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BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes.
Meier, Florian; Geyer, Philipp E; Virreira Winter, Sebastian; Cox, Juergen; Mann, Matthias.
Afiliação
  • Meier F; Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Geyer PE; Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Virreira Winter S; NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
  • Cox J; Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
  • Mann M; Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany.
Nat Methods ; 15(6): 440-448, 2018 06.
Article em En | MEDLINE | ID: mdl-29735998
ABSTRACT
Great advances have been made in sensitivity and acquisition speed on the Orbitrap mass analyzer, enabling increasingly deep proteome coverage. However, these advances have been mainly limited to the MS2 level, whereas ion beam sampling for the MS1 scans remains extremely inefficient. Here we report a data-acquisition method, termed BoxCar, in which filling multiple narrow mass-to-charge segments increases the mean ion injection time more than tenfold as compared to that of a standard full scan. In 1-h analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions, and it quantified more than 6,200 proteins in ten of ten replicates. In mouse brain tissue, we detected more than 10,000 proteins in only 100 min, and sensitivity extended into the low-attomolar range.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos / Bases de Dados de Proteínas / Proteômica Limite: Animals / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos / Bases de Dados de Proteínas / Proteômica Limite: Animals / Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article