RESUMO
The stem cell niche has a strong influence in the differentiation potential of human pluripotent stem cells with integrins playing a major role in communicating cells with the extracellular environment. However, it is not well understood how interactions between integrins and the extracellular matrix are involved in cardiac stem cell differentiation. To evaluate this, we performed a profile of integrins expression in two stages of cardiac differentiation: mesodermal progenitors and cardiomyocytes. We found an active regulation of the expression of different integrins during cardiac differentiation. In particular, integrin α5 subunit showed an increased expression in mesodermal progenitors, and a significant downregulation in cardiomyocytes. To analyze the effect of α5 subunit, we modified its expression by using a CRISPRi technique. After its downregulation, a significant impairment in the process of epithelial-to-mesenchymal transition was seen. Early mesoderm development was significantly affected due to a downregulation of key genes such as T Brachyury and TBX6. Furthermore, we observed that repression of integrin α5 during early stages led to a reduction in cardiomyocyte differentiation and impaired contractility. In summary, our results showed the link between changes in cell identity with the regulation of integrin α5 expression through the alteration of early stages of mesoderm commitment.
Assuntos
Células-Tronco Embrionárias Humanas/citologia , Integrina alfa5/genética , Miócitos Cardíacos/citologia , Sistemas CRISPR-Cas , Diferenciação Celular , Linhagem Celular , Regulação para Baixo , Regulação da Expressão Gênica no Desenvolvimento , Células HEK293 , Células-Tronco Embrionárias Humanas/metabolismo , Humanos , Miócitos Cardíacos/metabolismo , Células-Tronco Pluripotentes/citologia , Células-Tronco Pluripotentes/metabolismo , Nicho de Células-TroncoRESUMO
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.