Single-cell multiregion dissection of Alzheimer’s disease

In the paper

“In parallel, astrocytes uniquely upregulated DDIT4, PFKP and ADCY8, along with genes that suppress fatty acid metabolism (ANGPTL4) and promote lipid droplet storage of fatty acids (HILPDA), while microglia upregulated multiple glycogen-related genes (GBE1, UGP2 and PYGL)48 (Fig. 4m).”

What the sources say

• Disputed

  Split into 2 atoms — 0 confirmed, 0 partially supported, 1 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

  Checked by a reasoning model — may be wrong.

  cites [48] Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020). sourceExperimental — accuracy not yet measured

In the paper

“AD DEGs were consistent with published results both for region-specific DEGs and for DEGs computed jointly over all regions for multiple AD variables, and were further corroborated by comparisons with various independent studies11,12,15,19,47–53”

What the sources say

• ?Not checked — no full text of the sourcecites [11] Grubman, A. et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat. Neurosci. 22, 2087–2097 (2019). source
• Partly supported

  The differential-expression results section reports that bulk RNA-seq from the ROSMAP cohort validated the paper’s single-nucleus DEGs, and notes that an APOE finding was consistent with published model studies. But the study profiled one region—prefrontal cortex (Brodmann area 10)—and does not establish agreement for both region-specific and jointly analyzed multi-region DEGs across multiple AD variables, or comparisons with various independent studies. (LLM, conf=0.86)

  cites [12] Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). sourceExperimental — accuracy not yet measured
• Disputed

  The Discussion (page unavailable) describes a “near complete lack of overlap” in gene-expression results across studies and says differences in brain regions preclude proper comparisons with this temporal-cortex analysis. It notes that some individual effects were recapitulated, but does not report the claimed consistency of region-specific and jointly computed, all-region AD DEGs across multiple variables. (LLM, conf=0.85)

  Checked by a reasoning model — may be wrong.

  cites [15] Gabitto, M. I. et al. Integrated multimodal cell atlas of Alzheimer’s disease. Preprint at bioRxiv https://doi.org/10.1101/2023.05.08.539485 (2023). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results (page unavailable) report that synapse-related DEGs in entorhinal-cortex excitatory neurons were consistent with findings from two earlier studies. Our copy does not support the more specific claim about DEGs computed jointly across all regions for multiple AD variables, or corroboration of those DEGs against various independent studies. (LLM, conf=0.84)

  cites [19] Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results section, “Cross-Study Validation of the Transcriptomic Changes in AD Brains” (page unavailable), reports concordance between the paper’s AD DEGs and independent prefrontal- and temporal-cortex datasets, as well as another single-nucleus study. It does not report DEGs computed jointly over all brain regions for multiple AD variables, so that part of the attributed claim is unsupported. (LLM, conf=0.86)

  cites [47] Lau, S.-F., Cao, H., Fu, A. K. Y. & Ip, N. Y. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 117, 25800–25809 (2020). sourceExperimental — accuracy not yet measured
• Partly supported

  The Discussion (page unavailable) reports partial agreement between this paper’s AD gene signatures and a published study, and Extended Data Fig. 10 describes corroboration across independent cohorts. The paper does not establish consistency for both region-specific DEGs and DEGs computed jointly across all regions for multiple AD variables. (LLM, conf=0.82)

  cites [48] Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results section (page unavailable) compares hippocampal AD DEGs with several published cortex studies and reports region-specific differences, supporting the claim that independent studies were consulted. It does not report DEGs computed jointly across all regions for multiple AD variables; it also says few DEGs were shared across regions and that variable findings in prior studies need confirmation. (LLM, conf=0.85)

  cites [49] Su, Y. et al. A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan. Cell Stem Cell 29, 1594–1610 (2022). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results section, ‘Multi-omic characterization of AD cellular heterogeneity’ (page unavailable), reports late-stage AD DEGs by cell cluster and notes that NEAT1 upregulation agrees with a previous finding. This makes the paper a relevant independent AD DEG study, but our copy does not establish consistency with the citing authors’ region-specific and jointly computed DEGs across multiple AD variables. (LLM, conf=0.78)

  cites [50] Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results sections on regional preservation and network validation report similar AD-related protein co-expression patterns across brain regions and confirmation in an independent cohort (page not provided). The paper does not establish that region-specific or jointly computed differentially expressed genes (DEGs) agree with published DEG results; its analyses concern proteins rather than those gene-level comparisons. (LLM, conf=0.78)

  cites [51] Johnson, E. C. B. et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat. Med. 26, 769–780 (2020). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results sections on microglia and astrocytes report correspondence between disease-associated cell populations and previously published human and mouse AD datasets. That supports a limited comparison with independent studies, but the cited paper does not establish that region-specific DEGs and DEGs pooled across all regions were consistent with published results for multiple AD variables. Page numbers were not available in our copy. (LLM, conf=0.82)

  cites [52] Zeng, H. et al. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease. Nat. Neurosci. 26, 430–446 (2023). sourceExperimental — accuracy not yet measured

In the paper

“Tangles are first seen in the entorhinal cortex (EC) (Braak stages I–II), then the hippocampus and thalamus (Braak stages III–IV) and finally the neocortex (Braak stages V–VI), a sequence that is typically synchronous with cognitive decline from mild cognitive impairment to severe dementia1,2,4–7.”

