Astrocyte plays important roles in the pathogenesis of ischemic stroke and reperfusion injury. They intensively participate in the energy metabolism of the brain, while their heterogeneity and function after ischemic stroke remain controversial. By employing single-cell sequencing of mice cortex at 12 h after transient middle cerebral artery occlusion (tMCAO) and comparing with the similar published datasets of 24h after tMCAO, we uncover the cellular phenotypes and dynamic change of astrocytes at the acute phase of ischemic stroke. In this study, we separately identified 3 major subtypes of astrocytes at the 12 h-tMCAO-system and 24 h-tMCAO-system, indicated the significant differences in the expression of genes and metabolic pathways in the astrocytes between the two time nodes after ischemic stroke, and detected the major change in the energy metabolism. These results provided a comprehensive understanding of the characteristic changes of astrocytes after ischemic stroke and explored the potential astrocytic targets for neuroprotection.
Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.
Interlayer shear between graphene sheets plays an important role in graphene-based materials and devices, but the effect of in-plane deformation of graphene, which may depend on the graphene size, has not been fully understood. In this paper, the size effect on interlayer shear behavior between two graphene sheets is studied based on a non-linear shear-lag model with energy barrier analysis, in which both the lattice registry effect and the elastic deformation of graphene are taken into account, and molecular dynamics (MD) simulations are carried out to verify the model. Both theoretical prediction and MD simulations show that the maximum interlayer shear force of short graphene sheets increases with the graphene length and width. However, if the sheet length is beyond 20 nm, the maximum shear force cannot be further increased by increasing the graphene length due to the non-uniform relative displacement between graphene layers, which is caused by the in-plane deformation of graphene. The upper bound of the maximum shear force per unit graphene width is obtained analytically as a constant 5.6 N/m, suggesting that a small force can pull an infinite long graphene belt to slide on a graphene substrate. This study offers useful information for design and manufacture of graphene-based nano-devices and materials.
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