Biology subjects

Morante-Redolat, J. M.

Publications and source records attributed to Morante-Redolat, J. M..

5 recordsLinked to original sources

bioRxiv2026

Polo-like kinase 1 controls activation of adult neural stem cells and represents a druggable entry-point to boost adult neurogenesis

Neural stem cells (NSCs) persist in the mammalian brain after development, but their potential for replacing neural populations lost in brain damage is dramatically restricted. Quiescence, or the ability to transition in and out of the cell cycle is a hallmark of adult NSCs. It deepens as the organism ages and is regarded as a strategy to protect the reservoirs of NSC for the long term. Deciphering the molecular code of dormancy is a major interest in cellular and molecular biology of ageing and in regenerative medicine since it has important implications for potentially manipulating NSCs to improve tissular homeostasis. In this work we have identified the mitotic kinase Polo like kinase 1 (Plk1) as a novel intrinsic regulator of the dynamic transition between NSC quiescence and activation in adult neurogenic niches. Using quantitative phosphoproteomics to pinpoint mechanistical mediators of the role of Plk1, we have identified the quiescence/activation molecular switch formed by E3 ubiquitin ligase Huwe1 and its target, the transcription factor Ascl1/Mash1, as novel effectors of Plk1 in NSCs. We discover that Plk1 restrains the accumulation of Ascl1/Mash1 by acting upstream of Huwe1, providing a direct molecular connection between completion of the cell cycle and the re-establishment of a low activation state following NSC division, which supports the concept that quiescence is an actively maintained state. Moreover we demonstrate that pharmacological inhibition of Plk1 is sufficient to enhance adult neurogenesis in vivo, identifying Plk1 as a viable target to acutely boost endogenous stem cell activation.

neuroscience↗

bioRxiv2025

To join or not to join: handling biological replicates in long-read RNA sequencing data

Long-read RNA sequencing (lrRNA-seq) has revolutionized transcriptomics facilitating the study of alternative splicing and resulting in identification of thousands of novel transcripts. While isoform identification has received significant attention, the handling of biologically replicated lrRNA-seq datasets remains less explored. However, how multiple samples are combined in a lrRNA-seq study may strongly impact transcript identification. This study defines and evaluates two strategies for obtaining consensus transcriptomes from multi-sample lrRNA-seq data: "Join & Call", where reads from all samples are combined before transcript identification, and "Call & Join", where transcript identification is performed on individual samples before combining the resulting annotations. We applied these strategies to a highly replicated dataset of mouse brain and kidney tissues, using both PacBio and ONT technologies, across six widely used transcript reconstruction tools. Our results indicate that the optimal strategy depends on the chosen computational tool and research objective. We found that Join & Call is generally more suitable for discovering rarely occurring, novel isoforms, as pooling evidence increases confidence in calling lowly-expressed transcripts. Conversely, Call & Join is computationally more efficient and often preferable for highly replicated datasets when the investigation of rare novel transcripts is not the primary objective. Our findings provide a conceptual and practical framework for multi-sample transcriptome reconstruction, guiding best practices in the context of increasingly large-scale lrRNA-seq studies.

bioinformatics↗

bioRxiv2025

YAP/TAZ create a physical niche for the maintenance of adult neural stem cell quiescence

Adult stem cells inhabit specialized niches where local and systemic cues regulate their behavior. In the mouse ventricular-subventricular zone (V-SVZ), neural stem cells (NSCs) dynamically transition between quiescence and activation and reside amidst unique deposits of extracellular matrix (ECM) known as fractones. We show that NSCs that enter quiescence in response to BMP4 secrete a complex ECM that, on its own, is capable of inducing NSC quiescence. This specific ECM triggers the nuclear translocation of Yes-associated protein (YAP), to induce further ECM remodeling and adhesion. Together, the BMP-ECM-YAP pathway creates a two-step mechanism where a soluble and transient quiescence-inducing signal leads to the formation of a physical niche to maintain the quiescent state. In the intact niche, YAP and its paralog TAZ (Transcriptional coactivator with PDZ-binding motif) essentially sustain quiescence by preserving fractones and the characteristic structural organization. Moreover, our findings reveal a previously unrecognized role for YAP/TAZ in quiescence.

neuroscience↗

bioRxiv2023

AimSeg: a machine-learning-aided tool for axon, inner tongue and myelin segmentation

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements done manually from EM images - a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner myelin tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results show good performance segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth. Author SummaryMyelin is formed by specialised cells that wrap themselves around axons and has a major role in the function, protection, and maintenance of nerves. These functions are disturbed by demyelinating diseases, such as multiple sclerosis. In this work we present AimSeg, a new tool based on artificial intelligence algorithms (machine learning) to assess myelin growth on electron microscopy images. Whereas standard metrics and previous computational methods focus on quantifying compact myelin, AimSeg also quantifies the inner myelin tongue (uncompacted myelin). This structure has been largely overlooked despite the fact that it has an important role in the process of myelin growth (both during development and in the adult brain) and recent studies have reported morphological changes associated with some diseases. We report the performance of AimSeg, both as a fully automated approach and in an assisted segmentation workflow that enables the user to curate the results on-the-fly while reducing human intervention to the minimum. Therefore, AimSeg stands as a novel bioimage analysis tool that meets the challenges of assessing myelin growth by supporting both standard metrics for myelin evaluation and the quantification of the uncompacted myelin in different conditions.

bioinformatics↗

bioRxiv2022

The rates of adult neurogenesis and oligodendrogenesis are linked to cell cycle regulation through p27-dependent gene repression of SOX2

Cell differentiation involves profound changes in global gene expression that often have to occur in coordination with cell cycle exit. Because cyclin-dependent kinase inhibitor p27 reportedly regulates proliferation of neural progenitor cells in the subependymal neurogenic niche of the adult mouse brain, but can also have effects on gene expression, we decided to molecularly analyze its role in adult neurogenesis and oligodendrogenesis. At the cell level, we show that p27 restricts residual cyclin-dependent kinase activity after mitogen withdrawal to antagonize cycling, but it is not essential for cell cycle exit. Contrasting gene expression with chromatin accessibility, we find that p27 is coincidentally necessary to globally repress many genes involved in the transit from multipotentiality to differentiation, including those coding for neural progenitor transcription factors SOX2, OLIG2, and ASCL1. Our data reveal both direct association of p27 with regulatory sequences in the three genes and an additional hierarchical relationship where p27 repression of the Sox2 gene leads to reduced levels of SOX2-downstream targets Olig2 and Ascl1. In vivo, p27 is also required for the regulation of the proper level of SOX2 necessary for neuroblasts and oligodendroglial progenitor cells to timely exit cell cycle in a lineage-dependent manner.

neuroscience↗