Journal: eLife

Article Title: Single-cell profiling of trabecular meshwork identifies mitochondrial dysfunction in a glaucoma model that is protected by vitamin B3 treatment

doi: 10.7554/eLife.107161

Figure Lengend Snippet: ( A ) UMAPs of the integrated Columbia University and Duke University datasets. Cells from the different institutions occupy largely different but adjacent UMAP coordinates with some overlap, indicating batch effects. The difference was largest for TM3, but the marker genes were still conserved. Because tissue processing techniques can alter gene expression , the heatmap variation between institutes likely reflects differences in processing techniques (Methods) and suggests that TM3 cells are more susceptible to these effects than other cell types. ( B ) Analysis of Duke University data alone independently validates our findings presented in from Columbia University, including the presence of 3 TM cell clusters. ( C ) A heatmap comparing the top 20 marker genes of each TM cell subcluster for the Columbia (C57BL/6J strain only) and Duke datasets. Overall, there is strong overlap of TM cell gene expression between datasets. Discrepancies include certain marker genes (eg. Crym ) in the Columbia dataset that are not detected in the Duke dataset. These differences are associated with lower sequencing depth in the Duke data, and other technical factors/batch effects. ( D–E ) Dot plots showing similar TM subtype marker gene expression in the two datasets.

Article Snippet: Sequencing data can be visualized in the Broad Institute's Single Cell Portal under accession number SCP3243.

Techniques: Marker, Gene Expression, Sequencing

Journal: eLife

Article Title: Single-cell profiling of trabecular meshwork identifies mitochondrial dysfunction in a glaucoma model that is protected by vitamin B3 treatment

doi: 10.7554/eLife.107161

Figure Lengend Snippet: ( A ) A schematic representation of the pipeline used to identify open chromatin sites and active transcription factors (TFs). Individual cells were profiled using both single nucleus (sn) RNA and ATAC sequencing (multiome). TM cell clusters were identified using the snRNA-seq data, while significantly open chromatin regions were identified using the snATAC-seq data. Active TFs were determined based on the odds ratio of TF binding motifs within these open chromatin regions. Schematic created with BioRender.com (see here , here , and here ). ( B ) UMAP of subclusters derived from TM cell containing cluster (cluster 1) in the snRNA-seq data. ( C–E ) Example ATAC tracts for the promotor regions of selected marker genes for TM1 ( C ), TM2 ( D ), and TM3 ( E ). Each of these marker genes has greater promoter accessibility in the TM cell subtype in which its RNA expression is enriched (orange box). The aligned marker gene positions are shown. Coverage = normalized ATAC signal reads in transcription start site. CS = coverage scale. ( F ) Correlations between RNA expression levels (snRNA-seq dataset) of each TF and the chromatin accessibility levels (snATAC-seq dataset) of their respective predicted target binding motifs across all TM cell subtypes (see Materials and methods). Select TFs with strong positive (red) or negative correlations (blue) are named.

Article Snippet: Sequencing data can be visualized in the Broad Institute's Single Cell Portal under accession number SCP3243.

Techniques: Sequencing, Binding Assay, Derivative Assay, Marker, RNA Expression

Journal: eLife

Article Title: Single-cell profiling of trabecular meshwork identifies mitochondrial dysfunction in a glaucoma model that is protected by vitamin B3 treatment

doi: 10.7554/eLife.107161

Figure Lengend Snippet: ( A ) Overlap of marker gene expression for each TM cell subtype between single-cell (sc) and single-nucleus (sn) RNA sequencing (RNA-seq). Other than the expected technical drop out of genes (no detected expression) in the snRNA-seq dataset, there is general agreement. ( B ) Localization of significantly open chromatin regions (snATAC-seq). ( C ) Confusion matrix of clusters identified by snRNA-seq and snATAC-seq after separately clustering each data type using unbiased dimensional reduction. Colors indicate the fraction of cells identified in each ATAC cluster (row) which are also identified in each RNA cell type (columns), where darker colors represent stronger correspondence between RNA and ATAC clusters. There is a significant correlation between gene expression (RNA-based clustering) and chromatin accessibility (ATAC-based clustering) with adjusted Rand index of 0.20 (p<0.001, permutation test). ( D ) Heatmaps comparing the open chromatin score at a gene promoter (left panel, snATAC-seq) to the RNA expression (right panel, snRNA-seq). Individual genes are represented on the y-axis and individual cells are plotted on the x-axis (only TM cells included). The consistent heatmap patterns indicate a strong overlap between promoter chromatin states and RNA expression for individual genes across cell types, validating the quality of these multiome datasets.

Article Snippet: Sequencing data can be visualized in the Broad Institute's Single Cell Portal under accession number SCP3243.

Techniques: Marker, Gene Expression, RNA Sequencing, Expressing, RNA Expression

Journal: Mammalian Genome

Article Title: Exploration of shared gene signatures and molecular mechanisms between psoriasis and COVID-19: evidence from transcriptome data

doi: 10.1007/s00335-026-10194-8

Figure Lengend Snippet: Analysis of the hub genes expression at single cell level in COVID-19. A UMAP showing the major cell types in COVID-19 ( n = 37) and heathy controls ( n = 15) at single-cell transcriptomes. B Density plot of AUC values. C Boxplot comparing AUC values between COVID-19 and healthy controls

Article Snippet: Additionally, single-cell RNA sequencing (scRNA-seq) data of COVID-19 samples were obtained from the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell/study/SCP1289/ ).

Techniques: Expressing

Journal: Mammalian Genome

Article Title: Exploration of shared gene signatures and molecular mechanisms between psoriasis and COVID-19: evidence from transcriptome data

doi: 10.1007/s00335-026-10194-8

Figure Lengend Snippet: Analysis of the hub genes expression at single cell level in psoriasis. A UMAP showing major cell types and clusters in psoriasis patients ( n = 3) and healthy controls ( n = 3). B Density plot of AUC values. C Boxplot comparing AUC values between psoriasis and healthy controls

Article Snippet: Additionally, single-cell RNA sequencing (scRNA-seq) data of COVID-19 samples were obtained from the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell/study/SCP1289/ ).

Techniques: Expressing

Journal: Cancer Medicine

Article Title: Clinical Significance and Therapeutic Potential of Long Non‐Coding RNA H19 in Soft Tissue Sarcoma

doi: 10.1002/cam4.71305

Figure Lengend Snippet: H19 expression in different sarcoma subtypes. (A) H19 RNA‐seq expression data across cell lines from 38 different cancer types derived from the publicly available CCLE database. (B) H19 RNA‐seq expression data from 31 cancer types in tumor tissue derived from TCGA and GTEx data (SARC = sarcoma; TMP = Transcript Per Million). (C) The expression of H19 in 7 different sarcoma cell lines was measured by qRT‐PCR and normalized to the housekeeper genes GAPDH and U6 ( n = 3; mean ± SD). (D) Representative pictures of RNA in situ hybridization of H19 in the liposarcoma cell line SW872 showing a heterogenous expression pattern.

Article Snippet: Therefore, in the first screening step, we compared the occurrence and expression levels of H19 between different cancer types by using publicly available RNA‐seq data provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) that comprises expression data of cell lines originating from 38 different cancer types.

