Microglia coordinate cellular interactions during spinal cord repair in mice

Traumatic spinal cord injury (SCI) triggers a neuro-inflammatory response dominated by tissue-resident microglia and monocyte derived macrophages (MDMs). Since activated microglia and MDMs are morphologically identical and express similar phenotypic markers in vivo, identifying injury responses specifically coordinated by microglia has historically been challenging. Here, we pharmacologically depleted microglia and use anatomical, histopathological, tract tracing, bulk and single cell RNA sequencing to reveal the cellular and molecular responses to SCI controlled by microglia. We show that microglia are vital for SCI recovery and coordinate injury responses in CNS-resident glia and infiltrating leukocytes. Depleting microglia exacerbates tissue damage and worsens functional recovery. Conversely, restoring select microglia-dependent signaling axes, identified through sequencing data, in microglia depleted mice prevents secondary damage and promotes recovery. Additional bioinformatics analyses reveal that optimal repair after SCI might be achieved by co-opting key ligand-receptor interactions between microglia, astrocytes and MDMs.

D � The exact sample size (n) for each experimental group / condition, given as a discrete number and unit of measurement D � A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly D � The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
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Software and code
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Data analysis
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Data collection
Bulk tissue RNA-seq was carried out by the UCLA Neuroscience Genomics Core (UNGC). Reads were aligned to the mouse GRCm38 reference genome using STAR (v.2.4.0). Read counts for RefSeq genes (mm10) were generated by HTSeq v.0.6.1. Low-count genes were filtered and fragments per kilobase per million mapped reads (FPKM) values were generated. In total, 17,939 genes were identified. Single cell RNA sequencing was conducted on HiSeq4000 (Novogene). Fastq sequence files were de-multiplexed, aligned, and annotated using the mouse ENSMBL database and Cell Ranger software. Gene expression was counted using unique molecular identifier barcodes, and gene-cell matrices were constructed.
For bulk RNAseq, differentially expressed genes were identified using the DESeq2 (v l.14.1) and verified using Gene Network Analyst 3.0 (Zhou, et al., 2019). Pathway and network analysis was performed using: Web-based Gene Set Analysis Toolkit (WebGestalt), with the Gene Ontology Reference Genome set as the reference gene list (Liao, et al., 2019), Reactome pathway database (Jassal, et al., 2020), Genemania, and the The Bioinformatics and Evolutionary Genomics server. Fastq sequence files were de-multiplexed, aligned, and annotated using the mouse ENSMBL database and Cell Ranger v3.0 software. Data processing and visualizations were performed using Seurat package (v.3.2.0) in R (3. No field samples were used in this study No wild animals were used in this study X X X X X All surgical and postoperative care procedures were performed in accordance with The Ohio State University Institutional Animal Care and Use Committee. Adult female (8-10 week old) female C57BL/6J (WT) mice were purchased from Jackson Laboratories (RRID: ISMR_JAX:000664). Mice were age and weight-matched within experiments. Animals were housed under conventional ventilation conditions on a 12 hour light-dark cycle with ad libitum access to food and water. Room temperature was between 20-26 o C and humidity was between 30-70%.
Mice were anesthetized using 1.5x the surgical dose of anesthetic. Blood: Blood was collected via cardiac puncture with a 25G syringe and transferred to blood collcetion tubes coated with EDTA. A 50 A 50 μl sample of whole blood per mouse was used for flow cytometry. Spleen: The spleen was rapidly dissected, weighed, and placed in a small volume of DMEM. Spleens were minced with sterile dissection scissors and mashed through a 40 μm sterile cell filter using the plunger of a 3 ml syringe and rinsed with 10 ml of IMDM. Bone marrow (BM): Both femurs from each mouse were removed, cleaned, and placed in a small volume of DMEM. Pictures of representative bones were captured using an iPhone 6s and pseudocolored in ImageJ. Bone marrow cells were isolated by flushing bones with 10 ml of DMEM through a 40 μm sterile cell filter. Sample processing: Samples were processed as described previously74. Briefly, blood, BM and spleen samples were diluted 1:5 with NH4Cl red blood cell lysis buffer (StemCell Technologies, # 7850) and incubated for 5 mins at RT. Cells were centrifuged (300 x g for 4 mins) and resuspended in 0.1M PBS. Cells were then incubated with 1:100 zombie green viability dye (BioLegend, 423112) for the exclusion of dead cells. Cells were washed then resuspended in flow buffer (0.1M PBS with 2% FBS) containing rat anti-CD16/32 (1:200; BD Bioscience) for 10 mins on ice to block Fc receptors. Cells were then incubated with flow cytometry antibodies (see Key Resources Table) for 30 mins on ice, washed and resuspended in flow buffer. 10 μl of liquid counting beads (BD Biosciences, 335925) were added to allow for quantification of absolute cell numbers. Samples were processed on a BD Fortessa flow cytometer (BD Biosciences) running FACS Diva software (v9.0) (BD) and analyzed using FlowJo (v10.8.1) (BD). OneComp ebeads (ThermoFisher, #01-1111-41) were used to set voltage intensities and compensation thresholds to remove spectral overlap. Unstained controls, isotype controls, and fluorescence minus one controls were used to identify background staining levels and determine gate placement. Doublets were excluded based on linearity of FSC-A and FSC-H. From singlets, live cells were identified as the Zombie-FITClo/-population. Neutrophils were designated Ly6G+Ly6C+ cells and monocytes were designated Ly6G-Ly6C+ cells. CD11b and CD11c staining was also used to confirm cell identity. 10 μl of liquid counting beads (BD Biosciences, 335925) were added to allow for quantification of absolute cell numbers.
Unstained controls, isotype controls, and fluorescence minus one controls were used to identify background staining levels and determine gate placement. Doublets were excluded based on linearity of FSC-A and FSC-H. From singlets, live cells were identified as the Zombie-FITClo/population. Neutrophils were designated Ly6G+Ly6C+ cells and monocytes were designated Ly6G-Ly6C+ cells. CD11b and CD11c staining was also used to confirm cell identity.