Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

Dávila-Velderrain, José; Mathys, Hansruedi; Mohammadi, Shahin; Ruzicka, Brad; Jiang, Xueqiao; Ng, A.; Da, Bennett; Tsai, Li‐Huei; Kellis, M

doi:10.1101/2021.07.01.450715

Cited by 11 publications

(15 citation statements)

References 59 publications

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“…We analyzed snRNA-seq data by using ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32] (Supplementary Fig. 1), from striatal samples harvested from human striatum and from zQ175 and R6/2 HD model mice (Fig.…”

Section: Identification Of Earliest-depleted Spn Subtype In Human Hd ...mentioning

confidence: 99%

Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease

et al. 2023

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Striatal projection neurons (SPNs), which progressively degenerate in human patients with Huntington’s disease (HD), are classified along two axes: the canonical direct-indirect pathway division and the striosome-matrix compartmentation. It is well established that the indirect-pathway SPNs are susceptible to neurodegeneration and transcriptomic disturbances, but less is known about how the striosome-matrix axis is compromised in HD in relation to the canonical axis. Here we show, using single-nucleus RNA-sequencing data from male Grade 1 HD patient post-mortem brain samples and male zQ175 and R6/2 mouse models, that the two axes are multiplexed and differentially compromised in HD. In human HD, striosomal indirect-pathway SPNs are the most depleted SPN population. In mouse HD models, the transcriptomic distinctiveness of striosome-matrix SPNs is diminished more than that of direct-indirect pathway SPNs. Furthermore, the loss of striosome-matrix distinction is more prominent within indirect-pathway SPNs. These results open the possibility that the canonical direct-indirect pathway and striosome-matrix compartments are differentially compromised in late and early stages of disease progression, respectively, differentially contributing to the symptoms, thus calling for distinct therapeutic strategies.

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Section: Identification Of Earliest-depleted Spn Subtype In Human Hd ...mentioning

confidence: 99%

Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease

et al. 2023

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“…3), which seems a class of striatal interneuron according to our preliminary data (Pineda et al, in preparation). These cell types, identified by ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] , co-clustered perfectly atop each other between controls and the HD samples (magenta and cyan dots, Fig. 1A-C), affirming the consistency of cell-type annotations across phenotypes.…”

Section: Resultsmentioning

confidence: 69%

“…We analyzed snRNA-seq data by using ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (Extended Data Fig. 1), from striatal samples harvested from human striatum and from R6/2 and zQ175 HD model mice, and originally reported in an initial study without attention to the coordinated compartmental transcriptomics examined here (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We used the ACTIONet and scran R packages to normalize, batch correct, and cluster single-nucleus gene counts. Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph.…”

Section: Methodsmentioning

confidence: 99%

“…Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph. A modified version of the stochastic gradient descent-based layout method was used in the uniform manifold approximation and projection (UMAP) algorithm 59 , to visualize the ACTIONet graph.…”

Section: Methodsmentioning

confidence: 99%

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Huntington’s Disease Produces Multiplexed Transcriptional Vulnerabilities of Striatal D1-D2 and Striosome-Matrix Neurons

Matsushima

Pineda

Crittenden

et al. 2022

Preprint

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Striatal cell-type-specific vulnerability in Huntington’s disease (HD) preferentially affects dopamine D2R-expressing projection neurons (SPNs), compatible with manifest motor symptomatology in HD. Transcriptional studies of striatal striosome-matrix compartmentalization in HD are, however, limited, despite pathologic evidence for striosome vulnerability aligning with early mood symptomatology. We used single-nucleus RNA-sequencing on striatal samples from two murine models, and rare Grade 1 HD patient tissues, to examine striosome and matrix sub-clusters within parent D1 and D2 SPN clusters. In human HD, striosomal SPNs were the most depleted SPN population. Surprisingly, for both mouse models, transcriptomic distinctiveness was diminished more for striosome-matrix SPNs than for D1-D2 SPNs. Compartmental markers were dysregulated so as to cancel endogenous identities as striosomal or matrix SPNs, but markers for D1-D2 exhibited less identity obscuring. The canonical striosome-matrix as well as D1-D2 organizations of the striatum thus are both strongly, but differentially, compromised in HD and are targets for therapeutics.

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Artificial intelligence for dementia genetics and omics

Bettencourt,

Skene,

Bandres‐Ciga

et al. 2023

Alzheimer's & Dementia

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Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.Highlights We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

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Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer’s disease

Cited by 11 publications

References 59 publications

Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease

Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease

Huntington’s Disease Produces Multiplexed Transcriptional Vulnerabilities of Striatal D1-D2 and Striosome-Matrix Neurons

Artificial intelligence for dementia genetics and omics

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