Biology subjects

Koch, C.

Publications and source records attributed to Koch, C..

At least 55 records · Page 3Linked to original sources

bioRxiv2020

Mimicry is associated with procedural learning, not social bonding, neural systems in autism

Mimicry facilitates social bonding throughout the lifespan. Mimicry impairments in autism spectrum conditions (ASC) are widely reported, including differentiation of the brain networks associated with its social bonding and learning functions. This study examined associations between volumes of brain regions associated with social bonding versus procedural skill learning, and mimicry of gestures during a naturalistic interaction in ASC and neurotypical (NT) children. Consistent with predictions, results revealed reduced mimicry in ASC relative to the NT children. Mimicry frequency was negatively associated with autism symptom severity. Mimicry was predicted predominantly by the volume of procedural skill learning regions in ASC, and by bonding regions in NT. Further, bonding regions contributed significantly less to mimicry in ASC than in NT, while the contribution of learning regions was not different across groups. These findings suggest that associating mimicry with skill learning, rather than social bonding, may partially explain observed communication difficulties in ASC.

neuroscience↗

bioRxiv2020

Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology

Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of neurons in the brain. While these two modalities have distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging or electrophysiology. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging. This work explores which data transformations are most useful for explaining these modality-specific discrepancies. We show that the higher selectivity in imaging can be partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could not reconcile differences in responsiveness without sub-selecting neurons based on event rate or level of signal contamination. This suggests that differences in responsiveness more likely reflect neuronal sampling bias or cluster-merging artifacts during spike sorting of electrophysiological recordings, rather than flaws in event detection from fluorescence time series. This work establishes the dominant impacts of the two modalities respective biases on a set of functional metrics that are fundamental for characterizing sensory-evoked responses.

neuroscience↗

bioRxiv2020

Strongyle-resistant sheep express their potential across environments and leave limited scope for parasite plasticity

IntroductionDrug-resistant parasites threaten livestock production. Breeding more resistant hosts could be a sustainable control strategy. Environmental variation may however alter the expression of genetic potential and directional selection toward host resistance could initiate an arms race between the host and its parasites. Methods and ResultsWe created sheep lines with high or low resistance to Haemonchus contortus. We first exposed both lines to chronic stress or to the infection by another parasite Trichostrongylus colubriformis, to test for genotype-by-environment and genotype-by-parasite species interactions respectively. Overall, between-line divergence remained significant across environmental perturbations. But we found that the impact of chronic stress on H. contortus infection varied among families and that divergence was reduced during infection by T. colubriformis. Second, we quantified genomic and transcriptomic differences in H. contortus worms collected from both lines to identify components of an arms race. We found no evidence of genetic differentiation between worms from each line. But survival to more resistant hosts was associated with enhanced expression of cuticle collagen coding genes. DiscussionBreeding for resistance hence remains a sustainable strategy that requires to anticipate the effects of environmental perturbations and to monitor worm populations.

evolutionary biology↗

bioRxiv2020

Vimentin is Required for Tumor Progression and Metastasis in a Mouse Model of Non-Small Cell Lung Cancer

Vimentin, a type III intermediate filament, is highly expressed in aggressive epithelial cancers and is associated with increased rates of metastasis. We show that vimentin is causally required for lung cancer metastasis using a genetic mouse model of lung adenocarcinoma (LSL-KrasG12D;Tp53fl/fl, termed KPV+/+) crossed with vimentin-null mice (thereby creating KPV-/- mice). Both KPV+/+ and KPV-/- mice developed lung tumors, yet KPV-/- mice had delayed tumorigenesis and prolonged survival. KPV+/+ cells implanted in the flank metastasized to the lung while KPV-/- cells did not, providing additional evidence that vimentin is required for metastasis. Differential expression analysis of RNA-seq data demonstrated that KPV-/- cells had suppressed expression of genes that drive epithelial-to-mesenchymal transition, migration, and invasion, processes that are critical to the metastatic cascade. Integrative metabolomic and transcriptomic analysis revealed altered glutaminolysis, with KPV-/- cells accumulating glutathione, leading to impaired cell motility in response to oxidative stress. Together, these results show that loss of vimentin impairs epithelial-to-mesenchymal transition and regulation of the oxidative stress response, resulting in decreased metastasis in murine lung adenocarcinoma.

cancer biology↗

bioRxiv2020

Brain Modeling ToolKit: an Open Source Software Suite for Multiscale Modeling of Brain Circuits

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

neuroscience↗

bioRxiv2020

Multi-modal characterization and simulation of human epileptic circuitry

Temporal lobe epilepsy is the fourth most common neurological disorder with about 40% of patients not responding to pharmacological treatment. Increased cellular loss in the hippocampus is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions such as temporal lobe resection, the impact of the disease at the cellular level remains unclear in humans. Here we show that properties of hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from epilepsy patients. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume, surface area and spine density. Single-cell RNA sequencing combined with simulations ascribe the observed electrophysiological changes to gradual modification in three key ion channel conductances: BK, Cav2.2 and Kir2.1. In a bio-realistic computational network model, we show that the changes related to disease progression bring the circuit into a more excitable state. In turn, we observe that by reversing these changes in the three key conductances produces a less excitable, "early disease-like" state. These results provide mechanistic understanding of epilepsy in humans and will inform future therapies such as viral gene delivery to reverse the course of the disorder.

neuroscience↗

bioRxiv2020

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Investigating how visual inputs are encoded in visual cortex is important for elucidating the roles of cell populations in circuit computations. We here use a recently developed, large-scale model of mouse primary visual cortex (V1) and perturb both single neurons as well as functional- and cell-type defined population of neurons to mimic equivalent optogenetic perturbations. First, perturbations were performed to study the functional roles of layer 2/3 excitatory neurons in inter-laminar interactions. We observed activity changes consistent with the canonical cortical model (Douglas and Martin 1991). Second, single neuron perturbations in layer 2/3 revealed a center-surround inhibition-dominated effect, consistent with recent experiments. Finally, perturbations of multiple excitatory layer 2/3 neurons during visual stimuli of varying contrasts indicated that the V1 model has both efficient and robust coding features. The circuit transitions from predominantly broad like-to-like inhibition at high contrasts to predominantly specific like-to-like excitation at low contrasts. These in silico results demonstrate how the circuit can shift from redundancy reduction to robust codes as a function of stimulus contrast.

neuroscience↗

bioRxiv2020

Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined. HighlightsO_LIGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments. C_LIO_LIOptimized ion channel conductances along the cellular morphology ( all-active) are characteristic of model complexity and offer enhanced biophysical realism. C_LIO_LIIon channel conductance vectors of all-active models classify transcriptomically defined cell-types. C_LIO_LICell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes. C_LIO_LIA set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments. C_LIO_LIAll-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them. C_LI

neuroscience↗

bioRxiv2020

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet also produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process. Author SummaryAnimals have to adapt to environments with rich dynamics and maintain multiple types of memories. In this study, we focus on a visual change detection task in mice which requires short-term memory. Learning which features need to be maintained in short-term memory can be realized in a recurrent neural network by changing connections in the network, resulting in memory maintenance through persistent activity. However, in biological networks, a large diversity of time-dependent intrinsic mechanisms are also available. As an alternative to persistent neural activity, we find that learning to make use of internal adapting dynamics better matches both the observed neural activity and behavior of animals in this simple task. The presence of a large diversity of temporal traces could be one of the reasons for the diversity of cells observed. We believe that both learning to keep representations of relevant stimuli in persistent activity and learning to make use of intrinsic time-dependent mechanisms exist, and their relative use will be dependent on the exact task.

neuroscience↗

bioRxiv2019

Distinct transcriptomic cell types and neural circuits of the subiculum and prosubiculum along the dorsal-ventral axis

