Affiliation:
1. Biorheology Research Laboratory Menzies Health Institute Queensland, Griffith University Gold Coast Queensland Australia
2. Innovative Device & Engineering Applications (IDEA) Lab Texas Heart Institute Houston Texas USA
3. School of Pharmacy and Medical Science Griffith University Gold Coast Queensland Australia
Abstract
AbstractBackgroundVon Willebrand factor (VWF) is a critical glycoprotein in hemostasis and is an important factor in diagnosing bleeding disorders. Albeit the analysis of VWF is often compromised by inconsistent methodologies and challenges quantifying multimeric size. Current VWF multimer analysis methods are costly, time‐consuming, and often inconsistent; thus, demanding skilled professionals. This study aimed to streamline and optimize the VWF multimer analysis technique, making it more efficient and reproducible, particularly for identifying or predicting mechanical circulatory support (MCS) induced bleeding disorders.MethodsBlood samples from healthy volunteers were exposed to high shear forces via a Medtronic HeartWare ventricular assist device. VWF multimers were analyzed using vertical‐gel agarose electrophoresis and Western blotting. Differences in VWF distribution were determined using densitometry, and two methods of densitometric analysis were compared: proprietary software against open‐source software.ResultsUsing the developed method: (i) protocol duration was accelerated from three days (in classical methods) to ~ eight hours; (ii) the resolution of the high molecular weight (HMW) VWF multimers were substantially improved; and (iii) densitometric analysis tools were validated. Additionally, the densitometry analysis using two software types showed a strong correlation between results, with the proprietary software reporting slightly higher HMW VWF percentages.ConclusionThis methodology is recommended for affordable, accurate, and reproducible VWF multimer evaluations during MCS use and testing. Further research comparing this method with semi‐automated methods would provide additional insight and improve inter‐laboratory comparisons.
Funder
Australian Research Council