Shows improved single-bead encapsulation in microfluidic systems, highlighting advancements in protein quantification.
Droplet digital enzyme-linked immunosorbent assay (ddELISA) enables ultrasensitive protein quantification; however, its performance is often limited by low single-bead encapsulation efficiency due to Poisson statistics and significant background interference from empty droplets. We present an integrated microfluidic system that overcomes both limitations through synergistic magneto-inertial manipulation (SMIM). By coupling an external magnetic field with laminar inertial focusing, the system applies coordinated hydrodynamic lift and magnetophoretic forces on immunomagnetic beads, thereby enabling three interdependent functions within a continuous workflow: ordered single-file bead focusing, high-throughput single-bead encapsulation, and active sorting of bead-containing droplets. Under optimized conditions (30 μL min –1; 300 mT), magnetic beads are focused into a stable single-file train with a lateral deviation of only 0.95 ± 1.21 μm. This deterministic ordering prior to droplet generation fundamentally overcomes the Poisson limit, achieving single-bead encapsulation efficiency of 81.31%─a 2.21-fold improvement over stochastic loading─and a total encapsulation efficiency of 91.11%. The integrated magnetic sorting module generates localized high-gradient fields, capturing bead-containing droplets with 93.97 ± 1.18% efficiency while eliminating >91% of empty droplets. The platform’s quantitative capability was validated using a dual-color fluorescent bead assay ( R 2 = 0.9977; 0.50 – 1.75 × 10 7 beads mL –1 ) and an E. coli O157 ddELISA ( R 2 = 0.9999; LOD = 18.23 cells mL –1 ). This work establishes a fully integrated microfluidic platform that addresses the long-standing challenges of encapsulation inefficiency and empty-droplet interference in ddELISA by transforming bead encapsulation from a stochastic to a deterministic process, thereby providing a broadly applicable framework for high-throughput, single-particle-resolved digital bioanalysis.
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Chen et al. (2026) studied this question.
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