Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility

By Caleb A. Lareau, Fabiana M. Duarte, J. Chew, Vinay K. Kartha, Z. Burkett, Andrew S. Kohlway, Dmitry Pokholok, Martin J. Aryee, Frank Steemers, R. Lebofsky and 1 more. Nature Biotechnology, 2019

392 citations recorded by the source corpus.

Recent technical advancements have facilitated the mapping of epigenomes at single-cell resolution; however, the throughput and quality of these methods have limited their widespread adoption. Here we describe a high-quality (105 nuclear fragments per cell) droplet-microfluidics-based method for single-cell profiling of chromatin accessibility. We use this approach, named ‘droplet single-cell assay for transposase-accessible chromatin using sequencing’ (dscATAC-seq), to assay 46,653 cells for the unbiased discovery of cell types and regulatory elements in adult mouse brain. We further increase the throughput of this platform by combining it with combinatorial indexing (dsciATAC-seq), enabling single-cell studies at a massive scale. We demonstrate the utility of this approach by measuring chromatin accessibility across 136,463 resting and stimulated human bone marrow-derived cells to reveal changes in the cis- and trans-regulatory landscape across cell types and under stimulatory conditions at single-cell resolution. Altogether, we describe a total of 510,123 single-cell profiles, demonstrating the scalability and flexibility of this droplet-based platform. Combining microfluidics with combinatorial indexing enables rapid mapping of genome accessibility in hundreds of thousands of single cells.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e7cf3c1a584b16d5e15e5fbec7c05762385d669e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Rahul Satija
2. Tim Stuart
3. Bing Ren
4. Jason D. Buenrostro
5. Saket Kumar Choudhary

In Molecular Biology

1. Tim Stuart
2. Rahul Satija
3. Caleb A. Lareau
4. Saket Kumar Choudhary
5. Avi Srivastava
6. Shaista Madad
7. Paul J. Hoffman
8. Miguel Andrade

Citations in the corpus

• Comprehensive analysis of single cell ATAC-seq data with SnapATAC (2021, methodology): “More recently, single-cell ATAC-seq analysis has been demonstrated on droplet-based platforms 12,13 , enabling pro fi ling of chromatin accessibility from hundreds of thousands cells in a single experiment 13 .”
• Single-cell chromatin state analysis with Signac (2021, methodology): “Methods are now available that enable generation of very large scATAC-seq datasets 8 .”
• Assessment of computational methods for the analysis of single-cell ATAC-seq data (2019, methodology): “…computational methods have been developed to address the inherent sparsity and high dimensionality of single-cell ATAC-seq data, including BROCKMAN [5], chromVAR [4], Cicero [10], cisTopic [11], Cusano-vich2018 [1, 12, 13], Gene Scoring [14], scABC [15], Sca-sat [16], SCRAT [6], and SnapATAC [17].”
• Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “In particular, combinatorial indexing approaches can be readily applied to profile a single modality in hundreds of thousands of cells 72,73 but not for multiomic technologies.”