Biology subjects

Goepp, V.

Publications and source records attributed to Goepp, V..

2 recordsLinked to original sources

bioRxiv2025

"Smurf Mice": revolutionising our understanding of age-related and end-of-life animal physiology

Living animals reach their end-of-life through a stereotypic set of fascinating but poorly understood processes. The discovery, first in flies and later in nematodes and zebrafish, of the "Smurf phenotype" is a central tool for picking this complex "lock of biology", that one of ageing. Using the Smurfs, we have shown an evolutionarily conserved end-of-life transition across Drosophilids, nematodes and zebrafish. This tool has been key to identify the discontinuous nature of ageing and predict impending death from natural causes as well as from environmental stresses. This phenotype allowed us to discover that ageing is made up of two successive phases : a first phase where individuals are healthy and have no risk of mortality, but show an age-dependent and increasing risk of entering a second phase, characterized by the so-called hallmarks of ageing and a high risk of death. Here, we test whether these two consecutive phases of ageing separated by the Smurf transition are a conserved feature of ageing in the mammals using Mus musculus as a model. We performed a longitudinal longevity study using both males and females from two different mouse genetic backgrounds and by integrating physiological, metabolic and molecular measurements with the life history of approximately 150 mice. We show the existence of a phenotypic signature typical of the last phase of life, observable at any chronological age. Validating the two-phase ageing model in a mammalian organism allows better characterized the high risk of imminent death and would extend its implications to a broader range of species for ageing research.

physiology↗

bioRxiv2025

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and celltype annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate celltype annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

bioinformatics↗