The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index; 24 of the 27 sources its claims cite were checked for support.
Brain cell type–specific enhancer–promoter interactome maps and disease - risk association
Open accessAbstract
Linking enhancers to disease: Enhancers are genomic regions that regulate gene expression, sometimes in a cell-dependent manner. However, most of our knowledge of human brain cell–type enhancers derives from studies of bulk human brain tissue. Nott et al. examined chromatin and promoter activity in cell nuclei isolated from human brains. Genetic variants associated with brain traits and disease showed cell-specific patterns of enhancer enrichment. These data indicate that Alzheimer's disease is regulated by genetic variants within microglial cells, whereas psychiatric diseases tend to affect neurons. Science , this issue p. 1134
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
24 of 27 cited sources checked · 8 supported · 1 flagged · accuracy not yet measured
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“To better understand AD genetics and the microglia interactome, we distinguished likely causal variants from those in linkage disequilibrium by applying fine mapping and identified 261 credible set variants (18).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Disputedcites [18] Kunkle BW, Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature Genetics 51, 414– 430 (2019). 30820047 PMC6463297 sourceExperimental — accuracy not yet measured
The Results section, under “Functional interpretation of genetic variants” (page unavailable), confirms that the authors used fine mapping to identify credible sets. But it reports 345 plausible causal SNPs in the HLA-DRB1 locus alone, which contradicts a total of 261 credible-set variants. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Disputed
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Promoters associated with cell type signature genes preferentially exhibited corresponding H3K27ac profiles”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Could not verifycites [13] Wang D, Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY) 362, eaat8464 (2018). PMC6413328 30545857 sourceExperimental — accuracy not yet measured
The Transcriptome analysis: Bulk and single cell section describes cell-type expression signatures and marker genes, while the Enhancers section describes H3K27ac profiles. Neither passage reports whether promoters of those marker genes preferentially exhibit H3K27ac profiles corresponding to the same cell type. I searched for the stated promoter–signature-gene relationship and cell-type-specific promoter acetylation; page numbers were not available in our copy. (LLM, conf=0.73)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“We identified putative active promoters and enhancers in each cell type and found a one-to many relationship between promoters and enhancers (8).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Could not verifycites [8] Mumbach MR, HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature methods 13, 919 (2016). 27643841 PMC5501173 sourceExperimental — accuracy not yet measured
The Main Text (page not provided) associates some HiChIP loops with active enhancers and promoters, but does not report how many enhancers connect to a promoter in each cell type. Searches for a one-to-many promoter–enhancer relationship and for multiple enhancers per promoter found no passage addressing that relationship. (LLM, conf=0.73)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Promoter H3K27ac signal correlated with gene expression of the corresponding cell type more closely than ATAC-seq and H3K4me3 ChIP-seq”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Could not verifycites [12] Zhang Y, Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse. Neuron 89, 37– 53 (2016). 26687838 PMC4707064 sourceExperimental — accuracy not yet measured
The Results section on cell-type transcriptomes reports RNA-seq expression profiles and correlations between samples, not correlations between promoter marks and expression. Searches for H3K27ac, ATAC-seq, H3K4me3, and chromatin accessibility found no passage in our copy comparing those signals. (LLM, conf=0.72)
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“ATAC-seq and H3K27ac ChIP-seq profiles generated from PU.1 nuclei were highly correlated with those previously defined in ex vivo microglia”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Partly supportedcites [14] Gosselin D, An environment-dependent transcriptional network specifies human microglia identity. Science (New York, NY) 356, eaal3222 (2017). PMC5858585 28546318 sourceExperimental — accuracy not yet measured
The “Enhancer landscape of human microglia” section reports that ATAC-seq and ChIP-seq data, including H3K27ac, were highly correlated across human individuals and mouse biological replicates. It does not report a comparison between profiles generated from PU.1 nuclei and previously defined ex vivo microglia profiles; its PU.1 ChIP-seq experiments used microglia isolated from six individuals. (LLM, conf=0.85)
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The vast majority of these disease-risk genetic variants are located in non-coding regions of the genome (5).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [5] Maurano MT, Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190– 1195 (2012). 22955828 PMC3771521 source
The introductory passage following the Abstract states that the majority (~93%) of disease- and trait-associated variants identified through GWAS lie within noncoding sequence. That directly supports the citing sentence’s description of a vast majority of disease-risk variants, in the context of GWAS findings. (LLM, conf=0.94)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Next, we determined PLAC-interactions between active promoters and AD-risk credible set variants (19) and identified forty-one genes that were linked to these variants across cell types (fig. S8).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [19] Jansen IE, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature Genetics 51, 404– 413 (2019). 30617256 PMC6836675 source
The Results section, under “Functional interpretation of genetic variants” (page unavailable), says the source identified credible sets of causal SNPs from AD-associated variants. In the citing sentence, citation (19) identifies the variants used; the forty-one linked genes are presented as the citing authors’ own finding, not a finding attributed to this source. (LLM, conf=0.91)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Chromatin loops were identified between active promoters and distal regulatory regions in microglia, neurons and oligodendrocytes (26).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Partly supportedcites [26] Juric I, MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments. PLoS Comput Biol 15, e1006982 (2019). 30986246 PMC6483256 sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says distal enhancers frequently form chromatin loops with target-gene promoters, supporting the general type of interaction. The Results and Methods passages describe analyses in mouse embryonic stem cells and GM12878 cells, not microglia, neurons, or oligodendrocytes. Our copy therefore does not support the claim that these loops were identified in the three named cell types. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Most bulk brain enhancer regions identified by PsychENCODE overlapped with the nuclei cell type enhancers (94%) (13).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Could not verifycites [13] Wang D, Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY) 362, eaat8464 (2018). PMC6413328 30545857 sourceExperimental — accuracy not yet measured
The Enhancers section describes a reference set of 79,056 brain enhancers, but it does not report their overlap with enhancers from sorted nuclei. Searches for “94%,” “sorted nuclei enhancers,” and “cell type enhancers overlap bulk” found no passage establishing the claimed comparison. (LLM, conf=0.72)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In contrast, AD SNP heritability was most highly enriched in microglia regulatory elements (Fig. 1D), specifically microglia enhancers (18-23).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Partly supportedcites [18] Kunkle BW, Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature Genetics 51, 414– 430 (2019). 30820047 PMC6463297 sourceExperimental — accuracy not yet measured
The Results section on functional interpretation (page unavailable) reports AD SNP-heritability enrichment in H3K27ac marks and super enhancers, but does not identify these as microglial elements or say microglial enhancers had the strongest enrichment. The separate gene-set Results (page unavailable) report a suggestive microglia association, not microglial enhancer heritability enrichment. (LLM, conf=0.86)
- Partly supportedcites [19] Jansen IE, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature Genetics 51, 404– 413 (2019). 30617256 PMC6836675 sourceExperimental — accuracy not yet measured
The Results, under “Functional interpretation of genetic variants” (page unavailable), report AD SNP-heritability enrichment in H3K27ac marks and super enhancers, but do not identify these as microglial elements or say microglial enhancers had the strongest enrichment. The separate “Gene-sets implicated in AD and AD-by-proxy” section reports a microglial gene-expression association, not enrichment of SNP heritability in microglial regulatory elements. (LLM, conf=0.88)
- Partly supportedcites [20] Huang KL, A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease. Nat Neurosci 20, 1052– 1061 (2017). 28628103 PMC5759334 sourceExperimental — accuracy not yet measured
The Results section (page not available) reports enrichment of AD heritability in myeloid and B-lymphoid cells, including monocyte enhancers. It does not report that microglial regulatory elements were tested or that microglial enhancers showed the strongest enrichment; the Discussion instead calls myeloid cells the top candidate cell types. (LLM, conf=0.84)
- Partly supportedcites [21] Novikova G, Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways. bioRxiv, 694281 (2019). sourceExperimental — accuracy not yet measured
The section “AD risk alleles are specifically enriched in active enhancers of monocytes, macrophages and microglia” (page not available) reports enrichment in microglial active enhancers. It also reports enrichment in monocytes and macrophages; it does not establish that microglial regulatory elements had the strongest enrichment. (LLM, conf=0.86)
- Could not verifycites [22] Tansey KE, Hill MJ, Enrichment of schizophrenia heritability in both neuronal and glia cell regulatory elements. Transl Psychiatry 8, 7 (2018). 29317610 PMC5802526 sourceExperimental — accuracy not yet measured
The Results report enrichment of schizophrenia heritability at neuronal and non-neuronal H3K4me3 sites, not AD heritability. The Discussion mentions microglia only as one possible component of the NeuN-negative population. A search for AD and microglial enhancers found no passage assessing the claimed enrichment. (LLM, conf=0.76)
- Partly supportedcites [23] Tansey KE, Cameron D, Hill MJ, Genetic risk for Alzheimer's disease is concentrated in specific macrophage and microglial transcriptional networks. Genome Med 10, 14 (2018). 29482603 PMC5828245 sourceExperimental — accuracy not yet measured
The Results section on common-variant heritability reports significant enrichment in microglial open-chromatin regions containing SPI1 or Spdef motifs. It does not identify microglial enhancers as the most highly enriched regulatory elements; its heritability analysis groups regions by transcription-factor motif, not enhancer status. (LLM, conf=0.87)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
- Probably not a substantial claim. 5
- It may state two separate claims as one. 25
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The activity of enhancers depends on three-dimensional enhancer-promoter interactions (8)”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Partly supportedcites [8] Mumbach MR, HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature methods 13, 919 (2016). 27643841 PMC5501173 sourceExperimental — accuracy not yet measured
The Main Text reports that certain Oct4–Smc1a chromatin loops are associated with active enhancers and promoters. It does not establish that enhancer activity depends on, or requires, those physical interactions. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Unique enhancer repertoires underly particular patterns of gene expression and enable cell type-specific responses (7).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [7] Heinz S, Romanoski CE, Benner C, Glass CK, The selection and function of cell type-specific enhancers. Nature reviews Molecular cell biology 16, 144 (2015). 25650801 PMC4517609 source
The Introduction states that different cell types have different complements of active cis-regulatory elements and that pre-existing enhancer sets are largely responsible for cell type-specific gene expression and responses to external stimuli. This directly supports the citing sentence; no page number is provided in our copy. (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Clusters of multiple enhancers, referred to as super enhancers, are particularly important in driving the expression of cell identity genes (6).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [6] Hnisz D, Super-enhancers in the control of cell identity and disease. Cell 155, 934– 947 (2013). 24119843 PMC3841062 source
The Introduction describes super-enhancers as clusters of enhancers at genes controlling the pluripotent state. The Results section (‘Super-enhancers in many cell types’) explicitly says they drive expression of genes that control and define cell identity. (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“We found a strong enrichment of heritability for variants within neuronal enhancers and promoters for all psychiatric disorders and behavioral traits (Fig. 1D), which was substantially lower in PsychENCODE bulk brain enhancers (Fig. 1D) (13).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [13] Wang D, Comprehensive functional genomic resource and integrative model for the human brain. Science (New York, NY) 362, eaat8464 (2018). PMC6413328 30545857 source
The citation identifies the PsychENCODE bulk-brain enhancer set used for the citing authors’ Fig. 1D comparison. The source’s Enhancers section (page unavailable) describes its reference set of 79,056 prefrontal-cortex enhancers. The heritability comparison is presented as the citing authors’ own finding, not as a result attributed to this source. (LLM, conf=0.88)
- Could not verifycites [17] S. m. a. methods. sourceExperimental — accuracy not yet measured
The Methods section (‘Regulatory variant and eQTL analysis’; page unavailable) describes annotation of Alzheimer’s disease risk variants, not heritability enrichment across psychiatric disorders and behavioral traits. Searches for neuronal enhancers and promoters and PsychENCODE bulk-brain enhancers found no passage reporting the claimed comparison. (LLM, conf=0.76)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The BIN1 microglia-specific enhancer is PLAC-linked to the BIN1 promoter (Fig. 4A), binds to PU.1 (14) and contains the AD-risk variant rs6733839, which has the second highest AD-risk score after APOE and was fine-mapped as a casual variant (Fig. 3A).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Supportedcites [14] Gosselin D, An environment-dependent transcriptional network specifies human microglia identity. Science (New York, NY) 356, eaal3222 (2017). PMC5858585 28546318 source
Citation (14) is attached specifically to the claim that the BIN1 enhancer binds PU.1. In “Enhancer landscape of human microglia” (page not specified), the paper identifies Figure 3A as the BIN1-region track and reports PU.1 binding measured by ChIP-seq in that figure. The PLAC link, risk ranking, and fine-mapping claims are not attributed to this citation. (LLM, conf=0.85)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This finding that the most significant GWAS risk allele associated with BIN1 resides in a microglia-specific enhancer provides a rational for further investigation of its function in these cells (27).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Partly supportedcites [27] Crotti A, BIN1 favors the spreading of Tau via extracellular vesicles. Sci Rep 9, 9477 (2019). 31263146 PMC6603165 sourceExperimental — accuracy not yet measured
