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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 325 records · Page 18Linked to original sources

bioRxiv2017

Fast Intensity Adaptation Enhances the Encoding of Sound in Drosophila

To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here, we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antennas mean position. Song pattern recognition therefore requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.

neuroscience↗

bioRxiv2017

EGL-19 AND EXP-2 ION CHANNELS MODELLING WITHIN C. ELEGANS PHARYNGEAL MUSCLE CELL ALLOWS REPRODUCTION OF CA2+ DRIVEN ACTION POTENTIALS, BOTH SINGLE EVENTS AND TRAINS

One of the current problems at the interface between neuroscience, biophysics, and computational modeling is the reverse-engineering and reproduction of Caenorhabditis elegans using computer simulation. The aim of our research was to develop the computational models and techniques for solving this problem while participating in the international open science OpenWorm Project. We have suggested models of a typical C. elegans neuron and a pharyngeal muscle cell, which were constructed and optimized using the NEURON simulation environment. The available experimental data about EGL-19 and EXP-2 ion channels allowed the model of a muscle to reproduce the action potential time profile correctly. Also, the model of a neuron reproduces quite accurately the mechanism of neural signal transmission based on passive propagation. We believe our models to be promising for better representing the specifics of various nervous and muscular cell classes when adding the corresponding ion channel models. Moreover, they can be used to construct the networks of such elements.

neuroscience↗

bioRxiv2017

Decoupling cellular response to topography and stiffness in three dimensions

Biophysical aspects of in vivo tissue microenvironments include microscale mechanical properties, fibrillar alignment, and architecture or topography of the extracellular matrix (ECM). These aspects act in concert with chemical signals from a myriad of diverse ECM proteins to provide cues that drive cellular responses. Here, we used a bottom-up approach to build fibrillar architecture into 3D amorphous hydrogels using magnetic-field driven assembly of paramagnetic colloidal particles functionalized with three types of human ECM proteins found in vivo. We investigated if cells cultured in matrices comprised of fibrils of the same size and arranged in similar geometries will show similar behavior for each of the ECM proteins tested. We were able to resolve spatial heterogeneities in microscale mechanical properties near aligned fibers that were not observed in bulk tissue mechanics. We then used this platform to examine factors contributing to cell alignment in response to topographical cues in 3D laminin-rich matrices. Multiple human cell lines extended protrusions preferentially in directions parallel or perpendicular to aligned fibers independently of the ECM coating. Focal adhesion proteins, as measured by paxillin localization, were mainly diffuse in the cytoplasm, with few puncta localized at the protrusions. Integrin {beta}1 and fascin regulated protrusion extension but not protrusion alignment. Myosin II inhibition did not reduce observed protrusion length. Instead, cells with reduced myosin II activity generated protrusions in random orientations when cultured in hydrogels with aligned fibers. Similarly, myosin II dependence was observed in vivo, where cells no longer aligned along the abluminal surfaces of blood vessels upon treatment with blebbistatin. These data suggest that myosin II can regulate sensing of topography in 3D engineered matrices for both normal and transformed cells.

bioengineering↗

bioRxiv2018

Control of Spreading Depression with Electrical Fields

Spreading depression or depolarization is a large-scale pathological brain phenomenon related to migraine, stroke, hemorrhage and traumatic brain injury. Once initiated, spreading depression propagates across gray matter extruding potassium and other active molecules, collapsing the resting membrane electro-chemical gradient of cells leading to spike inactivation and cellular swelling, and propagates independently of synaptic transmission. We demonstrate the modulation, suppression and prevention of spreading depression utilizing applied transcortical DC electric fields in brain slices, measured with intrinsic optical imaging and potassium dye epifluorescence. We experimentally observe a surface-positive electric field induced forcing of spreading depression propagation to locations in cortex deeper than the unmodulated propagation path, whereby further propagation is confined and arrested even after field termination. The opposite surface-negative electric field polarity produces an increase in propagation velocity and a confinement of the wave to more superficial layers of cortex than the unmodulated propagation path. These field polarities are of opposite sign to the polarity that blocks neuronal spiking and seizures, and are consistent with biophysical models of spreading depression. The results demonstrate the potential feasibility of electrical control and prevention of spreading depression.

