cell rna seq data analysis  (Broad Clinical Labs)

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Minor macroscopic effects of Parp14 deficiency on the severity of salmonellosis. ( A, B ) Schematic representation of the single-animal experiment executed in this study. The blue box refers to mice, which were subjected to statistical comparisons throughout the study. The tissues marked with an asterisk were longitudinally cut into two pieces, one for histology and one for qPCR/RNA-Seq. Images were partially created with BioRender.com. ( C ) Weight change of the mice during the course of the experiment relative to day −1 (medians with interquartile range). No statistically significant differences between the infected wt and Parp14-deficient mice were detected. Statistical significance values are shown in the figure. Weights of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ). ( D ) Colon lengths at day 1 and day 5. ( E ) Spleen weights at day 1 and day 5. ( F ) Liver weights at day 1 and day 5. ( G–L ) Determination of viable bacteria in different tissues at day 1 and day 5. Bars in sub-panels D–L represent medians with interquartile range. All individual data points are shown. Statistical significance values for the differences between the infected wt and Parp14-deficient mice are shown in each D–L sub-panel. Fecal pellets were not obtained from all mice. Parameters of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ).

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: RNA Sequencing, Infection, Bacteria

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Quantitation of Parp14 expression in the mouse gastrointestinal tract. ( A ) The QuPath-based quantitation of Parp14 expression. Representative examples of the Parp14 stainings are shown in . The values on the y -axis refer to the means of DAB staining intensity, that is, the mean OD in the QuPath data output. Each dot refers to a single cell. The numbers of analyzed cells (mostly epithelial cells) are indicated on the x -axis (see ). The red lines above the data points refer to the mean values. Statistical analyses were conducted using the two-tailed unpaired t -test (NA, not applicable; fewer than three animals to compare, see ). One Salmonella -infected day 5 mouse was left out from the quantitation due to poor quality of the FFPE tissue block. ( B ) The qPCR data on relative Parp14 expression (means with standard deviation, statistics performed using two-tailed unpaired t -test). Samples were included in the data analysis if they passed the 0.5 standard deviation Ct filter for replicate runs. No statistical analyses were executed against the PBS groups because there were less than three data points/animal to compare (see ). The calibrators in each sub-panel are the mean dCq values of the day 1 Salmonella -infected mice.

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: Quantitation Assay, Expressing, Staining, Single Cell, Two Tailed Test, Infection, Blocking Assay, Standard Deviation

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Transcriptional signatures uniquely detected in S . Typhimurium-infected wt and Parp14-deficient mice. Data from a triplicate RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) The Venn diagrams of shared and unique genes that were detected to be expressed in the infected wt and Parp14-deficient mice (FPKM value >1). The integer is the number of genes detected to be expressed in both of the genotypes. ( B ) The pie charts of the numbers of identified GO terms based on the genotype-specific lists of expressed genes (BP, biological process; CC, cellular component; MF, molecular function; ). ( C–E ) Bar graph representation of all the identified GO BP terms with the genotype-specific lists of expressed genes. The BP terms are sorted based on the percentage of GO term gene values (number of detected genes in a particular BP term / number of all genes in particular BP term × 100). FDR refers to the false discovery rate value. An FDR value cut-off of <0.05 was used in the searches. The asterisks in the wt sub-panel ( D ) refer to the seven infection- and inflammation response-related BP terms. The sub-panel E displays the genes of these seven infection- and inflammation response-related BP terms. ( F–H ) Pathway-enrichment dot plot representations of all (KO sub-panel) and the top 10 (wt sub-panel) KEGG pathways identified with the genotype-specific lists of expressed genes. All the identified KEGG pathways with the corresponding gene lists are described in . The KEGG pathways are sorted based on the P -value. The count values refer to the number of genes that were detected in a particular KEGG pathway. The asterisk in the wt sub-panel ( G ) refers to the only KEGG pathway with a <0.05 P adj -value. The sub-panel H displays the genes of this IL-17 signaling pathway.

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: Infection, RNA Sequencing

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Hampered expression of four cytokines in the large intestine of S . Typhimurium-infected Parp14-deficient mice. Four hit genes of the large intestine bulk tissue RNA-Seq analysis ( Ccl2 , Ccl7 , Cxcl10 , Il1b ) were analyzed. Five other TaqMan qPCR assays on inflammation-associated genes were run in parallel. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the day 1 infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: Expressing, Infection, RNA Sequencing, Gene Expression, Two Tailed Test

