Spectral fingerprint diagnosis: Spatially independent analysis of biomarker patterns in homogeneous systems

High-throughput biomarker analysis traditionally relies on spatial distribution features, either naturally occurring or artificially engineered. Achieving multiplex detection in a homogeneous system without spatial distribution remains a challenge. Fluorescence-based polymerase chain reaction (PCR) exemplifies a spatially independent technology for targeted multiplex detection, but it is limited by spectral overlap. Here, we proposed spectral fingerprint PCR (sf-PCR), which leverages three-dimensional fluorescence spectral fingerprints to profile biomarker expression patterns. These fingerprints capture both the position and intensity of fluorescence peaks, increasing information density and offering a breakthrough in spectral overlap. In addition, sf-PCR exhibits linear superimposability and decodability, providing a solid foundation for data interpretability. Using a 10-plex sf-PCR model, we demonstrated sf-PCR's capacity to fundamentally overcome spectral overlap limitations. Furthermore, sf-PCR has demonstrated clinical potential in cancer diagnosis and respiratory pathogen detection. This work underscores the potential of spectral fingerprints to enhance information density and fundamentally resolve challenges in homogeneous system analysis.