Quantification of Transferrin in Human Serum Using Both QconCAT

Publication Date (Web): September 27, 2013 ... Transferrin levels obtained by both internal standards give overlapping, or nearly overlapping, uncerta...
1 downloads 0 Views 877KB Size
Download PDF
Article pubs.acs.org/ac

Quantification of Transferrin in Human Serum Using Both QconCAT and Synthetic Internal Standards Tyler A. Zimmerman,*,†,‡ Meiyao Wang,†,‡ Mark S. Lowenthal,† Illarion V. Turko,†,‡ and Karen W. Phinney† †

Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899 United States Institute for Bioscience and Biotechnology Research, 9600 Gudelsky Drive, Rockville, Maryland 20850 United States

‡

S Supporting Information *

ABSTRACT: Transferrin, an iron transport protein, is a clinically important biomarker in diseases such as iron-deficiency anemia. Current diagnostic methods for transferrin levels lack quantitative accuracy, suggesting the need for alternative approaches like LC-MS with isotope-labeled peptides as internal standards. Besides solid-phase synthesis, isotope-labeled peptides are also generated by a method called QconCAT where peptides are expressed from DNA in the presence of heavy isotope media. After evaluation of the expressed QconCAT, this study compares transferrin levels obtained by synthetic peptides versus QconCAT peptides as internal standards. Transferrin levels obtained by both internal standards give overlapping, or nearly overlapping, uncertainty values and are near ≈200 mg/dL of transferrin in human serum. Close agreement between the two methods suggests that the quantitative values are reasonable. Using QconCAT and synthetic peptides in parallel gives a refined focus on method development, and the resulting methods should be applicable to other clinically relevant proteins.

T

selectivity.14−17 While transferrin has recently been quantified in serum by LC-MS,17 the reported work utilized a single peptide for quantification. In the current study, we utilize a variety of labeled peptides and compare different labeling approaches. To quantify proteins in serum using mass spectrometry, the protein is first digested with trypsin and the tryptic peptides are quantified as measures of protein concentration. The isotopelabeled peptides as internal standards are generated in several ways, including digestion of labeled intact proteins expressed in culture,18 solid-phase peptide synthesis,19 or QconCAT methods.20,21 First, digestion of intact proteins is advantageous in that the protein internal standard is almost chemically identical to the analyte. Implementation of this method is hindered by expression difficulty and by variable posttranslational modifications including disulfide linkages.22 Second, isotope incorporation by solid-phase synthesis yields isolated peptides that are selected to avoid disulfide linkage sites and other PTMs. The disadvantage is that synthetic peptides do not account for digestion variability, and adherence of the small peptides to container walls has significant effects, leading to unexpected lower quantification values.19,23 Third, the QconCAT approach provides greater functionality by generating many peptides for a given protein at once, where multiple

ransferrin (≈80 kDa) is a clinically relevant glycoprotein that transports iron in the circulatory system,1 and its levels are affected in iron deficiency anemia,2 iron overload diseases,3 iron poisoning,4 and hemochromatosis type 1 (excessive iron absorption into tissues).5 Transferrin levels increase to compensate for lack of iron in iron-deficiency anemia6 but are conversely lower in iron overload disorders.7 When symptoms of such disorders are present, physicians specifically test for transferrin saturation in blood using the total iron-binding capacity (TIBC) test8,9 that indirectly and often inaccurately10−12 infers transferrin levels by colorimetric detection of iron. The accuracy of the TIBC test is further hindered by nonspecific binding of iron to other proteins like albumin. In comparison, direct measurement of transferrin levels was judged to be a better biomarker6 and is also diagnostically more robust than levels of ferritin, an iron storage protein. Therefore, transferrin levels in human serum have been measured by chemiluminescence,6 immunochemistry,9 and other clinical techniques.13 The accuracy of these routine clinical measurements exceeds that of the TIBC test, but they still report large standard deviations of about 40 to 50 mg/dL, which is not ideal when comparing normal levels of transferrin (≈200 mg/dL) to levels in iron-deficiency anemia (≈310 mg/ dL)6 and levels in iron overload hemochromatosis (≈112 mg/ dL).7 Given the limitations of existing methods, the investigation of an alternative approach is warranted. Mass spectrometry is attractive for this purpose because it removes the need for antibodies and gives low detection limits and good © XXXX American Chemical Society

Received: July 26, 2013 Accepted: September 27, 2013

A

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

method, both methods are used here in concert to provide further insight into method development.

