N Z Piety1, S C Gifford1, X Yang1, S S Shevkoplyas1. 1. Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, Houston, TX, USA.
Abstract
BACKGROUND AND OBJECTIVES: The morphology of red blood cells (RBCs) deteriorates progressively during hypothermic storage. The degree of deterioration varies between individual cells, resulting in a highly heterogeneous population of cells contained within each RBC unit. Current techniques capable of categorizing the morphology of individual stored RBCs are manual, laborious and error-prone procedures that limit the number of cells that can be studied. Our objective was to create a simple, automated system for high-throughput RBC morphology classification. MATERIALS AND METHODS: A simple microfluidic device, designed to enable rapid, consistent acquisition of images of optimally oriented RBCs, was fabricated using soft lithography. A custom image analysis algorithm was developed to categorize the morphology of each individual RBC in the acquired images. The system was used to determine morphology of individual RBCs in several RBC units stored hypothermically for 6-8 weeks. RESULTS: The system was used to automatically determine the distribution of cell diameter within each morphological class for >1 000 000 individual stored RBCs (speed: >10 000 cells/h; accuracy: 91·9% low resolution, 75·3% high resolution). Diameter mean and standard deviation by morphology class were as follows: discocyte 7·80 ± 0·49 μm, echinocyte 1 7·61 ± 0·63 μm, echinocyte 2 7·02 ± 0·61 μm, echinocyte 3 6·47 ± 0·42 μm, sphero-echinocyte 6·01 ± 0·26 μm, spherocyte 6·02 ± 0·27 μm, stomatocyte 1 6·95 ± 0·61 μm and stomatocyte 2 7·32 ± 0·47 μm. CONCLUSIONS: The automated morphology classification procedure described in this study is significantly simpler, faster and less subjective than conventional manual procedures. The ability to evaluate the morphology of individual RBCs automatically, rapidly and in statistically significant numbers enabled us to perform the most extensive study of stored RBC morphology to date.
BACKGROUND AND OBJECTIVES: The morphology of red blood cells (RBCs) deteriorates progressively during hypothermic storage. The degree of deterioration varies between individual cells, resulting in a highly heterogeneous population of cells contained within each RBC unit. Current techniques capable of categorizing the morphology of individual stored RBCs are manual, laborious and error-prone procedures that limit the number of cells that can be studied. Our objective was to create a simple, automated system for high-throughput RBC morphology classification. MATERIALS AND METHODS: A simple microfluidic device, designed to enable rapid, consistent acquisition of images of optimally oriented RBCs, was fabricated using soft lithography. A custom image analysis algorithm was developed to categorize the morphology of each individual RBC in the acquired images. The system was used to determine morphology of individual RBCs in several RBC units stored hypothermically for 6-8 weeks. RESULTS: The system was used to automatically determine the distribution of cell diameter within each morphological class for >1 000 000 individual stored RBCs (speed: >10 000 cells/h; accuracy: 91·9% low resolution, 75·3% high resolution). Diameter mean and standard deviation by morphology class were as follows: discocyte 7·80 ± 0·49 μm, echinocyte 1 7·61 ± 0·63 μm, echinocyte 2 7·02 ± 0·61 μm, echinocyte 3 6·47 ± 0·42 μm, sphero-echinocyte 6·01 ± 0·26 μm, spherocyte 6·02 ± 0·27 μm, stomatocyte 1 6·95 ± 0·61 μm and stomatocyte 2 7·32 ± 0·47 μm. CONCLUSIONS: The automated morphology classification procedure described in this study is significantly simpler, faster and less subjective than conventional manual procedures. The ability to evaluate the morphology of individual RBCs automatically, rapidly and in statistically significant numbers enabled us to perform the most extensive study of stored RBC morphology to date.
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