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Related Experiment Video

Updated: Jun 13, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

POPARI: Modeling multisample variation in spatial transcriptomics.

Shahul Alam¹, Tianming Zhou¹, Ellie Haber²

¹Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Biorxiv : the Preprint Server for Biology

|June 4, 2025

Summary

This summary is machine-generated.

Popari is a new computational tool that analyzes spatial transcriptomics data from multiple samples. It reveals how gene expression patterns change across tissues and conditions, offering insights into cell interactions and disease.

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Area of Science:

Computational Biology
Genomics
Spatial Transcriptomics

Background:

Integrating spatially-resolved transcriptomics (SRT) across biological samples is crucial for understanding in situ tissue architecture and cell-cell interactions.
Existing computational tools are limited for multisample SRT analysis, hindering the study of condition-specific spatial variations.

Purpose of the Study:

To introduce Popari, a novel probabilistic graphical model for factor-based decomposition of multisample SRT data.
To capture and analyze condition-specific changes in spatial organization and gene expression programs across multiple samples.

Main Methods:

Popari jointly learns spatial metagenes (gene expression programs) and their spatial affinities across samples.
Key innovations include a differential prior for spatial accordance regularization and spatial downsampling for multiresolution analysis.
The model was validated through simulations and applied to real-world datasets (mouse brain, thymus, ovarian cancer).

Main Results:

Popari outperforms existing methods in multisample and multi-resolution spatial metrics.
The tool successfully identified spatial metagene dynamics, spatial accordance, and cell identities in diverse biological samples.
Specific applications revealed AD-linked metagenes in mouse brain, V(D)J recombination and T cell proliferation in thymus, and malignant-immune interactions in ovarian cancer.

Conclusions:

Popari offers a general and interpretable framework for analyzing variations in multisample spatially-resolved transcriptomics data.
The method enhances the understanding of spatial organization and cell-cell interactions in complex biological systems.