Metabolite Conversion: Research Unveils New Imaging Technique for Useful Diagnostic Information, Effective Medical Applications

A team of researchers from the Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences has recently developed an imaging technique that uncovers metabolite transformation from individual cells.

Phys.org reported that specifically, the researchers had developed a fast, cost-oriented, and high-throughput approach capable of profiling active metabolic features from only a single isogenic cell sample.

Any population of the inherently matching cells could similarly present a great many different phenotypes. Such phenotypes can be described as "much more granular level by metabolites."

Discovering connections between metabolism-related phenotypes is exceptionally useful. For instance, the finding of a connection between an abundance of a specific metabolite type and a specific disease potentially provides a piece of very helpful information for diagnostics, as well as other medical applications.

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The 'Raman Microspectrometry' Technique

Employment of high-resolution mass spectrometry research across a "metabolome," or all metabolites' set, of a great many datasets, has been employed to determine these very metabolites characterizing a particular illness.

Nevertheless, such studies' strength is dependent, in general, on various samples, with every sample that contains a huge number of cells.

Specifically, a research team has developed a method that can quantitatively profile different metabolism-related phenotypes from only one snapshot of a single test tube that treats every cell as a single, distinctive sample.

The study authors used single-cell Raman micro spectrometry, as described in CRAIC Technologies, which exploits how light interacts with the chemical bonds in a molecule to allow identification of a cell's metabolic profile quickly minus changing or destroying it.

Meanwhile, laser light interacts with metabolite molecules, driving the laser photons' energy up or down. Furthermore, a so-called "landscape" of the thousands of peaks and valleys of photons bumped up or down arises that is particular metabolite molecules characteristic synthesized by the cell and, therefore, of its metabolic phenotype.

Predicting Metabolic Inter-Conversion

According to Professor Xu Jian of Single-Cell Center at QIBEBT, just like how a portrait can unveil a human individual's facial feature, Single-cell Raman Spectra or SCRS can reveal cellular phenotypes "in a landscape-like manner."

Jian, the corresponding author of the research, said, SCRS simultaneously is also revealing several metabolism-associated phenotypes of a cell in a specific state.

In a similar report, Florida News Times specified, the study authors call this a set of all SCRS or ramanome randomly sampled from a population of cells that are genetically identical, a metabolic snapshot of such a population at the single-cell resolution.

After that, by taking advantage of the inherent, generally present difference of metabolic activities among these distinctive cells, the study investigators proposed and presented the ability to unravel various between-phenotype associations, essentially forecasting a network of metabolite inter-conversion, from just dozens of cells from a single tube to isogenic cells.

This investigative framework, the researchers said, is called Intra-Ramanome Correlation Analysis or IRCA.

The IRCA Investigative Framework

Single-Cell Center's Dr. He Yuehui, the study's first author spearheading IRCA algorithm development said, one advantage of IRCA is that, rather than the conventional idea of treating each bottle or colony of cells as a single sample, now every cell turns into a single independent sample and this creates various incredibly exciting opportunities.

The researchers have since used IRCA through the ramanomes of different species of microbes, microalgae, and fungi which have high throughput and at low cost, presenting the common or general value of IRCA to the overabundance of cell types in nature.

Having proven the theoretical background underlying IRCA, the study authors are now hoping to see the approach unleashing a host of new data-driven scientific undertakings that unveil the concealed dynamic characteristics of cellular metabolism.

The study, Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells, is published in mBIO.

Related information about Raman micro spectrometry is shown on The MCT's YouTube video below: