Featured Publications
Global and Site-Specific Effect of Phosphorylation on Protein Turnover
Wu C, Ba Q, Lu D, Li W, Salovska B, Hou P, Mueller T, Rosenberger G, Gao E, Di Y, Zhou H, Fornasiero EF, Liu Y. Global and Site-Specific Effect of Phosphorylation on Protein Turnover. Developmental Cell 2020, 56: 111-124.e6. PMID: 33238149, PMCID: PMC7855865, DOI: 10.1016/j.devcel.2020.10.025.Peer-Reviewed Original ResearchConceptsProtein turnoverProtein lifetimeCyclin-dependent kinase substrateStable isotope-labeled amino acidsSite-specific phosphorylationPulse-labeling approachIsotope-labeled amino acidsMass spectrometry-based methodCell fitnessKinase substratePhosphorylation sitesPhosphorylated sitesProteomic methodsCell signalingSpectrometry-based methodsLive cellsAmino acidsPhosphositesRich resourceDisease biologyLabeling approachPhosphorylationModification typesGlutamic acidTurnover
2023
The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics
Huang X, Gan Z, Cui H, Lan T, Liu Y, Caron E, Shao W. The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics. Nucleic Acids Research 2023, 52: d1062-d1071. PMID: 38000392, PMCID: PMC10767952, DOI: 10.1093/nar/gkad1068.Peer-Reviewed Original Research
2022
Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover
Li W, Salovska B, Fornasiero E, Liu Y. Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover. Proteomics 2022, 23: e2100387. PMID: 36422574, PMCID: PMC10964180, DOI: 10.1002/pmic.202100387.Peer-Reviewed Original ResearchMeSH KeywordsIsotope LabelingMass SpectrometryPhosphorylationProtein Processing, Post-TranslationalProteolysisProteomeConceptsPost-translational modificationsProtein turnoverDynamic stable isotope labelingCell starvationStable isotope labelingData-independent acquisition mass spectrometryAcquisition mass spectrometryProteome levelTurnover diversityPhosphoproteomic datasetsPhosphorylation stoichiometryMetabolic labelingIsotope labelingMass spectrometryPhosphorylationAmino acidsCell culturesBiological perspectiveStarvationTurnoverTurnover measurementsRecent studiesSILACProteoformsPeptidoforms
2021
BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples
Salovska B, Li W, Di Y, Liu Y. BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples. Analytical Chemistry 2021, 93: 3103-3111. PMID: 33533601, PMCID: PMC8959401, DOI: 10.1021/acs.analchem.0c04293.Peer-Reviewed Original ResearchConceptsData-independent acquisitionProtein turnoverDIA mass spectrometryStable isotope labelingValuable biological insightsRelative protein quantificationSerum starvation stressIsotopic labeling approachSILAC experimentsStarvation stressConventional DIA methodGas-phase separation strategyBiological insightsDegradation regulationIsotope labelingCultured cellsAmino acidsDIA-MSProtein quantificationLabeling approachPeptide pairsCell culturesBiological investigationsMultiplexed acquisitionComplex samples
2020
SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles
Rosenberger G, Heusel M, Bludau I, Collins BC, Martelli C, Williams EG, Xue P, Liu Y, Aebersold R, Califano A. SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles. Cell Systems 2020, 11: 589-607.e8. PMID: 33333029, PMCID: PMC8034988, DOI: 10.1016/j.cels.2020.11.006.Peer-Reviewed Original ResearchConceptsProtein-protein interactionsProtein complexesCell statesProtein complex dynamicsNative protein complexesMacromolecular complex formationPaper's transparent peer review processProtein interaction networksSEC-SWATHMultiple cell statesNetwork-centric analysisCellular processesInteraction networksMass spectrometric data analysisProteome OrganizationMolecular mechanismsRegulatory roleMass spectrometric analysisNetwork-based studyMultiplexed characterizationComplex formationSpectrometric data analysisSpectrometric analysisAlgorithmic toolkitState-specific changesNAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses
Wang S, Li W, Hu L, Cheng J, Yang H, Liu Y. NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses. Nucleic Acids Research 2020, 48: e83-e83. PMID: 32526036, PMCID: PMC7641313, DOI: 10.1093/nar/gkaa498.Peer-Reviewed Original ResearchMeSH KeywordsCellsComputer SimulationDatasets as TopicFormaldehydeHumansMass SpectrometryMicrotubulesNocodazoleProtein PrecursorsProteomicsSoftwareSelection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments*
