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Turpin-Moreno, I.

Publications and source records attributed to Turpin-Moreno, I..

2 recordsLinked to original sources

MIF overexpression upon SARS-CoV-2 infection induces neural regeneration in human-derived brain organoids

While SARS-CoV-2 primarily targets the respiratory system, its neurological effects have become a significant clinical concern. Postmortem analyses reveal astrogliosis, neuronal death, and blood-brain barrier dysfunction, yet the interplay between neural injury and endogenous repair remains unclear. Here, we employed human embryonic stem cell-derived brain organoids to examine viral tropism, bystander effects, and regenerative responses following infection. Single-cell transcriptomics and histological assays showed that SARS-CoV-2 productively infects neurons, neural progenitors, astroglia, and choroid plexus cells, triggering widespread apoptosis and senescence in both infected and neighboring cells. Despite low infection rates, organoids activated robust regenerative programs, including axon guidance, Wnt pathway signaling in mature neurons, and radial glia proliferation. Importantly, macrophage migration inhibitory factor (MIF) emerged as a key mediator, being strongly upregulated in both infected and uninfected cells, particularly in the choroid plexus. Recombinant MIF promoted dendritic outgrowth and cortical progenitor activation in uninfected organoids. Computational analyses indicated that MIF stimulates neural regenerative via EGFR signaling and upregulates its own expression in non-infected cells. These findings identify MIF as a molecular link between SARS-CoV-2-induced neural damage and regenerative activation in cortical cells.

cell biology↗

Variability vs Phenotype: multimodal analysis of Dravet Syndrome Brain Organoids powered by Deep Learning

Brain organoids (BO) have risen as a reliable model for neurodelopmental disorders (ND), reproducing human brain development milestones. However, their significant intra- and inter-organoid variability compromises their use in advanced tasks such as drug testing. Overcoming experimental variability is crucial for models prone to variation, like unguided BO. BO modelling in Dravet Syndrome, a late-onset epileptic ND, represents a great challenge since BO variability accumulates with time, when phenotype shows in vitro. Leveraging deep learning, we developed ImPheNet, a predictive tool grounded in BO live imaging datasets. ImPheNet accurately classified phenotypes and assessed drug toxicity in BO derived from DS, revealing differences between genotypes and upon antiseizure drug exposure. These results are supported by transcriptomic and functional data, revealing an excitatory-inhibitory imbalance during the maturation of DS organoids. Altogether, our DL-predictive live imaging strategy, ImPheNet, emerges as a powerful tool enhancing BO research and advancing ND treatments.

developmental biology↗