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.

Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer’s disease

Abstract

Aberrant inflammation in the CNS has been implicated as a major player in the pathogenesis of human neurodegenerative disease. We developed a new approach to derive microglia from human pluripotent stem cells (hPSCs) and built a defined hPSC-derived tri-culture system containing pure populations of hPSC-derived microglia, astrocytes, and neurons to dissect cellular cross-talk along the neuroinflammatory axis in vitro. We used the tri-culture system to model neuroinflammation in Alzheimer’s disease with hPSCs harboring the APPSWE+/+ mutation and their isogenic control. We found that complement C3, a protein that is increased under inflammatory conditions and implicated in synaptic loss, is potentiated in tri-culture and further enhanced in APPSWE+/+ tri-cultures due to microglia initiating reciprocal signaling with astrocytes to produce excess C3. Our study defines the major cellular players contributing to increased C3 in Alzheimer’s disease and presents a broadly applicable platform to study neuroinflammation in human disease.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Patterning towards primitive hematopoiesis occurs during a narrow developmental window.
Fig. 2: Single-cell RNA sequencing validates the stages of microglial development within the in vitro differentiation.
Fig. 3: Two different methods to generate hPSC-derived microglia from the PMAC stage that are molecularly and functionally similar to primary microglia.
Fig. 4: hPSC-derived microglia cultured with hPSC-derived astrocytes and neurons builds a functional human tri-culture system that allows modeling the neuroinflammatory axis in vivo.
Fig. 5: Tri-culture model of Alzheimer’s disease shows increased C3 due to reciprocal signaling from microglia to astrocytes.

Data availability

All RNA-seq data, bulk and scRNA-seq, have been deposited to the Gene Expression Omnibus under GSE139549 and GSE139552. Publicly available RNA-seq datasets used include GSE85839 (ref. 10), GSE97744 (ref. 12), GSE89189 (ref. 11), GSE102335 (ref. 35) and GSE99074 (ref. 37). Other data and reagents in this study are available from the corresponding author upon reasonable request.

References

  1. 1.

    Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    CAS  PubMed  Google Scholar 

  2. 2.

    Lecours, C. et al. Microglial implication in Parkinson’s disease: loss of beneficial physiological roles or gain of inflammatory functions? Front. Cell. Neurosci. https://doi.org/10.3389/fncel.2018.00282 (2018).

  3. 3.

    Geloso, M. C. et al. The dual role of microglia in ALS: mechanisms and therapeutic approaches. Front. Aging Neurosci. https://doi.org/10.3389/fnagi.2017.00242 (2017).

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Smith, A. M. & Dragunow, M. The human side of microglia. Trends Neurosci. 37, 125–135 (2014).

    CAS  PubMed  Google Scholar 

  7. 7.

    Chambers, S. M. et al. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat. Biotechnol. 27, 275–280 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Qi, Y. et al. Combined small-molecule inhibition accelerates the derivation of functional cortical neurons from human pluripotent stem cells. Nat. Biotechnol. 35, 154–163 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Tchieu, J. et al. NFIA is a gliogenic switch enabling rapid derivation of functional human astrocytes from pluripotent stem cells. Nat. Biotechnol. 37, 267–275 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Muffat, J. et al. Efficient derivation of microglia-like cells from human pluripotent stem cells. Nat. Med. 22, 1358–1367 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Abud, E. M. et al. iPSC-derived human microglia-like cells to study neurological diseases. Neuron 94, 278–293 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Douvaras, P. et al. Directed differentiation of human pluripotent stem cells to microglia. Stem Cell Rep. 8, 1516–1524 (2017).

    CAS  Google Scholar 

  13. 13.

    Haenseler, W. et al. A highly efficient human pluripotent stem cell microglia model displays a neuronal co-culture-specific expression profile and inflammatory response. Stem Cell Rep. 8, 1727–1742 (2017).

    CAS  Google Scholar 

  14. 14.

