Journal: Journal of Cancer

Article Title: Discovery of Seven ROS-Sensitive Immune Checkpoints and 46 Ligands Mediating Immune Suppression Through T cell-APC Networks

doi: 10.7150/jca.128083

Figure Lengend Snippet: Knockout Screening Validates the Immunosuppressive Roles of Novel Immune Checkpoint Candidates Identified by Their Downregulation in Established Inhibitory IC Knockout Transcriptomic Datasets. (A). We first selected 25 well-established inhibitory immune checkpoints expressed on T cells and screened them across 16 GEO datasets containing knockouts of the top 10 inhibitory immune checkpoints. If the knockout of any of these top 10 checkpoints resulted in a decrease of more than 20% in the expression of other inhibitory checkpoints, indicative of immunosuppressive function. Five checkpoints—CTLA4, KLRG1, LAG3, PD1, and TIGIT—exhibited this key function and were used to refine the criteria for identifying novel inhibitory immune checkpoints. (B). We then screened newly identified 45 Treg- and 106 FOXP3⁺-specific plasma membrane proteins across the GEO knockout datasets of these five checkpoints. Genes that were downregulated at least three out of the five datasets were considered as potential inhibitory candidates. A total of seven such genes were identified (highlighted in grey): Ehd4, Cd200r1, Raph1, Bmpr2, Cd38, Cep55, and Prc1. Of these, the Treg-associated inhibitory group identified CEP55, while the FOXP3⁺ group identified Ehd4, Cd200r1, Raph1, Bmpr2, Cd38, and Prc1. (C). Figure C illustrates the expression patterns of five well-established inhibitory ICs in lymph node T cell subsets using single-cell RNA sequencing (scRNA-seq) data. These ICs including CTLA4, KLRG1, LAG3, PD1, and TIGIT were expressed across CD4⁺ T cells, CD8⁺ T cells, mitotic T cells, tissue-resident T cells, and regulatory T cells (Tregs). Figure D shows comparable expression profiles for seven newly identified inhibitory IC candidates: CEP55, CD38, EHD4, CD200R1, PRC1, RAPH1, and CD86 demonstrating similar distribution across the same T cell subsets. (E) Cross-species expression summary of seven newly identified immune checkpoint receptors in Tregs and conventional T cells.

Article Snippet: To examine this hypothesis, we searched for single cell RNA-sequencing (scRNA-Seq) data at MIT-Broad Institute Single Cell Portal database.

Techniques: Knock-Out, Expressing, Clinical Proteomics, Membrane, RNA Sequencing

Journal: Nature Communications

Article Title: Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis

doi: 10.1038/s41467-023-40727-7

Figure Lengend Snippet: a Workflow of sample collection and data analysis in this study. b Boxplots showing the scaled mean expression of inflammation signatures ( n = 42) in cells from different sample groups. The boxes indicate the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). The points indicate individual signatures. Similar patterns were observed in the PDAC scRNA-seq dataset from Peng et al. . c Uniform Manifold Approximation and Projection (UMAP) plot displaying the integrated cell map, which consists of 29 cell clusters from 12 annotated cell types. Cells are colored by clusters. d Dot plot showing representative marker genes across cell clusters. Dot size is proportional to the fraction of cells expressing specific genes. Color intensity corresponds to the relative expression of specific genes. e Bar plot showing the cell type abundance for samples from different groups, as measured by scRNA-seq data in this study or deconvoluted bulk RNA-seq data from Yang et al. . The error bar indicates standard error of the mean (s.e.m.). The p values are calculated using two-sided Wilcoxon rank-sum test. * p < 0.05; ** p < 0.01. The boxes indicate the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). f Bar plot displaying the heterogenicity of cell types among different patients based on Jensen-Shannon divergence (JSD) score. g UMAP showing the distribution of major cell types (above) and the number of differentially expressed genes (DEGs) in each cell type.

Article Snippet: The 10x Chromium single-cell RNA sequencing (scRNA-seq) data were processed using CellRanger (v3.1.0; 10x Genomics) for alignment, barcode assignment and unique molecular identifier (UMI) counting (using the genome reference set GRCh38-3.0.0).

