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TDP-43 condensates and lipid droplets regulate the reactivity of microglia and regeneration after traumatic brain injury

Nature Neuroscience volume 25, pages 1608–1625 (2022)Cite this article

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Abstract

Decreasing the activation of pathology-activated microglia is crucial to prevent chronic inflammation and tissue scarring. In this study, we used a stab wound injury model in zebrafish and identified an injury-induced microglial state characterized by the accumulation of lipid droplets and TAR DNA-binding protein of 43 kDa (TDP-43)+ condensates. Granulin-mediated clearance of both lipid droplets and TDP-43+ condensates was necessary and sufficient to promote the return of microglia back to the basal state and achieve scarless regeneration. Moreover, in postmortem cortical brain tissues from patients with traumatic brain injury, the extent of microglial activation correlated with the accumulation of lipid droplets and TDP-43+ condensates. Together, our results reveal a mechanism required for restoring microglia to a nonactivated state after injury, which has potential for new therapeutic applications in humans.

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Fig. 1: CNS injury induces pro-regenerative granulin a-expressing microglia.
Fig. 2: Granulins are required to limit prolonged microglial reactivity.
Fig. 3: Granulin deficiency induces prolonged glial cell reactivity and impaired neurogenesis.
Fig. 4: Grn-deficient microglia do not transition back to the homeostatic state.
Fig. 5: Clearance of TDP-43+ condensates resolves prolonged microglial reactivity.
Fig. 6: Injection of phase-separating TDP-43 mimics the phenotype of granulin deficiency.
Fig. 7: A decrease in TDP-43+ condensates restores homeostasis in Grn-deficient microglia.
Fig. 8: CNS injury induces formation of lipid droplets, TDP-43+ condensates and stress granules in the human brain.

Data availability

RNA-seq data from whole telencephali and FACS-isolated microglia can be found under the following GEO accession code: GSE144543 (ref. 45). scRNA-seq data can be found under the following GEO accession code: GSE179134. Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding author. Source data are provided with this paper.

Code availability

scRNA-seq analysis was performed according to codes previously released as open source codes on GitHub at the following links: https://github.com/theislab/single-cell-tutorial/blob/master/latest_notebook/Case-study_Mouse-intestinal-epithelium_1906.ipynb, https://github.com/brianhie/scanorama, https://github.com/theislab/scvelo_notebooks/blob/master/VelocityBasics.ipynb. Notebooks with codes are available from the corresponding author upon request.

References

  1. Dimou, L. & Götz, M. Glial cells as progenitors and stem cells: new roles in the healthy and diseased brain. Physiol. Rev. 94, 709–737 (2014).

    CAS  PubMed  Google Scholar 

  2. O’Shea, T. M., Burda, J. E. & Sofroniew, M. V. Cell biology of spinal cord injury and repair. J. Clin. Invest. 127, 3259–3270 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. Arvidsson, A., Collin, T., Kirik, D., Kokaia, Z. & Lindvall, O. Neuronal replacement from endogenous precursors in the adult brain after stroke. Nat. Med. 8, 963–970 (2002).

    CAS  PubMed  Google Scholar 

  4. Thored, P. et al. Persistent production of neurons from adult brain stem cells during recovery after stroke. Stem Cells 24, 739–747 (2006).

    CAS  PubMed  Google Scholar 

  5. Henriques, D., Moreira, R., Schwamborn, J., Pereira de Almeida, L. & Mendonça, L. S. Successes and hurdles in stem cells application and production for brain transplantation. Front. Neurosci. https://doi.org/10.3389/fnins.2019.01194 (2019).

  6. Grade, S. & Götz, M. Neuronal replacement therapy: previous achievements and challenges ahead. Regen. Med. 2, 1–11 (2017).

    Google Scholar 

  7. Adams, K. L. & Gallo, V. The diversity and disparity of the glial scar. Nat. Neurosci. 21, 9–15 (2018).

    CAS  PubMed  Google Scholar 

  8. Frik, J. et al. Cross-talk between monocyte invasion and astrocyte proliferation regulates scarring in brain injury. EMBO Rep. 19, 1–20 (2018).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Dias, D. O. et al. Reducing pericyte-derived scarring promotes recovery after spinal cord injury. Cell 173, 153–165 e22 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature https://doi.org/10.1038/s41586-020-2777-8 (2020).

  12. Koizumi, S., Ohsawa, K., Inoue, K. & Kohsaka, S. Purinergic receptors in microglia: functional modal shifts of microglia mediated by P2 and P1 receptors. Glia https://doi.org/10.1002/glia.22358 (2013).

  13. Michell-Robinson, M. A. et al. Roles of microglia in brain development, tissue maintenance and repair. Brain https://doi.org/10.1093/brain/awv066 (2015).

  14. Song, W. M. & Colonna, M. The identity and function of microglia in neurodegeneration. Nat. Immunol. https://doi.org/10.1038/s41590-018-0212-1 (2018).

  15. Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity https://doi.org/10.1016/j.immuni.2017.08.008 (2017).

  16. Deczkowska, A. et al. Disease-associated microglia: a universal immune sensor of neurodegeneration. Cell 173, 1073–1081 (2018).

    CAS  PubMed  Google Scholar 

  17. Barbosa, J. S. S. et al. Live imaging of adult neural stem cell behavior in the intact and injured zebrafish brain. Science 348, 789–793 (2015).

    CAS  PubMed  Google Scholar 

  18. Baumgart, E. V., Barbosa, J. S., Bally-cuif, L., Götz, M. & Ninkovic, J. Stab wound injury of the zebrafish telencephalon: a model for comparative analysis of reactive gliosis. Glia 60, 343–357 (2012).

    PubMed  Google Scholar 

  19. Kyritsis, N. et al. Acute inflammation initiates the regenerative response in the adult zebrafish brain. Science 338, 1353–1356 (2012).

    CAS  PubMed  Google Scholar 

  20. Kroehne, V., Freudenreich, D., Hans, S., Kaslin, J. & Brand, M. Regeneration of the adult zebrafish brain from neurogenic radial glia-type progenitors. Development 138, 4831–4841 (2011).

    CAS  PubMed  Google Scholar 

  21. Kishimoto, N., Shimizu, K. & Sawamoto, K. Neuronal regeneration in a zebrafish model of adult brain injury. Dis. Model. Mech. 5, 200–209 (2012).

    CAS  PubMed  Google Scholar 

  22. Di Giaimo, R. et al. The aryl hydrocarbon receptor pathway defines the time frame for restorative neurogenesis. Cell Rep. 25, 3241–3251 e5 (2018).

    PubMed  Google Scholar 

  23. Burda, J. E. & Sofroniew, M. V. Reactive gliosis and the multicellular response to CNS damage and disease. Neuron 81, 229–248 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Marschallinger, J. et al. Lipid-droplet-accumulating microglia represent a dysfunctional and proinflammatory state in the aging brain. Nat. Neurosci. https://doi.org/10.1038/s41593-019-0566-1 (2020).

  25. Mazaheri, F. et al. TREM2 deficiency impairs chemotaxis and microglial responses to neuronal injury. EMBO Rep. 18, 1186–1198 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Mazzolini, J. et al. Gene expression profiling reveals a conserved microglia signature in larval zebrafish. Glia 68, 298–315 (2020).

    PubMed  Google Scholar 

  27. Sanchez-Gonzalez, R. et al. Innate immune pathways promote oligodendrocyte progenitor cell recruitment to the injury site in adult zebrafish brain. Cells 11, 1–36 (2022).

    Google Scholar 

  28. Li, Y. et al. Microglia-organized scar-free spinal cord repair in neonatal mice. Nature 587, 613–618 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    CAS  PubMed  Google Scholar 

  30. Geirsdottir, L. et al. Cross-species single-cell analysis reveals divergence of the primate microglia program. Cell 179, 1609–1622.e16 (2019).

    CAS  PubMed  Google Scholar 

  31. Silva, N. J., Dorman, L. C., Vainchtein, I. D., Horneck, N. C. & Molofsky, A. V. In situ and transcriptomic identification of microglia in synapse-rich regions of the developing zebrafish brain. Nat. Commun. 12, 1–12 (2021).

    Google Scholar 

  32. Lange, C. et al. Single cell sequencing of radial glia progeny reveals the diversity of newborn neurons in the adult zebrafish brain. Development 147, 1–15 (2020).

    Google Scholar 

  33. Marisca, R. et al. Functionally distinct subgroups of oligodendrocyte precursor cells integrate neural activity and execute myelin formation. Nat. Neurosci. 23, 363–374 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0591-3 (2020).

  35. La Manno, G. et al. RNA velocity of single cells. Nature https://doi.org/10.1038/s41586-018-0414-6 (2018).

  36. Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 1–9 (2019).

    Google Scholar 

  37. Lauro, C. & Limatola, C. Metabolic reprograming of microglia in the regulation of the innate inflammatory response. Front. Immunol.https://doi.org/10.3389/fimmu.2020.00493 (2020).

  38. Dou, Y. et al. Microglial migration mediated by ATP-induced ATP release from lysosomes. Cell Res. https://doi.org/10.1038/cr.2012.10 (2012).

  39. He, Z., Ong, C. H. P., Halper, J. & Bateman, A. Progranulin is a mediator of the wound response. Nat. Med. 9, 225–229 (2003).

    CAS  PubMed  Google Scholar 

  40. Lui, H. et al. Progranulin deficiency promotes circuit-specific synaptic pruning by microglia via complement activation. Cell 165, 921–935 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Tanaka, Y., Matsuwaki, T., Yamanouchi, K. & Nishihara, M. Exacerbated inflammatory responses related to activated microglia after traumatic brain injury in progranulin-deficient mice. Neuroscience 231, 49–60 (2013).

    CAS  PubMed  Google Scholar 

  42. Zhang, J. et al. Neurotoxic microglia promote TDP-43 proteinopathy in progranulin deficiency. Nature 588, 459–465 (2020).

  43. Bosch, M. et al. Mammalian lipid droplets are innate immune hubs integrating cell metabolism and host defense. Science 370, 1–13 (2020).

  44. Solchenberger, B., Russell, C., Kremmer, E., Haass, C. & Schmid, B. Granulin knock out zebrafish lack frontotemporal lobar degeneration and neuronal ceroid lipofuscinosis pathology. PLoS ONE 10, e0118956 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Zambusi, A., Pelin Burhan, Ö., Di Giaimo, R., Schmid, B. & Ninkovic, J. Granulins regulate aging kinetics in the adult zebrafish telencephalon. Cells https://doi.org/10.3390/cells9020350 (2020).

  46. Simon, C., Lickert, H., Gotz, M. & Dimou, L. Sox10-iCreERT2: a mouse line to inducibly trace the neural crest and oligodendrocyte lineage. Genesis 50, 506–515 (2012).

    CAS  PubMed  Google Scholar 

  47. Buffo, A. et al. Expression pattern of the transcription factor Olig2 in response to brain injuries: implications for neuronal repair. Proc. Natl Acad. Sci. USA 102, 18183–18188 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Park, H. C. et al. Analysis of upstream elements in the huc promoter leads to the establishment of transgenic zebrafish with fluorescent neurons. Dev. Biol. 227, 279–293 (2000).

    CAS  PubMed  Google Scholar 

  49. Yu, H. et al. HSP70 chaperones RNA-free TDP-43 into anisotropic intranuclear liquid spherical shells. Science 371, 1–16 (2021).

  50. Gu, J. et al. Hsp70 chaperones TDP-43 in dynamic, liquid-like phase and prevents it from amyloid aggregation. Cell Res. 31, 1024–1027 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Thammisetty, S. S. et al. Age-related deregulation of TDP-43 after stroke enhances NF-κB-mediated inflammation and neuronal damage. J. Neuroinflammation 15, 1–15 (2018).

  52. Anderson, E. N. et al. Traumatic injury induces stress granule formation and enhances motor dysfunctions in ALS/FTD models. Hum. Mol. Genet. 27, 1366–1381 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Wiesner, D. et al. Reversible induction of TDP-43 granules in cortical neurons after traumatic injury. Exp. Neurol. 299, 15–25 (2018).

    CAS  PubMed  Google Scholar 

  54. Alberti, S. & Dormann, D. Liquid–liquid phase separation in disease. Annu. Rev. Genet. 53, 171–194 (2019).

    CAS  PubMed  Google Scholar 

  55. Wolozin, B. & Ivanov, P. Stress granules and neurodegeneration. Nat. Rev. Neurosci. 20, 649–666 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Zbinden, A., Pérez-Berlanga, M., De Rossi, P. & Polymenidou, M. Phase separation and neurodegenerative diseases: a disturbance in the force. Dev. Cell 55, 45–68 (2020).

    CAS  PubMed  Google Scholar 

  57. Gasset-Rosa, F. et al. Cytoplasmic TDP-43 de-mixing independent of stress granules drives inhibition of nuclear import, loss of nuclear TDP-43, and cell death. Neuron 102, 339–357.e7 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Beel, S. et al. Progranulin reduces insoluble TDP-43 levels, slows down axonal degeneration and prolongs survival in mutant TDP-43 mice. Mol. Neurodegener. 13, 1–9 (2018).

  59. Salazar, D. A. et al. The progranulin cleavage products, granulins, exacerbate TDP-43 toxicity and increase TDP-43 levels. J. Neurosci. 35, 9315–9328 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Bhopatkar, A. A., Uversky, V. N. & Rangachari, V. Granulins modulate liquid–liquid phase separation and aggregation of the prion-like C-terminal domain of the neurodegeneration-associated protein TDP-43. J. Biol. Chem. 295, 2506–2519 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Bhopatkar, A. A., Dhakal, S., Abernathy, H. G., Morgan, S. E. & Rangachari, V. Charge and redox states modulate granulin–TDP-43 coacervation toward phase separation or aggregation. Biophys. J. 121, 2107–2126 (2022).

  62. Hasegawa, M. et al. Phosphorylated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Ann. Neurol. 64, 60–70 (2008).

  63. Fox, J. D. & Waugh, D. S. Maltose-binding protein as a solubility enhancer. Methods Mol. Biol. 205, 99–117 (2003).

    CAS  PubMed  Google Scholar 

  64. Silva, L. A. Gda et al. Disease-linked TDP-43 hyperphosphorylation suppresses TDP-43 condensation and aggregation. EMBO J. 41, e108443 (2022).

    Google Scholar 

  65. Wheeler, R. J. et al. Small molecules for modulating protein driven liquid-liquid phase separation in treating neurodegenerative disease. Preprint at bioRxiv https://doi.org/10.1101/721001 (2019).

  66. Spiller, K. J. et al. Microglia-mediated recovery from ALS-relevant motor neuron degeneration in a mouse model of TDP-43 proteinopathy. Nat. Neurosci. 21, 329–340 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Hanisch, U.-K. & Kettenmann, H. Microglia: active sensor and versatile effector cells in the normal and pathologic brain. Nat. Neurosci. 10, 1387–1394 (2007).

