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High Sensitivity Cardiac Troponin I Detection via MP-Locked Aptamer and Multimeric DNAzyme-Coupled Hyperbranched Hybridization Chain Reaction

  • Sayantan Tripathy
  • , Sahil Sharma
  • , Ng Ka Wai
  • , Siddhant Jaitpal
  • , Anthony Dongchau
  • , Saugat Bhattacharyya
  • , Sonya R Wesselowski
  • , Ashley B. Saunders
  • , Gerard L. Coté
  • , Samuel B. Mabbott

Research output: Contribution to journalArticlepeer-review

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Abstract

Timely and sensitive detection of cardiac troponin I (cTnI) is critical for early diagnosis of myocardial infarction, particularly at the point-of-care. Herein, we present a novel colorimetric biosensing platform for high-sensitivity detection of cardiac troponin I (cTnI). The platform integrates magnetic particle (MP) anchored locked aptamers, stabilized by short complementary strands to minimize nonspecific folding and background activation prior to target binding, with hyperbranched hybridization chain reaction (HCR) and catalytic DNA (DNAzyme) nanocomplex–mediated signal amplification. This enzyme-free amplification system detects cTnI directly in 25–30 minutes, with a calculated detection limit of 0.25 ng/L, a wide dynamic range of 0.5–50,000 ng/L, and a coefficient of variation below 5% using just 25 µL of patient serum. The developed assay was evaluated using both human and canine serum samples. To assess classification performance, three distinct hyperparameter optimization strategies were applied to a reduced feature space. The model achieved an accuracy of 90.91% and a recall of 89.89% for human samples, and an accuracy of 83.33% with a recall of 85.71% for canine samples. Blind testing with human serum samples further confirmed the robustness of the platform, showing an overall accuracy of around 90%. This integrated biosensing and machine learning framework enables rapid and sensitive detection of cardiac troponin I, demonstrating strong potential for myocardial infarction diagnosis across species in a pre-clinical setting.
Original languageEnglish
Article numbere12096
Pages (from-to)1-22
Number of pages22
JournalSmall
Volume22
Issue number20
Early online date23 Feb 2026
DOIs
Publication statusPublished (in print/issue) - 7 Apr 2026

Bibliographical note

Publisher Copyright:
© 2026 The Author(s). Small published by Wiley-VCH GmbH.

Data Availability Statement

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

Funding

This work was supported by the National Science Foundation’s Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP, Award #1648451) and by the United States–Ireland–Northern Ireland R&D Partnership supplement. Figures were created with BioRender. We thank Dr. Aydogan Ozcan and Dr. Omai Garner (UCLA) for providing human samples.

FundersFunder number
1648451
University of California, Los Angeles

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure

    Keywords

    • cTnI
    • Machine Learning
    • Canine Data
    • Human Data
    • Genetic Algorithm
    • Particle Swarm Optimization
    • Grid Search
    • DNAzyme
    • hyperbranched hybridization chain reaction
    • myocardial infarction
    • locked aptamer
    • machine learning
    • Nucleic Acid Hybridization
    • Limit of Detection
    • Humans
    • Biosensing Techniques/methods
    • Troponin I/blood
    • DNA, Catalytic/chemistry
    • Animals
    • Myocardial Infarction/diagnosis
    • Dogs
    • Aptamers, Nucleotide/chemistry

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