Techniques: Expressing, Marker, RNA Sequencing

Journal: Nature Communications

Article Title: Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis

doi: 10.1038/s41467-023-40727-7

Figure Lengend Snippet: a UMAP showing the subtypes of myeloid cells, colored by subtypes. b Distribution of myeloid cells in different sample groups on the UMAP. Pie chart showing the proportion of three sample groups in each cell subcluster. c Dot plot illustrating the average expression and frequency of representative marker genes in each myeloid cell subcluster. d Feature plots showing the expression of selected cluster-specific genes. Cells with the highest expression level are colored red. e Dot plot illustrating DEGs in neutrophils and Lipid-associated macrophages (LAMs) from three sample groups (left). Boxplots showing the expression patterns of S100A8 , CXCL8 , SPP 1 , and APOC1 using the bulk RNA-seq dataset from Yang et al. . The number of samples in each group is in the legend. The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). f Immunofluorescent staining showing co-localization of CD68 (green), CCL18 (red), PanCK (yellow), and DAPI (blue) in PT and HM samples. Scale bars, 50 μm (left) and 20 μm (right). The bar plots show the quantification results, n = 3 patients with paired PT and HM samples. The error bar indicates standard error of the mean (s.e.m.). The p value is calculated with one-sided Wilcoxon rank-sum test. g Boxplot (top) showing the metabolic score of metabolic pathways in four LAM subclusters (LAM1-LAM4). The points indicate individual pathways ( n = 76). Dot plot (bottom) showing the metabolic activity analysis of all LAM subclusters by scMetabolism. The circle size and color darkness both represent the scaled metabolic score. The number of pathways in each category is indicated below the boxplot. The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). h Heatmap showing the scaled expression levels of a series of immune checkpoint genes in myeloid cell subtypes. Subtypes are grouped by sample source and myeloid cell type annotations (DC, LAM, macrophage, monocyte and neutrophil). Genes are grouped as receptor or ligand, inhibitory or stimulatory status and expected major lineage cell types known to express the gene (lymphocyte and myeloid).

Article Snippet: The 10x Chromium single-cell RNA sequencing (scRNA-seq) data were processed using CellRanger (v3.1.0; 10x Genomics) for alignment, barcode assignment and unique molecular identifier (UMI) counting (using the genome reference set GRCh38-3.0.0).

Techniques: Expressing, Marker, RNA Sequencing, Staining, Activity Assay

Journal: Journal of Virology

Article Title: Reanalysis of single-cell RNA sequencing data does not support herpes simplex virus 1 latency in non-neuronal ganglionic cells in mice

doi: 10.1128/jvi.01858-23

Figure Lengend Snippet: Quality control of scRNA-Seq datasets derived from pools of HSV-1 latently-infected mouse trigeminal ganglia. scRNA-Seq datasets generated by Wang et al. were obtained from uninfected (“Uninf-1) and two biological replicates of HSV-1-infected C57BL/6 mice (“Inf-1” and “Inf-2”). Each replicate was obtained by pooling dissociated cells – composed of a 1:1 mixture of CD45-enriched cells and the original cell suspension – from left and right TG from 15 animals (30 ganglia). ( A ) Using the filtered barcode matrices generated by Wang et al. , the quality of each dataset was assessed by (left) the number of unique genes detected per cell, (middle) the total number of RNA molecules (UMI) recovered per cell and (right) the proportion of reads per cell derived from mitochondrial RNAs. ( B ) Quality control filtering of these datasets dramatically reduced the total number of cells available for analysis, indicative that many dead/dying cells were present in the original single-cell suspensions of Inf-1 and Inf-2. Filtering parameters removed cells with less than 300 or more than 9,000 distinct expressed genes, and cells for which more than 15% of reads derived from mitochondrial RNAs. Inset: Number of cells before and after filtering.

Article Snippet: The core data supporting these claims was obtained by droplet-based scRNA-Seq analysis (10X Genomics platform) of TG from uninfected C57BL/6 mice (dataset: “Uninf-1”) and two biological replicate groups of C57BL/6 mice infected via the corneal route with 2 × 10 5 plaque-forming units/eye of HSV-1 strain McKrae 35 days earlier (datasets: “Inf-1” and “Inf-2”).

