Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.

Antineoplastic Agents / pharmacology Base Sequence Cell Line, Tumor Cell Survival / genetics Gene Expression Profiling / methods Gene Expression Regulation, Neoplastic / drug effects Humans Models, Statistical Neoplasms / drug therapy Polymorphism, Single Nucleotide Pyridones / pharmacology Pyrimidinones / pharmacology Single-Cell Analysis / methods

Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
27 08 2020
Historique:
received: 07 02 2020
accepted: 26 06 2020
entrez: 29 8 2020
pubmed: 29 8 2020
medline: 25 9 2020
Statut: epublish

Résumé

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment.

Identifiants

DOI: 10.1038/s41467-020-17440-w PMID: 32855387 PMC: PMC7453022
pubmed: 32855387
doi: 10.1038/s41467-020-17440-w
pii: 10.1038/s41467-020-17440-w
pmc: PMC7453022
doi:

Substances chimiques

Antineoplastic Agents 0
Pyridones 0
Pyrimidinones 0
trametinib 33E86K87QN

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4296

Subventions

Organisme : NCI NIH HHS
ID : K08 CA218420
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA250549
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States

Références

Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
pubmed: 28753430 pmcid: 5667678 doi: 10.1016/j.cell.2017.06.010
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
pubmed: 29083409 pmcid: 5709193 doi: 10.1038/ng.3984
McDonald, E. R. et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577.e10–592.e10 (2017).
doi: 10.1016/j.cell.2017.07.005
Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).
pubmed: 27397505 pmcid: 4967469 doi: 10.1016/j.cell.2016.06.017
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568, 511–516 (2019).
pubmed: 30971826 doi: 10.1038/s41586-019-1103-9
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
pubmed: 22460905 pmcid: 3320027 doi: 10.1038/nature11003
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
pubmed: 22460902 pmcid: 3349233 doi: 10.1038/nature11005
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).
pubmed: 27984732 pmcid: 5181115 doi: 10.1016/j.cell.2016.11.038
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867.e21–1882.e21 (2016).
doi: 10.1016/j.cell.2016.11.048
Subramanian, A. et al. A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437.e17–1452.e17 (2017).
doi: 10.1016/j.cell.2017.10.049
Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019).
pubmed: 31395745 pmcid: 6746554 doi: 10.1126/science.aax4438
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
pubmed: 17008526 doi: 10.1126/science.1132939
Lamb, J. et al. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 114, 323–334 (2003).
pubmed: 12914697 doi: 10.1016/S0092-8674(03)00570-1
Bush, E. C. et al. PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens. Nat. Commun. 8, 105 (2017).
pubmed: 28740083 pmcid: 5524642 doi: 10.1038/s41467-017-00136-z
Ye, C. et al. DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat. Commun. 9, 4307 (2018).
pubmed: 30333485 pmcid: 6192987 doi: 10.1038/s41467-018-06500-x
Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).
pubmed: 23993102 pmcid: 3954635 doi: 10.1016/j.cell.2013.08.003
Corsello, S. M. et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer https://doi.org/10.1038/s43018-019-0018-6 (2020).
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
pubmed: 26000487 pmcid: 4441768 doi: 10.1016/j.cell.2015.04.044
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
pubmed: 26000488 pmcid: 4481139 doi: 10.1016/j.cell.2015.05.002
Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).
pubmed: 26928769 pmcid: 5508574 doi: 10.1038/nbt.3460
Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single cell resolution. Science 367, 45–51 (2020).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
pubmed: 29227470 doi: 10.1038/nbt.4042
Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).
pubmed: 30567574 pmcid: 6300015 doi: 10.1186/s13059-018-1603-1
Vassilev, L. T. et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–848 (2004).
pubmed: 14704432 doi: 10.1126/science.1092472
Shi-Lin, D., Yuan, X., Zhan, S., Luo-Jia, T. & Chao-Yang, T. Trametinib, a novel MEK kinase inhibitor, suppresses lipopolysaccharide-induced tumor necrosis factor (TNF)-α production and endotoxin shock. Biochem. Biophys. Res. Commun. 458, 667–673 (2015).
