Automated collective motion analysis validates human keratinocyte stem cell cultures

Koji Kinoshita,Yann Barrandon,Shigeki Higashiyama,Daisuke Nanba,Emi K Nishimura,Yoshio Yanagihara,Takuya Munesue,Masaharu Isshiki,Fujio Toki

doi:10.1038/s41598-019-55279-4

Abstract

Identification and quality assurance of stem cells cultured in heterogeneous cell populations are indispensable for successful stem cell therapy. Here we present an image-processing pipeline for automated identification and quality assessment of human keratinocyte stem cells. When cultivated under appropriate conditions, human epidermal keratinocyte stem cells give rise to colonies and exhibit higher locomotive capacity as well as significant proliferative potential. Image processing and kernel density estimation were used to automatically extract the area of keratinocyte colonies from phase-contrast images of cultures containing feeder cells. The DeepFlow algorithm was then used to calculate locomotion speed of the colony area by analyzing serial images. This image-processing pipeline successfully identified keratinocyte stem cell colonies by measuring cell locomotion speed, and also assessed the effect of oligotrophic culture conditions and chemical inhibitors on keratinocyte behavior. Therefore, this study provides automated procedures for image-based quality control of stem cell cultures and high-throughput screening of small molecules targeting stem cells.

Highlights

We revealed that human epidermal keratinocyte stem cell colonies exhibit a higher locomotive phenotype[29]
We hypothesized that automated collective motion analysis in human keratinocyte stem cell cultures could be developed as follows: (1) Obtain time-series phase-contrast images of human keratinocyte colonies by time-lapse imaging of a culture containing both keratinocyte colonies and inactivated mouse 3T3 fibroblasts (Fig. 1a)
With the exception of data set #1, the correlation of the average speed of cells with the percentage of terminal colonies was maintained when the speed was calculated by DeepFlow algorithm (Fig. 5c). These results indicate that human keratinocyte stem cell colonies can be identified by measuring cell locomotion speed by a combination of automated colony area extraction and estimation of optical flow

Summary

Introduction

We revealed that human epidermal keratinocyte stem cell colonies exhibit a higher locomotive phenotype[29]. Human keratinocytes give rise to densely packed colonies, and the motion of individual cells within the colony has so far been analyzed by manual cell tracking. This process is extremely time-consuming, laborious, and at risk of human error. We provide an image-processing pipeline that noninvasively identifies human keratinocyte stem cells and validates the quality of human keratinocyte cultures for transplantation. This pipeline consists of two main modules: the identification of human keratinocyte colonies on the feeder layer of 3T3 cells, and the estimation of cell locomotion speed using optical flow

Methods

Results

Conclusion