Objective:To evaluate the therapeutic potential of targeting highly differentiated T cells in inclusion body myositis (IBM) patients, by establishing a high-resolution mapping of killer cell lectin-like receptor subfamily G member 1 (KLRG1+) within the T and NK cell compartments.Methods:Blood was collected from 51 IBM and 19 healthy age-matched donors. Peripheral blood mononuclear cells (PBMCs) were interrogated by flow cytometry using a 12-marker antibody panel. The panel allowed the delineation of naïve (Tn), central memory (Tcm), 4 stages of effector memory differentiation (Tem 1-4), and effector memory re-expressing CD45RA (TemRA) T cells, as well as total and subpopulations of NK cells based on the differential expression of CD16 and C56.Results:We found that a population of KLRG1+ Tem and TemRA cells were expanded in both the CD4+ and CD8+ T cell subpopulations in IBM patients. KLRG1 expression in CD8+ T cells increased with T cell differentiation with the lowest levels of expression in naïve T cells (Tn) and highest in highly differentiated TemRA and CD56+CD8+ T cells. The frequency of KLRG1+ total NK cells and subpopulations did not differ between IBM and healthy donors. IBM disease duration correlated with increased CD8+ T cell differentiation.Discussion:Collectively, our findings reveal that the selective expansion of blood KLRG1+ T cells in IBM patients is confined to the TemRA and Tem cellular compartments.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology.