Single-Cell Raman Spectroscopy Enables High-Resolution Detection and Semiquantification of Intracellular Cyanophycin across Biological and Environmental Samples

Cyanophycin is a nitrogen- and carbon-rich reserve biopolymer conserved across diverse microbial taxa and ecological habitats, yet its in situ distribution and ecological role remain poorly understood due to limitations in existing detection methods. Here, we present a high-resolution, label-free, and extraction-free method for detecting and semiquantifying intracellular cyanophycin using single-cell Raman spectroscopy (SCRS) integrated with explainable machine learning. Genetically engineered cyanophycin-producing and nonproducing strains of Acinetobacter baylyi and Escherichia coli are used to provide robust positive and negative controls. Our explainable machine learning approach uncovered that Raman peaks at 898.0, 982.0, 1230.0, and 1674.0 cm–1 should be used to identify intracellular cyanophycin. A linear dose–response relationship (R2 = 0.9924) confirmed the semiquantitative capability of SCRS for intracellular cyanophycin detection. In a proof-of-concept study using full-scale enhanced biological phosphorus removal system biomass spiked with engineered cyanophycin-producing A. baylyi ADP1-ISx, SCRS successfully detected cyanophycin-positive cells within complex microbial matrices. These findings establish SCRS as a powerful tool for noninvasive monitoring of nitrogen polymer storage in environmental microbiomes at the single-cell level, offering new opportunities for understanding and managing microbial nitrogen cycling in engineered ecosystems.