Biology subjects

Lee, K.

Publications and source records attributed to Lee, K..

At least 145 records · Page 8Linked to original sources

bioRxiv2023

The Brain Electroencephalogram Microdisplay for Precision Neurosurgery

Brain surgeries are among the most delicate clinical procedures and must be performed with the most technologically robust and advanced tools. When such surgical procedures are performed in functionally critical regions of the brain, functional mapping is applied as a standard practice that involves direct coordinated interactions between the neurosurgeon and the clinical neurology electrophysiology team. However, information flow during these interactions is commonly verbal as well as time consuming which in turn increases the duration and cost of the surgery, possibly compromising the patient outcomes. Additionally, the grids that measure brain activity and identify the boundaries of pathological versus functional brain regions suffer from low resolution (3-10 mm contact to contact spacing) with limited conformity to the brain surface. Here, we introduce a brain intracranial electroencephalogram microdisplay (Brain-iEEG-microdisplay) which conforms to the brain to measure the brain activity and display changes in near real-time (40 Hz refresh rate) on the surface of the brain in the surgical field. We used scalable engineered gallium nitride (GaN) substrates with 6" diameter to fabricate, encapsulate, and release free-standing arrays of up to 2048 GaN light emitting diodes (LEDs) in polyimide substrates. We then laminated the LED arrays on the back of micro-electrocorticography (ECoG) platinum nanorod grids (PtNRGrids) and developed hardware and software to perform near real-time intracranial EEG analysis and activation of light patterns that correspond to specific cortical activities. Using the Brain-iEEG-microdisplay, we precisely ideFSntified and displayed important cortical landmarks and pharmacologically induced pathological activities. In the rat model, we identified and displayed individual cortical columns corresponding to individual whiskers and the near real-time evolution of epileptic discharges. In the pig animal model, we demonstrated near real-time mapping and display of cortical functional boundaries using somatosensory evoked potentials (SSEP) and display of responses to direct electrical stimulation (DES) from the surface or within the brain tissue. Using a dual-color Brain-iEEG-microdisplay, we demonstrated co-registration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The Brain-iEEG-microdisplay holds the promise of increasing the efficiency of diagnosis and possibly surgical treatment, thereby reducing the cost and improving patient outcomes which would mark a major advancement in neurosurgery. These advances can also be translated to broader applications in neuro-oncology and neurophysiology. One Sentence SummaryA brain intracranial electroencephalogram microdisplay (Brain-iEEG-microdisplay) measures and displays real-time brain activity in the surgical field.

neuroscience↗

bioRxiv2023

A cortical information bottleneck during decision-making

Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas. SignificanceThe brain uses multiple areas for cognition, decision-making, and action, but it is unclear why cortical activity differs by brain area. Machine learning and information theory suggests that one benefit of multiple areas is that it provides an "information bottleneck" that compresses inputs into an optimal representation that is minimal and sufficient to solve the task. Combining experimental recordings from behaving animals and computational simulations, we show that later brain areas have a tendency to form such minimal sufficient representations of task inputs through preferential propagation of task-relevant information present in earlier areas. Our results thus provide insight into one possible reason why the brain uses multiple brain areas for supporting decision-making and action.

neuroscience↗

bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

neuroscience↗

bioRxiv2023

Disposition and Toxicity of 2,2',5,5'-Tetrachlorobiphenyl in Female Rats Following Intraperitoneal Exposure

The disposition and toxicity of lower chlorinated PCBs (LC-PCBs) with less than five chlorine substituents have received little attention. This study characterizes the distribution and metabolomic effects of PCB 52, an LC-PCB found in indoor and outdoor air, three weeks after intraperitoneal exposure of female Sprague Dawley rats to 0, 1, 10, or 100 mg/kg BW. PCB 52 exposure did not affect overall body weight. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis identified PCB 52 in all tissues investigated. Hydroxylated, sulfated, and methylated PCB metabolites, identified using GC-MS/MS and nontarget liquid chromatography-high resolution mass spectrometry (Nt-LCMS), were primarily found in the serum and liver of rats exposed to 100 mg/kg BW. Metabolomic analysis revealed minor effects on L-cysteine, glycine, cytosine, sphingosine, thymine, linoleic acid, orotic acid, L-histidine, and erythrose serum levels. Thus, the metabolism of PCB 52 and its effects on the metabolome must be considered in toxicity studies. HighlightsO_LIPCB 52 was present in adipose, brain, liver, and serum 3 weeks after PCB exposure C_LIO_LILiver and serum contained hydroxylated, sulfated, and methylated PCB 52 metabolites C_LIO_LIMetabolomics analysis revealed minor changes in endogenous serum metabolites C_LIO_LILevels of dopamine and its metabolites in the brain were not affected by PCB 52 C_LI

pharmacology and toxicology↗

bioRxiv2023

Albumin-binding recombinant human IL-18BP ameliorates macrophage activation syndrome and atopic dermatitis via direct IL-18 inactivation

Given the clinical success of cytokine blockade in managing diverse inflammatory human conditions, this approach could be exploited for numerous refractory or uncontrolled inflammatory conditions by identifying novel targets for functional blockade. IL-18, a pro-inflammatory cytokine, is relatively underestimated as a therapeutic target, despite accumulated evidence indicating the unique roles of IL-18 in acute and chronic inflammatory conditions, such as macrophage activation syndrome. Herein, we designed a new form of IL-18 blockade, i.e., APB-R3, a long-acting recombinant human IL-18BP linked to human albumin-binding Fab fragment, SL335, for extending half-life. We then explored the pharmacokinetics and pharmacodynamics of APB-R3. In addition to an extended serum half-life, APB-R3 alleviates liver inflammation and splenomegaly in a model of the macrophage activation syndrome induced in IL-18BP knockout mice. Moreover, APB-R3 substantially controlled skin inflammation in a model of atopic dermatitis. Thus, we report APB-R3 as a new potent IL-18 blocking agent that could be applied to treat IL-18-mediated inflammatory diseases.

immunology↗

bioRxiv2023

Synaptic architecture of leg and wing motor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

neuroscience↗

bioRxiv2023

Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on selecting features that differentiate known diseases or cell states, they often struggle to identify features that both preserve heterogeneity and reveal subtypes. To address this, we utilized deep metric learning-based feature embedding to explore the statistical properties of features crucial for preserving heterogeneity. Our analysis indicated that features with a notable difference in interquartile range (IQR) between classes hold important subtype information. Guided by this insight, we developed a statistical method called PHet (Preserving Heterogeneity), which employs iterative subsampling and differential analysis of IQR combined with Fishers method to identify a small set of features that preserve heterogeneity and enhance subtype clustering quality. Validation on public single-cell RNA-seq and microarray datasets demonstrated PHets ability to maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods. Furthermore, an analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells identified two distinct basal cell subtypes differentiating towards a luminal secretory phenotype using PHet-based features, demonstrating promising results in a real-data application. These results highlight PHets potential to enhance our understanding of disease mechanisms and cell differentiation, contributing significantly to the field of personalized medicine.

bioinformatics↗

bioRxiv2023

Molecular signatures associated with successful implantation of the human blastocyst

Embryo implantation in humans is remarkably inefficient for reasons that remain largely unexplained, and high rates of implantation failure remain one of the greatest obstacles in treating infertility. The volume of gene expression data available from human embryos has rapidly accumulated in recent years. However, prioritization of these data to identify the subset of genes that determine successful implantation remains a challenge, in part, because comprehensive analyses cannot be performed on the same embryos that are transferred. Here, we leverage clinical morphologic grading--known for decades to correlate with implantation potential--and transcriptome analyses of matched embryonic and abembryonic samples to identify genes and cell-cell interactions enriched and depleted in human blastocysts of good and poor morphology, genome-wide. Unexpectedly, we discovered that the greatest molecular difference was in the state of the extraembryonic primitive endoderm (PrE), with relative deficiencies in PrE development in embryos of poor morphology at the time of embryo transfer. Together, our results support a model in which implantation success is most strongly reflected by factors and signals from the embryonic compartment and suggest that deficiencies in PrE development, in particular, are common among embryos with reduced implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation.

developmental biology↗

bioRxiv2023

A Chemical Reaction Similarity-Based Prediction Algorithm Identifies the Multiple Taxa Required to Catalyze an Entire Metabolic Pathway of Dietary Flavonoids

