Quality Control Standards to validate the performance ...

Quality Control Standards to validate the performance of every aspect of Mass Spectrometry platform Amol Prakash1, Scott Peterman1 , David Sarracino1, Bryan Krastins1, Taha Rezai1, Michael Athanas2, Mary Lopez1 1Thermo Fisher Scientific, Cambridge, MA, USA 2Vast Scientific, Wayland, MA, USA

With the expansion of laboratories performing peptide/protein discovery and targeted quantitation, the need for routine protocols to verify instrument performance is critical. Adding to the complexity of the experiments is the desire for multiplexing experiments to monitor large number of proteins. This further elevates the need to constantly monitor system performance and inform the operator of any problem as soon as possible, so as to avoid loss of precious samples. Moreover, methods and libraries need to be transferred across laboratories, for which we need a protocol to check instrument performance from lab to lab.

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3. Data Processing

1. System Evaluation Peptide Trainer Kit

RAW TSQ Data

LC Reproducibility Peak Width RT:HF relationship Normalized Collision Energy (NCE)

Automated Data Processing, Verification, and Scoring

Targeted Protein List Complex SRM Assay (T-SRM combined with iSRM

2. Assay Creation

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FIGURE 4. Inter-laboratory comparison of Bonferroni corrected pvalues to determine the verification confidence. One spectral library was used to compare each data set of composite product ion spectra. The more negative the p-value, the greater the confidence in the spectral overlap between the library and composite spectra. RT

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A set of synthetic peptides were produced by Thermo Fisher Scientific (Ulm, GE) and mixed into an equal molar stock solution (80 fmol/µL) that can be spiked into each sample.. A yeast cell lysate digest was used as a complex digest mixture and targeted a large number of proteins and peptides. The stock solution concentration used by each lab is 0.5 µg/µL. A total of 2 µL was injected per experiment.

4. Method Refinement

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Sample Prep

Figure 1. Workflow used to characterize the LC-MS/MS set up in each laboratory that performed the analysis. The results of the peptide trainer kit were used to set up the optimal data acquisition settings for the multiplexed analysis.

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Methods

All experiments were performed using a TSQ Vantage triple quadrupole mass (Thermo Fisher Scientific, San Jose, CA). Each instrument has been tuned and calibrated using the standard protocols issued by the factory. Following calibration, each laboratory followed Scheme in Figure 1 to determine the optimum LC and MS instrumental parameters used to detect, quantify, and verify the list of targeted proteins and peptides in the yeast cell lysate digest. All of the method creations, data processing (verification and quantification), and method refinement steps are performed using the Pinpoint software package. The spectral libraries used to verify targeted peptide detection were created using an LTQ and processed by Proteomie Discoverer (Thermo Fisher Scientific, Bremen, GE). Calculation of the hydrophobicity factors for all peptides included in the study were obtained using the SSRCalc approach [3]. Verification was performed by acquiriing data using iSRM, quantitation was performed using the primary SRM transitions (2 per peptide) and the verification was performed on the data dependent acquisition of 6 additional SRM transitions per peptide (secondary SRM transitions). The spectral overlap between the composite SRM product ion and library spectra were evaluated in Pinpoint with a dot-product correlation coefficient and bonferroni p-value were calculated.3 The final assay contained 2816 SRM transitions to monitor 352 peptides from 108 yeast proteins. The LC systems used by each laboratory were the following: Vantage 1 used an Eksigent nano-Ultra system (Dublin, CA) with a Michrom 100 x 0.1 mm HPLC column packed with 90 Å Halo material and a flow rate was 700 nL/min. Vantage 2 used an Eksigent nano-LC system with a 100 x 0.075 mm Biobasic column (New Objective Inc. Woburn, MA) and a flow rate of 300 nL/min. Vantage 3 used a Surveyor MS Pump and CTC autosampler with a 150 x 1 mm Hypersil Gold column (3 µm) and a flow rate of 150 µL/min. The solvent system for each laboratory was comprised of A) 0.1% formic acid and B) MeCN (0.1% formic acid). A constant gradient of 3-75% B in 20 minutes was for the analysis.

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% Targeted Peptides

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FIGURE 3. Comparative RT breakdown for the targeted peptides across the three different laboratories. The theoretical RT was calculated using the linear equation determined using the peptide 70 kit. trainer

% Targeted Peptides

Introduction

FIGURE 2. Use of the peptide trainer kit to characterize the chromatographic performance for RT reproducibility, peak shapes as a function of mobile phase composition. The red line represents the % composition of the B solvent as a function of elution time. Two trainer set peptides are shown to demonstrate the measured variation in the LC parameters.

Results Utilization of a constant peptide trainer kit enabled cross-laboratory method set up, evaluation, and normalization to set up complex targeted protein quantitation methods without having to perform numerous method iteration. The primary sources of errors for peptide quantitation using triple quadrupole mass spectrometers is the LC performance and MS/MS performance within Q2. Figure 1 shows the NCE application contained with Pinpoint. The table lists the targeted peptides and the optimized collision energy evaluated from the individual breakdown curves. By incorporating peptides in the same precursor charge state that have a wide m/z values enables an efficient determination of the NCE as shown in the left-hand graph. This NCE equation was applied to calculate the Q2 collision energy for each of the targeted yeast cell lysate peptides. The peptide trainer kit was used to characterize the LC system and predict the RT used for T-SRM data acquisition. Figure 2 shows the concept for selecting trainer set peptides that have a wide distribution of hydrophobicity factors providing landmarks to be used to determine the relationship of retention times as a function of calculated hydrophobicity factors. Technical replicates were acquired for the peptide trainer set to evaluate RT and peak widths for peptides eluting at different %B compositions. Two examples are presented in Figure 2 to show peptides that elute early and late in the gradient profile with the measured RT and peak widths. The evaluation of the average peak widths and determination of the relationship of RT as a function of hydrophobicity factor enabled Pinpoint to predict the RT of each targeted peptide as well as the time window used to monitor the targeted peptides. Figure 3 shows the inter-laboratory comparison of predicting the T-SRM methods. Over 80% of the targeted peptides fell within a 6% error for the 352 targeted peptides. In addition to using RT as a means of confirmation, the acquisition of the data dependent composition SRM transitions were acquired using iSRM. The spectral overlap between the composite MS/MS and the library entry is evaluated using the dot-product correlation coefficient. From this value, p-value (false positive rate) is calculated within Pinpoint. Figure 4 shows the results for these p-values from the interlaboratory study. The lower the number correlates with the lower probability of randomly matching the composite MS/MS spectrum with the library. A score of 1E-5 approached a perfect match (no probability of a false positive) and the threshold for a real hit and a poor hit has been set at 5E-2. The results from each laboratory show over 80% of the peptides generated a confident composite MS/MS spectral match with the library. Evaluation of the peptides that had a p-value below the threshold generally had low signal intensity. A common spectral library was used for the correlation. Therefore, the uniformity of collisional activation was key to maintaining a high correlation across the instruments. The final measure of the instrument performance was AUC variance. Only the peptides that had a p-value greater than 5E-2 was considered for the %CV analysis. Despite the large number of targeted peptides (352 peptides from 108 proteins) being monitored in a short chromatographic gradient, ca. 80% of the peptides had %CV’s 10% or better for all three systems (Figure 5). By identifying the RT performance (peak widths and RT shifts) using the peptide trainer kit, shorter windows could be used to monitor the peptides resulting in less overlap between all of the targeted peptides. The smaller the number of SRM transitions per unit time results in longer dwell times per SRM transition that in turn yields better quantitation. These benefits are unique to the TSQ system as it is the only system to employ iSRM to maximize both quantitation and verification for large numbers of targeted peptides.

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Bonferroni Corrected P-Value FIGURE 5. Evaluation of the AUC variance for the targeted peptides. Each of the targeted peptides were verified using retention times, correlation coefficients, and bonferroni corrected p-values. 45 40

% Targeted Peptides

Mass spectrometry is becoming the most promising technology for discovering and validating protein/peptide biomarkers with eventual translation to clinics. To increase the effectiveness of setting up targeted assays and producing reproducible data across laboratories, optimization of the mass spectrometer and HPLC system is mandatory. We propose the incorporation of a set of synthetic peptides into the method development pipeline. These peptides measure the performance of many characteristics of the system thereby performing a complete quality check. Moreover, the peptide trainer set can be used to establish optimization protocols and standardize across multiple labs.

Relative Abundance

Overview

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Conclusion The workflow presented provides a uniform approach to characterizing and normalizing the entire experimental steps needed to perform targeted protein quantitation on large numbers of proteins and peptides. ƒ The entire workflow is controlled through the Pinpoint software. ƒ Creation and incorporation of the peptide trainer kit enables all laboratories to optimize LC and MS parameters on a common sample for direct comparison. ƒ Due to the large distribution of hydrophobicity factors from the peptide trainer kit, T-SRM experiments can be predicted, reducing the need for multiple method refinement steps. ƒ Incorporation of iSRM capabilities maximizes quantitation as well as qualitative analysis.

References 1.Prakash, A., Tomaela, D., Frewen, B., Merrihew, G., MacLean, B., Peterman, S., MacCoss, M. Expediting the Development of Targeted SRM Assays: Using Data from Shotgun Proteomics to Automate Method Development, J. Prot. Research, 8(6), 2009, pp. 2733-9 2.Peterman, S., Kiyonami, R., Prakash, A., Lopez, M., Rezai, Taha, Sarracino, D., Krastins, B., Athanas, M., Streamlining the Process of Biomarker Verification using Pinpoint Software, Thermo Fisher Scientific Application Note 470 3.Dwivedi, R., Spicer, V., Harder, M., Antonivici, M., Ens, W., Standing, K., Wilkins, J., Krokhin, O. Practical Implementation of 2D HPLC Scheme with Accurate Peptide Retention Prediction in Both Dimensions for High-Throughput Bottom-up Proteomics, Anal. Chem., 2008, 80, pp. 7036-7042

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