Skeletal muscle comprises 30–40% of the weight of a healthy human body and is required for voluntary movements in humans. Mature skeletal muscle is formed by multinuclear cells, which are called myofibers. Formation of myofibers depends on the proliferation, differentiation, and fusion of muscle progenitor cells during development and after injury. Muscle progenitor cells are derived from muscle satellite (stem) cells (MuSCs), which reside on the surface of the myofiber but beneath the basement membrane. MuSCs play a central role in postnatal maintenance, growth, repair, and regeneration of skeletal muscle. In sedentary adult muscle, MuSCs are mitotically quiescent, but are promptly activated in response to muscle injury. Physiological and chronological aging induces MuSC aging, leading to an impaired regenerative capability. Importantly, in pathological situations, repetitive muscle injury induces early impairment of MuSCs due to stem cell aging and leads to early impairment of regeneration ability. In this review, we discuss (1) the role of MuSCs in muscle regeneration, (2) stem cell aging under physiological and pathological conditions, and (3) prospects related to clinical applications of controlling MuSCs.

Long QT syndrome (LQTS) is an inheritable and life-threatening disease; however, it is often difficult to determine disease characteristics in sporadic cases with novel mutations, and more precise analysis is necessary for the successful development of evidence-based clinical therapies. This study thus sought to better characterize ion channel cardiac disorders using induced pluripotent stem cells (iPSCs). We reprogrammed somatic cells from a patient with sporadic LQTS and from controls, and differentiated them into cardiomyocytes through embryoid body (EB) formation. Electrophysiological analysis of the LQTS-iPSC-derived EBs using a multi-electrode array (MEA) system revealed a markedly prolonged field potential duration (FPD). The IKr blocker E4031 significantly prolonged FPD in control- and LQTS-iPSC-derived EBs and induced frequent severe arrhythmia only in LQTS-iPSC-derived EBs. The IKs blocker chromanol 293B did not prolong FPD in the LQTS-iPSC-derived EBs, but significantly prolonged FPD in the control EBs, suggesting the involvement of IKs disturbance in the patient. Patch-clamp analysis and immunostaining confirmed a dominant-negative role for 1893delC in IKs channels due to a trafficking deficiency in iPSC-derived cardiomyocytes and human embryonic kidney (HEK) cells. This study demonstrated that iPSCs could be useful to characterize LQTS disease as well as drug responses in the LQTS patient with a novel mutation. Such analyses may in turn lead to future progress in personalized medicine.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.

Background: Despite the accumulating genetic and molecular investigations into hypertrophic cardiomyopathy (HCM), it remains unclear how this condition develops and worsens pathologically and clinically in terms of the genetic–environmental interactions. Establishing a human disease model for HCM would help to elucidate these disease mechanisms; however, cardiomyocytes from patients are not easily obtained for basic research. Patient‐specific induced pluripotent stem cells ( iPSCs ) potentially hold much promise for deciphering the pathogenesis of HCM. The purpose of this study is to elucidate the interactions between genetic backgrounds and environmental factors involved in the disease progression of HCM. Methods and Results: We generated iPSC s from 3 patients with HCM and 3 healthy control subjects, and cardiomyocytes were differentiated. The HCM pathological phenotypes were characterized based on morphological properties and high‐speed video imaging. The differences between control and HCM iPSC ‐derived cardiomyocytes were mild under baseline conditions in pathological features. To identify candidate disease‐promoting environmental factors, the cardiomyocytes were stimulated by several cardiomyocyte hypertrophy‐promoting factors. Interestingly, endothelin‐1 strongly induced pathological phenotypes such as cardiomyocyte hypertrophy and intracellular myofibrillar disarray in the HCM iPSC ‐derived cardiomyocytes. We then reproduced these phenotypes in neonatal cardiomyocytes from the heterozygous Mybpc3 ‐targeted knock in mice. High‐speed video imaging with motion vector prediction depicted physiological contractile dynamics in the iPSC ‐derived cardiomyocytes, which revealed that self‐beating HCM iPSC ‐derived single cardiomyocytes stimulated by endothelin‐1 showed variable contractile directions. Conclusions: Interactions between the patient's genetic backgrounds and the environmental factor endothelin‐1 promote the HCM pathological phenotype and contractile variability in the HCM iPSC ‐derived cardiomyocytes.

Supercentenarians (those aged ≥110 years) are approaching the current human longevity limit by preventing or surviving major illness. Identifying specific biomarkers conducive to exceptional survival might provide insights into counter-regulatory mechanisms against aging-related disease. Here, we report associations between cardiovascular disease-related biomarkers and survival to the highest ages using a unique dataset of 1,427 oldest individuals from three longitudinal cohort studies, including 36 supercentenarians, 572 semi-supercentenarians (105–109 years), 288 centenarians (100–104 years), and 531 very old people (85–99 years). During follow-up, 1,000 participants (70.1%) died. Overall, N-terminal pro-B-type natriuretic peptide (NT-proBNP), interleukin-6, cystatin C and cholinesterase are associated with all-cause mortality independent of traditional cardiovascular risk factors and plasma albumin. Of these, low NT-proBNP levels are statistically associated with a survival advantage to supercentenarian age. Only low albumin is associated with high mortality across age groups. These findings expand our knowledge on the biology of human longevity.

Induced pluripotent stem cells (iPSC) are one the most prominent innovations of medical research in the last few decades. iPSCs can be easily generated from human somatic cells and have several potential uses in regenerative medicine, disease modeling, drug screening, and precision medicine. However, further innovation is still required to realize their full potential. Machine learning is an algorithm that learns from large datasets for pattern formation and classification. Deep learning, a form of machine learning, uses a multilayered neural network that mimics human neural circuit structure. Deep neural networks can automatically extract features from an image, although classical machine learning methods still require feature extraction by a human expert. Deep learning technology has developed recently; in particular, the accuracy of an image classification task by using a convolutional neural network (CNN) has exceeded that of humans since 2015. CNN is now used to address several tasks including medical issues. We believe that CNN would also have a great impact on the research of stem cell biology. iPSCs are utilized after their differentiation to specific cells, which are characterized by molecular techniques such as immunostaining or lineage tracing. Each cell shows a characteristic morphology; thus, a morphology-based identification system of cell type by CNN would be an alternative technique. The development of CNN enables the automation of identifying cell types from phase contrast microscope images without molecular labeling, which will be applied to several researches and medical science. Image classification is a strong field among deep learning tasks, and several medical tasks will be solved by deep learning-based programs in the future.