A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy. Issue 3 (12th September 2018)

• Record Type: Journal Article
• Authors: Xu, Jiabao; Potter, Michelle; Tomas, Cara; Elson, Joanna L.; Morten, Karl J.; Poulton, Joanna; Wang, Ning; Jin, Hanqing; Hou, Zhaoxu; Huang, Wei E.
• Is Part Of: Analyst. Volume 144:Issue 3(2019)
• Page Start: 913
• Page End: 920
• Publication Date: 2018-09-12
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c8an01437j
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Single-cell Raman microspectroscopy to detect phenylalanine as a potential biomarker for mitochondrial dysfunction and chronic fatigue syndrome. Abstract : Chronic fatigue syndrome (CFS), also called myalgic encephalomyelitis (ME), is a debilitating disorder characterized by physical and mental exhaustion. Mitochondrial and energetic dysfunction has been investigated in CFS patients due to a hallmark relationship with fatigue; however, no consistent conclusion has yet been achieved. Single-cell Raman spectra (SCRS) are label-free biochemical profiles, indicating phenotypic fingerprints of single cells. In this study, we applied a new approach using single-cell Raman microspectroscopy (SCRM) to examine ρ 0 cells that lack mitochondrial DNA (mtDNA), and peripheral blood mononuclear cells (PBMCs) from CFS patients and healthy controls. The experimental results show that Raman bands associated with phenylalanine in ρ 0 cells and CFS patient PBMCs were significantly higher than those of the wild-type model and healthy controls. As similar changes were observed in the ρ 0 cell model with a known deficiency in the mitochondrial respiratory chain as well as in CFS patients, our results suggest that the increase in cellular phenylalanine may be related to mitochondrial/energetic dysfunction in both systems. Interestingly, phenylalanine can be used as a potential biomarker for the diagnosis of CFS by SCRM. A machine learning classification model achieved an accuracy rate of 98% correctly assigning Raman spectra to either the CFS group or the control group. SCRM combined with a machine learning algorithm therefore has the potential to become a diagnostic tool for CFS.