Ten Years of Extracellular Matrix Proteomics: Accomplishments, Challenges, and Future Perspectives.
Ten Years of Extracellular Matrix Proteomics: Accomplishments, Challenges, and Future Perspectives.
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DOI：
10.1016/j.mcpro.2023.100528
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发表时间：
2023-04
影响因子：
7
通讯作者：
Naba, Alexandra
中科院分区：
文献类型：
--
作者：
Naba, Alexandra
细胞外基质（ECM）是由数百种蛋白质组成的复杂集合体，形成多细胞生物体的结构支架。除了其结构作用外，ECM还传递协调细胞表型的信号。ECM组成、丰度、结构或力学的改变与影响所有生理系统的疾病和病症（包括纤维化和癌症）有关。因此，破译ECM的蛋白质组成及其在病理生理学背景下如何变化是理解ECM在健康和疾病中的作用以及开发治疗策略以纠正致病ECM改变的第一步。潜在地，ECM也代表了一个巨大的、尚未开发的疾病生物标志物库。ECM蛋白的特征在于独特的生物化学性质，这阻碍了它们的研究：它们是大的，严重和独特的后修饰，并且高度不溶性。为了克服这些挑战，我们和其他人设计了基于质谱的蛋白质组学方法来定义组织的ECM组成或“基质体”。这篇评论的第一部分提供了ECM蛋白质组学研究的历史概述，并提出了最新的进展，现在允许健康和患病组织的ECM分析。第二部分强调了最近的例子，说明ECM蛋白质组学如何成为一个强大的发现管道，以确定预后癌症生物标志物。第三部分讨论了限制我们将研究结果转化为临床应用的能力的剩余挑战，并提出了克服这些挑战的方法。最后，该评论向读者介绍了可用于促进ECM蛋白质组学数据集解释的资源。ECM曾经被认为是不可穿透的。基于质谱的蛋白质组学已被证明是解码ECM的有力工具。鉴于在过去十年中取得的进展，有理由相信，深入探索基质体是触手可及的，我们可能很快就会看到ECM蛋白质组学的第一个翻译应用。ECM改变引起或伴随所有器官系统的疾病和病症。蛋白质组学是分析组织ECM组成的首选方法。ECM蛋白质组学可以鉴定新的预后和诊断生物标志物。ECM蛋白质组学可以发现在疾病病因中发挥功能作用的蛋白质。需要进一步的技术进步来捕获ECM蛋白质型的多样性。细胞外基质（ECM）是形成多细胞生物体的结构组织者的数百种蛋白质的复杂组装体。在过去的十年中，自下而上的蛋白质组学已成为分析ECM组成的首选方法。本文回顾了ECM蛋白质组学研究的最新进展，并说明了ECM蛋白质组学如何成为癌症生物标志物的强大发现渠道。本文还介绍了可用的资源，以促进ECM的研究，利用蛋白质组学。
The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely posttranslationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry–based proteomic approaches to define the ECM composition, or “matrisome,” of tissues. This first part of this review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Lastly, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. Mass spectrometry–based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics. ECM alterations cause or accompany diseases and disorders of all organ systems. Proteomics is a method of choice to profile the composition of the ECM of tissues. ECM proteomics can identify novel prognostic and diagnostic biomarkers. ECM proteomics can uncover proteins playing functional roles in disease etiology. Further technical advances are needed to capture the diversity of ECM proteoforms The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural organizer of multicellular organisms. Over the past decade, bottom-up proteomics has become the method of choice to profile the composition of the ECM. This article reviews the state of the art in ECM proteomics research and illustrates how ECM proteomics has emerged as a powerful discovery pipeline for cancer biomarkers. This review also introduces resources available to facilitate the study of the ECM using proteomics.
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