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.

CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states

Abstract

Astrocytes become reactive in response to insults to the central nervous system by adopting context-specific cellular signatures and outputs, but a systematic understanding of the underlying molecular mechanisms is lacking. In this study, we developed CRISPR interference screening in human induced pluripotent stem cell-derived astrocytes coupled to single-cell transcriptomics to systematically interrogate cytokine-induced inflammatory astrocyte reactivity. We found that autocrine–paracrine IL-6 and interferon signaling downstream of canonical NF-κB activation drove two distinct inflammatory reactive signatures, one promoted by STAT3 and the other inhibited by STAT3. These signatures overlapped with those observed in other experimental contexts, including mouse models, and their markers were upregulated in human brains in Alzheimer’s disease and hypoxic-ischemic encephalopathy. Furthermore, we validated that markers of these signatures were regulated by STAT3 in vivo using a mouse model of neuroinflammation. These results and the platform that we established have the potential to guide the development of therapeutics to selectively modulate different aspects of inflammatory astrocyte reactivity.

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: iAstrocytes perform canonical astrocyte functions and recapitulate key aspects of inflammatory reactivity.
Fig. 2: CRISPRi platform in iAstrocytes.
Fig. 3: CRISPRi screening and MRA uncover regulators of inflammatory reactivity.
Fig. 4: CROP-seq of iAstrocytes reveals two distinct inflammatory reactive signatures dependent on the canonical NF-kB pathway.
Fig. 5: IL-6 and IFNs act in an autocrine–paracrine manner downstream of IL-1α+TNF+C1q.
Fig. 6: Differential regulation of distinct inflammatory reactive signatures by cytokines and cellular factors.
Fig. 7: Integration of iAstrocyte single-cell data with published single-cell datasets shows overlap of inflammatory reactive signatures across species in diverse disease contexts.
Fig. 8: Markers of distinct inflammatory reactive signatures are upregulated in astrocytes in human AD and HIE and are regulated by Stat3 in a mouse model of neuroinflammation.

Data availability

Bulk RNA-seq data of hiPSC-derived astrocytes generated in this study shown in Extended Data Fig. 3 are available on the Gene Expression Omnibus (GEO) under accession code GSE182307. The raw single-cell RNA-seq data and UMI count matrices from the CROP-seq experiment are available on the GEO under accession code GSE182308. Processed data from the CRISPRi screens can also be interactively explored on CRISPRbrain (https://www.crisprbrain.org/screens/) by selecting ‘Astrocyte’ as the screening cell type. The public databases used in this study include Enrichr (https://maayanlab.cloud/Enrichr/), GENCODE (https://www.gencodegenes.org/human/), GEO (https://www.ncbi.nlm.nih.gov/geo/) and Synapse (https://www.synapse.org/). Previously published datasets available on the GEO that were reanalyzed in this study include GSE143598, GSE120411, GSE76097, GSE148611 and GSE130119; previously published datasets available on Synapse and reanalyzed in this study include syn21861229.

Code availability

The full analysis pipeline (including code and processed data objects) used for master regulator analysis, analysis of CROP-seq data and integration with previously published single-cell RNA-seq datasets is available at https://kampmannlab.ucsf.edu/inflammatory-reactive-astrocyte-analysis.

References

  1. Escartin, C. et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat. Neurosci. 24, 312–325 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sofroniew, M. V. & Vinters, H. V. Astrocytes: biology and pathology. Acta Neuropathol. 119, 7–35 (2010).

    Article  PubMed  Google Scholar 

  3. Burda, J. E. et al. Divergent transcriptional regulation of astrocyte reactivity across disorders. Nature 606, 557–564 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. Wang, Q., Tang, X. N. & Yenari, M. A. The inflammatory response in stroke. J. Neuroimmunol. 184, 53–68 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Hausmann, O. N. Post-traumatic inflammation following spinal cord injury. Spinal Cord 41, 369–378 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Ponath, G., Park, C. & Pitt, D. The role of astrocytes in multiple sclerosis. Front. Immunol. 9, 217 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Heneka, M. T. et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 14, 388–405 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Han, R. T., Kim, R. D., Molofsky, A. V. & Liddelow, S. A. Astrocyte–immune cell interactions in physiology and pathology. Immunity 54, 211–224 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. 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 

  10. Guttenplan, K. A. et al. Knockout of reactive astrocyte activating factors slows disease progression in an ALS mouse model. Nat. Commun. 11, 3753 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Guttenplan, K. A. et al. Neurotoxic reactive astrocytes drive neuronal death after retinal injury. Cell Rep. 31, 107776 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yun, S. P. et al. Block of A1 astrocyte conversion by microglia is neuroprotective in models of Parkinson’s disease. Nat. Med. 24, 931–938 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465–480 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. TCW, J. et al. An efficient platform for astrocyte differentiation from human induced pluripotent stem cells. Stem Cell Rep. 9, 600–614 (2017).

    Article  CAS  Google Scholar 

  17. Li, X. et al. Fast generation of functional subtype astrocytes from human pluripotent stem cells. Stem Cell Rep. 11, 998–1008 (2018).

    Article  CAS  Google Scholar 

  18. Williams, J. L. et al. Astrocyte–T cell crosstalk regulates region-specific neuroinflammation. Glia 68, 1361–1374 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gimenez, M. A., Sim, J. E. & Russell, J. H. TNFR1-dependent VCAM-1 expression by astrocytes exposes the CNS to destructive inflammation. J. Neuroimmunol. 151, 116–125 (2004).

    Article  CAS  PubMed  Google Scholar 

  20. Rosenman, S. J., Shrikant, P., Dubb, L., Benveniste, E. N. & Ransohoff, R. M. Cytokine-induced expression of vascular cell adhesion molecule-1 (VCAM-1) by astrocytes and astrocytoma cell lines. J. Immunol. 154, 1888–1899 (1995).

    CAS  PubMed  Google Scholar 

  21. Rubio, N., Sanz-Rodriguez, F. & Arevalo, M. A. Up-regulation of the vascular cell adhesion molecule-1 (VCAM-1) induced by Theilerʼs murine encephalomyelitis virus infection of murine brain astrocytes. Cell Commun. Adhes. 17, 57–68 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Labib, D. et al. Proteomic alterations and novel markers of neurotoxic reactive astrocytes in human induced pluripotent stem cell models. Front. Mol. Neurosci. 15, 870085 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kampmann, M. CRISPRi and CRISPRa screens in mammalian cells for precision biology and medicine. ACS Chem. Biol. 13, 406–416 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Castro, M. A. et al. Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat. Genet. 48, 12–21 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Fletcher, M. N. et al. Master regulators of FGFR2 signalling and breast cancer risk. Nat. Commun. 4, 2464 (2013).

    Article  PubMed  Google Scholar 

  27. Campbell, T. M. et al. FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness. Carcinogenesis 37, 741–750 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 1912–1934 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Liberti, S. et al. HuPho: the human phosphatase portal. FEBS J. 280, 379–387 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Shih, V. F., Tsui, R., Caldwell, A. & Hoffmann, A. A single NFκB system for both canonical and non-canonical signaling. Cell Res. 21, 86–102 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Liu, T., Zhang, L., Joo, D. & Sun, S. C. NF-κB signaling in inflammation. Signal Transduct. Target Ther. 2, 17023 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cardinaux, J. R., Allaman, I. & Magistretti, P. J. Pro-inflammatory cytokines induce the transcription factors C/EBPbeta and C/EBPdelta in astrocytes. Glia 29, 91–97 (2000).

    Article  CAS  PubMed  Google Scholar 

  35. Alonzi, T. et al. Essential role of STAT3 in the control of the acute-phase response as revealed by inducible gene inactivation [correction of activation] in the liver. Mol. Cell. Biol. 21, 1621–1632 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Herrmann, J. E. et al. STAT3 is a critical regulator of astrogliosis and scar formation after spinal cord injury. J. Neurosci. 28, 7231–7243 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195–200 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ben Haim, L. et al. The JAK/STAT3 pathway is a common inducer of astrocyte reactivity in Alzheimer’s and Huntington’s diseases. J. Neurosci. 35, 2817–2829 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang, Z. H. et al. C/EBPβ regulates delta-secretase expression and mediates pathogenesis in mouse models of Alzheimer’s disease. Nat. Commun. 9, 1784 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Strohmeyer, R., Shelton, J., Lougheed, C. & Breitkopf, T. CCAAT-enhancer binding protein-β expression and elevation in Alzheimer’s disease and microglial cell cultures. PLoS ONE 9, e86617 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Feng, H., Zhang, Y. B., Gui, J. F., Lemon, S. M. & Yamane, D. Interferon regulatory factor 1 (IRF1) and anti-pathogen innate immune responses. PLoS Pathog. 17, e1009220 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lehtonen, A., Matikainen, S. & Julkunen, I. Interferons up-regulate STAT1, STAT2, and IRF family transcription factor gene expression in human peripheral blood mononuclear cells and macrophages. J. Immunol. 159, 794–803 (1997).

    CAS  PubMed  Google Scholar 

  43. Ng, S. L. et al. IκB kinase ε (IKKε) regulates the balance between type I and type II interferon responses. Proc. Natl Acad. Sci. USA 108, 21170–21175 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hasel, P., Rose, I. V. L., Sadick, J. S., Kim, R. D. & Liddelow, S. A. Neuroinflammatory astrocyte subtypes in the mouse brain. Nat. Neurosci. 24, 1475–1487 (2021).

  45. Mayer-Barber, K. D. & Yan, B. Clash of the cytokine titans: counter-regulation of interleukin-1 and type I interferon-mediated inflammatory responses. Cell. Mol. Immunol. 14, 22–35 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Gan, W. et al. LATS suppresses mTORC1 activity to directly coordinate Hippo and mTORC1 pathways in growth control. Nat. Cell Biol. 22, 246–256 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. He, L. et al. mTORC1 promotes metabolic reprogramming by the suppression of GSK3-dependent Foxk1 phosphorylation. Mol. Cell 70, 949–960 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Banks, T. A. et al. A lymphotoxin-IFN-β axis essential for lymphocyte survival revealed during cytomegalovirus infection. J. Immunol. 174, 7217–7225 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Weichhart, T., Hengstschläger, M. & Linke, M. Regulation of innate immune cell function by mTOR. Nat. Rev. Immunol. 15, 599–614 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Balamurugan, K. et al. The tumour suppressor C/EBPδ inhibits FBXW7 expression and promotes mammary tumour metastasis. EMBO J. 29, 4106–4117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Heinrich, P. C., Castell, J. V. & Andus, T. Interleukin-6 and the acute phase response. Biochem. J. 265, 621–636 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sonderegger, I. et al. GM-CSF mediates autoimmunity by enhancing IL-6-dependent Th17 cell development and survival. J. Exp. Med. 205, 2281–2294 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Metzemaekers, M., Vanheule, V., Janssens, R., Struyf, S. & Proost, P. Overview of the mechanisms that may contribute to the non-redundant activities of interferon-inducible CXC chemokine receptor 3 ligands. Front. Immunol. 8, 1970 (2017).

    Article  PubMed  Google Scholar 

  55. Wang, Y., van Boxel-Dezaire, A. H., Cheon, H., Yang, J. & Stark, G. R. STAT3 activation in response to IL-6 is prolonged by the binding of IL-6 receptor to EGF receptor. Proc. Natl Acad. Sci. USA 110, 16975–16980 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Au-Yeung, N., Mandhana, R. & Horvath, C. M. Transcriptional regulation by STAT1 and STAT2 in the interferon JAK-STAT pathway. JAKSTAT 2, e23931 (2013).

    PubMed  PubMed Central  Google Scholar 

  57. Hungness, E. S. et al. Transcription factors C/EBP-β and -δ regulate IL-6 production in IL-1β-stimulated human enterocytes. J. Cell. Physiol. 192, 64–70 (2002).

    Article  CAS  PubMed  Google Scholar 

  58. Tsai, M. H., Pai, L. M. & Lee, C. K. Fine-tuning of type I interferon response by STAT3. Front Immunol. 10, 1448 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Michalska, A., Blaszczyk, K., Wesoly, J. & Bluyssen, H. A. R. A positive feedback amplifier circuit that regulates interferon (IFN)-stimulated gene expression and controls type I and type II IFN responses. Front. Immunol. 9, 1135 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Metwally, H. et al. Noncanonical STAT1 phosphorylation expands its transcriptional activity into promoting LPS-induced IL-6 and IL-12p40 production. Sci. Signal 13, eaay0574 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Nan, J., Wang, Y., Yang, J. & Stark, G. R. IRF9 and unphosphorylated STAT2 cooperate with NF-κB to drive IL6 expression. Proc. Natl Acad. Sci. USA 115, 3906–3911 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Haan, S., Keller, J. F., Behrmann, I., Heinrich, P. C. & Haan, C. Multiple reasons for an inefficient STAT1 response upon IL-6-type cytokine stimulation. Cell. Signal. 17, 1542–1550 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Qing, Y. & Stark, G. R. Alternative activation of STAT1 and STAT3 in response to interferon-γ. J. Biol. Chem. 279, 41679–41685 (2004).

    Article  CAS  PubMed  Google Scholar 

  64. van Boxel-Dezaire, A. H. et al. Major differences in the responses of primary human leukocyte subsets to IFN-β. J. Immunol. 185, 5888–5899 (2010).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Heppner, F. L., Ransohoff, R. M. & Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci. 16, 358–372 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Lyra, E. et al. Pro-inflammatory interleukin-6 signaling links cognitive impairments and peripheral metabolic alterations in Alzheimer’s disease. Transl. Psychiatry 11, 251 (2021).

    Article  Google Scholar 

  68. Roy, E. R. et al. Concerted type I interferon signaling in microglia and neural cells promotes memory impairment associated with amyloid β plaques. Immunity 55, 879–894 (2022).

  69. Eikelenboom, P., Hack, C. E., Rozemuller, J. M. & Stam, F. C. Complement activation in amyloid plaques in Alzheimer’s dementia. Virchows Arch. B Cell Pathol. Incl. Mol. Pathol. 56, 259–262 (1989).

    Article  CAS  PubMed  Google Scholar 

  70. Abraham, C. R., Selkoe, D. J. & Potter, H. Immunochemical identification of the serine protease inhibitor α1-antichymotrypsin in the brain amyloid deposits of Alzheimer’s disease. Cell 52, 487–501 (1988).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Orzyłowska, O., Oderfeld-Nowak, B., Zaremba, M., Januszewski, S. & Mossakowski, M. Prolonged and concomitant induction of astroglial immunoreactivity of interleukin-1beta and interleukin-6 in the rat hippocampus after transient global ischemia. Neurosci. Lett. 263, 72–76 (1999).

    Article  PubMed  Google Scholar 

  73. Deng, Y., Lu, J., Sivakumar, V., Ling, E. A. & Kaur, C. Amoeboid microglia in the periventricular white matter induce oligodendrocyte damage through expression of proinflammatory cytokines via MAP kinase signaling pathway in hypoxic neonatal rats. Brain Pathol. 18, 387–400 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Taher, N. A. B. et al. Altered distributions and functions of natural killer T cells and γδ T cells in neonates with neonatal encephalopathy, in school-age children at follow-up, and in children with cerebral palsy. J. Neuroimmunol. 356, 577597 (2021).

