ISSN: 0304-128X ISSN: 2233-9558
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In relation to this article, we declare that there is no conflict of interest.
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Received September 7, 2022
Revised October 23, 2022
Accepted November 8, 2022
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이미지 분석 프로그램을 이용한 액적 내 세포 계수 방법

Automated Bacterial Cell Counting Method in a Droplet Using ImageJ

충남대학교 공과대학 응용화학공학과 34134, 대전광역시 유성구 대학로 99
Department of Chemical Engineering and Applied Chemistry, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, 34134, Korea
Korean Chemical Engineering Research, May 2023, 61(2), 247-257(11), 10.9713/kcer.2023.61.2.247 Epub 31 May 2023
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본 연구에서는 이미지 분석 프로그램을 통해 액적 내 박테리아 세포의 개수를 측정하는 코딩-기반의 자동화된 세포 계수 방법을 제시하였다. 먼저, 형광 이미지 기반의 분석을 위하여, 형광단백질을 발현하는 균주를 담지한 액적을 형 성하고 이를 광학 및 형광 현미경을 이용하여 분석 결과를 나타냈다. 액적의 관찰을 용이하게 위하여 유리 그리드에 도포하고, 촬영한 광학 이미지를 통해 분석하고자 하는 영역(Region of Interest)을 지정하였다. 동일한 위치에서 촬영 한 형광 이미지에서 앞서 지정된 영역 속 특정 임계 값을 넘는 형광 신호를 계수하여 세포 수를 정량화 하였다. 또한 서로 다른 농도의 항생제를 처리한 액적 내 박테리아의 시간에 따른 세포 개수 변화의 차이를 추적하였다. 30분 간격 으로 동일한 위치에서의 형광 이미지들을 동시에 분석함으로써 시간에 따른 세포 개수 변화를 도출하였고, 본 계수법 의 성능을 실험적으로 검증하였다. 본 논문의 방법은 외부 분석 프로그램을 이용한 기존 방법 대비 분석 시간을 15배 가량 단축하고, 99%의 정확도를 보이는 것으로 확인되었다. 더 나아가 사용자의 연구의 방향에 맞춰 제시된 코드의 확장 수정을 통해 다양한 종류의 세포 계수 연구에 도움이 될 것으로 기대된다

Precise counting of cell number stands in important position within clinical and research laboratories. Conventional methods such as hemocytometer, migration/invasion assay, or automated cell counters have limited in analytical time, cost, and accuracy., which needs an alternative way with time-efficient in-situ approach to broaden the application avenue. Here, we present simple coding-based cell counting method using image analysis tool, freely available image software (ImageJ). Firstly, we encapsulated RFP-expressing bacteria in a droplet using microfluidic device and automatically performed fluorescence image-based analysis for the quantification of cell numbers. Also, time-lapse images were captured for tracking the change of cell numbers in a droplet containing different concentrations of antibiotics. This study confirms that our approach is approximately 15 times faster and provides more accurate number of cells in a droplet than the external analysis program method. We envision that it can be used to the development of high-throughput image-based cell counting analysis.


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