rna seq datasets  (Human Brain Project)

Journal: Life Medicine

Article Title: Single-cell analysis reveals essential lncRNAs regulating human trophoblast lineage differentiation

doi: 10.1093/lifemedi/lnag010

Figure Lengend Snippet: Transcriptomic landscape of lncRNAs in human placental trophoblasts. (A) UMAP plot showing first-trimester and term placental single-nucleus transcriptomes (CTB, cytotrophoblast; EVT, extravillous trophoblast; STB, syncytiotrophoblast; STR, stromal cell; e, early; l, late). (B) UMAP plot showing the distribution of first-trimester and term placental samples. (C) Pie plot of the fraction of lncRNAs and mRNAs in snRNA-seq and bulk RNA-seq, respectively. The value “ n ” indicates the average number of detected transcripts per cell for both lncRNAs and mRNAs based on the experimentally measured data. (D) Box plots of transcript counts in per cell type. Statistical significance was determined by two-sided Wilcoxon rank-sum tests (**** P < 0.0001). (E) Volcano plot of cell-type-specific lncRNAs across trophoblast subtypes. The x -axis represents the difference in the percentage of cells expressing each gene between the two compared cell types (Δ percentage = pct.1 − pct.2), while the y -axis indicates the log 2 (fold change) in average gene expression. This representation integrates both expression magnitude and expression prevalence at the single-cell level. (F) Heatmap of cell-type-specific lncRNA expression atlas across trophoblast populations. (G) UMAP plots showing the expression distribution of representative lncRNAs (Color gradient represents the average expression level per cell). (H) GO biological process terms enriched in protein-coding genes (PCGs) associated with stage-specific lncRNAs.

Article Snippet: For cross-platform validation: Three independent external datasets were utilized, including two single-cell RNA-seq datasets (accession numbers: HRA003309, GSE214607 ) and one single-nucleus RNA-seq dataset (singlecell.broadinstitute.org/single_cell/study/SCP2601).

Techniques: RNA Sequencing, Expressing, Gene Expression, Single Cell

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a Chow-Ruskey diagram of Non-CORE Active Enhancer (non-CORE)-associated genes and Clustered Open Regulatory Element (CORE)-associated genes in PBMC. B cells (red border), Monocytes (purple border), T cells (green border), NK cells (brown border). Monocytes represent the union of CORE-associated genes derived from CD14+ Monocytes and CD16+ Monocytes, while T cells represent the combined set of CORE-associated genes derived from CD4 Memory T cells and CD8 Effector T cells. The color of the borders around each intersection corresponds to the cell types whose genes overlap. The area of each intersection is proportional to the number of genes within the intersection. b Proportion of CORE constituents that overlap with H3K4me1 peaks, relative to the total number of enhancer candidates in each cell type. NK: NK cells, CD14_Mono: CD14+ Monocytes, B: B cells, CD8_Teff: CD8+ Effector T cells, CD4_Tmem: CD4+ Memory T cells. c Heatmap for cell type-specific master regulators. Black: gene present in the CORE-associated genes of the given cell type. White: absent. d Log2-transformed fold changes of normalized TPM of genes in each cell type. Normalized TPM values were retrieved from the PBMC Monaco dataset in the Human Protein Atlas (HPA). Left: comparison between typical enhancer (TE)-associated genes and super-enhancer (SE)-associated genes. Right: comparison between non-CORE-associated genes and CORE-associated genes. P-value was calculated by a two-sided Wilcoxon signed-rank test. e Representative RNA expression for GATA3 , EBF1 , and MAFB . Normalized TPM values were retrieved from the HPA PBMC dataset. CD4 T Mem: CD4+ Memory T cells, CD8 T Mem: CD8+ Memory T cells, NK: NK cells, B mem: Memory B cells, B nai: naïve B cells, cDC: classical Dendritic cells, CD14 Mono: CD14+ Monocytes, CD16 Mono: CD16+ Monocytes. f Enrichment analysis of known TF motifs within CORE and non-CORE constituents across cell types. Colored by enrichment score, modified MinMax-scaled statistical significance (-log10(P-value)).

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: Derivative Assay, Transformation Assay, Comparison, RNA Expression, Modification

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a Chow-Ruskey diagram of Non-CORE Active Enhancer (non-CORE)-associated genes and Clustered Open Regulatory Element (CORE)-associated genes in PBMC. B cells (red border), Monocytes (purple border), T cells (green border), NK cells (brown border). Monocytes represent the union of CORE-associated genes derived from CD14+ Monocytes and CD16+ Monocytes, while T cells represent the combined set of CORE-associated genes derived from CD4 Memory T cells and CD8 Effector T cells. The color of the borders around each intersection corresponds to the cell types whose genes overlap. The area of each intersection is proportional to the number of genes within the intersection. b Chow-Ruskey diagram of typical enhancer (TE)-associated genes and super-enhancer (SE)-associated genes. B cells (red border), Monocytes (purple border), T cells (green border), NK cells (brown border). Monocytes represent the union of SE-associated genes derived from CD14+ Monocytes and CD16+ Monocytes, while T cells represent the combined set of SE-associated genes derived from CD4+ T cells and CD8+ T cells. The color of the borders around each intersection corresponds to the cell types whose genes overlap. The area of each intersection is proportional to the number of genes within the intersection. c Log2-transformed fold changes of normalized TPM of genes in each cell type. Normalized TPM values were retrieved from the PBMC Monaco dataset in the Human Protein Atlas (HPA). Comparison between non-CORE-associated genes and CORE-associated genes using the potential option. P-value was calculated by a two-sided Wilcoxon signed-rank test. d Dot plot showing the classification performance of CORE in distinguishing SE and TE. Dots are colored by Area Under Curve (AUC), and the dot sizes indicate the specificity. SE derived from B: B cells, CD14: CD14+ Monocytes, CD16: CD16+ Monocytes, CD4: CD4+ T cells, CD4M: CD4+ Memory T cells, CD8: CD8+ T cells, NK: NK cells. e Representative ROC curves from CD16+ Monocytes. Left: CORE from the potential option, Right: CORE from the active option. The AUC values are indicated in the lower right corner of each plot.

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: Derivative Assay, Transformation Assay, Comparison

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a UMAP embedding of the PBMC scCUT&Tag-seq dataset, colored and annotated by clusters. b UMAP embedding of the PBMC scCUT&Tag-seq dataset, colored and annotated by cell types. CD14_Mono: CD14+ Monocytes, CD4_Tmem: CD4+ Memory T cells, CD8_Teff: CD8+ Effector T cells, B: B cells, NK: NK cells. c Chow-Ruskey diagram of typical enhancer (TE)-associated genes and super-enhancer (SE)-associated genes. B cells (red border), Monocytes (purple border), T cells (green border), NK cells (brown border). Monocytes represent the union of SE-associated genes derived from CD14+ Monocytes, while T cells represent the combined set of SE-associated genes derived from CD4+ Memory T cells and CD8+ Effector T cells. The color of the borders around each intersection corresponds to the cell types whose genes overlap. The area of each intersection is proportional to the number of genes within the intersection. d Heatmap for cell type-specific master regulators. Black: gene present in the SE-associated genes of the given cell type. White: absent. e Log2-transformed fold changes of normalized TPM of genes in each cell type. Normalized TPM values were retrieved from the PBMC Monaco dataset in the Human Protein Atlas (HPA). Comparison between TE-associated genes and SE-associated genes. P-value was calculated by a two-sided Wilcoxon signed-rank test.