peptides give confidence to the quantitative results. The shorter protein sequence of the QconCAT avoids the higher order structure of intact proteins, while still providing structural similarity to the analyte protein.24 Thus, the QconCAT approach accounts for some digestion variability, but whether the QconCAT is digested in the same manner as the analyte protein will depend on the peptides chosen as internal standards. Optimizing QconCAT peptides as internal standards requires an evaluation process, to determine if they accurately mirror the behavior of the analyte. The QconCAT approach is so named because peptides are expressed from a custom DNA sequence, where DNA sequences corresponding to the peptides are concatenated. Then, the expressed protein construct contains isotope-labeled peptides concatenated to each other, which are then released by trypsin digestion. The “Q” in QconCAT refers to quantitation or to the produced Q-peptides that act as internal standards.21 The QconCAT technology was originally designed for global proteomics where the expressed construct contains peptides from many different proteins. In this study, we alter this process so that the QconCAT construct contains peptides only from the target protein, transferrin. We also add flanking regions to the expressed construct. Flanking regions extend the amino acid sequence of each peptide by four residues in each direction, as the sequence would have occurred in the original protein.25−27 The amino acids directly surrounding the trypsin cleavage site are known to influence digestion efficiency, and a previous experiment on a QconCAT construct both with and without flanking regions showed the advantages of flanking regions.26 Flanking regions help the digestion efficiency of the QconCAT to more effectively mirror the digestion efficiency of the analyte, by helping trypsin to recognize its binding sites as they would occur in the intact protein. Trypsin digestion then separates the peptides from the flanking regions, releasing the labeled peptides as internal standards. This study compares traditional solid-phase synthesis to the QconCAT approach. While such a comparison was previously made,19 we update this comparison by adding flanking regions to the QconCAT and apply a quality control process to the calibration curves. We also alter the synthetic peptide protocol by adding concentrated bovine serum albumin to coat the container walls, which is known to prevent sample loss.23 These alterations allow updated comparisons between synthetic and QconCAT approaches. For either type of internal standard, eq 1 relates the chromatographic peak area ratio to the mole ratio, where A is analyte and S is internal standard. This equation is used to construct calibration curves and to quantify transferrin levels in human serum samples. pmolA peak areaA = pmol S peak areaS

■

EXPERIMENTAL SECTION Optimization of Multiple Reaction Monitoring (MRM). Purified human transferrin (Sigma Aldrich, St. Louis, MO) was diluted at 1.0 mg/mL in 50 mol/L ammonium bicarbonate buffer at pH 7.8. Trypsin digestion was done starting with addition of RapiGest surfactant (Waters Corp. Milford, MA). Before trypsin digestion, samples were alkylated and reduced to remove disulfide bonds, using 5 mmol/L dithiothreitol at 60 °C and 15 mmol/L iodoacetamide at room temperature. Samples were incubated overnight at 37 °C with porcine sequencing grade trypsin (Promega Corp., Madison, WI). Acid treatment was done at a 100 mmol/L final concentration of HCl. After centrifugation and vacuum drying to remove solvent, each sample was reconstituted in 0.1% (v/v, volume fraction) formic acid in water and placed in an autosampler vial for subsequent LC-MS measurements. A series of LC-MS runs were performed on the transferrin digest. The LC-MS system was an Agilent Technologies 1200 series coupled to an Applied Biosystems API 5000 triple quadrupole mass spectrometer with a QJet ion guide. The chromatographic mobile phase consisted of 0.1% formic acid in water mixed with 0.1% formic acid in acetonitrile (Burdick & Jackson, Muskegon, MI). The column was equilibrated over 2 h at a flow rate of 0.2 mL/min at 5% organic phase and then ramped to 95% over 65 min and then back to 5% over 5 min using a Supelco C18 HPLC column (2.1 mm × 150 mm, 3 μm packing) at a column temperature of 45 °C. The first LC-MS run was done in MS mode on the transferrin digest to detect the most intense tryptic peptides. The peptide sequences of the detected parent masses were used to create a list of possible multiple reaction monitoring (MRM) transitions. This calculation was done using in-house R language software. The second LC-MS run was set in MRM mode to detect possible fragmentation transitions (30 ms dwell time). The 2 to 3 most intense MRM transitions for each peptide were collated in a method. A series of MRM runs were done on the transferrin digest over a gradient of collision energies (in volts) of 10, 12, 15, 20, 25, 30, 35, 40, 50, and 60 to optimize the collision energy for each MRM transition. This was followed by another series of MRM runs at the optimized collision energies but over a gradient of declustering potentials of 40, 50, 60, 70, 80, 90, 100, 110, and 120 (in volts) to optimize this parameter. Isotope-Labeled Internal Standards. Synthetic isotopelabeled peptides were obtained commercially (GenScript, Piscataway, NJ). These two synthetic peptides are sufficient to provide comparisons to QconCAT measurements. The following peptides (HSTIFENL*ANK and DSAHGFL*K) were synthetically labeled with six 13C atoms on a leucine side chain (L*) to create a 6 Da mass shift. Stock solutions of these peptides were made in 50 mmol/L ammonium bicarbonate and aliquoted. The amino acid sequence of the QconCAT construct was designed to contain six tryptic peptides of transferrin (HSTIFENLANK, DSAHGFLK, SASDLTWDNLK, DGAGDVAFVK, APNHAVVTR, KPVDEYK; see Figure 1), along with flanking regions25,26 of four amino acids on each side. The QconCAT amino acid sequence was coded into the corresponding DNA sequence and incorporated into the pET21a expression vector, with codon optimization for E. coli

