The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Systemic Neutrophil Gelatinase-Associated Lipocalin Alterations in Chronic Pancreatitis: A Multicenter, Cross-Sectional Study

INTRODUCTION

Chronic pancreatitis (CP) is a progressive fibroinflammatory disorder lacking therapies and biomarkers. Neutrophil gelatinase-associated lipocalin (NGAL) is a proinflammatory cytokine elevated during inflammation that binds fatty acids (FAs) such as linoleic acid. We hypothesized that systemic NGAL could serve as a biomarker for CP and, with FAs, provide insights into inflammatory and metabolic alterations.

METHODS

NGAL was measured by immunoassay, and FA composition was measured by gas chromatography in plasma (n = 171) from a multicenter study, including controls (n = 50), acute and recurrent acute pancreatitis (AP/RAP) (n = 71), and CP (n = 50). Peripheral blood mononuclear cells (PBMCs) from controls (n = 16), AP/RAP (n = 17), and CP (n = 15) were measured by cytometry by time-of-flight.

RESULTS

Plasma NGAL was elevated in subjects with CP compared with controls (area under the curve [AUC] = 0.777) or AP/RAP (AUC = 0.754) in univariate and multivariate analyses with sex, age, body mass index, and smoking (control AUC = 0.874; AP/RAP AUC = 0.819). NGAL was elevated in CP and diabetes compared with CP without diabetes ( P < 0.001). NGAL + PBMC populations distinguished CP from controls (AUC = 0.950) or AP/RAP (AUC = 0.941). Linoleic acid was lower, whereas dihomo-γ-linolenic and adrenic acids were elevated in CP ( P < 0.05). Linoleic acid was elevated in CP with diabetes compared with CP subjects without diabetes ( P = 0.0471).

DISCUSSION

Elevated plasma NGAL and differences in NGAL + PBMCs indicate an immune response shift that may serve as biomarkers of CP. The potential interaction of FAs and NGAL levels provide insights into the metabolic pathophysiology and improve diagnostic classification of CP.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS

BACKGROUND

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time.

PURPOSE

We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS.

METHODS

The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising.

RESULTS

With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels.

CONCLUSIONS

The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

The Key Role of GSH in Keeping the Redox Balance in Mammalian Cells: Mechanisms and Significance of GSH in Detoxification via Formation of Conjugates

Glutathione (GSH) is a ubiquitous tripeptide that is biosynthesized in situ at high concentrations (1-5 mM) and involved in the regulation of cellular homeostasis via multiple mechanisms. The main known action of GSH is its antioxidant capacity, which aids in maintaining the redox cycle of cells. To this end, GSH peroxidases contribute to the scavenging of various forms of ROS and RNS. A generally underestimated mechanism of action of GSH is its direct nucleophilic interaction with electrophilic compounds yielding thioether GSH S-conjugates. Many compounds, including xenobiotics (such as NAPQI, simvastatin, cisplatin, and barbital) and intrinsic compounds (such as menadione, leukotrienes, prostaglandins, and dopamine), form covalent adducts with GSH leading mainly to their detoxification. In the present article, we wish to present the key role and significance of GSH in cellular redox biology. This includes an update on the formation of GSH-S conjugates or GSH adducts with emphasis given to the mechanism of reaction, the dependence on GST (GSH S-transferase), where this conjugation occurs in tissues, and its significance. The uncovering of the GSH adducts' formation enhances our knowledge of the human metabolome. GSH-hematin adducts were recently shown to have been formed spontaneously in multiples isomers at hemolysates, leading to structural destabilization of the endogenous toxin, hematin (free heme), which is derived from the released hemoglobin. Moreover, hemin (the form of oxidized heme) has been found to act through the Kelch-like ECH associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor-2 (Nrf2) signaling pathway as an epigenetic modulator of GSH metabolism. Last but not least, the implications of the genetic defects in GSH metabolism, recorded in hemolytic syndromes, cancer and other pathologies, are presented and discussed under the framework of conceptualizing that GSH S-conjugates could be regarded as signatures of the cellular metabolism in the diseased state.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

A gold nanoparticle-dye/poly(3-aminobenzylamine)/two dimensional MoSe2/graphene oxide electrode towards label-free electrochemical biosensor for simultaneous dual-mode detection of cancer antigen 15-3 and microRNA-21

A dual-mode electrochemical biosensor is successfully developed for simultaneous detection of two different kinds of breast cancer biomarkers, namely cancer antigen 15-3 (CA 15-3) and microRNA-21 (miRNA-21), for the first time. The sensor composes of a poly(3-aminobenzylamine)/two-dimensional (2D) molybdenum selenide/graphene oxide nanocomposite modified two-screen-printed carbon electrode array (dual electrode), functionalized individually with 2,3-diaminophenazine-gold nanoparticles and toluidine blue-gold nanoparticles. Both kinds of the redox probe-gold nanoparticles are employed as signaling molecules and supports for immobilization of anti-CA 15-3 antibodies and capture DNA-21 probes, respectively. Due to the good conductivity and high surface-to-volume ratio of the nanocomposite, high amount of the antibodies and capture probes can be immobilized on the modified dual-electrode, giving the efficient duplex detection. Consequently, the biosensor provides good selectivity, and high sensitivity for the dual target analyte detection. The experimental results show that this label-free biosensor exhibits good linear responses to the concentrations of both target analytes with the limits of detection (LODs) of 0.14 U mL^-1 and 1.2 fM for CA 15-3 and miRNA-21, respectively. This assay strategy has a great potential to be further developed for the simultaneous detection of a variety of miRNAs and protein biomarkers for point-of-care (POC) diagnostic applications.

