How a New Tool Advances Cellular Process Research

A new study published in Nature Communications presents a groundbreaking computational tool developed by researchers at the Carl R. Woese Institute for Genomic Biology. This tool is designed to extract biological connections from large transcriptomics datasets, advancing the study of cellular processes.

The behavior of living organisms is determined by their genome, a set of DNA instructions that governs growth, survival, and reproduction. Cells manage protein production by regulating the abundance of RNA transcripts, which in turn affects cellular functions and responses to the environment.

Transcriptomics involves the study of gene expression by cataloging RNA transcripts present in cells under various conditions. This method allows researchers to explore complex molecular interactions, understand diseases, and evaluate the effects of therapeutics.

A key technique in transcriptomics is spatial transcriptomics, which maps the locations of RNA transcripts within tissues. This approach provides insights into the spatial arrangement of molecules and cells. According to Hee-Sun Han, a professor at the University of Illinois Urbana-Champaign, while this technology creates highly detailed molecular maps, the scientific community has yet to fully utilize the data. In this study, the researchers used the co-localization patterns of molecules to identify potential molecular interactions and understand their functions.

Han partnered with Dave Zhao, a professor of statistics at Illinois, and Saurabh Sinha, a biomedical engineering professor at the Georgia Institute of Technology, to create InSTAnT, a new tool for analyzing intracellular spatial transcriptomics. This tool helps scientists break down complex biological systems by focusing on how components are positioned relative to each other, not just how many there are.

InSTAnT uses advanced statistical algorithms to identify proximal pairs—RNA transcripts found close to one another. By detecting these pairs, the toolkit uncovers how molecules might work together, shedding light on their potential functions.

Han compared the tool’s function to understanding societal structures—just as knowing who interacts with whom helps us understand how a society works, analyzing how molecules are positioned relative to each other helps reveal their roles in the cell.