Identification of the best housekeeping gene for RT-qPCR analysis of human pancreatic organoids

• Published in: PloS one Vol. 16; no. 12; p. e0260902
• Main Authors: Cherubini, Alessandro, Rusconi, Francesco, Lazzari, Lorenza
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.12.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Computer and Information Sciences; Diabetes; Drug screening; Efficiency; Engineering and Technology; Evaluation; Gene expression; Genes; Genes, Essential; Glyceraldehyde-3-phosphate dehydrogenase; Hematology; Humans; Laboratories; Medical research; Medicine; Medicine and Health Sciences; Organoids; Organoids - cytology; Organoids - metabolism; Pancreas; Pancreas - cytology; Pancreas - metabolism; Physical Sciences; Polymerase chain reaction; Real-Time Polymerase Chain Reaction - methods; Real-Time Polymerase Chain Reaction - standards; Reference Standards; Regenerative medicine; Reproducibility; Research and Analysis Methods; RNA, Messenger - analysis; RNA, Messenger - genetics; Scientific research; Variance analysis; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260902

In the last few years, there has been a considerable increase in the use of organoids, which is a new three-dimensional culture technology applied in scientific research. The main reasons for their extensive use are their plasticity and multiple applications, including in regenerative medicine and the screening of new drugs. The aim of this study was to better understand these structures by focusing on the choice of the best housekeeping gene (HKG) to perform accurate molecular analysis on such a heterogeneous system. This feature should not be underestimated because the inappropriate use of a HKG can lead to misleading data and incorrect results, especially when the subject of the study is innovative and not totally explored like organoids. We focused our attention on the newly described human pancreatic organoids (hPOs) and compared 12 well-known HKGs (ACTB, B2M, EF1α, GAPDH, GUSB, HPRT, PPIA, RNA18S, RPL13A TBP, UBC and YWHAZ). Four different statistical algorithms (NormFinder, geNorm, BestKeeper and ΔCt) were applied to estimate the expression stability of each HKG, and RefFinder was used to identify the most suitable genes for RT-qPCR data normalization. Our results showed that the intragroup and intergroup comparisons could influence the best choice of the HKG, making clear that the identification of a stable reference gene for accurate and reproducible RT-qPCR data normalization remains a critical issue. In summary, this is the first report on HKGs in human organoids, and this work provides a strong basis to pave the way for further gene analysis in hPOs.