posted on 2015-11-19, 08:50authored byGavin Scott Miller. Robertson
The prospect of reversing diabetes by the transplantation of isolated pancreatic islets thereby avoiding the major surgery and associated morbidity of whole pancreas transplants, led to the first reports of transplantation of dispersed human islets both purified and unpurified in the late 1970's. Reports of complications and even deaths following intraportal infusion of unpurified islets, led to intensified research into methods for purifying islets from the collagenase dispersed human pancreas. Separation on the basis of physical differences in the density of islets and exocrine tissue has proven to be the most effective method, and studies in Leicester pioneered the use of density gradients on the COBE 2991 cell processor for large scale islet purification. The first half of the work described in this thesis examines methods for maximising the differences in islet and exocrine densities using cold storage solutions, both at the time of organ retrieval and during pancreas processing. The improvement in human islet purification using University of Wisconsin solution and the components responsible for this are outlined. Simultaneously the mechanical process of density gradient purification was optimised by the introduction of continuous rather than discontinuous density gradients on the COBE. Despite this work, purified islet yields from a single donor pancreas remain insufficient for successful transplantation and the need to use more than one donor/transplant reduces both the number of transplants and the HLA matching possible. The second part of this work therefore examined the use of alternative immunomagnetic techniques to purify islets in a rat model. By coupling 4.5mum diameter magnetic beads to the exocrine fragments via very specific monoclonal antibody linkages, reliable and effective removal of 90% of the exocrine fragments was possible with islet yields of 60%. Immunomagnetic methods would therefore provide an alternative or adjunct to existing density dependent methods.
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College of Medicine, Biological Sciences and Psychology