- Open Access
Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration
- Tuan Leng Tay†1, 2, 3Email author,
- Jana Dautzenberg1,
- Dominic Grün4Email author and
- Marco Prinz1, 5Email author
© The Author(s). 2018
- Received: 9 August 2018
- Accepted: 11 August 2018
- Published: 5 September 2018
Microglia are brain immune cells that constantly survey their environment to maintain homeostasis. Enhanced microglial reactivity and proliferation are typical hallmarks of neurodegenerative diseases. Whether specific disease-linked microglial subsets exist during the entire course of neurodegeneration, including the recovery phase, is currently unclear. Taking a single-cell RNA-sequencing approach in a susceptibility gene-free model of nerve injury, we identified a microglial subpopulation that upon acute neurodegeneration shares a conserved gene regulatory profile compared to previously reported chronic and destructive neurodegeneration transgenic mouse models. Our data also revealed rapid shifts in gene regulation that defined microglial subsets at peak and resolution of neurodegeneration. Finally, our discovery of a unique transient microglial subpopulation at the onset of recovery may provide novel targets for modulating microglia-mediated restoration of brain health.
- Single-cell RNA analysis
Microglia are tissue-resident macrophages of the central nervous system (CNS) that act as the first line of defense upon disruption of CNS homeostasis. In contrast to the lattice-like organization of sparsely (< 0.5%) renewing microglial cells in the adult brain [3, 26, 27, 35, 43], heightened microglial reactivity and microgliosis are hallmarks of all neurodegenerative diseases regardless of severity, as exemplified in local neuronal damage and widespread neurodegeneration [10, 13, 32, 37, 43].
While adult microglia originate solely from the primitive yolk sac erythromyeloid progenitors without contribution from the peripheral hematopoietic stem cells [1, 11, 12, 22, 33], gene expression and single-cell transcriptomic studies [14, 29] suggest that total CNS parenchymal microglia are not functionally homogeneous. The relative contributions to neuroprotection and neurodegeneration by microglia in neurodegenerative diseases such as Alzheimer’s disease (AD), amyotrophic lateral sclerosis and multiple sclerosis remain contentious [38, 39]. Notably, we recently demonstrated that immediate activation and proliferation of microglial cells within one to two weeks of neuronal injury was not detrimental to the CNS but appeared vital to the timely recovery of tissue homeostasis and neural functions . Bulk RNA-sequencing (RNAseq) analyses of microglial cells of the facial nucleus (FN) from the unilateral facial nerve axotomy (FNX) model of acute neurodegeneration showed lesion-dependent gene regulation, while compensatory alterations observed in the contralateral FN were attributed to other CNS cell types . Recent reports based on single-cell analysis of microglial transcriptomes attributed specific cellular states to neurodegenerative diseases recapitulated in AD-like mouse models with chronic or severe CNS damage [21, 30]. Although these important studies highlighted the appearance of novel disease-associated microglial subtypes, they did not address the existence of distinct microglial populations during recovery due to the chronic and destructive characteristics of the transgenic mouse models used.
To define disease-associated populations of microglia more precisely, we took a single-cell RNAseq (scRNAseq) approach in the FNX model, which is not driven by any susceptibility gene. Indeed, a subset of disease-linked microglia from the ipsilateral FN was distinct from a homogenous cloud. Comparative analysis of single-cell transcriptomes across these three models of neurodegeneration furthermore established a strong conservation of the microglial gene regulatory profile ascribed to disease. Of high significance, we found temporal regulation of lesion-associated changes in our FNX model that distinguished microglia at peak and resolution of disease. In particular, we verified the emergence of a transient microglial cluster characterized by the upregulation of Apoe and Ccl5 at the onset of recovery in situ. Collectively, our findings highlight a potential new interpretation of disease-associated gene regulation that may be critical to the restoration of CNS homeostasis mediated by microglial cells.
Mice and treatments
CX3CR1GFP/+  mice were bred in specific-pathogen-free facility and given chow and water ad libitum. Unilateral facial nerve axotomy (FNX) at the stylomastoid foramen was performed in 8 weeks old female CX3CR1GFP/+mice described previously . Only female mice were used to allow comparisons of the scRNAseq data in this study with the bulk RNAseq analyses performed before . Mice were bred concurrently, received same-day operation and randomly assigned to each experimental group for sacrifice at the required time point. Animal experiments were approved by the Regional Council of Freiburg, Germany. Experimenters were blinded to all groups during data acquisition and analysis.
