Mass Spectrometry in Precision Medicine: Phenotypic Measurements Alongside Pharmacogenomics

Hyphenation of microflow chromatography with electrospray ionization mass spectrometry for bioanalytical applications focusing on low molecular weight compounds: A tutorial review

Benefits of miniaturized chromatography with various detection modes, such as increased sensitivity, chromatographic efficiency, and speed, were recognized nearly 50 years ago. Over the past two decades, this approach has experienced rapid growth, driven by the emergence of mass spectrometry applications serving -omics sciences and the need for analyzing minute volumes of precious samples with ever higher sensitivity. While nanoscale liquid chromatography (flow rates <1 μL/min) has gained widespread recognition in proteomics, the adoption of microscale setups (flow rates ranging from 1 to 100 μL/min) for low molecular weight compound applications, including metabolomics, has been surprisingly slow, despite the inherent advantages of the approach. Highly heterogeneous matrices and chemical structures accompanied by a relative lack of options for both selective sample preparation and user-friendly equipment are usually reported as major hindrances. To facilitate the wider implementation of microscale analyses, we present here a comprehensive tutorial encompassing important theoretical and practical considerations. We provide fundamental principles in micro-chromatography and guide the reader through the main elements of a microflow workflow, from LC pumps to ionization devices. Finally, based on both our literature overview and experience, illustrated by some in-house data, we highlight the critical importance of the ionization source design and its careful optimization to achieve significant sensitivity improvement.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Controllable-Swelling Microneedle-Assisted Ultrasensitive Paper Sensing Platforms for Personal Health Monitoring

Microneedle (MN) patches, which allow the extraction of skin interstitial fluid (ISF) without a pain sensation, are powerful tools for minimally invasive biofluid sampling. Herein, an MN-assisted paper-based sensing platform that enables rapid and painless biofluid analysis with ultrasensitive molecular recognition capacity is developed. First, a controllable-swelling MN patch is constructed through the engineering of a poly(ethylene glycol) diacrylate/methacrylated hyaluronic acid hydrogel; it combines rapid, sufficient extraction of ISF with excellent structural integrity. Notably, the analyte molecules in the needles can be recovered into a moist cellulose paper through spontaneous diffusion. More importantly, the paper can be functionalized with enzymatic colorimetric reagents or a plasmonic array, enabling a desired detection capacity-for example, the use of paper-based surface-enhanced Raman spectroscopy sensors leads to label-free, trace detection (sub-ppb level) of a diverse set of molecules (cefazolin, nicotine, paraquat, methylene blue). Finally, nicotine is selected as a model drug to evaluate the painless monitoring of three human volunteers. The changes in the nicotine levels can be tracked, with the levels varying significantly in response to the metabolism of drug in different volunteers. This as-designed minimally invasive sensing system should open up new opportunities for precision medicine, especially for personal healthcare monitoring.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Metabolomics application for the design of an optimal diet

Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

Point-of-care companion diagnostic tests for personalizing psychiatric medications: fulfilling an unmet clinical need

Over the last decade stable isotope-labeled substrates have been used as probes for rapid, point-of-care, non-invasive and user-friendly phenotype breath tests to evaluate activity of drug metabolizing enzymes. These diagnostic breath tests can potentially be used as companion diagnostics by physicians to personalize medications, especially psychiatric drugs with narrow therapeutic windows, to monitor the progress of disease severity, medication efficacy and to study in vivo the pharmacokinetics of xenobiotics. Several genotype tests have been approved by the FDA over the last 15 years for both cytochrome P450 2D6 and 2C19 enzymes, however they have not been cleared for use in personalizing medications since they fall woefully short in identifying all non-responders to drugs, especially for the CYP450 enzymes. CYP2D6 and CYP2C19 are among the most extensively studied drug metabolizing enzymes, involved in the metabolism of approximately 30% of FDA-approved drugs in clinical use, associated with large individual differences in medication efficacy or tolerability essentially due to phenoconversion. The development and commercialization via FDA approval of the non-invasive, rapid (<60 min), in vivo, phenotype diagnostic breath tests to evaluate polymorphic CYP2D6 and CYP2C19 enzyme activity by measuring exhaled ¹³CO₂ as a biomarker in breath will effectively resolve the currently unmet clinical need for individualized psychiatric drug therapy. Clinicians could personalize treatment options for patients based on the CYP2D6 and CYP2C19 phenotype by selecting the optimal medication at the right initial and subsequent maintenance dose for the desired clinical outcome (i.e. greatest efficacy and minimal side effects).

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Chronic hepatitis B virus (HBV) infection remains a global problem. Several HBV genotypes exist with different biology and geographical prevalence. Whilst the future aim of HBV treatment remains viral eradication, current treatment strategies aim to suppress the virus and prevent the progression of liver disease. Current strategies also involve identification of patients for treatment, namely those at risk of progressive liver disease. Identification of HBV genotype, HBV mutants and other predictive factors allow for tailoured treatments, and risk-surveillance pathways, such as hepatocellular cancer screening. In the future, these factors may enable stratification not only of treatment decisions, but also of patients at risk of higher relapse rates when current therapies are discontinued. Newer technologies, such as next-generation sequencing, to assess drug-resistant or immune escape variants and quasi-species heterogeneity in patients, may allow for more information-based treatment decisions between the clinician and the patient. This article serves to discuss how HBV genotypes and genetic variants impact not only upon the disease course and outcomes, but also current treatment strategies. Adopting a personalised genotypic approach may play a role in future strategies to combat the disease. Herein, we discuss new technologies that may allow more informed decision-making for response guided therapy in the battle against HBV.

Variations in plasma concentrations of tamoxifen metabolites and the effects of genetic polymorphisms on tamoxifen metabolism in Korean patients with breast cancer

Inter-individual variation in tamoxifen metabolism in breast cancer patients is caused by various genetic and clinical factors. We measured the plasma concentrations of tamoxifen and its metabolites and investigated genetic polymorphisms influencing those concentrations. We measured the concentrations of tamoxifen, endoxifen, N-desmethyltamoxifen (NDM), and 4-hydroxytamoxifen (4-OH tamoxifen) in 550 plasma specimens from 281 breast cancer patients treated with tamoxifen. Duplicate or triplicate specimens were obtained from 179 patients at 3-month intervals. In 80 patients, genotyping for tamoxifen metabolizing enzymes was performed using the DMET Plus array and long-range PCR. Plasma concentrations of tamoxifen and its metabolites showed wide variations among patients. The following genetic polymorphisms were associated with the plasma concentrations when body mass index and tamoxifen concentrations were considered as co-variables: CYP1A2 -2467delT, CYP2B6 genotype, CYP2D6 activity score (AS), and FMO3 441C>T. CYP2D6 AS and three variants in the SULT1E1 gene showed correlation with ratios of tamoxifen metabolites. CYP2D6 AS was the only variable that showed associations with both metabolite concentration and ratio: endoxifen (P < 0.001), NDM (P < 0.001), endoxifen/NDM (P < 0.001), NDM/tamoxifen (P < 0.001), and 4-OH tamoxifen/tamoxifen (P = 0.005). Serial measurements of 448 plasma concentrations in 179 patients at 3-month intervals showed wide intra-individual variation. Our study showed that genetic polymorphisms can in part determine the baseline concentrations of tamoxifen and its metabolites. However, marked intra-individual variations during follow-up monitoring were observed, and this could not be explained by genotype. Therefore, serial measurements of tamoxifen and its metabolites would be helpful in monitoring in vivo tamoxifen metabolic status.