FitFlow — a flow cytometry based method for understanding the cell state of cells that differ by dynamic phenotypes

Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage

David van Dijk, Riddhiman Dhar, Alsu M. Missarova, Lorena Espinar, William R. Blevins, Ben Lehner & Lucas B. Carey

Nature Communications volume 6, Article number: 7972 (2015)

Isogenic cells show a large degree of variability in growth rate, even when cultured in the same environment. Such cell-to-cell variability in growth can alter sensitivity to antibiotics, chemotherapy and environmental stress. To characterize transcriptional differences associated with this variability, we have developed a method—FitFlow—that enables the sorting of subpopulations by growth rate. The slow-growing subpopulation shows a transcriptional stress response, but, more surprisingly, these cells have reduced RNA polymerase fidelity and exhibit a DNA damage response. As DNA damage is often caused by oxidative stress, we test the addition of an antioxidant, and find that it reduces the size of the slow-growing population. More generally, we find a significantly altered transcriptome in the slow-growing subpopulation that only partially resembles that of cells growing slowly due to environmental and culture conditions. Slow-growing cells upregulate transposons and express more chromosomal, viral and plasmid-borne transcripts, and thus explore a larger genotypic—and so phenotypic — space.


(a) FitFlow method: single, chitinase-deficient, Hta2–GFP-expressing cells are suspended after brief sonication and G1 selection. Growth for several generations in liquid media results in microcolonies of cells that have a distribution of cell number. Flow cytometry of Hta2–GFP measurement reveals the microcolony size distribution. Subsequent sorting on Hta2–GFP abundance thus separates populations according to their single-cell growth rate. RNA-seq analysis of each bin of growth rate reveals gene expression patterns associated with variable stochastic growth.
(b) Microscopy at different time points shows microcolony formation.
(c) Flow cytometry of single cells (t=0 h) and microcolonies (t=4 h) shows the distribution cell number per microcolony. The HTA2–GFP fusion enables high-resolution measurement of DNA content as shown by separate G1 and G2 peaks at t=0 h.















We published the first transcriptome-level characterization of the differences in cellular state between slow and fast growing subpopulations. Little is known about the evolutionary consequences of non-genetic variability in fitness. In budding yeast, there is a slow growing subpopulation that is heat-shock resistant. In E. coli growing on the synthetic sugar lactulose, variability in the expression of genes involved in lactose metabolism generates growth rate variability within the population. Stochastic variability in gene expression can result in incomplete penetrance of some mutations. Without a fundamental understanding of the molecular causes of reversible phenotypic differences among isogenic cells, it is difficult to influence the outcomes of processes that depend on these transitions, which range from HIV infection to ‘stuck fermentations’ in wine.