Journal of Cerebral Blood Flow & Metabolism · 2015 · 59 citations · 44 references
Life SciencesNeurosciencePhysiologyCellular MetabolismCellular NeuroscienceMetabolismCellular EnergeticsCellular Energy MetabolismEnergy MetabolismMitochondrial Function In NeuronsMitochondrial MetabolismNeuronal MetabolismCellular Energy SensingCellular Fluorescence ImagingGlycerol-3-phosphate ShuttleIntracellular Calcium DynamicsMalate-aspartate ShuttleMetabolic Rate ChangesNicotinamide Adenine Dinucleotide HomeostasisTricarboxylic Acid Cycle Metabolism
Imaging NAD(P)H fluorescence provides a noninvasive, time‑resolved readout of mitochondrial function in neurons, and stimulation‑induced biphasic transients are shaped by calcium dynamics, TCA cycle activity, shuttles, oxygen supply, and ATP demand. The study aimed to assess how each of these processes contributes to NAD(P)H transients. To do so, the authors built and validated a detailed physiologic mathematical model of neuronal energy metabolism and used it to simulate single‑cell and slice responses under varied activity and nutrient conditions. The model reproduced all measured NAD(P)H responses, showing that cells with distinct metabolic states can generate similar transients and that spatially heterogeneous contributions explain slice recordings, thereby reconciling conflicting experimental results and deepening mechanistic insight.
Imaging of the cellular fluorescence of the reduced form of nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) is one of the few metabolic readouts that enable noninvasive and time-resolved monitoring of the functional status of mitochondria in neuronal tissues. Stimulation-induced transient changes in NAD(P)H fluorescence intensity frequently display a biphasic characteristic that is influenced by various molecular processes, e.g., intracellular calcium dynamics, tricarboxylic acid cycle activity, the malate–aspartate shuttle, the glycerol-3-phosphate shuttle, oxygen supply or adenosine triphosphate (ATP) demand. To evaluate the relative impact of these processes, we developed and validated a detailed physiologic mathematical model of the energy metabolism of neuronal cells and used the model to simulate metabolic changes of single cells and tissue slices under different settings of stimulus-induced activity and varying nutritional supply of glucose, pyruvate or lactate. Notably, all experimentally determined NAD(P)H responses could be reproduced with one and the same generic cellular model. Our computations reveal that (1) cells with quite different metabolic status may generate almost identical NAD(P)H responses and (2) cells of the same type may quite differently contribute to aggregate NAD(P)H responses recorded in brain slices, depending on the spatial location within the tissue. Our computational approach reconciles different and sometimes even controversial experimental findings and improves our mechanistic understanding of the metabolic changes underlying live-cell NAD(P)H fluorescence transients.
44