• Emma Callery

Student thesis: Unknown


Common variable immunodeficiency (CVID) is the most prevalent symptomatic primary immunodeficiency diagnosed in adults. CVID is considered to be a spectrum of different antibody deficiency disorders, with only 10-20% of cases attributed to monogenic mutations. Clinical features vary between patients. The majority have increased susceptibility to infection; however non-infectious complications (autoimmunity, inflammation and malignancy) have the greatest influence on morbidity and mortality. The laboratory features of low serum immunoglobulin G, A, and/or M, alongside poor vaccine responses contribute to the diagnosis of CVID; however the low specificity of these tests renders diagnosis one of exclusion. Diagnostic delay remains a significant problem worldwide. Immunophenotyping can be used to subgroup patients based on shared clinical features; however predictive biomarkers to identify patients at risk of severe disease have yet to be identified. In light of the limitations associated with current laboratory tests for CVID, a new diagnostic approach is needed. Vibrational spectroscopy is a powerful technique that can be used to characterise the molecular composition of a sample. Within a biological sample, important molecules such as lipids, carbohydrates, nucleic acids and proteins are held together by chemical bonds; these bonds will vibrate following excitation with infrared light. By measuring the vibrational energy of each molecule present in a sample, a unique spectrum, known as the ‘molecular fingerprint’ is generated. Fourier Transform infrared (FTIR) spectroscopy is a mode of vibrational spectroscopy gaining wider application in the clinical setting over the past decade. In this method, bond vibrations cause a change in the dipole moment of molecules; these vibrations can be quantified as spectral peaks. As disease-related changes in biological samples will be reflected in the molecular fingerprint, FTIR spectroscopy is a well-placed candidate for the investigation of disease. The experimental approach in this thesis uses FTIR spectroscopy to characterise the molecular fingerprint of blood serum and plasma samples of CVID patients. We examined two biologically relevant regions of the spectrum, Fingerprint (1800-900 cm-1) and High (3700-2800 cm-1). We determined the Fingerprint region to have superior performance for potential use as a diagnostic technique. Following the application of machine-learning algorithms, we successfully classified CVID patients from Healthy controls with sensitivities and specificities of 97% and 93%, respectively, for serum; and 94% and 95%, respectively, for plasma. Key spectral features capable of discriminating CVID patients from Healthy controls were identified for both serum and plasma samples; with a greater number of biomarkers associated with the Fingerprint region of the spectrum. Wavenumbers in regions indicative of nucleic acids (984 cm-1, 1053 cm-1, 1084 cm-1, 1115 cm-1, 1528 cm-1, 1639 cm-1), and a collagen-associated biomarker (1034 cm-1) were found to have statistically significant (p
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorPeter Arkwright (Supervisor)


  • Clinical translation
  • Biomarker
  • Diagnostics
  • Immunology
  • Machine Learning
  • FTIR
  • Primary immunodeficiency disorders
  • Vibrational Spectroscopy
  • Molecular Fingerprint
  • Common Variable Immune deficiency

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