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Related Experiment Video

Updated: Mar 19, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.

Qingyue Wang^1,2, Anqi Gao³, Yuying Li²

¹Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, Ireland.

Biorxiv : the Preprint Server for Biology

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces SR2P, a machine-learning tool that predicts spatial protein levels from RNA data. This advances tumor immunology research by enabling detailed analysis of spatial multi-omics data.

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Last Updated: Mar 19, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Published on: October 31, 2025

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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

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Published on: October 28, 2025

Area of Science:

Computational Biology
Immunology
Genomics

Background:

Spatial transcriptomics provides RNA expression but lacks protein data, hindering cell state identification.
Tumor microenvironment studies are limited by the scarcity of spatial multi-omics data and protein-RNA discordance in immune markers.
Existing spatial multi-omics technologies face technical and cost barriers.

Purpose of the Study:

To develop a computational framework for predicting spatial protein abundance from RNA expression data.
To overcome limitations in current spatial transcriptomics by enabling protein inference.
To enhance the analysis of immune cell states and signaling within the tumor microenvironment.

Main Methods:

Introduced SR2P, a stacking-based machine-learning framework.
Integrated 11 complementary predictive models.
Validated performance across multiple spatial multi-omics benchmarks.

Main Results:

SR2P consistently outperformed existing methods in predicting spatial protein abundance.
Successfully recovered macrophage-enriched regions in head-and-neck squamous cell carcinoma data.
Identified potential immune markers correlated with therapeutic response.

Conclusions:

SR2P enables accurate protein-abundance inference from RNA-only spatial data.
The framework extends the analytical power of spatial platforms for tumor immunology.
Facilitates deeper understanding of immune cell dynamics in the tumor microenvironment.