title: Cell streak imaging cytometry for rare cell detection.
abstract: Detection of rare cells, such as circulating tumor cells, have many clinical applications. To measure rare cells with increased sensitivity and improved data managements, we developed an imaging flow cytometer with a streak imaging mode capability. The new streak mode imaging mode utilizes low speed video to capture moving fluorescently labeled cells in a flow cell. Each moving cell is imaged on multiple pixels on each frame, where the cell path is marked as a streak line proportional to the length of the exposure. Finding rare cells (e.g., <1 cell/mL) requires measuring larger sample volumes to achieve higher sensitivity, therefore we combined streak mode imaging with a "wide" high throughput flow cell (e.g. flow rates set to 10 mL/min) in contrast to the conventional "narrow" hydrodynamic focusing cells typically used in cytometry that are inherently limited to low flow rates. The new flow cell is capable of analyzing 20 mL/min of fluorescently labeled cells. To further increase sensitivity, the signal to noise ratio of the images was also enhanced by combining three imaging methods: (1) background subtraction, (2) pixel binning, and (3) CMOS color channel selection. The streaking mode cytometer has been used for the analysis of SYTO-9 labeled THP-1 human monocytes in buffer and in blood. Samples of cells at 1 cell/mL and 0.1 cell/mL were analyzed in 30 mL with flow rates set to 10 mL/min and frame rates of 4 fps (frame per second). For the target of 1 cell/mL, an average concentration of 0.91 cell/mL was measured by cytometry, with a standard error of 0.03 (C(95) = 0.85-0.97). For the target of 0.1 cell/mL, an average concentration of 0.083 cell/mL was measured, with a standard error of 0.01 (C(95) = 0.065-0.102). Whole blood was also spiked with SYTO-9 labeled cells to a concentration of 10 cell/mL, and the average flow cytometry measurement was 8.7 cells/mL (i.e. 0.87 cells/mL in diluted blood) with a 95% CL of 8.1-9.2 cells/mL. This demonstrated the ability to detect rare cells in blood with high accuracy. Such detection approaches for rare cells have many potential clinical applications. Furthermore, the simplicity and low cost of this device may enable expansion of cell-based clinical diagnostics, especially in resource-poor settings.
authors.affiliation: Division of Biology, Office of Science and Engineering, FDA, Silver Spring, MD 20993, United States; University of Maryland, College Park, MD 20742, United States.
authors.forename: Joshua
authors.initials: J
authors.lastname: Balsam
authors.affiliation: University of Maryland, College Park, MD 20742, United States.
authors.forename: Hugh Alan
authors.initials: HA
authors.lastname: Bruck
authors.affiliation: Division of Biology, Office of Science and Engineering, FDA, Silver Spring, MD 20993, United States; Office of Cancer Complementary and Alternative Medicine, National Cancer Institute, Rockville, MD 20850, United States. Electronic address: rasoolya@mail.nih.gov.
authors.forename: Avraham
authors.initials: A
authors.lastname: Rasooly
mesh_terms: D002469:Cell Separation; D058948:Cell Tracking / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005434:Flow Cytometry; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D046210:Microfluidic Analytical Techniques / Q000295:instrumentation*; D018613:Microscopy, Confocal / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Flow cytometry; Image enhancement; Rare cells; Resource-poor settings; Wide-field imaging; mHealth