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Related Experiment Video

Updated: Jun 25, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved

Yuelei Zhang¹, Bianjiong Yu¹, Wenxuan Ming¹

¹Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.

Science Advances

|September 27, 2024

Summary

This summary is machine-generated.

SpaTopic, a new framework, analyzes spatial transcriptomics data to reveal tumor microenvironment structures. It accurately maps spatial domains, aiding in understanding tumor architecture and identifying key features like lymphoid structures.

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Published on: June 2, 2023

Area of Science:

Computational biology
Cancer research
Spatial transcriptomics

Background:

Tumor tissues possess complex spatial architectures within the tumor microenvironment (TME).
Spatially resolved transcriptomics (SRT) offers insights into TME cellular and molecular organization.
Identifying pathology-relevant spatial domains in SRT data remains a significant challenge.

Purpose of the Study:

Introduce SpaTopic, a statistical learning framework for harmonizing spot clustering and cell-type deconvolution.
Integrate single-cell transcriptomics and SRT data to stratify the TME into distinct spatial domains.
Enhance the annotation and interpretation of spatial architecture in tumor data.

Main Methods:

Developed SpaTopic, a statistical learning framework utilizing topic modeling.
Integrated single-cell transcriptomics with spatially resolved transcriptomics (SRT) data.
Applied SpaTopic to various tumor types for domain stratification and analysis.

Main Results:

SpaTopic successfully stratified the TME into spatial domains with coherent cellular organization.
Demonstrated accurate prediction of tertiary lymphoid structures and tumor boundaries across different tumor types.
Marker genes identified by SpaTopic were transferable to other datasets for spatial domain annotation.

Conclusions:

SpaTopic provides an innovative framework for analyzing tumor SRT data.
The method facilitates refined annotation and improved performance in understanding spatial architecture.
SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets.