- Email: [email protected]
Neuroscience: What Are Cortical Neurons Doing during Sleep?
Current Biology
Dispatches These considerations notwithstanding, the cytochrome P450 family is an attractive substrate for evolutionary adaptation by copy number variation [12], and perhaps an ancestral CYP2J19 gene was expressed in retina, skin, and liver, providing a substrate for increased visual resolution, color-based sexual selection and defense against environmental toxicants, respectively. If so, then the current situation in zebra finches — in which different paralogs appear to mediate the first two functions and the third function has been lost — could mean that red-beaked zebra finches are ‘dishonest’, at least when it comes to the redness of their beaks! Recent work from the Mundy group [13] indicates that CYP2J19 orthologs exist in turtles as well as in birds, and the authors suggest that an ancestral CYP2J19 (in dinosaurs) may have played an important role in color vision. Identification of CYP2J19 as a likely carotenoid ketolase is exciting for evolutionary biologists in addition to bird lovers because it has provided, for the first time, a foothold to explore and evaluate the molecular biology of sexual dimorphism. One of the more interesting areas to investigate are red riskins and red factor canaries. The former are sexually dimorphic but the latter are not, even though it is the red siskin version of CYP2J19 that makes red factor canaries red! An obvious question to ask is whether CYP2J19 is preferentially expressed in male vs. female red siskins; if so, that would point to trans-acting regulators of CYP2J19 as the proximate cause of sexual dimorphism. The zebra finch and the red factor canary CYP2J19 stories are wonderful examples of the awesome power of domestic animal genetics for understanding natural variation [14]. Of course, captive songbirds are a special type of domestic animal, bred and selected not for their utility in agriculture or hunting, but for their appeal as companions, and there are many examples of analogous situations with mammals. Thus, a uniquely human trait — our fascination with morphological diversity — has, over hundreds of years, created living resources to explore the biology that drives that diversity. As Darwin might have said, ‘‘vive la diffe´rence!’’
REFERENCES 1. Darwin, C. (1875). The Descent of Man, and Selection in Relation to Sex (New York: D. Appleton and Company). 2. Greitemeyer, T., Kastenmu¨ller, A., and Fischer, P. (2013). Romantic motives and risk-taking: an evolutionary approach. J. Risk Res. 16, 19–38. 3. Milam, E.L. (2010). Looking for a Few Good Males: Female Choice in Evolutionary Biology (Baltimore: Johns Hopkins University Press). 4. Lopes, R.J., Johnson, J.D., Toomey, M.B., Ferreira, M.S., Araujo, P.M., Melo-Ferreira, J., Andersson, L., Hill, G.E., Corbo, J.C., and Carneiro, M. (2016). Genetic basis for red coloration in birds. Curr. Biol. 26, 1427–1434. 5. Mundy, N.I., Stapley, J., Bennison, C., Tucker, R., Twyman, H., Kim, K.W., Burke, T., Birkhead, T.R., Andersson, S., and Slate, J. (2016). Red carotenoid coloration in the zebra finch is controlled by a cytochrome P450 gene cluster. Curr. Biol. 26, 1435–1440. 6. Brush, A.H. (1990). Metabolism of carotenoid pigments in birds. FASEB J. 4, 2969–2977. 7. Toomey, M.B., Collins, A.M., Frederiksen, R., Cornwall, M.C., Timlin, J.A., and Corbo, J.C. (2015). A complex carotenoid palette tunes avian colour vision. J. R. Soc. Interface 12, 20150563.
8. Birkhead, T. (2003). A Brand-New Bird: How Two Amateur Scientists Created the First Genetically Engineered Animal (New York: Basic Books). 9. Hosken, D.J., and House, C.M. (2011). Sexual selection. Curr. Biol. 21, R62–R65. 10. HIll, G.E. (1991). Plumage coloration is a sexually selected indicator of male quality. Nature 350, 337–340. 11. Blount, J.D., Metcalfe, N.B., Birkhead, T.R., and Surai, P.F. (2003). Carotenoid modulation of immune function and sexual attractiveness in zebra finches. Science 300, 125–127. 12. Good, R.T., Gramzow, L., Battlay, P., Sztal, T., Batterham, P., and Robin, C. (2014). The molecular evolution of cytochrome P450 genes within and between drosophila species. Genome Biol. Evol. 6, 1118–1134. 13. Twyman, H., Valenzuela, N., Literman, R., Andersson, S., and Mundy, N.I. (2016). Seeing red to being red: conserved genetic mechanism for red cone oil droplets and co-option for red coloration in birds and turtles. Proc Biol Sci. 283, pii: 20161208. 14. Andersson, L. (2016). Domestic animals as models for biomedical research. Ups J. Med. Sci. 121, 1–11.
Neuroscience: What Are Cortical Neurons Doing during Sleep? Barbara E. Jones McGill University, Department of Neurology and Neurosurgery, Montreal Neurological Institute, Montreal, Quebec, Canada Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2016.09.027
Through application of the latest calcium imaging techniques, a new study shows that pyramidal neurons generally decrease their activity during slow wave sleep and, remarkably, REM sleep, whereas parvalbumin interneurons increase their activity and could thus inhibit particular pyramidal cells during REM sleep. Much about sleep remains a mystery. Is it a state of rest for the brain or is it a state of activity during which neural processing occurs in a manner different from that during waking? Perhaps both, as a study reported in a recent issue of Current Biology by Niethard et al. [1] reveals by application of the latest calcium imaging technology, which allows the study of
neural activity in specific cell populations in the cerebral cortex of naturally sleeping/waking mice. Using ultrasensitive calcium sensor fluorescent proteins (GCaMP6f) in transgenic mice expressing proteins in specific neurons, the authors were able to image activity selectively in pyramidal cells and interneurons. They report novel findings
Current Biology 26, R1137–R1160, November 7, 2016 ª 2016 Elsevier Ltd. R1147
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Current Biology
Figure 1. Cortical neuron activity across the sleep–wake cycle. (A) Cortical neurons are made up of different excitatory (Ex) and inhibitory (In) neurons. SOM, somatostatin; PV, parvalbumin. (B) Electrophysiological recordings distinguish excitatory, presumed pyramidal cells, as regular spiking neurons, which discharge on average at slightly higher rates during slow wave sleep (SWS) and REM than wake (W) (left), and inhibitory interneurons as fast spiking neurons, which discharge on average at lower rates during slow wave sleep but much higher rates during REM sleep than wake (right) (adapted from Steriade et al. [5]). (C) The calcium imaging study by Niethard et al. [1] distinguished all unlabeled cells, including excitatory and some inhibitory neurons (green), which showed lower average calcium activity during slow wave sleep and REM than wake (topleft), SOM-labeled cells (orange), which showed a similar profile (top-right), and PV-labeled cells (red), which showed lower average calcium activity during slow wave sleep than wake but slightly higher activity during REM than SWS (top-right). Unlabeled cells and PV-labeled but not SOM-labeled cells which were most active (top 5%) during wake, were, however, also more active during REM sleep than during slow wave sleep (bottom-left and bottom-right) (extracted from Figure 5 of Niethard et al. [1]).
which appear to be at odds with previous electrophysiological results and conclusions, such as to suggest that most pyramidal neurons are less active during slow wave sleep but also during REM sleep, the state of dreaming, when they were previously thought to be as, if not more, active than during wakefulness. The cerebral cortex is comprised of excitatory neurons, which release glutamate and include pyramidal and stellate cells, and inhibitory neurons, which release GABA and include somatostatin, parvalbumin and other interneurons (Figure 1A) [2]. Using wide-field imaging of GCaMP6f expressed selectively in pyramidal neurons through a CaMKII promotor, Niethard et al. first show in the pyramidal cell population a decrease in calcium fluorescence when passing from wake into slow wave sleep and a further decrease passing into REM sleep. Using two photon imaging of the GCaMP6f expressed in all neurons through a synapsin promotor, they subsequently show different profiles of calcium
fluorescence in fluorescently labeled (tdTomato) somatostatin or parvalbumin interneurons, when compared to all other unlabeled neurons, which would include all excitatory and other inhibitory neurons (Figure 1C). Given the sensitivity of the GCaMP6f, the calcium activity should reflect the spiking activity of the neurons [3]. The authors thus report that the activity of pyramidal cells is highest during waking, lower during slow wave sleep and lowest during REM sleep (Figure 1C, top-left, green). The activity of somatostatin interneurons is similar to that of the other unlabeled neurons (Figure 1C, top-right, orange). In contrast, the activity of parvalbumin interneurons is highest during waking, lower during slow wave sleep but increases during REM sleep, such as to be almost as high as during waking (Figure 1C, top-right, red). That pyramidal neurons, along with other excitatory and some other inhibitory neurons, would be less active during slow wave sleep than during waking may not appear surprising; however, it is not exactly in agreement with the results of
R1148 Current Biology 26, R1137–R1160, November 7, 2016
previous electrophysiological studies. In the very first applications of single unit recording in the 1950s and 60s, it came as a surprise to find that cortical neurons were not quiet during slow wave sleep, which was considered to be a state of rest, but could be even more active than during waking [4]. As subsequently studied by multiple investigators, this change in activity was not found to be a simple change in rate of firing but rather in the mode of firing, going from a single spiking mode during wake to a bursting or phasic mode during slow wave sleep. The bursts of spikes were cross-correlated with the slow wave oscillations that characterize this phase of sleep on the electroencephalogram (EEG). As studied in depth by Steriade and colleagues, the phasic spiking is determined by up and down states of the membrane potential occurring in all cortical neurons during slow wave sleep [5]. Distinguished as regular spiking neurons, pyramidal cells were found to discharge in a single spiking mode during wake and a burst firing mode during slow wave sleep. Given this change in mode, the average spiking
Current Biology
