Screening Method Using Selected Reaction Monitoring for Targeted

Article pubs.acs.org/jpr

Screening Method Using Selected Reaction Monitoring for Targeted Proteomics Studies of Nasal Lavage Fluid Harriet Mörtstedt,* Monica H. Kåredal, Bo A. G. Jönsson, and Christian H. Lindh Department of Laboratory Medicine, Lund, Division of Occupational and Environmental Medicine, Lund University, SE-221 85 Lund, Sweden S Supporting Information *

ABSTRACT: Proteomic-based studies of nasal lavage fluid (NLF) may identify molecular pathways associated with disease pathology and new biomarker candidates of upper airway diseases. However, most studies have used rather tedious untargeted MS techniques. Selected reaction monitoring (SRM) is a sensitive and specific technique that can be used with high throughput. In this study, we developed a semiquantitative SRM-based method targeting 244 NLF proteins. The protein set was identified through a literature study in combination with untargeted LC−MS/MS analyses of trypsin-digested NLF samples. The SRM assays were designed using MS/MS data either downloaded from a proteomic data repository or experimentally obtained. Each protein is represented by one to five peptides, resulting in 708 SRM assays. Three to four transitions per assay were used to ensure analyte specificity. The majority (69%) of the assays showed good within-day precision (coefficient of variation ≤20%). The accuracy of the method was evaluated by analyzing four samples prepared with varying amounts of four proteins. Peptide and protein ratios were in good agreement with expected ratios. In conclusion, a high throughput screening method for relative quantification of 244 NLF proteins was developed. The method should be of general use in any proteomic study of the upper airways. KEYWORDS: targeted proteomics, selected reaction monitoring, nasal lavage fluid, airway diseases, biomarkers, mass spectrometry

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INTRODUCTION Proteomics has been extensively applied to study and identify mechanisms and biomarkers of a wide range of diseases.1 Most proteomics studies have been conducted on plasma and serum samples. When diseases in the upper airways are of interest, nasal lavage fluid (NLF) samples may be a better choice since they originate from the target organ. Nasal lavage fluid is obtained from the upper airways by a simple, rapid, and noninvasive technique.2 Samples can be collected repeatable over relatively short periods. Therefore, the method has been employed to study early and delayed nasal responses to various stimuli, e.g., chemicals, allergen, and glucocorticoids.3−5 Nasal lavage fluid has also been suggested to be useful for studies of the lower airways.6 In fact, it has been shown that NLF shares many proteins with bronchoalveolar lavage fluid (BALF).7 Several proteomic studies of samples originating from the nasal cavity have been conducted, using untargeted, global proteomic techniques to identify proteins of the upper airways and assess changes associated with disease or with chemical exposure.4,5,7−19 An important advantage of these untargeted strategies is the potential to discover novel proteins. They aim to analyze all components present in a sample. However, for several reasons, such studies often result in long lists of potential protein biomarkers that never reach clinical practice. Unbiased proteomic experiments have high false discovery rates due to many hypotheses being tested in few samples. Also, © XXXX American Chemical Society

since many proteins lack quantitative assays, new assays must be developed and development of assays for large numbers of biomarker candidates is a costly and lengthy process. In addition, most of the assays rely on immunoassays, which are limited by the availability of high quality antibodies.20,21 Selected reaction monitoring (SRM) is a technique that can be used to relatively quantify high numbers of proteins. It can be used for most proteins and in a multiplexed manner. In contrast to the untargeted proteomic techniques, which have limited sensitivity for low abundance proteins, SRM offers high selectivity and a broad dynamic range. Selected reaction monitoring experiments use two stages of mass filtering, resulting in a high selectivity. Also, no full-scan mass spectrometry (MS) spectra are acquired in SRM analysis, leading to higher sensitivity than achieved with conventional full-scan MS.1,22−28 Despite all of these attractive properties, SRM has not been widely used to study high numbers of proteins. Most SRMbased proteomic studies have focused on one or a few proteins. The major challenge of the SRM approach has been the large amount of time, effort, and money required to develop high quality SRM assays.26 Peptide and product ion selection is a crucial step for the success of the SRM experiment. Since a few Received: August 24, 2012

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dx.doi.org/10.1021/pr300802g | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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assay kit according to the manufacturer’s instructions. Samples were evaporated and dissolved in 50 mM ammonium acetate to a concentration of 2.5 mg/mL. Proteins were reduced with DTT (1.25 mg/mg protein) for 1 h at 55 °C and then alkylated with iodoacetamide (2.5 mg/mg protein) for 30 min at room temperature in darkness. Trypsin was dissolved in 0.5 mM hydrochloric acid, 25 mM ammonium acetate, and 1 mM calcium chloride. The trypsin solution was added to the proteins at a 1:50 wt:wt, trypsin:protein ratio, and the samples were incubated overnight at 37 °C. Digested peptides were evaporated to dryness and stored at −20 °C.

peptides are selected from ten to hundreds of tryptic peptides to represent the target protein25,29 this is a time-consuming and demanding step. Also, optimization of tune parameters, collision energies (CEs), and declustering potentials (DPs) requires a significant amount of time.30 Further, validation of the SRM assays is often challenging and expensive.25,26 In recent years, software tools for building and optimizing SRM assays have emerged to facilitate the development of SRM assays. Many of the available software tools rely on data from public proteomic repositories. They predict transitions either by searching a tandem mass spectrometry (MS/MS) data library downloaded from public repositories or based on experimental data uploaded by the user. Some of the software tools also allow the user to refine and optimize transitions using imported experimental data.30,31 Much focus on instrumental research and development has in the past few years resulted in more robust and sensitive mass spectrometers. Selected reaction monitoring is becoming increasingly popular in proteomics. In recent years, several studies have been published where larger numbers of proteins are being targeted using SRM.21,23,26,32−36 Most studies have focused on plasma proteins. To our knowledge, no previous study has targeted proteins of the airways. In this study, we developed SRM assays for high-throughput screening of large numbers of NLF proteins. We designed the assays with the software tool Skyline using MS/MS data either downloaded from a public proteomic data repository or experimentally obtained. We kept the sample preparation and separation techniques as simple as possible to increase throughput and robustness. The developed method could be used to validate and complement the results obtained in untargeted proteomic studies of NLF. Also, this method could be applied in studies of a wide variety of upper airway and potentially also lower airway diseases. It could be used to screen high numbers of patients for a large number of proteins.

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Mass Spectrometry Systems

Two MS platforms were used for data acquisition. The first MS platform was a 5500 QTRAP hybrid triple quadrupole/linear ion trap mass spectrometer equipped with a TurboIonSpray source (Applied Biosystems/MDS Sciex, Framingham, MA, U.S.) coupled to an online liquid chromatography (LC) system (UFLCXR; Shimadzu Corporation, Kyoto, Japan). The following MS settings were used: ionspray voltage, 5500 V; temperature of ion source, 450 °C. Air was used as nebulizer gas and pure nitrogen as auxiliary, curtain, and collision gas. Two different LC setups were used for this MS platform. The mobile phases for both setups were 0.1% FA in water (A) and 0.1% FA in ACN (B). In the first LC setup (microLC−MS/MS), samples were separated on a C18 column (1.0 mm ID × 100 mm, 1.5 μm) (VisionHT C18 Basic; Grace Vydac, Hesperia, CA, U.S.) using a gradient flow of 0.05 mL/min. The LC gradient was increased from 5% B to 10% B during the first 10 min, then from 10% B to 30% B for 50 min, from 30% B to 40% B for 15 min, and finally from 40% B to 99% B for 15 min. In the second LC setup (LC−MS/MS), samples were separated on a C18 column (2.1 mm ID × 50 mm, 3 μm) (VisionHT C18 CL; Grace Vydac, Hesperia, CA, U.S.) using a gradient flow of 0.2 mL/min. The LC gradient was increased from 5% B to 10% B during the first 10 min, then from 10% B to 30% B for 15 min, and from 30% B to 99% B for 5 min. The flow was kept isocratic at 99% B for 2 min and the column was then re-equilibrated at 5% B for 13 min. The second MS platform was a QSTAR pulsar hybrid quadrupole time-of-flight mass spectrometer (Applied Biosystems/MDS Sciex) with a nanoelectrospray (ES) source (Proxeon, Odense, Denmark) connected to an LC system with a capillary and a nano pump (1100 series, Agilent Technologies, Santa Clara, CA, U.S.) (nanoLC-Q-TOF). The mobile phases for both pumps were 0.1% FA in water (A) and 0.1% FA in ACN (B). Samples were injected with the capillary pump in 5% B at a flow rate of 10 μL/min onto a C18 trap column (5 mm × 0.3 mm, 5 μm) (ZORBAX; Agilent Technologies, Santa Clara, CA, U.S.). The peptides were then loaded and separated on a PicoFrit column (75 μm ID × 10 cm, 5 μm, 15 μm ID tip) (BioBasic C18; New Objective, Woburn, MA, U.S.) in 5% B at a flow rate of 200 nL/min using the nano pump. The samples were separated using a 90 min linear gradient from 5% B to 40% B, a 10 min linear gradient from 40% B to 50% B, and a 5 min linear gradient from 50% B to 70% B. The flow was then kept isocratic at 70% B for 5 min and brought back to 5% B within 5 min. The column was finally re-equilibrated at 5% B for 45 min.

