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
