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Macrophage-targeted therapy offers new options for intractable pancreatic ductal adenocarcinoma (PDAC), which has a low 5-year survival rate. However, the factors regulating the biological function and phenotype of macrophages in PDAC are incompletely understood. Here, we found that CD51 was positively associated with the poor prognosis of PDAC patients and was highly expressed on macrophages but not on pancreatic cancer cells. Subsequently, we found that CD51 was a marker of macrophages, which promoted the stemness of pancreatic cancer cells. Furthermore, knockdown of CD51 in macrophages drove macrophages toward an M1-like phenotype. Mechanistically, macrophage-expressed CD51 contributed to the acquisition of stemness traits of pancreatic cancer cells by regulating the TGF-β1/smad2/3 pathway. Our data demonstrate the central role played by macrophage-expressed CD51 in the acquisition of stemness traits of pancreatic cancer cells through the paracrine induction of TGF-β1. We first show the connection between the CD51/TGF-β1/smad2/3 axis and PDAC cancer stem cell properties and then indicate that CD51-targeted therapy is a new therapeutic modality for PDAC. [ABSTRACT FROM AUTHOR]

Single-cell mechanics have gained much attention due to its importance in a broad range of biological applications. Different experimental approaches have been used for measuring the mechanical properties of individual cells. However, the technical demands and time-consuming nature of these procedures have limited the throughput of single-cell measurement, necessitating the development of alternative computational approaches. Recently, single-cell deformability can be predicted using a convolutional neural network (CNN) model, shedding a light on using machine learning (ML) algorithms for high-throughput characterizations of single-cell mechanical properties. In this work, we developed a novel ML-based computational framework that can reproduce a physical microfluidic system to investigate the individual cell's deformability. The datasets for the training and testing of our model were generated using high-fidelity fluid-structure interaction (FSI) simulations. Our FSI-based ML approach of adopting CNN algorithms demonstrated a highly accurate prediction for the membrane stiffness of a microcapsule (maximum R2 = 0.98) based on its deformed shape. In this paper, we show that by applying physical constraints including the microcapsule's total surface area and total volume, we were able to build a physics-constrained ML model that possesses better convergence and higher stability during both training and validation. Finally, we found that ML models that used the three-dimensional geometry of the capsule as input could outperform the typical CNN models that relied solely on the two-dimensional images. We expect that this physics-constrained computational framework will serve as a basis for developing future tools for real-time biological applications through the integration of high-fidelity simulations with ML algorithms. • Integration of machine learning (ML) algorithms and fluid dynamics simulations to study individual cell's deformability. • This computational framework predicts accurately microcapsule's membrane stiffness based on its deformed shape. • Applying physical constraints on the ML models achieves better convergences during both training and validation. • CNN models using three-dimensional geometry as input outperform typical CNN models that solely use two-dimensional images. [ABSTRACT FROM AUTHOR]