RESUMEN
The Wnt/ß-catenin pathway is involved in development, cancer, and embryonic stem cell (ESC) maintenance; its dual role in stem cell self-renewal and differentiation is still controversial. Here, by applying an in vitro system enabling inducible gene expression control, we report that moderate induction of transcriptionally active exogenous ß-catenin in ß-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild-type cells, moderate chemical pre-activation of the Wnt/ß-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate ß-catenin levels in ß-catenin null mouse ESCs favor early stem cell commitment toward mesoderm if the exogenous protein is induced only in the "ground state" of pluripotency condition, or endoderm if the induction is maintained during the differentiation. Overall, our results confirm previous findings about the role of ß-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.
RESUMEN
The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.
Asunto(s)
Ciclo Celular/genética , Ciclinas/genética , Retroalimentación Fisiológica , GTP Fosfohidrolasas/genética , Regulación Fúngica de la Expresión Génica , Proteínas de la Membrana/genética , Proteínas de Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/genética , Algoritmos , Automatización de Laboratorios , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Ciclo Celular/efectos de los fármacos , Medios de Cultivo/química , Medios de Cultivo/farmacología , Ciclinas/metabolismo , GTP Fosfohidrolasas/metabolismo , Genes Reporteros , Proteínas Luminiscentes/genética , Proteínas Luminiscentes/metabolismo , Proteínas de la Membrana/metabolismo , Técnicas Analíticas Microfluídicas , Modelos Biológicos , Organismos Modificados Genéticamente , Saccharomyces cerevisiae/efectos de los fármacos , Saccharomyces cerevisiae/crecimiento & desarrollo , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Proteína Fluorescente RojaRESUMEN
Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.
