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.