Analysis of the immune response to sciatic nerve injury identifies efferocytosis as a key mechanism of nerve debridement
Ashley L Kalinski Choya Yoon Lucas D Huffman Patrick C Duncker Rafi Kohen Ryan Passino Hannah Hafner Craig Johnson Riki Kawaguchi Kevin S Carbajal
In the injured sciatic nerve, blood-derived monocytes and macrophages eat dying leukocytes, thereby contributing to nerve debridement and inflammation resolution, and this correlates with neuronal regeneration.
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eLife
Despite recent advances in our understanding of PNS injury-induced inflammation, a comparative analysis of the leukocyte subtypes within the injured sciatic nerve and axotomized DRGs does not yet exist. For identification and quantification of immune cell profiles at different post-injury time points, adult mice were subjected to a mid-thigh sciatic nerve crush (SNC) injury. SNC leads to axon transection, but preserves the surrounding epineurium (Figure 1A). Flow cytometry was used to assess the composition of injury-mobilized immune cell profiles in the nerve and DRGs (gating strategy is illustrated in Figure 1—figure supplement 1). To minimize sample contamination with circulating leukocytes, mice were perfused with physiological saline prior to tissue collection. The nerve trunk was harvested and divided into a proximal and distal segment. The distal segment included the injury site together with the distal nerve stump (Figure 1A). For comparison, the corresponding tissues from naive mice were collected. In naive mice, ~300 live leukocytes (CD45+) are detected within a ~ 5 mm nerve segment. At day 1 following SNC (d1), the number of CD45+ cells in the distal nerve increases sharply, peaks around 23,100 ± 180 cells at d3, and declines to 14,000 ± 200 at d7 (Figure 1B). Further analysis shows that granulocytes (GC), identified as CD45+CD11b+Ly6G+CD11c- cells, are absent from naive nerve, but increase to 7,800 ± 300 at d1. By d3, the number of GC dropped below 1000 (Figure 1C). A robust and prolonged increase of the Mo/Mac population (CD45+CD11b+Ly6G-CD11c-) is observed, reaching 7300 ± 120 cells at d1, peaking around 13,200 ± 240 at d3, and declining to 3200 ± 90 at d7 (Figure 1D). Monocyte-derived dendritic cells (MoDC), identified as CD45+CD11b+Ly6G-CD11c+ cells, increase more gradually. They are sparse at d1, reach 1100 ± 30 at d3, and 3400 ± 60 at d7 (Figure 1E). Few CD11b- conventional DC (cDC), identified as CD45+CD11b-Ly6G-CD11c+ cells, are present at d1 and d3 and cDC increase to 600 ± 20 at d7 (Figure 1F). The total number of lymphocytes (CD45+CD11b-CD11c-Ly6G-) is low, but significantly elevated at d1, d3, and d7 post-SNC (Figure 1G and H). In marked contrast to the distal nerve stump, flow cytometry of the proximal nerve stump shows that SNC does not significantly alter immune cells number or composition (Figure 1—figure supplement 2A–K). The sharp divide in myeloid cell distribution within the injured nerve is readily seen in longitudinal sections stained with anti-F4/80 (Figure 1—figure supplement 2L). The distal nerve stump was identified by anti-GFAP staining, a protein upregulated in repair Schwann cells (Figure 1—figure supplement 2L). In sum, SNC-elicited inflammation in the nerve is confined to the crush site and the distal nerve stump where severed fibers undergo rapid Wallerian degeneration. GC increase sharply and peak within 24 hr, followed by Mo/Mac, MoDC, and few lymphocytes.

Endoneurial Mac in the sciatic nerve and DRGs respond to injury (Mäurer et al., 2003; Müller et al., 2010); however, there are no reliable cell surface markers to distinguish between tissue resident and injury mobilized hematogenous immune cells that enter the nerve or axotomized DRGs. To examine cell origin, we employed parabiosis, that is conjoined wildtype (WT) and tdTomato (tdTom) reporter mice that share blood circulation. We chose parabiosis over bone marrow transplantation because of potential confounding effects caused by irradiation (Guimarães et al., 2019). One month after parabiosis surgery, both parabionts were subjected to unilateral SNC. Sciatic nerves, DRGs, and spinal cords were harvested at different post-injury time points (Figure 3A). Shared blood circulation was assessed by flow cytometry of the spleen, and revealed a myeloid cell (CD45+CD11b+) chimerism of 27.3 ± 1.5 (gating strategy is illustrated in Figure 3—figure supplement 1). At d3 following SNC, flow cytometric analysis of nerves isolated from WT parabionts identifies 28.4 ± 6.7% tdTom+ myeloid (CD45+CD11b+) cells (Figure 3B). Fractionation of myeloid cells into Mo/Mac (CD45+CD11b+Ly6G-CD11c-) and MoDC (CD45+CD11b+Ly6G-CD11c+) further revealed that 27.1 ± 6.9% of Mo/Mac and 30 ± 5.6% of MoDC are tdTom+ in the injured WT parabiont (Figure 3C). When coupled with ~27% chimerism (Figure 3—figure supplement 1C), this suggests that blood-borne cells make up the vast majority of immune cells in the injured nerve. Histological analysis of injured nerves from WT parabionts identified numerous tdTom+ cells (Figure 3D). During the first 24 hr, tdTom+ cells are confined to the injury site (data not shown). At d3 and d7, tdTom+ cells are preferentially found at the injury site but also present within the distal nerve stump where fibers undergo Wallerian degeneration (Figure 3D and Figure 6—figure supplement 2C). In the proximal nerve, very few tdTom+ cells are detected at any post-SNC time point (Figure 3D). A 2-week time course analysis of axotomized DRGs harvested from WT parabionts identified a modest and transient increase of tdTom+ cells (Figure 3E). DRG sections from naive mice revealed that the number of tdTom+ cells per field-of-view (4000 µm2) is very low. Following SNC, there is a modest, but statistically significant increase in tdTom+ cells at d3 and d7, but not at 14 days, suggesting that only a small number of hematogenous leukocytes enter axotomized DRGs (Figure 3F). Together these studies show that SNC-elicited intra-ganglionic increase of Iba1+ and F4/80+ immune profiles (Figure 2A) primarily occurs through mechanisms that involve DRG-resident macrophages, rather than hematogenous immune cells. Of note, during the first 2 weeks post-SNC, no tdTom+ cells were detected in the lumbar spinal cord (data not shown), suggesting that hematogenous immune cells do not significantly contribute to SNC-triggered spinal cord inflammation.

