Presenting metabolomics analyses: What’s in a number?

How the numbers behind tiny molecules reveal big insights into health, disease, and treatment

• Department of Cancer Research
• Cancer Metabolism Group

Metabolomics is the comprehensive study of small molecules within cells, tissues, and organisms, providing a detailed snapshot of metabolic processes. Quantitative analysis of this metabolic space allows scientists to decipher complex biochemical pathways, offering crucial information for understanding disease mechanisms and advancing therapeutic development.

In a recently published commentary in The EMBO Journal, Dr Johannes Meiser, Director of the Department of Cancer Research and Dr Christian Frezza from the University of Cologne discuss the challenges and common pitfalls of analyzing and presenting metabolomics data. Metabolomics measures molecules called metabolites that offer valuable insights into how cells function in both health and disease.

In their commentary, Dr Meiser and Dr Frezza caution that interpreting metabolomics data is not always straightforward. One of the main issues they discuss is the difference between relative and absolute quantification. Relative quantification compares metabolite levels between conditions (like healthy and diseased samples), while absolute quantification measures the exact amount of a metabolite. Misunderstanding these methods can lead to misleading conclusions.

They also explain how stable isotopes—safe, non-radioactive forms of elements—can be used to track metabolic pathways. This technique helps scientists understand how nutrients are processed in the body. Still, the authors emphasize the importance of careful planning to avoid errors when interpreting such data.

Numbers in metabolomics are not just data points – they are insights into the biochemical state of cells. Properly interpreting them allows us to understand how diseases develop and to move closer to effective therapies

explains Dr Meiser.

Via their commentary, Dr Meiser and Dr Frezza aim to guide scientists in accurately analyzing metabolomics data, which they feel will lead to better understanding of diseases like cancer and other metabolic disorders.