Computational methods for delineating subcellular and cellular spatial transcriptional heterogeneity along developmental trajectories

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Project Summary Biological differences between cells in healthy and diseased states are molecularly encoded in part by coordinated differences in gene expression. Gene expression differences between healthy and disease cell states may manifest as altered expression magnitudes of important regulatory factors, as well as aberrant alternative splicing of genes to produce protein isoforms with divergent functions. Likewise, the spatial localization of mRNAs within cells play important regulatory roles in modulating local protein translation that may be disrupted in disease. And finally, cells exist within diverse microenvironments where they signal and interact with different cells to maintain homeostasis within tissues. Quantitatively evaluating these different aspects of transcriptional heterogeneity between cells in healthy and diseased states is paramount to our understanding of disease etiology and the mechanisms for disease pathogenesis. Recent advancements in next-generation sequencing and imaging technologies are enabling investigators to quantitatively measure gene expression in individual cells at transcriptome-scale across different biological and disease settings in a high-throughput manner. As such, the ability to perform computational analysis is becoming increasingly paramount in order to extract biological insights from such data. My research program develops statistical approaches and computational tools to identify and characterize these aspects of transcriptional and spatial heterogeneity and quantitatively evaluate the functional consequences of this variation. Here, we will focus on developing computational tools to delineate 1) transcriptional heterogeneity across populations of cells, 2) subcellular spatial transcriptional heterogeneity within cells, and 3) spatial- contextual heterogeneity among cells in tissues. Specifically, I will build on my previous experience developing statistical approaches for unified clustering analysis in order to identify the appropriate normal cells for comparison with cells from transcriptionally heterogeneous diseased states. I will further build on my previous experience detecting alternative splicing to characterize aberrant alternative splicing within individual cells and assess how such alternative splicing may impact cellular function through subcellular localization. I will further assess how mRNA localization patterns may change through dynamic processes such as the cell-cycle and neuroglia maturation within tissues to impact cell-fate. Finally, I will assess how the spatial-contextual organization of cells within tissues may impact cell-cell communication networks. Although we focus on establishing proof of concept in model systems, pursuit of these research goals will result in the development of new computational methods available as open-source software that can be tailored and applied to address fundamental biological questions in a variety of disease settings.