Developing a safe and effective vaccine will be the principal way of controlling the COVID-19 pandemic. However, current COVID-19 vaccination trials are not adequately representing a diverse participant population in terms of age, ethnicity and comorbidities. Achieving the representative recruitment targets that are adequately powered to the study remains one of the greatest challenges in clinical trial management. To ensure accuracy and generalisability of the safety and efficacy conclusions generated by clinical trials, it is crucial to recruit patient cohorts as representative as possible of the future target population. Missing these targets can lead to reduced validity of the study results and can often slow down drug development leading to costly delays.

Mammalian cortex features a large diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity rules powerfully shape how each cell type participates in the cortical circuit, but comprehensively mapping connectivity at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy to investigate the connectivity of inhibitory neurons across a dense neuronal population spanning all layers of mouse visual cortex with synaptic resolution. We classified all 1183 excitatory neurons within a 100 micron column into anatomical subclasses using quantitative morphological and synapse features based on full dendritic reconstructions, finding both familiar subclasses corresponding to axonal projections and novel intralaminar distinctions based on synaptic properties. To relate these subclasses to single-cell connectivity, we reconstructed all 164 inhibitory interneurons in the same column, producing a wiring diagram of inhibition with more than 70,000 synapses. We found widespread cell-type-specific inhibition, including interneurons selectively targeting certain excitatory subpopulations among spatially intermingled neurons in layer 2/3, layer 5, and layer 6. Globally, inhibitory connectivity was organized into "motif groups," heterogeneous collections of cells that collectively target both perisomatic and dendritic compartments of the same combinations of excitatory subtypes. We also discovered a novel category of disinhibitory-specialist interneuron that preferentially targets basket cells. Collectively, our analysis revealed new organizing principles for cortical inhibition and will serve as a powerful foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.

One of the main drivers within the field of bottom-up synthetic biology is to develop artificial chemical machines, perhaps even living systems, that have programmable functionality. Numerous toolkits exist to generate giant unilamellar vesicle-based artificial cells. However, methods able to quantitatively measure their molecular constituents upon formation is an underdeveloped area. We report an artificial cell quality control (AC/QC) protocol using a microfluidic-based single-molecule approach, enabling the absolute quantification of encapsulated biomolecules. While the measured average encapsulation efficiency was 11.4 ± 6.8%, the AC/QC method allowed us to determine encapsulation efficiencies per vesicle, which varied significantly from 2.4 to 41%. We show that it is possible to achieve a desired concentration of biomolecule within each vesicle by commensurate compensation of its concentration in the seed emulsion. However, the variability in encapsulation efficiency suggests caution is necessary when using such vesicles as simplified biological models or standards.

Heterotrimeric G-proteins are ubiquitously expressed in several cancers, and they transduce signals from activated G-protein coupled receptors. These proteins have numerous biological functions, and they are becoming interesting target molecules in cancer therapy. Previously, we have shown that heterotrimeric G-protein subunit alphai2 (Gαi2) has an essential role in the migration and invasion of prostate cancer cells. Using a structure-based approach, we have synthesized optimized small molecule inhibitors that are able to prevent specifically the activation of the Gαi2 subunit, keeping the protein in its inactive GDP-bound state. We observed that two of the compounds (13 and 14) at 10 μΜ significantly inhibited the migratory behavior of the PC3 and DU145 prostate cancer cell lines. Additionally, compound 14 at 10 μΜ blocked the activation of Gαi2 in oxytocin-stimulated prostate cancer PC3 cells, and inhibited the migratory capability of DU145 cells overexpressing the constitutively active form of Gαi2, under basal and EGF-stimulated conditions. We also observed that the knockdown or inhibition of Gαi2 negatively regulated migration of renal and ovarian cancer cell lines. Our results suggest that small molecule inhibitors of Gαi2 have potential as leads for discovering novel anti-metastatic agents for attenuating the capability of cancer cells to spread and invade to distant sites.

Information from the retina is carried along the visual pathway with accuracy and spatial conservation as a result of topographically mapped axonal connections. The optic tectum in the midbrain is the primary region to which retinal ganglion cells project their axons in the chick. The two primary axes of the retina project independently onto the tectum using different sets of guidance cues to give rise to the retinotectal map. Specificity of the map is determined by attractive or repulsive interactions between molecular tags that are distributed in gradients in the retina and the tectum. Despite several studies, knowledge of the retinotectal guidance molecules is far from being complete. We screened for all molecules that are expressed differentially along the anterior-posterior and medial-lateral axes of the chick tectum using microarray based transcriptional profiling and identified several novel candidate retinotectal guidance molecules. Two such genes, encoding Wnt5a and Raldh2, the synthesizing enzymes for retinoic acid, were further analyzed for their function as putative regulators of retinotectal map formation. Wnt5a and retinoic acid were found to exhibit differential effects on the growth of axons from retinal explants derived from different quadrants of the retina. This screen also yielded a large number of genes expressed in a lamina-specific manner in the tectum, which may have other roles in tectal development. J. Comp. Neurol. 525:459-477, 2017. © 2016 Wiley Periodicals, Inc.

