Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes.
Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods.
Dicker
Lee
L
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Lin
Xihong
X
Ivanov
Alexander R
AR
D002853:Chromatography, Liquid / Q000379:methods*; D025341:Principal Component Analysis; D040901:Proteomics; D012015:Reference Standards; D053719:Tandem Mass Spectrometry / Q000379:methods*
Bioinformatics. 2001 Jun;17(6):520-5
11395428
Bioinformatics. 2006 Aug 1;22(15):1902-9
16766559
J Proteome Res. 2006 Sep;5(9):2339-47
16944946
J Proteome Res. 2008 Dec;7(12):5148-56
19367719
Anal Chem. 2002 Oct 15;74(20):5383-92
12403597
J Am Soc Mass Spectrom. 1994 Nov;5(11):976-89
24226387
Bioinformatics. 2009 Aug 15;25(16):2028-34
19535538
Mol Cell Proteomics. 2005 Oct;4(10):1487-502
15979981
Mol Syst Biol. 2007;3:79
17299416
Mol Cell Proteomics. 2006 Oct;5(10):1927-41
16857664
J Comput Biol. 2001;8(6):557-69
11747612
J Proteome Res. 2003 Jan-Feb;2(1):43-50
12643542
Nature. 2003 Mar 13;422(6928):198-207
12634793
J Proteome Res. 2007 Jul;6(7):2685-94
17559252
J Proteome Res. 2005 Jan-Feb;4(1):53-62
15707357
J Biomol Tech. 2008 Jul;19(3):189-99
19137106
Anal Chem. 2003 Sep 15;75(18):4818-26
14674459
Mol Cell Proteomics. 2004 Oct;3(10):984-97
15269249
Nat Biotechnol. 2008 Dec;26(12):1367-72
19029910
Science. 2006 Apr 14;312(5771):212-7
16614208
J Proteome Res. 2006 Nov;5(11):2909-18
17081042
Nat Biotechnol. 2004 Nov;22(11):1459-66
15529173
Mol Cell Proteomics. 2008 Dec;7(12):2373-85
18644780
Anal Chem. 2004 Jul 15;76(14):4193-201
15253663
Nat Biotechnol. 2010 Jan;28(1):83-9
20010810
BMC Bioinformatics. 2005 Jul 18;6:179
16026613
Curr Biol. 1993 Jun 1;3(6):327-32
15335725
Nature. 2007 Dec 13;450(7172):991-1000
18075578
Bioinformatics. 2008 Jul 1;24(13):1556-8
18453552
Mol Cell Proteomics. 2005 Sep;4(9):1265-72
15958392
Mol Cell Proteomics. 2008 Apr;7(4):631-44
18029349
Anal Chem. 2005 Oct 1;77(19):6218-24
16194081
Nat Biotechnol. 2007 Jan;25(1):117-24
17187058