EXPERIMENTAL SECTION

Reagents and Chemicals

Microcon centrifugal filters (3000 nominal molecular weight limit) were purchased from Millipore (Bedford, MA, U.S.). The micro bicinchoninic acid (BCA) protein assay kit was from Thermo Scientific (Rockford, IL, U.S.). Trypsin (sequencing grade) was purchased from Roche Diagnostics Gmbh (Mannheim, Germany). Calcium chloride, formic acid (FA), hydrochloric acid, and ammonium acetate were purchased from Merck (Darmstadt, Germany). Trifluoroacetic acid (TFA), dithiothreitol (DTT), iodoacetamide, human serum albumin (HSA) (99% purity), human serotransferrin (TF) (≥98% purity), human α-1-acid glycoprotein 1 (AGP 1) (99% purity) human apolipoprotein A-I (Apo A-I) (≥90% purity) and ovalbumin (≥90% purity) were purchased from Sigma-Aldrich (St. Louis, MO, U.S.). Acetonitrile (ACN) and methanol were purchased from Lab-scan (Dublin, Ireland). The synthetic standard peptides (95% purity) were purchased from Mimotopes (Melbourne, Australia). Sample Preparation

Nasal lavage fluid samples were collected from eight healthy donors using the procedure described by Diab et al.37 The samples from the different donors were pooled and desalted using Microcon centrifugal filters according to the manufacturer’s instructions. Total protein content in the pooled and desalted samples was determined using the micro BCA protein

Selection of the Protein Set

The workflow, from establishment of NLF proteins to development of the SRM method, is described in Scheme 1. A literature study was conducted to establish previously B



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Scheme 1. Development of Selected Reaction Monitoring (SRM) Assays for Proteins in Nasal Lavage Fluid (NLF)a

Scheme 1. continued linear CE and DP equations. (5) During previous steps, peptides not meeting the inclusion criteria were discarded, resulting in proteins represented by less than two peptides. New peptides were selected and SRM assays were designed by repeating steps 1 and 2. Collision energy and DP values were set according to the updated CE and DP equations. (6) The SRM detection window and target scan time were optimized.

identified proteins in the nasal cavity.4,7−18,38 The Protein Identifier Cross-Reference (PICR) service tool (www.ebi.ac.uk) was used to connect protein identifiers used in other protein databases to the protein identifiers (accession numbers) of the UniProtKB/SwissProt database. Furthermore, to include proteins previously not found in NLF, a pooled, trypsin-digested NLF sample (13.3 μg total protein injected) was analyzed by microLC−MS/MS in information-dependent acquisition (IDA) mode. A full MS scan was followed by MS/MS scans for the two most intense peaks. Ions with a charge state of +2 to +5 exceeding 15 000 counts were subjected to MS/MS. Enhanced resolution scanning was used to confirm charge states of the peptides. The settings are described in detail in Table 1. The resulting MS/MS data were analyzed using the ProteinPilot 3.0 software with the Paragon search algorithm (Applied Biosystems/MDS Sciex). The following settings were used: sample type, identification; cysteine alkylation, iodoacetamide; digestion, trypsin; instrument, 5500 QTRAP ESI; species, Homo sapiens; ID focus, biological modifications, and amino acid substitution; database, the SwissProt all species database (downloaded from www.uniprot.org/downloads on March 30th, 2009); search effort, thorough. Proteins with an unused protein score of >1.3 (confidence 95%) were selected for further analysis. To identify more low abundance proteins, peptides from a pooled trypsin-digested NLF sample were separated into 20 fractions by LC (Series 1050; Hewlett-Packard, Waldbronn, Germany). An amount of 1 mg tryptic digest was separated on a C18 column (2.1 mm × 50 mm, 4 μm; GENISIS; Grace Vydac, Hesperia, CA, U.S.) using a gradient flow of 0.2 mL/ min. The mobile phases were 0.1% TFA in water (A) and 0.1% TFA in methanol (B). The LC gradient was first kept isocratic at 5% B for 2 min. It was then increased from 5% B to 95% B during 40 min and was then kept isocratic at 5% B for 18 min. Fractions were collected every third min. The fractions were evaporated and dissolved in 50 μL water. The 20 fractions (8 μL injected) and a nonfractionated, trypsin-digested NLF sample (0.64 μg injected) were analyzed using nanoLC-Q-TOF in IDA mode. A full MS scan was followed by MS/MS scans for the three most intense peaks. Ions with a charge state of +2 to +5 exceeding 20 counts were subjected to MS/MS. The settings are described in detail in Table 1. The resulting MS/ MS data was analyzed using ProteinPilot 4.0 software with the Paragon search algorithm (Applied Biosystems/MDS Sciex). The following settings were used: sample type, identification; cysteine alkylation, iodoacetamide; digestion, trypsin; instrument, QSTAR ESI; species, Homo sapiens; ID focus, biological modifications, and amino acid substitution; database, the SwissProt all species database (downloaded from www. uniprot.org/downloads on September 21st, 2011); search effort, thorough. Proteins with an unused protein score of >1.3 (confidence 95%) were selected for further analysis.

a Potential NLF proteins were identified in a literature study and using untargeted liquid chromatography−tandem mass spectrometry (LC− MS/MS) analyses. The SRM assays were then developed in six steps (gray numbered text boxes): (1) Tryptic peptides were selected from a downloaded MS/MS library and from experimental data. For proteins lacking MS/MS data, peptides were chosen from the in silico digested protein. (2) Doubly and triply charged precursor ions and y- and b-ion fragments were acquired. The transitions of highest intensities were kept (three to five co-eluting transitions/peptide). Precursors with no co-eluting transitions were discarded. (3) and (4) Collision energy (CE) values and declustering potential (DP) values were optimized separately for each transition. The results were used to update the

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Table 1. Instrument Settings for the Liquid Chromatography−Tandem Mass Spectrometry (LC−MS/MS) Analyses by Information-Dependent Acquisition (IDA)a parameter MS instrument acquisition mode scan type range scan rate no. of transitions SRM detection window accumulation time or target scan time Q1 resolution Q3 resolution declustering potential (DP) collision energy (CE) scan type Q1 resolution scan rate accumulation time DP scan type range Q1 resolution scan rate accumulation time DP CE

microLC−MS/MS QTRAP5500 IDA triggering scan enhanced MS m/z: 300−1000 10 000 Da/s

nanoLC-Q-TOF QSTAR pulsar IDA

QTRAP5500 IDA

TOF MS m/z: 300−1400

scheduled SRM

dynamic unit

2s low

70 V

80 V

ER scan enhanced resolution open resolution 250 Da/s dynamic 70 V triggered scan enhanced product ion m/z: 100−1000 unit 10 000 Da/s dynamic 70 V CEd

LC−MS/MS

144−539 30 s, 60 s, 120 s, or 240 s 4s unit unit DPb CEc

none

none

TOF product ion m/z: 100−2000 low

enhanced product ion m/z: 100−1000 unit 10 000 Da/s dynamic 75 V(z=+2) or 85 V(z=+3) CEf

2s 80 V CEe

ER = enhanced resolution; LC-Q-TOF = liquid chromatography-quadrupole-time of flight; SRM = selection reaction monitoring. bOptimized or DP = 0.0716 m/z + 33.739. cOptimized or CE = 0.057 m/z − 4.300 (z = +2) and CE = 0.038 m/z + 2.187 (z = +3). dCE = 0.044 m/z + 5 (z = +2 or z unknown); CE = 0.050 m/z + 4 (z = +3); CE = 0.050 m/z + 3 (z = +4 and +5). eCE = 0.04 m/z + 5 (z = +2 or z unknown); CE = 0.04 m/z + 4 (z = +3); CE = 0.04 m/z + 2 (z = +4); CE = 0.04 m/z + 1 (z = +5). fCE = 0.057 m/z − 4.300 (z = +2); CE = 0.038 m/z + 2.187 (z = +3), where z is the charge of the peptides. a

Design of Selected Reaction Monitoring Assays

protein was lacking, peptides fulfilling the criteria mentioned above were chosen from the in silico digested protein. The tool UniMaP, described by Alexandridou et al.,40 was used to control if all selected peptides were unique for the targeted protein. (2) Transition selection: Transitions were generated by Skyline according to the following criteria: precursor ions (charge: +2 or +3) and y- and b-ion fragments in the m/z range 300−1250 were selected. Unscheduled SRM assays were exported and acquired. Each MS/MS method contained about 400 transitions (dwell time 4 ms). Collision energy and DP values were calculated according to the default formulas available for QTRAP4000 in Skyline:

