Abstract
The heterogeneity in mammalian cells signaling response is largely a result of pre‐existing cell‐to‐cell variability. It is unknown whether cell‐to‐cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single‐cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single‐cell level.
Highlights
Cell-to-cell variability in dynamic responses to stimuli is observed ubiquitously (Geva-Zatorsky et al, 2006; Cohen-Saidon et al, 2009; Tay et al, 2010; Selimkhanov et al, 2014), yet its underlying causes are still unknown
Another approach to identify functional cell state is based on a dynamical systems point of view, which argues that a cell can be represented as a dynamical system and that each cell state is an attractor within the cell state space (Furusawa & Kaneko, 2012)
While clustering based on time-series data is a valid and useful approach, in this case it did not lead to identification of cell states
Summary
Cell-to-cell variability in dynamic responses to stimuli is observed ubiquitously (Geva-Zatorsky et al, 2006; Cohen-Saidon et al, 2009; Tay et al, 2010; Selimkhanov et al, 2014), yet its underlying causes are still unknown. Two hypotheses can explain the observed cell-to-cell variability: The first is that variability is a result of accumulation of stochastic fluctuations in gene expression (Shibata & Fujimoto, 2005; Ladbury & Arold, 2012; Rhee et al, 2014), organelle composition (Oates, 2011), and other cellular factors where small numbers of molecules increase biochemical randomness Under this hypothesis, a clonal population will accumulate changes over time that would limit biochemical reaction accuracy. While the measured features have high predictive power, it does not provide an indication of distinct functional states Another approach to identify functional cell state is based on a dynamical systems point of view, which argues that a cell can be represented as a dynamical system and that each cell state is an attractor within the cell state space (Furusawa & Kaneko, 2012). The existence of distinct cell states can provide support for the existence of attractors and the possibility that structured heterogeneity contributes to the observed cellular variability
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
