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

AIM: While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. To address this challenge, we assembled an automated image analysis pipeline, FiNuTyper (Fiber and Nucleus Typer). METHODS: We integrated recently developed deep learning-based image segmentation methods, optimized for unbiased evaluation of fresh and postmortem human skeletal muscle, and utilized SERCA1 and SERCA2 as type-specific myonucleus and myofiber markers after validating them against the traditional use of MyHC isoforms. RESULTS: Parameters including cross-sectional area, myonuclei per fiber, myonuclear domain, central myonuclei per fiber, and grouped myofiber ratio were determined in a fiber-type-specific manner, revealing that a large degree of sex- and muscle-related heterogeneity could be detected using the pipeline. Our platform was also tested on pathological muscle tissue (ALS and IBM) and adapted for the detection of other resident cell types (leucocytes, satellite cells, capillary endothelium). CONCLUSION: In summary, we present an automated image analysis tool for the simultaneous quantification of myofiber and myonuclear types, to characterize the composition and structure of healthy and diseased human skeletal muscle.

User experience of self-reported computerized medical history taking for acute chest pain: The Clinical Expert Operating System Chest Pain Danderyd Study.

BACKGROUND AND OBJECTIVE: Chest pain is one of the most common complaints in emergency departments (EDs). Self-reported computerized history taking (CHT) programmes can be used for interpretation of the clinical significance of medical information coming directly from patients. The adoption of CHT in clinical practice depends on reactions and attitudes to the technology from patients and their belief that the technology will have benefits for their medical care. The study objective was to explore the user experience of the self-reported CHT programme Clinical Expert Operating System (CLEOS) in the setting of patients visiting an ED for acute chest pain. METHODS: This qualitative interview study is part of the ongoing CLEOS-Chest Pain Danderyd Study. A subset (n = 84) of the larger sample who had taken part in self-reported history taking during waiting times at the ED were contacted by telephone and n = 54 (64%) accepted participation. An interview guide with open-ended questions was used and the text was analysed using directed content analysis. RESULTS: The patients' experiences of the CLEOS programme were overall positive although some perceived it as extensive. The programme was well accepted and despite the busy environment, patients were highly motivated and deemed it helpful to make a diagnosis. Six categories of user experience emerged: The clinical context, The individual context, Time aspect, Acceptability of the programme, Usability of the programme and Perceptions of usefulness in a clinical setting. CONCLUSIONS: The programme was well accepted by most patients in the stressful environment at ED although some found it difficult to answer all the questions. Adjustments to the extent of an interview to better suit the context of the clinical use should be a future development of the programme. The findings suggest that CHT programmes can be integrated as a standard process for collecting self-reported medical history data in the ED setting.

The human skeletal muscle transcriptome: sex differences, alternative splicing, and tissue homogeneity assessed with RNA sequencing.

Human skeletal muscle health is important for quality of life and several chronic diseases, including type II diabetes, heart disease, and cancer. Skeletal muscle is a tissue widely used to study mechanisms behind different diseases and adaptive effects of controlled interventions. For such mechanistic studies, knowledge about the gene expression profiles in different states is essential. Since the baseline transcriptome has not been analyzed systematically, the purpose of this study was to provide a deep reference profile of female and male skeletal muscle. RNA sequencing data were analyzed from a large set of 45 resting human muscle biopsies. We provide extensive information on the skeletal muscle transcriptome, including 5 previously unannotated protein-coding transcripts. Global transcriptional tissue homogeneity was strikingly high, within both a specific muscle and the contralateral leg. We identified >23,000 known isoforms and found >5000 isoforms that differ between the sexes. The female and male transcriptome was enriched for genes associated with oxidative metabolism and protein catabolic processes, respectively. The data demonstrate remarkably high tissue homogeneity and provide a deep and extensive baseline reference for the human skeletal muscle transcriptome, with regard to alternative splicing, novel transcripts, and sex differences in functional ontology.transcriptome: sex differences, alternative splicing, and tissue homogeneity assessed with RNA sequencing.

An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform.

The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina HumanMethylation450 BeadChip is widely used to quantify DNA methylation; nevertheless, the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate data sets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished data sets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that: (1) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; (2) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); (3) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive data set suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450K arrays.