Digital Immunoassay for Biomarker Detection Based on Single-Particle Laser Ablation ICP MS.

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Nanotechnology

Background:

• Single-particle immunoassays offer superior sensitivity for detecting low-abundance biomarkers compared to traditional methods.
• Current digital detection primarily relies on counting individual labels, with limitations in precision and noise reduction.

Purpose of the Study:

• To develop a novel dot-blot particle-linked immunosorbent assay (PLISA) with a digital readout.
• To utilize laser ablation (LA) and single-particle inductively coupled plasma mass spectrometry (SP ICP MS) for precise counting of upconversion nanoparticle (UCNP) labels.
• To demonstrate the assay's capability in detecting low-abundance biomarkers, such as prostate-specific antigen (PSA).

Main Methods:

• A novel PLISA was developed utilizing UCNP labels on a nitrocellulose substrate.
• Laser ablation was employed for the desorption of intact UCNP labels.
• Single-particle ICP MS was used for precise counting of desorbed UCNPs, enabling digital signal processing.
• The method was optimized using human serum albumin and validated for PSA detection in clinical serum samples.

Main Results:

• Optimized LA SP ICP MS readout achieved limits of detection (LODs) of 0.12 ng/mL for human serum albumin.
• PLISA for prostate-specific antigen demonstrated ultra-low LODs of 0.3 pg/mL using the digital LA SP ICP MS readout.
• The digital readout significantly outperformed reference upconversion luminescence and analog LA ICP MS methods.
• Clinical sample analysis showed strong correlation with a reference method, confirming diagnostic potential.

Conclusions:

• The developed LA SP ICP MS-based PLISA offers a highly sensitive digital immunoassay for low-abundance biomarker detection.
• This particle-counting approach significantly enhances detection capabilities, surpassing conventional methods.
• The technology shows strong potential for accurate clinical diagnostics, particularly in cancer biomarker analysis.