In‐depth investigation in tau positron emission tomography tracers off‐target binding with voxel‐to‐voxel correlation analysis of tau and amyloid PET signal to histological iron and tau deposit in non‐Alzheimer tauopathies

Background Tau pathology strongly correlates with neuronal loss and clinical decline across tauopathies, emphasizing the need to detect and monitor tau pathology in living patients. Positron Emission Tomography(PET) using radiotracers like Flortaucipir ([18F]AV‐1451) enables good visualization of AD tau pathology. However, recent reports of its off‐targeting binding behavior raise concerns about the sensitivity and specificity of this tracer to visualize tau deposits in non‐AD tauopathies, especially with its similar pattern to iron accumulation. PET‐to‐autopsy validation has been challenging because of issues with tissue deformation and large‐scale pathological analysis. Here, we present i) an interdisciplinary approach combining immunohistochemistry, computer vision, and convolution neural network(CNN) to 3D‐reconstructed, billion‐pixel digital pathology tau and iron images of human brain hemispheres, co‐register them to PET and ii) preliminary PET‐to‐pathology voxel‐to‐voxel correlation analyses. Method We employed a pipeline developed in‐house combining whole‐brain processing, free‐floating immunohistochemistry and computer‐based algorithms for 2D and 3D registration. SlideNet, our CNN for segmenting tau inclusions in digital images (Fig. 1E,F), aims at generating quantitative, 3D‐tau‐maps. Iron maps are generated via Fiji's Weka segmentation (Fig. 1A‐B). Pipeline was applied to whole hemisphere sections in case#1(Cortico‐Basal‐Degeneration), case#3(TDP‐43), case#8(Frontaltemperal‐lobar‐disease w/ MAPT mutation), case#5(Alzheimer’s Disease) and case#7(Progressive‐Supranuclear‐Palsy) labeled by immunohistochemistry(CP‐13 antibody, Ser202, Fig. 2D)and Prussian Blue (Fig. 2A). Demographics of cases are shown in Table 1. We registered the resulting heatmaps (Fig. 1B,F) to MRI and PET coordinates (Fig. 3 & 1C,D,G,H), enabling voxel‐to‐voxel comparisons. Result CNN output three categories: tau in neurons, tau in glia/processes, and background (Fig. 2E). Weka segmentation output Iron in tissue and background (Fig. 2B). Obtaining the result with proper thresholding (Fig. 2C,F) We first registered the histology‐based iron maps, Tau and Amyloid PET scans onto the 3D MRI space (Fig. 1C‐D), following with registration of 3D Tau maps to PET scans and Iron maps to analyze the effect of iron to PET tracer binding quantitatively (Fig. 1G‐H). Preliminary correlation analysis generated between MRI resolution Iron map and PET scans via MATLAB. (Table 2) Conclusion Iron histological segmentations shows a mild positive association between iron deposit and Tau signal binding, showing that iron likely play a role in the Tau PET tracer off‐target binding. Ongoing regional analysis with Tau maps will provide more granular correlations, especially in areas known for off‐target binding, and we are finalizing it. (Table 2,3)