As ever-improving analytical techniques enable scientists to better understand gene-expression changes in Alzheimer’s disease, Vivek Swarup, University of California, Irvine, and colleagues realized they could double up on analytical power by combining multiple approaches. They paired single-nucleus Assay for Transposase-Accessible Chromatin (ATAC) sequencing with RNA sequencing. This allowed them to assess both how accessible genes are for transcription and which ones are actually being transcribed in the same brain samples from people who had died with advanced AD.
- Together, single-nucleus ATAC and RNA-Seq map gene expression in AD brains.
- Oligodendrocyte, astrocyte, microglia signals stood out.
- AD GWAS loci linked to glial genes based on DNA accessibility and transcription.
- Transcription factor SREBF1 activates target genes in oligodendrocytes.
In the July 8 Nature Genetics, they described subsets of glial cells that are unique to healthy or AD tissue. How? They matched sections of DNA that were open for transcription-factor binding to upregulated genes. By finding active genes near AD risk loci in cells from AD tissue, the scientists linked these loci to the genes they modify. “This provides a finer map of molecular changes that occur in this complex condition,” Nilüfer Ertekin-Taner, Mayo Clinic, Jacksonville, Florida, wrote (full comment below). The authors also modeled how microglia, astrocytes, and oligodendrocytes might have become diseased.
This is the first time single-nucleus RNA and ATAC-Seq have been used together in AD tissue. “This was not possible three years ago,” Carlos Cruchaga, Washington University, St. Louis, told Alzforum. Marcos Costa, Institut Pasteur de Lille in France, confirmed the study’s novelty. “This work provides a valuable tool to the scientific community, as there are only three to four single-nucleus RNA-Seq datasets on AD, to my knowledge, none of which combine chromatin accessibility,” he wrote (full comment below). Ryan Corces, University of California, San Francisco, agreed. “This will undoubtably be a resource for my lab and for other researchers,” he told Alzforum.
One recent study combined bulk RNA-Seq and ATAC-Seq to analyze hippocampal tissue from APP/PS1 mice (Wang et al., 2020). Another had done the same in primary astrocytes and three types of induced-pluripotent-stem-cell-derived neurons from a healthy person (Song et al., 2019). Two more studies tested human iPSC-derived neurons created from familial AD donors and human-embryonic-stem-cell-derived microglia with AD variants (Caldwell et al., 2020; Liu et al., 2020). However, none of those were done in tissue from a person with AD.
Co-first authors Samuel Morabito, Emily Miyoshi, Neethu Michael, and colleagues harnessed the precision of single-nucleus RNA and ATAC sequencing. In the latter technique, an enzyme binds to open chromatin and inserts a tag sequence (Oct 2020 news). Sequencing the DNA to reveal these tags shows researchers which bits are accessible to transcription factors and are likely being expressed. RNA-Seq confirms gene expression.
From Brain to Information. After being isolated from AD brain tissue, nuclei were sequenced and sorted by cell type. Gene expression and chromatin openness were assessed. Transcription factor networks connected open chromatin to regulated genes, cell changes were modeled from healthy to diseased, and GWAS loci were paired with the genes they influenced. [Courtesy of Morabito et al., Nature Genetics, 2021.]
The researchers obtained postmortem samples from the UC Irvine Alzheimer's Disease Research Center. They used prefrontal cortices from 12 people who had died with late-stage AD, and from eight older controls. They isolated, and analyzed chromatin accessibility in, a total of 130,418 nuclei and sequenced the RNA of 61,472 nuclei. This collection contained excitatory and inhibitory neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte precursor cells. The scientists compiled their sequencing data into a database for analysis and turned it into a searchable web app.
What did they find? In AD tissue, each cell type comprised distinct subpopulations with differentially expressed genes (DEGs) and differentially accessible chromatin regions. For example, there were more astrocytes expressing lots of GFAP and CHI3L, the latter encoding the inflammation marker and AD biomarker YKL-40, while there were fewer astrocytes expressing little GFAP, the Wnt inhibitor WIF1, and the metallopeptidase ADAMTS17. Two related microglial subsets were more numerous in AD, both of which expressed the perivascular macrophage marker CD163 and high levels of the transcriptional activation marker SPP1.
To learn what was happening to gene transcription in advanced AD, the scientists scoured healthy and diseased genomes for cis-regulatory elements. CREs are bits of noncoding DNA that regulate transcription of nearby genes. ATAC-Seq and RNA-Seq offer complementary intel on CREs: If a region of DNA turns up in both, then it can be transcribed and actually is. If that region contained a promoter, then the researchers tagged this as a CRE-gene pair. They found 56,552 CREs near 11,440 genes, averaging four CREs per gene.
More than half the CREs sat in introns. Some genes were expressed in multiple cell types but were controlled by a different CRE in each cell type; many were DEGs or genes upregulated in AD (see image below).
Gene Overlap. In each brain-cell type, some genes controlled by a cis-regulatory element (large circles) were unique to the cell type (medium circles) and/or altered in AD (small circles). The overlap between large and small circles indicates genes that are differently expressed and regulated in AD. [Courtesy of Morabito et al., Nature Genetics, 2021.]
To estimate how well transcription factors were able to access DNA at their target genes, the authors searched open regions for binding sites. Several were enriched in astrocytes, microglia, and excitatory neurons from AD tissue. The scientists focused on two genes: SPI1, which encodes the master transcriptional regulator PU.1 in microglia, and Nuclear Respiratory Factor (NRF1), which regulates mitochondrial function in oligodendrocytes. The two microglial subpopulations that were more numerous in advanced AD had more open SPI1 binding sites, yet fewer of its target genes expressed. This suggests that, in late-stage AD, SPI1 acts as a transcriptional repressor, the authors wrote. As for NRF1, it was dysregulated in certain oligodendrocyte subgroups, hinting at a possible role in mitochondrial dysfunction in AD, the authors suggest.
The researchers created cell-specific networks of transcription factors and genes they target, focusing on those that were open for expression in AD cells. SPI1 and NRF1 each regulate multiple AD DEGs and genes near AD risk loci in microglia and oligodendrocytes, respectively (see image below).
Transcription Networks. Late-stage AD microglia (left) and oligodendrocytes (right) have different transcription factors (big blue circles) and predicted target genes. [Courtesy of Morabito et al., Nature Genetics, 2021.]
How might the cells have morphed from healthy to diseased? The scientists strung together transcriptional and epigenetic information from microglia, astrocytes, and oligodendrocytes from cognitively normal people and from people with late-stage AD. Some of the former had mild plaque and tangle pathology, giving the scientists an intermediate point in a hypothetical cellular trajectory. Then, they used mathematical modeling to identify DEGs in each cell type at each stage. In this way, they constructed a disease scale for each cell type. “Every sample is a snapshot, so combining them into a continuum from healthy to AD allows us to figure out where cells are on their trajectory,” Swarup told Alzforum.
First, disease-associated oligodendrocytes. Such a subpopulation was recently identified in cortex tissue of 5xFAD mice and people with AD (Jan 2020 news). Morabito and colleagues split healthy and AD oligodendrocytes into three subpopulations based on unique gene expression: newly formed, myelin-forming, and mature cells. As cells progressed from healthy to diseased in the model, there were fewer newly formed and myelin-forming oligodendrocytes and more mature cells.
The researchers then scrutinized NRF1 and Sterol Regulatory Element Binding Factor 1 (SREBF1) in oligodendrocytes. They compared expression of both these transcription factors to expression of their respective target genes across the oligodendrocyte healthy-to-disease continuum. Oligodendrocytes from AD brain tissue upregulated NRF1 but expressed fewer of its target genes. This indicates that NRF1 acts as a repressor, the authors wrote.
As for SREBF1, it and its target genes were both downregulated in AD brain oligodendrocytes; expression going in the same direction indicates the factor normally acts as an activator. This was corroborated by two more analyses. A protein-protein interaction network in AD oligodendrocytes revealed fewer accessible SREBF1 binding sites and fewer copies of the protein itself. Likewise, single-nucleus, weighted gene co-expression analysis, aka WGCNA, also identified three subpopulations of oligodendrocytes from AD tissue with reduced expression of SREBF1 target genes and proteins. The authors noted that a physiological function of SREBF1 is to regulate cholesterol homeostasis, and Aβ has been previously proposed to inhibit its activation (Mohamed et al., 2018).
Likewise, the scientists spotted disease-associated astrocytes in these AD brains. Identified in 5xFAD amyloidosis mice, DAAs are a GFAP-rich subset of astrocytes that express a unique set of genes (Habib et al., 2020). To study what went on in the DAAs, the researchers correlated DEGs to two transcription factors: the master chromatin regulator CCCTC-binding factor (CTCF), and FOSL2, which encodes a subunit of the cell proliferation and differentiation regulator AP.1. CTCF was associated with DEGs in GFAP-low, aka healthy, astrocytes. It was downregulated in DAAs, while FOSL2 was up. Taken together, the authors propose that CTCF may promote homeostatic astrocytes while FOSL2 may stimulate DAA production.
Regarding disease-associated microglia, aka DAMs, in AD tissue they congregate around plaques and can be activated with or without TREM2 (June 2017 news; Sep 2017 news). In the late-stage AD brains analyzed here, microglia downregulated homeostatic genes; the number of TREM2-independent DAMs grew while TREM2-dependent cells became fewer.
What about AD risk loci? Compared to microglia from healthy controls, microglia from AD tissue, including DAMs, held more GWAS SNPs, including variants in APOE, BIN1, ADAM10, and SLC24A4. Lo and behold, gene regulation was different at these loci of microglia from AD brains.
These techniques could advance other questions in AD biology by using them on different samples. For example, Costa is interested in gene-expression changes in other brain regions, such as the entorhinal cortex and hippocampus. Those areas undergo more significant gene-expression alterations than the prefrontal cortex, Costa wrote, hence the authors may have missed important changes that could characterize advanced stages of AD pathology (full comment below). Moreover, that this study found its strongest signals in glia may reflect the extensive neuronal loss and gliosis in advanced AD. To this, Swarup replied that a follow-up paper on the neuronal results of this study is in the works.
Ertekin-Taner and Cruchaga also consider changes in preclinical or mild AD a priority. “We know there may be other processes ongoing at early disease stages, so it would be nice to see similar single-nucleus multi-omics studies on preclinical AD,” Cruchaga said.
Corces further mentioned a new commercial kit enabling researchers to do RNA and ATAC-Seq on the same nuclei, rather than splitting nuclei from a tissue sample between the two assays then manually integrating the data, as the authors had to do in this paper. “While this data was probably generated over a few years, this type of data can now be generated within a few months using the combination kit,” he told Alzforum. Ever-improving analytics, indeed.—Chelsea Weidman Burke
Research Models Citations
- Epigenomic Roadmap Points to Causal Genes
- Human and Mouse Microglia React Differently to Amyloid
- Hot DAM: Specific Microglia Engulf Plaques
- ApoE and Trem2 Flip a Microglial Switch in Neurodegenerative Disease
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