Unleashing the power of computational insights in revealing the complexity of biological systems in the new era of spatial multi-omics
This is an incremental review paper summarizing existing advances in spatial omics technologies and computational approaches for researchers in bioinformatics and systems biology.
This review examines how spatial multi-omics technologies and computational methods, including machine learning algorithms, enable comprehensive mapping of cellular heterogeneity and tissue architecture in biological systems, with applications in developmental biology, neuroscience, oncology, and evolutionary studies.
Recent advances in spatial omics technologies have revolutionized our ability to study biological systems with unprecedented resolution. By preserving the spatial context of molecular measurements, these methods enable comprehensive mapping of cellular heterogeneity, tissue architecture, and dynamic biological processes in developmental biology, neuroscience, oncology, and evolutionary studies. This review highlights a systematic overview of the continuous advancements in both technology and computational algorithms that are paving the way for a deeper, more systematic comprehension of the structure and mechanisms of mammalian tissues and organs by using spatial multi-omics. Our viewpoint demonstrates how advanced machine learning algorithms and multi-omics integrative modeling can decode complex biological processes, including the spatial organization and topological relationships of cells during organ development, as well as key molecular signatures and regulatory networks underlying tumorigenesis and metastasis. Finally, we outline future directions for technological innovation and modeling insights of spatial omics in precision medicine.