Coordinates: 44°25′56″N 68°17′25″W / 44.432091°N 68.290265°W / 44.432091; -68.290265The MDI Biological Laboratory is an independent non-profit biomedical research institution founded in 1898 and located in Salisbury Cove, Maine, on Mount Desert Island. Its mission is to improve human health and well- being through basic research, education, and development ventures that transform discoveries into cures. In 2013, the Laboratory was designated a Center for Biomedical Research Excellence (COBRE) by the National Institutes of Health, which awarded the Laboratory a grant of $13 million over five years to expand the institution’s research program. The MDI Biological Laboratory has a full-time staff of 63, and will offer 23 research training courses in 2014.
In this perspective, we explore the developmental mechanisms of muscle stem cells in multiple non-mammalian vertebrate species, and we compare these phenomena to those in mammals. Particularly, we discuss two recently described populations of noncanonical muscle progenitors in regenerative vertebrates: one in zebrafish and one in axolotl. We discuss the capabilities of these two populations during muscle development and regeneration, including the implications for muscle repair in mammals.
IgA nephropathy (IgAN) and celiac disease (CeD) are autoimmune disorders characterized by dysregulated immune responses; however, the molecular mechanisms underlying their comorbidity remain incompletely understood. Here, we integrated transcriptomic datasets from IgAN and CeD to perform differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning-based hub gene identification. The expression profiles and diagnostic performance of the identified hub genes were validated across multiple independent cohorts using receiver operating characteristic analysis, and their cellular localization was further explored using single-cell RNA sequencing data. In addition, we conducted clinical correlation analysis, immune infiltration profiling, therapeutic drug prediction, and constructed transcription factor-miRNA-mRNA regulatory networks. We identified ITGB2, CD74, and KLK1 as shared biomarkers with robust diagnostic performance (AUC > 0.7, with an AUC > 0.9 in the combined model). These genes were closely associated with immune dysregulation and disease progression, and four candidate therapeutic agents were predicted. Collectively, our findings provide novel insights into the shared pathogenic mechanisms and potential therapeutic strategies for IgAN and CeD.
Heparanase-1 (HPSE) is the only mammalian endo-β-D-glucuronidase that cleaves heparan sulfate (HS) polysaccharides on cell surfaces and in extracellular matrices. HPSE contributes to diverse pathological conditions, particularly in the processes driving injury to the luminal glycocalyx overlying vascular endothelial cells. Existing methods for assaying the activity of HPSE are insensitive and lack specificity. Here, we present a new method, Single Heparan Sulfate Substrate-Based Isotope Dilution Mass Spectrometry (SHS-IDMS), that permits the quantitative measurement of HPSE activity in complex biological matrices like plasma. The method involves the use of a structurally defined-HS 12-mer substrate that yields a consistent disaccharide product upon HPSE digestion. The disaccharide product is quantified using LC-MS/MS and a 13C-labeled disaccharide calibrant. The method demonstrates strong linearity, high sensitivity, and resistance to interference by heparanase-2 (HPSE-2). Using this method, we detected significant, quantitative differences in HPSE activity using 20 μL plasma samples from children with sepsis compared to samples from healthy children. Moreover, we found that plasma HPSE activity correlated with circulating levels of syndecan-1 and angiopoietin-2, supporting the hypothesis that HPSE may contribute to endothelial glycocalyx injury and pathological endothelial activation under septic conditions. The new method will advance research in the biology of this important enzyme.
Quantitative histological analysis of skeletal muscle morphometry provides critical insights into muscle physiology but remains labor-intensive and technically demanding. While recent developments in machine learning-based image segmentation techniques have facilitated large-scale tissue analysis, existing tools that automate muscle morphometry analysis are largely tailored to mammalian models, with limited applicability to teleosts. Moreover, there is a lack of effective tools for visualizing spatial organization and morphometric variability of teleost muscle fibers, a feature that is important for understanding hyperplastic muscle growth dynamics in teleosts. In this study, we benchmarked the performance of existing muscle morphometry analysis workflows and general-purpose cell segmentation algorithms in identifying individual muscle fibers in teleost muscles. We used these findings to inform the development of a new, teleost-optimized analysis tool. We show that cytoplasmic staining combined with machine learning-based cell segmentation offers a robust and accurate approach for automated muscle morphometry analysis in developing zebrafish. We also introduce fishROI – a Fiji plugin implemented in Jython, that streamlines both morphometric analysis and visualization. This tool accommodates shallow and deep learning-based segmentation techniques and incorporates novel quantification and visualization methods suited to teleost-specific muscle features, including mosaic hyperplasia dynamics. The plugin features an intuitive graphical user interface and is designed for flexibility, with minimal constraints regarding species, image quality, or staining protocol. Its modular architecture allows it to be used as a baseline for automated muscle morphometry analysis, while permitting integration with other tools and workflows to support a diverse range of analytical needs. This plugin will serve as a foundational platform for semi-automated muscle morphometry analysis in teleosts, enabling a combination of reliable image segmentation, quantification and visualization optimized for teleost muscles.
