A matrix-adapted RNA extraction strategy for targeted RT-qPCR analysis of periodontal ligament fibroblasts cultured on human amniotic membrane scaffolds.

Authors

Feiroz Humayara, Haslina Taib, Zurairah Berahim, Azlina Ahmad

Published in

Regenerative therapy. Volume 33. Pages 101165. Epub Jul 25, 2026.

Abstract

Human amniotic membrane (HAM) is a promising scaffold for periodontal tissue engineering, supporting favorable cellular responses of human periodontal ligament fibroblasts (HPDLFs). However, molecular characterization of cells cultured on HAM is frequently hindered by extracellular matrix-derived contaminants, leading to poor RNA yield and compromised purity that limit downstream gene expression analyses. This study aims to develop and validate a matrix-adapted RNA extraction strategy to enable reliable molecular profiling of HPDLFs cultured on HAM scaffolds.
HPDLFs were seeded onto de-epithelialized HAM, and cell viability was confirmed prior to RNA isolation. A systematic, multi-phase optimization of a phenol-based extraction protocol (GENEzol™ TriRNA Pure Kit) was undertaken to mitigate matrix-associated interference. RNA yield and purity were assessed spectrophotometrically, with functional validation performed using reverse transcription quantitative polymerase chain reaction (RT-qPCR).
The optimized protocol significantly enhanced RNA integrity and yield compared to the conventional method. Purity indices improved (A260/230: 0.73 ± 0.20 to 2.15 ± 0.40; A260/280: 1.41 ± 0.27 to 2.03 ± 0.18), while RNA concentration increased markedly. The proportion of samples meeting stringent quality criteria improved from 0% to 50%. Notably, mean RNA purity parameters were not significantly different from standard monolayer cultures for the selected measures assessed (p > 0.05).
The approach provides a practical framework for improving RNA recovery and targeted RT-qPCR validation in HAM-based culture systems which could set precedent for improving molecular investigations in periodontal tissue engineering and other scaffold-based models.

PMID:
42548905
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

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