Nature BiotechnologyGeneticsPeer-reviewed journal article
High-content single-cell combinatorial indexing
Ryan M. MulqueeniD, Dmitry Pokholok, Brendan L. O’ConnelliD, Casey A. ThorntoniD, Fan ZhangiD, Brian J. O’RoakiD, Jason M. LinkiD, Galip Gürkan YardımcıiD, Rosalie C. SearsiD, Frank J. Steemers, Andrew AdeyiD
Abstract
Single-cell genomics assays have emerged as a dominant platform for interrogating complex biological systems. Methods to capture various properties at the single-cell level typically suffer a tradeoff between cell count and information content, which is defined by the number of unique and usable reads acquired per cell. We and others have described workflows that utilize single-cell combinatorial indexing (sci)1, leveraging transposase-based library construction2 to assess a variety of genomic properties in high throughput; however, these techniques often produce sparse coverage for the property of interest. Here, we describe a novel adaptor-switching strategy, s3, capable of producing one-to-two order-of-magnitude improvements in usable reads obtained per cell for chromatin accessibility (s3-ATAC), whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), while retaining the same high-throughput capabilities of predecessor sci technologies. We apply s3 to produce high-coverage single-cell ATAC-seq profiles of mouse brain and human cortex tissue; and whole genome and chromatin contact maps for two low-passage patient-derived cell lines from a primary pancreatic tumor.
- organism: mouse
- organism: human
- assay: scATAC-seq
- assay: whole-genome sequencing
- assay: Hi-C
- research focus: variant calling
- research focus: genome assembly
- research focus: spatial domain detection
- approach: experimental assay
- Citations
- 98
- FWCI
- 5.33
- Field percentile
- top 2%
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