To gain insights into SNC-triggered genome wide transcriptional changes in DRGs, we carried out bulk RNA sequencing of ganglia harvested from naive, d1, d3, and d7 injured mice. To understand the modular network structure associated with peripheral axotomy, we carried out weighted gene co-expression network analysis (WGCNA) at different post-injury time points (Geschwind and Konopka, 2009; Zhang and Horvath, 2005). WGCNA permits identification of modules of highly co-expressed genes that likely function together. Focusing on prominently regulated gene modules, we find a previously described module (pink module [Chandran et al., 2016]), enriched for regeneration associated gene (RAG) products, including Jun, Fos, Stat3, Smad1, Atf3, among other genes. In addition, WGCNA identifies a large turquoise module (Figure 4A and B), which along with the pink module, is stably upregulated following SNC (Figure 4—figure supplement 1A and B). To annotate module function, we applied gene ontology (GO) enrichment analyses, which showed enrichment (Benjamini-corrected p values < 0.05) for several GO categories associated with immune system function in the turquoise module. The enrichment plot for GO regulation shows a strong upregulation for immune system processes (Figure 4C). The most significantly upregulated GO terms include cell activation, immune effector process, and defense response (Figure 4—figure supplement 1C). Ingenuity pathway analysis (IPA) identified several upstream activators, including cytokines and growth factors (IFNγ, TNF, IL1b, IL6, TGFβ1, IL10, IL4, IFNβ1, IL2) and the transcription regulators STAT1, STAT3, IRF7, RELA (Figure 4—figure supplement 1D). The upregulation of immune system processes in axotomized DRGs correlates with a modest ~1.5-fold increase of gene products encoding the canonical macrophage markers Itgam (CD11b), Aif1 (Iba1), and Adgre1 (F4/80) (Figure 4D–F). In comparison, expression levels and fold-upregulation of Atf3, Jun, and Stat3 are very robust (Figure 4G–I). Expression of the chemokine receptor Ccr2 and the receptor subunits for the GM-CSF receptor (Csf2ra and Csf2rb) are elevated in axotomized DRGs; however, expression levels are low, especially for Csf2rb (~1 fpkm) (Figure 4J–L). Moreover, some of the immune gene activity observed in axotomized DRGs may involve non-hematopoietic cells. Collectively, RNA-seq provides independent evidence that SNC triggers a remote inflammatory response in DRGs, however this does not result in a massive increase in transcripts encoding canonical Mac markers. This conclusion is consistent with flow cytometry (Figure 2B–G), Western blot analysis (Figure 2H and I), and 3D reconstruction of Mac (Figure 3G–K) in axotomized DRGs.

To de-convolute the cellular complexity of injured sciatic nerve tissue in an unbiased manner, we applied scRNA-seq to capture the transcriptional landscape at single-cell resolution. Because injury-induced expansion of the immune compartment peaks around d3 (Figure 1B), we chose this time point to dissect and process whole nerves for single cell capture, using the 10x Genomics platform. A total of 17,384 cells was sequenced with 16,204 used for downstream analysis after removing cells with fewer than 200 genes, more than 7500, or mitochondrial content greater than 25%. Median unique genes per cell was 2507. More than 20 different cell clusters were identified using shared nearest neighbor clustering algorithm. Results are visualized using Uniform Manifold Approximation and Projection (UMAP) for dimension reduction (Figure 5A). The top 100 genes enriched in each cluster (Figure 5—source data 1) were used to assign cluster-specific cell identities. Most prominently featured are immune cells, identified by their strong expression of Ptprc (encoding CD45). Innate immune cells (Itgam/CD11b) make up a median 42.22% (±1.39%), and lymphocytes less than 1.73%(±. 27%), of the cells in the injured nerve (Figure 5B and C). Other abundantly featured cell types include mesenchymal progenitor cells (MES). We identify three distinct MES subpopulations (Figure 5A), reminiscent of a recent study examining the nerve response to digit tip amputation (Carr et al., 2019). In the injured sciatic nerve, MES make up 18.49% (±0.98%) of cells and differentially express the markers Pdfgra and Sox9 (Figure 5D and E). MES are a rich source of extracellular matrix (ECM) molecules, including collagens (Col1a, Col3a, Col5a, Col6a), Fn1/fibronectin, Fbn1/fibrillin-1, Lamb2/laminin-b2, and numerous proteoglycans (Figure 5—figure supplement 1A). Individual MES clusters are identified as perineural MES (pMES) (Slc2a1/Glut1, Itgb4/integrin-β4, Stra6/stimulated by retinoic acid 6, Sfrp5/secreted frizzled related protein 5), endoneurial MES (eMES) (Wif1/Wnt inhibitory factor 1, Bmp7), and differentiating MES (dMES) (Gas1/Growth arrest-specific 1, Ly6a/SCA-1, Tnc/tenascin, Sfrp1/secreted frizzled-related protein 1). The dMES cluster is fused to a small population of fibroblasts (Fb) (Figure 5A). STRING Reactome pathway analysis for MES clusters identifies extracellular matrix organization as top hit (Figure 5—figure supplement 2). Further analysis revealed that cells in eMES, but not in clusters pMES and dMES, are neural crest derived (Carr et al., 2019; Gugala et al., 2018).

Figure 5—source data 1

List of top 100 cluster enriched genes for all cell clusters identified in the 3d post-SNC nerve.

https://cdn.elifesciences.org/articles/60223/elife-60223-fig5-data1-v3.xlsx

Three clusters of Schwann cells (SC1-3) represent 17.48% (±1.53%) of cells in the injured nerve (Figure 5A). Cluster SC1 contains proliferating cells marked by Mki67/Ki67 expression (Figure 5J) and many cells that strongly express Ncam1, Chl1/cell adhesion molecule L1-like, Erbb3, Epha5, Thbs2/thrombospondin-2, Tnc, Hbegf, and the BMP antagonist Sostdc1 (Figure 5F and Figure 5—figure supplement 3, Figure 5—source data 1). SC1 enriched transcription regulators (SC1-TR) include Zfp706, Tead1, Sox6, Nr2f1/COUP-TF (Figure 5K). SC3 cells express high levels of Ngfr/p75, Nrcam, Gfra1/GDNF family receptor alpha 1, Btc/betacellulin, Gjb1/connexin-32, Cryab/crystallin alpha B, Tnfrsf12a/Fn14, Gadd45b (Figure 5G and Figure 5—figure supplement 3, Figure 5—source data 1). SC3-TR include Sox4, Runx2, Hmga1, Jun, and the POU family member Pou3f1, a repressor of BMP and Wnt signaling, associated with a pro-myelinating cell state (Figure 5K). Cluster SC2, flanked by SC1 and SC3, expresses nes/nestin and Cryab. UMAP splits the SC2 cluster and places a subset of cells near MES cells, likely because of relatively higher expression in ECM encoding genes (Bgn, Dcn, and Fn1) compared to clusters SC1 and SC3. SC2 cells have a median 584 (±22) genes per cell and may have a higher degree of technical variation. STRING identified axon guidance and integrin cell surface interactions as top REACTOME pathways for SC1. Axon guidance, gap junction assembly, and microtubule-dependent trafficking are top hits for SC3 (Figure 5—figure supplement 3).

Cells associated with the nerve vasculature make up 14.2% (±3.19%). They include three clusters of endothelial cells (EC1-3), strongly expressing Pecam/CD31, representing 9.92% (±2.69%) of cells (Figure 5H and Figure 5—figure supplement 4). There are two pericyte cell clusters (PC1 and PC2) enriched for the pericyte markers (Pdgfrb, Rgs5) and vasculature-associated smooth muscle cells (Acta2, Des, Myl9, Mylk), representing 4.2% (±. 44%) (Figure 5I and Figure 5—figure supplement 5). A small cluster of chondrocyte-like cells (CL: Comp/cartilage oligomeric matrix protein, Col27a1, Jun) represents 0.5% (±0.44%). A cell cluster (3.09% (±1.08%)), designated Hyb, harbors few erythrocytes (Hba, Hbb) and some cell hybrids (Hyb). These cells had a median 521 (±27) expressed genes which was the lowest of any cell cluster and no clear identity could be assigned (Figure 5B).

Of relevance for neuronal regeneration, ECM components and numerous extracellular molecules known to regulate axon growth and regeneration are expressed by different cell types in the injured nerve (Figure 5—figure supplement 1A). MES and Fb are rich sources of gene products with neurotrophic and neurotropic properties, and thus may act in a paracrine fashion to regulate neuronal survival and direct axonal growth (Figure 5—figure supplement 1B). dMES express (Igf1, Ogn/osteoglycin, Nid1/Nidogen-1, Ntn1/netrin-1, Postn/periostin, Gdf10/BMP3b, Cxcl12/SDF1, Dcn/decorin, Grn/progranulin, Sparc/osteonectin, Lamb2/laminin-b2, Serpinf1), eMES (Spp1, Dcn, Nid1/nidogen-1, Sparc, Serpine2/glia-derived nexin, Lum/lumican, Gpc3/glycpican-3), and pMES (Ntn1, Cldn1/claudin-1, Efnb2/ephrin-b2, Mdk/midkine, Nid1, Sdc4/syndecan-4, Thbs4/thrombospondin-4, Gpc3). Repair Schwann cells in clusters SC1 and SC3 express high levels of cytokine receptor like factor 1 (Crlf1), and SC3 highly express cardiotrophin-like cytokine factor 1 (Clcf1). Crlf1 and Clcf1 are both members of the CNTF ligand family that signal through gp130. In addition, SC1 (Chl1, Ncam1, Nrn1/neuritin-1, Ptn/pleiotrophin, Sema3e, Sema7a, Reln/reelin), and SC3 (Reln, Dag1/dystroglycan, Gdnf, Nrcam, Sema3b) express numerous membrane-bound and soluble factors with known roles in axon growth and guidance. Subpopulations of myeloid cells exhibit high expression of the osteopontin-encoding gene, Spp1 and progranulin (Grn), powerful neurite outgrowth promoting factors (Figure 5—figure supplement 1B; Altmann et al., 2016; Wright et al., 2014). Taken together, scRNA-seq of injured nerve reveals that multiple cell types contribute to a large repertoire of extracellular molecules with neurotrophic and axon growth promoting properties.

The mononuclear phagocyte system (MPS) is comprised of Mo, Mac, and DC, cell types that are readily detected in the injured nerve by flow cytometry (Figure 1). UMAP, overlaid with Seurat-based clustering of scRNA-seq datasets, identified a connected continuum of seven cell clusters in the MPS (Mo, Mac1-5, and MoDC), characterized by strong expression of Itgam/CD11b (Figure 5C) and various degrees of the commonly used myeloid cell markers Adgre1/F4/80, Aif1/Iba1, Cd68, Cx3cr1 and Cd209a/DC-SIGN (Figure 6A–E). Cells in the MPS strongly express the myeloid lineage-defining transcription factor PU.1 (Spi1). The C/EBP family member TR (Cebpb) is expressed by Mo/Mac, but not dendritic cells (Figure 6F). Myeloid cells are a rich source of fibronectin, extracellular proteases, and hydrolases (Fn1, Tgfbi, Adam15, CtsC, CtsS, Gusb) and likely play a major role in ECM remodeling, cell adhesion, and fibrosis. Monocytes strongly express Ly6c2/Ly6C, Chil3/chitinase-like 3, Ifitm6/interferon-induced transmembrane protein 6, Itgal/integrin αL, Gsr/glutathione reductase, Hp/haptoglobin (Figure 6—figure supplement 1). In addition, they express the TRs Hif1a, Trps1, and Cebpb/C-EBPβ, a bZIP TR important for Mo survival (Figure 6F). In the UMAP plot, the Mo cluster is flanked by three macrophage subpopulations (Mac1-Mac3) (Figure 5A). Mac1 cells express (Fcgr2b/Fc gamma receptor 2b, Arg1/arginase-1, Ltc4s/leukotriene C4 synthase, Lpl/lipoprotein lipase, Camkk2). Mac2 (Cx3cr1, Ccr2, Csf1r) and Mac3 (Cx3cr1, Mrc1/CD206, Ccr2, Adgre1/F4/80, Csf1r, Cd38) express overlapping, yet distinct, sets of surface receptors (Figure 6—figure supplement 1). Of note, individual Mac subpopulations often co-express markers traditionally associated with M1-like and M2-like cells, indicating that these markers are of limited use to describe the more complex physiological states of Mac subpopulations in the injured nerve. Mac4 cells are characterized by high levels of Trem2/ triggering receptor expressed on myeloid cells 2, Arg1/arginase-1, Pf4/CXCL4, Stab1/stabilin-1, Cd68 (Figure 6—figure supplement 1) and express the TRs Cebpa, Mafb, Mef2a (Figure 6K). Cluster Mac5 is small, 239 cells, and harbors dividing (Mki67) myeloid cells with ‘stem-like’ features (Stmn1/ Stathmin-1, Top2a, Hmgb2, Tupp5) (Figure 5J, Figure 6—figure supplement 1, Figure 5—source data 1). In addition, a smaller group of dividing cells (Mki67, Top2a) is embedded in the MPS and located between clusters Mac2 and MoDC (Figure 6—figure supplement 2A and B). To distinguish between dividing nerve resident myeloid cells and dividing blood-derived myeloid cells, we subjected WT-tdTom parabionts to SNC (Figure 3A). At 3d post-SNC, WT nerves were analyzed for tdTom+ cells that co-stain with anti-Ki67 and anti-F4/80 (Figure 6—figure supplement 2C). TdTom+F4/80+Ki67+ cells were identified, indicating that blood-borne, stem-like myeloid cells are present in the injured sciatic nerve. Mac2 cells express high levels of MHCII genes (H2-Aa, H2-Ab1, H2-Eb1, M2-DM) and the CD74 invariant chain of MHCII (Cd74), typically associated with antigen presentation to CD4+ T cells. The MPS harbors monocyte-derived dendritic cells (MoDC), professional antigen-presenting cells, characterized by high level expression of MHCII genes, Itgax/CD11c, Itgb7/integrin-β7, Napsa/Napsin-A, and Cd209a/DC-SIGN (Figure 6E, Figure 6—figure supplement 1). Mac2 and MoDC express Ciita (Figure 6F), a class II transactivator, that promotes MHCII gene expression (Accolla et al., 2019). Few plasmacytoid DCs (pDC) (Siglech, Ly6d) and conventional DCs (cDC) (Clec9a, Xcr1, Itgae, Tlr3, Ifi205, Cd24a, Btla/CD272) are detected in the MPS (Figure 6—figure supplement 2D and E). cDC show enriched expression of the TRs Batf3, Id2, Irf5, Irf8, Mycl, Srebf2 (Figure 6F). DC clusters can readily be distinguished from other myeloid cells, based on their expression of Bcl11a, a TR that determines DC fate (Ippolito et al., 2014). Cells in the MoDC cluster show high expression of the TRs Nfkb1, Pou2f2, Runx1, Rel/c-Rel, and Ikbkb/IKKβ (Figure 6F). The GC cluster in the d3 nerve is small, 314 cells, and mainly includes neutrophils (S100a8, S100a9, Mmp9, Retnlg/Resistin-like gamma), intermingled with few eosinophils (Siglecf) (Figure 5A, Figure 6—figure supplement 1). Overall, the Seurat cluster analysis is in good agreement with the abundance and identity of immune cell profiles detected by flow cytometry and also reveals the presence of a large and connected continuum of cell states in the myeloid compartment (Figure 5A). To infer the most probable differentiation trajectories from Mo toward their descendants, we used Slingshot, a method for pseudo-time trajectory analysis (Street et al., 2018). The analysis reveals a bifurcated trajectory and provides independent evidence that blood-borne Mo entering the nerve give rise to different Mac subpopulations as well as MoDC. The predicted differentiation trajectory indicates that Mo first give rise to Mac3, and cells in cluster Mac3 then differentiate either into Mac1, Mac2, or Mac4 cells. Furthermore, Mac2 cells are predicted to differentiate into MoDC (Figure 6—figure supplement 3).

The ‘connected continuum’ of Mo/Mac in the injured nerve, as revealed by scRNA-seq, was independently verified by flow cytometry. The Mo/Mac population (CD45+CD11b+Ly6G-CD11c-) is highly plastic and can be subdivided based on surface levels of the lymphocyte antigen 6C (Ly6C). Ly6C is expressed at high levels on proinflammatory, circulating monocytes and is downregulated as they infiltrate tissues and mature into macrophages and dendritic cells (King et al., 2009). As expected, scRNA-seq of injured nerve shows that Ly6c2, the gene encoding Ly6C, is strongly expressed by Mo, but much less by Mac subpopulations (Figure 6G). Flow cytometry shows that naive nerve tissue harbors a small Mac population, mostly comprised of Ly6C- (70%) cells and few Ly6Cint (16%) and Ly6Chi (14%) cells (Figure 6H). At d1 post-SNC, the number of Mo/Mac increases sharply and Ly6C distribution is skewed toward classically activated Ly6Chi cells (50%), with fewer Ly6Cint (41%) and Ly6C- (9%) cells (Figure 6I). At d3, Ly6Chi (28%), Ly6Cint (47%), and Ly6C- (25%) cells are detected (Figure 6J) and at d7, the majority of Mo/Mac are non-classical Ly6C- (65%) and intermediate Ly6Cint (25%), with few Ly6Chi cells (10%) (Figure 6K). This shows that Ly6Chi Mo migrate into the injured nerve in large numbers and increase inflammation during the acute phase. Later, as nerve inflammation resolves, the Mo/Mac number and polarization gradually return back to pre-injury homeostatic levels (Figure 6L and M). Noteworthy, the Mo/Mac population in axotomized DRGs shows an opposite response with regard to surface Ly6C distribution. In naive DRGs, Mo/Mac are comprised of Ly6C- (30%), Ly6Cint (27%), and Ly6Chi (43%) cells. Upon SNC, the distribution shifts to 75%, 16%, and 9% on d1, to 53%, 20%, and 27% on d3, and 52%, 23%, and 25% on d7 (Figure 6—figure supplement 4). Together, these data show that SNC triggered inflammation in the nerve is massive and characterized by a short pro-inflammatory phase that rapidly transitions to a resolving state. A similar immune response is not observed in axotomized DRGs.

Mac subpopulations show overlapping, yet distinct, expression patterns of the canonical markers Adgre1(F4/80), Aif1(Iba1), Cd68, and Cx3cr1 (Figure 6A–D). Moreover, cells in Mac4 and some cells in clusters Mac1 and Mac3 express high levels of Arg1, while other Mac subpopulations do not (Figure 6N). To explore tissue distribution of Arg1+ cells relative to F4/80+ and CD68+ cells in naive and injured nerves, we subjected Arg1-YFP reporter mice to SNC. In naive mice, no YFP+ cells are observed (Figure 6O) while few F4/80+ and CD68+ are detected (Figure 6—figure supplement 5). At d1, few YFP+ cells accumulate near the injury site (data not shown) and at d3 many more are present (Figure 6P). Unexpectedly, YFP+ cells are confined to the nerve crush site and largely absent from the distal nerve stump. This stands in contrast to F4/80+ and CD68+ macrophages, found at the injury site and the distal nerve (Figure 6—figure supplement 5). At d7, only few Arg1-YFP+ cells are found at the injury site and none in the distal nerve stump (Figure 6Q). F4/80+ Mac, on the other hand, are more uniformly distributed within the injury site and distal nerve stump (Figure 6—figure supplement 5). This shows the existence of different immune compartments in the injured nerve. A subpopulation of Arg1+ macrophages (including cells in cluster Mac4) is preferentially localized to the crush site, whereas F4/80+ macrophages (including cells in cluster Mac2 and Mac3) are abundant in the distal nerve where fibers undergo Wallerian degeneration. Pathway analysis of cell clusters in the innate immune compartment reveals common functions in phagocytosis, phagosome, and endolysosomal digestion, but also highlights important differences (Figure 6—figure supplement 1). KEGG pathways specific for Mo include cytokine signaling and leukocyte trans-endothelial migration, providing independent evidence for their hematogenous origin. Mo are highly plastic and predicted to give rise to monocyte-derived Mac subpopulations in the injured nerve (Figure 6—figure supplement 3). Top KEGG pathways for Mac3 are chemokine signaling pathway, complement and coagulation cascades, and cytokine-cytokine receptor interaction (Figure 6—figure supplement 1). For Mac1 cells, complement and coagulation cascades, suggesting that Mac1 and Mac3 play roles in opsonization and blocking of endoneurial bleeding. For Mac2 cells, KEGG pathway analysis identified Leishmaniasis and Tuberculosis as top hits (Figure 6—figure supplement 1). For Mac4 cells, pathway analysis identified negative regulation of immune system processes and cholesterol metabolism. Cholesterol metabolism in Mac4 cells includes gene products that regulate reverse cholesterol transport (Abca1/ATP binding cassette subfamily A1, Abcg1/ATP-binding cassette subfamily G1, Ctsd/Cathepsin-D, Ctsb/Cathepsin-B), cholesterol and lipid storage (Plin2/perilipin), formation of cholesterol esters (Soat1), cholesterol ester hydrolysis and lipoprotein metabolism (Lipa/lipase-A, Nceh1/Neutral cholesterol hydrolase 1, Apoe/Apolipoprotein E) and intracellular cholesterol transport (Npc2/Niemann-Pick C2 and Scarb2/Scavenger receptor class B member 2) (Figure 6—figure supplement 6A–6I). The abundance of gene products that protect from cholesterol overloading (Haidar et al., 2006; Viaud et al., 2018; Wu et al., 2018), suggests that this cluster is comprised of cholesterol laden cells. Importantly, tissue resident macrophages in naive nerves (Wang et al., 2020), either do not express cholesterol regulatory gene products, or express them at significantly lower levels (Figure 6—figure supplement 6J–6S).

In the injured nerve, blood-borne phagocytes and repair SC collaborate in myelin removal. Repair SC use the receptor tyrosine kinases AXL and MerTK for myelin phagocytosis (Brosius Lutz et al., 2017). Clusters SC1 (Axlhi, Mertk-) and SC3 (Axllow, Mertkint) exhibit differential expression of these two receptors (Figure 7—figure supplement 1). Interestingly, Axl and Mertk expression in myeloid cells is very low, suggesting that innate immune cells and repair SC employ different mechanisms for myelin phagocytosis. Mac subclusters strongly express the myelin-binding receptors Lrp1 (low-density lipoprotein receptor-related protein 1), Pirb (paired Ig-like receptor B), Cd300lf (sphingomyelin receptor), and several scavenger receptors (Msr1, Cd36, Cd68), including high levels of opsonic receptors (Fcgr1, Fcgr3, Fcgr4, Fcer1g) that may contribute to phagocytosis of antibody marked myelin debris (Figure 7—figure supplement 1 and Figure 5—source data 1; Atwal et al., 2008; Grajchen et al., 2018; Izawa et al., 2014; Kuhlmann et al., 2002; Stiles et al., 2013). Compared to Mo/Mac of injured nerves, phagocytosis receptor expression is much lower in naive nerve Mac (Figure 7—figure supplement 1).

