# Unleashing the power of computational insights in revealing the complexity of biological systems in the new era of spatial multi-omics

arXiv: https://arxiv.org/abs/2509.13376 · HTML: https://mlsanity.com/paper/2509.13376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 182 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements](https://mlsanity.com/paper/1905.02269/) (2019)

## Abstract (arXiv)

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.