Faculty, Staff and Student Publications

Publication Date

3-1-2025

Journal

Nature Methods

DOI

10.1038/s41592-024-02574-2

PMID

39815104

PMCID

PMC12139046

PubMedCentral® Posted Date

9-1-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multi-modal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with sub-cellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics, and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a significant advancement in multi-modal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by sub-cellular resolution spatial omics technologies.

Keywords

Algorithms, Humans, Metabolomics, Computational Biology, Cluster Analysis, Genomics, Animals, Gene Expression Profiling, Software, multi-modal spatial omics, spatial transcriptomics, spatial multi-omics, deep learning

Comments

This article has been corrected. See Nat Methods. 2025 Jan 24. This article has been corrected. See Nat Methods. 2025 Mar 6.

Published Open-Access

yes

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.