Generalized ¿uctuation test for deciphering phenotypic switching within cell populations
The inherent probabilistic nature of biochemical reactions coupled with low-copy number components results in signi¿cant
random ¿uctuations (noise) in mRNA/protein levels inside individual cells. How cellular biochemical processes function
reliably in the face of such randomness is an intriguing fundamental problem. A long-term vision of our lab is to develop
new mathematical and computational tools for studying stochastic dynamics of cellular biochemical processes, and use these
tools to systematically understand how noise affects biological function and phenotype. As a consequence of noise in gene
product levels, single cells within an isoclonal population can differ in their expression pro¿le and reside in different pheno-
typic 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. Unexpectedly, phenotypic heterogeneity
within a population can play important functional roles in diverse biological processes, from driving genetically-identical
cells to different cell fates to allowing microbes and cancer cells to hedge their bets against uncertain environmental changes.
The Luria-Delbrück experiment, also called the “Fluctuation Test", introduced 75 years ago, demonstrated that genetic mu-
tations arise randomly in the absence of selection – rather than in response to selection – and led to a Nobel Prize. The
innovation of this project is to leverage this classical experiment in conjunction with mathematical modeling to char-
acterize reversible and irreversible switching between cell states. The key advantage of the proposed method is that it is
general enough to be applied to any proliferating cell type, and only involves making a single endpoint measurement. This
is especially important for scenarios where a measurement involves killing the cell (for example, assaying whether a bacte-
rial cell is in a drug-sensitive or drug-tolerant state or doing RNA-sequencing), and hence the state of the same cell cannot
be measured at different time points. The project will develop mathematical tools for characterizing phenotypic switching
between an arbitrary number of states using the ¿uctuation test, and such techniques will for the ¿rst time differentiate
between an irreversible cell-state transition via genetic alterations vs. a reversible epigenetic transition. These tools
will be ¿rst benchmarked with in-silico generated data and then applied on experimental datasets investigating diverse prob-
lems, including characterizing drug-tolerant states in bacterial/fungal cells, understanding differences in viral susceptibility
between single human cells within the same clonal population, and uncovering the transient dynamics of stem cell states
that bias individual cells to different differentiation fates. Our preliminary work reveals plasticity in drug-tolerant states
in bacterial, fungal, and cancer cells with different inheritance timescales. To understand the origins of cell states, the
project will develop computational tools for inferring causal interaction networks from single-cell expression data. These
tools will uncover how network topologies change across cell states and modeling the stochastic dynamics of underlying
biochemical networks will mechanistically capture transitions between states. Overall, tools developed through this project
will result in a fundamental understanding of how single-cell difference arises from stochastic epigenetic processes
without any changes to DNA, and drive translational approaches to perturb cell states for therapeutic bene¿t.