Confocal Raman microscopy can quantify advanced glycation end product (AGE) modifications in Bruch's membrane leading to accurate, nondestructive prediction of ocular aging

Abstract

The modification of proteins by nonenzymatic glycation leading to accumulation of advanced glycation end products (AGEs) is a well-established phenomenon of aging. In the eyes of elderly patients, these adducts have been observed in retinal pigment epithelium (RPE), particularly within the underlying pentalaminar substrate known as Bruch's membrane. AGEs have also been localized to age-related subcellular deposits (drusen and basal laminar deposits) and are thought to play a pathogenic role in progression of the major sight-threatening condition known as age-related macular degeneration (AMD). The current study has quantified AGEs in Bruch's membrane from postmortem eyes and established age-related correlations. In particular, we investigated the potential of confocal Raman microscopy to identify and quantify AGEs in Bruch's membrane in a nondestructive, analytical fashion. Bruch's membrane and the innermost layers of the underlying choroid (BM-Ch) were dissected from fresh postmortem eye-cups (n=56). AGE adducts were quantified from homogenized tissue using reverse-phase HPLC and GC/MS in combination with immunohistochemistry. For parallel Raman analysis, BM-Ch was flat-mounted on slides and evaluated using a Raman confocal microscope and spectra analyzed by a range of statistical approaches. Quantitative analysis showed that the AGEs pentosidine, carboxymethyllysine (CML), and carboxyethyllysine (CEL) occurred at significantly higher levels in BM-Ch with age (P<0.05-0.01). Defined Raman spectral "fingerprints" were identified for various AGEs and these were observed in the clinical samples using confocal Raman microscopy. The Raman data set successfully modeled AGEs and not only provided quantitative data that compared with conventional analytical approaches, but also provided new and complementary information via a nondestructive approach with high spatial resolution. It was shown that the Raman approach could be used to predict chronological age of the clinical samples (P<0.001) and a difference in the Raman spectra between genders was highly significant (P<0.000001). With further development, this Raman-based approach has the potential for noninvasive examination of AGE adducts in living eyes and ultimately to assess their precise pathogenic role in age-related diseases.

Integrated system for combined Raman spectroscopy–spectral domain optical coherence tomography

“…For example, recent work has demonstrated the sensitivity of RS to advanced glycation end products, which are molecules widely believed to play an important role in the ill-defined pathogenesis of age-related macular degeneration. 32 The expanding role of OCT in animal studies, coupled with the sensitivity of RS to biochemical features invisible to OCT, suggests that common-detector RS-OCT has the potential to become a valuable research tool for the study of retinal disease in animal models. The application of RS-OCT to human retinal analysis is not possible at this time, however, due to the fact that the laser exposure levels required to perform RS are well in excess of the Americal National Standards Institute (ANSI) standards.…”

Evaluation of Nonenzymatic Posttranslational Modification–Derived Products as Biomarkers of Molecular Aging of Proteins

“…Their use can be expected for monitoring molecular aging of plasma proteins, as PTMDPs generate specific spectral "fingerprints." Raman spectroscopy has been used recently for the evaluation of modifications in Bruch's membrane induced by AGEs, as a noninvasive prognostic tool for prediction of ocular aging (80 ).…”

Multivariate Analysis for the Processing of Signals

“…Glenn et al [43] published a method that exploited the principal components more directly for estimating the broad bandwidth backgrounds that contribute to a dataset where the signal of interest had medium bandwidths. These backgrounds were estimated on the PC and fed into a database that was used to perform a linear combination analysis on the individual spectra.…”

“…Figure 7 illustrates another benefit of exploiting SVD loadings for the purposes of baseline correction. This dataset, from Glenn et al [43] consists of Raman spectra measured on a thin collagenous membrane from the rear of the retina (light sensitive part of the eye). Beattie and McGarvey [39] used this dataset to compare a range of different background correction procedures (including linear interpolation, sequential polynomial, SVD based baseline correction and EMSC).…”