Closely related cell lines are color coded.

Article Snippet: Pre-indexed cells or nuclei are pooled and encapsulated into emulsion droplets using a standard microfluidic droplet generator (10x Genomics Chromium).

Techniques: RNA Sequencing, Comparison, Concentration Assay, Gene Expression, Marker

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: scRNAseq identifies key conserved populations in CPI colitis samples. (A) Schematic of study design. Peripheral blood or colon biopsies from all patients were, respectively, pooled, stained for CITE-seq, underwent scRNA-seq and TCR-seq workflows, and were later deconvoluted by patient single-nucleotide polymorphisms (SNPs) via demuxlet. A separate split of biopsy samples was barcoded, pooled, and analyzed via mass cytometry (CyTOF). (B–C) Uniform Manifold Approximation and Projection (UMAP) plots of total cells from biopsy (B) or blood (C) data. Coarse annotations are shown by color at left. Biopsy data is also separately shown by disease state and checkpoint inhibitor received. (D) Dot plots showing landmark genes for coarse annotations (top), CD4 + T-cell subsets (bottom left), and CD8+T cell subsets (bottom middle) in biopsy samples. Expression of immunotherapy targets are additionally shown in CD8 + T-cell subsets (bottom right). (E) Dot plots showing landmark genes for coarse annotations (left), CD4 + T-cell subsets (top right), and CD8+T cell subsets (bottom right) in blood samples. CITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; CPI, checkpoint inhibitors; CyTOF, cytometry by time-of-flight; HC, healthy controls; PD-1, programmed cell death protein 1; scRNA-seq, single-cell RNA sequencing; SNP, single-nucleotide polymorphisms; TCR-seq, T cell receptor sequencing; UC, ulcerative colitis.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Staining, Mass Cytometry, Expressing, Sequencing, Cytometry, RNA Sequencing

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: CPI colitis is associated with cytotoxic T-cell activity and myeloid dysregulation, distinct from ulcerative colitis. (A) Cell frequency of coarse annotated total immune cells by scRNA-seq (left) or CyTOF (right) in biopsies, stratified by disease (mean+SD; each dot represents one patient; *p<0.05 and q<0.1). (B) Number of scRNA-seq differentially-expressed (DE) genes (p<0.05 and log 2 (FC)>1 or <−1) between disease states, for each coarse immune population. (C) scRNA-seq DE genes in healthy versus CPI colitis biopsies by immune populations, with DE genes of interest labeled and color coded by category. (D) Number of DE genes (p<0.05 and |log 2 (FC)| >1) that are found uniquely in only healthy versus CPI colitis, only healthy versus ulcerative colitis (UC), or in both comparisons in biopsy samples, for each coarse immune population. (E) Overlapping DE genes (left) in both healthy versus CPI colitis (x axis) and healthy versus UC (y axis) as well as DE genes between UC and CPI colitis (right) with genes of interest labeled, for CD8 + T cells. CPI, checkpoint inhibitors; CyTOF, cytometry by time-of-flight; HC, healthy controls; scRNA-seq, single-cell RNA sequencing.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Activity Assay, Labeling, Cytometry, RNA Sequencing

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: The CPI colitis microenvironment is characterized by dendritic cell and macrophage dysregulation, and expanded CD4 + RM precursors that are clonally related to CD8 + RM and pathogenic cytotoxic T cells. (A) scRNA-seq-defined myeloid subpopulation frequencies in biopsies across disease states (mean+SD; each dot represents one patient; *p<0.05 and q<0.1). (B) scRNA-seq DE genes (p<0.05 and log 2 (FC)>1 or <−1) in cDC2 between healthy and CPI colitis, with select genes of interest labeled. (C) Heatmap showing DE genes from (B) for cDC2 as z-scores across individual patients (columns). (D) scRNA-seq-defined T-cell subtype frequencies in biopsies across disease states (mean+SD; formatted as in A). (E) DE genes in CD8 + RM cells between healthy and CPI colitis, with select genes of interest labeled. (F) Heatmap of DE genes from (E) for CD8 + RM across individual patients. (G) TCR network plots for two patients with CPI colitis (HS11, HS13). Single cells with RNA and TCR data are denoted by nodes with symbols encoding their functional annotation, and cells with a common TCRαβ CDR3 sequence are grouped together in a single cluster (shaded gray circles). Nodes from biopsy data are shown. cDC1, conventional type 1 dendritic cells; LAMP, lysosomal associated membrane protein; pDC, plasmacytoid DC; myeloid NOS, myeloid not otherwise specified; CPI, checkpoint inhibitors; DC, dendritic cell; DE, differential expressed; HC, healthy controls; RM, resident memory; scRNA-seq, single-cell RNA sequencing; TCR, T cell receptor; UC, ulcerative colitis.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Labeling, Functional Assay, Sequencing, Membrane, RNA Sequencing

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: Stromal and endothelial cells in CPI colitis uniquely dysregulate NAD + and tryptophan metabolism, while both CPI colitis and UC converge on loss of epithelial homeostasis. (A) Cell frequency of coarse-annotated total non-immune cells by scRNA-seq (left) or CyTOF (right) in biopsies, stratified by disease states (mean+SD; each dot represents one patient; *q<0.1). (B) Number of scRNA-seq differentially-expressed (DE) genes (p<0.05 and log 2 (FC)>1 or <−1) between disease states, for each non-immune population. (C) DE genes in healthy versus CPI colitis biopsies by non-immune populations, with DE genes of interest labeled. (D) Number of DE genes (p<0.05 and |log 2 (FC)| >1) that are found uniquely in either healthy versus CPI colitis, healthy versus ulcerative colitis (UC), or both comparisons in biopsy samples, for each non-immune population. (E–F) Overlapping DE genes (left) in both healthy versus CPI colitis (x axis) and healthy versus UC (y axis) as well as DE genes between UC and CPI colitis (right) with genes of interest labeled, for stromal cells (E) and epithelial cells (F). (G) Heatmap showing relative expression (z-score) of MADCAM1 in endothelial cells for individual patients (healthy controls, or patients with CPI colitis with distinct CPI exposure and immunosuppression). Fold change reported for comparisons with significant changes (p<0.05) relative to healthy controls (HS1-3). CPI, checkpoint inhibitors; CyTOF, cytometry by time-of-flight; HC, healthy controls; scRNA-seq, single-cell RNA sequencing.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Labeling, Expressing, Cytometry, RNA Sequencing

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: CPI colitis features global IFN-γ response including enhanced antigen presentation. (A) Single-cell RNA sequencing genes upregulated across the greatest number of coarse populations (p<0.05 and log 2 (FC)>1) in healthy versus CPI colitis. (B) Gene Set Enrichment Analysis showing significantly upregulated pathways (rows) across coarse cell populations (columns) relating to either interferon signaling (top rows) or antigen presentation (bottom rows). (C) DE genes between healthy and CPI colitis biopsies in epithelial absorptive colonocytes. Genes of interest are labeled. (D) Heatmap showing DE genes from (C) across individual patients. (E) Protein expression of HLA-DR by CyTOF in non-professional antigen-presenting populations (mean+SD; each dot represents one patient; *p<0.05 and q<0.1). CPI, checkpoint inhibitors; CyTOF, cytometry by time-of-flight; DE, differentially expressed; HC, healthy controls; IFN, interferon; MHC, major histocompatibility complex; UC, ulcerative colitis.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Immunopeptidomics, RNA Sequencing, Labeling, Expressing, Cytometry

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: Unbiased discovery reveals activated HLA-DR + CD38 + cytotoxic CD8 + and CD4 + RM populations. (A) Protein expression by unbiased CyTOF analysis of T-cell checkpoint/activation markers in CPI colitis biopsies, stratified by disease states (mean+SD; each dot represents one patient; *p<0.05 and q<0.1). (B) CyTOF UMAP plots of all live-cell events, color-coded by annotated unbiased clusters. Data is also shown separately in the inset below by disease state. (C) Feature plots of select marker expression projected on the UMAP space from (B) specifically CD4, CD8a, CD3 for T cells; EpCAM for epithelial cells; HLA-DR for class II antigen presentation and activated T cells; CD38 for activated T cells. (D) Non-immune (left) and immune (right) population frequencies based on unbiased clustering of CyTOF data, by disease state (formatted as in A). (E) CyTOF expression of manually gated HLA-DR and CD38 in T-cell subsets, by disease state. (Bar graph: formatted as in A); pie chart: per cent of each population shown). (F) scRNA-seq (RNA) and CITE-seq (protein) expression of CD38 and HLA-DR(A) in scRNA-seq defined T subpopulations. CITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; CPI, checkpoint inhibitors; CTLA-4, cytotoxic T-lymphocyte-associated antigen 4; CyTOF, cytometry by time-of-flight; EpCAM, epithelial cell adhesion molecule; HC, healthy controls; PD-1, programmed cell death protein 1; RM, resident memory; scRNA-seq, single-cell RNA sequencing; UC, ulcerative colitis; UMAP, Uniform Manifold Approximation and Projection; TIM3, T-cell immunoglobulin and mucin-domain containing 3; TIGIT, T cell immunoreceptor with immunoglobulin and ITIM domains; ICOS, inducible T cell costimulator.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Expressing, Activation Assay, Marker, Immunopeptidomics, Sequencing, Cytometry, RNA Sequencing

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: Checkpoint inhibitors colitis from αPD-1 and combination αPD-1/αCTLA-4 have distinct immunopathological features. (A–C) Single-cell RNA sequencing biopsy results for myeloid cells (top row), and CD8 + T cells (bottom row). (A) Number of DE genes (p<0.05 and log 2 (FC)>1 or <−1) between healthy, αPD-1 colitis, and αPD-1 + αCTLA-4 (combo) colitis. (B) DE genes (p<0.05 and log 2 (FC)>1 or <−1) between αPD-1 and combo (left) and overlapping DE genes (right) in both healthy versus αPD-1 (x axis) and healthy versus combo (y axis), with genes of interest labeled. (C) Subpopulation cell frequencies in αPD-1 and combo colitis patients. (mean+SD; each dot represents one patient). (D) DE genes between αPD-1 and combo, with genes related to IFN-γ signaling (left) or antigen presentation (right) labeled. cDC1, conventional type 1 dendritic cells; CTLA-4, cytotoxic T-lymphocyte-associated antigen 4; DC, dendritic cell; DE, differentially expressed; IFN, interferon; LAMP, lysosomal associated membrane protein; NOS, not otherwise specified; PD-1, programmed cell death protein 1; pDC, plasmacytoid dendritic cell.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: RNA Sequencing, Labeling, Immunopeptidomics, Membrane

Journal: Journal for Immunotherapy of Cancer

Article Title: Dysregulation of CD4 + and CD8 + resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis

doi: 10.1136/jitc-2023-008628

Figure Lengend Snippet: External validation underscores a conserved role for activated CD4 RM T cells and cDC dysregulation in CPI colitis across the steroid exposure spectrum. (A) Uniform Manifold Approximation and Projection (UMAP) plots generated from a data set of CD45 + -sorted colon biopsy cells, color coded by coarse annotations (right), and separately plotted by disease state in the inset at left. (B) Dot plots showing marker genes for coarse annotations. (C) Cell frequency of coarse-annotated cells by single-cell RNA sequencing in biopsies, stratified by disease states (mean+SD; each dot represents one patient; *p<0.05 and q<0.1). (D) Number of DE genes (p<0.05 and |log 2 (FC)| >1) that are found uniquely in either healthy versus CPI colitis, CPI only (no colitis) versus CPI colitis, or both comparisons, for each coarse population. (E) Number of DE genes (p<0.05 and log 2 (FC)>1 or <−1) between disease states, for each coarse population. (F) DE genes upregulated across the greatest number of coarse populations (p<0.05 and log 2 (FC)>1) in both healthy versus CPI colitis and CPI only versus CPI colitis. (G) Overlapping DE genes (p<0.05 and log 2 (FC)>1 or <−1) in both healthy versus CPI colitis (x axis) and CPI only versus CPI colitis (y axis) in select coarse populations, with genes of interest labeled. (H) UMAP plots generated from a data set of CD3 + -sorted colon biopsy cells, color coded by coarse annotations (right), and separately plotted by disease state in the inset at left. (I) Dot plots showing landmark genes for coarse T-cell annotations (top left), CD4 + T-cell subpopulations (top right), and CD8 + T-cell subpopulations (bottom left). Expression of immunotherapy targets additionally shown in CD8 + T-cell subsets (bottom right). (J) Cell frequency of T-cell subpopulations out of all annotated T cells, stratified by disease states (formatted as in C). (K) Number of DE genes that are found uniquely in either healthy versus CPI colitis, CPI only (no colitis) versus CPI colitis, or both comparisons, for each T-cell subpopulation. (L) Number of DE genes between disease states, for each T-cell subpopulation. (M) Overlapping DE genes in both healthy versus CPI colitis (x axis) and CPI only versus CPI colitis (y axis) in select T-cell subpopulations, with genes of interest labeled. cDC1, conventional type 1 dendritic cells; CPI, checkpoint inhibitors; DE, differentially-expressed; ILC3, type 3 innate lymphoid cells; NK, natural killer; NOS, not otherwise specified; pDC, plasmacytoid dendritic cell; RM, resident memory; GD, gamma delta.

