Recent progress in plasmonic nanoparticle-based biomarker detection and cytometry for the study of central nervous system disorders.

Cytometry A

Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.

Published: November 2021

AI Article Synopsis

  • * Plasmonic nanoparticles (PNPs), especially gold nanoparticles (AuNPs), have gained attention for their effectiveness in imaging and biomarker detection, with capabilities for sensitivity at the single-molecule level.
  • * The review highlights the potential of PNPs in developing rapid diagnostic methods for neurological diseases and introduces a novel digital cytometry approach that integrates dark-field imaging and machine learning for accurate biomarker quantification at the single-cell level.

Article Abstract

Neurological disorders affect hundreds of millions of people around the world, are often life-threatening, untreatable, and can result in debilitating symptoms. The high prevalence of these disorders, which feature biochemical or structural abnormalities in neuronal systems, has spurned innovations in both rapid and early detection to assist in the selection of appropriate treatment strategies to improve the patients' quality of life. Plasmonic nanoparticles (PNPs), a versatile and promising class of nanomaterials, are widely utilized in numerous imaging techniques, drug delivery systems, and biomarker detection methods. Recently, PNP-based nanoprobes have attracted considerable attention for the early diagnosis of neurological disorders. Gold nanoparticles (AuNPs), with high local surface plasmon resonance (LSPR) signals, have been particularly well exploited as probes for dynamic biomarker detection, with quantification sensitivity demonstrated down to the single-molecule level. In this review, we will discuss the possibilities of PNPs in the methodological development for rapid neurological disease identification. In addition, we will also describe a new digital cytometry method that combines dark-field imaging and machine learning for precise biomarker enumeration on single cells. The aim of this review is to attract researchers working on the future development of new plasmonic nanoprobe-based strategies for the diagnosis of neurological disorders.

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Source
http://dx.doi.org/10.1002/cyto.a.24489DOI Listing

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