Image-based single cell sorting automation in microfluidic droplets

Diversity in cell populations is much greater than what most of us believe. With Next Generation Sequencing (NGS) tools and single-cell omics, researchers can now investigate these populations in much more detail. Sample preparation for these studies usually involves isolation of cell subgroups with desired traits, expressions or markers. There are certain populations that can only be isolated by expert eyes, a microscope and a single-cell pipette. In this talk, I will present an automated approach to speed up this process using microdroplets. Single-cells encapsulated in droplets are imaged in flow cytometry after which a decision is made by a custom software to keep the cell or not. The sorting is achieved by a well-established technique, dielectrophoresis. Supervised machine learning translates expert knowledge for the software to aid in the decision making.