Quantifying Protein Measurands by Peptide Measurements: Where Do

Dec 12, 2014 - Abstract Image. Clinically actionable quantification of protein biomarkers by mass spectrometry (MS) requires analytical performance in...
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Quantifying Protein Measurands by Peptide Measurements: Where Do Errors Arise? Irene van den Broek,*,†,⊥ Fred P.H.T.M. Romijn,† Nico P.M. Smit,† Arnoud van der Laarse,†,‡ Jan W. Drijfhout,§ Yuri E.M. van der Burgt,∥ and Christa M. Cobbaert† †

Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands ‡ Department of Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands § Department of Immunohematology and Blood Transfusion, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands ∥ Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands S Supporting Information *

ABSTRACT: Clinically actionable quantification of protein biomarkers by mass spectrometry (MS) requires analytical performance in concordance with quality specifications for diagnostic tests. Laboratory-developed tests should, therefore, be validated in accordance with EN ISO 15189:2012 guidelines for medical laboratories to demonstrate competence and traceability along the entire workflow, including the selected standardization strategy and the phases before, during, and after proteolysis. In this study, bias and imprecision of a previously developed MS method for quantification of serum apolipoproteins A-I (Apo A-I) and B (Apo B) were thoroughly validated according to Clinical and Laboratory Standards Institute (CLSI) guidelines EP15-A2 and EP09-A3, using 100 patient sera and either stable-isotope labeled (SIL) peptides or SIL-Apo A-I as internal standard. The systematic overview of error components assigned sample preparation before the first 4 h of proteolysis as major source (∼85%) of within-sample imprecision without external calibration. No improvement in imprecision was observed with the use of SIL-Apo A-I instead of SIL-peptides. On the contrary, when the use of SIL-Apo A-I was combined with external calibration, imprecision improved significantly (from ∼9% to ∼6%) as a result of the normalization for matrix effects on linearity. A between-sample validation of bias in 100 patient sera further supported the presence of matrix effects on digestion completeness and additionally demonstrated specimen-specific biases associated with modified peptide sequences or alterations in protease activity. In conclusion, the presented overview of bias and imprecision components contributes to a better understanding of the sources of errors in MSbased protein quantification and provides valuable recommendations to assess and control analytical quality in concordance with the requirements for clinical use. KEYWORDS: analytical method validation, quantitative Clinical Chemistry Proteomics (qCCP), selected or multiple reaction monitoring (SRM/MRM), stable-isotope labeled peptide or protein, matrix effects, Apo A-I mutations, trypsin digestion efficiency

1. INTRODUCTION Quantitative mass spectrometry (MS) assays for protein biomarkers are typically based on selected or multiple reaction monitoring (SRM or MRM) strategies. Awareness is increasing that such MRM assays need analytical performance criteria which are fit-for-purpose.1−4 In this respect, MS-based protein quantifications for clinical diagnostics demand the highest grade of analytical quality and have been classified as Tier-1 assays1 or as a field named quantitative Clinical Chemistry Proteomics (qCCP).5 In addition, consideration of metrological traceability and the impact of analytical quality on clinical decisions might © XXXX American Chemical Society

necessitate even more-stringent criteria for accuracy (defined as trueness and precision) than the guidelines from regulatory agencies.6 The complexity of typical bottom-up workflows (i.e., from a protein-based sample via peptide-based analysis back to quantification of the protein measurand), nonetheless, introduces numerous specific sources of random (imprecision) and systematic (bias) errors: before (Phase A), during (Phase B), and after (Phase C) proteolysis, eventually extended by Received: September 19, 2014

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Figure 1. Factors that affect bias and imprecision in all phases of targeted MS-based peptide measurement: before (Phase A), during (Phase B), and after (Phase C) proteolysis. Imprecision without IS (σ2AA) consists of absolute variation at the protein (σ2prot) and peptide (σ2pep) level. Imprecision with IS (σ2RR) can consist of absolute variation (σ2) and variation relative to nonanalogous IS (σ2″) or analogous IS (σ2′), depending on the type of IS and time-point of addition (1: peptide, 2 + 3: (extended) peptide, 4: protein). The three phases can be extended by external calibration (Phase D), additionally affecting the variation in the calculated concentration (σ2CC). Bias is caused by absolute or relative loss of protein (δprot) and peptide (δpep), depending on the type and time-point of addition of the internal and/or external standard.

With regard to imprecision, it has been shown that enzymatic digestion (Phase B) is an important contributor to intra- and interlaboratory variability of targeted peptide measurements.9 In addition, the variability across different digestion protocols for various peptides and proteins has been generally

external calibration (Phase D). This four-phase-workflow (Figure 1) implies various standardization strategies, each with a different effect on the normalization of peptide- and protein-related bias and imprecision (together referred to as total error; TE = bias +1.65*imprecision7,8). B

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(δprot_B and δpep_B in Figure 1) is of particular concern when protein quantification is based on (nonanalogous) reference peptides, whether added before, concurrent with, or after digestion.24 In this context, it has been demonstrated that (recombinant) internal or external protein standards provide a better reflection of the true quantity of the target protein.20,23,25 However, it is important to note that minimizing bias of MSbased protein quantification requires considerable additional efforts to demonstrate that the recombinant protein, either used as internal or external standard, correctly mimics the behavior of the native protein.26,27 This identical behavior, or commutability, is crucial to allow metrological traceability of the reference standard to a standard of higher order and, as a consequence, to allow harmonization or standardization of MS-based protein assays.6 In this respect, external calibration with native matrix can provide the required commutability of the reference standard and has been described for single-21,28 or multiple-point29,30 calibration of MS-based protein assays. Nonetheless, metrological traceability of native serum calibrators requires a well-defined (protein) measurand as well as a reference method to assign values traceable to SI units. In addition, even the use of native external calibrators can introduce bias as a result of matrix effects that are not compensated by a (SIL-) internal standard. Here, we present a systematic overview of critical factors for bias and imprecision in a typical LC-MS/MS workflow for absolute quantification of protein biomarkers, using serum apolipoproteins A-I and B as model analytes. To that end, the laboratory-developed test has been thoroughly validated, following general guidance documents from the Clinical and Laboratory Standards Institute (CLSI)31−34 as a prerequisite for application in clinical patient care.

noticed,10,11 whereas the complexity of the digest after proteolysis furthermore challenges the robustness of the peptide measurement by LC-MS (Phase C) within and between laboratories.12,13 In this respect, targeted peptide measurement after enzymatic digestion requires the use of an internal standard that allows normalization for variations before (Phase A), during (Phase B), and after (Phase C) proteolysis. Several types of internal standards have been reviewed in detail14−16 and include stable-isotope labeled (SIL) peptides, full-length proteins, extended peptides (flanked by amino acids from the native protein sequence), or concatenated polypeptides. SIL-peptides are commonly added af ter digestion, but this approach only allows compensation for variability during peptide cleanup and LC-MS/MS analysis (as represented in Figure 1 by σ2′pep_C to define imprecision at the peptide level in Phase C, normalized by an analogous SIL-peptide). Addition of SIL-peptides before digestion, on the other hand, might additionally reduce imprecision caused by liquid handling and peptide decay.17 In Figure 1, the imprecision at the protein level in Phase A and B normalized to a nonanalogous SIL(extended) peptide is presented as σ2″prot_A and σ2″prot_B, respectively, whereas the imprecision at the peptide level in Phase B normalized to an analogous SIL-peptide is presented as σ2′pep_B. The highest level of normalization requires the use of an analogous SIL-protein that might compensate for imprecision of the total workflow, including protein purification and denaturation (σ2′prot_A) as well as protein cleavage (σ2′prot_B), as illustrated in Figure 1. However, a SIL-full length protein is not always available, and moreover, contradictory effects of SILprotein or extended peptide internal standards on imprecision have been observed for different methodologies and protein targets.13,18−20 The effect of the internal standardization approach on imprecision can be deduced from the difference between the variation in absolute (σ2AA) and relative MS response (σ2RR). In addition, when external calibration is applied to translate the peptide measurement into a protein concentration (Phase D), the effect of external calibration on imprecision can be derived from the difference between the variation in relative MS response (σ2RR) and calculated protein concentrations (σ2CC). External calibration can have a positive or negative effect on imprecision, for example, as a result of variations during preparation of the standard solutions. An evaluation of interlaboratory imprecision of MS-based protein quantification demonstrated, for example, that on-site preparation of the calibration curve had more impact on assay imprecision than the sample preparation, including trypsin digestion.21 In the same study, it was also shown that the use of single-point calibration with native serum reduced the interlaboratory variability compared to external calibration with spiked matrices. With regard to bias, it is clear that enzymatic digestion contributes to the complexity of LC-(MRM)-MS measurement (Phase C), in particular because of interferences in MRM ion transitions and ion suppression caused by other matrix components.13,22 Hence, minimizing bias at the peptide level in Phase C (represented as δpep_C in Figure 1) is key before considering trueness of the standardization approach. Such standardization can be based on an internal or external reference standard and, like the normalization strategies, can include calibration with (nonanalogous) peptides or extended peptides, or (analogous) intact proteins.23 Bias as a result of incomplete digestion or peptide decay during proteolysis