neuroscience↗

bioRxiv2017

Process proteomics of beer reveals a dynamic proteome with extensive modifications

Modern beer production is a complex industrial process. However, some of its biochemical details remain unclear. Using mass spectrometry proteomics, we have performed a global untargeted analysis of the proteins present across time during nano-scale beer production. Samples included sweet wort produced by a high temperature infusion mash, hopped wort, and bright beer. This analysis identified over 200 unique proteins from barley and yeast, emphasizing the complexity of the process and product. We then used data independent SWATH-MS to quantitatively compare the relative abundance of these proteins throughout the process. This identified large and significant changes in the proteome at each process step. These changes described enrichment of proteins by their biophysical properties, and identified the appearance of dominant yeast proteins during fermentation. Altered levels of malt modification also quantitatively changed the proteomes throughout the process. Detailed inspection of the proteomic data revealed that many proteins were modified by protease digestion, glycation, or oxidation during the processing steps. This work demonstrates the opportunities offered by modern mass spectrometry proteomics in understanding the ancient process of beer production.

biochemistry↗

bioRxiv2017

Global, multiplexed dendritic computations under in vivo-like conditions

Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex spatio-temporal synaptic input patterns. We used the hLN to predict the membrane potential of a detailed biophysical model of a L2/3 pyramidal cell receiving in vivo-like synaptic input and reproducing in vivo dendritic recordings. We found that more than 90% of the somatic response could be captured by linear integration followed a single global non-linearity. Multiplexing inputs into parallel processing channels could improve prediction accuracy by as much as additional layers of local non-linearities. These results provide a data-driven characterisation of a key building block of cortical circuit computations: dendritic integration and the input-output transformation of single neurons during in vivo-like conditions.

neuroscience↗

bioRxiv2017

A Parallel Processing Model of Drosophila Olfactory Sensory Neurons and Its Biological Validation

In the past two decades, a substantial amount of work characterized the odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized and, the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined.\n\nIdentity and concentration, two key aspects of olfactory coding, originate in the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model identity and concentration in OTP using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the ligand binding/dissociation process. We model the BSG as a Connor-Stevens point neuron.\n\nThe resulting combinatorial encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the combinatorial complexity of the transformation taking place in the antennae. The concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiology recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

neuroscience↗

bioRxiv2017

Neural computations combine low- and high-order motion cues similarly, in dragonfly and monkey

Visual motion analysis is fundamental to survival across the animal kingdom. In insects, our understanding of the underlying computations has centered on the Hassenstein-Reichardt motion detector, which computes two-point cross-correlation via multiplication; in mammalian cortex, it is postulated that a similar signal is computed by comparing matched squaring operations. Both of these operations are difficult to implement biophysically in a precise fashion; moreover, they fail to detect the more complex multipoint local motion cues present in the visual environment. Here, via single-unit recordings in two visual specialists, dragonfly \"(Odonata)\" and macaque, and via model simulations, we show that neuronal computations are not simply approximations to idealized behaviors forced by biological constraints, but rather, are signatures of a common computational strategy to capture multiple local motion cues. The similarity of motion computations at the neuronal level in the brains of two extremely dissimilar animals, with evolutionary divergence of over 700 Myr1, suggests convergence on a common computational scheme for detecting visual motion.

neuroscience↗

bioRxiv2018

Population model learned on different stimulus ensembles predicts network responses in the retina

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.

neuroscience↗

bioRxiv2018

Open source tools and standardized data in cancer systems biology

To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.

systems biology↗

bioRxiv2018

Expression of the Eight GABAA Receptor α Subunits in the Developing Zebrafish Central Nervous System

GABA is a robust regulator of both developing and mature neural networks. It exerts many of its effects through GABAA receptors, which are heteropentamers assembled from a large array of subunits encoded by distinct genes. In mammals, there are 19 different GABAA subunit types, which are divided into the , {beta}, {gamma}, {delta}, {varepsilon}, {pi}, {theta} and {rho} subfamilies. The immense diversity of GABAA receptors is not fully understood. However, it is known that specific isoforms, with their distinct biophysical properties and expression profiles, tune responses to GABA. Although larval zebrafish are well-established as a model system for neural circuit analysis, little is known about GABAA receptors diversity and expression in this system. Here, using database analysis, we show that the zebrafish genome contains at least 23 subunits. All but the mammalian {theta} and {varepsilon} subunits have at least one zebrafish ortholog, while five mammalian GABAA subunits have two zebrafish orthologs. Zebrafish contain one subunit, {beta}4, which does not have a clear mammalian ortholog. Similar to mammalian GABAA receptors, the zebrafish subfamily is the largest and most diverse of the subfamilies. In zebrafish there are eight subunits, and RNA in situ hybridization across early zebrafish development revealed that they demonstrate distinct patterns of expression in the brain, spinal cord, and retina. Some subunits were very broadly distributed, whereas others were restricted to small populations of cells. Subunit-specific expression patterns were similar between zebrafish, frogs and rodents, which suggests that the roles of different GABAA receptor isoforms are largely conserved among vertebrates. This study provides a platform to examine isoform specific roles of GABAA receptors within zebrafish neural circuits and it highlights the potential of this system to better understand the remarkable heterogeneity of GABAA receptors.

neuroscience↗

bioRxiv2018

The Viral protein Corona Directs Viral Pathogenesis and Amyloid Aggregation

Artificial nanoparticles accumulate a protein corona layer in biological fluids, which significantly influences their bioactivity. As nanosized obligate intracellular parasites, viruses share many biophysical properties with artificial nanoparticles in extracellular environments and here we show that respiratory syncytial virus (RSV) and herpes simplex virus 1 (HSV-1) accumulate a rich and distinctive protein corona in different biological fluids. Moreover, we show that corona pre-coating differentially affects viral infectivity and immune cell activation. Additionally, we demonstrate that viruses bind amyloidogenic peptides in their corona and catalyze amyloid formation via surface-assisted heterogeneous nucleation. Importantly, we show that HSV-1 catalyzes the aggregation of the amyloid beta peptide (A{beta}42), a major constituent of amyloid plaques in Alzheimers disease, in-vitro and in animal models. Our results highlight the viral protein corona as an acquired structural layer that is critical for viral-host interactions and illustrate a mechanistic convergence between viral and amyloid pathologies.

microbiology↗

bioRxiv2018

Spike sorting with Gaussian mixture models

The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows for isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a main challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant waveform features using a combination of common techniques (e.g., principal components and wavelets) and GMM fitting parameters (e.g., standard deviations and peak distances). Then, we developed an approach to perform unsupervised clustering using GMMs, which estimates cluster properties in a data-driven way. Our results show that the proposed GMM-based framework outperforms previously established methods when using realistic simulations of extracellular spikes and actual extracellular recordings to evaluate sorting performance. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface in MATLAB to run our algorithm, which allows for manual adjustment of the automatic results.

neuroscience↗

bioRxiv2018

Atomic force microscopy of phase separation on ruptured, giant unilamellar vesicles

Giant unilamellar vesicles (GUVs) and supported lipid bilayers (SLBs) are synthetic model systems widely used in biophysical studies of lipid membranes. Phase separation behaviors of lipid species in these two model systems differ due to the lipid-substrate interactions that are present only for SLBs. Therefore, GUVs are believed to resemble natural cell membranes more closely, and a very large body of literature focuses on applying nano-characterization techniques to quantify phase separation on GUVs. However, one important technique, atomic force microscopy (AFM), has not yet been used successfully to study phase separation on GUVs. In the present study, we report that in binary systems, certain phase domains on GUVs retain their original shapes and patterns after the GUVs rupture on glass surfaces. This enabled AFM experiments on phase domains from binary GUVs containing 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC) and either 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) or 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC). These DLPC/DSPC and DLPC/DPPC GUVs both presented two different gel phases, one of which (bright phase) included a relatively high concentration of DiI-C20 but excluded Bodipy-HPC, and the other of which (dark phase) excluded both probes. The bright phases are of interest because they seem to stabilize dark phases against coalescence. Results suggested that the gel phases labeled by DiI-C20 in the DLPC/DSPC membrane, which surround the dark gel phase, is an extra layer of membrane, indicating a highly curved structure that might stabilize the interior dark domains. This phenomenon was not found in the DLPC/DPPC membrane. These results show the utility of AFM on collapsed GUVs, and suggest a possible mechanism for stabilization of lipid domains.

biochemistry↗

bioRxiv2018

Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators

Interneurons are critical for the proper functioning of neural circuits and are typically considered to act as linear point neurons. However, exciting new findings reveal complex, sub- and/or supralinear computations in the dendrites of various interneuron types. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both the hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN.

neuroscience↗

bioRxiv2018

Enhancing resolution of natural methylome reprogramming behavior in plants

BackgroundNatural methylome reprogramming within chromatin involves changes in local energy landscapes that are subject to thermodynamic principles. Signal detection permits the discrimination of methylation signal from dynamic background noise that is induced by thermal fluctuation. Current genome-wide methylation analysis methods do not incorporate biophysical properties of DNA, and focus largely on DNA methylation density changes, which limits resolution of natural, more subtle methylome behavior in relation to gene activity.\n\nResultsWe present here a novel methylome analysis procedure, Methyl-IT, based on information thermodynamics and signal detection. Methylation analysis involves a signal detection step, and the method was designed to discriminate methylation regulatory signal from background variation. Comparisons with commonly used programs and two publicly available methylome datasets, involving stages of seed development and drought stress effects, were implemented. Information divergence between methylation levels from different groups, measured in terms of Hellinger divergence, provides discrimination power between control and treatment samples. Differentially informative methylation positions (DIMPs) achieved higher sensitivity and accuracy than standard differentially methylated positions (DMPs) identified by other methods. Differentially methylated genes (DMG) that are based on DIMPs were significantly enriched in biologically meaningful networks.\n\nConclusionsMethyl-IT analysis enhanced resolution of natural methylome reprogramming behavior to reveal network-associated responses, offering resolution of gene pathway influences not attainable with previous methods.

genomics↗

bioRxiv2018

Label-free assessment of red blood cell storage lesions by deep learning

Blood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.\n\nStored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.\n\nWe demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.\n\nDeep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

bioinformatics↗

bioRxiv2018

Quantifying Waddington’s epigenetic landscape: a comparison of single-cell potency measures

Over 60 years ago Waddington proposed an epigenetic landscape model of cellular differentiation, whereby cell-fate transitions are modelled as canalization events, with stable cell states occupying the basins or attractor states1, 2. A key ingredient of this landscape is the energy potential, or height3, which correlates with cell-potency. To date, very few explicit biophysical models for estimating single-cell potency have been proposed. Using 9 independent experiments, encompassing over 6,600 high-quality single-cell RNA-Seq profiles, we here demonstrate that single-cell potency can be approximated as the graph entropy of a Markov Chain process on a model signaling network. Our analysis highlights that other proposed single-cell potency measures are not robust, whilst also revealing that integration with orthogonal systems-level information improves potency estimates. Thus, this study provides a foundation for an improved systems-level understanding of single-cell potency, which may have profound implications for the discovery of novel stem-and progenitor cell phenotypes.

bioinformatics↗