Article Snippet: To study checkpoint inhibitor-induced colitis (CPI colitis) through an unbiased, multiomic lens, we performed multiplexed single-cell RNA sequencing (scRNA-seq) and T cell receptor (TCR) sequencing (TCR-seq) (10x Genomics), with barcoded antibody staining for protein features (TotalSeq-C, BioLegend), on patient colon biopsies and peripheral blood mononuclear cells (PBMCs) from healthy controls, patients with UC, and patients with CPI colitis ( , ).

Techniques: Biomarker Discovery, Generated, Marker, RNA Sequencing, Labeling, Expressing

Journal: EMBO Molecular Medicine

Article Title: IL ‐27 produced during acute malaria infection regulates Plasmodium ‐specific memory CD4 + T cells

doi: 10.15252/emmm.202317713

Figure Lengend Snippet: B6 mice were transferred with PbT‐II cells, infected with Pcc, and treated with control (IgG; n = 1 biological replicate) or anti‐IL‐27 mAb (α‐IL‐27; n = 1 biological replicate) between −1 and 5 days of infection. PbT‐II cells were purified from these mice 7 days after infection and single‐cell RNA sequencing (scRNA‐seq) analysis was performed. Details of the experiments are shown in Fig . UMAP plots of PbT‐II cells from IgG control ( n = 4,030) and anti‐IL‐27 mAb‐treated mice ( n = 7,476) after unsupervised clustering of pooled single‐cell data from the two groups, with clusters colored by gene expression profiles. UMAP clustering of PbT‐II cells colored by cell cycle profiles. Summary graph of proportions of PbT‐II cells in each cluster for IgG and anti‐IL‐27 mAb‐treated mice in (A). Dot plots showing the expression of Th1‐, Tfh‐, Tcmp‐related genes (Ciucci et al , ), and other genes of interest in each UMAP cluster of PbT‐II cells from IgG and anti‐IL‐27 mAb‐treated mice. Dot colors represent the intensity of expression, while dot size represents the proportion of cells with the corresponding expression. Violin plots showing the expression of Th1‐, Tfh‐, Tcmp‐, and proliferation‐associated genes in PbT‐II cells from IgG (light blue) and anti‐IL‐27 mAb (blue) treated mice. Ridge plots showing the expression of published Th1, Tfh, Tmem, and Tcmp CD4 + T cell signatures in each of the UMAP clusters in (A) based on (Ciucci et al , ). Source data are available online for this figure.

Article Snippet: Stained CD4 + T cells were washed using the recommended Cell Wash Protocol 1 in preparation for 10X Single Cell RNA sequencing (Chromium Next GEM Single Cell 5′ v2 Reagent kits (Dual Index); 10X Genomics).

Techniques: Infection, Control, Purification, RNA Sequencing, Gene Expression, Expressing

Journal: EMBO Molecular Medicine

Article Title: IL ‐27 produced during acute malaria infection regulates Plasmodium ‐specific memory CD4 + T cells

doi: 10.15252/emmm.202317713

Figure Lengend Snippet: B6 mice were transferred with PbT‐II cells, infected with Pcc, and were treated with either IgG or anti‐IL‐27 mAb between −1 and 7 days after infection ( n = 1 biological replicate per timepoint). PbT‐II cells were prepared from spleen at day 28 pi, stained for CD4/TCR/CD45.1 and for CD127, KLRG1, and CD49d with TotalSeq antibodies, sort purified, and processed for scRNA‐seq and CITE‐Seq analysis. Details of the experiment are found in Fig . A–G Comparative analysis of scRNA‐seq data from IgG and anti‐IL‐27 mAb‐treated PbT‐II cells. (A) UMAP plot colored of day 28 PbT‐II cells from IgG control ( n = 7,491) and anti‐IL‐27 mAb‐treated mice ( n = 4,944) after unsupervised clustering of pooled single cell data from the two groups, with clusters colored by gene expression profiles. Cluster labels were harmonized to reflect similar gene expression patterns in the clusters at day 7 pi (Fig ) and anti‐IL27 mAb day 7–28 PbT‐II analysis (Fig ). (B) UMAP clustering of PbT‐II cells colored by cell cycle profiles. (C) CITE‐seq analysis of PbT‐II cells for IgG2a (isotype control), CD127, KLRG1, and CD49d, shown in the same UMAP clustering as (A). (D) Proportions (%) of each cluster within PbT‐II cells, with bar graph sizes shown relative to the total number of PbT‐II cells in IgG (36.8 × 10 ) and anti‐IL‐27 mAb treated (265.7 × 10 ) mice. (E) Ridge plots of PbT‐II cells showing the expression of published CD4 + T cell signature genes (Ciucci et al , ). (F) Violin plots comparing the expression of the CD4 + T cell signature genes. (G) Dot plots showing the expression of Th1‐, Tfh‐, Tcmp‐, and proliferation‐associated genes in each cluster. Dot colors represent the intensity of expression, while dot size represents the proportion of cells with the corresponding expression. H Volcano plot of differentially expressed genes between major clusters 1* and 1** within PbT‐II cells from anti‐IL‐27‐treated mice and corresponding Gene Ontology enrichment analysis for the upregulated genes in each group using Metascape. Source data are available online for this figure.

Article Snippet: Stained CD4 + T cells were washed using the recommended Cell Wash Protocol 1 in preparation for 10X Single Cell RNA sequencing (Chromium Next GEM Single Cell 5′ v2 Reagent kits (Dual Index); 10X Genomics).

Techniques: Infection, Staining, Purification, Control, Gene Expression, Expressing

Journal: EMBO Molecular Medicine

Article Title: IL ‐27 produced during acute malaria infection regulates Plasmodium ‐specific memory CD4 + T cells

doi: 10.15252/emmm.202317713

Figure Lengend Snippet: B6 mice were transferred with PbT‐II cells, treated with IgG or anti‐IL‐27 mAb on day −1, 2 and 5 for day 7 analysis, while mice were treated with anti‐IL‐27 mAb on day −1, 2, 5, and 7 for day 14 and 28 analysis ( n = 1 biological replicate per timepoint). PbT‐II cells were purified and subjected to single‐cell RNA sequencing (scRNA‐seq) and CITE‐seq analysis. The ProjecTILs algorithm (Andreatta et al , ) was used to analyze CD4 + T cell states of PbT‐II cells based on a published reference atlas (Andreatta et al , ). A Experimental scheme. B Gating strategy for the sorting of PbT‐II cells for the scRNA‐seq experiments: Spleen cells were stained for CD4, TCRβ, and CD45.1 to distinguish PbT‐II cells and for TotalSeq IgG2a, CD127, KLRG1, and CD49d for CITE‐seq analysis. C Flow cytometry profiles for each PbT‐II sample analyzed for single‐cell transcriptomics. D, E Predicted distribution of the projected PbT‐II cells in IgG and anti‐IL‐27 mAb‐treated mice on day 7 (D) and day 28 (E) after Pcc infection as density contours in a UMAP plot of a CD4 + T cell reference map (Andreatta et al , ). The bar graphs represent the proportions of the PbT‐II cells projected in the indicated reference subtype.

Article Snippet: Stained CD4 + T cells were washed using the recommended Cell Wash Protocol 1 in preparation for 10X Single Cell RNA sequencing (Chromium Next GEM Single Cell 5′ v2 Reagent kits (Dual Index); 10X Genomics).

Techniques: Purification, RNA Sequencing, Staining, Flow Cytometry, Single-cell Transcriptomics, Infection

Journal: EMBO Molecular Medicine

Article Title: IL ‐27 produced during acute malaria infection regulates Plasmodium ‐specific memory CD4 + T cells

doi: 10.15252/emmm.202317713

Figure Lengend Snippet: scRNA‐seq data of PbT‐II cells from Pcc‐infected anti‐IL‐27 mAb‐treated mice (day7, 14, and 28) were pooled, and unsupervised clustering was performed. UMAP plot colored by gene expression clustering. Proportions (%) of each cluster for each time point. Feature plots of indicated genes across cell clusters as distributed in UMAP plots. Dot plots showing the expression of Th1‐, Tfh‐, Tmem‐, and proliferation‐associated genes in each cluster. Dot colors represent the intensity of expression, while dot size represents the proportion of cells with the corresponding expression. Ridge plots of PbT‐II cell clusters showing the expression of published CD4 + T cell signature genes (Ciucci et al , ).

Article Snippet: Stained CD4 + T cells were washed using the recommended Cell Wash Protocol 1 in preparation for 10X Single Cell RNA sequencing (Chromium Next GEM Single Cell 5′ v2 Reagent kits (Dual Index); 10X Genomics).

Techniques: Infection, Gene Expression, Expressing

Journal: EMBO Molecular Medicine

Article Title: IL ‐27 produced during acute malaria infection regulates Plasmodium ‐specific memory CD4 + T cells

doi: 10.15252/emmm.202317713

Figure Lengend Snippet:

Article Snippet: Stained CD4 + T cells were washed using the recommended Cell Wash Protocol 1 in preparation for 10X Single Cell RNA sequencing (Chromium Next GEM Single Cell 5′ v2 Reagent kits (Dual Index); 10X Genomics).

Techniques: Marker, Staining, FACS, Purification, Software, Cell Isolation