Biology subjects

Donner, C.

Publications and source records attributed to Donner, C..

4 recordsLinked to original sources

bioRxiv2024

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic and empirical ground-truth data sets, obtained from simulations and parallel single-cell patch-clamp and high-density microelectrode array (HD-MEA) recordings in vitro. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike train data. We find gross differences between different algorithms, and many algorithms have difficulties in detecting inhibitory connections. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms, and show how it improves network reconstruction accuracy and robustness. Overall, the eANN was robust across different dynamical regimes, with shorter recording time, and ameliorated the identification of synaptic connections, in particular inhibitory ones. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity. Author summaryThis study introduces an ensemble artificial neural network (eANN) to infer neuronal connectivity from multi-unit spike time recordings. We compare the eANN to previous algorithms and validate it using simulations and HD-MEA/patch-clamp datasets. The latter is obtained from three single-cell patch-clamp recordings and high-density microelectrode array (HD-MEA) measurements, in parallel. Our results demonstrate that the eANN outperforms all other algorithms across different dynamical regimes and provides a more accurate description of the underlying topological organization of the studied networks. We also provide a SHAP analysis of the trained eANN to understand which input features of the eANN contribute most to this superior performance. The eANN is a promising approach to improve connectivity inference from spike-train data.

neuroscience↗

bioRxiv2022

Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation

Parkinsons disease (PD) is a common debilitating neurodegenerative disorder, characterized by a progressive loss of dopaminergic (DA) neurons. Mutations, gene dosage increase, and single nucleotide polymorphisms in the -synuclein-encoding gene SNCA either cause or increase the risk for PD. However, neither the function of -synuclein in health and disease, nor its role throughout development is fully understood. Here, we introduce DeePhys, a new tool that allows for data-driven functional phenotyping of neuronal cell lines by combining electrophysiological features inferred from high-density microelectrode array (HD-MEA) recordings with a robust machine learning workflow. We apply DeePhys to human induced pluripotent stem cell (iPSC)-derived DA neuron-astrocyte co-cultures harboring the prominent SNCA mutation A53T and an isogenic control line. Moreover, we demonstrate how DeePhys can facilitate the assessment of cellular and network-level electrophysiological features to build functional phenotypes and to evaluate potential treatment interventions. We find that electrophysiological features across all scales proved to be highly specific for the A53T phenotype, enabled to predict the genotype and age of individual cultures with high accuracy, and revealed a mutant-like phenotype after downregulation of -synuclein.

neuroscience↗

bioRxiv2021

24-Nor-Ursodeoxycholic acid reshapes immunometabolism in CD8+ T cells and alleviates hepatic inflammation

Background & Aims24-NorUrsodeoxycholic acid (NorUDCA) is novel therapy for immune-mediated liver diseases such as primary sclerosing cholangitis (PSC) where dysregulated T cells including CD8+ T cells cause liver immunopathology. We hypothesized that NorUDCA may directly modulate CD8+ T cell effector function thus contributing to its therapeutic efficacy independent of anti-cholestatic effects. MethodsNorUDCA effects on CD8+ T cell function in vivo were investigated in a hepatic injury model system induced by excessive CD8+ T cell immune response upon non-cytolytic lymphocytic choriomeningitis virus (LCMV) infection. Mechanistic studies included molecular and biochemical approaches, flow cytometry and metabolic assays in mouse CD8+ T cells in vitro. Mass spectrometry (MS) was used to identify potential targets modulated by NorUDCA in CD8+ T cells. NorUDCA signaling effects observed in murine systems were validated in peripheral T cells from healthy volunteers and PSC patients. ResultsIn vivo NorUDCA ameliorated hepatic injury and systemic inflammation upon LCMV infection. Mechanistically, NorUDCA demonstrated a strong immunomodulatory efficacy in CD8+ T cells affecting lymphoblastogenesis, mTORC1 signaling and glycolysis of CD8+ T cells. With MS, we identified that NorUDCA regulates CD8+ T cells via targeting mTORC1. NorUDCAs impact on mTORC1 signaling was further confirmed in circulating human CD8+ T cells. ConclusionsNorUDCA possesses a yet-unrecognized direct modulatory potency on CD8+ T cells and attenuates excessive CD8+ T cell hepatic immunopathology. These findings may be relevant for treatment of immune-mediated liver diseases such as PSC and beyond.

molecular biology↗

bioRxiv2019

Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

Multi-neuronal spike-train data recorded in vivo often exhibit rich dynamics as well as considerable variability across cells and repetitions of identical experimental conditions (trials). Efforts to characterize and predict the population dynamics and the contributions of individual neurons require model-based tools. Abstract statistical models allow for principled parameter estimation and model selection, but possess only limited interpretive power because they typically do not incorporate prior biophysical constraints. Here we present a statistically principled approach based on a population of doubly-stochastic integrate-and-fire neurons, taking into account basic biophysics. This model class comprises an idealized description for the dynamics of the neuronal membrane voltage in response to fast independent and slower shared input fluctuations. To efficiently estimate the model parameters and compare different model variants we compute the likelihood of observed single-trail spike trains by leveraging analytical methods for spiking neuron models combined with inference techniques for hidden Markov models. This allows us to reconstruct the shared input variations, classify their dynamics, obtain precise spike rate estimates, and quantify how individual neurons couple to the low-dimensional overall population dynamics, all from a single trial. Extensive evaluations based on simulated data show that our method correctly identifies the dynamics of the shared input process and accurately estimates the model parameters. Validations on ground truth recordings of neurons in vitro demonstrate that our approach successfully reconstructs the dynamics of hidden inputs and yields improved fits compared to a typical phenomenological model. Finally, we apply the method to a neuronal population recorded in vivo, for which we assess the contributions of individual neurons to the overall spiking dynamics. Altogether, our work provides statistical inference tools for a class of reasonably constrained, mechanistic models and demonstrates the benefits of this approach to analyze measured spike train data.

neuroscience↗