Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
Open accessPublished version of a preprint
Abstract
ABSTRACT Despite rapid developments in single cell sequencing technology, sample-specific batch effects, detection of cell doublets, and the cost of generating massive datasets remain outstanding challenges. Here, we introduce cell “hashing”, where oligo-tagged antibodies against ubiquitously expressed surface proteins are used to 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 sample of origin, and robustly identify doublets originating from multiple samples. We demonstrate our approach by pooling eight human PBMC samples on a single run of the 10x Chromium system, substantially reducing our per-cell costs for library generation. Cell “hashing” is inspired by, and complementary to, elegant multiplexing strategies based on genetic variation, which we also leverage to validate our results. We therefore envision that our approach will help to generalize the benefits of single cell multiplexing to diverse samples and experimental designs.
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.
Integrity signals
Registry
Cites
Statistics
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SNPs can be obtained from gDNA using SNP arrays at a similar cost.
“In the absence of this RNA-Seq data, at a similar cost, SNPs can be obtained from gDNA using SNP arrays [17].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [17]
The Methods (PBMC genotyping; page unavailable) state that the authors purified genomic DNA and genotyped it with an Infinium CoreExome 24 array, supporting the SNP-array method. The Discussion (page unavailable) discusses sequencing and library-preparation costs, but does not compare SNP-array genotyping costs with obtaining SNPs from RNA-seq data. The “similar cost” qualification is therefore unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Integrated analysis of single cell transcriptomic data across conditions, technologies, and species
Butler, A., & Satija, R. (2017) · doi:10.1101/164889 · Cited by 13,200 · cited 1× · see citing works
“0, Satija Lab [29]) which enables the integrated processing of multi-modal (RNA, ADT, HTO) single cell datasets [31, 32].”
BackgroundSelf-citationIntegrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Self-citationIn situ click chemistry generation of cyclooxygenase-2 inhibitors
Bhardwaj, A., Kaur, J., Wuest, M., & Wuest, F. (2017) · Nature Communications · doi:10.1038/s41467-016-0009-6 · Cited by 7,448 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Self-citationSingle-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · see citing works
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationMultiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · cited 8× · see citing works
“[13], we “super-loaded” the 10x Genomics instrument, loading cells at a significantly higher concentration with an expected yield of 20,000 single cells and 5000 multiplets.”
BackgroundMethodResultInfluentialComprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Batch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356 · see citing works
Lake, B. B., Codeluppi, S., Yung, Y. C., Gao, D., Chun, J., Kharchenko, P. V., Linnarsson, S., & Zhang, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-04426-w · Cited by 271 · see citing works
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101 · see citing works
Flagged first, then by citations
Research works printed but not linked: 26
- [1]Stubbington MJT Rozenblatt-Rosen O Regev A Teichmann SA Single-cell transcriptomics to explore the immune system in health and disease Science 2017 358 58 63 10.1126/science.aan6828 28983043 PMC5654495 · DOI
- [2]Tanay A Regev A Scaling single-cell genomics from phenomenology to mechanism Nature 2017 541 331 338 10.1038/nature21350 28102262 PMC5438464 · DOI · Paper page