Flavonoids are polyphenolic phytochemicals abundant in plant-based, health-promoting foods. They are only partially absorbed in the small intestine, and gut microbiota plays a significant role in their metabolism. As flavonoids are not natural substrates of gut bacterial enzymes, reactions of flavonoid metabolism have been attributed to the ability of general classes of enzymes to metabolize non-natural substrates. To systematically characterize this promiscuous enzyme activity, we developed a prediction tool that is based on chemical reaction similarity. The tool takes a list of enzymes or organisms to match microbial enzymes with their non-native flavonoid substrates and orphan reactions. We successfully predicted the promiscuous activity of known flavonoid-metabolizing bacterial and plant enzymes. Next, we used this tool to identify the multiple taxa required to catalyze an entire metabolic pathway of dietary flavonoids. Tilianin is a flavonoid-O-glycoside having biological and pharmacological activities, including neuroprotection. Using our prediction tool, we defined a novel bacterial pathway of tilianin metabolism that includes O-deglycosylation to acacetin, demethylation of acacetin to apigenin, and hydrogenation of apigenin to naringenin. We predicted and confirmed using in vitro experiments and LC-MS techniques that Bifidobacterium longum subsp. animalis, Blautia coccoides and Flavonifractor plautii can catalyze this pathway. Prospectively, the prediction-validation methodology developed in this work could be used to systematically characterize gut microbial metabolism of dietary flavonoids and other phytochemicals. The bioactivities of flavonoids and their metabolic products can vary widely. We used an in vitro rat neuronal model to show that tilianin metabolites exhibit protective effect against H2O2 through reactive oxygen species (Delepine et al.) scavenging activity and thus, improve cell viability, while the parent compound, tilianin, was ineffective. These results are important to understand the gut microbiota-dependent physiological effects of dietary flavonoids.

microbiology↗

bioRxiv2023

Predictive coding with spiking neurons and feedforward gist signalling

Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neural network features such as a non-linear, continuous, and clock-driven function approximator as basic unit of computation. Therefore, we have developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. While adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: 1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and 2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high basal firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature. Author summaryHow does the brain seamlessly perceive the world, in the midst of chaotic sensory barrage? Rather than passively relaying information that sensory organs pick up from the external world along the cortical hierarchy for a series of feature extractions, it actively gathers statistical regularities from sensory inputs to track causal relationships between physical properties of external objects and the body. In other words, the brains perceptual apparatus is constantly trying to make sense of the incoming streams of sensory input and represent the subjects current situation by building and maintaining internal models of the world and body. While this constructivist theme in understanding perception has been pervasive across multiple disciplines from philosophy to psychology to computer science, a comprehensive theory of brain function called predictive coding aims at unifying neural implementations of perception. In this study, we present a biologically plausible neural network for predictive coding that uses spiking neurons, Hebbian learning, and a feedforward visual pathway to perform perceptual inference and learning on images. Not only does the model show that predictive coding is well behaved under the biological constraint of spiking neurons, but it also provides deep learning and neuromorphic communities with novel paradigms of learning and computational architectures inspired by the natures most intelligent system, the brain.

neuroscience↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2023

An extended wave of global mRNA deadenylation sets up a switch in translation regulation across the mammalian oocyte-to-embryo transition

The oocyte-to-embryo transition (OET) occurs in the absence of new transcription and relies on post-transcriptional gene regulation, including translational control by mRNA poly(A) tail regulation, where cytoplasmic polyadenylation activates translation and deadenylation leads to translational repression and decay. However, how the transcriptome-wide landscape of mRNA poly(A) tails shapes translation across the OET in mammals remains unknown. Here, we performed long-read RNA sequencing to uncover poly(A) tail lengths and mRNA abundance transcriptome-wide in mice across five stages of development from oocyte to embryo. Integrating these data with recently published ribosome profiling data, we demonstrate that poly(A) tail length is coupled to translational efficiency across the entire OET. We uncover an extended wave of global deadenylation during fertilization, which sets up a switch in translation control between the oocyte and embryo. In the oocyte, short-tailed maternal mRNAs that resist deadenylation in the oocyte are translationally activated, whereas large groups of mRNAs deadenylated without decay in the oocyte are later readenylated to drive translation activation in the early embryo. Our findings provide an important resource and insight into the mechanisms by which cytoplasmic polyadenylation and deadenylation dynamically shape poly(A) tail length in a stage-specific manner to orchestrate development from oocyte to embryo in mammals.

