Open AccessLabel‐free quality control and identification of human keratinocyte stem cells by deep learning‐based automated cell tracking
Author(s) -
Hirose Takuya,
Kotoku Jun'ichi,
Toki Fujio,
Nishimura Emi K.,
Nanba Daisuke
Publication year - 2021
Publication title -
stem cells
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.159
H-Index - 229
eISSN - 1549-4918
pISSN - 1066-5099
DOI - 10.1002/stem.3371
Subject(s) - stem cell , biology , keratinocyte , tracking (education) , regenerative medicine , identification (biology) , microbiology and biotechnology , computational biology , cell culture , artificial intelligence , computer science , genetics , psychology , pedagogy , botany
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 distinguish keratinocyte stem cell colonies from non‐stem cell‐derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology.