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Related Experiment Video

Updated: May 5, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex

Junsouk Choi, Jian Kang, Veerabhadran Baladandayuthapani

    Arxiv
    |May 2, 2025
    PubMed
    Summary

    We developed TopSpace, a new spatial topic model for analyzing tissue images. It identifies complex cellular structures like tertiary lymphoid structures (TLSs) and predicts patient survival in non-small cell lung cancer (NSCLC).

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    Area of Science:

    • Computational biology
    • Spatial transcriptomics
    • Bioinformatics

    Background:

    • Understanding tissue spatial architecture is crucial for disease pathology.
    • Multiplex imaging reveals cellular phenotypes and spatial distributions.
    • Existing methods struggle with nuanced cellular community analysis.

    Purpose of the Study:

    • To develop a novel spatial topic modeling framework for unsupervised discovery of spatial tissue structures.
    • To address limitations of hard clustering and adjacency-based models.
    • To analyze complex cellular interactions in multiplex imaging data.

    Main Methods:

    • Proposed TopSpace, a Bayesian spatial topic model integrating Gaussian processes with latent Dirichlet allocation.
    • Implemented a framework for multicellular mixed-membership clustering.
    • Utilized robust uncertainty quantification and data-driven determination of microenvironment numbers.

    Main Results:

    • TopSpace accurately recovers latent tissue microenvironments and spatial clustering patterns in simulations.
    • Outperformed existing methods in scenarios with varying spatial dependencies.
    • Identified tertiary lymphoid structures (TLSs) in non-small cell lung cancer (NSCLC) data, correlating with patient survival.

    Conclusions:

    • TopSpace provides a flexible and robust method for analyzing spatial tissue structures.
    • The model successfully captures complex cellular communities and their spatial relationships.
    • Spatial patterns identified by TopSpace have significant implications for predicting patient outcomes.