Analysis of B cell subsets following pancreatic islet cell transplantation in a patient with type 1 diabetes by cytometric fingerprinting |
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Authors: | Sekiguchi Debora R Sutter Jennifer A Rickels Michael R Naji Ali Liu Chengyang Propert Kathleen J Rogers Wade T Prak Eline T Luning |
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Affiliation: | a Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104b Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104c Division of Transplant Surgery, Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA 19104d Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104 |
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Abstract: | Manual gating of bivariate plots remains the most frequently used data analysis method in flow cytometry. However, gating is operator-dependent and cumbersome, particularly with the increasing complexity of modern multicolor immunophenotyping data. A method that can remove operator bias, enable systematic and thorough analysis of complex high-dimensional data, correlate temporal changes in different subsets and lead to biomarker discovery is needed. Here we apply such a method, called cytometric fingerprinting (CF), to data obtained on peripheral blood B cells from an adult patient with type-1 diabetes who underwent pancreatic islet transplantation. We establish that CF can be used to analyze longitudinal trends in immunophenotypic data, and show that results from CF are comparable to those obtained with traditional gating methods. Both methods reveal the appearance of transitional B cells and subsequent accumulation of more mature B cells following immunosuppression and transplantation. This pattern is consistent with a temporally ordered process of B cell auto-reconstitution. We also show the comparative efficiency of fingerprinting in recognizing relative changes in B cell subsets with respect to time, its ability to couple the data with statistical methods (agglomerative clustering) and its potential to define novel subsets. |
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Keywords: | CF, cytometric fingerprinting T1D, type 1 diabetes CIT, clinical islet transplantation. |
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