What the sources say

• ?Not checked — no full text of the sourcecites [1] Braak, H. & Braak, E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 (1995). source
• Partly supported

  Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

  cites [2] Wilson, R. S. et al. Temporal course and pathologic basis of unawareness of memory loss in dementia. Neurology 85, 984–991 (2015). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [4] Sonnen, J. A. et al. Pathological correlates of dementia in a longitudinal, population-based sample of aging. Ann. Neurol. 62, 406–413 (2007). source
• ?Not checked — no full text of the sourcecites [5] Bennett, D. A. et al. Natural history of mild cognitive impairment in older persons. Neurology 59, 198–205 (2002). source
• ?Not checked — no full text of the sourcecites [6] Bennett, D. A., Schneider, J. A., Wilson, R. S., Bienias, J. L. & Arnold, S. E. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch. Neurol. 61, 378–384 (2004). source
• ?Not checked — no full text of the sourcecites [7] Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta Neuropathol. 81, 261–268 (1991). source

In the paper

“Region-specific astrocyte subtypes were experimentally validated using RNA in situ hybridization (Fig. 2d) and confirmed by analysing a separate snRNA-seq dataset14”

What the sources say

• Partly supported

  The main text (Fig. 6A; Fig. S9E–F; page unavailable) identifies astrocyte clusters with distinct regional distributions. But our copy does not say those astrocyte subtypes were validated by RNA in situ hybridization or confirmed using a separate snRNA-seq dataset. Its in situ hybridization passages describe other cell populations, not validation of the astrocyte clusters. (LLM, conf=0.85)

  cites [14] Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023). sourceExperimental — accuracy not yet measured

In the paper

“We found that the thalamic–neocortex separation is conserved in mice and recapitulated both this divide and thalamic marker genes in independent single-cell, bulk and microarray data in both mice and humans8,39,40,43”

What the sources say

• Partly supported

  The ‘Global transcriptional architecture of the adult brain’ section reports distinct regional expression patterns in human microarray data, including a module specific to part of the thalamus and other modules enriched in neocortex. It does not establish the claimed thalamic–neocortical separation in mice or its recapitulation across independent single-cell and bulk datasets. Page numbers were not available in our copy. (LLM, conf=0.78)

  cites [8] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). sourceExperimental — accuracy not yet measured
• Supported

  The Results section, “Isolation and Molecular Analysis of Cells for an Adult Brain Cell Atlas” (page unavailable), identifies this work as an adult-mouse single-cell RNA-expression dataset spanning nine brain regions. The Results also discuss thalamic neurons and cortical cells. In this multi-citation sentence, the paper supports the mouse single-cell dataset used for the citing authors’ replication; the replication itself is the citing authors’ finding, and the human, bulk, and microarray datasets are not attributed to this source alone. (LLM, conf=0.85)

  cites [39] Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018). source
• Partly supported

  The Results sections on astroependymal cells and CNS neurons describe a molecular distinction between mouse telencephalic regions, including cortex, and the diencephalon, including thalamus. The paper also compares its mouse single-cell clusters with previously published datasets. Our copy does not report that the thalamic–neocortex divide was recapitulated across independent single-cell, bulk, and microarray data in both mice and humans. (LLM, conf=0.84)

  cites [40] Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Introduction (page unavailable) describes a taxonomy of adult mouse isocortex and hippocampal formation, not a thalamus–neocortex comparison. Searches for thalamic marker genes and for independent single-cell, bulk, and microarray data did not find a passage establishing the claimed separation in mice or humans. (LLM, conf=0.76)

  cites [43] Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021). sourceExperimental — accuracy not yet measured

In the paper

“EC-specific subtypes that clustered separately from neocortical subtypes for the same layers were often marked by expression of RELN, TOX3 and GPC5, and contained subtypes from both the lateral (L2 RELN+GPC5+) and medial (L2 TOX3+POSTN+) EC41–43”

What the sources say

• Partly supported

  The Results (Taxonomic relationship of neural cells across allo-, meso- and neo-cortex; page unavailable) identify EC-specific excitatory-neuron subtypes, including two layer-2 RELN-expressing subtypes that did not closely correspond to neocortical subtypes. The STAR Methods (Method details; page unavailable) also classify two layer-2 EC subtypes as RELN+. Our copy does not substantiate the claimed TOX3, GPC5 or POSTN markers, the specified marker combinations, or their lateral-versus-medial EC assignments. (LLM, conf=0.88)

  cites [41] Franjic, D. et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron 110, 452–469.e14 (2022). sourceExperimental — accuracy not yet measured
• Partly supported

  The Discussion (page not available) says the atlas identifies layers in the entorhinal area and proposes molecular layer definitions with subregional information. It does not report EC-specific subtypes clustering separately from neocortical subtypes for the same layers, or identify the claimed lateral and medial layer-2 subtypes by RELN, TOX3, GPC5, and POSTN. (LLM, conf=0.76)

  cites [42] Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020). sourceExperimental — accuracy not yet measured
• Partly supported

  The Results section, “Comparison of glutamatergic cell types between hippocampal formation and isocortex” (page unavailable), identifies distinct layer-2 supertypes in medial and lateral entorhinal cortex. It names them L2 IT ENTm Lef1 and L2 IT ENTl Chn2 (Grik1+), however; the searched passages do not establish the claimed RELN/GPC5 and TOX3/POSTN marker combinations or that these subtypes clustered separately from neocortical subtypes of the same layers. (LLM, conf=0.84)

  cites [43] Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021). sourceExperimental — accuracy not yet measured

In the paper

“Excitatory neurons in the TH were predominantly composed (74%) of a subtype (NXPH1+RNF220+) that was not observed in the neocortex and is predicted to be regulated by LHX9, SOX2, SHOX2 and TCF7L234,36”

What the sources say

• ?Could not verify

  The Results section on mouse visual cortex neurons discusses cortical activity programs, not the composition of thalamic excitatory neurons. Searches for NXPH1/RNF220, the 74% figure, and the proposed regulators found no passage in our copy addressing the claimed thalamic subtype or its absence from the neocortex. (LLM, conf=0.76)

  cites [34] Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The paper’s human-brain analysis describes DLX1/2-driven interneurons, and its Online Methods describe brain datasets, but neither passage addresses the claimed thalamic excitatory-neuron subtype. Searches for NXPH1, RNF220, the 74% figure, the listed regulators, and a thalamus–neocortex comparison found no supporting passage in our copy. (LLM, conf=0.76)

  cites [36] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). sourceExperimental — accuracy not yet measured

In the paper

“To further examine the cellular and regional heterogeneity of the human brain, we developed a scalable method, scdemon, which uses sample decorrelation to annotate both ubiquitous and rare gene expression programs in each major cell type, and used annotated modules to identify functional programs associated with specific pathological variables, including a glycolysis- and energy-metabolism-linked”

What the sources say

• ?Could not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  cites [48] Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020). sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [60] Magistretti, P. J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015). source

In the paper

“Microglia and immune cells showed consistently high scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG (Extended Data Fig. 9c).”

What the sources say

• Partly supported

  The Results section (‘Gene-sets implicated in AD and AD-by-proxy’; page unavailable) reports suggestive associations for immune-related tissues and microglia in two single-cell datasets. It does not report consistently high scores across brain regions or identify TPT1+ microglia and macrophages as top-scoring cells in the HC, thalamus or AG. (LLM, conf=0.84)

  cites [54] 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). sourceExperimental — accuracy not yet measured
• ?Could not verify

  The Results section on brain traits discusses hippocampal CA1 neurons, not high microglial or macrophage scores across the HC, thalamus and AG. Searches for TPT1, macrophages and those regions found no passage supporting the claimed ranking. This paper’s Extended Data Fig. 9c concerns metabolic pathways, not brain immune cells. (LLM, conf=0.77)

  cites [55] Zhang, M. J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat. Genet. 54, 1572–1580 (2022). sourceExperimental — accuracy not yet measured

In the paper

“We found that AD-risk genes were highly perturbed in AD—in particular for microglia, consistent with their enrichment for GWAS signal68—but few risk genes showed region-specific expression.”

What the sources say

• Supported

  The paper reports that AD-associated GWAS variants are enriched in immune-cell enhancer regions, with CD14+ immune cells showing the strongest enrichment (Abstract; page not provided). Citation 68 is attached to the GWAS-enrichment context; the sentence presents the gene-perturbation and region-specific-expression findings as the citing authors’ own results. (LLM, conf=0.88)

  cites [68] Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015). source

In the paper

“DEGs linked to non-vulnerable subtypes were enriched for a diverse set of functions, including ubiquitin-ligase binding, heat-shock-family chaperones, ER protein processing and mediators of neuronal death, whereas vulnerability-associated DEGs were highly enriched only for mitochondrial oxidative phosphorylation but included CRK and NEUROD2, which are both associated with Reelin signalling17,18”

What the sources say

• ?Could not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  cites [17] Chin, J. et al. Reelin depletion in the entorhinal cortex of human amyloid precursor protein transgenic mice and humans with Alzheimer’s disease. J. Neurosci. 27, 2727–2733 (2007). sourceExperimental — accuracy not yet measured
• ?Could not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  cites [18] Kobro-Flatmoen, A. et al. Re-emphasizing early Alzheimer’s disease pathology starting in select entorhinal neurons, with a special focus on mitophagy. Ageing Res. Rev. 67, 101307 (2021). sourceExperimental — accuracy not yet measured

In the paper

“Notably, choline oxidation to betaine generates a labile methyl group that can be used for homocysteine remethylation, resulting in methionine formation, which is subsequently transformed into the universal methyl donor S-adenosylmethionine67.”

What the sources say

• Supported

  The Introduction states that choline is oxidized to betaine, its labile methyl group can be used to remethylate homocysteine to methionine, and methionine is then converted to S-adenosylmethionine. This directly supports the attributed pathway. (LLM, conf=0.95)

  cites [67] Korsmo, H. W. et al. Maternal choline supplementation and high-fat feeding interact to influence DNA methylation in offspring in a time-specific manner. Front. Nutr. 9, 841787 (2022). source

In the paper

“We found that 74 genes (49%) were differentially expressed for at least one cell type, and multiple genes showed region-specific expression, including the lipid transporter ABCA7 (enriched in thalamus), the zinc-finger protein ZNF655 (EC) and the complement receptor CR1 (neocortex)56 (Extended Data Fig. 10).”

What the sources say

• Partly supported

  The GWAS discussion (page unavailable in our copy) identifies CR1 as a complement-receptor gene with suggestive evidence of association with Alzheimer’s disease. It does not report CR1 expression in neocortex or the stated regional enrichment of ABCA7 and ZNF655; searches for those genes and brain regions found no supporting passage. (LLM, conf=0.85)

  cites [56] Lambert, J.-C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet. 41, 1094–1099 (2009). sourceExperimental — accuracy not yet measured

In the paper

“ZCA is the transformation which maximizes the similarity of the transformed data to the original, which is achieved by setting W = C−1/2, where C is the covariance of X.”

