Single-cell multiregion dissection of Alzheimer’s disease

May combine two claims.

Entorhinal cortex-specific subtypes clustered separately from neocortical subtypes for the same layers and were often marked by RELN, TOX3 and GPC5, containing lateral (L2 RELN+GPC5+) and medial (L2 TOX3+POSTN+) EC subtypes.

“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”

• 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). sourcePartly 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)

  Experimental — accuracy not yet measured
• cites [42] Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020). sourceFetching the cited paper…
• cites [43] Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021). sourcePartly 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)

  Experimental — accuracy not yet measured

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

Probably not a substantial claim: may not pass S3 (load-bearing).

Studies have shown benefits of dietary choline intake and supplementation on cognitive performance in human individuals and in animal models.

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

• cites [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). sourceNot checked — no full text of the source
• cites [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). sourceNot checked — no full text of the source
• 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). sourceFetching the cited paper…
• cites [74] Nurk, E. et al. Plasma free choline, betaine and cognitive performance: the Hordaland Health Study. Br. J. Nutr. 109, 511–519 (2013). sourceFetching the cited paper…
• cites [75] Blusztajn, J. K., Slack, B. E. & Mellott, T. J. Neuroprotective actions of dietary choline. Nutrients 9, 815 (2017). sourceFetching the cited paper…
• cites [76] Liu, L. et al. Choline intake correlates with cognitive performance among elder adults in the United States. Behav. Neurol. 2021, 2962245 (2021). sourceFetching the cited paper…
• 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). sourceSupported
• cites [78] Sienski, G. et al. APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia. Sci. Transl. Med. 13, eaaz4564 (2021). sourcePartly 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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Bulk RNA-seq differential expression from ROSMAP used DESeq2 and edgeR, with age at death and post-mortem interval converted to z scores and included as covariates.

“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.”

• 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). sourceSupported
• 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). sourceSupported

May combine two claims.

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

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

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

Probably not a substantial claim: may not pass S3 (load-bearing).

The paper presents an online resource for navigating this atlas.

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

May combine two claims.

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

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

May combine two claims.

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 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”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

In hippocampus, entorhinal cortex and midtemporal cortex, caudal ganglionic eminence-derived GABAergic neurons (VIP+LAMP5+) were significantly more abundant than medial ganglionic eminence-derived neurons (SST+PVALB+), but these two clades were not significantly different in prefrontal cortex.

“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”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

Predicted cell-cell communication interactions were mostly shared across multiple regions, but the thalamus had multiple differential interactions.

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

May combine two claims.

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 for focal-adhesion-related genes.

“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”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

The thalamic-neocortex separation is conserved in mice and recapitulated in independent single-cell, bulk and microarray data in both mice and humans.

“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”

• cites [8] Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012). sourceNot checked yet

  Not checked in the free run.

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

  Not checked in the free run.

• cites [40] Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018). sourceNot checked yet

  Not checked in the free run.

• cites [43] Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Cells expressing these modules were enriched for cellular subtype identity (205 modules), brain region (156, with 77 thalamus specific and 34 entorhinal cortex specific), Alzheimer's disease status (73), APOE genotype (78) and sex (24).

“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)”

• AssumptionRests on an assumption: Gene module expression scores reflect functional biological programs.

May combine two claims.

Oligodendrocyte-lineage modules showed high regional specificity, and two OPC modules-thalamus-enriched M11 and entorhinal cortex-enriched M25-were marked by synapse-associated genes such as SEMA3D, SEMA6D, CNTN5, and GRIA4.

“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”

• AssumptionRests on an assumption: Gene module expression scores reflect functional biological programs.

May combine two claims.

Glutamatergic and GABAergic module scores matched the cortical excitatory and inhibitory split and were negatively correlated both across and within broad neuronal classes.

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

• AssumptionRests on an assumption: Gene module expression scores reflect functional biological programs.

May combine two claims.

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

“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)”

• AssumptionRests on an assumption: Gene module expression scores reflect functional biological programs.

May combine two claims.

Module-module correlations across samples revealed co-expressed programs, such as reactive astrocytes (M3) with cholesterol biosynthesis module M17 (r = 0.60), and glycolysis (M6) with AP-1 module M13 (r = 0.39).

“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)”

• AssumptionRests on an assumption: Gene module expression scores reflect functional biological programs.

May combine two claims.

The study profiled six brain regions with cell counts: EC 221,493; HC 221,415; TH 207,625; AG 220,409; MT 227,412; PFC 254,721; total 1.35 million transcriptomes after quality control.

“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”

• AssumptionRests on an assumption: Single-nucleus RNA-sequencing of post-mortem nuclei accurately reflects the cellular composition and transcriptomes of the human brain regions sampled.

May combine two claims.

Some inhibitory subtypes had regional biases: PVALB+HTR4+ and CUX2+MSR1+ enriched in neocortex, layer 6 SST+NPY+ in entorhinal cortex and hippocampus, and GPC5+RIT2+ in entorhinal cortex.

“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)”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

Excitatory neuron subtypes were consistent across individuals and were either highly region-specific to the hippocampus, entorhinal cortex and thalamus (7, 9 and 2 subtypes, respectively) or predominantly shared across neocortical regions (12 subtypes).

“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)”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

Excitatory neurons in the thalamus were predominantly composed (74%) of a subtype (NXPH1+RNF220+) not observed in the neocortex and predicted to be regulated by LHX9, SOX2, SHOX2 and TCF7L2.

“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”

• 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). sourceFetching the cited paper…
• cites [36] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

The study identified multiple transcriptionally distinct subsets for each major glial cell type and determined their characteristic marker genes.

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

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

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.

“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”

• AssumptionRests on an assumption: Cell type clusters annotated from transcriptomes correspond to bona fide biological cell types.

May combine two claims.

Neuritic-plaque-associated genes (190 genes) included IRS2, PDK4 and HIF3A, and genes enriched for immune response, chromatin regulation and lipid droplets.

“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.”

May combine two claims.

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).

“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).”

May combine two claims.

The study 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 program in glia associated with diffuse plaque burden.

“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”

• 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). sourceNot checked yet

  Not checked in the free run.

• cites [60] Magistretti, P. J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015). sourceNot checked — no full text of the source

May combine two claims.

Region-specific measurements of AD pathology identified 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.

“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.”

May combine two claims.

PNPLA6 and CHDH were both negatively associated with multiple measures of CR in astrocytes.

“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).”

• AssumptionThe authors state this as an assumption: The CR score, defined as the difference between observed cognition and cognition expected on the basis of pathology level, validly measures cognitive resilience.
• AssumptionThe authors state this as an assumption: Pathology measurements can be used to predict global cognitive function or rate of cognitive decline.

May combine two claims.

In microglia, region- and cell-type-specific changes 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.

“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).”

May combine two claims.

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.

“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).”

May combine two claims.

While the overall excitatory fraction was not associated with cognition, lower oligodendrocyte precursor cell fraction across regions and in particular in non-neocortex regions was significantly associated with impaired cognition.

“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”

May combine two claims.

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.

“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”

May combine two claims.

Astrocytes and inhibitory and excitatory neurons showed the highest number of DEGs over all regions, with the largest number of changes in the entorhinal cortex.

“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”

May combine two claims.

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

“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)”

May combine two claims.

Vulnerable neuronal subtypes were co-depleted specifically in individuals with Alzheimer's disease, with some of the strongest effects observed in established connections between the CA1, subiculum, EC-L3 and EC-L5.

“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”

• AssumptionRests on an assumption: Co-depletion of neuronal subtypes across connected regions reflects coordinated vulnerability rather than independent regional effects.

May combine two claims.

The glycolysis 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).