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Transcriptional signature downregulated in S . Typhimurium-infected Parp14-deficient mice. Data from triplicate bulk tissue RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) Inter-sample correlation heatmap based on the FPKM values of the DEGs in Parp14-deficient vs wt mice comparison. R 2 is the square of Pearson correlation coefficient ( R ). ( B ) Volcano plots of the DEGs. Specific information on the DEGs is given in . The x -axis shows the fold difference in gene expression between different samples, and the y -axis shows the statistical significance of the differences. Red dots represent upregulation genes, and green dots represent downregulation genes. The dashed line indicates the threshold line for statistically significant differential gene expression. The values marked with asterisks refer to the number of DEGs that were used for a stringent downstream data analysis, that is, UP genes, log2(FoldChange) > 0.5 and P adj < 0.05; DOWN genes, log2(FoldChange) < −0.5 and P adj < 0.05 . ( C ) GO term analysis with DEGs in Parp14-deficient vs wt mice comparison (BP, biological process; CC, cellular component; MF, molecular function; ). The GO terms were searched using the canonical Fisher’s test and an FDR value <0.05 filter. ( D ) Bar graph representations of the top 20 identified GO BP terms (all the 107 identified GO BP terms in ) sorted based on the percentage of GO term gene values (number of detected genes in a particular GO term / number of all genes in a particular GO term × 100). The black asterisks in the sub-panel refer to the PB terms with functional relevance to cell adhesion and cytoskeleton remodeling. ( E ) Pathway-enrichment dot plot representations of the top 10 identified KEGG pathways sorted based on the P -value. All the identified KEGG pathways with the corresponding gene lists are described in . The count values refer to the number of genes that were detected in a particular KEGG pathway. The black asterisk in the wt sub-panel refers to the KEGG pathways with a <0.05 P adj -value.

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: Infection, RNA Sequencing, Comparison, Gene Expression, Functional Assay

Journal: Microbiology Spectrum

Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

doi: 10.1128/spectrum.02971-25

Figure Lengend Snippet: Epithelial cell-specific transcriptomic signature downregulated in the large intestine of S . Typhimurium-infected Parp14-deficient mice. ( A ) The Venn diagrams of the shared and unique genes in two comparisons, that is (i) genes upregulated by infection in wt mice (single-cell data ) vs genes downregulated by infection in Parp14-deficient mice (bulk tissue data), and (ii) genes downregulated by infection in wt mice (single-cell data ) vs genes upregulated by infection in Parp14-deficient mice (bulk tissue data). ( B ) The key single-cell RNA-Seq differential expression metrics of the shared genes. The numbers behind the gene names indicate the rank numbers, for example, ApoA1 was the third highest upregulated gene in goblet cells. ( C ) The key bulk tissue differential expression metrics of the shared genes. ( D ) TaqMan qPCR validation of the four shared genes with small and large intestine samples at day 1 and day 5. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

Techniques: Infection, Single Cell, RNA Sequencing, Quantitative Proteomics, Biomarker Discovery, Gene Expression, Two Tailed Test

Journal: Inflammation

Article Title: Identifying Crucial Genes Associated with Pyroptosis in Lupus Nephritis

doi: 10.1007/s10753-025-02402-5

Figure Lengend Snippet: Single-Cell Analysis of GBP2 and EIF2AK2 Expression Dynamics in Lupus Nephritis. a t-SNE plot of single-cell sequencing data from kidney tissues. b-c Expression patterns of GBP2 and EIF2AK2 in different cell types between the control group and LN patients. d-f Expression patterns of GBP2 in the different LN ISN/RPS pathological classifications, Interstitial Inflammation, and Tubular Interstitial Fibrosis. g-i Expression patterns of EIF2AK2 in the different LN ISN/RPS pathological classifications, Interstitial Inflammation, and Tubular Interstitial Fibrosis. j t-SNE plot of single-cell sequencing data from kidney immune cells. k-l Expression patterns of GBP2 and EIF2AK2 in different immune cell types between the control group and LN patients. CE0: Epithelial cells, CD0: Dividing cells, CM0: Inflammatory CD16 + macrophages, CM2: Tissue-resident macrophages, CM3: Conventional dendritic cells, CT5b: CD56bright CD16 − NK cells, CT0a: Effector memory CD4 + T cells, CT3b: TFH-like cells, CT0b: Central memory CD4 + T cells, CT5a: Resident memory CD8 + T cells, CB2a: Naïve B cells, CT4: GZMK + CD8 + T cells, CT1: CD56dim CD16 + NK cells, CT3a: Treg cells, CT2: Cytotoxic T Lymphocytes, CM1: Phagocytic CD16 + macrophages, CB0: Activated B cells, CB2b: Plasmacytoid dendritic cells, CM4: M2-like CD16 + macrophages, CB1: Plasma cells and plasmablasts, CT6: ISG-high CD4 + T cells, CB3: ISG-high B cells

Article Snippet: Additionally, the distribution and expression characteristics of these hub genes within the kidney were examined using single-cell RNA sequencing (scRNA-seq) data ( https://singlecell.broadinstitute.org/single_cell/study/SCP279/amp-phase-1 ).

Techniques: Single-cell Analysis, Expressing, Single Cell, Sequencing, Control, Clinical Proteomics

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