(1)

While previous studies used QconCAT peptides to measure proteins in homogenized tissue samples,24,25,28 we present what seems to be the first application of QconCAT peptides to measurements of proteins in human serum. This study quantifies transferrin in human serum using QconCAT and synthetic peptides in parallel. If the obtained transferrin levels are in agreement between the two methods, this will add confidence to the quantitative values.29 The same experiment provides updated comparisons between the two isotopelabeling methods. While there are advantages to each labeling B

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

QconCAT construct was aliquoted and used below as an internal standard for transferrin. Sample Preparation. To prevent sample losses to the container, a concentrated bovine serum solution was added to coat the walls of the centrifuge tubes when preparing calibration solutions. It was not necessary to add bovine serum albumin to the human sera that already contain albumin. Using the same isotope-labeled peptides, two human serum samples were prepared, one from a single individual (Bioreclamation, Westbury, NY) and a pooled serum sample (NIST Standard Reference Material 909c). These sera were taken from healthy donors and should have normal transferrin levels. A 100-fold dilution of 20 mg of each serum was performed gravimetrically in 50 mol/L ammonium bicarbonate buffer. Constructing Calibration Curves. Purified human transferrin (Sigma Aldrich) as the calibrant was diluted so that the LC-MS injected transferrin concentrations were 4, 2, 1, 0.4, 0.2, and 0.04 pmol/μL to create a calibration curve. The isotopelabeled peptides were then added at a constant final concentration of 0.4 pmol/μL to all samples and six calibrant solutions. For the QconCAT internal standards, a separate calibration curve was created in the same way. The precise concentration of the QconCAT construct was not known, so a fixed mass of the QconCAT construct solution (20 mg) was added to each of the calibrants and serum samples. All the above dilutions were done gravimetrically to account for pipetting error. The recorded masses of pipetted volumes were used to correct the final quantitative calculations. Trypsin digestion was done on the sera and calibrants as described above. To fully dissolve the resulting serum digest, sonication was done in an ultrasonic bath for 20 min. This was followed by sample cleanup using C18 ZipTip pipet tips (Millipore Corp., Billerica, MA) and vacuum drying to remove solvent. The serum samples were reconstituted in 0.1% formic acid in water and placed in autosampler vials. LC-MS/MS Analysis. Finally, LC-MS/MS analysis was performed on the calibrants and serum samples using the above LC conditions and optimized MRM transition parameters for the unlabeled analyte, QconCAT, and synthetic internal standards. Data Analysis and Calculations. Peak area integrations were done manually on each MRM peak using Analyst 1.5 software (Applied Biosystems). A spreadsheet was used to calculate the peak area ratio and plot this against the mole ratio of transferrin in the calibrants. This created a calibration curve of six points for each MRM transition, which was fitted with a linear trendline. The equation of this trendline was used to determine transferrin concentration in the serum samples.

Figure 1. Amino acid sequence of the QconCAT construct. The six Qpeptides are highlighted in red. The intervening sequences are flanking regions of four amino acids on both sides of each Q-peptide. The green methionine corresponds to a start codon in the DNA sequence, which is necessary for gene expression.

cells. The DNA sequences corresponding to the above peptides were synthesized and placed into plasmids commercially (Biomatik, Cambridge, Ontario) and were reconstituted to 5 ng/μL in nuclease-free water. A total of 10 ng of the plasmid was added to E. coli cells and placed on ice. To express DNA into protein, the protein was overexpressed using the One Shot BL21 (DE3) Competent E. coli Kit (Invitrogen, Grand Island, NY). The M9 minimal media was used containing 1 g/L of 15 NH4Cl (Cambridge Isotope Laboratories, Andover, MA) as the only nitrogen source for E. coli culture. Initial inoculation was done for 25 mL of media, and the cell culture was grown for 12 to 14 h at 37 °C. Cells were collected by centrifugation at 5000g for 15 min and then washed 3 times in 10 mL of fresh 15 NH4Cl-containing M9 media. Cells were then transferred to 500 mL of fresh 15NH4Cl-containing M9 media and kept growing at 37 °C until the UV−vis optical density reached 0.6 to 0.8 at 600 nm. Protein overexpression was induced by 0.5 mmol/L of IPTG. After 4 h of growth, the cells were harvested by centrifugation at 5000g for 15 min and resuspended in Bug Buster extraction reagent with 0.05% (v/v) benzonase (EMD Biosciences, Darmstadt, Germany). After sonication, the inclusion body containing the QconCAT protein was collected by centrifugation at 7500g for 30 min. The inclusion body containing the QconCAT protein was dissolved in 8 mol/L urea, 50 mmol/L sodium phosphate buffer, pH 8.0, containing 0.3 mol/L NaCl. Protein purification based on a 6xHis-Tag was achieved on a Ni-NTA Agarose column (Qiagen, Valencia, CA). The washing and elution buffers were 8 mol/L urea and 50 mmol/L sodium phosphate (pH 8.0)/0.3 mol/L NaCl, containing 40 and 300 mmol/L imidazole, respectively. To verify protein expression of the QconCAT, a 15% SDSPAGE gel was run on the expressed QconCAT construct, which for verification purposes was expressed without using heavy isotope media. This was followed by in-gel trypsin digestion of the gel spot and MALDI-MS detection on a 4700 Proteomics Analyzer MALDI TOF/TOF instrument (AB Sciex, Framingham, MA). After expression was confirmed, the expression was repeated on a larger scale in the presence of 15N media. Incorporation of 15 N into the QconCAT was determined to be >99% using a MALDI-TOF spectrum of a representative QconCAT peptide 15N-DSAHGFLKVPPR which matched the isotopic distribution for >99% incorporation of 15N in this peptide as calculated by the NIST Isotope Enrichment Calculator v1.1 (http://www.nist.gov/mml/bmd/ bioanalytical/isoenrichcalc.cfm).30 Buffer exchange was done from 8 mol/L urea by an Amicon molecular weight centrifugal filter (Millipore, Billerica, MA) so that the QconCAT construct was dissolved in 0.5 mL of 50 mmol/L ammonium bicarbonate buffer. To determine a rough concentration value of total protein, a Bradford protein assay was done using a DC Protein Colorimetry Assay kit (Bio-Rad Laboratories, Hercules, CA). The UV−vis optical density was taken and gave the total protein level at 0.2 mg/mL. The

