Selection of stable expressed reference genes in native and vitrified/thawed human ovarian tissue for analysis by qRT-PCR and Western blot

To select reference genes with stable messenger RNA (mRNA) expression for quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) analysis of vitrified/thawed human ovarian tissue and to evaluate in human ovarian tissue the levels of key proteins which are commonly used as reference proteins. Pieces of ovarian tissue were obtained during laparoscopy from patients (n = 10, 24-36 years old) who suffered from types of cancer that does not affect reproductive system. Tissue strips from the intact group were immediately placed into liquid nitrogen. Tissue strips from the second group were successively placed into solutions with cryoprotective agents. Then, these strips were rapidly placed into liquid nitrogen. After thawing, ovarian tissue strips were cultured during 2 h in complete growth medium. Gene expression levels were measured using quantitative RT-PCR. Also, protein levels of three key reference genes were measured using Western blot. Statistical analysis of obtained data was performed by BestKeeper, NormFinder, and geNorm software utilities; correlation coefficients were also calculated. The most suitable reference genes for qRT-PCR analysis of human cortical ovarian tissue after cryopreservation by vitrification are genes of ribosomal proteins RPL4, RPLP0, RPS18, and heat shock protein HSP90AB1. The protein levels of three commonly used reference genes (ACTB, GAPDH, and HSP90) were measured in two groups of samples of human ovarian tissue: intact and vitrified/thawed. The levels of ACTB, GAPDH, and HSP90 proteins were similar in native and vitrified/thawed samples. Selection of suitable reference genes is the first aim of any research dedicated to the investigation of gene expression, because the interpretation of obtained results largely depends on selection of appropriate reference genes. Nowadays, there are many mathematical approaches allowing to select not only single reference gene but also a group of the most stably expressed reference genes. The use of mathematical models which take into account multiple reference genes will allow to obtain more accurate data on the expression of target genes.