Selection and validation of reference genes for qPCR analysis of differentiation and maturation of THP-1 cells into M1 macrophage-like cells.
Guoqiang RenMorten JuhlQiuyue PengTrine FinkSimone Riis PorsborgPublished in: Immunology and cell biology (2022)
For cell-based assays studying monocytes and macrophages, the immortalized monocyte cell line THP-1 is widely used and stimulated with phorbol 12-myristate 13-acetate, lipopolysaccharide (LPS) and/or interferon-γ (IFN-γ), after which it differentiates and polarizes into proinflammatory M1-like macrophages. For the quantification of this and the effect of different factors affecting these processes, the expression levels of various maturation markers are determined using reverse transcription-quantitative PCR. For this purpose, stably expressed reference genes are crucial. However, no studies evaluating the stability of reference genes in THP-1 cells stimulated with LPS and IFN-γ have been performed. Therefore, this paper describes the selection of the most used reference genes [RPL37A (ribosomal protein L37a), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), UBC (ubiquitin C), B2M (0β2-microbulin), ACTB (β-actin) and PPIA (cyclophilin A)], the in silico primer design, the analysis and the validation of these in accordance with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines and more recent recommendations for the validation of the stability of reference genes. Using the RefFinder platform, including the four most popular algorithms for reference gene validation, the Delta CT, BestKeeper, NormFinder and geNorm, we find the reference genes GAPDH and UBC to be the most stable. Furthermore, we demonstrate that the normalization of gene expression data using the least stable reference genes, ACTB and B2M, dramatically affects the interpretation of experimental data. Taken together, it is vital to validate the stability of reference genes under the specific experimental conditions used when utilizing the THP-1 monocyte model system.
Keyphrases
- genome wide
- genome wide identification
- dendritic cells
- gene expression
- bioinformatics analysis
- genome wide analysis
- inflammatory response
- induced apoptosis
- machine learning
- immune response
- magnetic resonance
- high throughput
- magnetic resonance imaging
- poor prognosis
- anti inflammatory
- stem cells
- real time pcr
- small molecule
- adipose tissue
- peripheral blood
- big data
- cell cycle arrest
- deep learning
- mesenchymal stem cells
- cell therapy
- lps induced
- pi k akt
- endoplasmic reticulum stress
- social media
- case control
- molecular dynamics simulations