The Results identify rs6733839 as the more statistically significant of two BIN1-associated AD risk alleles reported there. The Discussion says it lies near a PU.1 peak in human microglia and that the mouse Bin1 locus contains a microglia-specific super-enhancer; it does not establish that the human risk allele resides in a microglia-specific enhancer. The paper does point to microglia as a relevant cell type for further investigation. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These datasets clustered according to cell type of origin and exhibited cell type-specific patterns”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia but not in neurons or astrocytes.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Oligodendrocyte nuclei contain a low number of oligodendrocyte precursor cells, while neuronal nuclei represent a mixture of excitatory and inhibitory subtypes”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Strength:plain· not yet calibratedn = 6Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There were 219,509 significant unique interactions across cell types, and replicates clustered according to origin (fig. S6A, B; Table S5).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified 2,954 super-enhancers in microglia, neurons and oligodendrocytes, of which 83% had PLAC-interactions and were linked to promoters with elevated H3K27ac levels compared to promoters linked to regular enhancers (fig. S7A, B).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Active promoters linked to microglia, neuronal and oligodendrocyte enriched interactions were associated with gene ontology terms representative of each cell type, supporting the ability of the PLAC interactome to annotate cell type-specific promoter-enhancer interactions (Fig. 2C).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“In addition, H3K27ac-defined active promoters identified 288 human transcription factors that were active in a cell type-specific manner (fig. S4, Table S2) (24), several of which have been associated with disease (fig. S5; Table S4).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
What the sources say
- Not checked — no full text of the sourcecites [24] Lambert SA, The Human Transcription Factors. Cell 175, 598– 599 (2018). 30290144 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, gene expression of BIN1 was nearly absent in the BIN1enh_del PSC-derived microglia, whereas BIN1 expression in BIN1enh_del PSCs and PSC-derived neurons and astrocytes were equivalent to BIN1control cells (Figs. 4C; S12E; Table S8).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many super-enhancers harbored GWAS disease-risk variants and were connected to cell type-specific genes, suggesting that a subset of GWAS variants act on super-enhancers to affect gene expression (fig. S7A, Table S6).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Twenty-five of the PLAC-linked AD-risk genes were identified in microglia, of which 14 were not detected in the other cell types (Figs. 3B; S8).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whereas active promoters are largely shared between cell types (Fig. 1B), a relatively small fraction of active enhancers overlap between cell types (Fig. 1C), indicating that cell type specificity is mainly captured within the enhancer repertoire.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“BIN1 expression was unchanged in BIN1enh_del PSC-derived microglia precursor hematopoietic stem cells, indicating that microglia derivation was unaffected (fig S12F).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Protein-protein interaction (PPI) network analysis showed that microglia AD-risk genes identified by PLAC-seq were highly connected with GWAS-assigned genes and centered around APOE, whereas PPI networks for neurons and oligodendrocytes were smaller in scope (fig. S9C-E).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, BIN1 was dramatically reduced in BIN1enh_del microglia and not neurons and astrocytes (Fig. 4D, E).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Microglia AD-assigned genes were associated with gene ontology terms for immune function, whereas gene ontology terms for amyloid-beta processing were associated with neurons, microglia and oligodendrocytes (fig. S9F).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A strong H3K4me3 signal did not dictate that an interaction will occur (fig. S6C), suggesting that PLAC-seq captures a unique dimension of the chromatin conformation.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Non-coding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These findings revise and expand the genes likely to be influenced by non-coding variants in AD and suggest the probable cell types in which they function.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Disease-specific regulatory elements are likely to be influenced by genetic variation, which due to our limited sample size may partly explain the lack of overlap of a subset of risk alleles with the current regulatory atlases.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The present studies provide evidence that identification of cell type-specific promoter-enhancer interactomes enables substantial advances in interpretation of GWAS risk alleles associated with neurological and psychiatric diseases and establish a new resource for this purpose.”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Gene expression analysis of BIN1control and BIN1enh_del lines in PSC and PSC-derived microglia, neurons and astrocytes showed high correlation between samples, with clustering according to cell type (fig. S12C, D).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“PSC control (BIN1control) and BIN1 enhancer deletion (BIN1enh_del) lines had normal karyotypes (fig. S10D) and were differentiated into microglia, neurons and astrocytes (Figs. 4B, S11A-D, S12A, B).”
§ Brain cell type-specific enhancer-promoter interactome maps and disease risk association > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Variation in non-coding DNA substantially contributes to phenotypic differences, yet interpreting its function is difficult.
“Non-coding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging.”
The paper relies on this without support.
It is often not known which variants are causal or in which cell types disease-risk variants act.
“The causal variants and the specific cell type(s) in which the disease-risk variants may be active is often unclear.”
The paper relies on this without support.
The enhancer-promoter interactome and the relationship between promoters and distal regulatory regions in different brain cell types are largely unknown.
“the enhancer-promoter interactome of different brain cell types in vivo remain largely unknown.”
“The relationship between promoters and distal regulatory regions for different cell types in the brain is largely unknown.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“including the two loci with the strongest associations with LOAD: the BIN1 locus for which the functional linkage between rs6733839 and BIN1 has been well established7,23”
From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Genome -wide association meta -analysis studies (GWASs) 1,2 and interactome investigation 3 identified microglia gene expression patterns as strongly correlated with late-onset Alzheimer's disease (AD).”
From APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFβ-mediated checkpoints (2023) · cites this paper as [3]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PIRs were significantly enriched for open chromatin regions detected in our ATAC-seq experiments and for active chromatin marks from ex-vivo microglia22, suggesting a potential regulatory role (Suppl. Fig 1).”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [22]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Of the four cell types, H3K27ac was only enriched in microglia, suggesting that this region acts specifically as an enhancer in microglia.”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [22]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Also, the promoter and enhancer annotations were made using data from only 10 individuals and may have missed regulatory regions whose activity varies across individuals31.”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
Experimental — accuracy not yet measured
The cited source says what the claim attributes to it.
In the citing paper
“multiple sporadic AD risk variants were identified in microglial transcriptional enhancer regions in humans11”
From Amyloid-β induces lipid droplet-mediated microglial dysfunction via the enzyme DGAT2 in Alzheimer’s disease (2025) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“A significant proportion of these AD‐linked genes are associated with microglia and the immune response, reinforcing the necessity to understand the intersection of immunity and AD pathogenesis in the development of novel AD therapeutics.”
