A new age in scanning electron microscopy - Patologi (PDF)

A Sponsored Supplement to Science Sponsored by Produced by the Science/AAAS Custom Publishing Office A new age in scanning electron microscopy: Applications in the life sciences  able Resources • Anthropology • Astronomy • Atmospheric and Hydrospheric Sciences • Biological Sciences • Chemis ngineering • General Interest in Science and Engineering • Geology and Geography • History and Philosophy o omputing, and Communication • Linguistics and Language Science • Mathematics • Medical Sciences • Neur • Social, Economic, and Political Sciences • Societal Impacts of Science and Engineering • Statistics • Agricultur onomy • Atmospheric and Hydrospheric Sciences • Biological Sciences • Chemistry • Dentistry and Oral Health S t in Science and Engineering • Geology and Geography • History and Philosophy of Science • Industrial Science and ation • Linguistics and Language Science • Mathematics • Medical Sciences • Neuroscience • Pharmaceutical Scienc ciences • Societal Impacts of Science and Engineering • Statistics • Agriculture, Food, and Renewable Resour spheric Sciences • Biological Sciences • Chemistry • Dentistry and Oral Health Sciences • Education • Engineering Geology and Geography • History and Philosophy of Science • Industrial Science and Technology • Information, Computing, Mathematics • Medical Sciences • Neuroscience • Pharmaceutical Sciences • Physics • Psychology • Social, Economic e and Engineering • Statistics • Agriculture, Food, and Renewable Resources • Anthropology • Astronomy • Atmospheric and Hy Chemistry • Dentistry and Oral Health Sciences • Education • Engineering • General Interest in Science and Engineering cience • Industrial Science and Technology • Information, Computing, and Communication • Linguistics and Languag e • Pharmaceutical Sciences • Physics • Psychology • Social, Economic, and Political Sciences • Societal Impact and Renewable Resources • Anthropology • Astronomy • Atmospheric and Hydrospheric Sciences • Biological S Education • Engineering • General Interest in Science and Engineering • Geology and Geography • His e and Technology • Information, Computing, and Communication • Linguistics and Language Scienc Pharmaceutical Sciences • Physics • Psychology • Social, Economic, and Political Sciences • Societal Imp • Anthropology • Astronomy • Atmospheric and Hydrospheric S • Education • Engineering • y of Science • Industrial Science and Technology • Information, Computing, and Communication • Medical Sciences • Neuroscience • Pharmaceutical Sciences • Physics • Psychology • Social, Economic e and Engineering • Statistics • A Join AAAS Sections. They are the foundation of your AAAS membership • Be a subject-matter expert. • Represent your discipline. • Network with leaders in your field. aaas.org/sections AAAS MEMBERSHIP. MAKE THE CONNECTION.  1 ROGER GONCALVES, ASSOCIATE SALES DIRECTOR Custom Publishing Europe, Middle East, and India [email protected] +41-43-243-1358 © 2017 by The American Association for the Advancement of Science. All rights reserved. 29 September 2017 Editor: Sean Sanders, Ph.D. Proofreader/Copyeditor: Bob French Designer: Amy Hardcastle About the cover: A realistic, molecular-scale view of a synapse, showing a few hundred thousand proteins. The synapse organization was measured by a combination of electron microscopy, quantitative biochemistry, and superresolution microscopy. From the cover of the May 30, 2014 issue of Science. Image: Burkhard Rammner/Rizzoli Laboratory, University of Göttingen Medical Center. Original content (pages 29–42) has not been peer-reviewed or assessed by Science. These articles can be cited using the following format: [AUTHOR NAME(S)] in A new age in scanning electron microscopy: Applications in the life sciences (Science/ AAAS, Washington, DC, 2017), p. [xx-xx]. This booklet was produced by the Science/ AAAS Custom Publishing Office and sponsored by Carl Zeiss Microscopy GmbH, Germany A new age in scanning electron microscopy: Applications in the life sciences TABLE OF CONTENTS SCIENCE sciencemag.org Introductions 2 The rise of SEM in the life sciences Sean Sanders, Ph.D. Science/AAAS 3 3D electron microscopy for the life sciences Alexandra Elli, Ph.D. Eric Hummel, Ph.D. Jan Birkenbeil Carl Zeiss Microscopy GmbH, Germany Research articles 4 Ultrastructural evidence for synaptic scaling across the wake/sleep cycle Luisa de Vivo, Michele Bellesi, William Marshall et al. 8 Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER Jonathon Nixon-Abell, Christopher J. Obara, Aubrey V. Weigel et al. 20 Inflammation-induced disruption of SCS macrophages impairs B cell responses to secondary infection Mauro Gaya, Angelo Castello, Beatriz Montaner et al. 25 Development of the annelid axochord: Insights into notochord evolution Antonella Lauri, Thibaut Brunet, Mette Handberg-Thorsager et al. Technical note 29 Scanning Electron Microscopy in Life Sciences: Technical Solutions for Biological Applications Alexandra Elli, Ph.D.; Robert Kirmse, Ph.D.; Bernhard Zimmermann, Ph.D. Carl Zeiss Microscopy GmbH, Germany Questions and answers 32 A life of change in the brain: An interview with Jeff Lichtman Mike May Application note 34 Navigation of Bee Brains to Human Hip— Microscopy and the Modern Magellans Melissa L. Knothe Tate, Ph.D. University of New South Wales  sciencemag.org SCIENCE A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES 2 The rise of SEM in the life sciences The application of scanning electron microscopy in the biological sciences has enjoyed something of a renaissance. T he basic design and concept of the electron microscope (EM) have not changed much since its development in the 1930s. Nonetheless, incremental improvements in power and resolution have been achieved over the decades, even as prices have come down. More recently, however, and particularly in the last decade, the application of scanning electron microscopy in the biological sciences has enjoyed something of a renaissance, in part because of recent advances in EM technology. A quick PubMed search shows that the number of occurrences of the term “scanning electron microscope” (SEM) flatlined at around 150 per year through most of the 1980s and 1990s. Citations started to increase steadily beginning in the early 2000s, reaching over 1,100 in 2016—with no signs of slowing down. These rough statistics support the notion that there has been an uptick in the introduction of SEM technology into life science laboratories to solve previously intractable problems. One area that has benefited considerably is neuroscience. SEM and transmission EM have been applied to reveal physiological changes during invertebrate development that can shed light on vertebrate evolution (see page 25). More recent advances, such as serial block-face SEM (SBEM)—the imaging of a tissue block following iterative removal of ultrathin (10 nm–50 nm) sections of the top layer of tissue using an ultramicrotome integrated into the EM—have allowed for the 3D reconstruction of neural tissue sample images, enabling scientists to study neurons and their connections in unprecedented detail (see page 4). Another recent advancement in EM hardware is focused ion beam SEM (FIB-SEM), which is also providing new opportunities to image cellular and subcellular structures in 3D. Rather than using a microtome to cut out thin sections of tissue, an ion beam is applied to carefully remove ultrathin layers of tissue, allowing for reconstruction of z-stacks capable of resolving intracellular organization in fine detail (see page 8). Now that life science researchers have received a taste of what EM can do to drive their work forward, they are demanding increasingly advanced tools with more powerful capabilities. A number of companies—including ZEISS, the sponsor of this booklet—are stepping up to meet these demands and provide the technology needed to answer some of the most interesting biological questions, particularly in the field of neuroscience and neurological disease. No doubt there are many applications for this technology that have yet to be investigated and many new discoveries waiting to be made. All in all, this is a truly exciting time for the field of life sciences. Sean Sanders, Ph.D. Editor, Custom Publishing Science/AAAS  SCIENCE sciencemag.org INTRODUCTIONS “B y the help of microscopes, there is nothing so small, as to escape our inquiry; hence there is a new visible world discovered to the understanding.” The words of microscopy pioneer Robert Hooke fittingly reflect the great progress science has made in understanding the subcellular world in its most specific details. Technical advances in the centuries after Hooke have brought to light many fascinating aspects of the microworld. The rapid evolution of innovative techniques in light microscopy has opened new horizons, but also (and to a much larger extent) new inquiries. New routes have emerged to enter the subcellular space with the use of electron microscopes (EM). Starting with transmission electron microscopy (TEM) and the work of German physicist Ernst Ruska, electron microscopes became routine tools to investigate the open questions in life science. The next big step was bringing 3D techniques into electron microscopy, such as tilt-based tomography that enabled the investigation of the 3D ultrastructure of organelles. TEM, however, suffered from some limitations, including small sample volume and the fact that ultrastructural research was based primarily on TEM tomography or serial tomography. High-throughput and large-volume imaging methods using scanning electron microscopy (SEM) invigorated EM for the life sciences, making it possible to acquire large volumes in 3D using array tomography. Classical array tomography uses the sectioning capabilities of a microtome and—depending on the sample—typically reaches a z-resolution of 40 nm. A second groundbreaking development was the insertion of a microtome inside the SEM to section the block- surface after every image. Using this method, a z-resolution of 15 nm is achievable with samples of several hundred micrometers in height. A third approach has further improved z-resolution: Introduction of an ion gun into the SEM chamber allows researchers to get z-sections as thin as 3 nm. One further sophisticated approach is to connect the world of light and EM. Correlative microscopy opens completely unexplored opportunities that could generate a wealth of scientific knowledge. For 170 years, ZEISS has pioneered many of the breakthroughs in the field and is now connecting its entire portfolio of advanced light, electron, ion, and X-ray microscopes, enabling researchers to gain fresh insights into the processes underlying life itself. We hope you will find this collection of publications and peer- reviewed articles interesting and inspiring. At ZEISS, we believe that innovative electron microscopy and correlative techniques can make a big difference in your research, and we are excited to contribute to your success. Alexandra Elli, Ph.D. Eric Hummel, Ph.D. Jan Birkenbeil Carl Zeiss Microscopy GmbH, Germany [email protected] 3D electron microscopy for the life sciences The rapid evolution of innovative techniques in light microscopy has opened new horizons. 3  4 A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES sciencemag.org SCIENCE Originally published 3 February 2017 in SCIENCE \\\REPORT ◥ SLEEP RESEARCH Ultrastructural evidence for synaptic scaling across the wake/sleep cycle Luisa de Vivo,1 Michele Bellesi,1,2 William Marshall,1 Eric A. Bushong,3 Mark H. Ellisman,3,4 Giulio Tononi,1* Chiara Cirelli1* It is assumed that synaptic strengthening and weakening balance throughout learning to avoid runaway potentiation and memory interference. However, energetic and informational considerations suggest that potentiation should occur primarily during wake, when animals learn, and depression should occur during sleep.We measured 6920 synapses in mouse motor and sensory cortices using three-dimensional electron microscopy.The axon-spine interface (ASI) decreased ~18% after sleep compared with wake.This decrease was proportional to ASI size, which is indicative of scaling. Scaling was selective, sparing synapses that were large and lacked recycling endosomes. Similar scaling occurred for spine head volume, suggesting a distinction between weaker, more plastic synapses (~80%) and stronger, more stable synapses.These results support the hypothesis that a core function of sleep is to renormalize overall synaptic strength increased by wake. T he cerebral cortex in humans contains 16 billion neurons and in mice 14 million neurons (1), and each neuron harbors thou- sands of synapses (2). Of the billions of cortical synapses of adult mice, ~80% are excitatory, and the majority of these are on den- dritic spines (3). Spine size is tightly correlated with synaptic strength (3, 4); the area of the post- synaptic density (PSD), the area of the axon-spine interface (ASI), and the volume of the spine head (HV) are strongly correlated among themselves and with the number of vesicles in the presynapse (5–8), the number of synaptic AMPA receptors [AMPARs (9)], and the amplitude of AMPAR- mediated synaptic currents (10, 11). Changes in synaptic strength are the primary mechanisms mediating learning and memory (12, 13). Synaptic potentiation and depression must be balanced to avoid either saturation or obliteration of neural signaling and memory traces (14), and it is usually assumed that overall synaptic strength is regulated throughout learn- ing (15). The synaptic homeostasis hypothesis (SHY) (16) argues, however, that owing to energy and signaling requirements, learning should occur primarily through synaptic potentiation during wake, leading to a net increase in syn- aptic strength. This is because sparsely firing neurons can ensure that coincidences in their inputs learned during wake are signaled through- out the brain only if the connections relaying such coincidences are strengthened, not weak- ened. Overall synaptic renormalization by net weakening should occur during sleep, when animals are disconnected from the environment. The reason is that spontaneous neural activity can sample memories in a comprehensive and fair manner only if the brain is offline, without being at the mercy of current environmental inputs. Sleep can thus promote the acquisition, consolidation, and integration of new information as well as restore cellular function (16, 17). Becausestrongersynapses arelargerandweaker ones smaller (3, 4), SHY makes an intriguing pre- diction: Billions of cortical excitatory synapses should increase in size after wake and decrease after sleep, independent of circadian time. Further- more, although synaptic renormalization should affect a majority of synapses, it should also be selective, to allow for both stability and plasticity (16–18). We used serial block-face scanning electron microscopy (SBEM) (19) to obtain direct, high- resolution, three-dimensional (3D) volume mea- surements of synaptic size during the wake/sleep cycle and across thousands of synapses in two re- gions of mouse cortex. Brains were collected from three groupsof mice(fourmicepergroup)(Fig. 1A): S(sleep)micespentatleast75% ofthe first~7hours of the light period asleep; EW (enforced wake) mice were kept awake during that time by ex- posure to novel objects; and SW (spontaneous wake) mice spent at least 70% of the first ~7 hours of the dark period spontaneously awake (Fig. 1B). S mice were compared with both SW and EW mice in order to tease apart sleep/wake effects from potential confounding factors due to time of day, light exposure, and stimulation or stress associated with enforced wake. In each mouse, we sampled layer 2 of primary motor (M1) and primary somatosensory (S1) cortices. In these areas and layers, activity-dependent structural plasticity is well documented (3). Blocks of cor- tical tissue (~25 by 25 by 13–25 mm) were ac- quired and automatically aligned, and spiny dendritic segments were randomly selected within each block, balanced in size across groups (diameter = 0.86 ± 0.23 mm, mean ± SD) (table S1), and manually segmented by trained annotators blind to experimental condition (Fig. 1, C and D, and supplementary materials, materials and meth- ods). Within each dendritic segment, all protru- sions [also called “spines” (3)] were annotated, including spines forming synapses and a minori- ty that lacked synapses (~13% of all protrusions) (table S1). Across all mice, 168 dendritic segments were segmented (101 in M1 and 67 in S1) (Fig. 1D and fig. S1), for a total of 8427 spines, of which 7149 formed a synapse. Synapses were defined by the presence of a presynaptic bouton with at least two synaptic vesicles within a 50-nm distance from the cellular membrane facing the spine, a visible synaptic cleft, and a PSD. In spines form- ing a synapse, ASI, HV—as well as vesicles, tubules, and multivesicular bodies (MVBs) that together form the nonsmooth endoplasmic reticulum (non- SER) compartment (20)—and the spine appara- tus were segmented (Fig. 1, E and F) (supplemen- tary materials, materials and methods). After excluding incomplete synapses, 6920 spines with a synapse contributed to the final analysis (tables S1 and S2). ASI and PSD are strongly correlated with each other, and both become largeraftersynapticpoten- tiation (6–8). We focused on ASI—the surface of direct contact between axonal bouton and spine— asastructuralmeasureofsynapticstrengthbecause in SBEM images, its exact borders are easier to identify than those of the PSD (21). First, we asked whether ASI sizes change as a function of wake andsleep usinga linearmixed-effects (LME)model that included mouse and dendrite as random effects, condition (SW, EW, and S), and brain region (S1 and M1) as categorical fixed effects, and dendrite diameter as a linear fixed effect. Con- dition had a strongeffect onASI(c2= 10.159, df = 2, P = 0.0062), which did not interact with either brainregionordendritediameter.Posthocanalysis (adjusted for multiple comparisons)foundthatASI sizes after sleep were reduced on average by 18.9% relative to spontaneous wake (P = 0.001) and by 17.5% compared with enforced wake (P = 0.003) (Fig. 2A and supplementary materials, materials and Methods, LME model for ASI). Spontaneous and enforced wake did not differ (SW versus EW, –1.7%; P = 0.957). Thus, ASI sizes decrease with sleep on average by ~18% relative to both spon- taneous and enforced wake, independent of time of day. There was instead no difference across groups in the distribution of dendrite (P = 0.248) and mitochondrial (P = 0.445) diameters, ruling out overall tissue shrinkage after sleep (fig. S2). Consistent with the range of PSD and spine sizes in mouse somatosensory and auditory cortex (22, 23), the distribution of ASI sizes in our S1 and M1 samples was log-normal (Fig. 2B), a feature 1Department of Psychiatry, University of Wisconsin–Madison, 6001 Research Park Boulevard, Madison, WI 53719, USA. 2Department of Experimental and Clinical Medicine, Section of Neuroscience and Cell Biology, Università Politecnica delle Marche, Ancona, Italy. 3National Center for Microscopy and Imaging Research, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. 4Department of Neurosciences, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. *Corresponding author. Email: [email protected] (C.C.); [email protected] (G.T.) RESEARCH on May 16, 2017 http://science.sciencemag.org/ Downloaded from  5 SCIENCE sciencemag.org RESEARCH ARTICLES A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES thought to emerge from multiplicative dynamics (22). On the log scale, the S group showed an overall shift to the left relative to the SW and EW groups, suggesting that the decrease in ASI during sleep obeyed a scaling relationship (Fig. 2, B inset and C). Formal testing (supplementary materials, materials and methods) confirmed scaling, when sleep was compared with either spontaneous wake (average scaling –20.1%, P = 0.784) or enforced wake (average scaling –19.1%, P = 0.648). Monte Carlo simulations on boot- strapped data (supplementary materials, materials and methods) suggested that the change in ASI sizes between wake and sleep is not consistent with uniform scaling across all synapses but rather with selective scaling, in which a fraction of all synapses scales and the remaining portion does not. Of the models tested, the best fit was provided when the likelihood of scaling decreased qua- dratically with increasing ASI size (Fig. 2D). This model fitted the actual data best, assuming that a majority of all synapses (>80%) would scale and that a minority (<20%) would be less likely to do so (Fig. 2D). Do morphological features of synapses predict the likelihood of scaling? Given the results in Fig. 2D, we asked whether distinguishing between small to medium synapses (bottom 80%) versus large synapses would predict scaling versus no scaling. This distinction based on size was signif- icant (P = 0.009; small ASI: S versus SW –11.9%, P = 0.0002; S versus EW –12.5%, P = 0.0001; large ASI: S versus SW +0.7%, P = 0.999; S versus EW +2.0%, P = 0.994) (Fig. 3A) and robust for scaling fractions around 80% (supplementary materials, materials and methods). These results indicate that the ASIs of most synapsesdecrease duringsleep ina manner proportionaltotheirsize,and that the largest 20% of spines are less likely to scale. Plastic changes may preferentially occur in spines that contain recycling endosomes (24), whosepresencereflectsincreasedturnoverofmem- branes, glutamate receptors, and other proteins that are essential to support activity-dependent structural changes (13, 24, 25). Indeed, only spines containing vesicles, tubules, and multivesicular bodies (MVBs), most of which are considered of endosomal origin (20), showed significant scaling (P = 0.00003; vesicles/tubules, +: S versus SW –25.0%, P = 0.00001; S versus EW –20.9%, P = 0.0008; vesicles/tubules, –: S versus SW –2.9%, P = 0.985; S versus EW –2.8%, P = 0.989) (Fig. 3, B and C). Aspine’sstructuralplasticitymay beconstrained by the overall spine density of its dendritic branch (26). Although synaptic density by itself was un- affected by wake and sleep (P = 0.761), it inter- acted with the effect of sleep on ASI (P = 0.038); the ASI decrease with sleep was largest in less spiny dendrites (S versus SW = –36.4%; S versus EW = –25.3%) and smallest in dendrites with higher synaptic density (S versus SW = 7.8%; S versus EW = –8.2%) (Fig. 3D). In contrast, ASI decreased with sleep both in the spines with a spine apparatus (27)—a special- ization of SER involved in calcium regulation and synthesis of transmembrane proteins—and in those without it (Fig. 3E) (28). Although spines facing an axonal bouton with one or more mito- chondria were larger than spines lacking an axonal mitochondrion, scaling again occurred in both groups of spines (Fig. 3F). ASI size scales down between wake and sleep in