The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

• Published in: Frontiers in oncology Vol. 13; p. 1172314
• Main Authors: Lee, Ren Yuan, Ng, Chan Way, Rajapakse, Menaka Priyadharsani, Ang, Nicholas, Yeong, Joe Poh Sheng, Lau, Mai Chan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 01.05.2023
• Subjects: artificial intelligence; deep learning; machine learning; Oncology; spatial omics; spatial proteomics; tumour microenvironment; spatial proteomics; deep learning; digital pathology; spatial transcriptomics; tumour microenvironment; machine learning; spatial omics; artificial intelligence
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1172314

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.