2. Academic Validation
  3. Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures

Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures

  • ACS Sens. 2023 Oct 27;8(10):3781-3792. doi: 10.1021/acssensors.3c01234.
Jonathan Jeffet 1 2 3,  Sayan Mondal 2 3,  Amit Federbush 1 4,  Nadav Tenenboim 1 2 3,  Miriam Neaman 2 5,  Jasline Deek 2,  Yuval Ebenstein 2 6 3 7,  Yohai Bar-Sinai 1 4 7
Affiliations

Affiliations

  • 1 School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
  • 2 School of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
  • 3 Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 6997801, Israel.
  • 4 The Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel.
  • 5 Department of Hematology, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel.
  • 6 Department of Biomedical Engineering, Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
  • 7 Center for AI & Data Science (TAD), Tel Aviv University, Tel Aviv 6997801, Israel.
PMID: 37791886 DOI: 10.1021/acssensors.3c01234
Abstract

MicroRNAs (miRs) are small noncoding RNAs that regulate gene expression and are emerging as powerful indicators of diseases. MiRs are secreted in blood plasma and thus may report on systemic aberrations at an early stage via liquid biopsy analysis. We present a method for multiplexed single-molecule detection and quantification of a selected panel of miRs. The proposed assay does not depend on sequencing, requires less than 1 mL of blood, and provides fast results by direct analysis of native, unamplified miRs. This is enabled by a novel combination of compact spectral imaging and a machine learning-based detection scheme that allows simultaneous multiplexed classification of multiple miR targets per sample. The proposed end-to-end pipeline is extremely time efficient and cost-effective. We benchmark our method with synthetic mixtures of three target miRs, showcasing the ability to quantify and distinguish subtle ratio changes between miR targets.

Keywords

cancer diagnostics; circulating microRNA; machine learning; single-molecule; spectral imaging.

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