Mice were transcardially perfused with 20 ml ice-cold PBS. Pontine blocks were immediately cut in a coronal rodent brain matrix for acute isolation of single facial nuclei under the stereomicroscope. Brain tissue was gently mashed and resuspended in 20 ml ice-cold extraction buffer containing 1× HBSS, 1% fetal calf serum (FCS) and 1 mM EDTA, followed by the extraction of microglial cells in 5 ml 37% isotonic Percoll. Cells were labeled with antibodies CD45-BV421 (103,133, BioLegend), CD11b-BV605 (101,237, BioLegend) and MHC Class II-PE-Cy7 (107,630, BioLegend) in FACS buffer (1× PBS, 1% FCS). Single GFP+ CD45lo CD11b+ microglial cells were sorted into 384-well plates containing 240 nL of primer mix and 1.2 μl of Vapor-Lock (QIAGEN) PCR encapsulation barrier at the Influx™ cell sorter (Becton Dickinson) for subsequent RNA sequencing procedures.
Single-cell RNA amplification and library preparation
We used an automated and miniaturized version of the CEL-Seq2 protocol . Sixteen libraries (1536 single cells) were sequenced on two lanes (pair-end multiplexing run, 100 bp read length) of an Illumina HiSeq 2500 sequencing system generating 243,638,747 sequence fragments.
Quantification of transcript abundance
For the FNX experiment, paired end reads were aligned to the transcriptome using bwa (version 0.6.2-r126) with default parameters . The transcriptome contained all RefSeq gene models based on the mouse genome release mm10 downloaded from the UCSC genome browser comprising 31,201 isoforms derived from 23,538 gene loci . All isoforms of the same gene were merged to a single gene locus. The 50 bp right mate of each read pair was mapped to the ensemble of all gene loci and to the set of 92 ERCC spike-ins in sense direction . Reads that mapped to multiple loci were discarded. The 50 bp left read contains the barcode information: the first six bases corresponded to the unique molecular identifier (UMI) followed by six bases representing the cell specific barcode. The remainder of the left read contains a polyT stretch. Only the right read was used for quantification. For each cell barcode, the number of UMIs per transcript was counted and aggregated across all transcripts derived from the same gene locus. Based on binomial statistics, the number of observed UMIs was converted into transcript counts .
Single-cell RNA sequencing data analysis
Identification and visualization of different subpopulations as well as differential gene expression analysis was performed with the RaceID2 algorithm . Out of 1536 cells sequenced in the FNX experiment, 944 cells passed the quality thresholds. The median, minimum and maximum number of genes identified per cell are 1560, 858 and 2658, respectively. Down-sampling to 1500 transcripts was used for data normalization. Clustering was performed using k-medoids clustering without outlier identification. Ten clusters were identified based on the saturation of the average within-cluster dispersion. To compare our disease-associated clusters with a recently described microglia type associated with neurodegenerative disease (DAM), we obtained the raw data from scRNAseq of all immune cells in wild type (WT) and Alzheimer’s disease (AD) transgenic mouse brains . The AD mouse model expressed five human familial AD gene mutations (FAD). Results were obtained from a mix of male and female mice which showed no difference due to sex. Raw count files (henceforth referred to as the “FAD data set”) were downloaded from Gene Expression Omnibus (GEO): GSE98969  and analyzed using the RaceID2 algorithm . To exclude non-microglial cells from the FAD data set, only cells with UMI counts for Cst3 (UMI > 10) and Hexb (UMI > 5) (as defined in ) prior to normalization were retained for further analysis. Perivascular macrophages and monocytes (Cd74, UMIs ≥5), granulocytes (S100a9, UMIs ≥50) and mature B-cells (Cd79b, UMIs ≥3) were removed from the dataset. Downsampling to 700 UMIs was performed for data normalization.
The t-distributed stochastic neighbor embedding (t-SNE) algorithm was used for dimensional reduction and cell cluster visualization . Using the phyper function provided by the R software to perform a hypergeometric test, an enrichment score [−log10(p-value+ 10− 3)] was calculated for the FNX data to identify the enrichment of cells belonging to a group in a given cluster. Differentially expressed genes between the tail clusters (clusters 4, 8 and 9 in the FNX data) and cloud clusters were identified similar to a published method . First, negative binomial distributions reflecting the gene expression variability within each subgroup were inferred based on the background model for the expected transcript count variability computed by RaceID2 . Using these distributions, a P value for the observed difference in transcript counts between the two subgroups was calculated and multiple testing corrected by the Benjamini-Hochberg method.