■

RESULTS AND DISCUSSION To verify QconCAT expression, an SDS-PAGE gel was run after purification and shows the 11.8 kDa QconCAT construct at the expected gel band location (Figure S-1, Supporting Information). After in-gel digestion of this gel spot, a MALDIMS spectrum shows masses that match predicted masses from a theoretical digest of the QconCAT construct (Figure S-2, Supporting Information). An accompanying table identifies several of the Q-peptide peaks in the MALDI-MS spectrum. Also, the 15N isotope incorporation into the QconCAT was determined by MALDI-MS to be >99% (Figure S-3, Supporting Information). Thus, expression of the QconCAT C

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

Figure 2. Calibration curves of representative MRM transitions from the QconCAT data. Panels A, B, and C show good linearity, while Panel D shows poor linearity and a vastly different y-intercept. Thus, the HSTIFENLANK peptide in panel D was determined as unsuitable for quantification, but the other three peptides give ideal calibration curves.

μL. Using the QconCAT internal standards, calibration curves resulted from several different Q-peptides in Figure 2. To save space, the fifth peptide DSAHGLFK is not included in Figure 2 but is shown later in Figure 3. In Figure 2, representative calibration curves from three different Q-peptides show similar behavior and give linear trendlines (R2 > 0.98), whereas the

DNA into protein was successful, and the six Q-peptides (Figure 1) can be evaluated for use as internal standards for transferrin. The Q-peptide evaluation starts by verifying reasonable levels of detection. Of the six Q-peptides, five were sufficiently detectable in representative mass chromatograms (Figure S-4, Supporting Information), with the peptide elution order matching theoretical predictions (Figure S-5, Supporting Information). 31 The sixth Q-peptide of the sequence KPVDEYK had no QconCAT signal present at the lowest two concentration points on the calibration curve. Possible explanations for absence of signal for this peptide include a low digestion efficiency due to a proline residue next to the tryptic cleavage site (Figure 1), which is known to inhibit trypsin from binding.32,33 This peptide was included in the QconCAT because it showed good signal at higher concentrations in the initial optimization procedure. On the basis of the absence of signal at lower concentrations, it is apparent that such peptides containing a proline N-terminal to the basic site should be excluded when designing a QconCAT. For the remaining detectable Q-peptides, the calibration curves must be evaluated to determine if they result in linear calibration curves within the targeted concentration range. Calibration curves were constructed in the same way for both QconCAT and synthetic internal standards. The midpoint was chosen at 0.4 pmol/μL because this is an approximate transferrin concentration in human serum, 40 pmol/μL6 at 100-fold dilution, which gives an appropriate concentration for LC-MS injections. The end points of the calibration curve encompass 2 orders of magnitude between 0.04 and 4 pmol/

Figure 3. Overlay of MRM calibration curves, which were constructed from four different Q-peptides. The two MRM transitions of DSAHGLFK (abbreviated DSA) give calibration curves with lower slope values than the other peptides, suggesting a low digestion efficiency of this peptide from the QconCAT construct. Thus, only APNHAVVTR, DGAGDVAFVK, and SASDLTWDNLK were used for quantification. D