In addition to debris phagocytosis, myeloid cells participate in removal of apoptotic cells (AC), primarily dying neutrophils and other leukocytes. Phagocytic uptake of AC, called efferocytosis, is mediated by a range of specialized engulfment receptors and mechanisms for ingestion (Boada-Romero et al., 2020). AC are selectively recognized due to phosphatidylserine (PS) or calreticulin (Calr) accumulation on their surface; both function as strong ‘eat me’ signals (Figure 7A). Conversely, healthy cells display the ‘don’t eat me’ signal CD47 that binds to the cell surface receptor SIRPα (signal regulatory protein α) encoded by Sirpa, to block efferocytosis (Kourtzelis et al., 2020). Calr and Cd47 are boadly expressed by cells in the injured nerve, while Sirpa is largely confied to myeloid cells (Figure 7B). PS is directly recognized by cell surface receptors such as CD300 family members (Cd300a, Cd300lb, Cd300lf), stabilin-1 (Stab1), and oxidized-PS by the scavenger receptor Cd36, molecules that are expressed by phagocytes in the injured nerve (Figure 7C). Alternatively, PS binds indirectly, via bridging molecules, to engulfment receptors (Voss et al., 2015). Interestingly, in the injured sciatic nerve, numerous cell types express specific sets of bridging molecules, indicating that they may contribute in an autocrine or paracrine manner to AC removal. Bridging molecules prominently expressed include complement C1q components (C1qa, C1qb, C1qc, C1ra), annexins (Anxa1-5), pentraxin (Ptx3), thrombospondin 1 (Thbs1), collectin kidney protein 1 (Colec11), soluble collectin placenta 1 (Colec12), galectin-3/MAC-2 (Lgals3), growth arrest-specific 6 (Gas6), protein S (Pros1), milk fat globule-EGF factor 8 (Mfge8), and apolipoprotein E (Apoe) (Figure 7B). Bridging molecules that bind to PS are recognized by a large and diverse set of engulfment receptors on phagocytes, including Lrp1, Trem2, Dap12 (Tyrobp), C1q receptor (C1qr/Cd93), C3a receptor 1 (C3ar1), integrin αMβ2, (Itgam, Itgb2), integrin αv (Itgav), integrin β3 (Itgb3), CD14, and members of the scavenger receptor family (Cd68 and Msr1/Mac scavenger receptor 1) (Doran et al., 2020; Erriah et al., 2019; Korns et al., 2011). Strikingly, many of these engulfment receptors are expressed by myeloid cells and are particularly abundant in cluster Mac4 (Figure 7C). Indirect evidence that Mac4 cells eat AC corpses, is the strong expression of gene products that regulate lipid metabolism and mechanisms that protect cells from excessive cholesterol loading, such as reverse cholesterol transport and cholesterol esterification (Figure 6—figure supplement 6). To assess whether expression of gene products involved in efferocytosis are upregulated following nerve injury, we took advantage of recently published scRNA-seq data sets generated from naive mouse sciatic nerve tissue (Wang et al., 2020; Ydens et al., 2020). Importantly, bridging molecules and engulfment receptors are either not expressed by macrophages in the naive nerve, or expressed at much lower levels than in Mac4 cells in the injured nerve (Figure 7—figure supplement 2).

To directly test whether efferocytosis takes place in the injured nerve, we first examined the presence of AC corpses. Viability-dye labeling, combined with flow cytometry, identified an increase in AC at d3 and d7 post-SNC (Figure 7D). During nerve debridement, degenerated nerve fibers and AC corpses are removed. In order to distinguish between efferocytosis of dying leukocytes and phagocytosis of nerve fiber debris, we generated WTCD45.1-tdTomCD45.2 parabiotic mice (Figure 7E). Both mice in the parabiosis complex were subjected to bilateral SNC. At d3 post-SNC, live cells in the injured WTCD45.1 nerve were analyzed by flow cytometry (gating strategy is illustrated in Figure 3—figure supplement 1). All tdTom+ cells in the injured nerve of the WTCD45.1 parabiont are blood-borne immune cells. Moreover, cells that are CD45.1+tdTom+CD45.2- represent tdTom+ leukocytes that were eaten in the nerve by CD45.1+ phagocytes. In non-parabiotic (single) tdTom mice,~95% of myeloid cells (CD11b+) in the 3d injured nerve are tdTom+ (Figure 7F) and in the WTCD45.1 parabiont ~39% are CD11b+tdTom+ (Figure 7G). Importantly, in the WTCD45.1 parabiont, CD45.1+tdTom+CD45.2- (Q3) cells are readily detected in the injured nerve and such cells are not present in tdTom (single) mice (Figure 7H and I). This indicates that efferocytosis of apoptotic leukocytes takes place in the injured nerve. To determine which immune cell types eat apoptotic leukocytes, we analyzed CD45.1+tdTom+ cells for surface levels of Ly6C and CD11c to distinguish between maturing Mo/Mac (Ly6Chi to Ly6C-) and MoDC (CD11c+). Mo/Mac have the biggest appetite for tdTom+ apoptotic leukocytes, more so than MoDC, suggesting they remove the bulk of dying leukocytes (Figure 7K and M and Figure 7—figure supplement 3). As negative controls, non-parabiotic tdTomCD45.2 mice were processed in parallel (Figure 7J and L – Figure 7—figure supplement 3). Collectively, these studies show that efferocytosis of dying leukocytes takes place in the injured sciatic nerve, and thus, serves as an important mechanism to clear the nerve of AC corpses.

While PNS injury elicited inflammation is important for axon regeneration, it is not clear whether inflammation in the nerve or axotomized DRGs is a primary driver of peripheral axon regeneration, or conditioning-lesion-induced central axon growth (Figure 8A). Bulk RNA-seq of axotomized DRGs and scRNA-seq of injured nerve identified chemokine and cytokine ligand-receptor systems preferentially expressed in the injured nerve. GM-CSF signaling is of interested because this cytokine is present in the injured nerve and has been implicated in neuroprotection and axon repair (Be'eri et al., 1998; Franzen et al., 2004; Legacy et al., 2013). Moreover, GM-CSF increases surface expression of galectin-3 (Saada et al., 1996) and in non-neural tissues galectin-3 functions as a bridging molecule for efferocytosis of apoptotic immune cells (Erriah et al., 2019; Wright et al., 2017). Transcripts for the GM-CSF receptor subunits (Csf2ra and Csf2rb) are abundantly expressed by myeloid cells in the injured nerve (Figure 8B and C), but not in axotomized DRGs (Figure 4K and L). To assess the role in nerve-injury-triggered inflammation, we employed Csf2-/- mice (Figure 8—figure supplement 1) and subjected them to SNC. Flow cytometry was used to quantify immune cell profiles in naïve nerves and at 1d, 3d, and 7d post-SNC. In naïve WT and Csf2-/- mice, the number of endoneurial Mac is comparable, and the majority of them are Ly6C- or Ly6Cint cells (Figure 8D and E). In the d3 injured nerve, there is a strong increase in the Mo/Mac population, in both, WT and Csf2-/- mice (Figure 8F and G). However, when analyzed for surface Ly6C expression, significantly fewer Ly6C- cells are present in Csf2-/- mice. Conversely, the population of Ly6Chi cells is significantly elevated in Csf2-/- mice when compared to WT mice (Figure 8H). This indicates that Mo/Mac maturation and inflammation resolution in the injured nerve of Csf2-/- mice is significantly delayed. Delayed maturation is only observed in the Mo/Mac population, since analysis of surface Ly6C expression on MoDC is comparable between WT and Csf2-/- mice (Figure 8I).

To assess whether proper Mo/Mac maturation in the injured nerve is important for conditioning-lesion-induced regeneration of central axon projections, adult WT and Csf2-/- mice were either subjected to bilateral SNC or sham operated. Seven days later, a dorsal column lesion (DCL) was placed at cervical level 4 of the spinal cord. Five weeks following DCL, cholera toxin B (CTB) traced dorsal column axons were analyzed in longitudinal spinal cord sections (Figure 8A). DCL causes axon ‘die-back’ (Horn et al., 2008). In WT mice without conditioning lesion, there is a 600 ± 80 μm gap between the lesion center, and the most proximal, CTB labeled axons (Figure 8J and K). In WT mice that received a conditioning lesion, traced axons grew close to the spinal cord injury site (Figure 8J and K). In parallel processed Csf2-/- mice, without conditioning lesion, there is a 720 ± 120 μm gap between the lesion center, and the most proximal CTB-labeled axons (Figure 8J and K). However, in Csf2-/- mice subjected to a conditioning lesion, dorsal column axon regeneration is not significantly enhanced (Figure 8J and K). This shows that Csf2 is important for conditioning-lesion-induced central axon regeneration.

GM-CSF has pleiotropic functions and its receptors are found on hematopoietic cells, glial cells, and subsets of neurons (Donatien et al., 2018; Franzen et al., 2004). SNC leads to upregulation of GM-CSF in the nerve (Mirski et al., 2003) and acute administration of GM-CSF following SNC leads to a transient increase in PNS axon regeneration (Bombeiro et al., 2018). To assess whether loss of Csf2 attenuates neurite outgrowth in vitro, we cultured DRG neurons from adult WT and Csf2-/- mice. After 20 hr, many neurons with axons were identified in both WT and Csf2-/- cultures (Figure 8L). Quantification of axon growth did not identify Csf2 dependent differences in total axon length or the longest axon (Figure 8M). A second cohort of WT and Csf2-/- mice was subjected to a conditioning lesion 3d prior to harvesting of axotomized DRGs. In both WT and Csf2-/- cultures, neurite outgrowth is significantly increased when compared to DRGs prepared from naive mice (Figure 8M). Collectively, this shows that reduced axon regeneration in the dorsal columns of Csf2-/- mice is not due to loss of conditioning-lesion-induced activation of neuron-intrinsic growth programs and indicates that Csf2 promotes regeneration through cell non-autonomous, extrinsic mechanisms.

Rapid removal of AC corpses protects from secondary necrosis and is closely associated with the induction of immunological self-tolerance. Commensurate with this, the low presence of lymphocytes and Natural killer cells in the nerve indicates that the microenvironment is immunologically ‘cold’ and dominated by immunosuppressive mechanisms. We propose that efferocytosis in the injured nerve is key to switch from a pro-inflammatory environment to resolution and restoration of tissue integrity (Kourtzelis et al., 2020; Ortega-Gómez et al., 2013). At 3d post-SNC, expression of the pro-inflammatory cytokines and chemokines (Ifng, Il1a, Il1b, Tnf) is very low. Most myeloid cells express high levels of anti-inflammatory Cd52, a glycoprotein that binds to HMGB1 to suppress T cell function (Bandala-Sanchez et al., 2018; Rashidi et al., 2018). Trem2+Arg1+ cells are strongly enriched in cluster Macs4 and show gene signatures suggestive of myeloid-suppressive cells (Katzenelenbogen et al., 2020; Yurdagul et al., 2020). Further evidence for an immunosuppressive environment is the strong expression of Pirb by myeloid cells, a type one membrane protein with four cytoplasmic immunoreceptor tyrosine-based inhibitory motifs (ITIMs) that inhibit immune cell activation (van der Touw et al., 2017). Myeloid inhibitory C-type lectin-like receptor (Clec12a), Lair1 (leukocyte-associated Ig-like receptor-1), Fcgr2b (low affinity immunoglobulin gamma Fc region receptor IIb), and the CD300 family receptors Cd300a and Cd300lf, all of which contain ITIMs (Rozenberg et al., 2018), are strongly expressed, and thus, may reduce nerve inflammation. TGFβ is expressed by efferocytotic Mac in the lung (Yoon et al., 2015). In the injured sciatic nerve, Tgfb1 is expressed by myeloid cells and is important for axon regeneration (Clements et al., 2017; Kourtzelis et al., 2020). Cells in clusters Mac1, Mac2, and Mac3 express high levels of Rbpj, a TR that restrains ITAM (immunoreceptor tyrosine-based activation motif) signaling and promotes a, resolving Mac phenotype (Foldi et al., 2016). Mac4 cells express the transmembrane glycoprotein NMB (Gpnmb), a negative regulator of inflammation that has protective effects following tissue injury (Zhou et al., 2017). Of interest, in the 3d injured nerve, Mac1, Mac3, and Mac4 strongly express the TRs Maf/c-Maf and Mafb/MafB. MafB promotes reprogramming of macrophages into an M2-like, resolving phenotype (Kim, 2017) and c-Maf is a checkpoint that programs Mac and is critical for the acquisition of an immunosuppressive phenotype (Liu et al., 2020).

Parabiosis revealed massive infiltration of blood-borne immune cells into the injured nerve but not axotomized DRGs. This finding was independently confirmed by flow cytometry, Western blotting, 3D reconstruction of Iba1+ cells, and RNA-seq of axotomized DRGs. The small increase in hematogenous leukocytes in axotomized DRGs was unexpected, since infiltration of Mo/Mac is thought to be a key driver of conditioning lesion induced axon regeneration (Kwon et al., 2015; Richardson and Issa, 1984; Zigmond and Echevarria, 2019). Consistent with previous reports, sciatic nerve injury causes a strong increase in Iba1 immunoreactivity in DRGs. We provide evidence that increased Iba1 immunoreactivity is, at least in part, a reflection of macrophage morphological changes triggered by nerve injury. Additional mechanisms may include local myeloid cell proliferation (Yu et al., 2020) and infiltration of a small number of blood-borne myeloid cells.

SNC triggers an inflammatory response in the nerve and in axotomized DRGs, although quantitatively and qualitatively very different, it remains unclear which immune compartment is important for conditioning-lesion-elicited axon regeneration. To revisit this question, we took advantage of RNA-seq datasets generated from DRGs and nerves and searched for immune signaling pathways preferentially upregulated in the injured nerve, but not axotomized DRGs. Focusing on GM-CSF signaling, a cytokine that rapidly accumulates in the distal nerve stump (Mirski et al., 2003), we observed strong expression of both GM-CSF receptor subunits (Csf2ra and Csf2rb) in nerve macrophages but not axotomized DRGs. GM-CSF is known to promote Mo migration and Mac polarization (Ijaz et al., 2016; Vogel et al., 2015; Wicks and Roberts, 2016). Of interest, GM-CSF upregulates surface expression of galectin-3 on SC and Mac (Saada et al., 1996) and galectin-3 is thought to promote phagocytosis of myelin debris and participate in re-programming of Mac toward an anti-inflammatory phenotype (Erriah et al., 2019; Rotshenker, 2009). Recent evidence shows that galectin-3 promotes efferocytosis of neutrophils and promotes inflammation resolution (Quenum Zangbede et al., 2018; Wright et al., 2017). Following SNC in Csf2-/- mice, the ratio of Ly6Chi to Ly6C- Mo/Mac is significantly skewed toward the former. Functional studies with Csf2-/- mice highlight a critical role for conditioning-lesion-induced regenerative growth of severed dorsal column axons. Neurite outgrowth studies with primary DRG neurons suggest that the regenerative failure in Csf2-/- mice is not due to failed activation of DRG neuron intrinsic growth programs, but due to changes in extrinsic, environmental influences. Because Csf2 receptor expression is very low in axotomized DRGs, this suggests that Csf2-dependent accumulation of Ly6C- Mac in the nerve is important for conditioning-lesion-induced axon regeneration. We speculate that Csf2 functions non-cell autonomously in the injured nerve to generate an extracellular milieu capable to sustain neuron intrinsic growth programs activated by injury. In a similar vein, axotomy to corticospinal neurons is sufficient for the induction of neuron-intrinsic growth programs, but not maintenance. However, neuron-intrinsic growth programs in corticospinal neurons can be maintained by environmental cues released from stem cells grafted near the injury site (Kumamaru et al., 2018; Poplawski et al., 2020). While our studies demonstrate an important role for Csf2 in conditioning-lesion-induced axon regeneration, we cannot rule out potential contributions by DRG macrophages. However, the small number of hematogenous macrophages detected in axotomized DRGs suggests that potential pro-regenerative immune mechanisms would need to be exerted by tissue resident macrophages. We acknowledge that axon regeneration was examined in Csf2 global knock-out mice, and thus, it is possible that Csf2 deficiency affects immune cells before they enter the injured sciatic nerve (Hamilton, 2019) or within the injured spinal cord (Choi et al., 2014; Huang et al., 2009).

Taken together, we provide a comparative analysis of SNC-induced inflammation in the nerve and axotomized DRGs and identify two very different immune compartments, the former primarily comprised of hematogenous leukocytes and latter of tissue resident endoneurial Mac. Mac subpopulations in the injured nerve are not uniformly distributed, indicating the existence of specific immune microenvironments. Efferocytosis of dying leukocytes is observed in the injured nerve, and thus, contributes to nerve debridement and inflammation resolution. If this process is curtailed, conditioning-lesion-induced regeneration of DRG neuron central axons is impaired.

Primary DRG neuron cultures were fixed in 4% paraformaldehyde (PFA) in 1x PBS (Sigma-Aldrich, 158127) for 15 min at RT, followed by two brief rinses in PBS. Cells were permabilized in 0.3% Triton-X100 (Sigma, T8787) in PBS for 5 min at RT. Cells were incubated in blocking buffer, 2% FBS, 2% heat shock fraction V BSA (Fisher Scientific, BP1600), 0.3% Triton-x-100 in PBS for 1 hr. Cells were incubated with anti-Neurofilament heavy chain (NFH, 1:100; Aves Lab,) in blocking buffer overnight at 4°C and rinsed 3x in 0.3% triton-x-100 in PBS, 5 min each. Donkey anti-chicken Cy3 (1:200, Jackson Immunoresearch, 703-165-155) in blocking buffer was added for 45 min at room temperature. Cells were rinsed in PBS for 5 min. Hoechst 33342 (1:50,000 in PBS; Invitrogen, H3570) was added for 10 min at RT, followed by two washes in PBS. Cells were imaged on a Zeiss Axio Observer Z1 fitted with a Zeiss Axiocam 503 mono camera using the EC PlnN 10x objective. Single plane, tile scans were randomly acquired for each well. For immunofluorescence staining of neural tissues, mice were killed and perfused transcardially with ice-cold PBS for 2 min followed by ice-cold, freshly prepared 4% paraformaldehyde for 10 min. Spinal cord, sciatic nerves, and L4-L5 DRGs were collected and post-fixed in perfusion solution overnight. After that the solution was switched to 30% sucrose in PBS and tissues were kept at 4°C for at least 12 hr. Tissues were covered with tissue Tek (Electron Microscopy Sciences, 62550–01) and stored at −80°C. Spinal cord sections and longitudinal sciatic nerve sections were cut at 12 µm and DRGs at 10 µm thickness using a cryostat (Leica Biosystems, CM3050S). Sciatic nerve and DRG sections were mounted on Superfrost+ microscope slides (Fisher Scientific, 12-550-15) and air dried for at least 12 hr. Spinal cord sections were stained in 24-well plates as free floating sections. The following primary antibodies were used, anti-Iba1 (1:500; WAKO, 019–19741), anti-F4/80 (1:500; Thermo Fisher Scientific, MA1-91124), anti-CD68 (1:500, Abcam, ab125212), anti-GFAP (1:500, DAKO, Z0334), anti-SCG10 (1: 2,000, Novus Biological, NBP1-49461), anti-CTB (1: 10,000, List Biological Laboratories, #703).

Sciatic nerves and L3-L5 DRGs were dissected and lysed separately in radioimmunoprecipitation assay (RIPA) buffer (150 mM NaCl, 50 mM Tris, 1% NP-40, 3.5 mM sodium dodecyl sulfate, 12 mM sodium deoxycholate, pH 8.0) supplemented with 50 mM β-glycerophosphate (Sigma-Aldrich, G9422-100G), 1 mM Na3VO4 (Sigma-Aldrich, S6508-10G), and protease inhibitor cocktail (1:100, Sigma-Aldrich, P8340-5ML). Tissues were kept on ice, briefly homogenized with a motorized tissue homogenizer (RPI, 299200), and subjected to sonication (Fisher Scientific Sonic Dismembrator, Model 500) at 70% amplitude for 3 s. Tissue lysates were centrifuged at 15,000 rpm at 4°C for 10 min (Eppendorf, 5424R). The supernatant was transferred to a new tube and protein concentration was measured with a DC Protein Assay Kit (Bio-Rad, 5000111) using a photospectrometer at 750 nm (Molecular Devices, SpectraMax M5e). Samples were diluted with 2x Laemmli sample buffer (Bio-Rad, 1610737) containing 5% β-mercaptoethanol (EMD Millipore, 6010), boiled for 10 min at 100°C, and stored at −80°C for analysis. For SDS-PAGE, equal amounts of total protein (5–10 μg) were loaded per lane of a 15% gel. Separated proteins were transferred onto PVDF membrane (EMD Millipore, IPVH00010) for 2.5 hr at 200 mA in cold transfer buffer (25 mM TrisHCl, 192 mM Glycine, 10% Methanol). Membranes were blocked in 5% blotting-grade blocker (BioRad, 1706404) prepared in 1x TBS-T (TBS pH 7.4, containing 0.1% Tween- 20) for 1 hr at room temperature, and probed overnight at 4°C with the following primary antibodies diluted in 1x TBS-T with 3% BSA (Fisher Scientific, BP1600): α-CD11b (1:1000, Abcam, ab133357), α-ERK1/2 (1:5000, Cell Signaling Technologies, 9102). Horseradish peroxide (HRP)-conjugated α-rabbit secondary IgG (EMD Millipore, AP182P) were used. All HRP-conjugated secondary antibodies were diluted at half the dilution of the corresponding primary antibody in 3% BSA in 1x TBS-T, and the HRP signal was developed with various strengths of chemiluminescent substrates from Thermo Fisher Scientific (Pico Plus, 34580 or Femto, 34095) or from Li-COR Biosciences (926-95000). Protein band intensity was visualized and quantified in the linear range using LI-COR C-Digit (CDG-001313) and Image Studio Software (Version 5.2.5).

