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Visualization and Quantification of Mesenchymal Cell Adipogenic Differentiation Potential with a Lineage Specific Marker
Published on: March 31, 2018
Matrix- and Differentiation Stage-Dependent Variability of Reference Genes: Rethinking Validation Strategies in
Betina Todorova1, Zhenya Ivanova1, Natalia Grigorova1
1Department of Pharmacology, Animal Physiology, Biochemistry and Chemistry, Faculty of Veterinary Medicine, Trakia University, 6000 Stara Zagora, Bulgaria.
Abstract:
The present study evaluated the stability of candidate reference genes during adipogenic differentiation of 3T3-L1 cells cultured on different extracellular matrices. The aim was to investigate the effects of matrix composition and differentiation stage on the expression of candidate housekeeping genes and to compare validation strategies in dynamic in vitro models. Eleven candidate reference genes (18S, Actb, B2m, Gapdh, Hmbs, Hprt, Nono, Ppia, Rplp0, Tbp, and Ywhaz) were analyzed by RT-qPCR in 3T3-L1 cells cultured on TC, collagen, gelatin, and Matrigel at Days 7 and 14 of differentiation. Gene stability was assessed using geNorm, NormFinder, RefFinder, comparative ΔCt, BestKeeper, generalized linear model (GLM), linear mixed model (LMM), and correlation analyses with the adipogenic markers Pparg and Fasn. The results demonstrated that the expression of most housekeeping genes was influenced by matrix composition, differentiation stage, or their interaction. Actb and 18S exhibited the strongest condition-dependent variability and pronounced matrix sensitivity. Gapdh and Hprt showed significant correlations with both Pparg and Fasn, while Hmbs correlated with Fasn, suggesting that these reference genes may not be fully independent of adipogenic status. Ppia demonstrated markedly contrasting rankings across analytical approaches, highlighting limitations of single-method stability assessment. The findings confirm that universal housekeeping genes are unlikely to exist across different matrix conditions and differentiation stages. The results highlight the need for multi-level validation strategies and experimentally validated normalization panels to minimize normalization bias and avoid misleading RT-qPCR expression profiles. Functional validation identified B2m and Rplp0 as the most suitable two-gene normalization panel for the experimental model evaluated, whereas Tbp remained a strong complementary reference gene candidate.
