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Updated: Jan 10, 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

Computer Vision Methods for Spatial Transcriptomics: A Survey.

Junchao Zhu¹, Ruining Deng², Junlin Guo³

¹Department of Computer Science, Vanderbilt University, TN, USA.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

This summary is machine-generated.

Computer vision AI enhances spatial transcriptomics (ST) by integrating tissue morphology with gene expression data. This approach reduces costs, enables 3D analysis, and accelerates clinical applications of ST.

Keywords:

Artificial Intelligence Computational Pathology Computer Vision Spatial Transcriptomics

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Area of Science:

Computational Biology
Bioinformatics
Artificial Intelligence

Background:

Spatial transcriptomics (ST) measures gene expression and localization in tissues.
Current bioinformatics approaches often underutilize detailed tissue morphology.
Limitations of ST include high cost, 2D analysis, and limited clinical translation.

Purpose of the Study:

To systematically survey computer vision AI models applied to spatial transcriptomics analytics.
To explore how AI can bridge the gap between histology and molecular data.
To highlight the potential of AI to overcome ST limitations and advance research and clinical practice.

Main Methods:

Categorization of AI models based on architectures, learning paradigms, tasks, and datasets.
Review of AI techniques for integrating histological images with gene expression data.
Analysis of AI's role in virtual sequencing and 3D tissue reconstruction for ST.

Main Results:

Computer vision AI offers novel approaches beyond traditional bioinformatics for ST.
AI enables 'virtual sequencing' from histology, reducing costs and integrating pathology insights.
AI facilitates 3D reconstruction of tissues, advancing spatial omics analysis.

Conclusions:

AI-powered computer vision is transforming spatial transcriptomics analytics.
Vision-driven ST accelerates discovery in basic research and clinical translation.
This survey provides a panoramic view of AI in ST and future directions.