    CAS  PubMed  Google Scholar 

  68. Davalos, D. et al. ATP mediates rapid microglial response to local brain injury in vivo. Nat. Neurosci. https://doi.org/10.1038/nn1472 (2005).

  69. Nimmerjahn, A., Kirchhoff, F. & Helmchen, F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Neuroforum https://doi.org/10.1515/nf-2005-0304 (2005).

  70. Paolicelli, R. C. et al. Synaptic pruning by microglia is necessary for normal brain development. Science 333, 1456–1458 (2011).

    CAS  PubMed  Google Scholar 

  71. Van houcke, J. et al. Aging impairs the essential contributions of non-glial progenitors to neurorepair in the dorsal telencephalon of the killifish Nothobranchius furzeri. Aging Cell 20, e13464 (2021).

    PubMed  Google Scholar 

  72. Goritz, C. et al. A pericyte origin of spinal cord scar tissue. Science 333, 238–242 (2011).

    PubMed  Google Scholar 

  73. Altmann, C. et al. Progranulin promotes peripheral nerve regeneration and reinnervation: role of notch signaling. Mol. Neurodegener. 11, 69 (2016).

    PubMed  PubMed Central  Google Scholar 

  74. von Streitberg, A. et al. NG2-glia transiently overcome their homeostatic network and contribute to wound closure after brain injury. Front. Cell Dev. Biol. 9, 1–15 (2021).

  75. Marz, M., Schmidt, R., Rastegar, S. & Strahle, U. Regenerative response following stab injury in the adult zebrafish telencephalon. Dev. Dyn. 240, 2221–2231 (2011).

    PubMed  Google Scholar 

  76. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, 1–12 (2017).

    Google Scholar 

  77. Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176, 419–434 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Murakami, T. et al. ALS/FTD mutation-induced phase transition of FUS liquid droplets and reversible hydrogels into irreversible hydrogels impairs RNP granule function. Neuron 88, 678–690 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    CAS  PubMed  Google Scholar 

  81. Conicella, A. E., Zerze, G. H., Mittal, J. & Fawzi, N. L. ALS mutations disrupt phase separation mediated by α-helical structure in the TDP-43 low-complexity C-terminal domain. Structure 24, 1537–1549 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. McGurk, L. et al. Poly(ADP-ribose) prevents pathological phase separation of TDP-43 by promoting liquid demixing and stress granule localization. Mol. Cell 71, 703–717.e9 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Rhinn, H. & Abeliovich, A. Differential aging analysis in human cerebral cortex identifies variants in TMEM106B and GRN that regulate aging phenotypes. Cell Syst. 4, 404–415.e5 (2017).

    CAS  PubMed  Google Scholar 

  84. Zhu, J. et al. Conversion of proepithelin to epithelins: roles of SLPI and elastase in host defense and wound repair. Cell 111, 867–878 (2002).

    CAS  PubMed  Google Scholar 

  85. Jian, J., Konopka, J. & Liu, C. Insights into the role of progranulin in immunity, infection, and inflammation. J. Leukoc. Biol. https://doi.org/10.1189/jlb.0812429 (2013).

  86. Preuss, M. L. et al. A role for the RabA4b effector protein PI-4Kβ1 in polarized expansion of root hair cells in Arabidopsis thaliana. J. Cell Biol. 172, 991 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Markert, S. M. et al. 3D subcellular localization with superresolution array tomography on ultrathin sections of various species. Methods Cell. Biol. 140, 21–47 (2017).

    PubMed  Google Scholar 

  88. Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

    CAS  PubMed  Google Scholar 

  89. Luecken, M. D. & Theis, F. J. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Google Scholar 

  90. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 3, 861 (2018).

    Google Scholar 

  91. Wang, A. et al. A single N-terminal phosphomimic disrupts TDP-43 polymerization, phase separation, and RNA splicing. EMBO J. 37, 1–18 (2018).

    Google Scholar 

  92. Hofweber, M. et al. Phase separation of FUS is suppressed by its nuclear import receptor and arginine methylation. Cell 173, 706–719.e13 (2018).

    CAS  PubMed  Google Scholar 

  93. Schieweck, R. et al. Pumilio2 and Staufen2 selectively balance the synaptic proteome. Cell Rep. 35, 1–16 (2021).

    Google Scholar 

  94. Fischer, J. et al. Prospective isolation of adult neural stem cells from the mouse subependymal zone. Nat. Protoc. 6, 1981–1989 (2011).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank M. Götz and S. Stricker (Ludwig-Maximilians University, Munich) for their support toward this study, the experimental suggestions and critical reading of the manuscript. D.D. thanks E. Lemke for generously sharing laboratory space and infrastructure. Lastly, we thank all the members of the Neurogenesis and Regeneration group for experimental inputs, discussions and critical reading of the manuscript. We acknowledge the support of the following core facilities: the Bioimaging Core Facility at the BioMedical Center of LMU Munich, the Sequencing Facility at the Helmholtz Zentrum München and the Light Microscopy Core Facility of the Biocenter, JGU Mainz. This work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) by SFB 870 (J.N.); grant no. TRR274/1 (ID 408885537) (J.N.); SPP 1738 ‘Emerging roles of noncoding RNAs in nervous system development, plasticity & disease’ (J.N.); and SPP 1757 ‘Glial heterogeneity’ (J.N.); the Fritz Thyssen Foundation (J.N.); SPP 2191 ‘Molecular mechanisms of functional phase separation’ (ID 402723784, project no. 419139133) (J.N., D.D.); SPP 1935 ‘Deciphering the mRNP code: RNA-bound determinants of post-transcriptional gene regulation’ (J.N., M.K.); the Emmy Noether Programme (ID 246137224) (D.D.); the Heisenberg Programme (ID 442698351) (D.D.); the Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (grant no. EXC 2145/1010 SyNergy, ID 390857198) (J.N., D.D. and S.L.) and Ampro Helmholtz Alliance (J.N., D.D.); ReALity (Forschungsinitiative des Landes Rheinland-Pfalz) (D.D.); the Gutenberg Forschungskolleg (GFK) of JGU Mainz (D.D.); the Emmy Noether Programme (S.L.); and the Graduate School for Systemic Neurosciences GSN-LMU (A.Z., K.T.N., C.K., S.A. and Z.I.G.). The scanning electron microscope JEOL JSM-7500F and structured illumination microscope Zeiss Elyra S.1 SIM, both used for correlative light and electron microscopy imaging, were funded by the DFG, grant nos. 218894895 (INST 93/761-1 FUGG) (C.S.) and 261184502 (INST 93/823-1 FUGG) (C.S.), respectively.

Author information

Author notes

  1. These authors contributed equally: Alessandro Zambusi, Klara Tereza Novoselc, Dorothee Dormann, Jovica Ninkovic.

Authors and Affiliations

  1. Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany

    Alessandro Zambusi, Klara Tereza Novoselc, Saskia Hutten, Sofia Kalpazidou, Christina Koupourtidou, Rico Schieweck, Sven Aschenbroich, Lara Silva, Michael Kiebler, Dorothee Dormann & Jovica Ninkovic

  2. Graduate School of Systemic Neurosciences, LMU, Munich, Germany

    Alessandro Zambusi, Klara Tereza Novoselc, Christina Koupourtidou & Sven Aschenbroich

  3. Institute of Stem Cell Research, Helmholtz Zentrum Munich, Munich, Germany

    Alessandro Zambusi, Klara Tereza Novoselc, Swetlana Sirko & Jovica Ninkovic

  4. Faculty of Biology, Institute of Molecular Physiology, Johannes Gutenberg-Universität (JGU), Mainz, Germany

    Saskia Hutten, Lara Silva & Dorothee Dormann

  5. Comprehensive Pneumology Center (CPC), Institute of Lung Health and Immunity (LHI), Helmholtz Zentrum Munich, Munich, Germany

    Ayse Seda Yazgili & Silke Meiners

  6. Member of the German Center for Lung Research (DZL), Airway Research Center North (ARCN), Grosshansdorf, Germany

    Ayse Seda Yazgili & Silke Meiners

  7. University Hospital, LMU, Munich, Germany

    Ayse Seda Yazgili

  8. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Frauke van Bebber & Bettina Schmid

  9. Imaging Core Facility, Biocenter, University of Würzburg, Würzburg, Germany

    Gabriel Möller, Clara Tritscher & Christian Stigloher

  10. Institute of Pathology, Faculty of Medicine, TUM, Munich, Germany

    Claire Delbridge & Jürgen Schlegel

  11. Biomedical Center Munich (BMC), Institute of Physiological Genomics, LMU, Munich, Germany

    Swetlana Sirko

  12. Institute of Clinical Neuroimmunology, Klinikum der Universität München, LMU, Munich, Germany

    Zeynep Irem Günes & Sabine Liebscher

  13. Munich Cluster for Systems Neurology SyNergy, LMU, Munich, Germany

    Sabine Liebscher, Dorothee Dormann & Jovica Ninkovic

  14. Institute of Computational Biology, Helmholtz Zentrum Munich, Munich, Germany

    Hananeh Aliee & Fabian Theis

  15. Research Center Borstel/Leibniz Lung Center, Borstel, Germany

    Silke Meiners

  16. Institute of Experimental Medicine, Christian-Albrechts University Kiel, Kiel, Germany

    Silke Meiners

  17. Institute of Molecular Biology (IMB), Mainz, Germany

    Dorothee Dormann

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  1. Alessandro Zambusi
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Contributions

A.Z., K.T.N. and J.N. conceived the project and experiments. A.Z., K.T.N., S.H., S.K., R.S., S.A., L.S., A.S.Y., F.v.B., G.M., C.T., C.S., S.S., Z.I.G. and C.D. performed the experiments and analyzed the data. A.Z., K.T.N., C.K., H.A. and F.T. performed the bioinformatic analyses. A.Z., K.T.N. and J.N. wrote the manuscript with input from all authors. J.N., D.D., M.K., B.S., J.S., S.M. and S.L. supervised research and acquired funding.

Corresponding author

Correspondence to Jovica Ninkovic.

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Competing interests

The authors declare the following competing interests: F.T. consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. All other authors declare no competing interests.

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Peer review information

Nature Neuroscience thanks Lutgarde Arckens, Benjamin Wolozin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Identification of microglia in scRNA-seq dataset.

a, Representative images of Mpeg1:mCherry (magenta) and 4C4 (cyan) immunoreactivity in injured (3 and 7 dpi) Wt (Tg(mpeg1:mCherry;grna+/+;grnb+/+)) telencephali. Scale bars, 20 µm. b, Dot plot depicting evolutionary-conserved core microglial marker genes (Geirsdottir et al., Cell, 2019) in single cells isolated from intact and injured (3 and 7 dpi) Wt (grna+/+;grnb+/+) telencephali. Dot color, mean expression; dot size, fraction of cells. c, UMAP plots depicting gene set enrichment scores composed of evolutionary-conserved core microglial marker genes (from b), distinguishing microglial and macrophage cell populations in single cells isolated from intact and injured (3 and 7 dpi) Wt telencephali. Color bars, normalized expression level; each point represents a single cell.

Extended Data Fig. 2 Identification of main cell populations in scRNA-seq dataset.

a, Dot plot depicting the expression of characteristic cell-type marker genes identifying oligodendroglia, radial glia and neurons in single cells isolated from intact and injured (3 and 7 dpi) Wt (grna+/+;grnb+/+) telencephali. Dot color, mean expression; dot size, fraction of cells. b, UMAP plots depicting gene set enrichment scores composed of characteristic cell-type marker genes (from a) identifying oligodendroglia, radial glia and neurons in single cells isolated from intact and injured (3 and 7 dpi) Wt telencephali. Color bars, normalized expression level; each point represents a single cell.

Extended Data Fig. 3 Confirmation of microglial identity in scRNA-seq dataset.

a, UMAP plot depicting color-coded cellular clusters identified through single nuclei RNA-sequencing (snRNA-seq) of Wt cells, isolated from intact and injured (3 dpi) Wt (grna+/+;grnb+/+) telencephali. Cells are colored according to their cell type identity; each point represents a single nucleus. b, UMAP plots depicting gene set enrichment scores composed of characteristic cell-type marker genes identifying microglia, oligodendroglia, radial glia and neurons in single nuclei isolated from intact and injured (3 dpi) Wt telencephali. Color bars, normalized expression level; each point represents a single nucleus. Due to the low number of cells belonging to cluster 19 in our snRNA-seq dataset, it was not possible to clearly separate microglia from macrophages. c, UMAP plots depicting gene set enrichment scores from scRNA-seq (Extended Data Fig. 1b and Extended Data Fig. 2a) identifying microglia, oligodendroglia and radial glia isolated from intact and injured (3 and 7 dpi) Wt telencephali. Color bars, normalized expression level; each point represents a single cell. d, UMAP plots depicting gene set enrichment scores from snRNA-seq identifying microglia, oligodendroglia and radial glia populations in single cells, plotted in the scRNA-seq dataset. Color bars, normalized expression level; each point represents a single cell. e, Isolation procedure of FACS-purified Mpeg1+ cells for bulk RNA-seq analysis. f, Dot plots of evolutionary-conserved core microglial marker genes (from Extended Data Fig. 1b), expressed in FACS-purified Mpeg1:mCherry+ microglia vs whole telencephali. Data are shown as mean ± SEM. n = 3 and n = 5 for FACS-purified Mpeg1:mCherry+ microglia and whole telencephali, respectively. Each data point represents a distinct biological replicate.

Source data

Extended Data Fig. 4 Microglial characterization in injured brains.

a, Representative images of Mpeg1:mCherry (magenta) and 4C4 (cyan) immunoreactivity in injured (3 and 7 dpi) Grn-deficient Tg(mpeg1:mCherry;grna−/−;grnb−/−) telencephali. Scale bars, 20 µm. b, Representative images of Wt (grna+/+;grnb+/+) and Grn-deficient (grna-/-;grnb-/-) 4C4+ microglia (yellow), DAPI+ nuclei (cyan), scanning electron microscopy (SEM), final unbiased correlation (CLEM) and 3D reconstruction of single microglia at the injury site. Boxed areas are magnified. Scale bars, 10 μm.

Extended Data Fig. 5 Analysis of lipid droplets and lipid metabolism.

a, Representative images of 4C4 (red) and Plin3 (cyan) immunoreactivity with orthogonal projections at injury sites in Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) brains, displaying colocalization of Plin3+ lipid droplets and 4C4+ microglia. Scale bars, 20 µm. b, Dot plot depicting the proportion of Plin3+4C4+ double-positive lipid droplets among total Plin3+ lipid droplets at injury sites in Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) brains. Data are shown as mean ± SEM. n = 4 per group. Each point represents one animal. Significance was calculated with ordinary two-way ANOVA, with post-hoc Tukey’s test for multiple comparisons. c, Representative images of 4C4 (red) and BODIPY (cyan) reactivity at injury sites in Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) brains at 3 and 7 dpi. Scale bars, 20 µm. d, Heatmaps depicting phosphatidylcholine (PC) and phosphatidylethanolamine (PE) content in intact and injured (7 dpi) Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) telencephali. n = 5 per group (averaged). Scale bar, z-score.

