Biology subjects

Benisty, H.

Publications and source records attributed to Benisty, H..

12 recordsLinked to original sources

bioRxiv2026

Spatial remapping in the subicular complex and entorhinal cortex follows 1 low-dimensional geometric principles

Neurons in the hippocampal formation are part of the brains cognitive map, representing the spatial structure of the environment through coordinated activity across place cells, grid cells, border cells, head-direction cells and others1-5. Although remapping between environments has been extensively documented6-9, it remains unknown whether transitions between maps reflect unconstrained reorganization or obey a systematic transformation principle. To address this question, we recorded large neuronal populations from the subicular complex and entorhinal cortex in awake, behaving mice navigating environments spanning a wide range of geometries. We asked whether spatial representations across rooms could be related through a shared class of coordinate transformations. Despite pronounced heterogeneity and apparent randomness in single-cell remapping, population-level decoding across environments demonstrated a consistent low-dimensional affine transformation of coordinates, comprising rotation, scaling, shear, reflection, and translation. Thus, what appears as complex remapping at the level of individual neurons reduces to a compact geometric rule at the level of neural assemblies. These results indicate that the hippocampal formation maintains a structured internal coordinate template that is flexibly tailored to environmental geometry. This may serve as the organisms internal model of space.

neuroscience↗

bioRxiv2025

Unraveling Temporal Dynamics of SPSD matrices viaRiemmanian Multi-Scale Decomposition

Functional connectivity (FC) is fundamentally non-stationary, undergoing continuous reconfigurations that track shifting behavioral and cognitive states. Despite the importance of these transitions, existing analytical frameworks struggle to reconcile the high-dimensional nature of these reconfigurations with the need for structured trajectories and mechanistic interpretability. Specifically, identifying the precise network components responsible for driving these dynamics remains a significant challenge. To bridge this gap, we introduce RONI (Riemannian filtering Of Network Interactions), a geometry-aware, unsupervised framework that treats time-varying FC as a signal evolving on the manifold of symmetric positive semidefinite (SPSD) matrices. By defining intrinsic filtering operators along the SPSD geodesics, RONI enables a principled multiresolution decomposition of FC dynamics directly on the manifold. This representation allows for the isolation of "dynamic drivers" - specific network elements that dominate coherent connectivity modes across distinct temporal scales. We demonstrate the versatility of RONI by applying it to a diverse array of large-scale neural recordings, including hippocampal electrophysiology data, cortex-wide and dendritic calcium imaging, and human EEG. Across these varied modalities and spatio-temporal scales, RONI identifies biologically meaningful sub-networks that shape the geometry of the connectivity trajectory, providing a unified, interpretable, and quantitative framework for studying the evolution of distributed neural interactions.

neuroscience↗

bioRxiv2023

VTA projections to M1 are essential for reorganization of layer 2-3 network dynamics underlying motor learning

The primary motor cortex (M1) is crucial for motor skill learning. Previous studies demonstrated that skill acquisition requires dopaminergic VTA (ventral-tegmental area) signaling in M1, however little is known regarding the effect of these inputs at the neuronal and network levels. Using dexterity task, calcium imaging, chemogenetic silencing, and geometric data analysis, we demonstrate VTA-dependent reorganization of M1 layer 2-3 during motor learning. While average activity and average functional connectivity of layer 2-3 network remain stable during learning, the activity kinetics, the correlational configuration of functional connectivity, and average connectivity strength of layer 2-3 neurons gradually transform towards an expert configuration. In addition, task success-failure outcome signaling gradually emerges. Silencing VTA dopaminergic inputs to M1 during learning, prevents all these changes. Our findings demonstrate dopaminergic VTA-dependent formation of outcome signaling and new connectivity configuration of the layer 2-3 network, supporting reorganization of the M1 network for storing new motor skills.

neuroscience↗

bioRxiv2022

The Cousa objective: a long working distance air objective for multiphoton imaging in vivo

Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue, but applying this technique in vivo can be limited by short working distance water-immersion objectives. Here we present an ultra long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a > 4 mm2 field-of-view with submicron lateral resolution, and is compatible with commonly used multiphoton imaging systems. We share the full optical prescription, and report performance including in vivo 2-photon and 3-photon imaging in a range of species and preparations.

neuroscience↗

bioRxiv2022

Using protein-per-mRNA differences among human tissues in codon optimization

Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive. Here, we leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. In particular, we detect two clusters of tissues with an opposite pattern of codon preferences. We then use the identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In a human cell model, we provide evidence that codon optimization should indeed take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles. Altogether, CUSTOM could benefit biological and biotechnological research, such as the design of tissue-targeted therapies and vaccines.

systems biology↗

bioRxiv2022

Evolutionary conservation of A/T-ending codons reflects co-regulation of expression and complex formation.

BackgroundIn a wide variety of organisms, synonymous codons are used with different frequencies, a phenomenon known as codon bias that plays an important role in determining expression levels. However, the importance of codon bias to facilitate the simultaneous turnover of thousands of protein-coding transcripts to bring about phenotypic changes in cellular programs such as development, has not yet been investigated in detail. ResultsHere, we discover that genes with A/T-ending codon preferences are expressed coordinately and display a high codon conservation in mammals. This feature is not observed in genes enriched in G/C-ending codons. A paradigmatic case of this phenomenon is KRAS, from the RAS family, an A/T-rich gene with a high codon conservation (95%) in comparison to HRAS (76%). Also, we find that genes with similar codon composition are more likely to be part of the same protein complex, and that genes with A/T-ending codons are more prone to form protein complexes than those rich in G/C. The codon preferences of genes with A/T-ending codons are conserved among vertebrates. We propose that codon conservation, a feature of expression-coordinated transcripts, is linked to the high expression variation and coordination of tRNA isoacceptors reading A/T-ending codons. ConclusionsOur data indicate that cells exploit A/T-ending codons to generate coordinated, fine-tuned changes of protein-coding transcripts. We suggest that this orchestration contributes to tissue-specific and ontogenetic-specific expression, which can facilitate, for instance, timely protein complex formation.

evolutionary biology↗

bioRxiv2021

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly coupled to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed in awake animals. Here, we took advantage of recent developments in wide-field, mesoscopic calcium imaging to monitor neural activity across the neocortex of awake mice. We develop a novel "graph of graphs" approach to quantify rapidly time-varying functional connectivity and show that spontaneous behaviors are represented by fast changes in both the activity and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular resolution 2-photon microscopy also demonstrated that the correlations among neighboring neurons and between local and large-scale networks also encodes behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks that are not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insight into how behavioral information is represented across the mammalian neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

neuroscience↗

bioRxiv2020

Dual color mesoscopic imaging reveals spatiotemporally heterogeneous coordination of cholinergic and neocortical activity.

Variation in an animals behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent control of circuit dynamics may reflect neuromodulatory influences including acetylcholine (ACh). While early literature suggested ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. Additionally, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. We performed simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that increasing arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic release and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

neuroscience↗

bioRxiv2020

Cell-type specific outcome representation in primary motor cortex

Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, "success"- and "failure" related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trials outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

neuroscience↗

bioRxiv2019

Translational efficiency across healthy and tumor tissues is proliferation-related

BackgroundDifferent tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, as well as its effect on tissue-specific protein levels.\n\nResultsWe first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed tRNA expression in more than 8000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Relative Translation Efficiency. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues, which determines tissue-specific translation efficiency. Furthermore, the aberrant translational efficiency of ProCCA and GlyGGT in cancer, among other codons, which is partly regulated by the tRNA gene copy numbers and their promoter DNA methylation, is associated with poor patient survival.\n\nConclusionsThe distribution of tissue-specific tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program in tissues both in healthy and tumor states.

systems biology↗

bioRxiv2019

Proliferation specific codon usage facilitates oncogene translation

Tumors evolve under selection for gene mutations that give a growth advantage to the cancer cell. Intriguingly, some cancer genes are more often found mutated in tumors than their closely related family members. For example, KRAS mutations are more frequently observed in cancer in comparison to HRAS and NRAS. Here, we find that for RAS and six oncogene families, the most prevalent mutated members in cancer have a codon usage characteristic of genes involved in proliferation. The codon usage of KRAS is more adapted to be efficiently translated in proliferative cells than the codon usage of HRAS. We also show that the translation efficiency of KRAS varies between cell lines in a manner related to their tRNA expression. Altogether, our study demonstrates that a dynamic translation program contributes to shaping the expression profiles of oncogenes. We propose that codon bias related to cell proliferation contributes to the prevalence of mutations in certain members of oncogene families.

cancer biology↗

bioRxiv2019

NMDAR-dependent emergence of behavioral representation in primary visual cortex

Neocortical sensory areas are generally thought to faithfully represent external stimuli. However, a growing body of evidence suggests that cortical networks exhibit considerable functional plasticity over multiple temporal scales, allowing them to modify their output to reflect ongoing behavioral demands. Nevertheless, the dynamics of sensory and non-sensory representations during acquisition of stimulus-guided behavior are not well understood. We performed longitudinal 2-photon imaging of activity in primary visual cortex (V1) of mice learning a conditioned eyeblink task. We found that both excitatory and inhibitory neurons robustly encode the visual stimulus throughout training despite a significant experience-dependent reduction in response magnitude. In contrast, both pyramidal neurons and parvalbumin-expressing interneurons exhibit emergence of behavioral representation during learning. The plasticity of visual response magnitude and behavioral representation is abolished following loss of NMDA-type glutamate receptors. Overall, our findings demonstrate that V1 networks can dynamically multiplex distinct behaviorally relevant representations over the course of learning.

neuroscience↗