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Updated: May 1, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs.

Chunman Zuo^1,2,3,4, Junjie Xia⁵, Yupeng Xu⁵

¹School of Life Sciences, Sun Yat-sen University, Guangzhou, China. zuochm@mail.sysu.edu.cn.

Nature Communications

|June 16, 2025

Summary

We developed stClinic, a novel dynamic graph model for integrating spatial multi-omics and phenotype data. This approach uncovers clinically relevant cellular niches in tumors, improving our understanding of cancer progression and patient outcomes.

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Area of Science:

Computational biology
Cancer research
Bioinformatics

Background:

Spatial multi-omics (SMSMO) integration advances cellular niche understanding in tumors.
Challenges include data scale, diversity, heterogeneity, and small sample sizes, limiting clinical insights.

Purpose of the Study:

To introduce stClinic, a dynamic graph model for integrating SMSMO and phenotype data.
To uncover clinically relevant cellular niches and link them to disease manifestations.
To enhance understanding of tumor microenvironments and their clinical impact.

Main Methods:

stClinic aggregates information from neighboring nodes across slices using a Mixture-of-Gaussians prior.
It employs attention-based geometric statistical measures for slice characterization relative to the population.
Zero-shot learning is utilized for label annotation and multi-omics integration.

Main Results:

stClinic identifies aggressive tumor niches enriched with tumor-associated macrophages.
Favorable prognostic niches abundant in B and plasma cells were discovered.
A specific niche involving myeloid cells and fibroblasts driving colorectal cancer invasion was identified.

Conclusions:

stClinic effectively integrates multi-omics data to reveal clinically significant cellular niches.
The model aids in predicting cancer malignancy and patient prognosis.
Findings provide novel insights into tumor heterogeneity and therapeutic targets.