    Takata, K. et al. Induced pluripotent stem cell-derived primitive macrophages provide a platform for modeling tissue-resident macrophage differentiation and function. Immunity 47, 183–198 (2017).

    CAS  PubMed  Google Scholar 

  15. 15.

    Pandya, H. et al. Differentiation of human and murine induced pluripotent stem cells to microglia-like cells. Nat. Neurosci. 20, 753–759 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Brownjohn, P. W. et al. Functional studies of missense TREM2 mutations in human stem cell-derived microglia. Stem Cell Rep. 10, 1294–1307 (2018).

    CAS  Google Scholar 

  17. 17.

    Sturgeon, C. M., Ditadi, A., Awong, G., Kennedy, M. & Keller, G. Wnt signaling controls the specification of definitive and primitive hematopoiesis from human pluripotent stem cells. Nat. Biotechnol. 32, 554–561 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ginhoux, F. et al. Fate mapping analysis reveals that adult microglia derive from primitive macrophages. Science 330, 841–845 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ditadi, A. & Sturgeon, C. M. Directed differentiation of definitive hemogenic endothelium and hematopoietic progenitors from human pluripotent stem cells. Methods https://doi.org/10.1016/j.ymeth.2015.10.001 (2015).

  20. 20.

    Palis, J. Hematopoietic stem cell-independent hematopoiesis: emergence of erythroid, megakaryocyte and myeloid potential in the mammalian embryo. FEBS Lett. 590, 3965–3974 (2016).

    CAS  PubMed  Google Scholar 

  21. 21.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).

    CAS  PubMed  Google Scholar 

  24. 24.

    ES, N. et al. Differentiation of human embryonic stem cells to HOXA+ hemogenic vasculature that resembles the aorta-gonad-mesonephros. Nat. Biotechnol. 34, https://doi.org/10.1038/nbt.3702 (2016).

  25. 25.

    Mass, E. et al. Specification of tissue-resident macrophages during organogenesis. Science https://doi.org/10.1126/science.aaf4238 (2016).

  26. 26.

    Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbours. Nat. Biotechnol. 36, 421–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wang, Y. et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat. Immunol. 13, 753–760 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Bohlen, C. J. et al. Diverse requirements for microglial survival, specification and function revealed by defined-medium cultures. Neuron 94, 759–773 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Greter, M., Lelios, I. & Croxford, A. L. Microglia versus myeloid cell nomenclature during brain inflammation. Front. Immunol. https://doi.org/10.3389/fimmu.2015.00249 (2015).

  31. 31.

    Butovsky, O. et al. Identification of a unique TGF-β–dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    CAS  PubMed  Google Scholar 

  32. 32.

    Bennett, M. L. et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl Acad. Sci. USA 113, E1738–E1746 (2016).

    CAS  PubMed  Google Scholar 

  33. 33.

    Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).

    PubMed  Google Scholar 

  34. 34.

    Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science https://doi.org/10.1126/science.aal3222 (2017).

  35. 35.

    Ormel, P. R. et al. Microglia innately develop within cerebral organoids. Nat. Commun. 9, 4167 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Grubman, A. et al. A CX3CR1 reporter hESC line facilitates integrative analysis of in vitro-derived microglia and improved microglia identity upon neuron–glia co-culture. Stem Cell Reports https://doi.org/10.1016/j.stemcr.2020.04.007 (2020).

  37. 37.

    Galatro, T. F. et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. https://doi.org/10.1038/nn.4597 (2017).

  38. 38.

    Nimmerjahn, A., Kirchhoff, F. & Helmchen, F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 308, 1314–1318 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wake, H. & Fields, R. D. Physiological function of microglia. Neuron Glia Biol. 7, 1–3 (2011).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Stevens, B. et al. The classical complement cascade mediates CNS synapse elimination. Cell 131, 1164–1178 (2007).

    CAS  PubMed  Google Scholar 

  41. 41.

