Biology subjects

Weissbach, S.

Publications and source records attributed to Weissbach, S..

5 recordsLinked to original sources

bioRxiv2024

Premature upregulation of miR-92a's target RBFOX2 hijacks PTBP splicing and impairs cortical neuronal differentiation.

Alternative splicing is a crucial component of neuronal differentiation, yet the mechanisms that regulate splicing transitions during embryonic brain development remain incompletely understood. Here, we identify a post-transcriptional mechanism that times the expression of the splicing factor Rbfox2 during neurogenesis. RBFOX2 is normally expressed at low levels in neural progenitor cells (NPCs) and becomes upregulated in newborn neurons where it promotes neuronal differentiation. Unexpectedly, premature expression of Rbfox2 in NPCs of the embryonic mouse neocortex blocked their differentiation into neurons rather than promoting it. Genome-wide analysis revealed widespread alternative splicing changes enriched for NDD genes and associated with a hybrid NPC- and neuron-like splicing pattern that significantly deviates from the normal splicing developmental trajectory. Remarkably, premature Rbfox2 expression induced the inclusion of validated target exons that are otherwise repressed by PTBP2 pointing to an antagonistic splicing relationship. Integrative scRNA-seq analysis confirmed a negatively correlated expression between these two RNA-binding proteins (RBP) along differentiation pseudotime. Strikingly, we identified the NPC-specific miRNA 92a-3p as a regulator of the Rbfox2 expression switch: expression of miR-92a reduced RBFOX2 levels and reversed splicing patterns of target genes in vitro, while silencing miR-92a in vivo increased RBFOX2 expression in the embryonic cortex. Together, these findings reveal a previously unrecognized miRNA-RBP regulatory axis that ensures the proper timing of NPC-to-neuron splicing transitions in the developing cortex and provide new insights into splicing dysregulation as a contributing factor to the emergence of neurodevelopmental disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=152 HEIGHT=200 SRC="FIGDIR/small/614071v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@16248caorg.highwire.dtl.DTLVardef@198dd0dorg.highwire.dtl.DTLVardef@d8887aorg.highwire.dtl.DTLVardef@1e8419e_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract Schematic representation of the proposed splicing regulation for the transition of undifferentiated NPCs to neurons in the developing cerebral cortex.

genomics↗

bioRxiv2024

Neuroimage Denoiser for removing noise from transient fluorescent signals in functional imaging.

We developed Neuroimage Denoiser, a novel U-Net-based model that effectively removes noise from microscopic recordings of transient local fluorescent signals. The model makes the denoising process independent of the recording frequency and the kinetics of the sensor used. The framework is easy to use for denoising and training and has minimal hardware requirements, thus, making it accessible for an average laboratory to create a custom version specific to their experimental setup. Neuroimage Denoiser significantly enhances the quality of functional microscopy recordings by effectively removing noise, thereby facilitating a more accurate and reliable analysis of neural activity. Highlights- Neuroimage Denoiser is a deep learning framework to remove noise from functional microscopic recordings, particularly trained and tested for glutamate imaging - Neuroimage Denoiser balances the removal of noise while preserving the amplitude of responses - Neuroimage Denoiser operates without re-training for different sensors (when the localization is similar) and recording frequencies MotivationAccurate measurements of neuronal activity through functional imaging are critical in understanding mechanisms of synaptic plasticity and learning concerning changes in the molecular composition of single synapses. Traditional denoising methods, such as Gaussian or Median filters, indiscriminately smooth entire recordings, reducing temporal and spatial resolutions considerably. Existing frameworks are not suited to remove noise from glutamate recordings due to the fast dynamics of the sensor. Therefore, a specialized tool for the challenges imposed by glutamate recordings, i.e. faster dynamics, and synaptic localization, is needed.

bioinformatics↗

bioRxiv2024

Direct CCUT: A Versatile and Standardized Framework to Train 3C Deep Restoration Models

Chromatin Capture Experiments such as Hi-C and Micro-C have become popular methods for genome architecture exploration. Recently, also a protocol for long read sequencing, Pore-C, was introduced, allowing the characterization of three-dimensional chromatin structures using Oxford Nanopore Sequencing Technology. Here, we present a framework that focuses on the efficient reconstruction of low-resolution Pore-C data but can also process all other 3C data, such as Hi-C and Micro-C matrices, using models that can be trained on a consumer GPU. Furthermore, we integrate building blocks of popular super-resolution methods such as SWIN-Transformer or residual-in-residual-blocks to modify or build customized networks on the fly. Pre-built models were trained and evaluated on multiple publicly available gold-standard Micro-C and Pore-C datasets, allowing for fine-scale structure prediction. Our work aims to overcome the drawback of high sequencing costs to construct high resolution contact matrices, as well as the problem of mapping low-coverage libraries to high-resolution structures in the genome. Although there have been major breakthroughs regarding NGS-based methods for the reconstruction of high-resolution chromatin interaction matrices from low-resolution data, for data obtained by long-read sequencing, there is currently no solution to reconstruct missing and sparse information and to improve the quality. AvailabilityThe tool is available at (https://github.com/stasys-hub/CCUT)

bioinformatics↗

bioRxiv2024

Cortexa - a comprehensive resource for studying gene expression and alternative splicing in the murine brain.

MotivationGene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate devel-oping harmonized data handling and processing pipelines which in turn requires considerable computational resources and in-depth bioinformatics expertise. ResultsHere, we present Cortexa - a comprehensive web-portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform also provides SplicePCA - a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All gene expression and alternative splicing data have been processed in a standardized manner and they can also be downloaded for further in-depth down-stream analysis. AvailabilityThe data portal is available at https://cortexa-rna.com/ Contacthristo.todorov@uni-mainz.de.

genomics↗

bioRxiv2023

Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cerebral cortex

microRNAs are particularly important during brain development, however, the composition and temporal dynamics of miRNA regulatory networks are not sufficiently characterized. Here, we performed small RNA sequencing of mouse embryonic cortical samples at E14, E17, and P0 as well as in neural progenitor cells differentiated in vitro into neurons. Using co-expression network analysis, we detected clusters of miRNAs that were co-regulated at distinct developmental stages. miRNAs such as miR-92a/b acted as hubs during early, and miR-124 and miR-137 during late neurogenesis. Notably, validated targets of P0 hub miRNAs were enriched for down-regulated genes related to stem cell proliferation, negative regulation of neuronal differentiation and RNA splicing, among others, suggesting that miRNAs are particularly important for modulating transcriptional programs of crucial factors that guide the switch to neuronal differentiation. As most genes contain binding sites for more than one miRNA, we furthermore constructed a co-targeting network where numerous miRNAs shared more targets than expected by chance. Using luciferase reporter assays, we demonstrated that simultaneous binding of miRNA pairs to neurodevelopmentally relevant genes exerted an enhanced transcriptional silencing effect compared to single miRNAs. Taken together, our study provides a comprehensive resource of miRNA longitudinal expression changes during corticogenesis. Furthermore, we highlight several potential mechanisms through which miRNA regulatory networks can shape embryonic brain development.

developmental biology↗