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Single-cell RNA and protein profiling of immune cells from the mouse brain and its border tissues

Abstract

Brain–immune cross-talk and neuroinflammation critically shape brain physiology in health and disease. A detailed understanding of the brain immune landscape is essential for developing new treatments for neurological disorders. Single-cell technologies offer an unbiased assessment of the heterogeneity, dynamics and functions of immune cells. Here we provide a protocol that outlines all the steps involved in performing single-cell multi-omic analysis of the brain immune compartment. This includes a step-by-step description on how to microdissect the border regions of the mouse brain, together with dissociation protocols tailored to each of these tissues. These combine a high yield with minimal dissociation-induced gene expression changes. Next, we outline the steps involved for high-dimensional flow cytometry and droplet-based single-cell RNA sequencing via the 10x Genomics platform, which can be combined with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and offers a higher throughput than plate-based methods. Importantly, we detail how to implement CITE-seq with large antibody panels to obtain unbiased protein-expression screening coupled to transcriptome analysis. Finally, we describe the main steps involved in the analysis and interpretation of the data. This optimized workflow allows for a detailed assessment of immune cell heterogeneity and activation in the whole brain or specific border regions, at RNA and protein level. The wet lab workflow can be completed by properly trained researchers (with basic proficiency in cell and molecular biology) and takes between 6 and 11 h, depending on the chosen procedures. The computational analysis requires a background in bioinformatics and programming in R.

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Fig. 1: Schematic overview of the protocol.
Fig. 2: Dissection of the subdural meninges, CP and dura mater.
Fig. 3: Sorting of brain immune cells for single-cell sequencing.
Fig. 4: scRNA-seq analysis of dissected border tissues or whole brain samples.
Fig. 5: CITE-seq analysis of GL261 tumor-infiltrating immune cells.
Fig. 6: CITE-seq analysis of CD45- dura mater cells.
Fig. 7: Level of ambient RNA contamination and percentage of doublets in different mouse brain tissues.
Fig. 8: HD flow cytometry analysis of whole brain and border samples.

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Data availability

The scRNA-seq data shown in Fig. 4a,b,e are deposited at GEO under GSE128855; Fig. 4f under GSE157480. The CITE-seq data shown in Fig. 5 are deposited at GEO under GSE163120. The gene–cell count matrices of all these datasets can also be downloaded at www.brainimmuneatlas.org. The CITE-seq data shown in Fig. 6 are deposited at GEO under GSE191075.

Code availability

The R codes that were used for scRNA-seq and CITE-seq analyses can be found at Github: https://github.com/Movahedilab/Mouse_brain_borders_NatureProtocols.

References

  1. Salvador, A. F., de Lima, K. A. & Kipnis, J. Neuromodulation by the immune system: a focus on cytokines. Nat. Rev. Immunol. 21, 526–541 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Hammond, T. R., Robinton, D. & Stevens, B. Microglia and the brain: complementary partners in development and disease. Annu. Rev. Cell Dev. Biol. 34, 523–544 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Castellani, G. & Schwartz, M. Immunological features of non-neuronal brain cells: implications for Alzheimer’s disease immunotherapy. Trends Immunol. 41, 794–804 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Sampson, J. H., Gunn, M. D., Fecci, P. E. & Ashley, D. M. Brain immunology and immunotherapy in brain tumours. Nat. Rev. Cancer 20, 12–25 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Shechter, R., London, A. & Schwartz, M. Orchestrated leukocyte recruitment to immune-privileged sites: absolute barriers versus educational gates. Nat. Rev. Immunol. 13, 206–218 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Korin, B. et al. High-dimensional, single-cell characterization of the brain’s immune compartment. Nat. Neurosci. 20, 1300–1309 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 599 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    Article  PubMed  Google Scholar 

  9. Brioschi, S. et al. Heterogeneity of meningeal B cells reveals a lymphopoietic niche at the CNS borders. Science 373, eabf9277 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cugurra, A. et al. Skull and vertebral bone marrow are myeloid cell reservoirs for the meninges and CNS parenchyma. Science 373, eabf7844 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ajami, B. et al. Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models. Nat. Neurosci. 21, 541–551 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shemer, A. et al. Interleukin-10 prevents pathological microglia hyperactivation following peripheral endotoxin challenge. Immunity 53, 1033–1049 e1037 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Pombo Antunes, A. R. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat. Neurosci. 24, 595–610 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Kierdorf, K., Masuda, T., Jordao, M. J. C. & Prinz, M. Macrophages at CNS interfaces: ontogeny and function in health and disease. Nat. Rev. Neurosci. 20, 547–562 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Korin, B., Dubovik, T. & Rolls, A. Mass cytometry analysis of immune cells in the brain. Nat. Protoc. 13, 377–391 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271 e256 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Baruch, K. et al. Breaking immune tolerance by targeting Foxp3(+) regulatory T cells mitigates Alzheimer’s disease pathology. Nat. Commun. 6, 7967 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Movahedi, K. et al. Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6C(high) monocytes. Cancer Res. 70, 5728–5739 (2010).

    Article  CAS  PubMed  Google Scholar 

  23. Peptan, I. A., Hong, L. & Evans, C. A. Multiple differentiation potentials of neonatal dura mater-derived cells. Neurosurgery. 60, 346–352 (2007). discussion 352.

    Article  PubMed  Google Scholar 

  24. Ryg-Cornejo, V., Ioannidis, L. J. & Hansen, D. S. Isolation and analysis of brain-sequestered leukocytes from Plasmodium berghei ANKA-infected mice. J. Vis. Exp. 71, e50112 (2013).

    Google Scholar 

  25. Van Damme, H. et al. Therapeutic depletion of CCR8(+) tumor-infiltrating regulatory T cells elicits antitumor immunity and synergizes with anti-PD-1 therapy. J. Immunother. Cancer 9, e001749 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329 e316 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Rustenhoven, J. et al. Functional characterization of the dural sinuses as a neuroimmune interface. Cell 184, 1000–1016 e1027 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dani, N. et al. A cellular and spatial map of the choroid plexus across brain ventricles and ages. Cell 184, 3056–3074 e3021 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Golomb, S. M. et al. Multi-modal single-cell analysis reveals brain immune landscape plasticity during aging and gut microbiota dysbiosis. Cell Rep. 33, 108438 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  31. Zelco, A. et al. Single-cell atlas reveals meningeal leukocyte heterogeneity in the developing mouse brain. Genes Dev. 35, 1190–1207 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Li, Q. et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron 101, 207–223 e210 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Jordao, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Schafflick, D. et al. Single-cell profiling of CNS border compartment leukocytes reveals that B cells and their progenitors reside in non-diseased meninges. Nat. Neurosci. 24, 1225–1234 (2021).

    Article  CAS  PubMed  Google Scholar 

  35. Wang, Y. et al. Early developing B cells undergo negative selection by central nervous system-specific antigens in the meninges. Immunity 54, 2784–2794 e2786 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Brummelman, J. et al. Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry. Nat. Protoc. 14, 1946–1969 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Park, L. M., Lannigan, J. & Jaimes, M. C. OMIP-069: forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A 97, 1044–1051 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Takahashi, C. et al. Mass cytometry panel optimization through the designed distribution of signal interference. Cytometry A 91, 39–47 (2017).

    Article  CAS  PubMed  Google Scholar 

  40. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 e915 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Waylen, L. N., Nim, H. T., Martelotto, L. G. & Ramialison, M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun. Biol. 3, 602 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Grieshaber-Bouyer, R. et al. The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments. Nat. Commun. 12, 2856 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Monaco, G. et al. RNA-Seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types. Cell Rep. 26, 1627–1640 e1627 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Giordani, L. et al. High-dimensional single-cell cartography reveals novel skeletal muscle-resident cell populations. Mol. Cell 74, 609–621 e606 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Dahlin, J. S. et al. A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice. Blood 131, e1–e11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Millard, S. M. et al. Fragmentation of tissue-resident macrophages during isolation confounds analysis of single-cell preparations from mouse hematopoietic tissues. Cell Rep. 37, 110058 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Quintelier, K. et al. Analyzing high-dimensional cytometry data using FlowSOM. Nat. Protoc. 16, 3775–3801 (2021).

    Article  CAS  PubMed  Google Scholar 

  52. Louis, K. S. & Siegel, A. C. Cell viability analysis using trypan blue: manual and automated methods. Methods Mol. Biol. 740, 7–12 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. 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 

  54. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  PubMed  Google Scholar 

  55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).

    Article  PubMed Central  Google Scholar 

  58. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Polanski, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).

    CAS  PubMed  Google Scholar 

  60. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Germain, P., Lun, A., Macnair, W. & Robinson, M. Doublet identification in single-cell sequencing data using scDblFinder [version 2; peer review: 2 approved]. F1000Research 10, 979 (2022).

    Article  PubMed Central  Google Scholar 

  62. Xi, N. M. & Li, J. J. Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis. STAR Protoc. 2, 100699 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 e324 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Tung, P. Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 e1817 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  PubMed  Google Scholar 

  70. Cannoodt, R. et al.. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/079509v2 (2016).

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

    Article  CAS  PubMed  Google Scholar 

  72. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  77. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  78. Cha, J. & Lee, I. Single-cell network biology for resolving cellular heterogeneity in human diseases. Exp. Mol. Med. 52, 1798–1808 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by Innoviris (Attract grant BB2B 2015−2), Fonds Wetenschappelijk Onderzoek (grant no. 1506316 N) to K.M., a VLAIO grant (no. ImmCyte HBC.2016.0889) to K.M. and JAVG. K.M. is a Collen-Franqui research professor. I.S. is supported by an FWO postdoctoral fellowship. H.V.H. is supported by an FWO predoctoral fellowship. We thank Y. Elkrim, G. van Isterdael, the VIB Flow Core and the VIB Nucleomics Core for technical assistance, VIB Tech Watch and the single-cell accelerator program for support regarding single-cell technologies.

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I.S., H.V.H., K.D.V. and K.M. developed and optimized the protocol with input and help from C.L.S., J.A.V.G., Y.S., M.G., N.V. I.S., H.V.H., K.D.V., J.B., M.V.P., A.R.P.A. and N.V. performed wet-lab work and acquired scRNA-seq and/or CITE-seq data. I.S., D.K., L.M. and Y.S. analyzed the data and/or generated analysis pipelines. I.S., H.V.H., D.K. and K.M. wrote the manuscript. K.M. conceptualized and directed the study.

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Correspondence to Kiavash Movahedi.

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Key references using this protocol

Van Hove, H. et al. Nat. Neurosci. 22, 1021–1035 (2019): https://doi.org/10.1038/s41593-019-0393-4

Shemer, A. et al. Immunity 54, 1033–1049.E7 (2020): https://doi.org/10.1016/j.immuni.2020.09.018

Pombo Antunes, A. R. et al. Nat. Neurosci. 24, 595–610 (2021): https://doi.org/10.1038/s41593-020-00789-y

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Scheyltjens, I., Van Hove, H., De Vlaminck, K. et al. Single-cell RNA and protein profiling of immune cells from the mouse brain and its border tissues. Nat Protoc 17, 2354–2388 (2022). https://doi.org/10.1038/s41596-022-00716-4

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