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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
In the citing paper
“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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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
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Self-citationDose-Response Analysis Using R
Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0146021 · Cited by 3,833
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
ATP-citrate lyase links cellular metabolism to histone acetylation
Wellen, K. E., Hatzivassiliou, G., Sachdeva, U. M., Bui, T. V., Cross, J. R., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1164097 · Cited by 2,298
A Mammalian Histone Deacetylase Related to the Yeast Transcriptional Regulator Rpd3p
Taunton, J., Hassig, C. A., & Schreiber, S. L. (1996) · Science · doi:10.1126/science.272.5260.408 · Cited by 1,829
Brownell, J. E., Zhou, J., Ranalli, T. A., Kobayashi, R., Edmondson, D. G., Dent, S., & Allis, C. D. (1996) · Cell · doi:10.1016/s0092-8674(00)81063-6 · Cited by 1,636
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402
Self-citationMultiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248
Self-citationRare cell variability and drug-induced reprogramming as a mode of cancer drug resistance
Shaffer, S. M., Dunagin, M. C., Torborg, S. R., Torre, E. A., Emert, B., Krepler, C., Beqiri, M., Sproesser, K., Brafford, P. A., Xiao, M., et al. (2017) · Nature · doi:10.1038/nature22794 · Cited by 1,218
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046
Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis
Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M., & Sorger, P. K. (2009) · Nature · doi:10.1038/nature08012 · Cited by 1,023
Defining cell types and states with single-cell genomics
Trapnell, C. (2015) · Genome Research · doi:10.1101/gr.190595.115 · Cited by 813
Self-citationVäremo, L., Nielsen, J., & Nookaew, I. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt111 · Cited by 688
Comerford, S. A., Huang, Z., Du, X., Wang, Y., Cai, L., Witkiewicz, A. K., Walters, H., Tantawy, M. N., Fu, A., Manning, H. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.020 · Cited by 681
Multidimensional Drug Profiling By Automated Microscopy
Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., & Altschuler, S. J. (2004) · Science · doi:10.1126/science.1100709 · Cited by 660
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
Role of ABL family kinases in cancer: from leukaemia to solid tumours
Greuber, E. K., Smith-Pearson, P., Wang, J., & Pendergast, A. M. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3563 · Cited by 432
Origin and evolution of high throughput screening
Pereira, D. A., & Williams, J. A. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707373 · Cited by 367
Yu, C., Mannan, A. M., Yvone, G. M., Ross, K. N., Zhang, Y.-L., Marton, M. A., Taylor, B. R., Crenshaw, A., Gould, J. Z., Tamayo, P., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3460 · Cited by 362
Roe, J., Mercan, F., Rivera, K. D., Pappin, D., & Vakoc, C. R. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.011 · Cited by 343
Education reforms struggle against apartheid's legacy
Broach, J. R., & Thorner, J. W. (1996) · Nature · doi:10.1038/384014a0 · Cited by 292
High throughput screening in drug discovery
Carnero, A. (2006) · Clinical & Translational Oncology · doi:10.1007/s12094-006-0048-2 · Cited by 290
Histone Acetylation Regulates Intracellular pH
McBrian, M. A., Behbahan, I. S., Ferrari, R., Su, T., Huang, T.-W., Li, K., Hong, C. S., Christofk, H. R., Vogelauer, M., Seligson, D. B., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.025 · Cited by 272
Jost, M., Chen, Y., Gilbert, L. A., Horlbeck, M. A., Krenning, L., Menchon, G., Rai, A., Cho, M. Y., Stern, J. J., Prota, A. E., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.09.012 · Cited by 244
DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery
Ye, C., Ho, D. J., Neri, M., Yang, C., Kulkarni, T., Randhawa, R., Henault, M., Mostacci, N., Farmer, P., Renner, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06500-x · Cited by 222
Grosveld, G. C., Verwoerd, T. C., van Agthoven, T., de Klein, A., Ramachandran, K. L., Heisterkamp, N., Stam, K., & Groffen, J. (1986) · Molecular and Cellular Biology · doi:10.1128/mcb.6.2.607-616.1986 · Cited by 197
Loss of tumor suppressor NF1 activates HSF1 to promote carcinogenesis
Dai, C., Santagata, S., Tang, Z., Shi, J., Cao, J., Kwon, H., Bronson, R. T., Whitesell, L. J., & Lindquist, S. L. (2012) · Journal of Clinical Investigation · doi:10.1172/jci62727 · Cited by 131
Self-citationShin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101
PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens
Bush, E. C., Ray, F., Alvarez, M. J., Realubit, R. B., Hai, L., Karan, C., Califano, A., & Sims, P. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00136-z · Cited by 100
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Futamura, Y., Kawatani, M., Kazami, S., Tanaka, K., Muroi, M., Shimizu, T., Tomita, K., Watanabe, N., & Osada, H. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2012.10.014 · Cited by 91
Kang, J., Hsu, C.-H., Wu, Q., Liu, S., Coster, A. D., Posner, B. A., Altschuler, S. J., & Wu, L. F. (2015) · Nature Biotechnology · doi:10.1038/nbt.3419 · Cited by 82
Brazelle, W., Kreahling, J. M., Gemmer, J. Y., Ma, Y., Cress, W. D., Haura, E. B., & Altiok, S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014335 · Cited by 70
A high density assay format for the detection of novel cytotoxic agents in large chemical libraries
Shum, D., Radu, C., Kim, E., Cajuste, M., Shao, Y., Seshan, V., & Djaballah, H. (2008) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.1080/14756360701810082 · Cited by 59
High-Throughput Screening for Drug Discovery
Saxena, S. K. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.91075 · Cited by 46
Chromatin: a capacitor of acetate for integrated regulation of gene expression and cell physiology
Kurdistani, S. K. (2014) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2014.06.002 · Cited by 36
Huss, K. L., Blonigen, P. E., & Campbell, R. M. (2007) · SLAS DISCOVERY · doi:10.1177/1087057107300221 · Cited by 36
Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional SpacesPreprint
Gehring, J., Park, J. H., Chen, S., Thomson, M., & Pachter, L. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/315333 · Cited by 25
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Zhang, Y., Chen, H., Mo, H., Hu, X., Gao, R., Zhao, Y., Liu, B., Niu, L., Sun, X., Yu, X., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.010 · Cited by 783
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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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