Probing transient memory of cellular states using single-cell lineages

Abstract

The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria–Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

Development of adaptive anoikis resistance promotes metastasis that can be overcome by CDK8/19 Mediator kinase inhibition

“…76 . Using equations derived previously, 41 individual clonal expansion in attached culture for 20 generations before the first survival test in suspension ( Fig 2A,B P1) , and a transient heritability of approximately 10-11 generations for the resistant state ( Fig. 2D ), we obtain the model predicted fluctuation values to be much less than of 0.01 and 30-fold less than the observed fluctuations of 0 .…”

“…We thus used previously developed analytical formulas 38,40,41 to predict what levels of fluctuations would be expected from switching between an anoikis-sensitive state (cell death in suspension) and an anoikis-resistant state (cell survival and proliferation in suspension). If f is the fraction of cells in the resistant state then where T = 24 hr and growth rate where T d is the cell doubling time of anoikis resistant cells in suspension that is experimentally determined.…”

“…[36][37][38][39][40][41] We explored if a similar model of transient switching between cellular states could drive anoikis resistance. We thus used previously developed analytical formulas 38,40,41 to predict what levels of fluctuations would be expected from switching between an anoikis-sensitive state (cell death in suspension) and an anoikis-resistant state (cell survival and proliferation in suspension). If 𝑓 is the fraction of cells in the resistant state then 𝑓𝑒 !"…”

A heritable iron memory enables decision-making in Escherichia coli

“…The short duration of this heritability excludes epigenetic mechanisms ( 60 , 61 ) or bistable ( 62 ) and toggle switches ( 63 ). Although epigenetics might play a role in fur regulon ( 64 ), stochastic fluctuation ( 65 , 66 ) could explain the observed fluctuations within the population ( Fig. 5 A – D ).…”

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

“…While trajectory inference of single cell sequencing data can provide high granularity for understanding phenotypic heterogeneity in cancer, such approaches to understand plasticity of cancer cells over time must account for the destructive nature of sequencing. Alternatively, lineage tracing methods have long been used to understand the temporal dynamics of cell state in cancer populations, particularly during tumor initiation and in response to treatment ( Chakrabarti et al, 2018 ; Wagner et al, 2018 ; Weinreb et al, 2020 ; Wang et al, 2021 ; Singh and Saint-Antoine, 2023 ). Single cell time-lapse microscopy has been used to correlate cell state and fate, suggesting the existence of phenotype switching ( Bhola and Simon, 2009 ; Spencer et al, 2009 ; Bertaux et al, 2014 ; Chakrabarti et al, 2018 ).…”