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Longitudinal Urinary Protein Variability in Participants of the Space Flight Simulation Program Nina A. Khristenko, Irina M. Larina, and Bruno Domon J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00594 • Publication Date (Web): 30 Oct 2015 Downloaded from http://pubs.acs.org on November 3, 2015

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Journal of Proteome Research

Longitudinal Urinary Protein Variability in Participants of the Space Flight Simulation Program Nina A. Khristenko1,2; Irina M. Larina3 and Bruno Domon1,2* 1 Luxembourg Clinical Proteomics Center (LCP), L.I.H., Strassen, Luxembourg; 2 University of Luxembourg, Luxembourg; 3 Institute for BioMedicalProblems, Moscow, Russia KEYWORDS: targeted proteomics, urine, spectral library, quantification, normal protein concentration.

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ABSTRACT

Urine is a valuable material for the diagnosis of renal pathologies and to investigate the effects of their treatment. But, the protein abundance variability in the context of normal homeostasis remains a major challenge in urinary proteomics. In this study, the analysis of urine samples collected from healthy individuals, rigorously selected to take part in the MARS-500 spaceflight simulation program, provided an unique opportunity to estimate normal concentration ranges for an extended set of urinary proteins. In order to systematically identify and reliably quantify peptides/proteins across a large sample cohort a targeted mass spectrometry method was developed. The performance of parallel reaction monitoring (PRM) analyses was improved by implementing a tight control of the monitoring windows during LC-MS/MS runs, using an “onthe-fly” correction routine. The matching of the experimentally obtained MS/MS spectra with reference fragmentation patterns allowed for dependable peptide identifications. Following optimization and evaluation, the targeted method was applied to investigate the variability of protein abundances in 56 urine samples, collected from six volunteers participating in the MARS500 program. The intra-personal protein concentration ranges were determined for each individual and showed unexpectedly high abundance variation, with an averaged difference of one order of magnitude.


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INTRODUCTION The study of the urinary proteome is attractive as urine is considered to represent an ideal source of biomarkers for the diagnosis of renal pathologies and is available in a noninvasive manner. However, urinary biomarker studies suffer from the high variability in protein abundances. The recent progress to determine the persistently expressed urinary proteins using a mass spectrometry based approach. For instance, these stable and core proteins, characterized with the lowest variability, were defined by analyzing urine samples from apparently healthy volunteers, i.e., seven individuals for three consecutive days (Nagaraj, et al J Proteome Res 2011), or, 200 individuals with three urine samples each collected over four months (He, et al. Proteomics 2012). In the current study, estimated normal, longitudinal, protein variability provide the urinary proteomics community with complementary information. In order to define longitudinal normal protein concentration ranges in urine, a massspectrometric method developed in this study for systematic analysis was applied to an extended urinary protein screen across a large sample cohort. Thousands of proteins can now be analyzed and identified during one single analysis using data dependent acquisition (DDA)3. In spite of its success and broad acceptance, this method is subjected to several constraints implicit in the heuristics of the MS/MS acquisition, resulting in a bias towards the measurement of the most abundant peptides. In addition, precursor ion-based quantification may result in limited selectivity due to the presence of nearly isobaric interferences4. In this context, the systematic measurement of MS/MS spectra for all precursor ions using data independent acquisition (DIA) has been proposed as an alternative. The post-acquisition targeted data analysis allows querying the qualitative and quantitative information for the peptides of interest5. While being generic and powerful this method may also suffer from selectivity issues due to the co-isolation of multiple precursors, which often results in high complexity MS/MS spectra, consequently impairing the sensitivity of low abundant peptide measurements6. At present, robust quantitative analyses are commonly performed using targeted methods resulting in systematic measurements with a high degree of selectivity7. Targeted LC-MS/MS analyses, typically



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carried out using a triple quadrupole (QQQ) mass spectrometer operated in selected reaction monitoring (SRM) mode, have been extensively used for biomarker studies 8. For instance, biomarker candidates for bladder cancer were repeatedly analyzed in SRM experiments across 100s of urine samples9. The selectivity of quantification is dramatically increased by relying on one or several fragment ions, isolated with a narrow window, rather than on the precursor ions. More recently, this type of experiments have been performed on high resolution - accurate mass (HRAM) instruments10, 11

. The analyses in parallel reaction monitoring (PRM) mode, implemented on quadrupole-orbitrap or

quadrupole-time of flight (Q-TOF) mass spectrometers, resulted in measurements with increased selectivity due to the discrimination of the analytes from interferences11. In addition, the recording of full MS/MS spectra in PRM experiments allows for flexible data processing and an increased identification confidence. The challenge, however, is the multiplexed analysis of a large set of targets, which requires scheduling of the monitoring windows for the peptides during their elution, and in turn, mandates an excellent control of the chromatographic separation. A strategy was designed to correct “on-the-fly” i.e., during the actual acquisition, the targeted peptide elution windows to compensate for probable elution fluctuations12, 13. Landmark peptides, equally distributed along the chromatographic elution profile, are used to monitor drifts in the LC separation and to adjust the monitoring windows of the targeted peptides accordingly. In a typical targeted LC-MS/MS analysis a pair of peptides, i.e., the endogenous and its stable isotopically labelled (SIL) counterpart, are measured. The SIL internal standard is used both for identity confirmation, using its fragmentation pattern, and for the precise quantification of the endogenous peptides. Nevertheless, for the exploratory screening of a large number of peptides the systematic access to SIL peptides is not always trivial14. In this case, the analysis is conducted without the addition of a comprehensive set of internal standards and the identification is performed by comparing the experimental MS/MS spectra with existing reference spectra, ideally generated under similar instrumental conditions. The MS/MS spectra present in public repositories (such as NIST, PeptideAtlas15) are a valuable initial resource but the actual fragmentation patterns may vary