    Article  CAS  PubMed  Google Scholar 

  75. Liu, F. & McCullough, L. D. Inflammatory responses in hypoxic ischemic encephalopathy. Acta Pharmacol. Sin. 34, 1121–1130 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Sloan, S. A. et al. Human astrocyte maturation captured in 3D cerebral cortical spheroids derived from pluripotent stem cells. Neuron 95, 779–790 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Han, H. et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 46, D380–D386 (2018).

    Article  CAS  PubMed  Google Scholar 

  80. Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Barbar, L. et al. CD49f is a novel marker of functional and reactive human iPSC-derived astrocytes. Neuron 107, 436–453 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Miyaoka, Y. et al. Isolation of single-base genome-edited human iPS cells without antibiotic selection. Nat. Methods 11, 291–293 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. TCW, J. et al. Cholesterol and matrisome pathways dysregulated in astrocytes and microglia. Cell 185, 2213–2233 (2022).

    Article  CAS  PubMed  Google Scholar 

  85. Krencik, R. et al. Dysregulation of astrocyte extracellular signaling in Costello syndrome. Sci. Transl. Med. 7, 286ra266 (2015).

    Article  Google Scholar 

  86. Tian, R. et al. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104, 239–255 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Li, X. L. et al. Highly efficient genome editing via CRISPR–Cas9 in human pluripotent stem cells is achieved by transient BCL-XL overexpression. Nucleic Acids Res. 46, 10195–10215 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Cheng, C., Fass, D. M., Folz-Donahue, K., MacDonald, M. E. & Haggarty, S. J. Highly expandable human iPS cell-derived neural progenitor cells (NPC) and neurons for central nervous system disease modeling and high-throughput screening. Curr. Protoc. Hum. Genet. 92, 21.28.21 (2017).

    Google Scholar 

  89. Krencik, R. & Zhang, S. C. Directed differentiation of functional astroglial subtypes from human pluripotent stem cells. Nat. Protoc. 6, 1710–1717 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Bowles, K. R., TCW, J., Qian, L., Jadow, B. M. & Goate, A. M. Reduced variability of neural progenitor cells and improved purity of neuronal cultures using magnetic activated cell sorting. PLoS ONE 14, e0213374 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Wolf, J., Rose-John, S. & Garbers, C. Interleukin-6 and its receptors: a highly regulated and dynamic system. Cytokine 70, 11–20 (2014).

    Article  CAS  PubMed  Google Scholar 

  92. Neal, E. H. et al. A simplified, fully defined differentiation scheme for producing blood–brain barrier endothelial cells from human iPSCs. Stem Cell Rep. 12, 1380–1388 (2019).

    Article  CAS  Google Scholar 

  93. Fernandopulle, M. S. et al. Transcription factor-mediated differentiation of human iPSCs into neurons. Curr. Protoc. Cell Biol. 79, e51 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Niu, J. et al. Oligodendroglial ring finger protein Rnf43 is an essential injury-specific regulator of oligodendrocyte maturation. Neuron 109, 3104–3118 (2021).

  95. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Lachmann, A. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat. Commun. 9, 1366 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Lachmann, A., Xie, Z. & Ma’ayan, A. Elysium: RNA-seq alignment in the cloud. Preprint at https://www.biorxiv.org/content/10.1101/382937v1 (2018).

  98. Krawczyk, M. C. et al. Human astrocytes exhibit tumor microenvironment-, age-, and sex-related transcriptomic signatures. J. Neurosci. 42, 1587–1603 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Hill, A. J. et al. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15, 271–274 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Gaublomme, J. T. et al. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nat. Commun. 10, 2907 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2020).

  104. 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 

  105. Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).

    PubMed  Google Scholar 

  106. Torre, D., Lachmann, A. & Ma’ayan, A. BioJupies: automated generation of interactive notebooks for RNA-seq data analysis in the cloud. Cell Syst. 7, 556–561 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Perriot, S. et al. Human induced pluripotent stem cell-derived astrocytes are differentially activated by multiple sclerosis-associated cytokines. Stem Cell Rep. 11, 1199–1210 (2018).

    Article  CAS  Google Scholar 

  108. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  CAS  PubMed  Google Scholar 

  112. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).

    Article  PubMed  Google Scholar 

  114. Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–24 (2010).

    Article  Google Scholar 

  115. Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V. & Oldham, M. C. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat. Neurosci. 21, 1171–1184 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Huang, R. et al. The NCATS BioPlanet—an integrated platform for exploring the universe of cellular signaling pathways for toxicology, systems biology, and chemical genomics. Front. Pharm. 10, 445 (2019).

    Article  CAS  Google Scholar 

  117. Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank B. Desousa, V. Jovanovic, Z. Krejciova and N. Sun for contributions to preliminary studies and discussions. We thank A. Molofsky, A. Kao and M. Oldham for serving on K.L.ʼs thesis committee. We thank members of the Kampmann laboratory (G. Mohl, S. Sattler and O. Teter) for discussions and feedback on the manuscript. We thank B. Woo for cloning the transcription factors sgRNA library and B. Ramani for help with obtaining primary mouse astrocytes. We thank the Conklin laboratory for the gift of the WTC11 hiPSC line. This research was supported by National Institutes of Health (NIH) grant F30 AG066418 to K.L.; California Institute for Regenerative Medicine grant EDUC4-12812 and NIH grant T32 NS115706 to I.V.L.R.; Chan Zuckerberg Initiative Ben Barres Early Career Acceleration Awards to E.S.L. and M. Kampmann; NIH/NIND grants (R01NS097551, P01NS083513 and R21NS119954) to S.F.; and NIH grants P30 EY02162-39 and R03AG063157 to E.M.U. S.F. is a Harry Weaver Neuroscience Scholar of the National Multiple Sclerosis Society. The TCW-1E44 iPSC line was generated with the support of NIH NIA K01AG062683 (J.T.) and the Druckenmiller Fellowship from the New York Stem Cell Foundation (J.T.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

K.L. and M. Kampmann conceptualized and led the overall project and wrote the manuscript, with input from all co-authors. K.L. performed the majority of experiments, with support from B.R., and performed all data analysis. I.V.L.R. performed immunostaining of mouse tissue provided by Y.A. and S.W., with guidance from M.V.S. H.K. performed immunostaining of AD tissues provided by M.S.S. and co-culture experiments, with guidance from E.S.L., and W.X. performed immunostaining of HIE tissue, with guidance from S.F. W.R.F. also performed immunostaining of AD tissues provided by M.S.S. E.L. generated the CRISPRi TCW-1E44 hiPSC line. J.T. supplied the TCW-1E44 hiPSC line, with guidance from A.G. M. Koontz generated hiPSC-derived astrocytes using the methods of Krencik et al. and Li et al., with guidance from E.M.U., who also provided the 162D iPSC line. M. Krawcyzk and Y.Z. supplied unpublished human astrocyte RNA-seq data for master regulator analysis.

Corresponding authors

Correspondence to Kun Leng or Martin Kampmann.

Ethics declarations

Competing interests

M. Kampmann is an inventor on US Patent 11,254,933 related to CRISPRi and CRISPRa screening, serves on the scientific advisory boards of Engine Biosciences, Casma Therapeutics, Cajal Neuroscience and Alector and is an advisor to Modulo Bio and Recursion Therapeutics. J.T. co-founded Asmos Therapeutics, LLC, serves on the scientific advisory board of NeuCyte, Inc. and has consulted for FIND Genomics Inc., CareCureSystems Corporation, TheWell Biosciences Inc. and Aleta Neuroscience, LLC. A.G. serves on the scientific advisory board for Genentech and is a consultant to Muna Therapeutics. None of the other authors declare competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Valentina Fossati and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Additional characterization of iAstrocytes.

a, Empirical cumulative distribution functions of the mean expression of genes (averaged across experimental replicates, n = 3 wells) with astrocyte-specific expression (astrocyte fidelity > 40) or without astrocyte specific expression (astrocyte fidelity < 40) in iAstrocytes vs astrocytes generated using the TCW et al. protocol16 (TCW astrocytes). Genome-wide astrocyte fidelity scores were obtained from Kelley et al. TPM: transcripts per million. b, Relative expression (z-scored) of the top 50 genes with the highest astrocyte fidelity scores from Kelley et al.115 (organized by hierarchical clustering, see Methods) in iAstrocytes vs. TCW astrocytes (n = 3 experimental replicates corresponding to heatmap rows). Genes with statistically significant differential expression (adjusted P value < 0.1) between iAstrocytes and TCW astrocytes are marked with asterisks; P values were calculated and adjusted for multiple testing (false-discovery rate method) using DESeq2 (two-sided Wald test; see Methods). c, Heatmap of log-scaled transcripts per million (TPM) values of NFIA transcripts in human primary astrocytes from Zhang et al.93 d, Representative images from immunostaining of GFAP in iAstrocytes cultured alone or with iNeurons (n = 3 wells). In each case, an entire field of view is displayed (left) next to magnified sections (all at same scale) containing representative astrocyte morphologies (right). Scale bars correspond to 60 μm.

Extended Data Fig. 2 Validation of iAstrocyte differentiation from two additional hiPSC lines.

A, Representative images of immunofluorescence against GFAP, S100β, GLAST, Cx43, glutamine synthetase, or vimentin in iAstrocytes vs. TCW astrocytes derived from TCW-1E44 or 162D hiPSCs (scale bar: 60 μm). b,Quantification of GFAP, S100β, GLAST, Cx43, glutamine synthetase, or vimentin immunofluorescence intensity (n = 3 wells). c, Phagocytosis of pHrodo-labeled rat synaptosomes (median fluorescence intensity measured by flow cytometry) by iAstrocytes derived from TCW-1E44 or 162D hiPSCs in the absence (n = 5 wells) or presence (n = 1 well) of cytochalasin D (cytoD). d, Percent VCAM1+ cells in TCW-1E44 or 162D iAstrocytes treated with vehicle control vs. IL-1α+TNF+ C1q (n = 4 wells). e, Percentage of dead cells (measured by TO-PRO-3 permeability) in iNeurons incubated with conditioned media from TCW-1E44 or 162D iAstrocytes treated with vehicle control or IL-1α+TNF+ C1q (n = 12 wells). In panels b and c, P values were calculated using the two-sided Student’s t-test. In panels d and e, P values were calculated using the two-sided Mann-Whitney U test.

