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. 2019 Nov 14;11(2):525-533.
doi: 10.1039/c9sc03711j. eCollection 2020 Jan 14.

Serum Raman Spectroscopy as a Diagnostic Tool in Patients With Huntington's Disease

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Free PMC article

Serum Raman Spectroscopy as a Diagnostic Tool in Patients With Huntington's Disease

Anna Huefner et al. Chem Sci. .
Free PMC article

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.

Figures

Fig. 1
Fig. 1. Methodology with RS and SERS. (A) An incident laser light excites characteristic vibrations on interaction with the molecules (coloured symbols) in the sample (here illustrated for a drop of blood serum) resulting in Raman scattering which is then detected as a spectrum (B). While all molecules in the bulk of the sample can be interrogated with RS, only molecules in the direct vicinity of gold nanoparticles (AuNPs) (grey spheres) contribute to the SERS signature. Signals in SERS (B, black spectrum) are orders of magnitude stronger than in RS (B, blue spectrum), as well as surface-selective (cyan stars and red squares in A).
Fig. 2
Fig. 2. Tracking the progression of HD in cortical homogenates (A and B) and serum of transgenic R6/2 mice with SERS (C). (A) The AGERA shows both an increase in mutant Huntingtin aggregate size and intensity in the cortex of R6/2 mice as disease progresses. (B) Using a PCA approach we were able to separate WT littermates from transgenic littermates at 12 weeks of age; PC1 scores generated from SERS spectra of cortical homogenates of the R6/2 mice at different disease stages showed a similar progression profile to AGERA analysis. (C) Using a SERS approach on the serum, LD1 scores also correlated with the disease progression. Box and whiskers indicate estimated mean and standard deviation of multivariate ANOVA with post-hoc Bonferroni correction.
Fig. 3
Fig. 3. PC1 vs. PC2 scores represented as a scatter plot for RS (A) and SERS (B) analysis of serum from HD patients and healthy participants (ctrl) using the full spectral range (RS: 200–1900 cm–1, SERS: 400–1900 cm–1). (C and D) PC1 scores were used to compute the p value for group segregation between genotype (blue) and gender (red), to indicate spectral intervals of significant group segregation for RS (C) and SERS (D). The significance level of p = 0.05 is indicated by an orange dashed line. Results were analyzed using ANOVA with post-hoc Bonferroni correction.
Fig. 4
Fig. 4. Average RS (A) and SERS (B) spectra of serum from healthy control subjects (blue lines) and HD patients (red line) as well as their standard deviations (C and D, respectively). The different spectra of the averages for RS (black line, E) and SERS (black line, F) are within the standard deviation (C and D) of the average spectra (A and B). Hence, LD loadings for each of the spectral methods, respectively (coloured line in E and F), are also taken into account alongside the explained variance of the underlying PC1 scores (colouration of LD loadings line, see colour bar in E). Yellow marked regions indicate important peaks. Refer to ESI for peak assignments.
Fig. 5
Fig. 5. (A, B) The colour map shows the linear correlation of PC1 scores for spectral RS intervals (50 cm–1, x-axis) with different clinical HD assessment parameters (y-axis). High correlation is observed with patient's age (650–700 cm–1, 1750–1880 cm–1), DB (1600–1650 cm–1), CAG repeat size (300–350 cm–1, 1750–1850 cm–1) and TFC (1200–1250 cm–1). (C) The aR2 of the linear fit is colour-coded for each spectral interval and clinical parameter. Darker red colouration suggests better linear correlation.
Fig. 6
Fig. 6. The colour map shows the linear correlation of PC1 scores for SERS intervals (50 cm–1, x-axis) with different clinical HD assessment parameters (y-axis). PC1 scores from SERS were highly correlated with Indp levels (1300–1350 cm–1), CAG repeat length (1600–1650 cm–1), DB scores (700–750 cm–1), FA scores (1300–1350 cm–1), TFC (1300–1350 cm–1) and UHDRS motor scores (1200–1250 cm–1). The aR2 of the linear fit is colour-coded for each spectral interval and clinical parameter. Darker red colouration suggests better linear correlation.
Fig. 7
Fig. 7. Grouped UHDRS motor scores vs. PC1 scores (A) and LD1 scores (B) from the combined SERS spectral region 1200–1300 cm–1 and 1600–1700 cm–1. (A) A linear curve (aR2UHDRS = 0.97) has been fitted to the PC1 scores of manifest HD groups (UHDRS ≥ 5), which also has a good correlation for the pre-manifest group (UHDRS < 5). (B) In contrast, the LD1 scores significantly distinguishes (pcontrol-HD < 0.001) the control group from all four manifest HD groups, while the pre-manifest HD group appears to occupy a transitional state between the healthy controls and manifest HD subjects.

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