{"doi":"10.1101/2021.01.11.425995","title":"High-content single-cell combinatorial indexing","abstract":"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 construction 2 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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":214132,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9544,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":23602,"name":"Dmitry Pokholok","orcid":null,"position":1,"is_corresponding":false},{"id":585703,"name":"Brendan L. O’Connell","orcid":"0000-0001-7757-4561","position":2,"is_corresponding":false},{"id":585705,"name":"Casey Thornton","orcid":"0000-0003-3589-6911","position":3,"is_corresponding":false},{"id":108171,"name":"Fan Zhang","orcid":"0000-0003-4340-3435","position":4,"is_corresponding":false},{"id":585706,"name":"Brian J. O’Roak","orcid":"0000-0002-4141-0095","position":5,"is_corresponding":false},{"id":626485,"name":"Jason M. Link","orcid":"0000-0002-6892-0431","position":6,"is_corresponding":false},{"id":807562,"name":"Galip Gurkan Yardmici","orcid":null,"position":7,"is_corresponding":false},{"id":284346,"name":"Rosalie C. Sears","orcid":"0000-0003-1558-2413","position":8,"is_corresponding":false},{"id":94710,"name":"Frank J. Steemers","orcid":null,"position":9,"is_corresponding":false},{"id":15494,"name":"Andrew C. Adey","orcid":"0000-0001-7648-8717","position":10,"is_corresponding":false},{"id":15492,"name":"Ryan M. Mulqueen","orcid":"0000-0002-3903-7594","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:52:41.672172Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}