Dickson, Dennis W.

AbstractAimsThis study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning‐based decision tree classifier.MethodsParaffin‐embedded sections of the temporal cortex, motor cortex, caudate nucleus, globus pallidus, subthalamic nucleus, substantia nigra, red nucleus, and midbrain tectum from 1020 PSP and 199 CBD cases were assessed by phospho‐tau immunohistochemistry. The severity of tau lesions (i.e., neurofibrillary tangle, coiled body, tufted astrocyte or astrocytic plaque, and tau threads) was semi‐quantitatively scored in each region. Hierarchical cluster analysis was performed using tau pathology scores. A decision tree classifier was made with tau pathology scores using 914 cases. Cross‐validation was done using 305 cases. An additional ten cases were used for a validation study.ResultsCluster analysis displayed two distinct clusters; the first cluster included only CBD, and the other cluster included all PSP and six CBD cases. We built a decision tree, which used only seven decision nodes. The scores of tau threads in the caudate nucleus were the most decisive factor for predicting CBD. In a cross‐validation, 302 out of 305 cases were correctly diagnosed. In the pilot validation study, three investigators made a correct diagnosis in all cases using the decision tree.ConclusionRegardless of the morphology of astrocytic tau lesions, semi‐quantitative tau pathology scores in select brain regions are sufficient to distinguish PSP and CBD. The decision tree simplifies neuropathologic differential diagnosis of PSP and CBD.

Background: The relationship between cerebral microbleeds (CMBs) on hemosiderin-sensitive MRI sequences and cerebral amyloid angiopathy (CAA) remains unclear in population-based participants or in individuals with dementia. Objective: To determine whether CMBs on antemortem MRI correlate with CAA. Methods: We reviewed 54 consecutive participants with antemortem T2*GRE-MRI sequences and subsequent autopsy. CMBs were quantified on MRIs closest to death. Autopsy CAA burden was quantified in each region including leptomeningeal/cortical and capillary CAA. By a clustering approach, we examined the relationship among CAA variables and performed principal component analysis (PCA) for dimension reduction to produce two scores from these 15 interrelated predictors. Hurdle models assessed relationships between principal components and lobar CMBs. Results: MRI-based CMBs appeared in 20/54 (37%). 10 participants had ≥2 lobar-only CMBs. The first two components of the PCA analysis of the CAA variables explained 74% variability. The first rotated component (RPC1) consisted of leptomeningeal and cortical CAA and the second rotated component of capillary CAA (RPC2). Both the leptomeningeal and cortical component and the capillary component correlated with lobar-only CMBs. The capillary CAA component outperformed the leptomeningeal and cortical CAA component in predicting lobar CMBs. Both capillary and the leptomeningeal/cortical components correlated with number of lobar CMBs. Conclusion: Capillary and leptomeningeal/cortical scores correlated with lobar CMBs on MRI but lobar CMBs were more closely associated with the capillary component. The capillary component correlated with APOE ɛ4, highlighting lobar CMBs as one aspect of CAA phenotypic diversity. More CMBs also increase the probability of underlying CAA.

Abstract Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder associated with exposure to head trauma. In 2015, a panel of neuropathologists funded by the NINDS/NIBIB defined preliminary consensus neuropathological criteria for CTE, including the pathognomonic lesion of CTE as “an accumulation of abnormal hyperphosphorylated tau (p-tau) in neurons and astroglia distributed around small blood vessels at the depths of cortical sulci and in an irregular pattern,” based on review of 25 tauopathy cases. In 2016, the consensus panel met again to review and refine the preliminary criteria, with consideration around the minimum threshold for diagnosis and the reproducibility of a proposed pathological staging scheme. Eight neuropathologists evaluated 27 cases of tauopathies (17 CTE cases), blinded to clinical and demographic information. Generalized estimating equation analyses showed a statistically significant association between the raters and CTE diagnosis for both the blinded (OR = 72.11, 95% CI = 19.5–267.0) and unblinded rounds (OR = 256.91, 95% CI = 63.6–1558.6). Based on the challenges in assigning CTE stage, the panel proposed a working protocol including a minimum threshold for CTE diagnosis and an algorithm for the assessment of CTE severity as “Low CTE” or “High CTE” for use in future clinical, pathological, and molecular studies.

Abstract This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies.

