Biology subjects

Arnaud, E.

Publications and source records attributed to Arnaud, E..

2 recordsLinked to original sources

bioRxiv2025

Mobilizable adipose stromal cells fuel regenerative adipogenesis in injured muscle

Skeletal muscle regeneration is a highly orchestrated process involving the dynamic interplay of multiple cell types. Among these, fibro-adipogenic progenitors (FAPs), a population of resident mesenchymal stromal cells (MSCs), are essential for creating a supportive microenvironment that promotes satellite cell differentiation and modulates immune responses. Our recent work revealed that adipose stromal cells (ASCs) from subcutaneous adipose tissue (ScAT) infiltrate the injured muscle within the first 24h post-injury, contributing significantly to the regenerative process. Consequently, the FAP population in the regenerating muscle comprises both resident FAPs and infiltrated ASCs. In the present study, using single cell RNA-seq in a mouse model with trackable KikGR+ ASCs and bioinformatics analyses, we identify Limch1+/Prg4+ ASCs as the primary Mobilizable ASCs (Mob-ASCs) that migrate to and infiltrate the injury site. Notably, this migration is detectable as early as 14 hours post-injury. We demonstrate that these cells are pre-activated within the ScAT, primed to initiate both migratory and regenerative programs. Intriguingly, bioinformatic inference of key activated transcription factors suggested that adipogenesis is also activated in these cells. Leveraging supervised machine learning, we tracked the fate of Mob-ASCs within the regenerating muscle post-injury, where they continue to execute these programs. Importantly, these cells lineage is cued towards a fate of adipogenesis. In vivo, we observed transient generation of adipocytes with a peak at 7-9 days post-injury to which infiltrated ASCs contributed. In vitro, conditioned media assays further revealed that adipocytes derived from ASCs--but not those from FAPs--enhance myoblasts fusion. Collectively, our findings establish Limch1+/Prg4+ ASCs as the Mobilizable ASC population and suggest that their transient adipogenic differentiation is beneficial for muscle regeneration.

physiology↗

bioRxiv2019

COPO: a metadata platform for brokering FAIR data in the life sciences

Scientific innovation is increasingly reliant on data and computational resources. Much of todays life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.\n\nCOPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.\n\nAvailabilityCOPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics. A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org.

bioinformatics↗