Biology subjects

De Koninck, P.

Publications and source records attributed to De Koninck, P..

12 recordsLinked to original sources

bioRxiv2026

Signatures of brain-wide functional maturation in larval zebrafish

Brain development involves the coordinated organization of cellular activity into distributed functional networks, yet how this organization emerges across an entire vertebrate brain remains poorly understood. Here, we combined brain-wide structural and functional imaging to characterize brain maturation in larval zebrafish from 4 to 8 days post-fertilization. Brain growth was regionally heterogeneous and accompanied by progressive remodeling of mesoscale functional networks, with functional connectivity strengthening or weakening within distinct subnetworks. Spontaneous activity became more correlated in regions exhibiting pronounced neurogenesis, as revealed by HaloTag-based neuronal birthdating. Functional interactions became progressively less constrained by physical distance, while network organization became more modular, suggesting that long-range functional integration develops alongside increasing segregation of large-scale brain systems. At the cellular level, neuronal activity consolidated into larger, more modular functional ensembles whose combinations increasingly differentiated mesoscale modules. Visually responsive neurons redistributed along visual processing pathways, becoming more prevalent in regions further removed from retinal input, while motor-related activity declined broadly across the brain. The forebrain emerged as a major locus of maturation, exhibiting substantial anatomical expansion and a growing population of neurons displaying intrinsic activity fluctuations distinct from sensory and motor processes. Together, these findings reveal coordinated anatomical, cellular, and network-level signatures of early vertebrate brain maturation.

neuroscience↗

bioRxiv2025

Function aligns with geometry in locally connected neuronal networks

The geometry of the brain imposes fundamental constraints on neuronal network organization and dynamics, yet how these constraints give rise to observed patterns of brain activity remains unclear. Here, we investigate how geometric eigenmodes relate to functional connectivity gradients in three-dimensional neural systems using a combination of generative network simulations and cellular-resolution calcium imaging in larval zebrafish. We show that functional connectivity gradients emerging from network activity naturally align with the geometric eigenmodes of the underlying spatial embedding when connectivity is predominantly local. By systematically increasing the prevalence of long-range connections, we reveal a robust geometry-function correspondence that progressively deteriorates as local connectivity is disrupted. We then show that spatial filtering can artificially imprint geometric patterns on functional gradients, highlighting an important methodological confound. To validate our computational results, we conduct volumetric calcium imaging experiments at cellular resolution in the optic tectum of zebrafish larvae, uncovering functional gradients that closely align with geometric eigenmodes. As predicted from simulations, the eigenmode-gradient mapping exhibits a cutoff point that quantitatively reflects the spatial extent of the regions connectivity kernel, inferred from single-neuron morphologies. This geometry-functional alignment disappears at brain-wide scale, where long-range connections are more prevalent. Our findings demonstrate how short-range anatomical connectivity anchors functional connectivity gradients to the brains geometry.

neuroscience↗

bioRxiv2025

Enhancing STED Microscopy via Fluorescence Lifetime Unmixing and Filtering in Two-Species SPLIT-STED

Simultaneous super-resolution imaging of multiple fluorophores remains a major challenge in STimulated Emission Depletion (STED) microscopy due to spectral overlap of STED-compatible fluorophores. The combination of STED microscopy and Fluorescence Lifetime Imaging Microscopy (FLIM) offers a powerful alternative for super-resolved, multiplexed imaging of biological samples but is hindered by lifetime convergence at high depletion powers. Here, we present an analysis method, two-species Separation of Photons by LIfetime Tuning (SPLIT)-STED, that uses a linear system of equations in phasor-based STED-FLIM to enhance both fluorophore unmixing and spatial resolution. It defines the fluorescence signal as a mixture of three lifetime components: the two target fluorophores and a short-lifetime contribution from undepleted peripheral fluorescence photons. Two-species SPLIT-STED disentangles overlapping lifetimes and selectively filters low-resolution signal. The method enables accurate unmixing of spectrally overlapping fluorophores and, by enhancing resolution through lifetime-based filtering, allows the use of lower depletion powers, thereby improving fluorescence lifetime separation.