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

would adversely affect the quantitative results. At the same time, the QconCAT evaluation identified three Q-peptides that are suitable to measure transferrin and pointed out factors that should be examined when using QconCAT peptides. In parallel with QconCAT development, the synthetic isotope-labeled peptides DSAHGLFK and HSTIFENLANK were used to measure transferrin in human serum. These synthetic peptides provide a point of comparison against the results obtained by the QconCAT peptides. Therefore, sample preparation for LC-MS was done in a similar manner for both synthetic and QconCAT internal standards, as explained in the Experimental Section, to facilitate comparison of results. Table 2 lists all MRM transitions used for quantification, for both QconCAT and synthetic peptides. This table represents the optimized MRM method and is filtered down to suitable Qpeptides as explained above and to synthetic peptides that resulted in the best signal. Therefore, all MRM transitions in Table 3 were used to measure transferrin concentrations in serum. Transferrin levels were determined in two different serum samples, a healthy individual serum and a healthy pooled serum, using both synthetic and QconCAT peptides. The results are in Table 3. Some outlier data points were removed by a Q-test and in all cases because the serum sample resulted in low signal for a particular MRM transition. There are three reasons for an outlier: it is erroneous because the measurement failed to produce reasonable levels of signal, the regression model is incorrect, or the outlying data point is real but is just an improbable occurrence.35 Evidence is presented in the Supporting Information that the removed outliers are under the category of low signal. Although a calibration curve may have passed the above QconCAT evaluation, if the serum sample signal is low and falls outside the calibration range for an MRM transition, the data point must be removed. Of the original 26 data points in Table 3, only four low signal points were removed, and of those four, the three lowest-signal data points were from the synthetic DSAHGFLK peptide, revealing that some MRM transitions of this peptide are detectable in serum samples and others are not. Given that the main origin of low signal data points is the DSAHGFLK peptide, this suggests that larger numbers of serum samples can be quantified successfully if one controls for a high percentage of outliers from this peptide. After removal of low signal data, the remaining data resulted in the transferrin levels in Table 3. Interestingly, the Q-peptide SASDLTWDNLK in Table 3 gives higher transferrin values than those from other Qpeptides. This occurs for both MRM transitions of SASDLTWDNLK, so that interference from other ion species simultaneously occurring for both MRM transitions is an unlikely cause. Other sources of variation such as poor chromatography and inconsistent peak integration are also unlikely causes, as higher quantitative values for this peptide are seen over different LC-MS runs from two serum samples.36 One potential cause is the presence of an unexpected PTM or fragmentation pathway. However, it was decided to include SASDLTWDNLK in the results as the chromatography was ideal (Figure S-4, Supporting Information), no bias sources were evident, and the quantitative results reasonably agreed with the other Q-peptides. Table 3 shows encouragingly close results in terms of transferrin levels between QconCAT and synthetic peptides. For the pooled serum sample (NIST Standard Reference Material 909c), transferrin levels were obtained at (192.2 ±

fourth Q-peptide HSTIFENLANK in Figure 2D gives a calibration plot that is not sufficiently linear (R2 = 0.86) and a y-intercept of −2.1. This y-intercept differs from the other calibration plots that have y-intercepts near the origin. A separate MRM transition from the same HSTIFENLANK peptide produced a y-intercept of −6.4 (Figure S-6, Supporting Information), and such variability points to the unsuitability of this peptide for quantification. Given that other QconCAT peptides in the same experiment show linear behavior and the synthetic version of HSTIFENLANK gives linear plots (Figure S-7, Supporting Information), it is unclear why the QconCAT version gives a variable calibration curve. Because large differences are seen between two MRM transitions of the same Q-peptide, the cause might relate to the structure of the QconCAT construct. In fact, an aspartic acid residue at position P2′ relative to the cleavage site,34 as found in the C-terminal flanking region of HSTIFENLANK, is known to inhibit trypsin binding.17 So far, of the six Q-peptides, KPVDEYK was eliminated due to absence of signal and HSTIFENLANK was eliminated because of nonlinear calibration curves and variable y-intercepts, leaving four Q-peptides. These four remaining Qpeptides must be evaluated to determine if their calibration curves give reasonable slope values. A plot overlaying two MRM transitions from each of the four remaining Q-peptides is in Figure 3. Good linearity is seen for all 8 calibration plots (R2 > 0.98), but six MRM transitions from the three Q-peptides APNHAVVTR, DGAGDVAFVK, and SASDLTWDNLK have similar slopes, whereas the slopes of the two MRM transitions from DSAHGFLK are consistently lower. The interpretation of slope on this plot is that a higher slope means a higher digestion efficiency; see eq 1. Given that all Q-peptides were present in equimolar amounts in the QconCAT construct before digestion, the lower slope for DSAHGFLK points to a low digestion efficiency for this peptide. The lower digestion efficiency may result from a double proline, PP, sequence in the C-terminal flanking region for this peptide (see Figure 1) or perhaps its location within the QconCAT sequence inhibits trypsin binding by some unidentified mechanism.33 Thus, of the six total Q-peptides, three were eliminated for the reasons listed in Table 1, while the other three appear to be suitable internal standards to measure transferrin. Table 1. Summary of Which QconCAT Peptides Were Used to Quantify Transferrin in Serum QconCAT peptide

used for quantitation

reason

APNHAVVTR DGAGDVAFVK SASDLTWDNLK HSTIFENLANK KPVDEYK DSAHGFLK

used used used not used not used not used

good signal, linear calibration good signal, linear calibration good signal, linear calibration calibration not linear not detected, low signal digestion not efficient

The above evaluation of Q-peptides is comprehensive in three stages, verification of detectability, sufficient linearity of calibration curves, and sufficient slope of calibration curves. While the DSAHGFLK peptide passes the first two tests because it is detectable and shows linear calibration curves, it fails the third test by having a significantly lower slope. The QconCAT version of DSAHGLFK is thus determined to be unsuitable for quantitation. If it were the case that only the first two tests were used, this peptide would prove deceptive and E

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

Table 2. MRM Transitions Used to Quantify Transferrin and Their Optimized Instrumental Parameters analyte (m/z)

analyte (m/z)

internal standard (m/z)

internal standard (m/z)

ion

ion

collision energy

decluster potential

precursor

product

precursor

product

precursor

product

(volts)

(volts)

375.2 447.3 474.3 735.4 563.4 675.3 776.4 analyte (m/z)

327.2 327.2 327.2 495.2 495.2 632.3 632.3 internal standard (m/z)

381.2 454.2 481.3 743.4 569.3 683.3 785.4 internal standard (m/z)

3+ 3+ 3+ 2+ 2+ 2+ 2+

1+, y3 2+, y8 1+, y4 1+,y7 1+, y5 1+, y5 1+, y6

synthetic peptide transitionsa

322.2 322.2 322.2 489.7 489.7 625.3 625.3 analyte (m/z)

ion

ion

15 15 15 25 25 30 30 collision energy

50 50 50 110 120 100 90 decluster potential

precursor

product

precursor

product

precursor

product

(volts)

(volts)

DSAHGFL*K(l) DSAHGFL*K(2) DSAHGFL*K(3) DSAHGFL*K(4) HSTIFENL*ANK(1) HSTIFENL*ANK(2)

292.2 437.7 292.2 292.2 425.2 425.2

336.7 464.3 464.3 601.3 445.3 559.3

294.2 440.7 294.2 294.2 427.2 427.2

339.7 470.3 470.3 607.4 451.3 565.3

3+ 2+ 3+ 3+ 3+ 3+

2+,y6 1+, y4 1+, y4 1+, y5 1+, y4 1+,y5

12 20 25 25 15 15

70 110 60 60 100 100

QconCAT peptide transitions APNHAVVTR(l) APNHAVVTR(2) APNHAVVTR(3) DGAGDVAFVK(l) DGAGDVAFVK(2) SASDLTWDNLK(l) SASDLTWDNLK(2)

a

L* = 13C6.

uncertainty of transferrin levels seen here for both types of internal standards is better than the uncertainties generally reported by some clinical methods. When comparing QconCAT to synthetic peptides, one advantage of the QconCAT approach is that several Q-peptides are created simultaneously by gene expression, as opposed to synthetic internal standards that are synthesized individually, one peptide at a time. Therefore, the QconCAT approach has the potential to generate data from multiple peptides, adding confidence to the quantitative results. For an overall comparison between synthetic and QconCAT peptides, both give reasonable quantitative values in this study. The disadvantages of the QconCAT approach are largely overcome by the QconCAT evaluation process, which objectively narrows down the list of suitable peptides. For synthetic peptides, initial experiments without using BSA-coating to prevent sample losses failed to produce usable signal, but the addition of BSA improved the utility of the synthetic peptides. Thus, in the context of measuring a single analyte protein, the advantages between QconCAT and synthetic approaches remain related to cost, time, and effort.19 However, for both types of internal standard, trypsin digestion efficiency adds variability to the detected amounts of unlabeled analyte. Given that each method has associated variability, further confidence is added by using other orthogonal methods. In addition to the two methods presented here, synthetic and QconCAT labeling, a third method called inductively coupled plasma (ICP) mass spectrometry was used by our collaborators at NIST to measure transferrin. Transferrin levels were obtained by inductively coupled plasma mass spectrometry (ICPMS) by our collaborators at the Hollings Marine Laboratory (NIST). ICPMS indirectly measures transferrin levels by measuring iron content. Given that each transferrin molecule binds to two Fe3+ ions, this stoichiometry infers transferrin levels in human serum. Their result is in a submitted journal article for transferrin in SRM909c (Nuevo-Ordonez, Y., Davis, W.C., 2013, submitted) and closely matches our results for the same sample (data not shown). Our results also show reasonable agreement with previous transferrin measurements using LC-MS/MS and a single synthetic peptide as an internal standard.17 This diverse

Table 3. Transferrin Levels in Human Serum (in mg/dL) QconCAT MRM transition

SRM909C (pooled serum)

synthetic MRM transition

SRM909C (pooled serum)

