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Updated: Jan 14, 2026

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

Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method.

Quanyou Cai^1,2, Lihui Lin³, Xin Liu⁴

¹Center for Biomedical Digital Science, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.

Genome Research

|October 22, 2025

Summary

This summary is machine-generated.

Polyomino efficiently integrates single-cell and spatial transcriptomics data, even for millions of cells. This novel method improves speed and accuracy, revealing hidden gene expression patterns for biological insights.

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Last Updated: Jan 14, 2026

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

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

Author Spotlight: Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution

Author Spotlight: Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution

Published on: May 12, 2023

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Integrating single-cell and spatial transcriptomics enhances data quality.
Current methods face challenges with large datasets (millions of cells).

Purpose of the Study:

Introduce Polyomino, an intelligent region-allocation method for efficient data integration.
Optimize the mapping of single-cell data to spatial coordinates.

Main Methods:

Polyomino utilizes a region-of-interest (ROI) concept from image processing.
Employs gradient descent for cell allocation to structured spatial regions.
Optimizes integration for biological significance, speed, and accuracy.

Main Results:

Polyomino achieves 10 to 1000 times speed improvement over state-of-the-art methods.
Successfully integrates datasets with millions of cells in a single run.
Excels with sequencing artifacts like segmentation errors and imbalanced cell types.

Conclusions:

Polyomino enables efficient and accurate integration of large-scale single-cell and spatial transcriptomic data.
Uncovers previously hidden gene expression patterns in biological samples.
Provides new insights into organogenesis and tumor microenvironments.