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• W2891901614 abstract "Scientific Report11 September 2018Open Access Source DataTransparent process Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures Carole Sousa Carole Sousa NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Doctoral School of Science and Technology, University of Luxembourg, Esch-sur-Alzette, Luxembourg Search for more papers by this author Anna Golebiewska Anna Golebiewska NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Suresh K Poovathingal Suresh K Poovathingal Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Single Cell Analytics & Microfluidics Core, Vlaams Instituut voor Biotechnologie-KU Leuven, Leuven, Belgium Search for more papers by this author Tony Kaoma Tony Kaoma Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Yolanda Pires-Afonso Yolanda Pires-Afonso NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Doctoral School of Science and Technology, University of Luxembourg, Esch-sur-Alzette, Luxembourg Search for more papers by this author Silvia Martina Silvia Martina Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Djalil Coowar Djalil Coowar Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Francisco Azuaje Francisco Azuaje Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Alexander Skupin Alexander Skupin Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg National Centre for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA Search for more papers by this author Rudi Balling Rudi Balling Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Knut Biber Knut Biber orcid.org/0000-0002-8815-1705 Section Molecular Psychiatry, Department for Psychiatry and Psychotherapy, Laboratory of Translational Psychiatry, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany Section Medical Physiology, Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Search for more papers by this author Simone P Niclou Simone P Niclou orcid.org/0000-0002-3417-9534 NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Department of Biomedicine, KG Jebsen Brain Tumour Research Center, University of Bergen, Bergen, Norway Search for more papers by this author Alessandro Michelucci Corresponding Author Alessandro Michelucci [email protected] orcid.org/0000-0003-1230-061X NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Carole Sousa Carole Sousa NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Doctoral School of Science and Technology, University of Luxembourg, Esch-sur-Alzette, Luxembourg Search for more papers by this author Anna Golebiewska Anna Golebiewska NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Suresh K Poovathingal Suresh K Poovathingal Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Single Cell Analytics & Microfluidics Core, Vlaams Instituut voor Biotechnologie-KU Leuven, Leuven, Belgium Search for more papers by this author Tony Kaoma Tony Kaoma Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Yolanda Pires-Afonso Yolanda Pires-Afonso NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Doctoral School of Science and Technology, University of Luxembourg, Esch-sur-Alzette, Luxembourg Search for more papers by this author Silvia Martina Silvia Martina Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Djalil Coowar Djalil Coowar Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Francisco Azuaje Francisco Azuaje Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Search for more papers by this author Alexander Skupin Alexander Skupin Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg National Centre for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA Search for more papers by this author Rudi Balling Rudi Balling Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Knut Biber Knut Biber orcid.org/0000-0002-8815-1705 Section Molecular Psychiatry, Department for Psychiatry and Psychotherapy, Laboratory of Translational Psychiatry, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany Section Medical Physiology, Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Search for more papers by this author Simone P Niclou Simone P Niclou orcid.org/0000-0002-3417-9534 NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Department of Biomedicine, KG Jebsen Brain Tumour Research Center, University of Bergen, Bergen, Norway Search for more papers by this author Alessandro Michelucci Corresponding Author Alessandro Michelucci [email protected] orcid.org/0000-0003-1230-061X NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg Search for more papers by this author Author Information Carole Sousa1,2,3, Anna Golebiewska1, Suresh K Poovathingal2,4, Tony Kaoma5, Yolanda Pires-Afonso1,3, Silvia Martina2, Djalil Coowar2, Francisco Azuaje5, Alexander Skupin2,6, Rudi Balling2, Knut Biber7,8, Simone P Niclou1,9 and Alessandro Michelucci *,1,2 1NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg 2Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg 3Doctoral School of Science and Technology, University of Luxembourg, Esch-sur-Alzette, Luxembourg 4Single Cell Analytics & Microfluidics Core, Vlaams Instituut voor Biotechnologie-KU Leuven, Leuven, Belgium 5Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg 6National Centre for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA 7Section Molecular Psychiatry, Department for Psychiatry and Psychotherapy, Laboratory of Translational Psychiatry, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany 8Section Medical Physiology, Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands 9Department of Biomedicine, KG Jebsen Brain Tumour Research Center, University of Bergen, Bergen, Norway *Corresponding author. Tel: +352 26970 263; E-mail: [email protected]ih.lu EMBO Reports (2018)19:e46171https://doi.org/10.15252/embr.201846171 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Microglia are specialized parenchymal-resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are thought to worsen CNS diseases; nevertheless, their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single-cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)-injected mice. By excluding the contribution of other immune CNS-resident and peripheral cells, we show that microglia isolated from LPS-injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglial activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease-associated profiles. These results provide insights into microglial heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases. Synopsis Using single-cell transcriptomics and multicolour flow cytometry this study presents comprehensive profiles of microglia in LPS-injected mice, providing insight into microglia heterogeneity, and establishing a resource for the identification of specific phenotypes in CNS disorders. Microglia homeostatic signatures are mainly lost upon acute systemic inflammation. Inflammation-induced microglia segregate into two distinct reactive states. Inflammation-induced microglia signatures are distinct from neurodegenerative disease-associated profiles. Introduction The healthy brain hosts distinct and specialized populations of tissue-resident macrophages strategically placed in the parenchyma, perivascular spaces, meninges and choroid plexus where they coordinate homeostatic and immune surveillance functions 1. As the only parenchymal-resident immune cells of the central nervous system (CNS), microglia act as critical effectors and regulators of changes in the CNS during development and adult homeostasis. Their ontogeny, together with the absence of turnover from the periphery and the exceptional environment of the CNS, makes microglia a unique immune cell population 2. By sensing any disruption of CNS homeostasis, microglia rapidly change their gene expression programmes and functional profiles. Recent genome-wide transcriptional studies revealed a unique molecular signature selectively expressed in homeostatic microglia 3-6 that is lost in disease and during ageing 4, 7-17. Microglia coordinate immune responses between the periphery and the CNS as they perceive and propagate inflammatory signals initiated outside the CNS 18. A multitude of signals received from the CNS environment as well as from the periphery induce microglial responses towards phenotypes that ultimately may support or harm neuronal health 2, 19. Although neuroinflammation and its associated immune responses are often linked to neurodegeneration, the inflammatory response per se provides a primary, transient and self-limiting defence mechanism, by which harmful stimuli are resolved and tissue damage is repaired 20. Disruption of CNS homeostasis, neuronal deterioration and inflammation are common pathophysiological features of several neurodegenerative diseases. In this context, chronic inflammation is likely to be triggered by abnormal protein deposition, by signals elicited by injured neurons and synapses or by impaired pro- and anti-inflammatory regulatory mechanisms that ultimately exacerbate the neurodegenerative process 21. Dysfunctional microglial responses are believed to worsen CNS diseases 22; nevertheless, their impact during the neuroinflammatory processes remains largely obscure. In recent years, single-cell RNA sequencing investigations have emerged as a remarkable method to depict heterogeneous cell populations and measure cell-to-cell expression variability of thousands of genes 23-25. In the murine and human brains, single-cell RNA sequencing analyses have revealed neural and glial cell heterogeneity 26-30. Similarly, the complexity of immune cell types has been recently unravelled 31. However, although recent studies have elucidated microglia signatures associated with inflammatory conditions at the bulk level 4, 16, 32, it is still not clear whether all microglial cells uniformly react to the inflammatory stimuli. To elucidate the heterogeneity of microglial responses towards systemic inflammation, we here analysed the effect of a peripheral injection of the Gram-negative bacterial endotoxin lipopolysaccharide (LPS) in 3- to 4-month-old C57BL/6N mice using a combination of multicolour flow cytometry and single-cell RNA sequencing analyses. LPS is a well-known immunostimulant used to mimic inflammatory and infectious conditions inducing immune responses associated with sickness behaviour in mice and humans 33, 34. Notably, it has been shown that repeated peripheral injections of LPS in mice induce neurodegeneration, while a single-dose injection of LPS induces acute inflammatory, but not neurodegenerative processes 35. By our approach, we have identified distinct microglial activated profiles under acute inflammatory conditions, which differ from the recently described disease-associated phenotypes 14. Understanding the specific molecular triggers and the subsequent genetic programmes defining microglia under homeostatic, inflammatory and neurodegenerative conditions at the single-cell level is a fundamental step to further uncover the multifaceted nature of microglia, thus opening new windows to design novel therapeutic strategies to restore, for example, efficient inflammatory immune responses in CNS diseases. Results and Discussion Acutely isolated CD11b+CD45int cells express high levels of microglial homeostatic genes and represent a specific resident immune cell population Cell-specific transcriptomic analyses are critically dependent on isolation protocols to obtain pure populations resembling their physiological profiles. To characterize microglia close to their proper environment, mouse brains were mechanically dissociated into single-cell suspension with all the steps performed at 4°C 36. Since microglia in the mouse brain represent only 10% of the cells, CD11b+CD45int microglia were purified from other CNS and immune cells, including CD11b+CD45high macrophages and CD11b−CD45high lymphocytes, by FACS, as described previously (Figs 1A and EV1) 37. To verify accurate microglial enrichment, we compared gene expression levels of specific CNS cell type markers between RNA extracted from unsorted total brain cells and CD11b+CD45int sorted microglia (Fig 1B). We analysed the expression levels of microglial homeostatic genes (Olfml3, Fcrls, Tmem119, Siglech, Gpr34, P2ry12) as well as astrocytic (Gfap, Gjb6, Ntsr2, Aldh1l1), oligodendrocytic (Mobp, Mog, Cldn11) and neuronal (Tubb3, Vglut1, NeuN) markers. As expected, microglial markers were highly expressed in CD11b+CD45int sorted cells, whereas astrocytic, oligodendrocytic and neuronal markers were undetectable or detectable at background levels (Figs 1B and EV1). We next investigated whether CD11b+CD45int population contained resident non-parenchymal macrophages, such as perivascular macrophages. This was inferred using CD206 as an additional marker for resident macrophages 38. Under homeostatic conditions, CD11b+CD45int microglia contained only 0.04 ± 0.02% CD206+ cells, while CD11b+CD45high cells contained 24.7 ± 3.8% CD206+ resident macrophages (Fig 1C and D). Similar results were obtained for the dendritic cell marker CD11c and the monocytic markers Ly6C and CCR2 (Fig EV1). Taken together, these results show that our approach highly discriminates pure and not activated microglial populations from other resident CNS cells. Figure 1. Characterization of acutely isolated CD11b+CD45int cells FACS gating strategy representative of five independent experiments adopted to sort CD11b+CD45int microglia distinctly from CD11b+CD45high resident macrophages and CD11b−CD45high lymphocytes. Analysis of relative transcript levels of CD11b+CD45int FACS-sorted microglia compared with whole brain tissue by qPCR. Gene expression levels of microglia (Olfml3, Fcrls, Tmem119, Siglech, Gpr34, P2ry12), astrocyte (Gfap, Gjb6, Ntsr2, Aldh1l1), oligodendrocyte (Mobp, Mog, Cldn1) and neuron (Tubb3, Vglut1, NeuN) markers. Bars represent mean (n = 4; pool of one female and one male per biological replicate) of relative expression (Gapdh as housekeeping gene) ± SEM (*P < 0.05; **P < 0.01 by two-tailed Student's t-test). N.D., not detected. Representative quantification of CD206 expression in CD11b+CD45int microglia and CD11b+CD45high resident macrophages. Values denote the percentage of the mean ± SEM of five independent experiments. Representative images of two independent experiments showing microglia, resident macrophages and lymphocytes acquired with ImageStream imaging cytometer (Amnis) based on CD45, CD11b and CD206 expression levels (scale bar represents 7 μm). Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Microglial isolation and characterization FACS gating strategy representative of five independent experiments adopted to sort CD11b+CD45int microglial. Cells were distinguished from debris using forward (FSC-A) and side (SSC-A) scatters, followed by cell doublet and aggregate elimination (SSC-H/SSC-A). Dead cells were gated out by their strong positivity for the dead cell discrimination marker Hoechst. Single viable microglial cells were gated as CD11b+CD45int. Analysis of microglial purity by qPCR. Gene expression levels of microglial-specific genes (Itgb5, Sall1, Hexb, Tgfb, Aif1, Cx3cr1, Mertk, Ctss, Tyrobp, Trem2, Itgam, Itgax) in purified microglia compared to whole brain. Bars represent mean (n = 4; one female and one male per sample) of relative expression (Gapdh as housekeeping gene) ± SEM (*P < 0.05; **P < 0.01 by two-tailed Student's t-test). Quantification of CD11c, Ly6C and CCR2 expression in CD11b+CD45int microglia and CD11b+CD45high resident macrophages representative of five independent experiments. Values denote the percentage of the mean ± SEM of five independent experiments. Download figure Download PowerPoint Microglia isolated from LPS-injected mice show a classical activated pro-inflammatory profile accompanied by a decreased homeostatic signature The response of microglia towards specific pro- or anti-inflammatory cues in vitro has been extensively studied 39. Treatment of primary microglial cells with TGF-β, LPS or IL-4 generates, respectively, the so-called M0 homeostatic, M1 pro-inflammatory and M2 anti-inflammatory states defined by specific gene signatures 5, 40. However, our understanding towards the reaction of microglia under inflammatory conditions in vivo is only starting to emerge. To comprehensively investigate the effect of a systemic inflammatory and/or infectious state on microglia, we peripherally injected mice with LPS (4 μg/g body) 24 h prior analysis. It has been shown that a single-dose injection of LPS induces acute inflammatory, but not neurodegenerative processes 35. We isolated CD11b+CD45int cells from LPS-injected mice and compared mRNA levels of specific genes to the corresponding cells isolated from saline-injected control mice by qPCR. In agreement with previous studies 32, 41, the expression levels of homeostatic (e.g. Olfml3, Fcrls, Tmem119, Siglech, Gpr34, P2ry12, Mef2c), phagocytic (Tyrobp and Trem2) and anti-inflammatory genes (e.g. Mrc1 and Arg1) were highly decreased in microglia isolated from LPS-injected mice compared to untreated mice, while the classical pro-inflammatory genes (e.g. Il1b, Tnf and Ccl2) were markedly increased (Figs 2A and EV2). Notably, it has been recently shown that signals from the CNS microenvironment have considerable influence in shaping, maintaining and reinforcing microglial identity by regulating expression and establishing distinct chromatin landscapes surrounding enhancer regions 42-44. Changes in chromatin remodelers associate with changes in the expression of nearby genes. Specifically, MEF2C binding sites were shown to be over-enriched in enhancer regions of microglial-specific genes 42 and the loss of MEF2C was associated with priming of microglia 45. In line with these observations, Mef2c expression levels were highly decreased in microglia isolated from LPS-injected mice compared to naïve mice. Figure 2. LPS stimulation induces an intrinsic loss of the microglia homeostatic signature A–D. Three- to four-month-old C57BL/6N mice were treated with an acute dose of LPS (4 μg/g body) or vehicle (saline). Microglia (pool of two mice per group per replicate; one female and one male) were FACS-sorted 24 h later. (A) Gene expression levels of microglial homeostatic (Olfml3, Fcrls, Tmem119, Siglech, Gpr34, P2ry12, Mef2c), phagocytic (Tyrobp, Trem2) and inflammatory (Il1b, Tnf, Ccl2, Mrc1, Arg1) markers were analysed by qPCR. Bars represent mean of relative expression (% of saline; Gapdh as housekeeping gene) ± SEM (*P < 0.05; **P < 0.01 by two-tailed Student's t-test; n = 4). (B) Representative multicolour flow cytometry analysis of five independent experiments showing CD11b- and CD45-positive populations in single viable cells in saline or LPS-injected mouse brains. (C) Representative multicolour flow cytometry analysis showing the percentage of the mean ± SEM of five independent experiments of Ly6C- and CD206-expressing cells in CD11b+CD45int cells from saline or LPS-injected mice. (D) Gene expression levels of the monocytic markers Ly6c1 and Ccr2 in purified microglia (n = 4) and isolated bone marrow monocytes (n = 2) by qPCR. Bars represent mean of relative expression (Gapdh as housekeeping gene) ± SEM (**P < 0.01 by two-tailed Student's t-test). E. Primary adult microglia were cultivated in the presence of TGF-β (50 μg/ml) and M-CSF (10 ng/ml), while neonatal cells were stimulated for 24 h with TGF-β (50 μg/ml) followed by 6 h of stimulation with LPS (1 ng/ml) or left untreated. Expression levels of microglial homeostatic (Olfml3, Tmem119, Gpr34) and inflammatory (Il1b, Tnf, Ccl2) genes were analysed by qPCR. Bars represent mean of relative expression (Gapdh as housekeeping gene) ± SEM (*P < 0.05; **P < 0.01 by two-tailed Student's t-test). Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Ex vivo and in vitro microglial characterization A, B. Three- to four-month-old mice were treated with an acute dose of LPS (4 μg/g body) or vehicle (saline). Microglial (pool of two mice per group; one female and one male) were isolated 24 h later. (A) Gene expression levels of microglial homeostatic genes (Itgb5, Sall1, Hexb, Tgfb, Mertk, Ctss, Itgam, Cx3cr1) were analysed by qPCR. Bars represent mean of relative expression (% of saline; Gapdh as housekeeping gene) ± SEM (**P < 0.01 by two-tailed Student's t-test; n = 4). (B) Representative multicolour flow cytometry analysis of five independent experiments showing CD45, CD11b, CD86 and CD11c expression levels in CD11b+CD45int microglia of saline or LPS-injected mouse brains. C. Representative results of two independent experiments showing the purity of MACS-isolated bone marrow monocytes based on the expression levels of the monocytic marker Ly6C. D. Comparison of the homeostatic signature (Tmem119, Siglech, Gpr34, P2ry12) between primary and acutely isolated microglia. Primary adult microglia were cultivated in the presence of TGF-β (50 μg/ml) and M-CSF (10 ng/ml), while neonatal cells were treated for 24 h with TGF-β or left untreated. Gene expression levels were analysed by qPCR and normalized using Gapdh as housekeeping gene. Bars represent mean ± SEM (**P < 0.01 by two-tailed Student's t-test; n = 3). Download figure Download PowerPoint We verified that this signature is microglia-specific, and it is not affected by LPS-activated immune peripheral cells, such as lymphocytes (CD11b−CD45high cells) and peripheral monocytes/macrophages (CD11b+CD45high cells), as no significant differences were detected between cellular populations present in brains of saline- and LPS-injected mice (Figs 2B and EV2). Importantly, CD11b+CD45int FACS-gated cells contained very rare (< 0.25%) Ly6C+ putative monocytes and (< 0.1%) CD206+ putative resident macrophages (Fig 2C). Also, the expression of monocytic markers Ly6c1 and Ccr2 was very low in CD11b+CD45int microglia compared to bone marrow-isolated monocytes with no significant differences under LPS exposure (Figs 2D and EV2). In order to further assess that the decrease in the homeostatic signature under inflammatory conditions is not due to the presence of other immune cell types, but it is an intrinsic property of microglial cells, we also analysed the effect of LPS on cultivated microglial from adult and neonatal mice. As expected, the expression level of the homeostatic genes was markedly decreased in cultivated cells when compared to acutely isolated microglia (Fig EV2) 5. Thus, we cultivated adult microglia in the presence of TGF-β (50 μg/ml) and M-CSF (10 ng/ml) or neonatal cells with TGF-β 24 h prior treatment with LPS to induce the expression of the homeostatic genes, although at a lower extent than in ex vivo isolated cells (Fig EV2). Cells treated with LPS (1 ng/ml) for 6 h showed a dramatic decrease of the expression levels of the homeostatic gene markers, such as Olfml3, Tmem119 and Gpr34, accompanied by enhanced expression levels of inflammatory marker genes, such as Il1b, Tnf and Ccl2 both in adult and in neonatal microglia when compared to cells treated with TGF-β only (Fig 2E). In the healthy brain, TGF-β is expressed at low levels by both neurons and glial cells 46, 47, while its expression is increased upon injury 48, 49, hypoxia–ischaemia 50 and neurodegeneration 51, 52. SMAD and signal transducer and activator of transcription (STAT) proteins are key signal transducers and transcription factors controlling TGF-β downstream signalling 53. Specifically, STAT3 and suppressor of cytokine signalling 3 (SOCS3) regulate inflammatory responses 54. The binding of SOCS3 to both JAK kinase and the cytokine receptor results in the inhibition of STAT3 activation. In our analysis, microglial cells treated with LPS showed increased amounts of STAT3 phosphorylation along with upregulation of Socs3 expression levels compared to untreated cells (Appendix Fig S1). Taking advantage of the “harmonizome” collection of databases 55, we attested that more than 1/3 of the top 100 sensome genes 4 possess STAT3-binding sites in their promoter region. Hence, we hypothesized that the SOCS3-STAT3 antagonistic signalling may be responsible for the suppression of the homeostatic microglia signature and the concomitant shift towards the inflammatory profile 56. These results show that microglia isolated from LPS-injected mice display a classical activated pro-inflammatory profile associated with a decrease in the expression of the homeostatic genes. The decrease in the homeostatic signature under inflammatory conditions is an inherent facet of microglial in vivo and in vitro. Single-cell mRNA sequencing of CD11b+CD45int microglia isolated from LPS-injected mice reveals a global transcriptional shift and increased heterogeneity compared to steady state conditions Based on the observed differences in the targeted qPCR approach under steady state and LPS conditions, we next aimed to investigate microglial states at the genome-wide level and infer their transcriptomic heterogeneity at single-cell resolution, since studying a population of cells masks the differences among individual cells. For this purpose, FACS-sorted CD11b+CD45int cells from saline- or LPS-injected mice were analysed using the recently developed high-throughput droplet-based Drop-seq method 23. In Drop-seq, single cells and functionalized barcoded beads as cell identifiers are co-encapsulated into droplets followed by cDNA synthesis, amplification, library preparation and next-generation sequencing. First, we sought for differentially expressed genes between all LPS and all naïve/saline cells using MAST 57. We identified 2,405 differentially expressed genes between these two conditions with a false discovery rate (FDR) cut-off of 5% (Dataset EV1) and exemplified the top 100 differentially expressed genes in a heatmap (Fig 3A). Second, principal component analysis followed by two-dimensional t-distributed stochastic neighbour embedding (2D-tSNE) of the overall gene expression data of 1,247 analysed cells identified two main cell clusters that were independent of the 2D-tSNE parameters and library sizes (Appendix Fig S2). Microglia isolated from LPS-injected mice distinctly clustered from the corresponding steady state microglia presenting discrete gene expression signatures (Fig 3B; Dataset EV1). Intriguingly, we noticed from both analyses that, although mo" @default.
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• W2891901614 title "Single‐cell transcriptomics reveals distinct inflammation‐induced microglia signatures" @default.
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• W2891901614 doi "https://doi.org/10.15252/embr.201846171" @default.
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• W2891901614 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30206190" @default.
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