Tonsillar immune cells were loaded on to the 10X Genomics Chromium according to the manufacturer’s protocol using either the single-cell 3’ kit (v3) or the single-cell ATAC kit (v1). Cell surface labelling for scADT-seq libraries was performed with 12 oligo-labelled TotalSeq™ antibodies (BioLegend; Table S2 ). Library preparation was performed according to the manufacturer’s protocol prior to sequencing on either the Illumina NovaSeq 6000 or NextSeq 500 platforms. scRNA-seq libraries were sequenced with 28/10/10/90 bp cycles while scATAC-seq libraries were sequenced with 70/8/16/70 bp read configurations. BaseCall files were used to generate FASTQ files with either cellranger mkfastq (v3; 10X Genomics) or cellranger-atac (v1; 10X Genomics) prior to running cellranger count with the cellranger-GRCh38–3.0.0 reference or cellranger-atac count with the cellranger-atac-GRCh38–1.1.0 reference for scRNA-seq and scATAC-seq libraries respectively.
Cell ranger atac
Manufactured by 10x Genomics
Cell Ranger ATAC is a library preparation and analysis software suite for single-cell ATAC-seq. It automates the processing of single-cell ATAC-seq samples, including cell barcode identification, read alignment, and chromatin accessibility analysis.
Automatically generated - may contain errors
13 protocols using cell ranger atac
1
Multimodal Profiling of Tonsillar Immune Cells
2
Single-Cell ATAC-Seq Data Processing
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The raw sequencing data were demultiplexed using cellranger-atac mkfastq (Cell Ranger ATAC, version 1.1.0, 10x Genomics). scATAC-seq reads were aligned to the hg38 reference genome (GRCh38, version 1.1.0, 10x Genomics) and quantified using cellranger-atac count function with default parameters (Cell Ranger ATAC, version 1.1.0, 10x Genomics).
3
Demultiplexing and Quantifying Single-Cell ATAC-Seq
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The raw sequencing data was demultiplexed using cellranger-atac mkfastq (Cell Ranger ATAC, version 1.1.0, 10x Genomics). Single cell ATAC-seq reads were aligned to the hg38 reference genome (GRCh38, version 1.1.0, 10x Genomics) and quantified using cellranger-atac count function with default parameters (Cell Ranger ATAC, version 1.1.0, 10x Genomics).
4
Transcriptomics-based Single-cell Chromatin Profiling
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Cell Ranger ATAC from 10× Genomics was used to demultiplex raw base call files into FASTQ files and generate a filtered peak-barcode matrix containing detected cellular barcodes and a fragments file as in the BED format for each patient sample. These fragment lists were read into the R package ArchR [39 (link)] to perform quality control and doublet removal. To enrich for cellular barcodes, a threshold for log10(TSS enrichement + 1) was set manually to 0.9 for both scATAC-seq samples while a sample-specific threshold of log10(number of unique fragments) was estimated using a Gaussian Mixture Model (GMM) for each scATAC-seq sample, as implemented in the R package mclust [76 (link)]. Barcodes below these thresholds in any of these metrics were excluded before doublet detection step. ArchR’s addDoubletScores() function [39 (link)], with the knnMethod parameter of “UMAP”, was used to estimate doublet enrichment scores, and ArchR’s filterDoublets() function [39 (link)], with the filterRatio parameter of 1.0, was used to filter out cellular barcodes as doublets.
