IMAGING AND MONITORING ASTROCYTES IN HEALTH AND DISEASE Topic Editors Carole Escartin and Keith K. Murai CELLULAR NEUROSCIENCE Frontiers in Cellular Neuroscience November 2014 | Imaging and monitoring astrocytes in health and disease | 1 ABOUT FRONTIERS Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. FRONTIERS JOURNAL SERIES The Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. 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Individual articles may be downloaded and reproduced in accordance with the principles of the CC-BY licence subject to any copyright or other notices. They may not be re-sold as an e-book. As author or other contributor you grant a CC-BY licence to others to reproduce your articles, including any graphics and third-party materials supplied by you, in accordance with the Conditions for Website Use and subject to any copyright notices which you include in connection with your articles and materials. All copyright, and all rights therein, are protected by national and international copyright laws. The above represents a summary only. For the full conditions see the Conditions for Authors and the Conditions for Website Use. ISSN 1664-8714 ISBN 978-2-88919-393-6 DOI 10.3389/978-2-88919-393-6 Frontiers in Cellular Neuroscience November 2014 | Imaging and monitoring astrocytes in health and disease | 2 Astrocytes are key cellular partners to neurons in the brain. They play an important role in multiple processes such as neurotransmitter recycling, trophic support, antioxidant defense, ionic homeostasis, inflammatory modulation, neurovascular and neurometabolic coupling, neurogenesis, synapse formation and synaptic plasticity. In addition to their crucial involvement in normal brain physiology, it is well known that astrocytes adopt a reactive phenotype under most acute and chronic pathological conditions such as ischemia, trauma, brain cancer, epilepsy, demyelinating and neurodegenerative diseases. However, the functional impact of astrocyte reactivity is still unclear. During the last decades, the development of innovative approaches to study astrocytes has significantly improved our understanding of their prominent role in brain function and their contribution to disease states. In particular, new genetic tools, molecular probes, and imaging techniques that achieve high spatial and temporal resolution have revealed new insight into astrocyte functions in situ. This Research Topic provides a collection of cutting-edge techniques, approaches and models to study astrocytes in health and disease. It also suggests new directions to achieve discoveries on these fascinating cells. IMAGING AND MONITORING ASTROCYTES IN HEALTH AND DISEASE Image of a protoplasmic astrocyte expressing myristoylated enhanced green fluorescenct protein (EGFP) and glial fibrillary acidic protein (GFAP) in the adult mouse cortex. Note the fine astrocytic processes of the astrocyte which are known to associate with synapses and blood vessels. Topic Editors: Carole Escartin, Molecular Imaging Research Center, France Keith K. Murai, McGill University, Canada Frontiers in Cellular Neuroscience November 2014 | Imaging and monitoring astrocytes in health and disease | 3 Table of Contents 05 Imaging and Monitoring Astrocytes in Health and Disease Carole Escartin and Keith K. Murai 07 Small is Fast: Astrocytic Glucose and Lactate Metabolism at Cellular Resolution L. F. Barros, A. San Martín, T. Sotelo-Hitschfeld, R. Lerchundi, I. Fernández-Moncada, I. Ruminot, R. Gutiérrez, R. Valdebenito, S. Ceballo, K. Alegría, F. Baeza-Lehnert and D. Espinoza 15 Confocal Microscopy for Astrocyte in Vivo Imaging: Recycle and Reuse in Microscopy Alberto Pérez-Alvarez, Alfonso Araque and Eduardo D. Martín 25 Imaging the Microanatomy of Astrocyte–T-Cell Interactions in Immune- Mediated Inflammation Carlos Barcia Sr, Izaskun Mitxitorena, María A. Carrillo-de Sauvage, José-María Gallego, Ana Pérez-Vallés and Carlos Barcia Jr 35 Studying Subcellular Detail in Fixed Astrocytes: Dissociation of Morphologically Intact Glial Cells (DIMIGs) Julia Haseleu, Enrico Anlauf, Sandra Blaess, Elmar Endl and Amin Derouiche 45 How Do Astrocytes Shape Synaptic Transmission? Insights From Electrophysiology Glenn Dallérac, Oana Chever and Nathalie Rouach 64 Comparison of Unitary Exocytic Events in Pituitary Lactotrophs and in Astrocytes: Modeling the Discrete Open Fusion-Pore States Doron Kabaso, Jernej Jorga Č evski, Ana I. Calejo, Ajda Flašker, Alenka Guček, Marko Kreft and Robert Zorec 70 Astrocytes: Can they be the Missing Stars Linking Neuronal Activity to Neurofunctional Imaging Signals? Hirac Gurden 73 The Role of Astrocytes in CNS Tumors: Pre-Clinical Models and Novel Imaging Approaches Emma R. O’Brien, Clare Howarth and Nicola R. Sibson 86 New Tools for Investigating Astrocyte-to-Neuron Communication Dongdong Li, Cendra Agulhon, Elke Schmidt, Martin Oheim and Nicole Ropert 100 Recent Molecular Approaches to Understanding Astrocyte Function in vivo David Davila, Karine Thibault, Todd A Fiacco and Cendra Agulhon 120 Efficient Gene Delivery and Selective Transduction of Astrocytes in the Mammalian Brain Using Viral Vectors Nicolas Merienne, Juliette Le Douce, Emilie Faivre, Nicole Déglon and Gilles Bonvento Frontiers in Cellular Neuroscience November 2014 | Imaging and monitoring astrocytes in health and disease | 4 133 A Cellular Star Atlas: Using Astrocytes From Human Pluripotent Stem Cells for Disease Studies Robert Krencik and Erik M. Ullian 143 Multifunctional Role of Astrocytes as Gatekeepers of Neuronal Energy Supply Jillian L. Stobart and Christopher M. Anderson 164 Unraveling the Complex Metabolic Nature of Astrocytes Anne-Karine Bouzier-Sore and Luc Pellerin 177 D-Serine as a Gliotransmitter and Its Roles in Brain Development and Disease Marion R. Van Horn, Mari Sild and Edward S. Ruthazer EDITORIAL published: 12 March 2014 doi: 10.3389/fncel.2014.00074 Imaging and monitoring astrocytes in health and disease Carole Escartin 1 * and Keith K. Murai 2 * 1 CNRS CEA URA 2210 and CEA, DSV, I2BM, MIRCen, Fontenay-aux-Roses, France 2 Department of Neurology and Neurosurgery, Center for Research in Neuroscience, Montreal, Canada *Correspondence: carole.escartin@cea.fr; keith.murai@mcgill.ca Edited and reviewed by: Egidio D’Angelo, University of Pavia, Italy Keywords: neuron-astrocyte interactions, reactive astrocytes, in vivo analysis, high-resolution imaging, brain imaging, electrophysiology, gene transfer, transgenesis Astrocytes are key cellular partners to neurons in the brain. They play an important role in multiple processes such as neu- rotransmitter recycling, trophic support, antioxidant defence, ionic homeostasis, inflammatory modulation, neurovascular and neurometabolic coupling, neurogenesis, synapse formation, and synaptic plasticity. In addition to their crucial involvement in normal brain physiology, it is well known that astrocytes adopt a reactive phenotype under most acute and chronic pathologi- cal conditions such as ischemia, trauma, brain cancer, epilepsy, demyelinating, and neurodegenerative diseases. However, the functional impact of astrocyte reactivity is still unclear. During the last decades, the development of innovative approaches to study astrocytes has significantly improved our understanding of their prominent role in brain function and their contribution to disease states. In particular, new genetic tools, molecular probes, and imaging techniques that achieve high spatial and temporal resolution have revealed new insight into astrocyte functions in situ. This Research Topic illustrates how recent methodological advances have helped to uncover the role of astrocytes in health and disease. The articles assembled cover a range of approaches to both monitor astrocytes (high-resolution microscopy, live imag- ing, positron emission tomography, nuclear magnetic resonance, and electrophysiology) and manipulate their functional proper- ties (optogenetics, mouse transgenesis, viral gene transfer, and human stem cell differentiation). IMAGING AND MONITORING ASTROCYTES In their Technology report, Barros et al. (2013) discuss live imag- ing methods based on genetically-encoded optical biosensors to quantify, at the single-cell level and with high temporal resolu- tion, the concentration, and dynamics of intracellular metabo- lites. In their Methods article, Perez-Alvarez et al. (2013) present a detailed methodological procedure to make the most of standard confocal microscopy and perform real-time imaging of astrocytes in the intact mouse brain. Barcia et al. (2013) further illustrate the potency of confocal microscopy to image, in fixed tissues, the microanatomy of astrocyte interactions with immune cells during neuroinflammatory processes. In their Methods article, Haseleu et al. (2013) describe an original technique to study another fine subcellular feature of astrocytes: the peripheral astrocyte process. This method is based on the analysis by conventional microscopy of acutely-dissociated astrocytes from the mouse brain. Dall é rac et