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Plasmonic Molecular Entrapment for Label-Free Methylated DNA Detection and Machine-Learning Assisted Quantification
Muhammad Shalahuddin Al Ja'farawy1,2, Vo Thi Nhat Linh1, Chaewon Mun1
1Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, Gyeongnam, 51508, South Korea.
Summary
This study introduces a novel plasmonic molecular entrapment (PME) method for label-free DNA methylation detection. This technique, combined with machine learning, accurately diagnoses colorectal cancer from serum samples.
Area of Science:
- Biochemistry
- Nanotechnology
- Medical Diagnostics
Background:
- Epigenetic DNA methylation influences oncogene activation and tumor suppressor gene inactivation.
- Accurate, label-free quantification of DNA methylation is crucial for disease diagnosis and monitoring.
- Current methods for DNA methylation analysis require improvement in sensitivity and specificity.
Purpose of the Study:
- To develop a facile and label-free strategy for trapping and sensing DNA methylation using plasmonic molecular entrapment (PME) and Surface-Enhanced Raman Spectroscopy (SERS).
- To establish a highly sensitive and adaptable technique for profiling and quantifying DNA methylation.
- To demonstrate the feasibility of PME-SERS combined with machine learning for clinical disease diagnosis.
Main Methods:
- Developed a plasmonic molecular entrapment (PME) method assisted by SERS for label-free DNA methylation sensing.
- Utilized in situ surface growth of plasmonic particles to create hotspot sites around target analytes.
- Employed logistic regression (LR)-based machine learning for quantification and classification of methylation levels.
Main Results:
- The PME-SERS technique demonstrated significant signal enhancement due to strong electromagnetic fields at hotspot sites.
- The method effectively profiled and quantified DNA methylation with robust capabilities for DNA analysis.
- Logistic regression accurately classified methylation levels in colorectal cancer patient serum samples with high sensitivity, specificity, and accuracy.
Conclusions:
- The developed PME method combined with machine learning offers a promising sensing technique for disease screening and diagnosis.
- This approach represents a significant advancement in disease detection and patient care for methylation-related diseases.
- The label-free and sensitive nature of PME-SERS makes it adaptable for various DNA analysis applications.
