The long noncoding RNA MALAT1 predicts human islet isolation quality
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
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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Research Article Transplantation

The long noncoding RNA MALAT1 predicts human islet isolation quality

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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Figure 2

Key lncRNAs identified in islet quality stratified discovery sample set.

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Key lncRNAs identified in islet quality stratified discovery sample set....
Penalized regression analysis and bootstrapping were performed on the (6983) annotated lncRNAs and the identified lncRNAs across all group-wise comparisons (n = 18 human islet samples, categorized into 3 groups) as presented in A–C: (A) group 1 versus group 2, (B) group 1 versus group 3, and (C) group 2 versus group 3. (See Supplemental Table 4 for details; unequal-variance Student’s t test was used. Note: The lncRNA AC010987.5 was present at very low FPKM and hence is not shown in C.) *P < 0.05; **P < 0.01. Each red dot represents individual islet preparation. The horizontal line represents the mean while the polygons represent the estimated density of data (scatter plot). Two-tailed distribution, with 2-sample unequal-variance Student’s t test, was used to identify the difference for each lncRNA between each group-wise comparison. ROC curves: (D) group 1 versus group 2: MALAT1-1.1, MALAT1-9.1, and both MALAT1 variants (9.1 + 1.1); (E) group 1 versus group 3: MALAT1-1.1, MALAT1-2.1, MALAT1-9.1, and all the above 3 MALAT1 variants (1.1 + 2.1 + 9.1); (F) group 2 versus group 3: ENST00000450589.5 (GAS5), MALAT1-2.1, and both; GAS5 + MALAT1-2.1.