Subicular region plays important roles in spatial processing and many cognitive functions and these were mainly attributed to subiculum (Sub) rather than prosubiculum (PS). Using single-cell RNA-sequencing (scRNA-seq) technique we have identified up to 27 distinct transcriptomic clusters/cell types, which were registered to anatomical sub-domains in Sub and PS. Based on reliable molecular markers derived from transcriptomic clustering and in situ hybridization data, the precise boundaries of Sub and PS have been consistently defined along the dorsoventral (DV) axis. Using these borders to evaluate Cre-line specificity and tracer injections, we have found bona fide Sub projections topographically to structures important for spatial processing and navigation. In contrast, PS along DV axis sends its outputs to widespread brain regions crucial for motivation, emotion, reward, stress, anxiety and fear. Brain-wide cell-type specific projections of Sub and PS have also been revealed using specific Cre-lines. These results reveal two molecularly and anatomically distinct circuits centered in Sub and PS, respectively, providing a consistent explanation to historical data and a clearer foundation for future functional studies. HighlightsO_LI27 transcriptomic cell types identified in and spatially registered to "subicular" regions. C_LIO_LIAnatomic borders of "subicular" regions reliably determined along dorsal-ventral axis. C_LIO_LIDistinct cell types and circuits of full-length subiculum (Sub) and prosubiculum (PS). C_LIO_LIBrain-wide cell-type specific projections of Sub and PS revealed with specific Cre-lines. C_LI In BriefDing et al. show that mouse subiculum and prosubiculum are two distinct regions with differential transcriptomic cell types, subtypes, neural circuits and functional correlation. The former has obvious topographic projections to its main targets while the latter exhibits widespread projections to many subcortical regions associated with reward, emotion, stress and motivation.

neuroscience↗

bioRxiv2019

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging.

Multiphoton calcium imaging is commonly used to monitor the spiking of large populations of neurons. Recovering action potentials from fluorescence necessitates calibration experiments, often with simultaneous imaging and cell-attached recording. Here we performed calibration for imaging conditions matching those of the Allen Brain Observatory. We developed a novel crowd-sourced, algorithmic approach to quality control. Our final data set was 50 recordings from 35 neurons in 3 mouse lines. Our calibration indicated that 3 or more spikes were required to produce consistent changes in fluorescence. Moreover, neither a simple linear model nor a more complex biophysical model accurately predicted fluorescence for small numbers of spikes (1-3). We observed increases in fluorescence corresponding to prolonged depolarizations, particularly in Emx1-IRES-Cre mouse line crosses. Our results indicate that deriving spike times from fluorescence measurements may be an intractable problem in some mouse lines.

neuroscience↗

bioRxiv2019

Effects of Aging on the Encoding of Spatial Direction in the Human Brain

Human aging is characterized by impaired spatial cognition and reductions in the distinctiveness of category-specific fMRI activation patterns. Yet, little is know about age-related decline in neural distinctiveness of spatial information. Here, we asked whether neural tuning functions of walking direction are broadened in older versus younger adults. To test this idea, we developed a novel method that allowed us to investigate changes in fMRI-measured pattern similarity while participants navigated in different directions in a virtual spatial navigation task. We expected that directional tuning functions would be broader in older adults, and thus activation patterns that reflect neighboring directions would be less distinct as compared to non-adjacent directions. Because loss of distinctiveness leads to more confusions when information is read out by downstream areas, we analyzed predictions of a decoder trained on these representations and asked (1) whether decoder confusions between two directions increase proportionally to their angular similarity, (2) and how this effect may differ between age groups. Evidence for tuning-function-like signals was found in the retrosplenial complex and primary visual cortex. Significant age differences in tuning width, however, were only found in the primary visual cortex, suggesting that less precise visual information could lead to worse directional signals in older adults. Yet, age differences in visual tuning were not related to behavior. Instead, directional information encoded in RSC correlated with memory on task. These results shed new light on neural mechanisms underling age-related spatial navigation impairments and introduce a novel approach to measure tuning specificity using fMRI.

