# Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells

## TL;DR

This study introduces a new method called 'cross-expression' to analyze how genes in neighboring brain cells coordinate their activity, revealing insights into spatial gene expression patterns and disease-related gene interactions.

## Contribution

The novel 'cross-expression' method identifies coordinated gene expression between spatially adjacent cells without relying on cell type labels or curated databases.

## Key findings

- Cross-expression networks reveal gene communities enriched in spatial processes like synaptic signaling and GPCR activity.
- Genes Drd1 and Gpr6, linked to Parkinson’s disease, are cross-expressed in the striatum, suggesting a joint role in disease mechanisms.
- Ligand-receptor pairs and anatomical region markers are identified through cross-expression analysis.

## Abstract

Spatial transcriptomics allow us to ask a fundamental question: how do nearby cells orchestrate their gene expression? Rather than focus on how these cells (samples) communicate with each other, we reframe the problem to investigate how genes (features) coordinate their expression between neighboring cells. To this end, we introduce “cross-expression,” which models the degree to which genes coordinate their expression across spatially adjacent cells, avoiding the use of curated databases and cell type labels while controlling for cell-intrinsic processes.

We use multiple atlas-scale adult mouse brain datasets (~25 million cells, 695 slices from 52 brains, 8 technologies) to create an integrated, meta-analytic cross-expression network, whose communities are enriched in spatial processes such as synaptic signaling and G protein coupled receptor activity. Highlighting cross-expression’s biological utility, our network shows that genes Drd1 and Gpr6, which are individually implicated in Parkinson’s disease (PD), are cross-expressed within the striatum, hinting at their joint role in PD pathophysiology. It also recovers ligand-receptor pairs as cross-expressing genes and finds gene combinations that mark anatomical regions, thus complementing cell–cell communication approaches and marker gene-based region annotation, respectively.

We offer a gene-centric perspective to understand spatially coordinated expression between neighboring cells. Our method only requires the gene expression and cell location matrices to find cross-expressing gene pairs. The R package is available at https://github.com/gillislab/CrossExpression.

The online version contains supplementary material available at 10.1186/s13059-025-03747-8.