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Automated cell disruption is a reliable and effective method of isolating RNA from fresh snap-frozen normal and malignant oral mucosa samples.
- Source :
- Clinical Chemistry and Laboratory Medicine : Associated with FESCC and IFCC, Vol. 47, no. 3, p. 294-301 (2009)
- Publication Year :
- 2009
-
Abstract
- BACKGROUND: This study compared automated vs. manual tissue grinding in terms of RNA yield obtained from oral mucosa biopsies. METHODS: A total of 20 patients undergoing uvulectomy for sleep-related disorders and 10 patients undergoing biopsy for head and neck squamous cell carcinoma were enrolled in the study. Samples were collected, snap-frozen in liquid nitrogen, and divided into two parts of similar weight. Sample grinding was performed on one sample from each pair, either manually or using an automated cell disruptor. The performance and efficacy of each homogenization approach was compared in terms of total RNA yield (spectrophotometry, fluorometry), mRNA quantity [densitometry of specific TP53 amplicons and TP53 quantitative reverse-transcribed real-time PCR (qRT-PCR)], and mRNA quality (functional analysis of separated alleles in yeast). RESULTS: Although spectrophotometry and fluorometry results were comparable for both homogenization methods, TP53 expression values obtained by amplicon densitometry and qRT-PCR were significantly and consistently better after automated homogenization (p<0.005) for both uvula and tumor samples. Functional analysis of separated alleles in yeast results was better with the automated technique for tumor samples. CONCLUSIONS: Automated tissue homogenization appears to be a versatile, quick, and reliable method of cell disruption and is especially useful in the case of small malignant samples, which show unreliable results when processed by manual homogenization.
Details
- Database :
- OAIster
- Journal :
- Clinical Chemistry and Laboratory Medicine : Associated with FESCC and IFCC, Vol. 47, no. 3, p. 294-301 (2009)
- Notes :
- English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1130520024
- Document Type :
- Electronic Resource