Protocol for cellular age prediction in yeast and human single cells using transfer learning

Here, we present a protocol for predicting cellular age via computer vision analysis of cellular morphology and aging-related bioactivities from phase contrast microscopy images. We describe the steps for cultivating yeast cells, performing phase contrast microscopy of drug-treated yeast cells, and inducing senescence in human dermal fibroblasts. We detail the process of using the scCamAge Docker container, running the scCamAge model, applying the yeast-trained model to senescent human fibroblasts, and performing transfer learning to adapt scCamAge using human fibroblast data. For complete details on the use and execution of this protocol, please refer to Gautam et al. 1 • Protocol to predict cellular age using computer vision and transfer learning • Detailed steps for image acquisition, segmentation, and predictive modeling • Cross-species approach to predict aging in yeast and senescence in human cells Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present a protocol for predicting cellular age via computer vision analysis of cellular morphology and aging-related bioactivities from phase contrast microscopy images. We describe the steps for cultivating yeast cells, performing phase contrast microscopy of drug-treated yeast cells, and inducing senescence in human dermal fibroblasts. We detail the process of using the scCamAge Docker container, running the scCamAge model, applying the yeast-trained model to senescent human fibroblasts, and performing transfer learning to adapt scCamAge using human fibroblast data.