MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics

Abstract

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org. Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org. Ion mobility spectrometry (1Kanu A.B. Dwivedi P. Tam M. Matz L. Hill H.H. Ion mobility-mass spectrometry.J. Mass Spectrom. 2008; 43: 1-22Crossref PubMed Scopus (883) Google Scholar, 2Cumeras R. Figueras E. Davis C.E. Baumbach J.I. Gràcia I. Review on Ion Mobility Spectrometry. 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Chem. 2015; 87: 1422-1436Crossref PubMed Scopus (276) Google Scholar) (IMS) 1The abbreviations used are:IMSion mobility spectrometryLC-MSliquid chromatography-mass spectrometryTIMStrapped ion mobility spectrometryQTOFquadrupole time-of-flightMBRmatching between runsCCScollisional cross section1/K0inverse reduced ion mobilityMSmass spectrometryLC-IMS-MS/MSliquid chromatography-ion mobility spectrometry-tandem mass spectrometry. 1The abbreviations used are:IMSion mobility spectrometryLC-MSliquid chromatography-mass spectrometryTIMStrapped ion mobility spectrometryQTOFquadrupole time-of-flightMBRmatching between runsCCScollisional cross section1/K0inverse reduced ion mobilityMSmass spectrometryLC-IMS-MS/MSliquid chromatography-ion mobility spectrometry-tandem mass spectrometry. separates molecules in the gas phase by their collisional cross section (CCS) which is the effective area of a molecule quantifying the likelihood of scattering events with the gas. It can be coupled to mass spectrometry (MS) for which it constitutes a separation dimension in addition to mass over charge (m/z). Together with liquid chromatography (LC) and tandem mass spectrometry (MS/MS) one obtains LC-IMS-MS/MS shotgun proteomics, a promising strategy for the analysis of complex samples (4Valentine S.J. Plasencia M.D. Liu X. Krishnan M. Naylor S. Udseth H.R. Smith R.D. Clemmer D.E. Toward plasma proteome profiling with ion mobility-mass spectrometry.J. Proteome Res. 2006; 5: 2977-2984Crossref PubMed Scopus (128) Google Scholar, 5Baker E.S. Livesay E.A. Orton D.J. Moore R.J. Danielson W.F. Prior D.C. Ibrahim Y.M. LaMarche B.L. Mayampurath A.M. Schepmoes A.A. Hopkins D.F. Tang K. Smith R.D. Belov M.E. An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies.J. Proteome Res. 2010; 9: 997-1006Crossref PubMed Scopus (106) Google Scholar, 6Geromanos S.J. Hughes C. Ciavarini S. Vissers J.P.C. Langridge J.I. 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MaxQuant for analysis of large 2014; PubMed Scopus Google Scholar) and the algorithm J. I. N. Mann M. by and Cell Proteomics. 2014; 13: Full Text Full Text PDF PubMed Scopus Google Scholar) on label-free data. MaxQuant high peptide mass accuracies to algorithms J. A. Mann M. mass by of peptide mass Spectrom. 2011; PubMed Scopus Google Scholar, J. Mann M. Computational of and mass precision and accuracy for proteome in an Spectrom. PubMed Scopus Google Scholar). It S. Temu T. A. P. Mann M. Cox J. of LC-MS/MS proteomics data in 2015; PubMed Scopus Google Scholar) for the of the data and runs on and P. S. Rudolph J.D. P. C. F. Cox J. MaxQuant 2018; PubMed Scopus Google Scholar). The of this is to to MaxQuant of timsTOF Pro data. ion mobility information has used in J. P. H. S. proteomics.Nat. 2014; 11: PubMed Scopus Google we on a from the of data by the added dimension. the for the analysis to a is of this we describe the computational workflow of MaxQuant for ion shotgun proteomics which has for computational It is based on 4D feature detection in the space by the extra dimension by between runs is a in MaxQuant for MS1 features in shotgun proteomics, in to quantification of peptides and proteins over many It high mass accuracy and precise relative retention by non-linear retention alignment in to Here we a new matching algorithm that ion mobility values of peptide features on a timsTOF Pro for the retention we the for relative alignment of ion mobility values between LC-IMS-MS/MS and a alignment algorithm the MaxQuant the 4D features using CCS values significantly we a on the missing value problem in quantitative new their to data are in the protein extracts of from and to the of H. D.J. Moore R.J. M. Smith D.J. Smith R.D. and of a and proteomic Proteome Res. PubMed Scopus Google Scholar). the protein in and reduced with a of and with in by in the added in a protein of and by with to and peptides on and in a the protein of H. S. and E. in to a of and between the The plasma samples from and for the proteins using to the and and for with from their plasma samples for and a protein The and on an for the proteins for of plasma protein a A. D. of proteins in the of PubMed Scopus Google Scholar). added to the samples to a of and for added to a of and for The the protein and protein with for The protein in and The samples and for and samples with and using a in a and in using to in of in for to A high to the timsTOF an spectrometry quadrupole of mass spectrometer in and to phase with chromatography and peptides on the using a in a of A from for by for the and by a to for and a to for by of B. the plasma proteome the used to and a high by using the by from the on of the timsTOF Pro operated in the instrument an and trapped in the of the trapped ion mobility (TIMS) analyzer. 