Tsai TH, Choi M, Banfai B, Liu Y, MacLean B, Dunkley T, Vitek O. Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments*. Molecular & Cellular Proteomics 2020, 19: 944-959. PMID: 32234965, PMCID: PMC7261813, DOI: 10.1074/mcp.ra119.001792.Peer-Reviewed Original ResearchConceptsRelative protein quantificationData-independent acquisitionData-dependent acquisitionMass spectrometry-based proteomicsSpectrometry-based proteomicsProtein quantificationOverall protein profileAbundant proteinsProtein profilesManual curationProteinMass spectrometry experimentsReproducibility of conclusionsBiological investigationsAbundanceSpectrometry experimentsIsoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation
Salovska B, Zhu H, Gandhi T, Frank M, Li W, Rosenberger G, Wu C, Germain P, Zhou H, Hodny Z, Reiter L, Liu Y. Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation. Molecular Systems Biology 2020, 16: msb199170. PMID: 32175694, PMCID: PMC7073818, DOI: 10.15252/msb.20199170.Peer-Reviewed Original ResearchConceptsProtein degradationGenome-wide correlation analysisGene dosage variationProtein abundance levelsStable isotope-labeled amino acidsIndividual protein isoformsSpecific biological processesAlternative splicing isoformsData-independent acquisition mass spectrometryIsotope-labeled amino acidsAcquisition mass spectrometryProtein degradation ratesIntron retentionCellular functionsProtein isoformsSplicing isoformsCellular organellesTranscriptome variabilitySame geneTurnover controlRegulatory mechanismsBiological processesSpecific mRNAsTight associationAbundance levels
2019
motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs
Wang S, Cai Y, Cheng J, Li W, Liu Y, Yang H. motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs. Proteomics 2019, 19: e1900245. PMID: 31622013, DOI: 10.1002/pmic.201900245.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsComputational BiologyDatabases, ProteinHumansMass SpectrometryProtein Processing, Post-TranslationalProteomeProteomicsSoftwareConceptsUser-friendly web toolWeb softwarePublic datasetsBioinformatics backgroundLarge datasetsWeb toolMotif discoveryOptional featuresDatasetPresentation of motivesExponential growthLocation probabilitySoftwareKinase-substrate relationsModification sitesProtein post-translational modificationsPost-translational modificationsUsabilityUsersToolToolkitNetworkModification motifsPhosphoproteomic datasetsSite enrichmentAssessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition
Li W, Chi H, Salovska B, Wu C, Sun L, Rosenberger G, Liu Y. Assessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition. Journal Of The American Society For Mass Spectrometry 2019, 30: 1396-1405. PMID: 31147889, DOI: 10.1007/s13361-019-02243-1.Peer-Reviewed Original Research
2018
Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS
Sajic T, Liu Y, Arvaniti E, Surinova S, Williams EG, Schiess R, Hüttenhain R, Sethi A, Pan S, Brentnall TA, Chen R, Blattmann P, Friedrich B, Niméus E, Malander S, Omlin A, Gillessen S, Claassen M, Aebersold R. Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS. Cell Reports 2018, 23: 2819-2831.e5. PMID: 29847809, DOI: 10.1016/j.celrep.2018.04.114.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBlood PlateletsCarcinomaCohort StudiesGlycoproteinsHumansMass SpectrometryNeoplasm StagingOncogenesProteomeROC CurveConceptsN-glycoproteinsAvailable proteomic dataSWATH mass spectrometryImmense clinical interestCancer-type specificProteomic changesProteomic dataN-glycositesProteomic workflowParticular carcinomaProtein compositionClinical stageMetastatic stageBlood samplesPatient plasmaSolid carcinomasEarly cancer detectionSWATH-MSTumor tissueSystemic responseCarcinomaProteinEarly detectionClinical interestCancer
2017
Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry
Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW, Gingras AC, Held JM, Hirayama-Kurogi M, Hou G, Krisp C, Larsen B, Lin L, Liu S, Molloy MP, Moritz RL, Ohtsuki S, Schlapbach R, Selevsek N, Thomas SN, Tzeng SC, Zhang H, Aebersold R. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nature Communications 2017, 8: 291. PMID: 28827567, PMCID: PMC5566333, DOI: 10.1038/s41467-017-00249-5.Peer-Reviewed Original ResearchMeSH KeywordsHEK293 CellsHumansLaboratoriesLaboratory Proficiency TestingMass SpectrometryProteomeProteomicsReproducibility of ResultsConceptsSpectrometry studiesStatistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses
Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nature Methods 2017, 14: 921-927. PMID: 28825704, PMCID: PMC5581544, DOI: 10.1038/nmeth.4398.Peer-Reviewed Original ResearchInference and quantification of peptidoforms in large sample cohorts by SWATH-MS
Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS. Nature Biotechnology 2017, 35: 781-788. PMID: 28604659, PMCID: PMC5593115, DOI: 10.1038/nbt.3908.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsApolipoprotein A-IHumansMass SpectrometryPeptidesPhosphopeptidesProtein Processing, Post-TranslationalProteomicsTwinsConceptsPost-translational modificationsComparison of targeted proteomics approaches for detecting and quantifying proteins derived from human cancer tissues
Faktor J, Sucha R, Paralova V, Liu Y, Bouchal P. Comparison of targeted proteomics approaches for detecting and quantifying proteins derived from human cancer tissues. Proteomics 2017, 17 PMID: 27966270, DOI: 10.1002/pmic.201600323.Peer-Reviewed Original ResearchMeSH KeywordsHumansLimit of DetectionMass SpectrometryNeoplasmsProteinsProteomicsSignal-To-Noise RatioConceptsDifferent clinical-pathological characteristicsClinical pathological characteristicsHuman cancer tissuesClinical studiesCancer tissuesBreast tumorsTumor tissueCoefficient of varianceNumerous conditionsCancer researchMass spectrometry-based proteomic approachMultiple protein analytesProteomic approachTissueSequential window acquisition
2016
TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics
Röst HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, Collins BC, Gillet L, Testa G, Malmström L, Aebersold R. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nature Methods 2016, 13: 777-783. PMID: 27479329, PMCID: PMC5008461, DOI: 10.1038/nmeth.3954.Peer-Reviewed Original Research
2015
Multiplexed Targeted Mass Spectrometry-Based Assays for the Quantification of N‑Linked Glycosite-Containing Peptides in Serum
Thomas SN, Harlan R, Chen J, Aiyetan P, Liu Y, Sokoll LJ, Aebersold R, Chan DW, Zhang H. Multiplexed Targeted Mass Spectrometry-Based Assays for the Quantification of N‑Linked Glycosite-Containing Peptides in Serum. Analytical Chemistry 2015, 87: 10830-10838. PMID: 26451657, PMCID: PMC4708883, DOI: 10.1021/acs.analchem.5b02063.Peer-Reviewed Original ResearchMeSH KeywordsAgedChromatography, LiquidGlycosylationHumansMaleMass SpectrometryMiddle AgedPeptidesProstatic NeoplasmsConceptsGlycosite-containing peptidesClinical Proteomic Tumor Analysis ConsortiumParallel reaction monitoringNational Cancer Institute's Clinical Proteomic Tumor Analysis ConsortiumCommon protein modificationsProtein glycosylationProtein modificationBiological functionsAnalysis ConsortiumRelative abundanceTargeted Mass SpectrometryPRM assaysRobust assayPeak area ratioRelative peak area ratiosGlycoproteinReaction monitoringAssaysHuman serumMass spectrometryDisease statesPeptidesProstate cancer patient seraMS assayGlycosylationPrediction of colorectal cancer diagnosis based on circulating plasma proteins
Surinova S, Choi M, Tao S, Schüffler PJ, Chang CY, Clough T, Vysloužil K, Khoylou M, Srovnal J, Liu Y, Matondo M, Hüttenhain R, Weisser H, Buhmann JM, Hajdúch M, Brenner H, Vitek O, Aebersold R. Prediction of colorectal cancer diagnosis based on circulating plasma proteins. EMBO Molecular Medicine 2015, 7: 1166-1178. PMID: 26253081, PMCID: PMC4568950, DOI: 10.15252/emmm.201404873.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorClinical Laboratory TechniquesColorectal NeoplasmsHumansMass SpectrometryPlasmaConceptsColorectal cancer diagnosisBlood-based markersColorectal cancer detectionPatient's systemic circulationColorectal cancerSystemic circulationIndependent cohortCritical clinical needNon-invasive detectionDiagnostic valueClinical needTissue epitheliumCancer diagnosisPlasma samplesBiomarker signaturesPlasma proteinsBlood plasmaProtein biomarkersCancer detectionCandidate glycoproteinsClinical datasetsMass spectrometry-based approachCohortCancerDiagnosisUsing data‐independent, high‐resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications
Sajic T, Liu Y, Aebersold R. Using data‐independent, high‐resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications. Proteomics Clinical Applications 2015, 9: 307-321. PMID: 25504613, DOI: 10.1002/prca.201400117.Peer-Reviewed Original Research
2014
A repository of assays to quantify 10,000 human proteins by SWATH-MS
Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data 2014, 1: 140031. PMID: 25977788, PMCID: PMC4322573, DOI: 10.1038/sdata.2014.31.Peer-Reviewed Original Research