Biology subjects

Roqueiro, D.

Publications and source records attributed to Roqueiro, D..

3 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↗

bioRxiv2024

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

MotivationHigh-dimensional Bulk RNA sequencing (RNAseq) datasets pose a considerable challenge in identifying biologically relevant features for downstream analyses and data mining efforts. The standard approach involves differential gene expression (DGE) analysis, but its effectiveness can be limited depending on the data due to its univariate nature. In complex datasets, an alternative approach involves employing a variety of machine learning (ML) tools, which attempt to understand non-linear relationships between features and focus on generalizability rather than statistical significance. This approach will result in the generation of multiple feature lists, which might exhibit similarities in terms of classification performance metrics. Therefore, there is an urgent need for a cohesive workflow that seamlessly integrates robust feature selection using diverse ML methods while also evaluating the biological relevance of the resulting feature lists. This combined approach would enable the prioritization of the best-performing list, considering both sets of criteria. ResultsWe introduce GeneSelectR, an open-source R package that innovatively combines ML and bioinformatic data mining approaches for enhanced feature selection. With GeneSelectR, features can be selected from a normalized RNAseq dataset with a variety of ML methods and user-defined parameters. This is followed by an assessment of their biological relevance with Gene Ontology (GO) enrichment analysis, along with a semantic similarity analysis of the resulting GO terms. Additionally, similarity coefficients and fractions of the GO terms of interest are calculated. With this, GeneSelectR optimizes ML performance and rigorously assesses the biological relevance of the various lists, offering a means to prioritize feature lists with regard to the biological question. When applied to the TCGA-BRCA dataset, the GeneSelectR workflow generated several feature lists using different ML methods and a DGE analysis. By leveraging the various functions in GeneSelectR, the different lists could be evaluated based on both ML performance and biological relevance. This comprehensive evaluation facilitated the selection of the best-performing list, which exhibited both strong machine learning performance and high relevance to the biological question while maintaining a manageable number of highly specific features. AvailabilityThe package is available on CRAN. To install it, run: install.packages( GeneSelectR) Contactdzhakparov@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

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↗