Imaging Flow Cytometry Enabled by a Spatial-Frequency Filter

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 DESCRIPTION (provided by applicant): Fluorescence-activated-cell-sorting (FACS) or flow cytometry enables clinicians and researchers to quantitatively characterize the physical (cell size, shape, and granularity) and biochemical (DNA content, cell cycle distribution, cell surface markers, and viability) properties of cells, however FACS devices do not produce an image of the cell. Increasing sophistication of research assays now rely on the collection of cells based on their phenotypical and spatial characteristics; with the capabilities offered only by microscopic imaging cytometers having severe limits in throughput and lacking cell isolation. We propose an innovative, low cost design to combine the merits of FACS and microscopic imaging cytometry without the limits of each, offering the biomedical research and clinical community a unique tool to address the needs for current and emerging applications. The key innovation is based on a significant extension of the spatial-coding algorithms our team demonstrated in the past years. In the proposed design, we create a special filter with a matrix of periodic slits in front of each PMT detector. The resulting PMT signal is composed of the multiplexed cell signals modulated by the filter, which can subsequently be deconvolved to produce fluorescence and scatter generated from different areas of the cell: the image. In this Phase I program, we will integrate imaging technology into our existing WOLF Cell Sorter to produce the very first imaging cytometer with cell sorting capabilities. Since we use conventional, non-pixelated detectors (e.g. PMTs) found in conventional flow cytometers, this technology is compatible with existing flow cytometer architectures allowing for wide use. Equipped with cell imaging capabilities, researchers can track many important biological processes by analyzing not only the intensity but the localization of certain proteins within cytosolic, nuclear, or cell membrane domains and subdomains. With the rapidly developing capabilities of handling "big data", images of millions of single cells in a flow cytometer provide vast resources for research and disease analysis, and rapid growth has been predicted in the market of such high-content imaging cytometer cell sorters for emerging applications such as precision medicine. We believe the proposed design is a major breakthrough that can potentially revolutionize the field of flow cytometry, and its impact and ramifications on both fundamental biomedical research and clinical applications can be tremendous.