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Hirose, Takuya; Kotoku, Jun'ichi; Toki, Fujio; Nishimura, Emi K.; Nanba, Daisuke
Stem cells (Dayton, Ohio), August 2021, Letnik: 39, Številka: 8Journal Article
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. A deep learning‐based automated cell tracking (DeepACT) technology enables the evaluation of keratinocyte culture quality and the identification of keratinocyte stem cells using quantitative cell motion analysis. DeepACT comprises two main modules: identifying human keratinocytes at single‐cell resolution from phase‐contrast images of cultures through deep learning and tracking keratinocyte motion in the colony using a state‐space model.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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