Biology subjects

Bissen, D.

Publications and source records attributed to Bissen, D..

4 recordsLinked to original sources

bioRxiv2026

A hyperactivating mutation in formin-like 2 causes brain and vertebrae defects

Formins are a group of proteins that nucleate and polymerize actin monomers into filaments to promote cell morphology and migration. Mutations in formin-encoding genes cause a broad spectrum of human diseases, ranging from kidney disease to non-syndromic intellectual disability. Through a forward genetics screen, we identified a mouse strain (nmf418) with a homozygous mutation in a splice acceptor in Fmnl2 that causes hindlimb motor dysfunction, enlarged midbrain colliculi, cerebellar hypoplasia, and vertebral abnormalities in the thoracolumbar spinal cord. The nmf418 mutation causes skipping of exon 10, which encodes a portion of the diaphanous inhibitory domain (DID) required for FMNL2 autoinhibition, and results in dramatically lower FMNL2 protein levels. Consistent with a gain-of-function mechanism, mutant FMNL2 has altered enrichment at filopodia tips, increased actin polymerization activity, and its expression results in enhanced cell migration in vitro. Furthermore, none of the neural or skeletal phenotypes were recapitulated in Fmnl2 knockout mice. Transcriptomic analysis of the embryonic cerebellum suggests that complete loss of Fmnl2 triggers broad compensatory upregulation of actin regulatory pathways, while expression of mutant FMNL2 is associated with dysregulation of cell migration, motility pathways, and calcium homeostasis pathways. Taken together, these findings suggest that Fmnl2 is largely dispensable for overall mouse development but that its autoregulation is essential for proper formation of the mouse nervous and skeletal systems.

genetics↗

bioRxiv2025

Rapid prey capture learning drives a slow resetting of network activity in rodent binocular visual cortex

Neurons within primary visual cortex (V1) possess stable firing rate set points to which they faithfully return when perturbed by passive sensory manipulations. Chronic recordings in vivo suggest that these set points are present early in postnatal development and are stable through adulthood, raising the possibility that once they are established, they are largely immutable. Here we challenge this idea, using an ethological, vision-dependent prey capture learning paradigm in juvenile (critical period) rats. Juvenile rats rapidly (in one day) became proficient at catching crickets. This learning was impaired when V1 was inactivated, and was accompanied by an increase in the number of binocular V1 (V1b) neurons with firing reliably tied to specific behavioral epochs during hunts, indicating that hunting drives rapid functional plasticity within V1b. Remarkably, this rapid learning set in motion a slow, state-dependent increase in V1b firing, that began after learning was complete and persisted for days. This upward firing rate plasticity was gradual, occurred selectively during wake states, and in L2/3 was driven by a TNF-dependent increase in excitatory synapses and excitatory synaptic charge onto pyramidal neurons - all features of homeostatic forms of plasticity within V1. Finally, TNF inhibition administered after learning was complete diminished this increase in excitability and impaired the retention of hunting skills. Taken together, these data suggest that naturalistic learning in juvenile animals co-opts homeostatic forms of plasticity to reset firing rate setpoints within V1b, in a process that facilitates skill consolidation.

neuroscience↗

bioRxiv2025

Prey capture learning drives critical period-specific plasticity in mouse binocular visual cortex

Critical periods are developmental windows of high experience-dependent plasticity essential for the correct refinement of neuronal circuitry and function. While the consequences for the visual system of sensory deprivation during the critical period have been well-characterized, far less is known about the effects of enhanced sensory experience. Here, we use prey capture learning to assess structural and functional plasticity mediating visual learning in the primary visual cortex of critical period mice. We show that prey capture learning improves temporal frequency discrimination and drives a profound remodeling of visual circuitry through an increase in excitatory connectivity and spine turnover. This global and persistent rewiring is not observed in adult hunters and is mediated by TNF-dependent mechanisms. Our findings demonstrate that enhanced visual experience in a naturalistic paradigm during the critical period can drive structural plasticity to improve visual function, and promotes a long-lasting increase in spine dynamics that could enhance subsequent plasticity.

neuroscience↗

bioRxiv2023

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

neuroscience↗