The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease

Scalco, Rebeca; Saito, Naomi; Beckett, Laurel A.; Nguyen, My-Le; Huie, Emily; Wang, Hsin-Pei; Flaherty, D. L.; Honig, Lawrence S.; DeCarli, Charles; Rissman, Robert A.; Teich, Andrew F.; Jin, Lee‐Way; Dugger, Brittany N.

doi:10.1186/s40478-023-01574-1

Cited by 5 publications

(6 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, there is no consideration or comparison work on the diversity within Hispanic samples (e.g. Caribbean Hispanics, Mexicans, and others) 65 . Moreover, while our research has made considerable strides in characterizing the transcriptome associated with Braak staging, the resolution of insights is inherently limited using bulk RNA sequencing data.…”

Section: Discussionmentioning

confidence: 99%

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

Yang,

Cieza,

Reyes-Dumeyer

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundsAlzheimer’s Disease (AD) exhibits a complex molecular and phenotypic profile. Investigating gene expression plays a crucial role in unraveling the disease’s etiology and progression. Transcriptome data across ethnic groups lack, negatively impacting equity in intervention and research.MethodsWe employed 565 brains across six U.S. brain banks (N=399 non-Hispanic Whites, N=113 Hispanics, N=12 African Americans) to generated bulk RNA sequencing from prefrontal cortex. We sought to identify cross-ancestry and ancestry-specific differentially expressed genes (DEG) across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We further validated our findings using the Religious Orders Study/Memory Aging Project brains (ROS/MAP; N=1,095). We conducted Gene Set and Variation and Enrichment analysis (GSVA; GSEA). We employed a machine-learning approach for phenotype prediction and gene prioritization to construct a polytranscriptomics risk score (PTRS) splitting our sample into training and testing sub-samples, either randomly or by ethnicity (“ancestry-agnostic” and “ancestry-aware”, respectively). Lastly, we validated top DEG using single-nucleus RNA sequencing (snRNAseq) data.ResultsWe identified DEG associated with Braak staging: AD-known genesVGF(padj=3.78E-07) andADAMTS2(padj=1.21E-04) were consistently differentially expressed across statistical models, ethnicities, and replicated in ROS/MAP. Genes from the heat shock protein (HSP) family, e.g.HSPB7(padj=3.78E-07), were the top differentially expressed genes and replicated in ROS/MAP. Ethnic-stratified analyses prioritizedTNFSF14andSPOCD1as top Hispanics DEG. GSEA highlighted “Alzheimer disease” (padj=4.24E-06) and “TYROBP causal network in microglia” (padj=1.68E-08) pathways. Up- and down-regulated genes were enriched in several pathways (e.g. “Immune response activation signal pathways”, “Vesicle-mediated transport in synapse”, “cognition”). Ancestry-agnostic and ancestry-aware PTRS effectively classified brains (AUC=0.77 and 0.73 respectively) and replicated in ROS/MAP. snRNAseq validated prioritized genes, includingVGF (downregulated in neurons; padj=1.1 E-07).ConclusionsThis is the largest diverse transcriptome for AD in post-mortem tissue to our knowledge. We identified perturbated genes, pathways and network expressions in AD brains resulting in cross-ethnic and ethnic-specific findings, ultimately highlighting the diversity within AD pathogenesis. The latter underscores the need for an integrative and personalized approach in AD studies.

show abstract

Section: Discussionmentioning

confidence: 99%

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

Yang,

Cieza,

Reyes-Dumeyer

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We obtained de-identified autopsy brain tissue samples devoid of personal identifiers and compliant with HIPAA regulations consistent with previous practices [4,5,11,12,16,18]. The annotated dataset comprises a subset of 23 cases from 295 cases collected from three distinct Alzheimer’s Disease Research Centers (ADRCs): the University of California Davis ADRC, the Columbia ADRC, and the University of California San Diego ADRC, following published case-specific inclusion/exclusion criteria [18]. These cases came from a diverse pool of research subjects recruited from various sources, including the practices of participating neurologists and community-based recruitment.…”

Section: Methodsmentioning

confidence: 99%

“…Each ADRC prepared its FFPE sections, mounted the slides, and shipped unstained slides to the University of California Davis (UCD) for staining to minimize batch effects. As previously published [18], we performed all antibody staining procedures under laboratory best practice standards, meeting Federal, State of California, and UC Davis guidelines and regulations. We used appropriate positive and negative controls for each antibody in each run.…”

Section: Histology and Slide-level Assessmentsmentioning

confidence: 99%

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

Ghandian,

Albarghouthi,

Nava

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease. Accurate and efficient detection and quantification of NFTs in tissue samples aids in deeper phenotyping of Alzheimer disease and may reveal relationships with clinical, demographic, and genetic features. However, expert manual analysis can be time-consuming, subject to observer variability, and limited in handling the large amounts of data generated by modern imaging techniques. We present a scalable, open access, deep learning-based approach to quantify the NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. We trained a UNet model on 45 annotated 2400 μm by 1200 μm regions of interest (ROIs) selected from 15 unique WSIs of temporal cortex from Alzheimer disease cases from three institutes (University of California (UC)-Davis, UC-San Diego, and Columbia University). We developed a method to generate detailed segmentation ground truth masks at the pixel level directly from simple point annotations. The model achieved a precision of 0.53, recall of 0.60, and F1 score of 0.53 on a held-out test set of 7 WSIs, providing researchers with an efficient and reliable tool for NFT burden quantification. We compared this to an object detection model on the same dataset, which achieved comparable but more coarse-grained performance. Both models correlated with expert semi-quantitative scores at the whole-slide level. Our approach provides an open deep learning pipeline for detailed and scalable NFT spatial distribution and morphology analysis across large cohorts, which is not feasible through manual assessment.

show abstract

“… 37 Furthermore, AD‐related neuropathologies in frontal and temporal cortices have been reported to be higher in individuals of Hispanic decent compared to White individuals. 38 Another limitation is that sleep duration was self‐reported rather than objectively measured. However, this allows for data collection on a larger group of people and reduction in the rate of missing data.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Neuropathologic changes at age 90+ related to sleep duration 19 to 40 years earlier: The 90+ Study

Melikyan,

Kawas,

Paganini‐Hill

et al. 2024

Alzheimer's & Dementia

View full text Add to dashboard Cite

INTRODUCTIONWe investigated the association between sleep duration and neuropathologic changes 19 to 40 years later in oldest‐old (age 90+) participants of The 90+ Study.METHODSParticipants self‐reported sleep duration and underwent neuropathologic evaluation. We categorized sleep duration as < 7, 7 to 8 = reference, > 8 hours and dichotomized neuropathologic changes as present/absent. We estimated odds ratio (OR) and 95% confidence intervals (CI) using logistic regression.RESULTSIn 264 participants, mean age at sleep self‐report was 69 years, mean age at autopsy was 98 years, and mean interval between sleep self‐report and autopsy was 29 years (range: 19–40). Those reporting > 8 hours of sleep had lower likelihood of limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) inclusions (OR = 0.18; CI = 0.04–0.82) and amyloid beta deposits (OR = 0.34; 95% CI = 0.12–0.94).DISCUSSIONLong self‐reported sleep is associated with lower odds of neurodegenerative neuropathologic changes 19 to 40 years later in the oldest‐old, suggesting a potential role of sleep in accumulation of dementia‐related neuropathologies.Highlights Association of self‐reported sleep with non‐Alzheimer's disease neuropathologic changes has not been explored. Whether sleep duration is related to dementia neuropathologic changes decades later is unclear. Long self‐reported sleep is associated with lower odds of Alzheimer's disease neuropathologic change 19 to 40 years later in the oldest‐old. Long self‐reported sleep is associated with lower odds of limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change 19 to 40 years later in the oldest‐old.

show abstract

The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease

Cited by 5 publications

References 71 publications

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

MU-BRAIN: MUltiethnic Brain Rna-seq for Alzheimer INitiative

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

Neuropathologic changes at age 90+ related to sleep duration 19 to 40 years earlier: The 90+ Study

Contact Info

Product

Resources

About