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Related Experiment Video

Updated: Jun 17, 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

Discovery and generalization of tissue structures from spatial omics data.

Zhenqin Wu¹, Ayano Kondo¹, Monee McGrady¹

¹Enable Medicine, Menlo Park, CA 94025, USA.

Cell Reports Methods

|August 10, 2024

Summary

This summary is machine-generated.

We developed spatial cellular graph partitioning (SCGP), an unsupervised method to annotate tissue structures. SCGP accurately identifies functional units across diverse tissues and diseases, driving biological discovery.

Keywords:

CP: Systems biology artificial intelligence spatial omics unsupervised annotation

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

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Published on: March 25, 2020

Area of Science:

Spatial biology
Computational pathology
Bioinformatics

Background:

Tissues exhibit complex organization across multiple scales.
In situ molecular profiling technologies advance understanding of tissue structure-function relationships.
Consistent identification of functional units across datasets is challenging, often requiring manual annotation.

Purpose of the Study:

To present spatial cellular graph partitioning (SCGP), a flexible, unsupervised method for annotating tissue structures.
To introduce SCGP-Extension for generalizing tissue structure labels to new samples via data integration and discovery.
To enable robust and accurate identification of key functional units in spatial datasets.

Main Methods:

Spatial cellular graph partitioning (SCGP) for unsupervised annotation of tissue structures.
SCGP-Extension pipeline for generalizing labels to unseen samples.
Application across diverse tissue types and disease contexts.

Main Results:

SCGP demonstrates reliable and robust partitioning of spatial data.
Achieved best-in-class accuracy in identifying expertly annotated tissue structures.
Downstream analysis revealed disease-relevant insights in kidney, skin, and neoplastic diseases.

Conclusions:

SCGP provides a powerful tool for unsupervised annotation of spatial tissue data.
SCGP-Extension facilitates data integration and discovery across experiments.
The method has the potential to drive significant biological insight and discovery from spatial datasets.