Ultrasound generates both compressive and shear mechanical forces in soft tissues. However, the specific mechanisms by which these forces activate cellular processes remain unclear. Here we show that low-intensity focused ultrasound can activate the mechanosensitive RET signalling pathway. Specifically, in mouse colon tissues ex vivo and in vivo, focused ultrasound induced RET phosphorylation in colonic crypts cells, which correlated with markers of proliferation and stemness when using hours-long insonication. The activation of the RET pathway is non-thermal, is linearly related to acoustic pressure and is independent of radiation-force-induced shear strain in tissue. Our findings suggest that ultrasound could be used to regulate cell proliferation, particularly in the context of regenerative medicine, and highlight the importance of adhering to current ultrasound-safety regulations for medical imaging.
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.
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