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 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. K E Y W O R D S cell motion analysis, deep learning, keratinocyte stem cells, quality control, stem cell cultures 1 | INTRODUCTION Stem cells, including pluripotent and tissue-specific stem cells have various clinical applications. 1 The quality of the cultured stem cells is crucial for successful application in regenerative medicine. Hence, these stem cells must be maintained and expanded ex vivo under carefully controlled conditions. However, the quality control of stem cell cultures is currently based on visual inspection by individual cell culture experts, which hinders the standardization of stem cell cultures and large-scale stem cell manufacturing for applications in regenerative medicine. Takuya Hirose and Jun'ichi Kotoku contributed equally to this study.

EGFR-mediated epidermal stem cell motility drives skin regeneration through COL17A1 proteolysis

“…The alteration of keratinocyte stem cell dynamics is assumed to be involved in the age-dependent decline of skin regenerative capacity, but the underlying mechanism is unknown. Cell locomotion in human keratinocyte colonies was first reported >40 yr ago by Sun and Green (1976) , and recent studies have revealed that cell motility is associated with the proliferative capacity of human epidermal and oral keratinocytes ( Hirose et al, 2021 ; Hoshikawa et al, 2021 ; Hoshikawa et al, 2019 ; Kinoshita et al, 2019 ; Nanba et al, 2015 ; Tate et al, 2015 ). Here we demonstrated that an age-dependent decline of EGFR signaling impairs keratinocyte stem cell motility and reepithelialization through COL17A1 proteolysis.…”

Blank Spots in the Map of Human Skin: The Challenge for Xenotransplantation

“…The observation of human epidermal stem cells was complicated because of the impossibility of applying DNA labels or genetic modifications. The primary knowledge was obtained from keratinocyte culture: epidermal cells produced the different clones in vitro; stem cells formed large, self-renewing holoclones; whereas TA cells produced abortive clones because they divided a small number of times and underwent terminal differentiation [ 91 , 92 ]. Combining the xenografting approach with viral transfection visualized the epidermal clones directly in the human epidermis [ 93 ].…”

Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks

“…AI technologies have been applied in cell biology for the automation of intricate biological image recognition, which is only possible for skilled trainees with expertise. For example, in recent studies using microscope cell images, an AI model distinguished keratinocyte SC colonies from non-SC colonies [ 9 ]. AI accurately classified the state of cultured iPS cells into colonies, single cells, differentiated cells, and dead cells, as performed by a trainee with iPS cell culture expertise [ 10 ].…”