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due to the heterogeneity of the platforms (e.g., Q-trap or Q-TOF) used, the setting of parameters (e.g., fragmentation method or collision energy) and the sample quality (e.g., synthetic peptides, biological matrixes)16. Therefore, one of the first objectives of this study was to generate a high quality reference spectral library. The MS/MS spectra, acquired during the LC-MS/MS analysis of fractionated urine samples with moderate complexity, were subjected to a rigorous evaluation to ensure the high quality of the spectra. The main aim of this study was to devise a targeted approach using HRAM mass spectrometry by controlling on-the-fly the elution of each peptide, thus enabling the systematic analysis of more than 1000 peptides in a single LC-MS/MS run. Subsequently, the PRM method, combined with peptide identity confirmation by spectral comparison with the reference library, was applied to the analysis of a large set of urine samples. The systematic identification of peptides, as well as their reproducible quantification, were assessed. The novelty of the present study consists in determining the intra-personal variability of a large set of proteins across longitudinally collected urine samples from a group of males, rigorously selected after extensive physical, psychological and medical testing from 5,600 candidates. The selected individuals were participating in the internationally organized MARS-500 ground based space flight simulation program with the aim to study the psychological and physiological impact of living in isolation. Certain parameters of space flight simulation, such as microgravity and exposure to radiation were not considered directly in the MARS-500 program. The result of this extended protein screening across urine samples showed the average difference at one order of magnitude for intra-personal protein concentrations for all volunteers. The current study represents a first application of PRM analyses, using precise time-scheduled acquisition, to extended protein screening across a large biological sample cohort.



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EXPERIMENTAL SECTION All the reagents were purchased from Sigma-Aldrich (St. Louis, MO), unless otherwise specified. Urine samples collection Urine samples were collected weekly from six Caucasian males over 100 days of confinement, simulating an extended space travel in the frame of the MARS-500 program. The MARS-500 program was organized by the Institute for Biomedical Problems (IBMP, Moscow, Russia) and the European Space Agency (ESA) and approved by the corresponding Ethics Committees following the guidelines of the Declaration of Helsinki. The crewmembers were selected from over 5,600 initial candidates after passing extensive medical and psychological examinations, i.e., the same procedures as for astronauts/cosmonauts. The age of the participants was between 25 and 40 years old. The individuals were confined in enclosed modules of approximately 550 m3 with controlled air/oxygen partial pressure, artificial lighting, constant temperature at 24 oC and noise level at 60 -70 dB, i.e., the typical environment of the International Space Station. The crewmembers could only communicate with each other and had time-limited audio/video contact with the mission control. During isolation the individuals were subjected to a controlled diet and regulated sport activities. The urine sample collection was performed as previously described17. Briefly, midstream second morning urine was centrifuged at 2,000 g for 10 min at 4 oC and the supernatant stored at -80 °C. Sample preparation Three types of urine samples were prepared for subsequent LC-MS/MS analyses: (i) a pooled sample obtained by merging six aliquots of the desalted urine samples of the individuals, (ii) 12 fractions of the pooled urine sample obtained using off-gel electrophoresis and (iii) fifty-six individual urine samples. The protein concentration in urine was estimated using the pyrogallol assay. However, the low precision and poor robustness of this colorimetric assay, especially in the low protein concentration range (around 50 µg/mL), may impair the result and further data interpretation. Thus, the normalization was based on volume (i.e., 5 mL of raw urine for all the MARS-500 samples). This


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approach is applicable due to the nature of these samples, i.e., collected from well-controlled healthy individuals, with a relatively constant daily liquid intake and urine volume production. Therefore, identical sample volumes (5 mL) were used for the proteomic analyses. The sample preparation protocol was previously described18-20. Briefly, two exogenous yeast proteins (enolase 1 and alcohol dehydrogenase 1) were added to each sample at a final concentration of 20 pmol/µL in order to assess the protein extraction efficiency across the samples. The protein mixtures were precipitated by adding a 30% trichloroacetic acid solution to a final concentration of 6 % and incubated for 2 hours at 4 oC. Samples were centrifuged at 12,000 g for 15 min at 4 oC and the supernatant was discarded. The pellets were washed with 2.5 mL of ice-cold acetone to remove potentially interfering compounds. The centrifugation and washing procedures mentioned above were repeated once. A final centrifugation step was performed at 15,000 g for 20 min at room temperature and the supernatant was discarded. The pellets were air-dried in an oven with the thermostat set at 25 oC and stored at -80 oC. The protein pellets were re-suspended in 250 µL of denaturation buffer (8 M urea and 0.1 M ammonium bicarbonate). Disulfide bonds were reduced using dithiothreitol at a final concentration of 20 mM at 37 oC for 60 min. The cysteine residues were alkylated using iodoacetamide (IAM) at a final concentration of 80 mM at 25 oC for 90 min in the dark. The excess of IAM was quenched by adding N-acetyl-L-cysteine to a final concentration of 80 mM at 25 oC for 30 min. The samples were diluted with 0.1 M ammonium bicarbonate to obtain a final urea concentration below 2 M. The enzymatic digestion with trypsin (Promega, Madison, WI) was carried out overnight (13 hours) at 37 o

C, with a protein: enzyme ratio of 1:20. The proteolysis reaction was stopped by acidifying the

samples with 10 % formic acid to a final pH in the range 2-3 as measured by pH - indicator paper (Merck Millipore, Billerica, MA). The protein digest was desalted using tC18 solid phase extraction cartridges (WAT036820, Waters, Milford, MA) as previously described19 except for the elution step which was carried out with 80 % acetonitrile (ACN) containing 0.1 % formic acid (FA). Finally,



