A platform for a single socialized-cell analysis
For decades, biologists have studied phenomena at the scale of tissues or organs, to yield information averaged over a large number of cells. In the last 15 years, high-throughput single-cell analysis is now a widely used approach, thanks to progresses in analytical methods (PCR or sequencing) and miniaturized systems for the isolation and accurate positioning of individual cells . This breakthrough has notably contributed to essential discoveries in the fields of cancer biology, immunology, stem cell biology.
However, to perform single-cell analysis, cells must be isolated from their native environment, where they normally interact with neighbouring cells. These interactions are essential for multiple processes like cell differentiation, immune system activation or metastasis. Furthermore, these interactions have an impact on a cell molecular content and behaviour. Correlations between a cell environment, its interactions with neighbouring cells and its molecular content are therefore unfortunately lost in current single-cell analysis protocols.
In that context, we propose here to develop a high-throughput single-cell analysis platform allowing accessing this correlated information. In this project, the student will create compartmentalized cell niches containing a cell of interest and a controlled number of others cells, inside a microfluidic platform. Cell positioning , culture and characterization will be first performed using immortalized cell lines, followed with the study of cardiac stem cells and cardiomyocytes socialized in such a cell community.
Techniques: microfluidic design, microfabrication, microfluidic manipulation, cell culture, single-cell analysis, stem cell culture.
 Prakadan, S.M., Shalek, A.K., Weitz, D.A., 2017. Scaling by shrinking: empowering single-cell “omics” with microfluidic devices. Nature Reviews Genetics 18, 345–361. https://doi.org/10.1038/nrg.2017.15
 Tan, W.-H., Takeuchi, S., 2007. A trap-and-release integrated microfluidic system for dynamic microarray applications. Proc. Natl. Acad. Sci. U.S.A. 104, 1146–51. https://doi.org/10.1073/pnas.0606625104