Background: Transactive response DNA-binding protein of 43 kDa (TDP-43) is associated with memory impairment and overall cognitive decline. It is unclear how TDP-43 contributes to the rate of clinical decline. Objective: To determine whether cross-sectional and longitudinal cognitive and functional decline are associated with anatomical distribution of TDP-43 in the brain. Methods: Longitudinal clinical-neuropathologic autopsy cohort study of 385 initially cognitively normal/mildly impaired older adults prospectively followed until death. We investigated how TDP-43, amyloid-β (Aβ), tau neurofibrillary tangles (NFT), Lewy body disease (LBD), age, sex, and genetics are associated with clinical scores and rates of their longitudinal decline. Results: Of 385 participants, 260 (68%) had no TDP-43, 32 (8%) had TDP-43 limited to amygdala, and 93 (24%) had TDP-43 in the hippocampus and beyond. Higher TDP-43 and Braak NFT stages independently were associated with faster decline in global cognition, functional performance measured by Clinical Dementia Rating scale, and naming and episodic memory, whereas older age was associated with slower rate of cognitive, psychiatric, and functional decline. Cross-sectionally the following associations were found: higher TDP-43 and Braak NFT - worse performance; higher Aβ burden - worse global cognition, more behavioral changes, the latter also with higher LBD; older age - worse naming, lower frequency of behavioral changes; female sex - more impaired naming and better preserved episodic memory. There were no genetic associations. Conclusion: The association of TDP-43 distribution with decline in cognitive and functional performance suggests that TDP-43 is playing a role in the clinical progression to dementia. Further characterization of clinical features associated with TDP-43 can facilitate establishment of antemortem diagnosis.

AbstractIntron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome‐wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership‐Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon‐level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron‐retained mRNAs were identified using mass spectrometry. Further, we established AD‐specific intron expression Quantitative Trait Loci, and identified splicing‐related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms.

AbstractTo examine the length of a hexanucleotide expansion in C9orf72, which represents the most frequent genetic cause of frontotemporal lobar degeneration and motor neuron disease, we employed a targeted amplification-free long-read sequencing technology: No-Amp sequencing. In our cross-sectional study, we assessed cerebellar tissue from 28 well-characterized C9orf72 expansion carriers. We obtained 3507 on-target circular consensus sequencing reads, of which 814 bridged the C9orf72 repeat expansion (23%). Importantly, we observed a significant correlation between expansion sizes obtained using No-Amp sequencing and Southern blotting (P = 5.0 × 10−4). Interestingly, we also detected a significant survival advantage for individuals with smaller expansions (P = 0.004). Additionally, we uncovered that smaller expansions were significantly associated with higher levels of C9orf72 transcripts containing intron 1b (P = 0.003), poly(GP) proteins (P = 1.3 × 10− 5), and poly(GA) proteins (P = 0.005). Thorough examination of the composition of the expansion revealed that its GC content was extremely high (median: 100%) and that it was mainly composed of GGGGCC repeats (median: 96%), suggesting that expanded C9orf72 repeats are quite pure. Taken together, our findings demonstrate that No-Amp sequencing is a powerful tool that enables the discovery of relevant clinicopathological associations, highlighting the important role played by the cerebellar size of the expanded repeat in C9orf72-linked diseases.

Abstract Triggering receptor expressed on myeloid cell 2 (TREM2) is a surface receptor that, in the central nervous system, is exclusively expressed on microglia. TREM2 variants have been linked to increased risk for neurodegenerative diseases, but the functional effects of microglial TREM2 remain largely unknown. To this end, we investigated TAR-DNA binding protein 43 kDa (TDP-43)-related neurodegenerative disease via viral-mediated expression of human TDP-43 protein (hTDP-43) in mice or inducible expression of hTDP43 with defective nuclear localization signals in transgenic mice. We found that TREM2 deficiency impaired microglia phagocytic clearance of pathological TDP-43, and enhanced neuronal damage and motor function impairments. Mass cytometry analysis revealed that hTDP-43 induced a TREM2-dependent subpopulation of microglia with high CD11c expression and higher phagocytic ability. Using mass spectrometry, we further demonstrated an interaction between TDP-43 and TREM2, in vitro and in vivo, in hTDP-43-expressing transgenic mouse brains. We computationally identified the region within hTDP-43 that interacts with TREM2 and observed the potential interaction in ALS patient tissues. Our data reveal the novel interaction between TREM2 and TDP-43, highlighting that TDP-43 is a potential ligand for microglial TREM2 and the interaction mediates neuroprotection of microglial TREM2 in TDP-43-related neurodegeneration.