Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies

In the paper

“In addition, microglial functions vary across different ages, disease pathologies, and between sexes10–16.”

What the sources say

• Partly supported

  The Results section ('Identification of human microglia genes affected by aging'; page unavailable) reports age-associated changes in microglial transcriptional programs and their inferred functions. The Methods ('Identification of microglia enriched genes'; page unavailable) suggests that variation may reflect responses to differing amyloid and tau pathology, but does not establish functional differences across disease pathologies. The passages found do not show that microglial functions differ between sexes. (LLM, conf=0.84)

  cites [10] Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018). sourceExperimental — accuracy not yet measured
• Partly supported

  In “Identification of AD-associated cellular subpopulations” (page unavailable), the paper reports an AD-associated microglial state with markers distinct from those of non-AD aged microglia. “Sex-specific differential response to AD pathology” (page unavailable) reports that this microglial subpopulation was enriched with cells from female individuals. These findings support variation in microglial transcriptional states with AD pathology and sex, but the paper does not establish variation in microglial functions across different ages or directly measure the claimed functional differences. (LLM, conf=0.84)

  cites [11] Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). sourceExperimental — accuracy not yet measured
• Partly supported

  The ‘Aging and Neurodegenerative Disease Change the CNS Immune Cell Landscape’ section and Figure 5 (p. 389) report age- and Alzheimer’s-associated microglial changes; the Discussion also distinguishes the microglial phenotype in EAE from those in aging and Alzheimer’s disease. These passages support variation across ages and disease conditions, but our copy does not establish variation between sexes. The paper primarily measures phenotypic markers rather than directly comparing microglial functions. (LLM, conf=0.86)

  cites [12] Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 599 (2018). sourceExperimental — accuracy not yet measured
• Supported

  The Introduction describes different microglial roles during development and responses to injury, pathology, and aging. The Results report age- and injury-related changes in microglial states and cite evidence of sex differences in microglial function. This paper found little difference in microglial diversity between sexes, but that does not contradict the broader claim about function. (LLM, conf=0.85)

  cites [13] Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019). source
• Partly supported

  The Results report distinct microglial states in Alzheimer’s disease and ALS models, and a larger disease-associated microglia population in aged than young mice. Those findings support variation with age and disease, but the paper does not establish that microglial functions differ between sexes; its mention of male and female mice is not a sex comparison. The cited passages have section labels but no page numbers in our copy. (LLM, conf=0.88)

  cites [14] Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [15] Masuda, T. et al. Author correction: spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 568, E4 (2019). source
• Partly supported

  The age-associated changes section (p. 9) reports changes in microglial gene expression across donor ages, including genes linked to immune response and motility. The Discussion (p. 10) reports sex differences in microglial function in the developing brain, but finds only X- and Y-chromosome gene-expression differences in its human samples. It does not establish that microglial functions vary across disease pathologies; donors with overt CNS pathologies were excluded (Discussion, p. 9). (LLM, conf=0.85)

  cites [16] Galatro, T. F. et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. 20, 1162–1171 (2017). sourceExperimental — accuracy not yet measured

In the paper

“In addition, it is becoming increasingly clear that genetic risk can also be mediated through mRNA splicing28,29.”

What the sources say

• Supported

  The Abstract (page not provided) reports that splicing QTLs are major contributors to complex traits. The paper’s conclusion also identifies RNA splicing as a pathway through which genetic variation has phenotypic and pathogenic consequences, supporting the claim that genetic risk can be mediated through splicing. (LLM, conf=0.93)

  cites [28] Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016). source
• Supported

  The Results link the Alzheimer’s disease risk allele to increased CD33 exon 2 inclusion and suggest that altered splicing may be the locus’s primary effect on susceptibility. The Discussion likewise identifies altered CD33 splicing as a possible mechanism connecting the genetic variant to disease risk. Page numbers are not provided in our copy. (LLM, conf=0.91)

  cites [29] Raj, T. et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 23, 2729–2736 (2014). source

In the paper

“For instance, the CD33 locus in AD influences CD33 splicing, resulting in isoforms with different biological and likely pathological functions29.”

What the sources say

• Supported

  The Discussion reports that AD-associated CD33 variants alter exon 2 splicing and increase the full-length CD33M isoform. It also states that CD33M inhibits amyloid-beta uptake whereas the truncated CD33m isoform does not, linking the splicing difference to a possible mechanism of AD susceptibility. (LLM, conf=0.93)

  cites [29] Raj, T. et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 23, 2729–2736 (2014). source

In the paper

“Microglia are highly dynamic cells that are strongly influenced by different environmental signals which result in distinct phenotypes and functions across brain regions4–9.”

What the sources say

• Partly supported

  In “What may contribute to regional heterogeneity of microglia: nature versus nurture?” (page unavailable), the review describes microglia as highly sensitive to environmental changes and says local cues shape region-specific phenotypes. It treats the environment as a likely contributor, however, and allows for intrinsic mechanisms. In “Heterogeneity in microglial functions across CNS regions” (page unavailable), it reports regional functional differences but says direct evidence linking region-specific features to functions remains insufficient; the claim presents that environmental cause more definitively than the source does. (LLM, conf=0.85)

  cites [4] Tan, Y.-L., Yuan, Y. & Tian, L. Microglial regional heterogeneity and its role in the brain. Mol. Psychiatry 25, 351–367 (2020). sourceExperimental — accuracy not yet measured
• Supported

  The Introduction says microglia respond quickly to environmental changes, which can alter their states and functions. The Discussion reports distinct transcriptomic profiles in grey- and white-matter microglia and emphasizes regional differences in their responses to stimuli. Together, these passages support the paraphrase. (LLM, conf=0.91)

  cites [5] van der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019). source
• Partly supported

  The Introduction describes the local environment as a key influence on microglial phenotype, and the Results report regional differences in immune alertness and energy-metabolism gene expression. The Discussion, however, says local environmental differences *may* be important; it does not establish that environmental signals cause the regional differences or directly demonstrate distinct functions across regions. (LLM, conf=0.84)

  cites [6] Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016). sourceExperimental — accuracy not yet measured
• Supported

  The Introduction describes rapid changes in microglial morphology and activity in response to CNS insults. The Discussion (‘Maintenance of diverse microglial phenotypes’; page unavailable) reports that distinct phenotypes across basal ganglia nuclei reemerge after ablation and repopulation, indicating an ongoing role for local cues. This supports the claim, with the paper’s direct regional evidence coming from basal ganglia nuclei. (LLM, conf=0.91)

  cites [7] De Biase, L. M. et al. Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron 95, 341–356.e6 (2017). source
• Partly supported

  The Results section reports substantial age-related changes in microglia-specific gene expression and a distinct profile in the cerebellum. It also says that most regions were not strongly separated by those genes and that expression was more defined by age than by region. The paper does not establish that different environmental signals cause distinct microglial phenotypes and functions across brain regions. (LLM, conf=0.85)

  cites [8] Soreq, L. et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain aging. Cell Rep. 18, 557–570 (2017). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [9] Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019). source

In the paper

“Cluster 1 contained genes that were upregulated in the cortical regions compared to subcortical regions, such as P2RY12, CD36, and MRC1, and was enriched in genes that were downregulated in AD brain33 and in response to in vitro culture34.”

What the sources say

• Supported

  Citation 33 is attached to the description of genes downregulated in AD brain, not to the citing paper’s Cluster 1 analysis or the in vitro culture claim attributed to citation 34. In Results, “Defining the HAM Gene Expression Profile Using Frozen Tissues” reports 21 genes decreased in AD microglia; “Validation of the HAM Profile in Multiple Cortical Regions and Datasets” also reports decreased expression of the HAM-Down gene set in later-stage AD brain tissue. (LLM, conf=0.91)

  cites [33] Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020). source
• Supported

  Citation 34 is attached to the claim about downregulation in response to in vitro culture, not the cortical–subcortical comparison or the AD-brain finding. In “An environment-dependent transcriptional network,” the paper reports that 117 of 291 disorder-associated microglial transcripts were downregulated in vitro; the “Alterations of the human microglia transcriptome in vitro” section also reports reductions among AD-risk-associated genes. (LLM, conf=0.90)

  cites [34] Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017). source

In the paper

“For decades, changes in microglial density, morphology, and transcriptional state have been observed in postmortem brain tissue of patients with neurological and psychiatric disorders17–21.”

What the sources say

• ?Not checked — no full text of the sourcecites [17] McGeer, P. L. et al. Microglia in degenerative neurological disease. Glia 7, 84–92 (1993). source
• ?Not checked — no full text of the sourcecites [18] Kreutzberg, G. W. Microglia: a sensor for pathological events in the CNS. Trends Neurosci. 19, 312–318 (1996). source
• Partly supported

  The Microglia section (page not provided) reports changes in microglial labeling and morphology in postmortem schizophrenia brains, and discusses differences in microglial-marker mRNA expression. But this review focuses on schizophrenia, not neurological and psychiatric disorders generally, and it reports many studies with no change. The broader scope of the citing sentence is therefore not supported by this source alone. (LLM, conf=0.86)

  cites [19] Trépanier, M. O., Hopperton, K. E., Mizrahi, R., Mechawar, N. & Bazinet, R. P. Postmortem evidence of cerebral inflammation in schizophrenia: a systematic review. Mol. Psychiatry 21, 1009–1026 (2016). sourceExperimental — accuracy not yet measured
• Partly supported

  The Abstract reports altered activation-associated microglial markers in postmortem Alzheimer’s disease tissue, and the Results (High-throughput techniques: microarray and proteomics) report changes in microglia-associated gene-expression patterns. The Results (Microglia identified by morphology) report mixed findings on cell counts; they use morphology to identify cells, rather than establish a consistent morphological change. The review focuses on Alzheimer’s disease and does not substantiate the full claim across neurological and psychiatric disorders. (LLM, conf=0.85)

  cites [20] Hopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V. & Bazinet, R. P. Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review. Mol. Psychiatry 23, 177–198 (2018). sourceExperimental — accuracy not yet measured
• Partly supported

  The paper’s transcriptome analysis of postmortem ASD cortex reports increased expression of genes enriched in microglia (Abstract; page not provided), supporting the transcriptional-change part of the claim. Our copy does not report changes in microglial density or morphology, or establish the claim across neurological and psychiatric disorders over decades. (LLM, conf=0.87)

  cites [21] Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016). sourceExperimental — accuracy not yet measured

In the paper

“Cluster 1 genes were enriched in genes that were downregulated in AD brain and in response to in vitro culture.”

What the sources say

• ?Could not verify

  The Results section on the HAM profile reports genes decreased in AD microglia, but does not identify a ‘Cluster 1’ or test its enrichment. Searches for ‘Cluster 1’ and in vitro culture found no passage establishing the claimed relationship; the cited paper’s copy therefore does not verify this specific claim. (LLM, conf=0.72)

  cites [33] Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020). sourceExperimental — accuracy not yet measured
• Partly supported

  The ‘An environment-dependent transcriptional network’ section reports that some microglia transcripts associated with differential expression in AD and other disorders were downregulated in vitro. The ‘Human microglia transcriptomes’ section reports overlap with AD-related gene sets, but the cited passages do not identify a ‘Cluster 1’ or establish that its genes were enriched among genes downregulated in AD brain. (LLM, conf=0.82)

  cites [34] Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017). sourceExperimental — accuracy not yet measured

In the paper

“Recently, Young et al. constructed expression QTLs (eQTLs) in primary human microglia (n = 93 individuals/samples), and detected 401 eQTLs, some of which colocalized with AD loci, including BIN126.”

What the sources say

• Partly supported

  The ‘eQTL mapping in human microglia’ section reports data from 93 individuals, but says the study detected 585 eQTLs at 5% FDR, not 401. The ‘Fine-mapping primary microglia eQTLs using an in-vitro model’ section confirms that some primary-microglia eQTLs colocalized with Alzheimer’s disease risk loci, including BIN1. (LLM, conf=0.92)

  cites [26] Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021). sourceExperimental — accuracy not yet measured

In the paper

“We found that eQTL sharing was both cell type- and region-dependent (Fig. 4d), with the highest sharing between MiGA and the Young et al. microglia (π1 = 0.81–0.86), but with a lower sharing in the SVZ (π1 = 0.51).”

What the sources say

• Supported

  The citation identifies the Young et al. microglia dataset used in the citing authors’ comparison. The source’s “eQTL mapping in human microglia” section reports mapping eQTLs in primary human microglia. The π1 sharing estimates are presented as the citing authors’ own findings, not results attributed to Young et al. (LLM, conf=0.90)

  cites [26] Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021). source

In the paper

“No colocalization was observed in any other QTL dataset, though we note that USP6NL is expressed five-fold higher in microglia than in monocytes (MiGA median TPM = 15.77; MyND42 monocyte median TPM = 3.13).”

What the sources say

• ?Could not verify

  The Results section reports colocalization at 15 of 78 Parkinson’s disease loci, but does not address whether USP6NL colocalized in other QTL datasets. Searches for USP6NL and the stated MiGA/MyND42 median TPM values found no passage supporting the five-fold microglia–monocyte expression comparison. The retrieved passages have no page numbers. (LLM, conf=0.72)

  cites [42] Navarro, E. et al. Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells. Nat. Aging 1, 850–863 (2021). sourceExperimental — accuracy not yet measured

In the paper

“Together, these results highlight shared genetic regulation between microglia and monocytes, which is only partly captured in whole-tissue brain data22.”

What the sources say

• Partly supported

  The discussion passage (indexed under Abstract; page unavailable) suggests that monocytes may be proxies for resident microglia and says the two share many transcriptome patterns. But the study measured genetic effects in monocytes and T cells, not microglia, and our copy does not establish shared genetic regulation between microglia and monocytes or compare those effects with whole-tissue brain data. (LLM, conf=0.82)

  cites [22] Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014). sourceExperimental — accuracy not yet measured

In the paper

“The meta-analyzed eQTL colocalizes with the ECHDC3 locus in all 4 AD GWAS used in this study, with the highest PP4 (0.95) seen in 46 (Fig. 6c; Extended Data Fig. 3–4).”

What the sources say

• Supported

  The citation numbered 46 identifies the AD GWAS in which the citing authors observed their highest PP4; it does not attribute their colocalization analysis to this paper. The Results section, “UK Biobank parental and IGAP meta-analysis” (page unavailable), confirms that the source reports an AD GWAS meta-analysis. (LLM, conf=0.85)

  cites [46] Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018). source

In the paper

“The upregulated genes in chronological aging showed overrepresentation for genes in AD (e.g. MS4A6A, FCER1G, and CR1) or PD (e.g. BST1, PTPN22, and TNFSF13) GWAS loci, but not for genes in schizophrenia (SCZ) or bipolar disorder (BD) (Fig. 3d).”

What the sources say

• Partly supported

  The Results section (‘Follow-up of suggestive loci in additional samples’; page unavailable) identifies 30 bipolar-disorder GWAS loci, supporting use of this paper as a source for BD loci. It does not test whether genes upregulated in chronological aging are overrepresented in AD or PD loci but not SCZ or BD loci; that comparison is not supported by this source. (LLM, conf=0.83)

  cites [35] Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Results section (“TWAS for SCZ identifies new susceptibility genes”; page unavailable) reports schizophrenia-associated genes, but not an enrichment analysis of genes upregulated in chronological aging. Searches for chronological aging, AD/PD enrichment, and the named example genes found no passage addressing the claimed comparison with SCZ or BD. (LLM, conf=0.76)

  cites [36] Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Results section on TWAS identifies CR1 in a known Alzheimer’s disease locus, but does not say that genes upregulated with chronological aging are overrepresented in AD or PD GWAS loci. Searches for aging-related upregulation and for the PD, schizophrenia, and bipolar comparisons found no passage addressing the claimed enrichment pattern. No page numbers are available in our copy. (LLM, conf=0.72)

  cites [37] Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Results section reports enrichment of PD GWAS signals near genes expressed in particular tissues and among molecular QTLs, not among genes upregulated with chronological aging. Searches for chronological aging, the named genes, and SCZ/BD comparisons found no passage in our copy addressing the claimed four-disorder comparison; page numbers were not available. (LLM, conf=0.73)

  cites [38] Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019). sourceExperimental — accuracy not yet measured

In the paper

“The ECHDC3 locus has been associated with AD risk in several GWAS23,46,49.”

What the sources say

• Supported

  The Results section (‘Meta-analysis of Alzheimer’s disease GWAS’) says ECHDC3 was identified as a potential Alzheimer’s disease risk locus in several studies. It also reports a genome-wide-significant association for ECHDC3 in this meta-analysis, supporting the citing sentence’s broader wording. (LLM, conf=0.92)

  cites [23] Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). source
• ?Could not verify

  The Results sections on established and novel AD risk loci list other loci but do not mention ECHDC3. Searches for “ECHDC3” and its association with AD risk found no passage addressing the claimed locus or its appearance in several GWAS; page numbers are not available in our copy. (LLM, conf=0.72)

  cites [46] Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018). sourceExperimental — accuracy not yet measured
• Supported

  The Results section (“Genome-wide meta-analysis for AD status”; page not specified) identifies ECHDC3 among loci associated in this meta-analysis and says it had been discovered in earlier genetic studies of AD. This supports the claim that the locus has been associated with AD risk in several GWAS. (LLM, conf=0.91)

  cites [49] Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019). source

In the paper

“PLAC-seq56 revealed long-range connections between the enhancer and the P2RY12 promoter but not to MED12L (Fig. 6g).”

What the sources say

• Supported

  The citation is attached to “PLAC-seq,” identifying the source of the interactome data used in the citing paper’s Fig. 6g. In the supplied passage (section labelled Abstract; page unavailable), Nott et al. report using PLAC-seq to identify long-range enhancer–promoter loops in brain cell types. (LLM, conf=0.88)

  cites [56] Nott, A. et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019). source

In the paper

“The downregulated genes were significantly enriched for cell motility, polarity, IL-6 cytokine signaling (Fig. 3b), and for genes also downregulated following in vitro culture34 and in AD-derived microglia33 (Fig. 3c).”

What the sources say

• Supported

  Citation 34 is attached to the in vitro culture comparison, not the cell-motility, polarity, IL-6, or AD-derived microglia findings. In “Alterations of the human microglia transcriptome in vitro,” the source reports thousands of genes downregulated after human microglia were transferred to culture. The citing paper’s enrichment analysis is its own finding. (LLM, conf=0.91)

  cites [34] Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017). source
• Supported

  Citation 33 is attached to the comparison with genes downregulated in AD-derived microglia, not to the citing authors’ enrichment analysis. In Results, “Defining the HAM Gene Expression Profile Using Frozen Tissues” (page unavailable), the source reports 21 genes decreased in AD microglia relative to controls. (LLM, conf=0.91)

  cites [33] Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020). source

In the paper

“P2RY12 expression is lost upon microglia activation74, culture34”

What the sources say

• Supported

  The source supports the activation portion of the sentence: the Results (p. 15) report virtually undetectable P2Y12 immunoreactivity in microglia after LPS-induced activation, with reduced transcript levels. The Discussion (p. 21) likewise states that P2Y12 is down-regulated upon microglial activation. The culture portion has a separate citation and is not attributed to this source here. (LLM, conf=0.93)

  cites [74] Haynes, S. E. et al. The P2Y12 receptor regulates microglial activation by extracellular nucleotides. Nat. Neurosci. 9, 1512–1519 (2006). source
• Partly supported

  The ‘An environment-dependent transcriptional network’ section identifies mouse P2ry12 among genes downregulated in culture, supporting reduced expression upon culture. It does not establish that expression is completely lost, or specifically show P2RY12 loss in cultured human microglia. The activation claim is attached to a different citation and is not assessed here. (LLM, conf=0.87)

  cites [34] Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017). sourceExperimental — accuracy not yet measured

In the paper

“Overall, 11% of MiGA eQTL colocalizations could be reproduced in the Young et al. data, and 15% of the Young et al. colocalizations could be found in the MiGA data, at a relaxed PP4 > 0.5, whereas sharing between the two monocyte datasets41,42 was 17–24% with the same parameters (Supplementary Fig. 13).”

