Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Neuroinflammatory astrocyte subtypes in the mouse brain

Abstract

Astrocytes undergo an inflammatory transition after infections, acute injuries and chronic neurodegenerative diseases. How this transition is affected by time and sex, its heterogeneity at the single-cell level and how sub-states are spatially distributed in the brain remains unclear. In this study, we investigated transcriptome changes of mouse cortical astrocytes after an acute inflammatory stimulus using the bacterial cell wall endotoxin lipopolysaccharide. We identified fast transcriptomic changes in astrocytes occurring within hours that drastically change over time. By sequencing ~80,000 astrocytes at single-cell resolution, we show that inflammation causes a widespread response with subtypes of astrocytes undergoing distinct inflammatory transitions with defined transcriptomic profiles. We also attribute key sub-states of inflammation-induced reactive astrocytes to specific brain regions using spatial transcriptomics and in situ hybridization. Together, our datasets provide a powerful resource for profiling astrocyte heterogeneity and will be useful for understanding the biological importance of regionally constrained reactive astrocyte sub-states.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Astrocytes undergo a fast inflammatory transition that drastically changes over time.
Fig. 2: Astrocytic subtypes as identified by single-cell RNA-seq and localized using spatial transcriptomics in the brain of saline-treated mice.
Fig. 3: Inflammation causes a heterogenous, subtype-specific reactive transcriptomic response in astrocytes.
Fig. 4: Cluster 4 is an LPS super-responder that resides in the white matter.
Fig. 5: Cluster 8 is an LPS super-responder defined by interferon response genes and is closely associated with vessels, ventricles and the brain surface.
Fig. 6: Exposure of astrocytes to interferons recapitulates the gene expression pattern of Cluster 8.
Fig. 7: Interferon-responsive astrocytes are identifiable in mouse models of disease/injury.

Data availability

All sequencing data generated in association with this study are available in the Gene Expression Omnibus as a SuperSeries under accession number GSE148612. Mouse scRNA-seq is available under accession number GSE148611, mouse bulk RNA-seq is available under accession number GSE148610, mouse spatial transcriptomic under GSE165098 and rat bulk RNA-seq data are available under accession number GSE165069. Bulk sequencing of in vivo mouse astrocytes, in vivo mouse astrocyte scRNAseq pseudobulk and in vitro rat astrocyte bulk RNA-seq are available to search on www.gliaseq.com. scRNA-seq at single-cell resolution as well as spatial transcriptomic data are available on www.gliaseqpro.com. NanoString data are available on https://rosalind.onramp.bio/showcase/liddelow.

scRNA-seq data from other studies referenced in Fig. 7 and Extended Data Figs. 9 and 10 are available from the Gene Expression Omnibus under accession numbers GSE143758, GSE140511, GSE130119 and GSE139842.

Details of analysis pipeline libraries are listed in Methods and available at https://github.com/liddelowlab/Hasel_et_al.

References

  1. Nedergaard, M., Ransom, B. & Goldman, S. A. New roles for astrocytes: redefining the functional architecture of the brain. Trends Neurosci. 26, 523–530 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Magistretti, P. J. & Allaman, I. Lactate in the brain: from metabolic end-product to signalling molecule. Nat. Rev. Neurosci. 19, 235–249 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Alvarez, J. I., Katayama, T. & Prat, A. Glial influence on the blood brain barrier. Glia 61, 1939–1958 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zamanian, J. L. et al. Genomic analysis of reactive astrogliosis. J. Neurosci. 32, 6391–6410 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Karimi-Abdolrezaee, S. & Billakanti, R. Reactive astrogliosis after spinal cord injury-beneficial and detrimental effects. Mol. Neurobiol. 46, 251–264 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Liddelow, S. A. & Barres, B. A. Reactive astrocytes: production, function, and therapeutic potential. Immunity 46, 957–967 (2017).

    Article  CAS  PubMed  Google Scholar 

  8. Clarke, L. E. et al. Normal aging induces A1-like astrocyte reactivity. Proc. Natl Acad. Sci. USA 115, E1896–E1905 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chai, H. et al. Neural circuit-specialized astrocytes: transcriptomic, proteomic, morphological, and functional evidence. Neuron 95, 531–549.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bayraktar, O. A. et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 23, 500–509 (2020).

  11. Batiuk, M. Y. et al. Identification of region-specific astrocyte subtypes at single cell resolution. Nat. Commun. 11, 1–15 (2020).

    Article  CAS  Google Scholar 

  12. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hasel, P. et al. Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism. Nat. Commun. 8, 15132 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Buttini, M. & Boddeke, H. Peripheral lipopolysaccharide stimulation induces interleukin-1β messenger RNA in rat brain microglial cells. Neuroscience 65, 523–530 (1995).

    Article  CAS  PubMed  Google Scholar 

  17. Spencer, J. A. et al. Altered vascular remodeling in fibulin-5-deficient mice reveals a role of fibulin-5 in smooth muscle cell proliferation and migration. Proc. Natl Acad. Sci. USA 102, 2946–2951 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

  19. Pinheiro, I. et al. LPS resistance of SPRET/Ei mice is mediated by Gilz, encoded by the Tsc22d3 gene on the X chromosome. EMBO Mol. Med. 5, 456–470 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ronchetti, S., Migliorati, G. & Riccardi, C. GILZ as a mediator of the anti-inflammatory effects of glucocorticoids. Front. Endocrinol. (Lausanne) 6, 170 (2015).

    Article  Google Scholar 

  21. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Crowell, H. L. et al. Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 1–12 (2020).

    Article  CAS  Google Scholar 

  24. Mizrak, D. et al. Single-cell analysis of regional differences in adult V-SVZ neural stem cell lineages. Cell Rep. 26, 394–406 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Christopherson, K. S. et al. Thrombospondins are astrocyte-secreted proteins that promote CNS synaptogenesis. Cell 120, 421–433 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Kucukdereli, H. et al. Control of excitatory CNS synaptogenesis by astrocyte-secreted proteins Hevin and SPARC. Proc. Natl Acad. Sci. USA 108, E440–E449 (2011).

  27. Bar, O., Gelb, S., Atamny, K., Anzi, S. & Ben-Zvi, A. Angiomodulin (IGFBP7) is a cerebral specific angiocrine factor, but is probably not a blood–brain barrier inducer. Fluids Barriers CNS 17, 1–13 (2020).

    Article  CAS  Google Scholar 

  28. Lin, M. et al. Cell surface antigen CD109 is a novel member of the α2 macroglobulin/C3, C4, C5 family of thioester-containing proteins. Blood 99, 1683–1691 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Bizet, A. A. et al. The TGF-β co-receptor, CD109, promotes internalization and degradation of TGF-β receptors. Biochim. Biophys. Acta 1813, 742–753 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Hung, C. C. et al. Astrocytic GAP43 induced by the TLR4/NF-κB/STAT3 axis attenuates astrogliosis-mediated microglial activation and neurotoxicity. J. Neurosci. 36, 2027–2043 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Song, W., Li, D., Tao, L., Luo, Q. & Chen, L. Solute carrier transporters: the metabolic gatekeepers of immune cells. Acta Pharm. Sin. B 10, 61–78 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Furukawa, J. et al. Functional identification of SLC43A3 as an equilibrative nucleobase transporter involved in purine salvage in mammals. Sci. Rep. 5, 1–11 (2015).

    Google Scholar 

  33. Foo, L. C. et al. Development of a method for the purification and culture of rodent astrocytes. Neuron 71, 799–811 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Habib, N. et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat. Neurosci. 23, 701–706 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wheeler, M. A. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593–599 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zamboni, M., Llorens-Bobadilla, E., Magnusson, J. P. & Frisén, J. A widespread neurogenic potential of neocortical astrocytes is induced by injury. Cell Stem Cell 27, 605–617 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hamby, M. E. et al. Inflammatory mediators alter the astrocyte transcriptome and calcium signaling elicited by multiple G-protein-coupled receptors. J. Neurosci. 32, 14489–14510 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Saha, P., Sarkar, S., Paidi, R. K. & Biswas, S. C. TIMP-1: a key cytokine released from activated astrocytes protects neurons and ameliorates cognitive behaviours in a rodent model of Alzheimer’s disease. Brain. Behav. Immun. 87, 804–819 (2020).

  41. Ashutosh, C. Chao, Borgmann, K., Brew, K. & Ghorpade, A. Tissue inhibitor of metalloproteinases-1 protects human neurons from staurosporine and HIV-1-induced apoptosis: mechanisms and relevance to HIV-1-associated dementia. Cell Death Dis. 3, e332–e339 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Moore, C. S. et al. Astrocytic tissue inhibitor of metalloproteinase-1 (TIMP-1) promotes oligodendrocyte differentiation and enhances CNS myelination. J. Neurosci. 31, 6247–6254 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sørensen, T. L. et al. Expression of specific chemokines and chemokine receptors in the central nervous system of multiple sclerosis patients. J. Clin. Invest. 103, 807–815 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Halonen, S. K., Taylor, G. A. & Weiss, L. M. Gamma interferon-induced inhibition of Toxoplasma gondii in astrocytes is mediated by IGTP. Infect. Immun. 69, 5573–5576 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hidano, S. et al. STAT1 signaling in astrocytes is essential for control of infection in the central nervous system. MBio 7, 1–15 (2016).

    Article  Google Scholar 

  46. Gong, S. et al. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425, 917–925 (2003).

    Article  CAS  PubMed  Google Scholar 

  47. Blankenberg, D. et al. Manipulation of FASTQ data with galaxy. Bioinformatics 26, 1783–1785 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).

    Article  CAS  Google Scholar 

  51. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Perkins, J. R. et al. ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT–qPCR quantification cycle (Cq) data. BMC Genomics 13, 296 (2012).

  53. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all members of the Liddelow lab at NYU Grossman School of Medicine for support and advice and K. Guttenplan for careful review of the manuscript. We thank the staff at The Genome Technology Center at NYU Grossman School of Medicine. This shared resource is partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center. This research was supported by NYU Grossman School of Medicine (to S.A.L.), the Gifford Family Neuroimmune Consortium as part of the Cure Alzheimer’s Fund (to S.A.L.), the Blas Frangione Foundation (to S.A.L.), Alzheimer’s Research UK (to S.A.L.) and generous anonymous donors (to S.A.L.). Additional support was from the Neurodegenerative Diseases Consortium from MD Anderson (to P.H. and S.A.L.). J.S.S. was supported by the National Institutes of Health (T32 via T32AG052909 (Wisniewski, Scharfman)). Additionally, S.A.L. and J.S.S. were supported by the Alzheimer’s Disease Resource Center at NYU Langone Medical Center. This work was also supported, in part, by an unrestricted grant from Research to Prevent Blindness. We thank NanoString for gifting nCounter Glial Profiling Panel chips and reagents and for advice on analysis using the ROSALIND platform.

Author information

Authors and Affiliations

Authors

Contributions

P.H., J.S.S., R.D.K. and I.V.L.R. conducted experiments. P.H. analyzed data. P.H. and S.A.L planned experiments. P.H. and S.A.L. prepared the manuscript. All authors approve of the final version of the manuscript.

Corresponding authors

Correspondence to Philip Hasel or Shane A. Liddelow.

Ethics declarations

Competing interests

S.A.L. is a founder of AstronauTx Ltd. All other others declare no competing financial interests.

Additional information

Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 FACS strategy to purify GFP + astrocytes from Aldh1l1eGFP mice, initial clustering and cell type identification.

a, A single cell suspension of mouse cortex was prepared (see Methods) and cells from two hemispheres were recovered in 500 µL 1%BSA containing PBS. Cells were run on a Sony SH800Z with a 100 μm nozzle at 4 °C. Gating was set to capture single cells that were DAPI-negative (alive) and GFP-positive (astrocytes). 100.000 GFP + astrocytes were captured per mouse. Channels were: DAPI (405 nm) and GFP (488 nm). Abbreviations: SSC, side scatter; FSC, forward scatter, a, area; w, width. b,c, Initial clustering of all 91110 cells across all 12 animals identifies presence and percentage of non-astrocytic cells as defined by cell type marker genes in (d). d, Heatmap of normalized cell type marker genes across the identified cell type clusters.

Extended Data Fig. 2 Gene Ontology (GO) analysis of LPS-induced genes at 3 h, 24 h and 72 h after injection.

a, Genes induced ≥1 log2 fold at each time point with padj < 0.05 were used to perform GO-term analysis using Metascape18. Shown are the top 100 GO terms according to p-value and clustered by p-value at each time point. b, Shown are genes induced ≥1 log2 fold with padj < 0.05 for each time point (light and dark orange). Light orange are genes unique to the time point, dark orange are genes co-regulated at at least two time points. Dark blue lines connect genes that are co-regulated. Light blue lines connect genes with overlapping GO-terms.

