The evolving landscape of spatial proteomics technologies in the AI age

Although single-cell technologies have provided deep insights into cellular heterogeneity and complexity, they fall short in explaining how cells form tissue structures, a crucial aspect for understanding the principles of complex tissues. Recently, spatial transcriptomics has begun to fill this gap, allowing in situ studies of tissues at cellular and subcellular resolution. However, these genomic-level methods primarily provide indirect measurements of cellular states, as most biological processes are controlled by proteins. Therefore, spatial proteomics has the potential to revolutionize our understanding of biological processes, with significant implications for both basic cell biology and clinical applications. In this review, we provide an overview of the recent technical achievements and remaining challenges in spatial proteomics. Specifically, we categorize the techniques into three main types: antibody-based, LC-MS/MS-based, and imaging mass spectrometry-based. We describe each method in detail and discuss its strengths and weaknesses. We also discuss the emerging opportunities of artificial intelligence for spatial proteomics. Finally, we review key issues and suggest future directions for the advancement of spatial proteomics.