Distinct cellular states determine calcium signaling response

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.

Information transmission from NFkB signaling dynamics to gene expression

“…To analyze the effect of cellular heterogeneity on cellular information transmission we estimated the effect of biochemical variability on the accuracy of signal transduction using an information theoretic approach. The dominant source of variability in many signaling networks [41,47,68] and in NFkB specifically [69] is differences between cells in their underlying cell state (e.g. protein concentration, organelle structure, etc) between cells.…”

Calcium Activity Dynamics Correlate with Neuronal Phenotype at a Single Cell Level and in a Threshold-Dependent Manner

“…While the evidence demonstrating this mechanism of phenotype determination is compelling, many of these studies were performed on a population level rather than attempting to correlate calcium activity in single cells with a given phenotype. The importance of understanding calcium dynamics at the single-cell level has been established in recent work [43].…”

Oscillatory IL-2 stimulus reveals pertinent signaling timescales of T cell responsiveness

“…Cell-to-cell variability in signaling is commonly thought of as the result of accumulation of variation in protein at each level of a biochemical cascade. An alternate hypothesis to explain this variability states that rather than being caused by random variations in gene expression, it is caused by cellular convergence to specific attractor states within cell state space [ 46 ]. Such clustered heterogeneity could indicate the existence of distinct kinetic profiles within the cell population, allowing for the fulfillment of different functional needs.…”

“…Our experimental setup allowed us to focus on cellular response at the single cell level, in a manner that complemented the computational model by highlighting the variability of the in vitro cell response within a population. An intriguing approach to incorporate single cell information into computational models by single cell parameter estimation was recently suggested by Yao et al [ 46 ]. By using Bayesian parameter inference at the single-cell level, followed by inferred parameter clustering, these investigators detected existing cellular states within their population, explaining previously observed response variability.…”