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• W4380080094 abstract "Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Aging is a major risk factor for Alzheimer’s disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing in Drosophila with pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell-type-specific manner. Integration of cellular abundance and gene expression pinpoints nuclear factor kappa B signaling in neurons as a marker for cellular vulnerability. We also highlight the conservation of cell-type-specific transcriptional patterns between Drosophila and human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy. Editor's evaluation Wu et al. have provided a revised manuscript that presents important new findings that start to explain cell type vulnerability and the types of transcriptional changes that occur in the context of neurodegenerative diseases. They cleverly use Drosophila for this as they have access to numerous brain cells and exquisite genetic control. They present compelling evidence of transcriptional deregulation and affected pathways in relation to Tau toxicity in a well-controlled study. They also tested if affected pathways modify toxicity but were not successful, however, as pointed out, this can have different reasons. This paper is of broad interest to those in the field of neurodegeneration and neuronal disease and from a methodological point of view to single-cell biologists. https://doi.org/10.7554/eLife.85251.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by extracellular amyloid-beta neuritic plaques and intracellular tau neurofibrillary tangles (DeTure and Dickson, 2019; Scheltens et al., 2021). Tau neuropathological burden is strongly correlated with cognitive decline, synaptic loss, and neuronal death (Arriagada et al., 1992; Braak and Braak, 1991; Gómez-Isla et al., 1997). Cell-type-specific vulnerability is also an important driver of AD clinical manifestations, including its characteristic amnestic syndrome. Neurofibrillary tangles first appear in the transentorhinal cortex, entorhinal cortex, and CA1 region of the hippocampus, affecting resident pyramidal cells and excitatory glutamatergic neurons; cholinergic neurons of the basal forebrain are also particularly vulnerable (Mrdjen et al., 2019; Fu et al., 2018). Single-cell RNA-sequencing (scRNAseq) or single-nucleus RNA-sequencing (snRNAseq) are promising approaches to pinpoint cell-type-specific mechanisms in AD, including those that may underlie neuronal vulnerability (Mathys et al., 2019; Grubman et al., 2019; Lau et al., 2020; Zhou et al., 2020). Emerging data highlight altered transcriptional states and/or cell proportions for vulnerable versus resilient neurons, including excitatory or inhibitory neurons, respectively (Leng et al., 2021). snRNAseq profiles also implicate important roles for non-neuronal cells, including oligodendrocytes, astrocytes, and microglia (Grubman et al., 2019; Lau et al., 2020; Zhou et al., 2020). Microglial expression signatures, including genes with roles in innate immunity, are sharply increased in brains with AD pathology, and an important causal role in AD risk and pathogenesis is reinforced by findings from human genetics (Bohlen et al., 2019; Deczkowska et al., 2018; Bellenguez et al., 2022). One important limitation to gene expression studies from human postmortem tissue is that only cross-sectional analysis is possible, making it difficult to reconstruct dynamic changes over the full time course of disease. In fact, age is the most important risk factor for AD, which develops over decades (Masters et al., 2015; Villemagne et al., 2013). Another potential challenge is identifying molecularly specific changes since tau tangle pathology usually co-occurs with amyloid-beta plaques, along with other brain pathologies that can also cause dementia (e.g., Lewy bodies or infarcts) (Kapasi et al., 2017). By contrast, animal models permit experimentally controlled manipulations isolating specific triggers and their impact over time. For example, in mouse models of amyloid-beta pathology, scRNAseq and snRNAseq have implicated subpopulations of disease-associated microglia and astrocytes, and similar changes may also characterize brain aging (Keren-Shaul et al., 2017; Habib et al., 2020). Further, in tau transgenic models, activation of immune signaling by the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) transcription factor within microglia was found to be an important driver of pathological progression (Wang et al., 2022). We recently characterized tau- and aging-induced gene expression changes in a Drosophila melanogaster tauopathy model, revealing perturbations in many conserved pathways such as innate immune signaling (Mangleburg et al., 2020). Over 70% of tau-induced gene expression changes in flies