Precision culturomics enabled by unlabeled single-cell morphology and Raman spectra.

Abstract

Selective enrichment of target bacteria from complex communities, such as the human microbiome, has remained a challenge. Here, we report precision single-cell culturomics based on label-free morphology, Raman spectrometry, and Laser-Induced Forward Transfer (LIFT) technology. This approach operates at the level of single microbial cells, many generations before these cells form visible colonies. We develop a machine learning-based framework that achieves species-level identification of single cells in complex microbiome and achieve selective culturing for or against specific bacteria in fecal or vaginal samples, and quantify some of the cellular components based on Raman spectra. Genomic analysis of single-cell cultures reveals that short-term antibiotic use promotes both pre-existing resistance and de novo mutations of gut commensals, alongside convergent evolution across species. Our precision culturomics method provides a powerful tool for morphological, metabolic, and genomic analysis of microbial phenotypic variations at the single-cell level in microbiome studies.