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To date, numerous studies have been performed to elucidate the complex cellular dynamics in skin diseases, but few have attempted to characterize these cellular events under conditions similar to the native environment. To address this challenge, a three-dimensional (3D) multimodal analysis platform was developed for characterizing in vivo cellular dynamics in skin, which was then utilized to process in vivo wound healing data to demonstrate its applicability. Special attention is focused on in vivo biological parameters that are difficult to study with ex vivo analysis, including 3D cell tracking and techniques to connect biological information obtained from different imaging modalities. These results here open new possibilities for evaluating 3D cellular dynamics in vivo, and can potentially provide new tools for characterizing the skin microenvironment and pathologies in the future. 

Abstract Propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson’s correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12–15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties.