Journal: Cells

Article Title: scRNA-Seq of Cultured Human Amniotic Fluid from Fetuses with Spina Bifida Reveals the Origin and Heterogeneity of the Cellular Content

doi: 10.3390/cells12121577

Figure Lengend Snippet: Identification of fetal/maternal origin of the cell types detected in the cultured AF samples of this study. ( A ) Violin plot showing the expression of the male-specific gene RPS4Y1 per cluster in the merged analysis of the five AF samples included in the scRNA-seq analysis (two from male and three from female fetuses). The gene was expressed in all clusters, indicating that all detected cell types were of fetal origin (data from AF samples from four fetuses with spina bifida aperta (SBA) and one from a healthy fetus). ( B – F ) Violin plots showing the expression levels of RPS4Y1 per cluster in each of the five AF samples included in the scRNA-seq analysis. The gene was expressed in all clusters in the AF samples from the two male fetuses (m) but was absent in all clusters in the AF samples from the three female fetuses (f), indicating that all detected clusters (and therefore cell types) were of fetal origin. The cell clusters are shown in different colors and the legend indicates the assigned cell type.

Article Snippet: For single-cell transcriptomics analysis of cultured AF cells, the droplet-based 10X Genomics Chromium scRNA-seq platform was used [ ] at the Functional Genomics Center Zurich (University of Zurich, Zurich, Switzerland).

Techniques: Cell Culture, Expressing

Journal: Cells

Article Title: scRNA-Seq of Cultured Human Amniotic Fluid from Fetuses with Spina Bifida Reveals the Origin and Heterogeneity of the Cellular Content

doi: 10.3390/cells12121577

Figure Lengend Snippet: Neural-associated marker expression in one normal AF and in four AF from fetuses with neural tube defects. ( A ) Heatmap with the expression pattern of neural-cell-lineage markers and selected neuron- and neuroglia-associated markers in the 13 clusters and in each of the five AF samples that were subjected to scRNA-seq. The Z score indicates relative expression. ( B ) t-SNE plots of all single cells with the expression of representative neural-cell-lineage-specific marker genes. SOX2 and NES for neural progenitors, GAP43 for neurons and MAP2 for glial cells. Black dashed lines mark the neural clusters of interest. ( C ) Dot plot highlighting the expression of the neural-associated genes from ( A ) in each of the five AF samples included in the scRNA-seq analysis (one AF sample from a normal fetus and four from fetuses with SBA). ( D ) Weighted mean expression score of the neural-cell-lineage marker set from ( A ) in the one normal versus the four SBA samples. The score was computed by the package decoupleR .

Article Snippet: For single-cell transcriptomics analysis of cultured AF cells, the droplet-based 10X Genomics Chromium scRNA-seq platform was used [ ] at the Functional Genomics Center Zurich (University of Zurich, Zurich, Switzerland).

Techniques: Marker, Expressing

Journal: iScience

Article Title: Single-cell transcriptomics of neuroinflammation and cerebrovascular endothelial cells in the aged rat hippocampus

doi: 10.1016/j.isci.2025.113332

Figure Lengend Snippet: Single-cell transcriptome analysis of the microglia (A) UMAP shows the distribution of each subtype of microglia. (B) The sector graph shows the composition of cells in subclusters by groups. (C) Violin plot depicts the expression levels of known core signature genes for each microglia subcluster. (D) Representative immunofluorescence double staining images of NFKBIA (red), IBA1 (green), and nuclei were labeled with DAPI located in hippocampus in the NC and LPS groups. Scale bar = 75 μm or 25 μm. Quantitative analysis of the proportion of NFKBIA + cells in microglia (IBA1+) in hippocampal DG subregion. Data are shown as mean ± SEM, independent samples t-test, n = 4, ∗∗ p < 0.01. (E) Marker genes enriched KEGG pathway analyses in various microglia subpopulations. (F) GO analysis shows the top five signaling pathways across the four subpopulations, MG0, MG4, MG5 and MG7.

Article Snippet: Based on these results, we conducted single-cell transcriptome analysis (scRNA-seq) on aging rat hippocampus on day 3 after LPS or vehicle injection using the 10X Genomics platform to examine changes in the neuroinflammatory microenvironment ( H).

Techniques: Expressing, Immunofluorescence, Double Staining, Labeling, Marker, Protein-Protein interactions

Journal: iScience

Article Title: Single-cell transcriptomics of neuroinflammation and cerebrovascular endothelial cells in the aged rat hippocampus

doi: 10.1016/j.isci.2025.113332

Figure Lengend Snippet: Single-cell transcriptome analysis of the cerebral vascular endothelial cells (A–C) UMAP plot and bar plot showing the distribution of 6 subpopulations of cerebral vascular endothelial cells in the LPS and NC groups. (D) Violin plot shows the gene expression related to vascular origin (arterial, venous, and capillary), including arterial endothelial cell marker genes Fbln5, Bmx, Efnb2 , Vegfc . The venous endothelial cells highly expressed gene Nr2f and capillary endothelial cells highly expressed gene Rgcc and Slc16a1 . (E) Expression profiles of EC0 Marker genes including Mfge8, Lrg1, Lgals9, Cldn5, Ocln, Tjp1, Ddit4/Redd1, Mfsd2a are shown using the UMAP visualization approach. (F) Marker genes in the EC0 subpopulation are enriched with GO functional analysis. (G) Volcano plot depicts the DEGs at overall level of cerebral vascular endothelial cells between LPS and NC groups. DEGs (|log2(fold change)| > 1, p Value FDR <0.05, Difference = |pct.1- pct.2 | > 0.2) were colored (red for upregulated DEGs and blue for downregulated DEGs. (H and I) GO analysis shows the upregulated signaling pathway at overall level of cerebral vascular endothelial cells and EC0 subpopulation respectively.

Article Snippet: Based on these results, we conducted single-cell transcriptome analysis (scRNA-seq) on aging rat hippocampus on day 3 after LPS or vehicle injection using the 10X Genomics platform to examine changes in the neuroinflammatory microenvironment ( H).

Techniques: Gene Expression, Marker, Expressing, Functional Assay

Journal: Nature Communications

Article Title: Klf5 -adjacent super-enhancer functions as a 3D genome structure-dependent transcriptional driver to safeguard ESC identity

doi: 10.1038/s41467-025-60389-x

Figure Lengend Snippet: a Workflows for multi-omics analysis in ESCs and differentiated cells to identify SE patterns, including integrated epigenomics (H3K27ac), transcriptomics (scRNA sequencing) and three-dimensional genomics (Hi-C) analysis. b Analysis of SE regions based on H3K27ac ChIP-seq data. SEs were defined by signal levels above the inflection point of the curve. All H3K27ac ChIP-seq reads were aligned to the mouse genome assembly mm10 using Bowtie2, and ChIP-seq peaks were called by MACS with default parameters. The parameter “12.5 kb” was used as the maximum distance between the two regions to be sutured, and ROSE was used to distinguish SE from TSSs. H3K27ac ChIP-seq data: ESCs ( GSM6911328 , generated in this study), EBs ( GSM1816114 ), NPC ( GSM1603409 ), Mesoderm cells ( GSM1163099 ) and MEF ( GSM2912468 ). Publicly available ChIP-seq data have been listed in Supplementary Table . c Heatmap showing cell type-specific SE distribution. H3K27ac ChIP-seq signals in a ± 5 kb window for the same SE region in different cell types. d Gene ontology-biological process (GO-BP) analysis of cell-specific PSEAGs. Genes with the closest TSS to the SE (within a 50 kb window) were defined as predicted SE-associated genes (PSEAGs). GO-BP analyses were carried out using the online tool: DAVID , . e Gene regulatory network (GRN) analysis of SE-related TFs in ESCs. The red circle represents the master transcription factor (Seed), while the green four-sided diamonds are transcription factors (TFs) that interact with the Seed. GRN analyses were carried out using the online tool: NetworkAnalyst ( https://www.networkanalyst.ca/NetworkAnalyst/home.xhtml ) , . f UMAP embedding of single-cell RNA profiles (dots) of ESCs (green dashed line) and EBs (red dashed line). Color intensity reflects gene expression levels. g Hi-C heatmap ( GSE96107 ), H3K27ac ( GSM6911328 , generated in this study) and CTCF ( GSM699165 ) signals indicating co-localization of ESC SEs with adjacent master TFs ( Klf5 and Esrrb loci). The thick red line, located at the upper part of the H3K27ac peak, delineates the SE region, whereas the thick orange line, situated at the lower part of the Hi-C heatmap, delineates the TAD region. Hi-C data are analysed and presented using online tools: 3D Genome Browser ( https://3dgenome.fsm.northwestern.edu/ ) . Publicly available ChIP-seq data have been listed in Supplementary Table . h Proposed SE function model: SE may regulate ESC properties by promoting the activity of adjacent TFs within the same TAD. Taking Pou5f1 , Sox2 , Esrrb and Klf5 loci as examples in the illustration. Source data are provided as a Source Data file.

Article Snippet: Fig. 1 Identification of a class of SEs that coordinate cell fate may be mediated by adjacent TFs. a Workflows for multi-omics analysis in ESCs and differentiated cells to identify SE patterns, including integrated epigenomics (H3K27ac), transcriptomics (scRNA sequencing) and three-dimensional genomics (Hi-C) analysis. b Analysis of SE regions based on H3K27ac ChIP-seq data.

Techniques: Biomarker Discovery, Sequencing, Hi-C, ChIP-sequencing, Generated, Gene Expression, Activity Assay