Large‐scale quantitative neuropathological analysis across the spectrum of Alzheimer’s disease neuropathologic change

Abstract Background While traditional histologic neuropathological assessment of neurodegenerative disease has tremendous diagnostic value, insights into neurodegeneration’s pathophysiological underpinnings can be significantly enhanced with large‐scale quantitative analyses. As part of the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA‐AD), we present a neuropathology dataset across 84 brains spanning the spectrum of AD neuropathologic change donated by UW Alzheimer’s Disease Research Center and Adult Changes in Thought study participants. Method Human brain tissue was collected at rapid autopsy (postmortem interval <12 hours). One hemisphere (randomly selected) was embedded in alginate for uniform coronal slicing (4mm), with alternating slabs fixed in 10% neutral buffered formalin or frozen in a dry ice isopentane slurry. Superior and Middle Temporal Gyrus (MTG) was sampled from fixed slabs and subjected to standard processing, embedding, sectioning (5µm), histochemical (H&E/Luxol fast blue), and immunohistochemical (duplex pTau and pTDP‐43, Aβ and Iba1, and monoplex GFAP, a‐synuclein, and NeuN) labeling. HALO image analysis software (Indica Labs, Albuquerque NM) was used to define and quantify relevant cellular and pathological features within each cortical layer (Figures 1‐3). Result 84 individuals were included in these analyses (Table 1). While quantitative metrics of pTau and Aβ aligned with traditional neuropathological measures, they showed substantial variability within discrete diagnostic stages (Figure 4). These findings were robust to quantification (discrete count vs. total area stained) and normalization (total nuclei vs. total area analyzed) methods, and highlight the importance of deploying highly quantitative, continuous measures of pathology in the study of neurodegeneration. This unique resource provides spatially preserved measures of pathologic burden across the spectrum of AD with potential for correlative analysis across pathologies, clinical and biomarker parameters, and omics applications to better support the identification of vulnerable cell types in early stages of AD progression, the principal goal of SEA‐AD. Conclusion Here we present a large‐scale quantitative neuropathology dataset, which enables analysis of pathological proteins and cell types at unprecedented scale and accuracy. Little is known about the interactions of AD‐relevant cell types with pathologic peptides across disease progression. The presented analyses exemplify the potential of this unique publicly‐accessible resource to identify and quantify novel disease‐relevant relationships.