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samples were aliquoted, evaporated to dryness under vacuum using a centrifugal evaporator and stored at -20 oC. Fractionation of peptides by off-gel electrophoresis Aliquots of the first collected urine sample from each individuals were pooled after desalting and evaporated to dryness as described above. The peptide pellet, corresponding to a total protein amount of 300 µg, was re-suspended in 1.8 mL of the rehydration solution (i.e., glycerol solution, off-gel buffer pH 3-10 containing ampholytes, HPLC grade water) and loaded on the immobilized pH gradient (IPG) strip. The isoelectric focusing electrophoresis was carried out on a 3100 off-gel fractionator (Agilent Technologies, Santa Clara, CA) using a 12-wells IPG strip with pH 3-10. The separation process was performed according to the manufacturers’ instructions. Each of the 12 fractions, after completion of the separation, was transferred to an individual vial. In order to improve the peptide recovery from the IPG strips, the wells were refilled with 100 µL of rehydration solution without ampholytes and the isoelectric focusing was carried out for another 2 hours. These fractions were combined with the previously collected fractions. Each fraction was desalted as described above, aliquoted, concentrated to dryness and stored at -20 oC. LC-MS/MS analysis Samples were re-suspended in 5 % ACN containing 0.1 % FA to reach a final peptide concentration of 1 µg/µL. A mixture of fifteen synthetic tryptic peptides, isotopically labeled at the C-terminal amino acid residues (Peptide Retention Time Calibration Mixture, ThermoFisher Scientific, Rockford, IL), was added at a concentration of 50 fmol/µL to each sample prior to the LC-MS/MS analysis to assess the performance of the instrument (including the parameters affecting ion fragmentation such as the nitrogen pressure in the HCD cell) and to realign the elution windows of the targeted peptides during time-scheduled experiments. In addition, a set of 38 SIL peptides (ThermoFisher Scientific, Ulm, Germany) was added to each of the unfractionated individual urine samples to correct for ion signal intensity fluctuation due to technical variability across multiple LCMS/MS analyses (Supplementary Table 1). The LC-MS/MS analyses were performed as described


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previously13. Briefly, an Ultimate 3000 RSLC nano liquid chromatography (ThermoFisher Scientific) operated using a trap (Acclaim PepMap 2 cm × 75 μm i.d., C18, 3 μm, 100 Å, ThermoFisher Scientific) and an analytical (Acclaim PepMap RSLC 15 cm × 75 μm i.d., C18, 2 μm, 100 Å, ThermoFisher Scientific) column was used to separate the peptide mixture before the mass spectrometric analysis. The 350 ng of protein digest for the pooled samples (mixture of six individual samples) and the equivalent of 6 μl of initial urine volume for the individual samples were injected onto the trap column. The peptides were eluted using a linear gradient of 2 - 35 % solvent B (acetonitrile with 0.1 % (v/v) formic acid) with 0.5 % slope at a flow rate of 300 nL/min. The LC system was coupled to a Q-Exactive or Q-Exactive Plus (Thermo Scientific, Bremen, Germany) quadrupole-orbitrap mass spectrometer equipped with a nanoelectrospray source. The shotgun LC-MS/MS analysis of the fractionated pooled urine sample was performed using a QExactive instrument operated in DDA mode. The full scan MS1 spectra were recorded with a maximum fill time of 250 ms, an automatic gain control (AGC) value of 3E6 and an orbitrap resolution of 70,000 (at m/z = 200). The 15 most intense ions were iteratively isolated with a 2 Th window, then injected during a maximum fill time of 60 ms (target AGC = 1E6), and fragmented in the collision cell at a normalized collision energy (nCE) of 25. The MS/MS spectra were recorded at an orbitrap resolution of 17,500 (at m/z = 200). Ions with single charge or unassigned charge states were not selected for fragmentation and dynamic exclusion was set to 30 seconds. The targeted LC-MS/MS analyses of unfractionated urine samples were performed on a Q-Exactive Plus instrument operated in PRM mode. The full scan MS1 spectra were recorded with a maximum fill time of 100 ms, an AGC value of 3E6 and an orbitrap resolution of 70,000 (at m/z = 200). The targeted peptides were isolated with a 2 Th window, accumulated during a maximum fill time of 120 ms (target AGC = 1E6), and fragmented in the collision cell at a nCE of 25. The MS/MS spectra were recorded at an orbitrap resolution of 35,000 (at m/z = 200). Spectra annotation by database search



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The MS/MS spectra acquired in the LC-MS/MS analysis of fractionated pooled urine samples were assigned to peptide sequences using the Mascot search engine (Version 2.3.0, Matrix Science, London, UK) against the UniProtKB/Swiss-Prot database of canonical sequences (ver. 2011_08) embedded in the Proteome Discoverer software (Version 1.4, Thermo Scientific). The search was performed with the taxonomy Homo Sapiens, fixed modification for carbamidomethylated cysteine and tryptic peptides with no missed cleavages except for KP and RP. The precursor and fragment ions were annotated with 10 ppm and 0.05 Da mass tolerance, respectively. The false discovery rate (FDR) was estimated at the peptide spectrum matches (PSM) level. Design of the PRM method The necessary information to monitor the peptides of interest during the targeted LC-MS/MS analysis (i.e., precursor m/z, precursor charge, elution time) was extracted from the reference spectral library. Since the reference library was populated with tandem mass spectra originating from the independent LC-MS/MS analyses of off-gel fractions, the elution times of all the peptides were aligned to the elution times of the first analysis, using 15 SIL peptides as landmarks added to each fraction prior to the LC-MS/MS analyses. Briefly, the elution times for the 15 SIL peptides from the first LC-MS/MS file were used as reference, and a linear regression was applied to adjust the elution times in the following runs. A drift of the elution times was corrected for the entire set of urinary peptides across all the files stored in the spectral library, using the slope and offset derived from the linear regression. The corrected elution times of each peptide were used to precisely determine the centers of the monitoring windows for the scheduled LC-MS/MS analyses. To monitor 1586 targeted peptides (1540 urinary, 8 exogenous and 38 SIL peptides) in a single PRM analysis, the precision of the time scheduling was further improved by performing an “on-the-fly” correction of the elution windows, as previously described12, 13. In addition, the “off-line” elution window recalibration was applied on a regular basis in order to compensate for the major time shifts due to modifications of the system conditions (e.g., changing trap or analytical columns) and/or long intervals (e.g., one month or more) between LC-MS/MS analyses.