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A MaxQuant can be be to with data we used data we used E. and S. and proteins with of and protein and to and the peptide mass to a of The mass for and for MaxQuant mass to (13Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9154) Google Scholar). 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The alignment between the runs. are to an effective sample. The process by the samples from the and and samples are to the in accuracy that is by using ion mobility an for matching features between we a analysis of with are using retention with a matching in we to the that with large retention a likelihood of with retention We the of a with retention a and it is a that the is to of one of which is and one of the of and are a of the in used for are to the which to the the are the to the dependent we in the of the for and which are to a large in of a an to in in that the is to the of data by with the of ion mobility the a for MS1 features to be The for matching between runs is to the MS1 features for quantification samples and by that the missing value problem that shotgun proteomics of the of We the of the problem in of this we used data of of on the timsTOF Pro in the and with and with matching between runs. for of the the of protein and and with between runs. matching between on protein between runs increased this by on The of protein that in is increased by matching between runs by to the on the peptide level is of quantification between samples is a problem in plasma samples with their high dynamic range of and this end we plasma samples of with runs on a timsTOF Pro with a experimental the of protein and is for of the plasma samples in matching between runs this from to protein with an of The in of protein by matching between runs is on quantitative accuracy of label-free we a dataset with known ground by from in known to the strategy in the of data P. J. B. L. R. S. A software for label-free proteome quantification.Nat. Biotechnol. 2016; PubMed Scopus Google Scholar). proteins are to a proteins are to by a E. proteins by a The algorithm J. I. N. Mann M. by and Cell Proteomics. 2014; 13: Full Text Full Text PDF PubMed Scopus Google Scholar) can be to ion mobility enhanced data to timsTOF Pro data the signal of the 4D MS1 features is from LC-MS/MS The benchmark data of of samples The runs in a a with the which has with and the matching between runs The in the protein in the software by the between the are the intensity in and and with The data are in and The are on the that for the The of proteins from to by using the matching between runs. is in the are in quantification the end of the dynamic range. the matching to a in the of to a increased of the that the matching is with the in A in the to computational performance for large with many LC-IMS-MS/MS runs in one on for the computational to be on and software The of for a dataset the workflow can be in I. are feature the the The and the data and and the MaxQuant We the software on and data on a with of and of data in plasma proteome and and on for the and We implemented a novel computational workflow in MaxQuant that peptide CCS values to the missing value in shotgun The MaxQuant CCS algorithm added to the and ion from runs to It has on protein quantification which we to be with methods to proteome and with enhanced The of this and the MaxQuant software the with a platform for shotgun proteomics with and proteome the missing value problem of shotgun proteomics of is one of our is to computational performance of the with the data analysis is and with LC-MS/MS speed gains in software a on the of MaxQuant on We are in an process of computational in the workflow and the in this we a CCS aware MaxQuant that the for LC-MS/MS data. the of with in with feature detection and has the potential to of proteomics The of for the of retention and ion and MaxQuant S. R. P. F. L. M. A. P. Cox J. High for and data PubMed Scopus Google Scholar) has the potential to protein identification and The mass spectrometry proteomics data to the the with the dataset We Bache and from for their to the plasma proteome samples and Kosinski from for and LC-MS/MS with

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