Techniques: Expressing, RNA Sequencing, Derivative Assay, Quantitative RT-PCR, RNA In Situ Hybridization

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: bioRxiv

Article Title: Conserved Neuronal-like and Secretory Programs Define the Spatial Architecture of Gastroenteropancreatic Neuroendocrine Tumors

doi: 10.64898/2025.12.28.696762

Figure Lengend Snippet: (A) Multiome sample processing and sequencing workflow. Archival, flash-frozen NET samples were apportioned for nuclei isolation and subsequent joint single-nuclei RNA-sequencing and ATAC-sequencing using the 10x Genomics protocol. Formalin-fixed, paraffin-embedded (FFPE) slides were generated from each sample and used to generate spatial transcriptomics data using the 10x Genomics Visium protocol. (B) Clinical characteristics of the cohort, including tumor origin, stage, grade, treatment status, and availability of multi-omic data after quality control. (C) UMAP embeddings of snRNA-seq and snATAC-seq data colored by annotated cell types. Bar plots indicate the relative abundance of major cell populations across samples.

Article Snippet: Processed single-nuclei RNA-seq data will be deposited in the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ).

Techniques: Sequencing, Isolation, RNA Sequencing, Formalin-fixed Paraffin-Embedded, Generated, Control

Journal: bioRxiv

Article Title: Conserved Neuronal-like and Secretory Programs Define the Spatial Architecture of Gastroenteropancreatic Neuroendocrine Tumors

doi: 10.64898/2025.12.28.696762

Figure Lengend Snippet: (A,B) Violin plots showing cNMF program scores stratified by tumor type and clinical stage for siNETs (A) and pNETs (B). Differences between primary and metastatic tumors were assessed using a two-sided Wilcoxon rank-sum test. (C) Validation in an independent bulk RNA-seq pNET cohort showing decreased neuronal p-cNMF1 scores and increased secretory p-cNMF2 scores in metastatic samples. Statistical significance was assessed using a two-sided Wilcoxon rank-sum test (* = p value < 0.05, ** = p value < 0.01).

Article Snippet: Processed single-nuclei RNA-seq data will be deposited in the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ).

Techniques: Biomarker Discovery, RNA Sequencing

Journal: bioRxiv

Article Title: PKN2 regulates cell-junctions to limit colitis and colon tumour formation

doi: 10.64898/2025.12.15.694339

Figure Lengend Snippet: A-E) Immunohistochemistry analysis of CD45 (A), F4/80 (B), CD4 (C) and CD8 (D) staining in WT, HET and PKN2 KO adenomas formed during the AOM/1%DSS protocol. F) PKN2 expression from RNA sequencing analysis in adenomas by genotype. Statistical significance was determined using DESeq2 differential expression analysis (WT: n=5, HET: n=10, KO: n=18). Adenomas from the same individuals are coloured with the same colours. G) Gene set enrichment analysis of KO vs WT, KO vs HET and HET vs WT adenomas for the most significant Hallmark gene sets. The top 15 most significant gene sets across all comparisons are shown. H) Gene set enrichment analysis of KO vs WT for colorectal Sansom APC and WNT, McMurray TP53/HRAS, Cordenonsi YAP, Berenjeno Q63L-RhoA and mouse phenotypes decreased tumour growth size gene sets.

Article Snippet: All single cell RNA sequencing data was obtained from the Single Cell Portal from the Broad Institute.

Techniques: Immunohistochemistry, Staining, Expressing, RNA Sequencing, Quantitative Proteomics

Journal: bioRxiv

Article Title: PKN2 regulates cell-junctions to limit colitis and colon tumour formation

doi: 10.64898/2025.12.15.694339

Figure Lengend Snippet: A, B) Single cell RNA sequencing analysis of samples from normal colon (A), Crohn’s disease patients (B). Cell subtypes are coloured in the left panel with PKN2 expression labelled in the right panel. C) PCR genotyping of isolated colonic epithelial tissue two weeks following the tamoxifen treatment regime in Epi-iPKN2 KO mice. Diagram explaining band sizing is in . D, E) Colon length (D) and spleen weight (E) in Epi-iPKN2 KO mice following 5-days treatment with 2%DSS. Statistical significance was determined using a Dunnett’s multiple comparisons test (WT: n=5, HET: n=16, KO: n=8). F, G) Epithelial erosion (F) and goblet cell depletion (G) indicated by alcian blue staining in Epi-iPKN2 KO mice following 5-days treatment with 2%DSS. H-K) Average distal submucosa width (H, I) and cellularity (J, K) in global-iPKN2KO (H, J) and Epi-PKN2KO (I, K) mice following treatment with AOM/1%DSS (H, J) or 2%DSS (I, K). Statistical significance was determined using a Dunnett’s multiple comparisons test (H, J: WT: n= 4, HET: n=6, KO: n=8), (I, K: WT: n=5, HET: n=16, KO: n=8). L-N) CD45+ immune cell infiltration in distal crypts and submucosa global-iPKN2KO (N) and Epi-PKN2KO (L, N) mice following treatment with AOM/1%DSS (M) or 2%DSS (L, N). Statistical significance was determined using a Dunnett’s multiple comparisons test (M: WT: n=3, HET: n=5, KO: n=7) (N: WT: n=5, HET: n=16, KO: n=6).

Article Snippet: All single cell RNA sequencing data was obtained from the Single Cell Portal from the Broad Institute.

Techniques: RNA Sequencing, Expressing, Isolation, Staining

Journal: bioRxiv

Article Title: PKN2 regulates cell-junctions to limit colitis and colon tumour formation

doi: 10.64898/2025.12.15.694339

Figure Lengend Snippet: A) Principal component analysis of RNA sequencing data from wild-type and Rosa Cre/+ /PKN2 fl/fl primary mouse colon organoids treated with 4-OHT tamoxifen or a vehicle control. B) Differential gene expression analysis identifying the most significantly differentially expressed genes between vehicle control and tamoxifen treated PKN2-KO organoids, accounting for the effects of tamoxifen treatment on wild-type organoids. C) Significant differentially expressed genes between vehicle control and tamoxifen treated PKN2-KO organoids, accounting for the effects of tamoxifen treatment on wild-type organoids. Known cancer genes from the Cancer Gene Consensus are labelled. D) Hallmark gene set enrichment analysis comparing the response of PKN2 KO in primary mouse organoids and change in expression in organoids from patients with colitis. E) Upregulation in pathways from the GO collection of gene sets related to junction formation and regulation.

Article Snippet: All single cell RNA sequencing data was obtained from the Single Cell Portal from the Broad Institute.

Techniques: RNA Sequencing, Control, Gene Expression, Expressing

Journal: bioRxiv

Article Title: PKN2 regulates cell-junctions to limit colitis and colon tumour formation

doi: 10.64898/2025.12.15.694339

Figure Lengend Snippet: A) Principal component analysis of RNA sequencing data from DLD1 cells treated with a PKN2 targeting siRNA or a non-targeting control. B) Differential gene expression analysis identifying the most significantly differentially expressed genes between DLD1 cells treated with a PKN2 targeting siRNA or a non-targeting control. C) Hallmark gene set enrichment analysis comparing the response of PKN2 KO in primary mouse organoids and DLD1 cancer cells treated with a PKN2-targeting siRNA. D-F) Predictions for PKN2 gene function from the ARCHS4 database containing massively integrated RNA sequencing data. Predictions are divided into human phenotypes (D), mouse genome informatics (MGI) (E) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (F). G) Differential expression of cell junction genes in wild-type and Rosa Cre/+ /PKN2 fl/fl mouse primary organoids treated with 4-OHT or a vehicle control. Differential expression significance stars are from a comparison between vehicle control and tamoxifen treated PKN2-KO organoids, accounting for the effects of tamoxifen treatment on wild-type organoids. H, I) GSEA of hallmark apical junction gene set in TCGA-COAD (H) and TCGA-READ (I) ranks created from comparing PKN2-low and PKN2-high patient primary tumours.

Article Snippet: All single cell RNA sequencing data was obtained from the Single Cell Portal from the Broad Institute.

Techniques: RNA Sequencing, Control, Gene Expression, Quantitative Proteomics, Comparison