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Confirmation of the peptide identity via spectral matching All MS/MS spectra were extracted from MS files (.raw) using an in-house developed tool (C# programming language; the high level code is described in the supporting information) and stored in a local database. For spectral matching at least five fragment ions, with a mass deviation between experimental and reference (theoretical) m/z values below 10 ppm, were required 21. The experimental spectra fulfilling these conditions were considered as identified only if the contrast spectral angle θ (

⁄√

√

) between corresponding

experimental (IExp) and reference (IRef) fragment intensities was below 120, i.e., cos θ ≥ 0.9813, 21. Peptide quantification The chromatographic peak height of the most intense fragment, according to the ranking in the reference spectral library, was used to quantify the corresponding peptide. The estimation of the chromatographic peak heights was performed using the open source Skyline software (ver. 2.6)22; the MS/MS signals were extracted using the DIA filter with an isolation window set at 0.01 m/z. The peak boundaries selected by the program were manually reviewed and adjusted if needed. Processing the large data set The PRM analyses of the 56 individual urine samples in duplicate resulted in the acquisition of 112 MS files. The peptide identities were confirmed by spectral matching and the quantification was performed using the fragment ion intensity at the apex of the chromatographic profile. The run to run variability (e.g., injected sample volume or spray quality) was assessed using 15 landmark- and 38 SIL-peptides, spiked into the samples at a constant concentration, similar to approaches described previously23,

24

. Briefly, the correction factors were defined as the slope of the linear regressions

generated from the median intensities of each of these peptides (i.e., baseline), calculated using the five MS files with the lowest deviation, and their intensities in distinct MS files. The endogenous peptide signal intensities in each MS file were corrected using the corresponding correction factors.



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RESULTS AND DISCUSSION During the analysis of complex samples using a mass spectrometer with trapping capabilities the data acquired in a full scan MS1 often result in limited confidence in the peptide identity confirmation and in a reduced dynamic range for quantification. While the selected ion monitoring (SIM) mode expands the dynamic range (Gallien, et al. Mol Cell Proteomics 2012), analyses in PRM mode provide in addition a better confidence in peptide identity and additional selectivity. Nevertheless, the applicability of PRM to exploratory experiments remains limited to moderatescale studies, with a limited number of peptides targeted within a single LC-MS/MS run. Therefore, the main objective of this study was to develop targeted methods for high density protein screening across a large sample cohort (Figure 1A). The development of the targeted method and its application to high density protein screening included three main steps: 1) the creation of a reference spectral library, 2) the design of a high-density acquisition method and 3) the PRM analysis of complex biological samples. A secondary objective was to apply the method developed to the analysis of a large set of urine samples in order to define the distribution of normal protein concentration ranges in a stringently selected group of individuals under controlled conditions (Figure 1B).

Creation and curation of the spectral library The duplicated LC-MS/MS analyses of the 12 fractions of the urine protein digest obtained by offgel electrophoresis resulted in the acquisition of more than 240,000 MS/MS spectra. Among them, 45,000 were assigned to peptide sequences during a database search, i.e., peptide spectrum match (PSM) and yielded 15,000 non-redundant peptide sequences corresponding to more than 2,100 proteins. The LC-MS/MS analyses of the individual off-gel fractions, having a reduced complexity, not only drastically increased the number of identified peptides (by approximatively a factor five), compared to the analysis of unfractionated samples, but also resulted in MS/MS spectra of higher quality in agreement with a previous study25. The fractionation of the urine protein digest showed


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high separation efficiency, with 89 % of peptides identified out of a single well (Supplementary figure 1). The 45,000 assigned spectra from the fractionated samples constituted the basis for the reference library generation. The spectral library was built in a two-step process: first, the MS/MS spectra with the highest quality were automatically selected and, second, the remaining spectra were manually curated. The assigned spectra were manually accepted only if satisfying the empirically defined criteria: i) a minimum of six peaks annotated with “canonical” fragments (a, b, y types) within a 10 ppm mass tolerance; ii) at least 3 consecutive y- or b-ions (sequence tag) detected; iii) assignment of the majority of the intense ions; iv) in case of a favored fragmentation site (e.g., proline residue) the corresponding y-ion should exhibit an intense signal (among most intense peaks). Criteria such as assignment of the majority of the intense signals and an excess of Nterminal proline fragments are in agreement with observations made in a previous study26. Similarly, a sequence tag of at least three amino acids was demonstrated as an insurance for a high confidence peptide-spectrum match27, 28. Examples of accepted and rejected spectra are illustrated in Figure 2A. The parameters and thresholds for automated acceptance of the high quality MS/MS spectra were determined using a training set which included 1,452 MS/MS spectra, representing 1,000 peptides. This set was manually reviewed using the above mentioned criteria, leading to 1117 and 335 MS/MS spectra accepted and rejected, respectively. The main features to discriminate between the two categories were obtained by a logistic regression. Stringent thresholds were defined for all the relevant features to retain only accepted spectra. In addition to the presence of a sequence tag of more than 3 amino-acid, the automated selection of the high quality MS/MS applied the following parameters: signal above defined thresholds (4x105 for total ion current TIC), at least 40 % ion current annotated, at least 25 % of total intensity representing y-type ions, and a Mascot ion score above 25. The differentiation between manually accepted and rejected spectra was further validated with an independent test set, consisting of 735 MS/MS spectra, representing to 500 peptide



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sequences. The parameters and threshold values are associated with both the type of mass analyzer and the acquisition parameters, as modification of a single instrumental settings (e.g., injection time, collision energy etc.) can dramatically impact the TIC and the fragmentation patterns. Finally, over 14,000 MS/MS spectra corresponding to more than 5600 peptides were automatically accepted and transferred into the reference library. When multiple spectra per peptide were acceptable, the one with the highest intensity of fragments was manually set as a reference. The remaining 31,000 spectra obtained from the fractionated urine samples, corresponding to 9,400 peptides, underwent manual curation using the above mentioned criteria. In total, more than 2,000 peptides, with one reference spectrum per peptide, were added to the library. The curated reference library finally included around 7,700 non-redundant peptides, originating from 1,592 proteins. The manual reviewing process allowed for a significant number of well-annotated spectra to be rescued and included in the library in spite of their low search engine scores (Figure 2B). The presence of well-annotated spectra with a low score is a known issue which can be addressed by either refining the score29 or by MS/MS spectra computer aided validation30. Most of the proteins in the reference library (77%) were among the urinary proteins consistently detected at least in any two out of seven large studies of healthy human urine1, 31-36. In total, this reliable part of the urinary proteome is constituted of about 3300 proteins (27 % of overall identified 12,000 proteins). The reference library was uploaded in ProteomeXchange - MARS-500 – Accession number PXD003034.

Design of the targeted study of the urinary proteome The MS/MS spectra acquired in the PRM analyses were used both for peptide identity confirmation and for quantification based on fragment ion intensity. As a matter of fact, PRM analyses on the quadrupole-orbitrap platform result in a broader dynamic range of measurements as compared to a full scan MS1 due to the trapping capability of the instrument. In particular, during targeted LCMS/MS analysis the precursor ions are isolated for fragmentation regardless of the signal intensity


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in the full scan MS1. Using such an acquisition mode, peptides can be identified with a fragment pattern of high similarity to the reference, although the corresponding precursor ion has no- or a low-signal in the full scan MS1. In addition, quantification using MS1 signals may be compromised by the presence of nearly isobaric co-eluted interferences and even the analysis using an HRAM instrument may not fully overcome this issue10. In order to perform a reliable quantification of the analytes in the PRM mode using the chromatographic peak height, at least five data points across a peak should be collected37. As the average peak width at the base is 20 - 30 s for peptides eluting from a column packed with 2-3 µm particles, the MS cycle time should not exceed four seconds in order to capture the apex of the chromatographic peak. In this context, extended protein screening is problematic especially when fragments for each targeted peptide are monitored over the whole chromatographic elution. The time-scheduled acquisition overcomes this problem by analyzing the peptides of interest at their elution times resulting in a more effective use of the LC-MS/MS analysis time. However, the monitoring window in the time-scheduled targeted analysis is typically wider (2-4 minutes) than the actual elution of a peptide to ensure the proper capture of the signal in case of drift of the elution times between LC-MS/MS runs. The implementation of a precise elution time scheduling based on the “on-the-fly” correction allows a narrowing of the monitoring window width and thereby increasing the number of targeted peptides to be analyzed12, 13. In the present study, the protein dynamic range and the presence of background signals were taken into consideration during the design of the targeted method for the large-scale protein screening of urine. For PRM analyses of unfractionated urine samples, the fill time (and, accordingly, the transient length) was increased to 120 ms as compared to the fractionated sample analysis, to improve the selectivity and sensitivity of the measurements, which is critical for the detection of low abundant analytes13. To preserve an acceptable MS cycle time, i.e., below 4 seconds, with respect to the peptide elution profile the maximum number of targets monitored during each MS cycle was carefully adapted along the chromatographic separation using the equation:



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(Supplementary figure 2A). A maximum of 30 targets were thus followed per MS cycle in the densest region of the chromatographic peptide elution. Peptide quantification using the peak height instead of the entire elution profile allowed to reduce the monitoring window down to 20 seconds (Supplementary figure 2B) and consequently, to increase the scale of the PRM analysis. As a result, the precise time-scheduled PRM analysis enabled monitoring 1,586 peptides in a single LC-MS/MS run. Among them, 1,540 proteotypic peptides (PTPs) corresponding to 770 urinary proteins were monitored during the urine samples analysis along with the 46 exogenous and SIL peptides used for quality control of the urine sample preparation and LC-MS/MS analyses, respectively. The reference spectral library database was used as a source of information (precursor m/z, precursor charge, normalized elution time) needed for the PRM method development.

