Binary spatial-spectral encoding and decoding for ultra-multiplexed digital PCR: Breaking the fluorescence channel...

Digital PCR (dPCR) is widely used for absolute nucleic acid quantification because of its high sensitivity and precision. Its major implementations include droplet-based digital PCR (ddPCR), which generates stochastic droplet partitions, and chip-based digital PCR (cdPCR), which uses fixed-position microchamber arrays. Despite their different partition architectures, both formats remain fundamentally limited in multiplexing capacity by the number of available fluorescence channels (N). Existing strategies, such as amplitude modulation and probe-ratio encoding, partially increase capacity, but their reliance on analog fluorescence stratification makes them increasingly vulnerable to signal variation, spectral crosstalk, and cluster overlap as the target number increases. Here, we propose a binary spatial-spectral encoding and decoding framework for ultra-multiplexed dPCR. The strategy constructs target identities from the binary ON/OFF fluorescence states of the same spatially indexed partition across channels, expanding the theoretical detection capacity from N to 2^N-1. The key requirement is reliable preservation of partition identity across multi-channel readout. In this study, cdPCR was used as the first implementation platform because its fixed-position architecture naturally supports chamber-resolved cross-channel correspondence. Using this framework, we achieved simultaneous detection of nine respiratory pathogens with four fluorescence channels while maintaining accurate quantification across four orders of magnitude (R² = 0.9998). These results establish binary spatial-spectral decoding as a scalable strategy for multiplex dPCR, with potential extension to ddPCR through reliable cross-channel droplet tracking or re-identification.