Raman spectroscopy is a scattering technique that relies on the inelastic scattering of photons. The excitation of molecular bonds results in the shifts of the energy of laser photons and thus frequency change of the incident light. Therefore, a compound can have a specific Raman spectrum, while a Single-cell Raman Spectrum (SCRS) can depict the overall profile of metabolites in the cell, i.e., the metabolic state of the cell at a particular instance.

We have proposed the "Ramanome" concept, which is the collection of the SCRS from a cellular population (or a consortium, for “Meta-ramanome”) at a given instance. Each SCRS can have over 1500 Raman peaks, while each peak or combination of peaks can potentially represent a metabolic phenotype, therefore a ramanome data point would capture the single-cell-resolution metabolic phenome of the system at a certain state. Notably, within a ramanome, even though the genomes of the cells are identical, the SCRS of the cells are distinct; such intercellular heterogeneity of SCRS is an inherent feature of cellular systems, and is critical for single-cell analysis and interpretion.

We together with collaborators have demonstrated that ramanome can be employed to quantitatively profile a wide variety of metabolic phenotypes for individual cells, such as quantifying the intake rate of hydrogen- and carbon-containing substrates, determining the diversity and content of various Raman-sensitive intracellular products (pigments, triglycerides, starch, proteins, etc.), characterizing the environmental stress responses of cells (e.g., antimicrobial susceptibility of pathogens, mechanisms of microbial drug response, drug resistance and its mechanisms for tumor cells, etc.), detecting intercellular metabolism interactions, reconstructing intracellular metabolite interconversion networks (Intra-Ramanome Correlation Analysis; IRCA), and distinguishing different microbial (or microalgal) species. The application of ramanome is rapidly expanding.

References:

1. Yuehui He#, Xixian Wang#, Bo Ma*, Jian Xu*. Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution. Biotechnology Advances, 2019, 37(6):107388.

2. Jian Xu*, Bo Ma, Xiaoquan Su, Shi Huang, Xin Xu, Xuedong Zhou, Wei Huang, Rob Knight*. Emerging trends for microbiome analysis: from single-cell functional imaging to microbiome big data. Engineering, 2017, 3(1):66-70