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

A High-Resolution Method for Quantitative Molecular Analysis of Functionally Characterized Individual Synapses

Elucidating the molecular mechanisms underlying the functional diversity of synapses requires a high-resolution, sensitive, diffusion-free, quantitative localization method that allows the determination of many proteins in functionally characterized individual synapses. Array tomography permits the quantitative analysis of single synapses but has limited sensitivity, and its application to functionally characterized synapses is challenging. Here, we aim to overcome these limitations by searching the parameter space of different fixation, resin, embedding, etching, retrieval, and elution conditions. Our optimizations reveal that etching epoxy-resin-embedded ultrathin sections with Na-ethanolate and treating them with SDS dramatically increase the labeling efficiency of synaptic proteins. We also demonstrate that this method is ideal for the molecular characterization of individual synapses following paired recordings, two-photon [Ca²⁺] or glutamate-sensor (iGluSnFR) imaging. This method fills a missing gap in the toolbox of molecular and cellular neuroscience, helping us to reveal how molecular heterogeneity leads to diversity in function.

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Etching and antigen retrieval enhance immunoreactions in epoxy-resin-embedded tissue

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Biocytin-filled nerve cells can be visualized in epoxy-resin-embedded tissue

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Molecular composition of functionally characterized individual synapses is revealed

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Multiplexed, postembedding reactions are compatible with STED imaging

Super-resolution microscopy for analyzing neuromuscular junctions and synapses

Super-resolution microscopy techniques offer subdiffraction limited resolution that is two- to ten-fold improved compared to that offered by conventional confocal microscopy. This breakthrough in resolution for light microscopy has contributed to new findings in neuroscience and synapse biology. This review will focus on the Structured Illumination Microscopy (SIM), Stimulated emission depletion (STED) microscopy, and Stochastic optical reconstruction microscopy (STORM) / Single molecule localization microscopy (SMLM) techniques and compare them for the better understanding of their differences and their suitability for the analysis of synapse biology. In addition, we will discuss a few practical aspects of these microscopic techniques, including resolution, image acquisition speed, multicolor capability, and other advantages and disadvantages. Tips for the improvement of microscopy will be introduced; for example, information resources for recommended dyes, the limitations of multicolor analysis, and capabilities for live imaging. In addition, we will summarize how super-resolution microscopy has been used for analyses of neuromuscular junctions and synapses.

Q&A: Array tomography

Array tomography encompasses light and electron microscopy modalities that offer unparalleled opportunities to explore three-dimensional cellular architectures in extremely fine structural and molecular detail. Fluorescence array tomography achieves much higher resolution and molecular multiplexing than most other fluorescence microscopy methods, while electron array tomography can capture three-dimensional ultrastructure much more easily and rapidly than traditional serial-section electron microscopy methods. A correlative fluorescence/electron microscopy mode of array tomography furthermore offers a unique capacity to merge the molecular discrimination strengths of multichannel fluorescence microscopy with the ultrastructural imaging strengths of electron microscopy. This essay samples the first decade of array tomography, highlighting applications in neuroscience.

Characterizing synaptic protein development in human visual cortex enables alignment of synaptic age with rat visual cortex

Although many potential neuroplasticity based therapies have been developed in the lab, few have translated into established clinical treatments for human neurologic or neuropsychiatric diseases. Animal models, especially of the visual system, have shaped our understanding of neuroplasticity by characterizing the mechanisms that promote neural changes and defining timing of the sensitive period. The lack of knowledge about development of synaptic plasticity mechanisms in human cortex, and about alignment of synaptic age between animals and humans, has limited translation of neuroplasticity therapies. In this study, we quantified expression of a set of highly conserved pre- and post-synaptic proteins (Synapsin, Synaptophysin, PSD-95, Gephyrin) and found that synaptic development in human primary visual cortex (V1) continues into late childhood. Indeed, this is many years longer than suggested by neuroanatomical studies and points to a prolonged sensitive period for plasticity in human sensory cortex. In addition, during childhood we found waves of inter-individual variability that are different for the four proteins and include a stage during early development (<1 year) when only Gephyrin has high inter-individual variability. We also found that pre- and post-synaptic protein balances develop quickly, suggesting that maturation of certain synaptic functions happens within the 1 year or 2 of life. A multidimensional analysis (principle component analysis) showed that most of the variance was captured by the sum of the four synaptic proteins. We used that sum to compare development of human and rat visual cortex and identified a simple linear equation that provides robust alignment of synaptic age between humans and rats. Alignment of synaptic ages is important for age-appropriate targeting and effective translation of neuroplasticity therapies from the lab to the clinic.

Unraveling the architecture of the dorsal raphe synaptic neuropil using high-resolution neuroanatomy

The dorsal raphe nucleus (DRN), representing the main source of brain’s serotonin, is implicated in the pathophysiology and therapeutics of several mental disorders that can be debilitating and life-long including depression, anxiety and autism. The activity of DRN neurons is precisely regulated, both phasically and tonically, by excitatory glutamate and inhibitory GABAergic axons arising from extra-raphe areas as well as from local sources within the nucleus. Changes in serotonin neurotransmission associated with pathophysiology may be encoded by alterations within this network of regulatory afferents. However, the complex organization of the DRN circuitry remains still poorly understood. Using a recently developed high-resolution immunofluorescence technique called array tomography (AT) we quantitatively analyzed the relative contribution of different populations of glutamate axons originating from different brain regions to the excitatory drive of the DRN. Additionally, we examined the presence of GABA axons within the DRN and their possible association with glutamate axons. In this review, we summarize our findings on the architecture of the rodent DRN synaptic neuropil using high-resolution neuroanatomy, and discuss possible functional implications for the nucleus. Understanding of the synaptic architecture of neural circuits at high resolution will pave the way to understand how neural structure and function may be perturbed in pathological states.

Systematic, spatial imaging of large multimolecular assemblies and the emerging principles of supramolecular order in biological systems

Understanding biological systems at the level of their relational (emergent) molecular properties in functional protein networks relies on imaging methods, able to spatially resolve a tissue or a cell as a giant, non-random, topologically defined collection of interacting supermolecules executing myriads of subcellular mechanisms. Here, the development and findings of parameter-unlimited functional super-resolution microscopy are described-a technology based on the fluorescence imaging cycler (IC) principle capable of co-mapping thousands of distinct biomolecular assemblies at high spatial resolution and differentiation (<40 nm distances). It is shown that the subcellular and transcellular features of such supermolecules can be described at the compositional and constitutional levels; that the spatial connection, relational stoichiometry, and topology of supermolecules generate hitherto unrecognized functional self-segmentation of biological tissues; that hierarchical features, common to thousands of simultaneously imaged supermolecules, can be identified; and how the resulting supramolecular order relates to spatial coding of cellular functionalities in biological systems. A large body of observations with IC molecular systems microscopy collected over 20 years have disclosed principles governed by a law of supramolecular segregation of cellular functionalities. This pervades phenomena, such as exceptional orderliness, functional selectivity, combinatorial and spatial periodicity, and hierarchical organization of large molecular systems, across all species investigated so far. This insight is based on the high degree of specificity, selectivity, and sensitivity of molecular recognition processes for fluorescence imaging beyond the spectral resolution limit, using probe libraries controlled by ICs.

Retinal connectomics: towards complete, accurate networks

Connectomics is a strategy for mapping complex neural networks based on high-speed automated electron optical imaging, computational assembly of neural data volumes, web-based navigational tools to explore 10(12)-10(15) byte (terabyte to petabyte) image volumes, and annotation and markup tools to convert images into rich networks with cellular metadata. These collections of network data and associated metadata, analyzed using tools from graph theory and classification theory, can be merged with classical systems theory, giving a more completely parameterized view of how biologic information processing systems are implemented in retina and brain. Networks have two separable features: topology and connection attributes. The first findings from connectomics strongly validate the idea that the topologies of complete retinal networks are far more complex than the simple schematics that emerged from classical anatomy. In particular, connectomics has permitted an aggressive refactoring of the retinal inner plexiform layer, demonstrating that network function cannot be simply inferred from stratification; exposing the complex geometric rules for inserting different cells into a shared network; revealing unexpected bidirectional signaling pathways between mammalian rod and cone systems; documenting selective feedforward systems, novel candidate signaling architectures, new coupling motifs, and the highly complex architecture of the mammalian AII amacrine cell. This is but the beginning, as the underlying principles of connectomics are readily transferrable to non-neural cell complexes and provide new contexts for assessing intercellular communication.

Building retinal connectomes

Understanding vertebrate vision depends on knowing, in part, the complete network graph of at least one representative retina. Acquiring such graphs is the business of synaptic connectomics, emerging as a practical technology due to improvements in electron imaging platform control, management software for large-scale datasets, and availability of data storage. The optimal strategy for building complete connectomes uses transmission electron imaging with 2 nm or better resolution, molecular tags for cell identification, open-access data volumes for navigation, and annotation with open-source tools to build 3D cell libraries, complete network diagrams and connectivity databases. The first forays into retinal connectomics have shown that even nominally well-studied cells have much richer connection graphs than expected.