Identification of stable reference genes for quantitative real-time PCR in human fibroblasts from lymph nodes and synovium

Abstract

Abstract Real-time quantitative PCR (RT-qPCR) is a widely used method for gene expression analysis; however, its accuracy depends on the selection of stable reference genes for normalization. To date, no study has systematically identified suitable reference genes for primary human fibroblast-like synoviocytes (FLS) and lymph node stromal cells (LNSCs) in the context of rheumatoid arthritis (RA), despite the critical role of these stromal cells in disease pathogenesis. We evaluated the expression stability of 12 candidate reference genes in FLS and LNSCs subjected to various in vitro conditions, including inflammatory stimulation, senescence induction, and differentiation. Candidate genes were selected based on common usage and Genevestigator analysis of publicly available transcriptomic datasets. Expression stability was assessed using Clarida and RefFinder software, integrating geNorm, NormFinder, BestKeeper, and the comparative delta Ct method. The number and identity of recommended reference genes depended on cell type and experimental condition. In LNSCs, two genes, RPLP0 and POLR2G , were recommended across all tested conditions. In contrast, FLS exhibited condition-dependent variability, with POLR2G and TBP being recommended for untreated FLS. In contrast, combinations of three genes were recommended for FLS subjected to TNFαLTαβ stimulation ( EEF1A1 , NFIC and POLR2A ), senescence-related conditions ( EEF1A1 , GAPDH and NFIC) and adipogenesis ( POLR2G , EEF1A1 and TBP) . Comparison of normalization strategies in untreated FLS showed that reference gene choice influenced relative target gene expression patterns and their interpretation. This study provides the first systematic evaluation of reference genes for RT-qPCR normalization in human FLS and LNSCs. Our findings highlight the necessity of condition- and cell type-specific validation of reference genes to ensure accurate and reproducible gene expression analysis in studies of fibroblast biology and inflammatory disease.