PreprintCited by 111Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

Open access

Preprint — published as 10.1016/j.celrep.2020.03.063

Abstract

Summary: High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/700534

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 38

  1. 2014, Cell-type-resolved quantitative proteomics of murine liver, Cell Metab, 20, 1076, 10.1016/j.cmet.2014.11.002 · DOI · Paper page
  2. 10.1038/nbt.4096 · DOI · Paper page
  3. 10.1038/s41586-019-0969-x · DOI · Paper page
  4. 2016, Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding, The Journal of Immunology, 196, 924, 10.4049/jimmunol.1501928 · DOI · Paper page
  5. 10.1016/S1534-5807(03)00035-2 · DOI
  6. 2019, The RNA-Binding Protein YBX3 Controls Amino Acid Levels by Regulating SLC mRNA Abundance, Cell Rep, 27, 3097, 10.1016/j.celrep.2019.05.039 · DOI
  7. 10.1002/eji.201646632 · DOI
  8. 10.1182/blood-2002-01-0160 · DOI · Paper page
  9. 10.1126/science.1258367 · DOI · Paper page
  10. 10.1186/s13059-015-0844-5 · DOI · Paper page
  11. 2018, Selective Clustering Annotated using Modes of Projections, arXiv preprint
  12. 10.1038/nri3712 · DOI
  13. 2016, A Matter of Perspective: Moving from a Pre-omic to a Systems-Biology Vantage of Monocyte-Derived Cell Function and Nomenclature, Immunity, 44, 5, 10.1016/j.immuni.2015.12.020 · DOI
  14. 10.1084/jem.186.9.1407 · DOI · Paper page
  15. 10.1016/j.immuni.2015.05.018 · DOI
  16. 10.1126/science.1247651 · DOI · Paper page
  17. 10.1016/j.cell.2015.04.044 · DOI · Paper page
  18. 10.1016/j.cell.2015.05.047 · DOI · Paper page
  19. 10.1038/s41592-018-0308-4 · DOI
  20. 10.1016/j.cell.2015.05.002 · DOI · Paper page
  21. 2018, OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment, Cytometry A, 106, 255
  22. McInnes, L. , Healy, J. , and Melville, J.J.a.p.a. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. · DOI · Paper page
  23. 10.1146/annurev-immunol-032414-112008 · DOI
  24. 10.1038/nri.2017.49 · DOI
  25. 2012, Quality assurance for polychromatic flow cytometry using a suite of calibration beads, Nature protocols, 7, 2067, 10.1038/nprot.2012.126 · DOI · Paper page
  26. 10.1038/nbt.3973 · DOI · Paper page
  27. 10.1006/meth.1996.0104 · DOI
  28. 10.1146/annurev.immunol.22.012703.104702 · DOI · Paper page
  29. 10.1038/44385 · DOI · Paper page
  30. 2017, Mapping the human DC lineage through the integration of high-dimensional techniques, Science, 66, eaag3009
  31. Slichter, C.K. , McDavid, A. , Miller, H.W. , Finak, G. , Seymour, B.J. , McNevin, J.P. , Diaz, G. , Czartoski, J.L. , McElrath, M.J. , Gottardo, R. , et al. (2016). Distinct activation thresholds of human conventional and innate-like memory T cells. JCI Insight 1. · DOI
  32. 2017, Simultaneous epitope and transcriptome measurement in single cells, Nature Methods, 14, 865, 10.1038/nmeth.4380 · DOI · Paper page
  33. 2018, Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics, Genome Biol, 19, 224, 10.1186/s13059-018-1603-1 · DOI · Paper page
  34. 2019, Integrative single-cell analysis, Nat Rev Genet, 20, 257, 10.1038/s41576-019-0093-7 · DOI · Paper page
  35. 10.1038/s41577-018-0001-y · DOI
  36. 2017, Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors, Science, 356, 1
  37. 2017, Massively parallel digital transcriptional profiling of single cells, Nature communications, 8, 14049, 10.1038/ncomms14049 · DOI · Paper page
  38. 10.1016/j.immuni.2009.05.001 · DOI · Paper page

Metadata

Authors
Mair, F., Erickson, J. R., Voillet, V., Simoni, Y., Bi, T., Tyznik, A. J., Martin, J., Gottardo, R., Newell, E. W., & Prlic, M.
Affiliations
  • Fred Hutch Cancer Center (US)
  • BD Biosciences (United States) (US)
  • University of Washington (US)
Corresponding author
  • Newell, Evan W.
  • Prlic, Martin
Venue
Cell Reports
Publisher
openRxiv
Published
2019-07-14
Volume
31
Issue
1
Pages
107499-107499
ISSN
2211-1247 (ISSN-L)
OpenAlex
W2960849263
Cited by
111
References
1
Citations by year
2026: 10 · 2025: 17 · 2024: 11 · 2023: 16 · 2022: 24 · 2021: 20 · 2020: 13
FWCI
5.92
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cell Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • SSRN Electronic Journal (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Fred Hutchinson Cancer Research Center
  • National Institutes of Health
  • National Institute of Allergy and Infectious Diseases (R01AI121129, T32AI007509, U19AI128914, R01AI123323)
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 › Gene expression and cancer classification
Keywords
CITE-seq, single-cell multi-omics, protein expression, low-abundance transcripts, immune cell heterogeneity, data visualization
MeSH terms
Epitopes, Humans, RNA, Software, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Proteomics, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review