Probing Notch1-Dll4 signaling in regulating osteogenic differentiation of human mesenchymal stem cells using single cell nanobiosensor

Insights into the Notch signaling pathway in degenerative musculoskeletal disorders: Mechanisms and perspectives

Degenerative musculoskeletal disorders are a group of age-related diseases of the locomotive system that severely affects the patient's ability to work and cause adverse sequalae such as fractures and even death. The incidence and prevalence of degenerative musculoskeletal disorders is rising owing to the aging of the world's population. The Notch signaling pathway, which is expressed in almost all organ systems, extensively regulates cell proliferation and differentiation as well as cellular fate. Notch signaling shows increased activity in degenerative musculoskeletal disorders and retards the progression of degeneration to some extent. The review focuses on four major degenerative musculoskeletal disorders (osteoarthritis, intervertebral disc degeneration, osteoporosis, and sarcopenia) and summarizes the pathophysiological functions of Notch signaling in these disorders, especially its role in stem/progenitor cells in each disorder. Finally, a conclusion will be presented to explore the research and application of the perspectives on Notch signaling in degenerative musculoskeletal disorders.

Long non-coding RNA (lncRNA) MALAT1 in regulating osteogenic and adipogenic differentiation using a double-stranded gapmer locked nucleic acid nanobiosensor

Long non-coding RNAs (lncRNA) are non-protein coding RNA molecules that are longer than 200 nucleotides. The lncRNA molecule plays diverse roles in gene regulation, chromatin remodeling, and cellular processes, influencing various biological pathways. However, probing the complex dynamics of lncRNA in live cells is a challenging task. In this study, a double-stranded gapmer locked nucleic acid (ds-GapM-LNA) nanobiosensor is designed for visualizing the abundance and expression of lncRNA in live human bone-marrow-derived mesenchymal stem cells (hMSCs). The sensitivity, specificity, and stability were characterized. The results showed that this ds-GapM-LNA nanobiosensor has very good sensitivity, specificity, and stability, which allows for dissecting the regulatory roles of cellular processes during dynamic physiological events. By incorporating this nanobiosensor in living hMSC imaging, we elucidated lncRNA MALAT1 expression dynamics during osteogenic and adipogenic differentiation. The data reveal that lncRNA MALAT1 expression is correlated with distinct sub-stages of osteogenic and adipogenic differentiation.

Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs)

Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers. However, these methods are not only time-consuming and economically demanding, but also require the fixation of cells, resulting in the loss of temporal data. Consequently, there is an emerging need for a more efficient and precise approach to predict hMSCs differentiation in live cells, particularly for osteogenic and adipogenic differentiation. In response to this need, we developed innovative approaches that combine live-cell imaging with cutting-edge deep learning techniques, specifically employing a convolutional neural network (CNN) to meticulously classify osteogenic and adipogenic differentiation. Specifically, four notable pre-trained CNN models, VGG 19, Inception V3, ResNet 18, and ResNet 50, were developed and tested for identifying adipogenic and osteogenic differentiated cells based on cell morphology changes. We rigorously evaluated the performance of these four models concerning binary and multi-class classification of differentiated cells at various time intervals, focusing on pivotal metrics such as accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, precision, and F1-score. Among these four different models, ResNet 50 has proven to be the most effective choice with the highest accuracy (0.9572 for binary, 0.9474 for multi-class) and AUC (0.9958 for binary, 0.9836 for multi-class) in both multi-class and binary classification tasks. Although VGG 19 matched the accuracy of ResNet 50 in both tasks, ResNet 50 consistently outperformed it in terms of AUC, underscoring its superior effectiveness in identifying differentiated cells. Overall, our study demonstrated the capability to use a CNN approach to predict stem cell fate based on morphology changes, which will potentially provide insights for the application of cell-based therapy and advance our understanding of regenerative medicine.

DLL4-Notch signalling in acute-on-chronic liver failure: State of the art and perspectives

Acute-on-chronic liver failure (ACLF) is a syndrome characterized by acute decompensation of chronic liver disease associated with multiple-organ failures and high short-term mortality. Acute insults to patients with chronic liver disease can lead to ACLF, among which, hepatitis B virus-related ACLF is the most common type of liver failure in the Asia-Pacific region. Currently, immune-metabolism disorders and systemic inflammation are proposed to be the main mechanisms of ACLF. The resulting cholestasis and intrahepatic microcirculatory dysfunction accelerate the development of ACLF. Treatments targeting immune regulation, metabolic balance, microcirculation maintenance and bile duct repair can alleviate inflammation and restore the tissue structure. An increasing number of studies have demonstrated that delta-like ligand 4 (DLL4), one of the Notch signalling ligands, plays a vital role in immune regulation, metabolism, angiogenesis, and biliary regeneration, which participate in liver pathological and physiological processes. The detailed mechanism of the DLL4-Notch signalling pathway in ACLF has rarely been investigated. Here, we review the evidence showing that DLL4-Notch signalling is involved in ACLF and analyse the potential role of DLL4 in the treatment of ACLF.

Notch signaling and fluid shear stress in regulating osteogenic differentiation

Osteoporosis is a common bone and metabolic disease that is characterized by bone density loss and microstructural degeneration. Human bone marrow-derived mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes, which have been utilized extensively in the field of bone tissue engineering and cell-based therapy. Although fluid shear stress plays an important role in bone osteogenic differentiation, the cellular and molecular mechanisms underlying this effect remain poorly understood. Here, a locked nucleic acid (LNA)/DNA nanobiosensor was exploited to monitor mRNA gene expression of hMSCs that were exposed to physiologically relevant fluid shear stress to examine the regulatory role of Notch signaling during osteogenic differentiation. First, the effects of fluid shear stress on cell viability, proliferation, morphology, and osteogenic differentiation were investigated and compared. Our results showed shear stress modulates hMSCs morphology and osteogenic differentiation depending on the applied shear and duration. By incorporating this LNA/DNA nanobiosensor and alkaline phosphatase (ALP) staining, we further investigated the role of Notch signaling in regulating osteogenic differentiation. Pharmacological treatment is applied to disrupt Notch signaling to investigate the mechanisms that govern shear stress induced osteogenic differentiation. Our experimental results provide convincing evidence supporting that physiologically relevant shear stress regulates osteogenic differentiation through Notch signaling. Inhibition of Notch signaling mediates the effects of shear stress on osteogenic differentiation, with reduced ALP enzyme activity and decreased Dll4 mRNA expression. In conclusion, our results will add new information concerning osteogenic differentiation of hMSCs under shear stress and the regulatory role of Notch signaling. Further studies may elucidate the mechanisms underlying the mechanosensitive role of Notch signaling in stem cell differentiation.