Affinity-based recovery of metabolically active microbial cells using BioOrthogonal Non-Canonical amino acid tagging

Selective identification of translationally active cells remains a key challenge in linking microbial function to population dynamics. Bioorthogonal non-canonical amino acid tagging (BONCAT) enables pulse-labelling of newly synthesized proteins in live cells, providing a time-resolved of translational activity. While BONCAT is typically combined with fluorescent tagging and fluorescence-activated cell sorting (FACS), the reliance on high-end instrumentation and transparent matrices limits its use. Here, we present a fully benchtop workflow by coupling BONCAT with magnetic affinity-based cell separation (BONCAT–ABCS) to enable enrichment of translationally active Escherichia coli subpopulations from a mixed culture without specialised instrumentation. Using a diauxic growth model with glucose and lactose as carbon source and two near-isogenic strains (MV1300 (Lac-) and MV1717 (Lac+)), we quantified BONCAT-labelled and unlabelled fractions across five timepoints using AHA pulse labelling, click chemistry, and absolute qPCR targeting strain-specific chromosomal markers. BONCAT-labelled fractions exhibited significantly higher gene copy numbers and enrichment factors (up to 6.9-fold; p < 0.001, ANOVA with Tukey’s HSD) compared to non-labelled controls, with recovery efficiencies ranging from 12 to 21% and background capture < 4%. BONCAT-ABCS resolved physiological divergence during the diauxic shift, selectively enriching Lac+ cells during lactose metabolism while Lac- cells remained translationally inactive. Importantly, enrichment reflected activity-weighted population shifts rather than rare-cell amplification, highlighting the suitability of BONCAT–ABCS for bulk metabolic profiling. These results support BONCAT–ABCS as an accessible affinity-based enrichment strategy for quantifying translationally active subpopulations and highlight its potential application in bulk metabolic profiling, engineered microbial systems, microbiome analyses, and bioprocess monitoring.