An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy

The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile.     

Computational methods for ultrastructural analysis of synaptic complexes

Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.     

The gain in brain: novel imaging techniques and multiplexed proteomic imaging of brain tissue ultrastructure

The rapid accumulation of neuroproteomics data in recent years has prompted the emergence of novel antibody-based imaging methods that aim to understand the anatomical and functional context of the multitude of identified proteins. The pioneering field of ultrastructural multiplexed proteomic imaging now includes a number of high resolution methods, such as array tomography, stimulated emission depletion microscopy, stochastic optical reconstruction microscopy and automated transmission electron microscopy, which allow a detailed molecular characterization of individual synapses and subsynaptic structures within brain tissues for the first time. While all of these methods still face considerable limitations, a combined complementary approach building on the respective strengths of each method is possible and will enable fascinating research into the proteomic diversity of the nervous system.     

Volume electron microscopy for neuronal circuit reconstruction

The last decade has seen a rapid increase in the number of tools to acquire volume electron microscopy (EM) data. Several new scanning EM (SEM) imaging methods have emerged, and classical transmission EM (TEM) methods are being scaled up and automated. Here we summarize the new methods for acquiring large EM volumes, and discuss the tradeoffs in terms of resolution, acquisition speed, and reliability. We then assess each method's applicability to the problem of reconstructing anatomical connectivity between neurons, considering both the current capabilities and future prospects of the method. Finally, we argue that neuronal 'wiring diagrams' are likely necessary, but not sufficient, to understand the operation of most neuronal circuits: volume EM imaging will likely find its best application in combination with other methods in neuroscience, such as molecular biology, optogenetics, and physiology.     

Superresolution imaging of chemical synapses in the brain

Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here, we present a superresolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of?ten protein components of the presynaptic active?zone and the postsynaptic density. Variations?in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different?brain regions. Combination with optogenetics further?allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level.     

Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain

Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1-3] and the locations of coarser structures like cortical areas in complex vertebrate brains [4]. However, it remains unclear whether wiring economy can explain the placement of individual neurons in brains larger than that of C.?elegans. Indeed, given the greater number of neuronal interconnections in larger brains, simply minimizing the length of connections results in unrealistic configurations, with multiple neurons occupying the same position in space. Avoiding such configurations, or volume exclusion, repels neurons from each other, thus counteracting wiring economy. Here we test whether wiring economy together with volume exclusion can explain the placement of neurons in a module of the Drosophila melanogaster brain known as lamina cartridge [5-13]. We used newly developed techniques for semiautomated reconstruction from serial electron microscopy (EM) [14] to obtain the shapes of neurons, the location of synapses, and the resultant synaptic connectivity. We show that wiring length minimization and volume exclusion together can explain the structure of the lamina microcircuit. Therefore, even in brains larger than that of C.?elegans, at least for some circuits, optimization can play an important role in individual neuron placement.     

Single particle reconstruction of membrane proteins: A tool for understanding the 3D structure of disease-related macromolecules

Membrane proteins play important roles in cell functions such as neurotransmission, muscle contraction, and hormone secretion, but their structures are mostly undetermined. Several techniques have been developed to elucidate the structure of macromolecules; X-ray or electron crystallography, nuclear magnetic resonance spectroscopy, and high-resolution electron microscopy. Electron microscopy-based single particle reconstruction, a computer-aided structure determination method, reconstructs a three-dimensional (3D) structure from projections of monodispersed protein. A large number of particle images are picked up from EM films, aligned and classified to generate two-dimensional (2D) averages, and, using the Euler angle of each 2D average, reconstructed into a 3D structure. This method is challenging due to the necessity for close collaboration between classical biochemistry and innovative information technology, including parallel computing. However, recent progress in electron microscopy, mathematical algorithms, and computational ability has greatly increased the subjects that are considered to be primarily addressable using single particle reconstruction. Membrane proteins are one of these targets to which the single particle reconstruction is successfully applied for understanding of their structures. In this paper, we will introduce recently reconstructed channel-related proteins and discuss the applicability of this technique in understanding molecular structures and their roles in pathology.     

The integrative role of cryo electron microscopy in molecular and cellular structural biology

After gradually moving away from preparation methods prone to artefacts such as plastic embedding and negative staining for cell sections and single particles, the field of cryo electron microscopy (cryo‐EM) is now heading off at unprecedented speed towards high‐resolution analysis of biological objects of various sizes. This ‘revolution in resolution’ is happening largely thanks to new developments of new‐generation cameras used for recording the images in the cryo electron microscope which have much increased sensitivity being based on complementary metal oxide semiconductor devices. Combined with advanced image processing and 3D reconstruction, the cryo‐EM analysis of nucleoprotein complexes can provide unprecedented insights at molecular and atomic levels and address regulatory mechanisms in the cell. These advances reinforce the integrative role of cryo‐EM in synergy with other methods such as X‐ray crystallography, fluorescence imaging or focussed‐ion beam milling as exemplified here by some recent studies from our laboratory on ribosomes, viruses, chromatin and nuclear receptors. Such multi‐scale and multi‐resolution approaches allow integrating molecular and cellular levels when applied to purified or in situ macromolecular complexes, thus illustrating the trend of the field towards cellular structural biology.     

Derivation of a field equation of brain activity

We present a nonlinear field theory of the brain under realistic anatomical connectivity conditions describing the interaction between functional units within the brain. This macroscopic field theory is derived from the quasi-microscopic conversion properties of neural populations occurring at synapses and somas. Functional units are treated as inhomogeneities within a nonlinear neural tissue.     

The role of neurexins and neuroligins in the formation, maturation, and function of vertebrate synapses

Neurexins (NXs) and neuroligins (NLs) are transsynaptically interacting cell adhesion proteins that play a key role in the formation, maturation, activity-dependent validation, and maintenance of synapses. As complex alternative splicing processes in nerve cells generate a large number of NX and NLs variants, it has been proposed that a combinatorial interaction code generated by these variants may determine synapse identity and network connectivity during brain development. The functional importance of NXs and NLs is exemplified by the fact that mutations in NX and NL genes are associated with several neuropsychiatric disorders, most notably with autism. Accordingly, major research efforts have focused on the molecular mechanisms by which NXs and NLs operate at synapses. In this review, we summarize recent progress in this field and discuss emerging topics, such as the role of alternative interaction partners of NXs and NLs in synapse formation and function, and their relevance for synaptic plasticity in the mature brain. The novel findings highlight the fundamental importance of NX-NL interactions in a wide range of synaptic functions.     

Volume scanning electron microscopy for imaging biological ultrastructure

Electron microscopy (EM) has been a key imaging method to investigate biological ultrastructure for over six decades. In recent years, novel volume EM techniques have significantly advanced nanometre‐scale imaging of cells and tissues in three dimensions. Previously, this had depended on the slow and error‐prone manual tasks of cutting and handling large numbers of sections, and imaging them one‐by‐one with transmission EM. Now, automated volume imaging methods mostly based on scanning EM (SEM) allow faster and more reliable acquisition of serial images through tissue volumes and achieve higher z‐resolution. Various software tools have been developed to manipulate the acquired image stacks and facilitate quantitative analysis. Here, we introduce three volume SEM methods: serial block‐face electron microscopy (SBEM), focused ion beam SEM (FIB‐SEM) and automated tape‐collecting ultramicrotome SEM (ATUM‐SEM). We discuss and compare their capabilities, provide an overview of the full volume SEM workflow for obtaining 3D datasets and showcase different applications for biological research.     

Expansion microscopy facilitates quantitative super-resolution studies of cytoskeletal structures in kinetoplastid parasites

Expansion microscopy (ExM) has become a powerful super-resolution method in cell biology. It is a simple, yet robust approach, which does not require any instrumentation or reagents beyond those present in a standard microscopy facility. In this study, we used kinetoplastid parasites Trypanosoma brucei and Leishmania major, which possess a complex, yet well-defined microtubule-based cytoskeleton, to demonstrate that this method recapitulates faithfully morphology of structures as previously revealed by a combination of sophisticated electron microscopy (EM) approaches. Importantly, we also show that due to the rapidness of image acquisition and three-dimensional reconstruction of cellular volumes ExM is capable of complementing EM approaches by providing more quantitative data. This is demonstrated on examples of less well-appreciated microtubule structures, such as the neck microtubule of T. brucei or the pocket, cytosolic and multivesicular tubule-associated microtubules of L. major. We further demonstrate that ExM enables identifying cell types rare in a population, such as cells in mitosis and cytokinesis. Three-dimensional reconstruction of an entire volume of these cells provided details on the morphology of the mitotic spindle and the cleavage furrow. Finally, we show that established antibody markers of major cytoskeletal structures function well in ExM, which together with the ability to visualize proteins tagged with small epitope tags will facilitate studies of the kinetoplastid cytoskeleton.     

The orthogonal tilt reconstruction method: an approach to generating single-class volumes with no missing cone for ab initio reconstruction of asymmetric particles

Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the "missing cone" problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting data at +45 degrees and -45 degrees tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted data set is used for alignment and classification and the other set--which provides views orthogonal to those in the first--is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a heterogeneous data set and identifying the most homogeneous volumes.     