What the sources say

• Supported

  The citation identifies one of the monocyte eQTL datasets used in the citing authors’ comparison. The source’s Results section, “Identifying Context-Specific Local Genetic Regulatory Variants” (page not provided), reports monocyte expression data and cis-eQTL associations. The 17–24% sharing figure is presented as the citing authors’ comparison in their Supplementary Fig. 13, not as a result attributed to this source. (LLM, conf=0.90)

  cites [41] Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014). source
• Supported

  The Results section (‘PD common variants alter gene expression in monocytes’) describes cis-eQTL analysis of monocytes from 180 subjects, and the Methods (‘Quantitative Trait Loci Analysis’) describes the eQTL mapping. The citation attached to ‘two monocyte datasets’ identifies this paper as one of the datasets; the sharing percentages are presented as the citing authors’ comparison in their Supplementary Fig. 13, not as results attributed to this source. (LLM, conf=0.87)

  cites [42] Navarro, E. et al. Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells. Nat. Aging 1, 850–863 (2021). source

In the paper

“Regional and age-related differences in microglial density, morphology, gene, and protein expression have previously been described for both animals and humans5,6,9,60,61.”

What the sources say

• Partly supported

  The Discussion reports regional differences in human microglial gene expression and density, and the Introduction reports age-related gene-expression changes in humans. The cited paper does not establish the full combination of regional and age-related differences in density, morphology, and gene and protein expression for both animals and humans. The indexed passages provide section labels but no page numbers. (LLM, conf=0.86)

  cites [5] van der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019). sourceExperimental — accuracy not yet measured
• Partly supported

  The Introduction and Results (‘Ageing of microglia occurs in a region-dependent manner’; pages unavailable) describe regional differences and age-related changes in microglial gene expression in adult mice. The Discussion also reports regional variation in mouse microglial morphology and density. These passages do not establish the claimed differences in human microglia, so the ‘both animals and humans’ scope is not supported by this source. (LLM, conf=0.87)

  cites [6] Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [9] Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019). source
• ?Not checked — no full text of the sourcecites [60] Mittelbronn, M., Dietz, K., Schluesener, H. J. & Meyermann, R. Local distribution of microglia in the normal adult human central nervous system differs by up to one order of magnitude. Acta Neuropathol. 101, 249–255 (2001). source
• Partly supported

  The Discussion (page not specified) compares microglial gene-expression patterns in the subventricular zone and corpus callosum and suggests regional differences in microglial phenotype. Its reported changes in microglial number, morphology, and protein expression concern demyelination and remyelination in animals, not age-related differences. The passages do not establish the claimed differences across both animals and humans. (LLM, conf=0.86)

  cites [61] Olah, M. et al. Identification of a microglia phenotype supportive of remyelination. Glia 60, 306–321 (2012). sourceExperimental — accuracy not yet measured

In the paper

“Of interest also are a downregulation of C2, P2RY12, and P2RY13, key players in microglia-neuron interactions63,64, as well as genes related to age-related disorders: MS4A4A, MS4A6A, BST1 and P2RY12.”

What the sources say

• Partly supported

  The paper’s Abstract (page unavailable) shows that microglial P2RY12 helps recruit microglial processes to active synapses, supporting its role in microglia–neuron interactions. Extended Data Figure 10 reports reduced P2ry12 expression in mouse models of neurodegenerative disease, but it does not establish age-related downregulation of the listed genes or identify C2 and P2RY13 as key players in those interactions. (LLM, conf=0.88)

  cites [63] Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The citation is attached to the description of C2, P2RY12, and P2RY13 as key players in microglia–neuron interactions. The Discussion addresses myeloid-cell activity in Alzheimer’s disease, but our copy does not connect those three genes to microglia–neuron interactions. Searches for the gene names and related terms also found no passage establishing that point; the age-related downregulation appears to be the citing authors’ own observation. (LLM, conf=0.74)

  cites [64] Savage, J. C. et al. Nuclear receptors license phagocytosis by TREM2+ myeloid cells in mouse models of Alzheimer’s disease. J. Neurosci. 35, 6532–6543 (2015). sourceExperimental — accuracy not yet measured

In the paper

“Previous analyses have prioritized ECHDC3, as it is upregulated in AD post-mortem brains53,54, and an eQTL for ECHDC3 was seen in whole blood55, though it did not colocalize with the GWAS SNP23.”

What the sources say

• ?Could not verify

  The citation to this paper is attached to the claim that the ECHDC3 eQTL did not colocalize with the GWAS SNP. The Results section discusses brain cis eSNPs, but our copy does not report that ECHDC3-specific colocalization result (searched for ECHDC3, colocalization, and the whole-blood eQTL). (LLM, conf=0.75)

  cites [53] Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013). sourceExperimental — accuracy not yet measured
• Supported

  The citation to this paper is attached to the statement that ECHDC3 is upregulated in AD post-mortem brains, not to the separately cited eQTL or colocalization statements. The Results report significantly increased ECHDC3 transcript expression in AD brains compared with control brains. (LLM, conf=0.93)

  cites [54] Desikan, R. S. et al. Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 131, 2061–2069 (2015). source
• ?Could not verify

  The source reports whole-blood eQTLs generally (Abstract), but the passages in our copy do not identify an eQTL for ECHDC3. Searches for ECHDC3 and for an ECHDC3 whole-blood eQTL found no matching passage. The other parts of the sentence have different citations and are not judged here. (LLM, conf=0.72)

  cites [55] Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The citation is attached to the claim that the ECHDC3 eQTL did not colocalize with the GWAS SNP. The Results (Meta-analysis of Alzheimer’s disease GWAS) identify an ECHDC3 risk signal, and the Methods (Regulatory variant and eQTL analysis) describe colocalization testing, but neither reports a colocalization result for ECHDC3. Searches for ECHDC3 with eQTL and colocalization terms did not locate that result in our copy; page numbers were unavailable. (LLM, conf=0.72)

  cites [23] Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). sourceExperimental — accuracy not yet measured

In the paper

“The sharing between our microglia and previously published microglia26 was low (Fig. 5b), with only a few known loci in AD and PD (BIN1, PICALM, CHRNB1), presumably due to lower power in the Young et al. data compared to our multi-tissue meta-analysis.”

