Peripheral sensory neurons located in dorsal root ganglia (DRG) relay sensory information from the peripheral tissue to the brain. Satellite glial cells (SGCs) are unique glial cells in which they form an envelope that completely surround each sensory neuron.25,57–59 This organization allows for close bidirectional communication between SGC and their enwrapped soma. Satellite glial cells actively participate in the information processing of sensory signals.28 Morphological and molecular changes are elicited in SGCs by pathological conditions such as inflammation, chemotherapy-induced neuropathic pain, as well as nerve injuries.1,9,23,24,31,80 These studies also point to the contribution of SGCs to abnormal pain conditions under injurious conditions.19,25,28
Our recent analysis of SGCs at the single cell level revealed that SGCs share functional and molecular features with astrocytes.1,2 Despite great morphological differences, SGCs and astrocytes share many signaling mechanisms, including potassium buffering through the inwardly rectifying potassium channel Kir4.1 and intercellular signaling through gap junctions.26 Both cell types also undergo major changes under pathological conditions, which can have neuroprotective function but can also contribute to disease and chronic pain.26
Most of the available information on SGC function has been obtained in rodents, which limits the potential for clinical translation. Only a small number of studies have investigated the molecular characteristics of human SGC.21 Some evidence point to a role of SGC in human pathological conditions. In patients with Friedreich Ataxia, an autosomal recessive neurodegenerative disease, SGC proliferate, form gap junctions and abnormal multiple layers around the neurons,33,34 likely leading to alterations in the bidirectional communication between SGCs and neurons. Satellite glial cells also play a role in viral infection such as herpes simplex virus or varicella zoster virus.16,83 In HIV-1 infection of macaques, the virus that causes AIDS, in which peripheral neuropathy and pain are common, an upregulation of glial fibrillary acidic protein (GFAP) in SGCs was observed.44 The hemagglutinating encephalomyelitis virus belongs to the family of coronavirus and was shown to replicate within rat sensory neurons and accumulate in lysosome-like structures within SGCs, suggesting that SGCs may restrict the local diffusion of the virus.40 Dorsal root ganglion sensory neurons and their SGC coat represent a potential target for multiple viral invasions in the peripheral nervous system.
A better understanding of SGC responses to mechanical, chemical, and viral insults and how SGCs communicate with sensory neurons will be important for future targeted therapies to treat pathological nerve conditions. To facilitate translation of findings in rodent models, a direct comparison with human tissues is thus needed. Here, we present a single-cell level analysis of SGCs in humans, mice, and rats. We find that some of the key features of SGCs, including their similarities with astrocytes and the enrichment of biological pathways related to lipid metabolism and peroxisome proliferator–activated receptor-alpha (PPARα) signaling, are largely conserved between rodents and humans. We also find notable differences in ion channels and receptors expression, which may suggest differences in SGC–neuron communication and function in painful conditions and other peripheral neuropathies. Our study highlights the potential to leverage on rodent SGC properties and unravel novel mechanisms and potential targets for treating human nerve injuries and other pathological conditions.
2. Materials and methods
2.1. Animals and procedures
All animals were approved by the Washington University School of Medicine Institutional Animal Care and Use Committee under protocol A3381-01. All experiments were performed in accordance with the relevant guidelines and regulations. All experimental protocols involving rats and mice were approved by the Washington University School of Medicine (protocol #20180128). Mice and rats were housed and cared for in the Washington University School of Medicine animal care facility. This facility is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care and conforms to the PHS guidelines for Animal Care, Accreditation—7/18/97, USDA Accreditation— Registration # 43-R-008. Eight to 12-week-old female C57Bl/6 mice and adult male Lewis rats were used for single-cell RNA-sequencing (scRNAseq) studies.
2.2. Single-cell RNA sequencing in mice and rats
L4 and L5 DRG from mice (2 biological independent samples, n = 3 mice for each sample) and rats (2 biological independent samples, n = 2 rats for each sample) were collected into cold Hanks balanced salt solution (HBSS) with 5% Hepes and then transferred to warm papain solution and incubated for 20 minutes in 37°C. Dorsal root ganglia were washed in HBSS and incubated with collagenase for 20 minutes in 37°C. Ganglia were then mechanically dissociated to a single-cell suspension by triturating in culture medium (Neurobasal medium), with GlutaMAX, Pen-Strep, and B-27. Cells were washed in HBSS + Hepes + 0.1%BSA solution and passed through a 70-μm cell strainer. The Hoechst dye was added to distinguish live cells from debris, and cells were FACS sorted using MoFlo HTS with Cyclone (Beckman Coulter, Indianapolis, IN). Sorted cells were washed in HBSS + Hepes + 0.1% BSA solution and manually counted using a hemocytometer. The solution was adjusted to a concentration of 500 cells/μL and loaded on the 10x Chromium System. The mouse data set used in this study is the naïve data set used in our previous study.2
Single-cell RNAseq libraries were prepared using GemCode Single-Cell 3′ Gel Bead and Library Kit (10x Genomics). A digital expression matrix was obtained using 10x's CellRanger pipeline (build version 3.1.0) (Washington University Genome Technology Access Center). Quantification and statistical analysis were performed with Partek Flow package (build version 9.0.20.0417). Filtering criteria are low quality cells and potential doublets that were filtered out from analysis using the following parameters: total reads per cell: 600 to 15,000, expressed genes per cell: 500 to 4000, and mitochondrial reads <10%. A noise reduction was applied to remove low-expressing genes ≤1 count. Counts were normalized and presented in a logarithmic scale in the counts per million approach. We applied variance stabilizing transformation to count data using a regularized negative binomial regression model (Seurat::SCTransform) followed by removal of unwanted variation caused by known nuisance or batch factors (scale expression). The principal component analysis using the Louvain clustering algorithm was then undertaken followed by an unbiased clustering (graph-based clustering) algorithm implemented in Partek. Clustering was performed using the compute biomarkers algorithm, which computes the genes that are expressed highly when comparing each cluster. Seurat3 integration was used to obtain cell type markers that are conserved across samples, and clusters were assigned to a cell population by at least 3 established marker genes. Clusters are presented in a t-distributed stochastic neighbor embedding plot, using a dimensional reduction algorithm that shows groups of similar cells as clusters on a scatter plot. Differential gene expression analysis was performed using 3 different models: compute biomarkers following regularized negative binomial regression, nonparametric analysis of variance, and the Partek algorithm GSA that integrate multiple statistical models. Gene lists from each statistical model were intersected to remove potentially false positive genes. The intersected listed were then applied for all downstream analyses. A gene was considered differentially expressed if it has a false discovery rate step-up (P value adjusted) P ≤ 0.05 and a Log2fold-change ≥±2. The differentially expressed genes were subsequently analyzed for enrichment of Gene Ontology (GO) terms and the KEGG pathways using the Partek flow pathway analysis. Partek was also used to generate figures for t-distributed stochastic neighbor embedding and scatter plot representing gene expression.
2.3. Human tissue collection
For single nucleus RNAseq (snRNAseq) and transmission electron microscopy (TEM), human DRG were obtained from Anabios, Inc (San Diego, CA) (donor #1), or Mid-America Transplant (St. Louis, MO) (donors #2-5). L4-L5 DRG were extracted from tissue or organ donors less than 2 hours after aortic cross clamp and was immediately snap frozen and stored at −80°C until use. Details regarding the donors for snRNAseq are included below (Tables 1 and 2).
Table 1 -
Donor information for snRNAseq
BMI, body mass index; COD, cause of death; DRG, dorsal root ganglia.
Table 2 -
Donor information for immunohistochemistry studies
BMI, body mass index; COD, cause of death; DRG, dorsal root ganglia.
For immunohistochemistry studies, human DRG were obtained from organ donors with full legal consent for use of tissue in research and in compliance with procedures approved by Mid-America Transplant. The Human Research Protection Office at Washington University in St. Louis provided an institutional review board waiver. Details regarding the donors for immunohistochemistry are included below.
