Glial cells are increasingly recognized as active players that profoundly influence neuronal synaptic transmission by specialized signalling pathways. In particular, astrocytes have recently been shown to release small molecules such as the amino acids l-glutamate and d-serine as “gliotransmitters”, which directly control the efficacy of adjacent synapses. However, it is still controversial whether gliotransmitters are released from a cytosolic pool or by Ca2+-dependent exocytosis from secretory vesicles, i.e., by a mechanism similar to the release of synaptic vesicles in synapses. Here we report that rat cortical astrocytes contain storage vesicles that display morphological and biochemical features similar to neuronal synaptic vesicles. These vesicles share some, but not all, membrane proteins with synaptic vesicles including the SNARE synaptobrevin 2 and contain both l-glutamate and d-serine. Furthermore, they show uptake of l-glutamate and d-serine that is driven by a proton electrochemical gradient. d-Serine uptake is associated with vesicle acidification and is dependent on chloride. While l-serine is not transported, serine racemase, the synthesizing enzyme for d-serine, is anchored to the membrane of the vesicles allowing local generation of d-serine. Finally, we reveal a previously unexpected mutual vesicular synergy between d-serine and l-glutamate filling in glia vesicles. We conclude that astrocytes contain vesicles capable of storing and releasing d-serine, l-glutamate, and most likely other neuromodulators in an activity-dependent manner.

Single cell mass spectrometry (MS) is a rapidly emerging field in metabolic investigations. The inherent chemical complexity of most biological samples poses analytical challenges when using MS platforms to measure sample content without prior chemical separation. Here, a single-cell capillary electrophoresis (CE) system was coupled with electrospray ionization (ESI) MS to enable the simultaneous measurement of a vast array of endogenous compounds in over 50 identified and isolated large neurons from the Aplysia californica central nervous system. More than 300 distinct ion signals (m/z values) were detected from a single neuron in positive ion mode, 140 of which were selected for chemometric data analysis. Metabolic features were evaluated among six different neuron types (B1, B2, left pleural 1 (LPl1), metacerebral cell (MCC), R2, and R15), chosen for their various physiological functions. The results indicated chemical similarities among some neuron types (B1 to B2 and LPl1 to R2) and distinctive features for others (MCC and R15 cells). The quantitative nature of the MS platform allowed the comparison of metabolite levels for specific neurons. The CE-ESI-MS approach for examination of individual nanoliter-volume cells as described herein is readily adaptable to other volume-limited samples.

Metabolites are involved in a diverse range of intracellular processes, including a cell’s response to a changing extracellular environment. Using single-cell capillary electrophoresis coupled to electrospray ionization mass spectrometry, we investigated how placing individual identified neurons in culture affects their metabolic profile. First, glycerol-based cell stabilization was evaluated using metacerebral neurons from Aplysia californica; the measurement error was reduced from ∼24% relative standard deviation to ∼6% for glycerol-stabilized cells compared to those isolated without glycerol stabilization. In order to determine the changes induced by culturing, 14 freshly isolated and 11 overnight-cultured neurons of two metabolically distinct cell types from A. californica, the B1 and B2 buccal neurons, were characterized. Of the more than 300 distinctive cell-related signals detected, 35 compounds were selected for their known biological roles and compared among each measured cell. Unsupervised multivariate and statistical analysis revealed robust metabolic differences between these two identified neuron types. We then compared the changes induced by overnight culturing; metabolite concentrations were distinct for 26 compounds in the cultured B1 cells. In contrast, culturing had less influence on the metabolic profile of the B2 neurons, with only five compounds changing significantly. As a result of these culturing-induced changes, the metabolic composition of the B1 neurons became indistinguishable from the cultured B2 cells. This observation suggests that the two cell types differentially regulate their in vivo or in vitro metabolomes in response to a changing environment.

The development of automated non-targeted workflows for small molecule analyses is highly desirable in many areas of research and diagnostics. Sufficient mass and chromatographic resolution is necessary for the detectability of compounds and subsequent componentization and interpretation of ions. The mass accuracy and relative isotopic abundance are critical in correct molecular formulae generation for unknown compounds. While high-resolution instrumentation provides accurate mass information, sample complexity can greatly influence data quality and the measurement of compounds of interest. Two high-resolution instruments, an Orbitrap and a Q-TOF, were evaluated for mass accuracy and relative isotopic abundance with various concentrations of a standard mixture in four complex sample matrices. The overall average ± standard deviation of the mass accuracy was 1.06 ± 0.76 ppm and 1.62 ± 1.88 ppm for the Orbitrap and the Q-TOF, respectively; however, individual measurements were ± 5 ppm for the Orbitrap and greater than 10 ppm for the Q-TOF. Relative isotopic abundance measurements for A + 1 were within 5% of the theoretical value if the intensity of the monoisotopic peak was greater than 1E7 for the Orbitrap and 1E5 for the Q-TOF, where an increase in error is observed with a decrease in intensity. Furthermore, complicating factors were found in the data that would impact automated data analysis strategies, including coeluting species that interfere with detectability and relative isotopic abundance measurements. The implications of these findings will be discussed with an emphasis on reasonable expectations from these instruments, guidelines for experimental workflows, data analysis considerations, and software design for non-targeted analyses.

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The ability to identify contaminants or adulterants in diverse, complex sample matrixes is necessary in food safety. Thus, nontargeted screening approaches must be implemented to detect and identify unexpected, unknown hazardous compounds that may be present. Molecular formulas can be generated for detected compounds from high-resolution mass spectrometry data, but analysis can be lengthy when thousands of compounds are detected in a single sample. Efficient data mining methods to analyze these complex data sets are necessary given the inherent chemical diversity and variability of food matrixes. The aim of this work is to determine necessary requirements to successfully apply data analysis strategies to distinguish suspect and control samples. Infant formula and orange juice samples were analyzed with one lot of each matrix containing varying concentrations of a four compound mixture to represent a suspect sample set. Small molecular differences were parsed from the data, where analytes as low as 10 ppb were revealed. This was accomplished, in part, by analyzing a quality control standard, matrix spiked with an analytical standard mixture, technical replicates, a representative number of sample lots, and blanks within the sample sequence; this enabled the development of a data analysis workflow and ensured that the employed method is sufficient for mining relevant molecular features from the data.

Non-targeted analysis (NTA) workflows using mass spectrometry are gaining popularity in many disciplines, but universally accepted reporting standards are nonexistent. Current guidance addresses limited elements of NTA reportingmost notably, identification confidenceand is insufficient to ensure scientific transparency and reproducibility given the complexity of these methods. This lack of reporting standards hinders researchers' development of thorough study protocols and reviewers' ability to efficiently assess grant and manuscript submissions. To overcome these challenges, we developed the NTA Study Reporting Tool (SRT), an easy-touse, interdisciplinary framework for comprehensive NTA methods and results reporting. Eleven NTA practitioners reviewed eight published articles covering environmental, food, and health-based exposomic applications with the SRT. Overall, our analysis demonstrated that the SRT provides a valid structure to guide study design and manuscript writing, as well as to evaluate NTA reporting quality. Scores self-assigned by authors fell within the range of peer-reviewer scores, indicating that SRT use for self-evaluation will strengthen reporting practices. The results also highlighted NTA reporting areas that need immediate improvement, such as analytical sequence and quality assurance/quality control information. Although scores intentionally do not correspond to data/results quality, widespread implementation of the SRT could improve study design and standardize reporting practices, ultimately leading to broader use and acceptance of NTA data.