The accession code for the FNX data set is GEO:GSE90975, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90975.
Gene set enrichment analysis
Gene IDs of the differentially expressed genes between the tail (clusters 4, 8 and 9) and cloud clusters in the FNX data were converted to Entrez IDs using the clusterProfiler package . Gene set enrichment analysis was performed using the ReactomePA package . The fold-change for each gene between the cloud and tail clusters was calculated using the diffexpnb function of the RaceID2 algorithm and given as an argument to the gsePathway function to calculate enriched gene sets in the tail clusters.
Comparative single-cell transcriptomic analysis
In addition to the comparison of our FNX data set with the DAM signature from the FAD scRNAseq study , we included the neurodegeneration response genes identified in another recent scRNAseq report based on the transgenic mouse model for severe neurodegeneration known as CK-p25 . Male CK-p25 mice were analyzed. Withdrawal of doxycycline from the diet induces the CamKII promoter driven expression of p25, the calpain cleavage product of Cdk5 activator p35, and leads to apoptotic neuronal cell death. While the CK-p25 inducible mouse model is not based on genetic mutations associated with familial AD, the authors claimed that it recapitulates several aspects of AD pathology and the transcriptional profile of FAD mice . Data on neurodegeneration-associated differentially regulated genes identified in the respective FAD and CK-p25 studies were obtained from the Supplementary Table S3 (fold changes and P values; ) and Supplementary Table S4 (fold changes and Z scores, from which P values were calculated for the corresponding early and late response genes in Clusters 3 and 6; ). A total of 5820 genes were obtained after we overlapped the relevant genes in both studies with our FNX gene set for comparative assessment. Based on the Benjamini-Hochberg procedure, only genes with false discovery rate < 0.05 were considered for subsequent analysis.
Mice were transcardially perfused with 20 ml PBS. Brains were fixed overnight in 4% paraformaldehyde in PBS at 4°C and processed for frozen sectioning as before . A coronal rodent brain matrix (RBM-2000C, ASI Instruments) was used to obtain consistent blocks of pontine regions that included both facial nuclei. Cryosections (14-μm) were collected from the entire facial nuclei on coated glass slides and stored at − 20°C until use. Tissues were permeabilized in blocking solution (0.1% Triton-X 100, 5% bovine albumin, normal goat or normal donkey serum, and PBS) for 1 h at room temperature and incubated overnight at 4°C with primary antibodies: 1:200 rat anti-CD11b (ab8878, Abcam), 1:500 rabbit anti-IBA-1 (019–19,741, Wako), 1:200 goat anti-APOE (AB947, Merck), and 1:500 rabbit anti-CCL5 (RANTES) (710,001, ThermoFisher Scientific). Antigen retrieval was performed at 96 °C prior to APOE staining for 40 min in 10 mM citrate buffer at pH 9. Sections were incubated with corresponding secondary antibodies conjugated to 1:1000 Alexa Fluor 488 or Alexa Fluor 647 (Life Technologies) and 1:5000 nuclear counterstain 4′,6-diamidino-2-phenylindole (DAPI, Sigma) for 2 h at room temperature, and mounted in ProLong® Diamond Antifade Mountant (Life Technologies).
Microscopy and image analysis
GFP+ microglia were imaged using a 20X / 0.75 NA objective lens on the Keyence BZ − 9000 inverted fluorescence microscope and quantified using the BZ-II Analyzer. Three brain sections per mouse were analyzed. Confocal images of immunohistological preparations were acquired with the SP8 STED-WS (Leica Microsystems) using a HCX PL HCL PL APO C 20X/0.75 NA glycerine objective lens and the LAS X software. DAPI and Alexa Fluors 488 and 647 were excited by the UV Diode Laser 405 nm, Argon Laser 488 nm and WL 647 nm, respectively, and detected in sequential and simultaneous acquisition settings with the HyD detectors in the gating mode. The pinhole was set to one airy unit. Image stacks were sampled with a pixel size of 142 nm and in 1 μm z-steps.
Data are presented as mean ± SEM. GraphPad Prism5 was used for multiple comparisons using 2-way ANOVA with Bonferroni correction and paired t-tests. Differences were considered statistically significant at P < 0.05.
Single-cell analysis revealed stage-dependent microglial clusters during neurodegeneration
Contribution of facial nuclei microglia to single cell transcriptomic analysis
CD45lo CD11b+ GFP+ microglial cells
0 d contralateral
7 d contralateral
7 d lesion
30 d contralateral
30 d lesion
Common microglial gene regulatory profile across neurodegenerative diseases
Recovery-associated microglial subset arises during injury resolution
We believe that the interpretation of microglial upregulation of APOE during brain pathology is still up for dispute. High expression of APOE has been shown to be characteristic for a subtype of reactive microglia that appears in specific conditions of neurodegeneration in mice [6, 8, 19, 21, 23]. Multiple rodent studies demonstrated that genetic deletion or repression of APOE alleviated disease severity, as observed in the amelioration of experimental autoimmune encephalomyelitis (EAE) [25, 42], extension of lifespan in the SOD1 mouse model of amyotrophic lateral sclerosis , and protection from tau pathogenesis typical in AD . These results thus suggest a detrimental role of APOE in neurodegeneration. Studies of human brain autopsies  and humanized mouse models of tauopathy  relating to AD have however shown that different isoforms of APOE may alternate between being a risk factor or neuroprotective. Since Apoe is highly expressed in mouse astrocytes and microglia  and mainly expressed by astrocytes in human , it is unclear if the ablation of APOE in some or all cell types contribute similarly to CNS pathology. In agreement with the observation that the upregulation of Apoe during the initial DAM activation in the FAD model is independent of triggering receptor expressed on myeloid cells 2 (Trem2) , the FNX-dependent upregulation of Apoe in cluster C9 from the onset of recovery corresponds with no change in Trem2 expression (Additional file 5: Table S1). Our immunohistochemical results depicting C9 microglia that upregulate APOE (Fig. 3g) during recovery support the claim that switching on the TREM2-APOE pathway drives a non-homeostatic microglial phenotype . However, could the higher frequency of Apoe upregulation during early disease stage in the FAD model  represent a recovery-promoting microglial subtype? In the EAE study, overall levels of APOE transcript and protein in rat spinal cord reduced at onset, elevated during peak, and plateaued at the end of disease . Notably, a brain region-specific proteomic investigation of APOE protein levels and amyloid accumulation in three AD mouse models led the authors to predict that increased APOE detection drove amyloid clearance . There are few clues to date regarding the functional or mechanistic role of the chemoattractant and activating cytokine CCL5 or RANTES  particularly in microglia. The down-regulation of Cst3 seems to imply a loss of homeostatic microglial phenotype since it is typically considered a microglia signature gene . Astrocyte-secreted SPARC protein was described to be antagonistic to synaptogenic function , however it is presently unclear if the down-regulation of Sparc in microglia during recovery plays a supportive role for synaptogenesis. Overall, it remains to be investigated whether microglia carrying the recovery-associated gene signature are targeted for removal by local apoptosis and/or emigration  during reinstatement of steady state microglial network that accompany CNS regeneration.
In conclusion, our combinatorial analysis of microglia gene expression profiles across neurodegeneration models strongly implicates APOE in disease modulation. However, our FNX model opens a new window for further investigation into the significance of this and other pathways during microglia-directed disease amelioration and recovery of CNS health.
The authors thank Gen Lin for critical feedback and CEMT, University of Freiburg for excellent animal care. TLT was supported by the German Research Foundation (DFG, TA1029/1-1) and Ministry of Science, Research and the Arts of Baden-Württemberg (7532.21/2.1.6). MP is supported by the BMBF-funded competence network of multiple sclerosis (KKNMS), the Sobek Foundation, the Ernst-Jung Foundation, the DFG (SFB 992, SFB1160, SFB/TRR167, Reinhart-Koselleck-Grant) and the Ministry of Science, Research and Arts, Baden-Wuerttemberg (Sonderlinie “Neuroinflammation”).
The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the Open-Access Publishing funding programme.
Availability of data and materials
The primary pair-end sequencing files as well as expression count tables for the single-cell RNA-sequencing dataset reported here are available to download from GEO (accession number: GSE90975).
TLT and MP conceived the study. TLT, S and JD performed the experiments and analyses. TLT, DG, and MP provided supervision. TLT and MP wrote the manuscript. All authors read and approved the final manuscript.
All applicable national, and institutional guidelines for the care and use of animals were followed.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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