Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Cao, Junyue; Packer, Jonathan S.; Ramani, Vijay; Cusanovich, Darren A.; Huynh, Chau; Daza, Riza; Qiu, Xiaojie; Lee, Choli; Furlan, Scott N.; Steemers, Frank J.; Adey, Andrew; Waterston, Robert H.; Trapnell, Cole; Shendure, Jay

doi:10.1101/104844

PreprintCited by 1,402Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGenomics and Chromatin Dynamics

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

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Cao, Junyue, Packer, Jonathan S., Ramani, Vijay, Cusanovich, Darren A., Huynh, Chau, Daza, Riza, Qiu, Xiaojie, Lee, Choli +6 more·2017·Science

DOI 10.1101/104844PMC PMC5894354

Preprint — published as 10.1126/science.aam8940 · Paper page

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Abstract

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell ( S ingle cell C ombinatorial I ndexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

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Multiplexing reduces technical effects due to separate sample processing.
“suggesting multiplexing reduces technical effects due to separate sample processing22, 23.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [22]
Sample multiplexed scRNA-seq is an efficient method for massively parallel species-mixing experiments.
“Sample multiplexed scRNA-seq is another solution, establishing an efficient method for massively parallel species-mixing experiments [18–22].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [18]
For each cell type, variance across individuals is higher than variance across synthetic replicates (Lin's concordance = 0.007-0.20).
“for each cell type, the variance across individuals22, 30 is also higher than the variance across synthetic replicates (Lin’s concordance = 0.007–0.20)”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [22]
The ‘Identifying cell types’ section discusses variation between two C. elegans experiments and suggests developmental timing or preparation, rather than technical variation, as a cause. The ‘Robustness of sci-RNA-seq’ section reports correlations between experiments, but neither section compares variance across individuals with synthetic replicates or reports Lin’s concordance of 0.007–0.20 (searched those terms and the variance comparison). (LLM, conf=0.75)
Experimental — accuracy not yet measured
In typical single-cell RNA-sequencing experiments, at least a few percent of capture events are multiplets.
“In a typical scRNA-seq experiment, at least several percent of all capture events are multiplets (Cao et al., 2017; Klein et al., 2015; Macosko et al., 2015; Zheng et al., 2017).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Cao et al., 2017
The combinatorial barcoding method's utility has been demonstrated for DNA sequencing, RNA-seq and chromatin accessibility assays.
“The method's utility has been demonstrated for DNA sequencing (Vitak et al, 2017), RNA‐seq (Cao et al, 2017) and chromatin accessability assays (Cusanovich et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Cao et al, 2017

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Authors: Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., Adey, A., Waterston, R. H., Trapnell, C., & Shendure, J.
Affiliations: University of Washington (US)
Illumina (United States) (US)
Oregon Health & Science University (US)
Howard Hughes Medical Institute (US)
Corresponding author: Waterston, Robert H.
Trapnell, Cole
Shendure, Jay
ORCID: Cao, Junyue
Packer, Jonathan S.
Ramani, Vijay
Cusanovich, Darren A.
Daza, Riza
Qiu, Xiaojie
Lee, Choli
Furlan, Scott N.
Adey, Andrew
Waterston, Robert H.
Trapnell, Cole
Shendure, Jay
Venue: Science
Publisher: openRxiv
Published: 2017-02-02
Volume: 357
Issue: 6352
Pages: 661-667
ISSN: 0036-8075 (ISSN-L)
DOI: 10.1101/104844
PMC: PMC5894354
OpenAlex: W2747545374
Cited by: 1,402
References: 168
Citations by year: 2026: 86 · 2025: 139 · 2024: 189 · 2023: 153 · 2022: 171 · 2021: 186 · 2020: 214 · 2019: 115 · 2018: 118 · 2017: 31
FWCI: 53.86
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Science (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Howard Hughes Medical Institute (award336988)
W. M. Keck Foundation (award337002, award300298)
Paul G. Allen Family Foundation (award336997)
National Institutes of Health (award300293, award300285, award300296, award336992, award300288)
National Heart, Lung, and Blood Institute (award300306, T32HL007828)
National Human Genome Research Institute (DP1HG007811, U41HG007355, R01HG006283)
National Institute of General Medical Sciences (R01GM072675, R35GM124704)
National Institute of Diabetes and Digestive and Kidney Diseases (U54DK107979)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD088158)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords: single-cell transcriptomics, Caenorhabditis elegans, cell type atlas, neuronal cell types, rare cell types, transcription factor binding, ChIP-seq
MeSH terms: Animals, Cell Nucleus, Humans, Larva, Neurons, RNA, Transcription Factors, Caenorhabditis elegans, Sequence Analysis, RNA, NIH 3T3 Cells, Chromatin Immunoprecipitation, Mice, HEK293 Cells, Single-Cell Analysis, Transcriptome

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Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

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