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Regulatory networks that maintain functional, differentiated cell states are often dysregulated in tumor development. Here, we use single-cell epigenomics to profile chromatin state transitions in a mouse model of lung adenocarcinoma (LUAD). We identify an epigenomic continuum representing loss of cellular identity and progression toward a metastatic state. We define co-accessible regulatory programs and infer key activating and repressive chromatin regulators of these cell states. Among these co-accessibility programs, we identify a pre-metastatic transition, characterized by activation of RUNX transcription factors, which mediates extracellular matrix remodeling to promote metastasis and is predictive of survival across human LUAD patients. Together, these results demonstrate the power of single-cell epigenomics to identify regulatory programs to uncover mechanisms and key biomarkers of tumor progression., Competing Interests: Declaration of Interests T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific. He is also a co-Founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech, and Skyhawk Therapeutics. None of these affiliations represent a conflict of interest with respect to the design or execution of this study or interpretation of data presented in this manuscript. T.J. laboratory currently also receives funding from the Johnson & Johnson Lung Cancer Initiative and The Lustgarten Foundation for Pancreatic Cancer Research, but this funding did not support the research described in this manuscript. J.D.B. holds patents related to ATAC-seq and single-cell ATAC-seq and serves on the Scientific Advisory Board of CAMP4 Therapeutics and seqWell. A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and an SAB member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov, and Neogene Therapeutics., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10-45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level., Results: We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed., Conclusions: This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).