- [4]Velten L Human haematopoietic stem cell lineage commitment is a continuous process Nat Cell Biol 2017 19 271 281 10.1038/ncb3493 28319093 PMC5496982 · DOI · Paper page
- [5]Cao J Comprehensive single-cell transcriptional profiling of a multicellular organism Science 2017 357 661 667 10.1126/science.aam8940 28818938 PMC5894354 · DOI · Paper page
- [6]Karaiskos N The Drosophila embryo at single-cell transcriptome resolution Science 2017 8 eaan3235 eaan3214 28860209
- [7]Macosko EZ Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets Cell 2015 161 1202 1214 10.1016/j.cell.2015.05.002 26000488 PMC4481139 · DOI · Paper page
- [8]Klein AM Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells Cell 2015 161 1187 1201 10.1016/j.cell.2015.04.044 26000487 PMC4441768 · DOI · Paper page
- [12]Hicks SC, Townes FW, Teng M, Irizarry RA. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics. 2017. 10.1093/biostatistics/kxx053. PMC6215955 29121214 · DOI · Paper page
- [15]Mei HE Leipold MD Schulz AR Chester C Maecker HT Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry J Immunol 2015 194 2022 2031 10.4049/jimmunol.1402661 25609839 PMC4323739 · DOI
- [16]Lai L Ong R Li J Albani S A CD45-based barcoding approach to multiplex mass-cytometry (CyTOF) Cytometry 2015 87 369 374 10.1002/cyto.a.22640 25645694 PMC4670699 · DOI
- [17]Krutzik PO Nolan GP Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling Nat Meth 2006 3 361 368 10.1038/nmeth872 16628206 · DOI
- [18]Stoeckius M Simultaneous epitope and transcriptome measurement in single cells Nat Meth 2017 9 2579 2510 PMC5669064 28759029
- [19]van Buggenum JAGL, et al. A covalent and cleavable antibody- DNA conjugation strategy for sensitive protein detection via immuno-PCR. Nat Publ Group. 2016:1–12. 10.1038/srep22675. PMC4780193 26947912 · DOI · Paper page
- [20]Hulspas R Titration of fluorochrome-conjugated antibodies for labeling cell surface markers on live cells Curr Protoc Cytom 2010 Chapter 6 Unit 6 29 20938920
- [21]Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C. Tissue-based map of the human proteome. Science. 2015. 10.1126/science.1260419. 25613900 · DOI · Paper page
- [22]Hartmann FJ, Simonds EF, Bendall SC. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci Rep. 2018;8(10770). PMC6050312 30018331
- [25]Delley CL, liu L, Sarhan MF, Abate AR. Combined aptamer and transcriptome sequencing of single cells. bioRxiv. 2017:1–10. 10.1101/228338. PMC5811598 29440771 · DOI
- [26]Shin D Lee W Lee JH bioRxiv DB Multiplexed single-cell RNA-seq via transient barcoding for drug screening 2018 PMC6520024 31106268
- [27]Gehring J, Park JH, Chen S, Thomson M, Pachter L. Highly multiplexed single-cell RNA-seq for defining cell population and transcriptional spaces. bioRxiv. 2018:1–19. 10.1101/315333. · DOI
- [28]McGinnis, C. S. et al. MULTI-seq: scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices 1–34 (2018). doi:10.1101/387241. PMC6837808 31209384 · DOI
- [29]Stuart et al. Comprehensive integration of single cell data. biorxiv. 2018. 10.1101/460147. · DOI
- [30]Guo C, Biddy BA, Kamimoto K, Kong W, Morris SA. CellTag indexing: a genetic barcode-based multiplexing tool for single-cell technologies. 2018:1–20. 10.1101/335547. PMC6509836 31072405 · DOI
- [33]Blondel VD Guillaume J-L Lambiotte R Lefebvre E Fast unfolding of communities in large networks J Stat Mech 2008 2008 P10008 10.1088/1742-5468/2008/10/P10008 · DOI · Paper page
- [34]Levine JH Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis Cell 2015 162 184 197 10.1016/j.cell.2015.05.047 26095251 PMC4508757 · DOI · Paper page
- [36]Wright MN, Ziegler A. Ranger: a fast implementation of random forests for high dimensional data in C and R. J Stat Softw. 2017;77:1–17.
