Issue 3, 2025

Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)

Abstract

Ergothioneine (ERG) is a natural sulfur-containing amino acid found in many organisms, including humans. It accumulates at high concentrations in red blood cells and is distributed to various organs, including the brain. ERG has numerous health benefits and antioxidant capabilities, and it has been linked to various human physiological processes, such as anti-inflammatory, neuroprotective, and anti-aging effects. Accurate, rapid, and cost-effective quantification of ERG levels in human biofluids is crucial for understanding its role in oxidative stress-related diseases. Surface-enhanced Raman scattering (SERS) is an effective approach for measuring compounds at concentrations similar to those at which ERG is present in serum. However, while SERS has been used to characterize or detect ERG, quantification has not yet been achieved due to the variability in the signal enhancement that can arise during sample preparation and analysis. This study introduces a highly efficient and reliable technique for quickly (20 min is typical per sample) measuring ERG levels in human serum using SERS. This employs an internal standard highly specific for ERG which resulted in limit of quantification values of 0.71 μM. To validate this approach, we analysed real human serum with unknown ERG levels as a blind test set and primary reference levels of ERG were produced using a targeted UHPLC-MS/MS reference method.

Graphical abstract: Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)

Supplementary files

Article information

Article type
Paper
Submitted
11 Oct 2024
Accepted
27 Dec 2024
First published
30 Dec 2024
This article is Open Access
Creative Commons BY license

Analyst, 2025,150, 559-566

Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)

S. Fornasaro, N. Gotts, G. Venturotti, M. W. Muelas, I. Roberts, V. Sergo, R. Goodacre and A. Bonifacio, Analyst, 2025, 150, 559 DOI: 10.1039/D4AN01323A

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