Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nat Biotechnol. 2021 Jul;39(7):865-876. doi: 10.1038/s41587-021-00837-3. Epub 2021 Feb 22.
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 BRAF 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.
个体细胞之间的分子差异可能导致细胞命运的显著差异,例如癌细胞在药物治疗后是死亡还是存活。由于难以准确确定哪些可变分子特征导致了哪些细胞命运,这些最初的差异在很大程度上仍未被发现。因此,我们开发了 Rewind 方法,该方法将遗传条形码与 RNA 荧光原位杂交相结合,直接捕获产生感兴趣细胞行为的稀有细胞。将 Rewind 应用于 BRAF 黑色素瘤,我们追踪到耐药细胞命运可以追溯到其药物敏感前体的单细胞基因表达差异(初始频率约为 1:1000-1:10000 个细胞)以及药物治疗后 MAP 激酶信号的相对持久性。在这个稀有亚群中,我们揭示了一种丰富的亚结构,其中几个不同亚群之间的分子差异可以预测药物暴露后表型行为的未来差异,例如不同耐药克隆在药物治疗后的增殖能力。我们的研究结果揭示了隐藏的、罕见细胞的可变性,这种可变性是药物暴露后一系列潜在表型结果的基础。