- [37]Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung B, Smibert P, and Satija R. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108313 (2018). PMC6300015 30567574
Cited by
Sorted by citations
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Self-citationIntegrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Self-citationMcGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Joint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works
MAIT cells are imprinted by the microbiota in early life and promote tissue repair
Constantinides, M. G., Link, V. M., Tamoutounour, S., Wong, A. C., Perez-Chaparro, P. J., Han, S.-J., Chen, Y. E., Li, K., Farhat, S., Weckel, A., et al. (2019) · Science · doi:10.1126/science.aax6624 · Cited by 567 · see citing works
Mimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
Self-citationMimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Self-citationSouporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020) · Nature Methods · doi:10.1038/s41592-020-0820-1 · Cited by 506 · see citing works
Massively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401 · see citing works
Magen, A., Hamon, P., Fiaschi, N., Soong, B. Y., Park, M. D., Mattiuz, R., Humblin, E., Troncoso, L., D’souza, D., Dawson, T., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02345-0 · Cited by 429 · see citing works
Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation
Fabre, T., Barron, A. M. S., Christensen, S. M., Asano, S., Bound, K., Lech, M. P., Wadsworth, M. H., Chen, X., Wang, C., Wang, J., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.add8945 · Cited by 355 · see citing works
Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
Huang, Y., McCarthy, D. J., & Stegle, O. (2019) · Genome Biology · doi:10.1186/s13059-019-1865-2 · Cited by 348 · see citing works
Somatic mutations and cell identity linked by Genotyping of Transcriptomes
Nam, A. S., Kim, K.-T., Chaligne, R., Izzo, F., Ang, C., Taylor, J., Myers, R. M., Abu-Zeinah, G., Brand, R., Omans, N. D., et al. (2019) · Nature · doi:10.1038/s41586-019-1367-0 · Cited by 333 · see citing works
Self-citationChallenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
Neuronal diversity and convergence in a visual system developmental atlas
Özel, M. N., Simon, F., Jafari, S., Holguera, I., Chen, Y.-C., Benhra, N., El-Danaf, R. N., Kapuralin, K., Malin, J. A., Konstantinides, N., et al. (2021) · Nature · doi:10.1038/s41586-020-2879-3 · Cited by 237 · see citing works
Mechanisms of Resistance to Noncovalent Bruton’s Tyrosine Kinase Inhibitors
Wang, E., Mi, X., Thompson, M. C., Montoya, S., Notti, R. Q., Afaghani, J., Durham, B. H., Penson, A., Witkowski, M. T., Lu, S. X., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2114110 · Cited by 272 · see citing works
Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 238 · see citing works
“We hashed each cell line separately [25] prior to running our ECCITE-seq experiment.”
InfluentialSelf-citationBenchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208 · see citing works
Cottam, M. A., Caslin, H. L., Winn, N. C., & Hasty, A. H. (2022) · Nature Communications · doi:10.1038/s41467-022-30646-4 · Cited by 216 · see citing works
The spatiotemporal program of zonal liver regeneration following acute injury
Ben-Moshe, S., Veg, T., Manco, R., Dan, S., Papinutti, D., Lifshitz, A., Kolodziejczyk, A. A., Bahar Halpern, K., Elinav, E., & Itzkovitz, S. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.04.008 · Cited by 224 · see citing works
Normalizing and denoising protein expression data from droplet-based single cell profiling
Mulè, M. P., Martins, A. J., & Tsang, J. S. (2022) · Nature Communications · doi:10.1038/s41467-022-29356-8 · Cited by 197 · see citing works
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173 · see citing works
Experimental Considerations for Single-Cell RNA Sequencing Approaches
Nguyen, Q. H., Pervolarakis, N., Nee, K., & Kessenbrock, K. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00108 · Cited by 170 · see citing works
Bowles, K. R., Silva, M. C., Whitney, K., Bertucci, T., Berlind, J. E., Lai, J. D., Garza, J. C., Boles, N. C., Mahali, S., Strang, K. H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.003 · Cited by 175 · see citing works
Targeting p21Cip1 highly expressing cells in adipose tissue alleviates insulin resistance in obesity
Wang, L., Wang, B., Gasek, N. S., Zhou, Y., Cohn, R. L., Martin, D. E., Zuo, W., Flynn, W. F., Guo, C., Jellison, E. R., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.002 · Cited by 163 · see citing works
Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins
Gehring, J., Hwee Park, J., Chen, S., Thomson, M., & Pachter, L. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0372-z · Cited by 122 · see citing works
Chen, A. F., Parks, B., Kathiria, A. S., Ober-Reynolds, B., Goronzy, J. J., & Greenleaf, W. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01461-y · Cited by 139 · see citing works
Joint single-cell measurements of nuclear proteins and RNA in vivo
Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144 · see citing works
Self-citationMachine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · see citing works
Barriga, F. M., Tsanov, K. M., Ho, Y.-J., Sohail, N., Zhang, A., Baslan, T., Wuest, A. N., Del Priore, I., Meškauskaitė, B., Livshits, G., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00443-5 · Cited by 125 · see citing works
TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures
Tu, A. A., Gierahn, T. M., Monian, B., Morgan, D. M., Mehta, N. K., Ruiter, B., Shreffler, W. G., Shalek, A. K., & Love, J. C. (2019) · Nature Immunology · doi:10.1038/s41590-019-0544-5 · Cited by 128 · see citing works
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101 · see citing works
Autoreactive CD8+ T cells are restrained by an exhaustion-like program that is maintained by LAG3
Grebinoski, S., Zhang, Q., Cillo, A. R., Manne, S., Xiao, H., Brunazzi, E. A., Tabib, T., Cardello, C., Lian, C. G., Murphy, G. F., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01210-5 · Cited by 107 · see citing works
Trzupek, D., Dunstan, M., Cutler, A. J., Lee, M., Godfrey, L., Jarvis, L., Rainbow, D. B., Aschenbrenner, D., Jones, J. L., Uhlig, H. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00756-z · Cited by 105 · see citing works
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104 · see citing works
Self-citationBiologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq
Wessels, H.-H., Méndez-Mancilla, A., Hao, Y., Papalexi, E., Mauck, W. M., Lu, L., Morris, J. A., Mimitou, E. P., Smibert, P., Sanjana, N. E., et al. (2023) · Nature Methods · doi:10.1038/s41592-022-01705-x · Cited by 79 · see citing works
Self-citationAlveolar Differentiation Drives Resistance to <i>KRAS</i> Inhibition in Lung Adenocarcinoma
Li, Z., Zhuang, X., Pan, C.-H., Yan, Y., Thummalapalli, R., Hallin, J., Torborg, S., Singhal, A., Chang, J. C., Manchado, E., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0289 · Cited by 74 · see citing works
A dynamic atlas of immunocyte migration from the gut
Galván-Peña, S., Zhu, Y., Hanna, B. S., Mathis, D., & Benoist, C. (2024) · Science Immunology · doi:10.1126/sciimmunol.adi0672 · Cited by 74 · see citing works
Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition
Bues, J., Biočanin, M., Pezoldt, J., Dainese, R., Chrisnandy, A., Rezakhani, S., Saelens, W., Gardeux, V., Gupta, R., Sarkis, R., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01391-1 · Cited by 71 · see citing works
Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC
Fiskin, E., Lareau, C. A., Ludwig, L. S., Eraslan, G., Liu, F., Ring, A. M., Xavier, R. J., & Regev, A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01065-5 · Cited by 68 · see citing works
Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView
Kudo, T., Meireles, A. M., Moncada, R., Chen, Y., Wu, P., Gould, J., Hu, X., Kornfeld, O., Jesudason, R., Foo, C., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02391-0 · Cited by 74 · see citing works
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
Mandric, I., Schwarz, T., Majumdar, A., Hou, K., Briscoe, L., Perez, R., Subramaniam, M., Hafemeister, C., Satija, R., Ye, C. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19365-w · Cited by 62 · see citing works
Self-citationComparative 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., et al. (2022) · Genome Biology · doi:10.1186/s13059-022-02628-8 · Cited by 53 · see citing works
Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments
Bloom, J. D. (2018) · PeerJ · doi:10.7717/peerj.5578 · Cited by 46 · see citing works
Sample-multiplexing approaches for single-cell sequencing
Zhang, Y., Xu, S., Wen, Z., Gao, J., Li, S., Weissman, S. M., & Pan, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04482-0 · Cited by 40 · see citing works
Shangguan, S., Ehrenberg, P. K., Geretz, A., Yum, L., Kundu, G., May, K., Fourati, S., Nganou-Makamdop, K., Williams, L. D., Sawant, S., et al. (2021) · eLife · doi:10.7554/elife.69577 · Cited by 26 · see citing works
