Abstract
Human islet isolation is a cost- and resource-intensive program for generating islets for cell therapy in type 1 diabetes. However, only one-third of cadaveric pancreases get to clinical transplantation because of low quality/number of islets. There is a need to identify biomarkers that predict the quality of islets, before initiating their isolation. Here, we sequenced transcriptomes from 18 human islet preparations stratified into 3 groups (group 1: best quality/transplantable islets; group 2: intermediary quality; and group 3: inferior quality/nontransplantable islets) based on routine measurements, including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long noncoding RNAs (lncRNAs) that were significantly different across all group-wise comparisons (group 1 vs. group 2, group 2 vs. group 3, and group 1 vs. group 3). Two variants of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA were common across all comparisons. We then confirmed RNA-Seq findings in a validation set of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrated that assessing the levels of MALAT1 variants alone (receiver operator characteristic curve AUC: 0.83) offers higher specificity in predicting postisolation islet quality, further improving the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/BMI/North American Islet Donor Score. We present this resource of islet quality–stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical-grade (good manufacturing practice–grade) islet isolation.
Authors
Wilson K.M. Wong, Guozhi Jiang, Anja E. Sørensen, Yi Vee Chew, Cody Lee-Maynard, David Liuwantara, Lindy Williams, Philip J. O’Connell, Louise T. Dalgaard, Ronald C. Ma, Wayne J. Hawthorne, Mugdha V. Joglekar, Anandwardhan A. Hardikar
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