Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation.
Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation.
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DOI：
10.1016/j.mcpro.2023.100536
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发表时间：
2023-05
影响因子：
7
通讯作者：
Tao, W. Andy
中科院分区：
文献类型：
--
作者：
Hadisurya, Marco;Lee, Zheng-Chi;Luo, Zhuojun;Zhang, Guiyuan;Ding, Yajie;Zhang, Hao;Iliuk, Anton B.;Pili, Roberto;Boris, Ronald S.;Tao, W. Andy
将癌症信号方面的研究能力和知识转化为临床环境一直是缓慢和无效的。最近，细胞外小泡(EVS)已成为开发疾病磷酸蛋白标记物以监测疾病状态的一种有前途的来源。这项研究致力于建立一种稳健的数据独立获取(DIA)方法，使用质谱仪来描述尿液EV磷酸蛋白质组学在肾细胞癌(RCC)分级分化中的作用。我们研究了气相分馏库、直接DIA(无库)、禁区和几种不同的窗口方案。在建立了EV磷酸蛋白质组学的DIA质谱学方法后，我们应用该策略鉴定和定量了57名代表低度透明细胞肾癌、高级透明细胞肾癌、慢性肾脏疾病和健康对照组的尿液EV磷酸蛋白质组。功能磁珠能有效分离尿液中的EV，并用PolyMAC对EV磷酸肽进行富集化。我们量化了2584个独特的磷脂酶切位点，并观察到多种与癌症相关的重要途径，如ErbB信号、肾细胞癌和肌动蛋白细胞骨架的调节，仅在高级别透明细胞肾癌中上调。这些结果表明，利用我们优化的EV分离、磷酸肽富集法和DIA方法进行EV磷酸蛋白质组分析，为未来的临床应用提供了有力的工具。尿液EV磷酸蛋白质组气相分离(GPF)文库的优化集成EVTrap、PolyMAC和GPF DIA，用于数千种唯一的EV亚磷酸盐识别。线性判别分析(LDA)正确地对肾癌患者的分级进行了分类。慢性肾脏病(CKD)是肾细胞癌生物标志物筛查的较好对照。考虑到现有方法的局限性，有必要开发一种新的诊断技术用于肾癌的早期干预。在这里，我们努力采用现有的DIA方法来分析尿EV磷酸蛋白质组学，用于无创筛选肾细胞癌生物标记物。结合我们内部的EVTrap方法和PolyMAC富集法，我们对2584个独特的磷酸盐进行了量化。我们观察到了独特的上调的磷酸盐和区分健康对照(HC)、慢性肾脏疾病(CKD)、低级别和高级别透明细胞肾细胞癌的途径。
Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Optimal gas-phase fractionated (GPF) library for urinary EV phosphoproteomics. Integrating EVtrap, PolyMAC, and GPF DIA for thousands of unique EV phosphosite identification. Linear discriminant analysis (LDA) correctly clustered the grades of RCC patients. Chronic kidney disease (CKD) served as a better control for RCC biomarker screening. Considering the limitations of current approaches, it is necessary to develop a novel diagnostic technique for early intervention of renal cell carcinoma (RCC). Here, we made our efforts to adapt the existing DIA method to analyze urinary EV phosphoproteomics for noninvasive RCC biomarker screening. Combined with our in-house EVtrap method and PolyMAC enrichment, we quantified 2584 unique phosphosites. We observed unique upregulated phosphosites and pathways differentiating healthy control (HC), chronic kidney disease (CKD), low-grade, and high-grade clear cell RCC.
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DOI：
10.1136/bmj.g4797
发表时间：
2014-11-10
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Jonasch E;Gao J;Rathmell WK
通讯作者：
Rathmell WK
DOI：
10.1007/978-1-0716-0759-6_10
发表时间：
2021
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Chang WH;Cerione RA;Antonyak MA
通讯作者：
Antonyak MA
影响因子：
2.9
作者：
Iliuk, Anton B.;Arrington, Justine V.;Tao, Weiguo Andy
通讯作者：
Tao, Weiguo Andy
DOI：
10.1038/nrdp.2017.9
发表时间：
2017-03-09
期刊：
Nature reviews. Disease primers
影响因子：
--
作者：
Hsieh JJ;Purdue MP;Signoretti S;Swanton C;Albiges L;Schmidinger M;Heng DY;Larkin J;Ficarra V
通讯作者：
Ficarra V
影响因子：
5.6
作者：
Kalra H;Drummen GP;Mathivanan S
通讯作者：
Mathivanan S