Evolution of primary microcephaly genes and the enlargement of primate brains

Brain size, in relation to body size, has varied markedly during the evolution of mammals. In particular, a large cerebral cortex is a feature that distinguishes humans from our fellow primates. Such anatomical changes must have a basis in genetic alterations, but the molecular processes involved have yet to be defined. However, recent advances from the cloning of two human disease genes promise to make inroads in this important area. Microcephalin (MCPH1) and Abnormal spindle-like microcephaly associated (ASPM) are genes mutated in primary microcephaly, a human neurodevelopmental disorder. In this 'atavistic' condition, brain size is reduced in volume to a size comparable with that of early hominids. Hence, it has been proposed that these genes evolved adaptively with increasing primate brain size. Subsequent studies have lent weight to this hypothesis by showing that both genes have undergone positive selection during great ape evolution. Further functional characterisation of their proteins will contribute to an understanding of the molecular and evolutionary processes that have determined human brain size.     

Dynamic functional connectivity in the static connectome of Caenorhabditis elegans

A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.     

On Optical Detection of Densely Labeled Synapses in Neuropil and Mapping Connectivity with Combinatorially Multiplexed Fluorescent Synaptic Markers

We propose a new method for mapping neural connectivity optically, by utilizing Cre/Lox system Brainbow to tag synapses of different neurons with random mixtures of different fluorophores, such as GFP, YFP, etc., and then detecting patterns of fluorophores at different synapses using light microscopy (LM). Such patterns will immediately report the pre- and post-synaptic cells at each synaptic connection, without tracing neural projections from individual synapses to corresponding cell bodies. We simulate fluorescence from a population of densely labeled synapses in a block of hippocampal neuropil, completely reconstructed from electron microscopy data, and show that high-end LM is able to detect such patterns with over 95% accuracy. We conclude, therefore, that with the described approach neural connectivity in macroscopically large neural circuits can be mapped with great accuracy, in scalable manner, using fast optical tools, and straightforward image processing. Relying on an electron microscopy dataset, we also derive and explicitly enumerate the conditions that should be met to allow synaptic connectivity studies with high-resolution optical tools.     

Emergent functional properties of neuronal networks with controlled topology

The interplay between anatomical connectivity and dynamics in neural networks plays a key role in the functional properties of the brain and in the associated connectivity changes induced by neural diseases. However, a detailed experimental investigation of this interplay at both cellular and population scales in the living brain is limited by accessibility. Alternatively, to investigate the basic operational principles with morphological, electrophysiological and computational methods, the activity emerging from large in vitro networks of primary neurons organized with imposed topologies can be studied. Here, we validated the use of a new bio-printing approach, which effectively maintains the topology of hippocampal cultures in vitro and investigated, by patch-clamp and MEA electrophysiology, the emerging functional properties of these grid-confined networks. In spite of differences in the organization of physical connectivity, our bio-patterned grid networks retained the key properties of synaptic transmission, short-term plasticity and overall network activity with respect to random networks. Interestingly, the imposed grid topology resulted in a reinforcement of functional connections along orthogonal directions, shorter connectivity links and a greatly increased spiking probability in response to focal stimulation. These results clearly demonstrate that reliable functional studies can nowadays be performed on large neuronal networks in the presence of sustained changes in the physical network connectivity.     

Dendritic Nonlinearities Reduce Network Size Requirements and Mediate ON and OFF States of Persistent Activity in a PFC Microcircuit Model

Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC.     

Computational methods and challenges for large-scale circuit mapping

The connectivity architecture of neuronal circuits is essential to understand how brains work, yet our knowledge about the neuronal wiring diagrams remains limited and partial. Technical breakthroughs in labeling and imaging methods starting more than a century ago have advanced knowledge in the field. However, the volume of data associated with imaging a whole brain or a significant fraction thereof, with electron or light microscopy, has only recently become amenable to digital storage and analysis. A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1mm(3) of the brain at EM resolution is about half a petabyte. This has given rise to a new field of research, computational analysis of large-scale neuroanatomical data sets, with goals that include reconstructions of the morphology of individual neurons as well as entire circuits. The problems encountered include large data management, segmentation and 3D reconstruction, computational geometry and workflow management allowing for hybrid approaches combining manual and algorithmic processing. Here we review this growing field of neuronal data analysis with emphasis on reconstructing neurons from EM data cubes.     

Dendritic spines and linear networks

The function of the cortical microcircuitry is still mysterious. Using a bottom-up analysis based on the biophysics and connectivity of cortical neurons, we propose the hypothesis that the neocortex is essentially a linear integrator of inputs. Dendritic spines would slow the neuron and contribute to linearize input summation. Since excitatory axons are relatively straight, they appeared designed to help disperse information to a large number of recipient neurons, generating a distributed circuit. A linear summation regime will ensure the full benefit of a distributed connectivity matrix. Linear integration could also help the neocortex decode the sensory world and may have additional computational advantages. In this view, spines would be the anatomical signature of linear networks.