    Schafer, D. P. et al. Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74, 691–705 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Chen, Z. et al. Lipopolysaccharide-induced microglial activation and neuroprotection against experimental brain injury is independent of hematogenous TLR4. J. Neurosci. 32, 11706–11715 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Shi, Q. et al. Complement C3-deficient mice fail to display age-related hippocampal decline. J. Neurosci. 35, 13029–13042 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Wu, T. et al. Complement C3 is activated in human AD brain and is required for neurodegeneration in mouse models of amyloidosis and tauopathy. Cell Rep. 28, 2111–2123 (2019).

    CAS  PubMed  Google Scholar 

  45. 45.

    Shi, Q. et al. Complement C3 deficiency protects against neurodegeneration in aged plaque-rich APP/PS1 mice. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aaf6295 (2017).

  46. 46.

    Hong, S. et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Paquet, D. et al. Efficient introduction of specific homozygous and heterozygous mutations using CRISPR–Cas9. Nature 533, 125–129 (2016).

    CAS  PubMed  Google Scholar 

  48. 48.

    Afagh, A., Cummings, B. J., Cribbs, D. H., Cotman, C. W. & Tenner, A. J. Localization and cell association of C1Q in Alzheimer’s disease brain. Exp. Neurol. 138, 22–32 (1996).

    CAS  PubMed  Google Scholar 

  49. 49.

    Fonseca, M. I. et al. Cell-specific deletion of C1qa identifies microglia as the dominant source of C1Q in mouse brain. J. Neuroinflammation 14, 48 (2017).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Morizawa, Y. M. et al. Reactive astrocytes function as phagocytes after brain ischemia via ABCA1-mediated pathway. Nat. Commun. 8, 28 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

    CAS  PubMed  Google Scholar 

  53. 53.

    Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  PubMed  Google Scholar 

  54. 54.

    Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE 9, e98679 (2014).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548(2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Kinsella, R. J. et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011, bar030 (2011).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Sneeboer, M. A. M. et al. Microglia in postmortem brain tissue of patients with bipolar disorder are not immune activated. Transl. Psychiatry 9, 153 (2019).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to the members of the Studer laboratory for their helpful discussions and support for this project. Additionally, we thank the MSKCC flow cytometry core for cell sorting; A. Viale at Integrated Genomics Operation Core (MSKCC) for the RNA-seq studies; P. Zumbo and D. Betel at the Weill Cornell Medical College (WCMC) Applied Bioinformatics Core for MDS analysis; S. Ralph at the automated optical microscopy service with the Microscopy and Image Analysis Core (WCMC) for high-content imaging and analysis; V. Boyko at the Molecular Cytology Core (MSKCC) for help with confocal imaging and quantification; and the Molecular Cytogenetics Core (MSKCC) for karyotyping analysis. We also thank the team of the NBB for providing us with postmortem brain tissue and the Notarangelo laboratory at NIH for reprogramming the iPSC lines used to test reproducibility of the microglial differentiation (DBR1, IFNAR1, IL-10RB and STAT1). S.R.G. was supported by the Ruth L. Kirschstein Individual Predoctoral NRSA for MD/PhD Fellowship (1F30MH115616–01) and by a Medical Scientist Training Program grant from the NIH National Institute of General Medical Sciences (award no. T32GM007739) to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. J.T. was supported by a Tri-I Starr Stem Cell Scholar fellowship. N.S. was supported by a Glenn/AFAR Postdoctoral Fellowship; R.M.W. was supported by an F32 Ruth L. Kirschstein Postdoctoral fellowship (MH116590); and G.C was supported by an EMBO long-term postdoctoral fellowship and a NYSTEM postdoctoral fellowship. The work was supported in part by R21 NS084334, 1R01AG056298 to L.S. and the core grant P30CA008748.

Author information

Affiliations

Authors

Contributions

S.R.G.: conception, study design, data analysis and interpretation, writing of manuscript, development and execution of differentiation of microglia from hPSCs and development of tri-culture system. L. Sikkema: scRNA-seq bioinformatic analysis and interpretation. J.T.: differentiation of astrocytes from hPSCs. N.S.: generation of isogenic APPSWE+/+ hPSC lines. R.M.W.: generation of GPI-H2B-GFP hPSC line. O.H.: generation of microglia from multiple iPSC lines for reproducibility validation. G.C.: development of paradigm for cortical neuronal differentiation. M.S. and L.W.: human primary microglial isolation and RNA preparation. L.M.: sample processing for scRNA-seq. M.S.: development of Palantir. D.P.: conception and design of scRNA-seq data analysis and interpretation. P.Z. and D.B.: comparison of RNA-seq data to publicly available microglial datasets. L. Studer.: conception, study design, data analysis and interpretation, and writing of manuscript.

Corresponding author

Correspondence to Lorenz Studer.

Ethics declarations

Competing interests

L.S. holds equity and is a scientific co-founder and paid consultant of BlueRock Therapeutics. S.R.G. and L.S. are listed as inventors of a related patent application filed by the Memorial Sloan Kettering Cancer Center.

Additional information

Peer review information Nature Neuroscience thanks Hansang Cho and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Characterization of primitive hematopoiesis up to Day 10 of differentiation.

a, FACS analysis shows that addition of erythropoietin (EPO) from Day 6 to Day 10 of differentiation causes the emergence of CD235A + erythrocytes at Day 10 of differentiation, as well as a reduction in the percentage of Macro (macrophage precursor) fated cells. b, Brightfield images over days 4–10 of the differentiation show that round hematopoietic cells progressively proliferate in semi-suspension through day 10, black arrows point to hematopoietic cells. Scale bar = 100 µM (Day 4, 10) and 50 µM (Day 7). c, IF shows VE-cadherin+ hemogenic endothelium at Day 10 of differentiation. Scale bar = 100 µM. d, Imputed gene expression trends over pseudotime as calculated by Palantir of HOXA genes in the 3 differentiation trajectory arms show nearly absent HOXA1–7 expression, and minimal HOXA9 and 10 expression at low pseudotimes (~one log fold lower than genes in Fig. 2C). PMAC (PMAC arm, green), MK (megakaryocytic arm, pink), and ERY (erythrocyte arm, orange). e, Unimputed gene expression trends of HOXA1-10 genes show similar trends to imputed trends shown in A, nearly absent HOXA1-7 expression and minimal HOXA9-10 expression only at low pseudotimes in all differentiation trajectory arms. PMAC (PMAC arm, green), MK (megakaryocytic arm, pink), and ERY (erythrocyte arm, orange). f, Unimputed gene expression trends of key signature genes over pseudotime as calculated by Palantir show similar trends to imputed gene trends shown in Fig. 2C. Separate differentiation arms increasingly express their corresponding identity markers over pseudotime: ERY (GYPA, HBE1), MK (ITGA2B, ITGB3, GP1BA), and PMAC (CX3CR1, CSF1R, SPI1, PTPRC). MY (PMAC arm, green), MK (megakaryocytic arm, pink), and ERY (erythrocyte arm, orange). n = 6743 and error bars = SD for (A)-(C).

Extended Data Fig. 2 Heatmaps of EMP and PMAC signatures generated with unimputed data along PMAC, ERY, and MK arms.

