Rothberg, Darren M.

Abstract INTRODUCTION The locus coeruleus (LC) degenerates early in Alzheimer's disease (AD). However, the extent of rostrocaudal degeneration across clinicopathologic heterogeneity remains underexplored in AD. METHODS Using digital pathology, we quantified LC neuronal density and area at three neuroanatomic levels in a large AD series. RESULTS Analysis of neuropathologic AD subtypes revealed greater middle LC vulnerability in hippocampal sparing AD compared to typical and limbic predominant AD. Regression analyses identified distinct predictor variables associated with the degeneration of rostral and middle LC. Age at onset predicted 24% of the variability in rostral LC neuronal density, whereas Braak stage, brain weight, Lewy body disease, and age at onset accounted for 15% of the variability in middle LC. Analyses of clinical presentations revealed lower rostral LC neuronal density in non‐amnestic compared to amnestic AD cases. DISCUSSION These insights demonstrate greater LC degeneration in atypical clinicopathologic forms of AD, including hippocampal sparing, young‐onset, and non‐amnestic presentations.

Abstract Background The locus coeruleus (LC) degenerates early in Alzheimer's disease (AD), with profound volume and neuronal loss. The loss of noradrenergic LC projection neurons results in alterations in attention, memory, and arousal, and constitutes the first site of neurofibrillary tangle accumulation preceding the onset of cognitive symptoms. We sought to investigate the relationship between LC neuronal density and clinical heterogeneity in a neuropathologically‐diagnosed series of AD cases. Methods The FLorida Autopsied Multi‐Ethnic (FLAME) cohort database was queried for neuropathologically‐diagnosed AD cases with a clinical diagnosis of AD dementia or an atypical, non‐amnestic clinical syndrome (e.g., corticobasal syndrome, posterior cortical atrophy). AD cases were selected based on high AD neuropathologic change (ADNC) and lack of frontotemporal lobar degeneration or hippocampal sclerosis, resulting in 591 AD cases and an additional 74 none‐low ADNC controls. Databased clinical diagnosis for AD cases was classified as amnestic or non‐amnestic. Hematoxylin & eosin‐stained slides were digitized and neuronal density was quantified using an in‐house developed macro on 900µmx900µm region of interest using Aperio technology (Leica Biosystems). LC level was classified using neuroanatomic landmarks as rostral, middle, or caudal. Results Control cases had greater neuronal density in the LC compared to AD cases at all three levels, p <0.05. In AD cases, we observed an association between age and neuronal density. Examining these differences along the rostrocaudal‐axis, we found that a younger age at symptomatic onset was associated with lower LC neuronal density in the rostral (correlation coefficient: 0.48; p <0.001) and middle LC (correlation coefficient: 0.28; p <0.001), no association between age at onset and neuronal density was observed at the caudal level. Stratifying our analysis by clinical syndrome, we found fewer neurons in the rostral and middle LC in non‐amnestic AD compared to amnestic AD ( p = 0.006 and 0.029 respectively, Table 1). No significant differences between amnestic and non‐amnestic cases were observed in the caudal LC. Conclusions The locus coeruleus, especially the rostral and middle levels, are especially vulnerable in young‐onset and atypical forms of AD. The loss of noradrenergic neuromodulation may contribute to the atypical symptomatology.

Abstract Neurofibrillary tangles are dynamic neuropathologic hallmarks of Alzheimer’s disease with a hypothesized lifespan morphologically-defined by three maturity levels: pretangles, mature tangles, and ghost tangles. To better understand the progression of tangle pathophysiology, we characterized tangle maturity level predilection of 15 tau antibodies recognizing a broad range of linear, phosphorylation, conformational, and truncation epitopes in the hippocampus of 24 postmortem brains. We developed the tangle maturity scoring system to semi-quantitatively evaluate each tangle maturity level. Based on proportions of tangle maturity levels, we classified antibodies as “early” (mostly pretangles and mature tangles), “middling” (mature tangles with pretangles and ghost tangles), and “advanced” (mostly ghost tangles and mature tangles) tangle maturity markers. To summarize tangle maturity predilection, we developed the tangle maturity scale to integrate individual tangle maturity scores. Correlations showed stronger relationships between tangle maturity scale and subsector thickness for more advanced tangle maturity markers in CA1 and subiculum, whereas Braak tangle stage remained consistently correlated throughout markers of the tangle lifespan. To aid in scoring hippocampi, we used machine learning to recognize tangle maturity levels, which performed comparably to a domain expert and showed similar relationships by Spearman correlation. Pattern recognition software was used to assess tangle and neuritic tau burden separately, which generally correlated with Braak stage and neuronal counts. However, tangle-derived tau burden more consistently correlated with hippocampal subsector thickness. In conclusion, we developed manual and automated scoring systems to evaluate tangle maturity levels, demonstrating early 4R, phosphorylated, and oligomeric tau accumulation preceding more advanced 3R and truncated tau. Our study provides supportive evidence of disease-relevant ordering of tau posttranslational modifications in the brain, which may have implications for theragnostic development. These findings underscore the promise of computerized quantitative analyses (i.e., pathomics) for high-throughput feature extraction from whole-slide images to enhance our understanding of microscopically observed morphologic changes.