What the sources say

• Supported

  The citation identifies the previously published microglia dataset used for comparison. The Discussion describes this study as creating an eQTL map in primary human microglia. The low sharing and proposed power difference are conclusions of the citing paper, not findings attributed to Young et al. (LLM, conf=0.91)

  cites [26] Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021). source

In the paper

“A previous study prioritized P2RY12 at this locus due to an overlap with eQTLs in blood and brain57.”

What the sources say

• Supported

  In Results, Use Case Scenario 1 (Locus 16, rs11707416; page unavailable), the paper identifies P2RY12 as the primary candidate nominated by its browser data and reports overlap between GWAS and eQTL data in both brain and blood. This supports the citing sentence’s account of why the study prioritized P2RY12. (LLM, conf=0.92)

  cites [57] Grenn, F. P. et al. The Parkinson’s disease genome-wide association study locus browser. Mov. Disord. 35, 2056–2067 (2020). source

In the paper

“Duplicated and up to third-degree related samples were removed based on pairwise kinship coefficients estimated using KING97 (Supplementary Fig. 10a).”

What the sources say

• ?Could not verify

  The Findings > Performance comparisons passages describe PLINK runtimes and marker pruning, not removal of duplicated or related samples. Searches for KING, pairwise kinship coefficients, and third-degree relatedness found no passage in our copy addressing the claimed procedure. (LLM, conf=0.75)

  cites [97] Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). sourceExperimental — accuracy not yet measured

In the paper

“P2RY12 has been shown to play a role in microglia migration, activation, and neuronal activity64,75.”

What the sources say

• ?Could not verify

  The Discussion addresses TREM2-positive myeloid cells and their inflammatory phenotype, not a role for P2RY12 in microglial migration, activation, or neuronal activity. Searches of our copy for P2RY12, P2Y12, and those functions found no passage addressing the claimed P2RY12 role. (LLM, conf=0.76)

  cites [64] Savage, J. C. et al. Nuclear receptors license phagocytosis by TREM2+ myeloid cells in mouse models of Alzheimer’s disease. J. Neurosci. 35, 6532–6543 (2015). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Abstract and results passages discuss microglial gene-expression changes caused by tissue processing and enzymatic dissociation. Searches for P2RY12 and for purinergic-receptor involvement in migration or neuronal activity found no passage in our copy addressing the claimed roles. (LLM, conf=0.75)

  cites [75] Marsh, S. E. et al. Single cell sequencing reveals glial specific responses to tissue processing & enzymatic dissociation in mice and humans. Preprint at bioRxiv https://doi.org/10.1101/2020.12.03.408542 (2020). sourceExperimental — accuracy not yet measured

In the paper

“DNA samples were matched to the RNA-seq data to confirm the same donor origin using the MBV tool from QTLtools98 (Supplementary Fig. 10b) and sex mismatching samples were removed by comparing DNA inferred sex from PLINK to RNA gene expression of the UTY and XIST genes (Supplementary Fig. 10c).”

What the sources say

• ?Could not verify

  The Abstract describes a genotype-imputation service and its quality checks, while the Online Methods describe comparisons of imputation tools; neither addresses matching DNA samples to RNA-seq data or removing sex-mismatched samples. Searches for donor matching and for PLINK, UTY, and XIST found no passage supporting the claim. (LLM, conf=0.76)

  cites [98] Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016). sourceExperimental — accuracy not yet measured

In the paper

“We could not control for all these potential confounders, even though these factors could contribute to gene expression changes76,77.”

What the sources say

• Supported

  The Introduction identifies enzymatic dissociation as a source of artifacts in transcriptomic analyses. The Discussion reports that dissociation deregulated gene expression in brain cells, supporting the cited point that a potential confounding factor could contribute to gene-expression changes. The citing authors’ inability to control for their confounders is their own statement, not a finding attributed to this paper. (LLM, conf=0.85)

  cites [76] Mattei, D. et al. Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations. Int. J. Mol. Sci. 21, 7944 (2020). source
• Supported

  The Introduction says genetic variants contribute to variation in gene expression and that responses can differ with environmental stimuli. The Results section on reQTLs also identifies population structure and expression heterogeneity as systematic confounders. The citing authors’ statement that they could not control for all such factors is their own limitation, not a finding attributed to this paper. (LLM, conf=0.84)

  cites [77] Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014). source

In the paper

“Genetic ancestry of samples was confirmed by principal components analysis using the PLINK program99 and MDS (multidimensional scaling) values of study subjects were compared to those of 1000 Genome Project samples (Phase 3) (Supplementary Fig. 10d).”

What the sources say

• ?Could not verify

  The Online Methods (UK Biobank data set) mention PLINK for sample exclusions, and the Results (Phasing accuracy using the 1000 Genomes and HRC panels) use 1000 Genomes Phase 3 samples for phasing benchmarks. Neither passage reports confirming study subjects’ ancestry by principal components analysis or comparing their MDS values with 1000 Genomes samples. Searches for PCA, MDS, ancestry comparison, and PLINK did not locate that procedure in our copy. (LLM, conf=0.76)

  cites [99] Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016). sourceExperimental — accuracy not yet measured

In the paper

“Analysis of upstream regulators of the four clusters using Ingenuity Pathway Analysis (IPA) was inconclusive (Supplementary Table 9).”

In the paper

“Cluster 3 contained genes specifically downregulated in the SVZ (e.g. CX3CL1, CCR2, FCGR3B) and cluster 4 contained genes upregulated in SVZ (e.g. IL10, CLU, CD83), compared to the other three regions (Fig. 2e).”

In the paper

“We found that genes implicated in inflammatory processes were highly expressed in cluster 2 (Fig. 2f), whereas genes related to homeostatic functions of microglial cells were mainly present in cluster 1.”

In the paper

“176 transcripts in 132 genes had evidence of DTU (log odds ratio > 1; empirical FDR < 0.1), with a majority of transcripts coming from comparisons with the SVZ (Extended Data Fig. 1a).”

In the paper

“eQTLs for CASS4 are significant in both MiGA and monocytes (MyND) but with opposite directions of effect (Fig. 4g), suggesting that the causal variant is located in a complex regulatory element where both enhancing and repressing mechanisms are at play.”

In the paper

“We observed colocalization with eQTLs and sQTLs in MS4A6A, as well as eQTLs in MS4A4A and MS4A4E (Fig. 5f).”

In the paper

“Sharing with monocyte eQTLs was generally slightly lower than with microglia and sharing with bulk DLPFC eQTLs was lowest.”

In the paper

“By mapping sQTLs we have shown that the known AD risk association with CD33 exon 2 splicing is also present in microglia, and added disease associations that may act through splicing, such as MS4A6A in AD, SIPA1L2 and FAM49B in PD, IRF3 in SCZ, STK4 and GMIP in BD, and CD37 and EFCAB13 in MS.”

In the paper

“We also observe multiple splicing QTLs, some previously reported (CD33, FAM49B), and some unreported (MS4A6A, BST1).”

In the paper

“We found that 10 out of 17 genes colocalizing in AD, 8 out of 18 in PD, 4 out of 9 in SCZ, and 3 out of 17 in MS include SNPs that overlap with microglial enhancers (Fig. 6a; Extended Data Fig. 7; Supplementary Fig. 20).”

In the paper

“In the region-specific analysis, we observed between 67 and 199 genes with a cis-eQTL (eGenes) and 253 to 426 genes with a cis-sQTL (sGenes) per region (FDR < 0.05; Supplementary Table 17, Supplementary Fig. 11).”

In the paper

“Fine-mapping revealed that the lead GWAS SNP rs11707416 was suggested as a causal SNP by multiple fine-mapping tools (a consensus SNP) and is in perfect LD (r2 = 1) with the lead QTL SNP rs3732765 (Fig. 6g).”

In the paper

“The GWAS lead SNP and the QTL lead SNP are in moderate LD (r2 = 0.65), as are two of the three fine-mapped SNPs (Fig. 6d; Supplementary Table 22).”

In the paper

“Microglia from SVZ exhibited lower pairwise sharing of eQTLs with other regions, with the lowest sharing by magnitude observed between SVZ and MFG (41%), consistent with observed transcriptomic differences between these two regions.”

In the paper

“In each disease there are genes that appear to be microglia-specific (BIN1, PYCR2), shared between microglia and monocytes (CASS4, CTSB), and shared between microglia and brain (ZNF646, P2RY12).”

In the paper

“We overlaid all sQTL junctions that colocalized with the AD risk locus and found that the strongest colocalization signals highlighted a cluster of introns in the middle of the gene, with the 5’ intron in the cluster having the strongest colocalization.”

In the paper

“P2RY12 is an eQTL (lead SNP rs3732765) identified in the METASOFT meta-analysis, with the lead QTL SNP rs3732765-A decreasing P2RY12 expression (Fig. 6e).”

In the paper

“AD and PD had the highest number of colocalizing loci in each QTL dataset, compared to the other diseases (Fig. 5a, Supplementary Table 21), with 10–30% of loci containing at least one colocalized gene, depending on the stringency of the H4 posterior probability (PP4), with lower proportions observed in BD, SCZ and MS.”

In the paper

“Similarly, we found 225 transcripts from 150 genes exhibiting DTU with age (FDR < 0.1) (Extended Data Fig. 2a), where the balance between a long and short isoform shifts over age (Extended Data Fig. 2b).”

In the paper

“USP6NL harbors an expression QTL observed in all four microglial regions, with the lead QTL SNP rs7912495-G increasing USP6NL expression (Fig. 6b).”

In the paper

“For sQTLs, we found overall higher regional sharing effects compared to eQTLs, but still following the same trends as for eQTLs (Fig. 4b, lower triangle).”

In the paper

“Transcription factor binding motif analysis was inconclusive, with three of the tested SNPs rs143807787, rs74347557, and rs7912495 predicted to disrupt multiple motifs in different directions (Supplementary Table 23).”