small- and medium-sized synapses (~80% of the total pop- ulation) but is less likely to do so in synapses that are large (~20%) or in spines that contain no endosomes and is less marked in highly spiny dendrites. Because HV is also strongly correlated with synaptic strength, we investigated changes in HV as a function of wake and sleep using a linear model that included the same random and fixed effects as for ASI (supplementary materials, ma- terials and methods, LME model for HV). Results were consistent with those with ASI (c2 = 6.942, Fig. 1. Experimental groups and SBEM segmentation of cortical synapses. (A) The three experimental groups. SW, spontaneous wake at night; EW, wake during the day enforced by exposure to novel objects; S, sleep during the day. Arrowheads indicate time of brain collection. (B) Percent of wake in each mouse (four mice per group) during the last 6 hours before brain collection. (C) Schematic representation of mouse primary motor (M1, left) and somatosensory (S1, right) cortex,with the region of SBEM data collection indicated in layer 2 (blue box), and reconstruction of four spinydendritic segments in S1. (D) Some of the dendritic segments from SW, EW, and S mice reconstructed in this study (all segments are shown in fig. S1). Scale bar, 15 mm. (E and F) Raw image of a cortical spine containing a synapse and its 3D reconstruction. Spine head is in yellow, ASI is in red, and axonal bouton is in green. Scale bar, 350 nm.  6 A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES sciencemag.org SCIENCE Fig. 2. ASI size declines in sleep according to a scaling relationship. (A) (Left) Visualization of ASIs in one dendrite. Scale bar, 2.5 mm. (Right) Effect of condition. ASI size decreases in sleep (blue) relative to both spontaneous wake (orange) and enforced wake (red). ASI size is shown for all synapses, each represented by one dot. **P < 0.01. (B) Log-normal distribution of ASI sizes in the three experimental groups. (Inset) Same on a log scale. (C) The decrease in ASI size during sleep is due to scaling. (D) Monte Carlo simulations comparing different models of scaling. Size-dependent selective scaling (green) fits the actual data better than uniform scaling (asterisk) or selective scaling independent of size (brown) (supplementary materials, materials and methods). Fig. 3. Scaling of ASI size is selective. (A) The effect of sleep is present in small to medium synapses (80% of all synapses) but not in the largest ones (20% of all synapses). (B) The effect of sleep is present in spines with non-SER elements (vesicles, tubules, and multivesicular bodies, labeled “vesicles/ tubules”). (Top right) A multivesicular body (arrowhead) and a coated vesicle (asterisk). (Bottom right) A non-SER tubule (arrowhead). (C) the ASI decrease during sleep in spines with vesicles/tubules is due to scaling. (D) The decline of ASI size in sleep is greatest in the dendrites with the lowest synaptic density (range, 0.17 to 1.24/mm2). At the average value of synaptic density (vertical line; 0.70/mm2), the mean overall decrease is –17.3% (S versus SW –17.4%, P = 0.002; S versus EW–17.3%, P = 0.002). (E and F) ASI size declines in sleep independently of the presence of spine apparatus (asterisk) or mito- chondria in the axonal bouton (arrowheads). Scale bars, 500 nm. In all ex- perimental groups, spines containing a spine apparatus or facing an axonal bouton with mitochondria are larger than spines lacking these elements. **P < 0.01; ***P < 0.001.  7 SCIENCE sciencemag.org RESEARCH ARTICLES A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES df = 2, P = 0.031), with one additional interaction (condition × dendrite diameter): HV decreased most in the largest dendrites (S versus SW = –31.8%; S versus EW = –38.4%) and least in the smallest dendrites (S versus SW = –4.7%; S versus EW = 1.3%) (fig. S3, A and B). Like ASIs, HVs followed a log-normal distribution (fig. S3C), and as a group, only the spines with vesicles, tubules, and MVBs showed a significant down-scaling in HV after sleep at an average value of dendrite diam- eter (vesicles/tubules +: S versus SW = –20.8%, P = 0.0006; S versus EW = –14.3%, P = 0.045; vesicles/ tubules –: S versus SW = 6.4%, P = 0.776; S versus EW = 1.3%, P = 0.999) (fig. S3, D and E). The ultrastructural demonstration of up- and down-scaling of synapse sizes with wake and sleep supports the hypothesis that wake leads to a net increase in synaptic strength, whereas a core function of sleep is to renormalize synaptic strength through a net decrease (16). Ultra- structural analysis provides the morphological ground truth, but it is necessarily limited to small brain samples. However, synaptic scaling across the wake/sleep cycle is likely to be a general phenomenon, irrespective of species, brain region, and specific plasticity mechanisms (16). We found similar changes in two different cortical regions. Moreover, proteinlevelsofGluA1-containingAMPA receptors are higher after wake than after sleep (29) across the entire cerebral cortex. Also, the number of immunolabeled synaptic puncta increases with enriched wake and decreases with sleep in wide- spread regions of the fly brain (30). Last, electro- physiological markers of synaptic efficacy also increase broadly after wake and decrease after sleep (16). The scaling of synaptic size is not uniform, which is consistent with the requirement that learning during wake must potentiate synapses selectively and with the hypothesis that selective renormalization during sleep favors memory con- solidation, integration, and “smart” forgetting (16). We do not know how scaling is apportioned be- tween wake and sleep. During wake, there may be a selective up-scaling of a smaller proportion of synapses because learning is limited to a particular environment (31), whereas down-scaling during sleep may be broader because the brain can sample its memories comprehensively and fairly when it is offline (16). We also cannot rule out that a few synapses may up-scale in sleep (16, 17). Future studies labeling individual plastic events in the same synapses over wake and sleep may shed light on this issue. It will also be important to assess which molecular mechanisms are involved in the selective scaling of excitatory synapses in wake and sleep and to evaluate possible changes in inhibitory synapses (32). We found that the synapses that most likely escape scaling are those that are large, those that lack endosomes, as well as those in crowded den- dritic branches. These features may represent struc- tural markers [besides molecular markers (33)] of synapses and associated memory circuits that are either committed or relatively stable despite the profound daily remodeling. We do not know, however, to what extent and over which time scale synapses may switch between this smaller pool of stronger, more stable synapses and the larger pool of weaker, more plastic synapses. 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Schroeder for their contribution to the manual segmentation of SBEM images. This work was supported by NIH grants DP 1OD579 (G.T.), 1R01MH091326 (G.T.), 1R01MH099231 (G.T. and C.C.), 1P01NS083514 (G.T. and C.C.), and P41GM103412 for support of the National Center for Microscopy and Imaging research (M.H.E.). The other authors declare no competing financial interests. G.T. is involved in a research study in humans supported by Philips Respironics; this study is not related to the work presented in the current manuscript. The other authors declare no competing financial interests. Data (ASI and HV measures) are available at http://centerforsleepandconsciousness.med.wisc.edu. SUPPLEMENTARY MATERIALS www.sciencemag.org/content/355/6324/507/suppl/DC1 Materials and Methods Figs. S1 to S3 Tables S1 and S2 References (36–53) 19 July 2016; accepted 20 October 2016 10.1126/science.aah5982  8 A NEW AGE IN SCANNING ELECTRON MICROSCOPY: APPLICATIONS IN THE LIFE SCIENCES sciencemag.org SCIENCE RESEARCH ARTICLE ◥ CELLULAR STRUCTURE Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER Jonathon Nixon-Abell,1,2* Christopher J. Obara,3,4* Aubrey V. Weigel,3,4* Dong Li,4,5 Wesley R. Legant,4 C. Shan Xu,4 H. Amalia Pasolli,4 Kirsten Harvey,2 Harald F. Hess,4 Eric Betzig,4 Craig Blackstone,1†‡ Jennifer Lippincott-Schwartz3,4†‡ The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle that plays crucial roles in numerous cellular functions. We used emerging superresolution imaging technologies to clarify the morphology and dynamics of the peripheral ER, which contacts and modulates most other intracellular organelles. Peripheral components of the ER have classically been described as comprising both tubules and flat sheets. We show that this system consists almost exclusively of tubules at varying densities, including structures that we term ER matrices. Conventional optical imaging technologies had led to misidentification of these structures as sheets because of the dense clustering of tubular junctions and a previously uncharacterized rapid form of ER motion.The existence of ER matrices explains previous confounding evidence that had indicated the occurrence of ER “sheet” proliferation after overexpression of tubular junction–forming proteins. T he ER is a continuous, membranous net- work extending from the nuclear envelope to the outer periphery of cells; it plays vital roles in processes such as protein synthe- sis and folding, mitochondrial division, cal- cium storage and signaling, and lipid synthesis andtransfer. Inthe cellperiphery,theERisthought to exist as an elaborate membrane system that makes contact with nearly every other cellular organelle. Prevailing models of its structure pro- pose a complex arrangement of interconnected tubules and sheets, each of which is maintained by distinct mechanisms (1, 2). Numerous proteins are involved in maintaining this complex struc- tural organization. Membrane curvature-stabilizing proteins, including members of the reticulon (RTN) and REEP families, contain hydrophobic hairpin domains that are thought to be responsible for promoting curvature in ER tubules via scaffold- ing and hydrophobic wedging. Members of the atlastin (ATL) family of dynamin-related guano- sine triphosphatases (GTPases) are thought to mediate the formation of tubular three-way junc- tions, giving rise to the characteristic polygonal tubular network (3). Meanwhile, an alternative complement of proteins is proposed to regulate the structure of ER sheets, with p180, kinectin, and CLIMP63 all thought to play a role in shaping, helicoidal stacking, and luminal spacing (3). Mu- tations in many of these ER-shaping proteins are connected to a variety of human disease condi- tions, most notably the hereditary spastic pa- raplegias (4). Thus, characterizing ER morphology is critical to understanding the basic biology of cells in both health and disease. Determining the structure of the ER is challeng- ing because of limitations in our ability to visualize the intricate nature of its morphology. The pe- ripheral ER is particularly susceptible to this constraint, given its well-documented dynamic rearrangements and fine ultrastructure (5, 6). These characteristics impede attempts to derive functional information based on changes to ER structure. The recent development of various superresolution (SR) imaging approaches, however, offers an op- portunity to examine ER structure and dynamism with substantially improved spatiotemporalresolu- tion. Here, we used five different SR modalities, each