developmental biology↗

bioRxiv2023

Microbially-derived indole-3-acetate alleviates diet induced steatosis and inflammation in mice

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Western countries. There is growing evidence that dysbiosis of the intestinal microbiota and disruption of microbiota-host interactions contribute to the pathology of NAFLD. We previously demonstrated that gut microbiota derived tryptophan metabolite indole-3-acetate (I3A) was decreased in both cecum and liver of high-fat diet-fed mice and attenuated the expression of inflammatory cytokines in macrophages and TNF-a and fatty acid induced inflammatory responses in an aryl-hydrocarbon receptor (AhR) dependent manner in hepatocytes. In this study, we investigated the effect of orally administered I3A in a mouse model of diet induced NAFLD. Western diet (WD)-fed mice given sugar water (SW) with I3A showed dramatically decreased serum ALT, hepatic TG, liver steatosis, hepatocyte ballooning, lobular inflammation, and hepatic production of inflammatory cytokines, compared to WD-fed mice given only SW. Metagenomic analysis show that I3A administration did not significantly modify the intestinal microbiome, suggesting that I3As beneficial effects likely reflect the metabolites direct actions on the liver. Administration of I3A partially reversed WD induced alterations of liver metabolome and proteome, notably, decreasing expression of several enzymes in hepatic lipogenesis and {beta}- oxidation. Mechanistically, we also show that AMP-activated protein kinase (AMPK) mediates the anti-inflammatory effects of I3A in macrophages. The potency of I3A in alleviating liver steatosis and inflammation clearly demonstrates its potential as a therapeutic modality for preventing the progression of steatosis to NASH.

cell biology↗

bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

neuroscience↗

bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

neuroscience↗

bioRxiv2023

STORM imaging buffer with refractive index matched to standard immersion oil

Stochastic optical reconstruction microscopy (STORM) provides exceptional super-resolution imaging by sparsely blinking individual dye molecules in thiol-containing media. STORM is now well-established for imaging thin biological specimens, and recent technological advancements have expanded its use to thick tissues. While the use of mounting media with an oil refractive index has been shown to reduce light scattering within tissues and thus greatly improve imaging depth and resolution in optical microscopy, the refractive index of STORM imaging buffers is typically water-like and oil-index (OI) buffers have never been considered for this purpose. In this study, we report a 3-pyridinemethanol-based STORM buffer that matches the refractive index of standard immersion oil. Our OI buffer exhibits similar superior performance in terms of photoswitching of Alexa Flour 647 dye and STORM image quality in fixed cells as conventional STORM buffers, despite having a completely different refractive index. Interestingly, it shows remarkable stability for at least 25 days, and potentially longer, which will enable STORM imaging of a large number of cells on a single prepared slide, as well as larger field-of-view imaging through multiple field stitching. By achieving perfect index matching with oil immersion objectives, OI buffers can produce accurate nanoscale morphology of thin biological specimens, without the need for complex microscope calibrations across sample depth. More importantly, our STORM buffer is expected to play a crucial role in lightsheet STORM applications for thick tissues by reducing light scattering, thereby leading to improved imaging depth and localization performance.

bioengineering↗

bioRxiv2023

Genetic control of cell layer interactions in plants via tissue mechanics

Plant development depends on coordination of growth between different cell layers. Coordination may be mediated by molecular signalling or mechanical connectivity between cells, but evidence for genetic control via direct mechanics has been lacking. We show that a brassinosteroid-deficient dwarf mutant of the aquatic plant Utricularia gibba has twisted internal tissue, likely caused by a mechanical constraint from a slow-growing epidermis creating tissue stresses. This conclusion is supported by showing that inhibition of brassinosteroid action in an Arabidopsis mutant compromised for cell adhesion, enhances epidermal crack formation, an indicator of increased tissue tension. Thus, genes driving brassinosteroid synthesis can promote growth of internal tissue by reducing mechanical epidermal constraint, showing that tissue mechanics plays a key role in coordinating growth between cell layers. One-Sentence SummaryInternal twists in a mutant carnivorous plant reveal how genes control growth via tissue mechanics.

plant biology↗

bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