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Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures
Jonathan Jeffet1,2,3, Sayan Mondal2,3, Amit Federbush1,4
1School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
ACS Sensors
|October 4, 2023
Summary
We developed a novel method for detecting microRNAs (miRs) in blood plasma. This fast, cost-effective liquid biopsy technique uses spectral imaging and machine learning for early disease detection.
Area of Science:
- Biochemistry
- Molecular Biology
- Biotechnology
Background:
- MicroRNAs (miRs) are small noncoding RNAs regulating gene expression.
- Circulating miRs in blood plasma serve as potential biomarkers for early disease detection via liquid biopsy.
- Current detection methods can be complex and time-consuming.
Purpose of the Study:
- To develop a novel, rapid, and cost-effective method for multiplexed detection and quantification of microRNAs.
- To enable direct analysis of native, unamplified miRs from small blood volumes.
- To establish a new tool for early disease diagnostics using liquid biopsy.
Main Methods:
- A novel assay combining compact spectral imaging with a machine learning-based detection scheme.
- Multiplexed single-molecule detection and quantification of a selected panel of miRs.
- Direct analysis of native, unamplified miRs without sequencing.
Main Results:
- Demonstrated ability for simultaneous multiplexed classification of multiple miR targets.
- Achieved quantification and distinction of subtle ratio changes between miR targets using synthetic mixtures.
- Validated the method's time efficiency and cost-effectiveness.
Conclusions:
- The proposed method offers a significant advancement in microRNA analysis for diagnostics.
- This technique facilitates early disease detection through non-invasive liquid biopsy.
- The assay is time-efficient, cost-effective, and suitable for clinical applications.
