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 FISH to directly capture rare cells that give rise to cellular behaviors of interest. Applied to BRAF V600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1000–1:10,000 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 between several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones following drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time for a single cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the time of a fluctuation of gene expression or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that express together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors in multiple different cancer cell lines, for example, the ability to proliferate in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property. Main text:Cellular memory in biology, meaning the persistence of a cellular or organismal state over time, occurs over a wide range of timescales and can be induced by a variety of mechanisms. Genetic differences are one form of memory, encoding variation between organisms on multi-generational timescales. Within an organism, epigenetic mechanisms encode the differences between cell types in different tissues, with cells retaining memory of their state over a large number of cell divisions ( 1 ) . In contrast, recent measurements suggest that expression of many genes in single cells may have very little memory, displaying highly transient .

Molecular differences between individual cells can lead to dramatic differences in cell fate following an applied treatment, such as the difference between death versus survival of cancer cells upon receiving anti-cancer drugs. However, current strategies to retrospectively identify the cells that give rise to distinct rare behaviors and determine their distinguishing molecular characteristics remain limited. Here we describe Rewind, a methodology that combines genetic barcoding with an RNA-based readout to directly capture rare cells that give rise to cellular behaviors of interest, specifically the emergence of resistance to targeted cancer therapy. Using Rewind, we analyzed over 5 million cells to identify differences in gene expression and MAP-kinase signaling in single melanoma cells that mark a rare subpopulation of drug-naive cells (initial frequency of ~1:1000-1:10,000 cells) that ultimately gives rise to drug resistant clones. We further show that even within this rare subpopulation, molecular differences between single cells before the application of drug predict future differences in drug resistant behavior. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of cryptic variability that can underlie a range of distinct rare-cell phenotypic outcomes upon drug exposure. Applying Rewind to other rare biological phenomena, such as cancer metastasis, tissue regeneration, and stem cell reprogramming, may provide a means to map rare cellular states to the unique cellular fates to which they give rise.

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