2018 Fiscal Year Annual Research Report
High-throughput, single-cell optofluidic platform for point-of-care diagnosis
|Research Institution||The University of Tokyo |
合田 圭介 東京大学, 大学院理学系研究科(理学部), 教授 (70518696)
|Foreign Research Fellow
YAN SHENG 東京大学, 大学院理学系研究科, 外国人特別研究員
|Project Period (FY)
2018-10-12 – 2021-03-31
|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.
|Current Status of Research Progress
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
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.
|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.
Research Products (1 results)