Performance of the methodology for extended qualitative and quantitative analysis of urinary peptides The performance of the PRM method developed for large screening was evaluated in terms of systematic peptide/protein detection, identification and reliable quantification of urine samples. The targeted LC-MS/MS analyses of unfractionated pooled urine samples (the pool of the same individual aliquots, as was previously subjected to off-gel fractionation) resulted in the systematic identity confirmation, based on a high spectra similarity (spectral contrast angle ≤ 120), for 1,412 out of 1,586 targets, across four analytical replicates. Out of the remaining 174 peptides not systematically identified, 135 were detected in at least one LC-MS/MS analysis, whereas 39 were not detected in any replicate due to their signals being below the limit of detection (Figure 3). The latter was confirmed by their identification in the targeted LC-MS/MS analysis of the moderate complexity off-gel fractions. In light of the spectral matching, the low signal of fragment ions and the co-isolation of several precursor ions did not impact on the peptide identity confirmation. As an example, the identity of


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two co-eluting peptides with similar precursor mass to charge ratios (DTNRPLEPPSEFIVK with precursor at m/z 581.309 and WTLTAPPGYR, with precursors at 581.309 and 581.306, respectively) was confirmed by spectral matching with confounded MS/MS spectrum (Supplementary figure 3). The high success rate (89 %) in reproducible identification of the peptides obtained here outperformed those obtained with LC-MS/MS analyses in DDA mode, both using a conventional set up (about 60 %)4 and enhanced settings (about 75 % using a 50 cm analytical column and a 4 hours LC gradient)38. The approach allowed the identification of 768 out of 770 (99.7 %) targeted proteins with at least 1 PTP and more than 80 % of the proteins (621) with two PTP across all replicates. The protein abundances in unfractionated pooled urine samples were determined using the corresponding peptide(s) systematically detected and identified across all replicates, by extracting the chromatographic peak height of the most intense fragment. Five fragment ions with the lowest signal interference, based on a contrast spectral angle below 120, were used for peptide identity confirmation. The intensity of the most intense fragment ion was considered for quantification. To evaluate the reproducibility of peptide quantification, the coefficients of variation (CV) were calculated between replicates. Out of 1,586 targeted peptides, 1,412 were systematically identified, including 1,286 peptides quantified with CV values lower than 30 % across four replicates (Supplementary Table 2). Furthermore, 85 peptides were quantified with a CV value above 30 % across four replicates, presumably, due to a higher technical variability or fluctuations in the corresponding elution times. The results for the 41 remaining systematically identified peptides were excluded because the peak apices were not captured properly in any of the technical replicates. Overall, 81 % of the targeted peptides were systematically identified and reliably quantified with a CV below 30 % across four replicates (Figure 3). This allowed the quantification of 725 out of 770 targeted proteins. The dynamic range of the systematically identified and reliably quantified proteins covered at least three orders of magnitude based on their concentration in urine as previously reported in PeptideAtlas39. Within this range the highest concentration was detected for albumin (P02768) at 3.5 µg/mL and the lowest for Ephrin type-A receptor 1 (P21709) at 4.5 ng/mL (Supplementary figure 4).



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Analysis of urine samples from healthy individuals Now that a robust and verified LC-MS/MS method for the measurement of complex biological samples was established, a highly selective and sensitive extended protein screening was performed on urine samples from a large cohort. The targeted LC-MS/MS method was applied by increasing the minimum monitoring window to 30 seconds in order to compensate for peptide elution time fluctuations due to the variable individual sample backgrounds. In addition, three PTPs (if available), instead of two, were selected for each protein to reinforce the reliability of the protein quantification. Finally, over 1,300 targeted peptides corresponding to 480 proteins were monitored in a single LC-MS/MS run. The selection of these 480 proteins from the 1,500 proteins present in the reference library was based on their interest for the proteomic community (e.g., mentioned in the 6-7 large urine proteome studies, present in the core urinary proteome1 and in the stable urine protein panel2, etc.). The urine sample analysis performed here aimed to define the intra-personal variability of protein concentrations in the space program volunteers over time while being subjected to a controlled diet and mandatory sport activities. Out of the fifty-six samples collected over 15 weeks from six healthy volunteers two samples were excluded as the quality control for sample preparation was not fulfilled, i.e., they showed a significant difference in the exogenous protein precipitation efficiency. In order to control the systematic bias during the comparative study of urinary proteins, due to variations between MS runs, stable compounds (i.e., 15 landmark- and 38 SIL-peptides) were added at the same concentrations to each urine sample and used for signal correction in each of the raw files. Protein variability represents an important issue in the analyses of urinary proteomes, especially in the case of comparative studies. The analysis of urine collected during the MARS-500 program provided an unique opportunity to evaluate longitudinal changes in urinary protein expression profiles for the healthy volunteers living under standardized conditions. The protein concentration ranges were determined individually for each volunteer and showed on average a difference of one


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order of magnitude between the maximum and minimum signals measured (Figure 4A), after correction of the systematic errors between the individual MS runs. Whereas all of the individuals showed a similar distribution of the normal protein concentrations, a small subset of proteins were observed to be hypervariable, predominantly for two of the volunteers. The detailed analysis of 15 highly variable proteins revealed that at least seven of those are associated with inflammation processes (Supplementary Table 3). The expression profiles of these proteins over the confinement period showed a slow change rather than an acute, spike like increase (Figure 4B). Physiological studies also conducted in the framework of the MARS-500 program reported a dysregulation of the innate immune system albeit without evidence for a pathological conditions40. Presumably, the alteration of the innate immune state and the high variability of the inflammation related protein expression may be attributed to adaptive processes of the immune system caused by a confined environment containing mainly human associated microorganisms40 on one hand, and the enhanced stress levels during the flight simulation experiments, on the other hand. Indeed, the concentration of cortisol (stress hormone) measured in 24-hours urine was at the high end of a normal range, or slightly above the normal range, for the individuals during the confinement period41. Besides that, the disturbances in biorhythm, the constant temperature and noise level, the limited resources and communications, and boredom resulted in decreased positive emotion, motivation and overall psychological state, observed by decreased brain cortical activity via electroencephalographical (EEG) analysis and multiple questionnaires, which consequently leads to mild stress level42, 43. Therefore, the intra-personal protein variability appears to be significantly dependent on the physiological response. Consequently, the analysis of protein variability in healthy human urine is considered even more complicated than was initially expected. It is worth noticing that the main limitation of the current study is the small number of subjects (n=6), making it difficult to extend the results to the general population. In fact, the primary aim of the MARS-500 program was to model and prepare the manned spaceflight to Mars. Nevertheless, the observations and hypotheses presented above about stress induced immune response and



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consequently, an increased variation of inflammation related proteins are in agreement with multiple observations associated with a mild stress level. It was already accepted that variations in the urinary proteome are extensive as urine contains elimination products of internal homeostasis1.This study using a small, but highly controlled, group of selected individuals was focused on the estimation of the minimal protein abundance variability by avoiding any biases related to diet and sport activities. Even under such conditions the variation within this group remained significant, so one can anticipate that for less controlled individuals the protein abundance variability will be at least similar or moderately enhanced. This emphasizes a challenge for biomarker studies based on comparative analyses between a group of healthy individuals versus a disease. Whereas in plasma an up- or down regulation by a factor of 3-5 can cover a distribution of normal protein concentrations44, for comparative urine studies a factor of 10 in differential expression may still be within the normal range. Another stumbling block in urinary proteomics is the frequent presence of truncated proteins

45

. In

order to distinguish the intact proteins from the protein fragments the relative abundances of several corresponding proteotypic peptides spread along the sequence can be used. If the full-length protein is present, the abundance profiles of the two peptides originating from the same protein should be proportional46. Therefore, for 45 proteins, with different molecular weights, the targeted peptides were carefully selected to cover both N and C termini of the protein sequences within 30% from the beginning and the end, respectively. Interestingly, 32 of them demonstrated a high correlation between the peptides corresponding to the same protein, ensuring the presence of the full protein form in urine. As an example, for the anticoagulant protein Annexin A5, which is likely to be involved in the cell membrane reparation process47, the corresponding SEIDLFNIR and GTVTDFPGFDER peptides demonstrated a high correlation in their abundances, indicating the absence of protein truncation or modification (Supplementary figure 5A). Thus, the method presented here also allows the evaluation of the integrity of the proteins observed in urine and thus, represents an interesting tool to study the proteolysis activity occurring during urine formation.


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CONCLUSION Comparative urinary proteomics studies are challenging due to the high variability of protein concentrations in urine. The analysis of urine collected from healthy, well-controlled, individuals in the frame of the MARS-500 program has been an opportunity to determine the concentration ranges for a large set of proteins. For this purpose, a targeted MS method was developed and optimized for systematic, extended protein screening. The recently introduced strategy based on precise time-scheduling with “on-the-fly” elution time correction was employed to improve the efficiency of PRM analyses. In addition, confidence in the peptide identification was essential during explorative targeted analyses in the absence of stable isotopically labelled internal standards. Thus, in this study, confirmation of peptide identity was ensured by experimental spectral matching with a reference fragmentation pattern. In this context, a high quality manually curated spectral library containing about 7700 peptides, which corresponded to more than 1500 proteins, was generated. This library is a valuable resource both to design PRM methods and to confirm the peptide identity by spectral matching. The performance of the targeted by analysis of urine samples. Showed that 89% out of 1586 targeted peptides were systematic identified in four replicates thus outperforming conventional shotgun approaches in terms of robustness. Among the systematically identified targets 92 % were also reliably quantified. This novel approach allowed 480 proteins to be monitored across fifty-six urine samples longitudinally collected from healthy individuals in the context of the MARS-500 space flight simulation program. The intra-personal protein concentration ranges were determined for this small, but highly controlled, group of individuals and showed on average a difference of one order of magnitude. In addition, the intra-personal protein variability appears to be significantly dependent on the physiological response at the protein expression level. In conclusion, the analysis of protein variability in urine of healthy individuals appeared even more complicated than was initially expected.



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The observed change in intra-individual protein concentration of more than one order of magnitude will require an extended study with a much larger number of individuals to determine a normal distribution.


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Figures:

Figure 1. Workflow and experimental scheme applied to the qualitative and quantitative analysis of a large number of proteins.



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A. The prepared digest of urinary proteins was subjected to off-gel electrophoresis in order to obtain fractions of lower complexity. The LC-MS/MS analysis was perform in DDA mode based on selection of fifteen ions with highest intensity for fragmentation. The MS/MS spectra were assigned with peptide sequences using the Mascot database search. Further annotated spectra were curated using predefined acceptance criteria. The necessary information to design the targeted LC-MS/MS method was extracted from the reference spectral library. The targeted LC-MS/MS analyses in PRM mode were performed on a quadrupole-orbitrap instrument. The generated spectra were assigned to peptide sequences by spectral matching with the reference library. The quantification of peptides with confirmed identity was done based on the fragment ion intensity at the apex of the corresponding chromatogram. B. Schematic illustration of the type of samples and LC-MS/MS analyses performed in the current study.


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A

ETIEIETQVPEK 1.2E+5

√ y₃¹⁺(P)

VQALEEANNDLENK x

1E+6

₀b₂¹⁺ ₀b₃¹⁺ b₂¹⁺ ₀b₄¹⁺ y₁¹⁺ ₀a₂¹⁺ ₀a₄¹⁺ ₀a₃¹⁺ y₂¹⁺ *y₁¹⁺ a₂¹⁺ ₀y₃¹⁺ a₁¹⁺ b₄¹⁺ b₃¹⁺ ₀y₂¹⁺ ₀b₅¹⁺

√x

x x √

y₆¹⁺

600

700

√

y₇¹⁺

√y₈¹⁺

y₉¹⁺ ₀y₉¹⁺

y₁₀¹⁺

0.0E+0 100

200

300

400

500

800

900

√

1000 1100 1200 1300

0E+0

√

√

√

b 31+ oy 31+ y₂¹⁺ b₂¹⁺ y 4 1+ y₅¹⁺ 100

200

300

400

500

600

700

800

900

1000 1100 1200 1300

B # Assigned spectra

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MASCOT ion score Accepted manually – Accepted by MASCOT Rejected manually – Rejected by MASCOT Accepted manually – Rejected by MASCOT Rejected manually – Accepted by MASCOT

Figure 2. Distribution of accepted and rejected MS/MS spectra based on the Mascot ion score. A. Left panel: the manually accepted MS/MS spectrum was assigned to the peptide sequence ETIEIETQVPEK with a low Mascot ion score (17), despite the presence of a complete y-ion series from y1+ to y9+, including the intense y3+ ion corresponding to a favorable fragmentation site. In contrast, (right panel), a rejected MS/MS spectrum, assigned to the peptide VQALEEANNDLENK with a Mascot ion score of 51, exhibiting minimal annotation. B. The distribution of assigned spectra accepted (2909) and rejected (862) by manual curation, originating from a single analysis of the first off-gel fraction (pI range 4.3 ± 0.3), is presented as a function of their Mascot ion score.



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Figure 3. Targeted LC-MS/MS analysis of 1586 peptides in urine samples. Out of 1586 targets/peptides in the pie chart, 82% were systematically identified across four replicates and reproducibly quantified. Furthermore, 7% of the peptides were systematically identified across four replicates but showed high CVs, precluding their quantification, and 9% of the targeted peptides were identified in less than four runs, mostly in three replicates. The remaining 2% represent 39 peptides, which were not identified due to low signals (below the lowest limit of detection) or were not properly monitored due to elution before the “on-the-fly” time correction took place.


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A

Number of proteins

Concentration range, log2(max/min) Log scale

Person 1

Person 2

Intensity

1E+3

1E+3 1.5

Log scale 1E+9

1.8 Time 1.10 points 1.11 1.12 1.14

Person 3 1E+9

Intensity

1E+9

Intensity

1E+9

1E+3 2.7 2.8 Time 2.9 2.12 points2.13 2.14 2.15

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3.4 3.8 3.10 Time 3.11 points 3.12 3.14 3.15

Person 6

Person 5 1E+9

1E+9

Intensity

intensity

B

Intensity

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1E+3

1E+3

4.7 4.8 4.10 Time 4.11 points 4.12 4.14 4.15

1E+3

5.9

5.10 Time 5.11 points 5.12 5.13 5.14

6.3 6.5 6.7Time 6.9 points 6.10 6.11 6.12 6.14

Figure 4. The distribution of intra- individual protein concentration changes. A. Concentration ranges for 309 proteins determined of each individual. The distribution shows on average a difference of one order of magnitude (solid line) with a median of 7 (dash line) between the maximum and minimum signals measured. B. A small subset of proteins showed a very high variability, predominantly for the third and fifth individuals. The expression profiles of



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seven highly variable proteins, associated with inflammation processes, across the confinement period showed a slow change rather than an acute, spike like increase.


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SUPPORTING INFORMATION Supplementary figure 1. Efficiency of peptides separation using off-gel electrophoresis; Supplementary figure 2. Profile of the MS cycle time and the monitoring window duration across chromatographic elution in unfractionated pooled urine sample analysis; Supplementary figure 3. Selected example of a confounded MS/MS spectrum; Supplementary figure 4. Dynamic range of systematically analyzed proteins across four replicates; Supplementary figure 5. Linear plot illustrating the correlation of relative abundance between peptides corresponding to Annexin A5 across urine samples; Supplementary Table 1. List of 38 synthetic SIL peptides; Supplementary Table 2. Qualitative and quantitative analysis of 1586 targeted peptides; Supplementary Table 3. Concentration ranges for 309 proteins for each individual; Supporting Information. Spectra matching high-level code.

AUTHOR INFORMATION Corresponding Author *Correspondence: Bruno Domon, PhD., Luxembourg Clinical Proteomics Center (LCP), LIH, Strassen 1445, Luxembourg. E-mail: [email protected]. Fax: +35226970717

Author Contributions N.K. planned the experiments; performed the experiments, processed and interpreted the data; wrote the manuscript. I.L. provided the patient information and samples; interpreted the results from the physiological aspect. B.D. supervised the project; designed the experimental plan; participated in the writing of the manuscript.



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ACKNOWLEDGMENTS This work was supported by Luxembourg Fonds National de la Recherche (AFR grant # 1364383 to N.K. and PEARL grant to B.D.). N.K. expresses her deep gratitude to Drs Cédric Mesmin and Sébastien Gallien for their daily mentoring and their advice in the manuscript preparation. Suruchi Gutgutia and Sang Yoon Kim are thanked for software developments. We acknowledge Prof. Eugene Nikolaev for his support for this project (Moscow Institute of Physics and Technology); Drs Lyudmila Pastushkova and Igor Dobrokhotov for technical assistance in the sample collection and preparation. We are grateful to Prof. Eric Tschirhart, Drs Jan van Oostrum, Antoine Lesur, Elodie Duriez for helpful discussion and comments.


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Longitudinal Urinary Protein Variability in ... - ACS Publications

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