“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.”

May combine two claims.

In regions with high diffuse plaque, astrocytes upregulated glycolysis enzymes converting glucose-6-phosphate to pyruvate, while downregulating MPC1, the mitochondrial pyruvate transporter.

“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).”

• cites [60] Magistretti, P. J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015). sourceNot checked — no full text of the source

May combine two claims.

Microglia DEGs were broadly enriched for clathrin-coated endocytosis (up) and viral response (down), and had diverse region-specific enrichments including upregulation of MHC-II binding in entorhinal cortex and hippocampus, RNA processing in thalamus, glycolysis in prefrontal cortex and entorhinal cortex; and hippocampus-driven downregulation of phagocytosis, phospholipase signalling and protein-

“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”

May combine two claims.

Co-depletion included entorhinal L5 versus L5-projecting subiculum (Kendall's τ = 0.37 AD; −0.1 non-AD) or CA1 (τ = 0.42 AD; −0.16 non-AD); and CA1 versus L2-lateral EC (τ = 0.26 AD; −0.07 non-AD) and L3 RELN+ EC (τ = 0.24 AD; −0.13 non-AD).

“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”

• AssumptionRests on an assumption: Co-depletion of neuronal subtypes across connected regions reflects coordinated vulnerability rather than independent regional effects.

May combine two claims.

Microglia and immune cells showed consistently high GWAS scores across regions, with the top scores for the microglia TPT1+ subtype and macrophages in the HC, thalamus and AG.

“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).”

• 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). sourceNot checked yet

  Not checked in the free run.

• 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). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Microglia and oligodendrocyte precursor cell DEGs overlapped within the non-neocortex regions, and astrocyte and oligodendrocyte changes were more consistent across all regions.

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

May combine two claims.

Eight GWAS genes showed region-specific expression in microglia, including PLCG2 (EC), APOE and SORL1 (thalamus), and MS4A4A (midtemporal cortex).

“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).”

May combine two claims.

RELN expression was highly specific to two entorhinal cortex excitatory subtypes, while its downstream partner DAB1 was present across subtypes.

“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”

May combine two claims.

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%).

“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).”

May combine two claims.

RNAscope analysis 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.

“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).”

• AssumptionThe authors state this as an assumption: The CR score, defined as the difference between observed cognition and cognition expected on the basis of pathology level, validly measures cognitive resilience.
• AssumptionThe authors state this as an assumption: Pathology measurements can be used to predict global cognitive function or rate of cognitive decline.

May combine two claims.

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.

“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”

May combine two claims.

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.

“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).”

May combine two claims.

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).

“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).”

• 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). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Astrocytes contained more plaque-associated DEGs compared with other cell types, and their pathology-associated DEGs were enriched in expression modules, including metallostasis (M28) for plaque-associated DEGs and oxidative phosphorylation (M27) and chaperones (M8) for NFT-associated DEGs.

“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).”

May combine two claims.

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

“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)”

May combine two claims.

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

“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).”

May combine two claims.

A reactive astrocyte module (M3) was enriched in upregulated genes for plaques but in downregulated genes for NFTs.

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

May combine two claims.

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

“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.”

May combine two claims.

Annotating neuronal and glial subtypes by brain region revealed 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.

“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.”

May combine two claims.

AD-risk genes were highly perturbed in AD-in particular for microglia, consistent with their enrichment for GWAS signal-but few risk genes showed region-specific expression.

“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.”

• 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). sourceNot checked yet

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May combine two claims.

DEGs linked to non-vulnerable subtypes were enriched for 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 associated with Reelin signalling.

“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”

• 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). sourceNot checked yet

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• 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). sourceFetching the cited paper…

May combine two claims.

Neocortical L2-3 neurons were significantly less abundant in samples with high neurofibrillary tangle levels and in individuals with neocortical neurofibrillary tangle involvement.

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

May combine two claims.

The majority of region-specific DEGs were 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).

“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).”

May combine two claims.

Modules for heat-shock chaperones, glycolysis and oxidative phosphorylation were independently identified in multiple cell types, were correlated across cell types, and were enriched for upregulated DEGs.

“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).”

May combine two claims.

NFT-associated and downregulated DEGs were enriched for TCA cycle genes, whereas upregulated DEGs were enriched for unfolded protein response and lysosome-linked genes.

“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.”

May combine two claims.

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.

“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).”

May combine two claims.

Multiple familial AD and GWAS locus genes showed region-specific expression, including ABCA7 enriched in thalamus, ZNF655 in EC, and CR1 in neocortex.

“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).”

• 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). sourceNot checked yet

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May combine two claims.

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.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

DESeq2 and edgeR provided similar results for bulk RNA-seq differential expression.

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Tangles are first seen in the entorhinal cortex (Braak stages I-II), then the hippocampus and thalamus (Braak stages III-IV) and finally the neocortex (Braak stages V-VI), a sequence typically synchronous with cognitive decline from mild cognitive impairment to severe dementia.

“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.”

• cites [1] Braak, H. & Braak, E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 (1995). sourceNot checked — no full text of the source
• cites [2] Wilson, R. S. et al. Temporal course and pathologic basis of unawareness of memory loss in dementia. Neurology 85, 984–991 (2015). sourceFetching the cited paper…
• cites [4] Sonnen, J. A. et al. Pathological correlates of dementia in a longitudinal, population-based sample of aging. Ann. Neurol. 62, 406–413 (2007). sourceNot checked — no full text of the source
• cites [5] Bennett, D. A. et al. Natural history of mild cognitive impairment in older persons. Neurology 59, 198–205 (2002). sourceNot checked — no full text of the source
• cites [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). sourceNot checked — no full text of the source
• cites [7] Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta Neuropathol. 81, 261–268 (1991). sourceNot checked — no full text of the source

May combine two claims.

Region-specific astrocyte subtypes were experimentally validated using RNA in situ hybridization and confirmed by analysing a separate snRNA-seq dataset.

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

• cites [14] Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Variability in the major cell type composition between regions is a fundamental characteristic of the human brain and is not affected by Alzheimer's disease pathology.

“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.”

Classified as probably not a substantial claim.

S-adenosylmethionine is involved in the biosynthesis of spermidine.

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

Classified as probably not a substantial claim.

DEG analysis for each major cell type in every region and across regions used a negative binomial linear mixed model framework, adjusting for both known covariates and potential unknown batch effects.

“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)”

May combine two claims.

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 studies.

“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”

• cites [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). sourceNot checked — no full text of the source
• cites [12] Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). sourceNot checked yet

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• 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). sourceFetching the cited paper…
• cites [19] Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021). sourceNot checked yet

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• 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). sourceNot checked yet

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• 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). sourceNot checked yet

  Not checked in the free run.

• 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). sourceFetching the cited paper…
• cites [50] Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021). sourceNot checked yet

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• 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). sourceNot checked yet

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• 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). sourceFetching the cited paper…

Classified as probably not a substantial claim. May combine two claims.

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-adenosylmethionine.

“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.”

• 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). sourceFetching the cited paper…

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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

“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.”

• cites [101] Kessy, A., Lewin, A. & Strimmer, K. Optimal whitening and decorrelation. Am. Stat. 72, 309–314 (2018). sourceFetching the cited paper…

Classified as probably not a substantial claim. May combine two claims.

Differential expression analyses included only genes present in over 20% of cells; MAST and Nebula regressions included top 10 RUV components and covariates for sex, age of death, post-mortem interval, cell counts per gene, number of captured genes, and where applicable high-resolution cell subtype and brain region.

“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”

Classified as probably not a substantial claim. May combine two claims.

