Unraveling dynamic immunological landscapes in intracerebral hemorrhage: insights from single-cell and spatial transcriptomic profiling

Abstract

Intracerebral hemorrhage (ICH) poses a formidable challenge in stroke management, with limited therapeutic options, particularly in the realm of immune-targeted interventions. Clinical trials targeting immune responses post-ICH have encountered setbacks, potentially attributable to the substantial cellular heterogeneity and intricate intercellular networks within the brain. Here, we present a pioneering investigation utilizing single-cell RNA sequencing and spatial transcriptome profiling at hyperacute (1 h), acute (24 h), and subacute (7 days) intervals post-ICH, aimed at unraveling the dynamic immunological landscape and spatial distributions within the cerebral tissue. Our comprehensive analysis revealed distinct cell differentiation patterns among myeloid and lymphocyte populations, along with delineated spatial distributions across various brain regions. Notably, we identified a subset of lymphocytes characterized by the expression of Spp1 and Lyz2, termed macrophage-associated lymphocytes, which exhibited close interactions with myeloid cells. Specifically, we observed prominent interactions between Lgmn+Macro-T cells and microglia through the spp1–cd44 pathway during the acute phase post-ICH in the choroid plexus. These findings represent a significant advancement in our understanding of immune cell dynamics at single-cell resolution across distinct post-ICH time points, thereby laying the groundwork for exploring critical temporal windows and informing the development of targeted therapeutic strategies.

Keywords

intracerebral hemorrhage / single-cell sequencing / spatial transcriptome / immune microenvironment / myeloid and lymphocytes

References

1	W Johnson, O Onuma, M Owolabi, S Sachdev. Stroke: a global response is needed. Bull World Health Organ. 2016;94(9):634-634A.
2	R Liu, P Song, X Gu, et al. Comprehensive landscape of immune infiltration and aberrant pathway activation in ischemic stroke. Front Immunol. 2021;12:766724.
3	C Iadecola, MS Buckwalter, J Anrather. Immune responses to stroke: mechanisms, modulation, and therapeutic potential. J Clin Invest. 2020;130(6):2777-2788.
4	WE Allen, TR Blosser, ZA Sullivan, C Dulac, X Zhuang. Molecular and spatial signatures of mouse brain aging at single-cell resolution. Cell. 2023;186(1):194-208. e118.
5	RL Chua, S Lukassen, S Trump, et al. COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nat Biotechnol. 2020;38(8):970-979.
6	MU Wagenhauser, J Mulorz, KJ Krott, et al. Crosstalk of platelets with macrophages and fibroblasts aggravates inflammation, aortic wall stiffening, and osteopontin release in abdominal aortic aneurysm. Cardiovasc Res. 2024;120(4):417-432.
7	E Wieczorek, A Ozyhar. Transthyretin: from structural stability to osteoarticular and cardiovascular diseases. Cells. 2021;10(7):1768.
8	J Qi, H Sun, Y Zhang, et al. Single-cell and spatial analysis reveal interaction of FAP(+) fibroblasts and SPP1(+) macrophages in colorectal cancer. Nat Commun. 2022;13(1):1742.
9	J Zhu, Y Fan, Y Xiong, et al. Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. Exp Mol Med. 2022;54(11):2060-2076.
10	G Gerganova, A Riddell, AA Miller. CNS border-associated macrophages in the homeostatic and ischaemic brain. Pharmacol Ther. 2022;240:108220.
11	A Silvin, S Uderhardt, C Piot, et al. Dual ontogeny of disease-associated microglia and disease inflammatory macrophages in aging and neurodegeneration. Immunity. 2022;55(8):1448-1465. e1446.
12	A Morath, WW Schamel. alphabeta and gammadelta T cell receptors: similar but different. J Leukoc Biol. 2020;107(6):1045-1055.
13	BA Goods, MH Askenase, E Markarian, et al. Leukocyte dynamics after intracerebral hemorrhage in a living patient reveal rapid adaptations to tissue milieu. JCI Insight. 2021;6(6):e145857.
14	S Mei, Y Shao, Y Fang, et al. The changes of leukocytes in brain and blood after intracerebral hemorrhage. Front Immunol. 2021;12:617163.
15	HW Morrison, JA Filosa. A quantitative spatiotemporal analysis of microglia morphology during ischemic stroke and reperfusion. J Neuroinflammation. 2013;10:4.
16	MK Jha, S Jeon, M Jin, et al. The pivotal role played by lipocalin-2 in chronic inflammatory pain. Exp Neurol. 2014;254:41-53.
17	W Ni, M Zheng, G Xi, RF Keep, Y Hua. Role of lipocalin-2 in brain injury after intracerebral hemorrhage. J Cereb Blood Flow Metab. 2015;35(9):1454-1461.
18	M Luo, Z Qiu, X Tang, et al. Inhibiting cyclin B1-treated pontine infarction by suppressing proliferation of SPP1+ microglia. Mol Neurobiol. 2023;60(4):1782-1796.
19	A Uzdensky, S Demyanenko, G Fedorenko, T Lapteva, A Fedorenko. Protein profile and morphological alterations in penumbra after focal photothrombotic infarction in the rat cerebral cortex. Mol Neurobiol. 2017;54(6):4172-4188.
20	S Li, S Chen, Y Wang, et al. Direct targeting of DOCK4 by miRNA-181d in oxygen-glucose deprivation/reoxygenation-mediated neuronal injury. Lipids Health Dis. 2023;22(1):34.
21	A Garcia-Culebras, V Duran-Laforet, C Pena-Martinez, et al. Myeloid cells as therapeutic targets in neuroinflammation after stroke: specific roles of neutrophils and neutrophil-platelet interactions. J Cereb Blood Flow Metab. 2018;38(12):2150-2164.
22	LF Reichardt. Neurotrophin-regulated signalling pathways. Philos Trans R Soc Lond B Biol Sci. 2006;361(1473):1545-1564.
23	B Zhou, Y Luo, N Ji, C Hu, Y Lu. Orosomucoid 2 maintains hepatic lipid homeostasis through suppression of de novo lipogenesis. Nat Metab. 2022;4(9):1185-1201.
24	C Howells, AK West, RS Chung. Neuronal growth-inhibitory factor (metallothionein-3): evaluation of the biological function of growth-inhibitory factor in the injured and neurodegenerative brain. FEBS J. 2010;277(14):2931-2939.
25	E Kim, S Cho. Microglia and monocyte-derived macrophages in stroke. Neurotherapeutics. 2016;13(4):702-718.
26	Y Fang, L Huang, X Wang, et al. A new perspective on cerebrospinal fluid dynamics after subarachnoid hemorrhage: from normal physiology to pathophysiological changes. J Cereb Blood Flow Metab. 2022;42(4):543-558.
27	P Solar, I Klusakova, R Jancalek, P Dubovy, M Joukal. Subarachnoid hemorrhage induces dynamic immune cell reactions in the choroid plexus. Front Cell Neurosci. 2020;14:18.
28	Z Zhang, Q Tan, P Guo, et al. NLRP3 inflammasome-mediated choroid plexus hypersecretion contributes to hydrocephalus after intraventricular hemorrhage via phosphorylated NKCC1 channels. J Neuroinflammation. 2022;19(1):163.
29	J Cheng, J Zhang, Z Wu, X Sun. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19. Brief Bioinform. 2021;22(2):988-1005.
30	Y Hao, S Hao, E Andersen-Nissen, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587. e3529.
31	T Stuart, A Butler, P Hoffman, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902. e1821.
32	A Butler, P Hoffman, P Smibert, E Papalexi, R Satija. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420.
33	R Satija, JA Farrell, D Gennert, AF Schier, A Regev. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495-502.
34	T Wu, E Hu, S Xu, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141.
35	C Trapnell, D Cacchiarelli, J Grimsby, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-386.
36	X Qiu, A Hill, J Packer, D Lin, YA Ma, C Trapnell. Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017;14(3):309-315.
37	X Qiu, Q Mao, Y Tang, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979-982.
38	K Street, D Risso, RB Fletcher, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. Bmc Genomics [Electronic Resource]. 2018;19(1):477.
39	GS Gulati, SS Sikandar, DJ Wesche, et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science. 2020;367(6476):405-411.
40	Y Wang, R Wang, S Zhang, et al. iTALK: an R package to characterize and illustrate intercellular communication. Biorxiv. 2019:507871.
41	S Jin, CF Guerrero-Juarez, L Zhang, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.
42	Y Ma, X Zhou. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol. 2022;40(9):1349-1359.
43	R Moncada, D Barkley, F Wagner, et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol. 2020;38(3):333-342.
44	T Chen, X Chen, S Zhang, et al. The genome sequence archive family: toward explosive data growth and diverse data types. Genomics Proteomics Bioinformatics. 2021;19(4):578-583.
45	C-N Members, Partners. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res. 2022;50(D1):D27-D38.