Mass spectrometry: from proteomics to metabolomics and lipidomics

Mass Spectrometry-Based Proteomics in Clinical Diagnosis of Amyloidosis and Multiple Myeloma: A Review (2012-2024)

Proteomics is nowadays increasingly becoming part of the routine clinical practice of diagnostic laboratories, especially due to the advent of advanced mass spectrometry techniques. This review focuses on the application of proteomic analysis in the identification of pathological conditions in a hospital setting, with a particular focus on the analysis of protein biomarkers. In particular, the main purpose of the review is to highlight the challenges associated with the identification of specific disease-causing proteins, given their complex nature and the variety of posttranslational modifications (PTMs) they can undergo. PTMs, such as phosphorylation and glycosylation, play critical roles in protein function but can also lead to diseases if dysregulated. Proteomics plays an important role especially in various medical fields ranging from cardiology, internal medicine to hemato-oncology emphasizing the interdisciplinary nature of this field. Traditional methods such as electrophoretic or immunochemical methods have been mainstay in protein detection; however, these techniques are limited in terms of specificity and sensitivity. Examples include the diagnosis of multiple myeloma and the detection of its specific protein or amyloidosis, which relies heavily on these conventional methods, which sometimes lead to false positives or inadequate disease monitoring. Mass spectrometry in this respect emerges as a superior alternative, providing high sensitivity and specificity in the detection and quantification of specific protein sequences. This technique is particularly beneficial for monitoring minimal residual disease (MRD) in the diagnosis of multiple myeloma where traditional methods fall short. Furthermore mass spectrometry can provide precise typing of amyloid proteins, which is crucial for the appropriate treatment of amyloidosis. This review summarizes the opportunities for proteomic determination using mass spectrometry between 2012 and 2024, highlighting the transformative potential of mass spectrometry in clinical proteomics and encouraging its wider use in diagnostic laboratories.

Highly sensitive and rapid screening of radical electrochemical intermediates by chemical labeling untargeted mass spectrometry

Capturing and analyzing low-abundance, short-lived, and unstable radical intermediates during electrochemical (EC) reactions is important for understanding EC reaction mechanisms. Herein, we developed a chemical labeling strategy for rapid screening and highly sensitive analysis of EC radical intermediates by mass spectrometry (MS). Taking 5,5-dimethyl-1-pyrroline N-oxide (DMPO) labeling reagent as an example, DMPO can react with radical intermediates to form stable labeling products with characteristic fragmentation daughter ions or neutral loss in tandem MS, so the labeled intermediates can be screened by precursor ions scan or neutral loss scan. Further combined with our recently established floating electrochemical electrospray mass spectrometry platform (FE-ESI-MS), we realized online confirmation and in-depth analysis of radical intermediates, and accelerated the study of EC reaction mechanisms. We established the strategy based on the labeling analysis of radical intermediates produced by EC oxidation of acetaminophen and a mixed EC reaction system containing 10 substrates, and further studied the radical-radical cross coupling mechanism of sodium p-toluenesulfinate and 2-methyl-2H-indazole. The labeled products not only improved the MS response by more than 10 times, but also exhibited typical fragmentation behavior, which contributed to the rapid screening of radical intermediates. This chemical labeling strategy will increase the capability of MS for finding important EC intermediates in untargeted analysis and further investigating low abundant and short-lived EC intermediates in the future.

Lipidomics-based natural products for chronic kidney disease treatment

Chronic kidney disease (CKD) is by far the most prevalent disease in the world and is now a major global public health problem because of the increase in diabetes, hypertension and obesity. Traditional biomarkers of kidney function lack sensitivity and specificity for early detection and monitoring of CKD progression, necessitating more sensitive biomarkers for early diagnostic intervention. Dyslipidemia is a hallmark of CKD. Advancements in mass spectrometry (MS)-based lipidomics platforms have facilitated comprehensive analysis of lipids in biological samples and have revealed changes in the lipidome that are associated with metabolic disorders, which can be used as new biomarkers for kidney diseases. It is also critical for the discovery of new therapeutic targets and drugs. In this article, we focus on lipids in CKD, lipidomics methodologies and their applications in CKD. Additionally, we introduce novel biomarkers identified through lipidomics approaches and natural products derived from lipidomics for the treatment of CKD. We believe that our study makes a significant contribution to literature by demonstrating that natural products can improve CKD from a lipidomic perspective.

The Use of Omics in Untangling the Effect of Lifestyle Factors in Pregnancy and Gestational Diabetes: A Systematic Review

AIM

To synthesise the evidence from clinical trials and observational studies using omics techniques to investigate the impact of diet and lifestyle factors on metabolite profile in pregnancy, and in the prevention and management of gestational diabetes mellitus (GDM).

MATERIALS AND METHODS

A systematic literature search was performed using PubMed, Ovid, CINAHL, and Web of Science databases in October 2023 and updated in September 2024. Inclusion criteria were randomised controlled trials (RCT) or non-RCTs in pregnant women with or without GDM, that measured diet and lifestyle factors, and which applied post-transcriptional omics approaches. Risk of bias was assessed using the ROBINS-I for non-RCTs and ROB-2 tool for RCTs. The results of all studies are narratively synthesised.

RESULTS

Of 6293 studies identified, eight observational studies and three RCTs comprising 2639 pregnant women were included. Three studies reported on changes in diet-related metabolic phenotypes during pregnancy; however, the impact of certain foods on the metabolome and risk for GDM was less clear. Compared with women without GDM, women with GDM had a worse deterioration in metabolites, including saturated fatty acids, branched chain amino acids and purine degradation metabolites. There is limited evidence that conventional dietary treatment for GDM may modify the metabolome in women with GDM.

