Current technical approaches to brain energy metabolism

Abstract

Neuroscience is a technology-driven discipline and brain energy metabolism is no exception. Once satisfied with mapping metabolic pathways at organ level, we are now looking to learn what it is exactly that metabolic enzymes and transporters do and when, where do they reside, how are they regulated, and how do they relate to the specific functions of neurons, glial cells, and their subcellular domains and organelles, in different areas of the brain. Moreover, we aim to quantify the fluxes of metabolites within and between cells. Energy metabolism is not just a necessity for proper cell function and viability but plays specific roles in higher brain functions such as memory processing and behavior, whose mechanisms need to be understood at all hierarchical levels, from isolated proteins to whole subjects, in both health and disease. To this aim, the field takes advantage of diverse disciplines including anatomy, histology, physiology, biochemistry, bioenergetics, cellular biology, molecular biology, developmental biology, neurology, and mathematical modeling. This article presents a well-referenced synopsis of the technical side of brain energy metabolism research. Detail and jargon are avoided whenever possible and emphasis is given to comparative strengths, limitations, and weaknesses, information that is often not available in regular articles.

Keywords: in vitro; in vivo; organization level; spatio-temporal resolution.

Figure 1. Hierarchical organization of brain energy metabolism

Structural levels result from binding forces becoming weaker as small structures accrete into larger ones but also have an evolutionary explanation (Simon, 1962). The hierarchical organization of brain metabolism explains the success of reductionist investigative approaches.

Figure 2. Technical domains in brain energy metabolism research

Different techniques perform over different domains, which are in turn defined by a combination of organizational level, experimental control and relevance to human physiology. For example, electrophysiology achieves excellent experimental control at small and intermediate hierarchies by in vitro recording from single channels, single cells or small cell assemblies, but it is not practical in vivo and cannot be used in humans. MRS, in contrast, may be applied to human subjects, but has limited experimental control and provides time- and space-averaged measurements.

Figure 3. Examples of recent data in brain energy metabolism

A. Astrocytes in organotypic hippocampal slice culture were transfected with cDNA constructs encoding for hexokinase-ECFP and GLT1-mCherry using a gene-gun. B. In vivo [¹⁴C]-2-deoxyglucose uptake, obtained as described in Boussicault et al., 2014. C. ¹H-MRS data obtained in a rat, at rest or during whisker stimulation. The difference between the two conditions reveals an increase in tissue lactate. The BOLD fMRI response was elicited with the same paradigm. D. Rat neurons in culture were exposed to 3-¹³C₁ glucose so that the labeled carbon is lost via the PDH reaction but retained by the anaplerotic reactions of pyruvate carboxylase (PC) and malic enzyme (ME), from Divakaruni et al., 2017. E. Perturbation of hippocampal fuel fluxes during a memory task (adapted from Newman et al., 2011). F. Lactate-mediated depolarization and firing of a neuron in the locus coeruleus after optogenetic stimulation of neighboring astrocytes (adapted from Tang et al., 2014). G. The graph show the biphasic response to astrocytic lactate levels as detected with Laconic during local stimulation of the somatosensory cortex (from Sotelo-Hitschfeld et al., 2015). The top image shows the expression of Laconic and Pyronic in neurons and astrocytes of the somatosensory cortex using viral vectors (adapted from Machler et al., 2016). Bottom images were taken from a transgenic mouse expressing ATeam in neurons described in Trevisiol et al., 2017.