Publications

Comparing mathematical models offers a means to evaluate competing scientific theories. However, exact methods of model calibration are not applicable to many probabilistic models which simulate high-dimensional spatio-temporal data. Approximate Bayesian Computation is a widely-used method for parameter inference and model selection in such scenarios, and it may be combined with Topological Data Analysis to study models which simulate data with fine spatial structure.

Microtubules are stiff cytoskeletal polymers whose ability to rapidly switch between growth and disassembly relies on a metastable lattice. This metastability is also reflected in their sensitivity to environmental conditions and in intrinsic lattice dynamics, where spontaneous tubulin loss is balanced by tubulin incorporation from solution-a process that also enables microtubules to self-repair when damaged. Whether such intrinsic self-repair is sufficient to preserve microtubule integrity during dynamic molecular motor-induced buckling, which frequently occurs in cells, remains unclear. Here, we show that kinesin-driven microtubule buckling in vitro induces severe lattice damage, leading to extensive tubulin incorporation. In many cases, however, the damage exceeds the microtubules' capacity for self-repair, resulting in breakage. In contrast, microtubules survive continuous buckling substantially longer in the presence of intracellular factors. Our results identify the limits of intrinsic microtubule self-repair and demonstrate that additional cellular mechanisms are essential to maintain microtubule integrity under sustained mechanical load.

Meritxell Huch spoke with Cell Reports about her scientific journey, mentorship philosophy, and lab culture as well as her research on human liver regeneration. She discussed refined liver organoid models capturing cholangiocyte heterogeneity and cellular plasticity and shared perspectives on challenges in organoid research and modern science.

Lipid vesicles are important as minimal model systems for cellular compartmentalization. They drive major advances in deciphering biological mechanisms by molecular reconstitution; provide rational solutions for primitive compartmentalization in origin-of-life studies; form the basis of synthetic cells and drug delivery vehicles. The emulsion method is a well-established route for producing bilayer lipid vesicles. However, the application of this method in microfluidics requires complex and specialized machinery. The bulk method suffers from the need to physically manipulate the vesicles through oil layers for characterization that can damage the vesicles. Given this, we present a facile and robust method for on-chip production and manipulation of lipid vesicles by the emulsion method. We prepared a simple device that allows preparation, imaging, and collection of activated lipid vesicles. This technique combines minimal processing steps with maximum flexibility in lipid vesicle production and manipulation with direct imaging, thus fast-tracking production lines across disciplines.

The motivation for this paper is to detect when an irreducible projective variety 𝑉 is not toric. This is achieved by analyzing a Lie group and a Lie algebra associated with 𝑉 . If the dimension of 𝑉 is strictly less than the dimension of the aforementioned objects, then 𝑉 is not a toric variety. We provide an algorithm to compute the Lie algebra of an irreducible variety and use it to present examples of nontoric statistical models in algebraic statistics.

Recent advances in Bernstein-Walsh theory have extended Bernstein's Theorem to multiple dimensions, stating that a multivariate function can be approximated with a geometric rate in a downward-closed polynomial space if and only if it is analytic in a generalized Bernstein polyellipse. To compute approximations of this class of functions-which we term Bos-Levenberg-Trefethen-(BLT) functions-we extend the classic univariate Newton interpolation algorithm to arbitrary multivariate downward-closed polynomial spaces, while maintaining its quadratic runtime and linear storage complexity. The present generalization supports any choice of (nontensorial) unisolvent interpolation nodes, whose number coincides with the dimension of the chosen downward-closed space. We prove that by selecting Leja nodes, the optimal geometric approximation rates for BLT-functions are achieved and that these rates extend to the derivatives of the interpolants. Choosing a Euclidean degree results in downward-closed spaces whose dimension only grows sub-exponentially with spatial dimension, while delivering approximation rates close to or matching those of the tensorial maximum-degree case, hence mitigating the curse of dimensionality. Importantly, our constructive proof directly inspires an algorithm for multivariate polynomial interpolation. We implemented this algorithm as a Python package and use it here to validate our theoretical findings in numerical experiments. These experiments corroborate the superiority of multivariate Newton interpolation over state-of-the-art alternatives, and they suggest that Leja-ordered Chebyshev-Lobatto nodes offer the same approximation power as Leja nodes.

Incomplete reporting of microscopy methods undermines transparency, reproducibility, and data reuse. Despite recent initiatives, comprehensive, broadly endorsed, and accessible reporting guidelines are still lacking. Here, we present a bare minimal microscopy reporting requirements checklist that integrates human- and machine-readable input to provide clear, actionable guidance for researchers, reviewers, and publishers and to advance community standards in microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning (ML) are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap encompasses key aspects of how ML is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of ML for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

The length regulation of microtubules and their organization into complex arrays inside cells occurs through the activity of polymerases and depolymerases, as well as severing enzymes, such as spastin and katanin. Spastin and katanin hexamerize on the microtubule lattice, pull out single tubulin dimers in an ATP-dependent manner and eventually generate internal breaks in the microtubule. Although spastin has been shown to be regulated by post-translational tubulin modifications, the impact of microtubule lattice defects on the severing characteristics of spastin has not been explored. To address this, we prepared GMPCPP-stabilized microtubules with varying defect densities - introduced either through specific polymerization conditions or by end-to-end annealing - for subsequent in vitro severing assays. We found that: (1) the presence of defects accelerated the onset of the severing process; and (2) severing was twice as frequent in microtubule segments with defect sites when compared to random lattice segments. However, there was no evidence of preferential binding of spastin to defect sites. We therefore propose a severing mechanism in which defects do not actively promote microtubule severing but, instead, passively contribute to microtubule lattice instability. The defects thus facilitate the severing process by reducing the number of tubulin subunits that must be removed for severing to occur.

Early development across vertebrates and insects critically relies on robustly reorganizing the cytoplasm of fertilized eggs into individualized cells1,2. This intricate process is orchestrated by large microtubule structures that traverse the embryo, partitioning the cytoplasm into physically distinct and stable compartments3,4. Here, despite the robustness of embryonic development, we uncover an intrinsic instability in cytoplasmic partitioning driven by the microtubule cytoskeleton. By combining experiments in cytoplasmic extract and in vivo, we reveal that embryos circumvent this instability through two distinct mechanisms: either by matching the cell-cycle duration to the time needed for the instability to unfold or by limiting microtubule nucleation. These regulatory mechanisms give rise to two possible strategies to fill the cytoplasm, which we experimentally demonstrate in zebrafish and Drosophila embryos, respectively. In zebrafish embryos, unstable microtubule waves fill the geometry of the entire embryo from the first division. Conversely, in Drosophila embryos, stable microtubule asters resulting from reduced microtubule nucleation gradually fill the cytoplasm throughout multiple divisions. Our results indicate that the temporal control of microtubule dynamics could have driven the evolutionary emergence of species-specific mechanisms for effective cytoplasmic organization. Furthermore, our study unveils a fundamental synergy between physical instabilities and biological clocks, uncovering universal strategies for rapid, robust and efficient spatial ordering in biological systems.