Theoretical Framework for Absolute Quantification in Digital Immunoassays and Advancements in Microfluidic-Free Droplet-Based Digital Immunoassay Methodologies
Abstract
Current single-molecule immunoassays primarily rely on Poisson distribution models, which have limitations in achieving absolute quantification of target proteins. To address this, we developed a novel mathematical model that treats the capture of target proteins by magnetic beads as a discrete Poisson process, while the subsequent washing and secondary sampling steps are modeled using a hypergeometric distribution. Based on this combined distribution model, we constructed a new theoretical framework for digital immunoassays and proposed a calibration‑curve‑free absolute quantification method based on micro‑volume calculation. To validate the feasibility of this model, we established a digital immunoassay platform using a water‑in‑oil magnetic bead system without the need for complex microfluidic devices. This approach not only eliminates reliance on traditional calibration curves but also demonstrates enhanced feasibility and a broader dynamic range compared to existing methods such as Single Molecule Immunoassays and Electrochemiluminescence Immunoassays. Additionally, it allows for the determination of the binding efficiency and accurate blank coefficients of the detection system. Furthermore, the potential application of an internal calibration method within this system is explored. The proposed theoretical framework offers a new strategy to address the challenge of absolute quantification in protein immunoassays, provides important insights for advancing protein quantification methodologies, and supports the development of more precise detection techniques. It is expected to expand the application of digital immunoassays in biomedical research and clinical diagnostics.
Fields of Study & Subjects
Innovative Microfluidic and Catalytic Techniques Innovation
Electrowetting and Microfluidic Technologies
Advanced Biosensing Techniques and Applications
Publication details
- Journal / source
- ChemRxiv
- Publication date
- 5 January 2026
- Document type
- Preprint