Review



cell ranger single cell software suite  (10X Genomics)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 86
      Buy from Supplier

    Structured Review

    10X Genomics cell ranger single cell software suite
    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 <t>sequenced.</t> <t>Single-cell</t> 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.
    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+suite/pmc13168688-315-6-12?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    cell ranger single cell software suite - by Bioz Stars, 2026-07
    86/100 stars

    Images

    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

    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.
    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

    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.
    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



    Similar Products

    cell ranger single cell software suite  (10X Genomics)
    86
    10X Genomics cell ranger single cell software suite
    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 <t>sequenced.</t> <t>Single-cell</t> 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.
    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+suite/pmc13168688-315-6-12?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    cell ranger single cell software suite - by Bioz Stars, 2026-07
    86/100 stars
    		  Buy from Supplier
    cell ranger single cell software suite 6 1 0  (10X Genomics)
    86
    10X Genomics cell ranger single cell software suite 6 1 0
    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 <t>sequenced.</t> <t>Single-cell</t> 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.
    Cell Ranger Single Cell Software Suite 6 1 0, 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+suite/pmc12018128-85-10-17?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    cell ranger single cell software suite 6 1 0 - by Bioz Stars, 2026-07
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    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.

    Journal: Nature Communications

    Article Title: Whole-protein screening and multi-modal profiling of antigen-specific CD4 + T cells at single-cell resolution

    doi: 10.1038/s41467-026-72396-7

    Figure Lengend 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.

    Article Snippet: Raw data were processed using the Cell Ranger Single-Cell Software Suite (v3.1.0, 10X Genomics) with GRCh38 as the reference genome.

    Techniques: 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

    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.

    Journal: Nature Communications

    Article Title: Whole-protein screening and multi-modal profiling of antigen-specific CD4 + T cells at single-cell resolution

    doi: 10.1038/s41467-026-72396-7

    Figure Lengend 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.

    Article Snippet: Raw data were processed using the Cell Ranger Single-Cell Software Suite (v3.1.0, 10X Genomics) with GRCh38 as the reference genome.

    Techniques: Single Cell, Immunopeptidomics