Dispatches rate could actually increase slightly, though not significantly so, during slow wave sleep as compared to wake in pyramidal cells (Figure 1B, left). Fast spiking neurons, which would include parvalbumin and somatostatin interneurons, were also found to change their pattern of firing from a tonic to a phasic pattern during slow wave sleep, but exhibited a decrease in average spiking rate as compared to that during wake (Figure 1B, right). So, how can we explain the decrease in calcium activity for pyramidal along with interneurons during slow wave sleep in the present study? First, it is possible that the mouse, employed in the present study, is different from the cat, employed in the studies by Steriade and colleagues. However, similar discharge profiles have been obtained in rats across the sleep–wake cycle when distinguishing broad spike from narrow spike neurons, as characteristic of pyramidal and interneurons, respectively [6]. Second, it is unlikely that calcium imaging can faithfully reflect spiking. Although the GCaMP6f can perhaps reflect single spikes [3], it cannot do so with high frequency spike bursts (>100 spikes per sec intraburst) as would occur in the pyramidal neurons during the slow oscillation. However, GCaMP6f might better reflect the activity profile of the interneurons, which discharge in a fast, single spiking mode (20–60 spikes per sec) even during the phasic clustered discharge during slow wave sleep. Third, it is likely that the calcium images indirectly reflect the change in pattern of discharge during slow wave sleep. The most distinctive feature of this change is the silence imposed by the prolonged membrane hyperpolarization (of 0.3 to 0.5 sec) during the down states in all cortical neurons [5]. This recurrent prolonged hyperpolarization likely corresponds to the lower calcium fluorescence in the pyramidal as well as all other neurons during slow wave sleep. The decrease in calcium activity also appears to be correlated with the decrease (up to 40%) in glucose metabolism that occurs during slow wave sleep in the cerebral cortex of humans [7] and mice [8] and reflects a state of rest for cortical neurons. Such metabolic decreases during slow wave sleep could serve a homeostatic role of sleep for
cortical neurons [6], even though potentiation of synapses associated with memory consolidation can also occur with the slow oscillation discharge pattern in cortical circuits [9]. That pyramidal and other neurons are shown by calcium imaging to reduce their activity further during REM sleep relative to slow wave sleep and thus also waking is very surprising given opposite results in electrophysiological studies over many years. Both regular spiking and fast spiking neurons have been found to increase their average spiking rate during REM sleep as compared to slow wave sleep and even waking (Figure 1B) [5]. The increase in spiking is associated with the fast EEG pattern of activity in the cerebral cortex. So, we ask why the calcium images in the current study show decreasing activity in presumed excitatory and inhibitory, excepting parvalbumin, neurons during REM sleep? First, the activity profiles could again be different in the mouse compared to in the cat. However, relatively similar results to those in the cat were obtained in the rat for both types of neurons [6]. And a recent study reported decreases in firing rate of putative pyramidal cells during REM sleep in the cerebral cortex of rats [10]. Second, it appears that the differences concern particularly the activity of pyramidal and other, including somatostatin, neurons but less so that of the parvalbumin neurons for which an increase, albeit slight, in calcium activity was measured during REM sleep (Figure 1C, top). These results would suggest that parvalbumin neurons could inhibit the pyramidal cells during REM sleep, just as GABAergic and glycinergic neurons inhibit motor neurons and neurons of the behavioral arousal systems during this state [11]. Third, however, is the possibility that the electrophysiological studies sample neurons in a biased manner, as is intrinsic to this approach. In sleep–wake studies, those neurons which are active during wake are often selected for recording across the sleep– wake cycle. By contrast, the GCaMP imaging studies have the extraordinary capacity to image all neurons, along with specifically identified neurons, as in the current study, which makes it so special. With the latter possibility in mind, Niethard et al. examined selectively those excitatory or inhibitory neurons and the
identified parvalbumin and somatostatin neurons that were active during waking. Remarkably, they found that the wake-active neurons were also generally more active during REM than slow wave sleep and, among the parvalbumin neurons, as active as during wake (Figure 1C, bottom), which is more similar to what had been observed previously in the recorded regular spiking and fast spiking neurons (Figure 1B). These results are very exciting as they confirm the notion that particular neurons can replay during sleep what they have experienced or learned during waking, as has been shown to be the case in electrophysiological studies recording large arrays of neurons in hippocampus and neocortex [12–14]. Such replay in select networks of neurons could serve in the documented role of sleep in memory [15,16]. Moreover, the prominent activity of the parvalbumin interneurons and their presumed inhibition of most pyramidal cells could contribute to particular shaping of memories during REM sleep, as Niethard et al. suggest. REFERENCES 1. Niethard, N., Hasegawa, M., Itokazu, T., Oyanedel, C.N., Born, J., and Sato, T.R. (2016). Sleep-stage-specific regulation of cortical excitation and inhibition. Curr. Biol. 26, 2739–2749. 2. Petersen, C. (2014). Cell-type specific function of GABAergic neurons in layers 2 and 3 of mouse barrel cortex. Curr. Opin. Neurobiol. 26, 1–6. 3. Chen, T.W., Wardill, T.J., Sun, Y., Pulver, S.R., Renninger, S.L., Baohan, A., Schreiter, E.R., Kerr, R.A., Orger, M.B., Jayaraman, V., et al. (2013). Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300. 4. Evarts, E.V. (1964). Temporal patterns of discharge of pyramidal tract neurons during sleep and waking in the monkey. J. Neurophysiol. 27, 152–171. 5. Steriade, M., Timofeev, I., and Grenier, F. (2001). Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol. 85, 1969–1985. 6. Vyazovskiy, V.V., Olcese, U., Lazimy, Y.M., Faraguna, U., Esser, S.K., Williams, J.C., Cirelli, C., and Tononi, G. (2009). Cortical firing and sleep homeostasis. Neuron 63, 865–878. 7. Maquet, P., Dive, D., Salmon, E., Sadzot, B., Franco, G., Poirrier, R., von Frenckell, R., and Franck, G. (1990). Cerebral glucose utilization during sleep-wake cycle in man determined by positron emission tomography and [18F]2fluoro-2-deoxy-D-glucose method. Brain Res. 513, 136–143.
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Current Biology
Dispatches 8. Vyazovskiy, V.V., Cirelli, C., Tononi, G., and Tobler, I. (2008). Cortical metabolic rates as measured by 2-deoxyglucose-uptake are increased after waking and decreased after sleep in mice. Brain Res. Bull. 75, 591–597. 9. Chauvette, S., Seigneur, J., and Timofeev, I. (2012). Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron 75, 1105–1113. 10. Watson, B.O., Levenstein, D., Greene, J.P., Gelinas, J.N., and Buzsaki, G. (2016). Network
homeostasis and state dynamics of neocortical sleep. Neuron 90, 839–852. 11. Jones, B.E. (2011). Neurobiology of waking and sleeping. Handb. Clin. Neurol. 98, 131–149. 12. Wilson, M.A., and McNaughton, B.L. (1994). Reactivation of hippocampal ensemble memories during sleep. Science 265, 676–679. 13. Louie, K., and Wilson, M.A. (2001). Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156.
14. Ji, D., and Wilson, M.A. (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10, 100–107. 15. Aton, S.J., Seibt, J., Dumoulin, M., Jha, S.K., Steinmetz, N., Coleman, T., Naidoo, N., and Frank, M.G. (2009). Mechanisms of sleepdependent consolidation of cortical plasticity. Neuron 61, 454–466. 16. Rasch, B., and Born, J. (2013). About sleep’s role in memory. Physiol. Rev. 93, 681–766.
Cytokinesis: Going Super-Resolution in Live Cells Yajun Liu1 and Jian-Qiu Wu1,2,* 1Department
of Molecular Genetics, The Ohio State University, Columbus, OH 43210, USA of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH 43210, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2016.09.026 2Department
Super-resolution fluorescence microscopy has emerged as a powerful tool for studying molecular organization, but mostly in fixed cells. New work using high-speed fluorescence photoactivation localization microscopy now reveals the organization of cytokinesis nodes and contractile rings in live fission yeast cells. In recent years, super-resolution fluorescence microscopy has emerged as a state-of-the-art imaging method that exceeds the diffraction limit of light [1,2]. Common super-resolution techniques include structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), fluorescence photoactivation localization microscopy (FPALM or PALM), and stochastic optical reconstruction microscopy (STORM) [2]. These invaluable techniques can provide the information necessary for deciphering the 3D structure of multi-protein complexes, and also expand the toolbox for investigating biological molecules at the nanoscale level. However, super-resolution fluorescence microscopy with high spatial resolution has been used mostly in fixed cells due to its low temporal resolution, which has precluded imaging of live cells. In a new study published recently in PNAS, Laplante et al. [3] used high-speed quantitative FPALM to establish the molecular organization of the contractile ring and its precursor, the
cytokinesis nodes, in live fission yeast cells. Cytokinesis is essential for cell proliferation and differentiation. The process relies on the constriction of a contractile ring composed of multi-protein complexes in amoebas, fungi, and animal cells [4,5]. Besides actin filaments and myosin-II [6–8], the contractile ring also contains many other structural and regulatory proteins, including anillin, IQGAP, formins, and F-BAR proteins [4,9]. Currently, we are most knowledgeable about the cytokinetic machinery and mechanisms in the fission yeast Schizosaccharomyces pombe. In S. pombe, some of the major proteins present in the contractile ring assemble into discrete precursor structures called cytokinesis nodes around the division site before ring assembly [10,11]. The positioning marker and scaffolding protein anillin Mid1 first concentrates in the cortical nodes around the division site. Mid1 then recruits the myosin-II essential light chain (Cdc4) and the IQGAP Rng2. Rng2 subsequently recruits the myosin-II heavy chain (Myo2) and regulatory light
R1150 Current Biology 26, R1137–R1160, November 7, 2016 ª 2016 Elsevier Ltd.
