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single cell rna sequencing scrna seq dataset gse267718  (10X Genomics)

 
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    10X Genomics single cell rna sequencing scrna seq dataset gse267718
    Single Cell Rna Sequencing Scrna Seq Dataset Gse267718, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/single+cell+sequencing+datasets/pm42276585-115-18-29?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    single cell rna sequencing scrna seq dataset gse267718 - by Bioz Stars, 2026-06
    86/100 stars

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    a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the <t>human</t> <t>single-cell</t> <t>RNA</t> <t>sequencing</t> available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.
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    Image Search Results


    Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

    Journal: Frontiers in Immunology

    Article Title: Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

    doi: 10.3389/fimmu.2026.1705706

    Figure Lengend Snippet: Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

    Article Snippet: Single-cell RNA sequencing (scRNA-seq) datasets were obtained from GSE145086 and GSE233084 , both generated using the 10X Genomics platform ( , ).

    Techniques: Single Cell, Control, RNA Sequencing, Cell Cycle Assay, Expressing

    a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the human single-cell RNA sequencing available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.

    Journal: bioRxiv

    Article Title: Rare variants alter mitochondrial lipid homeostasis and neuronal excitability in PD patient-derived dopaminergic neurons

    doi: 10.64898/2026.04.10.717646

    Figure Lengend Snippet: a) Volcano plot showed the distribution of the most significant altered proteins comparing PD-derived mesDA neurons vs HS. Proteins showing statistically significant differences in expression are located in the top right (upregulated) and top left (downregulated) quadrants. The black line represents the p-value threshold set to p < 0.01. b) Heat map representation of the most 50 differentially expressed proteins highlighting the clusters of the two groups of analysis when comparing PD-derived mesDA to HS . c) Graphical representation of DEPs detected in PD-derived mesDA, up– (in red) and down– (in blue) regulated proteins that were also deregulated in SOX6+/AGTR1+ and in DA nuclei. Data from SOX6+/AGTR1+ and DA nuclei were available from the human single-cell RNA sequencing available dataset ( https://singlecell.broadinstitute.org/single_cell/app/genes ). d) Graphical representation of the most 20 enriched pathways from IPA analysis. e-g ) Heat map representation of the most dysregulated pathways within the PD-derived mesDA when compared to the HS.

    Article Snippet: However, considering the specific loss of the dopaminergic neurons of the Substantia Nigra pars compacta in PD patients, we tested the expression of the genes encoding for the 62 down-regulated proteins in the SNpc cells, by analyzing the human single-cell RNA sequencing available dataset [ ] ( https://singlecell.broadinstitute.org/single_cell/app/genes ).

    Techniques: Derivative Assay, Expressing, Single Cell, RNA Sequencing