Grape seed proanthocyanidins regulate mitophagy of endothelial cells and promote wound healing in mice through p-JNK/FOXO3a/ROS signal pathway.

Skin wound healing is a dynamic and complex process that involves multiple physiological and cellular events. Grape seed proanthocyanidins (GSP) have strong anti-oxidation and elimination of oxygen free radicals, and have been shown to significantly promote wound healing, but the underlying mechanism remains unclear. Studies have indicated that reactive oxygen species (ROS) acts as an upstream signal to induce mitophagy, suggesting that GSP can regulate mitophagy through the signal pathway. This study aimed to investigate whether GSP regulates mitophagy by down-regulating oxidative stress to promote wound healing. In vivo, GSP treatment accelerated wound healing, granulation tissue formation, collagen deposition, and angiogenesis in mice. Moreover, GSP down-regulated ROS levels and promoted the expression of antioxidant proteins by up-regulating the expression of p-JNK/FOXO3a protein, thereby regulating the expression of mitophagy-related proteins. In vitro, 4 µg/mL GSP showed no apparent toxic effects on cells and effectively reduce the oxidative stress damage of cells induced by H2O2. Western blot and superoxide anion fluorescence probe further confirmed that GSP effectively reduced Dihydroethidium content and up-regulated the expression of antioxidant proteins by activation of p-JNK/FOXO3a protein expression, thereby regulating mitophagy. Taken together, the findings from in vitro and in vivo experiments provide new insights into the promotion of wound healing by GSP.

Deep autoencoder enables interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.