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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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“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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“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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limma powers differential expression analyses for RNA-sequencing and microarray studies
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BEDTools: a flexible suite of utilities for comparing genomic features
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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
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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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Cui, T., Wang, X., Li, Z., Ding, J., You, L., Xu, Y., Xu, W., Wu, F., Ying, K., Ouyang, W., et al. (2026) · arXiv · doi:10.48550/arxiv.2609.14709
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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