Rapid Quantitative Assessment of the Pig Pancreas Biopsy Predicts Islet Yield

• Published in: Transplantation proceedings 2010-07, Vol.42 (6), p.2036-2039
• Main Authors: Anazawa, T, Balamurugan, A.N, Matsumoto, S, LaFreniere, S.A, O'Brien, T.D, Sutherland, D.E.R, Hering, B.J
• Format: Article
• Language: English
• Subjects: Aging - physiology; Animals; Biological and medical sciences; Biopsy - methods; Body Weight; Cell Separation - methods; Female; Fundamental and applied biological sciences. Psychology; Fundamental immunology; Islets of Langerhans - cytology; Male; Medical sciences; Organ Size; Pancreas - anatomy & histology; Pancreas - cytology; Pancreatic Ducts - cytology; Regression Analysis; Surgery; Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases; Swine; Tissue, organ and graft immunology
• ISSN: 0041-1345; 1873-2623
• DOI: 10.1016/j.transproceed.2010.05.113

Abstract Background The cost of islet procurement from donor pigs is increased by the use of organs that produce low yields. We developed an assessment system using dithizone-stained pig pancreas biopsies to enable the preselection of donor organs. Methods Pig pancreas biopsy slices were soaked in dithizone solution. The islets were evaluated before islet isolation by converting the islet counts (IC) to islet equivalents (IE), and then determining the IE/cm2 , IE/IC, % islets >150 μm, and % islets >200 μm. These parameters were evaluated in 3 different areas of the pancreas (duodenal, splenic, and connecting lobe; n = 42 each). Stepwise multivariate linear regression analysis was performed to assess for correlations with islet yield and decide which area of the pancreas had the most predictive value. To identify other predictors, including donor and islet isolation variables, we performed binary logistic regression analysis with significant variables from the univariate analysis (n = 67). For this analysis, the pigs were categorized into high (n = 23) and low (n = 44) yield groups. Results Stepwise multivariate linear regression analysis revealed that IE/cm2 of the splenic lobe significantly predicted islet yield. Binary logistic regression analysis indicated that the IE/mm2 of the splenic lobe was the only parameter that significantly correlated with successful pig islet isolations ( P = .01; odds ratio 3.605). Variables associated with donor and islet isolation, such as age, gender, ischemic time, or enzyme lot, were not significantly correlated with islet yield. Conclusion Our study suggests that the islet distribution of splenic lobe biopsies can be a reliable predictor of islet yield from pig pancreata.