Leveraging Spatial Location for Single-Cell Molecular and Morphological Characterization

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Abstract Innovative developments in single-cell sequencing technologies and techniques are providing increased resolution and novel ways to define and characterize cellular profiles. Despite this progress, linking different aspects of a cell’s identity, such as transcriptome, spatial location, morphology, and physiological response remains challenging. Spatial transcriptomic technologies, while providing transcriptomic data within a spatial framework, frequently must compromise achieving single-cell resolution in order to survey a wider panel of genes. Similarly, while techniques such as fluorescent micro-optical section tomography (fMOST) and functional ultrasound imaging (fUSI) provide detailed reconstructions of neuron morphology and physiological response, these data modalities lack the ability to simultaneously capture molecular information. As a result, while technological advances for each distinct modality continue to resolve finer and more complex cell type distinctions, cohesive cellular profiles that combine all aspects of a cell’s identity, from transcriptome to physiological response, have yet to be captured. Thus, understanding how the transcriptomic and morphological composition of a cell influences its physiological response is a key barrier for the field. This proposal aims to develop computational tools that will connect multiple facets of cellular identity. In Aim 1, we propose the addition of graph-regularization into the integrative non-negative matrix factorization algorithm (GRINMF). The use of GRINMF to include spatial information will result in more refined cell-type characterizations for cells assayed with spatial transcriptomics technologies. In Aim 2, we will validate a spatial deconvolution algorithm that leverages non-negative matrix factorization to calculate cell-type proportions within spatially registered transcriptomic data. We will anchor our derived cell-type proportion voxels in the same coordinate framework as a series of morphological and physiological datasets. By completing the proposed research, I will gain extensive experience in the development of algorithms to synthesize physiological, transcriptomic, and spatial data. This training will facilitate advancement of my communication, critical thinking, and translational technical skills, providing me with the tools necessary to pursue my ambition of becoming a research scientist at the interface of neuroscience and bioinformatics.