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

Abstract

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.