The importance of tryptic-like activity in purified enzyme blends for efficient islet isolation.

BACKGROUND: The isolation of islets from the human pancreas critically depends on an efficient enzyme blend. Previous studies have solely focused on the presence of collagenase and neutral protease/thermolysin. Despite improved characterization of these components, the lot-related variability in ef...

全面介绍

书目详细资料
Main Authors: Brandhorst, H, Friberg, A, Andersson, H, Felldin, M, Foss, A, Salmela, K, Lundgren, T, Tibell, A, Tufveson, G, Korsgren, O, Brandhorst, D
格式: Journal article
语言:English
出版: 2009
实物特征
总结:BACKGROUND: The isolation of islets from the human pancreas critically depends on an efficient enzyme blend. Previous studies have solely focused on the presence of collagenase and neutral protease/thermolysin. Despite improved characterization of these components, the lot-related variability in efficacy still persists suggesting that additional so far disregarded enzymes are required for efficient islet cleavage. METHODS: Varying activities of a tryptic-like enzyme were identified within collagenase NB1 lots, which were selected according to a matched ratio between tryptic-like and collagenase activity (TLA-ratio). Rat and human pancreata were processed with current standard procedures. RESULTS: Increasing the TLA-ratio from 1.3% to 10% reduced pancreas dissociation time in rats by 50% without affecting islet yield, viability, or posttransplant function in diabetic nude mice. Enhancing the TLA-ratio from 1.3% to 12.6% for human pancreas processing resulted in a significant reduction of recirculation time and increased incrementally human islet yield without affecting purity, in vitro function or recovery after culture. Optimized pancreas digestion correlated with a higher percentage of islet preparations fulfilling quality criteria for clinical transplantation. CONCLUSIONS: We conclude that TLA is an effective component that should be included in moderate amounts in enzyme blends for human islet isolation to optimize the efficiency and minimize the lot-related variability.