were also observed in normal aging. In this study, we deploy scRNAseq in Drosophila to map the cell-specific contributions of age- and tau-driven brain gene expression and identify NFκB signaling as a promising marker of neuronal vulnerability. Results Single-cell transcriptome profiles of the tau transgenic Drosophila brain Pan-neuronal expression of either wildtype or mutant forms of the human microtubule-associated protein tau (MAPT) gene in Drosophila recapitulates key features of AD and other tauopathies, including misfolded and hyperphosphorylated tau, age-dependent synaptic and neuron loss, and reduced survival (Wittmann et al., 2001). We performed scRNAseq of adult fly brains in tauR406W transgenic Drosophila (elav>tauR406W) and controls (elav-GAL4), including animals aged 1, 10, or 20 days (Figure 1—figure supplement 1A and B). The GAL4-UAS expression system is used to express human tau in neurons throughout the central nervous system (CNS) (Brand and Perrimon, 1993). The R406W variant in MAPT causes frontotemporal dementia with parkinsonism-17, an autosomal-dominant, neurodegenerative disorder with tau pathology (i.e., tauopathy). In flies, wild type and mutant forms of tau share conserved neurotoxic mechanisms and cause similar neurodegenerative phenotypes, but tauR406W induces a more robust transcriptional response and accelerated course (Wittmann et al., 2001; Bardai et al., 2018; Mangleburg et al., 2020). Following stringent quality control, transcriptome data from 48,111 single cells were available for our initial analyses, including from 6 total conditions (2 genotypes × 3 ages) (Figure 1—figure supplement 1C and E). In the integrated dataset, we identified 96 distinct cell clusters grouped by transcriptional signatures, and annotated cell-type identities to 59 clusters using available Drosophila brain scRNAseq reference data and established cell markers (Figure 1A, Figure 1—figure supplement 2, Figure 1—source data 1). As expected, most cells in the fly brain were neurons (CadN expression, n = 42,587), whereas glia were comparatively sparse (repo expression, n = 5524). Our dataset comprises a diverse range of cell types. Among all cell clusters, 49% were cholinergic neurons (VAChT), 20% were glutamatergic neurons (VGlut), 11% were GABAergic neurons (Gad1), and 7% were glia (repo, Gs2) (Figure 1B, Figure 1—figure supplement 3). We also identified several major glial subtypes in the fly brain (Kremer et al., 2017), including astrocyte-like, cortex, chiasm, subperineurial, perineurial, and ensheathing glia, along with a group of circulating macrophages (hemocytes). Overall, our findings are consistent with results from prior scRNAseq studies of whole adult Drosophila brains (Davie et al., 2018). Figure 1 with 3 supplements see all Download asset Open asset Single-cell RNA-sequencing of the adult Drosophila brain. (A) Uniform manifold approximation and projection (UMAP) plot displays unsupervised clustering of 48,111 cells, including from control (elav-GAL4/+) and elav>tauR406W transgenic animals (elav-GAL4/+; UAS-tauR406W/+) at 1, 10, and 20 days. Expression of neuron- and glia-specific marker genes, CadN and repo, respectively, is also shown. Cell cluster annotations identify heterogeneous optic lobe neuron types, including from the lamina (L1-5, T1, C2/3, Lawf, Lai), medulla (Tm/TmY, Mi, Dm, Pm, T2/3), and lobula (T4/T5, LC). Other identified neuron types include photoreceptors (ninaC, eya), dopaminergic neurons (DAT, Vmat, ple), and central brain mushroom body Kenyon cells (ey, Imp, sNFP, trio). (B) Violin plot showing cell-type marker expression across annotated cell clusters. Selected markers include Elav (neurons), repo/Gs2 (glia), Gad1 (GABA), VGlut (glutamate), VAChT (acetylcholine), and DAT/Vmat/ple (dopamine). See also Figure 1—figure supplements 1–3 and Figure 1—source data 1–4. Figure 1—source data 1 Drosophila scRNAseq cell cluster annotations. Cluster refers to the numeric ID assigned by Seurat when FindClusters resolution is set to 2, and the Annotation column notes the cell identity assignment. This table can be used to obtain cell identities of Seurat cluster IDs in the result tables below. https://cdn.elifesciences.org/articles/85251/elife-85251-fig1-data1-v2.xlsx Download elife-85251-fig1-data1-v2.xlsx Figure 1—source data 2 Cell cluster markers. Cluster markers are obtained by MAST differential expression analysis where each cell cluster is compared against all remaining cells. Only genes with a positive log2 fold change are displayed. Log2 fold change = expression fold change between a given cluster and all remaining cells. Pct.1 = percent of cells in the given cluster with non-zero expression of the gene. Pct.2 = percent of the remaining cells (not in the cluster) that have non-zero expression of the gene. Cluster ID = Seurat assigned cluster ID. BH-adjusted p-value = Benjamini–Hochberg-corrected p-value from the MAST differential expression analysis for each cluster. https://cdn.elifesciences.org/articles/85251/elife-85251-fig1-data2-v2.xlsx Download elife-85251-fig1-data2-v2.xlsx Figure 1—source data 3 Single-cell RNA-sequencing quality control parameters. Cell library metrics from the 10x Genomics Cell Ranger output. Sample = cell library labels. Libraries from the replication experiment are labeled with ‘rep’ behind the final underscore. See also Figure 1—figure supplement 1. Additional details on the data provided in each column can be found in 10x Genomics support materials: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/gex-metrics. https://cdn.elifesciences.org/articles/85251/elife-85251-fig1-data3-v2.xlsx Download elife-85251-fig1-data3-v2.xlsx Figure 1—source data 4 Drosophila cell-type expression markers. Table of established fly gene expression markers used as references for cell annotation. Genes = marker genes that can be used to identify a cell type. Cell type = Drosophila brain cell subpopulation. Reference = source publication used to obtain the genes. https://cdn.elifesciences.org/articles/85251/elife-85251-fig1-data4-v2.xlsx Download elife-85251-fig1-data4-v2.xlsx Tau drives changes in cell proportions in the brain Leveraging our scRNAseq data and pooling longitudinal samples to permit robust comparisons, we first assessed how tau affects the relative abundance of cell-type subpopulations in the adult brain. We found 16 neuronal and 6 glial clusters with statistically significant changes in cell abundance when comparing tau and controls (Figure 2A and B, Figure 2—source data 1). Cholinergic mushroom body Kenyon cell neurons in the central complex, which are important in learning and memory, were sharply reduced, likely consistent with developmental toxicity of tau, as noted in prior studies of Drosophila tauopathy models (Mershin et al., 2004; Kosmidis et al., 2010). In fact, seven excitatory neuronal clusters, including several cholinergic and glutamatergic cell types, demonstrated significant declines, whereas inhibitory neuronal subpopulations (e.g., Pm and Mi4 GABAergic cells in the visual system) appeared resilient. Conversely, cluster 12 cells appeared more abundant in tau flies; this non-annotated cell type was enriched for neuroendocrine expression markers, Ms and Hug, as well as a regulator of synaptic plasticity, Arc1 (Figure 1—source data 2). Interestingly, several glial cell types also appeared increased in the brains of tau animals. Ensheathing glia, which showed the largest potential increase, are localized to neuropil in the fly brain and mediate phagocytosis following neuronal injury (Doherty et al., 2009; Freeman, 2015). In order to confirm these observations, which were based on pooled data across timepoints, we generated additional scRNAseq profiles from 10-day-old elav>tauR406W and control flies in triplicate samples (69,128 cells; Figure 2—figure supplement 1). Overall, 13 out of the 22 significant cell abundance changes were also observed in this replication dataset, including the sharp reduction of excitatory neurons (e.g., Kenyon cells), and the increase in multiple glial clusters (e.g., ensheathing glia) (Figure 2—figure supplement 1B, Figure 2—source data 1). Non-replicated changes in cell-type abundance may be driven by data from earlier (1 day) or later (20 day) timepoints (Figure 2B). Although our experimental design limits cross-sectional analyses at 1 and 20 days, the observed changes in cell abundance were suggestive of a combination of both developmental tau toxicity and progressive, age-dependent neurodegeneration (e.g., neuronal clusters 1, 9, and 12, and astrocyte-like glia). Selected cell-type proportion changes were also recapitulated based on computational deconvolution of available bulk-tissue RNAseq from tauR406W and control flies at 1, 10, and 20 days by using an independent, published scRNAseq reference dataset (Figure 2—figure supplement 2). Figure 2 with 3 supplements see all Download asset Open asset Tau-triggered cell proportion changes in the adult brain. (A) Log2-fold change (log2FC) of normalized cell counts between elav>tauR406W (elav-GAL4/+; UAS-tauR406W/+) and control (elav-GAL4/+) animals. Timepoints are pooled for each cluster. Cell clusters with statistically significant changes (false discovery rate [FDR] < 0.05) are highlighted in black. Many of these cell abundance changes were replicated in an independent dataset generated from 10-day-old animals (Figure 2—figure supplement 1). Since cell-type abundance estimates are relative between clusters, we also performed an adjusted analysis in which glia were assumed to be unchanged (Figure 2—figure supplement 3A). (B) Plots highlight cluster cell counts with significant differences based on pooled timepoint comparisons between elav>tauR406W (red) and control (black) animals, including results for samples collected at 1 day (triangle), 10 days (cross-hatch square), or 20 days (filled square). See Figure 2—figure supplement 2 for complementary analysis based on deconvolution of bulk brain RNA-sequencing. (C) Whole-mount immunofluorescence of adult brains from 10-day-old flies. Glia are stained using the Anti-Repo antibody (red) in control (elav-GAL4/+) and elav>tauR406W transgenic flies. Full Z-stack projection is shown. Scale bar = 100 microns. See also Figure 2—figure supplement 3B for additional immunostains for nuclei and actin. (D) Quantification of glia (Repo-positive puncta), brain volume, and glial density is shown. Statistical analysis employed Welch’s T-test with n=9 animals per group and significance threshold p < 0.05. Error bars denote the 95% confidence interval. See also Figure 2—figure supplements 1–3 and Figure 2—source data 1. Figure 2—source data 1 Tau-triggered cell proportion changes. Analysis of cell abundance changes between elav>tauR406W and control animals as quantified by DESeq2. In the discovery dataset, the 1, 10, and 20-day timepoints are pooled, such that n = 3 values for each comparison. The replication dataset is comprised of n = 3 elav>tauR406W and control (elav-GAL4) animals all prepared at day 10. baseMean = mean of normalized cell counts for the given cell cluster across all samples. Log2FoldChange = log2 fold change of elav>tauR406W vs. control cell counts. lfcSE = standard error of log2 fold change value. Pvalue = p-value from Wald test of the genotype log2 fold change value. Differences in cell count are quantified by negative binomial GLM, such that count ~genotype + age. Padj = adjusted p values using the Benjamini–Hochberg procedure. Experiment = denotes if data is from the discovery or replication analysis. https://cdn.elifesciences.org/articles/85251/elife-85251-fig2-data1-v2.xlsx Download elife-85251-fig2-data1-v2.xlsx Similar to our Drosophila tauopathy model, snRNAseq from postmortem human brain tissue has consistently suggested AD-associated increases in glial cell abundance, including astrocytes, oligodendrocytes, microglia, and endothelial cells (Lau et al., 2020; Zhou et al., 2020). However, one major limitation of both scRNAseq and snRNAseq analysis is that cell-type abundance estimates are relative across the dataset. Therefore, a decline in neuronal subpopulations could lead to inflated abundance estimates of other, stable cell types. Indeed, whereas widespread neuronal loss is highly characteristic of AD (Davies and Maloney, 1976; Braak and Braak, 1991; Leng et al., 2021), systematic histopathological studies in postmortem brain tissue do not support an absolute increase in microglia or astrocyte numbers, but rather a proportional increase in reactive glia in diseased tissues (Serrano-Pozo et al., 2013; Davies et al., 2017; Paasila et al., 2019). We therefore computed confidence intervals for cell abundance changes under an alternative model in which glia were assumed to be unchanging (Figure 2—figure supplement 3A). In this more conservative, adjusted analysis, only the neuroendocrine group (cluster 12) was increased and 15 excitatory neuronal subtypes were decreased. In order to resolve the remaining ambiguity in potential glial cell changes, we performed immunofluorescence on whole-mount Drosophila brains (Figure 2C). Although the overall intensity of glial nuclear staining (anti-Repo) was increased in elav>tauR406W flies, quantification revealed no significant increase in absolute glial numbers. Instead, we found nominally increased glial density in tau animals after considering their reduced total brain volumes (Figure 2D). The increased intensity of antibody staining in tau brains may arise from enhanced antibody penetration since similar changes are also seen for other markers (Figure 2—figure supplement 3B and C). Moreover, increased repo gene expression was not observed in either scRNAseq or in our previously published bulk-tissue RNAseq (Mangleburg et al., 2020). Overall, our results suggest that the apparent increase in glial cell abundance from scRNAseq is likely a consequence of proportional changes in single-cell suspensions due to neuronal loss: in our replication dataset from 10-day-old flies, the proportion of neurons were reduced from 90% to 83% in control versus elav>tauR406W flies. While it is difficult to exclude more modest or selective regional changes, we conclude that similar to human postmortem tissue findings (Serrano-Pozo et al., 2013), absolute glial numbers are largely stable following tau expression in the Drosophila brain. Tau and aging exert cell-specific effects on brain gene expression To our knowledge, the specific contributions of tau and aging on gene expression across heterogeneous cell types in the adult brain have not been systematically examined. In order to define the impact of aging on brain gene expression, we first quantified cell-specific transcriptional signatures in control flies (elav-GAL4) by performing differential expression analyses between the three timepoints from matched cell clusters (Figure 3A, Figure 3—source data 1). Overall, we define 5998 unique, aging-induced differentially expressed genes. Based on Gene Ontology term enrichment, ribosome/protein translation and energy metabolism pathways were broadly dysregulated during aging, involving the majority of cell types (Figure 3—source data 2). We next used linear regression to examine tau-induced differential gene expression within each cell type, including adjustment for age as a covariate. Overall, a total of 5280 unique genes were differentially expressed in at least one or more cell types (Figure 3B, Figure 3—figure supplement 1A), and these results overlap significantly with our prior bulk RNA-seq in elav>tauR406W flies (Figure 3—figure supplement 2). Importantly, 93% of tau-induced differentially expressed genes (n = 4917 out of 5280) were also triggered by aging in control flies (among n = 5998 genes). However, tau and aging appeared to have markedly distinct impacts when considering the distribution of gene perturbations across heterogeneous cell types (Figure 3C). Whereas aging broadly perturbed gene expression, tau-triggered changes were sharply polarized to excitatory neurons and glia. Further, the overlap between tau and aging varied across clusters (range = 0–75%) and tau-specific signatures predominated in selected cell types. For example, cholinergic Kenyon cells from the α'/β' mushroom body lobes were among the most vulnerable cell types (Figure 2A) and also had the greatest number of tau-induced gene perturbations (Figure 3B), which were approximately equally divided between up- and downregulated changes (Figure 3—figure supplement 1A, Figure 3—source data 1). In fact, among 2289 tau-induced differentially expressed genes within α'/β' Kenyon cells, 2139 (93%) were unique to tau and not similarly triggered in the corresponding cell type in aging control animals. We confirmed that the number of differentially expressed genes and affected cell types does not correspond to the spatial pattern of MAPT transgene pan-neuronal expression in the brain (Figure 3—figure supplement 4); however, it is difficult to exclude the possibility that some vulnerable cell types with high MAPT expression might be inadvertently censored from our analyses. Figure 3 with 5 supplements see all Download asset Open asset Aging- versus tau-triggered brain gene expression changes. (A) Aging has widespread transcriptional effects on most brain cell types. Number of aging-induced differentially expressed genes (false discovery rate [FDR] < 0.05) within each cell cluster is shown, based on comparisons of day 1 vs. day 10 and day 10 vs. day 20 in control animals only (elav-GAL4/+). For each cell cluster, the number of gene expression changes unique to aging (white) or overlapping with tau-induced changes (gray) is highlighted. Labels for cell clusters with significant tau-induced cell abundance changes are shown in bold. (B) In contrast with aging, tau induces a more focal transcriptional response, with greater selectivity for excitatory neurons and glia. Number of tau-induced, differentially-expressed genes (FDR < 0.05) within each cell cluster is shown, based on regression models including age as a covariate and considering both control and elav>tauR406W animals (elav-GAL4/+; UAS-tauR406W/+) at 1, 10, and 20 days. For each cell cluster, the number of gene expression changes unique to tau (black) or overlapping with aging-induced changes (gray) is highlighted. Labels for cell clusters with significant tau-induced cell abundance changes are shown in bold. Tau-induced gene expression changes from single-cell profiles significantly overlap with prior analyses conducted using bulk brain RNA-sequencing (Figure 3—figure supplement 2). (C) Uniform manifold approximation and projection (UMAP) plots show the number of aging- (red) versus tau- (green) triggered differentially expressed genes within each cell cluster. Color intensity represents the number of differentially expressed genes. See also Figure 3—figure supplements 1–5 and Figure 3—source data 1–5. Figure 3—source data 1 Tau- and aging-triggered gene expression changes. Tau-induced differentially expressed genes were adjusted for aging by including a covariate in the regression model, based on comparisons of scRNAseq data elav>tauR406W vs. control (elav-GAL4) at 1, 10, and 20 days. Aging-induced differentially expressed genes are based on comparisons in control (elav-GAL4) flies, including between day 1 (d1) and day 10 (d10), and day 10 vs. day 20 (d20); comparisons are noted in age_comparisons column. The cell cluster being compared is denoted in the cluster column. Avg_logFC is the log2 fold change of gene expression between day 10 vs. day 1 or day 20 vs. day 10; in each entry, the former is the numerator, and the latter is the denominator. For tau vs. control comparisons, the numerator is tau, and the denominator is control. Pct.1 and Pct.2 refer to the percent of cells that have non-zero expression for the given gene in the numerator and denominator, respectively. P_val = uncorrected p-values from the MAST linear regression. Padj = Benjamini–Hochberg-adjusted p-values. Analysis = specifies either ‘control aging’ or ‘tau age-adjusted’ for the respective analyses. https://cdn.elifesciences.org/articles/85251/elife-85251-fig3-data1-v2.xlsx Download elife-85251-fig3-data1-v2.xlsx Figure 3—source data 2 Functional pathways from differential expression analysis. Significantly enriched functional terms based on overrepresentation analysis (ORA) of cell-specific differentially expressed gene sets, including from either (i) aging (controls), (ii) tau age-adjusted (elav>tauR406W vs. control (elav-GAL4)), or the (iii) ‘tau-specific’ gene set, which is the unique subset of genes from ii not seen in i. Genes used for functional enrichment analysis have a false discovery rate (FDR) < 0.05 in all differential expression analyses, and all functional enrichment terms listed have a hypergeometric FDR < 0.05. Analysis = source of gene set used for functional enrichment (i–iii, above). Age = relevant age groups of source comparison. Cluster = cell cluster source of gene set. Term_id = identifier of enrichment term. term = description of enriched term. FDR = FDR-corrected p-values. Database = database origin of term. https://cdn.elifesciences.org/articles/85251/elife-85251-fig3-data2-v2.xlsx Download elife-85251-fig3-data2-v2.xlsx Figure 3—source data 3 Tau-induced gene expression changes in the replication dataset. Cross-sectional replication analysis comparing differentially expressed genes in an independent dataset from day 10 (elav >tauR406W vs. control (elav-GAL4)). The cell cluster being compared is denoted in the cluster column. Avg_log2FC is the log2 fold change of gene expression between tau vs. control comparisons, the numerator is tau, and the denominator is control. Pct.1 and Pct.2 refer to the percent of cells that have non-zero expression for the named gene in the numerator and denominator, respectively. P_val = unadjusted, raw p-values from the MAST linear regression. Padj = Benjamini–Hochberg-adjusted p-values. https://cdn.elifesciences.org/articles/85251/elife-85251-fig3-data3-v2.xlsx Download elife-85251-fig3-data3-v2.xlsx Figure 3—source data 4 Cell-type-specific overlaps between tau-induced differentially expressed genes. Cell-cluster overlaps are quantified between the age-adjusted discovery data (Figure 3—source data 1) and the day 10 cross-sectional replication data (Figure 3—source data 3). Cluster = annotated cell identities or Seurat ID of unannotated clusters. ageAdj_discovery_DEG_n = number of tau-induced differentially expressed genes (FDR < 0.05) in the discovery dataset. d10_CS_replicate_DEG_n = number of differentially expressed genes in the replication dataset. Intersect = number of overlapping differentially expressed genes between results of the two comparisons. percent_of_original = percent of differentially expressed genes in the discovery dataset that is also observed in the replication dataset. Phyper = p-value of hypergeometric tests evaluating whether the number of overlapping genes observed is greater than by chance. tot_genes = total number of unique genes detected in each cell type and shared between datasets used for hypergeometric test. https://cdn.elifesciences.org/articles/85251/elife-85251-fig3-data4-v2.xlsx Download elife-85251-fig3-data4-v2.xlsx Figure 3—source data 5 Cross-sectional tau-induced differential expression. Cross-sectional analysis of tau-induced changes (elav>tauR406W vs. control (elav-GAL4)) from the discovery dataset at 1, 10, and 20 days; age = the age being compared. The specific cell cluster being compared is denoted in the cluster column. Avg_log2FC is the log2 fold change of gene expression between tau vs. control comparisons, the numerator is tau, and the denominator is control. Pct.1 and Pct.2 refer to the percent of cells that have non-zero expression for the named gene in the numerator and denominator, respectively. 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• W4380080094 title "Editor's evaluation: Tau polarizes an aging transcriptional signature to excitatory neurons and glia" @default.
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