having complementary strengths and weaknesses in the spatial and temporal domains, to examine ER structure and dynamics. A high-speed variation of structured illumination microscopy (SIM) al- lowed ER dynamics to be visualized at unprece- dented speeds and resolution. Three-dimensional SIM (3D-SIM) and Airyscan imaging allowed comparison of the fine distributions of different ER-shaping proteins. Finally, lattice light sheet- point accumulationforimaginginnanoscaletopog- raphy (LLS-PAINT)and focusedionbeam scanning electron microscopy (FIB-SEM) permitted 3D char- acterization of different ER structures. Thoroughly probing the ER in this manner provides unprece- dented information about the morphology and dynamics of this organelle, including the charac- terization of a previously underappreciated struc- ture within the peripheral ER. ER tubules and junctions undergo rapid motion in living cells ER tubules are known to undergo rapid struc- tural rearrangements, occurring over seconds or minutes, yet examination of these processes has typically been confined to the extension and re- traction of tubules and the formation of tubular three-way junctions (5, 6). To obtain a more com- prehensive picture of tubular motion, we used high-speed SIM with grazing incidence illumina- tion (GI-SIM; see materials and methods) (7). This live SR imaging modality (resolution ~100 nm) uses light beams counterpropagating just above the sample substrate to image cellular features near the basal plasma membrane at frequencies up to 40 Hz. This translates to a factor of 4 to 10 increase in acquisition speed, relative to what can be practically achieved with spinning-disk confocal microscopy for imaging the ER, and a factor of ~2 improvement in resolution. With GI-SIM, we imaged COS-7 cells express- ing an ER membrane marker (mEmerald-Sec61b, henceforth Sec61b) to track ER tubules. Increased spatiotemporal resolution revealed a novel form of ER motion consisting of remarkably rapid tu- bular fluctuations (Fig. 1 and movie S1). Using a modified skeletonization algorithm (8) to track the movement of ER tubules (Fig. 1B), we iden- tified oscillations with a mean peak-to-peak am- plitude of 70 ± 50 nm, occurring an average of 4 ± 1 times per second (means ± SD; n = 1755 tubules from 8 cells) (Fig. 1, C and D). Traditional imag- ing modalities have the ability to localize pre- cisely this tubular motion only if the tubules and junctions are sufficiently sparse. Also, a large proportion of this motion often occurs on too short a time scale to be effectively tracked using spinning-diskconfocalmicroscopyatimagingspeeds commonly reported in the literature (6, 9). This suggests that in dense regions, tubular ER motion and morphology are likely to be obscured when using traditional imaging modalities. These rapid fluctuations we observed in COS-7 cells were also found in an unrelated cell type (U-2 OS) as well as in COS-7 cells expressing a luminal ER marker (mEmerald-ER3, henceforth ER3; see materials and methods) instead of Sec61b (Fig. 1, C to E, and tables S1 and S2). In addition to the tubules themselves, three- way junctions also exhibited appreciable motion over very short time scales (Fig. 1, F and G, and movie S1). Three-way junctions were identified from the skeletonization of fluorescent ER images. Skeletonized pixels with exactly two neighbors were considered to be part of a branch (Fig. 1F, white), and pixels with more than two neighbors were considered junctions (Fig. 1F, overlaid with cyan dots). Three-way junctions were then treated as single particles and tracked (Fig. 1F, green). The time-averaged mean square displacement RESEARCH SCIENCE sciencemag.org 28 OCTOBER 2016 • VOL 354 ISSUE 6311 aaf3928-1 1Cell Biology Section, Neurogenetics Branch, National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, USA. 2Department of Pharmacology, UCL School of Pharmacy, University College London, London, UK. 3Cell Biology and Metabolism Program, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), Bethesda, MD, USA. 4Janelia Research Campus, Howard Hughes Medical Institute (HHMI), Ashburn, VA, USA. 5National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China. *These authors contributed equally to this work. †These authors contributed equally to this work. ‡Corresponding author. Email: [email protected] (C.B.); [email protected]. org (J.L.-S.) Originally published 28 October 2016 in SCIENCE RESEARCH ARTICLE ◥ CELLULAR STRUCTURE Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER Jonathon Nixon-Abell,1,2* Christopher J. Obara,3,4* Aubrey V. Weigel,3,4* Dong Li,4,5 Wesley R. Legant,4 C. Shan Xu,4 H. Amalia Pasolli,4 Kirsten Harvey,2 Harald F. Hess,4 Eric Betzig,4 Craig Blackstone,1†‡ Jennifer Lippincott-Schwartz3,4†‡ The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle that plays crucial roles in numerous cellular functions. We used emerging superresolution imaging technologies to clarify the morphology and dynamics of the peripheral ER, which contacts and modulates most other intracellular organelles. Peripheral components of the ER have classically been described as comprising both tubules and flat sheets. We show that this system consists almost exclusively of tubules at varying densities, including structures that we term ER matrices. Conventional optical imaging technologies had led to misidentification of these structures as sheets because of the dense clustering of tubular junctions and a previously uncharacterized rapid form of ER motion.The existence of ER matrices explains previous confounding evidence that had indicated the occurrence of ER “sheet” proliferation after overexpression of tubular junction–forming proteins. T he ER is a continuous, membranous net- work extending