From PLCG2 modulates TREM2 expression and signaling in response to Alzheimer's disease pathology (2025) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Genome-wide association studies (GWAS) have implicated functional alterations of microglia (MG), the macrophage-like hematopoietic cells in the brain, as a major driver of AD risk13.”
From Clonal hematopoiesis is associated with protection from Alzheimer’s disease (2023) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“Using these techniques with antibodies to enhancer- and promoter-associated histone modifications or those to RNA polymerase has also revealed large numbers of 3D enhancer–promoter contacts [244, 295–301].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [297]
The cited source says what the claim attributes to it.
In the citing paper
“Indeed, the causal SNP at the LOAD GWAS-implicated BIN1 locus resides within an enhancer that is only active in the microglia, and not other CNS cell types22.”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“Recently, two studies using proximity-ligation assays in either human primary or iPSC-derived microglia have reported 3D genomic interactions between GWAS-associated variants and gene promoters in LOAD22,65.”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“PLAC-seq56 revealed long-range connections between the enhancer and the P2RY12 promoter but not to MED12L (Fig. 6g).”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [56]
The cited source says what the claim attributes to it.
In the citing paper
“Using proximity ligation-assisted ChIP-seq (PLAC-seq) data56, we observed that the overlapping microglia enhancer region has extensive long-range connections to regions overlapping the USP6NL promoter and gene body.”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [56]
The cited source says what the claim attributes to it.
In the citing paper
“We observed significant enrichment of miR-QTLs in promoters active in human brain31.”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
The cited source says what the claim attributes to it.
In the citing paper
“The promoter and enhancer detection was done using pooled sorted single nuclei31, so the relative proportions of the cell type in bulk tissue does not create a bias in the promoter and enhancer distributions across cell types.”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“We prioritized this variant as it is in strong LD with the sentinel SNP at this locus (rs6069737, r2 = 0.97), and lies within open chromatin in both of our microglial cell models, as well as in ex vivo human microglia 22.”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus (2024) · cites this paper as [22]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Expression levels of miRs in the human brain were recently reported to be influenced by specific genetic variants, termed quantitative trait loci (miR-QTLs) 60 .”
From Human brain microRNAs exhibit cell-type specificity and age dependence (2026) · cites this paper as [60]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Considering enhancers for each cell type separately, miR-QTLs were enriched in enhancers in oligodendrocytes and neurons but not astrocytes; on the other hand, miR-QTLs were depleted in microglia enhancers (Figure 3).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Considering enhancers for each cell type separately, miR-QTLs were enriched in enhancers in oligodendrocytes and neurons but not astrocytes; on the other hand, miR-QTLs were depleted in microglia enhancers (Figure 3).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Considering enhancers for each cell type separately, miR-QTLs were enriched in enhancers in oligodendrocytes and neurons but not astrocytes; on the other hand, miR-QTLs were depleted in microglia enhancers (Figure 3).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Considering enhancers for each cell type separately, miR-QTLs were enriched in enhancers in oligodendrocytes and neurons but not astrocytes; on the other hand, miR-QTLs were depleted in microglia enhancers (Figure 3).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [31]
Experimental — accuracy not yet measuredIn the citing paper
“Intriguingly, variants in enhancers can exert cell-type specific effects on gene expression, as exemplified by Alzheimer’s Disease variants that affect BIN1 expression specifically in microglia, but not neurons or astrocytes5.”
From CRISPR-based functional genomics for neurological disease (2020) · cites this paper as [5]
In the citing paper
“genes expressed in other CNS cell types (e.g. neurons and astrocytes) have microglia-specific enhancers with risk polymorphisms that selectively affect myeloid expression (i.e. Bin1) [6].”
From Microglia facilitate loss of perineuronal nets in the Alzheimer's disease brain (2020) · cites this paper as [6]
In the citing paper
“Based on data from peripheral cells and select microglia-specific datasets [184, 192], it is likely the regulation of gene expression in microglia will emerge as critical to disease pathogenesis.”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [192]
In the citing paper
“This is supported by a recent identification of the enrichment of AD-associated variants in enhancers active in primary human microglia [192].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [192]
In the citing paper
“disease-associated SNPs in non-coding areas of the genome influence genes associated with diseases including Alzheimer’s Disease (AD)”
From Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration (2023) · cites this paper as [48]
In the citing paper
“Recently, promoter interaction profiles were inferred from three types of brain cells (neurons, oligodendrocytes, and microglia) obtained from the postnatal human cerebral cortex16.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [16]
In the citing paper
“Consistent with previous studies16,42,49, these genes were highly expressed in the postnatal brain samples (Fig. 4d) and microglia (Fig. 4e).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [16]
In the citing paper
“Nott et al. found that this SNP is causally implicated in the regulation of an AD risk gene, BIN1, in a microglia-specific manner16 as predicted by our analysis.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [16]
In the citing paper
“We and others have shown that enhancers often regulate distal genes4,15,16”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [16]
In the citing paper
“Indeed, many of the risk genes associated with AD mediate their effects through microglia, the brain‐resident macrophages (Glass et al, 2010; Heneka et al, 2015; Nott et al, 2019).”
From Mechanisms of DNA damage-mediated neurotoxicity in neurodegenerative disease (2022) · cites this paper as Nott et al, 2019
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Embryonic Stem Cell Lines Derived from Human Blastocysts
Thomson, J. A., Itskovitz‐Eldor, J., Shapiro, S. S., Waknitz, M. A., Swiergiel, J. J., Marshall, V. S., & Jones, J. M. (1998) · Science · doi:10.1126/science.282.5391.1145 · Cited by 15,947
Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-09234-6 · Cited by 14,611
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
Self-citationThe Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
Neuroinflammation in Alzheimer's disease
Heneka, M. T., Carson, M. J., Khoury, J. E., Landreth, G. E., Brosseron, F., Feinstein, D. L., Jacobs, A. H., Wyss-Coray, T., Vitorica, J., Ransohoff, R. M., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)70016-5 · Cited by 6,226
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 702
Neuroinflammation and neurodegeneration in human brain at single-cell resolution
Colonna, M., & Brioschi, S. (2019) · Nature reviews. Immunology · doi:10.1038/s41577-019-0262-0 · Cited by 71
BACE-1 inhibition facilitates the transition from homeostatic microglia to DAM-1
Singh, N., Benoit, M., Zhou, J., Das, B., Dávila-Velderrain, J., Kellis, M., Tsai, L., Hu, X., & Yan, R. (2022) · Science Advances · doi:10.1126/sciadv.abo1286 · Cited by 72
Liu, T., Zhu, B., Liu, Y., Zhang, X., Yin, J., Li, X., Jiang, L., Hodges, A. P., Rosenthal, S. B., Zhou, L., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20200474 · Cited by 64
Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation
Rao, S., Yao, Y., & Bauer, D. E. (2021) · Genome Medicine · doi:10.1186/s13073-021-00857-3 · Cited by 72
Reassessment of Pioglitazone for Alzheimer’s Disease
Saunders, A. M., Burns, D. K., & Gottschalk, W. K. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.666958 · Cited by 71
Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability
Voskobiynyk, Y., Roth, J. R., Cochran, J. N., Rush, T., Carullo, N. V. N., Mesina, J. S., Waqas, M., Vollmer, R. M., Day, J. J., McMahon, L. L., et al. (2020) · eLife · doi:10.7554/elife.57354 · Cited by 74
ABCA7 and the altered lipidostasis hypothesis of Alzheimer's disease
Lyssenko, N. N., & Praticò, D. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12220 · Cited by 67
A loss of mature microglial markers without immune activation in schizophrenia
Snijders, G. J. L., van Zuiden, W., Sneeboer, M. A. M., van Berlekom, A. B., van der Geest, A. T., Schnieder, T. P., MacIntyre, D. J., Hol, E. M., Kahn, R. S., & de Witte, L. D. (2021) · Glia · doi:10.1002/glia.23962 · Cited by 70
Somatic mosaicism reveals clonal distributions of neocortical development
Breuss, M. W., Yang, X., Schlachetzki, J. C. M., Antaki, D., Lana, A. J., Xu, X., Chung, C., Chai, G., Stanley, V., Song, Q., et al. (2022) · Nature · doi:10.1038/s41586-022-04602-7 · Cited by 67
Self-citationThe three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease
Bendl, J., Hauberg, M. E., Girdhar, K., Im, E., Vicari, J. M., Rahman, S., Fernando, M. B., Townsley, K. G., Dong, P., Misir, R., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01166-7 · Cited by 65
Dissecting the complexities of Alzheimer disease with in vitro models of the human brain
Blanchard, J., Victor, M. B., & Tsai, L. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00578-6 · Cited by 68
Liu, Z., Zhang, S., James, B. T., Galani, K., Mangan, R. J., Fass, S. B., Liang, C., Wagle, M. M., Boix, C. A., Tanigawa, Y., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.031 · Cited by 75
Li, P., Ensink, E., Lang, S., Marshall, L. L., Schilthuis, M., Lamp, J. A., Vega, I. E., & Labrie, V. (2020) · Genome biology · doi:10.1186/s13059-020-01960-1 · Cited by 65
Lambert, E., Saha, O., Landeira, B. S., de Farias, A. R. M., Hermant, X., Carrier, A., Pelletier, A., Gadaut, J., Davoine, L., Dupont, C., et al. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01285-5 · Cited by 70
Kapoor, M., Chao, M. J., Johnson, E. C., Novikova, G., Lai, D., Meyers, J. L., Schulman, J., Nürnberger, J. I., Porjesz, B., Liu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25392-y · Cited by 68
Heritability Enrichment Implicates Microglia in Parkinson's Disease Pathogenesis
Andersen, M. S., Bandrés‐Ciga, S., Reynolds, R. H., Hardy, J., Ryten, M., Krohn, L., Gan‐Or, Z., Holtman, I. R., & Pihlstrøm, L. (2021) · Annals of Neurology · doi:10.1002/ana.26032 · Cited by 66
Self-citationHall-Roberts, H., Agarwal, D., Obst, J., Smith, T. B., Monzón-Sandoval, J., Di Daniel, E., Webber, C., James, W. S., Mead, E., Davis, J. B., et al. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00709-z · Cited by 62
Genetic risk factors of ME/CFS: a critical review
Dibble, J. J., McGrath, S. J., & Ponting, C. P. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa169 · Cited by 62
Troutman, T. D., Kofman, E. R., & Glass, C. K. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.08.004 · Cited by 62
Self-citationNuclear RIPK1 promotes chromatin remodeling to mediate inflammatory response
Li, W., Shan, B., Zou, C., Wang, H., Zhang, M., Zhu, H., Naito, M. G., Xu, D., Manuel, V. J., Mifflin, L., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00673-3 · Cited by 61
Saha, O., de Farias, A. R. M., Pelletier, A., Siedlecki‐Wullich, D., Landeira, B. S., Gadaut, J., Carrier, A., Vreulx, A.-C., Guyot, K., Shen, Y., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02502-y · Cited by 65
The Expanding Cell Diversity of the Brain Vasculature
Ross, J. M., Kim, C. N., Allen, D. E., Crouch, E. E., Narsinh, K. H., Cooke, D. L., Abla, A. A., Nowakowski, T. J., & Winkler, E. A. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.600767 · Cited by 66
Wang, L., Nykänen, N.-P., Western, D., Gorijala, P., Timsina, J., Li, F., Wang, Z., Ali, M., Yang, C., Liu, M., et al. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00687-4 · Cited by 58
Cell-type-resolved mosaicism reveals clonal dynamics of the human forebrain
Chung, C., Yang, X., Hevner, R. F., Kennedy, K. L., Vong, K. I., Liu, Y., Patel, A. M., Nedunuri, R., Barton, S. T., Noël, G. P. J. C., et al. (2024) · Nature · doi:10.1038/s41586-024-07292-5 · Cited by 59
Self-citationMissing heritability in Parkinson’s disease: the emerging role of non-coding genetic variation
Ohnmacht, J., May, P., Sinkkonen, L., & Krüger, R. (2020) · Journal of Neural Transmission · doi:10.1007/s00702-020-02184-0 · Cited by 60
Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells
Navarro, E., Udine, E., Lopes, K. D. P., Parks, M., Riboldi, G., Schilder, B. M., Humphrey, J., Snijders, G. J. L., Vialle, R. A., Zhuang, M., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00110-x · Cited by 57
Suresh, H., Crow, M., Jorstad, N. L., Hodge, R. D., Lein, E. S., Dobin, A., Bakken, T. E., & Gillis, J. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02186-7 · Cited by 58
PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia
Kozlova, A., Zhang, S., Sudwarts, A., Zhang, H., Smirnou, S., Byeon, S. K., Thapa, C., Sun, X., Stephenson, K., Zhao, X., et al. (2025) · Nature · doi:10.1038/s41586-025-09486-x · Cited by 53
Adams, L., Song, M. K., Yuen, S., Tanaka, Y., & Kim, Y. (2024) · Nature Aging · doi:10.1038/s43587-024-00583-6 · Cited by 56
Microglial ontogeny, diversity and neurodevelopmental functions
Thion, M. S., & Garel, S. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.06.013 · Cited by 58
Podleśny-Drabiniok, A., Novikova, G., Liu, Y., Dunst, J., Temizer, R., Giannarelli, C., Marro, S., Kreslavsky, T., Marcora, E., & Goate, A. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46315-7 · Cited by 52
VPS10P Domain Receptors: Sorting Out Brain Health and Disease
Malik, A. R., & Willnow, T. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.08.003 · Cited by 56
Massively parallel techniques for cataloguing the regulome of the human brain
Townsley, K. G., Brennand, K., & Huckins, L. M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00740-1 · Cited by 52
A human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Maat, C. A., Losada, P. M., Chen, M. B., Agam, M., Schaum, N., Khoury, N., et al. (2021) · doi:10.1101/2021.04.26.441262 · Cited by 48
Han, R. T., Vainchtein, I. D., Schlachetzki, J. C. M., Cho, F. S., Dorman, L. C., Ahn, E., Kim, D. K., Barron, J. J., Nakao-Inoue, H., Molofsky, A. B., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20220605 · Cited by 56
Self-citationNeurodegeneration cell per cell
Balusu, S., Praschberger, R., Lauwers, E., Strooper, B. D., & Verstreken, P. (2023) · Neuron · doi:10.1016/j.neuron.2023.01.016 · Cited by 53
Farrell, K. W., Humphrey, J., Chang, T. S., Zhao, Y., Leung, Y. Y., Kuksa, P. P., Patil, V., Lee, W., Kuzma, A. B., Valladares, O., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52025-x · Cited by 52
DNA damage and its links to neuronal aging and degeneration
Delint‐Ramírez, I., & Madabhushi, R. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.001 · Cited by 57
Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases
Phongpreecha, T., Fernandez, R., Mrdjen, D., Culos, A., Gajera, C. R., Wawro, A. M., Stanley, N., Gaudilliere, B. L., Poston, K. L., Aghaeepour, N., et al. (2020) · Science Advances · doi:10.1126/sciadv.abd5575 · Cited by 52
Functional studies of GWAS variants are gaining momentum
Lichou, F., & Trynka, G. (2020) · Nature Communications · doi:10.1038/s41467-020-20188-y · Cited by 53
Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4
Hoeksema, M. A., Shen, Z., Holtman, I. R., Zheng, A., Spann, N. J., Cobo, I., Gymrek, M., & Glass, C. K. (2021) · Science Advances · doi:10.1126/sciadv.abf9808 · Cited by 51
Self-citationTipping points in neurodegeneration
Simons, M., Levin, J., & Dichgans, M. (2023) · Neuron · doi:10.1016/j.neuron.2023.05.031 · Cited by 52
Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants
McAfee, J. C., Lee, S., Lee, J., Bell, J., Krupa, O., Davis, J. E., Insigne, K. D., Bond, M. L., Zhao, N., Boyle, A. P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100404 · Cited by 53
Review of multi-omics data resources and integrative analysis for human brain disorders
Dong, X., Liu, C., & Dozmorov, M. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab024 · Cited by 47
Palermo, J., Chesi, A., Zimmerman, A. J., Sonti, S., Pahl, M. C., Lasconi, C., Brown, E., Pippin, J. A., Wells, A. D., Doldur-Balli, F., et al. (2023) · Science Advances · doi:10.1126/sciadv.abq0844 · Cited by 48
Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex
Lei, Y., Cheng, M., Li, Z., Zhuang, Z., Wu, L. L., Sun, Y., Han, L., Huang, Z., Wang, Y., Wang, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34413-3 · Cited by 47
Microglia in Health and Diseases: Integrative Hubs of the Central Nervous System (CNS)
Sierra, A., Miron, V. E., Paolicelli, R. C., & Ransohoff, R. M. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041366 · Cited by 47
Alzheimer’s genes in microglia: a risk worth investigating
Sudwarts, A., & Thinakaran, G. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00679-4 · Cited by 52
Cell type-specific potential pathogenic genes and functional pathways in Alzheimer’s Disease
Wang, X., & Li, L. (2021) · BMC Neurology · doi:10.1186/s12883-021-02407-1 · Cited by 47
The microglial lysosomal system in Alzheimer’s disease: Guardian against proteinopathy
Van Acker, Z. P., Perdok, A., Bretou, M., & Annaert, W. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101444 · Cited by 45
Walter, J., Bolognin, S., Poovathingal, S. K., Magni, S., Gérard, D., Antony, P. M. A., Nickels, S., Salamanca, L., Berger, E., Smits, L. M., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109864 · Cited by 47
echolocatoR : an automated end-to-end statistical and functional genomic fine-mapping pipeline
Schilder, B. M., Humphrey, J., & Raj, T. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab658 · Cited by 44
Genetic regulation of cell type–specific chromatin accessibility shapes brain disease etiology
Zeng, B., Bendl, J. M., Deng, C., Lee, D., Misir, R., Reach, S. M., Kleopoulos, S. P., Auluck, P. K., Marenco, S., Lewis, D. A., et al. (2024) · Science · doi:10.1126/science.adh4265 · Cited by 48
Weiss, F. D., Calderón, L., Wang, Y.-F., Georgieva, R., Guo, Y., Cvetešić, N., Kaur, M., Dharmalingam, G., Krantz, I. D., Lenhard, B., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23141-9 · Cited by 46
Gao, C., Shen, X., Tan, Y., & Chen, S. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02580-1 · Cited by 48
Haglund, A., Zuber, V., Abouzeid, M., Yang, Y., Ko, J. H., Wiemann, L., Otero‐Jimenez, M., Muhammed, L., Feleke, R., Nott, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02050-9 · Cited by 46
Self-citationUncovering the impact of noncoding variants in neurodegenerative brain diseases
Frydas, A., Wauters, E., van der Zee, J., & Broeckhoven, C. V. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.010 · Cited by 44
Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms
Schilder, B. M., Navarro, E., & Raj, T. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105580 · Cited by 45
Loss of forebrain BIN1 attenuates hippocampal pathology and neuroinflammation in a tauopathy model
Ponnusamy, M., Wang, S., Yuksel, M., Hansen, M. T., Blazier, D. M., McMillan, J. D., Zhang, X., Dammer, E. B., Collier, L. A., & Thinakaran, G. (2022) · Brain · doi:10.1093/brain/awac318 · Cited by 44
Yuan, W., Ma, S., Brown, J. R., Kim, K., Murek, V., Trastulla, L., Meissner, A., Lodato, S., Shetty, A. S., Levin, J. Z., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01123-4 · Cited by 42
Su, C., Argenziano, M., Lu, S., Pippin, J. A., Pahl, M. C., Leonard, M. E., Cousminer, D. L., Johnson, M. E., Lasconi, C., Wells, A. D., et al. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102000 · Cited by 41
Ma, Q., Shams, H., Didonna, A., Baranzini, S. E., Cree, B., Hauser, S. L., Henry, R. G., & Oksenberg, J. R. (2023) · Communications Biology · doi:10.1038/s42003-023-04713-5 · Cited by 45
Mulvey, B., Lagunas, T., & Dougherty, J. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.011 · Cited by 41
Jin, T., Rehani, P. R., Ying, M., Huang, J., Liu, S., Roussos, P., & Wang, D. (2021) · Genome Medicine · doi:10.1186/s13073-021-00908-9 · Cited by 42
Pihlstrøm, L., Shireby, G. L., Geut, H., Henriksen, S. P., Rozemüller, A. J. M., Tunold, J., Hannon, E. J., Francis, P. T., Thomas, A. J., Love, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32619-z · Cited by 42
Multimodal Omics Approaches to Aging and Age-Related Diseases
Ji, Q., Jiang, X., Wang, M., Xin, Z., Zhang, W., Qu, J. T., & Liu, G. (2024) · Phenomics · doi:10.1007/s43657-023-00125-x · Cited by 39
Epigenetic control of microglial immune responses
Scholz, R., Brösamle, D., Yuan, X., Beyer, M., & Neher, J. J. (2024) · Immunological Reviews · doi:10.1111/imr.13317 · Cited by 39
The shared genetic architecture and evolution of human language and musical rhythm
Alagöz, G., Eising, E., Mekki, Y., Bignardi, G., Fontanillas, P., Aslibekyan, S. W., Auton, A., Babalola, E., Bell, R. K., Bielenberg, J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02051-y · Cited by 42
Focus on your locus with a massively parallel reporter assay
McAfee, J. C., Bell, J., Krupa, O., Matoba, N., Stein, J. L., & Won, H. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09461-x · Cited by 40
Ramamurthy, E., Welch, G., Cheng, J., Yuan, Y., Gunsalus, L. M., Bennett, D. A., Tsai, L., & Pfenning, A. R. (2023) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.948456 · Cited by 39
Amyloid β Induces Lipid Droplet‐Mediated Microglial Dysfunction in Alzheimer’s Disease
Prakash, P., Manchanda, P., Paouri, E., Bisht, K., Sharma, K. P., Rajpoot, J., Wendt, V., Hossain, A., Wijewardhane, P. R., Randolph, C. E., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.075938 · Cited by 38
Tuncay, İ. O., Parmalee, N. L., Khalil, R., Kaur, K. J., Kumar, A., Jimale, M., Howe, J., Goodspeed, K., Evans, P., Alzghoul, L., et al. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00284-2 · Cited by 38
3D spatial genome organization in the nervous system: From development and plasticity to disease
Fujita, Y., Pather, S. R., Ming, G., & Song, H. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.004 · Cited by 37
An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases
Oguchi, A., Suzuki, A., Komatsu, S., Yoshitomi, H., Bhagat, S., Son, R., Bonnal, R. J. P., Kojima, S., Koido, M., Takeuchi, K., et al. (2024) · Science · doi:10.1126/science.add8394 · Cited by 40
Yao, M., Wei, Z., Nielsen, J. S., Ouyang, Y., Kakazu, A., Wang, H., Du, L., Li, R., Chu, T., Scafidi, S., et al. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106711 · Cited by 38
Caramello, A., Fancy, N. N., Tournerie, C., Eklund, M., Chau, V. M. N., Adair, E., Papageorgopoulou, M., Willumsen, N., Jackson, J., Hardy, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60328-w · Cited by 37
Fine-mapping of Parkinson’s disease susceptibility loci identifies putative causal variants
Schilder, B. M., & Raj, T. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab294 · Cited by 38
Microglial phagocytosis in Alzheimer disease
Brown, G. C., St George-Hyslop, P., Paolicelli, R. C., & Lemke, G. (2026) · Nature Reviews Neurology · doi:10.1038/s41582-025-01162-y · Cited by 36
Tools for studying human microglia: In vitro and in vivo strategies
Warden, A. S., Han, C. Z., Hansen, E. A., Trescott, S., Nguyen, C. T., Kim, R. Y., Schafer, D. M., Johnson, A., Wright, M., Ramírez, G., et al. (2022) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2022.10.008 · Cited by 38
Self-citationAPOE and immunity: Research highlights
Kloske, C. M., Barnum, C. J., Batista, A. F., Bradshaw, E. M., Brickman, A. M., Bu, G., Dennison, J. L., Gearon, M. D., Goate, A., Haass, C., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13020 · Cited by 38
Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches
Rahimzadeh, N., Srinivasan, S. S., Zhang, J., & Swarup, V. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13790 · Cited by 39
Humphrey, J., Brophy, E., Kosoy, R., Zeng, B., Coccia, E., Mattei, D., Ravi, A., Naito, T., Efthymiou, A. G., Navarro, E., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02099-0 · Cited by 37
Looking at neurodevelopment through a big data lens
Briscoe, J., & Marín, Ó. (2020) · Science · doi:10.1126/science.aaz8627 · Cited by 38
Golovina, E., Fadason, T., Lints, T., Walker, C. G., Vickers, M. H., & O'Sullivan, J. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-95447-z · Cited by 37
Meléndez-Ramírez, C., Durán, R. C., Barrios‐García, T., Giacoman-Lozano, M., López-Ornelas, A., Herrera-Gamboa, J. G., Estudillo, E., Soto‐Reyes, E., Velasco, I., & Treviño, V. (2021) · Scientific Reports · doi:10.1038/s41598-021-96263-1 · Cited by 38
Targeting epigenetics: A novel promise for Alzheimer’s disease treatment
Jeremic, D., Jiménez‐Díaz, L., & Navarro‐López, J. D. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102003 · Cited by 36
Identifying oligodendrocyte enhancers governing Plp1 expression
Kim, D., An, H., Fan, C., & Park, Y. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab184 · Cited by 39
The Genetic Drivers of Juvenile, Young, and Early‐Onset Parkinson's Disease in India
Andrews, S. V., Kukkle, P. L., Menon, R., Geetha, T. S., Goyal, V., Kandadai, R. M., Kumar, H., Borgohain, R., Mukherjee, A., Wadia, P. M., et al. (2023) · Movement Disorders · doi:10.1002/mds.29676 · Cited by 35
An overview of microglia ontogeny and maturation in the homeostatic and pathological brain
Mendes, M. S., & Majewska, A. K. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15225 · Cited by 35
Sánchez-Priego, C., Hu, R., Boshans, L. L., Lalli, M. A., Janas, J. A., Williams, S., Dong, Z., & Yang, N. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110877 · Cited by 35
Self-citationMatera, A., Compagnion, A.-C., Pedicone, C., Kotah, J. M., Ivanov, A., Monsorno, K., Labouèbe, G., Leggio, L., Pereira-Iglesias, M., Beule, D., et al. (2025) · Immunity · doi:10.1016/j.immuni.2024.11.003 · Cited by 34
Molecular pathways and diagnosis in spatially resolved Alzheimer’s hippocampal atlas
Wang, P., Han, L., Wang, L., Tao, Q., Guo, Z., Luo, T., He, Y., Xu, Z. P., Yu, J., Liu, Y., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.03.002 · Cited by 36
Diabetic phenotype in mouse and humans reduces the number of microglia around β-amyloid plaques
Natunen, T., Martiskainen, H., Marttinen, M., Gabbouj, S., Koivisto, H., Kemppainen, S., Kaipainen, S., Takalo, M., Svobodová, H., Leppänen, L., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00415-2 · Cited by 36
Intronic enhancer region governs transcript-specific Bdnf expression in rodent neurons
Tuvikene, J., Esvald, E., Rähni, A., Uustalu, K., Zhuravskaya, A., Avarlaid, A., Makeyev, E. V., & Timmusk, T. (2021) · eLife · doi:10.7554/elife.65161 · Cited by 35
Gupta, C., Xu, J., Jin, T., Khullar, S., Liu, X., Alatkar, S., Cheng, F., & Wang, D. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010287 · Cited by 33
Infection and inflammation: New perspectives on Alzheimer's disease
Whitson, H. E., Colton, C. A., Khoury, J. B. E., Gate, D., Goate, A., Heneka, M. T., Kaddurah‐Daouk, R., Klein, R. S., Shinohara, M. L., Sisodia, S. S., et al. (2022) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2022.100462 · Cited by 34
Human brain vascular multi-omics elucidates disease-risk associations
Reid, M. M., Menon, S., Liu, H., Zhou, H., Hu, Z., Frerich, S., Ding, B., Oveisgharan, S., Zhang, Z., Nelson, S., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.07.001 · Cited by 35
Rheinberger, M., Costa, A. L., Kampmann, M., Glavaš, D., Shytaj, I. L., Sreeram, S., Penzo, C., Tibroni, N., García‐Mesa, Y., Leskov, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112110 · Cited by 33
Rompala, G. R., Nagamatsu, S. T., Martínez‐Magaña, J. J., Núñez-Ríos, D. L., Wang, J., Girgenti, M. J., Krystal, J. H., Gelernter, J. E., Alvarez, V. E., Benedek, D. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40285-y · Cited by 33
Ramirez, M., Badayeva, Y., Yeung, J., Wu, J., Abdalla-Wyse, A., Yang, E., Trost, B., Scherer, S. W., & Goldowitz, D. (2022) · eLife · doi:10.7554/elife.74207 · Cited by 33
Metadata
- Authors
- Nott, A., Holtman, I. R., Coufal, N. G., Schlachetzki, J. C. M., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., Keulen, Z., Pasillas, M. P., O’Connor, C., Nickl, C. K., Schafer, S. T., Shen, Z., Rissman, R. A., Brewer, J. B., Gosselin, D., Gonda, D. D., Levy, M. L., Rosenfeld, M. G., McVicker, G., Gage, F. H., Ren, B., & Glass, C. K.
- Affiliations
- University of California San Diego (US)
- University Medical Center Groningen (NL)
- Salk Institute for Biological Studies (US)
- Ludwig Cancer Research (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Nott, Alexi
- Holtman, Inge R.
- Coufal, Nicole G.
- Glass, Christopher K.
- ORCID
- Nott, Alexi
- Holtman, Inge R.
- Coufal, Nicole G.
- Schlachetzki, Johannes C. M.
- Yu, Miao
- Hu, Rong
- Han, Claudia Z.
- Pena, Monique
- Xiao, Jiayang
- Wu, Yin
- Pasillas, Martina P.
- O’Connor, Carolyn
- Schafer, Simon T.
- Shen, Zeyang
- Rissman, Robert A.
- Gosselin, David
- Gonda, David D.
- Levy, Michael L.
- Rosenfeld, Michael G.
- McVicker, Graham
- Gage, Fred H.
- Ren, Bing
- Glass, Christopher K.
- Venue
- Science
- Published
- 2019-11-29
- Volume
- 366
- Issue
- 6469
- Pages
- 1134-1139
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7028213
- OpenAlex
- W2988958689
- Semantic Scholar
- 7a5a15720dccbd1ffaefb1a3e7b92e9e1219a946
- Cited by
- 699
- References
- 118
- Citations by year
- 2026: 95 · 2025: 110 · 2024: 92 · 2023: 101 · 2022: 120 · 2021: 111 · 2020: 65 · 2019: 5
- FWCI
- 23.15
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (acceptedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (NS109200-01, KL2TR001444, P30 014195, S10-OD023689)
- National Institute on Aging (AG057706-01, AG056511-01, AG062429-01, AG061060-01)
- National Cancer Institute (P30-CA014195)
- Netherlands Organization for Scientific Research (VENI research program)
- Engman Foundation
- American Heart Association / Allen Initiative in Brain Health and Cognitive Impairment
- JPB Foundation
- Cure Alzheimer's Fund
- Alzheimer's Association (AARF-18-531498)
- Cancer Research Institute (Irvington Postdoctoral Fellowship Program)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- cell type-specific enhancers, brain cell types, microglia, neurons, astrocytes, Alzheimer's disease, non-coding variants, BIN1, mental illness, regulatory elements
- MeSH terms
- Alzheimer Disease, Brain, Cells, Cultured, Chromatin, Enhancer Elements, Genetic, Humans, Nuclear Proteins, Promoter Regions, Genetic, Genetic Variation, Sequence Deletion, Microglia, Tumor Suppressor Proteins, Adaptor Proteins, Signal Transducing, Gene Regulatory Networks, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
