2018 Fiscal Year Annual Research Report
Study of glioma infiltration in complex microenvironment on an automated microfluidic chip
| Project/Area Number |
17J00362
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
TSAI Hsieh Fu 沖縄科学技術大学院大学, 科学技術研究科, 特別研究員(DC1)
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| Project Period (FY) |
2017-04-26 – 2020-03-31
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| Keywords | Hybrid / label-free / machine learning / cell tracking / single-cell segmentation / perivasculature |
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| Outline of Annual Research Achievements |
I have moderate progress in 2018.
1. An opensource software based on machine learning framework and Python programming language for all-in-one cell segmentation, tracking, and data analysis for cell migration study under stain-free phase contrast microscopy has been developed. The result is published on SoftwareX.
2. A novel hybrid PMMA/PDMS microdevice for high-throughput exp is developed and published on Biomedical microdevices.
The device combines the advantages of different materials and mitigate the disadvantages. A proof of concept study on glioblastoma-endothelial interaction is performed.
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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
I am on track for the research project. Although the proposed research plan on automated control platform as well as the protein effective charge characterization are slightly behind schedule. The developed software as well as experimental platforms are advancing and allowing me to study detailed effect of microenvironment to glioblastoma cell migration.
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| Strategy for Future Research Activity |
I intend to complete the characterization of native protein effective charge using IEF gel electrophoresis and capillary electrophoresis.
Due to the advanceness and enormous potential of the machine learning method we developed for the cell migration analysis, I will also focus on further applying the technique to probe cellular state without the need of molecular labelling.
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