Experimental and computational analysis of calcium dynamics in 22q11.2 deletion model astrocytes

Intracellular calcium dynamics in spontaneously active cells such as neurons or astrocytes is an information-rich readout of the physiological state of the cell. Methods for deriving mechanistic information from biological time courses, as well as for algorithmically extracting cellular activity time courses from imaging data, have significantly advanced in recent years but been mostly applied to neuronal data. At the same time, the role for astrocytes, a type of glial brain cells, in cognition and psychiatric diseases remains poorly understood. Using calcium imaging, computer vision, and Bayesian kinetic inference, we analyze calcium dynamics in primary astrocytes derived from control or Df1/+ mice, a model of 22q11.2 deletion syndrome (DiGeorge syndrome). Inference of highest-likelihood molecular kinetic characteristics from the intracellular calcium time courses pinpoints a significant change in the activity of the sarcoendoplasmic reticulum calcium ATPase (SERCA). Applying a SERCA inhibitor to the control cells reproduces the differences detected in the deletion-bearing cells. Our work identifies for the first time the molecular changes driving the calcium kinetics in 22q11.2 deletion model astrocytes. We conclude that Bayesian kinetic inference is a useful tool for mechanistic dissection of a complex cellular phenotype, calcium dynamics, in glial cells. This method has the potential to facilitate formulation of specific hypotheses concerning the underlying molecular mechanisms, prioritization of experiments testing such hypotheses, and, in the future, individualized functional molecular diagnostics.