chain (Rlc1) [10,12]. In addition, Mid1 also independently recruits the F-BAR protein Cdc15 [10]. Lastly, the formin Cdc12 is recruited for actin nucleation and assembly. Stochastic interactions between actin filaments and myosins condense these nodes into a compact contractile ring that is ready to carry out its function during cytokinesis [13]. Although protein composition in the contractile ring has been extensively studied, little is known about the 3D organization of these proteins. Understanding this fundamental question will shed light on how the contractile ring generates force and tension during cytokinesis and also provide information required for computational model simulations. Quantitative fluorescence microscopy using the spinning disk confocal system has estimated that each S. pombe cell has around 65 cytokinesis nodes [13]. One shortcoming of this method is the inability to fully resolve diffraction-limited cytokinesis nodes that are located in close proximity to each other. The single-molecule
Dispatches These considerations notwithstanding, the cytochrome P450 family is an attractive substrate for evolutionary adaptation by copy number variation [12], and perhaps an ancestral CYP2J19 gene was expressed in retina, skin, and liver, providing a substrate for increased visual resolution, color-based sexual selection and defense against environmental toxicants, respectively. If so, then the current situation in zebra finches — in which different paralogs appear to mediate the first two functions and the third function has been lost — could mean that red-beaked zebra finches are ‘dishonest’, at least when it comes to the redness of their beaks! Recent work from the Mundy group [13] indicates that CYP2J19 orthologs exist in turtles as well as in birds, and the authors suggest that an ancestral CYP2J19 (in dinosaurs) may have played an important role in color vision. Identification of CYP2J19 as a likely carotenoid ketolase is exciting for evolutionary biologists in addition to bird lovers because it has provided, for the first time, a foothold to explore and evaluate the molecular biology of sexual dimorphism. One of the more interesting areas to investigate are red riskins and red factor canaries. The former are sexually dimorphic but the latter are not, even though it is the red siskin version of CYP2J19 that makes red factor canaries red! An obvious question to ask is whether CYP2J19 is preferentially expressed in male vs. female red siskins; if so, that would point to trans-acting regulators of CYP2J19 as the proximate cause of sexual dimorphism. The zebra finch and the red factor canary CYP2J19 stories are wonderful examples of the awesome power of domestic animal genetics for understanding natural variation [14]. Of course, captive songbirds are a special type of domestic animal, bred and selected not for their utility in agriculture or hunting, but for their appeal as companions, and there are many examples of analogous situations with mammals. Thus, a uniquely human trait — our fascination with morphological diversity — has, over hundreds of years, created living resources to explore the biology that drives that diversity. As Darwin might have said, ‘‘vive la diffe´rence!’’
REFERENCES 1. Darwin, C. (1875). The Descent of Man, and Selection in Relation to Sex (New York: D. Appleton and Company). 2. Greitemeyer, T., Kastenmu¨ller, A., and Fischer, P. (2013). Romantic motives and risk-taking: an evolutionary approach. J. Risk Res. 16, 19–38. 3. Milam, E.L. (2010). Looking for a Few Good Males: Female Choice in Evolutionary Biology (Baltimore: Johns Hopkins University Press). 4. Lopes, R.J., Johnson, J.D., Toomey, M.B., Ferreira, M.S., Araujo, P.M., Melo-Ferreira, J., Andersson, L., Hill, G.E., Corbo, J.C., and Carneiro, M. (2016). Genetic basis for red coloration in birds. Curr. Biol. 26, 1427–1434. 5. Mundy, N.I., Stapley, J., Bennison, C., Tucker, R., Twyman, H., Kim, K.W., Burke, T., Birkhead, T.R., Andersson, S., and Slate, J. (2016). Red carotenoid coloration in the zebra finch is controlled by a cytochrome P450 gene cluster. Curr. Biol. 26, 1435–1440. 6. Brush, A.H. (1990). Metabolism of carotenoid pigments in birds. FASEB J. 4, 2969–2977. 7. Toomey, M.B., Collins, A.M., Frederiksen, R., Cornwall, M.C., Timlin, J.A., and Corbo, J.C. (2015). A complex carotenoid palette tunes avian colour vision. J. R. Soc. Interface 12, 20150563.
8. Birkhead, T. (2003). A Brand-New Bird: How Two Amateur Scientists Created the First Genetically Engineered Animal (New York: Basic Books). 9. Hosken, D.J., and House, C.M. (2011). Sexual selection. Curr. Biol. 21, R62–R65. 10. HIll, G.E. (1991). Plumage coloration is a sexually selected indicator of male quality. Nature 350, 337–340. 11. Blount, J.D., Metcalfe, N.B., Birkhead, T.R., and Surai, P.F. (2003). Carotenoid modulation of immune function and sexual attractiveness in zebra finches. Science 300, 125–127. 12. Good, R.T., Gramzow, L., Battlay, P., Sztal, T., Batterham, P., and Robin, C. (2014). The molecular evolution of cytochrome P450 genes within and between drosophila species. Genome Biol. Evol. 6, 1118–1134. 13. Twyman, H., Valenzuela, N., Literman, R., Andersson, S., and Mundy, N.I. (2016). Seeing red to being red: conserved genetic mechanism for red cone oil droplets and co-option for red coloration in birds and turtles. Proc Biol Sci. 283, pii: 20161208. 14. Andersson, L. (2016). Domestic animals as models for biomedical research. Ups J. Med. Sci. 121, 1–11.
Neuroscience: What Are Cortical Neurons Doing during Sleep? Barbara E. Jones McGill University, Department of Neurology and Neurosurgery, Montreal Neurological Institute, Montreal, Quebec, Canada Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2016.09.027
Through application of the latest calcium imaging techniques, a new study shows that pyramidal neurons generally decrease their activity during slow wave sleep and, remarkably, REM sleep, whereas parvalbumin interneurons increase their activity and could thus inhibit particular pyramidal cells during REM sleep. Much about sleep remains a mystery. Is it a state of rest for the brain or is it a state of activity during which neural processing occurs in a manner different from that during waking? Perhaps both, as a study reported in a recent issue of Current Biology by Niethard et al. [1] reveals by application of the latest calcium imaging technology, which allows the study of
neural activity in specific cell populations in the cerebral cortex of naturally sleeping/waking mice. Using ultrasensitive calcium sensor fluorescent proteins (GCaMP6f) in transgenic mice expressing proteins in specific neurons, the authors were able to image activity selectively in pyramidal cells and interneurons. They report novel findings
Current Biology 26, R1137–R1160, November 7, 2016 ª 2016 Elsevier Ltd. R1147
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Figure 1. Cortical neuron activity across the sleep–wake cycle. (A) Cortical neurons are made up of different excitatory (Ex) and inhibitory (In) neurons. SOM, somatostatin; PV, parvalbumin. (B) Electrophysiological recordings distinguish excitatory, presumed pyramidal cells, as regular spiking neurons, which discharge on average at slightly higher rates during slow wave sleep (SWS) and REM than wake (W) (left), and inhibitory interneurons as fast spiking neurons, which discharge on average at lower rates during slow wave sleep but much higher rates during REM sleep than wake (right) (adapted from Steriade et al. [5]). (C) The calcium imaging study by Niethard et al. [1] distinguished all unlabeled cells, including excitatory and some inhibitory neurons (green), which showed lower average calcium activity during slow wave sleep and REM than wake (topleft), SOM-labeled cells (orange), which showed a similar profile (top-right), and PV-labeled cells (red), which showed lower average calcium activity during slow wave sleep than wake but slightly higher activity during REM than SWS (top-right). Unlabeled cells and PV-labeled but not SOM-labeled cells which were most active (top 5%) during wake, were, however, also more active during REM sleep than during slow wave sleep (bottom-left and bottom-right) (extracted from Figure 5 of Niethard et al. [1]).
which appear to be at odds with previous electrophysiological results and conclusions, such as to suggest that most pyramidal neurons are less active during slow wave sleep but also during REM sleep, the state of dreaming, when they were previously thought to be as, if not more, active than during wakefulness. The cerebral cortex is comprised of excitatory neurons, which release glutamate and include pyramidal and stellate cells, and inhibitory neurons, which release GABA and include somatostatin, parvalbumin and other interneurons (Figure 1A) [2]. Using wide-field imaging of GCaMP6f expressed selectively in pyramidal neurons through a CaMKII promotor, Niethard et al. first show in the pyramidal cell population a decrease in calcium fluorescence when passing from wake into slow wave sleep and a further decrease passing into REM sleep. Using two photon imaging of the GCaMP6f expressed in all neurons through a synapsin promotor, they subsequently show different profiles of calcium
fluorescence in fluorescently labeled (tdTomato) somatostatin or parvalbumin interneurons, when compared to all other unlabeled neurons, which would include all excitatory and other inhibitory neurons (Figure 1C). Given the sensitivity of the GCaMP6f, the calcium activity should reflect the spiking activity of the neurons [3]. The authors thus report that the activity of pyramidal cells is highest during waking, lower during slow wave sleep and lowest during REM sleep (Figure 1C, top-left, green). The activity of somatostatin interneurons is similar to that of the other unlabeled neurons (Figure 1C, top-right, orange). In contrast, the activity of parvalbumin interneurons is highest during waking, lower during slow wave sleep but increases during REM sleep, such as to be almost as high as during waking (Figure 1C, top-right, red). That pyramidal neurons, along with other excitatory and some other inhibitory neurons, would be less active during slow wave sleep than during waking may not appear surprising; however, it is not exactly in agreement with the results of
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previous electrophysiological studies. In the very first applications of single unit recording in the 1950s and 60s, it came as a surprise to find that cortical neurons were not quiet during slow wave sleep, which was considered to be a state of rest, but could be even more active than during waking [4]. As subsequently studied by multiple investigators, this change in activity was not found to be a simple change in rate of firing but rather in the mode of firing, going from a single spiking mode during wake to a bursting or phasic mode during slow wave sleep. The bursts of spikes were cross-correlated with the slow wave oscillations that characterize this phase of sleep on the electroencephalogram (EEG). As studied in depth by Steriade and colleagues, the phasic spiking is determined by up and down states of the membrane potential occurring in all cortical neurons during slow wave sleep [5]. Distinguished as regular spiking neurons, pyramidal cells were found to discharge in a single spiking mode during wake and a burst firing mode during slow wave sleep. Given this change in mode, the average spiking
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Dispatches rate could actually increase slightly, though not significantly so, during slow wave sleep as compared to wake in pyramidal cells (Figure 1B, left). Fast spiking neurons, which would include parvalbumin and somatostatin interneurons, were also found to change their pattern of firing from a tonic to a phasic pattern during slow wave sleep, but exhibited a decrease in average spiking rate as compared to that during wake (Figure 1B, right). So, how can we explain the decrease in calcium activity for pyramidal along with interneurons during slow wave sleep in the present study? First, it is possible that the mouse, employed in the present study, is different from the cat, employed in the studies by Steriade and colleagues. However, similar discharge profiles have been obtained in rats across the sleep–wake cycle when distinguishing broad spike from narrow spike neurons, as characteristic of pyramidal and interneurons, respectively [6]. Second, it is unlikely that calcium imaging can faithfully reflect spiking. Although the GCaMP6f can perhaps reflect single spikes [3], it cannot do so with high frequency spike bursts (>100 spikes per sec intraburst) as would occur in the pyramidal neurons during the slow oscillation. However, GCaMP6f might better reflect the activity profile of the interneurons, which discharge in a fast, single spiking mode (20–60 spikes per sec) even during the phasic clustered discharge during slow wave sleep. Third, it is likely that the calcium images indirectly reflect the change in pattern of discharge during slow wave sleep. The most distinctive feature of this change is the silence imposed by the prolonged membrane hyperpolarization (of 0.3 to 0.5 sec) during the down states in all cortical neurons [5]. This recurrent prolonged hyperpolarization likely corresponds to the lower calcium fluorescence in the pyramidal as well as all other neurons during slow wave sleep. The decrease in calcium activity also appears to be correlated with the decrease (up to 40%) in glucose metabolism that occurs during slow wave sleep in the cerebral cortex of humans [7] and mice [8] and reflects a state of rest for cortical neurons. Such metabolic decreases during slow wave sleep could serve a homeostatic role of sleep for
cortical neurons [6], even though potentiation of synapses associated with memory consolidation can also occur with the slow oscillation discharge pattern in cortical circuits [9]. That pyramidal and other neurons are shown by calcium imaging to reduce their activity further during REM sleep relative to slow wave sleep and thus also waking is very surprising given opposite results in electrophysiological studies over many years. Both regular spiking and fast spiking neurons have been found to increase their average spiking rate during REM sleep as compared to slow wave sleep and even waking (Figure 1B) [5]. The increase in spiking is associated with the fast EEG pattern of activity in the cerebral cortex. So, we ask why the calcium images in the current study show decreasing activity in presumed excitatory and inhibitory, excepting parvalbumin, neurons during REM sleep? First, the activity profiles could again be different in the mouse compared to in the cat. However, relatively similar results to those in the cat were obtained in the rat for both types of neurons [6]. And a recent study reported decreases in firing rate of putative pyramidal cells during REM sleep in the cerebral cortex of rats [10]. Second, it appears that the differences concern particularly the activity of pyramidal and other, including somatostatin, neurons but less so that of the parvalbumin neurons for which an increase, albeit slight, in calcium activity was measured during REM sleep (Figure 1C, top). These results would suggest that parvalbumin neurons could inhibit the pyramidal cells during REM sleep, just as GABAergic and glycinergic neurons inhibit motor neurons and neurons of the behavioral arousal systems during this state [11]. Third, however, is the possibility that the electrophysiological studies sample neurons in a biased manner, as is intrinsic to this approach. In sleep–wake studies, those neurons which are active during wake are often selected for recording across the sleep– wake cycle. By contrast, the GCaMP imaging studies have the extraordinary capacity to image all neurons, along with specifically identified neurons, as in the current study, which makes it so special. With the latter possibility in mind, Niethard et al. examined selectively those excitatory or inhibitory neurons and the
identified parvalbumin and somatostatin neurons that were active during waking. Remarkably, they found that the wake-active neurons were also generally more active during REM than slow wave sleep and, among the parvalbumin neurons, as active as during wake (Figure 1C, bottom), which is more similar to what had been observed previously in the recorded regular spiking and fast spiking neurons (Figure 1B). These results are very exciting as they confirm the notion that particular neurons can replay during sleep what they have experienced or learned during waking, as has been shown to be the case in electrophysiological studies recording large arrays of neurons in hippocampus and neocortex [12–14]. Such replay in select networks of neurons could serve in the documented role of sleep in memory [15,16]. Moreover, the prominent activity of the parvalbumin interneurons and their presumed inhibition of most pyramidal cells could contribute to particular shaping of memories during REM sleep, as Niethard et al. suggest. REFERENCES 1. Niethard, N., Hasegawa, M., Itokazu, T., Oyanedel, C.N., Born, J., and Sato, T.R. (2016). Sleep-stage-specific regulation of cortical excitation and inhibition. Curr. Biol. 26, 2739–2749. 2. Petersen, C. (2014). Cell-type specific function of GABAergic neurons in layers 2 and 3 of mouse barrel cortex. Curr. Opin. Neurobiol. 26, 1–6. 3. Chen, T.W., Wardill, T.J., Sun, Y., Pulver, S.R., Renninger, S.L., Baohan, A., Schreiter, E.R., Kerr, R.A., Orger, M.B., Jayaraman, V., et al. (2013). Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300. 4. Evarts, E.V. (1964). Temporal patterns of discharge of pyramidal tract neurons during sleep and waking in the monkey. J. Neurophysiol. 27, 152–171. 5. Steriade, M., Timofeev, I., and Grenier, F. (2001). Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol. 85, 1969–1985. 6. Vyazovskiy, V.V., Olcese, U., Lazimy, Y.M., Faraguna, U., Esser, S.K., Williams, J.C., Cirelli, C., and Tononi, G. (2009). Cortical firing and sleep homeostasis. Neuron 63, 865–878. 7. Maquet, P., Dive, D., Salmon, E., Sadzot, B., Franco, G., Poirrier, R., von Frenckell, R., and Franck, G. (1990). Cerebral glucose utilization during sleep-wake cycle in man determined by positron emission tomography and [18F]2fluoro-2-deoxy-D-glucose method. Brain Res. 513, 136–143.
