Sungjin Park1, Kiyoung Lee2, Ie Byung Park2, Nan Hee Kim3, Seongcheol Cho4, Won Jong Rhee5, Yujin Oh6, Jimin Choi6, Seungyoon Nam7, Dae Ho Lee8. 1. Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea; Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon, Republic of Korea. 2. Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea. 3. Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea. 4. Department of Bioengineering and Nano-Bioengineering, Incheon National University, Incheon, Republic of Korea. 5. Department of Bioengineering and Nano-Bioengineering, Incheon National University, Incheon, Republic of Korea; Division of Bioengineering, Incheon National University, Incheon, Republic of Korea. 6. Gachon Biomedical & Convergence Institute, Gachon University Lee Gil Ya Cancer and Diabetes Institute, Incheon, Republic of Korea. 7. Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea; Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon, Republic of Korea; Department of Life Sciences, Gachon University, Seongnam, Republic of Korea. Electronic address: nams@gachon.ac.kr. 8. Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea; Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea; Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon, Republic of Korea. Electronic address: drhormone@naver.com.
Abstract
AIMS: MicroRNAs (miRNAs) that circulate in biological fluids are frequently enclosed in extracellular vesicles (EVs). However, urinary EVs and their cargo miRNAs have not been systematically studied according to their EV isolation methods. METHODS: In type 2 diabetes mellitus persons with diabetic nephropathy (n = 4), we compared miRNA species in urine EVs prepared by ultracentrifugation (UC), qEV original size exclusion column (qEV), ExoQuick-TC Plus (ExoQuick), and ultrafiltration using Amicon Ultra centrifugal filter devices (Amicons) 10 K and 100 K. EV miRNAs were profiled by next-generation sequencing (NGS). Additionally, we evaluated the correlations of EV miRNA expression between the urine and serum samples isolated by UC. RESULTS: From each of 100 ml of urine, the UC method yielded the highest number of EV miRNA species (233 ± 37.3), with the ExoQuick yielded the lowest (103 ± 17.4). Urine EV miRNA profiles were highly correlated between UC, qEV, ExoQuick and Amicon 10 K methods. EV miRNA profiles between the urine and serum samples showed variable correlations between the patients (paired sample number = 3, r = 0.39-0.72). CONCLUSIONS: UC, qEV, ExoQuick, and Amicon 10 K are acceptable for urinary EV isolation to profile miRNAs. Urine- and serum-derived EV miRNA profiles have variable correlations depending on specific patients.
AIMS: MicroRNAs (miRNAs) that circulate in biological fluids are frequently enclosed in extracellular vesicles (EVs). However, urinary EVs and their cargo miRNAs have not been systematically studied according to their EV isolation methods. METHODS: In type 2 diabetes mellituspersons with diabetic nephropathy (n = 4), we compared miRNA species in urine EVs prepared by ultracentrifugation (UC), qEV original size exclusion column (qEV), ExoQuick-TC Plus (ExoQuick), and ultrafiltration using Amicon Ultra centrifugal filter devices (Amicons) 10 K and 100 K. EV miRNAs were profiled by next-generation sequencing (NGS). Additionally, we evaluated the correlations of EV miRNA expression between the urine and serum samples isolated by UC. RESULTS: From each of 100 ml of urine, the UC method yielded the highest number of EV miRNA species (233 ± 37.3), with the ExoQuick yielded the lowest (103 ± 17.4). Urine EV miRNA profiles were highly correlated between UC, qEV, ExoQuick and Amicon 10 K methods. EV miRNA profiles between the urine and serum samples showed variable correlations between the patients (paired sample number = 3, r = 0.39-0.72). CONCLUSIONS: UC, qEV, ExoQuick, and Amicon 10 K are acceptable for urinary EV isolation to profile miRNAs. Urine- and serum-derived EV miRNA profiles have variable correlations depending on specific patients.
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