Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Meta data and accession ID for the scATAC-seq data used in simulation for pipeline tolerance evaluation.

An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.

The human adult intestinal system is a complex organ that is approximately 9 meters long and performs a variety of complex functions including digestion, nutrient absorption, and immune surveillance. We performed snATAC-seq on 8 regions of of the human intestine (duodenum, proximal-jejunum, mid-jejunum, ileum, ascending colon, transverse colon, descending colon, and sigmoid colon) from 9 donors (B001, B004, B005, B006, B008, B009, B010, B011, and B012). In the corresponding paper, we find cell compositions differ dramatically across regions of the intestine and demonstrate the complexity of epithelial subtypes. We map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization in the human intestine, and serve as an important reference map for understanding human biology and disease. Methods For a detailed description of each of the steps of protocols and processes to obtain this data see the detailed materials and methods in the associated manuscript. Briefly, intestine pieces from 8 different sites across the small intestine and colon were flash frozen. Nuclei were isolated from each sample and the resulting nuclei were processed with either 10x scRNA-seq using Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (10x Genomics, 1000121) or Chromium Next GEM Chip G Single Cell Kits (10x Genomics, 1000120) or 10x multiome sequencing using Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Kits (10x Genomics, 1000283).

Initial processing of snATAC-seq data was done with the Cell Ranger ATAC pipeline and initial processing of the mutiome data, including alignment and generation of fragments files and expression matrices, was performed with the Cell Ranger ARC pipeline. The fragments files from these pipelines are included here. Downstream processing was performed in R.

CD8 T cell exhaustion is a major barrier limiting anti-tumor therapy. Though checkpoint blockade temporarily improves exhausted CD8 T cell (Tex) function, the underlying epigenetic landscape of Tex remains largely unchanged, preventing their durable “reinvigoration.†Whereas the transcription factor (TF) TOX has been identified as a critical initiator of Tex epigenetic programming, it remains unclear whether TOX plays an ongoing role in preserving Tex biology after cells commit to exhaustion. Here, we decoupled the role of TOX in the initiation versus maintenance of CD8 T cell exhaustion by temporally deleting TOX in established Tex. Induced TOX ablation in committed Tex resulted in apoptotic-driven loss of Tex, reduced expression of inhibitory receptors including PD-1, and a pronounced decrease in terminally differentiated subsets of Tex cells. Simultaneous gene expression and epigenetic profiling revealed a critical role for TOX in ensuring ongoing chromatin accessibility and transcri..., Cells from inducible-Cre (Rosa26CreERT2/+Toxfl/fl P14) mice where TOX was temporally deleted from mature populations of LCMV-specific T exhausted cells after establishment of chronic LCMV infection 5 days post infection were subjected to scRNA and scATACseq coassay,naive cells and WT cells were used as controls. Analysis pipeline developed by Josephine Giles and vignettes published by Satija and Stuart labs.Transcript count and peak accessibility matrices deposited in GSE255042,GSE255043. Seurat/Signac was used to process the scRNA and scATACseq coassay data The processed Seurat/Signac object above was subsequently used for downstream RNA and ATAC analyses as described below: DEGs between TOX WT and iKO cells within each subset were identified using FindMarkers (Seurat, Signac), with a log2-fold-change threshold of 0, using the SCT assay. DACRs were identified using FindMarkers using the "LR" test, with a log2-fold-change threshold of 0.1, a min.pct of 0.05, and included the number of c..., , # Continuous expression of TOX safeguards exhausted CD8 T cell epigenetic fate

https://doi.org/10.5061/dryad.8kprr4xx9

Seurat/Signac pipeline for multiomic scRNA-seq and scATAC-seq dataset, generated following inducible TOX deletion in LCMV-Cl13

Author

Yinghui Jane Huang

Script information

Purpose: Generate and process Seurat/Signac object for downstream analyses Written: Nov 2021 through Oct 2022 Adapted from: Analysis pipeline developed by Josephine Giles and vignettes published by Satija and Stuart labs Input dataset: Transcript count and peak accessibility matrices deposited in GSE255042,GSE255043

Signac Object Generation

1) Create individual signac objects for each sample from the raw 10x cellranger output.

2) Merge individual objects to create one seurat object.

3) Add metadata to merged seurat object.

Following are the steps in the attached html file for analysis of the paired data (ATAC+RNA)

• Load fr...,

Running time of each QC and analysis step for scATAC datasets.

SummarySingle-cell ATAC-Seq data from human atherosclerosis. The files contain cells from endarterectomy samples from three donors, yielding approximately 7000 cells total. snATAC-Seq libraries were generated using the 10x Genomics scATAC-Seq v1 kit. The files were produced by the cellranger-atac v1.1 pipeline commands 'count' and 'aggr' (without normalization). The genome reference used was hg19 Reference v1.1.0. Journal publicationSingle-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS LociTiit Örd, Kadri Õunap, Lindsey Stolze, Rédouane Aherrahrou, Valtteri Nurminen, Ilakya Selvarajan, Anu Toropainen, Tapio Lönnberg, Einari Aavik, Seppo Yla-Herttuala, Mete Civelek, Casey E Romanoski, and Minna U KaikkonenCirculation Research, https://www.ahajournals.org/doi/abs/10.1161/CIRCRESAHA.121.318971Files availableThe file formats are described in detail at https://support.10xgenomics.com/single-cell-atac/software/pipelines/1.1/output/overview1) cloupe-manual_cell_type_added.cloupe: Loupe browser file for interactive browsing of the data. Includes a manual cell annotation into five principal cell lineages (smooth muscle cells, endothelial cells, macrophages, T/NK cells, and B/Plasma cells. For an overview of the Loupe Cell Browser ATAC software, please see https://support.10xgenomics.com/single-cell-atac/software/visualization/latest/what-is-loupe-cell-browser2) fragments.tsv.gz and fragments.tsv.gz.tbi: Fragments file and the corresponding index file. Contains ATAC fragment locations (cut sites) from the hg19 genome and the cell barcode associated with the fragment. Used for displaying coverage of cuts in genomic regions, and could be used for recalling of peaks. For full information of the table structure, please see https://support.10xgenomics.com/single-cell-atac/software/pipelines/1.1/output/fragments3) matrix.mtx, peaks.bed and barcodes.tsv: the peak-barcode matrix for cell-associated barcodes (contents of the "filtered_peak_bc_matrix" folder from cellranger-atac). Suitable for downstream analysis. For a full description of the matrix structure, please see https://support.10xgenomics.com/single-cell-atac/software/pipelines/1.1/output/matrices4) peak_annotation.tsv: annotations for the peaks called by cellranger-atac. For a description of how peaks were annotated to genes, please see https://support.10xgenomics.com/single-cell-atac/software/pipelines/1.1/output/annotation