a, Heatmaps showing unimputed counts of mouse yolk sac EMP PMAC signature genes25 along the PMAC arm ordered by pseudotime. There is increased expression of the EMP and PMAC signature genes over pseudotime along the PMAC trajectory, corresponding to the same increase in expression shown in Fig. 2D with imputed data. b, i) Heatmap of gene expression data from cells along the erythrocyte trajectory (ERY) ordered by pseudotime compared to mouse yolk sac EMP and PMAC gene signatures shows increased expression of EMP genes at pseudotimes corresponding to the in vitro human EMP/ERY clusters, but no increase in expression of the PMAC signature genes at any pseudotime. ii) Likewise, heatmap of imputed gene expression data from cells along the megakaryocytic trajectory (MK) ordered by pseudotime compared to mouse yolk sac EMP and PMAC gene signatures shows increased expression of EMP genes at pseudotimes corresponding to the human EMP/MK clusters, but no increase in expression of the PMAC signature at any pseudotime. c, Heatmaps generated from unimputed data show the same trends of increased expression of EMP signature genes at pseudotimes when EMP/ERY and EMP/MK clusters emerge, but no increased expression of the PMAC signature genes. Gene counts were individually scaled to range from 0 to 1 for all heatmaps.

Extended Data Fig. 3 Normalized counts of genes expressed by hPSC-derived microglia and hPSC-derived neurons in co-culture.

a, hPSC-derived neurons co-cultured with microglia express signature genes important for microglial maturation (IL-34, CSF1, CX3CL1, CD200, TGFB2, TGFB3), whereas hPSC-derived microglia express low levels or do not express these genes. neurons= hPSC-derived neurons, micro = hPSC-derived microglia from method ii of differentiation. n = 4 samples for neurons and n = 6 for microglia. Error bars = SD, center = mean. b, hPSC-derived microglia co-cultured with hPSC-derived neurons express a large panel of microglial genes at nearly the same levels as adult acutely isolated primary microglia, except TMEM119, P2RY12, and SALL1. primary = adult acutely isolated microglia, n = 4, macro = method ii, matured alone then co-cultured, n = 6, round = method i, direct co-culture, n = 6. Error bars = SD, center = mean.

Source data

Extended Data Fig. 4 Characterization and reproducibility of maturing primitive EMPs/PMACs to homogenous hPSC-derived microglia without co-culture.

a, Brightfield image of differentiating primitive EMPs/PMACs shows that by 11 days of culture in IL-34 and M-CSF, the cells are adherent on TC-treated plastic and display an elongated morphology. b, IF shows that all cells at day 11 uniformly express the myeloid transcription factor PU.1. Scale bar = 50 µM for (a) and (b). c, FACS analysis of day 10 differentiation cultures show a 37–51% induction of CD43 + CD45 + macrophage precursors that is reproducible across 4 different hPSC lines. d, After 11 days of culture in IL-34 and M-CSF, a pure population of IBA1 + cells is reproducible in 5 hPSC lines (including 4 iPSC lines). Scale bar = 100 µM. e, Each iPSC line used to test reproducibility of the microglia differentiation was derived from patient fibroblasts using a nonintegrating Sendai viral vector. f, Pairwise diffusion distances calculated between cells in the microglial sample after diffusion map embedding fall in a unimodal distribution. Pairwise distances calculated using different numbers of diffusion components are shown, with consistent results. g, In contrast, pairwise diffusion distances between cells in the heterogenous Day 10 sample have multiple peaks across different numbers of diffusion components.

Extended Data Fig. 5 GO pathway analysis and GSEA on differentially expressed genes between hPSC-derived microglia and acutely isolated adult primary microglia.

a, Heatmap of top 100 differentially expressed genes between hPSC-derived microglia derived from method ii and acutely isolated adult primary microglia using DESEQ. b, Table of top 100 differentially expressed genes between hPSC-derived microglia (method ii) and adult primary microglia using DESEQ. c, GO pathway analysis identifies neuronal developmental pathways as enriched in hPSC-derived microglia (method ii). d, GO pathways analysis identifies immune activation pathways as enriched in acutely isolated adult primary microglia. e-f, GSEA on 7 embryonic to adult microglial gene signatures33 (yolk sac, embryonic 1 and 2, postnatal 1 and 2, adult 1 and 2) reveals that hPSC-derived microglia (method ii) enrich for postnatal 1 and 2 signatures with NES = −1.46 and FDR = 0.14; NES = −1.52, FDR = 0.16. G-H) GSEA reveals that adult primary microglia enrich for adult 1 and 2 signatures with NES = −1.52 and FDR = 0.16; NES = −1.47, FDR = 0.09. Heatmaps show top 20 ranked genes from each gene signature in primary vs. hPSC-microglia (method ii).

