Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics

Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

What the BTBR/J mouse has taught us about diabetes and diabetic complications

Human and mouse genetics have delivered numerous diabetogenic loci, but it is mainly through the use of animal models that the pathophysiological basis for their contribution to diabetes has been investigated. More than 20 years ago, we serendipidously identified a mouse strain that could serve as a model of obesity-prone type 2 diabetes, the BTBR (Black and Tan Brachyury) mouse (BTBR T+ Itpr3tf/J, 2018) carrying the Lepob mutation. We went on to discover that the BTBR-Lepob mouse is an excellent model of diabetic nephropathy and is now widely used by nephrologists in academia and the pharmaceutical industry. In this review, we describe the motivation for developing this animal model, the many genes identified and the insights about diabetes and diabetes complications derived from >100 studies conducted in this remarkable animal model.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

Plasma Prostaglandin E2 Metabolite Levels Predict Type 2 Diabetes Status and One-Year Therapeutic Response Independent of Clinical Markers of Inflammation

Over half of patients with type 2 diabetes (T2D) are unable to achieve blood glucose targets despite therapeutic compliance, significantly increasing their risk of long-term complications. Discovering ways to identify and properly treat these individuals is a critical problem in the field. The arachidonic acid metabolite, prostaglandin E₂ (PGE₂), has shown great promise as a biomarker of β-cell dysfunction in T2D. PGE₂ synthesis, secretion, and downstream signaling are all upregulated in pancreatic islets isolated from T2D mice and human organ donors. In these islets, preventing β-cell PGE₂ signaling via a prostaglandin EP3 receptor antagonist significantly improves their glucose-stimulated and hormone-potentiated insulin secretion response. In this clinical cohort study, 167 participants, 35 non-diabetic, and 132 with T2D, were recruited from the University of Wisconsin Hospital and Clinics. At enrollment, a standard set of demographic, biometric, and clinical measurements were performed to quantify obesity status and glucose control. C reactive protein was measured to exclude acute inflammation/illness, and white cell count (WBC), erythrocyte sedimentation rate (ESR), and fasting triglycerides were used as markers of systemic inflammation. Finally, a plasma sample for research was used to determine circulating PGE₂ metabolite (PGEM) levels. At baseline, PGEM levels were not correlated with WBC and triglycerides, only weakly correlated with ESR, and were the strongest predictor of T2D disease status. One year after enrollment, blood glucose management was assessed by chart review, with a clinically-relevant change in hemoglobin A1c (HbA1c) defined as ≥0.5%. PGEM levels were strongly predictive of therapeutic response, independent of age, obesity, glucose control, and systemic inflammation at enrollment. Our results provide strong support for future research in this area.

Microbiota independent effects of oligosaccharides on Caco-2 cells -A semi-targeted metabolomics approach using DI-FT-ICR-MS coupled with pathway enrichment analysis

Milk oligosaccharides (MOS) and galactooligosaccharides (GOS) are associated with many benefits, including anti-microbial effects and immune-modulating properties. However, the cellular mechanisms of these are largely unknown. In this study, the effects of enriched GOS and MOS mixtures from caprine and bovine milk consisting mainly 6'-galactosyllactose, 3'-sialyllactose, and 6'-sialyllactose on Caco-2 cells were investigated, and the treatment-specific metabolomes were described. In the control, the cells were treated with a sugar mix consisting of one-third each of glucose, galactose and lactose. A local metabolomics workflow with pathway enrichment was established, which specifically addresses DI-FT-ICR-MS analyses and includes adaptations in terms of measurement technology and sample matrices. By including quality parameters, especially the isotope pattern, we increased the precision of annotation. The independence from online tools, the fast adaptability to changes in databases, and the specific adjustment to the measurement technology and biomaterial used, proved to be a great advantage. For the first time it was possible to find 71 active pathways in a Caco-2 cell experiment. These pathways were assigned to 12 main categories, with amino acid metabolism and carbohydrate metabolism being the most dominant categories in terms of the number of metabolites and metabolic pathways. Treatment of Caco-2 cells with high GOS and glucose contents resulted in significant effects on several metabolic pathways, whereas the MOS containing treatments resulted only for individual metabolites in significant changes. An effect based on bovine or caprine origin alone could not be observed. Thus, it was shown that MOS and GOS containing treatments can exert microbiome-independent effects on the metabolome of Caco-2 cells.

An Observation Study of Urinary Biomarkers Exploratory in Alzheimer's Disease using High Resolution Mass Spectrometry

Alzheimer's disease (AD) is regarded as a progressive neurodegenerative dementia, characterized by degeneration of distinct neuronal populations. A case-control study was carried out by using high resolution mass spectrometry to explore AD associated urinary metabolic biomarkers from 30 AD patients and 30 cognitively normal (CN) individuals, respectively. In total, 49 metabolites were determined and validated as known compounds by LC/MS analysis. With two sample t-test statistical analysis (p<0.05), 19 metabolites were shown to be significantly differed from AD to CN. A diagnostic model of receiver-operator characteristic (ROC) curve was constructed with a combination of 9 selected metabolites and yielded a separation with an area under the curve value of 0.976 between two groups. This study indicated urinary metabolites showed a significant expression between AD and CN. AD related metabolites enable to satisfy the diagnostic power of disease discrimination. Additionally, as a non-invasive approach, urine collection provides its convenience in clinical diagnosis of AD.

