Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing.

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Automated Computational Detection of Disease Activity in ANCA-Associated Glomerulonephritis Using Raman Spectroscopy: A Pilot Study.

Biospectroscopy offers the ability to simultaneously identify key biochemical changes in tissue associated with a given pathological state to facilitate biomarker extraction and automated detection of key lesions. Herein, we evaluated the application of machine learning in conjunction with Raman spectroscopy as an innovative low-cost technique for the automated computational detection of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (AAGN). Consecutive patients with active AAGN and those in disease remission were recruited from a single UK centre. In those with active disease, renal biopsy samples were collected together with a paired urine sample. Urine samples were collected immediately prior to biopsy. Amongst those in remission at the time of recruitment, archived renal tissue samples representative of biopsies taken during an active disease period were obtained. In total, twenty-eight tissue samples were included in the analysis. Following supervised classification according to recorded histological data, spectral data from unstained tissue samples were able to discriminate disease activity with a high degree of accuracy on blind predictive modelling: F-score 95% for >25% interstitial fibrosis and tubular atrophy (sensitivity 100%, specificity 90%, area under ROC 0.98), 100% for necrotising glomerular lesions (sensitivity 100%, specificity 100%, area under ROC 1) and 100% for interstitial infiltrate (sensitivity 100%, specificity 100%, area under ROC 0.97). Corresponding spectrochemical changes in paired urine samples were limited. Future larger study is required, inclusive of assigned variables according to novel non-invasive biomarkers as well as the application of forward feature extraction algorithms to predict clinical outcomes based on spectral features.

Distinguishing active from quiescent disease in ANCA-associated vasculitis using attenuated total reflection Fourier-transform infrared spectroscopy.

The current lack of a reliable biomarker of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA) associated vasculitis poses a significant clinical unmet need when determining relapsing or persisting disease. In this study, we demonstrate for the first time that attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy offers a novel and functional candidate biomarker, distinguishing active from quiescent disease with a high degree of accuracy. Paired blood and urine samples were collected within a single UK centre from patients with active disease, disease remission, disease controls and healthy controls. Three key biofluids were evaluated; plasma, serum and urine, with subsequent chemometric analysis and blind predictive model validation. Spectrochemical interrogation proved plasma to be the most conducive biofluid, with excellent separation between the two categories on PC2 direction (AUC 0.901) and 100% sensitivity (F-score 92.3%) for disease remission and 85.7% specificity (F-score 92.3%) for active disease on blind predictive modelling. This was independent of organ system involvement and current ANCA status, with similar findings observed on comparative analysis following successful remission-induction therapy (AUC > 0.9, 100% sensitivity for disease remission, F-score 75%). This promising technique is clinically translatable and warrants future larger study with longitudinal data, potentially aiding earlier intervention and individualisation of treatment.

Biomarkers in ANCA-Associated Vasculitis: Potential Pitfalls and Future Prospects.

Over the past 3 decades, significant advancements in the understanding of the pathophysiology of ANCA-associated vasculitis has led to the development of a multitude of potential candidate biomarkers. Accompanied by the advent of increasingly effective therapeutic strategies, the need for a dependable biomarker to help determine the extent of disease activity and risk of relapse is ever present. Implementation of such a biomarker would enable tailored therapy, optimizing disease control while helping to mitigate unnecessary exposure to therapy and potential treatment-related damage. Although far from perfect, ANCA serology and B-cell population are the two main staple biomarker tools widely used in practice to help supplement clinical assessment. Over recent years, the application and progress of more novel biomarker tools have arisen in both organ-limited and multisystem disease, including genomics, urinary proteins, degradation products of the alternative complement system, cytokines, metabolomics, and biospectroscopy. Validation studies and clinical translation of these tools are required, with serial assessment of disease activity and determination of therapy according to biomarker status correlated with patient outcomes.

New approach to investigate Common Variable Immunodeficiency patients using spectrochemical analysis of blood.

Common variable immune deficiency (CVID) is a primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and various complications. The clinical heterogeneity of CVID has hindered identification of an underlying immune defect; diagnosis relies on clinical judgement, alongside evidence-based criteria. The lack of pathognomonic clinical or laboratory features leads to average diagnostic delays of 5 years or more from the onset. Vibrational spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy have recently gained increasing clinical importance, being rapid-, non-invasive and inexpensive methods to obtain information on the content of biological samples. This has led us to apply FTIR spectroscopy to the investigation of blood samples from a cohort of CVID patients; revealing spectral features capable of stratifying CVID patients from healthy controls with sensitivities and specificities of 97% and 93%, respectively for serum, and 94% and 95%, respectively for plasma. Furthermore we identified several discriminating spectral biomarkers; 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 (1528 cm-1), which may represent future candidate biomarkers and provide new knowledge on the aetiology of CVID. This proof-of-concept study provides a basis for developing a novel diagnostic tool for CVID.

Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma.

