Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics - PubMed (original) (raw)
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
Marlon Stoeckius et al. Genome Biol. 2018.
Abstract
Despite rapid developments in single cell sequencing, sample-specific batch effects, detection of cell multiplets, and experimental costs remain outstanding challenges. Here, we introduce Cell Hashing, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its original sample, robustly identify cross-sample multiplets, and "super-load" commercial droplet-based systems for significant cost reduction. We validate our approach using a complementary genetic approach and demonstrate how hashing can generalize the benefits of single cell multiplexing to diverse samples and experimental designs.
Conflict of interest statement
Ethics approval and consent to participate
Not applicable for this study.
Competing interests
MS, PS and BHL have filed a patent application based on this work (US provisional patent application 62/515-180). BZY is an employee at BioLegend Inc., which is the exclusive licensee of the New York Genome Center patent application related to this work. All other authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figures
Fig. 1
Sample multiplexing using DNA-barcoded antibodies. a Schematic overview of sample multiplexing by Cell Hashing. Cells from different samples are incubated with DNA-barcoded antibodies recognizing ubiquitous cell surface proteins. Distinct barcodes (referred to as hashtag-oligos, HTO) on the antibodies allow pooling of multiple samples into one scRNA-seq experiment. After sequencing, cells can be assigned to their sample of origin based on HTO levels (“Methods” section). b Representative scatter plot showing raw counts for HTO A and HTO B across all cell barcodes. Both axes are clipped at 99.9% quantiles to exclude visual outliers. c Heatmap of scaled (_z_-scores) normalized HTO values based on our classifications. Multiplets express more than one HTO. Negative populations contain HEK293T and mouse NIH-3T3 cells that were spiked into the experiments as negative controls. d tSNE embedding of the HTO dataset. Cells are colored and labeled based on our classifications. Eight singlet clusters and all 28 cross-sample doublet clusters are clearly present. e Distribution of RNA UMIs per cell barcode in cells that were characterized as singlets (red), multiplets (violet) or negatives (grey). f Transcriptome-based clustering of single-cell expression profiles reveals distinct immune cell populations interspersed across donors. B, B cells; T, T cells; NK, natural killer cells; mono, monocytes; DC, dendritic cells. Cells are colored based on their HTO classification (donor ID), as in d
Fig. 2
Validation of Cell Hashing using demuxlet. a Row-normalized “confusion matrix” comparing demuxlet and HTO classifications. Each value on the diagonal represents the fraction of barcodes for a given HTO classification that received an identical classification from demuxlet. b Count distribution of the most highly expressed HTO for groups of concordant and discordant singlets. Both groups have identical classification strength based on Cell Hashing. c Discordant singlets have lower UMI counts, suggesting that a lack of sequencing depth contributed to “ambiguous” calls from demuxlet. d RNA UMI distributions for discordant and concordant multiplets. Only concordant multiplets exhibit increased molecular complexity, suggesting that both methods are conservatively overcalling multiplets in discordant cases. e In support of this, demuxlet assigns lower multiplets posterior probabilities to discordant calls
Fig. 3
Cell Hashing enables efficient experimental optimization and identification of low-quality cells. a–c We performed a titration series to assess optimal staining concentrations for a panel of CITE-seq immunophenotyping antibodies. Normalized ADT counts for CD8 (a), CD45RA (b), and CD4 (c) are depicted for the different concentrations used per test. d Titration curve depicting the staining index (SI; “Methods” section) for these three antibodies across the titration series. The signal/noise ratio for these antibodies begins to saturate at levels similar to manufacturer recommended staining concentrations typical for flow cytometry antibodies. e Cells with low UMI counts can be distinguished from ambient RNA using HTO classifications. Classified singlets group into canonical hematopoietic populations. f Barcodes classified as “negative” do not group into clusters and likely represent “empty” droplets containing only ambient RNA
Similar articles
- Nuclei multiplexing with barcoded antibodies for single-nucleus genomics.
Gaublomme JT, Li B, McCabe C, Knecht A, Yang Y, Drokhlyansky E, Van Wittenberghe N, Waldman J, Dionne D, Nguyen L, De Jager PL, Yeung B, Zhao X, Habib N, Rozenblatt-Rosen O, Regev A. Gaublomme JT, et al. Nat Commun. 2019 Jul 2;10(1):2907. doi: 10.1038/s41467-019-10756-2. Nat Commun. 2019. PMID: 31266958 Free PMC article. - Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq.
Mylka V, Matetovici I, Poovathingal S, Aerts J, Vandamme N, Seurinck R, Verstaen K, Hulselmans G, Van den Hoecke S, Scheyltjens I, Movahedi K, Wils H, Reumers J, Van Houdt J, Aerts S, Saeys Y. Mylka V, et al. Genome Biol. 2022 Feb 16;23(1):55. doi: 10.1186/s13059-022-02628-8. Genome Biol. 2022. PMID: 35172874 Free PMC article. - hadge: a comprehensive pipeline for donor deconvolution in single-cell studies.
Curion F, Wu X, Heumos L, André MMG, Halle L, Ozols M, Grant-Peters M, Rich-Griffin C, Yeung HY, Dendrou CA, Schiller HB, Theis FJ. Curion F, et al. Genome Biol. 2024 Apr 26;25(1):109. doi: 10.1186/s13059-024-03249-z. Genome Biol. 2024. PMID: 38671451 Free PMC article. - Sample-multiplexing approaches for single-cell sequencing.
Zhang Y, Xu S, Wen Z, Gao J, Li S, Weissman SM, Pan X. Zhang Y, et al. Cell Mol Life Sci. 2022 Aug 5;79(8):466. doi: 10.1007/s00018-022-04482-0. Cell Mol Life Sci. 2022. PMID: 35927335 Free PMC article. Review. - Turning single cells into microarrays by super-resolution barcoding.
Cai L. Cai L. Brief Funct Genomics. 2013 Mar;12(2):75-80. doi: 10.1093/bfgp/els054. Epub 2012 Nov 22. Brief Funct Genomics. 2013. PMID: 23178478 Free PMC article. Review.
Cited by
- Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView.
Kudo T, Meireles AM, Moncada R, Chen Y, Wu P, Gould J, Hu X, Kornfeld O, Jesudason R, Foo C, Höckendorf B, Corrada Bravo H, Town JP, Wei R, Rios A, Chandrasekar V, Heinlein M, Chuong AS, Cai S, Lu CS, Coelho P, Mis M, Celen C, Kljavin N, Jiang J, Richmond D, Thakore P, Benito-Gutiérrez E, Geiger-Schuller K, Hleap JS, Kayagaki N, de Sousa E Melo F, McGinnis L, Li B, Singh A, Garraway L, Rozenblatt-Rosen O, Regev A, Lubeck E. Kudo T, et al. Nat Biotechnol. 2024 Oct 7. doi: 10.1038/s41587-024-02391-0. Online ahead of print. Nat Biotechnol. 2024. PMID: 39375449 - ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.
Thomas RA, Fiorini MR, Amiri S, Fon EA, Farhan SMK. Thomas RA, et al. BMC Bioinformatics. 2024 Oct 1;25(1):319. doi: 10.1186/s12859-024-05935-y. BMC Bioinformatics. 2024. PMID: 39354372 Free PMC article. - scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison.
Ferrena A, Zheng XY, Jackson K, Hoang B, Morrow BE, Zheng D. Ferrena A, et al. NAR Genom Bioinform. 2024 Sep 28;6(4):lqae134. doi: 10.1093/nargab/lqae134. eCollection 2024 Sep. NAR Genom Bioinform. 2024. PMID: 39345754 Free PMC article. - Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition.
Mitchel J, Gordon MG, Perez RK, Biederstedt E, Bueno R, Ye CJ, Kharchenko PV. Mitchel J, et al. Nat Biotechnol. 2024 Sep 23. doi: 10.1038/s41587-024-02411-z. Online ahead of print. Nat Biotechnol. 2024. PMID: 39313646 - Immunological landscape of human lymphoid explants during measles virus infection.
Acklin JA, Patel AR, Kurland AP, Horiuchi S, Moss AS, DeGrace EJ, Ikegame S, Carmichael J, Kowdle S, Thibault PA, Imai N, Ueno H, Tweel B, Johnson JR, Rosenberg BR, Lee B, Lim JK. Acklin JA, et al. JCI Insight. 2024 Jul 25;9(17):e172261. doi: 10.1172/jci.insight.172261. JCI Insight. 2024. PMID: 39253971 Free PMC article.
References
Publication types
MeSH terms
Substances
Grants and funding
- DP2-HG-009623/HG/NHGRI NIH HHS/United States
- R01 MH071679/MH/NIMH NIH HHS/United States
- DP2 HG009623/HG/NHGRI NIH HHS/United States
- R35 NS097404/NS/NINDS NIH HHS/United States
- OT2 OD026673/OD/NIH HHS/United States
- R21 HG009748/HG/NHGRI NIH HHS/United States
- NIHR21-HG-009748/HG/NHGRI NIH HHS/United States
LinkOut - more resources
Full Text Sources
Other Literature Sources