Kwon Yumi, Fulcher James M, Paša-Tolić Ljiljana, Qian Wei-Jun
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.
Expert Rev Proteomics. 2024 Dec 25:1-10. doi: 10.1080/14789450.2024.2445809.
Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level.
This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution.
The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. Achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.
空间生物学是一个新兴的跨学科领域，通过使用空间组学技术促进生物学发现。空间转录组学、空间基因组学（如基因突变和表观遗传标记）、多重免疫荧光以及空间代谢组学/脂质组学的最新进展，使得在组织中对基因表达、遗传变异、蛋白质表达以及代谢物/脂质谱进行高分辨率的空间分析成为可能。这些进展有助于在分子水平上更深入地理解组织微环境中的空间组织。
本报告概述了基于非靶向、自下而上的质谱（MS）的空间蛋白质组学工作流程。它突出了组织解剖、样品处理、生物信息学以及液相色谱（LC）-MS技术方面的最新进展，这些进展正在推动空间蛋白质组学向细胞分辨率发展。
基于非靶向MS的空间蛋白质组学领域正在迅速发展，前景广阔。为了充分实现空间蛋白质组学的潜力，推进数据分析以及开发细胞或亚细胞水平的自动化和智能组织解剖，同时对数千个样品进行高通量LC-MS分析至关重要。要实现这些目标，需要在组织解剖技术、LC-MS仪器以及计算工具方面取得重大进展。
