Suppr超能文献

Preparation of noradrenaline-storing organelles from bovine sympathetic ganglia: biochemical and morphological evaluation of partly purified large dense cored vesicles.

作者信息

Miserez B, De Block J, Cortvrindt R, Van Marck E, De Potter W P

机构信息

Department of Medicine, University of Antwerp (UIA), Wilrijk, Belgium.

出版信息

Neurochem Int. 1992 Jun;20(4):577-82. doi: 10.1016/0197-0186(92)90037-r.

Abstract

Large dense cored vesicles from bovine sympathetic ganglia were isolated and partly purified. Biochemical and morphological evaluation of the present vesicle-preparation revealed that it represents a convenient fraction for the characterization of perikaryal noradrenergic vesicles. Homogenates of bovine stellate ganglia were subjected to differential centrifugation and D2O-sucrose density gradient centrifugation. Biochemical evaluation of gradient fractions was performed by measuring marker enzyme activities reflecting subcellular contamination, while morphological evaluation was performed by electron microscopic analysis of the isolated fractions. Both techniques revealed that the vesicle-preparation was, at first, still considerably contaminated by mitochondria and lysosomes. An improved purification could be achieved by subjecting this fraction to an additional centrifugation under iso-osmotic conditions, also applied for the preparation of highly purified splenic nerve vesicles. The resulting vesicle-fraction was almost completely free of contaminating enzyme activities and consisted merely of large dense cored vesicles as revealed by electron microscopic observations (50-70% purity). Neuropeptide Y and chromogranin A were enriched more than 50 times as compared to the total homogenate. Although the purity of these vesicles was still not satisfactory for direct chemical analysis, this vesicle-preparation seemed very well suited for immunological characterization of perikaryal large dense cored vesicles.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验