Structural Heterogeneity of TDP-43 Fragments in Alzheimer’s Disease and Primary Age-Related Tauopathy by Artificial Intelligence (AI)-Based 3D Segmentation

AI Summary

AI 3D segmentation reveals amygdala centromedial highest pTDP-43 burden in lAD and PART; TDP-N predominates in amygdala basolateral in PART.
Hippocampal subfields show fragment-specific patterns: CA2/3 with greater pTDP-43, while TDP-N is enriched in dentate gyrus and subiculum.
TDP-C is the only fragment differing between PART and lAD in CA2/3, dentate gyrus and subiculum, indicating disease-specific aggregation.

AI Summary

TDP-43 in AD is either associated with cognitive impairment or a protective-life prolonging impact, and yet the localization, cellular and fragment characteristics of TDP-43 need to be determined.

We investigated the relationships between TDP-43 volumetric inclusion burden in low likelihood AD (lAD) and definite PART by immunostaining against phosphorylated TDP-43 (pTDP-43), TDP-43 C terminal (TDP-C) and TDP-43 N-terminal (TDP-N) fragments combined with 3D confocal imaging taken from eight regions: amygdala (basolateral [amygdala-BL] and centromedial amygdala [amygdala-CM]), the hippocampus (Cornu Ammonis [CA]-1, CA2/3, CA4, dentate gyrus [DG] and subiculum [SUB]) and entorhinal cortex (ERC) and artificial intelligence (AI)-based segmentation via object recognition, reconstruction and quantification.

We found amygdala-CM in lAD and PART to have the overall greatest burden of pTDP-43 whereas TDP-N burden in amygdala-BL of PART cases was greater than other TDP-43 fragments.

Basic summary

Neuropathol Appl Neurobiol. 2026 Aug;52(4):e70096. doi: 10.1111/nan.70096.

ABSTRACT

TAR DNA-binding protein 43 (TDP-43) inclusions are defining pathological features of frontotemporal lobar degeneration (FTLD) but are also often observed in Alzheimer’s disease (AD) and primary age-related tauopathy (PART). TDP-43 in AD is either associated with cognitive impairment or a protective-life prolonging impact, and yet the localization, cellular and fragment characteristics of TDP-43 need to be determined. We investigated the relationships between TDP-43 volumetric inclusion burden in low likelihood AD (lAD) and definite PART by immunostaining against phosphorylated TDP-43 (pTDP-43), TDP-43 C terminal (TDP-C) and TDP-43 N-terminal (TDP-N) fragments combined with 3D confocal imaging taken from eight regions: amygdala (basolateral [amygdala-BL] and centromedial amygdala [amygdala-CM]), the hippocampus (Cornu Ammonis [CA]-1, CA2/3, CA4, dentate gyrus [DG] and subiculum [SUB]) and entorhinal cortex (ERC) and artificial intelligence (AI)-based segmentation via object recognition, reconstruction and quantification. We found amygdala-CM in lAD and PART to have the overall greatest burden of pTDP-43 whereas TDP-N burden in amygdala-BL of PART cases was greater than other TDP-43 fragments. There was no difference in TDP-43 burden in hippocampal subfields in PART. However, CA2/3 region showed greater pTDP-43 burden while TDP-N stood out in DG and SUB. Multiple comparisons among the groups revealed that TDP-C was the only fragment showing differences among PART and lAD in CA2/3, DG and SUB regions. Overall, unbiased AI-based volumetric burden analysis pipeline demonstrated unique fragment aggregation patterns in the neurodegenerative processes of PART and AD.

PMID:42576167 | DOI:10.1111/nan.70096

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