Differential expression for CR in PFC used muscat with pseudobulk aggregation, edgeR, covariates for age at death and post-mortem interval, and Benjamini-Hochberg correction performed locally per cell type.

“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”

• 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). sourceNot checked yet

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• 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). sourceNot checked yet

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Classified as probably not a substantial claim. May combine two claims.

Dietary supplementation with the polyamine spermidine prolongs life span and health span in several animal models, and spermidine has also been shown to enhance memory performance and counteract age-related cognitive decline.

“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.”

• cites [66] Madeo, F., Eisenberg, T., Pietrocola, F. & Kroemer, G. Spermidine in health and disease. Science 29, 1243–1252 (2018). sourceNot checked — no full text of the source
• cites [79] Sigrist, S. J. et al. Spermidine-triggered autophagy ameliorates memory during aging. Autophagy 10, 178–179 (2014). sourceFetching the cited paper…
• cites [80] Schroeder, S. et al. Dietary spermidine improves cognitive function. Cell Rep. 35, 108985 (2021). sourceNot checked yet

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• 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). sourceFetching the cited paper…

Probably not a substantial claim: may not pass S3 (load-bearing).

P values in the comparison with the previously published proteomics study of AD were adjusted for multiple testing using the Benjamini-Hochberg method as implemented in the R function p.adjust.

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

Probably not a substantial claim: may not pass S3 (load-bearing).

Isotropic fractionation and read depth cut-offs may bias cell recovery based on their nuclear content.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

P values in the inter-regional comparison of AD pathology-associated gene sets in glial cells were adjusted for multiple hypotheses testing using the Benjamini-Hochberg method as implemented in the R function p.adjust.

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

Classified as probably not a substantial claim. May combine two claims.

Disease-relevance scores were calculated per cell against a recent Alzheimer's GWAS using scDRS, and overlap with microglia/immune modules was tested by hypergeometric test with Benjamini-Hochberg correction.

“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”

• 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). sourceNot checked yet

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• 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). sourceNot checked yet

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• 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). sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim. May combine two claims.

To control for inflated correlation estimates in highly sparse genes, the authors bin estimated correlations for each gene pair by their sparsities and use smoothed estimates to z-score the correlation matrix.

“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).”

• AssumptionRests on an assumption: Correlation values will often be inflated for pairs of sparsely expressed genes.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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

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

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Nuclear RNA may not fully capture microglial states or localized transcriptomic changes.

“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.”

• 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). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

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 binding.

“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.”

• 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). sourceNot checked yet

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• 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). sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

The permutation test for DEG overlap preserved the expression-level distribution by randomly sampling significant DEGs from each of 10 expression subsets and repeating this for 1,000 iterations.

“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.”

Classified as probably not a substantial claim.

Effects of cell fraction on cognitive performance were modelled with Gaussian linear regression including covariates for age, sex, APOE-ε4 and post-mortem interval, with FDR correction.

“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).”

Classified as probably not a substantial claim. May combine two claims.

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)).

“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)).”

Classified as probably not a substantial claim.

The final set of differential genes in each analysis comprised genes that were significant and concordant in both MAST and Nebula results.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Pathology burden is based on per-sample averages instead of on the spatial context of each cell.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

No statistical methods were used to predetermine sample size.

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

Classified as probably not a substantial claim. May combine two claims.

To robustly estimate modules for each set of cells, the authors performed a grid search for the optimal number of SVD components and computed z-scored matrices for each of 10 bootstraps, selecting 90% of batches per bootstrap and using only genes expressed in over 5% of cells.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Researchers were blinded to animal genotype in the immunohistochemistry experiments.

“Researchers were blinded to animal genotype.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Sample sizes in the immunohistochemistry experiments were determined without power analysis calculation or randomization.

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

Probably not a substantial claim: may not pass S3 (load-bearing).

The number of components K for cNMF was determined using a diagnostic plot of solution stability and Frobenius reconstruction error as a function of K.

“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.”

May combine two claims.

The sample-wise covariance can be analytically approximated using the SVD of XT, yielding Cn ≈ VnSn2VnT/g and Cn−1/2 = g1/2VnSn−1VnT.

“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.”

• AssumptionRests on an assumption: Computing and storing the n by n sample-wise covariance is prohibitively expensive for modern datasets with n > 1 × 10^6.

Classified as probably not a substantial claim.

MAST analysis of CR genes in astrocytes from other brain regions included covariates for cellular detection rate, age at death, post-mortem interval and sex, a random effect for individual, and FDR correction.

“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”

Classified as probably not a substantial claim.

Compositional differences in AD versus non-AD were modelled using quasi-binomial regression of cell counts relative to total cells, adjusting for brain region and sex, with FDR correction.

“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”

May combine two claims.

The SVD-based approximation commonly drops out the largest identity program in the data due to the decorrelation approach, so the authors allow computing CZCA = g × VSpVT/n for any p to tune the relative involvement of larger eigenvalue components.

“As this approximation commonly drops out the largest identity program in the data due to the decorrelation approach, we allow computing CZCA = g × VSpVT/n, for any p, to tune the relative involvement of the larger eigenvalue components of the SVD.”

Probably not a substantial claim: may not pass S3 (load-bearing).

In both chromogenic and fluorescence RNAscope experiments, puncta were manually counted by researchers blinded to the experimental group of each image.

“For both chromogenic and fluorescence RNAscope experiments, puncta were manually counted by researchers blinded to the experimental group of each image.”

Classified as probably not a substantial claim.

Vulnerable inhibitory neuron subtypes were identified by quasi-binomial regression of cell counts relative to total cells against NFT density, including age, sex and post-mortem interval as covariates, with Benjamini-Hochberg correction.

“To identify vulnerable inhibitory neuron subtypes, we examined the association between the relative abundance of cell types and the measure of NFT density (variable tangles). We used a quasi-binomial regression model to model the number of cells belonging to a specific cell type in a given sample (individual study participant), relative to the total number of cells in that sample. We fitted the”

Probably not a substantial claim: may not pass S3 (load-bearing).

For the module-module network across microglial and immune modules, the authors used Pearson correlations of logged module scores at the aggregated subtype-by-sample level with a one-sided test and Padj < 0.01 cutoff.

“To create the module–module network across microglial and immune modules, we calculated module–module score Pearson correlations using the logged module scores at the aggregated subtype by sample level, using a one-sided test with Padj < 0.01 as a cut-off105.”

• cites [105] Student. Probable error of a correlation coefficient. Biometrika 6, 302–310 (1908). sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

P values from the linear mixed-effects models were adjusted for multiple hypothesis testing using the Bonferroni method.

“To account for multiple hypothesis testing, the obtained P values were further adjusted using the Bonferroni method.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

To confirm CR/CDR differential expression results, the authors also evaluated associations between gene expression and global cognitive function or its rate of change adjusting for AD pathology as a covariate, performing 16 tests in total.

“To confirm the differential gene expression results based on the CR and CDR scores, we also evaluated the association between gene expression and global cognitive function or the rate of change of global cognitive function adjusting for AD pathology as a covariate. The AD pathology measures considered as a covariate were global AD pathology (gpath), neuritic plaque burden (plaq_n), NFT burden (nft”

Probably not a substantial claim: may not pass S3 (load-bearing).

Comparisons of fractional abundances of neuronal subtype pairs between two regions in AD individuals used beta regression controlling for sex, APOE genotype, post-mortem interval and age of death, with FDR correction.

“We compared the fractional abundances of pairs of neuronal subtypes between two regions using Kendall’s τ only in individuals with AD (NIA-Reagan score). Significance was assessed using beta regression (R library betareg) controlling for sex, APOE genotype, post-mortem interval and age of death, and we adjusted P values using p.adjust in R with the fdr method.”