2018 Fiscal Year Annual Research Report
High-throughput, single-cell optofluidic platform for point-of-care diagnosis
| Project/Area Number |
18F18716
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| Research Institution | The University of Tokyo |
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Principal Investigator |
合田 圭介 東京大学, 大学院理学系研究科(理学部), 教授 (70518696)
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| Co-Investigator(Kenkyū-buntansha) |
YAN SHENG 東京大学, 大学院理学系研究科, 外国人特別研究員
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| Project Period (FY) |
2018-10-12 – 2021-03-31
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| Keywords | Optofluidics |
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| Outline of Annual Research Achievements |
An optofluidic sorter has been developed. This platform integrates time-stretch microscopy, microfluidic sorter and deep learning.
The microfluidic sorter can effectively remove >99% of blood cells and collect >80% of tumor cells, which enables the detection of cancerous cells in whole blood without any sample preparation.
The images of isolated cancer cells were on-chip captured by the optofluidic time-stretch microscopy and then transferred to a convolutional neural network for classification.
Currently, the detection limit of cancer cells can be low as 200 cells/mL. Besides, this platform also classifies the viable and non-viable cancer cells based on their morphology, which cannot be achieve by the conventional microfluidic sorter.
Our method takes advantage of the microfluidic sorter and machine learning-assisted time-stretch microscopy to process blood samples in an undiluted and label-free manner.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The optofluidic time-stretch microscope has been etablished in my group. The JSPS fellow, Dr Sheng Yan, can access the clean-room facilities to fabricate microfluidc devices.
After ethics and bio-safety training, he can prepare the cell lines and get blood samples from a clinical collaborator.
He can organise his timeline and manage his experiments as scheduled.
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| Strategy for Future Research Activity |
Based on the preliminary results, we aim to apply the optofluidic sorter for drug screening and identification of platelet aggregates.
Drugs are effectively way for the cancer therapy. The drug-treated cancerous cells will have morphological changes, which will be a useful feature to evaluate the drugs.
The drug-treated blood will be introduced to our develop optofluidic sorter, where the cancer cells can be isolated from blood cells and captured by the ultra-fast camera.
The images of drug-treated cancer cells will be sent to a convolutional neural network to identify the cancer cells with morphological changes.
This can be a highly-effective way for drug screening.
Also, thrombotic diseases can also be diagnosed by screening the platelet aggregates in the whole blood using this optofluidic sorter.
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