Multiomics Method Enabled by Sequential Metabolomics and Proteomics for Human Pluripotent Stem-Cell-Derived Cardiomyocytes

Human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs) show immense promise for patient-specific disease modeling, cardiotoxicity screening, and regenerative therapy development. However, thus far, hPSC-CMs in culture have not recapitulated the structural or functional properties of adult CMs in vivo. To gain global insight into hPSC-CM biology, we established a multiomics method for analyzing the hPSC-CM metabolome and proteome from the same cell culture, creating multidimensional profiles of hPSC-CMs. Specifically, we developed a sequential extraction to capture metabolites and proteins from the same hPSC-CM monolayer cultures and analyzed these extracts using high-resolution mass spectrometry. Using this method, we annotated 205 metabolites/lipids and 4319 proteins from 106 cells with high reproducibility. We further integrated the proteome and metabolome measurements to create network profiles of molecular phenotypes for hPSC-CMs. Out of 310 pathways identified using metabolomics and proteomics, 40 pathways were considered significantly overrepresented (false-discovery-rate-corrected p ≤ 0.05). Highly populated pathways included those involved in protein synthesis (ribosome, spliceosome), ATP generation (oxidative phosphorylation), and cardiac muscle contraction. This multiomics method achieves a deep coverage of metabolites and proteins, creating a multidimensional view of the hPSC-CM phenotype, which provides a strong technological foundation to advance the understanding of hPSC-CM biology. Raw data are available in the MassIVE repository with identifier MSV000088010.

Human Islet Expression Levels of Prostaglandin E2 Synthetic Enzymes, But Not Prostaglandin EP3 Receptor, Are Positively Correlated with Markers of β-Cell Function and Mass in Nondiabetic Obesity

Elevated islet production of prostaglandin E₂ (PGE₂), an arachidonic acid metabolite, and expression of prostaglandin E₂ receptor subtype EP3 (EP3) are well-known contributors to the β-cell dysfunction of type 2 diabetes (T2D). Yet, many of the same pathophysiological conditions exist in obesity, and little is known about how the PGE₂ production and signaling pathway influences nondiabetic β-cell function. In this work, plasma arachidonic acid and PGE₂ metabolite levels were quantified in a cohort of nondiabetic and T2D human subjects to identify their relationship with glycemic control, obesity, and systemic inflammation. In order to link these findings to processes happening at the islet level, cadaveric human islets were subject to gene expression and functional assays. Interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2) mRNA levels, but not those of EP3, positively correlated with donor body mass index (BMI). IL-6 expression also strongly correlated with the expression of COX-2 and other PGE₂ synthetic pathway genes. Insulin secretion assays using an EP3-specific antagonist confirmed functionally relevant upregulation of PGE₂ production. Yet, islets from obese donors were not dysfunctional, secreting just as much insulin in basal and stimulatory conditions as those from nonobese donors as a percent of content. Islet insulin content, on the other hand, was increased with both donor BMI and islet COX-2 expression, while EP3 expression was unaffected. We conclude that upregulated islet PGE₂ production may be part of the β-cell adaption response to obesity and insulin resistance that only becomes dysfunctional when both ligand and receptor are highly expressed in T2D.

Systemic Metabolic Alterations Correlate with Islet-Level Prostaglandin E2 Production and Signaling Mechanisms That Predict β-Cell Dysfunction in a Mouse Model of Type 2 Diabetes

The transition from β-cell compensation to β-cell failure is not well understood. Previous works by our group and others have demonstrated a role for Prostaglandin EP3 receptor (EP3), encoded by the Ptger3 gene, in the loss of functional β-cell mass in Type 2 diabetes (T2D). The primary endogenous EP3 ligand is the arachidonic acid metabolite prostaglandin E₂ (PGE₂). Expression of the pancreatic islet EP3 and PGE₂ synthetic enzymes and/or PGE₂ excretion itself have all been shown to be upregulated in primary mouse and human islets isolated from animals or human organ donors with established T2D compared to nondiabetic controls. In this study, we took advantage of a rare and fleeting phenotype in which a subset of Black and Tan BRachyury (BTBR) mice homozygous for the Leptin^ob/ob mutation—a strong genetic model of T2D—were entirely protected from fasting hyperglycemia even with equal obesity and insulin resistance as their hyperglycemic littermates. Utilizing this model, we found numerous alterations in full-body metabolic parameters in T2D-protected mice (e.g., gut microbiome composition, circulating pancreatic and incretin hormones, and markers of systemic inflammation) that correlate with improvements in EP3-mediated β-cell dysfunction.