Roberts David S, Loo Joseph A, Tsybin Yury O, Liu Xiaowen, Wu Si, Chamot-Rooke Julia, Agar Jeffrey N, Paša-Tolić Ljiljana, Smith Lloyd M, Ge Ying
Department of Chemistry, Stanford University, Stanford, CA, USA.
Sarafan ChEM-H, Stanford University, Stanford, CA, USA.
Nat Rev Methods Primers. 2024;4(1). doi: 10.1038/s43586-024-00318-2. Epub 2024 Jun 13.
Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.
蛋白质异构体由翻译后修饰、基因多态性和RNA剪接变体产生，在生物学中作为驱动因素发挥着关键作用。了解蛋白质异构体对于揭示生物系统的复杂性以及弥合基因型和表型之间的差距至关重要。通过分析未经消化的完整蛋白质，自上而下蛋白质组学（TDP）提供了蛋白质组的整体视图，能够解读蛋白质功能、揭示疾病机制并推动精准医学发展。本入门指南探讨了TDP，包括其基本原理、最新进展以及对未来的展望。实验部分讨论了仪器设备、样品制备、完整蛋白质分离、串联质谱技术和数据收集。结果部分介绍了如何解读原始数据、可视化完整蛋白质谱以及进行数据分析。此外，还总结了蛋白质异构体的鉴定、表征和定量方法，以及统计分析方法。描述了各种应用，包括人类蛋白质异构体计划以及生物医学、生物制药和临床科学领域的应用。同时还讨论了测量重现性、局限性以及前瞻性观点，概述了该领域可以取得进展的方向，包括潜在的未来应用。
