Cell ranger

Manufactured by 10x Genomics

Sourced in United States

Cell Ranger is a software suite that enables the analysis of single-cell transcriptomics data. It provides tools for sample demultiplexing, barcode processing, gene counting, and data aggregation.

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Lab products found in correlation

Novaseq 6000, by Illumina (16 mentions) Hiseq 4000, by Illumina (15 mentions) Chromium controller, by 10x Genomics (9 mentions) Nextseq, by Illumina (6 mentions) Gel bead multiplex kit, by 10x Genomics (6 mentions) Hiseq x, by Illumina (4 mentions) Chromium single cell 5 library, by 10x Genomics (4 mentions) Hiseq 2500, by Illumina (4 mentions) Loupe cell browser, by 10x Genomics (4 mentions) Loupe browser, by 10x Genomics (4 mentions) 2100 bioanalyzer, by Agilent Technologies (4 mentions) Seurat, by 10x Genomics (3 mentions) Bcl2fastq2, by Illumina (3 mentions) Loupe vdj browser, by 10x Genomics (3 mentions) Antibody hashtag 1, by BioLegend (3 mentions) Novaseq, by Illumina (3 mentions) Novaseq 6000, by 10x Genomics (3 mentions) Cell ranger pipeline, by 10x Genomics (3 mentions) Fastqc, by Babraham Bioinformatics (3 mentions) Chromium single cell 3 reagent kits v3, by 10x Genomics (2 mentions)

308 protocols using cell ranger

Comprehensive Single-Cell Transcriptome Analysis

Cited 2 times

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Raw base call (BCL) files were demultiplexed to generate Fastq files using the Cell Ranger mkfastq pipeline within Cell Ranger (10x Genomics). Whole-transcriptome Fastq files were processed using the standard Cell Ranger pipeline (10x genomics) within Cell Ranger 2.1.1 or Cell Ranger 3.0.2. In brief, Cell Ranger count performs read alignment, filtering, barcode and unique molecular identifier (UMI) counting, and determination of putative cells. The final output of Cell Ranger (the molecule per cell count matrix) was then analysed in R using the package Seurat^{60 (link),61 (link)} (3.0) as described below. For targeted transcriptomics data, Fastq files were processed via the standard Rhapsody analysis pipeline (BD Biosciences) on Seven Bridges (www.sevenbridges.com). In brief, after read filtering, reads are aligned to a reference genome and annotated, barcodes and UMIs are counted, followed by determining putative cells. The final output (molecule per cell count matrix) was also analysed in R using Seurat^{60 (link),61 (link)} (version 3.0) as described below. For 5′ VDJ sequencing experiments, the output after Cell Ranger vdj was analysed using the Loupe VDJ browser v3 (10x Genomics). For the SMART-Seq v4 experiments, Fastq files were aligned to the GRCh38 reference genome as described in more detail above.

Mair F., Erickson J.R., Frutoso M., Konecny A.J., Greene E., Voillet V., Maurice N.J., Rongvaux A., Dixon D., Barber B., Gottardo R, & Prlic M. (2022). Extricating human tumour immune alterations from tissue inflammation. Nature, 605(7911), 728-735.

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Antigen-Specific BCR Repertoire Analysis

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We used a modified version of our previously described pipeline to use paired-end FASTQ files of oligo libraries as input, process and annotate reads for cell barcode, UMI, and antigen barcode, and generate a cell barcode - antigen barcode UMI count matrix. BCR contigs were processed using Cell Ranger (10X Genomics) using GRCh38 as reference. Antigen barcode libraries were also processed using Cell Ranger (10X Genomics). The overlapping cell barcodes between the two libraries were used as the basis of the subsequent analysis. We removed cell barcodes that had only non-functional heavy chain sequences as well as cells with multiple functional heavy chain sequences and/or multiple functional light chain sequences, reasoning that these may be multiplets. Additionally, we aligned the BCR contigs (filtered_contigs.fasta file output by Cell Ranger, 10X Genomics) to IMGT reference genes using HighV-Quest38.^{67 (link)} The output of HighV-Quest was parsed using ChangeO and merged with an antigen barcode UMI count matrix.^{68 (link)} Finally, we determined the LIBRA-seq score for each antigen in the library for every cell as previously described.^{26 (link)}

Pilewski K.A., Wall S., Richardson S.I., Manamela N.P., Clark K., Hermanus T., Binshtein E., Venkat R., Sautto G.A., Kramer K.J., Shiakolas A.R., Setliff I., Salas J., Mapengo R.E., Suryadevara N., Brannon J.R., Beebout C.J., Parks R., Raju N., Frumento N., Walker L.M., Fechter E.F., Qin J.S., Murji A.A., Janowska K., Thakur B., Lindenberger J., May A.J., Huang X., Sammour S., Acharya P., Carnahan R.H., Ross T.M., Haynes B.F., Hadjifrangiskou M., Crowe JE J.r., Bailey J.R., Kalams S., Morris L, & Georgiev I.S. (2023). Functional HIV-1/HCV cross-reactive antibodies isolated from a chronically co-infected donor. Cell reports, 42(2), 112044.

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Integrated B Cell Receptor and Antigen Profiling

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Our established pipeline was followed, which takes paired-end FASTQ files of oligonucleotide libraries as input, to process and annotate reads for cell barcodes, unique molecular identifiers (UMIs) and antigen barcodes, resulting in a cell barcode-antigen barcode UMI count matrix (Setliff, Shiakolas et al. 2019 (link)). B cell receptor contigs were processed using CellRanger (10x Genomics) and GRCh38 as reference, while the antigen barcode libraries were also processed using CellRanger (10x Genomics). The cell barcodes that overlapped between the two libraries formed the basis of the subsequent analysis. Cell barcodes that had only non-functional heavy chain sequences as well as cells with multiple functional heavy chain sequences and/or multiple functional light chain sequences, were eliminated, reasoning that these may be multiplets. We also aligned the B cell receptor contigs (filtered_contigs.fasta file output by CellRanger, 10x Genomics) to IMGT reference genes using HighV-Quest (Alamyar, Duroux et al. 2012 (link)). The output of HighV-Quest was parsed using ChangeO (Gupta, Vander Heiden et al. 2015 (link)), and combined with an antigen barcode UMI count matrix. Finally, we determined the LIBRA-seq score for each antigen in the library for every cell as previously described (Setliff, Shiakolas et al. 2019 (link)).

Johnson N.V., Wall S.C., Kramer K.J., Holt C.M., Periasamy S., Richardson S., Suryadevara N., Andreano E., Paciello I., Pierleoni G., Piccini G., Huang Y., Ge P., Allen J.D., Uno N., Shiakolas A.R., Pilewski K.A., Nargi R.S., Sutton R.E., Abu-Shmais A.A., Parks R., Haynes B.F., Carnahan R.H., Crowe JE J.r., Montomoli E., Rappuoli R., Bukreyev A., Ross T.M., Sautto G.A., McLellan J.S, & Georgiev I.S. (2024). Discovery and Characterization of a Pan-betacoronavirus S2-binding antibody. bioRxiv.

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Paired-end FASTQ processing for BCR-antigen mapping

Cited 1 time

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We utilized and modified our previously described pipeline to use paired-end FASTQ files of oligo libraries as input, process and annotate reads for cell barcode, unique molecular identifier (UMI), and antigen barcode, and generate a cell barcode - antigen barcode UMI count matrix (Setliff et al., 2019 (link)). BCR contigs were processed using Cell Ranger (10X Genomics) using GRCh38 as reference. Antigen barcode libraries were also processed using Cell Ranger (10X Genomics). The overlapping cell barcodes between the two libraries were used as the basis of the subsequent analysis. We removed cell barcodes that had only non-functional heavy chain sequences as well as cells with multiple functional heavy chain sequences and/or multiple functional light chain sequences, reasoning that these may be multiplets. Additionally, we aligned the BCR contigs (filtered_contigs.fasta file output by Cell Ranger, 10X Genomics) to IMGT reference genes using HighV-Quest (Alamyar et al., 2012 (link)). The output of HighV-Quest was parsed using ChangeO (Gupta et al., 2015 (link)) and merged with an antigen barcode UMI count matrix. Finally, we determined the LIBRA-seq score for each antigen in the library for every cell as previously described (Setliff et al., 2019 (link)).