AbstractBackgroundAlzheimer’s disease (AD) is neuropathologically heterogeneous and can be objectively classified along a spectrum of corticolimbic tangle distribution as hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. The olfactory bulb is an early area of tau accumulation with a direct connection to the amygdala. Although tau pathology has been identified in the olfactory bulb, its association with AD subtypes remains unclear. To investigate these differences, tau markers were characterized within the olfactory bulb among AD subtypes.MethodThe cohort included 39 AD cases (HpSp AD: n = 15, typical AD: n = 12, limbic AD: n = 12). Immunofluorescence staining was conducted on 5 µm thick formalin‐fixed paraffin‐embedded olfactory bulb sections using phosphorylated tau (AT8 & PHF‐1) and C‐terminal tau (2E9) antibodies. Slides were digitally scanned on the PhenoImager™ HT slide scanner and analyzed using QuPath Digital Analysis software, quantifying tau burden as a percentage of positive staining.ResultIn whole olfactory bulbs, AT8 was observed as intrasomal tangle‐like immunoreactivity with some extrasomal neuritic staining. AT8 burden did not differ among subtypes (HpSp AD median = 0.28% [interquartile range 0.014‐1.7%], typical AD = 0.28% [0.027‐2.24%], limbic AD = 0.39% [interquartile range 0.39‐0.74%]). PHF‐1 was observed as intrasomal tangle‐like immunoreactivity with abundant extrasomal neuritic pathology. PHF‐1 was observed to be highest in limbic AD (HpSp AD = 3.5% [0.63‐11%)], typical AD = 3.2% [0.38‐13%], and limbic AD = 5.3% [1.97‐10%]) and differed from typical AD (p<0.05). 2E9 was observed as intrasomal tangle‐like immunoreactivity with minimal extrasomal staining and not found to differ among subtypes (HpSp AD = 0.18% [0.036‐2.2%], typical AD = 0.15% [0.021‐2.4%], and limbic AD = 0.29% [0.0030‐8.2%]).ConclusionAbundant tau accumulation was observed among all AD subtypes with some suggestion of higher tau burden in limbic predominant AD. Given the direct connection between olfactory bulb and amygdala, it is interesting to speculate of an environmental contribution to limbic predominant AD through olfactory bulb damage. Future studies will expand the cohort to include AD cases with Lewy body disease co‐pathology and primary tauopathies with a focus on the anterior olfactory nucleus as the primary site of tau pathology.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total β‐amyloid Aβ42 relative to Aβ40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total ß‐amyloid Aß42 relative to Aß40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

AbstractBackgroundTau aggregation is the major cause of several neurodegenerative tauopathies. Tau interaction with other proteins affects the formation of tau aggregates with seeding activity but less is known about its effects on tau‐seed properties. Our previous study revealed that Bassoon (BSN), a presynaptic protein, interacts with tau‐seed, exacerbating its toxicity in vivo. Bsndownregulation reduced tau spreading and overall pathology. Intriguingly, a parallel study associated missense mutations in BSN with tau aggregation in patients, prompting an investigation into the influence of genetic mutations in BSN on tau pathology for potential therapeutic insights.MethodWe generated a knock‐in mouse model (BSNKI) harboring the disease‐associated p.Pro3866Ala mutation in endogenous Bsn. Cognitive and motor abilities were assessed in aged heterozygous and homozygous BSNKI mice, followed by analyses of BSN and tau patterns, gliosis, and gene expression changes in their brains. Additionally, we validated our findings in a human BSN mutation carrier.ResultAt 10 months, BSNKI mice displayed motor impairments on the rotarod, and grip strength assays compared to WT mice. Their brains displayed somatic BSN and pathological tau accumulation, with the gene expression changes indicating alterations in microglia activation, protein degradation pathways, complement activation, and synapse pruning. We also observed an accumulation of pathological tau at the synapses and synapse engulfment by microglia. Histopathological analyses revealed robust microglia activation and co‐deposition of proteasomal subunits with BSN. The human BSN mutation carrier displayed inclusions of BSN and tau pathology similar to observations in the BSNKI model.ConclusionOur BSNKI mouse model, reflecting a disease‐associated BSN mutation, revealed motor impairments and pathological tau and BSN deposits, mirroring observations in BSN mutation carriers. Notably, BSN seems to play a dual role, promoting tau aggregation and sequestering protein degradation molecules, leading to tau and protein accumulation at the soma and synapse, triggering microgliosis and neuroinflammation. These findings propose BSN as a promising therapeutic target for tauopathies, underscoring the need for further exploration to elucidate underlying mechanisms and therapeutic implications.

AbstractBackgroundThere is a gap in knowledge concerning which tangle maturity levels (Moloney, Alzheimers Dement, 2021) are associated with antemortem measures (tau‐PET, MRI, cognitive testing). We hypothesized antemortem measures would be more strongly correlated with markers recognizing middling to advanced tangle maturity. We additionally sought to assess the strength of the relationship with tangle and neuritic pathology recognized by tangle maturity markers.MethodPosterior hippocampi from n = 30 cases with antemortem measures ≤3 years prior to death were cut (5µm) and immunostained with tau antibodies to evaluate early (AT8), middling (PHF‐1), or advanced (2E9) tangle maturity. GENIE (pattern recognition software) separated tangle from neuritic burden (Figure). Hippocampus, parahippocampus, and fusiform were analyzed to correlate digital pathology with flortaucipir tau‐PET standard uptake value ratio (SUVr, cerebellar crus normalization), MRI volume/thickness, and cognitive measures which included Auditory Verbal Learning Test (AVLT) delayed recall, Mini‐Mental State Exam (MMSE), and Clinical Dementia Rating (CDR).ResultThe strongest relationships observed with flortaucipir were with PHF‐1 and 2E9 burden (Table). AT8 tangle and neuritic burden correlations with flortaucipir were relatively similar in the hippocampus but were stronger with neuritic pathology in parahippocampus and fusiform. PHF‐1 tangle and neuritic burden correlations were relatively similar with flortaucipir. 2E9 correlations were stronger with neuritic pathology in hippocampus. MRI measures were more strongly correlated with PHF‐1 and 2E9 burden. AT8 tangle and neuritic burden were correlated with fusiform thickness. The correlations between MRI measures and 2E9 tangle and neuritic burden were similar. Correlations between cognitive measures were similar between tangle maturity markers. AVLT delayed recall correlated with all tangle maturity markers, but neuritic burden was stronger for AT8 and 2E9 in hippocampus compared to tangle burden. MMSE correlated with neuritic burden for all tangle maturity markers. CDR remained statistically non‐significant.ConclusionInterpretation of the strength of the relationship between tau burden and antemortem measures differed across tangle maturity markers. Neuroimaging measures may be more sensitive to middle/advanced maturity markers with limited differences between tangle and neuritic pathology. These findings may be biologically relevant and imply either a limited recognition of tangles depending on antibody epitope, and/or may represent the clinicoradiologic impact of increasing neuritic burden.

AbstractBackgroundPSEN1 mutations are the most common genetic cause for autosomal dominant forms of Alzheimer’s disease patients with an age at onset below 65 years (ADAD). Even though all PSEN1 mutations lead to an increased Aβ processing and production, there is a large variability in phenotypic presentation even in individuals with the same mutation. Splicing isoforms in AD genes are reported to be associated with the disease, however little is known about correlation between splicing pattern and phenotypic presentation (e.g. variable age onset). We hypothesized that cellular and transcriptomic alterations differ amongst patients with different phenotypic presentation. In a group of 8 ADAD with the same PSEN1 p.G206A mutation, we investigate the association between brain cell type proportion, gene expression, and splicing isoforms with age at onset, Aβ plaque type and severity of AD neuropathologic change.MethodsEight Hispanic ADAD cases with PSEN1 p.G206A mutation were identified in the Mayo Clinic brain bank housed in Jacksonville, FL. Standard (immuno)histochemical stains, including H&E and thioflavin S, were performed to evaluate AD neuropathologic change. We focused on distinct Aβ plaque types in an association cortex (inferior parietal gyrus) and primary cortex (motor). We carried‐out single‐nuclei short and long read RNA sequencing on both brain regions. We will stratify our transcriptomic analyses based on neuropathology and phenotypic presentation.ResultsWe observed large variability in age at onset (median = 53 years; range: 42‐59 years). Furthermore, disease duration was reportedly variable (median = 10 years; range: 8‐17). All cases had a Braak tangle stage VI and Thal amyloid phase 5. Only 3 patients carried at least one APOE*ε4 risk allele, suggesting that its effect was minimal on ages at onset and death. Different plaque types were observed for short versus long survivors. In short survivors, plaques were predominantly fibrillar with an eosinophilic radial component, whereas in longer survivors predominantly cored plaques were observed.ConclusionLarge phenotypic variability within patients with the same mutation is observed suggesting the involvement of yet to be discovered genetic modifiers. Understanding cellular transcriptomic events in ADAD is essential to future development of therapeutic approaches and development of biomarkers.