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ELLA: modeling subcellular spatial variation of gene expression within cells in high-resolution spatial transcriptomics

Jade Xiaoqing Wang,Xiang Zhou

Published 2025 in Nature Communications

ABSTRACT

Spatial transcriptomics technologies are becoming increasingly high-resolution, enabling gene expression measurement at the subcellular level. Here, we present subcellular expression localization analysis (ELLA), a statistical framework for modeling subcellular mRNA localization and detecting spatially variable genes within cells. ELLA uses an over-dispersed nonhomogeneous Poisson process to model spatial count data with a unified cellular coordinate system to anchor diverse cellular morphologies, demonstrating effective type I error control and high power in simulations. In real data applications, ELLA identifies genes with distinct subcellular localization and associate these patterns to key mRNA characteristics: nuclear-enriched genes exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, while cytoplasmic- or membrane-enriched genes frequently encode ribosomal proteins or contain signal peptides. ELLA also uncovers dynamic subcellular localization changes across the cell cycle. Overall, ELLA is a powerful, robust, and scalable tool for subcellular spatial expression analysis across high-resolution spatial transcriptomics platforms. Authors introduce ELLA, a computational framework that maps gene expression within cells at subcellular resolution, uncovering localization patterns and revealing how mRNA features and the cell cycle shape spatial gene regulation.

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