TY - CONF
T1 - Metabolic changes in chemotherapeutically treated Hodgkin Lymphoma cells: complementary analytics by SERS and GC-MS
T2 - Data4OMICs Symposium 2023
AU - Zimmermann, Daniel
AU - Lilek, David
AU - Minarik , Alfred
AU - Herbinger, Birgit
AU - Prohaska, Katerina
PY - 2023/6
Y1 - 2023/6
N2 - Oncometabolites, substances which accumulate in cells due mutations in genes of Krebs cycle enzymes, have been recognized as cancer biomarkers. There is an essential need for simple, user independent hands–on sample preparation, robust measurement and fast data analysis. Raman spectroscopy with its fast, label-free and non-destructive detection of chemical fingerprints provide such a tool and possess the potential to investigate metabolic changes in single cells. Surface-enhanced Raman spectroscopy (SERS) further allows the mapping of metabolic changes at the level of organelles, and the discovery of novel biomarkers, to differentiate between healthy and degenerated cells at the different development stages. SERS findings regarding metabolic changes in chemotherapeutically treated Hodgkin Lymphoma cells need confirmation by complementary analysis. Untreated control and cells treated with the cytostatic Etoposide were analysed using GC-MS to identify specific metabolites with significantly different concentration between these two classes. Ideally, changes in the cellular Raman spectra could then be matched to these differences. In total, 69 unique compounds were identified, of which 37 were found in at least 50% of samples. Out of these 37 metabolites, 8 are significantly different (p = 0.05) in concentration between classes. Three of these significant metabolites – aspartic acid, valine and glutamic acid – are amino acids, which are all more prevalent in Etoposide treated samples. This could potentially correspond to the stronger protein signal found in the Raman spectra of treated cells. While GC-MS is able to resolve individual compounds, when using Raman spectroscopy, it is often only possible to identify groups of substances. In the case of SERS, affinity towards the substrate also determines which compounds are detected, with heteroatomic molecules often dominating the spectrum. It is therefore no coincidence that the most influential SERS signals belong to proteins and the nucleobase Adenine, both of which contain a large number of nitrogen atoms. On the other hand, Raman spectroscopy can provide a more complete picture of the cellular constituents, as it is not limited to low-molecular-weight metabolites and can detect larger biomolecules as well. Therefore, Raman spectroscopy/SERS and GC-MS should be considered complementary techniques, rather than being directly compared. Another future goal will be the establishment of metabolite databases for both Raman spectroscopy and GC-MS to make the assignment of metabolites to signals simpler and more robust.
AB - Oncometabolites, substances which accumulate in cells due mutations in genes of Krebs cycle enzymes, have been recognized as cancer biomarkers. There is an essential need for simple, user independent hands–on sample preparation, robust measurement and fast data analysis. Raman spectroscopy with its fast, label-free and non-destructive detection of chemical fingerprints provide such a tool and possess the potential to investigate metabolic changes in single cells. Surface-enhanced Raman spectroscopy (SERS) further allows the mapping of metabolic changes at the level of organelles, and the discovery of novel biomarkers, to differentiate between healthy and degenerated cells at the different development stages. SERS findings regarding metabolic changes in chemotherapeutically treated Hodgkin Lymphoma cells need confirmation by complementary analysis. Untreated control and cells treated with the cytostatic Etoposide were analysed using GC-MS to identify specific metabolites with significantly different concentration between these two classes. Ideally, changes in the cellular Raman spectra could then be matched to these differences. In total, 69 unique compounds were identified, of which 37 were found in at least 50% of samples. Out of these 37 metabolites, 8 are significantly different (p = 0.05) in concentration between classes. Three of these significant metabolites – aspartic acid, valine and glutamic acid – are amino acids, which are all more prevalent in Etoposide treated samples. This could potentially correspond to the stronger protein signal found in the Raman spectra of treated cells. While GC-MS is able to resolve individual compounds, when using Raman spectroscopy, it is often only possible to identify groups of substances. In the case of SERS, affinity towards the substrate also determines which compounds are detected, with heteroatomic molecules often dominating the spectrum. It is therefore no coincidence that the most influential SERS signals belong to proteins and the nucleobase Adenine, both of which contain a large number of nitrogen atoms. On the other hand, Raman spectroscopy can provide a more complete picture of the cellular constituents, as it is not limited to low-molecular-weight metabolites and can detect larger biomolecules as well. Therefore, Raman spectroscopy/SERS and GC-MS should be considered complementary techniques, rather than being directly compared. Another future goal will be the establishment of metabolite databases for both Raman spectroscopy and GC-MS to make the assignment of metabolites to signals simpler and more robust.
M3 - Poster
ER -