Techniques: Derivative Assay, Infection, Generated, Suspension

Journal: EMBO Reports

Article Title: TMPRSS11B promotes an acidified microenvironment and immune suppression in squamous lung cancer

doi: 10.1038/s44319-025-00631-1

Figure Lengend Snippet: ( A ) RNA sequencing analysis of tumors from various mouse models of lung cancer. The y-axis represents the normalized counts for Tmprss11b . One-way ANOVA with Dunnett’s multiple comparisons test was used for the statistical analysis (RPR2 mice n = 5; RPM mice n = 15; SNL mice n = 4; LP mice n = 6; SL mice n = 9; KP mice n = 8, biological replicates), **** P < 0.0001 (RPR2, RPM, KP), * P = 0.0133 (LP). Plot represents mean ± SD. ( B ) Schematic representation of Ad-Cre mediated tumor induction in SNL mice. Figure created in BioRender. ( C ) Representative MRI images of the mice in ( B ), 3- & 4-months post infection. Red outlines denote tumors (Biological replicates n > 3). ( D ) Representative H&E images of SNL mouse lung, 7 months post infection with Ad-Cre showing distinct regions of LUSC and mucinous LUAD (Biological replicates n > 3). Scale bar, 100 μm. ( E ) H&E image of SNL mouse lung (11 months post infection with Ad-Cre) and RNAscope of Tmprss11b on a serial section. Left, red outline denotes squamous tumors based on H&E staining. Right, yellow outline denotes regions with Tmprss11b expression (red) corresponding to the regions of squamous tumors. The staining was repeated three times with serial sections (technical replicates) and with lung sections from different mice ( n = 4, biological replicates). Scale bar, 2 mm. ( F ) Zoom-in of ( E ) showing Tmprss11b expression by RNAScope in squamous tumors (top panel) and normal lung (bottom panel). Scale bar, 200 μm. ( G ) Representative H&E image with annotations and RNAscope analysis of Tmprss11b (red) and Sox2 (green) in SNL lung sections. Scale bar, 400 μm. .

Article Snippet: We obtained reference single-cell RNA sequencing (scRNA-seq) data from The Tabla Muris Consortium ( Nature 2018) (Schaum et al, ) and spatial transcriptomics (ST) data from relevant datasets.

Techniques: RNA Sequencing, Infection, RNAscope, Staining, Expressing

Journal: EMBO Reports

Article Title: TMPRSS11B promotes an acidified microenvironment and immune suppression in squamous lung cancer

doi: 10.1038/s44319-025-00631-1

Figure Lengend Snippet: ( A ) Top downregulated Keratin genes from differential gene expression analysis of control shRNA versus Tmprss11b shRNA bulk RNA sequencing from the KLN205 syngeneic experiment in Fig. . The log2FC change depicts the reduction in expression of the indicated genes in the Tmprss11b knockdown tumors compared to the control. ( B ) Top Keratin genes from the differential gene expression (DEG) analysis of the Tmprss11b -high versus low in LUSC spatial data from SNL lung tumors. ( C ) Top keratin genes from the differential gene expression (DEG) analysis of the Tmprss11b -high LUSC versus LUAD spatial data from SNL lung tumors. ( D ) Top Keratin genes from the differential gene expression (DEG) analysis of TMPRSS11B -high versus low LUSC human tumors from TCGA. ( E ) Venn diagram depicting overlapping Keratin genes from the gene lists in ( A – D ).

Article Snippet: We obtained reference single-cell RNA sequencing (scRNA-seq) data from The Tabla Muris Consortium ( Nature 2018) (Schaum et al, ) and spatial transcriptomics (ST) data from relevant datasets.

Techniques: Gene Expression, Control, shRNA, RNA Sequencing, Expressing, Knockdown

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet: A Schematic work flow for 10× Genomics scRNASeq of pooled SCN slices after 3 days in culture. For each sequencing run, ca. 17 organotypic SCN slices were pooled into a single sample and their cells dissociated using papain to obtain single‐cell suspension of ˜8,000 cells/µl. Dispersed single cells, along with barcoded 10× Genomics Chromium Single Cell 3′ v2 technology gel beads, were partitioned into water‐in‐oil droplets. Within these droplets, reverse transcription and amplification steps generated cDNA libraries for the barcoded single cells. B t‐SNE plot from 13,324 sequenced SCN cells collected at CT7.5, across three independent sequencing runs. Each sequenced cell marked with a unique barcode is represented as a single dot. This dimensionality reduction method aims to maintain both local and global structure of the data by clustering data points of highest similarity nearest to each other. Cell clusters recognised by the graph‐based clustering algorithm are further colour‐coded to highlight the cell types: putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells. C Heatmap of the top 5 up‐regulated genes that most distinguish the transcriptional clusters identified in B. The degree of up‐regulation is measured as the log 2‐fold change ratio of gene expression calculated for cells of each cluster and normalised to a size factor accounting for the total transcript count of each sequenced cell across each cluster. D t‐SNE plot from 16,996 SCN sequenced cells collected at CT15.5, across two independent sequencing runs. As for B, the cell types, putative SCN neurons, extra‐SCN hypothalamic neurons, astrocytes, oligodendrocytes, radial glia, microglia, ependymocytes and endothelial cells, were identified. E As C, but for cell clusters identified at CT15.5. Source data are available online for this figure.

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: Sequencing, Suspension, Reverse Transcription, Amplification, Generated, Gene Expression

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet: A Schematic view of populations of interest from Venn analysis of scRNAseq data from daytime SCN neurons. B–E Representative RNAScope in situ hybridisation images from SCN cryostat sections to reveal overlap/non‐overlap of neuropeptide/receptor‐expressing cells. (B) Cells expressing Prok2 (green) and/or Avp (red). (C) Cells expressing Vipr2 and/or ProkR2 . (D) Potential autocrine signalling in cells expressing Prok2 + and/or ProkR2 + . (E) Cells expressing Av p and/or Avpr1a . Left: 40×, right 63×, scale bar: 100 µm. For each gene set, panel A and B show magnified 63× images. White arrows highlight the overlap‐/non‐overlap of neuropeptide/ receptor‐expressing cells. F, G Inferred topology of neuropeptidergic signalling axes between identified neuronal sub‐populations in SCN from circadian day (F) or circadian night (G). Inter‐cluster signalling is unscaled as connections are weighted by highest expression count measured across the entire dataset. Clusters are numbered according to their size and are annotated in the overview t‐SNE for day (bottom left) and night (top right), respectively. Source data are available online for this figure.

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: RNAscope, In Situ, Hybridization, Expressing

Journal: The EMBO Journal

Article Title: Single‐cell transcriptomics of suprachiasmatic nuclei reveal a Prokineticin‐driven circadian network

doi: 10.15252/embj.2021108614

Figure Lengend Snippet:

Article Snippet: Codes used to analyse scRNASeq data are publicly available from 10× Genomics at “ https://support.10xgenomics.com/single‐cell‐gene‐expression/software/overview/welcome .” Viral reagents generated in this study have been deposited with Addgene, AAV1.pProk2.Cre.T2A.mCherry Plasmid #169013 and AAV1.pProkR2.Cre.T2A.Venus Plasmid #169014.

Techniques: Plasmid Preparation, Recombinant, Modification, RNAscope, Multiplex Assay, Software

Journal: Science (New York, N.Y.)

Article Title: In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes

doi: 10.1126/science.aaz6063

Figure Lengend Snippet: (A) Schematics of the in vivo Perturb-Seq platform, which introduces mutations in individual genes in utero at E12.5, followed by transcriptomic profiling of the cellular progeny of these perturbed cells at P7 via single-cell RNA sequencing (scRNA-seq). (B) tSNE of five major cell populations identified in the Perturb-Seq cells. (C) In vivo Perturb-Seq lentiviral vector carrying an mCherry reporter drives detectable expression within 24h, and can sparsely infect brain cells across many brain regions. Scale bar is 1000μm. (D) Cell-type analysis of in vivo Perturb-Seq of ASD/ND de novo risk genes. Canonical marker genes were used to identify major cell clusters (left), and cell-type distribution in each perturbation group (right). Negative control (GFP) is highlighted by a black rectangle. (E) tSNEs showing the subclusters of each of the five major cell types, identified by re-clustering each cell type separately.

Article Snippet: Based on mouse cortical single-cell RNA sequencing (scRNA-seq) data, the orthologs of these ASD/ND risk genes are expressed in diverse cell types ( fig. S2 ) (E18.5 data from the 10x Genomics public dataset ( 10 ); P7 data from this work).

Techniques: In Vivo, In Utero, RNA Sequencing, Plasmid Preparation, Expressing, Marker, Negative Control