Jump to ContentJump to Main Navigation

Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Editorial Board Member: Gillery, Philippe / Kazmierczak, Steven / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Schlattmann, Peter / Whitfield, John B.

12 Issues per year


IMPACT FACTOR 2013: 2.955
Rank 5 out of 29 in category Medical Laboratory Technology in the 2013 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR): 0.860
Source Normalized Impact per Paper (SNIP): 1.046

VolumeIssuePage

Issues

Automated cell disruption is a reliable and effective method of isolating RNA from fresh snap-frozen normal and malignant oral mucosa samples

Sébastien Van der Vorst1a / Anne-France Dekairelle2 / Léonid Irenge3 / Marc Hamoir4 / Annie Robert5 / Jean-Luc Gala6a

1Center for Applied Molecular Technology, Université catholique de Louvain, Brussels, Belgium and Department of Head and Neck Surgery, Head and Neck Oncology Program, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium

2Center for Applied Molecular Technology, Université catholique de Louvain, Brussels, Belgium

3Center for Applied Molecular Technology, Université catholique de Louvain, Brussels, Belgium and Defense Laboratories Department, Belgian Armed Forces, Brussels, Belgium

4Department of Head and Neck Surgery, Head and Neck Oncology Program, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium

5Epidemiology and Biostatistics, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium

6Center for Applied Molecular Technology, Université catholique de Louvain, Brussels, Belgium and Defense Laboratories Department, Belgian Armed Forces, Brussels, Belgium

Corresponding author: Jean-Luc Gala, MD, PhD, Center for Applied Molecular Technology, BP 30.46, 30 Clos Chapelle-aux-Champs, 1200 Brussels, Belgium Phone: +32-2-764-3165, Fax: +32-2-764-3166,

Citation Information: Clinical Chemistry and Laboratory Medicine. Volume 47, Issue 3, Pages 294–301, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: 10.1515/CCLM.2009.070, February 2009

Publication History

Received:
2008-10-20
Accepted:
2008-12-12
Published Online:
2009-02-05

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.

Clin Chem Lab Med 2009;47:294–301.

Keywords: head and neck squamous cell carcinoma; homogenization; RNA extraction; TP53

Comments (0)

Please log in or register to comment.
Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.