Amphid sensilla are the primary olfactory, chemoreceptive, and thermoreceptive organs in nematodes. Their function is well described for the model organism Caenorhabditis elegans, but it is not clear to what extent we can generalize these findings to distantly related nematodes of medical, economic, and agricultural importance. Current detailed descriptions of anatomy and sensory function are limited to nematodes that recent molecular phylogenies would place in the same taxonomic family, the Rhabditidae. Using serial thin-section transmission electron microscopy, we reconstructed the anatomy of the amphid sensilla in the more distantly related nematode, Acrobeles complexus (Cephalobidae). Amphid structure is broadly conserved in number and arrangement of cells. Details of cell anatomy differ, particularly for the sensory neurite termini. We identify an additional sensory neuron not found in the amphid of C. elegans and propose homology with the C. elegans interneuron AUA. Hypotheses of homology for the remaining sensory neurons are also proposed based on comparisons between C. elegans, Strongyloides stercoralis, and Haemonchus contortus.

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.

To determine the seroprevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antibody status amongst healthcare workers (HCWs) working through the first wave of the Coronavirus (COVID-19) pandemic in 2020. To examine the association of seroprevalence and self-reported COVID-19 symptoms with occupation, sex, and ethnicity; and how these factors were associated with physical and mental wellbeing.

Serial section electron microscopy (ssEM), a technique where volumes of tissue can be anatomically reconstructed by imaging consecutive tissue slices, has proven to be a powerful tool for the investigation of brain anatomy. Between the process of cutting the slices, or "sections," and imaging them, however, handling 10°-106 delicate sections remains a bottleneck in ssEM, especially for batches in the "mesoscale" regime, i.e., 102-103 sections. We present a tissue section handling device that transports and positions sections, accurately and repeatability, for automated, robotic section pick-up and placement onto an imaging substrate. The device interfaces with a conventional ultramicrotomy diamond knife, accomplishing in-line, exact-constraint trapping of sections with 100-μm repeatability. An associated mathematical model includes capillary-based and Stokes-based forces, accurately describing observed behavior and fundamentally extends the modeling of water-air interface forces. Using the device, we demonstrate and describe the limits of reliable handling of hundreds of slices onto a variety of electron and light microscopy substrates without significant defects (n = 8 datasets composed of 126 serial sections in an automated fashion with an average loss rate and throughput of 0.50% and 63 s/section, respectively. In total, this work represents an automated mesoscale serial sectioning system for scalable 3D-EM connectomics.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response 5 properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-10 function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video 15 stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feed-20 back connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the 25 spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and 30 function.

Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in "batches." Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (~40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 ± 110 μm (1 s.d.), y-direction: 60 ± 150 μm (1 s.d.)) with a mean cycle time of 43 s ± 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies.

Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months. Over 26,500 ultrathin tissue sections from the same block were imaged, yielding a dataset of more than 2 petabytes. The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpixel per sec with six microscopes running in parallel. This work demonstrates the feasibility of acquiring EM datasets at the scale of cortical microcircuits in multiple brain regions and species.

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

The type 2 iodothyronine deiodinase (D2) converts the pro-hormone thyroxine into T3 within target tissues. D2 is essential for a full thermogenic response of brown adipose tissue (BAT), and mice with a disrupted Dio2 gene (D2KO) have an impaired response to cold. BAT is also activated by overfeeding.

The nematodes C. elegans and P. pacificus populate diverse habitats and display distinct patterns of behavior. To understand how their nervous systems have diverged, we undertook a detailed examination of the neuroanatomy of the chemosensory system of P. pacificus. Using independent features such as cell body position, axon projections and lipophilic dye uptake, we have assigned homologies between the amphid neurons, their first-layer interneurons, and several internal receptor neurons of P. pacificus and C. elegans. We found that neuronal number and soma position are highly conserved. However, the morphological elaborations of several amphid cilia are different between them, most notably in the absence of 'winged' cilia morphology in P. pacificus. We established a synaptic wiring diagram of amphid sensory neurons and amphid interneurons in P. pacificus and found striking patterns of conservation and divergence in connectivity relative to C. elegans, but very little changes in relative neighborhood of neuronal processes. These findings demonstrate the existence of several constraints in patterning the nervous system and suggest that major substrates for evolutionary novelty lie in the alterations of dendritic structures and synaptic connectivity.

A key challenge towards a successful COVID-19 vaccine uptake is vaccine hesitancy. We examine and provide novel insights on the key drivers and barriers towards COVID-19 vaccine uptake.

This study aims to determine differences in the on- and off-tumor microbiota between patients with right- and left-sided colorectal cancer. Microbiome profiling of tumor and tumor-adjacent biopsies from patients with right-sided (n = 17) and left-sided (n = 7) colorectal adenocarcinoma was performed using 16S ribosomal RNA sequencing. Off-tumor alpha and beta diversity were significantly different between right- and left-sided colorectal cancer patients. However, no differences in on-tumor diversity were observed between tumor locations. Comparing the off-tumor microbiota showed the right colon to be enriched with species of the Lachnoclostridium, Selenomonas, and Ruminococcus genera. Whereas the left colon is enriched with Epsilonbacteraeota phylum, Campylobacteria class, and Pasteurellales and Campylobacterales orders, in contrast, the on-tumor microbiota showed relatively fewer differences in bacterial taxonomy between tumor sites, with left tumors being enriched with Methylophilaceae and Vadin BE97 families and Alloprevotella, Intestinibacter, Romboutsia, and Ruminococcus 2 genera. Patients with left-sided colorectal cancer had large taxonomic differences between their paired on- and off-tumor microbiota, while patients with right-sided colorectal cancer showed relatively fewer taxonomic differences. Collectively, this suggests that the right and left colon show distinctive bacterial populations; however, the presence of a colonic tumor leads to a more consistent microbiota between locations.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.