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Regenerative therapy · 2026

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

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Humayara Feiroz, Taib Haslina, Berahim Zurairah, Ahmad Azlina

Аннотация

INTRODUCTION: 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.

METHODS: 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).

RESULTS: 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).

CONCLUSIONS: 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.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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