Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors
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 of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and DelbrÃ¼ckâ€™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 are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. 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 Overall design: We generated clonal cell lines of WM989 (BRAFV600E melanoma), WM983b (BRAFV600E melanoma), MDA-MB-231 (triple negative breast cancer), and PC9 (EGFR del Glu746-Ala750 lung cancer) from single-cell bottlenecks. Starting with these parental, clonal cell lines (WM989-A6, WM983B-E9, MDA-MB-231-D4 and PC9-D11) we isolated a single cell, let it proliferate until reaching roughly 100-200 cells, then plated these cells into a 96 well plate at a dilution in which we expected roughly 0.5 cells per well. From these wells, we isolated ~100 subclones for further expansion, excluding wells that were seeded with more than 1 cell.  Of these, we aimed for 48 subclones from each cell line for downstream analysis. We grew the subclones until they reached a minimum of around 100,000 cells, with some reaching as high as roughly 200,000 cells. At that point, we used miRNAeasy RNA extraction kit (Qiagen 217004) to isolate RNA from each subclone, followed by library preparation using the NEBNext Poly(A) Magnetic Isolation Module and NEBNext Ultra RNA sequencing library prep kit for Illumina (NEB). For WM989-A6, WM983b-E9, and MDA-MB-231 cell lines, at the time of RNA isolation for the subclones, we also isolated 48 separate samples of 100,000 cells from the parental line and prepared these samples for RNA sequencing as controls.