bioinformatics↗

bioRxiv2025

A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy

Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a variety of tasks. The performance increase they offer is obtained without elaborated specific trainings for domains such as natural language processing and computer vision. However, their application in specialized fields like biomedical imaging and fluorescence microscopy remains difficult due to distribution shifts and the scarcity of high-quality annotated datasets. The high cost of data acquisition and the requirement for in-domain expertise further exacerbate this challenge in microscopy. To address this we introduce STED-FM, a foundation model specifically designed for super-resolution STimulated Emission Depletion (STED) microscopy. STED-FM leverages a Vision Transformer architecture trained at scale with Masked Autoencoding on a new dataset of nearly one million STED images. STED-FM learns expressive latent representations without requiring extensive annotations, yielding robust performance across diverse downstream microscopy image analysis tasks. Unsupervised experiments demonstrate the discriminative structure of its learned latent space. These representations can be leveraged for multiple downstream applications, including fully supervised classification and segmentation with reduced annotation requirements. Moreover, STED-FM representations enhance the performance of deep learning-based image denoising and improve the quality of images generated by diffusion models, enabling latent attribute manipulation for the data-driven discovery of subtle nanostructures and phenotypes, as well as algorithmic super-resolution. Moreover, its powerful structure retrieval capabilities are integrated into automated STED microscopy acquisition pipelines, paving the way for smart microscopy. In sum, we demonstrate that STED-FM lays a robust foundation for state-of-the-art algorithms across a wide array of tasks, establishing it as a highly valuable and scalable resource for researchers in super-resolution microscopy.

bioinformatics↗

bioRxiv2024

Structural and genetic determinants of zebrafish functional brain networks

Network science has significantly advanced our understanding of brain networks across species, revealing universal connectivity principles. While human studies based on magnetic resonance imaging (MRI) have established several network principles at macroscopic scales, recent breakthroughs, including high-amplitude regional co-activation patterns and spatially contiguous functional gradients, remain unexplored at cellular resolution in animal models. Here, we employ whole-brain functional imaging at cellular resolution in larval zebrafish, combined with anatomical and spatial genetic expression profile databases, to investigate the structural and genetic basis of functional brain networks. We show that mesoscopic functional connectivity (FC) is a robust measure of brain activity that captures the individuality of larvae. Using a public dataset of thousands of single-neuron reconstructions, we reveal a strong coupling between FC and structural connectivity (SC). Numerous properties of the connectome that account for indirect pathways and diffusion mechanisms individually and collectively predict interregional correlations. The hierarchical modular structure of SC and FC significantly overlaps in space, and modules identified within the connectome constrain the shape of both spontaneous and stimulus-driven activity patterns. Using visual stimuli and tail monitoring, we identify a functional network gradient that maps onto the sensorimotor function of brain regions. Finally, we identify a set of genes whose co-expression in brain regions significantly predicts regional FC. Our findings reproduce several key features of mammalian brain networks in zebrafish, demonstrating the potential for studying large-scale network phenomena in smaller, optically accessible vertebrate brains.

neuroscience↗

bioRxiv2024

Development of sensorimotor responses in larval zebrafish: a comparison between wild-type and GCaMP6s transgenic line

During early development, zebrafish larvae exhibit stereotypical behaviors, which rapidly become more complex. Thus, the generation of mutant transgenic lines that maintain transparency throughout their larval stage and that can be used to record brain activity has offered strategic opportunities to investigate the underlying neural correlates of behavior establishment. However, few studies have documented the behavioral profile of these lines during larval development. Here, we set up a behavioral characterization using diverse stimuli (light and vibration) throughout larval development to compare the responses of a transgenic strain expressing a pan-neuronal calcium indicator (GCaMP6s) with that of a wild-type strain. Interestingly, we report a drastic switch in behavioral responses to light transitions at 11 days post-fertilization (dpf) and to vibration stimuli at 14 dpf in both lines. These data highlight a specific time window of behavioral complexification. Meanwhile, we found no major difference in the maturation of sensorimotor responses between GCaMP6s and wild-type strains. Thus, these results support using GCaMP6s strain in investigating the neural mechanisms underlying the developmental maturation of sensorimotor responses. We observed nevertheless some minor differences that suggest careful attention should be taken when using mutant/transgenic lines for behavioral studies. Highlights- Longitudinal investigation of sensorimotor responses by zebrafish during their larval development - During the second week of development, larval zebrafish switch their motor response to light transition - Pan-neuronal nuclear expression of GCaMP6s has little impact on larval fish response to various stimuli

neuroscience↗

bioRxiv2024

Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning

Ca2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed analysis of dynamic processes across various scales. Enhanced by high-contrast optical microscopy and fluorescent Ca2+ sensors, this technique can be used to reveal localized Ca2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Nets performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.

bioinformatics↗

bioRxiv2021

MICRA-Net: MICRoscopy Analysis Neural Network to solve detection, classification, and segmentation from a single simple auxiliary task

High throughput quantitative analysis of microscopy images presents a challenge due to the complexity of the image content and the difficulty to retrieve precisely annotated datasets. In this paper we introduce a weakly-supervised MICRoscopy Analysis neural network (MICRA-Net) that can be trained on a simple main classification task using image-level annotations to solve multiple the more complex auxiliary semantic segmentation task and other associated tasks such as detection or enumeration. MICRA-Net relies on the latent information embedded within a trained model to achieve performances similar to state-of-the-art architectures when no precisely annotated dataset is available. This learnt information is extracted from the network using gradient class activation maps, which are combined to generate detailed feature maps of the biological structures of interest. We demonstrate how MICRA-Net significantly alleviates the Expert annotation process on various microscopy datasets and can be used for high-throughput quantitative analysis of microscopy images.

bioinformatics↗

bioRxiv2020

Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons

The nanoscale organization of the F-actin cytoskeleton in neurons comprises membrane-associated periodical rings, bundles, and longitudinal fibers. The F-actin rings have been observed predominantly in axons but only sporadically in dendrites, where fluorescence nanoscopy reveals various patterns of F-actin arranged in mixed patches. These complex dendritic F-actin patterns pose a challenge for investigating quantitatively their regulatory mechanisms. We developed here a weakly supervised deep learning segmentation approach of fluorescence nanoscopy images of F-actin in cultured hippocampal neurons. This approach enabled the quantitative assessment of F-actin remodeling, revealing the disappearance of the rings during neuronal activity in dendrites, but not in axons. The dendritic F-actin cytoskeleton of activated neurons remodeled into longitudinal fibers. We show that this activity-dependent remodeling involves Ca2+ and NMDA-dependent mechanisms. This highly dynamic restructuring of dendritic F-actin based submembrane lattice into longitudinal fibers may serve to support activity-dependent membrane remodeling, protein trafficking and neuronal plasticity.

neuroscience↗

bioRxiv2020

Nano-positioning and tubuline conformation determine transport of mitochondria along microtubules

Correct spatiotemporal distribution of organelles and vesicles is crucial for healthy cell functioning and is regulated by intracellular transport mechanisms. Controlled transport of bulky mitochondria is especially important in polarized cells such as neurons that rely on these organelles to locally produce energy and buffer calcium. Mitochondrial transport requires and depends on microtubules which fill much of the available axonal space. How mitochondrial transport is affected by their position within the microtubule bundles is not known. Here, we found that anterograde transport, driven by kinesin motors, is susceptible to the molecular conformation of tubulin both in vitro and in vivo. Anterograde velocities negatively correlate with the density of elongated tubulin dimers, similar to GTP-tubulin, that are more straight and rigid. The impact of the tubulin conformation depends primarily on where a mitochondrion is positioned, either within or at the rim of microtubule bundle. Increasing elongated tubulin levels lowers the number of motile anterograde mitochondria within the microtubule bundle and increases anterograde transport speed at the microtubule bundle rim. We demonstrate that the increased kinesin step processivity on microtubules consisting of elongated dimers underlies increased mitochondrial dynamics. Our work indicates that the molecular conformation of tubulin controls mitochondrial motility and as such locally regulates the distribution of mitochondria along axons.

neuroscience↗

bioRxiv2020

The dynamic interplay between ATP/ADP levels and autophagy sustain neuronal migration in vivo

Cell migration is a dynamic process that entails extensive protein synthesis and recycling, structural remodeling, and a considerable bioenergetic demand. Autophagy is one of the pathways that maintain cellular homeostasis. Time-lapse imaging of autophagosomes and ATP/ADP levels in migrating cells in the rostral migratory stream of mice revealed that decrease in ATP levels force cells into the stationary phase and induce autophagy. Genetic impairment of autophagy in neuroblasts using either inducible conditional mice or CRISPR/Cas9 gene editing decreased cell migration due to the longer duration of the stationary phase. Autophagy is modulated in response to migration-promoting and inhibiting molecular cues and is required for the recycling of focal adhesions. Our results show that autophagy and energy consumption act in concert in migrating cells to dynamically regulate the pace and periodicity of the migratory and stationary phases in order to sustain neuronal migration. HighlightsO_LIADP levels dynamically change during cell migration C_LIO_LIA decrease in ATP levels leads to cell pausing and autophagy induction via AMPK C_LIO_LIAutophagy is required to sustain neuronal migration by recycling focal adhesions C_LIO_LIAutophagy level is dynamically modulated by migration-promoting and inhibiting cues C_LI

neuroscience↗

bioRxiv2018

Activity-dependent netrin-1 secretion drives synaptic insertion of GluA1-containing AMPA receptors in the hippocampus.

Dynamic trafficking of -amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid glutamate receptors (AMPARs) to synapses is critical for activity-dependent synaptic plasticity underlying learning and memory, however the identity of key molecular effectors remains elusive. Here, we demonstrate that membrane depolarization and N-methyl-D-aspartate receptor (NMDAR) activation triggers secretion of the chemotropic guidance cue netrin-1 from dendrites. Using selective genetic deletion, we show that netrin-1 expression by excitatory neurons is required for NMDAR-dependent long-term potentiation (LTP) in the adult hippocampus. Further, we demonstrate that application of exogenous netrin-1 is sufficient to trigger the potentiation of excitatory glutamatergic transmission at hippocampal Schaffer collateral synapses via Ca2+-dependent recruitment of GluA1-containing AMPARs, promoting the maturation of immature or nascent synapses. These findings identify a central role for activity-dependent release of netrin-1 as a critical effector of synaptic plasticity in the adult hippocampus.

neuroscience↗