Abstract
Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci (‘s3’), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-assay for transposase-accessible chromatin, s3-ATAC) in human cortical and mouse whole-brain tissues, with mouse datasets demonstrating a six- to 13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole-genome sequencing (s3-WGS) and to whole-genome plus chromatin conformation (s3-GCC) yields 148- and 14.8-fold improvements, respectively, in usable reads per cell compared with sci-DNA-sequencing and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.
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Data availability
The data discussed in this publication have been deposited in the National Center for Biotechnology Information’s (NCBI’s) Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE174226. External single-cell ATAC datasets were downloaded from GEO sample accession number GSM2668124 for snATAC, and external sites for dscATAC (https://github.com/buenrostrolab/dscATAC_analysis_code/blob/master/mousebrain/data/mousebrain-master_dataframe.rds) and 10X Genomics scATAC (https://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_v1_adult_brain_fresh_5k). The external single-cell WGS dataset was downloaded from NCBI BioProject PRJNA326698 (https://www.ncbi.nlm.nih.gov/sra/SRX2005587). Single-cell HiC datasets were downloaded from the 4D Nucleosome project (https://data.4dnucleome.org/publications/048d4558-2cac-41d2-ac6e-ff2ac3f007c4/#expsets-table). External bulk HiC datasets have been downloaded from the ENCODE consortium’s data portal, https://www.encodeproject.org/ via accession codes ENCSR194SRI, ENCSR346DCU, ENCSR444WCZ and ENCSR079VIJ. Source data are provided with this paper.
Code availability
Code and custom scripts used in this study are available at https://github.com/adeylab/scitools and https://mulqueenr.github.io/.
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Acknowledgements
We thank other members of the Adey Laboratory as well as J. Shendure and C. Trapnell for helpful suggestions and feedback. This work was funded by grants R01DA047237 (NIH/NIDA) and R35GM124704 (NIH/NIGMS) to A.C.A. and R01MH113926 (NIH/NIMH) to B.J.O. We also thank the Oregon Brain Bank for the donated biological sample used in this study.
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Contributions
R.M.M., D.P., F.J.S. and A.C.A. conceived the study. R.M.M. performed all s3 experiments and led all analysis under the supervision of A.C.A. D.P. and F.Z. performed additional experiments under the supervision of F.J.S. B.L.O. and G.G.Y. contributed to the design and analysis of chromatin conformation s3-GCC protocol and datasets. B.J.O. provided support for R.M.M. and advice on analysis. C.A.T. contributed to the analysis of cell types in the s3-ATAC datasets. J.L. generated PDCL cell lines and performed characterization of the lines under supervision of R.C.S. J.L. and R.C.S. contributed to the analysis of PDAC s3-WGS and s3-GCC datasets. The paper was written by R.M.M. and A.C.A. All authors reviewed and contributed to the paper.
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Competing interests
D.P., F.Z. and F.J.S. are employees of Scale Bio. R.M.M., D.P., F.Z., F.J.S. and A.C.A. are authors on licensed patents that cover components of the technologies described in this paper. This potential conflict of interest for A.C.A. and R.M.M. has been reviewed and managed by OHSU.
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Supplementary Information
Supplementary Figs. 1–3.
Supplementary Data 1
Mouse brain ATAC-seq peaks.
Supplementary Data 2
s3-ATAC differential accessibility and cell type classification.
Supplementary Data 3
s3-GCC A/B compartment eigenvector plots.
Supplementary Tables
Supplementary Tables 1–8.
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Mulqueen, R.M., Pokholok, D., O’Connell, B.L. et al. High-content single-cell combinatorial indexing. Nat Biotechnol 39, 1574–1580 (2021). https://doi.org/10.1038/s41587-021-00962-z
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DOI: https://doi.org/10.1038/s41587-021-00962-z
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