iFACS: Imaging Florescence Activated Cell Sorter to sort cells based on images

Period of Performance: 09/30/2017 - 08/31/2018


Phase 2 SBIR

Recipient Firm

Nanosort, Inc.
San Diego, CA 92122
Principal Investigator
Principal Investigator


iFACS: Imaging Florescence Activated Cell Sorter to sort cells based on images NanoCellect Biomedical, Inc. RESEARCH & RELATED Other Project Information 7. PROJECT SUMMARY Advances in reagents (e.g. CRiSPR) and analytical tools (e.g. flow cytometers) have improved the ability to alter and characterize cellular phenotypes. Ultimately, many key applications in biomedicine require efficient and accurate isolation of cell populations according to features contained in high content images. Unfortunately, microscopic laser microdissection systems have a throughput that is too slow to be practical in many applications; while the existing flow cytometers that can sort cells (fluorescence-activated cell sorters or FACS) provide only size, internal complexity, and fluorescence intensity information and lack the rich data of imaging. Another critical limitation is that the existing flow cytometers that can image, cannot sort cells. NanoCellect has made a highly affordable FACS to increase access to this important high-throughput tool for cell analysis. Here we propose to enhance our existing low-cost FACS with the ability to image cells and sort them based on image features. This will allow users to pursue new strategies in drug screening and mechanism of action research; as well as work with suspension cell lines, such as those that dominate the recent advances in immuno-oncology. In Phase I research, we have demonstrated the world's first imaging flow cytometer with cell sorting capabilities (iFACS) in a unique design of space-time coding with an optical spatial filter. The approach adds negligible cost to the system for the desirable features of cell imaging and sorting. To fully realize the enormous potential of the design and to meet the demands for most applications, in Phase II we will develop high-throughput image-based cell sorting with innovative image-guided gating schemes supported by machine learning and interactive user/machine interface. Essentially, image-based flow cytometry gating uses similar cell isolation criteria as the techniques of laser capture microdissection or cell aspiration to isolate cells of interest, with 10,000X throughput improvements to 1000+ cells per second. We envision such unique capabilities will become common, default features for tomorrow's users as the tool becomes as intuitive and ubiquitous as fluorescent microscopy. The proposed iFACS will be transformative and benefit numerous biomedical applications, such as isolation of cells based on organelle translocation, cell cycle analyses, detection and counting of phagocytosed particles, and protein co-localization, to name just a few.