Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

Abstract

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease.

Tissue Strain Reorganizes Collagen With a Switchlike Response That Regulates Neuronal Extracellular Signal-Regulated Kinase Phosphorylation In Vitro: Implications for Ligamentous Injury and Mechanotransduction

“…All animal procedures were approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania. Cortical neurons were isolated from embryonic day 18 Sprague-Dawley rats as described previously [56]. Briefly, timed pregnant rats were anesthetized with 5% CO 2 and terminated by cervical dislocation.…”

Functional investigation of neural circuits in larval zebrafish

“…These were compared to confocal images of RGC axons of It was observed in our recordings that shadows across the illumination plane could flicker due to minor movements of the animal or blood circulation. These flickers added to the noise of the fluorescence signal and as such, a knowledge-based detection method, similar to that proposed by Patel et al (2015) was used to detect only significant neuronal firing. Specifically, the time-varying correlation coefficient between the fluorescence trace for each cell and an 'example spike' was calculated.…”

“…Cross-correlation provides another method for detecting groups of cells with common characteristics (Eggermont, 2006;Mukamel et al, 2008;Patel et al, 2015).…”

Anatomical and functional analysis of non-retinal tectal afferents in larval zebrafish

“…Because the experimental paradigms generating these datasets are often diverse, some approaches will produce more sound interpretations than others, and often there is a need for iteratively implementing algorithms for a particular analysis. These complexities are illustrated by the sheer number of software tools available for analysing neural data generated from the use of GECIs, none of which have become a standard practice in the field Kaifosh et al, 2014;Patel et al, 2015),.…”

“…Numerous toolkits exist for generating data from these experiments (Francis et al, 2014;Freeman et al, 2014;Kaifosh et al, 2014;Patel et al, 2015), but as alluded to above, the complexity of the datasets being analysed often means that tailored analytical approaches are needed, depending on what features of the data need to be extracted. Nevertheless, the use of bioinformatics to analyse optophysiology datasets has resulted in remarkable insights into how neural circuits in the brain can generate behaviour.…”