pubmed: 25684183 doi: 10.1016/j.bbrc.2015.01.160
Lulli, D., Carbone, M. L. & Pastore, S. The MEK inhibitors trametinib and cobimetinib induce a type I interferon response in human keratinocytes. Int. J. Mol. Sci. 18, 2227 (2017).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
pubmed: 31068700 pmcid: 6697103 doi: 10.1038/s41586-019-1186-3
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452 pmcid: 4944528 doi: 10.1126/science.aad0501
King, C. et al. LY2606368 causes replication catastrophe and antitumor effects through CHK1-dependent mechanisms. Mol. Cancer Ther. 14, 2004–2013 (2015).
pubmed: 26141948 doi: 10.1158/1535-7163.MCT-14-1037
Kinker, G. S. et al. Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity. Preprint at https://www.biorxiv.org/content/10.1101/807552v1 (2019).
Shin, D., Lee, W., Lee, J. H. & Bang, D. Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. Sci. Adv. 5, eaav2249 (2019).
pubmed: 31106268 pmcid: 6520024 doi: 10.1126/sciadv.aav2249
Lim, C. P., Jain, N. & Cao, X. Stress-induced immediate-early gene, egr-1, involves activation of p38/JNK1. Oncogene 16, 2915–2926 (1998).
pubmed: 9671412 doi: 10.1038/sj.onc.1201834
Huang, Y., McCarthy, D. J. & Stegle, O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 20, 273 https://doi.org/10.1186/s13059-019-1865-2 (2019).
Xu, J. et al. Genotype-free demultiplexing of pooled single-cell RNA-seq. Genome Biol. 20, 290 (2019).
pubmed: 31856883 pmcid: 6921391 doi: 10.1186/s13059-019-1852-7
Szalai, B. et al. Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction. Nucleic Acids Res. 47, 10010–10026 (2019).
pubmed: 31552418 pmcid: 6821211 doi: 10.1093/nar/gkz805
Jones, A., Tsherniak, A. & McFarland, J. Post-perturbational transcriptional signatures of cancer cell line vulnerabilities. Preprint at https://www.biorxiv.org/content/10.1101/2020.03.04.976217v1 (2020).
Kodack, D. P. et al. Primary patient-derived cancer cells and their potential for personalized cancer patient care. Cell Rep. 21, 3298–3309 (2017).
pubmed: 29241554 pmcid: 5745232 doi: 10.1016/j.celrep.2017.11.051
Tseng, Y.-Y. & Boehm, J. S. From cell lines to living biosensors: new opportunities to prioritize cancer dependencies using ex vivo tumor cultures. Curr. Opin. Genet. Dev. 54, 33–40 (2019).
pubmed: 30928774 doi: 10.1016/j.gde.2019.02.007
Li, B. et al. Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq. Preprint at https://www.biorxiv.org/content/10.1101/823682v1 (2019).
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
pubmed: 20808728 pmcid: 2929880 doi: 10.18637/jss.v033.i01
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016).
pubmed: 27818791 pmcid: 5096736 doi: 10.32614/RJ-2016-021
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179 pmcid: 6700744 doi: 10.1038/nbt.4096
Broad DepMap. DepMap 19Q3 Public. Figshare https://doi.org/10.6084/m9.figshare.9201770.v3 (2019).
Crowell, H. L. et al. On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. Preprint at https://www.biorxiv.org/content/10.1101/713412v1 (2019).
Lun, A. T. L. & Marioni, J. C. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics 18, 451–464 (2017).
pubmed: 28334062 pmcid: 5862359 doi: 10.1093/biostatistics/kxw055
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308 doi: 10.1093/bioinformatics/btp616
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249 pmcid: 4053721 doi: 10.1186/gb-2014-15-2-r29
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792 pmcid: 4402510 doi: 10.1093/nar/gkv007
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).
Lun, A. T. L., Chen, Y. & Smyth, G. K. It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).
pubmed: 27008025 doi: 10.1007/978-1-4939-3578-9_19
Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393 pmcid: 3106198 doi: 10.1093/bioinformatics/btr260
Janitza, S., Celik, E. & Boulesteix, A.-L. A computationally fast variable importance test for random forests for high-dimensional data. Adv. Data Anal. Classif. https://doi.org/10.1007/s11634-016-0276-4 (2016).
Gaublomme, J. T. et al. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nat. Commun. 10, 2907 (2019).
pubmed: 31266958 pmcid: 6606589 doi: 10.1038/s41467-019-10756-2
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423 pmcid: 6927181 doi: 10.1186/s13059-019-1874-1
Cancer Data Science. MIX-seq data. Figshare. Dataset at https://doi.org/10.6084/m9.figshare.10298696.v1 (2019).

Auteurs

James M McFarland (JM)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
ORCID: 0000-0001-9978-480X

Brenton R Paolella (BR)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
ORCID: 0000-0003-3394-828X

Allison Warren (A)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Kathryn Geiger-Schuller (K)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
ORCID: 0000-0002-6705-0681

Tsukasa Shibue (T)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Michael Rothberg (M)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Olena Kuksenko (O)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

William N Colgan (WN)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
ORCID: 0000-0002-8328-4672

Andrew Jones (A)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Emily Chambers (E)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Danielle Dionne (D)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Samantha Bender (S)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Brian M Wolpin (BM)

Harvard Medical School, Boston, 02115, MA, USA.
Brigham and Women's Hospital, Boston, 02115, MA, USA.
Department of Medical Oncology, Dana Farber Cancer Institute, Boston, 02115, MA, USA.

Mahmoud Ghandi (M)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
ORCID: 0000-0003-1897-2265

Itay Tirosh (I)

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Orit Rozenblatt-Rosen (O)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Jennifer A Roth (JA)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Todd R Golub (TR)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Harvard Medical School, Boston, 02115, MA, USA.
Department of Pediatric Oncology, Dana Farber Cancer Institute, Boston, 02115, MA, USA.
Howard Hughes Medical Institute, Chevy Chase, 20815, MD, USA.
ORCID: 0000-0003-0113-2403

Aviv Regev (A)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.
Howard Hughes Medical Institute, Chevy Chase, 20815, MD, USA.
Koch Institute of Integrative Cancer Research, Cambridge, 021242, MA, USA.
Department of Biology, MIT, Cambridge, 021242, MA, USA.
ORCID: 0000-0003-3293-3158

Andrew J Aguirre (AJ)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA. andrew_aguirre@dfci.harvard.edu.
Harvard Medical School, Boston, 02115, MA, USA. andrew_aguirre@dfci.harvard.edu.
Brigham and Women's Hospital, Boston, 02115, MA, USA. andrew_aguirre@dfci.harvard.edu.
Department of Medical Oncology, Dana Farber Cancer Institute, Boston, 02115, MA, USA. andrew_aguirre@dfci.harvard.edu.
ORCID: 0000-0002-0701-6203

Francisca Vazquez (F)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA. vazquez@broadinstitute.org.
ORCID: 0000-0002-2857-4685

Aviad Tsherniak (A)

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA. aviad@broadinstitute.org.
ORCID: 0000-0002-3797-1877

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Actions chimiques et utilisations › Actions pharmacologiques › Utilisations thérapeutiques › Antinéoplasiques › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Sciences de l'information › Sources d'information › Services d'information › Documentation › Données de séquences moléculaires › Séquence nucléotidique › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Cellules › Cellules cultivées › Cellules cancéreuses en culture › Lignée cellulaire tumorale › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Phénomènes physiologiques cellulaires › Survie cellulaire › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de profil d'expression de gènes › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Régulation de l'expression des gènes tumoraux › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Modèles statistiques › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Tumeurs › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Composés hétérocycliques › Composés hétéromonocycliques › Pyridines › Pyridones › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Composés hétérocycliques › Composés hétéromonocycliques › Pyrimidines › Pyrimidinones › Multiplexed single-cell transcriptional...
questionsmedicales.fr › Techniques d'investigation › Techniques cytologiques › Analyse sur cellule unique › Multiplexed single-cell transcriptional...