Biology subjects

Zaccone, A.

Publications and source records attributed to Zaccone, A..

3 recordsLinked to original sources

bioRxiv2026

A Geometrical Class of Population Growth Models, across Scales and Population Structures

We develop a general geometrical framework for growth processes in which the growth rate depends exponentially on the number of active growth-supporting components. The framework separates the fraction of active components from the size of the accessible component space, allowing qualitatively different growth laws to arise from the same underlying construction. Bacterial population growth provides an example of a fixed-size component space controlled primarily by activation and deactivation, whereas examples such as human population growth or speciation provide an expanding component space in which interactions between individuals promote further growth. This motivates a Replicative geometrical law, dX/dt= X exp({beta} X^{gamma}), whose exponent characterizes the scaling of the effective growth-supporting component space and is tested against historical human-population regimes and eukaryotic lineage-through-time data. For innovation indicators such as patents and arXiv manuscript submissions, where an existing item need not itself reproduce, the same construction motivates a Direct Geometrical production law without the prefactor X. The framework therefore provides a common description of fixed and expanding component spaces while distinguishing Replicative from Direct Geometrical growth.

biophysics↗

bioRxiv2026

Quantitative assessment of mesoscale cellular order and organization in the mouse hippocampus

The hippocampus is characterized by a stereotypical macroscopic structure, where the nuclei are densely and heterogeneously packed among different subregions of the hippocampus. Despite the fact that tissue-specific cellular organization has been implicated in neural function, it has been technically challenging to quantitatively analyze mesoscopic cellular organization in the hippocampus due to its high cellular density. To overcome this technical hurdle, we developed Computational Biophysical Histomorphometry Software (CBHS), an automated image-analysis pipeline, aimed at quantifying nuclear shape and the order of the cellular ensemble in high-density areas. When applied to the subfields of hippocampus, we found that denser regions, most notably the dentate gyrus, were the most positionally, but least orientationally ordered. Nuclear shape exhibited a dependence on the local environment in a packing-dependent manner. This association was cell-type specific, with neurons, but not astrocytes displaying nuclear shape that varied with neighbour proximity, although astrocytes demonstrated greater intrinsic shape variance. The results reveal the presence of reproducible mesoscale cell packing order in hippocampal tissue, and are consistent with a nucleus-driven mechanical coupling between neighbouring cells. The present study provides a quantitative framework with which to understand mesoscopic tissue organization, thus enabling the formulation of testable hypotheses for future investigation.

neuroscience↗

bioRxiv2021

Direct Observation of Anisotropy-Driven Formation of Amyloid Protein Core-Shell Structures in Real-time by Super-resolution Microscopy

The misfolding of proteins and their aggregation in the form of fibrils or amyloid-like spherulites are involved in a spectrum of pathological abnormalities. Our current understanding of protein amyloid aggregation mechanisms has primarily relied on the use of spectrometric methods to determine the average growth rates and diffraction limited microscopes with low temporal resolution to observe the large-scale morphologies of intermediates. We developed a REal-time kinetics via binding and Photobleaching LOcalisation Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of a microscopically identical aggregates, e.g. amyloid spherulites, may follow distinct pathways. Specifically, spherulites do not exclusively grow isotropically but, surprisingly, may also grow anisotropically, following similar pathways as reported for minerals and polymers. Combining our technique with machine learning approaches, we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape at the level of single aggregate morphology. Our unifying framework for the detection and analysis of spherulite growth can be extended to other self-assembled systems characterized by a high degree of heterogeneity, disentangling the broad spectrum of diverse morphologies at the single-molecule level.

biophysics↗