In the paper

“In addition, there are 4 other SNPs prioritized by fine-mapping, 2 of which were in perfect or very high LD with the lead QTL SNP.”

In the paper

“We have identified specific disease colocalizations that may not be captured in monocytes or bulk brain tissue, like BIN1, USP6NL, and PICALM in AD, P2RY12 in PD, PLXNB2 in MS, and IFRD1 in SCZ.”

In the paper

“We mapped sQTLs with between 0 and 20 PEER factors as covariates in our QTL model and determined 5 to be optimal in MFG, STG and THA. 0 PEER factors were used for SVZ (Supplementary Fig. 11).”

In the paper

“Transcripts form 2 k-means clusters”

In the paper

“We tested numbers of PEER factors from 0 to 20 and found that between 5 and 10 factors produced the largest number of eGenes in each region (Supplementary Fig. 11).”

In the paper

“Heatmap of relative transcript usage between regions using all 176 transcripts from pairwise comparisons of differential transcript usage (DTU; empirical FDR < 0.1)”

In the paper

“Notably, there was no colocalization of the upstream ECHDC3 gene in any tested cell type, suggesting that USP6NL is the AD risk gene at this locus.”

In the paper

“The CD33 risk locus has been implicated in AD susceptibility59.”

What the sources say

• ?Not checked — no full text of the sourcecites [59] Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019). source

In the paper

“Microglia from the SVZ had the most region-specific effects with 1,045, most likely because the transcriptomic profile of this region is most distinct (Supplementary Table 20).”

In the paper

“We demonstrate that transcriptional heterogeneity in human microglia varies between brain regions and across aging.”

In the paper

“This approach allowed us to identify candidate functional variants in multiple disease susceptibility loci that modulate microglia-specific enhancer activity and regulate causal gene expression, which in turn likely modify disease risk by altering the function of microglia (or other myeloid cells) in the brain.”

In the paper

“Together, our results indicate that the microglial phenotype ages in a generally uniform manner across brain regions, with a distinct aging trajectory observed in a minority of genes.”

In the paper

“This suggests that splicing QTLs are less cell type-specific, presumably due to the association with distinct types of regulatory elements.”

In the paper

“We generated a catalog of eQTLs and sQTLs in microglia and thereby validated and extended the list of disease genes and putative causal variants underlying risk for neurodegenerative and psychiatric diseases.”

In the paper

“Previous analyses in peripheral monocytes found association between lead GWAS SNP rs3865444 and the inclusion of CD33 exon 259.”

What the sources say

• ?Not checked — no full text of the sourcecites [59] Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019). source

In the paper

“rs2162254-A is associated with an increased usage of this intron, which may result in increased production of the shorter isoforms, which could have a downstream consequence on MS4A6A protein function.”

In the paper

“We have generated an atlas of genetic effects on the human microglia transcriptome, which allowed us to identify potential causal genes and variants underlying risk for neurodegenerative and psychiatric diseases.”

In the paper

“The investigators were not blinded for group allocation (diagnosis, sex, age etc.) during data analysis since adjustment for these factors was necessary for the data analyses.”

In the paper

“All autopsies were performed with written consent from the legal next-of-kin.”

In the paper

“Furthermore, our ability to detect additional disease-associated eQTLs may be obscured due to the use of bulk RNA-sequencing data.”

In the paper

“Our findings represent mechanistic hypotheses that can now be tested with further experimental work at both the level of individual variants and the candidate genes.”

In the paper

“The age range of the 100 donors is between 21 and 103 years old”

In the paper

“Besides neuroinflammation, hypoxia, and long postmortem intervals, technical artifacts (enzymatic digestion, temperature changes, sorting) may cause microglial activation.”

In the paper

“58 of them were female”

In the paper

“We sorted the microglial cells with CD11b+ beads. This marker is not restricted to microglia and may capture small fractions of other myeloid cells.”

In the paper

“We increased power by combining the four regions in a meta-analysis, with the caveat of not adjusting for shared donors, which will have increased our false discovery rate.”

In the paper

“The study was performed under IRB-approved guidance and regulations to keep all patient information strictly de-identified.”

In the paper

“RNA-seq quality control was performed applying filters to remove samples: 1) samples with less than 10M reads aligned from STAR; 2) samples with more than 20% of the reads aligned to ribosomal regions; 3) samples with less than 10% of the reads mapping to coding regions; 4) samples from brain regions with fewer than 20 donors.”

In the paper

“The permission to collect human brain material was obtained from the Ethical Committee of the VU University Medical Center, Amsterdam, The Netherlands, and the Mount Sinai Institutional Review Board (IRB).”

In the paper

“All research conformed to the principles of the Helsinki Declaration.”

In the paper

“All analyses were adjusted for age, gender, and other covariates.”

In the paper

“In total, 59 out of 314 samples were excluded due to insufficient RNA-seq quality or insufficient sample size by brain region.”

In the paper

“Informed consent for autopsy, the use of brain tissue and accompanied clinical information for research purposes was obtained per donor ante-mortem.”

In the paper

“To select high-quality data, we applied an initial genotyping quality control using bcftools (v1.9) and vcftools (v0.1.15), keeping SNPs with call rate > 95%, minor allele frequency (MAF) > 5%, Hardy-Weinberg equilibrium (HWE) P-value > 1 × 10−6, and sample call rate > 95%.”