from the nuclear envelope to the outer periphery of cells; it plays vital roles in processes such as protein synthe- sis and folding, mitochondrial division, cal- cium storage and signaling, and lipid synthesis andtransfer. Inthe cellperiphery,theERisthought to exist as an elaborate membrane system that makes contact with nearly every other cellular organelle. Prevailing models of its structure pro- pose a complex arrangement of interconnected tubules and sheets, each of which is maintained by distinct mechanisms (1, 2). Numerous proteins are involved in maintaining this complex struc- tural organization. Membrane curvature-stabilizing proteins, including members of the reticulon (RTN) and REEP families, contain hydrophobic hairpin domains that are thought to be responsible for promoting curvature in ER tubules via scaffold- ing and hydrophobic wedging. Members of the atlastin (ATL) family of dynamin-related guano- sine triphosphatases (GTPases) are thought to mediate the formation of tubular three-way junc- tions, giving rise to the characteristic polygonal tubular network (3). Meanwhile, an alternative complement of proteins is proposed to regulate the structure of ER sheets, with p180, kinectin, and CLIMP63 all thought to play a role in shaping, helicoidal stacking, and luminal spacing (3). Mu- tations in many of these ER-shaping proteins are connected to a variety of human disease condi- tions, most notably the hereditary spastic pa- raplegias (4). Thus, characterizing ER morphology is critical to understanding the basic biology of cells in both health and disease. Determining the structure of the ER is challeng- ing because of limitations in our ability to visualize the intricate nature of its morphology. The pe- ripheral ER is particularly susceptible to this constraint, given its well-documented dynamic rearrangements and fine ultrastructure (5, 6). These characteristics impede attempts to derive functional information based on changes to ER structure. The recent development of various superresolution (SR) imaging approaches, however, offers an op- portunity to examine ER structure and dynamism with substantially improved spatiotemporalresolu- tion. Here, we used five different SR modalities, each having complementary strengths and weaknesses in the spatial and temporal domains, to examine ER structure and dynamics. A high-speed variation of structured illumination microscopy (SIM) al- lowed ER dynamics to be visualized at unprece- dented speeds and resolution. Three-dimensional SIM (3D-SIM) and Airyscan imaging allowed comparison of the fine distributions of different ER-shaping proteins. Finally, lattice light sheet- point accumulationforimaginginnanoscaletopog- raphy (LLS-PAINT)and focusedionbeam scanning electron microscopy (FIB-SEM) permitted 3D char- acterization of different ER structures. Thoroughly probing the ER in this manner provides unprece- dented information about the morphology and dynamics of this organelle, including the charac- terization of a previously underappreciated struc- ture within the peripheral ER. ER tubules and junctions undergo rapid motion in living cells ER tubules are known to undergo rapid struc- tural rearrangements, occurring over seconds or minutes, yet examination of these processes has typically been confined to the extension and re- traction of tubules and the formation of tubular three-way junctions (5, 6). To obtain a more com- prehensive picture of tubular motion, we used high-speed SIM with grazing incidence illumina- tion (GI-SIM; see materials and methods) (7). This live SR imaging modality (resolution ~100 nm) uses light beams counterpropagating just above the sample substrate to image cellular features near the basal plasma membrane at frequencies up to 40 Hz. This translates to a factor of 4 to 10 increase in acquisition speed, relative to what can be practically achieved with spinning-disk confocal microscopy for imaging the ER, and a factor of ~2 improvement in resolution. With GI-SIM, we imaged COS-7 cells express- ing an ER membrane marker (mEmerald-Sec61b, henceforth Sec61b) to track ER tubules. Increased spatiotemporal resolution revealed a novel form of ER motion consisting of remarkably rapid tu- bular fluctuations (Fig. 1 and movie S1). Using a modified skeletonization algorithm (8) to track the movement of ER tubules (Fig. 1B), we iden- tified oscillations with a mean peak-to-peak am- plitude of 70 ± 50 nm, occurring an average of 4 ± 1 times per second (means ± SD; n = 1755 tubules from 8 cells) (Fig. 1, C and D). Traditional imag- ing modalities have the ability to localize pre- cisely this tubular motion only if the tubules and junctions are sufficiently sparse. Also, a large proportion of this motion often occurs on too short a time scale to be effectively tracked using spinning-diskconfocalmicroscopyatimagingspeeds commonly reported in the literature (6, 9). This suggests that in dense regions, tubular ER motion and morphology are likely to be obscured when using traditional imaging modalities. These rapid fluctuations we observed in COS-7 cells were also found in an unrelated cell type (U-2 OS) as well as in COS-7 cells expressing a luminal ER marker (mEmerald-ER3, henceforth ER3; see materials and methods) instead of Sec61b (Fig. 1, C to E, and tables S1 and S2). In addition to the tubules themselves, three- way junctions also exhibited appreciable motion over very short time scales (Fig. 1, F and G, and movie S1). Three-way junctions were identified from the skeletonization of fluorescent ER images. Skeletonized pixels with exactly two neighbors were considered to be part of a branch (Fig. 1F, white), and pixels with more than two neighbors were considered junctions (Fig. 1F, overlaid with cyan dots)....