Biology subjects

Masson, J.-B.

Publications and source records attributed to Masson, J.-B..

18 recordsLinked to original sources

bioRxiv2024

Competition between glycine and GABAA receptors for gephyrin controls their equilibrium populations at inhibitory synapses

Glycine and GABA receptors are ligand-gated chloride channels that mediate inhibitory neurotransmission throughout the central nervous system. The receptors co-localise widely at inhibitory synapses in the spinal cord and in the brainstem due to their interaction with an overlapping binding site of the synaptic scaffold protein gephyrin, pointing to a direct competition between the different receptor types. We have put this hypothesis to the test using single molecule approaches to measure receptor-gephyrin interactions in cells and in vitro. We explored the effects of receptor competition at inhibitory synapses in living neurons by measuring the change in the accumulation and effective stabilisation energy of glycine receptors in the presence of interfering GABA receptor complexes through single molecule tracking and diffusion analysis. Secondly, using molecular tweezers, we quantified the thermodynamic properties of receptor-gephyrin binding, demonstrating direct and reversible competition through the addition of interacting peptides in solution. The relatively low affinity of GABA receptor subunits for gephyrin compared to the glycine receptor raises interesting questions about the role of this competition in synaptic plasticity. We hypothesize that GABA and glycine receptor competition constitutes a molecular system designed to reconcile synapse stability and plasticity at mixed inhibitory synapses.

neuroscience↗

bioRxiv2024

Statistical signature of subtle behavioural changes inlarge-scale behavioural assays

The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours. These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines. Author SummaryThere is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation.Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation [1]. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardized stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally. However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches. We introduce a suite of statistical methods that utilize individual behavioural data and the overarching structure of the behavioural screen to deduce subtle behavioural changes from raw data. Given our studys extensive number of larvae, addressing and preempting potential challenges in body shape recognition is critical for enhancing behaviour detection. To this end, we have adopted a physics-informed inference model. Our first group of techniques enables robust statistical analysis within a learned continuous behaviour latent space, facilitating the detection of subtle behavioural shifts relative to reference genetic lines. A second array of methods probes for subtle variations in action sequences by comparing them to a bespoke generative model. Together, these strategies have enabled us to construct representations of behavioural patterns specific to a lineage and identify a roster of "hit" neurons with the potential to influence behaviour subtly.

neuroscience↗

bioRxiv2024

LarvaTagger: Manual and automatic tagging of Drosophila larval behaviour

MotivationAs more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. ResultsWe reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. AvailabilityAll the code is free and open source. Docker images are also available. See git-lab.pasteur.fr/nyx/LarvaTagger.jl.

bioinformatics↗

bioRxiv2024

Pervasive sublethal effects of agrochemicals as contributing factors to insect decline

Insect biomass is declining across the globe at an alarming rate. Climate change and the widespread use of pesticides have been hypothesized as two underlying drivers. However, the lack of systematic experimental studies across chemicals and species limits our causal understanding of this problem. Here, we employed a chemical library encompassing 1024 different molecules--including insecticides, herbicides, fungicides, and plant growth inhibitors --to investigate how insect populations are affected by varying concentrations of pesticides, focusing on sublethal doses. Using a controlled laboratory pipeline for Drosophila melanogaster, we found that 57% of these chemicals affect the behavior of larvae at sublethal concentrations, and an even higher proportion compromises long-term survivability after acute exposure. Consistent with these results, we observed that exposure to chemicals at doses orders of magnitude below lethality induced widespread phosphorylation changes across the larval proteome. The effects of agrochemicals were amplified when the ambient temperature was increased by four degrees. We also tested the synergistic effects of multiple chemicals at doses found widely in nature and observed fitness-reducing changes in larval developmental time, behavior, and reproduction. Finally, we expanded our investigation to additional fly species, mosquitos, and butterflies and detected similar behavioral alterations triggered by pesticides at sublethal concentrations. Our results provide experimental evidence that strongly suggests sublethal doses of agrochemicals coupled with changes in environmental temperatures are contributing to the global decline in insect populations. We anticipate that our assays can contribute to improving chemical safety assessment, better protect the environment, secure food supplies, and safeguard animal and human health, as well as understand our rapidly changing world.

ecology↗

bioRxiv2023

Feeding-state dependent modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila

Animals feeding state changes behavioral priorities and thus influences even non-feeding related decisions. How is the feeding state information transmitted to non-feeding related circuits and what are the circuit mechanisms involved in biasing non-feeding related decisions remains an open question. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by feeding state changes. We found that this is achieved by differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and less frequently a protective type of response if larvae are fed sugar compared to when they are fed a balanced diet. The information about the internal state is conveyed to the inhibitory neurons through homologues of the vertebrate neuropeptide Y known to be involved in regulating feeding behavior.

