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Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors

Nature Neuroscience volume 26, pages 1352–1364 (2023)Cite this article

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Abstract

Major depressive disorder represents a serious public health challenge worldwide; however, the underlying cellular and molecular mechanisms are mostly unknown. Here, we profile the dorsolateral prefrontal cortex of female cynomolgus macaques with social stress-associated depressive-like behaviors using single-nucleus RNA-sequencing and spatial transcriptomics. We find gene expression changes associated with depressive-like behaviors mostly in microglia, and we report a pro-inflammatory microglia subpopulation enriched in the depressive-like condition. Single-nucleus RNA-sequencing data result in the identification of six enriched gene modules associated with depressive-like behaviors, and these modules are further resolved by spatial transcriptomics. Gene modules associated with huddle and sit alone behaviors are expressed in neurons and oligodendrocytes of the superficial cortical layer, while gene modules associated with locomotion and amicable behaviors are enriched in microglia and astrocytes in mid-to-deep cortical layers. The depressive-like behavior associated microglia subpopulation is enriched in deep cortical layers. In summary, our findings show cell-type and cortical layer-specific gene expression changes and identify one microglia subpopulation associated with depressive-like behaviors in female non-human primates.

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Fig. 1: Schematic of the experimental design and analysis.
Fig. 2: Evaluation of behavior-associated phenotypes and identification of cell types.
Fig. 3: Most DEG derived from glia were assembled mainly into coexpression gene clusters.
Fig. 4: Identification of pro-inflammatory microglial subpopulation enriched in DL.
Fig. 5: ST regions were identified with layer structure in dlPFC.
Fig. 6: ST regions were associated with depressive-like phenotypes.

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Data availability

All sequencing data generated by this study are available in the Gene Expression Omnibus as a SuperSeries (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201687).

Code availability

The code for analysis and generating figures can be found at https://github.com/JWu-brainstudy/Macaca-sn-ana2022 and at https://doi.org/10.5281/zenodo.8015827 (ref. 100).

References

  1. Frankish, H., Boyce, N. & Horton, R. Mental health for all: a global goal. Lancet 392, 1493–1494 (2018).

    PubMed  Google Scholar 

  2. Malhi, G. S. & Mann, J. J. Depression. Lancet 392, 2299–2312 (2018).

    PubMed  Google Scholar 

  3. Price, R. B. & Duman, R. Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model. Mol. Psychiatr. 25, 530–543 (2020).

    Google Scholar 

  4. Fan, Z. et al. Neural mechanism underlying depressive-like state associated with social status loss. Cell 186, 560–576.e17 (2023).

    CAS  PubMed  Google Scholar 

  5. Jia, X. N., Gao, Z. H. & Hu, H. L. Microglia in depression: current perspectives. Sci. China Life Sci. 64, 911–925 (2021).

    CAS  PubMed  Google Scholar 

  6. Klawonn, A. et al. Microglial activation elicits a negative affective state through prostaglandin-mediated modulation of striatal neurons. Immunity 54, 225–234.e6 (2021).

    CAS  PubMed  Google Scholar 

  7. Li, S. et al. Microglial NLRP3 inflammasome activates neurotoxic astrocytes in depression-like mice. Cell Rep. 41, 111532 (2022).

    CAS  PubMed  Google Scholar 

  8. Ofengeim, D., Giagtzoglou, N., Huh, D., Zou, C. & Yuan, J. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol. Med. 23, 563–576 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    CAS  PubMed  Google Scholar 

  10. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E. & Cohen, J. D. The neural basis of economic decision-making in the ultimatum game. Science 300, 1755–1758 (2003).

    CAS  PubMed  Google Scholar 

  11. Gold, P. W. The organization of the stress system and its dysregulation in depressive illness. Mol. Psychiatr. 20, 32–47 (2015).

    CAS  Google Scholar 

  12. Vansteensel, M. J. et al. Brain-computer interfacing based on cognitive control. Ann. Neurol. 67, 809–816 (2010).

    PubMed  Google Scholar 

  13. Chen, W. T. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991.e19 (2020).

    CAS  PubMed  Google Scholar 

  14. Feder, A., Nestler, E. J. & Charney, D. S. Psychobiology and molecular genetics of resilience. Nat. Rev. Neurosci. 10, 446–457 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Slavich, G. M. & Irwin, M. R. From stress to inflammation and major depressive disorder: a social signal transduction theory of depression. Psychol. Bull. 140, 774–815 (2014).

    PubMed  PubMed Central  Google Scholar 

  16. Yang, Y. et al. Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554, 317–322 (2018).

    CAS  PubMed  Google Scholar 

  17. Cui, Y. et al. Astroglial Kir4.1 in the lateral habenula drives neuronal bursts in depression. Nature 554, 323–327 (2018).

    CAS  PubMed  Google Scholar 

  18. Canuto, A. et al. Anxiety disorders in old age: psychiatric comorbidities, quality of life, and prevalence according to age, gender, and country. Am. J. Geriatr. Psychiatry 26, 174–185 (2018).

    PubMed  Google Scholar 

  19. Hassard, J., Teoh, K. R. H., Visockaite, G., Dewe, P. & Cox, T. The cost of work-related stress to society: a systematic review. J. Occup. Health Psychol. 23, 1–17 (2018).

    PubMed  Google Scholar 

  20. Xu, F. et al. Construction and validation of a systematic ethogram of Macaca fascicularis in a free enclosure. PLoS One 7, e37486 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Zheng, P. et al. The gut microbiome modulates gut-brain axis glycerophospholipid metabolism in a region-specific manner in a nonhuman primate model of depression. Mol. Psychiatr. 26, 2380–2392 (2021).

    CAS  Google Scholar 

  22. Wu, J. et al. Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model. Transl. Psychiatry 12, 74 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Xu, F. et al. Macaques exhibit a naturally-occurring depression similar to humans. Sci. Rep. 5, 9220 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Felger, J. C. et al. Effects of interferon-alpha on rhesus monkeys: a nonhuman primate model of cytokine-induced depression. Biol. Psychiatry 62, 1324–1333 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, S. et al. Single-cell transcriptomic atlas of primate ovarian aging. Cell 180, 585–600.e19 (2020).

    CAS  PubMed  Google Scholar 

  26. Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    CAS  PubMed  Google Scholar 

  28. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Saiselet, M. et al. Transcriptional output, cell-type densities, and normalization in spatial transcriptomics. J. Mol. Cell. Biol. 12, 906–908 (2020).

    PubMed  PubMed Central  Google Scholar 

  30. Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).

    CAS  PubMed  Google Scholar 

  31. Nagy, C. et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nat. Neurosci. 23, 771–781 (2020).

    CAS  PubMed  Google Scholar 

  32. Mathys, H. et al. Author Correction: Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 571, 332–337 (2019).

    Google Scholar 

  33. Huang, L. J. et al. Multiomics analyses reveal a critical role of selenium in controlling T cell differentiation in Crohn’s disease. Immunity 54, 1728–1744.e7 (2021).

    CAS  PubMed  Google Scholar 

  34. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347–353 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Pinero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxf.) 2015, bav028 (2015).

    Google Scholar 

  37. Gutierrez-Sacristan, A. et al. PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics 31, 3075–3077 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Marsh, S. E. et al. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nat. Neurosci. 25, 306–316 (2022).

    CAS  PubMed  Google Scholar 

  39. Panicker, N. et al. Fyn kinase regulates misfolded α-synuclein uptake and NLRP3 inflammasome activation in microglia. J. Exp. Med. 216, 1411–1430 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Liao, L. et al. Disrupting RhoA activity by blocking Arhgef3 expression mitigates microglia-induced neuroinflammation post spinal cord contusion. J. Neuroimmunol. 359, 577688 (2021).

    CAS  PubMed  Google Scholar 

  41. Redondo, M. et al. Effect of phosphodiesterase 7 (PDE7) inhibitors in experimental autoimmune encephalomyelitis mice. Discovery of a new chemically diverse family of compounds. J. Med. Chem. 55, 3274–3284 (2012).

