Optimal sample handling techniques for tissue preparation and storage, RNA extraction and quantification, and target gene detection are crucial for reliable gene expression analysis. Methods for measuring low-expressing genes, such as interferons, in human cervical samples are not described in the scientific literature. To detect interferon mRNA in human cervical samples we obtained normal and dysplastic frozen and formalin-fixed cervical biopsies from colposcopy. Histopathological diagnosis was performed by one pathologist. Cervical keratinocytes were isolated using laser capture microdissection. Immortalized keratinocytes transduced with or devoid of an HPV oncogene were used for initial method development. RNA from samples was extracted and integrity tested to compare tissue storage and extraction methods. The expression of five housekeeping genes was analyzed in cell lines and patient tissue to permit normalization between samples using quantitative real-time polymerase chain reaction. The usefulness of cDNA amplification was assessed for the detection of low-expressing interferon κ in cervical tissue. Here we report optimal tissue storage conditions, RNA extraction, sample normalization, and transcript amplification, as well as the sensitivity of quantitative real-time polymerase chain reaction and laser capture microdissection, for interferon κ detection in cervical tissue. Without these optimized techniques, interferon κ detection would be unattainable in cervical samples.
KeywordsCervical keratinocytes; Frozen and formalin-fixed cervical tissue; Housekeeping genes; Interferons; Interferon κ; Laser capture microdissection; Quantitative real-time polymerase chain reaction Quantitative real-time polymerase chain reaction (qRT-PCR) 1 for the molecular analysis of disease is a powerful and widely used tool. Extraction of high-quality RNA for use in gene expression analysis techniques such as reverse transcription (RT), qRT-PCR, and cDNA microarrays is of great importance. Although obtaining high-quality RNA from cell lines is relatively straightforward, the complexity and heterogeneity of human tissue pose considerable challenges for linking gene expression patterns with disease state. Nonetheless,