Extended Data Fig. 3 iAstrocytes respond to IL-1α+TNF+ C1q in a highly similar manner as hiPSC-derived astrocytes generated using different protocols and primary mouse astrocytes.

a, Principal component (PC) analysis plot of the gene expression profiles (top 5000 variable genes) of iAstrocytes vs. astrocytes derived using the protocols from TCW et al.16 (TCW astrocytes), Li et al.17 (Li astrocytes), or Krencik et al.89 (Krencik astrocytes), as well as iPSC-derived neurons (iNeurons) and neural progenitor cells (NPCs), treated with vehicle control or IL-1α+TNF+ C1q (n = 3 wells for astrocyte samples, n = 2 wells for iNeuron and NPC samples). b, Number of differential expressed genes (DEGs) induced by IL-1α+TNF+ C1q. c, Log2-fold-changes of pan-reactive, A1 reactive, and A2 reactive genes defined in Liddlelow et al.9 in hiPSC-derived astrocytes from this study. d, Overlap of upregulated and downregulated DEGs induced by IL-1α+TNF+ C1q among hiPSC-derived astrocytes from this study. e, Overlap of upregulated and downregulated DEGs induced by IL-1α+TNF+ C1q from hiPSC-derived astrocytes from this study compared to DEGs from inflammatory reactive astrocytes in other studies. f-g, Phagocytosis of pHrodo-labeled synaptosomes (f; n = 3 wells for −cytoD, n = 1 well for +cytoD) or induction of cell-surface VCAM1 (g; n = 6 wells) by iAstrocytes compared to Li and Krencik astrocytes. cytoD: cytochalasin D. h-i, Induction of VCAM1 expression by IL-1α+TNF+ C1q in primary mouse astrocytes measured by flow cytometry in this study (h; n = 4 wells) or primary mouse astrocytes measured by RNA-seq in Guttenplan et al.10 and Hasel et al.44 (i; n = 3 mice). In panel e, P values were calculated using the two-sided Fisher’s exact test and adjusted for multiple testing using the Benjamini-Hochberg method. In panel and i, P values were calculated using the two-sided Student’s t-test. In panels f-h, P values were calculated using beta regression (two-sided Wald test; see Methods).

Extended Data Fig. 4 sgRNA abundance distribution in CRISPRi screens, comparison of phenotypes from VCAM1 and phagocytosis CRISPRi screens, and validation of selected hits from CRISPRi screens.

a, sgRNA abundance distribution for the CRISPRi screens shown in Fig. 3. b, Gene scores (see Methods) from the phagocytosis vs. VCAM1 CRISPRi screen against transcription factors (left) or the druggable genome (right) in iAstrocytes treated with IL-1α+TNF+ C1q. c-d, Phagocytosis of pHrodo-labeled synaptosomes (c) or induction of cell-surface VCAM1 (d) by iAstrocytes transduced with non-targeting sgRNA (NTC) vs. sgRNAs targeting selected hits from the screens shown in Fig. 3, treated with vehicle control or IL-1α+TNF+ C1q (n = 6 wells for NTC, n = 3 wells for knockdowns). MFI: median fluorescence intensity measured by flow cytometry. In panels c and d, P values were calculated by linear regression (two-sided Wald test; see Methods) and adjusted for multiple testing (Padj; Holm’s method) per family of tests (all comparisons within a plot).

Extended Data Fig. 5 Additional analyses of CROP-seq data.

a, Expression levels of the top cluster markers of non-targeting control (NTC) sgRNA-transduced iAstrocytes shown in Fig. 4a. b-c, Cellular pathway (BioPlanet116) enrichment analysis of Cluster 3 and 4 markers (b) and cell type marker (Descartes117) enrichment analysis of Cluster 5 and 6 markers (c) of NTC sgRNA-transduced iAstrocytes shown in Fig. 4a. P values were calculated using the two-sided Fisher’s exact test and corrected for multiple testing using the Benjamini-Hochberg method. d, The degree of regulator knockdown (left) or the number of differentially expressed genes (DEGs) whose differential expression induced IL-1α+TNF+ C1q is significantly altered by regulator knockdown. e, Hierarchical clustering of the P-value-weighted log-fold-changes (gene score) of the union of knockdown-associated DEGs from panel d; DEGs associated with ABCE1 knockdown were excluded due to a significant number of DEGs also being caused by ABCE1 knockdown in vehicle control-treated iAstrocytes.

Extended Data Fig. 6 Enrichment analysis of CROP-seq knockdown-associated gene modules.

Cellular pathway (MSigDB78) and upstream transcription factor (TRRUST79) enrichment analysis of gene modules from Extended Data Fig. 4e; TF – transcription factor. P values were calculated using the two-sided Fisher’s exact test and adjusted for multiple testing using the Benjamini-Hochberg method.

