Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA fluorescence in situ hybridization to directly capture rare cells that give rise to cellular behaviors of interest. Applying Rewind to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1,000–1:10,000 cells) and relative persistence of MAP kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences among several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones after drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.
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All RNA sequencing data generated for this study are available at the Gene Expression Omnibus (accession no. GSE161300). Additional sequencing and imaging data are available on Dropbox at https://www.dropbox.com/sh/mmeg3mckrpridu3/AAALBaMLoJsJiQC2-lrVY0Cva?dl=0 and upon reasonable request to the corresponding author.
Software used to segment cells and quantify RNA spots is available at https://github.com/arjunrajlaboratory/rajlabimagetools. Software used to stitch, segment and quantify scan images of resistant colonies is available at https://github.com/arjunrajlaboratory/colonycounting_v2. Additional custom image analysis scripts are available at https://github.com/arjunrajlaboratory/timemachineimageanalysis. The pipeline used for barcode sequencing analysis is available at https://github.com/arjunrajlaboratory/timemachine.
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We thank C. Bartman, A. Anguierra, J. Murray, N. Zhang, L. Cai, B. Stanger, A. Coté, K. Kiani, E. Sanford and Y. Goyal, along with other members of the Raj laboratory, for many useful suggestions. We thank the Flow Cytometry Core Laboratory at the Children’s Hospital of Philadelphia Research Institute for assistance in designing and performing FACS, including F. Tuluc for several helpful discussions. B.L.E. acknowledges support from NIH training grants F30 CA236129, T32 GM007170 and T32 HG000046; E.A.T. acknowledges support from R01 CA238237; I.P.D. acknowledges support from NIH 4DN U01 HL129998 and the NIH Center for Photogenomics (RM1 HG007743); C.L.J. acknowledges support from NIH T32 DK007780 and F30 HG010822; N.J. acknowledges support from NIH F30 HD103378; S.M.S. acknowledges support from DP5 OD028144; and A.R. acknowledges support from R01 CA238237, NIH Director’s Transformative Research Award R01 GM137425, R01 CA232256, NSF CAREER 1350601, P30 CA016520, SPORE P50 CA174523, NIH U01 CA227550, NIH 4DN U01 HL129998, the NIH Center for Photogenomics (RM1 HG007743) and the Tara Miller Foundation.
A.R. receives consulting income, and A.R. and S.M.S. receive royalties, related to Stellaris RNA FISH probes.
Peer review information Nature Biotechnology thanks Leor Weinberger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Emert, B.L., Cote, C.J., Torre, E.A. et al. Variability within rare cell states enables multiple paths toward drug resistance. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00837-3