Nature Genetics, a multi-omics analysis of brain tissue in patients with Alzheimer's disease at single nucleus resolution

Written | Qi human brain is composed of multiple heterogeneous cell subsets.
Recent studies have provided more precise molecular features in normal brains, but our understanding of cell heterogeneity in diseased brains is still limited
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For example, Alzheimer's disease (AD), although some snRNA-seq (mononuclear transcriptome) studies have been completed in human and mouse models, revealing its specific transcriptional changes, but these disease-related cell subtypes The regulatory factors have not yet been determined
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Although a lot of work intersects GWAS signal and functional genomics analysis, such as ATAC-seq, the resolution of these studies is obviously limited by the heterogeneity of cell types
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On July 8, 2021, the Vivek Swarup team from the University of California published an article titled Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease in the journal Nature Genetics.
This study focuses on AD patients and cognitive health SnATAC-seq and snRNA-seq were performed on the brain tissue samples of the control group, which defined AD-related gene regulatory programs at the epigenome and transcriptome levels, and provided a powerful tool for insight into the heterogeneous brain of cells, helping us to find A new biological approach to induce neurodegeneration
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First, the author performed a single-core resolution multi-omics analysis of 191,890 brain tissue nuclei from patients with advanced AD and an age-matched cognitive health control group, directly integrating snATAC-seq and snRNA-seq data sets, and at the same time Perform transcription factor blot analysis to further clarify cell type-specific transcription factor regulation
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Based on this experimental design, the author believes that candidate cis-regulatory elements (cCREs) target genes can be identified in a specific cell population, so cis-co-regulatory elements were constructed for each cell type of the two sets of samples.
Accessibility networks (cis co-accessibility networks, CCANs) [1] to clarify the cis-regulatory structure in the advanced AD group
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Through this method, the authors found that there is a significant overlap between cCREs-linked genes and cell type markers and up-regulated genes in the AD group, suggesting that cCREs play a key role in disease-related gene expression changes
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Next, the author used Monocle3 [2] to perform pseudo-time trajectory analysis on the integrated data of snATAC-seq and snRNA-seq in oligodendrocytes, microglia and astrocytes, and used variational loop automatic Encoders (recurrent variational Autoencoder, RVAE) [3] model gene expression and chromatin access dynamics to reveal the molecular mechanisms that drive the heterogeneity of glial cells in AD
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The authors discovered two key transcription factors in oligodendrocytes: NRF1 and SREBF1.
Among them, NRF1 motif variability is up-regulated in AD, while SREBF1 is the opposite.
In addition, NRF1 is negatively correlated with target genes at the end of the trajectory.
SREBF1 is positively correlated with the target gene at the beginning and end of the trajectory, indicating that SREBF1 acts as a transcriptional activator in the entire trajectory
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Similarly, the author also used the integrated data set to construct a microglia trajectory, and observed that the transcription factor SPI1 motif trajectory was negatively correlated with genes at the end of the trajectory, which also supports the previous findings that SPI1 is in advanced AD Play an inhibitory role
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The motif variability of CTCF and FOSL2 in astrocytes was down-regulated and up-regulated in the AD group, respectively.
Among them, the motif variability of FOSL2 was correlated with disease-associated astrocyte gene characteristics (disease-associated astrocyte, DAAs) and genes at the end of the locus are positively correlated, suggesting that FOSL2 may be an activator of DAA signaling
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In general, this method can reveal the role of transcription factors in regulating cell states (such as DAA)
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In order to further understand the genetic risk signals of AD, the authors performed cell type-specific linkage disequilibrium score regression analysis (LDSC) on snATAC-seq clustering [4]
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The authors found that the "star molecule" APOE locus in AD changes in the cis-regulating chromatin network in microglia and astrocyte diseases
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Subsequently, the authors developed a scWGCNA based on the weighted gene co-expression analysis (WGCNA) method, and identified three oligodendrocyte modules rich in SREBF1 target genes, and the expression of these target genes decreased in AD, emphasizing the Under the background of AD, the transcription factor SREBF1 in oligodendrocytes, which is almost unresearched, is worthy of future research, and it also proves that this method can generate new insights into diseases
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All in all, this comprehensive multi-omics analysis for advanced AD provides a unique perspective for understanding the cellular heterogeneity of disease pathogenesis, especially when confirming the mechanism of complex diseases such as AD, it is necessary to analyze the epigenome and transcription Strict understanding of the cell population-specific gene regulation system at the group level
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This study provides the possibility for future research on AD by identifying cell type-specific gl-cCREs that may mediate changes in the gene regulation of advanced AD, and transcription factors that may bind to these gl-cCREs in specific cell types.
New target
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Original link: https://doi.
org/10.
1038/s41588-021-00894-z Platemaker: 11 References 1.
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Mitra, R.
& Maclean, AL RVAgene: generative modeling of gene expression time-series data.
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375436 (2020).
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Finucane, HK et al .
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.
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50, 621–629 (2018).
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