Single-cell Analysis with Microfluidic Device

Featuring John F. Zhong, Ph.D.

Department of Pathology

Keck School of Medicine

University of Southern California



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Abstract:

Performing biochemical analyses on single cells is critical for cancer diagnosis and evaluation of cancer treatment with circulating tumor cells (CTCs). Single-cell analysis allows multiple biochemical analysis to be performed on the rare CTCs to determine the origin and development dynamic of CTC. We have developed microfluidic devices to perform nanoliter scale biomedical analysis in order to minimize materials loss on single-cells analysis. Our microfluidic devices convert single-cell mRNA-to-cDNA in 10-nanoliter reactors, and can simultaneously process 32 single cells with 5-fold higher efficiency compared to bulk assay. However, manipulating single cells via controlling fluid flow inside microfluidic devices is complicated and inefficient, especially when manipulating a large number of cells. To address these issues for enabling large scale single-cell analysis, we designed new devices with various features to enable rapid and efficient single-cell analysis. These new devices enable precise and non-contact manipulation of a large number of significantly improves the simultaneous processing capacity. With these new devices a large number of single-cells can be processed simultaneously for multiple assays inside a single integrated device.



About the Speaker:

John F. Zhong, Ph.D., is an assistant professor in the Department of Pathology within the Keck School of Medicine at University of Southern California. His primary research interest is on the gene regulation of cell fate, with a particular focus on how gene-gene interactions determine cell fates, such as stem cell differentiation. In order to study a stem cell’s fate after receiving environmental signals, his laboratory has developed microfluidic devices to study the gene expression of a single cell. Zhong received a B.S. degree in molecular biology from CSU San Jose, an M.S. degree in computational biology from USC, and a Ph.D. degree in biochemistry and molecular biology from USC.