RESUMO
Current approaches for dynamic profiling of single cells rely on dissociated cultures, which lack important biological features existing in tissues. Organotypic slice cultures preserve aspects of structural and synaptic organisation within the brain and are amenable to microscopy, but established techniques are not well adapted for high throughput or longitudinal single cell analysis. Here we developed a custom-built, automated confocal imaging platform, with improved organotypic slice culture and maintenance. The approach enables fully automated image acquisition and four-dimensional tracking of morphological changes within individual cells in organotypic cultures from rodent and human primary tissues for at least 3 weeks. To validate this system, we analysed neurons expressing a disease-associated version of huntingtin (HTT586Q138-EGFP), and observed that they displayed hallmarks of Huntington's disease and died sooner than controls. By facilitating longitudinal single-cell analyses of neuronal physiology, our system bridges scales necessary to attain statistical power to detect developmental and disease phenotypes.
Assuntos
Rastreamento de Células/métodos , Hipocampo/ultraestrutura , Doença de Huntington/patologia , Microscopia Confocal/métodos , Neurônios/ultraestrutura , Análise de Célula Única/métodos , Substituição de Aminoácidos , Animais , Animais Recém-Nascidos , Diferenciação Celular , Rastreamento de Células/instrumentação , Expressão Gênica , Hipocampo/metabolismo , Hipocampo/patologia , Humanos , Proteína Huntingtina/genética , Proteína Huntingtina/metabolismo , Doença de Huntington/genética , Doença de Huntington/metabolismo , Camundongos , Camundongos Endogâmicos C57BL , Microscopia Confocal/instrumentação , Modelos Biológicos , Células-Tronco Neurais/metabolismo , Células-Tronco Neurais/ultraestrutura , Neurônios/metabolismo , Cultura Primária de Células , Análise de Célula Única/instrumentação , Técnicas de Cultura de TecidosRESUMO
Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.
