Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

Morabito, Samuel; Miyoshi, Emily; Michael, Neethu; Shahin, Saba; Martini, Alessandra Cadete; Head, Elizabeth; Silva, Justine; Leavy, Kelsey; Perez‐Rosendahl, Mari; Swarup, Vivek

doi:10.1038/s41588-021-00894-z

Cited by 352 publications

(513 citation statements)

References 72 publications

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“…Many recent studies of pleiotropy, colocalization and causality have focused on molecular phenotypes such as gene expression, chromatin accessibility or DNA methylation ( Umans et al, 2020 ; Vuckovic et al, 2020 ; Ye et al, 2020 ; Morabito et al, 2021 ). Numerous QTLs for various molecular phenotypes have been identified for these classes of traits (most prominently expression QTLs or eQTLs).…”

Section: Empirical Demonstrations Of the Existence And Nature Of Pleiotropymentioning

confidence: 99%

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Koch

Sunyaev

2021

Front. Genet.

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Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

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Section: Empirical Demonstrations Of the Existence And Nature Of Pleiotropymentioning

confidence: 99%

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Koch

Sunyaev

2021

Front. Genet.

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“…Although DEG analysis identified genes which were statistically enriched in aggregated cell-types (Figure 1 and Figure 2), it did not provide how transcriptional states of single cells transition between distinct biological states. Pseudotime (trajectory) analysis has been widely utilized to compute transition of transcriptional states of single cells in various biological processes including development and disease (Cao et al, 2019;Morabito et al, 2021;Qiu et al, 2017;Rossi et al, 2019). To elucidate the cell-type specific progression of transcriptional dynamics of single cells during puberty, we performed pseudotime analysis, which measures transcriptional progression of single cells through a biological process (puberty) by learning a principal graph from the combinatorial gene expression in single cells in UMAP space (Cao et al, 2019;Qiu et al, 2017).…”

Section: Pubertal Transcriptional Trajectories In Vgatmentioning

confidence: 99%

Pubertal sex hormones control transcriptional trajectories in the medial preoptic area

Hashikawa

Liu

et al. 2021

Preprint

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Pubertal maturation aids development of emotion, cognition, and reproduction. We investigated transcriptional dynamics in the medial preoptic area (MPOA), a hypothalamic center for reproductive behaviors, in male and female mice at single-cell resolution (scRNAseq) during puberty. Defined subsets of neurons expressing Slc32a1 and Esr1 (Vgat+ Esr1+) were the most transcriptionally dynamic compared to other cell types throughout puberty. These cell type specific transcriptional progressions towards adulthood were bidirectionally controlled by the levels of circulating testosterone and estradiol. Selective deletion of Esr1 in Slc32a1-expressing cells in the MPOA prior to puberty arrested transcriptional progression and revealed a sexually dimorphic gene-regulatory network governed by Esr1. Deletion of Esr1 in Vgat+ cells prevented the development of mating behavior in both sexes. These analyses reveal both sexually common and dimorphic transcriptional progressions during puberty as well as their regulatory mechanisms, which have important implications towards understanding adaptative and maladaptive processes governing adolescent brain development.

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“…We highlighted enriched GO-terms (FDR < 5e-7) forming connected PPI clusters, revealing the interplay of glycolysis, hypoxia, unfolded protein response, and translation with the general stress response ( Fig 1E , Table S1). To identify all genes co-regulated with stress, we applied scWGCNA (single cell weighted gene co-expression network analysis, (Morabito et al, 2021)) and found 12 gene modules (Appendix Figure S1A), one of which was specific to stressed cells (Stress module Fig 1F ). Gene set enrichment analysis (GSEA) on the stress module identified the strongest enrichment for “response to hypoxia”, “cellular response to ER stress”, and GO-terms of glycolytic processes ( Fig 1G , Appendix Figure S1B).…”

Section: Resultsmentioning

confidence: 99%

Cellular stress in brain organoids is limited to a distinct and bioinformatically removable subpopulation

Vértesy

Eichmüller

Naas

et al. 2022

Preprint

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Organoids enable disease modeling in complex and structured human tissue, in vitro. Like most 3D models, they lack sufficient oxygen supply, leading to cellular stress. These negative effects are particularly prominent in complex models, like brain organoids, where they can prevent proper lineage commitment. Here, we analyze brain organoid and fetal single cell RNA sequencing (scRNAseq) data from published and new datasets totaling over 190,000 cells. We describe a unique stress signature found in all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined organoid cell population, and present Gruffi, an algorithm that uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. Our data show that adverse effects of cell stress can be corrected by bioinformatic analysis, improving developmental trajectories and resemblance to fetal data.

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Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

Cited by 352 publications

References 72 publications

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Pubertal sex hormones control transcriptional trajectories in the medial preoptic area

Cellular stress in brain organoids is limited to a distinct and bioinformatically removable subpopulation

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