Specificity of cortical synaptic connectivity: Emphasis on perspectives gained from quantitative electron microscopy

This report traces the historical development of concepts regarding the specificity of synaptic connectivity in the cerebral cortex as viewed primarily from the perspective of electron microscopy. The occurrence of stereotypical patterns of connection (e.g., contrasting synaptic patterns on the surfaces of spiny vs. non-spiny neurons, the general consistency with which axonal pathways impinge on and originate within specific cortical areas and layers, triadic synaptic relationships) implies that cortical connectivity is highly structured. The high degree of order characterizing many aspects of cortical organization is mirrored by an equally ordered arrangement of synaptic connections between specific types of neurons. This observation is based on quantitative electron microscopic studies of synapses between identified neurons and from the results of correlative anatomical/electrophysiological investigations. The recognition of recurring synaptic patterns and responses between specific neurons has generated increased support for the notion of specificity of synaptic connections at the expense of randomness, but the role of specificity in cortical function is an unresolved question. At the core of cortical processing lie myriad possibilities for computation provided by the wealth of synaptic connections involving each cortical neuron. Specificity, by limiting possibilities for connection, can impose an order on synaptic interactions even as processes of dynamic selection or synaptic remodeling ensure the constant formation and dissolution of cortical circuits. These operations make maximal use of the richness of cortical synaptic connections to produce a highly flexible system, irrespective of the degree of randomness or specificity that obtains for cortical wiring at any particular time.     

Correlative 3D imaging of whole mammalian cells with light and electron microscopy

We report methodological advances that extend the current capabilities of ion-abrasion scanning electron microscopy (IA-SEM), also known as focused ion beam scanning electron microscopy, a newly emerging technology for high resolution imaging of large biological specimens in 3D. We establish protocols that enable the routine generation of 3D image stacks of entire plastic-embedded mammalian cells by IA-SEM at resolutions of ∼10–20 nm at high contrast and with minimal artifacts from the focused ion beam. We build on these advances by describing a detailed approach for carrying out correlative live confocal microscopy and IA-SEM on the same cells. Finally, we demonstrate that by combining correlative imaging with newly developed tools for automated image processing, small 100 nm-sized entities such as HIV-1 or gold beads can be localized in SEM image stacks of whole mammalian cells. We anticipate that these methods will add to the arsenal of tools available for investigating mechanisms underlying host-pathogen interactions, and more generally, the 3D subcellular architecture of mammalian cells and tissues.     

Pleural space thickness in situ by light microscopy in five mammalian species

The thickness of the pleural space was measured by a focusing method using a light microscope (X157, 2.5-micron depth of focus). In anesthetized animals, thin transparent parietal pleural windows were made by dissection of intercostal muscle. Multiple postmortem measurements were made of the combined thickness of the pleural space and the window by focusing in sequence on the lung surface and on 1- to 2-micron tantulum particles sprayed on the window. The window thickness was measured after creating a pneumothorax and retracting the lungs. In supine rabbits the pleural space measured at various heights on the costal surface was of uniform thickness (16 micron) except for a thicker region (62 micron) located within 3 mm of the most dependent part of the lung. The thicker region reverted to the uniform thickness after it was placed in a nondependent position by inverting the animal from the supine to prone position, indicating fluid drainage by gravity. In the prone position near midchest, pleural space thickness (t) averaged 6.9 micron in the mouse, 10.2 in the rat, 17.2 in the rabbit, 18.3 in the cat, and 23.6 in the dog. Animals of larger body mass (M, kg) had a wider pleural space: t = 13.1 X M0.20. There was no contact between the two pleurae, indicating that fluid lubrication facilitates sliding between the lung and chest wall. Based on the t vs. M relationship and estimates of the viscous flow of pleural liquid, pleural fluid exchange rate would be proportional to body mass and the work of sliding as a fraction of the work of breathing would be smaller in larger animals.     

EGFR signaling modulates synaptic connectivity via Gurken

Synaptic target selection is critical for establishing functional neuronal circuits. The mechanisms regulating target selection remain incompletely understood. We describe a role for the EGF receptor and its ligand Gurken in target selection of octopaminergic Type II neurons in the Drosophila neuromuscular system. Mutants in happyhour, a regulator of EGFR signaling, form ectopic Type II neuromuscular junctions. These ectopic innervations are due to inappropriate target selection. We demonstrate that EGFR signaling is necessary and sufficient to inhibit synaptic target selection by these octopaminergic Type II neurons, and that the EGFR ligand Gurken is the postsynaptic, muscle-derived repulsive cue. These results identify a new pathway mediating cell-type and branch-specific synaptic repulsion, a novel role for EGFR signaling in synaptic target selection, and an unexpected role for Gurken as a muscle-secreted repulsive ligand.     

Geometry and structural plasticity of synaptic connectivity

Changes in synaptic connectivity patterns through the formation and elimination of dendritic spines may contribute to structural plasticity in the brain. We characterize this contribution quantitatively by estimating the number of different synaptic connectivity patterns attainable without major arbor remodeling. This number depends on the ratio of the synapses on a dendrite to the axons that pass within a spine length of that dendrite. We call this ratio the filling fraction and calculate it from geometrical analysis and anatomical data. The filling fraction is 0.26 in mouse neocortex, 0.22-0.34 in rat hippocampus. In the macaque visual cortex, the filling fraction increases by a factor of 1.6-1.8 from area V1 to areas V2, V4, and 7a. Since the filling fraction is much smaller than 1, spine remodeling can make a large contribution to structural plasticity.     

Distinguishing signal from autofluorescence in cryogenic correlated light and electron microscopy of mammalian cells

In cryogenic correlated light and electron microscopy (cryo-CLEM), frozen targets of interest are identified and located on EM grids by fluorescence microscopy and then imaged at higher resolution by cryo-EM. Whilst working with these methods, we discovered that a variety of mammalian cells exhibit strong punctate autofluorescence when imaged under cryogenic conditions (80?K). Autofluorescence originated from multilamellar bodies (MLBs) and secretory granules. Here we describe a method to distinguish fluorescent protein tags from these autofluorescent sources based on the narrower emission spectrum of the former. The method is first tested on mitochondria and then applied to examine the ultrastructural variability of secretory granules within insulin-secreting pancreatic beta-cell-derived INS-1E cells.     

Fast state-space methods for inferring dendritic synaptic connectivity

We present fast methods for filtering voltage measurements and performing optimal inference of the location and strength of synaptic connections in large dendritic trees. Given noisy, subsampled voltage observations we develop fast l ₁-penalized regression methods for Kalman state-space models of the neuron voltage dynamics. The value of the l ₁-penalty parameter is chosen using cross-validation or, for low signal-to-noise ratio, a Mallows’ C _p -like criterion. Using low-rank approximations, we reduce the inference runtime from cubic to linear in the number of dendritic compartments. We also present an alternative, fully Bayesian approach to the inference problem using a spike-and-slab prior. We illustrate our results with simulations on toy and real neuronal geometries. We consider observation schemes that either scan the dendritic geometry uniformly or measure linear combinations of voltages across several locations with random coefficients. For the latter, we show how to choose the coefficients to offset the correlation between successive measurements imposed by the neuron dynamics. This results in a “compressed sensing” observation scheme, with an important reduction in the number of measurements required to infer the synaptic weights.     

Genome architecture enables local adaptation of Atlantic cod despite high connectivity

