Integrative Single-Plaque Analysis Reveals Signature Aβ and Lipid Profiles in the Alzheimer's Brain

Abstract

Cerebral accumulation of amyloid-β (Aβ) initiates molecular and cellular cascades that lead to Alzheimer's disease (AD). However, amyloid deposition does not invariably lead to dementia. Amyloid-positive but cognitively unaffected (AP-CU) individuals present widespread amyloid pathology, suggesting that molecular signatures more complex than the total amyloid burden are required to better differentiate AD from AP-CU cases. Motivated by the essential role of Aβ and the key lipid involvement in AD pathogenesis, we applied multimodal mass spectrometry imaging (MSI) and machine learning (ML) to investigate amyloid plaque heterogeneity, regarding Aβ and lipid composition, in AP-CU versus AD brain samples at the single-plaque level. Instead of focusing on a population mean, our analytical approach allowed the investigation of large populations of plaques at the single-plaque level. We found that different (sub)populations of amyloid plaques, differing in Aβ and lipid composition, coexist in the brain samples studied. The integration of MSI data with ML-based feature extraction further revealed that plaque-associated gangliosides GM2 and GM1, as well as Aβ_1-38, but not Aβ_1-42, are relevant differentiators between the investigated pathologies. The pinpointed differences may guide further fundamental research investigating the role of amyloid plaque heterogeneity in AD pathogenesis/progression and may provide molecular clues for further development of emerging immunotherapies to effectively target toxic amyloid assemblies in AD therapy. Our study exemplifies how an integrative analytical strategy facilitates the unraveling of complex biochemical phenomena, advancing our understanding of AD from an analytical perspective and offering potential avenues for the refinement of diagnostic tools.

Figure 1

Ganglioside isoforms are enriched in a subset of plaques from sporadic Alzheimer’s disease (SAD) patients (A, B, E, F) but less so in plaques from AP-CU cases (C, D, G, H). MALDI-MS ion images of m/z 4129 ± 6 (A, C) and m/z 4515 ± 6 (B, D) corresponding to Aβ_1–38 and Aβ_1–42 peptides, respectively. MALDI-MS ion images of m/z 1544.86 ± 0.1 corresponding to GM1(36:1) (E, G). Merged views (F, H). Arrows indicate amyloid plaques containing Aβ_1–38 and other short Aβ species. Arrowheads indicate small Aβ_1–42- and Aβ_4–42-rich plaques. (I) Average spectrum of plaque pixels corresponding to peptides marked by a white rectangle in panels (F) and (H) in red and cyan for SAD and AP-CU cases, respectively. (J) Average spectrum of ganglioside species of plaque pixels marked by the same white rectangle. (K) Mean apparent plaque size per sample; n(SAD) = 8, n(AP-CU) = 9, t test, *p < 0.05. (L) Distribution of total Aβ and total GM plaque intensities (kernel density estimate). Total Aβ (top panel) is comparable in both sample types, whereas total GM (bottom panel) displays high intensities in SAD but low intensities in AP-CU (red and blue, respectively). The dashed line marks the mean per measurement run. Amyloid plaques were considered “low” or “high” in total GM below or above this threshold. (M) Distribution of kernel density estimates for all detected Aβs vs total GM intensity per plaque (high and low total GM contents shown in upper and low panels, respectively).

Figure 2

Single-plaque analysis reveals accumulation of gangliosides in a subgroup of plaques from sporadic Alzheimer’s disease (SAD) but not AP-CU cases. Statistical analysis of Aβ peptide and ganglioside (GM) composition using plaquepicker software. (A) On a global level mean (tissue-wide) composition of plaques per sample in SAD and AP-CU cases, only Aβ_1–38 and GM2(36:1) were marginally elevated in SAD vs AP-CU. (B) Single-plaque composition: Plaques were grouped into two clusters, independent of sample type, using k-means clustering. (C) Mean composition of plaques from clusters 1 and 2. In cluster 1 plaques, Aβ_1–38 and all ganglioside species were significantly elevated (p < 0.001). In cluster 2, Aβ_1–42 and Aβ_3–42pE (p < 0.01) as well as Aβ₄₄₂ (p < 0.001) were significantly higher. Unpaired t test: *p < 0.05, **p < 0.01, ***p < 0.001. All p-values were adjusted using the BenjaminiHochberg method, n(AP-CU) = 8, n(SAD) = 9.

Figure 3

Machine learning (ML) reveals Aβ_1–38 and GM1(36:1) as the best differentiators between SAD and AP-CU conditions.(AC) SHAP values indicating the importance and direction of all variables for elastic net (A), multilayer perceptron (MLP) (B), and extreme gradient boosting (xgBoost) model (C). Each point represents a single observation, with the color indicating the respective feature value (Z-score of measured intensity), whereas the x position indicates the associated SHAP value. Positive SHAP values favor predictions for the SAD class, where negative SHAP values indicate associations with the AP-CU class.