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. 2022 Feb 16;23:55. doi: 10.1186/s13059-022-02628-8

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1. — Classification accuracy on human cells and nuclei with an overview of the experimental setup. A Overall classification accuracy (OCA) for all tested conditions and demultiplexed functions was calculated using freemuxlet demultiplexing as ground truth. SD represents variations of OCA across 4 cell lines or 3 individuals for PBMCs. B Four cancer cell lines or PBMCs were used to extract cells or nuclei to process further with the different labeling methods as indicated on the scheme. After pooling, the samples were run on a 10x Genomics Chromium platform and libraries were sequenced. LMO: lipid-modified oligonucleotides; CMO: cholesterol-modified oligonucleotides. “Pre-sort labeling”—labeling with hashing reagents followed by one wash and live/dead sorting with subsequent loading of the cells on a 10x Genomics chip. Also for these 2 PBMC samples cDNA libraries were generated using dual sample indexing (for all other samples single-sample indexing used)