al. (2013) discuss how astrocytes are not silent in the brain and how studying astrocytes by electrophysiological recordings provides insight into their complex communication with neu- rons at the synapse. In their Original research article, Kabaso et al. (2013) use electrophysiology, this time combined with modeling, to describe the mechanical properties of vesicular release from astrocytes. On the larger imaging scale, two articles present brain imag- ing techniques applied to the study of astrocytes. In his opinion article, Gurden (2013) discusses the evidence that astrocytes have a pivotal position to translate neuronal activity into hyperemic and blood oxygenation level dependent (BOLD) signals, which are measured by functional neuroimaging techniques. O’Brien et al. (2013) present several brain imaging methods to study astrocyte interactions with cerebral tumors in situ , including bio- luminescence, fluorescent labeling of astrocytes, single photon emission computed tomography, positron emission tomography, and magnetic resonance imaging. MANIPULATING ASTROCYTES Li et al. (2013) provide a detailed review of new genetic and imaging tools to study neuron-astrocyte communication at the tripartite synapse. Central to this field, is the physiological manip- ulation of calcium levels in astrocytes and its precise monitoring with high spatial and temporal resolution. Davila et al. (2013) review the current molecular approaches to overexpress or down- regulate genes in astrocytes in vivo using mouse transgenesis or gene transfer. They illustrate the potency of these techniques to decipher astrocyte contribution to brain function. Merienne et al. (2013) describe the recently-developed viral vectors to achieve selective gene transfer in astrocytes in situ . These versatile tools can be used to model brain diseases involving astrocytes or to test astrocyte-based therapeutic strategies. Krencik and Ullian (2013) present the robustness and limits of using astrocytes derived from human pluripotent stem cells (hPSCs) to model or treat neurode- velopmental diseases. They provide a complete set of guidelines to optimize experiments with these cells. MULTIDISCIPLINARY APPROACHES TO STUDY THE COMPLEX FEATURES OF ASTROCYTES Finally, three Review articles extensively describe the multidisci- plinary approach undertaken to understand some complex fea- tures of astrocytes. Stobart and Anderson (2013) describe our present knowledge on astrocyte contributions to neurometabolic and neurovascular coupling, and discuss how their dysfunc- tion could participate to brain disorders. Through their his- torical review, Bouzier-Sore and Pellerin (2013) illustrate how Frontiers in Cellular Neuroscience www.frontiersin.org March 2014 | Volume 8 | Article 74 | CELLULAR NEUROSCIENCE 5 Escartin and Murai Imaging and monitoring astrocytes in health and disease the combination of biochemical analysis, live cellular imaging, magnetic resonance spectroscopy, transcriptomics, and metabolic modeling has contributed to the characterization of the unique metabolic features of astrocytes. Last but not least, Van Horn et al. (2013) provide a historical description of the discovery of D-serine, as a crucial gliotransmitter with multiple roles in brain development and function. Overall, this Research Topic provides a collection of cutting- edge techniques, approaches, and models to study astrocytes in health and disease. It also suggests new directions to achieve discoveries on these fascinating cells. ACKNOWLEDGMENTS We thank Dr. Todd Farmer for providing the image of the astro- cyte for this Research Topic. REFERENCES Barcia, C. Sr., Mitxitorena, I., Carrillo-De Sauvage, M. A., Gallego, J. M., Perez- Valles, A., and Barcia, C. Jr. (2013). Imaging the microanatomy of astrocyte-T- cell interactions in immune-mediated inflammation. Front. Cell. Neurosci. 7:58. doi: 10.3389/fncel.2013.00058 Barros, L. F., San Martin, A., Sotelo-Hitschfeld, T., Lerchundi, R., Fernandez- Moncada, I., Ruminot, I., et al. (2013). Small is fast: astrocytic glucose and lactate metabolism at cellular resolution. Front. Cell. Neurosci. 7:27. doi: 10.3389/fncel.2013.00027 Bouzier-Sore, A. K., and Pellerin, L. (2013). Unraveling the complex metabolic nature of astrocytes. Front. Cell. Neurosci. 7:179. doi: 10.3389/fncel. 2013.00179 Dall é rac, G., Chever, O., and Rouach, N. (2013). How do astrocytes shape synaptic transmission? Insights from electrophysiology. Front. Cell. Neurosci. 7:159. doi: 10.3389/fncel.2013.00159 Davila, D., Thibault, K., Fiacco, T. A., and Agulhon, C. (2013). Recent molecu- lar approaches to understanding astrocyte function. Front. Cell. Neurosci. 7:272. doi: 10.3389/fncel.2013.00272 Gurden, H. (2013). Astrocytes: can they be the missing stars linking neuronal activity to neurofunctional imaging signals? Front. Cell. Neurosci. 7:21. doi: 10.3389/fncel.2013.00021 Haseleu, J., Anlauf, E., Blaess, S., Endl, E., and Derouiche, A. (2013). Studying sub- cellular detail in fixed astrocytes: dissociation of morphologically intact glial cells (DIMIGs). Front. Cell. Neurosci. 7:54. doi: 10.3389/fncel.2013.00054 Kabaso, D., Jorgacevski, J., Calejo, A. I., Flasker, A., Gucek, A., Kreft, M., et al. (2013). Comparison of unitary exocytic events in pituitary lactotrophs and in astrocytes: modeling the discrete open fusion-pore states. Front. Cell. Neurosci. 7:33. doi: 10.3389/fncel.2013.00033 Krencik, R., and Ullian, E. M. (2013). A cellular star atlas: using astrocytes from human pluripotent stem cells for disease studies. Front. Cell. Neurosci. 7:25. doi: 10.3389/fncel.2013.00025 Li, D., Agulhon, C., Schmidt, E., Oheim, M., and Ropert, N. (2013). New tools for investigating astrocyte-to-neuron communication. Front. Cell. Neurosci. 7:193. doi: 10.3389/fncel.2013.00193 Merienne, N., Le Douce, J., Faivre, E., Deglon, N., and Bonvento, G. (2013). Efficient gene delivery and selective transduction of astrocytes in the mammalian brain using viral vectors. Front. Cell. Neurosci. 7:106. doi: 10.3389/fncel.2013.00106 O’Brien, E. R., Howarth, C., and Sibson, N. R. (2013). The role of astrocytes in CNS tumors: pre-clinical models and novel imaging approaches. Front. Cell. Neurosci. 7:40. doi: 10.3389/fncel.2013.00040 Perez-Alvarez, A., Araque, A., and Martin, E. D. (2013). Confocal microscopy for astrocyte in vivo imaging: recycle and reuse in microscopy. Front. Cell. Neurosci. 7:51. doi: 10.3389/fncel.2013.00051 Stobart, J. L., and Anderson, C. M. (2013). Multifunctional role of astrocytes as gatekeepers of neuronal energy supply. Front. Cell. Neurosci. 7:38. doi: 10.3389/fncel.2013.00038 Van Horn, M. R., Sild, M., and Ruthazer, E. S. (2013). D-serine as a gliotransmitter and its roles in brain development and disease. Front. Cell. Neurosci. 7:39. doi: 10.3389/fncel.2013.00039 Received: 10 February 2014; accepted: 19 February 2014; published online: 12 March 2014. Citation: Escartin C and Murai KK (2014) Imaging and monitoring astrocytes in health and disease. Front. Cell. Neurosci. 8 :74. doi: 10.3389/fncel.2014.00074 This article was submitted to the journal Frontiers in Cellular Neuroscience. Copyright © 2014 Escartin and Murai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, dis- tribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Cellular Neuroscience www.frontiersin.org March 2014 | Volume 8 | Article 74 | 6 TECHNOLOGY REPORT published: 22 March 2013 doi: 10.3389/fncel.2013.00027 Small is fast: astrocytic glucose and lactate metabolism at cellular resolution L. F. Barros 1 *, A. San Martín 1,2 , T. Sotelo-Hitschfeld 1,2 , R. Lerchundi 1,2 , I. Fernández-Moncada 1,2 , I. Ruminot 1,2 , R. Gutiérrez 1,2 , R. Valdebenito 1 , S. Ceballo 1 , K. Alegría 1 , F. Baeza-Lehnert 1,2 and D. Espinoza 1,2 1 Centro de Estudios Científicos, Valdivia, Chile 2 Universidad Austral de Chile, Valdivia, Chile Edited by: Carole Escartin, MIRCen, France Reviewed by: Carole Escartin, MIRCen, France Keith Murai, McGill University, Canada *Correspondence: L. F . Barros, Centro de Estudios Científicos, Arturo Prat 514, Casilla 1469, Valdivia, Chile. e-mail: fbarros@cecs.cl Brain tissue is highly dynamic in terms of electrical activity and energy demand. Relevant energy metabolites have turnover times ranging from milliseconds to seconds and are rapidly exchanged between cells and within cells. Until recently these fast metabolic events were inaccessible, because standard isotopic techniques require use of populations of cells and/or involve integration times of tens of minutes. Thanks to fluorescent probes and recently available genetically-encoded optical nanosensors, this Technology Report shows how it is now possible to monitor the concentration of metabolites in real-time and in single cells. In combination with ad hoc inhibitor-stop protocols, these probes have revealed a key role for K + in the acute stimulation of astrocytic glycolysis by synaptic activity. They have also permitted detection of the Warburg effect in single cancer cells. Genetically-encoded nanosensors currently exist for glucose, lactate, NADH and ATP , and it is envisaged that other metabolite nanosensors will soon be available. These optical tools together with improved expression systems and in vivo imaging, herald an exciting era of single-cell metabolic analysis. Keywords: FRET, FLII12Pglu-700 δμ 6, laconic, glycolysis, mitochondria, flux, cancer metabolism INTRODUCTION It is hard to overestimate the importance of resolution. In the absence of sufficient temporal resolution transient events go undetected. Without enough spatial resolution, opposite changes in neighboring compartments may cancel out. Classic biochem- istry mapped metabolic pathways and characterized the behavior of purified enzymes in test tubes. Decades before cell sorting, enzymes had to be extracted from whole brain homogenates. Metabolites were measured, again in whole tissue extracts, and a handful of enzymes thought to catalyze far-from-equilibrium reactions were deemed to control flux. With the introduction of radioisotopes in research during the 1950s and non-invasive techniques for their detection, particularly PET and NMRS, con- centrations and fluxes could be estimated in living humans, thus permitting biochemical investigation of brain disease. Meanwhile, progress in molecular biology, immunohistochemistry, and the introduction of cell cultures, revealed previously unsuspected complexities, with numerous isoforms for metabolic enzymes and transporters plus cell-specific post-translational modifica- tions. At present, it is evident that neurons and glial cells differ metabolically as much as they differ functionally, but little is known regarding subtypes of neurons and astrocytes and their interaction with oligodendrocytes (Funfschilling et al., 2012; Lee et al., 2012). Of clinical importance are regional variations in metabolism across the brain that may help to explain susceptibil- ity to neurodegeneration (Vaishnavi et al., 2010; Vlassenko et al., 2010; Bero et al., 2011). Over the last decade, fluorescence microscopy-based tech- niques with high spatiotemporal resolution have been introduced for the study of energy metabolism in cultured cells and in brain tissue slices. Genetically-encoded sensors are becoming available and it is now possible to measure, glucose, lactate, NADH, and ATP in individual cells with sub-second resolution. The present work describes how some of these sensors may be used in combi- nation with transport inhibitors, to quantify metabolic flux and investigate the regulation of astrocytic glycolysis in response to neuronal activity. HOW LOCAL AND HOW FAST IS BRAIN METABOLISM? The average rate of glucose utilization in human gray matter has been estimated at 8.8 μ M/s (Huang et al., 1980; Gjedde and Diemer, 1983), ten times higher than the body’s average. With this value and the known stoichiometry of the glucose oxidation (C 6 H 12 O 6 + 6O 2 → 6CO 2 + 6H 2 O) and coupled reactions, it is possible to obtain an estimate of flux at different points in the metabolic chain. As the glucose molecule proceeds through gly- colysis and the Krebs cycle, its free energy is split into smaller packets and the molar flux rises, reaching a maximum at ATP, with 31 molecules produced for each glucose molecule consumed ( Figure 1 ). In addition to flux, metabolite dynamics are deter- mined by concentration, so that the smaller the concentration, the larger the impact of a given flux on the metabolite pool. The ratio between concentration and flux is known as the turnover time and is a useful parameter of how dynamic a metabolite is. Frontiers in Cellular Neuroscience www.frontiersin.org March 2013 | Volume 7 | Article 27 | CELLULAR NEUROSCIENCE 7 Barros et al. Imaging glucose and lactate dynamics The turnover time can be thought of as the time that a metabo- lite pool would last if production were to stop while consumption remained constant. As shown in Table 1 , brain tissue glucose and lactate have turnover times in the order of 2 min whereas ATP and oxygen have turnover times of a few seconds, whereas for NADH it is just 7 ms. The turnover time reflect sensitivity of a given FIGURE 1 | Stoichiometry of glucose oxidation. The schematic represents the oxidation of glucose to CO 2 , where the width of the arrows is proportional to flux. Cytosolic NADH is assumed to transfer its electrons to mitochondria through both the malate-aspartate shuttle (rendering 3 ATPs per NADH) and the glycerol phosphate shuttle (rendering 2 ATPs per NADH). metabolite pool to flux perturbation. According to the simulation shown in