Extended Data Fig. 3 Filtering of scRNAseq samples based on per sample count data.

a, Histograms showing total counts, total features per counts as well as the percentage of mitochondrial counts for each of the 12 single-cell RNA-seq samples. b, Scatter plot showing total counts (x-axis) and total features by counts (y-axis) before and after filtering.

Extended Data Fig. 4 QC and validation of six Visium spatial transcriptomics sections from saline- and LPS-treated animals.

a, H&E staining of 10 μm coronal sections from three saline-treated and three LPS-treated mice. b, FeaturePlot showing number of genes across all tissue covered spots across the six brains sections. c, Violin plot quantifying the FeatuePlot in (b) showing the number of genes across the six brain sections. d, Violin plot showing the number of reads per brain section. e, t-SNE of the six brain sections, three saline- and three LPS-treated. Each dot represents one spot of a Visium Gene Expression Slide capture area. f, t-SNE as in (e) but with saline- and LPS-treated animals grouped. g, Violin plot highlighting an inflammatory module created from the top LPS-responsive genes shows inflammatory gene induction in brain sections from LPS- but not saline-treated animals (* = p < 0.05, two-tailed t-test on spot-averages of inflammatory gene modules). h, FeaturePlots showing expression of Timp1, Igtp, Gfap and Serpina3n across the six brain sections.

Extended Data Fig. 5 Astrocytic subtypes differentially express layer-specific genes that can be found in brain sections using spatial transcriptomics.

a, Heatmap showing normalized cluster averages of upper layer or lower layer/subpial/white matter astrocyte genes as identified in Bayraktar et. al. 202010. b, Identification of brain location of upper layer or lower layer/subpial/white matter astrocytes using modules of layer-specific genes using Visium spatial transcriptomics (a). c, Example genes from (a) shows the gene distribution of upper versus lower layer/subpial/white matter genes across the coronal brain section. d, Layer-specific pyramidal neuron genes were taken from scRNA-seq data from Zeisel et. al. 201512, subset into modules and highlighted in a coronal Visium section from a saline-treated animal. e, Visium showing the expression of Mbp to highlight presence of white matter tracts in the coronal brain section.

Extended Data Fig. 6 Multimodal quantification of Cluster 8 gene Igtp and Cluster 4 gene Timp1.

a, Representative sagittal section from female P30 mouse 24 h after LPS injection highlighting Igtp (red, top), Slc1a3 (cyan, middle) and co-localization (bottom) using RNAScope based on 4 saline- and 4 LPS-injected animals. Scale bar = 250 μm. b, Magnification of area in (a) indicated by yellow box. Yellow arrows indicate Igtp + astrocytes in Layer1 and surface of the brain. Scale bar = 100 μm. Inset shows a magnification of a single Igtp + astrocyte. Scale bar = 10 μm c, Quantification of Igtp + /Slc1a3 + astrocyte numbers using RNAScope in Layer1 of the cortex. A cell is identified as Igtp + if it expresses ≥20 dots/cell. P-value calculated with Mann-Whitney test, n = 4 saline-treated mice, n = 4 LPS-treated mice, 2-3 sections per mouse. Bar graphs show the mean, error bars SEM. d, Igtp expression in FACS-purified astrocytes 3 h (n = 4 saline, 5 LPS), 24 h (n = 5 saline, 4 LPS) and 72 h (n = 4 saline, 3 LPS) after LPS injection. * = padj<0.05, DESeq2, compared to corresponding saline-injected mice. Exact padj values can be found in Supplementary Table 1. N indicates number of animals, bar graphs show the mean, error bars are SEM. e, Igtp expression as quantified with Visium. Log Igtp levels (left) and percentage of spots with detectable Igtp levels as well as scaled average expression (right), across all spots from 3 saline- and 3 LPS-treated animals. f, Representative sagittal section from female P30 mouse 24 h after LPS injection highlighting Timp1 (red, top), Slc1a3 (cyan, middle) and co-localization (bottom) using RNAScope based on 4 saline- and 4 LPS-injected animals. Scale bar = 250 μm. g, Magnification of area in (f) indicated with a yellow box. White dashed line demarcates transition from white to gray matter. Scale bar = 50 μm. h, Quantification of Timp1 + /Slc1a3 + astrocyte numbers using RNAScope in corpus callosum. A cell is identified as Timp1 + if it expresses ≥20 dots/cell. P-value calculated with Mann-Whitney test, n = 4 saline-treated mice, n = 4 LPS-treated mice, 2-3 sections per mouse. Bar graphs show the mean, error bars SEM. i, Timp1 expression in FACS-purified astrocytes 3 h (n = 4 saline, 5 LPS), 24 h (n = 5 saline, 4 LPS) and 72 h (n = 4 saline, 3 LPS) after LPS injection. * = padj<0.05, DESeq2, compared to corresponding saline-injected mice. Exact padj values can be found in Supplementary Table 1. N indicates number of animals, bar graphs show the mean, error bars are SEM. e, Timp1 expression as quantified with Visium. Log Timp1 levels (left) and percentage of spots with detectable Igtp levels as well as scaled average expression (right), across all spots from 3 saline- and 3 LPS-treated animals.

Extended Data Fig. 7 There are few sex-specific differences in astrocytic gene expression during inflammation.

a, FPKM scatter plot showing all genes expressed >0.5 FPKM in astrocytes purified from male and female saline-injected Aldh1l1eGFP mice based on bulk sequencing. Genes that are differentially expressed with padj < 0.05 are highlighted in purple. Circled are the highest expressed genes according to FPKM specific to males and females, Eif2s3y and Xist, respectively. b, Bar graphs showing mean FPKM values of genes differentially expressed (* = padj<0.05, DESeq2) between male (n = 10) and female mice (n = 9). Circles are single animals, error bars are SEM. c, t-SNE feature plot highlighting female-specific gene Xist and male-specific gene Eif2s3y. d, Heatmap showing z-normalized FPKM levels of sex-specific LPS-induced genes. e, Scatter plot comparing log2 fold inductions due to LPS compared to saline-injected mice based on bulk sequencing. Genes included are >10 FPKM and padj<0.05 in either males or females as a consequence of LPS injection. f, Bar graphs showing mean FPKM values of example genes only induced in female mice (* = padj < 0.05, # = NS compared to male saline, DESeq2) Far1, Cdh19 and Tsc22d3. Circles are single animals, error bars are SEM.

Extended Data Fig. 8 Nanostring® Glial Profiling Panel was used to validate lowly abundant transcripts discovered using single cell RNASeq experiments.

a, Schematic for Nanostring Glial Profile Panel experiments. Brain sections from P30 Aldh1l1eGFP mice injected with saline or lipopolysaccharide (LPS) were collected at 3, 24, or 72 h. b, Principle Component (PC) plot of all samples. c, Heatmap of expression level z-scores for 720 genes included in Panel across time since LPS injection. d, Heatmap of z-scores of differentially expressed genes following LPS injection. e, PC plot of genes identified from single cell RNA sequencing (scRNASeq) experiments and enriched in Cluster 8. f, Heatmap of Cluster 8 enriched transcripts. g, Fold-change in expression of Cluster 8 enriched genes with time following LPS injection (compared to the previous time point). h-l, Bar plots showing raw expression values (normalized to chip control transcripts) for individual genes enriched in Cluster 8 (error bars are SEM). While many genes are enriched by 3 h and have lowered expression levels by 24 h, for example Igtp (h), Iigp1 (i), others maintain increased expression for several days (j-l).n = 5 animals per condition. m, PC plot of genes identified from scRNASeq experiments and enriched in Cluster 4. n, Heatmap of Cluster 4 enriched transcripts. N = 5 biological replicates per group. o, Fold-change in expression of Cluster 4 enriched genes with time following LPS injection (compared to the previous time point). p-s, Bar plots showing raw expression values (normalized to Nanostring chip control transcripts) for individual genes enriched in Cluster 4 (error bars are SEM). Abbreviations: LPS, lipopolysaccharide; PC, principle component; QC, quality control (bioAnalyzer chip); scRNASeq, single cell RNA sequencing = 5 biological replicates per group. Prediction ellipses in b,e,m represent probability = 0.95.

Extended Data Fig. 9 White matter-associated astrocytes are not well-defined in integrated datasets.

Integration of current study with published astrocyte datasets to visualize white matter-associated astrocytes a, t-SNE projection of 89,420 astrocytes identified by unique astrocyte clusters (top) and original dataset (bottom). b, Dotplot showing genes enriched in white matter-associated astrocytes, split by dataset and cluster. Data: 5xFAD34 (purple), Trem2-/-/5xFAD35 7-month (red) and 15-month (yellow), and current study (blue). c, Violin plots of white matter-associated astrocyte transcripts split by dataset and disease. Only data from integrated cluster 7 (that is, integrated white matter-associated astrocyte subpopulation) are visualized. d, t-SNE projection of 100,471 astrocytes identified by unique astrocyte clusters (top) and original dataset (bottom). e, Dotplot showing genes enriched in white matter-associated astrocytes, split by dataset and cluster. Data: EAE from two isolated populations36 - one un-sorted (green) and the other GfapTdT-sorted (orange), and current study (blue). f, Violin plots of white matter-associated astrocyte transcripts split by dataset and disease. Only data from integrated cluster 8 (that is, integrated white matter-associated astrocyte subpopulation) are visualized. g, t-SNE projection of 83,684 astrocytes identified by unique astrocyte clusters (top) and original dataset (bottom). h, Dotplot showing genes enriched in white matter-associated astrocytes, split by dataset and cluster. Data: stab wound37 (pink) and current study (blue). i, Violin plots of white matter-associated astrocyte transcripts split by dataset and model. Only data from integrated cluster 5 (that is, integrated white matter-associated astrocyte subpopulation) are visualized. Abbreviations: 5X, 5xFAD; AD, Alzheimer’s disease; B6, C57BL/6 J mice; CNT, control; EAE, experimental autoimmune encephalomyelitis; LPS, lipopolysaccharide; CFA, complete Freund’s adjuvant.

Extended Data Fig. 10 Dataset quality assessment: Genes, UMI counts, and percentage of mitochondrial genes per cell split by cluster and by dataset.

t-SNE projections of astrocytes and violin plots of quality control metrics for AD models (5xFAD, Trem2-/-, and Trem2-/-/5xFAD) (a), a model of demyelination (experimental autoimmune encephalomyelitis, EAE) (b), and an acute injury/stab wound model (c) split by astrocyte cluster (top) and by dataset (bottom). Habib et al. (2020 – 5xFAD)34 data are in purple, Zhou et al. (2020 – 5xFAD, Trem2-/-, and Trem2-/-/5xFAD)35 7-month time point data are in red, Zhou et al. (2020 – 5xFAD) 15-month time point data are in yellow, Wheeler et al. (2020 – EAE)36 B6 sample data are in green, Wheeler et al. (2020 – EAE) TdTomatoGFAP-sorted sample data are in orange, Zamboni et al. (2020 – stab wound)37 data are in pink, and the current study data (acute inflammation using lipopolysaccharide, LPS) are in blue. Abbreviations: AD, Alzheimer’s disease; B6, C57BL/6 J mice; GFAP, TdTomatoGFAP mice; EAE, experimental autoimmune encephalomyelitis; LPS, lipopolysaccharide; Trem2-/-, Trem2 knockout mice; Trem2-/-/5X, Trem2 knockout 5xFAD mice; UMI, unique molecular identifier.

Supplementary information

Reporting Summary

Supplementary Table 1

RNA-seq analysis of FACS-purified astrocytes after injection of saline or LPS at 3 h, 24 h and 72 h

Supplementary Table 2

LPS-induced genes in astrocytes unique or overlapping at 3 h, 24 h or 72 h after LPS injection

Supplementary Table 3

GO term analysis of genes induced in astrocytes (log2 fold ≥ 1, Padj < 0.05) following 3 h, 24 h and 72 h after LPS injection

Supplementary Table 4

Number of cell types and number of astrocytes per cluster for the 12 scRNA-seq samples

Supplementary Table 5

Gene enrichment across the ten scRNA-seq clusters

Supplementary Table 6

Cluster-specific LPS-responsive genes as calculated with edgeR using pseudobulk counts

Supplementary Table 7

RNA-seq analysis of immunopanned rat astrocytes exposed to type I and type II interferons as well as TNFa/IL1a/C1q for 24 h

Supplementary Table 8

RNA-seq analysis of sex-specific LPS-responsive genes in FACS-purified astrocytes

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hasel, P., Rose, I.V.L., Sadick, J.S. et al. Neuroinflammatory astrocyte subtypes in the mouse brain. Nat Neurosci 24, 1475–1487 (2021). https://doi.org/10.1038/s41593-021-00905-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00905-6

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing