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Human islet isolation is a cost-/resource-intensive program generating islets for cell therapy in Type 1 diabetes. However, only a third of cadaveric pancreas get to clinical transplantation due to low quality/number of islets. There is a need to identify biomarker(s) that predict the quality of islets, prior to initiating their isolation. Here, we sequenced transcriptome from 18 human islet preparations stratified into three groups (Gr.1: Best quality/transplantable islets, Gr.2: Intermediary quality, Gr.3: Inferior quality/non-transplantable islets) based on routine measurements including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long-non-coding(lnc)RNAs significantly different across all group-wise comparisons (Gr1VsGr2, Gr2vsGr3, Gr1vsGr3). Two variants of Metastasis-Associated Lung Adenocarcinoma Transcript-1(MALAT1) lncRNA were common across all comparisons. We confirmed RNA-seq findings in a "validation set" of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrate that assessing the levels of MALAT1 variants alone (ROC curve AUC: 0.83) offers highest specificity in predicting post-isolation islet quality and improves the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/Body Mass Index(BMI)/North American Islet Donor Score(NAIDS). 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 (GMP)-grade islet isolation.