Chinese scholar mBio issued a paper: New Ramanome technology reveals the transformation network of single-cell metabolites

Researchers at the Single Cell Center of Qingdao Institute of Bioenergy and Process Technology, Chinese Academy of Sciences have developed a fast, low-cost and high-throughput technology that can analyze dynamic metabolic characteristics from only a homogenous cell sample

The research was published on mBio on August 31

Any group of genetically identical cells can also show many different phenotypes

It is very useful to find correlations between metabolic-related phenotypes

High-resolution mass spectrometry studies of all metabolites through "metabolites" or large data sets have been used to identify these metabolites that characterize a certain disease

However, the strength of this type of research generally depends on many samples, each containing a large number of cells

Researchers use single-cell Raman microspectroscopy to use how light interacts with chemical bonds in molecules to quickly identify the metabolic profile of cells without changing or damaging the cells

The corresponding author of the study, Professor Xu Jian of the QIBEBT Single Cell Center, said: “Just like a portrait can reveal a person’s multiple facial features, single-cell Raman spectroscopy (SCRS) can be used in a landscape-like manner.

Researchers call it "ramanome", which is a collection of all SCRS randomly selected from a genetically identical cell population.

Using the inherent, ubiquitous changes in the metabolic activity of these individual cells, the researchers proposed and demonstrated its ability to unravel the numerous between-phenotype links, basically predicting the metabolite inter-conversion network, from dozens of cells from one tube.

"The beauty of an IRCA, instead of the traditional concept of taking each bottle or group of cells as a "sample", now each cell is an independent "sample", which creates many incredibly exciting opportunities.

Since then, the research team has applied IRCA to the ramomes of many different types of bacteria, microalgae and fungi, with high throughput and low cost, proving the universal value of IRCA to various types of cells in nature

After proving the theoretical framework of IRCA, researchers now hope to see this technology release a large number of new data-driven scientific efforts to reveal the hidden dynamics of cell metabolism