Kramer K.J., Johnson N.V., Shiakolas A.R., Suryadevara N., Periasamy S., Raju N., Williams J.K., Wrapp D., Zost S.J., Walker L.M., Wall S.C., Holt C.M., Hsieh C.L., Sutton R.E., Paulo A., Nargi R.S., Davidson E., Doranz B.J., Crowe JE J.r., Bukreyev A., Carnahan R.H., McLellan J.S, & Georgiev I.S. (2021). Potent neutralization of SARS-CoV-2 variants of concern by an antibody with an uncommon genetic signature and structural mode of spike recognition. Cell Reports, 37(1), 109784.

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Single-Cell 5' Gene Expression Profiling

Cited 3 times

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The 5′ gene expression alignment from sorted PBMC was performed using the 10x Genomics Cell Ranger pipeline [45] (link). Sample demultiplexing, alignment, barcode/UMI filtering, and duplicate compression was performed using the Cell Ranger software package (10x Genomics, CA, v2.1.0) and bcl2fastq2 (Illumina, CA, v2.20) according to the manufacturer's recommendations, using the default settings and mkfastq/count commands, respectively. Transcript alignment was performed against a human reference library generated using the Cell Ranger mkref command and the Ensembl GRCh38 v87 top-level genome FASTA and the corresponding Ensembl v87 gene GTF.
Multi-sample integration, data normalization, visualization, and differential gene expression was performed using the R package Seurat (v3.0) [46 (link),47 (link)]. Non-viable cells and doublets were filtered from the dataset by removing cells with >10% mitochondrial RNA content, and those cells expressing fewer than 200 or more than 6,000 unique genes. Differential gene expression analysis was performed using a Wilcoxon rank sum test with Bonferoni correction to control for False Discovery Rate (FDR).

Waickman A.T., Gromowski G.D., Rutvisuttinunt W., Li T., Siegfried H., Victor K., Kuklis C., Gomootsukavadee M., McCracken M.K., Gabriel B., Mathew A., Grinyo i Escuer A., Fouch M.E., Liang J., Fernandez S., Davidson E., Doranz B.J., Srikiatkhachorn A., Endy T., Thomas S.J., Ellison D., Rothman A.L., Jarman R.G., Currier J.R, & Friberg H. (2020). Transcriptional and clonal characterization of B cell plasmablast diversity following primary and secondary natural DENV infection. EBioMedicine, 54, 102733.

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Comprehensive Immunoglobulin Clonotype Analysis

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Sorted B cell immunoglobulin clonotype identification, alignment, and annotation was performed using the 10x Genomics Cell Ranger pipeline. Sample demultiplexing and clonotype alignment was performed using the Cell Ranger software package (10x Genomics, CA, v2.1.0) and bcl2fastq2 (Illumina, CA, v2.20) according to the manufacturer's recommendations, using the default settings and mkfastq/vdj commands, respectively. Immunoglobulin clonotype alignment was performed against a filtered human V(D)J reference library generated using the Cell Ranger mkvdjref command and the Ensembl GRCh38 v87 top-level genome FASTA and the corresponding Ensembl v87 gene GTF. Immunoglobulin clonotype visualization, diversity assessment, and analysis were performed using either the Loupe VDJ Browser (10x Genomics, CA, v2.0.0) or custom R code. Paired immunoglobulin clonotype identity, hypermutation burden, and clonal lineage was assessed using the software package BRILIA, with a 15% sequence similarity threshold for clonal lineage assignment [48] (link). Heavy chain restricted framework and CDR hypermutation burdens were calculated using the IMGT/HighV-QUEST server [49] (link).

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Single-Cell RNA-Seq Data Processing

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The sequencing data were processed using the Cell Ranger (V5.0.1) software from 10x Genomics, and the downstream data were split into format for sequence and quality files using the CellRanger mkfastq command, which compared the reads of each of the barcodes to the GRCh38 reference genome. The unique molecular identifier counts for each barcode were determined, and the gene expression matrix of all the cells was obtained.

Song Y., Zhang Q., Ban R., Zhao X., Sun H., Lin J., Guo T., Wang T., Xia K., Xin Z., Zhang G., Jia X, & Xia Z. (2024). Single-nucleus RNA sequencing reveals that macrophages and smooth muscle cells promote carotid atherosclerosis progression through mitochondrial autophagy. Medicine, 103(7), e37171.

