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Massively multiplex chemical transcriptomics at single-cell resolution
Open accessAbstract
Single-cell chemical transcriptomics: Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Srivatsan et al. now add another layer of information and complexity by combining single-cell transcriptomics with oligo hashing and small molecule screening in a method called sci-Plex. Because screening many chemical compounds requires the ability to profile many cells, and because screens perturb cells in many different ways, the authors demonstrate the effects of 188 compounds in three cancer lines. The sci-Plex method can capture gene expression profiles from thousands of experimental conditions in a single experiment. Science , this issue p. 45
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“The most extensive drug dataset is sci-Plex 3, which includes 188 drugs tested across three cell lines52; 107 of those perturbations had significant effects on cell states according to E-test analysis (Supplementary Table 3).”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [52]
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“along with the ability to couple these measurements with systematic perturbations.27–29”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [28]
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“Here, high-throughput perturbation screens—based on RNA-seq,28,83,90 optical pooled screens (OPS),29,39,91 or other technologies—offer a rich resource through which the AIVC can be trained to predict these effects.”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [28]
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“It has been shown that compounds eliciting similar transcriptional signatures tend to share mechanisms of action.170,207−209”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [170]
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“As HDAC inhibitors act partly through the deprivation of acetyl-CoA, we expect that the relative abundance of acetyl-CoA precursors within a cell would modulate the effect of HDAC inhibitor exposure, and acetyl-CoA precursors can be considered confounders8 (Fig. 3e).”
From Causal identification of single-cell experimental perturbation effects with CINEMA-OT (2023) · cites this paper as [8]
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“This holds for a large set of major mechanisms: we find that epigenetic, tyrosine kinase signaling, and protein formation compounds are clustered together by the model, which suggests the effectiveness of drugs with these mechanisms on these three cancer cell lines which is in line with the findings in the original publication (Srivatsan et al, 2020).”
From Predicting cellular responses to complex perturbations in high‐throughput screens (2023) · cites this paper as Srivatsan et al, 2020
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“Furthermore, we observed that chemCPA generalized particularly well for compounds that belonged to the histone deacetylation pathway (Fig 4C), which is in line with the original sci‐Plex publication (Srivatsan et al, 2020) and general support within this perturbation dataset.”
From Predicting cellular responses to complex perturbations in high‐throughput screens (2023) · cites this paper as Srivatsan et al, 2020
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Self-citationUMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
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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
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Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194
Wu, Y., & Xie, L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.030 · Cited by 220
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Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Miao, H., Zhang, Y., et al. (2024) · Cell Research · doi:10.1038/s41422-024-01034-y · Cited by 148
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Zhang, J. M., Ubas, A. A., de Borja, R., Svensson, V., Thomas, N., Thakar, N., Lai, I. K., Winters, A. F., Khan, U., Jones, M. G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.20.639398 · Cited by 146
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Nomura, S. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00844-3 · Cited by 47
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Kana, O., Nault, R., Filipovic, D., Marri, D., Zacharewski, T., & Bhattacharya, S. (2023) · Patterns · doi:10.1016/j.patter.2023.100817 · Cited by 44
Method of moments framework for differential expression analysis of single-cell RNA sequencing data
Kim, M. C., Gate, R. E., Lee, D. S., Tolopko, A., Lu, A. C., Gordon, E. D., Shifrut, E., García-Nieto, P. E., Marson, A., Ntranos, V., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.044 · Cited by 47
CellFlow enables generative single-cell phenotype modeling with flow matchingPreprint
Klein, D., Fleck, J. S., Bobrovskiy, D. M., Zimmermann, L., Becker, S., Palma, A., Dony, L., Tejada-Lapuerta, A., Huguet, G., Lin, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648220 · Cited by 65
Transcriptional Inhibition of Host Viral Entry Proteins as a Therapeutic Strategy for SARS-CoV-2Preprint
Wang, X., Dhindsa, R. S., Povysil, G., Zoghbi, A. W., Motelow, J. E., Hostyk, J. A., & Goldstein, D. B. (2020) · Preprints.org · doi:10.20944/preprints202003.0360.v1 · Cited by 41
Epigenetic modulators provide a path to understanding disease and therapeutic opportunityPreprint
Honer, M. A., Ferman, B. I., Gray, Z. H., Bondarenko, E. A., & Whetstine, J. R. (2024) · Genes & Development · doi:10.1101/gad.351444.123 · Cited by 44
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
High-throughput Microwell-seq 2.0 profiles massively multiplexed chemical perturbation
Chen, H., Liao, Y., Zhang, G., Sun, Z., Yang, L. X., Fang, X., Sun, H., Ma, L., Fu, Y., Li, J., et al. (2021) · Cell Discovery · doi:10.1038/s41421-021-00333-7 · Cited by 38
Squidiff: predicting cellular development and responses to perturbations using a diffusion model
He, S., Zhu, Y., Tavakol, D. N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02877-y · Cited by 38
Benchmarking algorithms for generalizable single-cell perturbation response prediction
Wei, Z., Wang, Y., Gao, Y., Wang, S., Li, P., Si, D., Gao, Y., Wu, S., Li, D., Dong, K., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02980-0 · Cited by 42
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Tomé, I., Francisco, V., Fernandes, H., & Ferreira, L. (2021) · APL Bioengineering · doi:10.1063/5.0057204 · Cited by 39
Yu, H., & Welch, J. D. (2021) · Genome Biology · doi:10.1186/s13059-021-02373-4 · Cited by 38
Tresenrider, A. M., Sridhar, A., Eldred, K. C., Cuschieri, S. A., Hoffer, D., Trapnell, C., & Reh, T. A. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100548 · Cited by 39
Self-citationHope, J. L., Otero, D. C., Bae, E., Stairiker, C. J., Palete, A. B., Faso, H. A., Lin, M. P., Henriquez, M. L., Roy, S. B., Seo, H., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112436 · Cited by 39
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Fangma, Y., Liu, M., Liao, J., Chen, Z., & Zheng, Y. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.04.003 · Cited by 39
Yin, K., Zhao, M., Lin, L., Chen, Y., Huang, S., Zhu, C., Liang, X., Lin, F., Wei, H., Zeng, H., et al. (2022) · Small Methods · doi:10.1002/smtd.202200341 · Cited by 35
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Li, J., Ho, D. J., Hénault, M., Yang, C., Neri, M., Ge, R., Renner, S., Mansur, L., Lindeman, A., Kelly, B., et al. (2022) · ACS Chemical Biology · doi:10.1021/acschembio.1c00920 · Cited by 35
A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery
Dini, A., Barker, H. R., Piki, E., Sharma, S., Raivola, J., Murumägi, A., & Ungureanu, D. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01761-8 · Cited by 40
Caporale, N., Castaldi, D., Rigoli, M. T., Cheroni, C., Valenti, A., Stucchi, S., Lessi, M., Bulgheresi, D., Trattaro, S., Pezzali, M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02555-5 · Cited by 37
Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility
Wang, K., Xiao, Z., Yan, Y., Ye, R., Hu, M. S., Bai, S., Sei, E., Qiao, Y., Chen, H., Lim, B., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.09.026 · Cited by 33
Are sex hormones promising candidates to explain sex disparities in the COVID-19 pandemic?
Brandi, M. L. (2021) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-021-09692-8 · Cited by 35
Spatially resolved transcriptomics: advances and applications
Duan, H., Cheng, T., & Cheng, H. (2022) · Blood Science · doi:10.1097/bs9.0000000000000141 · Cited by 34
deMULTIplex2: robust sample demultiplexing for scRNA-seq
Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Genome biology · doi:10.1186/s13059-024-03177-y · Cited by 32
Understanding glioblastoma at the single-cell level: Recent advances and future challenges
Yabo, Y. A., & Heiland, D. H. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002640 · Cited by 38
SPARK-seq: A high-throughput platform for aptamer discovery and kinetic profiling
Luo, G., Song, J. T., Fu, Y., Jiang, Y., Gao, Y., Zhong, Z., Li, L., Wei, Y., Jia, H., Guo, L., et al. (2026) · Science · doi:10.1126/science.adv6127 · Cited by 33
Singh, R., Wu, A. P., Mudide, A., & Berger, B. (2024) · Cell Systems · doi:10.1016/j.cels.2024.04.005 · Cited by 35
Pérez, R. F., Tezanos, P., Peñarroya, A., González-Ramón, A., Urdinguio, R. G., Gancedo-Verdejo, J., Tejedor, J. R., Santamarina‐Ojeda, P., Alba‐Linares, J. J., Sainz‐Ledo, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49608-z · Cited by 34
Metadata
- Authors
- 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., Zhang, F., Steemers, F., Shendure, J., & Trapnell, C.
- Affiliations
- University of Washington (US)
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- University of California, San Francisco (US)
- Illumina (United States) (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Srivatsan, Sanjay R.
- Ramani, Vijay
- Shendure, Jay
- Trapnell, Cole
- ORCID
- Venue
- Science
- Published
- 2020-01-03
- Volume
- 367
- Issue
- 6473
- Pages
- 45-51
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7289078
- OpenAlex
- W2993905360
- Cited by
- 401
- References
- 48
- Citations by year
- 2026: 76 · 2025: 78 · 2024: 69 · 2023: 66 · 2022: 42 · 2021: 48 · 2020: 20
- FWCI
- 9.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DGE-1258485)
- Howard Hughes Medical Institute
- National Human Genome Research Institute (DP1HG007811, R01HG006283)
- W.M. Keck Foundation
- Paul G. Allen Frontiers Group
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD088158)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- chemical screen, single-cell transcriptomics, intercellular heterogeneity, cancer cell lines, histone deacetylase inhibitors, acetate, chromatin, off-target effects
- MeSH terms
- A549 Cells, RNA-Seq, Acetates, Cell Nucleus, Chromatin, Humans, Neoplasms, K562 Cells, Genomics, Histone Deacetylase Inhibitors, High-Throughput Screening Assays, Single-Cell Analysis, Transcriptome, MCF-7 Cells
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