Normalization Challenges Across Adipocyte Differentiation and Lipid-Modulating Treatments: Identifying Reliable Housekeeping Genes

Accurate normalization of RT-qPCR data requires selecting stable internal control genes, particularly in models characterized by dynamic metabolic transitions, such as 3T3-L1 adipocytes. The current study compares the expression stability of nine widely used housekeeping genes (HKGs) (peptidylprolyl isomerase A (<i>Ppia</i>), glyceraldehyde-3-phosphate dehydrogenase (<i>Gapdh</i>), beta-2 microglobulin (<i>B2M</i>), ribosomal protein, large, P0 (<i>36b4</i>), hydroxymethylbilane synthase (<i>Hmbs</i>), hypoxanthine guanine phosphoribosyl transferase (<i>Hprt</i>), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (<i>Ywhaz</i>), 18S ribosomal RNA (<i>18S</i>), and β-actin (<i>Actb</i>)) across key stages of differentiation (days 0, 9, and 18) and under treatments with palmitic acid and docosahexaenoic acid. Stability was assessed using four classical algorithms-geNorm, NormFinder, BestKeeper, and RefFinder-supplemented by the ΔCt method, conventional statistical testing, correlation, and regression analysis relative to two target genes, fatty acid-binding protein 4 (<i>Fabp4</i>) and sterol regulatory element binding transcription factor 1 (<i>Srebf1</i>). The obtained data indicate that no single HKG remains universally stable across these experimental conditions, and the expression of traditionally used reference genes (<i>Gapdh</i>, <i>Actb</i>, <i>Hprt</i>, <i>18S</i>) is highly influenced by both the stage of adipogenesis and exposure to lipid-modulating factors. In contrast, <i>Ppia</i>, <i>36b4</i>, and <i>B2M</i>-despite some of them being underestimated in use as references-consistently display the lowest variability across most analytical tools, forming a reliable and functionally diverse normalization panel. It should be noted that our initial stability assessment revealed apparent discrepancies among mathematical evaluation methods, emphasizing the need for a holistic, multiple-level approach strategy. The applied combination of algorithmic and statistical methods provides a more rigorous and objective framework for assessing the stability of reference genes, which is highly recommended in such a complex adipocyte-based model.