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Spatial quantification of the synaptic activity phenotype across large populations of neurons with Markov random fields.

ABSTRACT: Motivation:The collective and co-ordinated synaptic activity of large neuronal populations is relevant to neuronal development as well as a range of neurological diseases. Quantification of synaptically-mediated neuronal signalling permits further downstream analysis as well as potential application in target validation and in vitro screening assays. Our aim is to develop a phenotypic quantification for neuronal activity imaging data of large populations of neurons, in particular relating to the spatial component of the activity. Results:We extend the use of Markov random field (MRF) models to achieve this aim. In particular, we consider Bayesian posterior densities of model parameters in Gaussian MRFs to directly model changes in calcium fluorescence intensity rather than using spike trains. The basis of our model is defining neuron 'neighbours' by the relative spatial positions of the neuronal somata as obtained from the image data whereas previously this has been limited to defining an artificial square grid across the field of view and spike binning. We demonstrate that our spatial phenotypic quantification is applicable for both in vitro and in vivo data consisting of thousands of neurons over hundreds of time points. We show how our approach provides insight beyond that attained by conventional spike counting and discuss how it could be used to facilitate screening assays for modifiers of disease-associated defects of communication between cells. Availability and implementation:We supply the MATLAB code and data to obtain all of the results in the paper. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Robinson S

PROVIDER: S-EPMC6137993 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

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Spatial quantification of the synaptic activity phenotype across large populations of neurons with Markov random fields.

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