检出限
分子生物学
共焦
线粒体DNA
化学
拉曼光谱
DNA
拉曼散射
聚合酶链反应
流式细胞术
胶体金
荧光
生物标志物
生物
显微镜
数字聚合酶链反应
共焦显微镜
子宫内膜异位症
比色法
金标准(测试)
色谱法
突变体
放大器
染色
样品制备
凝胶电泳
基因组DNA
实时聚合酶链反应
生物物理学
定量分析(化学)
荧光显微镜
病理
临床诊断
分析化学(期刊)
作者
Laura M. Rey Gomez,Kazi Morshed Alom,Audrey Nadalini,Su Su Thae Hnit,Benjamin Clark,Nana Lyu,Rena Hirani,Sian Sloan-Dennison,Stacey Laing,Cicely Rathmell,D. J. Creasey,Dieter Bingemann,Jonathan Faircloth,Neil C. Shand,D. Graham,Karen Faulds,Yuling Wang
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2026-03-12
标识
DOI:10.1021/acssensors.5c04674
摘要
Endometriosis (EMT) is an incurable and painful chronic illness that affects approximately 10% of people assigned female at birth worldwide. Currently, EMT takes on average 5-7 years to diagnose after histological confirmation with a tissue sample collected via laparoscopy. Therefore, there is a demand for developing new and powerful detection tools that can work in conjunction with imaging techniques to make EMT diagnosis more accessible and reduce diagnostic delays. This study proposes a proof-of-concept lateral flow assay (LFA) for the detection of an EMT biomarker based on mitochondrial DNA deletion mutations found in cell-free mitochondria in plasma. Surface-enhanced resonance Raman scattering (SERRS) was integrated with the LFA to increase sensitivity and allow quantitation using gold nanoparticles functionalised with the near-infrared fluorescent dye NIR 4f. The SERRS-LFA was compared to gel electrophoresis and colorimetry in a typical polymerase chain reaction (PCR) workflow. SERRS signals were analysed using confocal Raman microscopy in combination with digital SERRS. This digital SERRS-LFA demonstrated a limit of detection (LOD) of 5.6 × 102 input copies, about 109-fold lower than the reference colorimetric method. When mutant DNA was tested as abundance of background wild type DNA, an LOD of 0.35% was obtained, about 14-fold lower than the reference colorimetric method. Finally, quantitative analysis with a handheld Raman reader demonstrated the potential of bringing future amplification-free SERRS-based LFAs to point-of-care settings.
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