Figure 2 , a 100% increase in the rate of consumption would reduce the respective brain pools with a half time of about 1 min for glucose and lactate, 0.3 and 1.5 s for O 2 and ATP and 5 ms for NADH. Taken in combination with the diffusion coeffi- cient, the turnover time also helps to reveal how local a metabolite may be if its diffusion were not restricted by membranes. For instance, during its turnover time, the average glucose or lactate molecule can diffuse several hundreds of micrometeres along the cytosol of a neuron, roughly the diameter of a cortical column or a cortical barrel, whereas variations in cytosolic NADH in a den- drite will not be sensed by its soma located just a few micrometers away ( Table 1 ). The above considerations help to establish a priori the min- imum spatial and temporal resolutions required to order to characterize metabolism. Glucose and lactate dynamics have to be sampled in seconds, whereas monitoring ATP and O 2 may need techniques that resolve hundreds of milliseconds. To avoid miss- ing NADH fluctuations, millisecond sampling will be required. In terms of size, monitoring cytosolic glucose, lactate, O 2 or ATP demand single-cell resolution, whereas cytosolic NADH and metabolites inside small membrane compartments such as mito- chondria demand sub-cellular resolution. One may think that even higher resolution may be needed to characterize the imme- diate neighborhood of metabolic enzymes and transporters, but this is not the case. Glucose, lactate, O 2 , ATP, and any other molecules present at micromolar levels or higher are not expected to form microdomains or nanodomains, because the build up or Table 1 | Dynamics of selected metabolites in brain tissue. Metabolite Glucose Lactate plus pyruvate ATP O 2 NADH Stoichiometry * 1 2 31 6 2 Concentration ( μ M) 1000 a 2000 b 1250 c 30 d 0.13 e Flux f ( μ M/s) 8.8 17 .6 273 53 17 .6 Turnover time g (s) 114 114 4.6 0.6 0.007 Diffusion coefficient ( D ) ( μ m 2 /s) 500 h 130 i 500 j 2000 k 500 l Average distance traveled over 1 and 10 turnover times m ( μ m) 585 and 1849 298 and 943 117 and 371 85 and 268 5 and 14 Generation of nanodomains No No No No Yes * Whole tissue stoichiometry is given for glucose, lactate/pyruvate, ATP and oxygen, while cytosolic stoichiometry is given for NADH. a Human brain tissue (Barros et al., 2007). b Human brain tissue (Dienel and Cruz, 2004). c HeLa cells, MIN6 cells, and COSM6 cells (Zamaraeva et al., 2005) and references therein. d Human brain tissue (Buxton, 2010). e COS7 cells (Zhang et al., 2002). f The glucose flux was calculated using non-invasive measurements in human gray matter (Huang et al., 1980) and the glucose distribution volume (Gjedde and Diemer, 1983). The flux of the other metabolites was calculated as the product of the glucose flux and the respective stoichiometry. For NADH the cytosolic flux is given. g Turnover time is concentration divided by flux. h Isotopic deoxyglucose in rat vagus nerve (Vega et al., 2003). i NMRS in rat brain tissue (Pfeuffer et al., 2000). j NMRS in rat skeletal muscle (de Graaf et al., 2000). k Oxygen electrode measurements in rat brain tissue (Baumgartl and Lubbers, 1983). l Assumed to be equal to that of ATP m Estimated assuming Brownian diffusion according to the Einstein’s equation in three dimensions (distance 2 = 6 × D × turnover time). Frontiers in Cellular Neuroscience www.frontiersin.org March 2013 | Volume 7 | Article 27 | 8 Barros et al. Imaging glucose and lactate dynamics FIGURE 2 | Simulation of the response of metabolites to an instant rise in consumption. The dynamics of each metabolite were simulated independently using the concentration and steady-state flux in Table 1 and the differential equation: d metabolite/dt = production–metabolite × C , where C is the rate constant of consumption. At time zero, C was increased by 100% while production was kept constant, resulting in a 50% decrease in the size of the pool. The speed at which the new steady-state is reached varies dramatically between different metabolites. The inset shows the same data over an extended timescale. The differential equation was solved by numerical simulation using Madonna software. depletion of metabolites in the immediate vicinity of the proteins handling these molecules is negligible compared to the powerful mixing effect of diffusion in short distances (Barros and Martinez, 2007; Martinez et al., 2010). For these abundant molecules, the cytosol within an astrocyte or a neuronal soma is expected to behave as a well-mixed