# Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER

## TL;DR

This paper introduces SPER, a new method to identify genes that regulate cell type composition in tissues using spatial transcriptomics data.

## Contribution

SPER is the first method to systematically identify paracrine regulators of cell type composition from spatial transcriptomics.

## Key findings

- SPER accurately detects paracrine drivers of cellular abundance in simulated data.
- Genes identified by SPER are enriched for extracellular secretion and receptor-ligand interactions.
- SPER's results are validated using spatial transcriptomics data from mouse brain and human lung.

## Abstract

A defining characteristic of biological tissue is its cell type composition. Many pathologies and chronic diseases are associated with perturbations from the homeostatic composition, and these transformations can lead to aberrant or deleterious tissue function. Spatial transcriptomics enables the concurrent measurement of gene expression and cell type composition, providing an opportunity to identify transcripts that co-vary with and potentially influence nearby cell composition. However, no method yet exists to systematically identify such intercellular regulatory factors.

Here, we develop Spatial Paired Expression Ratio (SPER), a computational approach to evaluate the spatial dependence between transcript abundance and cell type proportions in spatial transcriptomics. We demonstrate the ability of SPER to accurately detect paracrine drivers of cellular abundance using simulated data. Using publicly available spatial transcriptomics data from mouse brain and human lung, we show that genes identified by SPER show statistical enrichment for both extracellular secretion and participation in known receptor-ligand interactions, supporting their potential role as compositional regulators. Taken together, SPER represents a general approach to discover paracrine drivers of cellular compositional changes from spatial transcriptomics.

The methods are implemented in R and available at: https://github.com/TianxiaoNYU/SPER.