Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California 90069, United States.
Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States.
J Am Soc Mass Spectrom. 2023 Sep 6;34(9):1858-1867. doi: 10.1021/jasms.3c00072. Epub 2023 Jul 18.
Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.
骨骼肌是调节全身代谢的主要组织,由多种细胞(纤维)类型组成。衰老和几种疾病会以不同的方式影响各种纤维类型,因此,以纤维类型特异性的方式研究蛋白质组的变化至关重要。最近在分离的单个肌肉纤维蛋白质组学方面的突破开始揭示纤维之间的异质性。然而,现有的程序既缓慢又费力,每个肌肉纤维需要 2 小时的质谱时间;50 根纤维大约需要 4 天的时间来分析。因此,要捕捉个体内和个体间纤维的高变异性,需要在高通量单个肌肉纤维蛋白质组学方面取得进展。在这里,我们使用单细胞蛋白质组学方法,在 15 分钟的总仪器时间内实现单个肌肉纤维蛋白质组的定量。作为概念验证,我们展示了来自两个健康个体的 53 个分离骨骼肌纤维的数据,这些数据在 13.25 小时内进行了分析。通过适应单细胞数据分析技术来整合数据,我们可以可靠地分离 1 型和 2A 纤维。94 种蛋白质在聚类之间存在统计学差异,表明参与脂肪酸氧化、氧化磷酸化以及肌肉结构和收缩功能的蛋白质发生了改变。我们的结果表明,与以前的单个纤维方法相比,这种方法在数据收集和样品制备方面都显著更快,同时保持了足够的蛋白质组深度。我们预计该检测方法将能够对数百个人的单个肌肉纤维进行未来的研究,这在以前由于通量限制是不可能的。
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