compartment. NADH is different, because its cytosolic concentration is very low. For example, considering a cytosolic NADH of 130 nM ( u ), a diffusion coefficient ( D ) of 500 μ m 2 s − 1 ( Table 1 ), and that a single lactate dehydrogenase enzyme (LDH) produces or consumes lactate at a rate of 260 s − 1 ( q , Barros and Martinez, 2007), the relative amplitude ( AMP ) of the local NADH nanodomain may be estimated using the equa- tion AMP = 1 ± q /( u × D × a ), where a is the radius of the catalytic site (Martinez et al., 2010). According to this formula and assuming a radius of 0.5 nm (Barros and Martinez, 2007), LDH is predicted to create a local nanodomain in which the con- centration of NADH is twice that of the bulk cytosolic NADH when the enzyme is consuming lactate, whereas LDH should deplete its vicinity of NADH when the enzyme is producing lac- tate. Thus, an accurate characterization of NADH dynamics will require nanometer resolution. FAST GLUCOSE DYNAMICS MEASURED WITH FLUORESCENT GLUCOSE ANALOGS 2-deoxyglucose is a glucose analog that is transported into cells by the GLUT glucose transporters and then phosphorylated by hex- okinase, but is not metabolized further to any significant extent. Detected in cultured cells by scintillation counting, by autoradio- graphy in laboratory animals and non-invasively in humans with FDG-PET, radiolabeled 2-deoxyglucose has wide applications in research and clinical medicine. However, like other radioiso- topes, it has limited spatiotemporal resolution, with detection requiring cell populations and typical sampling intervals in excess of 10 min. Fluorescent analogs of glucose have been used to character- ize the transport and metabolism of glucose at high resolution by means of microscopy (Kim et al., 2012). The most popu- lar analogs are 2-NBDG and 6-NBDG. These compounds are comprised of a glucose moiety in which a fluorescent nitroben- zoxydiazoamine (NBD) group replaces the hydroxyl group at carbon 2 or 6. Both are substrates of GLUT carriers but only 2-NBDG can be phosphorylated by hexokinase. As demonstrated with 6-NBDG, the bulky hydrophobic NBD group increases the affinity of binding to GLUTs, but impairs translocation of the binding site to a larger extent (Barros et al., 2009a), making trans- port of 6-NBDG by GLUT1 and GLUT3, respectively 100 and 16 times slower than that of glucose (Jakoby et al., 2012). The low efficiency of translocation provides an important experimen- tal advantage because it permits the use of confocal microscopy to monitor uptake in real time over a period of several minutes, a time window in which agonists can be applied to investigate acute modulation of glucose transporters (Loaiza et al., 2003; Porras et al., 2004, 2008). These fluorescent glucose tracers have also been used to characterize glucose uptake in many other mammalian cell types including erythrocytes, fibroblasts, smooth muscle cells, enterocytes, cardiomyocytes, endothelium, lympho- cytes, pancreatic beta cells, adipocytes, and tumor cells (Barros et al., 2009a; Kim et al., 2012). Imaging of 2- and 6-NBDG by multiphoton microscopy has been used to study the transport and metabolism of glucose in cerebellar and hippocampal slices (Barros et al., 2009b; Jakoby et al., 2012) and to detect a stimu- latory effect of neural activity on glucose transport in astrocytes in the somatosensorial cortex in vivo (Chuquet et al., 2010). Long-term ( > 10 min) incubation with 2-NBDG followed by a washout period to remove unphosphorylated 2-NBDG is infor- mative about glucose consumption, but 2-NBDG cannot be used to monitor metabolism in real time, because both the phospho- rylated and unphosphorylated form of the analog are fluorescent, making it impossible to differentiate between the two. However, single-cell real-time monitoring of glucose metabolism is now possible with a genetically-encoded FRET glucose nanosensor. GLUCOSE METABOLISM MEASURED WITH A GENETICALLY-ENCODED FRET NANOSENSOR Ten years ago Wolf Frommer and colleagues introduced the first FRET glucose nanosensor (Fehr et al., 2003), making an improved version available in 2008 (Takanaga et al., 2008). Since then, vari- ous research groups have made fluorescent nanosensors specific for ATP (Berg et al., 2009; Imamura et al., 2009) and NADH Frontiers in Cellular Neuroscience www.frontiersin.org March 2013 | Volume 7 | Article 27 | 9 Barros et al. Imaging glucose and lactate dynamics (Hung et al., 2011; Zhao et al., 2011) and we have developed a FRET nanosensor for lactate (San Martín et al., 2013). The glucose and lactate nanosensors are of the same principle. They comprise a bacterial protein that binds the analyte, sandwiched between two fluorescent proteins with overlapping emission and excitation spectra that undergo FRET. Binding of the analyte to the bacterial protein induces a conformational change that modi- fies the distance between the fluorescent protein and/or its relative orientation, resulting in a change in FRET efficiency which can be calibrated. Figures 3A , B shows cultured astrocytes express- ing the glucose nanosensor (FLII12Pglu-700 μδ 6) and the lactate nanosensor (Laconic), with the typical cytosolic distribution and exclusion of nuclei and organelles. In Figure 3C , Laconic has been targeted to the nucleus and FLII12Pglu-700 μδ 6 to the cytosol of HEK293 cells, which permits the use of confocal microscopy to simultaneously monitor glucose and lactate in the same cell. All mammalian cells metabolize glucose but they differ in their handling of lactate. Some cells are lactate exporters, while oth- ers are lactate importers ( Figure 4A ). The concentration of a metabolite, absolute or relative, may be interesting in itself, as it informs about the balance between production and consump- tion (Fehr et al., 2003; Bittner et al., 2010, 2011; Takanaga and Frommer, 2010; Kovacic et al., 2011; Prebil et al., 2011). However, concentration gives no information about flux. For instance, a FIGURE 3 | Expression of the glucose and lactate sensors in astrocytes and HEK293 cells. (A,B) μδ 6 and the FRET lactate nanosensor Laconic were expressed in astrocytes using a custom-made adenoviral vector (Vector Biolabs). Confocal images correspond to green emission at 535 nm (Venus) as excited with a 488 nm argon laser. Bar represents 20 μ m. (C) HEK293 cells were co-transfected with FLII12Pglu700 μδ 6 and nuclear-targeted Laconic. The confocal images show the CFP and mTFP emissions at 480 nm (blue channel) and the Citrine and Venus emissions at 535 nm (green channel) of a cell expressing only Laconic (top) and a cell expressing both sensors (bottom). Scale bar is 20 μ m. decrease in intracellular glucose may be due to an inhibition of GLUT-mediated transport (with flux decrease) or a stimulation of hexokinase (with flux increase). Moreover, the rate of depar- ture from the steady-state is sensitive to the degree of GLUT or hexokinase modulation but equally sensitive to resting flux (Barros et al., 2013), with a fast cell reacting more quickly than a slow cell to the same degree of stimulation (Barros et al., 2013). However, by eliminating the contribution of transport with a GLUT blocker like cytochalasin B, the ambiguity is lifted as the glucose concentration is forced to decrease with a rate equal to that of glucose consumption ( Figure 4B ). This is a protocol that can be applied repeatedly and which has been validated in astro- cytes, neurons, muscle cells, fibroblasts, adipocytes, and tumor cells (Bittner et al., 2010). Most cells express a high affinity iso- form of hexokinase, with a K D of 50 μ M, and in the presence of millimolar extracellular glucose maintain steady-state intra- cellular glucose levels of 0.5 mM or higher, so that hexokinase runs at V max . This explains the linear decrease in glucose con- centration during the GLUT block. When applied to astrocytes in culture and in organotypical hippocampal slices, this GLUT-stop technique revealed that glycolysis is strongly and reversibly stim- ulated within seconds of exposure to elevated extracellular K + , a cation that is released by active neurons (Bittner et al., 2011). Further work showed that K + activates astrocytic glycolysis by a sequence of events that begin with plasma membrane depo- larization, then stimulation of the electrogenic Na + /bicarbonate co-transporter NBCe1 and intracellular alkalinization (Ruminot et al., 2011). We also confirmed the stimulatory effect of gluta- mate on glycolysis that was detected two decades ago by Pellerin and Magistretti using 2-deoxyglucose (Pellerin and Magistretti, 1994; Pellerin et al., 2007) and showed that the effect of glutamate on glucose consumption develops over minutes and persists long after withdrawal of the neurotransmitter (Bittner et al., 2011). More recently, the same method detected fast changes in the rate of astrocytic glycolysis in response to variations in extra- cellular lactate (Sotelo-Hitschfeld et al., 2012), a phenomenon that may be relevant for the local distribution of fuel in brain tiss