Abstract
Endothelial dysfunction, as occurs in pre-clinical atherosclerosis and organ transplant rejection, is characterized by increased endothelial cell (EC) surface expression of inflammatory markers including intercellular adhesion molecule-1 (ICAM). Previously, we have shown that lipid-based ultrasound contrast microbubbles (MBs) selectively adhere to inflamed ECs when conjugated to anti-ICAM monoclonal antibodies (Ab). Here, we hypothesized that the adhesion of targeted MBs to ECs from flow is shear-rate dependent. Microbubbles were conjugated via avidin/biotin bridging chemistry to anti-ICAM mAb (60,000 Ab/MB surface density). In a parallel plate chamber, MBs were perfused across coverslips of normal or interleukin-1β-activated cultured human coronary artery ECs at selected wall shear rates for 3 min and washed. Adhesion to ECs was quantified in 20 random microscopic fields per coverslip (mean MBs/cell±SD). Adhesion decreased with increasing shear rate to both normal (100s-1=0.84±1.7, 175s-1=0.58±0.40, 250s-1=0.40±0.45, 350s-1=0.16±0.14, 500s-1=0.04±0.02) and activated ECs (100s-1=3.0±2.7, 175s-1=2.6±0.77, 250s-1=2.1±1.3, 350s-1=1.2±0.90, 500s-1=0.49±0.09). Adherence was greater to activated than normal ECs (p<0.001), and decreased with increasing shear rate (p=0.02). These data demonstrate that adhesion of targeted MBs to ECs is highly dependent upon the local shear environment. This relationship has important implications for the design of targeted contrast agents for ultrasonic assessment of endothelial dysfunction.
Original language | English (US) |
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Pages (from-to) | 899-900 |
Number of pages | 2 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 2 |
State | Published - Dec 1 2002 |
Event | Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States Duration: Oct 23 2002 → Oct 26 2002 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics