Proteome Analysis of Human Neutrophil Granulocytes From Patients With Monogenic Disease Using Data-independent Acquisition
2019; Elsevier BV; Volume: 18; Issue: 4 Linguagem: Inglês
10.1074/mcp.ra118.001141
ISSN1535-9484
AutoresPiotr Grabowski, Sebastian Hesse, Sebastian Hollizeck, Meino Rohlfs, Uta Behrends, Roya Sherkat, Hannah Tamary, Ekrem Ünal, Raz Somech, Türkan Patıroğlu, Stefan Canzar, Jutte van der Werff ten Bosch, Christoph Klein, Juri Rappsilber,
Tópico(s)S100 Proteins and Annexins
ResumoNeutrophil granulocytes are critical mediators of innate immunity and tissue regeneration. Rare diseases of neutrophil granulocytes may affect their differentiation and/or functions. However, there are very few validated diagnostic tests assessing the functions of neutrophil granulocytes in these diseases. Here, we set out to probe omics analysis as a novel diagnostic platform for patients with defective differentiation and function of neutrophil granulocytes. We analyzed highly purified neutrophil granulocytes from 68 healthy individuals and 16 patients with rare monogenic diseases. Cells were isolated from fresh venous blood (purity >99%) and used to create a spectral library covering almost 8000 proteins using strong cation exchange fractionation. Patient neutrophil samples were then analyzed by data-independent acquisition proteomics, quantifying 4154 proteins in each sample. Neutrophils with mutations in the neutrophil elastase gene ELANE showed large proteome changes that suggest these mutations may affect maturation of neutrophil granulocytes and initiate misfolded protein response and cellular stress mechanisms. In contrast, only few proteins changed in patients with leukocyte adhesion deficiency (LAD) and chronic granulomatous disease (CGD). Strikingly, neutrophil transcriptome analysis showed no correlation with its proteome. In case of two patients with undetermined genetic causes, proteome analysis guided the targeted genetic diagnostics and uncovered the underlying genomic mutations. Data-independent acquisition proteomics may help to define novel pathomechanisms in neutrophil diseases and provide a clinically useful diagnostic dimension. Neutrophil granulocytes are critical mediators of innate immunity and tissue regeneration. Rare diseases of neutrophil granulocytes may affect their differentiation and/or functions. However, there are very few validated diagnostic tests assessing the functions of neutrophil granulocytes in these diseases. Here, we set out to probe omics analysis as a novel diagnostic platform for patients with defective differentiation and function of neutrophil granulocytes. We analyzed highly purified neutrophil granulocytes from 68 healthy individuals and 16 patients with rare monogenic diseases. Cells were isolated from fresh venous blood (purity >99%) and used to create a spectral library covering almost 8000 proteins using strong cation exchange fractionation. Patient neutrophil samples were then analyzed by data-independent acquisition proteomics, quantifying 4154 proteins in each sample. Neutrophils with mutations in the neutrophil elastase gene ELANE showed large proteome changes that suggest these mutations may affect maturation of neutrophil granulocytes and initiate misfolded protein response and cellular stress mechanisms. In contrast, only few proteins changed in patients with leukocyte adhesion deficiency (LAD) and chronic granulomatous disease (CGD). Strikingly, neutrophil transcriptome analysis showed no correlation with its proteome. In case of two patients with undetermined genetic causes, proteome analysis guided the targeted genetic diagnostics and uncovered the underlying genomic mutations. Data-independent acquisition proteomics may help to define novel pathomechanisms in neutrophil diseases and provide a clinically useful diagnostic dimension. Neutrophil granulocytes constitute the most abundant population of nucleated cells in human blood. Whereas their role in defense against microbes has been known for more than a century, their sophisticated roles in tissue remodeling, cancer and chronic inflammation has emerged only recently (1Borregaard N. Neutrophils, from marrow to microbes.Immunity. 2010; 33: 657-670Abstract Full Text Full Text PDF PubMed Scopus (951) Google Scholar, 2Nauseef W.M. Borregaard N. Neutrophils at work.Nat. Immunol. 2014; 15: 602-611Crossref PubMed Scopus (590) Google Scholar). Rare diseases of neutrophil granulocytes may affect their differentiation and/or function. Severe congenital neutropenia (SCN) 1The abbreviations used are:SCNsevere congenital neutropeniaABCammonium bicarbonateCANacetonitrileANCabsolute neutrophil countsCGDchronic granulomatous diseaseDDAdata-dependent acquisitionDIAdata-independent acquisitionDTTdithiothreitolEDTAethylenediaminetetraacetic acidFASPfilter-aided sample prepFDRfalse discovery rateGOgene ontologyIAAiodoacetamideLADleukocyte adhesion deficiencyNBTnitroblue tetrazoliumPBSphosphate-buffered salinePCAprincipal component analysisPCCPearson correlation coefficientSCXstrong cation exchangeSDSsodium dodecyl sulfateTFAtrifluoroacetic acid. 1The abbreviations used are:SCNsevere congenital neutropeniaABCammonium bicarbonateCANacetonitrileANCabsolute neutrophil countsCGDchronic granulomatous diseaseDDAdata-dependent acquisitionDIAdata-independent acquisitionDTTdithiothreitolEDTAethylenediaminetetraacetic acidFASPfilter-aided sample prepFDRfalse discovery rateGOgene ontologyIAAiodoacetamideLADleukocyte adhesion deficiencyNBTnitroblue tetrazoliumPBSphosphate-buffered salinePCAprincipal component analysisPCCPearson correlation coefficientSCXstrong cation exchangeSDSsodium dodecyl sulfateTFAtrifluoroacetic acid. comprises a heterogeneous group of monogenic disorders characterized by aberrant premature apoptosis of myeloid progenitor cells (3Klein C. 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In striking contrast to their prominent role in health and disease, there are very few validated diagnostic tests assessing the function of neutrophil granulocytes. Although quantitative studies (i.e. differential blood counts) are among the most common laboratory tests, validated qualitative studies are virtually absent, except for measurement of NADPH-oxidase activity for CGD and expression of defined cell surface markers to diagnose LAD. Genetic sequencing assays, based on defined panels, exome, or whole genome sequencing are the gold standard for the diagnosis of monogenic diseases, yielding conclusive results in up to 25–50% of patients (6Taylor J.C. Martin H.C. Lise S. Broxholme J. Cazier J.-B. Rimmer A. Kanapin A. Lunter G. Fiddy S. Allan C. Aricescu A.R. Attar M. Babbs C. Becq J. Beeson D. Bento C. Bignell P. Blair E. Buckle V.J. Bull K. Cais O. Cario H. Chapel H. Copley R.R. Cornall R. Craft J. Dahan K. Davenport E.E. Dendrou C. Devuyst O. Fenwick A.L. Flint J. Fugger L. 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Immunol. 2017; 18: 583-593Crossref PubMed Scopus (194) Google Scholar) quantified significantly fewer proteins compared with our study. We here systematically analyze proteome changes in neutrophils from patients with different monogenic diseases and demonstrate the usefulness of next-generation proteomics for guiding clinical genetic diagnostics. The total number of analyzed neutrophil samples was 84 (68 healthy controls and 16 patients). The patient samples were measured without replication. The size of the healthy control group allowed to average out biological variation. The rationale for choosing the large healthy control group was that it allowed to better estimate parameters of the Gaussian curves fitted to protein expression profiles for outlier detection in the two unclear clinical cases. Differential protein expression analysis was performed with limma R package (24Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43: e47Crossref PubMed Scopus (15342) Google Scholar), while blocking for batch effects. Patients were recruited from pediatric centers in Germany (LMU University, Dr. von Hauner Children's Hospital; and TU University, Department of Pediatrics), Turkey (Erciyes University, Fevzi Mercan Children's Hospital, Kayseri), Iran (Isfahan University, Imam Hossein Children's Hospital, Isfahan) and Israel (Schneider Children's Medical Center of Israel, Tel Aviv). Informed consent was given by the parents or legal guardians in accordance with the Declaration of Helsinki and European legislation. Children were asked for their informed assent. The study was approved by the LMU Munich ethics committee as well as ethics boards of local clinical institutions. Blood was drawn from patients and healthy donors into EDTA-containing collection tubes (clinical standard tubes for anticoagulation, Sarstedt, Nümbrecht, Germany, 04.1915.100) and immediately processed within a 4 h time window. Neutrophils were isolated with the MACSexpress human neutrophil isolation kit (Miltenyi, Bergisch Gladbach, Germany, 130-104-434) according to the vendor's protocol. For total erythrocyte depletion the MACSxpress Erythrocyte Depletion Kit (Miltenyi, 130-098-196) was used according to the vendor's protocol. After isolation, neutrophils were twice washed in PBS (Gibco, Paisley, Scotland, UK, 14190250), microscopically counted in a hemocytometer and divided into aliquots of 1 × 106 or 2.5 × 105 cells. A cytospin stained with May-Grunwald Giemsa for cell purity control was prepared when possible. After pelleting, the supernatant was removed and 5 μl of 25x protease inhibitor was added (Roche, Penzberg, Germany, 04693159001). Cells were then frozen in a −80 °C freezer before being transferred to storage in liquid nitrogen until final proteome preparation. Purified neutrophil samples were processed with the Filter-Aided Sample Preparation (FASP) method as follows: ∼106 cells were lysed using 50 μl of SDS lysis buffer (0.5% SDS, 0.1 m DTT in 0.1 m Tris-HCl pH 7.6) and sonicated for 30 s using a Branson Ultrasonics 250A analog sonifier (10% duty cycle, energy level 1). Samples were then heated for 5 min at 95 °C in a heating block. Subsequently, 150 μl of UA buffer (8 m urea in 0.1 m Tris-HCl pH 8.5) were added to the samples to a total volume of 200 μl, loaded onto 0.5 ml Microcon 30 kDa-cutoff Ultracel membrane filters (Merck, Germany, catalog number MRCF0R030) and spun down in an Eppendorf 5418 centrifuge for 20 min at 14,000 rcf. Next, 200 μl of UA buffer was added and the centrifugation repeated. Subsequently, 50 μl of IAA buffer (0.05 m iodoacetamide in UA buffer) were added to the filters and incubated in darkness for 20 min at room temperature. Next, two washing steps using 150 μl and 200 μl of UA buffer were performed, each time spinning down the samples for 20 min at 14,000 rcf. As a final washing step, the samples were washed twice with 200 μl of ABC buffer (50 mm ammonium bicarbonate in ddH2O) and spun down as described above. The filters with washed samples were then transported to new collector tubes and MS-grade trypsin (Thermo Fisher, Germany, catalogue number 90057) in digestion buffer (1 m urea in 0.1 m Tris-HCl 8.5) was added in 1:100 ratio. The filters were wrapped in parafilm to prevent drying and placed in a wet chamber for overnight digestion at 37 °C. Finally, the samples were spun down for 15 min at 14,000 rcf, 50 μl of ABC buffer were added to the filters and the centrifugation step repeated. The samples were then acidified to pH ∼2.5 using 20% TFA (trifluoroacetic acid). The peptide yield was estimated using Thermo Fisher NanoDrop 2000c. The samples were cleaned-up and desalted using a C18-StageTip approach as described in (25Rappsilber J. Mann M. Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.Nat. Protoc. 2007; 2: 1896-1906Crossref PubMed Scopus (2569) Google Scholar) and stored at −20 °C. To create a comprehensive neutrophil spectral library, selected healthy donor and patient peptide samples were eluted from StageTips using 80% acetonitrile (ACN) in 0.1% formic acid, dried in Eppendorf Concentrator plus 5305, reconstituted in SCX buffer A (5 mm K2HPO4 in 10% ACN) and pooled. 100 μg of pooled peptides were separated on a Shimadzu LC-20AD HPLC system using a PolyLC PolySULFOETHYL-A SCX column (100 × 2.1 mm, 3 μm beads, 300 Å pores) with a 12 min nonlinear gradient of SCX buffer B (1 m KCl, 5 mm K2HPO4 in 10% ACN) while collecting fractions every 15 s. The fractions were then concentrated, reconstituted in 0.1% TFA and desalted using C18-StageTips. Lower complexity fractions were pooled together prior to mass spectrometric analysis. The spectral library SCX fractions were analyzed on Thermo Fisher QExactive HF mass spectrometer coupled to a Dionex UltiMate 3000 HPLC system using a 50 cm C-18 Thermo Fisher EasySpray column (catalog number ES803), heated to 50 °C. Roughly 2 μg of peptides was loaded onto the column for each run. All samples contained spiked-in iRT peptides (Biognosys, Switzerland, catalog number Ki-3002-2) for retention-time alignment. A 135-min gradient was used as follows: the flow rate was set to 300 nl/min, start at 2% buffer B (80% ACN in 0.1% FA) with a linear increase to 35% B for 90 min followed by a linear increase to 41% until 102 min and to 99% B until 104 min with a hold at 99% B until 120 min for washing. The gradient was then reduced to 2% B at 120 min and held at 2% for 15 min for column re-equilibration. A Top10 DDA method was used as follows: 1 full MS1 scan between 350 and 1300 m/z at resolution of 120,000, with AGC target of 3e6, maximum injection time (IT) of 50 ms and a default charge state of 2. Ten most intense peaks were selected for MS2 fragmentation using resolution 15,000, AGC target of 1e5 and a maximum IT of 80 ms. The isolation window was set to 1.6 m/z, fixed first mass to 100 m/z and a normalized collision energy (NCE) to 30. Additionally, a dynamic exclusion of 30 s was set. Peak lists obtained from DDA MS/MS spectra were identified using X! Tandem Vengeance (2015.12.15.2) (26Craig R. Beavis R.C. TANDEM: matching proteins with tandem mass spectra.Bioinformatics. 2004; 20: 1466-1467Crossref PubMed Scopus (1987) Google Scholar), Andromeda version 1.5.3.4 (27Cox J. Neuhauser N. Michalski A. Scheltema R.A. Olsen J.V. Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment.J. Proteome Res. 2011; 10: 1794-1805Crossref PubMed Scopus (3448) Google Scholar), MS Amanda version 1.0.0.7501 (28Dorfer V. Pichler P. Stranzl T. Stadlmann J. Taus T. Winkler S. Mechtler K. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra.J. 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The identification settings were as follows: trypsin, specific, with a maximum of 2 missed cleavages. 10.0 ppm as MS1 and 20 ppm as MS2 tolerances; fixed modifications: carbamidomethylation of C (+57.021464 Da); variable modifications: acetylation of protein N-term (+42.010565 Da), oxidation of M (+15.994915 Da); fixed modifications during refinement procedure: carbamidomethylation of C (+57.021464 Da); variable modifications during refinement procedure: pyrolidone from E (–18.010565 Da), pyrolidone from Q (–17.026549 Da), pyrolidone from carbamidomethylated C (–17.026549 Da). Peptides and proteins were inferred from the spectrum identification results using PeptideShaker version 1.16.11 (32Vaudel M. Burkhart J.M. Zahedi R.P. Oveland E. Berven F.S. Sickmann A. Martens L. Barsnes H. PeptideShaker enables reanalysis of MS-derived proteomics data sets.Nat. Biotechnol. 2015; 33: 22-24Crossref PubMed Scopus (357) Google Scholar). Peptide Spectrum Matches (PSMs), peptides and proteins were validated at a 1% False Discovery Rate (FDR) estimated using the decoy hit distribution. All engine-specific settings were set kept as default. The list of peptides identified in this study can be found in the supplemental Table S6. The data-independent acquisitions were performed on the same equipment as the spectral library DDA measurements using the exact same chromatography conditions. Each patient sample was measured once because of the size of the cohort. All the samples contained spiked-in iRT peptides (Biognosys, catalogue number Ki-3002–2) for retention-time alignment. The mass spectrometry settings were as follows: one MS1 scan was performed between 350 and 1300 m/z at resolution of 120,000, AGC target of 5e6 and a maximum IT of 100 ms, followed by ten 12.5 m/z MS2 windows, ten 37.5 m/z MS2 windows and a final single 450 m/z MS2 window (21 MS2 windows total). In case of all the MS2 windows the resolution was set to 30,000, AGC target to 1e6, maximum IT to "auto" and the collision energy to 30. Both MS1 and MS2 scans were recorded in profile mode. The DIA method resulted in a median of 7 data points per peak. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (33Vizcaíno J.A. Csordas A. Del-Toro N. Dianes J.A. Griss J. Lavidas I. Mayer G. Perez-Riverol Y. Reisinger F. Ternent T. Xu Q.-W. Wang R. Hermjakob H. 2016 update of the PRIDE database and its related tools.Nucleic Acids Res. 2016; 44: 11033Crossref PubMed Scopus (21) Google Scholar) partner repository with the data set identifier PXD010701. Biognosys Spectronaut 11 was used for DIA search and protein quantification. The DIA raw files were converted into HTRMS format using Biognosys HTRMS converter. Our sample-specific SCX spectral library containing 119193 precursors, 87757 peptides and 7977 proteins, was imported. Minimum of 3 up to 6 best fragments per peptide were used. The DIA search and quantification were performed with the following settings: using precision iRT and nonlinear iRT calibration, MS1 and MS2 mass tolerance strategy were set to "Dynamic," XIC RT extraction window was set to "Dynamic." Precursor FDR was set to 1% and protein FDR was set to 5% (using decoy method set to "inverse"). Data filtering was set to "Qvalue percentile 0.5," cross-run normalization was set to "Qvalue complete." Peptide quantification was performed using mean precursor quantity (using up to 3 top precursors) and the area under the MS2 signal. Protein quantification was performed using mean peptide quantity (using up to 3 top peptides per protein). Protein inference was set to automatic. All settings for our Spectronaut analyses can be found in the Spectronaut experiment file (.sne) uploaded to the PRIDE archive with raw files. As a final filtering step, known contaminants according to MaxQuant (34Tyanova S. Temu T. Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.Nat. Protoc. 2016; 11: 2301Crossref PubMed Scopus (1871) Google Scholar) were removed from the list of quantified proteins. Limma R package (24Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43: e47Crossref PubMed Scopus (15342) Google Scholar) was used to perform differential protein expression analysis using empirical Bayes moderation. The log2-transformed expression values were normally distributed. In order to increase the power of the analysis, the extra term (sample processing date) was added to the model for blocking as a mean of batch-effect control. The expression matrix used in this analysis was not processed by the ComBat algorithm. Only proteins identified by two or more peptides were used for differential abundance testing. Hits with Benjamini-Hochberg P-adjusted value <0.01 were considered statistically significant. Log2-transformed protein expression values in healthy donors were used to fit a Gaussian model for each protein using R package MASS (35Venables W.N. Ripley B.D. Modern Applied Statistics with S. Springer-Verlag New York, New York2002Crossref Google Scholar). Proteins quantified with at least three peptides were used for increased stringency. Protein expression values of each of the two patients were used to calculate probability that a given protein is expressed similarly to healthy d
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