Source data

Extended Data Fig. 6 Analysis of glial cell reactivity after dexamethasone treatment.

a, Representative images of Olig2:DsRed (magenta) and Sox10 (cyan) immunoreactivity in injured (3 and 7 dpi) Wt (Tg(olig2:DsRed;grna+/+;grnb+/+)) and Grn-deficient (Tg(olig2:DsRed;grna−/−;grnb−/−) telencephali. Scale bars, 20 µm. b, Experimental paradigm of MeOH and dexamethasone manipulations in Grn-deficient telencephali at 3 dpi. c, Representative images of 4C4 (red) and Sox10 (cyan) immunoreactivity in MeOH- and dexamethasone-treated Grn-deficient (grna−/−;grnb−/−) brains at 3 dpi. Scale bars, 100 µm or 20 µm (magnifications). d, Dot plot depicting the number of Sox10+ oligodendroglia at injury sites in MeOH- and dexamethasone-treated Grn-deficient (grna−/−;grnb−/−) brains at 3 dpi. Data are shown as mean ± SEM. n = 4 per group. Each point represents one animal. Significance was calculated using Student’s t-test.

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Extended Data Fig. 7 Characterization of TDP-43 behavior in intact and injured brains.

a, Representative images of HuC/D (magenta) and TDP-43 (cyan) immunoreactivity in intact Wt (grna+/+;grnb+/+) telencephalon. Scale bars, 20 µm. b, Representative images of DAPI (magenta) and TDP-43 (cyan) immunoreactivity in injured Wt (grna+/+;grnb+/+) telencephalon at 3 dpi. Scale bars, 20 µm. Red arrowheads indicate examples of extranuclear TDP-43+ condensates; white arrowheads indicate examples of nuclear TDP-43+ signal. c, Representative images of 4C4 (magenta) and TDP-43 (cyan) immunoreactivity with orthogonal projections at injury sites in Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) brains at 3 dpi, displaying colocalization of TDP-43+ condensates with 4C4+ microglia. Scale bars, 20 µm. d, Representative images of phosphoTDP-43 (cyan), Plin3 (green) and Lamp1 (magenta) immunoreactivity in injured (3 dpi) Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) telencephali. Scale bars, 20 µm. e, Experimental paradigm of intraparenchymal recombinant PGRN injection in Grn-deficient brain. f, Representative images of 4C4 (magenta) and TDP-43 (cyan) immunoreactivity at injury sites in vehicle- and PGRN-injected Grn-deficient (grna−/−;grnb−/−) brains. Scale bars, 20 µm. g,h, Dot plots depicting the numbers of TDP-43+ condensates (g) and TDP-43+ condensates in 4C4+ microglia (h) at injury sites in Wt (grna+/+;grnb+/+), Grn-deficient (grna−/−;grnb−/−) and PGRN-injected Grn-deficient (grna−/−;grnb−/−) brains at 7 dpi. Data are shown as mean ± SEM. n = 4 per group. Each point represents one animal. Significance was calculated with ordinary one-way ANOVA, with post-hoc Tukey’s test for multiple comparisons.

Source data

Extended Data Fig. 8 Characterization of injected phase-separated TDP-43.

a, Experimental paradigm of intraparenchymal injections in vehicle, soluble TDP-43, phase-separated TDP-43, FUS and A488−conjugated TDP-43 in Wt (grna+/+;grnb+/+) brains. b, Schematic representation of soluble TDP-43 and phase-separated TDP-43. c, Representative bright-field images of phase-separated TDP-43 (cleaved) and soluble TDP-43 (uncleaved). Scale bars, 50 µm (upper images) or 25 µm (lower images). d, Representative images of 4C4 (magenta), TDP-43 (cyan) and A488−conjugated TDP-43 (red) immunoreactivity with orthogonal projections in Wt (grna+/+;grnb+/+) injured (7 dpi) telencephalon injected with phase-separated A488-conjugated TDP-43. Scale bars, 20 µm. Yellow arrowheads indicate examples of extranuclear TDP-43+A488+ condensates; white arrowheads indicate examples of extranuclear TDP-43+A488- condensates. e, Representative images of HuC/D (green) immunoreactivity and TUNEL (magenta) signal in injured (3 dpi) Wt (grna+/+;grnb+/+) and Grn-deficient (grna−/−;grnb−/−) telencephali. Scale bars, 20 µm. f, Representative images of 4C4 (magenta), TDP-43 (cyan) and Plin3 (green) immunoreactivity in Grn-deficient (grna−/−;grnb−/−) injured (7 dpi) telencephalon injected with phase-separated TDP-43. Scale bars, 20 µm. g,h, Dot plots depicting the total numbers of Plin3+ lipid droplets (g) and TDP-43+ condensates in 4C4+ microglia (h) at injury sites in TDP-43-injected Grn-deficient (grna−/−;grnb−/−) brains. Data are shown as mean ± SEM. n = 4 per group. Each point represents one animal. Significance was calculated with Brown-Forsythe and Welch ANOVA tests, with post-hoc Dunnett’s test for multiple comparisons (g) and with ordinary one-way ANOVA, with post-hoc Tukey’s test for multiple comparisons (h). i, Representative images of 4C4 (magenta) and Plin3 (green) immunoreactivity in Wt (grna+/+;grnb+/+) injured (7 dpi) telencephali injected with soluble FUS or phase-separated FUS. Scale bars, 20 µm. j,k, Dot plots depicting the total numbers of Plin3+ lipid droplets (j) and Plin3+ lipid droplets in 4C4+ microglia (k) at injury sites in vehicle- and FUS-injected Wt (grna+/+;grnb+/+) brains. Data are shown as mean ± SEM. n = 4 per group. Each point represents one animal. Significance was calculated with ordinary one-way ANOVA, with post-hoc Tukey’s test for multiple comparisons.

Source data

Extended Data Fig. 9 Morphological analysis of microglia in intact and injured brains.

a, Representative images of 4C4+ microglia in Wt (grna+/+;grnb+/+) or Grn-deficient (grna−/−;grnb−/−) intact, injured (7 dpi), and phase-separated TDP-43- or FUS-injected (grna+/+;grnb+/+) brains. Scale bars, 20 µm. b–d, Violin plots depicting the number of main processes (b), area of somata (c) and average process length (d) of 4C4+ microglia in telencephalic parenchyma of Wt (grna+/+;grnb+/+) or Grn-deficient (grna−/−;grnb−/−) intact, injured and TDP-43- or FUS-injected (grna+/+;grnb+/+) brains. Group sizes are indicated in the violin plots. Each point represents one cell. Significance was calculated with Kruskal-Wallis test, with post-hoc Dunn’s test for multiple comparisons.

Source data

Extended Data Fig. 10 Analysis of protein biosynthesis in vitro and in vivo after lipoamide treatment.

a, Scheme of microglial cell line preparation for polysome profiling. b, Polysome profiles of differently treated microglial cell lines. c, Dot plot depicting the polysome/monosome ratio in microglial cell lines, treated with different concentrations of DMSO or lipoamide, or harringtonine as a control. Data are shown as mean ± SEM. n = 4 per group. Each point represents one replicate. Significance was calculated with Brown-Forsythe and Welch ANOVA tests, with post-hoc Dunnett’s test for multiple comparisons. d, Scheme of cerebroventricular injections of OPP and CHX in DMSO-treated or lipoamide-treated brains, followed by FACS analysis. Abbreviations: OPP = O-propargyl-puromycin, CHX = cycloheximide. e, FACS plots depicting average intensity of the signal in OPP+ cells in different conditions in vivo, indicating actively translating cells in the adult zebrafish telencephalon. f, Dot plot of OPP+ intensity in DMSO- and lipoamide-treated brains. Data are shown as mean ± SEM. n = 5 per group. Each point represents one animal. Significance was calculated with unpaired Student’s t-test.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Identity, cell numbers and proportions of scRNA-seq clusters, according to genotype, conditions and timepoints. Related to Figs. 1, 4, 6 and 7; Extended Data Figs. 1 and 2.

Supplementary Table 2

Terms and pathways from enriched genes (P < 0.05 and fold change > 2) in Wt microglia at 3 dpi. Related to Fig. 1.

Supplementary Table 3

Terms and pathways from upregulated and downregulated genes (P < 0.05 and fold change < −2 or >2) identified comparing each Wt microglial cluster with all the others. Related to Fig. 1.

Supplementary Table 4

Terms and pathways from upregulated genes (P < 0.05 and fold change > 2) in Grn-deficient versus Wt microglia at 7 dpi and in MG0 versus MG2 microglial clusters. Related to Fig. 4.

Supplementary Table 5

Terms and pathways from upregulated genes (P < 0.05 and fold change > 2) in Wt microglia injected with phase-separated TDP-43 versus Wt microglia at 7 dpi. Related to Fig. 6.

Supplementary Table 6

Terms and pathways from downregulated genes (P < 0.05 and fold change < −2) in MG9 versus remaining microglial clusters and in lipoamide-treated Grn-deficient versus Grn-deficient microglia at 7 dpi. Related to Fig. 7.

Supplementary Video 1

Microglial and oligodendroglial cell reactivity in Wt telencephalon at 7 dpi. Related to Fig. 3.

Supplementary Video 2

Microglial and oligodendroglial cell reactivity in Grn-deficient telencephalon at 7 dpi. Related to Fig. 3.

Supplementary Video 3

Microglial and oligodendroglial cell reactivity in Grn-deficient telencephalon at 31 dpi. Related to Fig. 3.

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Zambusi, A., Novoselc, K.T., Hutten, S. et al. TDP-43 condensates and lipid droplets regulate the reactivity of microglia and regeneration after traumatic brain injury. Nat Neurosci 25, 1608–1625 (2022). https://doi.org/10.1038/s41593-022-01199-y

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