neuroscience↗

bioRxiv2023

Neural circuits underlying context-dependent competition between defensive actions in Drosophila larva

To ensure their survival, animals must be able to respond adaptively to threats within their environment. However, the precise neural circuit mechanisms that underlie such flexible defensive behaviors remain poorly understood. Using neuronal manipulations, machine-learning-based behavioral detection, Electron Microscopy (EM) connectomics and calcium imaging in Drosophila larva, we have mapped the second-order interneurons differentially involved in the competition between different defensive actions and the main pathways to the motor side putatively involved in inhibiting startle-type behaviors and promoting escape behaviors in a context dependent manner. We found that mechanosensory stimulation modulates the nociceptive escape sequences and inhibits C-shape bends and Rolls in favor of startle-like behaviors. This suggests a competition between mechanosensory-induced startle responses and escape behaviors. Structural and functional connectivity revealed that the second order interneurons receive their main input from projection neurons that integrate mechanosensory and nociceptive stimuli. The analysis of their postsynaptic connectivity in EM revealed that they make indirect connections to the pre-motor and motor neurons. Finally, we identify a pair of descending neurons that could promote modulate the escape sequence and promote startle behaviors. Altogether, these results characterize the pathways involved in the Startle and Escape competition, modulated by the sensory context.

neuroscience↗

bioRxiv2023

Quantitative classification of energy landscapes inferred from single nanoparticle tracking of membrane receptors inside nanodomains reveals confinement functional and molecular features

The cell membrane organization has been hypothesized for a long time to have an essential functional role, through the control of membrane receptor confinement in micro- or nanodomains. Several mechanisms have been proposed to account for these properties, though some features of the resulting organization have remained controversial, notably the nature, size, and stability of cholesterol- and sphingolipid-rich domains called rafts. Here, we quantitatively probed the energy landscape experienced by single nanoparticle-labeled membrane receptors - epidermal growth factor receptors (EGFR), transferrin receptors (TfR), and receptors of {varepsilon}-toxin produced by C. perfringens and -toxin of C.Septicum (CP{varepsilon}TR and CSTR, respectively) - through the development of new computational methods. By establishing a new analysis pipeline combining Bayesian inference, decision trees and clustering approaches, we indeed systematically classified single protein trajectories according to the type of confining energy landscape. This revealed the existence of only two distinct organization modalities: (A) confinement in a quadratic energy landscape for EGF, CP{varepsilon}T and CST receptors and (B) free diffusion in confinement domains resulting from the steric hindrance due to F-actin barriers for transferrin receptors. The characterization of confinement energy landscapes by Bayesian inference furthermore revealed the role of interactions with the domain environment in cholesterol- and sphingolipid-rich domains with (in the case of EGFR) or without (for CP{varepsilon}T and CST receptors) parallel interactions with F-actin, to regulate the confinement energy depth. Strikingly, these two distinct mechanisms result in the same organization type (A). We furthermore revealed that the apparent domain sizes for these receptor trajectories resulted from Brownian exploration of the energy landscape in a steady-state like regime at a common effective temperature, independently of the underlying molecular mechanisms. These results highlight that the membrane organization in confinement domains may be more adequately described as interaction hotspots rather than rafts with abrupt domain boundaries. Altogether, these results establish a new computational approach, which paves the way to the constitution of an atlas of energy landscape of membrane proteins and of their control mechanisms, and support a new general model for functional receptor confinement in membrane nanodomains.

biophysics↗

bioRxiv2022

Motion of single molecular tethers reveals dynamic subdomains at ER-mitochondria contact sites

To coordinate cellular physiology, eukaryotic cells rely on the inter-organelle transfer of molecules at specialized organelle-organelle contact sites1,2. Endoplasmic reticulum-mitochondria contact sites (ERMCSs) are particularly vital communication hubs, playing key roles in the exchange of signaling molecules, lipids, and metabolites3. ERMCSs are maintained by interactions between complementary tethering molecules on the surface of each organelle4,5. However, due to the extreme sensitivity of these membrane interfaces to experimental perturbation6,7, a clear understanding of their nanoscale structure and regulation is still lacking. Here, we combine 3D electron microscopy with high-speed molecular tracking of a model organelle tether, VAPB, to map the structure and diffusion landscape of ERMCSs. From EM reconstructions, we identified subdomains within the contact site where ER membranes dramatically deform to match local mitochondrial curvature. In parallel live cell experiments, we observed that the VAPB tethers that mediate this interface were not immobile, but rather highly dynamic, entering and leaving the site in seconds. These subdomains enlarged during nutrient stress, indicating ERMCSs can readily remodel under different physiological conditions. An ALS-associated mutation in VAPB altered the normal fluidity of contact sites, likely perturbing effective communication across the contact site and preventing remodeling. These results establish high speed single molecule imaging as a new tool for mapping the structure of contact site interfaces and suggest that the diffusion landscape of VAPB is a crucial component of ERMCS homeostasis.

cell biology↗

bioRxiv2022

A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a methodology for comparing biomolecule dynamics observed in different experimental conditions without beforehand identifying the model generating the recorded random walks. We introduce a two-step statistical testing scheme. We first use simulation-based inference to train a graph neural network to learn a fixed-length latent representation of recorded random walks. As a second step, we use a maximum mean discrepancy statistical test on the vectors of learnt features to compare biological conditions. This procedure allows us to characterise sets of random walks regardless of their generating models. We initially tested our approach on numerical trajectories. We then demonstrated its ability to detect changes in -synuclein dynamics at synapses in cultured cortical neurons in response to membrane depolarisation. Using our methodology, we identify the domains in the latent space where the variations between conditions are the most significant, which provides a way of interpreting the detected differences in terms of single trajectory characteristics. Our data show that changes in -synuclein dynamics between the chosen conditions are largely driven by increased protein mobility in the depolarised state. Author summaryThe continuous refinement of methods for single molecule tracking in live cells advance our understanding of how biomolecules move inside cells. Analysing the trajectories of single molecules is complicated by their highly erratic and noisy nature and thus requires the use of statistical models of their motion. However, it is often not possible to unambiguously determine a model from a set of short and noisy trajectories. Furthermore, the heterogeneous nature of the cellular environment means that the molecules motion is often not properly described by a single model. In this paper we develop a new statistical testing scheme to detect changes in biomolecule dynamics within organelles without needing to identify a model of their motion. We train a graph neural network on large-scale simulations of random walks to learn a latent representation that captures relevant physical properties of a trajectory. We use a kernel-based statistical test within that latent space to compare the properties of two sets of trajectories recorded under different biological conditions. We apply our approach to detect differences in the dynamics of -synuclein, a presynaptic protein, in axons and boutons during synaptic stimulation. This represents an important step towards automated single-molecule-based read-out of pharmacological action.

biophysics↗

bioRxiv2021

Serotonergic neurons mediate operant conditioning in Drosophila larvae

Observed across species, operant conditioning facilitates learned associations between behaviours and outcomes, biasing future action selection to maximise reward and avoid punishment. To elucidate the underlying neural mechanisms, we built a high-throughput tracker for Drosophila melanogaster larvae, combining real-time behaviour detection with closed-loop optogenetic and thermogenetic stimulation capabilities. We demonstrate operant conditioning in Drosophila larvae by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Specifically, we establish that the ventral nerve cord is necessary for this memory formation. Our results extend the role of serotonergic neurons for learning in insects as well as the existence of learning circuits outside the mushroom body. This work supports future studies on the function of serotonin and the mechanisms underlying operant conditioning at both circuit and cellular levels.

neuroscience↗

bioRxiv2020

Dynamic spatiotemporal coordination of neural stem cell fate decisions through local feedback in the adult vertebrate brain

Neural stem cell (NSC) populations persist in the adult vertebrate brain over a life time, and their homeostasis is controlled at the population level. The nature and properties of these coordination mechanisms remain unknown. Here we combine dynamic imaging of entire NSC populations in their in vivo niche over weeks, pharmacological manipulations, mathematical modeling and spatial statistics, and demonstrate that NSCs use spatiotemporally resolved local feedbacks to coordinate their decision to divide. These involve a Notch-mediated inhibition from transient neural progenitors, and a dispersion effect from dividing NSCs themselves, exerted with a delay of 9-12 days. Simulations from a stochastic NSC lattice model capturing these interactions demonstrate that they are linked by lineage progression and control the spatiotemporal distribution of output neurons. These results highlight how local and temporally delayed interactions occurring between brain germinal cells generate self-propagating dynamics that maintain NSC population homeostasis with specific spatiotemporal correlations.

