Biology subjects

Zuccotti, M.

Publications and source records attributed to Zuccotti, M..

3 recordsLinked to original sources

bioRxiv2025

NanoCT-confocal mapping of mouse ovaries reveals lifespan-persistent symmetric organizational rules of primary-to-preovulatory follicles

Mammalian ovaries are organized into follicles integrated with the surrounding tissue, collectively shaping an architecture continuously remodeled through cycles of growth and elimination. Combining nano-Computed Tomography with 3D digital modeling, we deciphered spatial rules governing follicle organization in the mouse ovary across lifespan. We find that primary to preovulatory follicles adopt a quantitatively symmetric arrangement along the anterior-posterior and dorsal-ventral axes, established during prepuberty before secondary follicle vascularization and preserved through adulthood and aging, regardless of follicle health or atresia. A novel nanoCT-confocal pipeline revealed that follicles enclosing oocytes with transcriptionally-active or transcriptionally-inactive chromatin states follow divergent fates of growth, atresia, or developmental competence, yet retaining a symmetric distribution. Mathematical modeling of follicle growth and elimination from prepuberty to adulthood identified transition rates between follicle stages that support gonadal symmetry. These findings suggest an evolutionarily optimized developmental program ensuring follicle recruitment and high ovulatory output, a hallmark of polytocous species.

developmental biology↗

bioRxiv2025

LAM/TREM2+ macrophages release extracellular vesicles and extracellular lipid droplets which modulate the phenotype of recipient macrophages and homeostasis of skeletal muscle cells

The polarization of tissue-resident macrophages is influenced by a variety of signals from the immune system and the local tissue environment, including nutrition. Although it is known that the quality and quantity of ingested lipids have a significant effect on the lipid composition of extracellular vesicles and their fate, it is unknown how the nutritional environment modifies the release and the function of macrophage-derived EVs. In this study, we used a combination of palmitate and oleate (1:2, FFA) to generate lipid-associated TREM2-expressing macrophages (LAM/TREM2+) in vitro. Using various electron microscopy techniques (TEM, SEM, CryoEM) and fluorophores, we found that FFA overload not only induces lipid storage in LAM/TREM2+ macrophages, but also alters their morphology and reduces the diversity and the number of the lipid-derived structures they release. In addition, LAM/TREM2+ macrophages accumulated lipid droplets (LDs) below the plasma membrane and we discovered for the first time that they export and disseminate full LDs into their environment, in addition to extracellular vesicles, by using a cellular pathway associated to CD81. The use of 14C-palmitate confirmed the presence of 14C-triacylglycerols in the large extracellular vesicle pellet. LAM/TREM2+ macrophage-derived EVs induced TREM2 and Il-10 expression in recipient M0 macrophages. These data provide potential insights into how dietary factors and metabolic perturbations can shape the functions of macrophage-derived EVs in the context of metabolic diseases such as diabetes and obesity. In addition, LAM/TREM2+ macrophage-derived EVs modulated insulin-sensitivity, mitochondrial oxidative capacity, lipid profiles and the expressions of genes encoding extracellular matrix components in recipient skeletal muscle cells. Although previously postulated but never demonstrated, these data also highlight the LAM/TREM2+ macrophage-derived EVs as important players in SkM tissue renewal and metabolic homeostasis.

cell biology↗

bioRxiv2020

Harnessing cytoplasmic particles movement of the human early embryo analysed by advanced imaging and artificial intelligence to predict development to blastocyst stage

Research QuestionProgress in artificial intelligence (AI) and advanced image analysis offers unique opportunities to develop novel embryo assessment approaches. In this study, we tested the hypothesis that such technologies can extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst. DesignIn a proof-of principle study, an artificial neural network (ANN) approach was undertaken to assess retrospectively 230 human preimplantation embryos. After ICSI, embryos were subjected to time-lapse monitoring for 44 hours. For comparison as a standard embryo assessment methodology, a single senior embryologist assessed each embryo to predict development to blastocyst stage (BL) based on a single picture frame taken at 42 hours of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest (NoBL), cytoplasm movement velocity (CMV) was recorded by time-lapse monitoring during the first 44 hours of culture and analysed with a Particle Image Velocimetry (PIV) algorithm to extract quantitative information. Three main AI approaches, the k-Nearest Neighbor (k-NN), the Long-Short Term Memory Neural Network (LSTM-NN) and the hybrid ensemble classifier (HyEC) were employed to classify the two embryo classes. ResultsBlind operator assessment classified each embryo in terms of ability of development to blastocyst, reaching a 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. After integration of results from AI models together with the blind operator classification, the performance metrics improved significantly, with a 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score. ConclusionsThe present study suggests the possibility to predict human blastocyst development at early cleavage stages by detection of CMV and AI analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.

developmental biology↗