Mass spectrometry based metabolomics of volume-restricted in-vivo brain samples: Actual status and the way forward

1-Octanol-assisted ultra-small volume droplet microfluidics with nanoelectrospray ionization mass spectrometry

BACKGROUND

Droplet microfluidics with push-pull and microdialysis sampling from brain slices, cultured cells and engineered tissues produce low volume mass limited samples containing analytes sampled from the extracellular space. This sampling approach coupled to mass spectrometry (MS) detection allows evaluation of time-dependent chemical changes. Our goal is an approach for continuous sampling and segregation of extracellular samples into picoliter droplets followed by the characterization of the droplets using nanoelectrospray ionization (nESI) MS. The main focus here is the optimization of the carrier oil for the microfluidic device that neither affects the stability of picoliter droplets nor compatibility with MS detection of a range of analytes.

RESULTS

We developed and characterized a 1-octanol-assisted ultra-small volume droplet microfluidic nESI MS system for the analysis of neurotransmitters in distinct samples including cerebrospinal fluid (CSF). The use of a 1-octanol oil phase was effective for generation of aqueous droplets as small as 65 pL and enabled detection of acetylcholine (ACh) and gamma-aminobutyric acid (GABA) in water and artificial CSF. Continuous MS analysis of droplets for extended periods up to 220 min validated the long-term stability of droplet generation and analyte detection by nESI-MS. As an example, ACh response demonstrated a linear working range (R2 = 0.99) between 0.4 μM and 25 μM with a limit of detection of 370 nM (24 amol), enabling its quantitation in rodent CSF.

SIGNIFICANCE

The established droplet microfluidics - nESI MS approach allows the analysis of microenvironments at high spatiotemporal resolution. The approach may allow microsampling and monitoring of spatiotemporal dynamics of neurochemicals and drugs in the brain and spinal cord of live animals.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

Biomarkers in psychiatric disorders

Psychiatric disorders represent a significant socioeconomic and healthcare burden worldwide. Of these, schizophrenia, bipolar disorder, major depressive disorder and anxiety are among the most prevalent. Unfortunately, diagnosis remains problematic and largely complicated by the lack of disease specific biomarkers. Accordingly, much research has focused on elucidating these conditions to more fully understand underlying pathophysiology and potentially identify biomarkers, especially those of early stage disease. In this chapter, we review current status of this endeavor as well as the potential development of novel biomarkers for clinical applications and future research study.

Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

INTRODUCTION

Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.

OBJECTIVES

This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.

METHODS

We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.

RESULTS

MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.

CONCLUSION

These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

Mapping of Fatty Aldehydes in the Diabetic Rat Brain Using On-Tissue Chemical Derivatization and Air-Flow-Assisted Desorption Electrospray Ionization-Mass Spectrometry Imaging

Fatty aldehydes (FALs) are involved in various biological processes, and their abnormal metabolism is related to the occurrence and development of neurological diseases. Because of their low ionization efficiency, methods for in situ detection and mass spectrometry imaging (MSI) analysis of FALs remain underreported. On-tissue chemical tagging of hardly ionizable target analytes with easily ionized moieties can improve ionization efficiency and detection sensitivity in MSI experiments. In this study, an on-tissue chemical derivatization-air-flow-assisted desorption electrospray ionization-MSI method was developed to visualize FALs in the rat brain. The method showed high sensitivity and specificity, allowing the use of in situ high-resolution MS³ to identify FALs. The methodology was applied to investigate the region-specific distribution of FALs in the brains of control and diabetic encephalopathy (DE) rats. In DE rats, FALs were found to be significantly enriched in various brain regions, especially in the cerebral cortex, hippocampus, and amygdala. Thus, increased FAL levels and oxidative stress occurred in a region-dependent manner, which may contribute to cognitive function deficits in DE. In summary, we provide a novel method for the in situ detection of FALs in biological tissues as well as new insights into the potential pathogenesis of DE.

V, Joseph D, Meitei HY, Asampille G, Kumar P, Gowda G. A. N, Kalthur G, Kovacic B, Adiga SK. ICSI in non-male factor infertility patients does not alter metabolomic signature in sibling embryos as evidenced by sensitivity enhanced nuclear magnetic resonance (NMR) spectroscopy

Intracytoplasmic sperm injection (ICSI) was developed to overcome male factor infertility, however, there recently has been an increasing trend in ICSI usage irrespective of the etiology, demonstrating an overuse of this insemination technique. There is a limited knowledge on the behaviour of ICSI derived embryos in non-male factor infertility patients. Metabolomic assessment of preimplantation embryos in conjunction with morphological evaluation can provide better understanding of embryonic behaviour. Hence, this study was undertaken to explore if there are any metabolomic differences between IVF and ICSI derived sibling day-5 blastocysts from non-male factor infertility patients. This prospective study included nineteen couples with non-male factor infertility undergoing Assisted Reproductive Technology. The sibling oocytes retrieved from each patient were randomly assigned to two groups and inseminated either by IVF or ICSI. Spent culture media (SCM) in which embryos were cultured up to day 5 were collected and investigated using sensitivity enhanced NMR based metabolite profiling utilizing high resolution (800 MHz) NMR equipped with cryogenically cooled micro-coil (1.7 mm) probe. The metabolomic signature between IVF and ICSI derived sibling blastocysts was assessed. A significant reduction in the concentrations of pyruvate, citrate, glucose and lysine were observed in both IVF and ICSI sibling embryos compared to medium control (P< 0.05-0.001). Further, histidine and valine level was found lower in ICSI embryos compared to medium control (P<0.05) during 96 hours of in vitro culture. Notably, between IVF and ICSI SCM, no significant difference in the concentration of the metabolites was found. Our results suggest that ICSI in non-male factor does not alter the SCM metabolomic signature during 96 hours of embryonic development.