Rethinking massive multiplexing in whispering gallery mode biosensing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917144948637696 |
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| author | Saetchnikov, Ivan Tcherniavskaia, Elina Ostendorf, Andreas Saetchnikov, Anton |
| author_facet | Saetchnikov, Ivan Tcherniavskaia, Elina Ostendorf, Andreas Saetchnikov, Anton |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_12421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rethinking massive multiplexing in whispering gallery mode biosensing Saetchnikov, Ivan Tcherniavskaia, Elina Ostendorf, Andreas Saetchnikov, Anton Optics Biological Physics Chemical Physics Data Analysis, Statistics and Probability Instrumentation and Detectors 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. |
| title | Rethinking massive multiplexing in whispering gallery mode biosensing |
| topic | Optics Biological Physics Chemical Physics Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2512.12421 |