Recent advances of droplet-based microfluidics for engineering artificial cells.

Artificial cells, synthetic cells, or minimal cells are microengineered cell-like structures that mimic the biological functions of cells. Artificial cells are typically biological or polymeric membranes where biologically active components, including proteins, genes, and enzymes, are encapsulated. The goal of engineering artificial cells is to build a living cell with the least amount of parts and complexity. Artificial cells hold great potential for several applications, including membrane protein interactions, gene expression, biomaterials, and drug development. It is critical to generate robust, stable artificial cells using high throughput, easy-to-control, and flexible techniques. Recently, droplet-based microfluidic techniques have shown great potential for the synthesis of vesicles and artificial cells. Here, we summarized the recent advances in droplet-based microfluidic techniques for the fabrication of vesicles and artificial cells. We first reviewed the different types of droplet-based microfluidic devices, including flow-focusing, T-junction, and coflowing. Next, we discussed the formation of multi-compartmental vesicles and artificial cells based on droplet-based microfluidics. The applications of artificial cells for studying gene expression dynamics, artificial cell-cell communications, and mechanobiology are highlighted and discussed. Finally, the current challenges and future outlook of droplet-based microfluidic methods for engineering artificial cells are discussed. This review will provide insights into scientific research in synthetic biology, microfluidic devices, membrane interactions, and mechanobiology.

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.

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.

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.