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Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.
Fröhlich, Klemens; Furrer, Regula; Schori, Christian; Handschin, Christoph; Schmidt, Alexander.
Afiliação
  • Fröhlich K; Proteomics Core Facility, Biozentrum Basel, University of Basel, 4056 Basel, Switzerland.
  • Furrer R; Biozentrum Basel, University of Basel, 4056 Basel, Switzerland.
  • Schori C; Proteomics Core Facility, Biozentrum Basel, University of Basel, 4056 Basel, Switzerland.
  • Handschin C; Biozentrum Basel, University of Basel, 4056 Basel, Switzerland.
  • Schmidt A; Proteomics Core Facility, Biozentrum Basel, University of Basel, 4056 Basel, Switzerland.
J Proteome Res ; 23(3): 1028-1038, 2024 03 01.
Article em En | MEDLINE | ID: mdl-38275131
ABSTRACT
In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteoma Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteoma Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article