Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells
Abstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self‐report, questionnaires, and subjective measures to receive a diagnos...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202302146 |
| _version_ | 1797648688087040000 |
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| author | Jiabao Xu Tiffany Lodge Caroline Kingdon James W. L. Strong John Maclennan Eliana Lacerda Slawomir Kujawski Pawel Zalewski Wei E. Huang Karl J. Morten |
| author_facet | Jiabao Xu Tiffany Lodge Caroline Kingdon James W. L. Strong John Maclennan Eliana Lacerda Slawomir Kujawski Pawel Zalewski Wei E. Huang Karl J. Morten |
| author_sort | Jiabao Xu |
| collection | DOAJ |
| description | Abstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self‐report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single‐cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post‐treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS. |
| first_indexed | 2024-03-11T15:35:33Z |
| format | Article |
| id | doaj.art-e64e3d5fa7774b50b117e525bc11f7a5 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-11T15:35:33Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-e64e3d5fa7774b50b117e525bc11f7a52023-10-26T20:10:11ZengWileyAdvanced Science2198-38442023-10-011030n/an/a10.1002/advs.202302146Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear CellsJiabao Xu0Tiffany Lodge1Caroline Kingdon2James W. L. Strong3John Maclennan4Eliana Lacerda5Slawomir Kujawski6Pawel Zalewski7Wei E. Huang8Karl J. Morten9Department of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ UKNuffield Department of Women's and Reproductive Health University of Oxford The Women Centre John Radcliffe Hospital Headley Way, Headington Oxford OX3 9DU UKFaculty of Infectious Diseases London School of Hygiene and Tropical Medicine Keppel St London WC1E 7HT UKNuffield Department of Women's and Reproductive Health University of Oxford The Women Centre John Radcliffe Hospital Headley Way, Headington Oxford OX3 9DU UKSoft Cell Biological Research Attwood Innovation Center 453 S 600 E St. George UT 84770 USAFaculty of Infectious Diseases London School of Hygiene and Tropical Medicine Keppel St London WC1E 7HT UKDepartment of Exercise Physiology and Functional Anatomy Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Torun Swietojanska 20 Bydgoszcz 85‐077 PolandDepartment of Exercise Physiology and Functional Anatomy Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Torun Swietojanska 20 Bydgoszcz 85‐077 PolandDepartment of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ UKNuffield Department of Women's and Reproductive Health University of Oxford The Women Centre John Radcliffe Hospital Headley Way, Headington Oxford OX3 9DU UKAbstract Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self‐report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single‐cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post‐treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.https://doi.org/10.1002/advs.202302146machine learningmitochondriamultiple sclerosismyalgic encephalomyelitis/chronic fatigue syndromeperipheral blood mononuclear cellsRaman microspectroscopy |
| spellingShingle | Jiabao Xu Tiffany Lodge Caroline Kingdon James W. L. Strong John Maclennan Eliana Lacerda Slawomir Kujawski Pawel Zalewski Wei E. Huang Karl J. Morten Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells Advanced Science machine learning mitochondria multiple sclerosis myalgic encephalomyelitis/chronic fatigue syndrome peripheral blood mononuclear cells Raman microspectroscopy |
| title | Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells |
| title_full | Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells |
| title_fullStr | Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells |
| title_full_unstemmed | Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells |
| title_short | Developing a Blood Cell‐Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells |
| title_sort | developing a blood cell based diagnostic test for myalgic encephalomyelitis chronic fatigue syndrome using peripheral blood mononuclear cells |
| topic | machine learning mitochondria multiple sclerosis myalgic encephalomyelitis/chronic fatigue syndrome peripheral blood mononuclear cells Raman microspectroscopy |
| url | https://doi.org/10.1002/advs.202302146 |
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