Microbiologia Medica (Jun 2014)

Pre-analytical and post-analytical evaluation in the era of molecular diagnosis of sexually transmitted diseases: cellularity control and internal control

  • Loria Bianchi,
  • Zaleida Napoli,
  • Simona Turrisi,
  • Swan Donati,
  • Riccardo Lari

DOI
https://doi.org/10.4081/mm.2014.4718
Journal volume & issue
Vol. 29, no. 1

Abstract

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Background. Increase of molecular tests performed on DNA extracted from various biological materials should not be carried out without an adequate standardization of the pre-analytical and post-analytical phase. Materials and Methods. Aim of this study was to evaluate the role of internal control (IC) to standardize pre-analytical phase and the role of cellularity control (CC) in the suitability evaluation of biological matrices, and their influence on false negative results. 120 cervical swabs (CS) were pre-treated and extracted following 3 different protocols. Extraction performance was evaluated by amplification of: IC, added in each mix extraction; human gene HPRT1 (CC) with RT-PCR to quantify sample cellularity; L1 region of HPV with SPF10 primers. 135 urine, 135 urethral swabs, 553 CS and 332 ThinPrep swabs (TP) were tested for C. trachomatis (CT) and U. parvum (UP) with RT-PCR and for HPV by endpoint-PCR. Samples were also tested for cellularity. Results. Extraction protocol with highest average cellularity (Ac)/sample showed lowest number of samples with inhibitors; highest HPV positivity was achieved by protocol with greatest Ac/PCR. CS and TP under 300.000 cells/sample showed a significant decrease of UP (P<0.01) and HPV (P<0.005) positivity. Female urine under 40.000 cells/mL were inadequate to detect UP (P<0.05). Conclusions. Our data show that IC and CC allow optimization of pre-analytical phase, with an increase of analytical quality. Cellularity/sample allows better sample adequacy evaluation, crucial to avoid false negative results, while cellularity/PCR allows better optimization of PCR amplification. Further data are required to define the optimal cut-off for result normalization.

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