Extended Data Fig. 6 Transcriptomic comparison of hPSC-derived microglia to microglia from previously published differentiation protocols.

a, MDS analysis using published datasets from 4 different microglial protocols10,11,12,35 and 1 study profiling acutely isolated adult primary microglia from postmortem human brain tissue37 reveals that hPSC-derived microglia from both method i (mg1) and method ii (mg2) cluster near the microglia differentiated from published protocols as well as near fetal microglia. adultmg = acutely isolated adult primary microglia sequenced in our study, mg1 = hPSC-derived microglia from method i, mg2 = hPSC-derived microglia from method ii of our study. adultmg_(ormel, galatro, abud) = adult microglia from Ormel et al35, Galatro et al37, Abud et al11, fetal_mg(abud, douv, muffat) = fetal microglia from Abud et al11, Douvaras et al12, Muffat et al10, mg_(abud, douv, muffat, ormel) = hPSC-microglia from Abud et al11, Douvaras et al12, Muffat et al10, and Ormel et al35. Dimension 1 vs. 2 separates the adult primary microglia from the fetal microglia/hPSC-derived microglia. b, Dimension 2 vs. 3 separates out the adult primary microglia and fetal microglia cultured in serum used in Abud et al11. Our hPSC-derived microglia (mg1, mg2) cluster near the other differentiated microglia (mg_ormel, mg_douv, mg_muffat) and fetal microglia (fetalmg_douv, fetalmg_douv+serum, fetalmg_muffat+serum).

Extended Data Fig. 7 Microglia perform efficient phagocytosis of zymosan-coated beads and mature hPSC-derived neurons form synapses in vitro.

a, Fluorescent microscopy shows that microglial cells contain zymosan-conjugated fluorescent beads as inclusions within 4 hours of incubation. Scale bar = 70 µM. b, Astrocyte control does not contain fluorescent bead inclusions after 4 hr of incubation. Scale bar = 280 µM. c, IF shows that hPSC-derived cortical neurons at day 70 express a punctate distribution of the pre-synaptic SYNI and post-synaptic HOMER1. Putative synapses are stained where both SYNI and HOMER1 are side-by-side (white arrow). d, IF shows that day 70 hPSC-derived cortical neurons also express the post-synaptic marker PSD95 in a punctate distribution. Scale bar = 50 µM (c) and (d).

Extended Data Fig. 8 Optimization of tri-culture ratio and culture media to maximize cell survival and minimize cell activation at baseline.

a, IF shows that tri-cultures with increased numbers of astrocytes plated (50 K) show fewer microglial cells (IBA1 + ) attached at Day 7 than with tri-cultures containing fewer (25 K) astrocytes. Scale bar = 200 µM. b, NB/BAGC and NB:N2 base media formulations show the lowest secretion of C3 in the tri-culture by ELISA. Addition of DMEM base, F12 supplement, low glucose, high glutamine, and high pyruvate all increase C3 levels in baseline tri-cultures. n = 2 technical replicates of cell culture supernatant. c, Control staining for CC3. IF of hPSC-derived neurons killed by 70% methanol incubation for 30 minutes shows bright CC3 + whereas IF of fixed cells without the methanol treatment does not. Scale bar = 100 µM.

Source data

Extended Data Fig. 9 Characterization of C3 KO hPSC line and C3 KO-derived microglia and activation properties of conditioned medium and C3 addition to microglia and astrocytes.

a, Cell scoring by ImageExpress microscopy shows higher %IBA1 + / DAPI in microglia/neuron (M/N) cultures vs. tri-cultures at day 7. n = 4 distinct cell culture wells. Error bars = SD, center = mean. b, Cytokine ELISA panel shows the secretion of inflammatory cytokines upon LPS stimulation only in cultures containing microglia, including IL-10, IL-6, TNFa, GM-CSF, IL1B, and IFNy. n = 3 cell culture supernatants. Error bars = SD, center = mean. c, Sanger sequencing shows that the C3 KO hPSC line has a 7 bp deletion (red) near the targeted PAM site (blue), guide is marked in green. d, IF shows that the C3 KO hPSC line expresses the pluripotency markers of SOX2, NANOG, and OCT4. Scale bar = 100 µM. e, i) FACS analysis shows that microglia differentiated from the C3 KO line are a pure population expressing CD11B + and CX3CR1 + . ii) IF shows that these cells are all IBA1 + and PU.1 + . Scale bar = 100 µM. f, ELISA shows that C3 KO microglia (densely cultured alone) do not secrete C3 protein as compared to WT microglia. n = 2 cell culture supernatants. g, Cell scoring by ImageExpress shows similar numbers of IBA1 + microglia and GFAP + astrocytes between the different tri-cultures (TRI, C3KOM, C3KOA). n = 4 distinct cell culture wells. Error bars = SD, center = mean. h, 48hrs of astrocyte-conditioned medium (ACM) treatment induces C3 expression in hPSC-derived microglia, normalized to untreated microglia. **p = 0.0037, two-tailed t-test, n = 3. 48 hrs of microglia-conditioned medium (MCM) treatment induces C3 in astrocytes, normalized to untreated astrocytes, n = 3 independent experiments. *p = 0.0122, two-tailed t-test. Error bars = SD, center = mean. i, C3 (1ug/mL) addition to microglia induces C3 expression after 48 hr, normalized to untreated microglia. *p = 0.0217, two-tailed t-test, n = 3 independent experiments. C3 addition to astrocytes does not induce C3 expression after 48 hr of treatment, n = 3. Error bars = SD, center = mean.

Source data

Extended Data Fig. 10 Characterization of the APPSWE+/+ and WT isogenic lines.

a, Sanger sequencing shows that the APPSWE+/+ line is homozygous for the GA > TC mutation as compared to the isogenic wildtype line. b, IF shows that both lines express the pluripotency markers of SOX2, NANOG, and OCT4. Scale bar = 100 µM. c, Quantification of amyloid peptides 38, 40, and 42 in neuronal cultures shows that APPSWE+/+ neurons have higher levels of all peptides as compared to isogenic WT neurons. n = 3 cell culture supernatants. d, Increased C1Q secretion by ELISA in APPSWE+/+ tri-cultures, ****p < 0.0001, two-way ANOVA with Sidak’s post hoc test, n = 3 cell culture supernatants, error bars = SD, center = mean. e, Increased C1Q secretion in C3KOM but not C3KOA tri-cultures. n = 3 cell culture supernatants, error bars = SD, center = mean.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2

Reporting Summary

Supplementary Video 1

GFP+ microglia in a co-culture of neurons. GFP+ microglia are motile and survey the surrounding co-culture of hPSC-derived neurons, moving and retracting processes.

Supplementary Video 2

Brightfield overlay of hPSC-derived neurons in GFP+ microglia and neuronal co-culture. GFP+ and untagged microglia use their processes to sample the surrounding neurons.

Supplementary Video 3

Microglial phagocytosis of zymosan-coated GFP+ beads. Microglial cells incubated with the yeast antigen zymosan-coated beads effectively phagocytose the beads over 16 h.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed western blot.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guttikonda, S.R., Sikkema, L., Tchieu, J. et al. Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer’s disease. Nat Neurosci 24, 343–354 (2021). https://doi.org/10.1038/s41593-020-00796-z

Download citation

Further reading

Search

Quick links