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BCR-Antigen Sequencing and Annotation

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We utilized our previously described pipeline to use paired-end FASTQ files of oligo libraries as input, process and annotate reads for cell barcode, UMI, and antigen barcode, and generate a cell barcode - antigen barcode UMI count matrix^{43 (link)}. BCR contigs were processed using Cell Ranger v5.0.0 (10X Genomics) using GRCh38 as reference. Antigen barcode libraries were also processed using Cell Ranger (10X Genomics). The overlapping cell barcodes between the two libraries were used as the basis of the subsequent analysis. We removed cell barcodes that had only non-functional heavy chain sequences as well as cells with multiple functional heavy chain sequences. Additionally, we aligned the BCR contigs (filtered_contigs.fasta file output by Cell Ranger, 10X Genomics) to IMGT reference genes using HighV-Quest^{96 (link)}. The output of HighV-Quest was parsed using Change-O^{97 (link)}. and merged with an antigen barcode UMI count matrix. Finally, we determined the LIBRA-seq score for each antigen in the library for every cell as previously described^{32 (link)}.

Kramer K.J., Wilfong E.M., Voss K., Barone S.M., Shiakolas A.R., Raju N., Roe C.E., Suryadevara N., Walker L.M., Wall S.C., Paulo A., Schaefer S., Dahunsi D., Westlake C.S., Crowe JE J.r., Carnahan R.H., Rathmell J.C., Bonami R.H., Georgiev I.S, & Irish J.M. (2022). Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine. Nature Communications, 13, 3466.

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Single-cell transcriptomics of infected cells

Cited 3 times

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5′ gene expression alignment from in vitro infected DC-SIGN expressing CEM.NK^R cells or sorted PBMC was performed using the 10x Genomics Cell Ranger pipeline^{49 (link)}. Sample demultiplexing, alignment, barcode/UMI filtering, and duplicate compression was performed using the Cell Ranger software package (10× Genomics, CA, v2.1.0, https://support.10xgenomics.com/single-cell-gene-expression) and bcl2fastq2 (Illumina, CA, v2.20, bcl2fastq-conversion-software-v2-20.html">https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html) according to the manufacturer’s recommendations, using the default settings and mkfastq/count commands, respectively. Transcript alignment was performed against a human reference library generated using the Cell Ranger mkref command and the Ensembl GRCh38 v87 top-level genome FASTA and the corresponding Ensembl v87 gene GTF. Multi-sample integration, data normalization, visualization, and differential gene expression was performed using the R package Seurat (v3.0, https://satijalab.org/seurat)^{50 (link),51 (link)}.

Sanborn M.A., Li T., Victor K., Siegfried H., Fung C., Rothman A.L., Srikiatkhachorn A., Fernandez S., Ellison D., Jarman R.G., Friberg H., Maljkovic Berry I., Currier J.R, & Waickman A.T. (2020). Analysis of cell-associated DENV RNA by oligo(dT) primed 5’ capture scRNAseq. Scientific Reports, 10, 9047.

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Transcriptomic Analysis of Populus Genes

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Gene Ontology (GO) annotations of A. thaliana genes were obtained from TAIR (The Arabidopsis Information Resource) database [91 ]. The corresponding GO terms of each P. trichocarpa gene were assigned by the best hit to A. thaliana genes provided in Phytozome database [92 ]. In each GO term analysis, the set of P. trichocarpa genes related to the target GO terms with all descendants (child terms) would be used. For each gene, the local transcript abundance at each cell point was estimated with the weighted average of normalized UMI counts calculated using Gaussian kernel smoother. The Euclidean distance used in the kernel smoother was based on the top 10 principal components from Cell Ranger (10x Genomics). The relative transcript abundances were then defined as the ratio between the local transcript abundance of each cell and the mean local transcript abundance. The mean relative transcript abundance in the gene set of interest was calculated for each cell. The results were visualized as a heatmap on the UMAP from Cell Ranger (10x Genomics). Total 27, 69, and 43 P. trichocarpa genes were used for the analyses of “meristem initiation,” “cellulose biosynthetic process,” and “hemicellulose metabolic process,” respectively.

Tung C.C., Kuo S.C., Yang C.L., Yu J.H., Huang C.E., Liou P.C., Sun Y.H., Shuai P., Su J.C., Ku C, & Lin Y.C. (2023). Single-cell transcriptomics unveils xylem cell development and evolution. Genome Biology, 24, 3.

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