An imaging method for biological samples will help identify cancerous tissue

Scientists gathered around the international software project MZmine, led by Dr. Robin Schmid and Dr. Tomáš Pluskal from the Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, have come up with a new piece of software that significantly speeds up and simplifies the identification of chemicals in tissues. It enables researchers to confidently recognize chemicals and visualize their distribution in organs. Compared to established workflows, the new pipeline requires less work in the laboratory to gain similar insights into the chemistry happening within, for example, tumours and inflammatory lesions. The paper presenting this software to the global scientific community has just been published in the influential scientific journal Nature Communications.

At IOCB Prague, Dr. Robin Schmid is mostly involved in developing the software SIMSEF (spatial ion mobility-scheduled exhaustive fragmentation) together with his colleagues from the group of Prof. Dr. Karst at the University of Münster, Germany, where he was previously based. The work is done in collaboration with the developers of the timsTOF fleX state-of-the-art mass spectrometer from Bruker Daltonics, which enables high-level elucidation of the composition of molecules by measuring the mobility of ions.

‘Up until now, scientists could tell with a mass spectrometer that this was the formula of the molecule they were looking at, but then when they looked at the database and tried to identify this substance, it was very difficult. The reason is that biological samples can contain numerous distinct lipids and combinations of molecules that are often biologically very different. Now, thanks to the new algorithm, it is possible to look inside the molecule, learn what it is made up of and even compare images with each other,’ explains Robin Schmid. This can provide important information, for example that a specific part of the brain contains a different type of lipid than another part of this essential organ and that this particular lipid is absent in other tissue.

The SIMSEF algorithm was developed in collaboration with medical experts from German and Swiss universities. Crucially for medical doctors, the detection of clinical biomarkers for diagnostic purposes is also significantly accelerated. Learning very quickly, for example, whether they are dealing with a lipid or metabolite that is only found in cancerous tissue plays a critical role in deciding on further treatment. Needless to say, this has great implications for its outcome.

Read more here.

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