Literature DB >> 31465043

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy.

Jan Muntel1, Tejas Gandhi, Lynn Verbeke, Oliver M Bernhardt, Tobias Treiber, Roland Bruderer, Lukas Reiter.   

Abstract

Comprehensive proteome quantification is crucial for a better understanding of underlying mechanisms of diseases. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for comprehensive proteome quantification due to its power and versatility. Even though great advances have been made in recent years, full proteome coverage for complex samples remains challenging due to the high dynamic range of protein expression. Additionally, when studying disease regulatory proteins, biomarkers or potential drug targets are often low abundant, such as for instance kinases and transcription factors. Here, we show that with improvements in chromatography and data analysis the single shot proteome coverage can go beyond 10 000 proteins in human tissue. In a testis cancer study, we quantified 11 200 proteins using data independent acquisition (DIA). This depth was achieved with a false discovery rate of 1% which was experimentally validated using a two species test. We introduce the concept of hybrid libraries which combines the strength of direct searching of DIA data as well as the use of large project-specific or published DDA data sets. Remarkably deep proteome coverage is possible using hybrid libraries without the additional burden of creating a project-specific library. Within the testis cancer set, we found a large proportion of proteins in an altered expression (in total: 3351; 1453 increased in cancer). Many of these proteins could be linked to the hallmarks of cancer. For example, the complement system was downregulated which helps to evade the immune response and chromosomal replication was upregulated indicating a dysregulated cell cycle.

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Year:  2019        PMID: 31465043     DOI: 10.1039/c9mo00082h

Source DB:  PubMed          Journal:  Mol Omics        ISSN: 2515-4184


  24 in total

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6.  Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

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Journal:  Nat Biotechnol       Date:  2021-03-25       Impact factor: 54.908

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Authors:  Leila Reyes; Manuel A Sanchez-Garcia; Tyler Morrison; Andy J M Howden; Emily R Watts; Simone Arienti; Pranvera Sadiku; Patricia Coelho; Ananda S Mirchandani; Ailiang Zhang; David Hope; Sarah K Clark; Jo Singleton; Shonna Johnston; Robert Grecian; Azin Poon; Sarah McNamara; Isla Harper; Max Head Fourman; Alejandro J Brenes; Shalini Pathak; Amy Lloyd; Giovanny Rodriguez Blanco; Alex von Kriegsheim; Bart Ghesquiere; Wesley Vermaelen; Camila T Cologna; Kevin Dhaliwal; Nik Hirani; David H Dockrell; Moira K B Whyte; David Griffith; Doreen A Cantrell; Sarah R Walmsley
Journal:  Wellcome Open Res       Date:  2021-05-20
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