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DART-ID increases single-cell proteome coverage.
Chen, Albert Tian; Franks, Alexander; Slavov, Nikolai.
Afiliación
  • Chen AT; Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America.
  • Franks A; Barnett Institute, Northeastern University, Boston, Massachusetts, United States of America.
  • Slavov N; Department of Statistics and Applied Probability, University of California Santa Barbara, California, United States of America.
PLoS Comput Biol ; 15(7): e1007082, 2019 07.
Article en En | MEDLINE | ID: mdl-31260443
ABSTRACT
Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http//dart-id.slavovlab.net.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Proteoma / Análisis de la Célula Individual Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Proteoma / Análisis de la Célula Individual Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos