Modeling differential sub-cellular localization of transcripts as a regulatory mechanism of translation and cell fate

Abstract Description: To gain their unique biological function, plant cells regulate protein biosynthesis through gene activation and repression along with multiple post-transcriptional, translational, and post-translational mechanisms. Additionally, the differential trafficking and subcellular localization of mRNAs have been reported as a complementary regulatory mechanism of the biology of fungi, yeast, and animal cells. However, studies comprehensively reporting the impact of mRNA localization in plant cells are lacking.

Here, we set to mathematically model the spatial distribution of sub-cellular cytosolic transcripts across multiple cell types and developmental stages. Through the use of high-resolution spatial transcriptomic technology, we first report the comprehensive and differential mapping of millions of plant transcripts between the nuclear and cytoplasmic compartments of various soybean nodule cell types. We then characterize key mathematical features of these transcriptomic spatial distributions using Topological Data Analysis (TDA). TDA offers a comprehensive pattern-quantifying framework that is robust to variations in cell shape, size, and orientation. TDA thus provides us with a common ground to mathematically compare and contrast intrinsic differences in sub-cellular transcript distributions and mRNA localization patterns across cell types and expressed genes.

Our analyses reveal distinct patterns and spatial distributions of plant transcripts between the nucleus and cytoplasm, varying both between and within genes, as well as across different cell types. We believe this differential distribution is an additional, less understood, regulatory mechanism controlling protein translation and localization, cell identity, and cell state and reveals the influence of the sub-compartmentalization of transcripts as another post-transcriptional regulatory mechanism.

Equity and Inclusion: I am a mathematician and data scientist turned biologist. My main research focuses in understanding the diverse shapes, and patterns found in plant-based 2D and 3D image datasets using diverse tools from mathematics and data science. I focus on adapting and creating mathematically precise techniques to compute, model, and compare the totality of morphological information across multiple temporal and spatial scales from a variety of inputs, such as microscopy, X-ray CT scanning, or RGB imaging. My goal is to facilitate the use of novel morphological tools in a way accessible to biologists, data scientists, and mathematicians alike.