De-multiplexing, alignment to the mm10 mouse transcriptome and UMI-collapsing were performed using the Cellranger toolkit (version 2.1.0, 10X Genomics). Subsequent analysis was performed with R package Seurat v3 (Butler et al., 2018 (link)). For downstream processing we filtered out low quality cells that had (1) a low number (< 500) of unique detected genes, and (2) a high mitochondrial content (15%) determined by the ratio of reads mapping to the mitochondria. A small proportion of cells were identified as contamination by macrophages, innate lymphoid cells, intraepithelial lymphocytes and fibroblasts, and were excluded from downstream analysis. To account for differences in sequencing depth across cells, UMI counts were normalized by the total number of UMIs per cell and converted to transcripts-per-10,000 before being log transformed (henceforth “log(TP10K+1)”).
Cellranger toolkit
Manufactured by 10x Genomics
Sourced in United States
The Cellranger toolkit is a software suite developed by 10x Genomics for processing data generated from their single-cell sequencing platforms. Its core function is to provide tools for sample demultiplexing, gene expression quantification, and other data analysis tasks related to single-cell genomics experiments.
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31 protocols using cellranger toolkit
1
Single-cell transcriptome analysis of mouse
2
Single-cell RNA-seq data processing
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Following library generation and sequencing, raw sequencing data were de‐multiplexed and mapped to the mouse reference genome (mm10) using the CellRanger toolkit (10X Genomics, version 2.1.0). Gene expression matrices were then generated from both control and EC‐Foxc‐DKO mice using CellRanger. The matrix files were then utilized for data processing and downstream analysis using the BIOMEX browser‐based software platform and its incorporated packages developed in R (Taverna et al, 2020 (link)). Quality control and data pretreatment was performed in BIOMEX with the following manually set parameters: (i) genes with a row average of < 0.001 were excluded for downstream analysis and (ii) cells in which over 10% of unique molecular identifiers (UMIs) were derived from the mitochondrial genome were considered as dead cells and removed from downstream analysis. The data were then normalized in BIOMEX using a similar methodology to the NormalizeData function as implemented in the Seurat package (Satija et al, 2015 (link)).
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Single-Cell RNA-Seq Data Processing and Normalization
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Single-cell RNA-seq of mouse organoids
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Single-Cell RNA-Seq Data Processing and Normalization
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Demultiplexing, alignment to the mm10 transcriptome and UMI-collapsing were performed using the Cellranger toolkit (version 1.0.1) provided by 10X Genomics. For each cell, we quantified the number of genes for which at least one read was mapped, and then excluded all cells with either fewer than 800 detected genes. Expression values Ei,j for gene i in cell j were calculated by dividing UMI count values for gene i by the sum of the UMI counts in cell j, to normalize for differences in coverage, and then multiplying by 10,000 to create TPM-like values, and finally taking log transform to compute log2(TPM+1) values. Batch correction was performed using ComBat46 (link) as implemented in the R package sva47 (link), using the default parametric adjustment mode. The output was a corrected expression matrix, which was used as input to further analysis.
Selection of variable genes was performed by fitting a generalized linear model to the relationship between the squared co-efficient of variation (CV) and the mean expression level in log/log space, and selecting genes that significantly deviated (P<0.05) from the fitted curve, as previously described48 (link).
Selection of variable genes was performed by fitting a generalized linear model to the relationship between the squared co-efficient of variation (CV) and the mean expression level in log/log space, and selecting genes that significantly deviated (P<0.05) from the fitted curve, as previously described48 (link).
6
Single-Cell Transcriptome Profiling of Tumor Samples
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Samples prepared as described above were submitted to the Cancer Genomics Shared Resource in the Wake Forest Baptist Medical Center Comprehensive Cancer Center for single-cell sequencing and analysis. Samples with ≥60% viable cells were processed for cDNA library construction using the 10× Genomics Chromium platform (10× Genomics, Pleasanton, CA, USA) with v3 chemistry. Indexed libraries were paired-end sequenced on an Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA) targeting 2500 cells per sample at a median read depth of 100,000 reads per cell. Raw bcl files were converted to fastq for read demultiplexing, alignment, and counting using the CellRanger toolkit (10× Genomics, Pleasanton, CA, USA). Data QC parameters were applied as previously described [34 (link)] to select high-quality cellular transcriptomes. Data dimensionality reduction (t-SNE) and clustering (K-means) algorithms were used to assess cell-to-cell relationships. Immune cell identities were assigned as previously described [34 (link)]. Cross-sample cell populations were compared for differences in gene expression using negative binomial models with FDR correction.
7
Scalable Single-Cell RNA-seq Analysis
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De-multiplexing, alignment to the mm10 transcriptome and unique molecular identifier (UMI)-collapsing were performed using the Cellranger toolkit (version 2.1.1, chemistry V2, or version 3.0.2, chemistry V3) provided by 10X Genomics for chemistry Single Cell 3’, and run using cloud computing on the Terra platform (https://Terra.bio ). Since nuclear RNA is expected to include roughly equal proportions of intronic and exonic reads, we built and aligned reads to genome references with pre-mRNA annotations, which account for both exons and introns. For every nucleus, we quantified the number of genes for which at least one read was mapped, and then excluded all nuclei with fewer than 400 detected genes. Genes that were detected in fewer than 10 nuclei were excluded. Expression values Ei,j for gene i in cell j were calculated by dividing UMI counts for gene i by the sum of the UMI counts in nucleus j, to normalize for differences in coverage, and then multiplying by 10,000 to create TPM-like values, and finally computing log2(TP10K + 1) (using the NormalizeData function from the Seurat22 (link) package version 2.3.4).
8
Integrative scRNA-seq Analysis of Human Blood and Tumor
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Reads were aligned and the UMI matrix was generated using the Cell Ranger toolkit (version 3.0.2; 10X Genomics) and the reference genome GRCh37 (hg19). Then the gene expression matrices for all peripheral blood, tumor and normal samples were combined in R (version 3.6.1) and converted to a Seurat object using the Seurat package in R (version 3.2.2). Cells were removed if no more than 200 genes or if more than 6000 genes were found to be expressed, or if >10% of UMIs corresponded to the mitochondrial genome. The number of cells retained for each feature was calculated, and only genes that were expressed in at least five cells per feature were retained. We normalized the count matrix of remaining cells to TP10K using the “NormalizeData” function in the Seurat package.
We corrected for batch effects using the “FindIntegrationAnchors” function in the Seurat package as recommended: we scaled each dataset, selected 2000 HVGs as input to compute integration anchors, then integrated the batches using the anchors. Linear regression was used to log-normalize gene expression matrices to total cellular read-counts and mitochondrial read-counts using the “ScaleData” function.
We corrected for batch effects using the “FindIntegrationAnchors” function in the Seurat package as recommended: we scaled each dataset, selected 2000 HVGs as input to compute integration anchors, then integrated the batches using the anchors. Linear regression was used to log-normalize gene expression matrices to total cellular read-counts and mitochondrial read-counts using the “ScaleData” function.
9
Single-Cell Transcriptomic Quality Control
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The Cell Ranger toolkit (10x Genomics, v3.1.0) was utilized to align reads to the mm10 (v3.0.0) reference genome and generate the matrix of genes versus cells. Further quality control of each cell was applied as follows: (1) The number of unique molecular identifier (UMI) counts > 1000; (2) The number of detected genes > 300; (3) The proportion of mitochondrial gene expression <10%. A total of 31880 cells were included in further downstream analysis.
10
Single-cell RNA-seq data analysis
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After library construction and sequencing, raw sequencing data were de-multiplexed and mapped to the mouse reference genome (mm10) using the CellRanger toolkit (10X Genomics, version 4.0.0). Gene expression matrices were then generated from Foxc2fl/fl control and NC-Foxc2-/- mice. The matrix files were then used for data processing and downstream analysis using the BIOMEX browser-based software platform and its incorporated packages developed in R (76 (link)). Quality control and data pretreatment was performed in BIOMEX with the following manually set parameters: (i) genes with a row average of <0.005 were excluded for downstream analysis and (ii) cells in which over 8% of unique molecular identifiers were derived from the mitochondrial genome were considered as dead cells and removed from downstream analysis. The data were then normalized in BIOMEX using similar methodology to the NormalizeData function as implemented in the Seurat package (77 (link)).
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