Research Areas

Cells function within the context of tissue architecture and continuously interact with their local environment. The transcriptome of a cell varies in accordance with the location and the environment. Cells express genes related to cell communication and genes involved in downstream pathways. Interaction with the neighboring cells influences the transcriptome of a cell. Spatial transcriptomics (ST), by measuring the transcriptome of cells as well as their locations, can contribute to our understanding of how tissue architecture and cell-cell interaction influence the transcriptome.

We apply image processing and machine-learning algorithms to single-cell and spatial genomics data to understand gene regulatory rules involved in cell-cell interactions.

Physical contact between neighboring cells is known to induce transcriptional changes in the interacting partners. Accurate measurement of these cell-cell, contact-based influences on the transcriptome is a very difficult experimental task. However, determining such transcriptional changes will highly enhance our understanding for the developmental processes. Current scRNAseq technology isolates the tissue into individual cells, making it hard to determine the potential transcriptomic changes due to its interacting partners. We combine physically interacting cells followed by RNA sequencing and computational algorithms to identify cell-type, contact-dependent transcriptional profiles. We also use approaches to label physically interacting cells. Our study suggests a new way to study cell-cell interactions for development and disease.