Modeling subcellular gene expression patterns

Analysis at the subcellular level, examining individual genes one at a time, across possibly tens of thousands of cells.

December 17, 2025 By Kevin Matyi

Sondra White

Executive Director, College of Arts & Sciences Marketing & Communications

sondra@tamu.edu

979-458-3296

Modeling subcellular gene expression patterns

Various graphs and images depicting various types of data ELLA has processed. — ELLA’s data output, split into various types of graphs to better show patterns between mRNA function and location within a cell. | Image: Courtesy Jade Wang

Dr. Jade Wang, a Texas A&M University researcher in the department of statistics, in collaboration with Dr. Xiang Zhou of Yale University, has recently developed ELLA (subcellular Expression Localization Analysis), a powerful, robust and scalable statistical method designed to model mRNA localization in cells and detect genes that change based on their location within a cell.

By modeling how mRNA is localized across a cell, researchers can interrogate wither it is concentrated around the nucleus, enriched at the cell membrane or scattered throughout the cytoplasm.

ELLA became possible due to recent advancements in high-resolution spatial transcriptomics, the emerging field of study combining biology and statistics. In four major spatial transcriptomics datasets, ELLA found several key, consistent biological patterns, underscoring a fundamental link between mRNA’s function and location.

Due to various programmatic efficiencies such as a pre-processing step to create a unified cellular coordinate system, ELLA is computationally efficient and easily scalable to tens of thousands of genes measured in tens of thousands of cells.

The research was supported by the National Institutes of Health (NIH) Grants R01HG009124, R01GM126553, R01HG011883, and R01GM144960. ELLA’s code, implemented in Python, is publicly available, released under a Massachusetts Institute of Technology license.