. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease. Nat Genet. 2021 Jul 8; PubMed.

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  1. This work is an important step toward filling three important gaps in the study of gene expression alterations in Alzheimer’s disease:

    • Understanding whether gene expression alterations observed in previous studies based on tissue analysis (bulk RNA-seq) are a consequence of changes in cellular composition (neurodegeneration plus gliosis, for example).
    • Identifying cell-type specific changes in gene expression.
    • Characterizing possible epigenetic regulators of gene expression, which is a prerequisite for linking GWAS hits to gene-expression alterations observed in the diseased brain.

    In principle, the methodology used is appropriate to address these points, though two aspects of the work temper my enthusiasm. First, the brain region used to perform the snATAC-Seq is perhaps not ideal. We and other researchers have shown that gene-expression alterations in the prefrontal cortex are less significant than those observed in brain regions affected at earlier stages of AD, such as the entorhinal cortex or hippocampus. Thus, the authors are likely missing important gene-expression alterations that could characterize AD pathology at advanced stages. Second, there is a strong bias toward glial cells, mainly because these cells (especially oligodendrocytes) are overrepresented in the samples. Consequently, there is little discussion about gene-expression alterations in neurons, which are the functional units of our brains. Glial cells are important, but I wonder whether a “transcriptomic characterization of the AD brain” can neglect neuronal cells.

    On the other hand, the data generated and now publicly available, is an interesting resource to evaluate gene expression alterations in specific cell types of the AD brain. To my knowledge, there are only three to four snRNA-Seq datasets on AD, and none of them combine chromatin accessibility. Thus, the work provides a valuable tool to the scientific community.  

    The work also has merit in raising discussion about the identification of disease-associated microglia (DAM) in the human brain using snRNA-Seq, which has been recently challenged by other researchers.

    As for the link between GWAS hits and gene-expression alterations observed in the AD brain, I think we need to await new studies on other brain regions and with larger number of brain samples obtained from patients at different stages of the disease. With the data presented in this work, possible causal links between the genetic variants identified in GWAS and the gene-expression alterations in AD remain elusive.

    View all comments by Marcos Costa
  2. In this paper, the authors provide a comprehensive characterization of human prefrontal cortex tissue from 12 Alzheimer’s disease and eight control brains by both single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-Seq) and RNA sequencing (RNA-Seq, subset of 11 AD and seven control brains). Through a series of bioinformatics analyses, they integrate these data modalities from 191,890 nuclei (130,418 for snATAC-Seq and 61,472 for snRNA-Seq):

    1. to identify cell cluster-specific, differentially expressed genes (DEGs) and differentially accessible chromatin regions in AD;
    2. to build cis co-accessibility networks (CCANs) of regulatory elements and their target genes;
    3. to construct cell-type specific transcription factor (TF) regulatory networks with a particular focus on TFs SPI1 in microglial and NRF1 in oligodendrocyte networks;
    4. to perform pseudotime trajectory analysis and investigate correlations of select TFs with genes at different pseudotime stages of cell-specific trajectories;
    5. to integrate their single-nucleus data with disease GWAS variants; and
    6. to generate co-expression networks using aggregate expression levels from their single nucleus data.

    Their findings build upon prior work on the transcriptional and genomic landscape of AD brains by adding an important layer of chromatin-accessibility data, performing both snATAC-Seq and snRNA-Seq in the same samples and focusing on both cis and trans regulatory elements.

    Prior brain gene-expression quantitative trait loci (eQTL) studies integrated with AD risk GWAS have discovered many significant eQTL that also influence AD risk, highlighting the potential role of regulatory disease risk variants in AD (Zou et al., 2010; Zou et al., 2012; Allen et al., 2015; Carrasquillo et al., 2017). Brain transcriptome studies utilizing bulk RNA-Seq data (Allen et al., 2016; Wang et al., 2018; De Jager et al., 2018) revealed myriad perturbations in transcripts and transcriptional networks implicating pathways and cell types including myelination/oligodendrocytes (McKenzie et al., 2017; Allen et al., 2018) and innate immunity/microglia (Zhang et al., 2013). 

    Both deconvolution analyses of bulk transcriptome data (McKenzie et al., 2018; Wang et al., 2020) and recent single-cell/single-nucleus transcriptome studies (Mathys et al., 2019; Zhou et al., 2020; Lau et al., 2020; Olah et al., 2020) are beginning to uncover the complexity of the cell-specific expression changes in AD. These studies underscore the variety of cellular subtypes annotated as clusters in sn/scRNA-Seq data and their distinct transcriptional changes in AD, thereby providing a finer map of the molecular changes that occur in this complex condition.   

    This study confirms prior observations pertinent to cell proportion and cell-specific transcriptional changes in AD, but extends beyond these observations to bioinformatic “matching” of transcripts to both cis- and trans-regulatory elements. This provides potential, testable regulatory mechanisms that underlie transcriptional dysregulation in AD. The regulatory elements, their target genes, and biological pathways identified in this study could serve as novel therapeutic or biomarker targets and also be leveraged to model various cell-specific dysfunction in AD.

    Despite the wealth of data and analytic paradigms afforded by this study, its main shortcoming is the small sample size. This limits generalization with respect to the full spectrum of AD. The extent to which the transcriptome and epigenetic changes in end-stage autopsy brain samples reflect disease mechanisms that initiate and propagate AD is an ongoing question we must collectively try to answer in this field.

    Future studies on larger autopsy cohorts, racially and ethnically diverse patient populations, as well as longitudinal peripheral tissue samples are necessary to address these concerns. In the meantime, integrative multi-omics studies at the single-cell level can be expected to continue to expand our understanding of the molecular complexities of AD.

    References:

    . Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease. Neurology. 2010 Feb 9;74(6):480-6. PubMed.

    . Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 2012;8(6):e1002707. PubMed.

    . Late-onset Alzheimer disease risk variants mark brain regulatory loci. Neurol Genet. 2015 Aug;1(2):e15. Epub 2015 Jul 23 PubMed.

    . A candidate regulatory variant at the TREM gene cluster associates with decreased Alzheimer's disease risk and increased TREML1 and TREM2 brain gene expression. Alzheimers Dement. 2017 Jun;13(6):663-673. Epub 2016 Dec 8 PubMed.

    . Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases. Sci Data. 2016 Oct 11;3:160089. PubMed.

    . The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease. Sci Data. 2018 Sep 11;5:180185. PubMed.

    . A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research. Sci Data. 2018 Aug 7;5:180142. PubMed.

    . Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener. 2017 Nov 6;12(1):82. PubMed.

    . Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases. Alzheimers Dement. 2018 Mar;14(3):352-366. Epub 2017 Oct 31 PubMed.

    . Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell. 2013 Apr 25;153(3):707-20. PubMed.

    . Brain Cell Type Specific Gene Expression and Co-expression Network Architectures. Sci Rep. 2018 Jun 11;8(1):8868. PubMed.

    . Deciphering cellular transcriptional alterations in Alzheimer's disease brains. Mol Neurodegener. 2020 Jul 13;15(1):38. PubMed.

    . Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019 Jun;570(7761):332-337. Epub 2019 May 1 PubMed.

    . Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease. Nat Med. 2020 Jan;26(1):131-142. Epub 2020 Jan 13 PubMed.

    . Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer's disease. Proc Natl Acad Sci U S A. 2020 Oct 13;117(41):25800-25809. Epub 2020 Sep 28 PubMed.

    . Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer's disease. Nat Commun. 2020 Nov 30;11(1):6129. PubMed.

    View all comments by Nilufer Ertekin-Taner

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