Rethinking massive multiplexing in whispering gallery mode biosensing

TL;DR

This paper presents a novel WGM biosensing platform with over 10,000 microresonators and a deep learning framework that enables accurate, scalable, and calibration-free multiplexed detection of biological analytes in complex media.

Contribution

It introduces a massively multiplexed WGM biosensing platform combined with a hybrid deep learning approach for robust, calibration-free analyte identification and quantification across diverse sensor chips.

Findings

Achieved 99.3% accuracy in solution identification.

Demonstrated 10^-4 relative prediction error in quantification.

Supported over 200 hours of sensing data from nine different chips.

Abstract

Accurate, label-free quantification of multiple analytes in complex biological media remains a major challenge due to limited multiplexing, signal cross-correlations, and inconsistency across sensor samples and measurement runs. We introduce a multiplexed whispering-gallery-mode (WGM) biosensing framework that overcomes these barriers by jointly advancing photonic integration and data analytics. Our glass-chip platform enables massive, parallelized and flexible multiplexing of >10000 microresonators organized into up to 100 sensing channels, with universal and modular chip design and detection hardware, while maintaining loaded Q-factors of 10^6. Our novel hybrid deep-learning framework BioCCF that integrates domain adaptation with cross-channel fusion enables harmonization of responses across sensing chips and extraction of nonlinear correlations in complex mixtures. Using a highly heterogeneous dataset comprising over 200 hours of sensing data acquired from nine chips with different channel configurations, biological replicates, and repeated regeneration cycles, we demonstrate recalibration-free identification of solution (99.3\% accuracy) and quantification of immunoglobulin G components with relative prediction error of 10^-4 under 5 min. The affordability and modularity of the platform enable distributed data acquisition and aggregation into shared repositories, providing a pathway toward continuously improving model generalization, cross-validation and a scalable, community-driven paradigm for biosensing.