APNHAVVTR(l) APNHAVVTR(2) DGAGDVAFVK(l) DGAGDVAFVK(2) SASDLTWDNLK(l) SASDLTWDNLK(2) mean st. dev. CV (%) QconCAT MRM transition

172.4 191.8 181.8 189.4 220.0 198.0 192.2 16.2 8.4 serum (individual)

DSAHGFLK(2) DSAHGFLK(4) HSTIFENLANK(l) HSTIFENLANK(2)

218.7 243.1 187.5 159.3

APNHAVVTR(l) APNHAVVTR(2) APNHAVVTR(3) DGAGDVAFVK(l) DGAGDVAFVK(2) SASDLTWDNLK(l) SASDLTWDNLK(2) mean st. dev. CV (%)

241.7 214.5 224.5 235.3 240.1 270.8 260.3 241.0 19.4 8.1

DSAHGFLK(l) DSAHGFLK(2) DSAHGFLK(3) HSTIFENLANK(l) HSTIFENLANK(2)

211.8 189.6 194.0 206.3 216.1

mean st. dev. CV (%)

203.6 11.4 5.6

mean st. dev. CV (%) synthetic MRM transition

202.2 36.5 18.0 serum (individual)

16.2) mg/dL with QconCAT peptides and (202.2 ± 36.5) mg/ dL with synthetic peptides. The reported uncertainties correspond to variability over the MRM transitions and do not correspond to replicate measurements. The uncertainty ranges of these values show high overlap, and this mutual agreement between different types of internal standards points to good quantitative accuracy. For the individual serum sample, the transferrin levels in Table 3 are similar between QconCAT and synthetic peptides, albeit not quite as close as for SRM909c. The two uncertainty ranges almost but not quite overlap for the individual donor sample. If the measurements were repeated, one expects that these values would converge. Considering the usual diagnostic ranges for transferrin in a variety of clinical conditions,6,7 the F

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

Article

(8) Yamanishi, H.; Iyama, S.; Yamaguchi, Y.; Kanakura, Y.; Iwatani, Y. Clin. Chem. 2003, 49, 175−178. (9) Gambino, R.; Desvarieux, E.; Orth, M.; Matan, H.; Ackattupathil, T.; Lijoi, E.; Wimmer, C.; Bower, J.; Gunter, E. Clin. Chem. 1997, 43, 2408−2412. (10) Roberts, W. L.; Smith, P. T.; Martin, W. J.; Rainey, P. M. Clin. Pathol. 1999, 112, 657−664. (11) Siff, J. E.; Meldon, S. W.; Tomassoni, A. J. Ann. Emerg. Med. 1999, 33, 73−76. (12) Tenenbein, M.; Yatscoff, R. W. Am. J. Dis. Child. 1991, 145, 437−439. (13) Schellenberg, F.; Martin, M.; Cacès, E.; Bénard, J. Y.; Weill, J. Clin. Chem. 1996, 42, 551−557. (14) Pan, S.; Aebersold, R.; Chen, R.; Rush, J.; Goodlett, D. R.; McIntosh, M. W.; Zhang, J.; Brentnall, T. A. J. Proteome Res. 2009, 8, 787−797. (15) Hale, J. E. Int. J. Proteomics 2013, Article ID: 219452. (16) Arsene, C. G.; Ohlendorf, R.; Burkitt, W.; Pritchard, C.; Henrion, A.; O’Connor, G.; Bunk, D. M.; Gttler, B. Anal. Chem. 2008, 80, 4154−4160. (17) Yu, Y.; Xu, J.; Liu, Y.; Chen, Y. J. Chromatogr., B: Anal. Technol. Biomed. Life Sci. 2012, 902, 10−15. (18) Hanke, S.; Besir, H.; Oesterhelt, D.; Mann, M. J. Proteome Res. 2008, 7, 1118−1130. (19) Mirzaei, H.; McBee, J. K.; Watts, J.; Aebersold, R. Mol. Cell. Proteomics 2008, 7, 813−823. (20) Pratt, J. M.; Simpson, D. M.; Doherty, M. K.; Rivers, J.; Gaskell, S. J.; Beynon, R. J. Nat. Protoc. 2006, 1, 1029−1043. (21) Beynon, R. J.; Doherty, M. K.; Pratt, J. M.; Gaskell, S. J. Nat. Methods 2005, 2, 587−589. (22) Chen, C. H. Anal. Chim. Acta 2008, 22, 16−36. (23) Goebel-Stengel, M.; Stengel, A.; Taché, Y.; Reeve, J. R. J. Anal. Biochem. 2011, 414, 38−46. (24) Rivers, J.; Simpson, D. M.; Robertson, D. H.; Gaskell, S. J.; Beynon, R. J. Mol. Cell. Proteomics 2007, 6, 1416−1427. (25) Chen, J.; Wang, M.; Turko, I. V. Mol. Neurodegener. 2012, 7, 1− 6. (26) Kito, K.; Ota, K.; Fujita, T.; Ito, T. J. Proteome Res. 2007, 6, 792−800. (27) Nanavati, D.; Gucek, M.; Milne, J. L.; Subramaniam, S.; Markey, S. P. Mol. Cell. Proteomics 2008, 7, 442−447. (28) Bislev, S. L.; Kusebauch, U.; Codrea, M. C.; Beynon, R. J.; Harman, V. M.; Røntved, C. M.; Aebersold, R.; Moritz, R. L.; E, B. J. Proteome Res. 2012, 11, 1832−1843. (29) Demond, W.; Kenley, R. A.; Italien, J. L.; Lokensgard, D.; Weilersbacher, G.; Herman, K. AAPS PharmSciTech 2000, 1, 50−59. (30) Kilpatrick, E. L.; Liao, W. L.; Camara, J. E.; Turko, I. V.; Bunk, D. M. Protein Expression Purif. 2012, 85, 94−99. (31) Spicer, V.; Yamchuk, A.; Cortens, J.; Sousa, S.; Ens, W.; Standing, K. G.; Wilkins, J. A.; Krokhin, O. V. Anal. Chem. 2007, 79, 8762−8768. (32) Rodriguez, J.; Gupta, N.; Smith, R. D.; Pevzner, P. A. J. Proteome Res. 2008, 7, 300−305. (33) Olsen, J. V.; Ong, S. E.; Mann, M. Mol. Cell. Proteomics 2004, 3, 608−614. (34) Siepen, J. A.; Keevil, E. J.; Knight, D.; Hubbard, S. J. J. Proteome Res. 2007, 6, 399−408. (35) Howson, C.; Urbach, P. Scientific Reasoning: The Bayesian Approach; 3rd ed.; Open Court: Chicago, 2006. (36) Abbatiello, S. E.; Mani, D. R.; Keshishian, H.; Carr, S. A. Clin. Chem. 2010, 56, 291−305.

array of methods (synthetic, QconCAT, and ICPMS) converging on similar transferrin levels suggests that the obtained values are reasonable, but further study is needed to report quantitative values with certainty. Using synthetic and QconCAT peptides in parallel allows a refined focus on method development. For example, the synthetic version of HSTIFENLANK worked ideally, while the QconCAT version gave variable results, thus using both types of peptides reveals that the variability stems from the QconCAT sequence and not the isolated peptide sequence. This also suggests the need for completely separate experiments to uncover the physical mechanism behind the variable results of certain peptides, which is a future direction of this study. However, the above QconCAT evaluation was able to identify and remove such variable-response peptides from the quantification, giving higher confidence in the reported measurements. Such a QconCAT evaluation process could be used, along with synthetic peptides, to measure other clinically relevant proteins.

■

ASSOCIATED CONTENT

* Supporting Information S

Additional material as described in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Address

T.A. Zimmerman: Department of Chemistry, Northwestern University, Evanston, IL, U.S.A. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors thank Dr. Eric L. Kilpatrick and Alyssa Florwick for assistance. T.A.Z. acknowledges a National Research Council (NRC)/NIST postdoctoral research associateship. Certain commercial materials, instruments, and equipment are identified in this manuscript in order to specify the experimental procedure as completely as possible. In no case does such identification imply a recommendation or endorsement by the National Institute of Standards and Technology nor does it imply that the materials, instruments, or equipment identified is necessarily the best available for the purpose.

■

REFERENCES

(1) Brandsma, M. E.; Jevnikar, A. M.; Ma, S. Biotechnol. Adv. 2011, 29, 230−238. (2) Karlsson, T.; Sjöö, F.; Kedinge-Cyrus, B.; Bäckström, B. J. Intern. Med. 2010, 267, 331−334. (3) Wallace, D. F.; Summerville, L.; Crampton, E. M.; Frazer, D. M.; Anderson, G. J.; Subramaniam, V. N. Hepatology 2009, 60, 1992− 2000. (4) Tenenbein, M. Toxicol. Lett. 1998, 102−103, 653−656. (5) Feder, J. N.; Penny, D. M.; Irrinki, A.; Lee, V. K.; Lebron, J. A.; Watson, N.; Tsuchihashi, Z.; Sigal, E.; Bjorkman, P. J.; Schatzman, R. C. Proc. Natl. Acad. Sci. U.S.A. 1998, 95, 1472−1477. (6) Castel, R.; Tax, M. G.; Droogendijk, J.; Leers, M. P.; Beukers, R.; Levin, M. D.; Sonneveld, P.; Berendes, P. B. Clin. Chem. Lab. Med. 2012, 50, 1343−1349. (7) Vannotti, A.; Blanc, B. Schweiz. Med. Wochenschr. 1964, 93, 1189−1191. G

dx.doi.org/10.1021/ac402326v | Anal. Chem. XXXX, XXX, XXX−XXX