cell ranger single cell software suite (10X Genomics)
86
Structured Review
10X Genomics
cell ranger single cell software suite
Cell Ranger Single Cell Software Suite, 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/cell+ranger+single-cell+software/cellranger/pmc13168688-315-6-12
Average 86 stars, based on 1 article reviews
Cell Ranger Single Cell Software Suite, 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/cell+ranger+single-cell+software/cellranger/pmc13168688-315-6-12
Average 86 stars, based on 1 article reviews
cell ranger single cell software suite - by Bioz Stars,
2026-09
86/100 stars
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1) Product Images from "Whole-protein screening and multi-modal profiling of antigen-specific CD4 + T cells at single-cell resolution"
Article Title: Whole-protein screening and multi-modal profiling of antigen-specific CD4 + T cells at single-cell resolution
Journal: Nature Communications
doi: 10.1038/s41467-026-72396-7
Figure Legend Snippet: a Design of an SCT library with overlapping 15-mer peptides spanning the entire RBD of the SARS-CoV-2 spike protein. b . Schematic overview for the experimental workflow of high-throughput screening. 50 PBMC samples from 22 HLA-DR1+ participants were barcoded by timepoint, pooled, enriched for CD4 + T cells, stained with the 64-element SCT-dextramer pool, FACS-sorted, and sequenced. Single-cell analysis included RNA expression profiling, TCRαβ sequencing, antigen-MHC pairing, and timepoint identification. Comparison of genetic variants between transcriptome and whole-genome sequencing (WGS) was used to demultiplex patient identity (Methods). Created in BioRender . c Correlation between SCT expression levels, quantity of antigen-specific cells captured, and predictions from class II antigen-presentation algorithms. SCT expression quantified prior to purification ( n = 2–4 biological replicates). Data were shown as mean ± SEM. %Rank_EL, percentile rank of eluted ligands. d Clonal and patient diversity in CD4 + T cell responses to each SARS-CoV-2 antigen. Antigens are color-coded based on their parent protein. e Tetramer binding validation of SARS-CoV-2-specific CD4 TCRs. A representative subset of SARS-CoV-2 TCRs ( n = 10) from Fig. 2d were selected and validated through SCT tetramer binding assays with biological triplicates. A DR1-restricted influenza A M1 17–31 SCT was used as the negative control. f Peptide-pulsed activation of SARS-CoV-2 CD4 TCRs in NFAT-GFP Jurkats ( n = 3). e , f P < 0.0001 for each group relative to the negative control peptide, determined by one-tailed independent t -test assuming equal variances. Exact P values are provided in the Source Data. All replicates are biological replicates. All bar plots ( n = 3) are presented as mean values ± SD. Source data are provided as a Source Data file.
Techniques Used: High Throughput Screening Assay, Staining, Single-cell Analysis, RNA Expression, Sequencing, Comparison, Expressing, Immunopeptidomics, Purification, Binding Assay, Biomarker Discovery, Negative Control, Activation Assay, One-tailed Test
Figure Legend Snippet: a Graphical description of the high-dimensional data extracted from each single antigen-specific CD4 + T cell. Created in BioRender . b UMAP projection of single-cell transcriptomic profiles from SARS-CoV-2-specific CD4 + T cells. c Longitudinal trajectories of CD4 + T cell phenotypes stratified by antigen clusters. Antigens are grouped based on early response patterns at timepoint AC, and the corresponding CD4 + T cell phenotypes are tracked over time to reveal the immunological dynamics for each cluster. d Immunogenicity ranking of DRB1*01:01-restricted SARS-CoV-2 antigens. Each property contributing to immunogenicity was scored on a log 2 scale (0 to 5) and summed to generate a composite score. e Correlation of donor immunogenicity score with antibody level. Scatter plots fitted with linear regression lines and 95% confidence intervals (gray shaded areas) showing the correlation of donor immunogenicity score with RBD antibody titers. Statistical significance was determined using a two-sided Pearson correlation; correlation coefficients ( R ) and P values are shown. f Phenotypic evolution of CD4 + T cells induced by antigens of low, medium, and high immunogenicity. Source data are provided as a Source Data file.
Techniques Used: Single Cell, Immunopeptidomics
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