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Biomolecules
Volume 13
Issue 6
10.3390/biom13060895
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Article

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

by
Kevin Meng-Lin
1,†,
Choong-Yong Ung
1,†,
Cheng Zhang
1,
Taylor M. Weiskittel
1,
Philip Wisniewski
1,
Zhuofei Zhang
1,
Shyang-Hong Tan
1,
Kok-Siong Yeo
2,
Shizhen Zhu
2,
Cristina Correia
1,* and
Hu Li
1,*
1
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
2
Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomolecules 2023, 13(6), 895; https://doi.org/10.3390/biom13060895
Submission received: 27 April 2023 / Revised: 23 May 2023 / Accepted: 23 May 2023 / Published: 27 May 2023
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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Abstract

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.
Keywords: spatial transcriptomics; artificial intelligence; spatial gene regulation; cellular niche spatial transcriptomics; artificial intelligence; spatial gene regulation; cellular niche

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MDPI and ACS Style

Meng-Lin, K.; Ung, C.-Y.; Zhang, C.; Weiskittel, T.M.; Wisniewski, P.; Zhang, Z.; Tan, S.-H.; Yeo, K.-S.; Zhu, S.; Correia, C.; et al. SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes. Biomolecules 2023, 13, 895. https://doi.org/10.3390/biom13060895

AMA Style

Meng-Lin K, Ung C-Y, Zhang C, Weiskittel TM, Wisniewski P, Zhang Z, Tan S-H, Yeo K-S, Zhu S, Correia C, et al. SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes. Biomolecules. 2023; 13(6):895. https://doi.org/10.3390/biom13060895

Chicago/Turabian Style

Meng-Lin, Kevin, Choong-Yong Ung, Cheng Zhang, Taylor M. Weiskittel, Philip Wisniewski, Zhuofei Zhang, Shyang-Hong Tan, Kok-Siong Yeo, Shizhen Zhu, Cristina Correia, and et al. 2023. "SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes" Biomolecules 13, no. 6: 895. https://doi.org/10.3390/biom13060895

APA Style

Meng-Lin, K., Ung, C.-Y., Zhang, C., Weiskittel, T. M., Wisniewski, P., Zhang, Z., Tan, S.-H., Yeo, K.-S., Zhu, S., Correia, C., & Li, H. (2023). SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes. Biomolecules, 13(6), 895. https://doi.org/10.3390/biom13060895

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