The open source software tool Skyline v. 1.1, (https://skyline. gs.washington.edu/labkey/project/home/software/Skyline/ begin.view), described by Maclean et al,39 was used to build the SRM assays. FASTA sequences for the proteins were downloaded from the UniProt database (www.uniprot.org) and imported into Skyline. An MS/MS Spectral Library was created from a human data set (downloaded from http:// proteome.gs.washington.edu/software/bibliospec/ documentation/libs.html, August fifth, 2010). All data acquisition was conducted with LC−MS/MS in SRM mode (LC− SRM−MS) on pooled NLF samples (33 μg total protein digest was injected each run). The SRM assays were developed in six steps (Scheme 1). (1) First peptide selection: Peptides, five per protein (if five peptides were available), were selected. Only fully tryptic peptides, with no missed cleavages (except where the Lys or Arg residue was next to a Pro residue), with a length of 6−25 amino acids and with Cys residues alkylated, were considered. The choice of peptides was based on data from the downloaded MS/MS Spectral Library. The library supplied an intensity-based ranking value for each peptide included in the library. The highest ranked peptides were selected. In addition, peptides were also selected from available MS/MS data obtained in the IDA-MS experiments described above. When data for a

doubly charged precursors: CE = 0.057m /z − 4.265

(1)

triply charged precursors: CE = 0.031m /z + 7.082

(2)

doubly and triply charged precursors: DP = 0.0729m /z + 31.117

(3)

where m/z is the mass-to-charge ratio of the precursor ion. The resulting data was imported into Skyline and D



(3)

(4)

(5)

(6)

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manually examined. Precursors with no coeluting transitions or precursors with signal intensities 1.3 (confidence >95%) were considered identified. Relative Accuracy

Three samples A, B, and C and a reference sample (ref) were prepared in water (490 μg total protein) with varying amounts of HSA (A/B/C/ref: 100 μg/350 μg/250 μg/200 μg), TF (A/ B/C/ref: 300 μg/50 μg/175 μg/235 μg), AGP 1 (A/B/C/ref: 20 μg/75 μg/35 μg/50 μg), and Apo A-I (A/B/C/ref: 70 μg/ 15 μg/30 μg/5 μg). The protein mixtures were reduced, alkylated, and trypsin-digested as described above. The samples were analyzed (11 μg total protein digest injected) with LC− SRM−MS using the assays targeted toward the respective proteins. The samples were prepared in duplicate and each sample was analyzed twice. The resulting data was imported into Skyline and manually reviewed. The transitions of highest intensity were integrated and mean peak areas of duplicate analyses and duplicate samples were calculated. Peptide ratios were calculated by relating mean peak areas of the three samples to the reference sample. Relative protein quantities were then calculated according to the weighted geometric mean: weighted geometric mean =

∑ wi × xi ∑ wi

(7)

where xi is the log10 (peptide ratio)i, and wi is the weight for the i:th peptide ratio calculated according to − wi =

1 %error

(8)

where % error is calculated according to the following: ⎛ ⎛ 2 ⎛ errorZ ⎞2 ⎞⎟ errorY ⎞⎟ ⎟ %error = ⎜⎜ ⎜ +⎜ × 100 ⎝ areaZ ⎠ ⎟⎠ ⎝ ⎝ areaY ⎠

(9)

where error Y and Z are the standard errors (SEx) for peak areas Y and Z, calculated according to the following: s SE x = i (10) n where si is the standard deviation (SD) for the peak area of the i:th peptide and n is the number of the contributing peak areas. E



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different identifiers or, in some cases, just by protein name. The PICR service tool was used to translate the identified proteins into UniProtKB/SwissProt accession numbers. However, some of the proteins identified in the studies could not be translated into a UniProtKB/SwissProt accession number and were therefore not included in this study. To include proteins not previously found in NLF, a pooled trypsin-digested sample was analyzed by microLC−MS/MS in IDA mode. In total, 47 proteins (Supporting Information Table S2) were identified and eight of these had not been identified in any of the studies. Also, in order to identify more low abundance proteins, a pooled trypsin-digested NLF sample was fractionated into 20 fractions and each fraction was analyzed by nanoLC-Q-TOF in IDA mode. The nanoLC-Q-TOF analyses resulted in the identification of 82 proteins (Supporting Information Table S3), 18 of which had not been described in any of the studies. Altogether, 331 proteins (Supporting Information Table S1) were identified as potentially present in NLF, 305 from the literature study and 26 additional proteins from the IDA−MS analyses.

The proteins were relatively quantified using two to four peptides per protein. Precision

The within- and between-day precisions were evaluated in three separate experiments. In the first experiment, 10 aliquots of a pooled NLF sample were reduced, alkylated, and trypsindigested separately, as described above. The samples (33.7 μg total protein digest injected) were analyzed at the same occasion using the developed method in replicates of two. The resulting data were imported into Skyline and manually reviewed. For a peptide to be considered detected, all three transitions had to coelute and exceed 500 counts. The transition with highest intensity was integrated in Skyline. The within-day precision in NLF samples for each assay was estimated as the coefficients of variation (CVs) of mean peak areas. In the second experiment, six aliquots of a pooled NLF sample were reduced, alkylated, and trypsin-digested at six different occasions during a month, as described above. The samples (24 μg total protein digest injected) were analyzed at the same occasion using the assays targeting the four selected proteins HSA, TF, AGP 1, and Apo A-I. The transition with highest intensity was integrated in Skyline. The between-day precision for trypsin digestion in NLF samples was estimated as the CVs of peak areas. In the third experiment, the digested samples A and B from the relative accuracy experiment described above were pooled (1:1). The sample (11 μg total protein digest injected) was then analyzed at 19 different occasions during eleven days with the assays targeting HSA, TF, AGP 1, and Apo A-I. The transition with highest intensity was integrated in Skyline. The between-day precision was estimated as the CVs of peak areas. The limit of detection (LOD) was studied for the two Apolipoprotein A-I peptides, DLATVYVDVLK and VSFLSALEEYTK, and the two Lactotransferrin peptides, DGAGDVAFIR and FQLFGSPSGQK, using synthetic standard peptides. The synthetic standard peptides were dissolved in water, serial diluted, and spiked into samples containing trypsin digested ovalbumin (0.02 mg/mL) at final concentrations of 0.02−11 pg/μL (injected amount: 0.2, 0.5, 1.4, 4.1, 12, 37, 111 pg). Ten samples containing trypsin digested ovalbumin (0.02 mg/mL) were used as blank samples. The samples were analyzed with the assays targeting the two peptides in replicates of two. The transition of highest intensity was integrated and the mean peak area of the two replicates was used for the quantitative measurements. LOD was defined as the peptide concentration giving a signal equal to the blank signal plus three standard deviations of the blank. To test how many of the targeted proteins that can be detected in a single NLF sample, the method was applied on two NLF samples obtained from a study conducted by Diab et al.37 The samples were desalted, reduced, alkylated, and trypsindigested, as described above. The samples were then analyzed (20 μg total protein digest injected) with the developed SRM method (slightly modified).

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Design of Selected Reaction Monitoring Assays

The initial protein list consisted of 331 proteins potentially present in NLF. However, a high number of precursors and their corresponding proteins were discarded. Only precursors with at least three coeluting transitions with intensities exceeding 500 counts were included. Also, some proteins did not have any unique peptides of adequate length when trypsin was used as digestion enzyme. In the end, it was possible to generate scheduled SRM assays for 708 tryptic peptides representing 244 proteins (the first 244 proteins in Supporting Information Table S1). One to five peptides per protein were used and 207 of the proteins were represented by two or more peptides. Three to four transitions per peptide were used to ensure analyte specificity, resulting in 2146 transitions in total (Supporting Information Table S4 and data S5). Collision energy and DP parameters were individually optimized using Skyline. The results from these experiments were used to update the linear CE and DP equations. The updated equations were as follows: doubly charged precursors: CE = 0.057m /z − 4.300(R2 = 0.92)

(4)

triply charged precursors: CE = 0.038m /z + 2.187(R2 = 0.82)

(5)

doubly and triply charged precursors: DP = 0.0716m /z + 33.739(R2 = 0.87)

(6)

where m/z is the mass-to-charge ratio of the precursor ion, and R2 is the coefficient of determination from the respective regression analyses. Method Validation

Absolute analyte specificity was demonstrated using selected reaction monitoring-triggered MS/MS scans (Figure 1). Absolute analyte specificity could be demonstrated for 133 assays representing 71 proteins using SRM-triggered MS/MS scans (Table 2). Sixty-eight of these proteins had previously been described in at least one of the studies, but the three proteins Long palate, lung, and nasal epithelium carcinomaassociated protein 4 (P59827), Mammaglobin-A (Q13296),

RESULTS

Identification of NLF Proteins

The literature study resulted in a list of 305 proteins previously identified in the nasal cavity (NLF4,7−17,38 or nasal mucus18) or in BALF7,15,16 samples (Supporting Information Table S1). The 14 studies were based on different protein databases and between them, identified proteins were expressed using F



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pg on column) for DGAGDVAFIR, and 0.3 fmol (0.4 pg on column) FQLFGSPSGQK. To test how many of the targeted proteins the developed method can detect in a single NLF sample, two samples from another study37 were analyzed. The method was able to detect 207 and 211proteins, respectively, in the two samples.

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DISCUSSION Proteomic studies are commonly performed using either an untargeted, global proteomic strategy where an attempt is made to analyze all proteins present in a sample, or a targeted proteomic strategy where a smaller set of proteins is selected for analysis. In this study, we tried to combine the discovery potential of the untargeted strategy with the high throughput, selectivity, sensitivity, and quantitative capabilities of the SRMbased, targeted strategy. The purpose was to develop a semiquantitative method targeting high numbers of proteins that can be detected in NLF. Such multiplexed SRM assays can be used to screen hundreds of samples for hundreds of proteins. More precise and accurate quantitative assays can then be developed for a defined subset of proteins. In this work, the protein set was based both on the scientific literature and on identifications made in the untargeted IDA− MS analyses. The 14 studies included in the literature study varied regarding both sample type and area of interest. Nasal lavage fluid,4,7−17,38 nasal mucus,18 or BALF7,15,16 were used as study material and airway diseases such as rhinitis8,9,12 and sinusitis,11,18 as well as exposures like persulfates4 and tobacco smoke13,15,16 were studied. The developed method is therefore intended to be of general use in any proteomic study of the upper and potentially also lower airways. However, it has to be borne in mind that only samples from healthy volunteers were used to build and design the assays of the method. This could have biased the set of proteins being targeted. It is likely that subjects with an airway disease express a different set of proteins compared to healthy subjects. Therefore, when applying the method to a new study, we should always start by analyzing pooled samples with an untargeted strategy to identify any proteins not included in the method. Selected reaction monitoring assays can then be developed and added to the method for the new proteins. This could over time lead to development of a comprehensive assay platform for proteins of the upper airways. Selected reaction monitoring assays were developed for 244 NLF proteins targeting 708 tryptic peptides. The majority of the proteins were represented by two or more peptides. Peptides containing residues susceptible to post-translational or chemically induced modifications were not excluded. Therefore, it is particularly important to monitor numerous peptides per protein. It is often recommended that peptides containing methionine, tryptophan or/and cysteine residues are avoided due to their susceptibility for chemically induced modifications. Interestingly, no remarkable difference in CV was found between methionine, tryptophan, and/or cysteine containing peptides (median CV: 12%) and all detected peptides (median CV: 11%). Absolute analyte specificity was demonstrated for 133 of the 708 assays using SRM-triggered MS/MS scans. For many assays, absolute analyte specificity could not be demonstrated, either due to poor quality of MS/MS scans or because of a lack of MS/MS scans. Therefore, to minimize the risk of monitoring the wrong peptide, numerous coeluting transitions per peptide were used. In the final method, three to four transitions per peptide were used. It is generally recognized

Figure 1. Validation of transitions. Absolute analyte specificity for the selected reaction monitoring (SRM) assays was demonstrated using SRM-triggered MS/MS scans. (A) Selected reaction monitoring chromatogram with three transitions. (B) Tandem mass spectrometry (MS/MS) spectrum for the m/z 692.8 ion triggered by the SRM peaks above. The MS/MS spectrum shows that the transitions are targeted toward the antileukoproteinase peptide CLDPVDTPNPTR.

and Mesothelin (Q13421) had not been described in any of the 14 studies. Relative accuracy of the method was evaluated by analyzing three samples and a reference sample prepared with varying and known amounts of HSA, TF, AGP 1, and Apo A-I. The proteins were relatively quantified using two to four peptides per protein. The vast majority of peptide and protein ratios were in good agreement with expected ratios (Table 3). The within- and between-day precisions were evaluated in three separate experiments. Median CV for within-day precision was 11% (range 1−51%). Among the 708 targeted peptides, 572 representing 228 proteins were detected in at least one of the samples. Almost 70% (489 assays) of the assays had a CV ≤ 20% (Table 2 and Supporting Information Table S4). Chromatographic elution times were reproducible for the majority of peptides. The median RT shift within runs was ±3 s (range ±0−22 s). Between-day precision for trypsin digestion in NLF samples was also evaluated for twelve selected peptides. Median CV was determined to 21% (range 8−34%). In the final experiment, between-day precision was evaluated by analyzing the same protein mixture sample at different days. Median CV for the twelve targeted peptides was 13% (range 8−32%, Table 3). Limit of detection values for the four evaluated peptides were estimated to 10 fmol (13 pg on column) for DLATVYVDVLK, 4 fmol (5 pg on column) for VSFLSALEEYTK, 0.6 fmol (0.6 G



Article

Table 2. Selected Reaction Monitoring (SRM) Assays Designed for the Detection of 71 Proteins in Nasal Lavage Fluid (NLF)a,b precision transition no.

protein name

accession

1

mammaglobin-B

O75556

2 3

secretoglobin family 1D member 1 secretoglobin family 1D member 2

O95968 O95969

4

haptoglobin

P00738

5 6 7 8 9

complement factor B α-1-antitrypsin complement C3 cystatin-C cystatin-S

P00751 P01009 P01024 P01034 P01036

10 11

cystatin-SN immunoglobulin J chain

P01037 P01591

12

polymeric immunoglobulin receptor

P01833

13

Ig κ chain C region

P01834

14

Ig γ-1 chain C region

P01857

15

Ig γ-2 chain C region

P01859

16 17

Ig μ chain C region Ig α-1 chain C region

P01871 P01876

18

Ig α-2 chain C region

P01877

19 20

keratin type II cytoskeletal 6A apolipoprotein A-I

P02538 P02647

21 22 23 24

fibrinogen α chain β-2-glycoprotein 1 α-2-HS-glycoprotein serum albumin

P02671 P02749 P02765 P02768

25 26

vitamin D-binding protein serotransferrin

P02774 P02787

27

lactotransferrin

P02788

28

antileukoproteinase

P03973

peptide

RT (min)

conf. (%)

CV (%)

ELLQEFIDSDAAAEAMGK LLEDMVEK QCFLNQSHR TINSDISIPEYK APLEAVAAK FDAPPEAVAAK SLIAEVLVK TEGDGVYTLNNEK VGYVSGWGR DISEVVTPR SVLGQLGITK TELRPGETLNVNFLLR ALDFAVGEYNK IIPGGIYDADLNDEWVQR QLCSFEIYEVPWEDR RPLQVLR IIPGGIYNADLNDEWVQR CYTAVVPLVYGGETK IIVPLNNR SSEDPNEDIVER DGSFSVVITGLR GGCITLISSEGYVSSK GSVTFHCALGPEVANVAK SGTASVVCLLNNFYPR TVAAPSVFIFPPSDEQLK VDNALQSGNSQESVTEQDSK VYACEVTHQGLSSPVTK GPSVFPLAPSSK TPEVTCVVVDVSHEDPEVK CCVECPPCPAPPVAGPSVFLFPPKPK VVSVLTVVHQDWLNGK QVGSGVTTDQVQAEAK DASGVTFTWTPSSGK TFTCTAAYPESK TPLTATLSK DASGATFTWTPSSGK HYTNPSQDVTVPCPVPPPPPCCHPR SGFSSVSVSR DLATVYVDVLK QGLLPVLESFK VSFLSALEEYTK GSESGIFTNTK VCPFAGILENGAVR TVVQPSVGAAAGPVVPPCPGR CCTESLVNR DLGEENFK TYETTLEK LCDNLSTK DYELLCLDGTR EGYYGYTGAFR SASDLTWDNLK WCAVSEHEATK DGAGDVAFIR FQLFGSPSGQK LADFALLCLDGK THYYAVAVVK AGVCPPK

22.6 14.3 5.4 16.7 7.6 12.1 19.5 10.0 13.9 13.3 17.8 21.1 16.9 20.7 22.2 6.9 20.1 18.7 14.4 8.2 20.2 17.1 16.7 23.9 22.2 8.2 13.4 17.0 17.9 21.3 21.5 9.6 17.7 10.6 11.6 16.0 15.4 10.1 21.5 22.7 23.6 9.5 20.6 16.5 6.1 9.7 6.1 5.3 19.1 15.8 16.6 5.8 15.2 17.3 21.9 11.3 2.6

99.0 99.0 97.2 99.0 99.0 99.0 99.0 98.6 99.0 99.0 99.0 99.0 99.0 99.0 99.0 99.0 99.0 99.0 95.0 99.0 99.0 99.0 97.9 99.0 99.0 99.0 99.0 99.0 99.0 99.0 98.5 99.0 99.0 99.0 99.0 99.0 99.0 99.0 99.0 97.7 99.0 98.4 99.0 99.0 99.0 99.0 96.3 98.5 99.0 99.0 99.0 97.6 99.0 99.0 99.0 99.0 98.7

25 23 5 5 14 9 21 15 6 9 14 19 21 9 30 16 24 8 15 3 8 7 18 18 20 14 9 14 19 10 27 5 16 4 9 5 22 13 14 8 22 3 20 8 5 11 5 5 20 6 6 4 6 7 50 6 3

H

mean peak area

Q1 (m/z)

Q3 (m/z)

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

646.6 488.8 595.3 690.4 435.3 558.3 486.3 720.3 490.8 508.3 508.3 624.7 613.8 1037.5 657.6 441.3 1037.0 828.9 469.8 695.3 625.8 829.4 619.6 899.5 649.3 712.7 626.0 593.8 713.7 970.1 598.7 809.4 770.9 688.3 466.3 756.9 970.4 506.8 618.3 615.9 693.9 570.8 751.9 1008.5 569.8 476.2 492.7 475.7 677.8 642.3 625.3 659.3 510.8 598.3 668.4 575.8 364.7

677.3 750.3 641.3 736.4 588.3 685.4 771.5 881.4 562.3 787.4 716.4 662.4 709.4 1059.5 702.3 725.5 1059.5 1062.6 613.3 971.5 658.4 943.4 1030.5 1196.6 913.5 893.4 444.3 699.4 472.3 709.2 997.5 1090.5 1111.5 866.4 620.4 1111.5 1214.6 721.4 736.4 819.5 940.5 610.3 928.5 683.3 818.4 723.3 720.4 677.3 333.2 771.4 776.4 347.1 777.4 660.3 705.4 402.2 341.2

9.7 2.9 7.3 3.2 5.9 4.2 7.8 5.4 1.3 2.5 1.3 8.4 4.4 1.7 8.1 9.8 6.0 9.4 1.3 4.6 2.5 9.2 1.6 1.1 2.7 8.6 1.0 6.3 4.2 2.2 1.5 1.3 1.1 5.6 3.0 5.1 7.1 6.2 2.5 9.0 3.9 1.5 3.8 5.3 6.4 1.7 7.2 2.8 2.6 3.0 1.9 1.7 2.8 4.2 5.8 2.8 1.3

105 106 105 106 105 105 105 105 106 104 105 104 105 104 104 105 103 105 107 106 106 105 106 106 106 105 105 105 105 105 105 105 107 106 107 105 104 104 105 105 105 105 104 104 106 107 106 105 105 105 105 105 106 106 104 105 107



Article

Table 2. continued precision transition no.

29 30 31

protein name

accession

32 33 34

cystatin-B α-1B-glycoprotein glyceraldehyde-3-phosphate dehydrogenase heat shock protein β-1 myeloperoxidase gelsolin

P04080 P04217 P04406 P04792 P05164 P06396

35 36 37 38 39 40

α-enolase neutrophil elastase complement factor H thioredoxin clusterin uteroglobin

P06733 P08246 P08603 P10599 P10909 P11684

41

prolactin-inducible protein

P12273

42 43

P13987 P14555

44

CD59 glycoprotein phospholipase A2 membraneassociated transcobalamin-1

45 46

cofilin-1 zinc-α-2-glycoprotein

P23528 P25311

47 48 49 50 51

P26038 P31025 P31944 P54108 P59827

52

moesin lipocalin-1 caspase-14 cysteine-rich secretory protein 3 long palate, lung, and nasal epithelium carcinoma-associated protein 4 triosephosphate isomerase

53

lysozyme C

P61626

54

β-2-microglobulin

P61769

55 56 57

hemoglobin subunit α mucin-5AC (fragments) fatty acid-binding protein epidermal

P69905 P98088 Q01469

58 59

mammaglobin-A mesothelin

Q13296 Q13421

60

calcyphosin

Q13938

P20061

P60174

RT (min)

conf. (%)

CV (%)

CCMGMCGK CCPDTCGIK CLDPVDTPNPTR SQVVAGTNYFIK ATWSGAVLAGR VGVNGFGR

3.4 3.5 13.1 16.5 15.4 9.2

99.0 99.0 99.0 99.0 95.3 99.0

22 7 8 8 13 6

VSLDVNHFAPDELTVK IANVFTNAFR AGALNSNDAFVLK HVVPNEVVVQR TGAQELLR TIAPALVSK SNVCTLVR NGFYPATR VGEFSGANK ELDESLQVAER IAQSSLCN LVDTLPQKPR FYTIEILK TVQIAAVVDVIR YTACLCDDNPK AGLQVYNK AAATCFAR

19.0 19.0 16.9 9.5 9.7 12.3 9.1 8.8 3.6 13.9 4.7 9.2 19.9 21.1 8.3 7.2 4.4

99.0 99.0 99.0 99.0 99.0 98.0 97.2 97.8 98.1 97.3 99.0 97.4 97.6 99.0 99.0 99.0 99.0

GTSAVNVVLSLK LKPLLNTMIQSNYNR NGENLEVR TYWELLSGGEPLSQGAGSYVVR NIILEEGK AYLEEECPATLR HVEDVPAFQALGSLNDLQFFR ALTSELANAR VTMLISGR TNPEIQSTLR YEDLYSNCK YGEILESEGSIR

19.3 17.6 4.8 22.6 11.6 14.8 25.5 12.1 13.4 10.3 7.2 15.3

IAVAAQNCYK SNVSDAVAQSTR ATNYNAGDR GISLANWMCLAK QYVQGCGV STDYGIFQINSR YWCNDGK SNFLNCYVSGFHPSDIEVDLLK VNHVTLSQPK VGAHAGEYGAEALER GPSGVPLR ELGVGIALR FEETTADGR TTQFSCTLGEK TINPQVSK ALGGLACDLPGR FVAESAEVLLPR GSLLSEADVR GASGIQGLAR

7.6 6.8 1.4 22.9 8.2 18.1 3.5 23.9 5.1 10.6 6.7 17.3 2.8 11.3 3.7 16.4 19.0 14.8 9.2

peptide

I

mean peak area

Q1 (m/z)

Q3 (m/z)

6.4 2.3 3.4 5.4 5.0 4.7

× × × × × ×

105 107 106 104 104 105

502.2 555.7 692.8 663.9 544.8 403.2

552.2 790.4 996.5 913.5 643.4 706.4

ND 20 9 5 6 7 3 − 11 20 5 3 23 18 5 5 5

ND 2.9 1.6 5.0 7.4 3.4 2.9 1.6 9.0 1.9 5.3 2.2 1.0 5.6 5.7 1.1 4.0

× × × × × × × × × × × × × × × ×

104 105 105 105 105 105 105 104 105 105 107 106 105 105 105 105

595.3 576.8 660.4 425.9 444.3 450.3 474.8 463.2 454.7 644.8 446.7 389.6 513.8 642.4 678.8 446.7 434.2

872.5 755.4 1007.5 501.3 658.4 685.4 648.3 444.3 623.3 375.2 487.3 625.4 716.5 842.5 1092.4 651.3 654.3

99.0 95.3 99.0 98.3 96.8 99.0 99.0 97.5 99.0 98.9 97.7 97.5

7 9 3 13 15 31 22 11 17 4 21 21

2.5 1.6 2.1 2.3 3.6 2.3 7.2 1.1 6.4 3.5 1.1 5.4

× × × × × × × × × × × ×

105 105 106 105 105 105 105 104 106 105 105 105

594.4 602.3 465.7 790.4 458.3 726.3 801.7 523.3 438.8 579.8 596.3 676.8

871.6 781.4 630.4 808.4 688.4 557.3 1196.6 544.3 676.4 943.5 671.3 350.1

99.0 99.0 99.0 99.0 95.2 99.0 96.4 99.0 99.0 99.0 98.6 99.0 99.0 97.9 99.0 99.0 99.0 97.2 99.0

5 5 14 48 14 9 24 20 6 6 4 4 10 5 7 6 17 ND 15

5.1 1.1 5.8 1.1 9.5 1.8 2.9 3.0 1.4 2.0 3.3 1.0 9.2 2.0 5.1 7.7 5.1 ND 2.2

× × × × × × × × × × × × × × × × ×

104 105 106 106 106 107 106 105 106 106 105 106 104 105 106 104 104

569.3 617.8 491.2 682.3 455.7 700.8 471.7 852.1 561.8 510.6 391.7 464.3 513.2 636.3 443.8 600.3 665.9 523.8 465.3

854.4 934.5 532.2 993.5 392.2 764.4 593.2 1128.6 351.2 488.3 628.4 529.3 749.3 941.4 672.4 329.2 884.5 676.3 544.3

× 105



Article

Table 2. continued precision transition no.

protein name

accession

peptide

RT (min)

conf. (%)

CV (%)

mean peak area

Q1 (m/z)

Q3 (m/z)

14.7 5.6 21.1 15.4 23.0 11.6 7.5 11.3

99.0 99.0 99.0 99.0 99.0 98.9 99.0 99.0

13 13 11 10 36 18 7 11

1.5 2.8 7.1 5.9 5.0 5.6 1.4 1.7

× × × × × × × ×

105 105 105 105 104 104 106 105

616.8 840.8 1045.0 462.2 865.4 597.2 543.8 609.3

817.4 560.3 843.5 377.2 842.4 886.4 857.5 659.3

61

WAP four-disulfide core domain protein 2

Q14508

62 63 64 65

proline-rich protein 4 UPF0762 protein C6orf58 lipocalin-15 vitelline membrane outer layer protein 1 homologue bactericidal/permeabilityincreasing protein-like 1 long palate, lung, and nasal epithelium carcinoma-associated protein 1 zymogen granule protein 16 homologue B

Q16378 Q6P5S2 Q6UWW0 Q7Z5L0

SGDGVVTVDDLR DQCQVDSQCPGQMK EGSCPQVNINFPQLGLCR VSCVTPNF FPSVSLQEASSFFQR FCYDVSSCR TQDVSPQALK GLGDDTALNDAR

Q8N4F0

AGALNLDITGQLR

19.4

99.0

28

5.3 × 104

671.4

802.4

Q8TDL5

ALGFEAAESSLTK SGVPVSLVK

17.4 14.4

98.8 98.3

15 3

1.7 × 105 7.0 × 105

662.3 443.3

1139.6 642.4

Q96DA0

69

extracellular glycoprotein lacritin

Q9GZZ8

70

mucin-5B

Q9HC84

71

deleted in malignant brain tumors 1 protein

Q9UGM3

LGDSWDVK VSVGLLLVK GMHGGVPGGK QELNPLK QFIENGSEFAQK SILLTEQALAK AAGGAVCEQPLGLECR LSCLGASLQK SEQLGGDVESYDK FGQGSGPIVLDDVR VDVVLGPIQLQTPPR

12.4 19.7 1.5 9.6 14.3 17.2 14.8 12.7 11.2 17.9 21.8

99.0 99.0 99.0 97.7 98.3 99.0 99.0 99.0 97.9 99.0 99.0

9 21 14 3 17 5 7 15 5 20 5

3.7 8.6 1.3 4.5 6.3 1.4 1.1 7.0 5.7 9.0 1.3

× × × × × × × × × × ×

460.2 464.3 448.7 421.2 699.3 593.9 844.4 538.8 713.8 730.4 816.5

806.4 642.5 571.3 357.2 1009.5 760.4 634.3 603.3 969.4 617.3 314.2

66 67 68

105 105 106 107 105 107 105 104 104 105 106

a Protein name, SwissProt accession number, tryptic peptide sequence, and retention times (RTs) are presented. Absolute analyte specificity of the SRM assays was demonstrated using SRM-triggered, tandem mass spectrometry (MS/MS) scans. Confidence (Conf.), expressed in percentage for the resulting peptide identification, is shown. Coefficients of variation (CV) values for the analyses of 10 replicate NLF samples, each separately digested, are shown together with mean peak areas of the 10 analyses and the transition (precursor m/z (Q1) and fragment m/z (Q3)) used for CV calculations. bND = not detected.

Table 3. Evaluation of Relative Accuracya protein ratiosb (expected) protein name

peptide ratiosc

CV (%)

accession

A:ref

B:ref

C:ref

peptide

A:ref

B:ref

C:ref

serum albumin

P02768

3.7 (3.5)

3.4 (2.5)

2.3 (2.0)

serotransferrin

P02787

0.2 (0.2)

0.7 (0.6)

0.9 (0.8)

α-1-acid glycoprotein 1

P02763

4.6 (3.8)

3.1 (1.8)

2.2 (2.5)

apolipoprotein A-I

P02647

0.2 (0.2)

0.4 (0.4)

0.1 (0.1)

DLGEENFK TYETTLEK CCTESLVNR WCAVSEHEATK SASDLTWDNLK EGYYGYTGAFR DYELLCLDGTR ENGTISR SDVVYTDWK DLATVYVDVLK QGLLPVLESFK VSFLSALEEYTK

2.6 4.9 3.6 0.2 0.2 0.3 0.4 3.2 6.7 0.2 0.2 0.3

2.3 4.5 3.3 0.7 0.6 0.7 0.9 2.7 3.6 0.4 0.4 0.4

2.0 2.6 2.3 0.9 0.9 1.0 1.0 2.3 2.2 0.1 0.1 0.1

17 19 32 13 11 10 13 14 8 14 11 11

a Relative quantification of a protein mix using selected reaction monitoring (SRM). Three samples (A, B, and C) and a reference (ref) sample were prepared in water with varying amounts of albumin, serotransferrin, α-1-acid glycoprotein 1, and apolipoprotein A-I. The samples were analyzed using the developed assays for the targeted peptides and the proteins were then relatively quantified. Between-day precision was evaluated by analyzing a mixture of sample A and B (1:1) 19 times during eleven days. The between-day precision for each assay was estimated as the coefficient of variation (CV) of peak areas. bThe weighted geometric mean of the individual peptide ratios was calculated as an estimate for relative protein quantities. cPeptide ratios were calculated by relating mean peak areas of the three samples to the reference sample.

that validation of SRM assays with SRM-triggered MS/MS scans can be challenging because full MS/MS spectrum acquisition is less sensitive than the SRM assay itself. An alternative approach is to use synthetic peptides to demonstrate absolute analyte specificity. However, the cost for pure

synthetic peptides is high, often limiting the number of peptides and, consequently, proteins being quantified.25 Ideally, all SRM assays should have been validated. However, at this point, it is not necessary to invest the amount of time and money required for validation of all 708 assays. This method is J



Article

proteins are targeted, using information from previous experiments in combination with systematic testing of numerous highly ranked peptides is probably the best approach. When a small number of peptides is measured, it is common to spend considerable time optimizing CE and DP values individually for each transition, but when larger numbers of peptides are targeted, linear equations for CE and DP values are often used to save time.30 It has been shown that individual optimization of CE and DP values can increase transition signal intensities.25,30 Also, linear equations recommended by the manufacturer or reported in the literature can differ from optimized linear equations derived empirically.30 In the present study, despite the high number of transitions contained in the method, CE and DP values were individually optimized for the majority of transitions using the CE and DP optimization tool available in Skyline. The empirically determined CE and DP optima were also used to update the linear equations for DP and CE. The updated equations were highly predictive (R2 ≥ 0.82) for both charge states. The CE equation for doubly charged peptides and the DP equation were very similar to the default equations available in Skyline. By contrast, the updated CE equation for triply charged peptides differed from the default equation, demonstrating the need for empirically deriving these equations even when similar MS platforms are used. As a final step, the SRM detection window and target scan time were optimized. These settings should be adjusted after peak width and variability in RTs. However, since they cannot be set individually for each peptide, they have to be chosen so that they fit the majority of the peptides. The aim was for the SRM detection window to be as narrow as possible. This would decrease the number of transitions scanned for simultaneously. Also, we aimed to keep the target scan time as long as possible to achieve high sensitivity without restricting the quantitative capabilities. At least eight data points need to be recorded per peak to ensure precise quantitation.25 The optimization resulted in settings suitable for most peptides. A few peaks were partly missed due to shifts in RTs in combination with wide peaks. Also, too few data points were recorded for some narrow peaks. In this work, peptides were grouped into four scheduled MS/MS methods in order to decrease the number of transitions scanned for simultaneously. This grouping was based solely on the individual peptide RTs. This was a good strategy but could have been further extended by basing the grouping on both RTs and peak width. The SRM detection window and target scan times could then have been set differently for the four methods. Three separate precision experiments were used to assess different sources of variation. The majority of the evaluated assays showed acceptable within-day and between-day precision (CV ≤ 20%). Higher CVs (median CV: 21%; range 8−34%) were obtained for between-day precision of trypsin digestion in NLF samples, identifying the trypsin digestion step as the largest source of variation. However, when the samples are digested at the same occasion, illustrated by the first precision experiment, the precision improves. Thus, the results from these experiments demonstrated reproducible assays and, when samples are digested simultaneously, reproducible trypsin digestion. Interestingly, within-day precision was still acceptable (CV ≤ 20%) for peptides with peak areas down to 5 × 103. The highest peak areas measured were up to 5 × 107, demonstrating a dynamic range around 4 orders of magnitude. Similar results have been obtained before.23 Furthermore, when the method

intended as a semiquantitative screening method that can be used to define smaller subsets of proteins. The assays targeting these subsets of proteins will then undergo more rigorous validation and can be further developed into fully quantitative assays. Several studies have shown that SRM can, with the appropriate internal standards, accurately quantify selected proteins in complex mixtures with great reproducibility and high throughput.20,23,24,33,34 To date, SRM has not been widely used to study large sets of proteins, due to the amount of time and effort required to develop high quality SRM assays.26 However, in recent years, new software tools for designing SRM assays have emerged. As a consequence, SRM has become increasingly employed for targeted studies of larger protein sets.21,23,26,32−36 In this work, the open source software Skyline was used to build and optimize SRM assays and also to review the resulting experimental data. The software facilitated this process to a great extent and significantly shortened the development time. The most challenging step was the selection of suitable peptides. Only a few peptides were selected from a high number of possible peptide candidates. The initial intention was to base the peptide selection primarily on the downloaded MS/MS Spectral Library and on MS/MS data obtained in the IDA−MS experiments. The use of available MS/MS data did indeed facilitate this process. However, peptides with high ranking did not always result in high-responding peptides; therefore, new peptides had to be selected and evaluated. Consequently, in the end, peptides were selected from previous information in combination with systematic testing of numerous highly ranked peptides. In total, about 3000 peptides were tested with a failure rate for omitting peptides of around 75%. The ranking system used for peptide selection also contained information about fragment ions, but fragment ions of high rank did for many peptides not result in the highest responding transitions. Therefore, all singly charged b- and yions in a given mass window range of selected peptides were systematically tested. The peptide fragmentation pattern is dependent on the instrument type as well as operating parameters. This pattern influences the observability of a peptide differently in a discovery experiment compared to an SRM experiment. In discovery experiments, peptides yielding many fragments of similar intensities are often identified with high confidence. By contrast, peptides yielding a few predominant fragments result in the highest signal intensities in SRM experiments.25 Therefore, as was seen here, peptides best representing a protein in an SRM experiment may not always be frequently observed in a discovery experiment. In addition, it has been shown that differences in denaturing strategies,41,42 digestion duration,42 and trypsin purchased from different vendors43 affect the outcome of trypsin digestion. It is likely that different sample preparation procedures have been used in the MS/MS experiments from where the data in the downloaded MS/MS spectral library originated in comparison to the procedures used in this work. Thus, differences in sample preparation procedures could also be a probable explanation for why the peptide ranking system supplied by the library did not always concur with the best responding peptides in this work. The choice of peptides and transitions is crucial for the success of the SRM assay and when the number of targeted proteins is lower, systematically testing all possible peptides and fragments may be the best strategy. However, in this study, over 300 proteins were initially targeted and such strategy would have been too time demanding. Therefore, when large sets of K



Article

throughput due to simple sample preparation and a robust method. The method should be of general use in any proteomic study of the upper and potentially also lower airways. At this point, the method is best suited for studies where the study subjects are used as their own controls. The method can be continually extended by adding SRM assays targeting new proteins. This could over time lead to the development of a comprehensive assay platform for proteins of the upper airways.

was applied on two single NLF samples, over 200 proteins were detected in each sample, even though pooled samples from a completely different set of individuals were used to build and design the assays of the method. LOD values, estimated for four selected peptides, were in the lower fmol-range. The result indicates acceptable sensitivity even though better sensitivity for SRM based analyses of plasma digests with limit of quantitation values down to amol-levels have been presented by others.20,33,34 An attempt was also made to evaluate the accuracy of the method. Three samples and a reference sample were prepared with varying amounts of HSA, TF, AGP 1, and Apo A-I. Peptide and protein ratios were in good agreement with expected ratios, demonstrating the accuracy of the method. However, it should be noted that a digested NLF sample, containing hundreds of proteins in varying amounts, is far more complex than the digest of the four protein sample used to study accuracy in this work. It is likely that the assays will be less accurate when monitoring low abundance proteins in a complex sample. Nasal lavage is a simple and rapid technique that is well tolerated by most subjects. It has been widely used in research, but not as widely in clinical practice. A wide range of mediators, cytokines, and chemokines have been measured in different nasal lavage studies. However, the lack of a standardized procedure makes comparison of absolute levels between different laboratories problematic. Yet, when the study subjects are used as their own controls, the method can be successfully employed2. Dilution effects associated with nasal lavage have also been addressed as a problem.44 This problem may be solved by normalizing against an endogenous dilution marker, e.g., albumin, total protein abundance, or urea.45,46 Also, several exogenous dilution markers, e.g., inulin47 or lithium chloride,48 which are added to the washing fluid before sample collection, have been used.2 In this study, unknown dilution was corrected for by normalizing samples against total protein abundances. At this point, this SRM method is best suited for studies where each subject is their own control. The method can be used to monitor nasal effects of various exposures and treatments, where samples are collected before and after exposure/ treatment. Before developing this SRM method into a fully quantitative method a standardized procedure for nasal lavage, taking into account possible dilution effects has to be developed. In this work, sample preparation and separation techniques were kept as simple as possible to increase throughput and robustness, as well as minimizing potential error sources. The evaluated assays demonstrated good accuracy, and precision was good for the majority of the assays. Also, RTs for most peptides were very stable. However, the high throughput, robustness, and good reproducibility may to some extent limit the sensitivity. It has to be kept in mind that this method should be used as a semiquantitative screening method to define a smaller set of proteins. Therefore, to enable analysis of many samples, high throughput, robustness, and reproducibility are, at this point, more important than the highest possible sensitivity. More precise and sensitive quantitative assays can then be developed for a subset of the proteins.

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ASSOCIATED CONTENT

* Supporting Information S

Tables S1−S4 and data S5. Table S1: Proteins detected in nasal lavage fluid. Table S2: Proteins identified in nasal lavage fluid (NLF) samples using microLC−MS/MS by information dependent acquisition (IDA). Table S3: Proteins identified in nasal lavage fluid (NLF) samples using nanoLC-Q-TOF by information dependent acquisition (IDA). Table S4: Selected reaction monitoring assays designed for the detection of 244 proteins in nasal lavage fluid (NLF). Data S5: Skyline document with optimized transitions for the detection of 244 proteins in nasal lavage fluid (NLF). This material is available free of charge via the Internet, at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: +46-46-173198. Fax: +46-46-173180. E-mail: harriet. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank E. Assarsson, K. Diab, P. Tallving, and E. Åkerberg for assistance with the nasal lavage. This work was supported by the Swedish Council for Working Life and Social Research (Grant No. 2005-0687), the AFA Foundation, and Lund University, Sweden.

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ABBREVIATIONS AGP 1, α-1-acid glycoprotein 1; Apo A-I, apolipoprotein A-I; BALF, bronchoalveolar lavage fluid; BCA, bicinchoninic acid; CE, collision energy; CV, coefficient of variation; DP, declustering potential; ES, electrospray; FA, formic acid; HSA, human serum albumin; IDA, information-dependent acquisition; IDA-MS, tandem mass spectrometry by information-dependent acquisition; LOD, limit of detection; LC−MS/ MS, liquid chromatography−tandem mass spectrometry; LCQ-TOF-MS; liquid chromatography-quadrupole-time-of-flight mass spectrometry; LC-SRM-MS, liquid chromatography− tandem mass spectrometry by selected reaction monitoring; MS/MS, tandem mass spectrometry; NLF, nasal lavage fluid; PICR, Protein Identifier Cross-Reference; RT, retention time; SD, standard deviation; SE, standard error; SRM, selected reaction monitoring; TF, serotransferrin

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REFERENCES

(1) Walsh, G. M.; Rogalski, J. C.; Klockenbusch, C.; Kast, J. Mass spectrometry-based proteomics in biomedical research: Emerging technologies and future strategies. Exp. Rev. Mol. Med. 2010, 12, e30. (2) Quirce, S.; Lemière, C.; De Blay, F.; Del Pozo, V.; Gerth Van Wijk, R.; Maestrelli, P.; Pauli, G.; Pignatti, P.; Raulf-Heimsoth, M.; Sastre, J.; Storaas, T.; Moscato, G. Noninvasive methods for

CONCLUSIONS The developed method can be used for relative quantification of the targeted NLF proteins. The method is reproducible and accurate, and many samples can be measured with high L



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assessment of airway inflammation in occupational settings. Allergy 2010, 65, 445−458. (3) Howarth, P. H.; Persson, C. G. A.; Meltzer, E. O.; Jacobson, M. R.; Durham, S. R.; Silkoff, P. E. Objective monitoring of nasal airway inflammation in rhinitis. J. Allergy Clin. Immunol. 2005, 115, 414−441. (4) Karedal, M. H.; Mortstedt, H.; Jeppsson, M. C.; Diab, K. K.; Nielsen, J.; Jonsson, B. a. G.; Lindh, C. H. Time-dependent proteomic itraq analysis of nasal lavage of hairdressers challenged by persulfate. J. Proteome Res. 2010, 9, 5620−5628. (5) Wang, H.; Chavali, S.; Mobini, R.; Muraro, A.; Barbon, F.; Boldrin, D.; Aberg, N.; Benson, M. A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis. Allergy 2011, 66, 132−140. (6) Persson, C. G. A.; Svensson, C.; Greiff, L.; Andersson, M.; Wollmer, P.; Alkner, U.; Erjefalt, I. The use of the nose to study the inflammatory response of the respiratory-tract. Thorax 1992, 47, 993− 1000. (7) Lindahl, M.; Stahlbom, B.; Tagesson, C. 2-Dimensional gelelectrophoresis of nasal and bronchoalveolar lavage fluids after occupational exposure. Electrophoresis 1995, 16, 1199−1204. (8) Benson, L. M.; Mason, C. J.; Friedman, O.; Kita, H.; Bergen, H. R.; Plager, D. A. Extensive fractionation and identification of proteins within nasal lavage fluids from allergic rhinitis and asthmatic chronic rhinosinusitis patients. J. Sep. Sci. 2009, 32, 44−56. (9) Bryborn, M.; Adner, M.; Cardell, L. O. Psoriasin, one of several new proteins identified in nasal lavage fluid from allergic and nonallergic individuals using 2-dimensional gel electrophoresis and mass spectrometry. Respir. Res. 2005, 6, 118. (10) Casado, B.; Pannell, L. K.; Iadarola, P.; Baraniuk, J. N. Identification of human nasal mucous proteins using proteomics. Proteomics 2005, 5, 2949−2959. (11) Casado, B.; Pannell, L. K.; Viglio, S.; Iadarola, P.; Baraniuk, J. N. Analysis of the sinusitis nasal lavage fluid proteome using capillary liquid chromatography interfaced to electrospray ionization-quadrupole time of flight-tandem mass spectrometry. Electrophoresis 2004, 25, 1386−1393. (12) Ghafouri, B.; Irander, K.; Lindbom, J.; Tagesson, C.; Lindahl, M. Comparative proteomics of nasal fluid in seasonal allergic rhinitis. J. Proteome Res. 2006, 5, 330−338. (13) Ghafouri, B.; Stahlbom, B.; Tagesson, C.; Lindahl, M. Newly identified proteins in human nasal lavage fluid from non-smokers and smokers using two-dimensional gel electrophoresis and peptide mass fingerprinting. Proteomics 2002, 2, 112−120. (14) Lindahl, M.; Irander, K.; Tagesson, C.; Stahlbom, B. Nasal lavage fluid and proteomics as means to identify the effects of the irritating epoxy chemical dimethylbenzylamine. Biomarkers 2004, 9, 56−70. (15) Lindahl, M.; Stahlbom, B.; Svartz, J.; Tagesson, C. Protein patterns of human nasal and bronchoalveolar lavage fluids analyzed with two-dimensional gel electrophoresis. Electrophoresis 1998, 19, 3222−3229. (16) Lindahl, M.; Stahlbom, B.; Tagesson, C. Newly identified proteins in human nasal and bronchoalveolar lavage fluids: Potential biomedical and clinical applications. Electrophoresis 1999, 20, 3670− 3676. (17) Lindahl, M.; Stahlbom, B.; Tagesson, C. Identification of a new potential airway irritation marker, palate lung nasal epithelial clone protein, in human nasal lavage fluid with two-dimensional electrophoresis and matrix-assisted laser desorption/ionization-time of flight. Electrophoresis 2001, 22, 1795−1800. (18) Tewfik, M. A.; Latterich, M.; Difalco, M. R.; Samaha, M. Proteomics of nasal mucus in chronic rhinosinusitis. Am. J. Rhinol. 2007, 21, 680−685. (19) Wang, H.; Gottfries, J.; Barrenas, F.; Benson, M. Identification of novel biomarkers in seasonal allergic rhinitis by combining proteomic, multivariate and pathway analysis. Plos One 2011, 6, e23563. (20) Keshishian, H.; Addona, T.; Burgess, M.; Kuhn, E.; Carr, S. A. Quantitative, multiplexed assays for low abundance proteins in plasma

by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics 2007, 6, 2212−2229. (21) Whiteaker, J. R.; Lin, C. W.; Kennedy, J.; Hou, L. M.; Trute, M.; Sokal, I.; Yan, P.; Schoenherr, R. M.; Zhao, L.; Voytovich, U. J.; KellySpratt, K. S.; Krasnoselsky, A.; Gafken, P. R.; Hogan, J. M.; Jones, L. A.; Wang, P.; Amon, L.; Chodosh, L. A.; Nelson, P. S.; Mcintosh, M. W.; Kemp, C. J.; Paulovich, A. G. A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat. Biotechnol. 2011, 29, 625−634. (22) Addona, T. A.; Abbatiello, S. E.; Schilling, B.; Skates, S. J.; Mani, D. R.; Bunk, D. M.; Spiegelman, C. H.; Zimmerman, L. J.; Ham, A.-J. L.; Keshishian, H.; Hall, S. C.; Allen, S.; Blackman, R. K.; Borchers, C. H.; Buck, C.; Cardasis, H. L.; Cusack, M. P.; Dodder, N. G.; Gibson, B. W.; Held, J. M.; Hiltke, T.; Jackson, A.; Johansen, E. B.; Kinsinger, C. R.; Li, J.; Mesri, M.; Neubert, T. A.; Niles, R. K.; Pulsipher, T. C.; Ransohoff, D.; Rodriguez, H.; Rudnick, P. A.; Smith, D.; Tabb, D. L.; Tegeler, T. J.; Variyath, A. M.; Vega-Montoto, L. J.; Wahlander, A.; Waldemarson, S.; Wang, M.; Whiteaker, J. R.; Zhao, L.; Anderson, N. L.; Fisher, S. J.; Liebler, D. C.; Paulovich, A. G.; Regnier, F. E.; Tempst, P.; Carr, S. A. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 2009, 27, 633−641. (23) Anderson, L.; Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 2006, 5, 573−588. (24) Brun, V.; Dupuis, A.; Adrait, A.; Marcellin, M.; Thomas, D.; Court, M.; Vandenesch, F.; Garin, J. Isotope-labeled protein standards. Mol. Cell. Proteomics 2007, 6, 2139−2149. (25) Lange, V.; Picotti, P.; Domon, B.; Aebersold, R. Selected reaction monitoring for quantitative proteomics: A tutorial. Mol. Syst. Biol. 2008, 4, 222. (26) Picotti, P.; Rinner, O.; Stallmach, R.; Dautel, F.; Farrah, T.; Domon, B.; Wenschuh, H.; Aebersold, R. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 2010, 7, 43−46. (27) Prakash, A.; Tomazela, D. M.; Frewen, B.; Maclean, B.; Merrihew, G.; Peterman, S.; Maccoss, M. J. Expediting the development of targeted srm assays: Using data from shotgun proteomics to automate method development. J. Proteome Res. 2009, 8, 2733−2739. (28) Zhang, H. X.; Liu, Q. F.; Zimmerman, L. J.; Ham, A. J. L.; Slebos, R. J. C.; Rahman, J.; Kikuchi, T.; Massion, P. P.; Carbone, D. P.; Billheimer, D.; Liebler, D. C. Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Mol. Cell. Proteomics 2011, 10, 1−17. (29) Picotti, P.; Aebersold, R.; Domon, B. The implications of proteolytic background for shotgun proteomics. Mol. Cell. Proteomics 2007, 6, 1589−1598. (30) Maclean, B.; Tomazela, D. M.; Abbatiello, S. E.; Zhang, S.; Whiteaker, J. R.; Paulovich, A. G.; Carr, S. A.; Maccoss, M. J. Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (srm) mass spectrometry. Anal. Chem. 2010, 82, 10116−10124. (31) Cham, J. A.; Bianco, L.; Bessant, C. Free computational resources for designing selected reaction monitoring transitions. Proteomics 2010, 10, 1106−1126. (32) Ang, C. S.; Rothacker, J.; Patsiouras, H.; Gibbs, P.; Burgess, A. W.; Nice, E. C. Use of multiple reaction monitoring for multiplex analysis of colorectal cancer-associated proteins in human feces. Electrophoresis 2011, 32, 1926−1938. (33) Domanski, D.; Percy, A. J.; Yang, J. C.; Chambers, A. G.; Hill, J. S.; Freue, G. V. C.; Borchers, C. H. MRM-based multiplexed quantitation of 67 putative cardiovascular disease biomarkers in human plasma. Proteomics 2012, 12, 1222−1243. (34) Kuzyk, M. A.; Smith, D.; Yang, J.; Cross, T. J.; Jackson, A. M.; Hardie, D. B.; Anderson, N. L.; Borchers, C. H. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol. Cell. Proteomics 2009, 8, 1860−1877. M



Article

(35) Picotti, P.; Bodenmiller, B.; Mueller, L. N.; Domon, B.; Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 2009, 138, 795−806. (36) Stergachis, A. B.; Maclean, B.; Lee, K.; Stamatoyannopoulos, J. A.; Maccoss, M. J. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 2011, 8, 1041−1043. (37) Diab, K. K.; Truedsson, L.; Albin, M.; Nielsen, J. Persulphate challenge in female hairdressers with nasal hyperreactivity suggests immune cell, but no ige reaction. Int. Arch. Occup. Environ. Health 2009, 82, 771−777. (38) Johannesson, G.; Lindh, C.; Nielsen, J.; Bjork, B.; Rosqvist, S.; Jonsson, B. a. G. In vivo conjugation of nasal lavage proteins by hexahydrophthalic anhydride. Toxicol. Appl. Pharmacol. 2004, 194, 69−78. (39) Maclean, B.; Tomazela, D. M.; Shulman, N.; Chambers, M.; Finney, G. L.; Frewen, B.; Kern, R.; Tabb, D. L.; Liebler, D. C.; Maccoss, M. J. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26, 966−968. (40) Alexandridou, A.; Tsangaris, G. T.; Vougas, K.; Nikita, K.; Spyrou, G. Unimap: Finding unique mass and peptide signatures in the human proteome. Bioinformatics 2009, 25, 3035−3037. (41) Chen, E. I.; Cociorva, D.; Norris, J. L.; Yates, J. R. Optimization of mass spectrometry-compatible surfactants for shotgun proteomics. J. Proteome Res. 2007, 6, 2529−2538. (42) Proc, J. L.; Kuzyk, M. A.; Hardie, D. B.; Yang, J.; Smith, D. S.; Jackson, A. M.; Parker, C. E.; Borchers, C. H. A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin. J. Proteome Res. 2010, 9, 5422−5437. (43) Burkhart, J. M.; Schumbrutzki, C.; Wortelkamp, S.; Sickmann, A.; Zahedi, R. P. Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on msbased proteomics. J. Proteomics 2012, 75, 1454−1462. (44) Riechelmann, H.; Deutschle, T.; Friemel, E.; Gross, H.-J.; Bachem, M. Biological markers in nasal secretions. Eur. Respir. J. 2003, 21, 600−605. (45) Heikkinen, T.; Shenoy, M.; Goldblum, R. M.; Chonmaitree, T. Quantification of cytokines and inflammatory mediators in samples of nasopharyngeal secretions with unknown dilution. Pediatr. Res. 1999, 45, 230−234. (46) Kaulbach, H. C.; White, M. V.; Igarashi, Y.; Hahn, B. K.; Kaliner, M. A. Estimation of nasal epithelial lining fluid using urea as a marker. J. Allergy Clin. Immunol. 1993, 92, 457−465. (47) Balfour-Lynn, I.; Noah, T. L. Importance of using markers of dilution when measuring inflammatory mediators in nasal lavage fluid [with reply]. J. Infect. Dis. 1996, 173, 1049−1050. (48) Virolainen, A.; Makela, M. J.; Esko, E.; Jero, J.; Alfthan, G.; Sundvall, J.; Leinonen, M. New method to assess dilution of secretions for immunological and microbiological assays. J. Clin. Microbiol. 1993, 31, 1382−1384.

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Screening Method Using Selected Reaction Monitoring for Targeted

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