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: Derivative Assay, Transformation Assay, Comparison

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a NMF analysis of H3K27ac ChIP-seq signals within CORE to distinguish between CRC and Normal samples. Dots are colored by disease state: CRC (red color), Normal (blue color). b Log2-transformed fold changes of H3K27ac ChIP-seq signals between CRC and Normal within CORE, SE, and TE. Dots and borderlines are colored by region categories: CORE (red color), SE (blue color), TE (grey color). c Enrichment analysis of de novo TF motifs in CORE and non-CORE constituents. Left: TF motif enrichment analysis using non-CORE constituents, Right: TF motif enrichment analysis using CORE constituents. The FOXM1 gene is highlighted in red. d TCGA survival analysis for MACC1 and EGFR genes. P-values were calculated using the Log-rank test. Lines are colored by expression level status: High gene expression (red color), Low gene expression (blue color). Survival curves for the MACC1 and EGFR genes were obtained from RNA-seq and RPPA data, respectively. e Track visualization result for the USP7 gene with scATAC-seq signals normalized by Reads in TSS. Colored and annotated by disease state: CRC (Colorectal cancer epithelial cells; red color), Normal (Normal epithelial cells; blue color).

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: ChIP-sequencing, Transformation Assay, Expressing, Gene Expression, RNA Sequencing

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: TCGA survival analysis for CORE-associated genes. a ANO1 , b ARL4C , c FOXD1 , d PRDM15 , e HOXC10 , f ISM1 , g BRD2 , h KDM4B . P-values were calculated using the Log-rank test. Lines are colored by expression level status: High gene expression (red color), Low gene expression (blue color). Survival curves were obtained from RNA-seq data.

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: Expressing, Gene Expression, RNA Sequencing

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a Proportion of CORE- and SE-associated genes that overlap with metastasis-related genes, relative to the total number of metastasis-related genes. Target (Bulk): differentially expressed genes between metastatic tumor and primary CRC in bulk RNA-seq data overlapping with target genes in TF regulons from scRNA-seq data, TF (scRNA): differential TFs between metastatic tumor and primary CRC based on TF regulon activity in scRNA-seq data. b Box plots showing weighted degree and weighted transitivity (weighted local clustering coefficients) in CORE only, SE only, and Other peaks. CORE only: H3K4me1 peaks located exclusively within CORE, SE only: H3K4me1 peaks located exclusively within SE, Other peaks: H3K4me1 peaks that do not fall into either CORE or SE. Correlation coefficients are used as edge weights in the co-accessibility network. c Log2-transformed fold changes of mean weighted degree and mean clustering coefficient between CORE and SE. DEGR: Weighted degree (W.D), LCLC: Weighted transitivity (W.T). d Fold changes of the number of metastasis-related genes that overlap with the annotated genes from peaks with a high weighted local clustering coefficient, relative to the number of metastasis-related genes that overlap with the annotated genes from peaks with a high weighted degree. Exp: Expected fold changes; the number of annotated genes from peaks with a high weighted clustering coefficient divided by the number of annotated genes from peaks with a high weighted degree, Obs: Observed fold changes. Target (Bulk): differentially expressed genes between metastatic tumor and primary CRC in bulk RNA-seq data overlapping with target genes in TF regulons from scRNA-seq data, TF (scRNA): differential TFs between metastatic tumor and primary CRC based on TF regulon activity in scRNA-seq data.

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: RNA Sequencing, Activity Assay, Transformation Assay

Journal: bioRxiv

Article Title: Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

doi: 10.64898/2026.03.17.712366

Figure Lengend Snippet: a Track visualization result for the USP7 gene with H3K27ac HiChIP interactions from HT29 cells and normalized H3K27ac ChIP-seq signals in CRC and Normal samples. Colored and annotated by disease state: CRC (Colorectal cancer epithelial cells; red color), Normal (Normal epithelial cells; blue color). b Track visualization of RNA-seq from Left Colon ontology for in silico enhancer knockout within CORE near USP7 gene. c The magnitude of the predicted effect in RNA-seq reads from Left Colon ontology for in silico enhancer knockout within CORE near USP7 gene.

Article Snippet: To investigate cell type-specific transcriptional regulation of CORE-associated and SE-associated genes in PBMC, we utilized the Monaco PBMC RNA-seq dataset and the HPA PBMC RNA-seq dataset from the Human Protein Atlas (HPA; https://www.proteinatlas.org/ ) [ ].

Techniques: HiChIP, ChIP-sequencing, RNA Sequencing, In Silico, Knock-Out