Tandem mass spectroscopy in diagnosis and clinical research

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

A comprehensive review on anticancer evaluation techniques

The development of effective anticancer strategies and the improvement of our understanding of cancer need analytical tools. Utilizing a variety of analytical approaches while investigating anti-cancer medicines gives us a thorough understanding of the traits and mechanisms concerned to cancer cells, which enables us to develop potent treatments to combat them. The importance of anticancer research may be attributed to various analytical techniques that contributes to the identification of therapeutic targets and the assessment of medication efficacy, which are crucial things in expanding our understanding of cancer biology. The study looks at methods that are often used in cancer research, including cell viability assays, clonogenic assay, flow cytometry, 2D electrophoresis, microarray, immunofluorescence, western blot caspase activation assay, bioinformatics, etc. The fundamentals, applications, and how each technique analytical advances our understanding of cancer are briefly reviewed.

Recent Advances in the Detection of Food Toxins Using Mass Spectrometry

Edibles are the only source of nutrients and energy for humans. However, ingredients of edibles have undergone many physicochemical changes during preparation and storage. Aging, hydrolysis, oxidation, and rancidity are some of the major changes that not only change the native flavor, texture, and taste of food but also destroy the nutritive value and jeopardize public health. The major reasons for the production of harmful metabolites, chemicals, and toxins are poor processing, inappropriate storage, and microbial spoilage, which are lethal to consumers. In addition, the emergence of new pollutants has intensified the need for advanced and rapid food analysis techniques to detect such toxins. The issue with the detection of toxins in food samples is the nonvolatile nature and absence of detectable chromophores; hence, normal conventional techniques need additional derivatization. Mass spectrometry (MS) offers high sensitivity, selectivity, and capability to handle complex mixtures, making it an ideal analytical technique for the identification and quantification of food toxins. Recent technological advancements, such as high-resolution MS and tandem mass spectrometry (MS/MS), have significantly improved sensitivity, enabling the detection of food toxins at ultralow levels. Moreover, the emergence of ambient ionization techniques has facilitated rapid in situ analysis of samples with lower time and resources. Despite numerous advantages, the widespread adoption of MS in routine food safety monitoring faces certain challenges such as instrument cost, complexity, data analysis, and standardization of methods. Nevertheless, the continuous advancements in MS-technology and its integration with complementary techniques hold promising prospects for revolutionizing food safety monitoring. This review discusses the application of MS in detecting various food toxins including mycotoxins, marine biotoxins, and plant-derived toxins. It also explores the implementation of untargeted approaches, such as metabolomics and proteomics, for the discovery of novel and emerging food toxins, enhancing our understanding of potential hazards in the food supply chain.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

Interest of the faecal and plasma matrix for monitoring the exposure of wildlife or domestic animals to anticoagulant rodenticides

Anticoagulant rodenticides (ARs), particularly second-generation compounds (SGAR), are known to be a potential threat to unintended species due to their tissue persistence. The liver is the storage tissue of ARs and is a matrix of choice in diagnosing exposure and intoxication of non-target fauna. However, it is only available on dead animals. Blood and faeces can be used on living animals. These two biological matrices were compared in terms of their relevance to exposure to ARs. In addressing this question, we compared the faecal, plasma and liver concentrations of bromadiolone, one of the SGAR frequently implicated in wildlife exposure. We studied this comparison at the individual level and at the population level, considering three influencing factors: dose, sex and time. Our findings demonstrate that faecal analyses are more valuable than plasma analyses for monitoring AR exposure of domestic and wild animals, even if faecal concentrations cannot be correlated with liver concentrations.

Estimating the Prevalence of Rare Diseases: Long-Chain Fatty Acid Oxidation Disorders as an Illustrative Example

Introduction

Determining the epidemiology of disease is critical for multiple reasons, including to perform risk assessment, compare disease rates in varying populations, support diagnostic decisions, evaluate health care needs and disease burden, and determine the economic benefit of treatment. However, establishing epidemiological measures for rare diseases can be difficult owing to small patient populations, variable diagnostic techniques, and potential disease and diagnostic heterogeneity. To determine the epidemiology of rare diseases, investigators often develop estimation models to account for missing or unobtainable data, and to ensure that their findings are representative of their desired patient population.

Methods

A modeling methodology to estimate the prevalence of rare diseases in one such population—patients with long-chain fatty acid oxidation disorders (LC-FAOD)—as an illustrative example of its applicability.

Results

The proposed model begins with reliable source data from newborn screening reports and applies to them key modifiers. These modifiers include changes in population growth over time and variable standardization rates of LC-FAOD screening that lead to (1) a confirmed diagnosis and (2) improvements in standards of care and survival estimates relative to the general population. The model also makes necessary assumptions to allow the broad applicability of the estimation of LC-FAOD prevalence, including rates of diagnosed versus undiagnosed patients in the USA over time.

Conclusions

Although each rare disease is unique, the approach described here and demonstrated in the estimation of LC-FAOD prevalence provides the necessary tools to calculate key epidemiological estimates useful in performing risk assessment analyses; comparing disease rates between different subgroups of people; supporting diagnostic decisions; planning health care needs; comparing disease burden, including cost; and determining the economic benefit of treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-022-02186-2.

Applications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research

Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.

Precision Medicine: An Emerging Paradigm for Improved Diagnosis and Safe Therapy in Pediatric Oncology

Cancer is a lethal disease that kills a great number of people each year. Standard treatments such as chemotherapy or radiation are only effective in a small percentage of individuals due to illness variability. Tumors can be caused by a variety of genetic factors and express a variety of proteins depending on the individual. Because of developments in high-throughput technology, there has been a flood of large-scale biological data produced in recent decades. As a result, the focus of medical research has evolved. It was a once-in-a-lifetime chance for translational research to explore molecular alterations across the entire genome. In this setting, precision medicine was developed, and the possibility of better diagnostic and treatment tools became a reality. This is especially true in the case of cancer, which is becoming more prevalent around the world. The goal of this study is to look at precision medicine technology and its applications to cancer, with a focus on children. The inherent diversity of cancer lends itself to the rapidly expanding field of precision and personalized medicine.

Inborn Errors of Metabolism-Approach to Diagnosis and Management in Neonates

Inborn errors of metabolism (IEM), otherwise known as inherited metabolic disorders (IMD), are individually rare, but collectively common. IEM pose a challenge to diagnosis, as neonates present with nonspecific signs. A high index of suspicion is essential. Knowledge on clinical presentation may be life saving, especially for conditions that are treatable. It is important for the first-line physicians not to miss treatable disorders. Simplified classification and algorithmic approach help in the clinical setting. This article describes the classification of IEM into three groups, namely group 1 - intoxication disorders, group 2 - energy defects, and group 3 - storage disorders. Clinical presentations of IEM in the neonatal period, a quick guide to the diagnosis with the help of baseline investigations (glucose, arterial blood gas, lactate, ammonia, and ketone abbreviated as GALAK), a tabulated guide to the diagnosis with the help of tandem mass spectrometry (TMS), and gas chromatography and mass spectrometry (GCMS) are summarized in this article. Four principles of therapy that include substrate reduction, provision of deficient metabolites, disposal of toxic metabolites, and increase in enzyme activity are elaborated with particular stress to the diet management. In addition, a list of medications used in the treatment of different disorders classified according to Society for the Study of IEM (SSIEM) is presented.

Mitochondrial Function as a Potential Tool for Assessing Function, Quality and Adulteration in Medicinal Herbal Teas

Quality control has been a significant issue in herbal medicine since herbs became widely used to heal. Modern technologies have improved the methods of evaluating the quality of medicinal herbs but the methods of adulterating them have also grown in sophistication. In this paper we undertook a comprehensive literature search to identify the key analytical techniques used in the quality control of herbal medicine, reviewing their uses and limitations. We also present a new tool, based on mitochondrial profiling, that can be used to measure medicinal herbal quality. Besides being fundamental to the energy metabolism required for most cellular activities, mitochondria play a direct role in cellular signalling, apoptosis, stress responses, inflammation, cancer, ageing, and neurological function, mirroring some of the most common reasons people take herbal medicines. A fingerprint of the specific mitochondrial effects of medicinal herbs can be documented in order to assess their potential efficacy, detect adulterations that modulate these effects and determine the relative potency of batches. Furthermore, through this method it will be possible to assess whole herbs or complex formulas thus avoiding the issues inherent in identifying active ingredients which may be complex or unknown. Thus, while current analytical methods focus on determining the chemical quality of herbal medicines, including adulteration and contamination, mitochondrial functional analysis offers a new way of determining the quality of plant derived products that is more closely linked to the biological activity of a product and its potential clinical effectiveness.

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring

The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term "annotated peak" is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations.

Dispersive Liquid-Liquid Microextraction Based Preconcentration of Selected Pesticides and Escitalopram Oxalate, Haloperidol, and Olanzapine from Wastewater Samples Prior to Determination by GC-MS

Background

Determination of emerging pollutants including pharmaceuticals, pesticides, industrial chemicals and hormones in different environmental samples is very important for human health.

Objective

Experimental design enabled parameters to be evaluated for their effects onextraction output as well as their interactive effects.

Method

A multivariate experimental design was used to attain optimum conditions of a dispersive liquid-liquid microextraction method for preconcentration of pesticides and pharmaceuticals for determination by GC-MS.

Results

The optimum parameters suggested by the design model were 200 µL of chloroform, 1.96 mL of ethanol, and 40 s vortexing period. LOD and LOQ were calculated using linear calibration plots of the analytes developed in the standard concentration range of 2.0 μg/L–2.0 mg/L.

Conclusions

Enhancement in detection power of the analytes recorded by the optimized method with respect to direct GC-MS determination (based on LOD values) was in the range of 3.6 and 539 folds. Spiked recovery experiments for municipal, medical, and synthetic wastewater samples yielded low recovery results when calculated against aqueous standard solutions. Matrix matched calibration standards were used to mitigate interferences from the waste samples and the percent recoveries obtained were close to 100%. This established accuracy and applicability of the developed method.

Highlights

The detection limits were found between 0.50 ng/mL and 37 ng/mL. An accurate, simple and sensitive analytical method was developed for the analytes.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Recent developments in metabolomics-based research in understanding transgenic grass metabolism

BACKGROUND

Transgenic herbicide-resistant (HR) turfgrass together with its associated, broad spectrum herbicides promise cheap, selective and efficient weed control by excluding infested weeds resulting in turf lawn with high uniformity and aesthetic value. The concept of this "weeding program" initiated from modern biotechnology has been widely implemented in several principal crops including maize, soybean, canola and cotton as early as the 1990s. Transgenic HR turfgrass classified as a genetically modified organism (GMO) has undoubtedly caused public concern with respect to its biosafety and legalities similar to well-established HR crops. Nevertheless, applying metabolomics-based approaches which focuses on the identification of the global metabolic state of a biological system in response to either internal or external stimuli can also provide a comprehensive characterization of transgenic grass metabolism and its involvement in biosecurity and public perception.

AIM OF REVIEW

This review summaries the recent applications of metabolomics applied to HR crops to predict the molecular and physiological phenotypes of HR turfgrass species, glyphosate-resistant Kentucky bluegrass (Poa pratensis L.) and glufosinate-resistant creeping bentgrass (Agrotis stonifera L.). Additionally, this review also presents background knowledge with respect to the application of metabolomics, transformation of HR crops and its biosafety concerns, turfgrass botanical knowledge and its economic and aesthetic value.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to demonstrate the molecular and physiological phenotypes of HR turfgrass based on several lines of evidence primarily derived from metabolomics data applied to HR crops to identify alterations on HR turfgrass metabolism as a result of genetic modification that confers resistant traits.