Bulk VDJ sequencing data had adapters trimmed by Trimmomatic v0.39 in single-end mode, clipping Illumina TruSeq adapters with default settings and filtering reads with an average quality score < 30.55 (link) Clonotypes were called using MiXCR v2.1.5 with the recommended settings for 5′ RACE (RNA alignment to V gene transcripts with P region).56 (link) Single-cell sequencing data was processed using the Cellranger v5.0.1 (10x Genomics) pipeline and aligned to the mm10 VDJ reference. The MiXCR clonotype output or Cell Ranger AIRR-formated output (bulk and single cell VDJ analyses, respectively) were used as inputs to Immunarch v0.6.6 R package for calculating summary statistics, diversity metrics, and repertoire overlaps.
Cellranger v5
Manufactured by 10x Genomics
CellRanger v5.0.0 is a software suite developed by 10x Genomics for processing and analyzing single-cell RNA sequencing data. It provides a comprehensive workflow for extracting and quantifying gene expression profiles from individual cells.
Automatically generated - may contain errors
Lab products found in correlation
Next gem single cell 3 gem kit v3, by 10x Genomics (3 mentions)
Novaseq, by Illumina (3 mentions)
Truseq, by Illumina (1 mentions)
Cell ranger software, by 10x Genomics (1 mentions)
Bioanalyzer system, by Agilent Technologies (1 mentions)
Novaseq 6000, by Illumina (1 mentions)
Cell ranger, by 10x Genomics (1 mentions)
Chromium single cell a chip, by 10x Genomics (1 mentions)
Chromium single cell 3 reagent kit v3, by 10x Genomics (1 mentions)
Nextseq 500, by Illumina (1 mentions)
12 protocols using cellranger v5
1
Bulk and Single-cell VDJ Sequencing Analysis
2
High-quality single-cell RNA-seq workflow
3
Single-cell RNA-seq of Mouse Cells
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Sequencing data from 10x Genomics was processed with the Cell Ranger software for each sample. Raw data in FASTQ format was processed and aligned to the mm10 mouse reference genome (https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz ) with the Cell Ranger v5.0.1 pipeline(10x Genomics, https://www.10xgenom.ics.com/ ). Doublet cells were removed using DoubletFinder R package (v2.0.3)63 (link) with the recommended doublet rate by the pipeline. Then filter low-quality cells, we retained cells: (1) expressing 500–7000 genes, (2) less than 10% of reads mapped to mitochondrial genes, and (3) less than 40% of reads mapped to ribosome genes. We sequenced 74,754 single cells, and retained 52,836 cells after the quality-control process of the primary sequencing data.
4
Single-cell RNA-seq of Endothelial Cells
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scRNA-seq was performed using a 10x Next GEM Single-Cell 3′ GEM kit v3.1 (10x Genomics) according to the manufacturer’s protocol. Briefly, an equal number of FACS-isolated CD31+CD45low cells from three biological replicates per condition were pooled, centrifuged (4 °C, 300g, 5 min), resuspended at ~1,000 cells per µl and immediately loaded into the 10x Chromium controller. Separate 10x Genomics reactions were used for each organ, timepoint and condition, with ECs pooled from three mice per group. Generated libraries were sequenced on an Illumina NovaSeq with >27.5 × 103 reads per cell followed by demultiplexing and mapping to the mouse genome (build mm10) using CellRanger v5.0 (10x Genomics).
5
Single-cell Transcriptomics of SARS-CoV-2 Infected Cells
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After enzymatic fragmentation and size selection, resulting double-stranded cDNA amplicons optimized for library construction were subjected to adaptor ligation and sample index PCRs needed for Illumina bridge amplification and sequencing according to the manufacturer’s instruction (10x genomics). Single cell libraries were quantified using Qubit (Thermo Fisher) and quality-controlled using the Bioanalyzer System (Agilent). Sequencing was performed on a Novaseq 6000 (Illumina), aiming for 200 Million reads per library (read1: 28, read2: 150 nucleotides). Data were analysed using CellRanger v5.0 (10x Genomics) using hamster and SARS-CoV-2 genome scaffolds, and the R packages Seurat v4.0 (Hao et al., 2021 (link)) and DoRothEA v3.12 (Holland et al., 2020 (link)) were used for cell clustering, annotation, and transcription factor activity analysis. Median gene number detected per cell ranged between 2000 and 4400, with 3800–18500 median UMI counts per cell. Gene set variation analysis (GSVA) was performed using the GSVA R package (Hänzelmann et al., 2013 ) and gene set enrichment analysis was performed using the clusterProfiler R package (Wu et al., 2021 (link)).
6
Single-cell RNA-seq of Endothelial Cells
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scRNA-seq was performed using a 10x Next GEM Single-Cell 3′ GEM kit v3.1 (10x Genomics) according to the manufacturer’s protocol. Briefly, an equal number of FACS-isolated CD31+CD45low cells from three biological replicates per condition were pooled, centrifuged (4 °C, 300g, 5 min), resuspended at ~1,000 cells per µl and immediately loaded into the 10x Chromium controller. Separate 10x Genomics reactions were used for each organ, timepoint and condition, with ECs pooled from three mice per group. Generated libraries were sequenced on an Illumina NovaSeq with >27.5 × 103 reads per cell followed by demultiplexing and mapping to the mouse genome (build mm10) using CellRanger v5.0 (10x Genomics).
7
Single-cell RNA-seq of Passage 5 and 20 Mouse PCs
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One set of PCs isolated from 6 mice was used for the P5 timepoint, while a different, second set of PCs were used for the P20 timepoint. Single cell RNA-seq was performed using 10 × Next GEM Single Cell 3′ GEM kit v3.1 (10 × Genomics; Pleasanton, CA, USA) according to the manufacturer’s protocol. Briefly, passage 5 PCs and passage 20 PCs as well as freshly sorted brain PCs were spun down (4°C, 300 g, for 5 min), resuspended at ∼1000 cells/μl, and immediately loaded into the 10 × Chromium controller. Generated libraries were sequenced on an Illumina NovaSeq with > 45.6 × 103 reads per cell followed by de-multiplexing and mapping to the mouse genome (build mm10) using CellRanger v5.0 (10 × Genomics).
8
TCR Clonotype Analysis Pipeline
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Raw sequencing files were aligned and annotated by using CellRanger v5.0.0 (10× Genomics). After removal of cells lacking a TCR alpha or TCR beta chain, or expressing two or more TCR alpha/TCR beta chains. TCR clonotypes were assigned based on the CDR3α and CDR3β nucleotide sequences.
9
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 matrix43 (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-Quest96 (link). The output of HighV-Quest was parsed using Change-O97 (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 described32 (link).
10
Single-cell RNA-seq of mouse cells
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After digestion, the single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. A hemocytometer was used to manually count the cells to determine the concentration of the suspension. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, Pleasanton, CA) following the manufacturer’s protocol.67 Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes with <5% doublet rate. Libraries were sequenced on the NextSeq 500 (Illumina, San Diego, CA).68 The sequencing data was aligned to the mouse reference genome (mm10) using CellRanger v5.0.0 (10x Genomics).67
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