Evaluation of genetic demultiplexing of single-cell sequencing data from model species
Cardiello, J. F., Joven Araus, A., Giatrellis, S., Helsens, C., Simon, A., & Leigh, N. D. (2023) · Life Science Alliance · doi:10.26508/lsa.202301979 · Cited by 21 · see citing works
“The most common method for scRNA-seq pooling is cell hashing, in which cells from each sample are labeled with antibodies (Stoeckius et al, 2018), lipids (McGinnis et al, 2019), or chemicals (Gehring et al, 2020) tethered to an oligonucleotide label that links gene expression data from each cell to…”
MethodDemuxalot: scaled up genetic demultiplexing for single-cell sequencing
Rogozhnikov, A., Ramkumar, P., Shah, K., Bedi, R., Kato, S., & Escola, G. S. (2021) · doi:10.1101/2021.05.22.443646 · Cited by 19 · see citing works
Helios represses megakaryocyte priming in hematopoietic stem and progenitor cells
Cova, G., Taroni, C., Deau, M.-C., Cai, Q., Mittelheisser, V., Philipps, M., Jung, M., Cerciat, M., Le Gras, S., Thibault-Carpentier, C., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20202317 · Cited by 17 · see citing works
Improved ClickTags enable live-cell barcoding for highly multiplexed single cell sequencing
Zhao, X., Sun, S., Yu, W., Zhu, W., Zhao, Z., Zhou, Y., Ding, X., Fang, N., Yang, R., & Li, J. P. (2022) · RSC Chemical Biology · doi:10.1039/d2cb00046f · Cited by 8 · see citing works
Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression
Nassiri, I., Kwok, A. J., Bhandari, A., Bull, K. R., Garner, L. C., Klenerman, P., Webber, C., Parkkinen, L., Lee, A. W., Wu, Y., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae085 · Cited by 8 · see citing works
Determinants, mechanisms, and functional outcomes of myeloid cell diversity in cancer
Caronni, N., Montaldo, E., Mezzanzanica, L., Cilenti, F., Genua, M., & Ostuni, R. (2021) · Immunological Reviews · doi:10.1111/imr.12944 · Cited by 6 · see citing works
Type 1 interferon perturbates clonal competition by reshaping human blood development
Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02751-3 · Cited by 3 · see citing works
Self-citationLabel-free single-cell RNA multiplexing leveraging genetic variability
Hoeft, K., Bleckwehl, T., Schumacher, D., Kim, H., Meyer, R., Long, Q., Zhang, L., Möller, C., Clahsen-van Groningen, M. C., Babler, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54270-6 · Cited by 3 · see citing works
Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data
Fu, Y., Youness, M., Virzì, A., Song, X., Tubeeckx, M. R. L., De Keulenaer, G. W., Heidbuchel, H., Segers, V. F. M., Sipido, K. R., Thienpont, B., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf371 · Cited by 2 · see citing works
Lebeaupin, C., Donahue, K. L., Lal, S., Christensen, S. M., Asano, S., Wadsworth, M. H., Bound, K., De, S., McMahon, J., Schlerman, F., et al. (2025) · bioRxiv (preprint) · doi:10.64898/2025.12.17.694415
Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing
Meier, M., Howitt, G., Grima, C. V., Dixit, G., King, H., Gubbels, L., Levi, E., Davies, K., Shanthikumar, S., Neeland, M. R., et al. (2026) · doi:10.64898/2026.10.13.758369
“As scRNA-seq experiments continue to grow in scale, multiplexing technology allows for cells from different samples to be pooled in a single capture [8].”
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · see citing works
- Authors
- Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R.
- Affiliations
- New York Genome Center (US)
- BioLegend (United States) (US)
- Corresponding author
- Smibert, Peter
- Satija, Rahul
- ORCID
- Venue
- Genome Biology
- Published
- 2018-12
- Volume
- 19
- Issue
- 1
- Pages
- 224-224
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC6300015
- OpenAlex
- W2785085486
- Cited by
- 1,046
- References
- 14
- Citations by year
- 2026: 110 · 2025: 143 · 2024: 192 · 2023: 150 · 2022: 161 · 2021: 140 · 2020: 90 · 2019: 40 · 2018: 18 · 2013: 1
- FWCI
- 26.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Human Genome Research Institute (DP2-HG-009623, NIHR21-HG-009748)
- Chan Zuckerberg Initiative (HCA-A1704-01895, HCA2-A-1708-02755)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Keywords
- cell hashing, doublet detection, single-cell genomics, cost reduction
- MeSH terms
- Animals, Humans, Immunologic Techniques, Oligonucleotides, Staining and Labeling, 3T3 Cells, Genomics, Mice, HEK293 Cells, Single-Cell Analysis
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