5
ATAC-seq analysis of activated monocytes
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Basecall files were used to generate FASTQ files with cellranger-atac (v1; 10 X Genomics). Reads were aligned to the human genome using cellranger-atac count with the cellranger-atac-GRCh38.1.1.0 reference. Mapped Tn5 insertion sites from cellranger were read into ArchR (version 1.0.1) R package retaining barcodes with at least 1000 fragments per cell and a TSS enrichment score >4. Doublets were identified and filtered using addDoubletScores and filterDoublets (filter ratio = 1.4) respectively before iterative LSI dimensionality reduction (iterations = 2, res = 0.2, variable features = 25000, dim = 30). Clustering was then performed (addClusters, res = 0.8) before UMAP dimensionality reduction (nNeighbors = 30, metric = cosine, minDist = 0.4). One cluster enriched for high doublet scores and was removed. Peaks for each cluster were calculated using MACS2, using the addReproduciblePeakSet function. Marker peaks for each cluster and differential peaks with stimulation were calculated using the getMarkerFeatures function using the Wilcoxon test. A cluster of activated monocytes was identified by pileups and feature plots of canonical cytokine and activation markers.
6
Unbiased Profiling of LAM Lung Chromatin
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snATAC-seq of LAM lung was processed using Cell Ranger ATAC with GRCh38 reference (10x Genomics). ATAC cells passing the following QC criteria were included in downstream analysis using Signac (50 ), including: (i) at least 1000 but less than 20,000 fragments in peaks, (ii) greater than 10% of fragments in peaks, (iii) less than 5% of fragments in ENCODE blacklist regions for GRCh38 reference, (iv) ratio of mononucleosomal to nucleosome-free fragments of less than 2, and (v) transcription start site enrichment score greater than 2. The QC criteria were selected on the basis of inspection of distributions of each QC metric. Unbiased cell clustering based on ATAC peaks was performed using the Leiden algorithm. Cell clusters were assigned to cell types using label transfer method in Signac using our analyzed scRNA-seq of LAM lung. Cell type–specific differentially accessible peaks and overrepresented motifs were identified using the FindMarkers function in Seurat using the “logistic regression” method. Signac ClosestFeature function was used to find closest genes to ATAC peak regions. HOMER was used to annotate peak regions with nearest genes under default parameters. Motif enrichment was performed with HOMER using the hg38 assembly.
7
Chromatin Accessibility Analysis Using Cell Ranger ATAC
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All preprocessing steps were performed using “Cell Ranger ATAC version 1.2.0” (10X Genomics). Read filtering, alignment, peak calling, and count matrix generation from fastq files were done per sample using ‘cellranger-atac count’. Reference genome assemblies mm10 (refdata-cellranger-atac-mm10-1.2.0) provided by 10xGenomics were used for samples. All further analysis steps were performed in R (Version 4.0.0). Fragments were loaded into R using the package Seurat10 (link). The R package Signac (version 1.3.0, https://github.com/timoast/signac ) was used for normalization and dimensionality reduction. The peak-barcode matrix was then binarized and normalized using the implementation of the TF-IDF transformation described in (RunTFIDF (method = 1)). Subsequently, singular value decomposition was run (RunSVD) on the upper quartile of accessible peaks (FindTopFeatures (min.cutoff = ‘q75’)). The first 20 components from the SVD reduction were used for secondary dimensionality reduction with UMAP.
8
ATAC-seq Quality Control and Doublet Removal
9
Single-cell ATAC-seq analysis of mouse interneurons
10
ATAC-seq Analysis Pipeline with Signac
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All preprocessing steps were performed using “Cell Ranger ATAC version 1.2.0” (10X Genomics). Read filtering, alignment, peak calling, and count matrix generation from fastq files were done per sample using ‘cellranger-atac count’. Reference genome assemblies mm10 (refdata-cellranger-atac-mm10–1.2.0) provided by 10xGenomics were used for samples. All further analysis steps were performed in R (Version 4.0.0). Fragments were loaded into R using the package Seurat10 (link). The R package Signac (version 1.3.0, https://github.com/timoast/signac ) was used for normalization and dimensionality reduction. The peak-barcode matrix was then binarized and normalized using the implementation of the TF-IDF transformation described in (RunTFIDF (method = 1)). Subsequently, singular value decomposition was run (RunSVD) on the upper quartile of accessible peaks (FindTopFeatures (min.cutoff = ‘q75’)). The first 20 components from the SVD reduction were used for secondary dimensionality reduction with UMAP.
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