Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer.

Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.

Metabolomic and lipidomic characterization of an X-chromosome deletion disorder in neural progenitor cells by UHPLC-HRMS.

INTRODUCTION: Intellectual disorders involving deletions of the X chromosome present a difficult task in the determination of a connection between symptoms and metabolites that could lead to treatment options. One specific disorder of X-chromosomal deletion, Fragile X syndrome, is the most frequently occurring of intellectual disabilities. Previous metabolomic studies have been limited to mouse models that may not have sufficiently revealed the full biochemical diversity of the disease in humans. OBJECTIVES: The primary objective of this study was to elucidate the human biochemistry in X-chromosomal deletion disorders through metabolomic and lipidomic profiling, using cells from a X-deletion patient as a representative case. METHODS: Metabolomic and lipidomic analysis was performed by UHPLC-HRMS on neural progenitor (NP) cells isolated from an afflicted female patient versus normal neural progenitor cells. RESULTS: Results showed perturbations in several metabolic pathways, including those of arginine and proline, that significantly impact both neurotransmitter generation and overall brain function. Coincidently, dysregulation was observed for lipids involved in both cellular structure and membrane integrity. The trends of observed metabolomic changes, as well as lipidomic profiling from identified features, are discussed. CONCLUSION: The lipidomic and metabolomic profiles of NP cell samples exhibited significant differentiation associated with partial deletion of the X chromosome. These findings suggest that rare X-chromosomal deletion disorders are not only a mental disorder limited to alterations in local neuronal functions, but are also metabolic diseases.

Rapid Diagnosis of Prostate Cancer Disease Progression Using Paper Spray Ionization Mass Spectrometry.

The limitation of prostate specific antigen (PSA) for prostate cancer (PC) diagnosis is well-recognized. The Gleason score (GS) has been the most widely used grading system for prostate tumor differentiation and represents the best-established prognostic indicator for prostate cancer progression. However, a rapid and sensitive noninvasive diagnostic marker that differentiates GS-based prostate cancer disease progression is needed. As PC is becoming a leading cause of cancer related death for men in the U.S. and worldwide, an immediate need exists for an improved, sensitive, noninvasive, and rapid diagnostic test for PC screening. Here, we employed paper spray ionization-mass spectrometry (PSI MS)-based global metabolomics of urine liquid biopsies to distinguish between healthy (negative for any prostate specific health problems) and progressive PC states (low grade PC such as GS6 and high-grade PC such as GS7, GS8, and GS9). For PSI-MS-based direct untargeted metabolic investigation, a raw urine sample was directly pipetted onto a triangular paper substrate, without any additional sample preparation. Multivariate statistical analysis revealed distinct GS-specific metabolic signatures compared to a healthy control. Variable importance in projection from partial least-squares-discriminant analysis showed distinct metabolic patterns that were correlatively elevated with progressive disease and could serve as biomarkers for diagnosis of prostate cancer risk categorization.

Identification and quantification of honeybee venom constituents by multiplatform metabolomics.

Honeybee (Apis mellifera) venom (HBV) has been a subject of extensive proteomics research; however, scarce information on its metabolite composition can be found in the literature. The aim of the study was to identify and quantify the metabolites present in HBV. To gain the highest metabolite coverage, three different mass spectrometry (MS)-based methodologies were applied. In the first step, untargeted metabolomics was used, which employed high-resolution, accurate-mass Orbitrap MS. It allowed obtaining a broad overview of HBV metabolic components. Then, two targeted metabolomics approaches, which employed triple quadrupole MS, were applied to quantify metabolites in HBV samples. The untargeted metabolomics not only confirmed the presence of amines, amino acids, carbohydrates, and organic acids in HBV, but also provided information on venom components from other metabolite classes (e.g., nucleosides, alcohols, purine and pyrimidine derivatives). The combination of three MS-based metabolomics platforms facilitated the identification of 214 metabolites in HBV samples, among which 138 were quantified. The obtaining of the wide free amino acid profiles of HBV is one of the project's achievements. Our study contributed significantly to broadening the knowledge about HBV composition and should be continued to obtain the most comprehensive metabolite profile of HBV.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics.

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS.

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics.

INTRODUCTION: LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics. OBJECTIVES: We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections. METHODS: Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity. RESULTS: Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency. CONCLUSIONS: Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

Strain-Level Differentiation of Bacteria by Paper Spray Ionization Mass Spectrometry.

Paper spray ionization mass spectrometry (PSI-MS) is a relatively new analytical technique allowing for rapid mass spectrometric analysis of biological samples with little or no sample preparation. The expeditious nature of the analysis and minimal requirement for sample preparation make PSI-MS a promising avenue for future clinical assays with one potential application in the identification of different types of bacteria. Although past PSI-MS studies have demonstrated the ability to distinguish between bacteria of different species and morphological classes, achieving within-species strain-level differentiation has never been performed. In this report, we demonstrate the first strain-level bacterial differentiation by PSI-MS with the mammalian intestinal bacterium Oxalobacter formigenes ( Oxf). This novel application holds promising clinical significance as it could be used to differentiate between pathogenic bacteria and their harmless, commensal relatives, saving time and money in clinical diagnostics. Both whole cells and cell lysates of  Oxf strains HC1 and OxWR were analyzed using the Prosolia Velox 360TM PSI source coupled to a Thermo Scientific Q Exactive high-resolution mass spectrometer with a rapid 30 s analytical method. Multivariate statistical analysis followed by examination of significant features provided for and confirmed differentiation between Oxf HC1 and OxWR. We report a panel of strain-exclusive metabolites that could serve as potential strain-indicating biomarkers.

Study of early stage non-small-cell lung cancer using Orbitrap-based global serum metabolomics.

PURPOSE: The aim of the project was to apply ultra-high-performance liquid chromatography-quadrupole-Orbitrap-high-resolution mass spectrometry for serum metabolite profiling of non-small-cell lung cancer (NSCLC). This Orbitrap-based methodology has been applied for a study of NSCLC potential markers for the first time. METHODS: After extraction using protein precipitation, sera were separated on the ACE Excel 2 C18-PFP (100 × 2.1 mm, 2.0 µm) column using gradient elution and analyzed within the range of 70-1000 m/z. Only patients with early stage disease (stages IA-IIB) were included in the study, providing opportunity to find biomarkers for early lung cancer detection. The resulting metabolite profiles were subjected to univariate and multivariate statistical tests. RESULTS: 36 features were found significantly changed between NSCLC group and controls after FDR adjustment and 19 were identified using various metabolite databases (in-house library, HMDB, mzCloud). The study revealed a number of NSCLC biomarker candidates which belong to such compound classes as acylcarnitines, organic acids, and amino acids. Multivariate ROC curve built using 12 identified metabolites was characterized by AUC = 0.836 (0.722-0.946). There were no significant differences in the serum metabolite profiles between two most common histological types of lung cancer-adenocarcinoma and squamous cell carcinoma. CONCLUSIONS: Through identification of novel potential tumor markers, Orbitrap-based global metabolic profiling is a useful strategy in cancer research. Our study can accelerate development of new diagnostic and therapeutic strategies in NSCLC. The metabolites involved in discrimination between NSCLC patients and the control subjects should be further explored using a targeted approach.