Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer's disease

Abstract

Background: Blood-based biomarkers are gaining grounds for the detection of Alzheimer's disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel.

Methods: The NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [¹¹C] PiB PET, [¹⁸F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers.

Results: NULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878-0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype.

Conclusions: Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

Keywords: Amyloid pathology; NULISA with next-generation sequencing readout (NULISAseq); NUcleic acid-Linked Immuno-Sandwich Assay (NULISA); Neurodegeneration; Plasma biomarkers; Preclinical Alzheimer’s disease; Proteomics; Tau pathology.

Fig. 1

Technical performance of the NULISAseq CNS disease panel. A Box plots illustrating the detectability of 116 targets in 176 plasma samples collected from 113 MYHAT-NI participants. The y-axis represents NPQ-LOD, where values > 0 indicate detectability. For each box plot, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the ' + ' marker symbol. Data points were considered outliers if they were greater than q3 + 1.5 × (q3 – q1) or less than q1 – 1.5 × (q3 – q1), where q1 and q3 are the 25th and 75th percentiles of the sample data. B-C Histogram distributions of intra-plate (B) and inter-plate (C) coefficient of variations (CVs). D-E Scatterplot distributions between abundance rank and intra-plate (D) or inter-plate (E) CVs. Intra- and inter-plate CVs were calculated based on results of a pooled plasma sample (SC), measured in duplicates separately in two different plates. Abundance rank was based on the mass spectrometry-estimated protein abundance in the Human Protein Atlas (downloaded on 12/24/2023). F Scatterplot distributions illustrating the correlation of protein levels measured using NULISAseq and Simoa method. Rho and p values were determined using Spearman rank-based correlation. Purple lines indicated the least square regression lines. Abbreviations: NPQ, NULISA Protein Quantification, represents the log2-transformation of normalized target counts; LOD, limit of detection. Simoa measured concentration (fg/ml) was also log2-transformed for this analysis

Fig. 2

Cross-sectional association of NULISAseq targets with amyloid pathology (A). A Volcano plot of -log10 (p-value) versus log2(fold change) comparing biomarker abundances (NPQ) in samples from A + participants (n = 49) vs. A- controls (n = 127). Significant targets are shown in red (higher in A +) or blue (lower in A +) circles. Grey circles represent non-significant targets. NPQ (NULISA Protein Quantification) represents the log2-transformation of normalized target counts. B Boxplot distributions of significant NULISAseq targets, separated by A status and visit. P-values on top of the boxplots were for the whole data combining both visits and were determined using linear mixed models (random intercepts) with NPQs as the dependent variable, visit-specific A status as the independent variables, adjusting for covariates age, sex, and APOE ε4 carrier status. Significance determination was based on p-value < 0.005, corresponding to ~ 8% FDR

Fig. 3

Longitudinal association between NULISAseq targets and amyloid pathology (A). A Boxplots illustrating the distribution of yearly biomarker abundance change by A status. P-values were based on two-sided Wilcoxon rank-sum tests. B Scatterplots for the correlation between yearly longitudinal Aβ PET SUVR change and baseline biomarker levels. The strength of the correlation was assessed based on Spearman’s ranks. Purple lines indicated the least square regression lines

Fig. 4

Association of NULISAseq targets with tau pathology (T). A Boxplots of NULISAseq targets with significant cross-sectional associations with T status, separated by T status and visit. P-values on top of the boxplots were for the whole data combining both visits and were determined using linear mixed models (random intercepts) with NPQs as the dependent variable, visit-specific T status as the independent variables, adjusting for covariates age, sex and APOE ε4 carrier status. Significance determination was based on p-value < 0.005, corresponding to ~ 9% FDR. B Boxplots illustrating the distribution of yearly biomarker abundance change by T status. P-values were based on two-sided Wilcoxon rank-sum tests

Fig. 5

Association of NULISAseq targets with neurodegeneration (N). A Heatmaps illustrating the abundance levels of NULISAseq with significant univariate associations with N status (unadjusted for covariates). The NPQ values were standardized for each protein target using z-scores. B Boxplots of selected NULISAseq targets, separated by N status and visit. P-values on top of the boxplots were for the whole data combining both visits and were determined using linear mixed models (random intercepts) with NPQs as the dependent variable, visit-specific N status as the independent variables, adjusting for covariates age, sex, and APOE ε4 carrier status. C Boxplots illustrating the distribution of yearly biomarker abundance change by N status. P-values were based on two-sided Wilcoxon rank-sum tests