Streamlining large-scale MRM database development through automated software tools and aeMRM on the novus V55 system

Abstract

Abstract

This technical note describes a streamlined workflow for the development of a large-panel MRM database comprised of >700 pesticides. Software features in SCIEX OS software, such as automated compound optimization (ACO), accelerated the optimization and creation of a database with >2,100 MRM transitions. These MRM transitions were further authenticated using high-resolution HR-MS/MS spectra, providing additional confidence in precursor and product ion selection. Leveraging accelerated MRM (aeMRM) on the novus V55 system, sub-to-low ms pause and dwell times and fast polarity switching enabled unscheduled MRM acquisition of the entire panel in a single injection, significantly expediting retention time (RT) discovery and conserving reference materials and /or analytical standards (Figure 1).

Key-features

Key benefits

Key benefits of SCIEX OS ACO and aeMRM for large-panel MRM method development

• Automated method development: SCIEX OS ACO rapidly determined optimal MRM transitions, while minimizing manual tuning of compound parameters.
• Large-panel pesticides MRM library: A final MRM database, comprised of 1975 positive and 127 negative transitions, was curated and validated against HR-MS/MS fragmentation data.
• Accelerated RT discovery: aeMRM on the novus V55 system enabled unscheduled monitoring of all transitions in a single injection, significantly speeding up RT discovery.
• Optimized sMRM cycle time with SCIEX OS: Software visualization of MRM concurrency helped preserve cycle time for higher analyte coverage without data quality trade-off.

Figure 1. Large-panel MRM method development using software automation and aeMRM on the novus V55 system. Compound optimization was performed using automated compound optimization (ACO) in SCIEX OS software, resulting in a large-panel pesticide MRM database of 2,101 MRM transitions, with 2–3 fragments per target analyte. Retention times (RTs) were initially identified using aeMRM on the novus V55 system, followed by MS/MS-based confirmation on the ZenoTOF 7600+ system. Software features, such as RT import and concurrency visualization plots, facilitated the creation of a large panel scheduled MRM (sMRM) method for quantitation on the novus V55 system.

Introduction

Introduction

The demand for comprehensive targeted analysis in food testing continues to grow due to the discovery of new analytes and regulatory updates. Laboratories are also under increasing pressure to improve throughput and sustainability initiatives, driving the adoption of large-panel methods that consolidate multiple assays into a single run and reduce instrument time and operational costs. Multiple reaction monitoring (MRM) remains the gold standard approach for targeted quantitation, but developing and managing large panels with hundreds of analytes, such as those recently emerged in the food testing community^1-3, presents significant challenges. For example, identifying optimal MRM transitions and source and compound-specific parameter optimization become increasingly cumbersome with larger panels.

Software tools, such as automated compound optimization (ACO) in SCIEX OS, can streamline these processes by automating the iterative ramping of each parameter and adding the optimized values to an existing or a new in-house MRM database. Here,the use of aeMRM on the novus V55 system⁴ enabled rapid RT discovery of >2,100 transitions in a single injection, which were then authenticated by HR-MS/MS fragmentation data. The final pesticides MRM library enabled scalable method development with optimized parameters for 1,975 positive and 127 negative transitions that can be readily incorporated into targeted scheduled MRM (sMRM) assays.

Methods

Methods

Standards: The Smart Solutions^TM v700 LC PestiMix Kit 3 from LGC Standards was used to prepare 10 individual intermediate stock standards at 1 μg/mL in acetonitrile. Intermediate mixes were prepared either in acetonitrile or methanol, while calibration standards were prepared with the same solvent composition as mobile phase A.

Chromatography: Two separate gradients were developed for RT discovery on the novus V55 system and MRM authentication on the ZenoTOF 7600+ system (Tables 1 and 2). Chromatographic separation was performed on an ExionAE LC system using the Phenomenex Luna Omega Polar C18 column in 2 different configurations (RT discovery: 100 x 2.1 mm, 1.6 µm, P/N: 00D- 4748-AN; MRM authentication: 100 x 2.1 mm, 3 µm, P/N: 00D- 4760-AN).