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Dispatches 8. Vyazovskiy, V.V., Cirelli, C., Tononi, G., and Tobler, I. (2008). Cortical metabolic rates as measured by 2-deoxyglucose-uptake are increased after waking and decreased after sleep in mice. Brain Res. Bull. 75, 591–597. 9. Chauvette, S., Seigneur, J., and Timofeev, I. (2012). Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron 75, 1105–1113. 10. Watson, B.O., Levenstein, D., Greene, J.P., Gelinas, J.N., and Buzsaki, G. (2016). Network
homeostasis and state dynamics of neocortical sleep. Neuron 90, 839–852. 11. Jones, B.E. (2011). Neurobiology of waking and sleeping. Handb. Clin. Neurol. 98, 131–149. 12. Wilson, M.A., and McNaughton, B.L. (1994). Reactivation of hippocampal ensemble memories during sleep. Science 265, 676–679. 13. Louie, K., and Wilson, M.A. (2001). Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156.
14. Ji, D., and Wilson, M.A. (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10, 100–107. 15. Aton, S.J., Seibt, J., Dumoulin, M., Jha, S.K., Steinmetz, N., Coleman, T., Naidoo, N., and Frank, M.G. (2009). Mechanisms of sleepdependent consolidation of cortical plasticity. Neuron 61, 454–466. 16. Rasch, B., and Born, J. (2013). About sleep’s role in memory. Physiol. Rev. 93, 681–766.
Cytokinesis: Going Super-Resolution in Live Cells Yajun Liu1 and Jian-Qiu Wu1,2,* 1Department
of Molecular Genetics, The Ohio State University, Columbus, OH 43210, USA of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH 43210, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2016.09.026 2Department
Super-resolution fluorescence microscopy has emerged as a powerful tool for studying molecular organization, but mostly in fixed cells. New work using high-speed fluorescence photoactivation localization microscopy now reveals the organization of cytokinesis nodes and contractile rings in live fission yeast cells. In recent years, super-resolution fluorescence microscopy has emerged as a state-of-the-art imaging method that exceeds the diffraction limit of light [1,2]. Common super-resolution techniques include structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), fluorescence photoactivation localization microscopy (FPALM or PALM), and stochastic optical reconstruction microscopy (STORM) [2]. These invaluable techniques can provide the information necessary for deciphering the 3D structure of multi-protein complexes, and also expand the toolbox for investigating biological molecules at the nanoscale level. However, super-resolution fluorescence microscopy with high spatial resolution has been used mostly in fixed cells due to its low temporal resolution, which has precluded imaging of live cells. In a new study published recently in PNAS, Laplante et al. [3] used high-speed quantitative FPALM to establish the molecular organization of the contractile ring and its precursor, the
cytokinesis nodes, in live fission yeast cells. Cytokinesis is essential for cell proliferation and differentiation. The process relies on the constriction of a contractile ring composed of multi-protein complexes in amoebas, fungi, and animal cells [4,5]. Besides actin filaments and myosin-II [6–8], the contractile ring also contains many other structural and regulatory proteins, including anillin, IQGAP, formins, and F-BAR proteins [4,9]. Currently, we are most knowledgeable about the cytokinetic machinery and mechanisms in the fission yeast Schizosaccharomyces pombe. In S. pombe, some of the major proteins present in the contractile ring assemble into discrete precursor structures called cytokinesis nodes around the division site before ring assembly [10,11]. The positioning marker and scaffolding protein anillin Mid1 first concentrates in the cortical nodes around the division site. Mid1 then recruits the myosin-II essential light chain (Cdc4) and the IQGAP Rng2. Rng2 subsequently recruits the myosin-II heavy chain (Myo2) and regulatory light
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chain (Rlc1) [10,12]. In addition, Mid1 also independently recruits the F-BAR protein Cdc15 [10]. Lastly, the formin Cdc12 is recruited for actin nucleation and assembly. Stochastic interactions between actin filaments and myosins condense these nodes into a compact contractile ring that is ready to carry out its function during cytokinesis [13]. Although protein composition in the contractile ring has been extensively studied, little is known about the 3D organization of these proteins. Understanding this fundamental question will shed light on how the contractile ring generates force and tension during cytokinesis and also provide information required for computational model simulations. Quantitative fluorescence microscopy using the spinning disk confocal system has estimated that each S. pombe cell has around 65 cytokinesis nodes [13]. One shortcoming of this method is the inability to fully resolve diffraction-limited cytokinesis nodes that are located in close proximity to each other. The single-molecule