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Updated: May 26, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

A comprehensive survey of computer vision methods for spatial transcriptomics.

Junchao Zhu¹, Ruining Deng², Junlin Guo³

¹Department of Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN 37235, USA.

Briefings in Bioinformatics

|May 25, 2026

Summary

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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 virtual sequencing, and advances 3D spatial omics analysis for research and clinical applications.

Area of Science:

Computational Biology
Bioinformatics
Artificial Intelligence

Background:

Spatial transcriptomics (ST) measures gene expression and spatial localization in tissues.
Current bioinformatics approaches in ST often underutilize detailed morphological information.
Limitations of ST include high cost, limited clinical use, and 2D analysis of 3D tissues.

Purpose of the Study:

To survey computer vision AI models applied to spatial transcriptomics analytics.
To explore how AI can integrate histological morphology with molecular data in ST.
To address key limitations of ST, such as cost and clinical applicability.

Main Methods:

Systematic survey and categorization of computer vision AI models for ST.
Analysis of approaches based on architectures, learning paradigms, tasks, and datasets.

Keywords:

computational pathology computer vision medical image analysis spatial transcriptomics

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Last Updated: May 26, 2026

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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

Published on: July 6, 2022

Tracing the technological evolution of vision-driven ST.

Main Results:

Computer vision AI offers novel approaches beyond conventional bioinformatics for ST.
AI models can predict ST from histology (virtual sequencing), reducing costs.
AI enables 3D reconstruction of tissue models for advanced spatial omics analysis.

Conclusions:

Computer vision AI significantly enhances spatial transcriptomics analytics.
Vision-driven ST has the potential to transform basic research and clinical practice.
This survey provides a panoramic perspective on the field and future directions.