Fluid biomarkers of white matter hyperintensities in cerebrovascular disease and neurodegeneration: a systematic review protocol

OBJECTIVE

The goal of this systematic review is to evaluate the association between fluid biomarkers and white matter hyperintensities (WMH) in cerebrovascular disease and neurodegenerative disorders. While previous research has examined the etiology of WMH in specific diseases, we propose a comprehensive framework encompassing WMH of both vascular and non-vascular origin.

INTRODUCTION

Although WMH have been mostly described in aging populations with cerebrovascular disease, extensive lesions also occur in non-vascular diseases. Such lesions are traditionally treated as a separate pathological entity from vascular ones, but recent work has challenged the appropriateness of that framework when probing WMH etiology. Comparing biomarkers associated with WMH across various pathologies may improve our understanding of their etiology.

INCLUSION CRITERIA

The review will focus on cerebrovascular disease and neurodegenerative disorders and exclude infectious, metabolic, drug-induced, or radiation-induced white matter diseases. Original, peer-reviewed research on the relationship of WMH on magnetic resonance imaging with blood/cerebrospinal fluid biomarkers will be considered for inclusion. Postmortem studies will guide the selection of biomarkers of interest and the interpretation of our findings. Genomic markers will be excluded.

METHODS

The review will be conducted in accordance with PRISMA and JBI guidelines. English articles of interest published between 2000 and 2020 will be identified in MEDLINE and Embase. Two reviewers will perform abstract and full-text screening, standardized data extraction, and quality assessments of the selected studies. The relationship between each biomarker and WMH burden will be meta-analyzed, if possible, with subgroup or meta-regression analyses to assess differences between diseases.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42020218298.

Temporal metabolic response yields a dynamic biosignature of inflammation

Chronic low-grade inflammation is a subclinical condition directly and indirectly linked to the development of a wide range of diseases responsible for the vast majority of morbidity. To examine mechanisms coupled to chronic disease, a group of overweight and obese human subjects without known inflammatory diseases participated in a high-fat meal challenge as an acute inflammation stimulus. Analysis of serum metabolites grouped by baseline cytokine levels revealed that single samples had little power in differentiating groups. However, an analysis that incorporated temporal response separated inflammatory response phenotypes and allowed us to create a metabolic signature of inflammation which revealed metabolic components that are crucial to a cytokine-mediated inflammation response. The use of temporal response, rather than a single time point, improved metabolomic prediction of high postprandial inflammation responses and led to the development of a dynamic biosignature as a potential tool for stratifying risk to a wide range of diseases.

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Dynamic responses often provide insight into disease pathology

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Temporal metabolic responses to acute inflammation were explored in obese people

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Temporal metabolite levels differentiated low, mid, and high inflammation groups

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Inflammation-linked metabolites were shown to be predictors of cytokine responses

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

Dried Blood Spot (DBS) Methodology Study for Biomarker Discovery in Lysosomal Storage Disease (LSD)

Lysosomal storage diseases (LSDs) are a heterogeneous group of inherited metabolic diseases caused by mutations in genes encoding for proteins involved in the lysosomal degradation of macromolecules. They occur in approximately 1 in 5000 live births and pose a lifelong risk. Therefore, to achieve the maximum benefit from LSDs therapies, a fast and early diagnosis of the disease is required. In this framework, biomarker discovery is a significant factor in disease diagnosis and in predicting its outcomes. On the other hand, the dried blood spot (DBS) based metabolomics platform can open up new pathways for studying non-directional hypothesis approaches to biomarker discovery. This study aims to increase the efficiency of the developed methods for biomarker development in the context of rare diseases, with an improved impact on the reliability of the detected compounds. Thereby, we conducted two independent experiments and integrated them into the screening of the human blood metabolome: (1) comparison of EDTA blood and filter cards in terms of their suitability for metabolomics studies; (2) optimization of the extraction method: a side-by-side comparison of a series of buffers to the best utility to the disease of interest. The findings were compared to previous studies across parameters such as metabolite coverage, sample type suitability, and stability. The results indicate that measurements of metabolites are susceptible to differences in pre-analytical conditions and extraction solvents. This proposed approach can increase the positive rate of the future development of biomarkers. Altogether, the procedure can be easily adapted and applied to other studies, where the limited number of samples is a common barrier.