neuroscience↗

bioRxiv2020

DIVA: natural navigation inside 3D images usingvirtual reality

As three-dimensional microscopy becomes commonplace in biological re-search, there is an increasing need for researchers to be able to view experimental image stacks in a natural three-dimensional viewing context. Through stereoscopy and motion tracking, commercial virtual reality headsets provide a solution to this important visualization challenge by allowing researchers to view volumetric objects in an entirely intuitive fashion. With this motivation, we present DIVA, a user-friendly software tool that automatically creates detailed three-dimensional reconstructions of raw experimental image stacks that are integrated in virtual reality. In DIVAs immersive virtual environment, users can view, manipulate and perform volumetric measurements on their microscopy images as they would to real physical objects. In contrast to similar solutions, our software provides high-quality volume rendering with native TIFF file compatibility. We benchmark the software with diverse image types including those generated by confocal, light-sheet and electron microscopy. DIVA is available at https://diva.pasteur.fr and will be regularly updated.

bioinformatics↗

bioRxiv2020

Genuage: a virtual reality platform for visualizing and analyzing multidimensional point cloud data

The quantity of experimentally recorded point cloud data, such generated in single-molecule experiments, is increasing continuously in both size and dimension. Gaining an intuitive understanding of the data and facilitating multi-dimensional data analysis remains a challenge. It is especially challenging when static distribution properties are not predictive of dynamical properties. Here, we report a new open-source software platform, Genuage, that enables the easy perception, interaction and analysis of complex multidimensional point cloud datasets by leveraging virtual reality. We illustrate the benefit of the Genuage with examples of three-dimensional static and dynamic localization microscopy datasets, as well as some synthetic datasets. Genuage has a large breadth of usage modes, due to its compatibility with arbitrary multidimensional data types extending beyond the single-molecule research community.

biophysics↗

bioRxiv2019

Comparative single-cell transcriptomics of complete insect nervous systems

Molecular profiles of neurons influence information processing, but bridging the gap between genes, circuits, and behavior has been very difficult. Furthermore, the behavioral state of an animal continuously changes across development and as a result of sensory experience. How behavioral state influences molecular cell state is poorly understood. Here we present a complete atlas of the Drosophila larval central nervous system composed of over 200,000 single cells across four developmental stages. We develop polyseq, a python package, to perform cell-type analyses. We use single-molecule RNA-FISH to validate our scRNAseq findings. To investigate how internal state affects cell state, we optogentically altered internal state with high-throughput behavior protocols designed to mimic wasp sting and over activation of the memory system. We found nervous system-wide and neuron-specific gene expression changes. This resource is valuable for developmental biology and neuroscience, and it advances our understanding of how genes, neurons, and circuits generate behavior.

neuroscience↗

bioRxiv2018

Live single molecule microscopy of HIV-1 assembly in host T cells reveals a spatio-temporal effect of the viral genome.

Monitoring virus assembly dynamic at the nanoscale level in host cells remains a major challenge. Human Immunodeficiency Virus type 1 (HIV-1) components are addressed to the plasma membrane where they assemble to form spherical particles of 100nm in diameter. HIV-1 Gag protein expression alone is sufficient to produce virus-like particles (VLPs) that resemble immature virus. Here, we monitored Gag assembly in host CD4 T lymphocytes using single molecule dynamics microscopy and energy mapping. A workflow allowing long time recordings of single Gag molecule localization, diffusion and effective energy maps was developed for robust quantitative analysis of HIV assembly and budding. Comparison of numerous cell plasma membrane assembling platforms in cells expressing wild type or assembly-defective Gag proteins showed that VLP formation last 15 minutes, with an assembly time of 5 minutes, and that the nucleocapsid domain is mandatory. Importantly, it reveals that the viral genome coordinates spatio-temporally HIV-1 assembly.

biophysics↗

bioRxiv2017

Mapping neurons and brain regions underlying sensorimotor decisions and sequences

A fundamental property of the nervous systems across the animal kingdom is the ability to select appropriate actions and sequences of actions in response to sensory cues. Many behaviors are physically mutually exclusive so commitment to one requires complete suppression of others. Further, because many behaviors are serially organized into sequences of actions, mechanisms must also exist that control transitions from one action to the next. The circuit mechanisms by which nervous systems achieve behavioral choice, stability and transitions are still incompletely understood. A key step in understanding these functions is to identify neurons and brain areas involved in controlling behavioral choice, stability and transitions. To do this, we developed an approach where we combined a large-scale neuronal inactivation screen with an automated action detection of sensorimotor decisions and sequences in response to a sensory cue. We characterized the response of wild-type larvae to a mechanosensory stimulus and found they can respond to the stimulus with a probabilistic sequence of 4 possible actions (head-cast, head-retraction, back-up and stop). We analyzed behaviors from around three hundred thousand larvae (N=2.9x105) where we selectively silenced small numbers of neuron types and individual neuron types systematically across the nervous system using a library of Drosophila GAL4 lines and determined the effect of these manipulations on larval mechanosensory responses. We identified neurons and brain-regions that when inactivated affected Drosophila larval sensorimotor decisions and sequence transitions between the different actions. Specifically, we identified 51 candidate lines for sensory processing and 24 candidate lines for competitive interactions. We also detected phenotype categories for sequence transitions consistent with a model of sequence generation where transitions and reversals are independently controlled. These findings provide the basis for understanding how sensorimotor decisions and sequence transition are controlled by the nervous system.

neuroscience↗

bioRxiv2017

Olfactory coding in the turbulent realm

Long-distance olfactory search behaviors depend on odor detection dynamics. Due to turbulence, olfactory signals travel as bursts of variable concentration and spacing and are characterized by long-tail distributions of odor/no-odor events, challenging the computing capacities of olfactory systems. How animals encode complex olfactory scenes to track the plume far from the source remains unclear. Here we focus on the coding of the plume temporal dynamics in moths. We compare responses of olfactory receptor neurons (ORNs) and antennal lobe projection neurons (PNs) to sequences of pheromone stimuli either with white-noise patterns or with realistic turbulent temporal structures simulating a large range of distances (8 to 64 m) from the odor source. For the first time, we analyze what information is extracted by the olfactory system at large distances from the source. Neuronal responses are analyzed using linear-nonlinear models fitted with white-noise stimuli and used for predicting responses to turbulent stimuli. We found that neuronal firing rate is less correlated with the dynamic odor time course when distance to the source increases because of improper coding during long odor and no-odor events that characterize large distances. Rapid adaptation during long puffs does not preclude however the detection of puff transitions in PNs. Individual PNs but not individual ORNs encode the onset and offset of odor puffs for any temporal structure of stimuli. A higher spontaneous firing rate coupled to an inhibition phase at the end of PN responses contributes to this coding property. This allows PNs to decode the temporal structure of the odor plume at any distance to the source, an essential piece of information moths can use in their tracking behavior.\n\nAuthor SummaryLong-distance olfactory search is a difficult task because atmospheric turbulence erases global gradients and makes the plume discontinuous. The dynamics of odor detections is the sole information about the position of the source. Male moths successfully track female pheromone plumes at large distances. Here we show that the moth olfactory system encodes olfactory scenes simulating variable distances from the odor source by characterizing puff onsets and offsets. A single projection neuron is sufficient to provide an accurate representation of the dynamic pheromone time course at any distance to the source while this information seems to be encoded at the population level in olfactory receptor neurons.

neuroscience↗

bioRxiv2017

Gradients Of Rac1 Nanoclusters Support Spatial Patterns Of Rac1 Signaling

The dynamics of the cytoskeleton and cell shape relies on the coordinated activation of RhoGTPase molecular switches. Among them, Rac1 participates to the orchestration in space and time of actin branching and protrusion/retraction cycles of the lamellipodia at the cell front during mesenchymal migration. Biosensor imaging has revealed a graded concentration of active GTP-loaded Rac1 in protruding regions of the cell. Here, using single molecule imaging and super-resolution microscopy, we reveal an additional supramolecular organization of Rac1. We find that, similarly to H-Ras, Rac1 partitions and is immobilized into nanoclusters of 50-100 molecules each. These nanoclusters assemble due to the interaction of the polybasic tail of Rac1 with the phosphoinositide lipids PIP2 and PIP3. The additional interactions with GEFs, GAPs, downstream effectors, and possibly other partners are responsible for an enrichment of Rac1 nanoclusters in protruding regions of the cell. Using optogenetics and micropatterning tools, we find that activation of Rac1 leads to its immobilization in nanoclusters and that the local level of Rac1 activity matches the local density of nanoclusters. Altogether, our results show that subcellular patterns of Rac1 activity are supported by gradients of signaling nanodomains of heterogeneous molecular composition, which presumably act as discrete signaling platforms. This finding implies that graded distributions of nanoclusters might encode spatial information.\n\nSignificance statementThe plasma membrane of eukaryotic cells is a highly organized surface where hundreds of incoming signals are transduced to the intracellular space. How cells encode faithfully this myriad of signals is a fundamental question. Here we show that Rac1, a critical membrane-bound protein involved in the regulation of cytoskeletal dynamics, forms small aggregates together with other regulating proteins. These supramolecular assemblies, called nanoclusters, are the \"quantal\" units of signaling. By increasing the local concentration, nanoclusters set thresholds for downstream signaling and ensure the fidelity of information transduction. We found that Rac1 nanoclusters are distributed as spatial gradients matching the patterns of Rac1 activity. We propose that cells can encode positional information through distributed signaling quanta, hereby ensuring spatial fidelity.

biophysics↗