Enablers and challenges of spatial omics, a melting pot of technologies

Author:

Alexandrov Theodore123ORCID,Saez‐Rodriguez Julio24ORCID,Saka Sinem K5ORCID

Affiliation:

1. Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Germany

2. Molecular Medicine Partnership Unit European Molecular Biology Laboratory Heidelberg Germany

3. BioInnovation Institute Copenhagen Denmark

4. Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine Heidelberg University Heidelberg Germany

5. Genome Biology Unit European Molecular Biology Laboratory Heidelberg Germany

Abstract

AbstractSpatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

Funder

Michael J. Fox Foundation for Parkinson's Research

Stavros Niarchos Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Molecular Advances in Microbial Metabolism 2.0;International Journal of Molecular Sciences;2024-01-22

2. THItoGene: a deep learning method for predicting spatial transcriptomics from histological images;Briefings in Bioinformatics;2023-11-22

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