Adaptation to local conditions is a fundamental process in evolution; however, mechanisms maintaining local adaptation despite high gene flow are still poorly understood. Marine ecosystems provide a wide array of diverse habitats that frequently promote ecological adaptation even in species characterized by strong levels of gene flow. As one example, populations of the marine fish Atlantic cod (Gadus morhua) are highly connected due to immense dispersal capabilities but nevertheless show local adaptation in several key traits. By combining population genomic analyses based on 12K single nucleotide polymorphisms with larval dispersal patterns inferred using a biophysical ocean model, we show that Atlantic cod individuals residing in sheltered estuarine habitats of Scandinavian fjords mainly belong to offshore oceanic populations with considerable connectivity between these diverse ecosystems. Nevertheless, we also find evidence for discrete fjord populations that are genetically differentiated from offshore populations, indicative of local adaptation, the degree of which appears to be influenced by connectivity. Analyses of the genomic architecture reveal a significant overrepresentation of a large ~5 Mb chromosomal rearrangement in fjord cod, previously proposed to comprise genes critical for the survival at low salinities. This suggests that despite considerable connectivity with offshore populations, local adaptation to fjord environments may be enabled by suppression of recombination in the rearranged region. Our study provides new insights into the potential of local adaptation in high gene flow species within fine geographical scales and highlights the importance of genome architecture in analyses of ecological adaptation.     

"Nanosized voltmeter" enables cellular-wide electric field mapping

Previously, all biological measurements of intracellular electric fields (E fields), using voltage dyes or patch/voltage clamps, were confined to cellular membranes, which account for <0.1% of the total cellular volume. These membrane-dependent techniques also frequently require lengthy calibration steps for each cell or cell type measured. A new 30-nm "photonic voltmeter", 1000-fold smaller than existing voltmeters, enables, to our knowledge, the first complete three-dimensional E field profiling throughout the entire volume of living cells. These nanodevices are calibrated externally and then applied for E field determinations inside any live cell or cellular compartment, with no further calibration steps. The results indicate that the E fields from the mitochondrial membranes penetrate much deeper into the cytosol than previously estimated, indicating that, electrically, the cytoplasm cannot be described as a simple homogeneous solution, as often approximated, but should rather be thought of as a complex, heterogeneous hydrogel, with distinct microdomains.     

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

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A phenomenological theory of spatially structured local synaptic connectivity

The structure of local synaptic circuits is the key to understanding cortical function and how neuronal functional modules such as cortical columns are formed. The central problem in deciphering cortical microcircuits is the quantification of synaptic connectivity between neuron pairs. I present a theoretical model that accounts for the axon and dendrite morphologies of pre- and postsynaptic cells and provides the average number of synaptic contacts formed between them as a function of their relative locations in three-dimensional space. An important aspect of the current approach is the representation of a complex structure of an axonal/dendritic arbor as a superposition of basic structures—synaptic clouds. Each cloud has three structural parameters that can be directly estimated from two-dimensional drawings of the underlying arbor. Using empirical data available in literature, I applied this theory to three morphologically different types of cell pairs. I found that, within a wide range of cell separations, the theory is in very good agreement with empirical data on (i) axonal–dendritic contacts of pyramidal cells and (ii) somatic synapses formed by the axons of inhibitory interneurons. Since for many types of neurons plane arborization drawings are available from literature, this theory can provide a practical means for quantitatively deriving local synaptic circuits based on the actual observed densities of specific types of neurons and their morphologies. It can also have significant implications for computational models of cortical networks by making it possible to wire up simulated neural networks in a realistic fashion.     

Multivariate phenotype analysis enables genome-wide inference of mammalian gene function

The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines.There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype–gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene–phenotype map with good sensitivity.A factor analysis of the MV model’s fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.

The function of the majority of genes in the human and mouse genomes is unknown, and illuminating this "dark genome" is a major challenge for the biomedical sciences. This study shows that multi-dimensional phenotypes from single-gene knockout mouse lines can be analysed at a genome-wide scale both to increase power and infer missing phenotypes.     

High-density mapping of single-molecule trajectories with photoactivated localization microscopy

We combined photoactivated localization microscopy (PALM) with live-cell single-particle tracking to create a new method termed sptPALM. We created spatially resolved maps of single-molecule motions by imaging the membrane proteins Gag and VSVG, and obtained several orders of magnitude more trajectories per cell than traditional single-particle tracking enables. By probing distinct subsets of molecules, sptPALM can provide insight into the origins of spatial and temporal heterogeneities in membranes.