Role of Metabotropic Glutamate Receptors (mGluRs) in the Regulation of Cellular Calcium Signaling: Theory, Protocols, and Data Analysis

Glutamate is the primary fast excitatory neurotransmitter which is responsible for neuronal communication, memory formation, and learning. It activates metabotropic glutamate receptors (mGluRs), a family of G protein-coupled receptors, which lead to oscillations in cytosolic calcium (Ca2+) through a variety of signaling cascades. Mechanisms of these cascades and their modulation by drugs are well understood, but there are limited methodological guidelines available for systematic study of Ca2+-spiking behavior and quantification of spiking information. Neurons are known to exhibit an extensive variety of Ca2+-spiking behavior from bursting spikes to sustained plateau, thus constituting the oscillations into high-dimensional time-series data, comprising of heterogeneous features (amplitude, frequency, inter-spike interval, etc.). This chapter addresses the signaling pathways involved in mGluR-mediated Ca2+-spiking along with a system of nonlinear ordinary differential equation used for explaining glutamate-induced-Ca2+-responses. We also discuss the modulatory effect of mGluRs on other channels and receptors involved in Ca2+-signaling. The chapter specifically focuses on the details of the imaging systems and parameters that can be used for high-resolution-Ca2+-imaging for monitoring neural activity. Here, we provide a detailed workflow of algorithms for various data analysis tools including clustering, measuring correlations, and probability density function (PDF) fitting for analyzing the heterogeneous data obtained from mGluR-mediated-Ca2+-spiking in primary hippocampal neurons. Moreover, we provide sample codes and MATLAB functions that can be used for automated analysis of large-scale-Ca2+-spiking data. Since there is a high level of heterogeneity present in dissociated cultured neurons, we provide a protocol for grouping the data and random sampling to perform the statistical analysis using clustering and PDF fitting