Dataset Information

Serum Raman spectroscopy as a diagnostic tool in patients with Huntington's disease.

ABSTRACT: Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by an abnormal CAG expansion in exon 1 of the huntingtin (HTT) gene. Given its genetic basis it is possible to study patients both in the pre-manifest and manifest stages of the condition. While disease onset can be modelled using CAG repeat size, there are no easily accessible biomarkers that can objectively track disease progression. Here, we employed a holistic approach using spectral profiles generated using both surface-enhanced Raman spectroscopy (SERS) and Raman Spectroscopy (RS), on the serum of healthy participants and HD patients covering a wide spectrum of disease stages. We found that there was both genotype- and gender-specific segregation on using the full range in the fingerprint region with both SERS and RS. On a more detailed interrogation using specific spectral intervals, SERS revealed significant correlations with disease progression, in particular progression from pre-manifest through to advanced HD was associated with serum molecules related to protein misfolding and nucleotide catabolism. Thus, this study shows the potential of Raman spectroscopy-based techniques for stratification of patients and, of SERS, in particular, to track disease status through provision of 'spectral' biomarkers in HD, with clinical applications for other diseases and trials looking at disease modifying therapies.

SUBMITTER: Huefner A

PROVIDER: S-EPMC7067270 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Serum Raman spectroscopy as a diagnostic tool in patients with Huntington's disease.

Huefner Anna A Kuan Wei-Li WL Mason Sarah L SL Mahajan Sumeet S Barker Roger A RA

Chemical science 20191114 2

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by an abnormal CAG expansion in exon 1 of the huntingtin (<i>HTT</i>) gene. Given its genetic basis it is possible to study patients both in the pre-manifest and manifest stages of the condition. While disease onset can be modelled using CAG repeat size, there are no easily accessible biomarkers that can objectively track disease progression. Here, we employed a holistic approach using spectral profiles generate ...[more]

PMID: 32190272

Similar Datasets

Project description:Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Project description:Inflammatory bowel diseases (IBD) are chronic inflammatory disorders that can be categorized as ulcerative colitis (UC), Crohn’s disease (CD) and indeterminate colitis (IC). Deep remission has been shown to improve disease outcome. There may be a lack of concordance between endoscopic and histologic remission. IBD patients with long standing colitis are at risk of developing dysplasia and colorectal cancer (CRC). However, it can be challenging to diagnose dysplasia in IBD patients during colonoscopy, as dysplasia frequently manifests as non-pedunculated lesions that present with only subtle visible changes or are even invisible due to the surrounding inflammation, scarring, pseudopolyps, or hyperplasia. Although white light endoscopy and chromoendoscopy are the current standard modality of imaging, there is still a gap to be bridged, in terms of improving endoscopic diagnosis of dysplasia and improving concordance of endoscopic and histologic remission. Raman spectroscopy is an inelastic light scattering technique provide specific fingerprints of molecular compositions and structures of biological tissues. It may be able to provide additional diagnostic information over standard endoscopy. A second-generation Raman endoscope system for improving in vivo tissue characterization and diagnosis during colonoscopy has been developed (SPECTRA IMDx system). Preliminary data suggested its utility in the diagnosis of colorectal neoplasia during colonoscopy. There is currently a lack of data concerning the application of this novel technology in the context of IBD. Specifically, whether the spectral signals generated can be used to better classify disease remission, and thus achieve higher concordance with histology when compared to standard endoscopy. It is also unclear whether this technology can be used to differentiate dysplastic mucosa from non-dysplastic mucosal in IBD patients. Hypotheses 1. Raman spectroscopy based artificial intelligence system has the potential to be used to differentiate disease remission from active mucosal inflammation and hence improve concordance between endoscopic and histologic remission, with the potential to decrease the need for random biopsies real-time during colonoscopy. 2. Raman spectroscopy based artificial intelligence system has the potential to differentiate dysplastic mucosa in IBD patients (low grade and high grade dysplasia; colorectal cancer) from non-dysplastic mucosa. real-time during colonoscopy.

Dataset Information

Serum Raman spectroscopy as a diagnostic tool in patients with Huntington's disease.

Publications

Serum Raman spectroscopy as a diagnostic tool in patients with Huntington's disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets