bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2022‒06‒05
thirteen papers selected by
Sofia Costa
Matterworks
  1. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.
  2. Quantitation of non-derivatized free amino acids for detecting inborn errors of metabolism by incorporating mixed-mode chromatography with tandem mass spectrometry.
  3. MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities.
  4. Comprehensive pseudotargeted metabolomics analysis based on two-phase liquid extraction-UHPLC-MS/MS for the investigation of depressive rats.
  5. Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility.
  6. Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping.
  7. Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.
  8. Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics.
  9. Fast and highly sensitive determination of tetrahydrocannabinol (THC) metabolites in hair using liquid chromatography-multistage mass spectrometry (LC-MS3 ).
  10. Acoustic ejection mass spectrometry: fundamentals and applications in high-throughput drug discovery.
  11. A multi-dimensional liquid chromatography/high-resolution mass spectrometry approach combined with computational data processing for the comprehensive characterization of the multicomponents from Cuscuta chinensis.
  12. Validation of an LC-MS/MS method for the quantification IOA-289 in human plasma and its application in a first-in-human clinical trial.
  13. Validation of an automated UPLC-MS/MS method for methylmalonic acid in serum/plasma and its application on clinical samples.
  1. Mass Spectrom Rev. 2022 May 29. e21785
    Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.
    Chao-Jung Chen, Der-Yen Lee, Jiaxin Yu, Yu-Ning Lin, Tsung-Min Lin.
      The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.
    Keywords:  LC-MS; derivatization; disease marker; machine learning; metabolomics
    DOI:  https://doi.org/10.1002/mas.21785
  2. J Mass Spectrom Adv Clin Lab. 2022 Aug;25 1-11
    Quantitation of non-derivatized free amino acids for detecting inborn errors of metabolism by incorporating mixed-mode chromatography with tandem mass spectrometry.
    Patrick D DeArmond, Dustin R Bunch.
      Introduction: Amino acids are critical biomarkers for many inborn errors of metabolism, but amino acid analysis is challenging due to the range of chemical properties inherent in these small molecules. Techniques are available for amino acid analysis, but they can suffer from long run times, laborious derivatization, and/or poor resolution of isobaric compounds.Objective: To develop and validate a method for the quantitation of a non-derivatized free amino acid profile in both plasma and urine samples using mixed-mode chromatography and tandem mass spectrometry.
    Methods: Chromatographic conditions were optimized to separate leucine, isoleucine, and allo-isoleucine and maintain analytical runtime at less than 15 min. Sample preparation included a quick protein precipitation followed by LC-MS/MS analysis. Matrix effects, interferences, linearity, carryover, acceptable dilution limits, precision, accuracy, and stability were evaluated in both plasma and urine specimen types.
    Results: A total of 38 amino acids and related compounds were successfully quantitated with this method. In addition, argininosuccinic acid was qualitatively analyzed. A full clinical validation was performed that included method comparison to a reference laboratory for plasma and urine with Deming regression slopes ranging from 0.38 to 1.26.
    Conclusion: This method represents an alternative to derivatization-based methods, especially in urine samples where interference from metabolites and medications is prevalent.
    Keywords:  AMR, analytical measurement range; AQC, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate; ASA, argininosuccinic acid; Allo-isoleucine; Amino acid; CEX, cation exchange; CS, Fcerebrospinal fluid; CV, coefficient of variation; GABA, gamma-aminobutyric acid; GC/MS, gas chromatography-mass spectrometry; HCl, hydrochloric acid; HILIC, hydrophilic interaction liquid chromatography; IS, internal standard; Inborn errors of metabolism; LC-MS/MS; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LLOQ, lower limit of quantitation; MM, mixed-mode; MS/MS, tandem mass spectrometry; MSU, Dmaple syrup urine disease; Maple syrup urine disease; QC, quality control; RPL, Creversed phase liquid chromatography; ULO, Qupper limit of quantitation
    DOI:  https://doi.org/10.1016/j.jmsacl.2022.05.002
  3. Bioinformatics. 2022 May 26. pii: btac355. [Epub ahead of print]
    MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities.
    Huaxu Yu, Tao Huan.
      MOTIVATION: Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.RESULTS: MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.
    AVAILABILITY AND IMPLEMENTATION: The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btac355
  4. J Sep Sci. 2022 Jun 01.
    Comprehensive pseudotargeted metabolomics analysis based on two-phase liquid extraction-UHPLC-MS/MS for the investigation of depressive rats.
    Minhan Huang, Ting Zhou.
      Pseudotargeted analysis combines the advantages of untargeted and targeted metabolomics methods. This study proposed a comprehensive pseudotargeted metabolomics method based on two-phase liquid extraction using ultra-high-performance liquid chromatography-tandem mass spectrometry. Two-phase liquid extraction, composed of both aqueous and organic phases, extracted a wide range of metabolites from polar to nonpolar in plasma samples. Besides, the two phases were combined and detected in a single injection to save analytical time. A total of 486 potential metabolites were detected by the developed approach. Compared with the conventional methanol-based protein precipitation method, the two-phase liquid extraction method significantly increased the metabolite coverage by 20.29%. Besides, the proposed pseudotargeted metabolomics method exhibited higher sensitivity and better repeatability than the untargeted method. Finally, we applied the established pseudotargeted method to the metabolomics study of depressive rats and screened 53 differential variables. Sixteen determined differential metabolites were mainly in four metabolic pathways, including glycerophospholipid, arachidonic acid, sphingolipid metabolisms, pentose and glucuronate interconversions. The results indicated that the pseudotargeted method based on two-phase liquid extraction broadened the metabolite coverage with good sensitivity and repeatability, exhibiting significant potential for discovering differential metabolites in metabolomics studies. This article is protected by copyright. All rights reserved.
    Keywords:  Depression; Pseudotargeted metabolomics; Two-phase liquid extraction; Ultra-high performance liquid chromatography-tandem mass spectrometry
    DOI:  https://doi.org/10.1002/jssc.202200255
  5. J Am Soc Mass Spectrom. 2022 Jun 02.
    Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility.
    Bailey S Rose, Jody C May, Allison R Reardon, John A McLean.
      Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.
    DOI:  https://doi.org/10.1021/jasms.2c00067
  6. ACS Meas Sci Au. 2022 Feb 16. 2(1): 67-75
    Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping.
    Melanie T Odenkirk, Brian M Horman, James N Dodds, Heather B Patisaul, Erin S Baker.
      While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.
    Keywords:  Brain; Cortex; Lipidomics; Mass spectrometry; Tissue
    DOI:  https://doi.org/10.1021/acsmeasuresciau.1c00035
  7. Environ Sci Technol. 2022 Jun 02.
    Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.
    MaKayla Foster, Markace Rainey, Chandler Watson, James N Dodds, Kaylie I Kirkwood, Facundo M Fernández, Erin S Baker.
      The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.
    Keywords:  PFAS; ion mobility spectrometry; machine learning; mass defect; mass spectrometry; per- and polyfluoroalkyl substances; xenobiotics
    DOI:  https://doi.org/10.1021/acs.est.2c00201
  8. Proteomics. 2022 Jun 02. e2100328
    Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics.
    María Moran-Garrido, Sandra M Camunas-Alberca, Alberto Gil-de-la Fuente, Antonio Mariscal, Ana Gradillas, Coral Barbas, Jorge Sáiz.
      Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.
    Keywords:  Acquisition; Data; Databases ; Ion Mobility; Lipidomics; Mass Spectrometry
    DOI:  https://doi.org/10.1002/pmic.202100328
  9. Drug Test Anal. 2022 Jun 01.
    Fast and highly sensitive determination of tetrahydrocannabinol (THC) metabolites in hair using liquid chromatography-multistage mass spectrometry (LC-MS3 ).
    Petra Hehet, Thomas Franz, Natalie Kunert, Frank Musshoff.
      In hair analysis, identification of 11-nor-9-carboxy-∆9 -tetrahydrocannabinol (THC-COOH), one of the major endogenously formed metabolites of the psychoactive cannabinoid tetrahydrocannabinol (THC), is considered unambiguous proof of cannabis consumption. Due to the complex hair matrix and low target concentrations of THC-COOH in hair, this kind of investigation represents a great analytical challenge. The aim of this work was to establish a fast, simple, and reliable LC-MS3 routine method for sensitive detection of THC-COOH in hair samples. Furthermore, the LC-MS3 method developed also included the detection of derivatized 11-hydroxy-∆9 -THC (11-OH-THC) as an additional marker of cannabis use. Hair sample preparation prior to detection of the two THC metabolites was based on digestion of the hair matrix under alkaline conditions followed by an optimized liquid-liquid extraction (LLE) procedure. Sample preparation by LLE proved to be more suitable than solid-phase extraction (SPE) due to less laborious and time-consuming steps while still yielding satisfactory results. A significant improvement in analytical detection was introduced by multistage fragmentation (MS3 ), which led to enhanced sensitivity and selectivity and thus low limits of quantification (0.1 pg/mg hair). The MS3 method included two transitions for THC-COOH (m/z 343 → 299 → 245 and m/z 343 → 299 → 191) encompassing the quantifier (m/z 245) and the qualifier ion (m/z 191). The method was fully validated, and successful application to authentic toxicology case samples was demonstrated by the analysis of more than 2000 hair samples from cannabis users with THC-COOH concentrations determined ranging from 0.1 to >15 pg/mg hair.
    Keywords:  11-hydroxy-∆9-THC (11-OH-THC); 11-nor-9-carboxy-∆9- tetrahydrocannabinol (THC-COOH); LC-MS3; cannabis; hair analysis; liquid-liquid extraction (LLE)
    DOI:  https://doi.org/10.1002/dta.3330
  10. Expert Opin Drug Discov. 2022 May 31. 1-13
    Acoustic ejection mass spectrometry: fundamentals and applications in high-throughput drug discovery.
    Chang Liu.
      INTRODUCTION: Acoustic ejection mass spectrometry (AEMS) is an electrospray ionization-mass spectrometry (ESI-MS) based analytical platform that provides high analytical throughput and data quality. It has been applied in numerous drug discovery workflows, such as high-throughput screening (HTS), absorption, distribution, metabolism, and excretion (ADME) profiling, pharmacokinetics (PK) analysis, compound quality control (QC), and the high throughput readout of medical chemistry samples.AREAS COVERED: This paper introduces the working principle of AEMS technology. The analytical performance, system tuning and method development workflows, and representative applications on high-throughput drug discovery assays are introduced.
    EXPERT OPINION: The fast sample readout speed, high reproducibility, wide compound coverage, minimum carryover, and the tolerance to complex sample matrices of AEMS have demonstrated its advantages over other technologies in this field, with broad applicability to a diverse range of drug discovery workflows. However, the current platform still has challenges for some assays due to having less sensitivity compared with liquid chromatography-mass spectrometry (LC-MS), and the lack of chromatographic separation for isomeric interferences. Despite the remaining hurdles, AEMS is becoming an attractive high-throughput analytical platform in drug discovery. Additional system developments would enable its further adoption in the future.
    Keywords:  High-throughput analysis; acoustic dropout ejection; drug discovery; mass spectrometry; sampling interface
    DOI:  https://doi.org/10.1080/17460441.2022.2084069
  11. J Chromatogr A. 2022 May 19. pii: S0021-9673(22)00355-7. [Epub ahead of print]1675 463162
    A multi-dimensional liquid chromatography/high-resolution mass spectrometry approach combined with computational data processing for the comprehensive characterization of the multicomponents from Cuscuta chinensis.
    Miao Wang, Xiao-Yan Xu, Hong-da Wang, Hui-Min Wang, Mei-Yu Liu, Wan-di Hu, Bo-Xue Chen, Mei-Ting Jiang, Jing Qi, Xiao-Hang Li, Wen-Zhi Yang, Xiu-Mei Gao.
      Challenges encountered in plant metabolites characterization by liquid chromatography/mass spectrometry can arise from the insufficient chromatography separation, the lack of specific database, and low reliability of identification because of the ubiquitous isomerism. Herein, we present an integral approach, by combining comprehensive off-line two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), automatic peak annotation, molecular networking, and collision cross section (CCS) prediction, aimed to improve the resolution and reliability in MS-oriented metabolites characterization. Using the seeds of Cuscuta chinensis as a case, the configuration of an XBridge Amide column of hydrophilic interaction chromatography (HILIC) and a Zorbax SB-Aq column of reversed-phase chromatography (RPC), in an off-line mode, showed the orthogonality of 0.73 and effective peak capacity of 4361. Data-independent high-definition MSE (HDMSE) in the negative mode could enable high-coverage MS2 data acquisition and enhance the ions resolution, while computational peak annotation workflows facilitated by UNIFITM and Global Natural Products Social Molecular Network (GNPS) could efficiently characterize the targeted and untargeted compound analogs. A total of 302 compounds were identified or tentatively characterized, and 109 thereof were unreported. Moreover, CCS prediction (www.allccs.zhulab.cn) provided more possibilities to distinguish 12 pairs of isomers in the lack of reference standards. The 2D-LC/IM-QTOF-MS approach enabled the collection of five dimension of data related to each component (tR by HILIC and RPC, CCS, m/z in MS1 and MS2), and the intelligent metabolites characterization with more reliable MS data. Conclusively, the established integral strategy can be utilized in metabolome analysis to support the quality control of herbal medicines.
    Keywords:  Cuscuta chinensis; High-definition MS(E); Ion mobility/quadrupole time-of-flight mass spectrometry; Molecular networking; Off-line two-dimensional liquid chromatography
    DOI:  https://doi.org/10.1016/j.chroma.2022.463162
  12. J Pharm Biomed Anal. 2022 May 10. pii: S0731-7085(22)00250-3. [Epub ahead of print]217 114829
    Validation of an LC-MS/MS method for the quantification IOA-289 in human plasma and its application in a first-in-human clinical trial.
    M Mameli, J Franchi, G Calusi, M A Deken, Z Johnson, L van der Veen, M Lahn, A Vezzelli, R Cardin, A Greco, M Breda.
      IOA-289 is a novel small molecule inhibitor of autotaxin developed as a first-in-class therapy of fibrotic pathologies including cancer. A method for quantitation of IOA-289 in human plasma was developed using a stable isotope labeled compound ([13C4]IOA-289) as internal standard. The analytes were extracted from human plasma by protein precipitation and the analysis was performed by liquid chromatography coupled with tandem mass spectrometric detection (LCMS/MS). The chromatographic separation was performed with a gradient elution from a BEH C18 column and under these conditions the retention time and the run time were 1 and 2 min, respectively. The assay was fully validated over the range 3-3000 ng/mL, proved to be accurate, precise and selective and was successfully applied to quantitate IOA-289 in plasma samples from subjects in a first-in-humanclinical trial.
    Keywords:  ATX inhibitor; Autotaxin (ATX); Human plasma validation; IOA-289; LC-MS/MS
    DOI:  https://doi.org/10.1016/j.jpba.2022.114829
  13. Scand J Clin Lab Invest. 2022 May 30. 1-6
    Validation of an automated UPLC-MS/MS method for methylmalonic acid in serum/plasma and its application on clinical samples.
    Yufang Zheng, Mats Bergström.
      BACKGROUND: Vitamin B12 is essential for cell function and only accessible in food for mammals. To monitor vitamin B12 deficiency, the substance methylmalonic acid (MMA) is used. Since MMA in serum/plasma samples is a frequently requested analyte at clinical laboratories the analytical method was therefore, improved and validated on a 96 well plate.METHODS: Using a Tecan robot a working solution of acetonitrile containing MMA-D3 was added to plasma/serum samples. The solution was shaken for 1 min and then centrifuged for 10 min. The supernatant was transferred to another plate and evaporated with nitrogen gas. The residual was redissolved with 0.2% formic acid in MilliQ-water and the plate was shaken for 1-min prior LC-MS/MS analysis.
    RESULTS: The total analysis time was 3 min, retention time for MMA was 1.1 min and was well separated from the interfering substance succinic acid. The calibrator curve was 0.044-1.63 µmol/L, which was also the linear range and LLOQ was 0.044 µmol/L. The within and between-run CV were 3-7%. Age dependent clinical cut-offs at 0.28 and 0.36 µmol/L for individuals < 50 years > respectively were applied. In 404 clinical routine samples the found concentration for MMA > 0.28 µmol/L was 10%, > 0.4 µmol/L was 7% and only 1% was >0.7 µmol/L.
    CONCLUSIONS: This automated method was successfully implemented in the laboratory analysing MMA in clinical routine samples.
    Keywords:  Mass spectrometry; high performance liquid chromatography; method validation; methylmalonic acid; reference levels; vitamin B12
    DOI:  https://doi.org/10.1080/00365513.2022.2079558
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