DART-ID increases single-cell proteome coverage

被引:52
作者
Chen, Albert Tian [1 ,2 ]
Franks, Alexander [3 ]
Slavov, Nikolai [1 ,2 ,4 ]
机构
[1] Northeastern Univ, Dept Bioengn, Boston, MA 02115 USA
[2] Northeastern Univ, Barnett Inst, Boston, MA 02115 USA
[3] Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA
[4] Northeastern Univ, Dept Biol, Boston, MA 02115 USA
基金
美国国家卫生研究院;
关键词
DATA-INDEPENDENT ACQUISITION; RETENTION TIME PREDICTION; REVERSED-PHASE HPLC; PEPTIDE RETENTION; LIQUID-CHROMATOGRAPHY; ACCURATE MASS; STATISTICAL-MODEL; TARGETED ANALYSIS; IDENTIFICATION; INFORMATION;
D O I
10.1371/journal.pcbi.1007082
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页数:30
相关论文
共 64 条
[1]   moFF: a robust and automated approach to extract peptide ion intensities [J].
Argentini, Andrea ;
Goeminne, Ludger J. E. ;
Verheggen, Kenneth ;
Hulstaert, Niels ;
Staes, An ;
Clement, Lieven ;
Martens, Lennart .
NATURE METHODS, 2016, 13 (12) :962-965
[2]   Prediction of peptide retention at different HPLC conditions from multiple linear regression models [J].
Baczek, T ;
Wiczling, P ;
Marszall, M ;
Vander Heyden, Y ;
Kaliszan, R .
JOURNAL OF PROTEOME RESEARCH, 2005, 4 (02) :555-563
[3]  
Bernhardt OM, 2012, SPECTRONAUT FAST EFF, P1
[4]   Fragmentation-free LC-MS can identify hundreds of proteins [J].
Bochet, Pascal ;
Ruegheimer, Frank ;
Guina, Tina ;
Brooks, Peter ;
Goodlett, David ;
Clote, Peter ;
Schwikowski, Benno .
PROTEOMICS, 2011, 11 (01) :22-32
[5]   High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation [J].
Bruderer, Roland ;
Bernhardt, Oliver M. ;
Gandhi, Tejas ;
Reiter, Lukas .
PROTEOMICS, 2016, 16 (15-16) :2246-2256
[6]   SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation [J].
Budnik, Bogdan ;
Levy, Ezra ;
Harmange, Guillaume ;
Slavov, Nikolai .
GENOME BIOLOGY, 2018, 19
[7]  
Carpenter B., 2017, Stan: A probabilistic programming language
[8]   ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments [J].
Choi, Meena ;
Eren-Dogu, Zeynep F. ;
Colangelo, Christopher ;
Cottrell, John ;
Hoopmann, Michael R. ;
Kapp, Eugene A. ;
Kim, Sangtae ;
Lam, Henry ;
Neubert, Thomas A. ;
Palmblad, Magnus ;
Phinney, Brett S. ;
Weintraub, Susan T. ;
MacLean, Brendan ;
Vitek, Olga .
JOURNAL OF PROTEOME RESEARCH, 2017, 16 (02) :945-957
[9]   Utility of accurate mass tags for proteome-wide protein identification [J].
Conrads, TP ;
Anderson, GA ;
Veenstra, TD ;
Pasa-Tolic, L ;
Smith, RD .
ANALYTICAL CHEMISTRY, 2000, 72 (14) :3349-3354
[10]   Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ [J].
Cox, Juergen ;
Hein, Marco Y. ;
Luber, Christian A. ;
Paron, Igor ;
Nagaraj, Nagarjuna ;
Mann, Matthias .
MOLECULAR & CELLULAR PROTEOMICS, 2014, 13 (09) :2513-2526