Biology subjects

Municio-Diaz, C.

Publications and source records attributed to Municio-Diaz, C..

3 recordsLinked to original sources

bioRxiv2026

A Cell Size-Dependent Competition Between Geometry and Polarity Governs Nuclear and Spindle positioning in Early Embryos

Nuclei and mitotic spindles are actively positioned at defined locations within cells to regulate cell polarity, division and multicellular morphogenesis1-4. Forces generated by cytoskeleton networks regulate the positioning of these organelles and are commonly influenced by extrinsic cues such as cell geometry or polarity5-12. To date, however, most studies have investigated this problem in one given cell type, hampering our understanding for how mechanical systems that position nuclei and spindles may scale during multicellular development. We tracked the spatiotemporal behaviour of centrosomes, nuclei and spindles in early sea urchin embryos from the 1-cell to the [~]1000 cells blastula stage. We found that they are initially located at cell centers, but that they undergo a progressive decentration towards the embryo apical surface, as cells become smaller during development. This apical shift is mediated by microtubule (MTs) pulling forces which are influenced by both cell shapes and apical polarity domains. Using 3D mathematical models and embryo dissections, we propose that MT centering forces that derive from cell geometry decay in strength during development as a consequence of cell size reduction, allowing apical polarity decentering forces to take over. Our results support a self-organized scenario in which polarity cues progressively outcompete cell geometry, to modulate the overall balance of MT forces and pattern nuclear and spindle positioning throughout early embryo development.

cell biology↗

bioRxiv2025

A CROSS-SPECIES ANALYSIS OF CELL WALL MECHANOSENSORS

The cell wall (CW) protects fungal cells from various mechanical challenges making its integrity essential for cell survival. CW integrity is monitored by transmembrane sensors that activate downstream effectors to promote CW synthesis in response to injuries. Sensors of the WSC family are found in most fungi, and share a conserved architecture, with a cytoplasmic tail, a single transmembrane domain and a long Serine Threonine Rich domain (STR) prolonged by a WSC domain, both embedded in the CW. In response to forces applied onto the CW, these extracellular domains promote force detection, sensor clustering and cell survival. Interestingly, Wsc sensors exhibit variations in domain sequence and size among different fungal species. To understand how these variations impact mechanosensing, we heterologously expressed Wsc sensors taken from S. cerevisiae and C. albicans, in the fission yeast S. pombe. Remarkably, we found that a subset of these foreign sensors could cluster at sites of CW compression, but that others failed, suggesting divergences in mechanosensing abilities. By swapping sensor domains, we demonstrate that both the cytoplasmic tail and STR influence mechanosensation. These findings reveal a high level of functional plasticity in fungal sensors, and identify tuneable modules that may regulate mechanosensing of various CWs. SIGNIFICANCE STATEMENTO_LICell Wall Mechanosensors of the WSC family are present in most fungi, but whether they can detect mechanical stimuli in a foreign cell wall of a distant fungal species is unknown. C_LIO_LIThe authors heterologously expressed Wsc sensors taken from S. cerevisiae and C. albicans in the fission yeast S. pombe and demonstrate that a subset of foreign sensors can probe mechanical stress in its Cell Wall. C_LIO_LIThis work highlights a remarkable plasticity in mechanosensors ability to detect mechanical stress in the Cell Wall and identifies domains involved in regulating mechanosensing. C_LI

cell biology↗

bioRxiv2025

Inferring diffusion, reaction, and exchange parameters from imperfect FRAP

Fluorescence recovery after photobleaching (FRAP) is broadly used to investigate the dynamics of molecules in cells and tissues, notably to quantify diffusion coefficients. FRAP is based on the spatiotemporal imaging of fluorescent molecules following an initial bleaching of fluorescence in a region of the sample. Although a large number of methods have been developed to infer kinetic parameters from experiments, it is still a challenge to fully characterize molecular dynamics from noisy experiments in which diffusion is coupled to other molecular processes or in which the initial bleaching profile is not perfectly controlled. To address this challenge, we have developed HiFRAP to quantify the reaction-(or exchange-)diffusion kinetic parameters from FRAP under imperfect experimental conditions. HiFRAP is based on a low-rank approximation of a kernel related to the model Greens function and is implemented as an ImageJ/Python macro for (potentially curved) one-dimensional systems and for two-dimensional systems. To the best of our knowledge, HiFRAP offers features that have not been combined together: making no assumption on the initial bleaching profile, which does not need to be known; accounting for the limitation of the optical setup by diffraction; inferring several kinetic parameters from a single experiment; providing errors on parameter estimation; and testing model goodness. In the future, our approach could be applied to other dynamical processes described by linear partial differential equations, which could be useful beyond FRAP, in experiments where the concentration fields are monitored over space and time. SIGNIFICANCEFluorescence recovery after photobleaching (FRAP) is a microscopy approach that is widely used to investigate the diffusion and transport of molecules in life sciences and in material sciences. Numerous methods have been developed to derive kinetic parameters such as diffusion and binding coefficients. However, these methods suffer from limitations associated with experimental constraints, such as technical noise or an imperfectly known initial condition. To circumvent these limitations, we developed a comprehensive approach to estimate several kinetic parameters from a single experiment, to assess the precision of estimation, and to test whether the underlying model is well-suited. We implemented this approach in HiFRAP, an ImageJ/Python macro of broad applicability to one- and two-dimensional systems.

biophysics↗