For gene expression analysis of naive and axotomized DRGs, we carried out bulk RNA sequencing of L3-L5 ganglia harvested from naïve mice (n = 3), d1 (n = 3), d3 (n = 3), and d7 (n = 3) following bilateral SNC. For each data point, 18 ganglia were collected form three mice, pooled, flash frozen and lysed in Trizol solution for RNA extraction (Chandran et al., 2016). RNA-sequencing was carried out using TrueSeq RiboZero gold (stranded) kit (Illumina). Libraries were indexed and sequenced over two lanes using HiSeq4000 (Illumina) with 75 bp paired end reads. Quality control (QC) was performed on base qualities and nucleotide composition of sequences, to identify potential problems in library preparation or sequencing. Sequence quality for the dataset described here was sufficient that no reads were trimmed or filtered before input to the alignment stage. Reads were aligned to the latest Mouse mm10 reference genome (GRCm38.75) using the STAR spliced read aligner (version 2.4.0). Average input read counts were 58.8M per sample (range 53.4M to 66.2M) and average percentage of uniquely aligned reads was 86.3% (range 83.8% to 88.0%). Raw reads were filtered for low expressed genes and normalized by variance stabilization transformation method. Unwanted variation was removed by RUVSeq (1.20.0) with k = 1. Differentially expressed genes were identified using the bioconductor package limma (3.42.2) with FDR < 0.1 and the resulting gene lists were used as input for Ingenuity pathway analysis (Qiagen). Weighted gene co-expression network analysis was conducted using WGCNA R-package (ver 1.69). Soft thresholding power of 18 was used to calculate network adjacency. CutHeight of 0.3 was used to merge similar co-expression modules. Enrichment analysis for gene set was performed with GSEA (ver 2.2.2) using MsigDB (ver 7.0). Normalized enrichment score (NES) was used to assess enrichment of gene sets.

Mice were transcardially perfused with ice-cold PBS for 5 min to flush out all blood cells in circulation. The sciatic nerve trunk was harvested and a segment that contains the injury site and the distal nerve stump, up to the branch point of the tibial nerve, used for further processing. A minimum of three mice (six nerves) was used to obtain sufficient cells for analysis using the 10x Genomics platform. The collected nerve segments were cut into small pieces with microscissers and incubated in 1 ml PBS supplemented with collagenase (4 mg/ml Worthington Biochemical, LS004176), dispase (2 mg/ml, Sigma-Aldrich, D4693), and actinomycin D (45 µM, Sigma Aldrich, A1410) for 30–45 min at 37°C in a 15-mL conical tube. Tissues were gently triturated with a P1000 pipette every 10 min. Next, samples were rinsed in DMEM with 10% FBS and spun down at 650 g for 5 min before removing supernatant. The resulting pellet was gently re-suspended in 1 mL of 27% Percoll (Sigma Aldrich, P4937) in PBS. Then 3 ml of 27% Percoll were added to bring the final volume to 4 ml. Samples were spun at 900 g for 20 min with no brake in a clinical centrifuge (Beckman Coulter Allegra 6R). The top layers (with myelin and other debris) were carefully aspirated. The final 100 µl were resuspended in 1 ml of PBS with 2% FBS and filtered through a pre-washed 40 µm Falcon filter (Corning, 352340) with an additional 5 ml of PBS with 2% FBS. Cells were pelleted at 650 g for 5 min at 4°C. The supernatant was removed and the cell pellet resuspended in 180 µl of MACS buffer (Miltenyi, 130-091-376) diluted 1:20 in PBS (final bovine serum albumin [BSA] was 0.5%) and 10 µl of myelin removal beads were added (Miltenyi, 30-096-731). To remove all myelin debris, cells were incubated with myelin depletion beads for 15 min at 4°C with intermitted tapping. Cells were rinsed in 5 ml of MACS buffer, gently inverted several times and spun at 300 g for 10 min. Cells were separated from myelin beads using the MidiMACS separator (Miltenyi, 130-042-302) and LS columns (Miltenyi, 130-042-401). The flow through solution with the cells was centrifuged and the cells resuspended in 50 µl of Hanks balanced salt solution (Gibco, 14025092) supplemented with 0.04% BSA (Fisher Scientific, BP1600). The cell number and live/dead ratio was determined using propidium iodine labeling and a hemocytometer.

Raw scRNAseq data were processed using the 10x Genomics CellRanger softeware version 3.1.0. The CellRanger ‘mkfastq’ function was used for de-multiplexing and generating FASTQ files from raw BCLs. The CellRanger 'count’ function, with default settings was used with the mm10 reference supplied by 10x Genomics, to align reads and generate single cell feature counts. Per sample, approximately 5800 cells with a median of 2507 genes per cell were obtained. CellRanger filtered cells and counts were used for downstream analysis in Seurat version 3.1.2 implemented in R version 3.6.2. Cells were excluded if they had fewer than 200 features, more than 7500, or the mitochondrial content was more than 25%. Reads from multiple samples were merged and normalized following a standard Seurat SCTransform integration pipeline (Hafemeister and Satija, 2019); mitochondrial mapping percentage was regressed out during the SCTransform normalization step. Principal component analysis was performed on the top 3000 variable genes and the top 30 principle components were used for downstream analysis. A K-nearest neighbor graph was produced using Euclidean distances. The Louvain algorithm was used with resolution set to. five to group cells together. Non-linear dimensional reduction was done using UMAP. The top 100 genes for each cluster, determined by Seurat’s FindAllMarkers function and the Wilcoxon Rank Sum test, were submitted to version 11 of the string-db.org to determine functional enrichment; referred to as STRING analysis.

To model developmental trajectories of cells that comprise the mononuclear phagocyte system (MPS), the Bioconductor package, slingshot version 1.4.0 was used. The integrated Seurat object was subset to include only MPS cells and slingshot was instructed to start from monocytes. The pseudo-time from the three slingshot constructed lineages were used in random regression forest to reveal the most influential genes, on pseudo-time. Random forests were implemented with the Ranger package of R from 1400 trees, 200 genes at each node, and the Gini index, ‘impurity’, measure for gene importance. The bulk RNA-seq and scRNA-seq data is available online in the Gene Expression Omnibus (GEO) database (GSE153762).

Cell identities, as defined above, were saved for the 3d injured nerve. Single-cell transcriptomes from YFP.pos and YFP.neg macrophage populations identified in naïve peripheral nerve tissue (Wang et al., 2020), were downloaded and given the label Mac_Naive. The log2 transformed raw counts of the 3d injured Mac1-5 and Mo as well as the Mac_Naive cells were subjected to batch correction using the ComBat function from the Bioconductor ‘sva’ package (Leek et al., 2012). Injured nerve Mo/Mac and naïve Mac made up the two batches and the following arguments were passed to ComBat: mod = NULL, par.prior = TRUE, mean.only = FALSE, prior.plots = FALSE. After batch correction each cell type and gene had a highly repeated minimum number near 0. To aid in plotting and determining ‘percent expressed’ this value was replaced with 0. The average expression for each gene and each cell type was calculated for the purpose of making dotplots. Any cell type with more than 85% zeros was not given a dot. The dots represent percent expressed by radius and average expression, scaled across cell type, by color.