Biology subjects

Piraud, M.

Publications and source records attributed to Piraud, M..

14 recordsLinked to original sources

bioRxiv2026

A scalable human neuromuscular organoid platform enables lineage-specific analysis of drug responses in spinal muscular atrophy.

Scalable human models that capture interactions between distinct tissues remain limited, constraining mechanistic insight and therapeutic prediction. Here, we established a scalable, automation-compatible human neuromuscular organoid (NMO) platform that enables integrated analysis of neuronal and muscle lineages in spinal muscular atrophy (SMA). Patient-derived NMOs reproducibly self-organise into spinal cord and skeletal muscle compartments and form functional neuromuscular circuits. SMA NMOs recapitulate early disease features, including reduced survival motor neuron (SMN) protein levels and impaired neuromuscular junction (NMJ) maturation. Single-nucleus RNA sequencing identifies lineage-specific transcriptional changes across neuronal and muscle compartments preceding functional deficits. Using this platform, we compared two clinically relevant SMN2 splicing modulators and observed distinct, cell-type-dependent responses. While both compounds increased SMN levels and NMJ number, only one enhanced myofiber growth and improved contractile function. These findings highlight muscle maturation, rather than NMJ number alone, as a key determinant of functional recovery and establish NMOs as a scalable system for studying cell-type-specific therapeutic responses.

bioengineering↗

bioRxiv2026

Lifespan Analysis of the Lung Epithelium Reveals Inflammatory Reprogramming and Regenerative Decline

Aging is a major risk factor for chronic lung diseases, associated with chronic low-grade inflammation (inflammaging) and impaired epithelial regeneration. How epithelial-intrinsic aging intersects with inflammaging across the lifespan remains poorly understood. Here, we systematically analyzed lung epithelial cells from neonatal, young adult, and aged mice to define age-dependent changes in regenerative capacity. RNA sequencing revealed lifespan-associated shifts characterized by early repression of developmental and WNT/{beta}-catenin programs and progressive activation of DNA damage, inflammation, and senescence signatures. Functionally, neonatal epithelial cells exhibited markedly enhanced organoid-forming capacity compared with young and aged cells. Aged organoids maintained a pro-inflammatory secretory profile indicative of cell-intrinsic inflammaging, and transfer of the aged secretome or TNF- to young cultures significantly impaired regeneration. Comparison of freshly isolated cells and long-term organoid cultures revealed sustained repression of regenerative pathways with age, consistent with stable epigenetic imprinting. Pharmacological inhibition of DNA methylation and WNT signalling partially restored regenerative capacity in adult organoids. Together, these findings identify epigenetic reprogramming and epithelial-intrinsic inflammaging as key determinants of age-dependent regenerative decline.

cell biology↗

bioRxiv2025

Cortex-Wide Preservation of Multi-Stimulus Information across Degrees of Network Synchronization

Sensory-evoked cortical responses vary with global network dynamics, yet the link between cortical state and stimulus processing remains unclear. Here, we introduce a paradigm that jointly decodes stimulus identity and network state from wide-field calcium imaging in mice undergoing multisensory and optogenetic stimulation across isoflurane-induced transitions from compartmentalized to synchronized activity. Effective dimensionality, a summary measure of network complexity, correlated well with anesthesia depth, while non-linear contrastive learning achieved >97% stimulus decoding accuracy across all states. Individual cortical regions maintained [≥]82.5% accuracy even during deep anesthesia with prominent slow waves. Preservation of stimulus-specific information extended throughout the cortex, demonstrating that distinct representations remain decodable within synchronized networks. Direct optogenetic cortical stimulation exhibited state-invariant decoding performance, contrasting with anesthesia-dependent decline observed for sensory stimuli. Multi-stimulus cortical representations remain decodable across varying levels of network synchronization, with implications for brain-machine interfaces and clinical tools that assess preserved cortical responsiveness under variable arousal conditions.

neuroscience↗

bioRxiv2025

Guided maturation of human neuromuscular organoids via electrical stimulation

Organoids derived from human pluripotent stem cells (hPSCs) are emerging as powerful models for studying development and disease. Despite their physiological relevance, the predictive power of organoids remains limited by the immature state of the constituent cells, posing a major challenge for mechanistic studies of adult physiology and late-onset diseases and disorders. Here, we establish a strategy for enhancing the maturation status of human neuromuscular organoids (NMOs) through chronic Electrical Pulse Stimulation (EPS). We demonstrate that low-frequency EPS, applied early on during NMO development and maintained over several weeks, promotes structural and functional maturation of neuromuscular junctions (NMJs). Independent of stimulation waveform dynamics, EPS-trained NMOs (EPS-NMOs) displayed stronger and more frequent spontaneous contractions that persisted long after stimulation ceased. Quantitative imaging and transcriptomic analyses revealed a robust improvement in EPS-NMO skeletal muscle and neural tissue morphology, coordinated regulation of lineage-specific biomarkers, and upregulation of gene programmes associated with mature neuromuscular function. Mechanobiological measurements further demonstrated increased EPS-NMO tissue stiffness and faster relaxation dynamics, consistent with advanced excitation-contraction coupling and force generation. Collectively, these findings establish EPS as a powerful, non-invasive, and on-demand modality for driving the morphological and functional maturation of complex organoid systems.

bioengineering↗

bioRxiv2025

Formation of amyloid-like HTTex1 aggregates in neurons, downregulation of synaptic proteins and early mortality of Huntington's disease flies are causally linked

Amyloidogenic mutant huntingtin exon-1 (mHTTex1) protein aggregates with pathogenic polyglutamine (polyQ) tracts are the potential root cause of Huntingtons disease (HD). Here, we assessed the gain-of-function toxicity of mHTTex1 aggregation in neurons of HD transgenic flies. We show that the rate of mHTTex1 aggregation in neurons and early mortality of HD transgenic flies are correlated. We observed sequestration of key synaptic proteins into amyloid-like mHTTex1 aggregates and a concomitant decrease of their transcript levels, suggesting that progressive mHTTex1 aggregate stress in neurons leads to an impairment of synaptic function. Machine learning-based data analysis revealed that the abundance of synaptic proteins such as the vesicular monoamine transporter Vmat in the brain is predictive of fly survival. RNAi knockdown of Vmat-encoding transcripts in neurons with pathogenic amyloid-like HTTex1Q97 aggregates further shortened the lifespan of HD flies, supporting the hypothesis that mHTTex1 aggregation drives impairment of synaptic processes and pathogenesis of HD.

molecular biology↗

bioRxiv2024

Deep Learning and 3D Imaging Reveal Whole-Body Alterations in Obesity

Many diseases, such as obesity, have systemic effects that impact multiple organ systems throughout the body. However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here, we developed a suite of deep learning-based image analysis algorithms (MouseMapper) and integrated it with tissue clearing and light-sheet microscopy to enable a comprehensive analysis of diseases impacting diverse systems across the mouse body. This approach enables the quantitative analysis of cellular and structural changes across the entire mouse body at unprecedented resolution and scale, including tracking nerves over several centimeters in whole animal bodies. To demonstrate its power, we applied MouseMapper to study nervous and immune systems in high-fat diet induced obesity. We uncovered widespread changes in both immune cell distribution and nerve structures, including alterations in the trigeminal nerve characterized by a reduced number of nerve endings in obese mice. These structural abnormalities were associated with functional deficits of whisker sensing and proteomic changes in the trigeminal ganglion, primarily affecting pathways related to axon growth and the complement system. Additionally, we found heterogeneity in obesity-induced whole-body inflammation across different tissues and organs. Our study demonstrates MouseMappers capability to discover and quantify pathological alterations at the whole-body level, offering a powerful approach for investigating the systemic impacts of various diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/608300v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@17d2601org.highwire.dtl.DTLVardef@14a2d3dorg.highwire.dtl.DTLVardef@e04114org.highwire.dtl.DTLVardef@d0b8b5_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe developed MouseMapper: AI-driven pipeline for whole-body structural analysis in mice C_LIO_LIMouseMapper revealed obesity-induced changes in nerves and immune cells across multiple organs C_LIO_LIMouseMapper identified facial nerve alterations in obesity linked to impaired whisker sensitivity C_LIO_LIMouseMapper has the potential for holistic 3D analysis of systemic diseases C_LI Supplementary Videos can be seen at: http://discotechnologies.org/MouseMapper/

systems biology↗

bioRxiv2024

DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties

With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages of the development process. Given the current success of deep generative models across domains, we here investigated their application to the property-based proposal of new small molecules for drug development. Specifically, we trained a latent diffusion model -- DrugDiff -- paired with predictor guidance to generate novel compounds with a variety of desired molecular properties. The architecture was designed to be highly flexible and easily adaptable to future scenarios. Our experiments showed successful generation of unique, diverse and novel small molecules with targeted properties. The code is available at https://github.com/MarieOestreich/DrugDiff.