neuroscience↗

bioRxiv2019

Relationship between spiking activity and simultaneously recorded fluorescence signals in transgenic mice expressing GCaMP6

Two-photon calcium imaging is often used with genetically encoded calcium indicators (GECIs) to investigate neural dynamics, but the relationship between fluorescence and action potentials (spikes) remains unclear. Pioneering work linked electrophysiology and calcium imaging in vivo with viral GECI expression, albeit in a small number of cells. Here we characterized the spikefluorescence transfer function in vivo of 91 layer 2/3 pyramidal neurons in primary visual cortex in four transgenic mouse lines expressing GCaMP6s or GCaMP6f. We found that GCaMP6s cells have spike-triggered fluorescence responses of larger amplitude, lower variability and greater single-spike detectability than GCaMP6f. Mean single-spike detection rates at high spatiotemporal resolution measured in our data was >70% for GCaMP6s and ~40-50% for GCaMP6f (at 5% false positive rate). These rates are estimated to decrease to 25-35% for GCaMP6f under generally used population imaging conditions. Our ground-truth dataset thus supports more refined inference of neuronal activity from calcium imaging.

neuroscience↗

bioRxiv2019

Complete single neuron reconstruction reveals morphological diversity in molecularly defined claustral and cortical neuron types

Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

neuroscience↗

bioRxiv2019

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWStructural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic model of the mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically-detailed or point-neurons, with identical network connectivity. Both variants were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. While constructing and tuning these networks to recapitulate experimental data, we identified a set of rules governing cell-class specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single cell abstraction, spatially extended or point-models, both perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

neuroscience↗

bioRxiv2019

Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identified a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results predict VENs are a regionally distinctive type of ET neuron, and we additionally describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

neuroscience↗

bioRxiv2019

Lupin (Lupinus sp.) seeds exert anthelmintic activity associated with their alkaloid content

The growing expansion range of drug resistant parasitic nematode populations threatens the sustainability of ruminant farming worldwide. In this context, nutraceuticals, i.e. feed that would both fulfil dietary requirements while ensuring parasite control, would contribute to increase farming sustainability in developed and low resource settings. In this study, we characterized the anthelmintic potential of lupin seed extracts against major ruminant trichostrongylids, i.e. H. contortus and T. circumcincta. Our observations showed that total seed extracts from commercially available lupin varieties significantly inhibited larval migration. This anthelmintic effect was sustained on multidrug resistant field isolate and across parasite species and was mediated by the seed alkaloid content. Analytical chemistry revealed a set of four lupanine and sparteine-derivatives with anthelmintic activity and electrophysiology assays on recombinant nematode acetylcholine receptors suggested an antagonistic mode of action for lupin alkaloids. While commercial lupin seeds did not exert direct anthelmintic effect in H. contortus infected lupin-fed ewes and goats, it significantly dampened blood production losses suffered by goats. Lupin seed extracts hence provide a working basis for the development of novel anthelmintic compounds able to break drug resistance in the field, while they could be used to increase the resilience of infected dairy ruminants.

pharmacology and toxicology↗

bioRxiv2018

Evolution of regulatory networks controlling adaptive traits in cichlids

Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is still known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically-diverse species. Using a novel approach to reconstruct GRNs in vertebrate species, we aimed to study GRN evolution in representative species of the most striking example of an adaptive radiation, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids is attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. To investigate these mechanisms across species at a genome-wide scale, our novel network-based approach identifies ancestral and extant gene co-expression modules along a phylogeny, and by integrating associated regulators, predicts candidate regulatory regions implicated in traits under selection in cichlids. As a case study, we present data from a well-studied adaptive trait - the visual system - for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate the plausibility of their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species, and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Our approach revealed numerous novel potential candidate regulatory regions across cichlid genomes with no prior association, as well as those with previously reported associations to known adaptive evolutionary traits, thus providing proof of concept.

evolutionary biology↗