Spectral Wavelet-feature Analysis and Classification Assisted Denoising for enhancing magnetic resonance spectroscopy

Magnetic resonance spectroscopy (MRS) is capable of revealing important biochemical and metabolic information of tissues noninvasively. However, the low concentrations of metabolites often lead to poor signal-to-noise ratio (SNR) and a long acquisition time. Therefore, the applications of MRS in detection and quantitative measurements of metabolites in vivo remain limited. Reducing or even eliminating noise can improve SNR sufficiently to obtain high quality spectra in addition to increasing the number of signal averaging (NSA) or the field strength, both of which are limited in clinical applications. We present a Spectral Wavelet-feature ANalysis and Classification Assisted Denoising (SWANCAD) approach to differentiate signal and noise peaks in magnetic resonance spectra based on their respective wavelet features, followed by removing the identified noise components to improve SNR. The performance of this new denoising approach was evaluated by measuring and comparing SNRs and quantified metabolite levels of low NSA spectra (e.g. NSA = 8) before and after denoising using the SWANCAD approach or by conventional spectral fitting and denoising methods, such as LCModel and wavelet threshold methods, as well as the high NSA spectra (e.g. NSA = 192) recorded in the same sampling volumes. The results demonstrated that SWANCAD offers a more effective way to detect the signals and improve SNR by removing noise from the noisy spectra collected with low NSA or in the subminute scan time (e.g. NSA = 8 or 16 s). The potential applications of SWANCAD include using low NSA to accelerate MRS acquisition while maintaining adequate spectroscopic information for detection and quantification of the metabolites of interest when a limited time is available for an MRS examination in the clinical setting.

Tick-Tock Consider the Clock: The Influence of Circadian and External Cycles on Time of Day Variation in the Human Metabolome-A Review

The past decade has seen a large influx of work investigating time of day variation in different human biofluid and tissue metabolomes. The driver of this daily variation can be endogenous circadian rhythms driven by the central and/or peripheral clocks, or exogenous diurnal rhythms driven by behavioural and environmental cycles, which manifest as regular 24 h cycles of metabolite concentrations. This review, of all published studies to date, establishes the extent of daily variation with regard to the number and identity of 'rhythmic' metabolites observed in blood, saliva, urine, breath, and skeletal muscle. The probable sources driving such variation, in addition to what metabolite classes are most susceptible in adhering to or uncoupling from such cycles is described in addition to a compiled list of common rhythmic metabolites. The reviewed studies show that the metabolome undergoes significant time of day variation, primarily observed for amino acids and multiple lipid classes. Such 24 h rhythms, driven by various factors discussed herein, are an additional source of intra/inter-individual variation and are thus highly pertinent to all studies applying untargeted and targeted metabolomics platforms, particularly for the construction of biomarker panels. The potential implications are discussed alongside proposed minimum reporting criteria suggested to acknowledge time of day variation as a potential influence of results and to facilitate improved reproducibility.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.

Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation

Over the past decades, the genome and proteome have been widely explored for biomarker discovery and personalized medicine. However, there is still a large need for improved diagnostics and stratification strategies for a wide range of diseases. Post-translational modification of proteins by glycosylation affects protein structure and function, and glycosylation has been implicated in many prevalent human diseases. Numerous proteins for which the plasma levels are nowadays evaluated in clinical practice are glycoproteins. While the glycosylation of these proteins often changes with disease, their glycosylation status is largely ignored in the clinical setting. Hence, the implementation of glycomic markers in the clinic is still in its infancy. This is for a large part caused by the high complexity of protein glycosylation itself and of the analytical techniques required for their robust quantification. Mass spectrometry-based workflows are particularly suitable for the quantification of glycans and glycoproteins, but still require advances for their transformation from a biomedical research setting to a clinical laboratory. In this review, we describe why and how glycomics is expected to find its role in clinical tests and the status of current mass spectrometry-based methods for clinical glycomics.

Five Easy Metrics of Data Quality for LC-MS-Based Global Metabolomics

Data quality in global metabolomics is of great importance for biomarker discovery and system biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar liquid chromatography coupled with mass spectrometry (LC-MS) platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis QI software and then analyzed using five important data quality measures, including retention time drift, the number of compounds detected, missing values, and MS reproducibility (2 measures). The detected compounds were fit to a γ distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus the percentage of total compounds detected and intraclass correlation coefficient (ICC) versus compound abundance were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary table of results gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters.

Small molecule biomarkers for neonatal hypoxic ischemic encephalopathy

Hypoxic Ischemic Encephalopathy (HIE) is one of the most deleterious conditions in the perinatal period and the access to small molecule biomarkers aiding accurate diagnosis and disease staging, progress monitoring, and early outcome prognosis could provide relevant advances towards the development of personalized therapies. The emergence of metabolomics, the "omics" technology enabling the holistic study of small molecules, for biomarker discovery employing different analytical platforms, animal models and study populations has drastically increased the number and diversity of small molecules proposed as candidate biomarkers. However, the use of very few compounds has been implemented in clinical guidelines and authorized medical devices. In this work we review different approaches employed for discovering HIE-related small molecule biomarkers. Their role in associated biochemical disease mechanisms as well as the way towards their translation into the clinical practice are discussed.