2. MATERIALS AND METHODS Reagents and Chemicals

Recombinant, SIL-Apo A-I (ISprot) with [13C615N2]-Lys and [13C615N4]-Arg incorporated amino acids was provided as lyophilized protein (10 μg/vial) by Sigma-Aldrich (SigmaAldrich, St. Louis, MO, U.S.A.). SIL-peptides (ISpep) were synthesized in-house for five and four signature peptides from Apo A-I and Apo B, respectively, by incorporation of [13C6, 15 N1]-Val or [13C7, 15N1]-Leu (Sigma-Aldrich). All solvents and reagents used during sample preparation and LC-MS/MS analysis were from the highest analytic grade available and were freshly prepared on each day of analysis. DL-dithiothreitol (DTT), iodoacetamide (IAA), and urea were purchased from Sigma-Aldrich (Zwijndrecht, The Netherlands). Ammonium bicarbonate and formic acid were from Fluka (Buchs, Switzerland). Sequencing-grade modified porcine trypsin (LOT 81313) was purchased from Promega (Madison, WI, U.S.A.), and methanol absolute from Biosolve (Valkenswaard, The Netherlands). Serum Samples

In total, five normo- and three hypertriglyceridemic sera were obtained from the Dutch Foundation for Quality Assessment in Medical Laboratories (SKML, Nijmegen, The Netherlands, www.skml.nl/verificatiematerialen/lipiden-trueness-verificator). The preparation and application of three normotriglyceridemic sera (NTG1 (LOT 2009.0361), NTG2 (LOT 2009.0362), and NTG3 (LOT 2009.0363) for use as external serum calibrators have been described previously,17,30 whereas the additional normo- and hypertriglyceridemic sera (NTG4 (LOT C

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bias, and total allowable error (TEa) were, respectively, 5.0, 5.6, and 13.7% for Apo A-I, and 5.3, 9.0, and 17.4% for Apo B.

2013.2061), NTG5 (LOT 2013.2062), HTG1 (LOT 2013.2063), HTG2 (LOT 2013.2064), and HTG3 (LOT 2013.2065)) were prepared accordingly. The five normotriglyceridemic sera were value-assigned for Apo A-I and Apo B by the Northwest Lipid Metabolism and Diabetes Research Laboratories (Seattle, WA, U.S.A.) to guarantee traceability to WHO-international reference material SP1-01 and SP3-07, respectively.35,36 Apo A-I and Apo B concentrations in the three hypertriglyceridemic sera were value-assigned by immunoturbidimetric analysis (ITA) on a Cobas Integra 800 (Roche Diagnostics, Mannheim, Germany). Therefore, HTG1, HTG2, and HTG3 were measured in duplicate on three different days with calibration based on the value-assigned normotriglyceridemic sera. In addition, clinical left-over patient sera with cholesterol levels ± 10% in calculated concentrations based on quantifier or qualifier were identified, except for a small number of sera with low levels of Apo A-I (10% was observed in only two (NTG) sera. K

