Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line
- Creators
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Su, Yapeng
- Ko, Melissa E.
- Cheng, Hanjun
- Zhu, Ronghui
- Xue, Min
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Wang, Jessica
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Lee, Jihoon W.
- Frankiw, Luke
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Xu, Alexander
- Wong, Stephanie
- Robert, Lidia
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Takata, Kaitlyn
- Yuan, Dan
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Lu, Yue
- Huang, Sui
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Ribas, Antoni
- Levine, Raphael
- Nolan, Garry P.
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Wei, Wei
- Plevritis, Sylvia K.
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Li, Guideng
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Baltimore, David
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Heath, James R.
Abstract
The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAF^(V600E) mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.
Additional Information
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 03 April 2019; Accepted 02 April 2020; Published 11 May 2020. We thank members of the Heath and Baltimore laboratories for helpful comments on the manuscript. We acknowledge the following agencies and foundations for support: NIH Grants U54 CA199090 (to J.R.H., D.B., W.W., and A.R.), U01 CA217655 (to J.R.H. and W.W.), P01 CA168585 (to A.R.), R35 CA197633 (to A.R.), and U54CA209971 (to S.K.P.); the Dr. Robert Vigen Memorial Fund, the Ressler Family Fund, and Ken and Donna Schultz (A.R.); the Jean Perkins Foundation (J.R.H.); Andy Hill CARE Fund (W.W.); and ISB Innovator Award (Y.S.). L.R. was supported by the V Foundation-Gil Nickel Family Endowed Fellowship and a scholarship from SEOM. M.E.K. was supported by the National Cancer Institute of the National Institutes of Health under Award Number F99 CA212231 and Stanford University's Diversifying Academia, Recruiting Excellence Fellowship. We acknowledge Rochelle Diamond and the Caltech Flow Cytometry Cell Sorting Facility for FACS analysis and advice. Data availability: All the data supporting the findings of this study are available within the article and its Supplementary Information files, and from the corresponding author upon reasonable request. Raw data for underlying Figs. 3c–f, h–i and 5a–e, and Supplementary Figs. 3, 4, 5, 6, and 21a–c are provided in the Source Data file. Code availability: Custom code for analysis of raw CSV output of the SCBC measurements was written in R and has been made available/open-source via GitHub (https://github.com/mesako/Melanoma-Publication). The code used to generate the exact FLOW-MAP graphs, the single-cell clustering, the calculation of SNAI/Ic indices for individual cell clusters, and the correlation network analysis is available upon request. Author Contributions: J.R.H. and Y.S. conceived the study. Y.S. and G.L. designed the experiments. Y.S., G.L., H.C., R.Z, M.X., J.W., S.W., J.L., K.T., J.W.L., L.K., A.X., L.F., Y.L., D.Y., and L.R. performed the experiments. M.E.K., Y.S., W.W., and R.L. analyzed and interpreted the data. J.R.H., W.W., S.K.P., G.P.N, S.H., and A.R. provided conceptual advice on data analysis and interpretation. Y.S., G.L., M.E.K., and J.R.H. wrote the manuscript. J.R.H., D.B., and G.L. supervised this study. Competing interests: J.R.H. and A.R. are affiliated with Isoplexis, which is seeking to commercialize the single-cell barcode chip technology. The remaining authors declare no competing interests.Attached Files
Published - s41467-020-15956-9.pdf
Supplemental Material - 41467_2020_15956_MOESM1_ESM.pdf
Supplemental Material - 41467_2020_15956_MOESM2_ESM.pdf
Supplemental Material - 41467_2020_15956_MOESM3_ESM.xlsx
Files
Additional details
- PMCID
- PMC7214418
- Eprint ID
- 103103
- Resolver ID
- CaltechAUTHORS:20200511-122023000
- NIH
- U54 CA199090
- NIH
- U01 CA217655
- NIH
- P01 CA168585
- NIH
- R35 CA197633
- NIH
- U54CA209971
- Dr. Robert Vigen Memorial Fund
- Ressler Family Fund
- Ken and Donna Schultz
- Jean Perkins Foundation
- Andy Hill CARE Fund
- ISB Innovator Award
- V Foundation-Gil Nickel Family Endowed Fellowship
- Sociedad Española de Oncología Médica (SEOM)
- NIH
- F99 CA212231
- Stanford University
- Created
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2020-05-11Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering