Counting and Recognizing Single Bacterial Cells by a Lanthanide-Encoding Inductively Coupled Plasma Mass Spectrometric Approach.

Counting and recognizing single bacterial cells are crucial to the diagnosis of bacterium-induced disease and study of cell-to-cell variability as well as the related antibiotic resistance mechanism. A higher sensitive and selective method has always been desired for a more accurate single bacterial cell analysis. We report a lanthanide-encoding inductively coupled plasma (ICP) mass spectrometric approach for counting and recognizing single bacterial cells for the first time. When noncanonical alkyne-d-alanine ( aDA) was added to five typical bacterial strains of Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, Shigella dysenteriae, and Vibrio parahemolyticus, aDA was metabolically assembled into the peptidoglycan layer-supported bacterial cell wall followed by post-clickable europium-tagging with 1,4,7,10-tetraazacyclododecane-1,4,7-tris-acetic acid-10-azidopropyl ethylacetamide-europium complex ( azide-DOTA-Eu). Such Eu-tagged bacterial cells can be deemed as Eu-engineered particles, delivering more than 5 orders of magnitude self-signal-amplification outcome relative to the single bacterial cells themselves when 151/153Eu is determined by single particle ICP mass spectrometry ( spICPMS). This metabolic assembly of aDA mediated Eu-encoding signal amplification strategy breaks through the detection limit of spICPMS and ensures that we directly count a single bacterial cell. The individual bacterial strains we counted can be simultaneously recognized through their corresponding lanthanide (Ln)-coded polyclonal antibody (Ln = 139La, 141Pr, 142Nd, 152Sm, and 160Gd, respectively), serving as a specific bacterial identification (Ln- pAb-ID). Moreover, the developed approach was applied to show the different behavior between genetically identical Staphylococcus aureus under the treatments of vancomycin and Ag nanoparticles, demonstrating that such a lanthanide-encoding spICPMS approach provided a new way to discover still ambiguous cell-to-cell variability.