Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

ABSTRACT: Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology for noninvasive quality control and identification of cultured human stem cells. The combination of deep learning-based cascading cell detection and Kalman filter algorithm-based tracking successfully tracked the individual cells within the densely packed human epidermal keratinocyte colonies in the phase-contrast images of the culture. DeepACT rapidly analyzed the motion of individual keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT can distinguis

SUBMITTER: Hirose T 

PROVIDER: S-EPMC8359832 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature