Multimodal Spatial Omics: From Data Acquisition to Computational Integration

TL;DR

This paper reviews recent advances in spatial omics technologies and computational methods for integrating multimodal molecular and imaging data to better understand tissue architecture.

Contribution

It provides a comprehensive overview of computational strategies, including probabilistic and deep learning approaches, for integrating multimodal spatial omics datasets.

Findings

Summarizes key computational methods for data integration.

Highlights challenges and solutions in multimodal data analysis.

Emphasizes the importance of deep learning in recent methods.

Abstract

Recent developments in spatial omics technologies have enabled the generation of high dimensional molecular data, such as transcriptomes, proteomes, and epigenomes, within their spatial tissue context, either through coprofiling on the same slice or through serial tissue sections. These datasets, which are often complemented by images, have given rise to multimodal frameworks that capture both the cellular and architectural complexity of tissues across multiple molecular layers. Integration in such multimodal data poses significant computational challenges due to differences in scale, resolution, and data modality. In this review, we present a comprehensive overview of computational methods developed to integrate multimodal spatial omics and imaging datasets. We highlight key algorithmic principles underlying these methods, ranging from probabilistic to the latest deep learning approaches.