Introduction

Methods

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Mass spectrometry: Analysis was performed using electrospray ionization on the novus V55 system for the RT discovery workflow and on the ZenoTOF 7600+ system for the MRM authentication workflow. ACO in SCIEX OS software was used to automate the optimization of MRM transition pairs and compound-dependent parameters on the novus V55 system. For the RT discovery workflow, data was acquired by unscheduled MRM with the optimized source and gas conditions (Table 3) and compound-dependent parameters, with at least 2–3 transitions per analyte. Leveraging aeMRM on the novus V55 system, a polarity switching time of 10 ms, a pause time of 0.7 ms and a minimum dwell time of 1 ms were used. For the MRM authentication workflow, data was acquired by SWATH data-independent acquisition (DIA), using the parameters outlined in Table 4 and with 23 SWATH windows, spanning a start/stop mass range of 50–1000 Da.

Results and discussion

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Data acquisition and processing: Data was acquired and processed using SCIEX OS software (version 5.0). The SCIEX NIST 2023 tandem mass library and the SCIEX Pesticides high resolution MS/MS spectral library were used to perform library searching in the Analytics module of SCIEX OS software.

Automated compound optimization in SCIEX OS

Conventional compound optimization is a largely manual process involving iterative tuning of parameters for each analyte, creating a significant bottleneck for large-panel MRM method development. This typically starts with a precursor ion scan to confirm the presence of the target precursors and their m/z during which the declustering (DP) and entrance (EP) potentials are ramped to find the optimal values. Next, a product ion scan is performed to identify the most intense fragments for each precursor, followed by optimization of the collision energy (CE) and collision exit potential (CXP). Once the precursor and product ion pairs have been identified for an analyte, an MRM scan can be used to optimize the source and gas parameters.

The ACOfeature in SCIEX OS software combines these steps in an automated workflow by performing sequential optimization of the precursor ion conditions, fragment ion selection and compound-dependent parameters, with the results stored in a database for subsequent method creation (Figure 2). The precursor masses of the target analytes are either entered manually or calculated from the molecular formula, with the optimization set to find up to 5 fragments per precursor. Using flow injection analysis (FIA), the analytes are injected directly into the mass spectrometer without chromatographic separation, generating a broad peak during which the precursor/fragment ions and compound parameters are optimized based on signal intensity. Based on the parameter ramp settings, such as the start/stop range and the step interval, ACO provides the total injection count or the total sample volume required to optimize all the analytes, allowing the user to prepare sufficient amounts of standards in advance. Upon completion of ACO, the summary report shows how many transitions are successfully optimized and added to the compound database, enabling the user to revisit those that failed during optimization.

Here, ACO was used to identify up to 5 transitions and optimize their parameters for each of the 724 pesticides in the Smart Solutions v700 LC PestiMix Kit 3. An MRMdatabase, consisting of 3882 positive and 180 negative transitions, was initially generated, then further authenticated using HR-MS/MS fragmentation data.

Figure 2. Automated compound optimization (ACO) workflow in SCIEX OS software. (1) ACO prerequisites include an LC system configured for flow injection analysis (FIA) and user input of target compound data, preferences for parameter ramping and fragment identification. (2) Upon starting the optimization, ACO identifies and ranks the most intense fragment ions for each target precursor, then sequentially ramps the compound-dependent parameters. The software provides a warning for failed optimizations, such as when the signal intensity has exceeded the threshold, to inform the user to dilute their standards before restarting ACO.

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Figure 2 (cont’d). Automated compound optimization (ACO) workflow in SCIEX OS software. (3) Upon completion of the ACO process, a report is generated with a summary of the optimized results and a count of completed optimizations. (4) The final step of ACO shows a list of all optimized MRM transitions and their parameters that have been added to the compound database. The list can be filtered and edited by deleting or importing transitions from other ACO-generated databases, while the list itself can also be exported for sharing.

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MRM authentication by HR-MS/MS

Orthogonal HR-MS/MS data were used to authenticate the MRM database by confirming the exact masses and fragmentation patterns of precursor/fragment ion pairs, providing increased confidence of the MRM assignment. Figures 3 and 4 demonstrate this workflow for 2 example pesticides, vamidothion and thiabendazole. In addition to exhibiting the same RT of 4.99 min, all 5 transitions identified for vamidothion during ACO were found to be present in the HR-MS/MS spectrum (Figure 3). The isotopic pattern and library confirmation further corroborated the transition assignment. In contrast, the 5 transitions of thiabendazole exhibited 2 different RTs of 4.7 min and 6.9 min (Figure 4). Comparison against the MS/MS spectrum confirmed the 3 transitions (m/z 202 > 65, 202 > 131 and 202 > 175) , eluting at 4.7 min, were correct.

Using this authentication workflow, the final MRM database was revised to 1975 positive and 127 negative transitions, with 2–3 fragments assigned to each pesticide. The availability of multiple transitions here strengthens method robustness by providing alternative ions for analyte confirmation and interference mitigation across diverse sample matrices. This database provides a starting point for food laboratories seeking to develop and expand multi-residue screening methods.

Figure 3. HR-MS/MS-based authentication of MRM database. Comparison of representative extracted ion chromatograms (XICs) of vamidothion acquired on the novus V55 system against their corresponding XIC, MS and MS/MS spectra acquired on the ZenoTOF 7600+ system. The 5 fragments identified from ACO were compared against the MS/MS fragmentation data. A green checkmark indicates the transition has been correctly assigned based on the presence of the fragment in the MS/MS spectrum, which is further corroborated by library confirmation. A red checkmark indicates the transition is not correct.

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Figure 4. HR-MS/MS-based authentication of MRM database. Comparison of representative extracted ion chromatograms (XICs) of thiabendazole acquired on the novus V55 system against their corresponding XIC, MS and MS/MS spectra acquired on the ZenoTOF 7600+ system. The 5 fragments identified from ACO were compared against the MS/MS fragmentation data. A green checkmark indicates the transition has been correctly assigned based on the presence of the fragment in the MS/MS spectrum, which is further corroborated by library confirmation. A red checkmark indicates the transition is not correct.

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High-throughput RT discovery

Conventional large-panel MRM method development requires labour-intensive preparation of numerous standards, while RT verification of thousands of transitions across a single run necessitates multiple injections. The LGC PestiMix kit simplifies standards preparation with pre-mixed multi-analyte solutions, reducing analyst time and risk of error. By coupling the comprehensive analyte coverage of the PestiMix standards with the high-speed acquisition of aeMRM on the novus V55 system,⁵ RT discovery for thousands of transitions was determined in a single injection, significantly accelerating method development while conserving analytical standards.

Figure 5 demonstrates the creation of an unscheduled MRM method comprised of the 2,102 transitions imported from the ACO MRM database. Coupling a pause time of 0.7 ms and a dwell time of 1 ms from aeMRM acquisition with fast polarity switching enabled RT identification of the entire panel, including 2–3 transitions for each pesticide .

For certain transitions exhibiting multiple LC peaks ,such as phorate and flonicamid (Figure 5), the inclusion of additional transitions helped to confirm their RTs, reducing the need for reinjections. Although the very low pause and dwell times used may not provide sufficient data point density for quantitation, they are adequate for reliable RT identification, enabling rapid RT discovery of all analytes in both polarities in a single injection. The resulting RTs were incorporated into the development of a 15-min sMRM method, using visualization plots in SCIEX OS software to balance between concurrency and optimal cycle times (Figure 5). The method overview, concurrency, cycle time and dwell time plots dynamically update with changes to the acquisition method, enabling the method developer to visualize the impact of real-time modifications to parameters , such as pause time, dwell time, RT window size and panel size.

Figure 5. Leveraging aeMRM for sMRM method development. After importing 2102 optimized transitions from the ACO MRM database into the acquisition method, a pause time of 0.7 ms, minimum dwell time of 1 ms and settling time of 10 ms were used to enable unscheduled MRM acquisition with polarity switching in a single injection. This enabled simultaneous monitoring of the entire target panel, including multiple transitions for each pesticide, thereby significantly expediting RT identification and confirmation. Software visualization tools in SCIEX OS software facilitated cycle time optimization in the final sMRM method.

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Conclusion

Conclusion

This work features an end-to-end workflow, involving ACO and aeMRM, to help streamline MRM method development for food laboratories seeking to expand their multi-residue panels.

• ACO in SCIEX OS alleviated a major method development bottleneck by automating fragment selection and parameter optimization, generating a large-panel pesticides database.
• HR-MS/MS-based authentication strengthened the database quality by confirming the transition assignments based on mass error, fragmentation patterns and library searching.
• Sub-to-low ms pause and dwell times used for aeMRM RT discovery helped reduce previous multi-injection workflows into a single-injection method, greatly reducing time and standards consumption normally required for large-panel method development.
• SCIEX OS visualization tools further streamlined final sMRM method creation by enabling real-time optimization of concurrency and cycle time

References

References

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