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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Kevin Meng-Lin¹, Choong-Yong Ung¹, Cheng Zhang¹

¹Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.

|June 28, 2023

Summary

This summary is machine-generated.

We developed Spatially Informed Artificial Intelligence (SPIN-AI) to identify spatially predictive genes (SPGs) that better capture cellular organization than spatially variable genes (SVGs). SPIN-AI offers a novel approach for understanding spatial biology and gene function in tissues.

Keywords:

artificial intelligence cellular niche spatial gene regulation spatial transcriptomics

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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

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Spatially resolved sequencing reveals cellular organization.
Spatially variable genes (SVGs) identify genes with spatially varying expression.
SVGs do not capture all spatial organization information.

Purpose of the Study:

To develop a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs).
To demonstrate SPIN-AI's ability to predict cellular spatial organization using spatial transcriptomic data.
To compare SPGs with SVGs for biological relevance in cellular niches.

Main Methods:

Devised a deep learning model named Spatially Informed Artificial Intelligence (SPIN-AI).
Applied SPIN-AI to spatial transcriptomic data from squamous cell carcinoma (SCC).
Analyzed and compared identified SPGs with previously identified SVGs.

Main Results:

SPIN-AI successfully identified SPGs that predict cellular spatial organization.
SPGs recapitulated SCC biology and identified distinct genes compared to SVGs.
A significant number of ribosomal genes were identified as SPGs but not SVGs.

Conclusions:

SPGs, identified by SPIN-AI, capture more biologically relevant information than SVGs for predicting cellular organization.
SPIN-AI provides a powerful tool for detecting SPGs and understanding the biological processes governing cellular organization.
This approach has broad applications in spatial transcriptomics and systems biology research.