    CAS  PubMed  Google Scholar 

  42. Brisac, C. et al. IQGAP2 is a novel interferon-alpha antiviral effector gene acting non-conventionally through the NF-kappaB pathway. J. Hepatol. 65, 972–979 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Yaari, G., Bolen, C. R., Thakar, J. & Kleinstein, S. H. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Res. 41, e170 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Meng, H. L., Yaari, G., Bolen, C. R., Avey, S. & Kleinstein, S. H. Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE). PLoS Comput. Biol. 15, e1006899 (2019).

    PubMed  PubMed Central  Google Scholar 

  45. Schwabenland, M. et al. Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions. Immunity 54, 1594–1610.e11 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, Y. et al. Expression of OX40 ligand in microglia activated by IFN-gamma sustains a protective CD4+ T-cell response in vitro. Cell Immunol. 251, 86–92 (2008).

    CAS  PubMed  Google Scholar 

  47. Frakes, A. E. et al. Microglia induce motor neuron death via the classical NF-κB pathway in amyotrophic lateral sclerosis. Neuron 81, 1009–1023 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Jin, X. et al. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. David, D. J. et al. Neurogenesis-dependent and -independent effects of fluoxetine in an animal model of anxiety/depression. Neuron 62, 479–493 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang, Q., Jie, W., Liu, J. H., Yang, J. M. & Gao, T. M. An astroglial basis of major depressive disorder? An overview. Glia 65, 1227–1250 (2017).

    PubMed  Google Scholar 

  53. Jurga, A. M., Paleczna, M. & Kuter, K. Z. Overview of general and discriminating markers of differential microglia phenotypes. Front. Cell. Neurosci. 14, 198 (2020).

    PubMed  PubMed Central  Google Scholar 

  54. Hayley, S., Hakim, A. M. & Albert, P. R. Depression, dementia and immune dysregulation. Brain 144, 746–760 (2021).

    PubMed  Google Scholar 

  55. Prinz, M. & Priller, J. Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat. Rev. Neurosci. 15, 300–312 (2014).

    CAS  PubMed  Google Scholar 

  56. Wang, Y. M. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Mosher, K. I. & Wyss-Coray, T. Microglial dysfunction in brain aging and Alzheimer’s disease. Biochem. Pharmacol. 88, 594–604 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Kendler, K. S., Hettema, J. M., Butera, F., Gardner, C. O. & Prescott, C. A. Life event dimensions of loss, humiliation, entrapment, and danger in the prediction of onsets of major depression and generalized anxiety. Arch. Gen. Psychiatry 60, 789–796 (2003).

    PubMed  Google Scholar 

  59. van Goozen, S. H. et al. Salivary cortisol and cardiovascular activity during stress in oppositional-defiant disorder boys and normal controls. Biol. Psychiatry 43, 531–539 (1998).

    PubMed  Google Scholar 

  60. Anisman, H. & Merali, Z. Cytokines, stress, and depressive illness. Brain Behav. Immun. 16, 513–524 (2002).

    CAS  PubMed  Google Scholar 

  61. Camacho-Arroyo, I., Lopez-Griego, L. & Morales-Montor, J. The role of cytokines in the regulation of neurotransmission. Neuroimmunomodulation 16, 1–12 (2009).

    CAS  PubMed  Google Scholar 

  62. Zhang, Q. et al. Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature 464, 104–107 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Galyamina, A. G., Kovalenko, I. L., Smagin, D. A. & Kudryavtsev, N. N. Altered expression of neurotransmitters systems’ genes in the ventral tegmental area of depressive male mice: data of RNA-Seq. Zh. Vyssh. Nerv. Deiat. Im I. P. Pavlova 67, 113–128 (2017).

    CAS  PubMed  Google Scholar 

  64. Nagy, C. et al. Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. Mol. Psychiatr. 20, 320–328 (2015).

    CAS  Google Scholar 

  65. O’Connor, J. A. & Hemby, S. E. Elevated GRIA1 mRNA expression in Layer II/III and V pyramidal cells of the DLPFC in schizophrenia. Schizophr. Res 97, 277–288 (2007).

    PubMed  PubMed Central  Google Scholar 

  66. Wohleb, E. S. et al. Re-establishment of anxiety in stress-sensitized mice is caused by monocyte trafficking from the spleen to the brain. Biol. Psychiatry 75, 970–981 (2014).

    CAS  PubMed  Google Scholar 

  67. Scangos, K. W. et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat. Med. 27, 1696–1700 (2021).

    CAS  PubMed  Google Scholar 

  68. Scangos, K. W., Makhoul, G. S., Sugrue, L. P., Chang, E. F. & Krystal, A. D. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat. Med. 27, 229–231 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Sebille, S. B. et al. Normal and pathological neuronal distribution of the human mesencephalic locomotor region. Mov. Disord. 34, 218–227 (2019).

    CAS  PubMed  Google Scholar 

  70. Fuller, P., Sherman, D., Pedersen, N. P., Saper, C. B. & Lu, J. Reassessment of the structural basis of the ascending arousal system. J. Comp. Neurol. 519, 933–956 (2011).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  72. Zeng, H. K. et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell 149, 483–496 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. He, Z. et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat. Neurosci. 20, 886–895 (2017).

    CAS  PubMed  Google Scholar 

  74. Kessler, R. C. Epidemiology of women and depression. J. Affect Disord. 74, 5–13 (2003).

    PubMed  Google Scholar 

  75. Williams, E. S., Mazei-Robison, M. & Robison, A. J. Sex differences in major depressive disorder (MDD) and preclinical animal models for the study of depression. Cold Spring Harb. Perspect. Biol. 14, a039198 (2022).

    CAS  PubMed  Google Scholar 

  76. Moieni, M. et al. Sex differences in depressive and socioemotional responses to an inflammatory challenge: implications for sex differences in depression. Neuropsychopharmacology 40, 1709–1716 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Bollinger, J. L., Bergeon Burns, C. M. & Wellman, C. L. Differential effects of stress on microglial cell activation in male and female medial prefrontal cortex. Brain Behav. Immun. 52, 88–97 (2016).

    CAS  PubMed  Google Scholar 

  78. Weatheall, D. The Use of Non-human Primates in Research (Academy of Medical Sciences, London, 2006).

    Google Scholar 

  79. Kikuchi, T. et al. Human iPS cell-derived dopaminergic neurons function in a primate Parkinson’s disease model. Nature 548, 592–596 (2017).

    CAS  PubMed  Google Scholar 

  80. Chu, X. Preliminary validation of natural depression in macaques with acute treatments of the fast-acting antidepressant ketamine. Behav. Brain Res 360, 60–68 (2019).

    CAS  PubMed  Google Scholar 

  81. Gammell, M. P., De Vries, H., Jennings, D. J., Carlin, C. M. & Hayden, T. J. David’s score: a more appropriate dominance ranking method than Clutton-Brock et al.’s index. Anim. Behav. 66, 601–605 (2003).

    Google Scholar 

  82. Morrill, K. et al. Ancestry-inclusive dog genomics challenges popular breed stereotypes. Science 376, eabk0639 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Dijkman, K. et al. Prevention of tuberculosis infection and disease by local BCG in repeatedly exposed rhesus macaques. Nat. Med. 25, 255–262 (2019).

    CAS  PubMed  Google Scholar 

  84. Sorrells, S. F. et al. Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature 555, 377–381 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Saleem, K. S. & Logothetis, N. K. A Combined MRI and Histology Atlas of the Rhesus Monkey Brain in Stereotaxic Coordinates (Academic Press, 2012).

  86. BrainInfo. University of Washington http://braininfo.rprc.washington.edu/ (2016).

  87. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Zhang, L. et al. Molecular taxonomy of the primate amygdala via single-nucleus RNA sequencing analysis. Sci. Bull. 66, 1379–1383 (2021).

    CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Zhang, W. et al. The zinc finger protein Miz1 suppresses liver tumorigenesis by restricting hepatocyte-driven macrophage activation and inflammation. Immunity 54, 1168–1185.e8 (2021).

    CAS  PubMed  Google Scholar 

  91. Paulson, K. G. et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9, 3868 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Peng, M. et al. Single-cell transcriptomic landscape reveals the differences in cell differentiation and immune microenvironment of papillary thyroid carcinoma between genders. Cell Biosci. 11, 39 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).

    CAS  PubMed  Google Scholar 

  94. Pinero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2017).

    CAS  PubMed  Google Scholar 

  95. Zhang, X. X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).

    CAS  PubMed  Google Scholar 

  96. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Taylor, R. A., Toivanen, R. & Risbridger, G. P. Stem cells in prostate cancer: treating the root of the problem. Endocr. Relat. Cancer 17, R273–R285 (2010).

    CAS  PubMed  Google Scholar 

  98. Luo, Y. P. et al. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells. Cell 161, 1175–1186 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Wu, J. Single-nucleus and spatial transcriptomic atlas of depressive-like cynomolgus macaques. Zenodo https://doi.org/10.5281/zenodo.8015827 (2023).

Download references

Acknowledgements

This work was supported by the National Key R&D Program of China (2017YFA0505700 to P.X.), Projects of International Cooperation and Exchanges NSFC (81820108015 to P.X.), Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT320002 to P.X.), the Natural Science Foundation Project of China (81971296 and 82171523 to P.Z., 82101596 to J.P., 82201688 to J.W., 82201683 to L.L.), Chongqing Science and Technology Commission (cstc2019 jcyjjqX0009 to P.Z., cstc2021 jscx-msxm0026 to J.W.), Program for Youth Innovation in Future Medicine, Chongqing Medical University to P.Z., China Postdoctoral Science Foundation (2020TQ0393 to L.L., 2021TQ0396 and 2021MD703928 to H.Z., 2021MD693926 to H.W., 2022MD713717 to J.W.), Chongqing Talents Plan for Young Talents (CQYC202105017 to P.Z.) and institutional funds from the State University of New York (SUNY) Upstate Medical University. This paper is subject to the SUNY Open Access Policy. We thank J. Hu (ShanghaiTech University), T. F. Yuan (Shanghai Jiao Tong University), J. Yang and Y. He (Beijing Anding Hospital, Capital Medical University) for technical advice and helpful discussions.

Author information

Author notes

  1. These authors contributed equally: Jing Wu, Yifan Li, Yu Huang.

  2. These authors jointly supervised this work: Peng Zheng, Peng Xie.

Authors and Affiliations

  1. NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Jing Wu, Yifan Li, Yu Huang, Lanxiang Liu, Hanping Zhang, Xunmin Tan, Ke Cheng, Yiyun Liu, Juncai Pu, Haiyang Wang, Qingyuan Wu & Peng Xie

  2. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Jing Wu, Yifan Li, Yu Huang, Hanping Zhang, Xunmin Tan, Yiyun Liu, Juncai Pu, Haiyang Wang, Peng Zheng & Peng Xie

  3. Jinfeng Laboratory, Chongqing, China

    Jing Wu, Yifan Li, Yu Huang, Lanxiang Liu, Hanping Zhang, Xunmin Tan, Yiyun Liu, Haiyang Wang, Peng Zheng & Peng Xie

  4. Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China

    Lanxiang Liu

  5. McGill Group for Suicide Studies, Douglas Mental Health University Institute, Montreal, Quebec, Canada

    Corina Nagy & Gustavo Turecki

  6. Department of Neurology, Chongqing University Three Gorges Hospital, Chongqing, China

    Qingyuan Wu

  7. Department of Psychiatry, College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA

    Seth W. Perry, Ma-Li Wong & Julio Licinio

  8. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Gustavo Turecki

  9. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

    Gustavo Turecki

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  1. Jing Wu
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  2. Yifan Li
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  4. Lanxiang Liu
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  5. Hanping Zhang
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  6. Corina Nagy
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  7. Xunmin Tan
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  8. Ke Cheng
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  9. Yiyun Liu
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  10. Juncai Pu
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  11. Haiyang Wang
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  17. Peng Zheng
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  18. Peng Xie
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P.X. and P.Z. designed the experiments. J.W., Y.H., H.Z., K.C. and H.W. collected the dIPFC of macaques. J.W., Y.L., Y.H. and J.P. performed the sc- and snRNA-seq analysis. J.W., Y.L., Y.H. and L.L. performed the ST analysis. J.W., Y.L., Y.H., X.T., Y.L. and Q.W. observed animal behaviors. J.W. and P.Z. drafted the manuscript. P.X., P.Z., J.W., S.W.P., M.-L.W., J.L., C.N. and G.T. revised the manuscript for intellectual content.

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Correspondence to Peng Zheng or Peng Xie.

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Extended data

Extended Data Fig. 1 Schematic of macaque behavioral observation.

a-b, conflicts were recorded with two webcams on either side of the enclosure. The top three populations with the highest confliction behaviors were screened for further social rank evaluation; detailed depressive-like behaviors were recorded with three webcams to eliminate the coverage holes. The depressive-like behaviors of individuals in the top 3 and bottom 4 were analyzed. A trough was located in the middle of each enclosure. Each enclosure measures 8.0 m × 3.0 m × 3.0 m (L×W×H). c, the social rank was stable with or without male presence calculated by David’s score. Two-sided Pearson correlation test, error bands represent the 95% confidence interval of linear model.

Extended Data Fig. 2 Nuclei clustering, cell type annotation, and cell-specific genes.

a, QC analysis of snRNA-seq; 8,750 doublets and 136,231 single nuclei were identified. b, heatmap indicated the enrichment of expressional signatures between macaque and human single-nuclei clusters in dlPFC. All the nuclei clusters were identified using graph-based clustering method through Seurat. The annotation of macaque cell types was displayed at the top, and the annotation of human cell types was displayed at the right of the heatmap. The cell type annotation of human dlPFC clusters was independently performed and previously published34. c, dissected UMAP plot showed a coincident distribution of nuclei clustering of in the three groups. d, Venn diagram showed only 5 overlapped DEGs between neuron and glial cells. e, Venn diagram showed 12 overlapped DEGs in 6 cell types and 207 unique DEGs. f, KEGG enrichment analysis showed differential involved pathways of cell-specific DEGs.

Extended Data Fig. 3 DEGs and associated signaling pathways in each cell type.

a, volcano plot of DEGs. DEGs were identified with the Wilcox rank-sum test (two-sided) in R package Seurat, and the significance threshold was logFC > 0.25, FDR < 0.05, and min.pct > 0.1. b, KEGG enrichment analysis in each cell type. Blue and red bars indicated the proportion of up and down-regulated DEGs in involved pathways (Hyper geometric test). c, The number of specific activated receptor–ligand pairs in dlPFC of macaques. The activated pairs in DLs were significantly higher (~4 fold) than in controls. Microglia involved the primary (54%, 56/104) DLs-specific communications. The background communication pairs were identified shared pairs between DLs and controls.

Extended Data Fig. 4 Depressive pattern and rank pattern DEGs involved in different processes.

a, DEG with D-pattern (depressive pattern, left) showed differential expression in the DLs and non-DLs groups (RES and controls), which was specifically associated with depressive-like behaviors. DEG with R-pattern (rank pattern, right) showed differential expression between high rank (controls) and low rank (DL and RES), which was specifically associated with social stress. b-c, enrichment analysis showed different involved pathways between D-pattern and R-pattern. D-pattern DEGs were mainly contributed by Mic and involved the pro-inflammatory pathways. R-pattern DEGs were mainly contributed by Ast and involved the synthesis of neurotransmitter.

Extended Data Fig. 5 Microglial subpopulations were not significantly activated by dissociation enzyme and considered as ‘artifactual’.

a, the number (left) or proportion (right) of microglia were not significantly different between these three groups. b, boxplot showed a significantly increased PIMID proportion in DLs (Controls, n = 7; DLs, n = 7; RES, n = 5. box=25-75th percentiles, whiskers=Tukey, horizontal line in box=median). c, activation score based on the exAM signature gene modules and plotted on UMAP coordinates. d, expression of exAM gene markers in microglia subpopulations. 10 genes (SOCS3, CCL3, CCL4, KLF2, HIST2H2AA1, HIST1H4I, HSPA1A, HSPA1B, HIST1H2BC, HIST1H1C) were not expressed in any microglia. e, scoring results of microglia subpopulations based on the activation score in each subpopulation.

Extended Data Fig. 6 PIMID expression signatures.

a, normalized expression of 4 marker genes of PIMID. b, overview of dlPFC slices. Each slice contained clear cortical structures, including edges, grey matter and white matter. n = 5 per group. c, Immunofluorescence staining and quantification of IQGAP2+ microglia in dlPFC. IQGAP2+IBA1+ colocalization cells were identified as Mic03 cells. IQGAP2 was identified as a gene marker of Mic03 (FDR = 1.095 e-212) in sn-RNA data. IBA1 was a widely used microglial marker. The IQGAP2+ microglia were identified following a criterion of IQGAP2+IBA1+ cells with clear microglial features including long processes and a central soma. Two representative cells were photographed at 40X magnification, IQGAP2+IBA1+(left, yellow arrow) and IBA1+ microglia (right, white arrow).

Extended Data Fig. 7 Functional profiling of PIMID.

a, activated and inactivated gene sets in PIMID, identified by QUSAGE. b-c, KEGG (b) and GO (c) enrichment of PIMID marker genes indicated pro-inflammatory processes (Hyper geometric test). GO terms, BP, biological process; MF, molecular function; CC, cell component.

Extended Data Fig. 8 Digestion-obtained microglia exhibited expression signatures and differences between groups similar to microglial nuclei.

a-b, UMAP visualization showing clustering of 12,374 digested living cells, colored by unsupervised cluster (a) and annotated cell types (b). c, gene markers for annotating major cell types. d, digested microglial cluster8 (Sc08) and microglial nuclei cluster3 (Mic03, also called PIMID) were highly aligned in coordinate space (highlighted by red circles), also see Fig. 4j. e, mapping relationship between the subpopulations of microglial cells and nuclei. The color depth indicates the odds ratio (OR). Sc08 and Sn03 were highly identical (FDR = 1.21×10−134, OR = 18.19, Fisher exact test, two-sided). f, QUSAGE analysis shows the pro-inflammatory profiles both in subcluster 8 using enzymic digestion. g-i, a higher amount and proportion of Sc08 were detected in DLs in digested microglial cells (n = 1 per group).

Extended Data Fig. 9 Unsupervised clustering of dlPFC ST hybridization spots showed layer distribution.

a, HE staining of dlPFC ST slices (Controls, n = 3; DLs, n = 2; RESs, n = 3). The boxes indicated the hybridized spot area (6.5 mm×6.5 mm). b-i, spot clusters showed layer distribution. Left, the merged plots of HE-stained image and in situ clusters. Right, the UMAP plots of each cluster.

Extended Data Fig. 10 Depressive-like behavioral and spatial location of cell-type-specific WGCNA modules.

a-f, the top heatmap indicates the behavioral association of Ast(a), Mic(b), Oli(c), Exn(d), Int(e) and OPC(f). The color (blue to red) indicates the coefficient between the expression of gene modules and depressive-like behaviors (*p < 0.05, #p < 0.01, Pearson correlation, two-sided); the bottom heatmap indicates the enrichment of gene modules in the dlPFC ST regions, color depth indicates the Fisher exact test odds ratio. g, a table summarizing depressive-like behaviors and associated gene modules. Check marks indicate whether a gene module identified in cell types significantly correlated with depressive-like behaviors. Red check marks indicate the depressive-like behavior-associated gene modules were also spatially specific in some ST regions.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8.

Reporting Summary

Supplementary Tables 1–15

Supplementary Tables 1–15.

Supplementary Tables 16–27

Supplementary Tables 16–27.

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Wu, J., Li, Y., Huang, Y. et al. Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors. Nat Neurosci 26, 1352–1364 (2023). https://doi.org/10.1038/s41593-023-01379-4

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