Age-dependent changes in the cerebrospinal fluid proteome by slow off-rate modified aptamer array

Abstract

An important precondition for the successful development of diagnostic assays of cerebrospinal fluid (CSF) biomarkers of age-related neurodegenerative diseases is an understanding of the dynamic nature of the CSF proteome during the normal aging process. In this study, a novel proteomic technology was used to quantify hundreds of proteins simultaneously in the CSF from 90 cognitively normal adults 21 to 85 years of age. SomaLogic's highly multiplexed proteomic platform can measure more than 800 proteins simultaneously from small volumes of biological fluids using novel slow off-rate modified aptamer (SOMAmer) protein affinity reagents with sensitivity, specificity, and dynamic ranges that meet or exceed those of enzyme-linked immunosorbent assays. In the first application of this technology to CSF, we detected 248 proteins that possessed signals greater than twofold over background. Several novel correlations between detected protein concentrations and age were discovered that indicate that both inflammation and response to injury in the central nervous system may increase with age. Applying this powerful proteomic approach to CSF provides potential new insight into the aging of the human central nervous system that may have utility in discovering new disease-related changes in the CSF proteome.

Figure 1

Percent change in relative fluorescence unit signal between subjects 21 and 85 years of age, estimated from linear regression, plotted against Pearson correlation coefficients for biomarkers. Analytes with significant age correlation after Bonferroni correction are shown with open boxes, and remaining analytes are shown with crosses. Points corresponding to four biomarkers with large and significant age-dependent changes are labeled.

Figure 2

Plots of SOMAmer signal (y axis, relative fluorescence units) versus age (x axis, years) for selected biomarkers. Female subjects (closed circles) and male subjects (open triangles) are identified in each plot.

Figure 3

A: Screeplot of principal component analysis from entire biomarker dataset. The first 10 principal components, which cumulatively account for 70% of the total variance in the data set, are shown. B: The first two principal components are plotted after DSGA to remove variation observed in subjects 20 to 40 years of age.

Figure 4

A: Principal component regression prediction of age by biomarker signature using only the first principal component. B: Results of leave-one-out (LOO) cross-validation of principal component regression. Root-mean-squared error of prediction (y axis) is plotted versus the number of components (x axis) included in the model. C: Principal component regression prediction of age by biomarker signature using the first 38 principal components, showing improved model prediction.