BACKGROUND: The HepaRG cell line is widely used as an alternative for primary human hepatocytes for numerous applications, including drug screening, and is progressively gaining importance as a human-relevant cell source. Consequently, increasing numbers of experiments are being performed with this cell line, including real-time quantitative PCR (RT-qPCR) experiments for gene expression studies. CONTENT: When RT-qPCR experiments are performed, results are reliable only when attention is paid to several critical aspects, including a proper normalization strategy. Therefore, in 2011 we determined the most optimal reference genes for gene expression studies in the HepaRG cell system, according to the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. This study additionally provided clear evidence that the use of a single reference gene [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein S18 (RPS18), or actin, beta (ACTB)] was insufficient for normalization in HepaRG cells. Our screening of relevant studies published after our study suggested that the findings of our study were completely ignored. SUMMARY: In none of the 24 reviewed studies was a proper normalization method used. Only 1 reference gene was included for normalization in 21 out of the 24 reported studies we screened, with RPS18 and GAPDH used most frequently, followed by hypoxanthine phosphoribosyltransferase 1 (HPRT1), glutathione synthetase (GSS) (hGus), β-2 microglobin (B2M), and acidic ribosomal phosphoprotein P0 (36B4). For 2 studies the use of multiple reference genes (2 and 3) was reported, but these had not been prevalidated for expression stability in HepaRG cells. In 1 study, there was no evidence that any reference gene had been used. Current RT-qPCR gene expression studies in HepaRG cells are being performed without adequate consideration or evaluation of reference genes. Such studies can yield erroneous and biologically irrelevant results.
BACKGROUND: The HepaRG cell line is widely used as an alternative for primary human hepatocytes for numerous applications, including drug screening, and is progressively gaining importance as a human-relevant cell source. Consequently, increasing numbers of experiments are being performed with this cell line, including real-time quantitative PCR (RT-qPCR) experiments for gene expression studies. CONTENT: When RT-qPCR experiments are performed, results are reliable only when attention is paid to several critical aspects, including a proper normalization strategy. Therefore, in 2011 we determined the most optimal reference genes for gene expression studies in the HepaRG cell system, according to the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. This study additionally provided clear evidence that the use of a single reference gene [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein S18 (RPS18), or actin, beta (ACTB)] was insufficient for normalization in HepaRG cells. Our screening of relevant studies published after our study suggested that the findings of our study were completely ignored. SUMMARY: In none of the 24 reviewed studies was a proper normalization method used. Only 1 reference gene was included for normalization in 21 out of the 24 reported studies we screened, with RPS18 and GAPDH used most frequently, followed by hypoxanthine phosphoribosyltransferase 1 (HPRT1), glutathione synthetase (GSS) (hGus), β-2 microglobin (B2M), and acidic ribosomal phosphoprotein P0 (36B4). For 2 studies the use of multiple reference genes (2 and 3) was reported, but these had not been prevalidated for expression stability in HepaRG cells. In 1 study, there was no evidence that any reference gene had been used. Current RT-qPCR gene expression studies in HepaRG cells are being performed without adequate consideration or evaluation of reference genes. Such studies can yield erroneous and biologically irrelevant results.
Authors: Eva Malatinkova; Ward De Spiegelaere; Pawel Bonczkowski; Maja Kiselinova; Karen Vervisch; Wim Trypsteen; Margaret Johnson; Chris Verhofstede; Danny de Looze; Charles Murray; Sabine Kinloch-de Loes; Linos Vandekerckhove Journal: Elife Date: 2015-10-06 Impact factor: 8.140
Authors: Kristin Hieronymus; Benjamin Dorschner; Felix Schulze; Neeta L Vora; Joel S Parker; Jennifer Lucia Winkler; Angela Rösen-Wolff; Stefan Winkler Journal: BMC Genomics Date: 2021-06-30 Impact factor: 3.969
Authors: M Schaeck; W De Spiegelaere; J De Craene; W Van den Broeck; B De Spiegeleer; C Burvenich; F Haesebrouck; A Decostere Journal: Sci Rep Date: 2016-02-17 Impact factor: 4.379