Robust diagnosis of ovarian cancer is crucial to improve patient outcomes. The lack of a single and accurate diagnostic approach necessitates the advent of novel methods in the field. In the present study, two spectroscopic techniques, Raman and surface-enhanced Raman spectroscopy (SERS) using silver nanoparticles, have been employed to identify signatures linked to cancer in blood. Blood plasma samples were collected from 27 patients with ovarian cancer and 28 with benign gynecological conditions, the majority of which had a prolapse. Early ovarian cancer cases were also included in the cohort (n = 17). The derived information was processed to account for differences between cancerous and healthy individuals and a support vector machine (SVM) algorithm was applied for classification. A subgroup analysis using CA-125 levels was also conducted to rule out that the observed segregation was due to CA-125 differences between patients and controls. Both techniques provided satisfactory diagnostic accuracy for the detection of ovarian cancer, with spontaneous Raman achieving 94% sensitivity and 96% specificity and SERS 87% sensitivity and 89% specificity. For early ovarian cancer, Raman achieved sensitivity and specificity of 93% and 97%, respectively, while SERS had 80% sensitivity and 94% specificity. Five spectral biomarkers were detected by both techniques and could be utilised as a panel of markers indicating carcinogenesis. CA-125 levels did not seem to undermine the high classification accuracies. This minimally invasive test may provide an alternative diagnostic and screening tool for ovarian cancer that is superior to other established blood-based biomarkers.

Correlation between CD8+ T cells specific for prostate-specific antigen and level of disease in patients with prostate cancer.

Modest work has been performed to improve the sensitivity of residual disease detection or investigate the contribution that the immune system makes in controlling metastatic tumor growth, in particular, the frequency and biological actions of peptide-specific CD8+ T lymphocytes in limiting metastatic disease and/or maintaining remission. Fifty-three peripheral blood samples from 32 prostate cancer (PC) patients were investigated for the presence of circulating prostate-specific antigen (PSA)-expressing cells (CPECs) using a highly sensitive and specific assay combining immunomagnetic epithelial cell enrichment with nested RT-PCR of PSA mRNA. Using HLA-A2 tetramer complexes, frequency of CD8+ T cells specific for PSA-derived peptides was determined. Additionally, serum concentrations of PSA and testosterone were measured. CPECs were detected in 26% of peripheral blood samples from PC patients. CD8+ T cells specific for PSA-derived peptides were detected at low frequency in HLA-A2-positive PC patients. The correlation between these PSA-specific CD8+ T cells and residual prostate tumor cells and clinical measures was investigated. Our data suggest that frequency of PSA-specific CD8+ T cells is correlated to CPECs, but not to serum PSA level.

Differential CTLs specific for prostate-specific antigen in healthy donors and patients with prostate cancer.

Induction of CTL responses specific for prostate-specific antigen (PSA)-derived peptides in healthy individuals and patients with prostate cancer (PC) was investigated. Eight PSA-derived peptides that have the potential to bind HLA-A2 molecules were examined. Peripheral blood lymphocytes isolated from HLA-A2-positive volunteers were expanded using autologous mature, PSA-derived peptide-pulsed dendritic cells. The expansion of IFN-gamma-secreting CD8+ T cells specific for three of the eight PSA-derived peptides (PSA-2(108-117), PSA-4(141-150) and PSA-6(146-154)) was detected in healthy individuals, but not in patients with PC. Using HLA-A2/peptide tetramers, the PSA-specific CD8+ T cells were detectable at low frequency both in healthy individuals and patients with PC. Using flow cytometric cytotoxicity assays, the expanded effectors from healthy individuals were able to kill the PSA-expressing epithelial cell line LNCaP and the peptide-pulsed T2 cells in a MHC class I-restricted manner without involving NK activity. However, such killing by effectors expanded from prostatectomized patients involved a complete or a significant NK activity. Specific recognition of PSA-derived peptides in healthy individuals may occur by an adaptive CTL immune response, while such recognition in PC patients may additionally or alternatively be mediated by an innate NK immune response. In conclusion, our work indicates that the PSA-specific CD8+ T cells exist in both healthy individuals and PC patients, but they have impaired function in patients as they failed to release IFN-gamma and to kill targets without involving NK activity.

Human monocyte isolation methods influence cytokine production from in vitro generated dendritic cells.

There is growing interest in the in vitro generation of dendritic cells (DC) from peripheral blood monocytes, but the effect of the method chosen to isolate CD14+ monocytes for subsequent DC generation is poorly documented. The method used to isolate monocytes may have an impact on the subsequent function of DC by affecting their ability to express costimulatory molecules (CD80/86), maturation marker (CD83) and/or to produce important immunomodulatory cytokines. In this study, we show that the positive selection of monocytes by anti-CD14-coated microbeads inhibits the lipopolysaccharide (LPS)-induced production of interleukin (IL)-12, IL-10 and tumour necrosis factor-alpha (TNF-alpha) from human DC. However, when DC were grown from monocytes isolated by plastic adherence, LPS induced the production of much higher levels of these cytokines. DC derived from adherence-isolated monocytes induced the development of potent cytotoxic T lymphocytes of the Tc1 subset specific for influenza matrix protein, as confirmed by interferon-gamma (IFN-gamma) enzyme-linked immunosorbent spot-forming cell assay (ELISPOT), cytotoxicity assay, major histocompatibility complex (MHC)-peptide tetrameric complexes and T helper 1/T helper 2 (Th1/Th2) cytokine production assays.