Embracing Poisson\nEncapsulation Statistics for Improved\nDroplet Digital Immunoassay

Digital immunoassays enable the detection of protein\nbiomarkers\nwith very low concentrations, but the analysis stringently requires\nsingle-bead encapsulation. Low bead density has been adopted to minimize\nmultiple-bead encapsulations, but the trade-off is the low droplet\neffectiveness (∼10%) in droplet-based assays. Here we report\nthe method of inclusive droplet digital ELISA (iddELISA) that embraces\nall types of encapsulations by factoring in their varied “on–off”\nprobabilities in the statistical inference. We derived the statistical\nmodel, optimized the bead encapsulation and immunoreaction, and developed\nan image analysis pipeline for accurate droplet and bead recognition,\nshowing that approximately 40% of the droplets could be used in the\nanalysis. Using the detection of SARS-CoV-2 nucleocapsid protein as\na demonstration, the iddELISA achieved a limit of detection of 0.71\nfg/mL, which was much lower than conventional ELISA as well as droplet\ndigital ELISA. By effectively incorporating multiple bead encapsulations,\nthe iddELISA simplified the digital immunoassay while improving the\ncounting efficiency and sensitivity, representing a unique concept\nin digital immunoassays.