Research
Keywords: imaging flow cytometry, cell sorting, fluorescence, microscopy, image analysis, cell biology (cancer, yeast, microalgae), liquid biopsy, swimming
Imaging Flow Cytometry enables high-throughput acquisition of images of cells, making it possible to analyze cellular morphologies or distributions of molecules.
Link to article: Imaging system
Link to article: Application for cancer cell detection
Intelligent image-activated cell sorting or iIACS enables rapid selections of cells of interest decided by image analysis from a large population of cells. Link to the article
Main project: Detection of various forms of cancer cells in blood by imaging flow cytometry
Background: Cancer cells in the primary tumor can be disassociated and invade the surrounding blood vessels. These cells are known as circulating tumor cells (CTCs), which can migrate in the blood stream, proliferate at distant sites, and then form additional tumors beyond the primary site. Detection of CTCs based on surface proteins or filtration have enabled gaining insight into the mechanism of the cancer migration or prognosticate the cancer patients. However, CTCs are heterogeneous in protein expressions or morphological features, making it challenging to detect all of them.
Result: To detect and analyze various forms of cancer cells in blood, I used a high-throughput optomechanical imaging flow cytometer and 5-aminolevulinic acid (5-ALA). 5-ALA induces autofluorescence in cancer cells, making them detectable by fluorescence imaging. With this strategy, I could detect various types of cancer cells such as EpCAM-positive/negative cancer cells, those larger than leukocytes, those as small as leukocytes. Particularly, component cells in clusters were well characterized in terms of surface proteins or the autofluorescence. Furthermore, the evaluation of the detection capability of rare cells and demonstration with clinical samples were performed to show the potential of the method for CTC study.
What I did in this project:
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Design of experiment, data acquisition, analysis, evaluation, paper writing, generating graphs
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Introducing additional laser and objectives into the VIFFI flow cytometer and optical alignment.
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Modification of the data acquisition PC and the program (LabVIEW)
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Developing program for the analysis (Python)
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Cell culture and antibody staining
Journal article: https://pubs.rsc.org/en/content/articlelanding/2023/lc/d2lc00856d
Projects I joined
Optomechanical imaging flow cytometry for sensitive detection of fluorescence
(Virtual-freezing fluorescence imaging flow cytometry: VIFFI)
VIFFI is a technique that can stabilize the fluorescence images of cells flowing as fast as 1 m/s with a high sensitivity of 50 MESF. In this project, I introduced an additional laser line and obtained fluorescence images of several types of cells as demonstrations.
Journal article: https://www.nature.com/articles/s41467-020-14929-2
Cell sorting based on real-time image processing
(Intelligent image-activated cell sorting 2.0: iIACS 2.0)
My colleagues and I reported an upgraded version of iIACS system with a higher sensitivity in fluorescence detection for imaging. Based on my experience with the VIFFI system, I contributed to figuring out and solving technical problems during the experimental demonstration of the highly integrated iIACS. Furthermore, I performed the first sorting experiment of cell sample.
Journal article: https://pubs.rsc.org/en/content/articlelanding/2020/lc/d0lc00080a#!divAbstract
Image restoration by deep learning (Deep imaging flow cytometry)
An imaging restoration algorithm has been developed to surpass the trade-off relation between the throughput and spatial resolution in imaging flow cytometry. I contributed to this project in the experimental design, modification of VIFFI, and optical alignment.
Journal article: https://pubs.rsc.org/en/Content/ArticleLanding/2022/LC/D1LC01043C