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Figure 8. Response for SIL-AKPALEDLR (A) and SIL-DYVSQFEGSALGK (B) from ISprot (left, in red) or ISpep (right, in blue), normalized to the daily average of the IS response in the calibration samples and HTG1, HTG2, and HTG3. To exclude bias from outliers, only samples with less than 10% deviation between two duplicates were included as the average of both samples. The black lines indicate the average normalized IS response ±2*SD across all included clinical sera. The black circles indicate the average normalized IS response ± SD in the sera used for external calibration (NTG1, NTG2, NTG3, NTG4, and NTG5). NTG4 is the sample with the highest Apo A-I concentration.

as well as SIL-Apo A-I (Figure 7), suggesting a compensation by SIL-Apo A-I for the observed specimen-specific alterations in digestion efficiency. SIL-Apo A-I furthermore demonstrated a significant improvement in CVdd and TE due to external calibration (% D) compared to the use of SIL-peptide internal standards (Figure 2 and 4). In addition, with the use of ISprot instead of ISpep, an apparent reduction in the (negative) bias for quantification of Apo A-I was observed in samples with high Apo A-I concentrations, such as the calibrator NTG4 and clinical sample NTG_s11 (Figures 2, 3, and 5). A closer look at the ISprot response in the sera used for calibration revealed that the SIL-peptide recovery was consistently lower in calibrator NTG4 than in the other sera used for calibration (Figure 8). The lower response for SIL-peptides from SIL-Apo A-I in sample NTG4 suggests incompleteness of digestion, at least in this sample, and agrees with the observed negative bias for the same sample in the calibration curve based on ISpep (Figure 3). The response of SIL-peptides from Apo A-I that did not require protein cleavage (i.e., ISpep) was, on the other hand, constant across all sera used for external calibration (Figure 8) and indicates that the observed reduction in response of peptides from SIL-Apo A-I was not related to matrix effects on LC-MS/ MS analysis (Phase C) but, most likely, to matrix effects on

On the basis of the number of observed peptide-specific outliers in this study with 100 clinical sera, it is strongly recommended to include at least two, and preferably three, peptides per protein to monitor specimen-specific effects on absolute peptide recovery. INTERNAL STANDARD TYPE AND MATRIX EFFECTS. Because a SIL-full length protein is subjected to the same denaturation, reduction, alkylation, and digestion conditions as the endogenous protein, compensation for digestion variability is expected. Nonetheless, the use of SIL-Apo A-I did, in our experiments, not reduce the imprecision of targeted peptide measurements when compared to the use of SIL-peptide internal standards (Supplementary Figure 2B,F). On the other hand, the use of an intact SIL-protein demonstrated to correct for specimen-specific alterations in digestion efficiency associated with altered protease activities that would otherwise cause a significant negative bias. For example, quantification of Apo A-I with VQPYLDDFQK and DYVSQFEGSALGK in the samples NTG_s22 and NTG_s44 provided a negative bias compared to quantification based on the other peptides with ISpep though not with ISprot (Figure 6A,C). Accordingly, the observed increase in the fraction of peptides with missed cleavages (i.e., VQPYLDDFQKK and DSGRDYVSQFEGSALGK) in the same samples was observed for endogenous L

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Journal of Proteome Research protein unfolding and/or protein cleavage (Phase A and B). Interestingly, the ISprot response among all clinical sera showed a trend to lower peptide recovery in samples with elevated Apo A-I concentrations (Figure 8). Although these observations suggest enhanced normalization by SIL-Apo A-I for incomplete denaturation or digestion (δprot_A or δprot_B in Figure 1) of endogenous Apo A-I, it is worth noting that the fraction of peptides with missed cleavages, e.g., DSGRDYVSQFEGSALGK, VQPYLDDFQKK, and VSFLSALEEYTKK, differ between endogenous and SILApo A-I (Figure 7), supporting the suggested differences in denaturation and protein cleavage between endogenous and SIL-Apo A-I. Effectiveness and reproducibility of protein unfolding, therefore, require specific attention to improve the stoichiometry of peptide release (from recombinant (SIL) as well as native proteins) and, potentially, improve accuracy of protein quantification.

reduced bias and CVdd, and allowed accurate quantification within the minimal total allowable error. Because the systematic evaluation of between-sample effects by analysis of 100 patients sera pointed toward matrix effects on protein denaturation and cleavage as the key to further reduce bias, and sample preparation before digestion as the main contributor to imprecision, future research will focus on optimization of denaturation and digestion conditions to minimize matrix effects on the abundance of the targeted peptides from Apo A-I and Apo B and, consequently, improve linearity. Promising strategies to optimize protein unfolding include the use of alternative denaturants such as sodium deoxycholate that has demonstrated to yield higher peptide recoveries for various apolipoproteins.11,29,38 In addition, protein cleavage might be improved by the use of multiple rounds of trypsin,23,28 a combination of Lys-C and trypsin,39 or by the use of immobilized trypsin.37,40 In order to facilitate clinical application to large sample cohorts and to potentially further reduce imprecision, protein unfolding and cleavage will be further optimized with simultaneous automation of liquid handling and digestion procedures.

4. CONCLUSIONS Our detailed validation of the analytical performance for quantification of Apo A-I and Apo B, using different internal and external peptide and protein standards, short and long digestion times, multiple signature peptides from the same protein, and 100 clinical sera, provided a systematic overview of the most important contributors to bias and imprecision in a typical LC-MS/MS workflow for protein quantification. Validation of bias and imprecision according to CLSI guidelines demonstrated that true and precise, clinically actionable, quantification of protein biomarkers is feasible, even considering highly stringent analytical quality criteria based on biological variation. Nonetheless, two major sources of bias have been highlighted that have so far been underestimated in MS-based protein quantification: (1) matrix effects on digestion efficiency and (2) modifications and mutations in the peptide sequence. It was, for example, demonstrated that although peptide recovery from protein digestion can be highly reproducible within the same sample, nonlinear relations between protein concentration and peptide response can occur when the signature peptide is not consistently liberated from one sample to another. In addition, a comparison of quantitative results between up to ten different peptides from the same protein could identify various peptidespecific biases as a result of mutations or modifications in the peptide sequence, as well as specimen-specific alterations in cleavage efficiency. Our study furthermore demonstrated that the major advantage of an intact SIL-protein internal standard is not the normalization for within-sample variability, but the normalization for bias as a result of matrix effects on digestion efficiency. Illustratively, the lower absolute response of SILpeptides released from SIL-Apo A-I in a small number of sera with relatively high Apo A-I concentrations (including one serum used for calibration) well-agreed with the observed negative bias in the same sera for Apo A-I quantification with SIL-peptides. This compensation of SIL-Apo A-I for matrix effects on peptide recovery particularly improved the linear correlation between protein quantity and MS signal. In addition, peptide-specific biases that were observed with the use of SIL-peptides in 2 out of 100 sera were not observed with the use of SIL-Apo A-I, further supporting the compensatory effect of SIL-Apo A-I for poor digestion efficiency as a result of altered protease activity. As a result, the use of SIL-Apo A-I

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ASSOCIATED CONTENT

S Supporting Information *

Supplemental data and methods as noted in the text. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +31 71 526 6257. Fax: +31 71 5266753. Present Address ⊥

P.O. Box 9600, 2300 RC Leiden, the Netherlands.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Kevin Ray from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO) for kind supply of SIL-Apo A-I. We furthermore acknowledge Martin Haex (Agilent Technologies, Amstelveen, The Netherlands) for his support during MS method development, and Andrew Few and Rainer Nietsche (Agilent Technologies, Waldbronn, Germany) for their support with the experiments on the Bravo liquid handling platform.

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ABBREVIATIONS

AA:Absolute MS response area; Apo A-I:Apolipoprotein A-I; Apo B:Apolipoprotein B; CC:Calculated concentration; CLSI:Clinical and Laboratory Standards Institute; CVdd:Between-day coefficient of variation; CVwr:Within-run coefficient of variation; HTG:Hypertriglyceridemic; IQR:Interquartile range; IS:Internal standard; ISpep:Stable-isotope labeled internal standard peptide(s); ISprot:Stable-isotope labeled fulllength protein internal standard; ITA:Immunoturbidimetric analysis; MRM:Multiple reaction monitoring; NTG:Normotriglyceridemic; RR:Relative MS response; SI:Système International; SIL:Stable-isotope labeled; TE:Total error; TEa:Total allowable error M

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