Label-Free Pathogen Detection Based on Yttrium-Doped Carbon Nanoparticles up to Single-Cell Resolution

Maha Alafeef, Ketan Dighe, Dipanjan Pan

Research output: Contribution to journalArticlepeer-review

Abstract

The capability to detect bacteria at a low cell density is critical to prevent the delay in therapeutic intervention and to avoid the emergence of antibiotic-resistant species. Till date, significant advancement has been made to develop a sensing platform for rapid and reliable bacterial detection. However, critical requirements, that is, limit of detection, fast time of response, ultrasensitivity with high reproducibility, and the ability to distinguish between bacterial strains are yet to be met within a single sensing platform. In this contribution, we present a novel label-free sensor based on pH-sensitive fluorescent yttrium-doped carbon nanoparticles (YCNPs) embedded in agarose that can rapidly and accurately detect and discriminate pathogens in real time. The developed sensor matrix presented pH-triggered aggregation-induced emission quenching of YCNPs in a wide pH range. When the pH decreased from 10.0 to 4.0, the fluorescence of the matrix decreased linearly (R2 = 0.9229). The sensor 's high sensitivity in a physiologically relevant pH range enables the monitoring of the presence of live pathogens to single-cell resolution. In addition, the 3D matrix sensor showed low cytotoxicity and long stability (>30 days). Besides, the YCNP platform is stable for several hours (5 h) in a complex medium and does not alter the bacterial activities, allowing real-time monitoring of bacterial growth with a small sample volume (100 μL) and rapid response time (25 min). Furthermore, using machine learning-assisted tools, different bacterial strains with various cell densities were discriminated with an accuracy of almost 100%. Moreover, blends of pathogens and a real-world sample can also be identified accurately, thus enabling the sensor to provide fast and reliable pathogen information for clinical decisions and allowing continuous monitoring of infectious disease trends.

Original languageEnglish (US)
Pages (from-to)42943-42955
Number of pages13
JournalACS Applied Materials and Interfaces
Volume11
Issue number46
DOIs
StatePublished - Nov 20 2019

Keywords

  • carbon nanoparticles
  • fluorescence quenching
  • machine learning
  • pH sensor
  • pathogen detection

ASJC Scopus subject areas

  • Materials Science(all)

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