Review of the Use of Liquid Chromatography-Tandem Mass Spectrometry in Clinical Laboratories: Part II-Operations

Detecting M-Protein via Mass Spectrometry and Affinity Beads: Enrichment With Mixed Kappa-Lambda Beads Enables Prompt Application in Clinical Laboratories

Background

Detecting monoclonal protein (M-protein), a hallmark of plasma cell disorders, traditionally relies on methods such as protein electrophoresis, immune-electrophoresis, and immunofixation electrophoresis (IFE). Mass spectrometry (MS)-based methods, such as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and electrospray ionization-quadrupole time-of-flight (ESI-qTOF) MS, have emerged as sensitive methods. We explored the M-protein-detection efficacies of different MS techniques.

Methods

To isolate immunoglobulin and light chain proteins, six types of beads (IgG, IgA, IgM, kappa, lambda, and mixed kappa and lambda) were used to prepare samples along with CaptureSelect nanobody affinity beads (NBs). After purification, both MALDI-TOF MS and liquid chromatography coupled with Synapt G2 ESI-qTOF high-resolution MS analysis were performed. We purified 25 normal and 25 abnormal IFE samples using NBs and MALDI-TOF MS (NB-MALDI-TOF).

Results

Abnormal samples showed monoclonal peaks, whereas normal samples showed polyclonal peaks. The IgG and mixed kappa and lambda beads showed monoclonal peaks following the use of daratumumab (an IgG/kappa type of monoclonal antibody) with both MALDI-TOF and ESI-qTOF MS analysis. The limits of detection for MALDI-TOF MS and ESI-qTOF MS were established as 0.1 g/dL and 0.025 g/dL, respectively. NB-MALDI-TOF and IFE exhibited comparable sensitivity and specificity (92% and 92%, respectively).

Conclusions

NBs for M-protein detection, particularly with mixed kappa-lambda beads, identified monoclonal peaks with both MALDI-TOF and ESI-qTOF analyses. Qualitative analysis using MALDI-TOF yielded results comparable with that of IFE. NB-MALDI-TOF might be used as an alternative method to replace conventional tests (such as IFE) to detect M-protein with high sensitivity.

Suppressing the background of LC-ESI-MS analysis of permethylated glycans using the active background ion reduction device

Mass spectrometry (MS) has revolutionized analytical chemistry, enabling precise identification and quantification of chemical species, which is pivotal for biomarker discovery and understanding complex biological systems. Despite its versatility, the presence of background ions in MS analysis hinders the sensitive detection of low-abundance analytes. Therefore, studies aimed at lowering background ion levels have become increasingly important. Here, we utilized the commercially available Active Background Ion Reduction Device (ABIRD) to suppress background ions and assess its effect on the liquid chromatography-electrospray ionization (LC-ESI)-MS analyses of N-glycans on the Q Exactive HF mass spectrometer. We also investigated the effect of different solvent vapors in the ESI source on N-glycan analysis by MS. ABIRD generally had no effect on high-mannose and neutral structures but reduced the intensity of some structures that contained sialic acid, fucose, or both when methanol vapor filled the ESI source. Based on our findings on the highest number of identified N-glycans from human serum, methanol vapor in the ion source compartment may enhance N-glycan LC-ESI-MS analyses by improving the desolvation of droplets formed during the ESI process due to its high volatility. This protocol may be further validated and extended to advanced bottom-up proteomic/glycoproteomic studies for the analysis of peptide/glycopeptide ions by MS.

Surreptitious pipetting errors on a vendor-programmed liquid handler

Liquid handlers (LHs) have become common in both clinical and academic laboratories for the preparation and manipulation of samples. In theory, these systems offer the potential for reduced error due to the elimination of mis-pipetting errors. In reality, these systems still have potential for mis-pipetting and require careful validation by the end user. In this case report, we describe two instances where inappropriate pipetting by a vendor-programmed LH were observed. In each case, the worklist that was obtained from the LH failed to reflect what had actually been pipetted and as such these instances represented significant near misses with substantial potential for patient harm. Neither of these instances were caught during the laboratory's validation studies of the LH. Laboratories should be aware of the potential for mis-pipetting by LHs. LH vendors should work to ensure the worklists reflect what was pipetted (instead of what was intended to be pipetted) and end users must ensure robust validation studies that take into account as many "real world" scenarios as possible.

Long- and Short-Term Glucosphingosine (lyso-Gb1) Dynamics in Gaucher Patients Undergoing Enzyme Replacement Therapy

Background: Gaucher disease (GD) is a lysosomal storage disorder caused by mutations in the GBA1 gene, leading to β-glucocerebrosidase deficiency and glucosylceramide accumulation. Methods: We analyzed short- and long-term dynamics of lyso-glucosylceramide (lyso-Gb1) in a large cohort of GD patients undergoing enzyme replacement therapy (ERT). Results: Eight-years analysis of lyso-Gb1 revealed statistically insignificant variability in the biomarker across the years and relatively high individual variability in patients' results. GD type 1 (GD1) patients exhibited higher variability compared to GD type 3 (GD3) patients (coefficients of variation: 34% and 23%, respectively; p-value = 0.0003). We also investigated the short-term response of the biomarker to enzyme replacement therapy (ERT), measuring lyso-Gb1 right before and 30 min after treatment administration. We tested 20 GD patients (16 GD1, 4 GD3) and observed a rapid and significant reduction in lyso-Gb1 levels (average decrease of 17%; p-value < 0.0001). This immediate response reaffirms the efficacy of ERT in reducing substrate accumulation in GD patients but, on the other hand, suggests the biomarker's instability between the infusions. Conclusions: These findings underscore lyso-Gb1's potential as a reliable biomarker for monitoring efficacy of treatment. However, individual variability and dry blood spot (DBS) testing limitations urge a further refinement in clinical application. Our study contributes valuable insights into GD patient management, emphasizing the evolving role of biomarkers in personalized medicine.

Serum trace elements during treatment in pancreatic cancer patients and their associations with cancer prognosis

BACKGROUND & AIMS

In this study, we assessed serum trace element concentrations in patients with pancreatic cancer and compared the results to those of healthy controls and patients with chronic pancreatitis. We evaluated the association between trace element concentrations during cancer treatment and the risk of cancer progression and mortality in pancreatic cancer patients.

METHODS

A retrospective cohort study was conducted at a tertiary center in Korea. Serum trace element concentrations of cobalt (Co), copper (Cu), selenium (Se), and zinc (Zn) were measured at diagnosis using an inductively coupled plasma-mass spectrometry in 124 patients with pancreatic cancer, 50 patients with chronic pancreatitis, and 120 healthy controls. Trace elements were measured after a median of 282.5 (95% confidence interval [CI], 224.0-326.5) days from treatment initiation to assess changes in trace element concentrations during treatment.

RESULTS

Serum Co concentrations were significantly higher in patients with chronic pancreatitis and pancreatic cancer compared to healthy controls, while serum Se concentrations were significantly lower. During treatment, serum concentrations of Cu, Se, and Zn significantly decreased in patients with pancreatic cancer. During the follow-up (median 152.5; 95% CI, 142.8-160.0 months), 85.5% of patients experienced progression or relapse, and 84.7% of patients died. Patients with decreased Se and Zn concentrations during treatment had a higher mortality (hazard ratio [HR], 2.10; 95% CI, 1.31-3.38; P = 0.0020 for Se; HR, 1.72; 95% CI, 1.06-2.79; P = 0.0269 for Zn) compared to those with unchanged or increased trace element concentrations during treatment. Patients with a greater reduction in Zn concentrations during treatment had a higher mortality than those with a smaller reduction (HR, 1.59; 95% CI, 1.01-2.52; P = 0.0483). Patients whose Zn status changed from normal to deficient during treatment had an increased mortality (HR, 1.76; 95% CI, 1.16-2.67, P = 0.0084). Patients with multiple (≥2) trace element deficiencies after treatment had poorer outcomes than those with no or single trace element deficiency.

CONCLUSIONS

This study revealed that decreases in Se and Zn concentrations during cancer treatment were associated with adverse outcomes in terms of cancer progression and mortality in patients with pancreatic cancer. Further prospective investigations are recommended.

Simultaneous determination of diquat, paraquat, glufosinate, and glyphosate in plasma by liquid chromatography/tandem mass spectrometry: from method development to clinical application

Diquat (DQ), paraquat (PQ), glufosinate (GLU), and glyphosate (GLYP) are commonly used herbicides that have been confirmed to be toxic to humans. Rapid and accurate measurements of these toxicants in clinical practice are beneficial for the correct diagnosis and timely treatment of herbicide-poisoned patients. The present study aimed to establish an efficient, convenient, and reliable method to achieve the simultaneous quantification of DQ, PQ, GLU, and GLYP in human plasma using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without using derivatization or ion-pairing reagents. DQ, PQ, GLU, and GLYP were extracted by the rapid protein precipitation and liquid-liquid extraction method and then separated and detected by LC-MS/MS. Subsequently, linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, extraction recovery, matrix effect, dilution integrity, and stability were evaluated to validate the method based on the FDA criteria. Finally, the validated method was applied to real plasma samples collected from 166 Chinese patients with herbicide poisoning. The results showed satisfactory linearity with low LOD (1 ng/mL for DQ and PQ, 5 ng/mL for GLU, and 10 ng/mL for GLYP, respectively) and low LOQ (5 ng/mL for DQ and PQ, 25 ng/mL for GLU and GLYP, respectively). In addition, the precision, accuracy, extraction recovery, and stability of the method were acceptable. The matrix effect was not observed in the analyzed samples. Moreover, the developed method was successfully applied to determine the target compounds in real plasma samples. These data provided reliable evidence for the application of this LC-MS/MS method for clinical poisoning detection.

Simultaneous Measurement and Distribution Analysis of Urinary Nicotine, Cotinine, Trans-3'-Hydroxycotinine, Nornicotine, Anabasine, and Total Nicotine Equivalents in a Large Korean Population

Measurement of multiple nicotine metabolites and total nicotine equivalents (TNE) might be a more reliable strategy for tobacco exposure verification than measuring single urinary cotinine alone. We simultaneously measured nicotine, cotinine, 3-OH cotinine, nornicotine, and anabasine using 19,874 urine samples collected from the Korean National Health and Nutrition Examination Survey. Of all samples, 18.6% were positive for cotinine, 17.4% for nicotine, 17.3% for nornicotine, 17.6% for 3-OH cotinine, and 13.2% for anabasine. Of the cotinine negative samples, less than 0.3% were positive for all nicotine metabolites, but not for anabasine (5.7%). The agreement of the classification of smoking status by cotinine combined with nicotine metabolites was 0.982-0.994 (Cohen's kappa). TNE3 (the molar sum of urinary nicotine, cotinine, and 3-OH cotinine) was most strongly correlated with cotinine compared to the other nicotine metabolites; however, anabasine was less strongly correlated with other biomarkers. Among anabasine-positive samples, 30% were negative for nicotine or its metabolites, and 25% were undetectable. Our study shows that the single measurement of urinary cotinine is simple and has a comparable classification of smoking status to differentiate between current smokers and non-smokers relative to the measurement of multiple nicotine metabolites. However, measurement of multiple nicotine metabolites and TNE3 could be useful for monitoring exposure to low-level or secondhand smoke exposure and for determining individual differences in nicotine metabolism. Geometric or cultural factors should be considered for the differentiation of tobacco use from patients with nicotine replacement therapy by anabasine.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models

BACKGROUND

Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia.

OBJECTIVE

This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index.

STUDY DESIGN

This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate.

RESULTS

First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes.

CONCLUSION

Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Quantitative tandem mass spectrometry in the clinical laboratory: Regulation and opportunity for validation of laboratory developed tests

Tandem mass spectrometry is an important analytical tool for clinical laboratories, but tests developed and validated in-house (laboratory developed tests, or LDTs) require special consideration. In late 2022, the forecast for United States (U.S.) federal regulation of LDTs changed unexpectedly when the VALID Act was not passed by the U.S. Congress. This Act would have modified the Food and Drug Administration's (FDA's) role to increase regulatory oversight for LDT providers. In this revised context, we review optimization of quantitative mass spectrometry LDT validation and suggest avenues other than an additional FDA mandate to achieve uniform best practice. Common challenges, logistical barriers, and recommendations for easing the burden of best-quality quantitative mass spectrometry LDT method validation are discussed.

Clinical Usefulness of Ultraperformance Liquid Chromatography-Tandem Mass Spectrometry Method for Low Serum Testosterone Measurement

Background

Mass spectrometry methods exhibit higher accuracy and lower variability than immunoassays at low testosterone concentrations. We developed and validated an ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) assay for quantifying serum total testosterone.

Methods

We used an ExionLC UPLC (Sciex, Framingham, MA, USA) system and a Sciex Triple Quad 6500+ (Sciex) MS/MS system in electrospray ionization and positive ion modes with multiple reaction monitoring transitions to evaluate precision, accuracy, linearity, lower limit of quantitation (LLOQ), carryover, ion suppression, stability, and reference intervals. For method comparison, we measured serum testosterone concentrations using this method in 40 subjects whose testosterone concentrations ranged from 0.14 to 55.48 nmol/L as determined using the Architect i2000 immunoassay (Abbott Diagnostics, Abbott Park, IL, USA) and in an additional 160 sera with testosterone concentrations <1.67 nmol/L.

Results

The intra- and inter-run precision CVs were <2.81%, and the accuracy bias values were <3.85%, which were all acceptable. The verified linear interval was 0.03-180.84 nmol/L; the LLOQ was 0.03 nmol/L. No significant carryover and ion suppression were observed. The testosterone in serum was stable at 4°C, at -20°C, and after three freeze-thaw cycles. The reference intervals were successfully verified. The correlation was good at testosterone concentrations of 0.14-55.48 nmol/L; however, the Architect assay showed positive percent bias at concentrations <1.67 nmol/L.

Conclusions

The UPLC-MS/MS assay shows acceptable performance, with a lower LLOQ than the immunoassay. This method will enable the quantitation of low testosterone concentrations.

Calibration Practices in Clinical Mass Spectrometry: Review and Recommendations

Background

Calibration is a critical component for the reliability, accuracy, and precision of mass spectrometry measurements. Optimal practice in the construction, evaluation, and implementation of a new calibration curve is often underappreciated. This systematic review examined how calibration practices are applied to liquid chromatography-tandem mass spectrometry measurement procedures.

Methods

The electronic database PubMed was searched from the date of database inception to April 1, 2022. The search terms used were "calibration," "mass spectrometry," and "regression." Twenty-one articles were identified and included in this review, following evaluation of the titles, abstracts, full text, and reference lists of the search results.

Results

The use of matrix-matched calibrators and stable isotope-labeled internal standards helps to mitigate the impact of matrix effects. A higher number of calibration standards or replicate measurements improves the mapping of the detector response and hence the accuracy and precision of the regression model. Constructing a calibration curve with each analytical batch recharacterizes the instrument detector but does not reduce the actual variability. The analytical response and measurand concentrations should be considered when constructing a calibration curve, along with subsequent use of quality controls to confirm assay performance. It is important to assess the linearity of the calibration curve by using actual experimental data and appropriate statistics. The heteroscedasticity of the calibration data should be investigated, and appropriate weighting should be applied during regression modeling.

Conclusions

This review provides an outline and guidance for optimal calibration practices in clinical mass spectrometry laboratories.

Liquid chromatography–tandem mass spectrometry for clinical diagnostics

Mass spectrometry is a powerful analytical tool used for the analysis of a wide range of substances and matrices; it is increasingly utilized for clinical applications in laboratory medicine. This Primer includes an overview of basic mass spectrometry concepts, focusing primarily on tandem mass spectrometry. We discuss experimental considerations and quality management, and provide an overview of some key applications in the clinic. Lastly, the Primer discusses significant challenges for implementation of mass spectrometry in clinical laboratories and provides an outlook of where there are emerging clinical applications for this technology.

Tandem mass spectrometry is increasingly utilized for clinical applications in laboratory medicine. In this Primer, Thomas et al. discuss experimental considerations and quality management for implementing clinical tandem mass spectrometry in the clinic with an overview of some key applications.