Extended Data Fig. 7 C3 and IFIT3 expression and cytokine production in iAstrocytes derived from multiple hiPSC lines.

a, Transcript levels of IFIT3 overlaid onto the UMAP embedding from Fig. 3a. b, Representative immunofluorescence images of C3 and IFIT3 staining (scale bar: 60 μm). c, Percent IFIT3−/C3+, IFIT3+ /C3−, or IFIT3+ /C3+ cells measured by immunofluorescence in iAstrocytes derived from multiple hiPSC lines (WTC11, TCW-1E44, 162D) treated with vehicle control vs. all possible combinations of IL-1α, TNF, and C1q, in the absence (n = 3 wells per condition) or presence of additional IL-6/IL6R chimera (25 ng/mL) or IFN-β (5 ng/mL) added concurrently (n = 4 wells per condition). D, Concentration of IFN-β, IL-6, CXCL10, or GM-CSF in conditioned media from iAstrocytes derived from multiple hiPSC lines (WTC11, 162D) treated with vehicle control vs. all possible combinations of IL-1α, TNF, and C1q (n = 4 wells). For panels c a, P values were calculated using beta regression (two-sided Wald test; see Methods). For panel d, P values were calculated using linear regression (two-sided Wald test; see Methods). P values were adjusted for multiple testing (Padj; Holm’s method) per family of tests (all comparisons within a plot).

Extended Data Fig. 8 Validation of STAT3, CEBPB, NFKB2, and IRF1 knockdown in iAstrocytes derived from multiple hiPSC lines.

a, Percent VCAM1−/C3+, VCAM1+ /C3−, or VCAM1+ /C3+ cells measured by flow cytometry)in iAstrocytes derived from multiple hiPSC lines (TCW-1E44 and 162D) transduced with non-targeting sgRNA (NTC) or sgRNAs targeting STAT3, CEBPB, NFKB2, or IRF1 (n = 6 wells). b, Combined statistical analysis of the effect of STAT3, CEBPB, NFKB2, or IRF1 knockdown compared to NTC in IL-1α+TNF+ C1q-treated iAstrocytes derived from multiple hiPSC lines (WTC11, TCW-1E44, 162D). For panels a and b, P values were calculated using beta regression (two-sided Wald test; see Methods) and adjusted for multiple testing (Padj; Holm’s method) per family of tests (all comparisons within a plot or table).

Extended Data Fig. 9 Effect of small molecule modulators of STAT3 or STAT1/2 activity.

a, Representative immunofluorescence images of phospho-STAT3 (Y705) staining in vehicle control vs. IL-1α+TNF+ C1q-treated iAstrocytes. Scale bar corresponds to 20 μm. b, Phospo-STAT3 (Y705) levels measured by flow cytometry in iAstrocytes treated with vehicle control vs. IL-1α+TNF+ C1q in the presence of increasing doses of napabucasin (n = 6 wells for 0 μM napabucasin, n = 3 for napabucasin > 0 μM). MFI: median fluorescence intensity. c, Percent VCAM1−/C3+, VCAM1+ /C3−, or VCAM1+ /C3+ cells measured by flow cytometry)in iAstrocytes treated with vehicle control vs. IL-1α+TNF+ C1q in the presence of increasing doses of napabucasin (n = 6 wells for 0 μM napabucasin, n = 3 for napabucasin > 0 μM). d, Percent VCAM1−/C3+, VCAM1+ /C3−, or VCAM1+ /C3+ cells measured by flow cytometry in iAstrocytes treated with vehicle control vs. IL-1α+TNF+ C1q, with or without concurrent RGFP966 treatment (n = 6 wells). In panels b-d, P values were calculated using linear regression for MFI values or beta regression (two-sided Wald test; see Methods) for percentages and adjusted for multiple testing (Padj; Holm’s method) per family of tests (all comparisons within a plot).

Extended Data Fig. 10 Overlap analysis of IRAS1 and IRAS2 markers with external datasets.

a-c, Overlap analysis (Fisher’s exact test; see Methods) of differentially expressed genes (DEGs) between IRAS1 vs. IRAS2 with DEGs between IRAS1- and IRAS2-co-clustering astrocytes from Barbar et al.81 (a), Wheeler et al.82 (b), or Hasel et al.44 (c). d, Overlap analysis of DEGs between IRAS1 vs. IRAS2 with DEGs between astrocytes from Stat3 astrocyte-specific conditional knockout (cKO) mice vs. wild-type (WT) mice subject to spinal cord injury (SCI) from Anderson et al.37. e-g, Module expression score (see Methods) of IRAS1 or IRAS2 markers overlaid onto the UMAP embedding of Barbar et al.81 (e), Wheeler et al.82 (f), or Hasel et al.44 (g) astrocytes from Fig. 7d, h, and l, respectively. h, Module expression score of upregulated vs. downregulated DEGs between astrocytes from Stat3 cKO SCI vs. WT SCI mice from Anderson et al.37 overlaid onto the UMAP embedding of iAstrocytes from Fig. 4a. i, Cellular pathway (MSigDB78) and upstream transcription factor (TRRUST79) enrichment analysis of upregulated vs. downregulated DEGs between astrocytes from Stat3 cKO SCI vs. WT SCI mice from Anderson et al.37. For panels a-d and i, P values were calculated using the two-sided Fisher’s exact test and corrected for multiple testing using the Benjamini-Hochberg method.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Supplementary Text

Reporting Summary

Supplementary Table 1

Bulk RNA-seq differential gene expression analysis results. From this study: iAstrocytes and hiPSC-derived astrocytes generated using the protocols from TCW et al.16, Li et al.17 and Krencik et al.88 treated with vehicle versus IL-1α+TNF+C1q. From external datasets: immunopanned primary mouse astrocytes treated with vehicle versus IL-1α+TNF+C1q from Guttenplan et al.10, hiPSC-derived astrocytes treated with vehicle versus IL-1β from Perriot et al.107 and human cerebral organoid-derived astrocytes treated with vehicle versus IL-1α+TNF+C1q from Barbar et al.18. For data from hiPSC-derived astrocytes in this study, expression was quantified using Salmon, and DEGs were called using DESeq2. For external datasets, the DEGs from Gutteplan et al. were downloaded from the supplementary table included in GSE143598, and raw data from Perriot et al. and Barbar et al. were analyzed with BioJupies106 to call DEGs. P values from DESeq2 or BioJupies were calculated using the two-sided Wald test and corrected for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 2

Phenotype scores and P values from CRISPRi screens. See the first tab of the Excel file for a description of the contents of the remaining tabs and information regarding the columns in each tab. P values were calculated using the two-sided Mann–Whitney U-test.

Supplementary Table 3

Hits from MRA and associated scores and statistics. See Castro et al.26 for interpretation of the activity score. The mean LFC score of a regulon is calculated by averaging the LFC of all DEGs induced by IL-1α+TNF+C1q in that regulon. The P values reported were calculated from two-tailed gene set enrichment analysis as detailed in Castro et al.26 and Campbell et al.28.

Supplementary Table 4

Cluster markers of iAstrocytes in Fig. 4a and differential expression analysis between IRAS1 and IRAS2 iAstrocytes. The column ‘avg_diff’ contains the magnitude of the difference in the mean Pearson residual values of a given gene between cells in the cluster of interest versus all other cells or between IRAS2 (cluster 2) versus IRAS1 (cluster 1) iAstrocytes. The columns ‘pct.1’ and ‘pct.2’ contain the percent of cells expressing the gene of interest in the cluster of interest (‘pct.1’) or all other cells (‘pct.2’) or in IRAS2 (‘pct.1’) iAstrocytes or IRAS1 (‘pct.2’) iAstrocytes. P values were calculated using the two-sided Student’s t-test and adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 5

Enrichment analysis of IRAS1 and IRAS2 markers. Enrichment results for the MSigDB79 and TRRUST80 libraries were downloaded from Enrichr81. See Chen et al.81 for how the ‘combined score’ is calculated. P values were calculated using the two-sided Fisher’s exact test and adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 6

Differential gene expression analysis results from knockdown of regulators in CROP-seq experiment. The results shown correspond to the interaction term between cytokine treatment and regulator knockdown—that is, they reflect how regulator knockdown affects the change in gene expression induced by IL-1α+TNF+C1q. See the first tab of the Excel file for the number of cells recovered for each knockdown. P values were calculated using the two-sided Wald test and adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 7

Gene modules and enrichment analysis of gene modules from Extended Data Fig. 4d. Enrichment results for the MSigDB79 and TRRUST80 libraries were downloaded from Enrichr81. See Chen et al.81 for how the ‘combined score’ is calculated.

Supplementary Table 8

Cluster markers and DEGs from integrated analysis of iAstrocytes with Barbar et al., Wheeler et al. or Hasel et al. astrocytes. Cluster markers and DEGs are derived using only astrocytes from Barbar et al.18, Wheeler et al.104 or Hasel et al.45. The column ‘avg_diff’ contains the magnitude of the difference in the mean Pearson residual values of a given gene between cells in the cluster of interest versus all other cells or between two clusters of interest (the first cluster minus the second cluster). The columns ‘pct.1’ and ‘pct.2’ contain the percent of cells expressing the gene of interest in the cluster of interest (‘pct.1’) or all other cells (‘pct.2’) or the two clusters of interest. P values were calculated using the two-sided Student’s t-test and adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 9

Differential gene expression analysis between Stat3 cKO SCI versus WT SCI astrocyte transcriptomes from Anderson et al. and enrichment analysis of DEGs. The raw data from Anderson et al.38 were analyzed with BioJupies106 to call DEGs between Stat3 cKO SCI versus WT SCI astrocyte transcriptomes. Enrichment results for the MSigDB79 and TRRUST80 libraries were downloaded from Enrichr81. See Chen et al.81 for how the ‘combined score’ is calculated. P values were calculated using the two-sided Fisher’s exact test and adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 10

Clinical metadata of human neuropathology samples. For the AD cohort, Braak stage is provided for each sample. Additional neuropathology scoring (for example, Thal and CERAD scores) are provided if available.

Supplementary Table 11

Metadata of astrocyte bulk RNA-seq samples used for co-expression network reconstruction in MRA. Samples from publicly available datasets can be cross-referenced with the GEO using their GSM accession numbers.

Supplementary Table 12

sgRNA information for CRISPRi libraries. sgRNA_ID, a unique ID for each sgRNA that includes the gene target, orientation, genomic coordinate and the TSS targeted. Gene_target, the gene targeted by the sgRNA. TSS, the transcriptional start site targeted by the sgRNA. sgRNA_sequence, the protospacer sequence of the sgRNA.

Supplementary Table 13

Information on mice used for immunostaining

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Leng, K., Rose, I.V.L., Kim, H. et al. CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states. Nat Neurosci 25, 1528–1542 (2022). https://doi.org/10.1038/s41593-022-01180-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01180-9

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