What the sources say

• Supported

  Section 6.1, “ZCA-Mahalanobis whitening,” defines similarity as the squared distance between the original and whitened vectors and shows that minimizing it uniquely yields ZCA whitening. Its proof gives W = Σ⁻¹ᐟ², where Σ is the covariance matrix. (LLM, conf=0.93)

  cites [101] Kessy, A., Lewin, A. & Strimmer, K. Optimal whitening and decorrelation. Am. Stat. 72, 309–314 (2018). source

In the paper

“We performed differential expression analyses using the R package muscat to identify genes associated with CR in the PFC117. Low-expressed genes were excluded and only genes with more than one count in at least ten cells were considered. To take advantage of robust bulk RNA-seq differential expression frameworks, such as edgeR118, in a first step, muscat aggregates measurements for each sample (in”

What the sources say

• Supported

  The citation identifies muscat as the R package used in the citing authors’ analysis. The source’s Methods (‘Aggregation-based methods,’ page unavailable) describes muscat’s sample-by-subpopulation pseudobulk aggregation and edgeR-based testing. The PFC/CR analysis, covariates, and correction procedure describe the citing authors’ own implementation, rather than findings attributed to this source. (LLM, conf=0.88)

  cites [117] Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020). source
• Supported

  Citation 118 identifies edgeR as an example of a differential-expression framework, not as the source for the authors’ PFC analysis choices. The Introduction describes edgeR as software for replicated count-based expression data applicable to RNA-seq, and the Model section describes its differential-expression analysis. (LLM, conf=0.94)

  cites [118] Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). source

In the paper

“Similarly, dietary supplementation with the polyamine spermidine prolongs life span and health span in several animal models66, and spermidine has also been shown to enhance memory performance and counteract age-related cognitive decline79–81.”

What the sources say

• ?Not checked — no full text of the sourcecites [66] Madeo, F., Eisenberg, T., Pietrocola, F. & Kroemer, G. Spermidine in health and disease. Science 29, 1243–1252 (2018). source
• Supported

  The opening passage (p. 178) reports that spermidine treatment rescued age-induced memory impairment in fruit flies. A later passage (p. 178) says spermidine-fed 30-day-old flies maintained short- and intermediate-term memory performance comparable to young flies. This supports the memory claim to which this source is cited; the demonstrated effect is in flies. (LLM, conf=0.91)

  cites [79] Sigrist, S. J. et al. Spermidine-triggered autophagy ameliorates memory during aging. Autophagy 10, 178–179 (2014). source
• Supported

  The cognition claim is the part of the sentence attributed to this source; the lifespan claim carries a separate citation. The Summary (p. 2) reports improved spatial learning in aged mice and enhanced cognition in flies, while the Introduction (p. 3) reports that dietary spermidine suppresses age-induced memory impairment in flies. (LLM, conf=0.93)

  cites [80] Schroeder, S. et al. Dietary spermidine improves cognitive function. Cell Rep. 35, 108985 (2021). source
• Supported

  The lifespan claim is separately cited to reference 66; this source supports the memory and cognitive-decline clause. Its Introduction (‘Spermidine and its effects’; page not supplied) reports improved memory in supplemented fruit flies compared with same-age controls and describes positive effects on age-related memory loss. Its Abstract also reports improved cognitive performance among older adults with mild or moderate dementia receiving the higher dose. (LLM, conf=0.91)

  cites [81] Pekar, T. et al. The positive effect of spermidine in older adults suffering from dementia: first results of a 3-month trial. Wien. Klin. Wochenschr. 133, 484–491 (2021). source

In the paper

“This finding aligns with studies showing benefits of dietary choline intake and supplementation on cognitive performance in human individuals and in animal models71–78.”

What the sources say

• ?Not checked — no full text of the sourcecites [71] Teather, L. A. & Wurtman, R. J. Dietary cytidine (5′)-diphosphocholine supplementation protects against development of memory deficits in aging rats. Prog. Neuropsychopharmacol. Biol. Psychiatry 27, 711–717 (2003). source
• ?Not checked — no full text of the sourcecites [72] Poly, C. et al. The relation of dietary choline to cognitive performance and white-matter hyperintensity in the Framingham Offspring Cohort. Am. J. Clin. Nutr. 94, 1584–1591 (2011). source
• Supported

  The Discussion (page not available in our copy) reports that long-term dietary CDP-choline supplementation protected rats reared in impoverished conditions from spatial-memory impairment. This supports the animal-model part of the claim; this citation is one of several attached to the broader sentence and need not itself establish a benefit in humans. (LLM, conf=0.90)

  cites [73] Teather, L. A. & Wurtman, R. J. Dietary CDP-choline supplementation prevents memory impairment caused by impoverished environmental conditions in rats. Learn. Mem. 12, 39 (2005). source
• Partly supported

  The passages indexed under Abstract (page unavailable) report improved memory performance after choline supplementation in rats and an association between higher dietary choline intake and better cognitive performance in a human cohort. They do not establish that choline intake caused the human benefit; they also say controlled trials found no clinical value for choline or phosphatidylcholine in treating cognitive dysfunction in Alzheimer’s disease. This source therefore supports the animal supplementation finding and a human dietary association, but not a general claim of supplementation benefits in humans. (LLM, conf=0.87)

  cites [74] Nurk, E. et al. Plasma free choline, betaine and cognitive performance: the Hordaland Health Study. Br. J. Nutr. 109, 511–519 (2013). sourceExperimental — accuracy not yet measured
• Supported

  The Conclusions state that high choline intake enhances cognitive performance in multiple animal models and some human studies. Section 2.1 describes improved cognition in prenatally choline-supplemented rats and an association between higher maternal choline intake and better childhood memory in humans; the human finding is observational. (LLM, conf=0.91)

  cites [75] Blusztajn, J. K., Slack, B. E. & Mellott, T. J. Neuroprotective actions of dietary choline. Nutrients 9, 815 (2017). source
• Supported

  The Introduction reports improved spatial memory with choline supplementation in an animal study and an association between supplementation and improved cognitive function in human adults. The Discussion also reports an association between dietary choline intake and better cognitive performance among older adults. The paper cautions that its own cross-sectional findings cannot establish causation. (LLM, conf=0.90)

  cites [76] Liu, L. et al. Choline intake correlates with cognitive performance among elder adults in the United States. Behav. Neurol. 2021, 2962245 (2021). source
• Supported

  The Introduction reports that animal experiments found improved cognitive performance with dietary choline supplementation. It also reports positive associations between dietary choline intake and cognitive performance in two human populations. (LLM, conf=0.92)

  cites [77] Yuan, J. et al. Is dietary choline intake related to dementia and Alzheimer’s disease risks? Results from the Framingham Heart Study. Am. J. Clin. Nutr. 116, 1201–1207 (2022). source
• Partly supported

  The Discussion (page not provided) reports greater efficacy of choline supplementation in clinical trials involving people with Alzheimer’s disease who carry APOE4; the cited trial’s title identifies cognitive performance as an outcome. The paper’s own Results concern lipid defects in cultured cells, while the animal-related study listed in its References reports improved Alzheimer’s pathology, not cognitive performance. The claim’s animal-cognition component is therefore not supported by our copy. (LLM, conf=0.82)

  cites [78] Sienski, G. et al. APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia. Sci. Transl. Med. 13, eaaz4564 (2021). sourceExperimental — accuracy not yet measured

In the paper

“We calculated the disease-relevance score of each cell in the dataset against a recent Alzheimer’s GWAS, using scDRS (based on MAGMA)54,55,116. For the scDRS results, we counted the fraction of cells with significant scDRS scores (FDR < 0.05) in each cell type, subtype and region. To test for overlap with microglia/immune modules, we compared the set of immune cells with significant expression of”

What the sources say

• Supported

  The Results section (‘Genome-wide meta-analysis for AD status’; page unavailable) reports a genome-wide meta-analysis of Alzheimer’s disease, confirming that this source is an AD GWAS. The citation identifies the GWAS used by the citing authors; their scDRS scoring and overlap test are not attributed to this source. (LLM, conf=0.92)

  cites [54] 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
• Supported

  The Results section (“Overview of methods”) identifies scDRS as a method for scoring individual cells against GWAS-derived disease genes and states that it uses MAGMA to select those genes. The citation supports the method identification; the citing authors describe the Alzheimer’s analysis and overlap test as their own procedures. (LLM, conf=0.94)

  cites [55] Zhang, M. J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat. Genet. 54, 1572–1580 (2022). source
• Supported

  This citation identifies MAGMA, the GWAS analysis tool named as a basis for scDRS. The Materials and Methods section (‘Analysis of summary SNP statistics’; page unavailable) describes MAGMA’s use of GWAS SNP statistics for gene analysis. The Alzheimer’s cell scores and overlap test are the citing authors’ own procedures, not findings attributed to this paper. (LLM, conf=0.88)

  cites [116] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source

In the paper

“Our study has several limitations: isotropic fractionation and read depth cut-offs may bias cell recovery based on their nuclear content; nuclear RNA may not fully capture microglial states82 or localized transcriptomic changes; and pathology burden is based on per-sample averages instead of on the spatial context of each cell.”

What the sources say

• Supported

  The Discussion says that single-nucleus RNA-seq misses genes associated with microglial activation and is therefore not suitable for assessing cellular state in human microglia. The Results section also reports that differences among some microglial subpopulations were not convincingly recovered. Citation 82 is attached to the microglial-states claim, not the separate point about localized transcriptomic changes. (LLM, conf=0.93)

  cites [82] Thrupp, N. et al. Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 32, 108189 (2020). source

In the paper

“Notably, the RELN variant enhanced its binding to glycosaminoglycans (GAGs) and NRP1, and the APOE variant decreased binding to GAGs, potentially affecting their ability to compete for receptor binding69,70.”

What the sources say

• Partly supported

  The Abstract (page not provided) reports that ApoE3 Christchurch had the lowest heparin binding among the ApoE isoforms tested, supporting reduced APOE-variant binding to a GAG-related molecule. The paper does not report enhanced RELN-variant binding to GAGs or NRP1, or test whether the variants compete for receptor binding. (LLM, conf=0.88)

  cites [69] Arboleda-Velasquez, J. F. et al. Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: a case report. Nat. Med. 25, 1680–1683 (2019). sourceExperimental — accuracy not yet measured
• Supported

  The Discussion states that the RELN-COLBOS variant enhances RELN binding to GAGs and NRP1, while the APOECh variant impairs APOE binding to GAGs. It also describes a possible competitive advantage for RELN in receptor binding, matching the claim’s uncertainty. Page numbers are not provided in the supplied copy. (LLM, conf=0.94)

  cites [70] Lopera, F. et al. Resilience to autosomal dominant Alzheimer’s disease in a Reelin-COLBOS heterozygous man. Nat. Med. 29, 1243–1252 (2023). source

In the paper

“Differential expression analysis of bulk RNA-seq data from the ROSMAP cohorts was performed using DESeq2119 (plotted) and edgeR118. Age at death and post-mortem interval were converted into z scores and included as covariates in the regression equation.”

What the sources say

• Supported

  The citation is attached to DESeq2, identifying the method used by the citing authors; it is not offered as evidence for their ROSMAP analysis or covariate choices. The cited paper’s Materials and methods section identifies DESeq2 as an R/Bioconductor package for differential expression analysis. (LLM, conf=0.92)

  cites [119] Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). source
• Supported

  The citation is attached to “edgeR,” identifying the software used rather than documenting the citing authors’ ROSMAP analysis. The Introduction identifies edgeR as a package for count-based expression data applicable to RNA-seq. The ROSMAP dataset and covariate choices are the citing authors’ own methods, not claims attributed to this source. (LLM, conf=0.94)

  cites [118] Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). source

In the paper

“Among glial cell types, astrocytes had the highest regional heterogeneity, containing both highly neocortex-enriched (GRM3+DPP10+) and thalamus-enriched (LUZP2+DCLK1+) subtypes”

In the paper

“present an online resource for navigating this atlas (http://compbio.mit.edu/ad_multiregion)”

In the paper

“The top thalamus-specific interactions were between excitatory NXPH1 and neuronal NRXN1 or NRXN3, whereas inhibitory neurons expressed NXPH1 in the other regions”

In the paper

“In contrast to astrocytes, immune cells showed little regional specificity and oligodendrocyte-lineage cells had thalamus-enriched subtypes with minor transcriptomic differences to neocortex-enriched subtypes”

In the paper

“Moreover, in the HC, EC and MT, caudal ganglionic eminence-derived GABAergic neurons (VIP+LAMP5+) were significantly more abundant than medial ganglionic eminence-derived neurons (SST+PVALB+), but these two major clades were not significantly different in the PFC”

In the paper

“Predicted cell–cell communication interactions were mostly shared across multiple regions, but the thalamus had multiple differential interactions”

In the paper

“Cortical astrocytes were enriched for markers involved in glutamate processing and transport, whereas hippocampus- and anterior thalamus-enriched DCLK1 astrocytes had lower glutamate transporter activity and were enriched instead for focal-adhesion-related genes”

In the paper

“Cells expressing these modules were enriched for diverse aspects of our dataset, including cellular subtype identity (205 modules), brain region (156, with 77 thalamus specific and 34 EC specific), AD status (73), APOE genotype (78) and sex (24)”

In the paper

“Oligodendrocyte-lineage modules showed high regional specificity, and two OPC modules—thalamus-enriched M11 and EC-enriched M25—were marked by synapse-associated genes such as neural adhesion-related SEMA3D, SEMA6D and CNTN5, and glutamate receptor GRIA4”

In the paper

“These scores matched the cortical excitatory and inhibitory split and were negatively correlated both across and within broad neuronal classes”

In the paper

“Immune modules included identity programs, such as for T cells (M6), macrophages (M7) and cycling microglia (MKI67+, M5) as well as modules found across immune cells and enriched for genes involved in NF-κB signalling (M18), interferon (M20), p53 and DNA damage response (M22) and TGFβ signalling (M14)”

In the paper

“Module–module correlations across samples revealed co-expressed programs, such as reactive astrocytes (M3, marked by TPST1, CLIC4 and EMP1) with cholesterol biosynthesis module M17 (r = 0.60), and glycolysis (M6) with AP-1 module M13 (r = 0.39, including FOS/JUN and ubiquitin)”

In the paper

“From these 48 individuals, we profiled six brain regions: the EC (221,493 cells), which is affected in early AD (stages I–II); the HC (221,415) and TH (207,625), which are affected in mid-AD (stages III–IV); and the AG (220,409), MT (227,412) and PFC (254,721), which are affected in late AD (stages V–VI), for a total of 1.35 million transcriptomes of independent nuclei after removing doublets, low”

In the paper

“although some inhibitory subtypes had regional biases, including PVALB+HTR4+ and CUX2+MSR1+ (enriched in neocortex), layer 6 SST+NPY+ (EC and HC) and GPC5+RIT2+ (EC)”

In the paper

“We first characterized the regional diversity of excitatory neuron subtypes, which were consistent across individuals and were either highly region-specific to the HC, EC and TH (7, 9 and 2 subtypes, respectively) or were predominantly shared across neocortical regions (12 subtypes)”

In the paper

“We identified multiple transcriptionally distinct subsets for each major glial cell type and determined their characteristic marker genes”

In the paper

“Thalamic astrocytes (LUZP2+) expressed GABA-uptake genes SLC6A1 and SLC6A11 at much higher levels compared with other subtypes, even though the proportion of inhibitory neurons was not markedly higher in the thalamus”

In the paper

“Neuritic-plaque-associated genes (190 genes) included the energy-homeostasis-regulating genes IRS2, PDK4 and HIF3A, and genes enriched for immune response, chromatin regulation and lipid droplets.”

In the paper

“The glycolysis modules were enriched among diffuse plaque DEGs in microglia and astrocytes and shared a set of genes that included canonical glycolysis genes (PDK1/3/4, PFKL/P, PKM and PGK1), anaerobic glycolysis enzymes (TPI1 and LDHA) and stress-induced genes (EGLN1, DDIT4, VEGFA and BNIP3L) (Fig. 4k–m and Extended Data Fig. 6a,b).”

In the paper

“We used region-specific measurements of AD pathology to identify changes in gene expression associated with neurofibrillary tau tangle or amyloid-β plaque burden, including plaque-associated upregulation of metallostasis in astrocytes and of the electron-transport chain in excitatory neurons.”

In the paper

“Conversely, PNPLA6, which encodes a phospholipase that catalyses the hydrolysis of intracellular phosphatidylcholine, a major membrane lipid, generating GPC, and CHDH, which encodes choline dehydrogenase, an enzyme that catalyses the conversion of choline to betaine aldehyde, were both negatively associated with multiple measures of CR in astrocytes (Fig. 5c–f and Supplementary Fig. 43a–d).”

In the paper

“In microglia, these included PPARG and MSR1, upregulated in the HC, each associated with microglia polarization, as well as upregulation of lipoprotein modifier APOC1 and downregulation of transcription factor FOXP2 in the EC (Extended Data Fig. 8f and Supplementary Fig. 40).”

In the paper

“Consistently, NFT-associated genes (374 genes, differentially expressed in 2+ cell types) included PLCG2, CLU and CTNNA2 (in oligodendrocytes and OPCs) and mitochondrial subunits, and were enriched for ER protein processing, electron transport and cadherin binding (Fig. 4d,e).”

In the paper

“Notably, while the overall excitatory fraction was not associated with cognition, lower OPC fraction across regions and, in particular, in non-neocortex regions was significantly associated with impaired cognition”

In the paper

“Excitatory DEGs were strongly enriched for electron-transport functional terms across regions and showed weak region-specific enrichments for protein-folding-, ubiquitination- and synapse-associated terms”

In the paper

“Astrocytes and inhibitory and excitatory neurons showed the highest number of DEGs over all of the regions, with the largest number of changes in the EC”

In the paper

“These included Reelin signalling pathway genes RELN and DAB1; kinase-associated genes MAP2K5, PRKCA and SPHKAP; and multiple genes associated with heparan sulfate proteoglycan biosynthesis (including HS6ST3, XYLT1 and NDST3)”

In the paper

“We found that vulnerable neuronal subtypes were co-depleted specifically in individuals with AD, with some of the strongest effects observed in established connections between the CA1, subiculum, EC–L3 and EC–L5”

In the paper

“The pathway peaked very early in the EC (ABC score of 1, low levels of AD pathology), later in the HC and midtemporal cortex (intermediate levels), and very late in the PFC (high levels) (Supplementary Fig. 42c), suggesting that the glial metabolic response to AD may not be coordinated globally.”

In the paper

“In regions with high diffuse plaque, astrocytes upregulated glycolysis enzymes converting glucose-6-phosphate to pyruvate, while downregulating MPC1, the mitochondrial pyruvate transporter60 (Fig. 4n).”

What the sources say

• ?Not checked — no full text of the sourcecites [60] Magistretti, P. J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015). source

In the paper

“While microglia DEGs were broadly enriched for clathrin-coated endocytosis (up) and viral response (down), they also had diverse region-specific enrichments, including upregulation of major histocompatibility complex type II (MHC-II) binding in the EC and HC, RNA processing in thalamus and glycolysis in the PFC and EC; and HC-driven downregulation of phagocytosis, phospholipase signalling and”

In the paper

“These included co-depletion for entorhinal L5 versus L5-projecting subiculum (Kendall’s τ = 0.37 (AD); −0.1 (non-AD)) or CA1 (τ = 0.42 (AD) and −0.16 (non-AD)); and for CA1 versus L2-lateral EC (LEC, τ = 0.26 (AD) and −0.07 (non-AD)) and L3 RELN+ (τ = 0.24 (AD) and −0.13 (non-AD)) EC neurons”

In the paper

“By contrast, microglia and OPC DEGs overlapped within the non-neocortex regions, and astrocyte and oligodendrocyte changes were more consistent across all regions”

In the paper

“We identified eight GWAS genes with region-specific expression in microglia, including PLCG2 (EC), APOE and SORL1 (thalamus), and MS4A4A (midtemporal cortex) (Extended Data Fig. 9c–f).”

In the paper

“Notably, while RELN expression, which is typically restricted to inhibitory neurons, was highly specific to two EC excitatory subtypes, its downstream partner DAB1 was present across subtypes”

In the paper

“Agreement between NFT and plaque DEGs was highest in the EC and HC for all cell types (average adjusted R2 of 67% in both) and lowest in the PFC (43%) and AG (21%), consistent with late-AD NFT appearance in the neocortex (Fig. 4b).”

In the paper

“We found a significant decrease in PNPLA6 and CHDH transcripts and a significant increase in GPCPD1 transcripts in cognitively resilient individuals, in agreement with the differential expression results (Fig. 5j–l).”

In the paper

“Notably, vulnerable inhibitory neuron subtypes expressed significantly higher levels of the Reelin signalling pathway components RELN and DAB1, mirroring the observed higher expression of these two genes in vulnerable excitatory neuron subtypes”

In the paper

“Broadly shared genes across cell types showed region-specific enrichment, including for DNA damage (EC), amyloid-β binding and iron transport (HC) and glycolysis (thalamus) in upregulated genes as well as for phospholipid biosynthesis and autophagy in downregulated genes (Extended Data Fig. 8e).”

In the paper

“Finally, astrocytes contained more plaque-associated DEGs compared with other cell types, and their pathology-associated DEGs were enriched in our expression modules, including in metallostasis (M28) for plaque-associated DEGs and oxidative phosphorylation (M27) and chaperones (M8) for NFT-associated DEGs (Fig. 4c,h–j).”

In the paper

“Some DEGs associated with non-vulnerable subtypes had higher differential effect sizes in the vulnerable subtypes, and showed additional enrichment for aerobic glycolysis (including PGK1, LDHB and SLC2A3) and clathrin-mediated endocytosis (including AP2M1/AP2S1, OCRL and COPS8)”

In the paper

“Modules including ECM, adhesion and neurogenesis-related genes were much lower in AD (M1 and M11), while the modules for specific astrocyte subtypes (M7, DCLK1+; and M24, DPP10+) were enriched for upregulated DEGs (Fig. 4k).”

In the paper

“Interestingly, a reactive astrocyte module (M3) was enriched in upregulated genes for plaques but in downregulated genes for NFTs (Fig. 4j).”

In the paper

“Among all of the cell types, astrocytes and microglia showed the largest differential changes for these genes in regions with high neuritic plaque density, for example, for APOE, HLA-DRA, PILRA and SORT1, and showed the most response to diffuse plaque.”

In the paper

“By annotating neuronal and glial subtypes by brain region, we found significant compositional differences between regions, including a subtype of thalamic GABAergic neurons (MEIS2+FOXP2+) that is molecularly distinct from the canonical subclasses of inhibitory neurons in the neocortex.”

In the paper

“Neocortical L2–3 neurons were also significantly less abundant in samples with high NFT levels and in individuals with neocortical NFT involvement”

In the paper

“The majority of region-specific DEGs was either broadly shared (on average, 11% of genes were differentially expressed in 3+ cell types in a region) or were in cell-type-specific programs (40% of DEGs were in 3+ regions for a cell type) (Extended Data Fig. 8d,e).”

In the paper

“We independently identified modules for heat-shock chaperones, glycolysis and oxidative phosphorylation in multiple cell types, which were correlated across cell types and were enriched for upregulated DEGs (Fig. 4k, Extended Data Figs. 6a,b and 11g and Supplementary Fig. 42a,b).”

In the paper

“On the other hand, NFT-associated and downregulated DEGs were enriched for TCA cycle genes, whereas upregulated DEGs were enriched for unfolded protein response and lysosome-linked genes.”

In the paper

“GWAS and familial AD genes were maximally expressed (75 genes) and differentially expressed (30 genes) in microglia, and 25 genes were differentially expressed in at least three cell types, including upregulated CLU, PLCG2 and SORT1, and downregulated DENND6A (Extended Data Fig. 10).”

In the paper

“Notably, vulnerable excitatory neurons shared expression of genes involved in Reelin signalling and heparan sulfate proteoglycan biosynthesis, both of which were also predictive of inhibitory neuron vulnerability to AD.”

In the paper

“Both approaches (DESeq2 and edgeR) provided similar results.”

In the paper

“suggesting that variability in the major cell type composition between regions is a fundamental characteristic of the human brain and is not affected by AD pathology.”

In the paper

“S-adenosylmethionine is involved in the biosynthesis of spermidine, linking choline metabolism and polyamine biosynthesis in astrocytes in CR to AD pathology (Fig. 5m).”

In the paper

“we computed DEGs for each major cell type in every region alone and across regions using a negative binomial linear mixed model framework, adjusting for both known covariates and potential unknown batch effects (Methods)”

In the paper

“For all methods, we subset the tested genes to only genes present in over 20% of cells. For MAST and Nebula, we calculated and included in the regression the top 10 components of unwanted variation calculated using RUV on the pseudo-bulk-level data (individuals by regions). For these methods, we also included as covariates the individual’s sex, age of death and post-mortem interval, each cell’s”

In the paper

“P values were adjusted for multiple testing using the Benjamini–Hochberg method as implemented in the R function p.adjust.”

In the paper

“Our study has several limitations: isotropic fractionation and read depth cut-offs may bias cell recovery based on their nuclear content; nuclear RNA may not fully capture microglial states82 or localized transcriptomic changes; and pathology burden is based on per-sample averages instead of on the spatial context of each cell.”

In the paper

“P values were adjusted for multiple hypotheses testing using the Benjamini–Hochberg method as implemented in the R function p.adjust.”

In the paper

“To control for inflated correlation estimates in highly sparse genes, we bin the estimated correlations (CZCA) for each pair of genes according to their sparsities (fraction of cells expressing the gene, binned on a log10 scale).”

In the paper

“The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment.”

In the paper

“Subsequently, we randomly sampled the determined number of significant DEGs from each of the 10 subsets, ensuring that the expression level distribution of the DEGs was preserved in the random samples. This random sampling step was repeated for a total of 1,000 iterations.”

In the paper

“We modelled the effects of fraction of cells on cognitive performance in multiple domains with gaussian linear regression of cognitive performance on last visit versus the log10-transformed fraction of cells in the subtype or major cell type jointly with covariates for age, sex, APOE-ε4 and post-mortem interval, with false-discovery rate P-value correction (p.adjust in R).”

In the paper

“Individuals were balanced between sexes (male:female ratios 13:13 in AD, 11:11 in NoAD), matched for age (median, 86.6 years (AD) and 86.0 years (no AD)) and post-mortem interval (median, 5.9 h (AD) and 6.3 h (no AD)).”

In the paper

“For our final set of differential genes in each analysis, we took all genes that were significant (Padj > 0.05) and concordant in both the MAST and Nebula results.”

In the paper

“Our study has several limitations: isotropic fractionation and read depth cut-offs may bias cell recovery based on their nuclear content; nuclear RNA may not fully capture microglial states82 or localized transcriptomic changes; and pathology burden is based on per-sample averages instead of on the spatial context of each cell.”

In the paper

“No statistical methods were used to predetermine sample size.”

In the paper

“To robustly estimate modules for each set of cells in our analyses, we first performed a grid search for the optimal number of SVD components for cells of that type. We then computed the z-scored matrices for each of 10 bootstraps, selecting 90% of batches for each bootstrap and using only genes expressed in over 5% of cells in the full dataset for the cell type.”

In the paper

“Researchers were blinded to animal genotype.”

In the paper

“Sample sizes were determined on the basis of previous work from our laboratory, without power analysis calculation or randomization.”

In the paper

“The number of components (K) to use for cNMF was determined on the basis of a diagnostic plot showing the stability of the solution and the Frobenius reconstruction error as a function of K.”

In the paper

“Instead, we can analytically approximate the covariance with the SVD of XT = UnSnVnT as Cn ≈ (UnSnVnT)T(UnSnVnT)/g = VnSn2VnT/g and therefore Cn−1/2 = g1/2VnSn−1VnT.”

In the paper

“We used the model-based analysis of single-cell transcriptomics (MAST) tool to investigate whether the CR genes identified in PFC astrocytes were also associated with CR in astrocytes from other regions of the human brain. To ensure robust analysis, we initially filtered the genes under investigation, selecting only those with more than one count in at least 10 cells. The analytical model”

In the paper

“We calculated compositional differences in individuals with AD versus individuals without AD (or AD dementia versus no dementia) by modelling the number of cells of a certain cell type or subtype in a specific sample (individual by region) relative to the total number of cells using a quasi-binomial regression model. We modelled AD status by binary ascertainment variables (cogdx 4–5, NIA-Reagan”