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singlecell rna seq data  (10X Genomics)

 
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    10X Genomics singlecell rna seq data
    Singlecell Rna Seq Data, 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/singlecell+rna-seq/rna+sequencing+singlecell/pm41271691-408-26-29
    Average 86 stars, based on 1 article reviews
    singlecell rna seq data - by Bioz Stars, 2026-11
    86/100 stars

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    Related Articles

    RNA Sequencing:

    Article Title: Remodeling of the immune microenvironment is linked to adverse outcome in pediatric T cell acute lymphoblastic leukemia.
    Article Snippet: .. Publicly available RNA-seq datasets from healthy donors To compare CD45+ immune cells from adult healthy donors (our data) and pediatric healthy donors, we processed publicly available singlecell RNA-seq data (10x Genomics). ..

    Article Title: Protocol for characterizing craniopharyngioma subtypes and their microenvironments using single-cell RNA sequencing and immunohistochemistry.
    Article Snippet: Kono et al., STAR Protocols 6, 103760 June 20, 2025 a 2025 The Author(s).. Published by Here, we present a comprehensive protocol for analyzing craniopharyngioma subtypes and their tumor microenvironments at single-cell resolution.. We describe tumor tissue dissociation, singlecell isolation, RNA sequencing library preparation, and bioinformatics analysis procedures.

    Article Title: In-depth comparison of human serous cavity resident macrophages and dendritic cells to their murine counterparts
    Article Snippet: .. We sorted CD45+ peritoneal cells and carried out singlecell RNA sequencing (scRNA-seq) using the 10X Genomics platform. ..

    Article Title: Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs
    Article Snippet: .. Among the most widely adopted of these are singlecell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), commercialised by 10x Genomics ( ). ..

    Article Title: Human Macrophage Long Intergenic Noncoding RNA, <i>SIMALR</i> , Suppresses Inflammatory Macrophage Apoptosis via NTN1 (Netrin-1)
    Article Snippet: .. When we probed the public 10X Genomics Chromium 3ʹ singlecell RNA-seq data from human plaque immune cells,12 we were unable to detect SIMALR, likely due to this technology’s low sequencing depth. .. Likewise, when we analyzed our own 10X Genomics Chromium 3ʹ single-cell RNA-seq data from 3 human atherosclerotic plaques,20 we were unable to detect SIMLAR.

    Article Title: ASH1L in Hepatoma Cells and Hepatic Stellate Cells Promotes Fibrosis-Associated Hepatocellular Carcinoma by Modulating Tumor-Associated Macrophages.
    Article Snippet: Single-Cell Data Analysis: Mouse liver tissue digestion and single-cell sequencing were performed by Novogene (Beijing, China). .. The raw singlecell RNA-seq data were processed using the 10X Genomics Cell Ranger toolkit (version 4.1.0), which includes reading alignment from FASTQ files to the human reference genome (GRCh38, v3.0.0, obtained from 10X Genomics) and generation of a gene-cell unique molecular identifier (UMI) matrix using the “cell ranger count” function. ..

    Article Title: Cutaneous dysbiosis characterizes the post-allogeneic hematopoietic stem cell transplantation period.
    Article Snippet: .. After quality check, cells were immediately used for cDNA amplification and chromium library construction using singlecell RNA-seq library kit v2 according to the manufacturers’ recommendations (10X Genomics, CA) by the Molecular Genomics Core facility at the Duke Molecular Physiology Institute. cDNA libraries were sequenced on the Illumina HiSeq 4,000 with read length of 150 bp by the Duke Center for Genomics and Computational Biology core facility. ..

    other:

    Article Title: Chemically-induced osteogenic cells for bone tissue engineering and disease modeling.
    Article Snippet: Cell reprogramming can satisfy the demands of obtaining specific cell types for applications such as tissue regeneration and disease modeling.. Here we report the reprogramming of human fibroblasts to produce chemically-induced osteogenic cells (ciOG), and explore the potential uses of ciOG in bone repair and disease treatment.. A chemical cocktail of RepSox, forskolin, and phenamil was used for osteogenic induction of fibroblasts by activation of RUNX2 expression.

