Biology subjects

Cochet-Escartin, O.

Publications and source records attributed to Cochet-Escartin, O..

11 recordsLinked to original sources

bioRxiv2026

Microscopic collisions govern macroscopic aerotactic spreading through emergent transport coefficients

Collective migration is often attributed to complex cell-cell communication, yet coordinated multicellular dynamics can also emerge in organisms lacking social interactions. Continuum descriptions successfully capture such behaviors but generally rely on phenomenological transport coefficients whose microscopic origin remains elusive. Here we show that these coefficients emerge from cell-cell collisions and can be independently determined from experiments. We investigate the aerotactic dispersal of confined colonies of the solitary amoeba Acanthamoeba castellanii, in which oxygen consumption continuously reshapes the local environment to generate self-organized guidance cues. Combining quantitative videomicroscopy, oxygen mapping with phosphorescent sensing films, and oxygen-controlled microfluidics, we directly measure both the oxygen landscape and the aerotactic response governing colony expansion. We then develop a Patlak-Keller-Segel (PKS) -type drift-diffusion model whose transport coefficients are entirely determined from independent experiments and microscopic-to-macroscopic modeling approach, without adjustable parameters. The model quantitatively reproduces the observed dynamics, including the coexistence of a fast ballistic front and a trailing transient accumulation band. We show that both diffusion and aerotactic transport are modulated by the same collision-controlled mechanism through density-dependent changes in cell persistence. More generally, our results establish transport coefficients as measurable emergent properties bridging microscopic cell dynamics and macroscopic collective behavior, providing a predictive framework for oxygen-guided migration in living active matter.

biophysics↗

bioRxiv2025

Wnt signaling modulates tissue mechanics, actin order, and regeneration in Hydra vulgaris

Hydra vulgaris is a powerful model organism in the study of axial patterning and regeneration. While recent models emphasize the importance of mechanical cues in establishing the body axis of Hydra tissue spheres, the precise role of the Wnt pathway, known to be central to axial patterning, in regulating tissue mechanics and cytoskeletal organization remains unclear. In this paper, we pharmacologically modulated the canonical Wnt pathway using Alsterpaullone and iCRT14 and assessed their effects on regeneration, tissue rheology, osmotic oscillations and actin organization in Hydra tissue spheres. We found that Wnt activation prevents full regeneration, softens the constitutive tissues, disrupts osmotic oscillations, leads to an accumulation of dense actin filaments but impairs their orientational order. Conversely, we found that Wnt inhibition partially impairs regeneration and reduces actin filament presence while preserving alignment. These results support the underlying hypotheses of two existing mechano-chemical models of Hydra patterning, suggesting that Wnt-driven feedbacks on both tissue stiffness and actin order could contribute to the robust emergence of body axes. Our findings highlight the possibility of redundant and coupled mechanisms in the Hydra patterning system, potentially explaining its robustness.

biophysics↗

bioRxiv2025

Global characterization of Dictyostelium discoideum gene expression changes under hypoxic conditions

Aerobic eukaryotes utilize O2 to oxidize metabolites and generate ATP. The protist kingdom lacks the Hypoxia Inducible Factor-dependent transcriptional response network to accommodate low O2 though possesses a HIF prolyl hydroxylase that initially mediates the response. To address the scale and scope of hypoxic responses in protists, we characterized transcriptomic and proteomic changes when the social amoeba Dictyostelium is subjected to low (1%) O2 under nutritive conditions over 24 h followed by reoxygenation. Remarkably, 32% of the transcripts quantified were differentially expressed during hypoxia, with greatest changes associated with early (1 h) and late phases (24 h). Protein changes were modestly correlated with and generally lagged behind transcriptional changes. Correlated changes were observed for transcripts and proteins associated with various metabolic, anabolic, and catabolic pathways, as well as chromosome organization, cell cycling, vesicular trafficking, and signaling. Analysis of 4 marker genes showed extremely rapid responses that were graded over a range of O2 levels, with differential responses to inhibitors affecting protein synthesis and mitochondria. Overall, the amoebal response to a low but non-toxic O2-level resulted in massive remodeling of the transcriptome and proteome.

genomics↗

bioRxiv2025

Density-Dependent Cell Migration in the Absence of Social Interactions: A Case Study of Acanthamoeba castellanii

Cell migration is often influenced by intercellular or social interactions, ranging from long-range diffusive cues to direct contacts that can trigger biochemical signaling within the cell and affect the cell protruding activity or direction of turns. Here we study the density-dependent migration of the amoeba Acanthamoeba castellanii (Ac), a unicellular eukaryote that moves without social interactions. Using experiments and mean free path theory, we characterize how collisions affect motility parameters in crowded environments. We identify the collision rate as a key parameter linking cell density to the collision-induced reorientation rate, and we show its consistency across multiple independent approaches. Our findings reveal that the intrinsic migration speed remains constant, while persistence time and effective diffusion are entirely governed by collisions. At high densities, cells exhibit nearly ballistic trajectories between collisions, a behavior rarely reported in eukaryotes. These results establish Ac as a minimal model for motility in the absence of biochemical signaling, with implications for testing behaviors in complex crowded environments and pre-jamming dynamics.

biophysics↗

bioRxiv2023

Mechanical characterization of regenerating Hydra tissue spheres

Hydra vulgaris, long known for its remarkable regenerative capabilities, is also a longstanding source of inspiration for models of spontaneous patterning. Recently, it became clear that early patterning during Hydra regeneration is an integrated mechano-chemical process where morphogen dynamics is influenced by tissue mechanics. One roadblock to understand Hydra self-organization is our lack of knowledge about the mechanical properties of these organisms. In this paper, we combined microfluidic developments to perform parallelized microaspiration rheological experiments and numerical simulations to characterize these mechanical properties. We found three different behaviors depending on the applied stresses: an elastic response, a visco-elastic one and tissue rupture. Using models of deformable shells, we quantify their Youngs modulus, shear viscosity as well as the critical stresses required to switch between behaviors. Based on these experimental results, we propose a description of the tissue mechanics during normal regeneration. Our results provide a first step towards the development of original mechano-chemical models of patterning grounded in quantitative, experimental data. Statement of significanceHydra vulgaris is a remarkable organism thanks to its regenerative abilities. One can cut this animal into several pieces which will reform a full Hydra in a few days. In this process, the pieces have to define a new organizing axis. Recently, researchers have shown that this axis definition is under mechanical control. One roadblock to understand the relationship between tissue mechanics and Hydra biology is our lack of knowledge about the mechanical state of this organism. Here, we perform a mechanical characterization using a combination of microaspiration setups and numerical simulations. We finally propose a description of what happens at the mechanical level during Hydra regeneration, allowing quantitative approaches questioning the role of mechanical cues in axis definition.

biophysics↗

bioRxiv2022

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues

Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes such as cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses requires experimental tools to measure these cues. In the context of large scale tissues, this can be achieved by segmenting individual cells to extract their shapes and deformations which in turn inform on their mechanical environment. Historically, this has been done by segmentation methods which are well known to be time consuming and error prone. In this context however, one doesnt necessarily require a cell-level description and a coarse grained approach can be more efficient while using tools different than segmentation. The advent of machine learning and deep neural networks has revolutionized the field of image analysis in recent years, including in biomedical research. With the democratization of these techniques, more and more researchers are trying to apply them to their own biological systems. In this paper, we tackle a problem of cell shape measurement thanks to a large annotated dataset. We develop simple CNNs which we thoroughly optimize in terms of architecture and complexity to question construction rules usually applied. We find that increasing the complexity of the networks rapidly no longer yields improvements in performance and that the number of kernels in each convolutional layer is the most important parameter to achieve good results. In addition, we compare our step-by-step approach with transfer learning and find that our simple, optimized CNNs give better predictions, are faster in training and analysis and dont require more technical knowledge to be implemented. Overall, we offer a rational roadmap to develop optimized models and argue that we should limit the complexity of such models. We conclude by illustrating this strategy on a similar problem and dataset.

bioinformatics↗

bioRxiv2022

Microphase separation of living cells

Self-organization of cells is central to a variety of biological systems and physical concepts of condensed matter have proven instrumental in deciphering some of their properties. Here we show that microphase separation, long studied in polymeric materials and other inert systems, has a natural counterpart in living cells. When placed below a millimetric film of liquid nutritive medium, a quasi two-dimensional, high-density population of Dictyostelium discoideum cells spontaneously assemble into compact domains. Their typical size of 100 m is governed by a balance between competing interactions: an adhesion acting as a short-range attraction and promoting aggregation, and an effective long-range repulsion stemming from aerotaxis in near anoxic condition. Experimental data, a simple model and cell-based simulations all support this scenario. Our findings establish a generic mechanism for self-organization of living cells and highlight oxygen regulation as an emergent organizing principle for biological matter.

biophysics↗

bioRxiv2022

Machine learning-based detection of label-free cancer stem-like cell fate

Most imaging methods rely on labelling biological samples in order to provide specific and easily detectable features. However, label-free imaging is a non-invasive and non-toxic alternative that requires accurate image analysis algorithm based on cell morphology. Such analysis has to deal with a high image variability while fewer features are extractable, so far, fast analysis of label-free brightfield microscopy (LFBM) images remains a challenging task. With the development of microfabricated devices during the last decades, high throughput image generation makes it possible to use machine learning-based algorithms in order to analyse LFBM images. Fast algorithms are also crucially needed to analyze high throughput experiments. In this paper, we provide a data-driven study in order to assess the complexity of LFBM time-lapses monitoring isolated cancer stem-like cells (CSCs) fate in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. Several image analysis algorithms of increasing processing capacities were tested: a classical computer vision algorithm (CCVA), a shallow learning-based algorithm (SLBA) and a deep learning-based algorithm (DLBA). We show that our optimized DLBA has by far the best accuracy compared to CCVA and SLBA, is at least as accurate as other state-of-the-art DLBAs while being faster. With this study, we demonstrate that optimizing our DLBA accordingly to the image analysis problem can overall provide better results than pretrained models. Such a fast and accurate DLBA is therefore compatible with the generation of high throughput data and opens the route for on-the-fly analysis of CSC fate from LFBM time-lapses.

bioinformatics↗

bioRxiv2020

Hypoxia triggers collective aerotactic migration in Dictyostelium discoideum

It is well known that eukaryotic cells can sense oxygen (O2) and adapt their metabolism accordingly. It is less known that they can also move towards regions of higher oxygen level (aerotaxis). Using a self-generated hypoxic assay, we show that the social amoeba Dictyostelium discoideum displays a spectacular aerotactic behavior. When a cell colony is covered by a coverglass, cells quickly consume the available O2 and the ones close to the periphery move directionally outward forming a dense ring keeping a constant speed and density. To confirm that O2 is the main molecular player in this seemingly collective process, we combined two technological developments, porphyrin based O2 sensing films and microfluidic O2 gradient generators. We showed that Dictyostelium cells exhibit aerotactic and aerokinetic (increased speed at low O2) response in an extremely low range of O2 concentration (0-1.5%) indicative of a very efficient detection mechanism. The various cell behaviors under self-generated or imposed O2 gradients were modeled with a very satisfactory quantitative agreement using an in silico cellular Potts model built on experimental observations. This computational model was complemented with a parsimonious Go or Grow partial differential equation (PDE) model. In both models, we found that the collective migration of a dense ring can be explained by the interplay between cell division and the modulation of aerotaxis, without the need for cell-cell communication.

biophysics↗

bioRxiv2019

Pharmacological or genetic targeting of Transient Receptor Potential (TRP) channels can disrupt the planarian escape response

In response to noxious stimuli, planarians cease their typical ciliary gliding and exhibit an oscillatory type of locomotion called scrunching. We have previously characterized the biomechanics of scrunching and shown that it is induced by specific stimuli, such as amputation, noxious heat, and extreme pH. Because these specific inducers are known to activate Transient Receptor Potential (TRP) channels in other systems, we hypothesized that TRP channels control scrunching. We found that chemicals known to activate TRPA1 (allyl isothiocyanate (AITC) and hydrogen peroxide) and TRPV (capsaicin and anandamide) in other systems induce scrunching in the planarian species Dugesia japonica and, except for anandamide, in Schmidtea mediterranea. To confirm that these responses were specific to either TRPA1 or TRPV, respectively, we tried to block scrunching using selective TRPA1 or TRPV antagonists and RNA interference (RNAi) mediated knockdown. Unexpectedly, co-treatment with a mammalian TRPA1 antagonist, HC-030031, enhanced AITC-induced scrunching by decreasing the latency time, suggesting an agonistic relationship in planarians. We further confirmed that TRPA1 in both species is necessary for AITC-induced scrunching using RNAi. Conversely, while co-treatment of a mammalian TRPV antagonist, SB-366791, also enhanced capsaicin-induced reactions in D. japonica, combined knockdown of two previously identified D. japonica TRPV genes (DjTRPVa and DjTRPVb) did not inhibit capsaicin-induced scrunching. Surprisingly, RNAi of either DjTRPAa or DjTRPVa/DjTRPVb disrupted scrunching induced by the endocannabinoid and TRPV agonist, anandamide. Overall, our results show that although scrunching induction can involve different initial pathways for sensing stimuli, this behaviors signature dynamical features are independent of the inducer, implying that scrunching is a stereotypical planarian escape behavior in response to various noxious stimuli that converge on a single downstream pathway. Understanding which aspects of nociception are conserved or not across different organisms can provide insight into the underlying regulatory mechanisms to better understand pain sensation.

animal behavior and cognition↗

bioRxiv2017

Forces driving cell sorting in Hydra

Cell sorting, whereby a heterogeneous cell mixture organizes into distinct tissues, is a fundamental patterning process in development. So far, most studies of cell sorting have relied either on 2-dimensional cellular aggregates, in vitro situations that do not have a direct counterpart in vivo, or were focused on the properties of single cells. Here, we report the first multiscale experimental study on 3-dimensional regenerating Hydra aggregates, capable of reforming a full animal. By quantifying the kinematics of single cell and whole aggregate behaviors, we show that no differences in cell motility exist among cell types and that sorting dynamics follow a power law. Moreover, we measure the physical properties of separated tissues and determine their viscosities and surface tensions. Based on our experimental results and numerical simulations, we conclude that tissue interfacial tensions are sufficient to explain Hydra cell sorting. Doing so, we illustrate DArcy Thompsons central idea that biological organization can be understood through physical principles, an idea which is currently re-shaping the field of developmental biology.\n\nSummary statementHydra regenerates after dissociation into single cells. We show how physical mechanisms can explain the first step of regeneration, whereby ectodermal and endodermal cells sort out to form distinct tissue layers.

biophysics↗