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Sundberg, C. J.

Publications and source records attributed to Sundberg, C. J..

2 recordsLinked to original sources

Molecular profiling of high-level athlete skeletal muscle after acute exercise - a systems biology approach

Life-long high-level exercise training leads to improvements in physical performance and multi-tissue adaptation following changes in molecular pathways. While skeletal muscle baseline differences between exercise-trained and untrained individuals have been previously investigated, it remains unclear how acute exercise multi-omics are influenced by training history. We recruited and extensively characterized 24 individuals categorized as endurance athletes, strength athletes or control subjects. Multi-omics profiling was performed from skeletal muscle before and at three time-points after endurance or resistance exercise sessions. Timeseries multi-omics analysis revealed distinct differences in molecular processes such as fatty- and amino acid metabolism and for transcription factors such as HIF1A and the MYF-family between both exercise history and acute form of exercise. Furthermore, we found a "transcriptional specialization effect" by transcriptional narrowing and intensification. Finally, we performed multi-omics network analysis and clustering, providing a novel resource of skeletal muscle transcriptomic and metabolomic profiling in highly trained and untrained individuals.

physiology↗

FiNuTyper: an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle

SummaryWhile manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. We assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer), from recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle. We validated and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers. Parameters including myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber type-specific manner, revealing a large degree of gender- and muscle-related heterogeneity. Our platform was also tested on pathological muscle tissue (ALS) and adapted for the detection of other resident cell types (leukocytes, satellite cells, capillary endothelium). In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition of healthy and diseased human skeletal muscle. HighlightsO_LIA deep learning-based automated platform for skeletal muscle microscopic analysis C_LIO_LIHigh-fidelity identification and characterization of myonuclei and myofibers C_LIO_LIValidation of SERCA1 and SERCA2 as markers for myofiber and myonuclear subtypes C_LIO_LICharacterization of healthy and pathological human skeletal muscle tissue features C_LIO_LIAdaptations provided for studies on other resident cell types like satellite cells C_LI eTOC BlurbAn automated platform for unbiased analysis of skeletal muscle immunohistochemical images, focusing on type-specific myofiber-myonucleus relationships, facilitating high-throughput studies of healthy and diseased tissues.

cell biology↗