bioRxiv Science⌕ Search

Biology subjects

Harridge, S. D. R.

Publications and source records attributed to Harridge, S. D. R..

4 recordsLinked to original sources

CD55-CD319-CX3CR1 flow cytometry gating strategy recapitulates scRNA-seq-defined memory CD8 T cell subpopulations

Human CD8 T cells have traditionally been classified into naive, central memory, effector memory, and terminal effector subsets using CCR7 and CD45RA expression, a framework that has guided immunological research and clinical immune monitoring for nearly three decades. However, recent single-cell studies have revealed transcriptionally distinct CD8 T cell populations, including GZMK-, GZMB-, and central memory-like states, raising important questions regarding their relationship to canonical flow cytometric subsets. Here, we systematically integrated transcriptomic, epigenetic, and phenotypic analyses to evaluate the correspondence between these classification schemes. We demonstrate that conventional CCR7-CD45RA gating generates heterogeneous populations containing extensive mixtures of transcriptionally and epigenetically distinct CD8 T cell states, resulting in poor resolution of biologically meaningful subsets. To address this limitation, we developed a surface-marker framework based on CD55, CD319, and CX3CR1 that accurately identifies transcriptionally defined human CD8 T cell populations using standard flow cytometry. This strategy enables direct isolation of viable cells, including GZMK-expressing cells increasingly implicated in aging, chronic inflammation, autoimmunity, and cancer, which previously could only be identified using intracellular staining or single-cell sequencing. Functional characterization of purified subsets revealed marked differences in proliferative capacity, cytokine production, and cytotoxic activity, demonstrating that transcriptionally defined states possess distinct immune functions. Together, these findings establish a biologically grounded framework for CD8 T cell classification and provide a practical platform for mechanistic studies, biomarker discovery, and cellular immunotherapy applications.

immunology↗

Increased utrophin expression in healthy and DMD patient derived myoblasts in response to ERK1/2 and EZH2 inhibitor treatment

BackgroundThe X-linked muscle wasting disorder Duchenne muscular dystrophy (DMD) is a progressive and ultimately fatal disease caused by loss of function mutations in the dystrophin (DMD) gene. Upregulation of utrophin (UTRN), an embryonic homologue of dystrophin, has been proposed as a therapeutic option that could ameliorate disease. We previously generated a bioluminescent screen for utrophin-upregulating compounds using a mouse reporter of endogenous utrophin expression and discovered that inhibition of ERK1/2 and EZH2, increases utrophin expression in myoblasts. MethodologyHere we extend this analysis to show that treatment of human myoblasts with the ERK1/2 inhibitor LY3214996 and the EZH2 inhibitor GSK503, increases UTRN expression in primary and immortalised myoblasts derived from healthy volunteers and DMD patients. ResultsShort-term (24 hours) inhibition of ERK1/2 and EZH2 resulted in increased expression of utrophin in proliferating myoblasts. Surprisingly, in patient-derived samples, but not healthy controls, increased UTRN expression was sustained following drug removal and in vitro differentiation. Furthermore, dystrophin deficient myoblasts have altered expression of myogenic transcription factors MYOD1 and MYOG and proliferation marker Ki67, signalling an altered regenerative capacity of these cells, while ERK1/2 inhibition, alone or combined with EZH2i, reversed this transcriptional signature. ConclusionsTreatment with ERK1/2 and EZH2 inhibitors could offer a therapeutic option for DMD by increasing UTRN and MYOD1 expression. We propose that this may compensate for DMD loss and help restore productive muscle differentiation and regeneration.

pharmacology and toxicology↗

Exercise induces myonuclear remodelling in humans independently of age

Age-related decline in skeletal muscle structure and function can be mitigated by regular exercise. However, the precise mechanisms that govern this are not fully understood. The nucleus plays an active role in translating forces into biochemical signals (mechanotransduction), with nuclear lamina protein Lamin A regulating nuclear shape, nuclear mechanics, and ultimately gene expression. Defective Lamin A expression causes muscle pathologies and premature ageing syndromes, but the roles of nuclear structure and function in physiological ageing and in exercise adaptations remain obscure. Here, we isolated single muscle fibres and carried out detailed morphological and functional analyses on myonuclei from young and older exercise-trained individuals. Strikingly, myonuclei from trained individuals were more spherical, less deformable, and contained a thicker nuclear lamina than untrained individuals. Complementary to this, exercise resulted in increased levels of Lamin A and increased myonuclear stiffness in mice. We conclude that exercise is associated with myonuclear remodelling, independently of age, which may contribute to the preservative effects of exercise on muscle function throughout the lifespan. Key pointsO_LIThe nucleus plays an active role in translating forces into biochemical signals C_LIO_LIMyonuclear aberrations in a group of muscular dystrophies called laminopathies suggest that the shape and mechanical properties of myonuclei are important for maintaining muscle function. C_LIO_LIHere, we present striking differences in myonuclear shape and mechanics associated with exercise, in both young and old humans. C_LIO_LIMyonuclei from trained individuals were more spherical, less deformable, and contained a thicker nuclear lamina than untrained individuals. C_LIO_LIWe conclude that exercise is associated with age-independent myonuclear remodelling, which may help to maintain muscle function throughout the lifespan. C_LI

cell biology↗

SIMON: open-source knowledge discovery platform

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

systems biology↗