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Lefebvre, A. E. Y. T.-S.

Publications and source records attributed to Lefebvre, A. E. Y. T.-S..

3 recordsLinked to original sources

Imageomics defines granular morphological changes of human skin with age and reveals a rejuvenating effect of xenografting

Rejuvenating aging human skin is a major therapeutic goal, but objective, quantitative measures of intrinsic aging are limited. We performed a cross-sectional histological study of UV-protected buttock and abdominal skin in adults spanning multiple decades of life to identify features that reliably track age. Epidermal thickness measured between rete ridges was unchanged, but rete ridge size declined linearly with age: ridges became shorter and thinner in both sites, though rete ridge number decreased only in the abdomen. Consistent with these structural changes, proliferative cells (Ki67+) per ridge and expression of integrin {beta}4 (ITGB4), a putative stem-cell marker, were reduced in aged skin. We combined these biomarkers into a predictive model that estimated skin age more accurately than any single marker. To test whether the model detects longitudinal change, we analyzed aged abdominal skin before and after xenografting onto young or aged mice, a procedure previously reported to rejuvenate human skin in young but not aged recipient mice. Both individual biomarkers and the imaging model indicated rejuvenation regardless of host age; however, notably, engraftment efficiency was lower in aged hosts, with surviving grafts showing younger histological phenotypes. These results provide quantitative criteria for assessing intrinsic skin aging and suggest that the process of engraftment itself is sufficient to induce rejuvenation-like changes.

pathology↗

Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies

Diversity Outbred (DO) mice are a powerful model system for mapping complex traits due to their high genetic diversity and mapping resolution. However, while there are extensive tools available for standard genetic analysis in DO mice, fewer techniques have been implemented to facilitate integrated, cross-study analysis. Here, we implement Haseman-Elston regression to estimate genetic correlations among 7,233 phenotypes measured across eleven independent DO mouse studies. We used this network of genetic correlations to cluster phenotypes according to shared genetics, which enhanced the power to detect quantitative trait loci (QTL). This approach empowered the detection of 884 QTL for 383 meta-phenotypes, explaining an average of 40.36% of the total genetic variance per mega-analysis. We leveraged this network for insights into specific areas of biology, including lifespan, frailty, immune composition, histological and functional lung phenotypes, and histological phenotypes of the aorta. We found the genetics of lifespan to share limited correlation with the genetics of frailty but stronger correlation with the genetics of immune cell composition. Additionally, mega-analyses driven by genetic correlations identified candidate genes (e.g. Cdkn2b) associated with degraded extracellular matrix in the aorta. Finally, an ensemble of genetic analyses implicated pulmonary neuroendocrine cell signaling and/or differentiation as a key driver of multiple lung pathophenotypes.

genetics↗

Automated Quantitative Assessment of Elastic Fibers in Verhoeff-Van Gieson-Stained Mouse Aorta Histological Images

The mechanical resilience of the aortic wall hinges on the organisation of its concentric elastic laminae, yet histological evaluation of these fibers remains largely qualitative and observer-dependent. We present a fully automated, stain-aware pipeline that transforms Verhoeff-Van Gieson (VVG) whole-slide images of mouse aortae into reproducible, quantitative maps of elastic-fiber architecture. Leveraging optical-density deconvolution to disentangle elastin from collagen, the workflow couples multi-resolution processing with graph-based skeletonisation to preserve gigapixel detail while scaling efficiently. It returns pixel-level measurements of fiber thickness, tortuosity, lamina count and network complexity, together with validation snapshots for transparent quality control. By eliminating observer bias and delivering high-throughput morphometry, our framework enables powered genotype-phenotype screens in genetically diverse mouse populations and provides objective read-outs for interventions aimed at preserving matrix integrity. The modular codebase is open-source, readily extendable to other elastin-rich tissues or stains, and forms a bridge between qualitative microscopy and biomechanical phenotyping--setting the stage for large-scale, data-driven exploration of vascular structure-function relationships.

pathology↗