Stored red blood cell (sRBC) morphology is currently scored manually by technicians in a slow labor intensive process prone to error. This project proposes a way to simplify, automate, and expedite the morphology scoring process by using a novel microfluidic device that I designed to facilitate the flow of a single layer of red blood cells (RBCs). The appearance of this flow allows for the capture of a series of high clarity images captured via digital camera coupled to a microscope that are ideally suited for image analysis algorithm-based morphological scoring. During storage, RBCs heterogeneously shift from the form of discocyte to the reversibly altered form of discoechinocyte as storage lesion progresses. Beyond this level of degradation, the cell assumes the form of a spheroechinocyte or spherocyte and becomes irreparably damaged. The microfluidic device and image analysis algorithm developed in this research classified the individual morphology of 5000 RBCs taken from storage into the physiologically relevant category of either “discocyte,” “reversibly changed,” or “irreversibly changed.” This process took only 15 minutes. The accuracy in classification was verified as 92.6% in a separate trial when compared against classification of the same sample images via manual inspection. The morphological distribution of the RBC population remained consistent in both cases. The findings of this project suggest that microfluidic device assisted automated image analysis can provide a quick and effective way to quantitatively estimate the viability of a sRBC population and the extent of storage lesion endured. This technology could provide augmented RBC storage and transfusion research capabilities and have clinical applications, such as the ability to conveniently differentiate between the transfusion qualities of two sRBC units of the same age. / acase@tulane.edu
| Identifer | oai:union.ndltd.org:TULANE/oai:http://digitallibrary.tulane.edu/:tulane_26836 |
| Date | January 2013 |
| Contributors | Triscott, Mathew I. (Author), Shevkoplyas, Sergey (Thesis advisor) |
| Publisher | Tulane University |
| Source Sets | Tulane University |
| Language | English |
| Detected Language | English |
| Rights | Copyright is in accordance with U.S. Copyright law |
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