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- korean
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received September 7, 2022
Revised October 23, 2022
Accepted November 8, 2022
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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이미지 분석 프로그램을 이용한 액적 내 세포 계수 방법
Automated Bacterial Cell Counting Method in a Droplet Using ImageJ
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Abstract
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.
Keywords
References
2. Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J.,Zhao, X., Jiang, T. and Grzegorzek, M., “A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches,”Artif. Intell. Rev., 55, 2875-2944(2022).
3. Camacho-Fernández, C., Hervás, D., Rivas-Sendra, A., Marín, M.and Seguí-Simarro, J. M., “Comparison of Six Different Methods to Calculate Cell Densities,” Plant Methods 14, 1-15(2018).
4. Vembadi, A., Menachery, A. and Qasaimeh, M. A., “Cell Cytometry: Review and Perspective on Biotechnological Advances,”Front. Bioeng. Biotechnol., 7, 147(2019).
5. Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang,I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A.,Moffat, J., Golland, P. and Sabatini, D. M., “Cellprofiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes,” Genome Biol., 7, R100(2006).
6. Rajapaksha, P., Elbourne, A., Gangadoo, S., Brown, R., Cozzolino,D. and Chapman, J., “A Review of Methods for the Detection of Pathogenic Microorganisms,” Analyst, 144, 396-411(2019).
7. Geissmann, Q., “Opencfu, a New Free and Open-source Software to Count Cell Colonies and Other Circular Objects,” PLoS One, 8, e54072(2013).
8. Lee, H.-C., “Automatic Counting of Yeast Cells in Baker’s Yeast Culture Using pc Camera and Conventional Light Microscope,”KSBB Journal, 26, 87-91(2011).
9. Suh, J.-S., Lee, C., Pan, Y., Wang, Y., Jung, Y. and Kim, T.-J.,“A Study on Single Cell Polarized Signals Using Polydimethylsiloxane-based Micropatterned Channel System,” Korean Chem.Eng. Res., 58, 122-126(2020).
10. Lu, H., Caen, O., Vrignon, J., Zonta, E., El Harrak, Z., Nizard,P., Baret, J.-C. and Taly, V., “High Throughput Single Cell Counting in Droplet-based Microfluidics,” Sci. Rep., 7, 1-9(2017).
11. Khorshidi, M. A., Rajeswari, P. K. P., Wählby, C., Joensson, H.N. and Svahn, H. A., “Automated Analysis of Dynamic Behavior of Single Cells in Picoliter Droplets,” Lab Chip, 14, 931-937(2014).
12. Yang, I. J. and Lim, S. I., “Site-specific Dye-labeling of the Bacterial Cell Surface by Bioconjugation and Self-assembly,” Korean Chem. Eng. Res., 60, 398-406(2022).
13. Ganguly, R., Lee, B., Kang, S., Kim, Y. S., Jeong, S.-G., Kim, J.S., Park, S. Y., Yohei, Y. and Lee, C.-S., “Microfluidic Single-cell Trapping and Cultivation for the Analysis of Host-viral Interactions,” Biotechnol. Bioprocess Eng., 26, 179-187(2021).
14. Ahn, K., Kerbage, C., Hunt, T. P., Westervelt, R., Link, D. R. and Weitz, D. A., “Dielectrophoretic Manipulation of Drops for High-speed Microfluidic Sorting Devices,” Appl. Phys. Lett., 88,024104(2006).
15. Brouzes, E., Medkova, M., Savenelli, N., Marran, D., Twardowski,M., Hutchison, J. B., Rothberg, J. M., Link, D. R., Perrimon, N. and Samuels, M. L., “Droplet Microfluidic Technology for Single-cell High-throughput Screening,” PNAS, 106, 14195-14200(2009).
16. Lozano-Gerona, J. and García-Otín, Á. L., “ImageJ-based Semiautomatic Method to Analyze Senescence in Cell Culture,” Anal.Biochem., 543, 30-32(2018).
17. Grishagin, I. V., “Automatic Cell Counting with ImageJ,” Anal.Biochem., 63-65(2015).
18. Nichele, L., Persichetti, V., Lucidi, M. and Cincotti, G., “Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting”, OSA Contin., 3, 1417-1427(2020).
19. Rueden, C. T., Schindelin, J., Hiner, M. C., DeZonia, B. E., Walter, A. E., Arena, E. T. and Eliceiri, K. W., “Imagej2: Imagej for the Next Generation of Scientific Image Data,” BMC Bioinformatics 18, 1-26(2017).
20. Schindelin, J., Rueden, C. T., Hiner, M. C. and Eliceiri, K. W.,“The Imagej Ecosystem: An Open Platform for Biomedical Image
Analysis,” Molecular Reproduction and Development 82, 518-529(2015).
21. Vosatka, K. W., Lavenus, S. B. and Logue, J. S., “A Novel Fiji/imagej Plugin for the Rapid Analysis of Blebbing Cells,” PloS one 17, e0267740(2022).
22. Jin, S. H., Kim, T., Oh, D., Kang, K.-K. and Lee, C.-S., “Preparation of Monodisperse Pegda Microparticles Using a Dispensing Needle Based Microfluidic Device,” Korean Chem. Eng. Res.,57, 58-64(2019).
23. Lee, H. J., Jang, S. H., Chang, S. M. and Kim, J. M., “Study on Polymerization Condition of Water-based Acrylic Adhesion,”Korean Chem. Eng. Res., 48, 609-614(2010).
24. Jeong, H.-H., Noh, Y.-M., Jang, S.-C. and Lee, C.-S., “Dropletbased Microfluidic Device for High-throughput Screening,”Korean Chem. Eng. Res., 52, 141-153(2014).
25. Moon, S., Ceyhan, E., Gurkan, U. A. and Demirci, U., “Statistical Modeling of Single Target Cell Encapsulation,” PloS one, 6, e21580 (2011).
26. Labanieh, L., Nguyen, T. N., Zhao, W. and Kang, D.-K., “Floating Droplet Array: An Ultrahigh-throughput Device for Droplet Trapping, Real-time Analysis and Recovery,” Micromachines, 6,1469-1482(2015).
27. Shemesh, J., Ben Arye, T., Avesar, J., Kang, J. H., Fine, A.,Super, M., Meller, A., Ingber, D. E. and Levenberg, S., “Stationary Nanoliter Droplet Array with a Substrate of Choice for Single Adherent/nonadherent Cell Incubation and Analysis,” PNAS, 111,11293-11298(2014).
28. Taylor, D., Verdon, N., Lomax, P., Allen, R. J. and Titmuss, S.,“Tracking the Stochastic Growth of Bacterial Populations in Microfluidic Droplets,” Phys. Bio., 19, 026003(2022).
29. Nge, P. N., Rogers, C. I. and Woolley, A. T., “Advances in Microfluidic Materials, Functions, Integration, and Applications,”Chem. Rev., 113, 2550-2583(2013).