bioinformatics↗

bioRxiv2024

TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks

Accurately annotating molecular function to enzymes remains challenging. Computational methods can aid in this and allow for high-throughput annotation. Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if for certain sequences a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from (predicted) enzyme structures and predict Enzyme Commission (EC) classes. Using the message passing frameworks from SchNet and DimeNet++, we include distance and angle information to improve the predictive performance compared to regular 2D graph neural networks. We obtained significantly improved EC classification prediction (F-score: 0.72) without fold bias at residue and atomic resolutions and trained networks that can classify both experimental and computationally generated enzyme structures for a vast functional space (> 800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. By investigating the importance of each graph node to the predictive performance, we see that TopEC networks learn from an interplay between biochemical features and local shape-dependent features.

bioinformatics↗

bioRxiv2023

MISATO - Machine learning dataset for structure-based drug discovery

Large language models (LLMs) have greatly enhanced our ability to understand biology and chemistry. Yet, relatively few robust methods have been reported for structure-based drug discovery. Highly precise biomolecule-ligand interaction datasets are urgently needed in particular for LLMs, that require extensive training data. We present MISATO, the first dataset that combines quantum mechanics properties of small molecules and associated molecular dynamics simulations of about 20000 experimental protein-ligand complexes. Starting from the PDBbind dataset, semi-empirical quantum mechanics was used to systematically refine these structures. The largest collection to date of molecular dynamics traces of protein-ligand complexes in explicit water are included, accumulating to 170 s. We give ML baseline models and simple Python data loaders, and aim to foster a thriving community around MISATO (https://github.com/t7morgen/misato-dataset). An easy entry point for ML experts is provided without the need of deep domain expertise to enable the next generation of drug discovery AI models.

bioinformatics↗

bioRxiv2023

Formate Promotes Invasion and Metastasis by Activating Fatty Acid Synthesis and Matrix Metalloproteinases

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon cycle, in which the third carbon of serine is oxidized to formate. We have previously shown that formate production in cancer cells often exceeds the anabolic demand, resulting in formate overflow. Furthermore, we observed that high extracellular formate promotes the in vitro invasiveness of glioblastoma (GBM) cells. However, additional data supporting this in vitro observation and mechanistic details remained elusive so far. In the present study, we now demonstrate that inhibition of formate overflow results in a decreased invasiveness of GBM cells ex vivo and in vivo. Additionally, we observed that exposure to exogeneous formate can induce a transiently stable pro-invasive phenotype that results in increased metastasis formation in vivo. All in all, these results suggest that a local formate increase within the tumor microenvironment may be one factor that can promote cancer cell motility and dissemination. Mechanistically, we uncover a previously undescribed interplay where formate acts as a trigger to alter fatty acid metabolism and matrix metalloproteinase (MMP) activity which in turn impacts cancer cell invasiveness. We thus highlight the role of formate as a pro-invasive metabolite. Gaining a deeper understanding of formate overflow and how it promotes invasion in cancer, may open new therapeutic opportunities to prevent cancer cell dissmination.

cancer biology↗

bioRxiv2022

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

bioinformatics↗

bioRxiv2022

Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning

CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

bioinformatics↗

bioRxiv2019

Automated analysis of whole brain vasculature using machine learning

Tissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC=\"FIGDIR/small/613257_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (51K):\norg.highwire.dtl.DTLVardef@8c79eborg.highwire.dtl.DTLVardef@984d3aorg.highwire.dtl.DTLVardef@f62d9borg.highwire.dtl.DTLVardef@2c20bc_HPS_FORMAT_FIGEXP M_FIG Supporting material of VesSAP is available at http://DISCOtechnologies.org/VesSAP\n\nC_FIG

neuroscience↗

bioRxiv2019

Towards Quantitative Imaging Biomarkers of Tumor Dissemination: a Multi-scale Parametric Modeling of Multiple Myeloma

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.

bioinformatics↗