    Sequencing:

    Article Title: Human Macrophage Long Intergenic Noncoding RNA, <i>SIMALR</i> , Suppresses Inflammatory Macrophage Apoptosis via NTN1 (Netrin-1)
    Article Snippet: .. When we probed the public 10X Genomics Chromium 3ʹ singlecell RNA-seq data from human plaque immune cells,12 we were unable to detect SIMALR, likely due to this technology’s low sequencing depth. .. Likewise, when we analyzed our own 10X Genomics Chromium 3ʹ single-cell RNA-seq data from 3 human atherosclerotic plaques,20 we were unable to detect SIMLAR.

    Amplification:

    Article Title: Cutaneous dysbiosis characterizes the post-allogeneic hematopoietic stem cell transplantation period.
    Article Snippet: .. After quality check, cells were immediately used for cDNA amplification and chromium library construction using singlecell RNA-seq library kit v2 according to the manufacturers’ recommendations (10X Genomics, CA) by the Molecular Genomics Core facility at the Duke Molecular Physiology Institute. cDNA libraries were sequenced on the Illumina HiSeq 4,000 with read length of 150 bp by the Duke Center for Genomics and Computational Biology core facility. ..



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    Image Search Results


    A–C) Split violin plots display the distribution of key sequencing and cell quality metrics, with red representing snRNA-seq and blue representing scRNA-seq. Each pair of samples is connected by a line. Samples are coloured by the laboratory to indicate batch origin (A, B, C, F, H, I, L). Median number of genes detected per cell for each sample (A), Library size per sample after removing MT genes (B) and Sequencing saturation (C). D–E) Scatter plots showing an example of the correlation between library size and number of genes per cell in scRNA-seq (D) and snRNA-seq (E), coloured by cell type. Scatter plots for all samples are shown in Figure S7. F) Split violin plots showing proportion of reads mapped to intronic regions per sample. G) Distribution of total UMI counts per gene length bin across 50 gene length bins across all 22 samples. H–I) Split violin plots comparing the percentage of MT gene content (H) and ambient RNA (I). J) Correlation between the percentage of detected doublets and the total number of cells recovered per sample. K) Boxplots showing Jaccard scores representing similarity of HVGs identified within each method, based on the variance-stabilising transformation method. L) Comparison of the average number of clusters detected in three comparable resolutions in each sample.

    Journal: bioRxiv

    Article Title: Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

    doi: 10.1101/2025.09.08.675012

    Figure Lengend Snippet: A–C) Split violin plots display the distribution of key sequencing and cell quality metrics, with red representing snRNA-seq and blue representing scRNA-seq. Each pair of samples is connected by a line. Samples are coloured by the laboratory to indicate batch origin (A, B, C, F, H, I, L). Median number of genes detected per cell for each sample (A), Library size per sample after removing MT genes (B) and Sequencing saturation (C). D–E) Scatter plots showing an example of the correlation between library size and number of genes per cell in scRNA-seq (D) and snRNA-seq (E), coloured by cell type. Scatter plots for all samples are shown in Figure S7. F) Split violin plots showing proportion of reads mapped to intronic regions per sample. G) Distribution of total UMI counts per gene length bin across 50 gene length bins across all 22 samples. H–I) Split violin plots comparing the percentage of MT gene content (H) and ambient RNA (I). J) Correlation between the percentage of detected doublets and the total number of cells recovered per sample. K) Boxplots showing Jaccard scores representing similarity of HVGs identified within each method, based on the variance-stabilising transformation method. L) Comparison of the average number of clusters detected in three comparable resolutions in each sample.

    Article Snippet: Among the most widely adopted of these are singlecell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), commercialised by 10x Genomics ( ).

    Techniques: Sequencing, Transformation Assay, Comparison