CONCLUSIONS

Metabolome profiles in pregnancy may be altered by certain dietary choices; however, it is inconclusive whether improved diet related metabolite profiles have a beneficial impact in the prevention or management of GDM. High quality studies with larger sample sizes are needed to better understand the role that maternal nutrition plays in modulating the maternal metabolome, not only for a healthy pregnancy but also for the prevention and management of GDM.

Quantitative Proteomics and Metabolomics of Culture Medium from Single Human Embryo Reveal Embryo Quality-Related Multiomics Biomarkers

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

Hollow Cathode Discharge Ionization Mass Spectrometry: Detection, Quantification and Gas Phase Ion-Molecule Reactions of Explosives and Related Compounds

Mass spectrometry (MS) has become an essential analytical method in every sector of science and technology. Because of its unique ability to provide direct molecular structure information on analytes, an extra method is rarely required. This review describes fabrication of a variable-pressure hollow cathode discharge (HCD) ion source for MS in detection, quantification and investigation of gas-phase ion molecule reactions of explosives and related compounds using air as a carrier gas. The HCD ion source has been designed in such a way that by altering the ion source pressures, the system can generate both HCD and conventional GD. This design enables for the selective detection and quantification of explosives at trace to ultra-trace levels. The pressure-dependent HCD ion source has also been used to investigate ion-molecule reactions in the gas phase of explosives and related compounds. The mechanism of ion formation in explosive reactions is also discussed.

Nanometabolomics Elucidated Biological Prospective of Mo4/3B2-x Nanosheets: Toward Metabolic Reprogramming of Amino Acid Metabolism

Mo4/3B2-x nanosheets are newly developed, and 2D transition metal borides (MBene) were reported in 2021, but there is no report on their further applications and modification; hence, this article sheds light on the significance of potential biological prospects for future biomedical applications. Therefore, elucidation of the biocompatibility, biotoxicology, and bioactivity of Mo4/3B2-x nanosheets has been an urgent need to be fulfilled. Nanometabolomics (also referred as nanomaterials-based metabolomics) was first proposed and utilized in our previous work, which specialized in interpreting nanomaterials-induced metabolic reprogramming through aqueous metabolomics and lipidomics approach. Hence, nanometabolomics could be considered as a novel concept combining nanoscience and metabolomics to provide bioinformation on nanomaterials' biomedical applications. In this work, the safe range of concentration (<50 mg/L) with good biosafety toward human umbilical vein endothelial cells (HUVECs) was discovered. The low concentration (5 mg/L) and high concentration (50 mg/L) of Mo4/3B2-x nanosheets were utilized for the in vitro Mo4/3B2-x-cell interaction. Nanometabolomics has elucidated the biological prospective of Mo4/3B2-x nanosheets via monitoring its biocompatibility and metabolic shift of HUVECs. The results revealed that 50 mg/L Mo4/3B2-x nanosheets could lead to a stronger alteration of amino acid metabolism with disturbance of the corresponding amino acid-related pathways (including amino acid metabolism, amino acid degradation, fatty acid biosynthesis, and lipid biosynthesis and metabolism). These interesting results were closely involved with the oxidative stress and production of excess ROS. This work could be regarded as a pathbreaking study on Mo4/3B2-x nanosheets at a biological level, which also designates their further biochemical, medical, and industrial application and development based on nanometabolomics bioinformation.

Integrated multi-omics and machine learning approach reveals lipid metabolic biomarkers and signaling in age-related meibomian gland dysfunction

Meibomian gland dysfunction (MGD) is a prevalent inflammatory disorder of the ocular surface that significantly impacts patients' vision and quality of life. The underlying mechanism of aging and MGD remains largely uncharacterized. The aim of this work is to investigate lipid metabolic alterations in age-related MGD (ARMGD) through integrated proteomics, lipidomics and machine learning (ML) approach. For this purpose, we collected samples of female mouse meibomian glands (MGs) dissected from eyelids at age two months (n = 9) and two years (n = 9) for proteomic and lipidomic profilings using the liquid chromatography with tandem mass spectrometry (LC-MS/MS) method. To further identify ARMGD-related lipid biomarkers, ML model was established using the least absolute shrinkage and selection operator (LASSO) algorithm. For proteomic profiling, 375 differentially expressed proteins were detected. Functional analyses indicated the leading role of cholesterol biosynthesis in the aging process of MGs. Several proteins were proposed as potential biomarkers, including lanosterol synthase (Lss), 24-dehydrocholesterol reductase (Dhcr24), and farnesyl diphosphate farnesyl transferase 1 (Fdft1). Concomitantly, lipidomic analysis unveiled 47 lipid species that were differentially expressed and clustered into four classes. The most notable age-related alterations involved a decline in cholesteryl esters (ChE) levels and an increase in triradylglycerols (TG) levels, accompanied by significant differences in their lipid unsaturation patterns. Through ML construction, it was confirmed that ChE(26:0), ChE(26:1), and ChE(30:1) represent the most promising diagnostic molecules. The present study identified essential proteins, lipids, and signaling pathways in age-related MGD (ARMGD), providing a reference landscape to facilitate novel strategies for the disease transformation.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Integrating metabolomics and lipidomics revealed a decrease in plasma fatty acids but an increase in triglycerides in children with drug-refractory epilepsy

OBJECTIVE

The drug-refractory epilepsy (DRE) in children is commonly observed but the underlying mechanisms remain elusive. We examined whether fatty acids (FAs) and lipids are potentially associated with the pharmacoresistance to valproic acid (VPA) therapy.

METHODS

This single-center, retrospective cohort study was conducted using data from pediatric patients collected between May 2019 and December 2019 at the Children's Hospital of Nanjing Medical University. Ninety plasma samples from 53 responders with VPA monotherapy (RE group) and 37 non-responders with VPA polytherapy (NR group) were collected. Non-targeted metabolomics and lipidomics analysis for those plasma samples were performed to compare the potential differences of small metabolites and lipids between the two groups. Plasma metabolites and lipids passing the threshold of variable importance in projection value >1, fold change >1.2 or <0.8, and p-value <0.05 were regarded as statistically different substances.

RESULTS

A total of 204 small metabolites and 433 lipids comprising 16 different lipid subclasses were identified. The well-established partial least squares-discriminant analysis (PLS-DA) revealed a good separation of the RE from the NR group. The FAs and glycerophospholipids status were significantly decreased in the NR group, but their triglycerides (TG) levels were significantly increased. The trend of TG levels in routine laboratory tests was in line with the lipidomics analysis. Meanwhile, cases from the NR group were characterized by a decreased level of citric acid and L-thyroxine, but with an increased level of glucose and 2-oxoglutarate. The top two enriched metabolic pathways involved in the DRE condition were biosynthesis of unsaturated FAs and linoleic acid metabolism.

SIGNIFICANCE

The results of this study suggested an association between metabolism of FAs and the medically intractable epilepsy. Such novel findings might propose a potential mechanism linked to the energy metabolism. Ketogenic acid and FAs supplementation might therefore be high-priority strategies for DRE management.

Integrated Transcriptome and Metabolome Analysis Reveals the Regulatory Mechanisms of FASN in Geese Granulosa Cells

FASN plays a critical role in lipid metabolism, which is involved in regulating ovarian follicular development. However, the molecular mechanisms of how FASN regulate the function of ovarian follicular cells still remain elusive. In this study, by overexpression or interference of FASN in pre-hierarchical follicle granulosa cells (phGCs) and hierarchical follicle granulosa cells (hGCs), we analyzed their effects on the granulosa cell transcriptome and metabolome profiles using RNA-Seq and LC-MS/MS, respectively. The results showed that overexpression of FASN promoted proinflammatory factors expression by activating TLR3/IRF7 and TLR3/NF-κB pathways in phGCs, but only by activating TLR3/IRF7 pathways in hGCs. Then, necroptosis and apoptosis were triggered through the JAK/STAT1 pathway (induced by inflammatory factors) and BAK/caspase-7 pathway, respectively. The combined analysis of the metabolome and transcriptome revealed that FASN affected the demand of GCs for 5-hydroxytryptamine (5-HT) by activating the neuroactive ligand-receptor interaction pathway in two categorized GCs and only altering the metabolic pathway of tryptophan in phGCs, and ultimately participated in regulating the physiological function of geese GCs. Taken together, this study showed that the mechanisms of FASN regulating the physiological function of geese phGCs and hGCs were similar, but they also had some different characteristics.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

A companion to the preclinical common data elements for proteomics, lipidomics, and metabolomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. This is the second in a two-part series of omics papers, with the other including genomics, transcriptomics, and epigenomics. The aim of the CDEs was to improve the standardization of experimental designs across a range of epilepsy research-related methods. We have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for proteomics, lipidomics, and metabolomics of samples from rodent models and people with epilepsy. We discuss the important elements that need to be considered for the proteomics, lipidomics, and metabolomics methodologies, providing a rationale for the parameters that should be documented.

Spectroscopic methods for COVID-19 detection and early diagnosis

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.

Impact of intracellular response regulator QseB in quorum sensing regulatory network in a clinical isolate SC1401 of Glaesserella parasuis

Two component systems (TCS) mediate specific responses to different conditions and/or pressures. In the quorum sensing Glaesserella parasuis (QSE) BC TCS, qseB, as a response regulator, is closely related to the transcriptional regulation of multiple downstream genes. In this study, the effects of qseB gene deletion, which encodes the response regulator of population density sensing in G. parasuis, were studied through biological characteristics and metabolomic analysis. Based on previous research, we further explored the virulence of ΔqseB mutant strains through cell morphology, adhesion and invasion. The ΔqseB mutant and parent strains were sequenced by metabolome and combined with the previous transcriptome sequencing results for joint analysis. This study aims to clarify the regulatory effect of QseB on the virulence of G. parasuis and lay the foundation for revealing the pathogenic mechanism of G. parasuis. We detected 476 different metabolites, of which 30 metabolites (6.3%) had a significant difference in abundance between SC1401 and ΔqseB (p < 0.05). We conducted a comparative analysis of pathway enrichment on the transcriptome and metabolome, and found that the two omics participate in seven metabolic pathways together. The top 10 KEGG pathways with the largest number of genes and metabolites identified in this experiment are ABC transporters, Biosynthesis of secondary metabolites, Cysteine and methionine metabolism, Purine metabolism, Pyrimidine metabolism, Metabolic pathways, and Nicotinate and nicotinamide metabolism. Analysis of metabolome sequencing results showed that differential metabolites were also enriched in metabolic pathways, such as Purine metabolism, cGMP-PKG signaling pathway and cAMP signaling pathway, which were not found in transcriptome sequencing data. The internal coloration of the mutant strain ΔqseB was uneven, and the adhesion and invasion ability of PAM cell lines were significantly reduced. We speculate that QseB may affect the adhesion and invasion ability of Glaesserella parasuis by influencing substance transport and signal transduction.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end-stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non-albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever-evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre-disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time-course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high-throughput metabolomic techniques will be discussed.

OUP accepted manuscript

Background

COVID-19 is a highly contagious respiratory disease that can be transmitted through human exhaled breath. It has caused immense loss and has challenged the healthcare sector. It has affected the economy of countries and thereby affected numerous sectors. Analysis of human breath samples is an attractive strategy for rapid diagnosis of COVID-19 by monitoring breath biomarkers.

Content

Breath collection is a noninvasive process. Various technologies are employed for detection of breath biomarkers like mass spectrometry, biosensors, artificial learning, and machine learning. These tools have low turnaround time, robustness, and provide onsite results. Also, MS-based approaches are promising tools with high speed, specificity, sensitivity, reproducibility, and broader coverage, as well as its coupling with various chromatographic separation techniques providing better clinical and biochemical understanding of COVID-19 using breath samples.

Summary

Herein, we have tried to review the MS-based approaches as well as other techniques used for the analysis of breath samples for COVID-19 diagnosis. We have also highlighted the different breath analyzers being developed for COVID-19 detection.

Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics

Humans have a long-standing coexistence with microorganisms. In particular, the microbial community that populates the human gastrointestinal tract has emerged as a critical player in governing human health and disease. DNA and RNA sequencing techniques that map taxonomical composition and genomic potential of the gut community have become invaluable for microbiome research. However, deriving a biochemical understanding of how activities of the gut microbiome shape host development and physiology requires an expanded experimental design that goes beyond these approaches. In this review, we explore advances in high-throughput techniques based on liquid chromatography-mass spectrometry. These omics methods for the identification of proteins and metabolites have enabled direct characterisation of gut microbiome functions and the crosstalk with the host. We discuss current metaproteomics and metabolomics workflows for producing functional profiles, the existing methodological challenges and limitations, and recent studies utilising these techniques with a special focus on early life gut microbiome.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

As an ancient infectious disease, tuberculosis (TB) is still the leading cause of death from a single infectious agent worldwide. Latent TB infection (LTBI) has been recognized as the largest source of new TB cases and is one of the biggest obstacles to achieving the aim of the End TB Strategy. The latest data indicate that a considerable percentage of the population with LTBI and the lack of differential diagnosis between LTBI and active TB (aTB) may be potential reasons for the high TB morbidity and mortality in countries with high TB burdens. The tuberculin skin test (TST) has been used to diagnose TB for > 100 years, but it fails to distinguish patients with LTBI from those with aTB and people who have received Bacillus Calmette–Guérin vaccination. To overcome the limitations of TST, several new skin tests and interferon-gamma release assays have been developed, such as the Diaskintest, C-Tb skin test, EC-Test, and T-cell spot of the TB assay, QuantiFERON-TB Gold In-Tube, QuantiFERON-TB Gold-Plus, LIAISON QuantiFERON-TB Gold Plus test, and LIOFeron TB/LTBI. However, these methods cannot distinguish LTBI from aTB. To investigate the reasons why all these methods cannot distinguish LTBI from aTB, we have explained the concept and definition of LTBI and expounded on the immunological mechanism of LTBI in this review. In addition, we have outlined the research status, future directions, and challenges of LTBI differential diagnosis, including novel biomarkers derived from Mycobacterium tuberculosis and hosts, new models and algorithms, omics technologies, and microbiota.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

Mass Spectrometry-Based Human Breath Analysis: Towards COVID-19 Diagnosis and Research

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath. Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers. Mass spectrometry (MS)-based approaches offer a promising analytical platform for human breath analysis due to their high speed, specificity, sensitivity, reproducibility, and broad coverage, as well as its versatile coupling methods with different chromatographic separation, and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19. Herein, we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples, including metabolites, proteins, microorganisms, and elements. New features of breath sampling and analysis are highlighted. Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.

Electrospray Ionization-Mass Spectrometry of Synthetic Polymers Functionalized with Carboxylic Acid End-Groups

Electrospray ionization-mass spectrometry (ESI-MS) of low-charging synthetic polymers typically produces mass spectra exhibiting a bias toward the low-mass region of the polymer mass distribution. To examine the origin(s) of this ionization bias, narrow dispersity polystyrene polymers (Đ < 1.10) were prepared with ionizable carboxylic acid end-groups at one or both chain termini. The mixture complexity was further reduced through preparative size-exclusion chromatography (SEC), and these well-defined polymers were subjected to negative ion ESI-MS on a high-resolution instrument with a mass-to-charge (m/z) range up to 8000. Incorporation of one carboxylic acid end-group facilitated the generation of singly charged [M - H]^- ions across the entire range of the mass analyzer. The comparison of mass spectra with size-exclusion chromatograms of the same polymer revealed an ionization bias toward lower masses, which was partially overcome through fractionation, modification of electrospray solvent, and increased declustering potentials. Incorporation of a second ionizable moiety within polymers of equivalent size facilitated multiply charged [M - 2H]^2- ion formation with significantly improved ionization efficiency, spectral coverage of the molar mass distribution, and minimal cluster ion formation. These findings indicate that increased charging of polymers through multiple, well-defined sites of ionization can enhance volatilization and ionization of higher-mass polymers. Generation of higher-molecular-weight polymers in low-charge states-while possible under ideal conditions-competes ineffectively with either nonspecific, multiple-charging of similar sized polymers or ionization of the smaller polymers in the distribution.

Untargeted metabolomics and lipidomics analysis identified the role of FOXA1 in remodeling the metabolic pattern of BaP-transformed 16HBE cells

Benzo[a]pyrene (BaP) is a strong carcinogen for lung cancer, and forkhead-box A1 (FOXA1) plays an oncogenic role in BaP-transformed cell THBEc1. To explore the remodeling of metabolic pattern caused by BaP-induced transformation and the possible role FOXA1 might play in it, we compared metabolic patterns between THBEc1 cells and control using untargeted metabolomics and lipidomics analysis, and determined the effects of FOXA1 knockout on the metabolic pattern of THBEc1 cells. Metabolomics and lipidomics identified a total of 15 and 46 differential metabolites and lipids between THBEc1 and 16HBE cells, respectively, and a total of 4 and 1 differential metabolites and lipids between FOXA1 knockout cell THBEc1-ΔFOXA1-c34 and control cell THBEc1-ctrl, respectively. Analysis results of metabolites and metabolic pathways indicated the metabolic pattern remodeling may be related to the alteration in glucose metabolism during BaP-induced transformation. Western blotting revealed the up-regulation of enolase-2 (ENO2), pyruvate carboxylase (PCB), aconitase-2 (ACO2) and phosphorylated extracellular signal-regulated kinase 1/2 (p-ERK1/2) (Thr202/Tyr204), the down-regulation of succinate dehydrogenase complex subunit A (SDHA) and phosphoenolpyruvate carboxykinase 2 (PCK2) in THBEc1 cells. The detection results of metabolites related to glucose metabolism demonstrated the decreasing of lactic acid content in cells, lactic acid production in culture medium and citric acid content in mitochondria, and the increasing of ATP production of THBEc1 cells. FOXA1 knockout partially reversed the changes of ENO2, SDHA, PCK2 and p-ERK1/2 (Thr202/Tyr204) levels, lactic acid release, citric acid content in mitochondria of THBEc1 cells. In conclusion, FOXA1 knockout partially reversed the remodeling of glucose metabolism caused by BaP-induced malignant transformation. Our findings provide a clue for the possible role of FOXA1 in glucose metabolism regulation.

Systematic evaluation of sample preparation strategy for GC-MS-based plasma metabolomics and its application in osteoarthritis

Sample preparation plays a crucial part in plasma metabolomics. In order to obtain an optimal sample extraction method for gas chromatography mass spectrometry (GC-MS)-based plasma metabolomics, five different extraction strategies including protein precipitation, liquid-liquid extraction and solid-phase extraction were evaluated systematically for both plasma untargeted- and targeted-metabolomics. The comprehensive evaluation revealed that the all-in-one sample preparation method, MeOH-MTBE-H₂O (1:5:1.5, v/v/v), was the optimal extraction method for both untargeted- and targeted-metabolomics. Next, the optimal sample preparation protocol was applied in plasma metabolomics of osteoarthritis (OA). A panel containing cholesterol, lactic acid, stearic acid, alpha-tocopherol and oxalic acid was selected as candidate biomarker to distinguish OA patients from healthy controls (HC) based on the support vector machine (SVM) classification model. The discriminating capability of the candidate biomarker panel was further validated successfully with logistic regression and principal components analysis (PCA) analysis. Therefore, the panel could potentially act as diagnostic biomarker for osteoarthritis.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Investigating biological effects of multidimensional carboxylated carbon-based nanomaterials on human lung A549 cells revealed via non-targeted metabolomics approach

The biological responses of multidimensional carboxylated carbon-based nanomaterials (c-CBNs), including carboxylated graphene, carbon nanotube, and fullerene, on human lung A549 cells were investigated by using metabolomics technology. The structure and components of c-CBNs were characterized, and their biological effects were evaluated through cell apoptosis and viability analysis. Additionally, the metabolomics analysis of the nanomaterial-cell interaction system was performed using the established platform combining liquid chromatography-mass spectrometry (LC-MS) with the bioinformatics system. Results revealed that all tested c-CBNs demonstrated some biological effects in our cell model. However, significant metabolomic alterations induced by c-CBNs were also observed mainly in amino acids, organic acids, glycerophospholipids, and glycerolipids. Further, under the tested concentrations, the multiple dimensions of c-CBNs played a major role in determining the metabolic process in various interaction modes. This study provides an advanced alternative for evaluating metabolic effects of multidimensional nanomaterials through metabolomics technology considering the association between dimension and metabolic characteristics.

Unveiling the Structure and Reactivity of Fatty-Acid Based (Nano)materials Thanks to Efficient and Scalable 17O and 18O-Isotopic Labeling Schemes

Fatty acids are ubiquitous in biological systems and widely used in materials science, including for the formulation of drugs and the surface-functionalization of nanoparticles. However, important questions regarding the structure and reactivity of these molecules are still to be elucidated, including their mode of binding to certain metal cations or materials surfaces. In this context, we have developed novel, efficient, user-friendly, and cost-effective synthetic protocols based on ball-milling, for the 17O and 18O isotopic labeling of two key fatty acids which are widely used in (nano)materials science, namely stearic and oleic acid. Labeled molecules were analyzed by 1H and 13C solution NMR, IR spectroscopy, and mass spectrometry (ESI-TOF and LC-MS), as well as 17O solid state NMR (for the 17O labeled species). In both cases, the labeling procedures were scaled-up to produce up to gram quantities of 17O- or 18O-enriched molecules in just half-a-day, with very good synthetic yields (all ≥84%) and enrichment levels (up to an average of 46% per carboxylic oxygen). The 17O-labeled oleic acid was then used for the synthesis of a metal soap (Zn-oleate) and the surface-functionalization of ZnO nanoparticles (NPs), which were characterized for the first time by high-resolution 17O NMR (at 14.1 and 35.2 T). This allowed very detailed insight into (i) the coordination mode of the oleate ligand in Zn-oleate to be achieved (including information on Zn···O distances) and (ii) the mode of attachment of oleic-acid at the surface of ZnO (including novel information on its photoreactivity upon UV-irradiation). Overall, this work demonstrates the high interest of these fatty acid-enrichment protocols for understanding the structure and reactivity of a variety of functional (nano)materials systems using high resolution analyses like 17O NMR.

Metabolic Flux Analysis-Linking Isotope Labeling and Metabolic Fluxes

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Ambient mass spectrometry for the molecular diagnosis of lung cancer

Lung cancer is one of the most common malignancies and the leading cause of cancer-related death worldwide. Among the technologies suitable for the rapid and accurate molecular diagnosis of lung cancer, ambient mass spectrometry (AMS) has gained increasing interest as it allows the direct profiling of molecular information from various biological samples (e.g., tissue, serum, urine and sputum) in real-time and with minimal or no sample pretreatment. This minireview summarizes the applications of AMS in lung cancer studies (including tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment), and discusses the challenges and perspectives of AMS in the clinical precision molecular diagnosis of lung cancer.

Structure determination of conjugated linoleic and linolenic acids

Conjugated linoleic and linolenic acids (CLA and CLnA) can be found in dairy, ruminant meat and oilseeds, these types of unsaturated fatty acids consist of various positional and geometrical isomers, and have demonstrated health-promoting potential for human beings. Extensive reviews have reported the physiological effects of CLA, CLnA, while little is known regarding their isomer-specific effects. However, the isomers are difficult to identify, owing to (i) the similar retention time in common chromatographic methods; and (ii) the isomers are highly sensitive to high temperature, pH changes, and oxidation. The uncertainties in molecular structure have hindered investigations on the physiological effects of CLA and CLnA. Therefore, this review presents a summary of the currently available technologies for the structural determination of CLA and CLnA, including the presence confirmation, double bond position determination, and the potential stereo-isomer determination. Special focus has been projected to the novel techniques for structure determination of CLA and CLnA. Some possible future directions are also proposed.

A prospective study of serum metabolomic and lipidomic changes in myopic children and adolescents

BACKGROUND

Myopia is a prevalent eye disorder, especially among children and adolescents in eastern Asian countries. Multiple measures have already been taken to prevent and treat myopia, including atropine and dopamine. However, the serum metabolic picture of myopia has not yet been studied as a whole and remains largely unclear. In this paper, a prospective and panoramic study was carried out to find out the whole serum metabolomic and lipidomic picture of myopia.

METHODS

With untargeted mass spectrometry (MS), myopia among 211 children and adolescents was studied. The MS features were first grouped across the samples. Then, compound annotation was carried out based on these features. Finally, the metabolite features were mapped to pathways, whose biological functions in myopia were studied and discussed.

RESULTS

A total of 275 metabolite features were derived from 92 aligned MS peak groups with significant fold changes, and then mapped to 33 pathways. By a comprehensive consideration of significance, fold change, importance score and appearance in different omics, 9 pathways were selected, and their biological functions were further analyzed. Among these selected pathways, 5 pathways were related with oxidative stress, a validated phenomenon during myopia development, while 5 pathways were related with dopamine receptor D2, whose molecular function in myopia treatment is not fully understood. A total of 177 metabolite features from 45 peak groups were related with the studied pathways.

CONCLUSION

This prospective study shed light on the whole picture of metabolomic mechanism underlying myopia and provided guidance to further elucidation of compounds and pathways in this whole picture.

Proteomics and Metabolomics for Cystic Fibrosis Research

The aim of this review article is to introduce the reader to the state-of-the-art of the contribution that proteomics and metabolomics sciences are currently providing for cystic fibrosis (CF) research: from the understanding of cystic fibrosis transmembrane conductance regulator (CFTR) biology to biomarker discovery for CF diagnosis. Our work particularly focuses on CFTR post-translational modifications and their role in cellular trafficking as well as on studies that allowed the identification of CFTR molecular interactors. We also show how metabolomics is currently helping biomarker discovery in CF. The most recent advances in these fields are covered by this review, as well as some considerations on possible future scenarios for new applications.

Multi-Omics Technologies Applied to Tuberculosis Drug Discovery

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

A straightforward gel-free proteomics pipeline assisted by liquid isoelectric focusing (OFFGEL) and mass spectrometry analysis to study bovine meat proteome

Bovine sarcoplasmic sub-proteome was studied through a straightforward gel-free pipeline supported by liquid isoelectric focusing (OFFGEL) protein fractionation coupled to liquid chromatography-mass spectrometry (LC-MS) analysis. Full-MS and data-dependent MS/MS analyses were simultaneously performed by a conventional three-dimensional ion-trap addressing targeted quantitative and untargeted qualitative research, respectively. There were unambiguously identified 47 proteins distributed along 12 OFFGEL fractions assayed. Regarding intermediate- and high-abundant peptides, bulky quantitative data processing performed by MZmine 2 freeware yielded a satisfactory linearity and coefficient of variation with r2 in the 0.95–0.99 range and about 25%, respectively. Up to 41 peptides from 20 identified proteins were relatively quantified throughout OFFGEL fractions. This reliable, flexible and affordable gel-free proteomic approach could be readily implemented by industry to improve quality assessment of protein-based food products.

Center-of-Mass iso-Energetic Collision-Induced Decomposition in Tandem Triple Quadrupole Mass Spectrometry

Two scan modes of the triple quadrupole tandem mass spectrometer, namely Collision Induced Dissociation Precursor Ion scan and Neutral Loss scan, allow selectively pinpointing, in a complex mixture, compounds that feature specific chemical groups, which yield characteristic fragment ions or are lost as distinctive neutral fragments. This feature of the triple quadrupole tandem mass spectrometer allows the non-target screening of mixtures for classes of components. The effective (center-of-mass) energy to achieve specific fragmentation depends on the inter-quadrupole voltage (laboratory-frame collision energy) and on the masses of the precursor molecular ion and of the collision gas, through a non-linear relationship. Thus, in a class of homologous compounds, precursor ions activated at the same laboratory-frame collision energy face different center-of-mass collision energy, and therefore the same fragmentation channel operates with different degrees of efficiency. This article reports a linear equation to calculate the laboratory-frame collision energy necessary to operate Collision-Induced Dissociation at the same center-of-mass energy on closely related compounds with different molecular mass. A routine triple quadrupole tandem mass spectrometer can operate this novel feature (iso-energetic collision-induced dissociation scan; i-CID) to analyze mixtures of endogenous metabolites by Precursor Ion and Neutral Loss scans. The latter experiment also entails the hitherto unprecedented synchronized scanning of all three quadrupoles of the triple quadrupole tandem mass spectrometer. To exemplify the application of this technique, this article shows two proof-of-principle approaches to the determination of biological mixtures, one by Precursor Ion analysis on alpha amino acid derivatized with a popular chromophore, and the other on modified nucleosides with a Neutral Fragment Loss scan.

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single-tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large-scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.

Clinical Application of Metabolomics in Pancreatic Diseases: A Mini-Review

Metabolomics is a powerful new analytical method to describe the set of metabolites within cellular tissue and bodily fluids. Metabolomics can uncover detailed information about metabolic changes in organisms. The morphology of these metabolites represents the metabolic processes that occur in cells, such as anabolism, catabolism, inhomogeneous natural absorption and metabolism, detoxification, and metabolism of biomass energy. Because the metabolites of different diseases are different, the specificity of the changes can be found by metabolomics testing, which provides a new source of biomarkers for the early identification of diseases and the difference between benign and malignant states. Metabolomics has a wide application potential in pancreatic diseases, including early detection, diagnosis, and identification of pancreatic diseases. However, there are few studies on metabolomics in pancreatic diseases in the literature. This article reviews the application of metabolomics in the diagnosis, prognosis, treatment, and evaluation of pancreatic diseases.

Parallel Detection of Fundamental and Sixth Harmonic Signals Using an ICR Cell with Dipole and Sixth Harmonic Detectors

Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is a powerful instrument for high-resolution analysis of biomolecules. However, relatively long signal acquisition periods are needed to achieve mass spectra with high resolution. The use of multiple detector electrodes for detection of harmonic frequencies has been introduced as one approach to increase scan rate for a given resolving power or to obtain increased resolving power for a given detection period. The achieved resolving power and scan rate increase linearly with the order of detected harmonic signals. In recent years, ICR cell geometries have been investigated to increase the order of the harmonic frequencies and enhance harmonic signal intensities. In this study, we demonstrated PCB-based ICR cell designs with dipole and sixth harmonic detectors for parallel detection of fundamental and harmonic (6f) signals. The sixth harmonic signals from the sixth harmonic detector showed an expected 6 times higher resolving power with (M + 3H)3+ charge state insulin ions as compared with that from fundamental signals from the dipole detector. Moreover, the insulin isotopic peaks with sixth harmonic frequency signals acquired with the sixth harmonic detector were resolved for a 40 ms data acquisition period but unresolved with the same duration dipole detector signals, corresponding to a 6-fold improvement in achievable spectral acquisition rates for a given resolving power.

The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat formation as they are the progenitors of adipocytes. In this work, we used human MSCs, received from surgery waste, and differentiated them into fat adipocytes. The combination of several layers of information coming from lipidomics, metabolomics and proteomics enabled network analysis of the biochemical pathways in adipogenesis. Simultaneous analysis of metabolites, lipids, and proteins in cell culture is challenging due to the compound’s chemical difference, so most studies involve separate analysis with unimolecular strategies. In this study, we employed a multimolecular approach using a two–phase extraction to monitor the crosstalk between lipid metabolism and protein-based signaling in a single sample (~10⁵ cells). We developed an innovative analytical workflow including standardization with in-house produced ¹³C isotopically labeled compounds, hyphenated high-end mass spectrometry (high-resolution Orbitrap MS), and chromatography (HILIC, RP) for simultaneous untargeted screening and targeted quantification. Metabolite and lipid concentrations ranged over three to four orders of magnitude and were detected down to the low fmol (absolute on column) level. Biological validation and data interpretation of the multiomics workflow was performed based on proteomics network reconstruction, metabolic modelling (MetaboAnalyst 4.0), and pathway analysis (OmicsNet). Comparing MSCs and adipocytes, we observed significant regulation of different metabolites and lipids such as triglycerides, gangliosides, and carnitine with 113 fully reprogrammed pathways. The observed changes are in accordance with literature findings dealing with adipogenic differentiation of MSC. These results are a proof of principle for the power of multimolecular extraction combined with orthogonal LC-MS assays and network construction. Considering the analytical and biological validation performed in this study, we conclude that the proposed multiomics workflow is ideally suited for comprehensive follow-up studies on adipogenesis and is fit for purpose for different applications with a high potential to understand the complex pathophysiology of diseases.

Stress and drug abuse-related disorders: The promising therapeutic value of neurosteroids focus on pregnenolone-progesterone-allopregnanolone pathway

The pregnenolone-progesterone-allopregnanolone pathway is receiving increasing attention in research on the role of neurosteroids in pathophysiology, particularly in stress-related and drug use disorders. These disorders involve an allostatic change that may result from deficiencies in allostasis or adaptive responses, and may be downregulated by adjustments in neurotransmission by neurosteroids. The following is an overview of findings that assess how pregnenolone and/or allopregnanolone concentrations are altered in animal models of stress and after consumption of alcohol or cannabis-type drugs, as well as in patients with depression, anxiety, post-traumatic stress disorder or psychosis and/or in those diagnosed with alcohol or cannabis use disorders. Preclinical and clinical evidence shows that pregnenolone and allopregnanolone, operating according to a different or common pharmacological profile involving GABAergic and/or endocannabinoid system, may be relevant biomarkers of psychiatric disorders for therapeutic purposes. Hence, ongoing clinical trials implicate synthetic analogs of pregnenolone or allopregnanolone, and also modulators of neurosteroidogenesis.

Postnatal gestational age estimation via newborn screening analysis: application and potential

Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.

Tutorial on lipidomics

The mainstream of lipidomics involves mass spectrometry-based, systematic, and large-scale studies of the structure, composition, and quantity of lipids in biological systems such as organs, cells, and body fluids. As increasingly more researchers in broad fields are beginning to pay attention to and actively learn about the lipidomic technology, some introduction on the topic is needed to help the newcomers to better understand the field. This tutorial seeks to introduce the basic knowledge about lipidomics and to provide readers with some core ideas and the most important approaches for studying the field.

Analysis of the Antiproliferative Effect of Ankaferd Hemostat on Caco-2 Colon Cancer Cells via LC/MS Shotgun Proteomics Approach

Ankaferd hemostat (ABS), a traditional herbal extract, is a hemostatic agent used for wound healing and bleeding treatment. A standardized form of plants contains many biomolecules. In recent years, previous studies have demonstrated the antineoplastic effect of ABS. In the present work, we focused on the mechanism of its antineoplastic effect over Caco-2 colon cancer cells. The LC/MS-based proteomics method was used to understand the effect of ABS at the protein level. The results were evaluated with gene ontology, protein interaction, and pathway analysis. As shown by our results, ABS altered glucose, fatty acids, and protein metabolism. Moreover, ABS affects the cell cycle machinery. Moreover, we found that ABS induced critical cancer target and suppressor proteins such as carboxyl-terminal hydrolase 1, 60S ribosomal protein L5, Tumor protein D52-like2, karyopherin alpha 2, and protein deglycase DJ-1. In conclusion, the proteomics results indicated that ABS affects various cancer targets and suppressor proteins. Moreover ABS has systematical effect on cell metabolism and cell cycle in Caco-2 cells, suggesting that it could be used as an antineoplastic agent.

Membrane Protein-Lipid Interactions Probed Using Mass Spectrometry

Membrane proteins that exist in lipid bilayers are not isolated molecular entities. The lipid molecules that surround them play crucial roles in maintaining their full structural and functional integrity. Research directed at investigating these critical lipid-protein interactions is developing rapidly. Advancements in both instrumentation and software, as well as in key biophysical and biochemical techniques, are accelerating the field. In this review, we provide a brief outline of structural techniques used to probe protein-lipid interactions and focus on the molecular aspects of these interactions obtained from native mass spectrometry (native MS). We highlight examples in which lipids have been shown to modulate membrane protein structure and show how native MS has emerged as a complementary technique to X-ray crystallography and cryo-electron microscopy. We conclude with a short perspective on future developments that aim to better understand protein-lipid interactions in the native environment.

A Peptide-Level Fully Annotated Data Set for Quantitative Evaluation of Precursor-Aware Mass Spectrometry Data Processing Algorithms

Modern label-free quantitative mass spectrometry workflows are complex experimental chains for devising the composition of biological samples. With benchtop and in silico experimental steps that each have a significant effect on the accuracy, coverage, and statistical significance of the study result, it is crucial to understand the efficacy and biases of each protocol decision. Although many studies have been conducted on wet lab experimental protocols, postacquisition data processing methods have not been adequately evaluated in large part due to a lack of available ground truth data. In this study, we provide a novel ground truth data set for mass spectrometry data analysis at the precursor (MS1) signal level comprised of isolated peptide signals from UPS2, a popular complex standard for proteomics analysis, requiring more than 1000 h of manual curation. The data set consists of more than 62 million points with 1,294,008 grouped into 57,518 extracted ion chromatograms and those grouped into 14,111 isotopic envelopes. This data set can be used to evaluate many aspects of mass spectrometry data processing, including precursor mapping and signal extraction algorithms.