2.4. Single-nucleus RNA sequencing from the human sample
To make the tissue suitable for nuclei isolation, the entire DRG was processed into smaller pieces by cryopulverization using the CryoPrep (Covaris; CP02). Nuclei were isolated according to Martelotto with some modifications.45 We elected to apportion the cryopulverized tissue and process the portions in parallel using 2 different buffers to evaluate potential effects on nuclei representation. One homogenization buffer was EZ Nuclei Lysis Buffer (Sigma; NUC101-1KT) with 0.5% RNasin Plus (Promega; N2615), 0.5% SUPERase-In (ThermoFisher; AM2696), and 1 mM Dithiothreitol (DTT).22 The other homogenization buffer was CHAPS detergent, salts, and Tris buffer (CST) buffer (NaCl2 146 mM, Tris HCl pH 7,5 10 mM, CaCl2 1 mM, MgCl2 21 mM, 0.49% 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS) (Millipore-Sigma), 0.01% BSA, 0.5% SUPERasin-in, and 0.5% RNasin Plus), as described in Slyper et al.68 Using the EZ buffer, the samples were homogenized on ice using 18 strokes of Pestle A followed by 18 strokes of Pestle B. The homogenate was filtered through a 50 μm filter (Sysmex; 04-004-2327) into a 2 mL microcentrifuge tube (Eppendorf; 022431048). An additional 0.5 mL of homogenization buffer was used to wash the Dounce homogenizer and filter. The sample was then placed on ice, whereas the remaining samples were processed. The sample was centrifuged at 500g at 4°C for 5 minutes to obtain a crude pellet containing spinal nuclei. The supernatant was removed and discarded, being careful to not disturb the pellet. The pellet was resuspended in 1.5 mL of homogenization buffer and allowed to sit on ice for 5 minutes at 500g, 4°C for 5 minutes. This wash step was repeated twice more for a total of 3 washes. The final pellet was resuspended in 0.5 mL of nuclei resuspension buffer (NRB) containing 6 μM 4′,6-diamidino-2-phenylindole (ThermoFisher; D1306). The suspension was filtered through a 20 μm filter (Sysmex; 04-004-2325) into a polypropylene tube and kept on ice. Using the CST buffer, the samples were homogenized on ice using 18 strokes of Pestle A followed by 18 strokes of Pestle B in 1 mL of CST buffer. The homogenate was filtered through a 50 μm filter into a 15 mL conical. An additional 1 mL was used to wash the filter and then 3 mL of CST was added, bringing the total volume to 5 mL. The suspension was spun down at 500g for 5 minutes at 4°C. The supernatant was removed, and the pellet was resuspended in 0.5 mL of CST containing 6 μM of 4′,6-diamidino-2-phenylindole. The suspension was filtered through a 20 μm filter into a polypropylene tube and kept on ice. Fluorescence-activated nuclear sorting (FANS) was performed to purify nuclei from debris on a FACSAria II (BD). Gates were set to isolate DAPI+ singlet nuclei based on forward scatter and side scatter as well as fluorescence intensity. The instrument was set to 45 pounds per square inch of pressure, and a 85 μm nozzle was used, with sterile phosphate-buffered saline (PBS) sheath fluid. Nuclei were sorted into a 1.5 mL microcentrifuge tube containing 15 μL of NRB at 4°C. For each sample, 18,000 events were sorted into the collection tube. The sorted nuclei and NRB total volume were approximately 45 μL, allowing for the entire loading of the suspension into the Chromium Single Cell 3′ v3 solution (10x Genomics) without any further manipulation. 10x libraries were processed according to the manufacturer's instructions. Completed libraries were run on the Novaseq 6000 (Illumina). A digital expression matrix was obtained using the 10X CellRanger pipeline as above.
2.5. Tissue preparation and immunohistochemistry
Mice were perfused with PBS buffer, followed by 4% paraformaldehyde. After isolation of mouse DRG, the tissue was postfixed using 4% paraformaldehyde for 1 hour at room temperature. Tissue was then washed in PBS and cryoprotected using 30% sucrose solution at 4°C overnight. Next, the tissue was embedded in optimal cutting temperature, frozen, and mounted for cryosectioning. Mouse frozen sections were cut at 12 μm for subsequent staining. Freshly dissected human DRG were sectioned to 40 μm and fixed and stored as free floating in a cryoprotectant. Mouse DRG sections mounted on slides and human floating DRG sections were washed 3 times in PBS and then blocked in solution containing 10% donkey serum in 0.1% Triton-PBS for 1 hour. Next, sections were incubated overnight in blocking solution containing primary antibody. The next day, sections were washed 3 times with PBS and then incubated in blocking solution containing a secondary antibody for 1 hour at room temperature. Finally, sections were washed 3 times with PBS and mounted using ProLong Gold Antifade (Thermo Fisher Scientific). Images were acquired at ×10 or ×20 using a Nikon TE2000E inverted microscope. Antibodies were as follows: TUJ1 (Tubb3/βIII tubulin) antibody (BioLegend catalog #802001, RRID:AB_291637), FABP7 (Thermo Fisher Scientific Cat #PA5-24949, RRID:AB_2542449), and fatty acid synthase (FASN) (Abcam, Catalog #ab128870). Stained sections with only secondary antibody were used as controls. Quantification of SGC markers in human and mouse DRG sections were performed in ImageJ, where the % of neurons surrounded by at least 1 SGC expressing the indicated markers out of total number of neurons in each section was quantified. n = 4 biological independent replicates. Unpaired t test. Data are presented as mean values ±SD.
2.6. Transmission electron microscopy of mice and human dorsal root ganglia
Mice were perfused with 2.5% glutaraldehyde with 4% paraformaldehyde in 0.1 M cacodylate buffer, and DRG were drop fixed to the same fixation buffer for a postfix. A secondary fix was performed with 1% osmium tetroxide. Freshly collected human DRG samples were drop fixed in 2.5% glutaraldehyde + 2% paraformaldehyde in 0.15 M (final concentration) cacodylate buffer pH 7.4 with 2 mM calcium chloride overnight at 4°C. Samples were then vibratomed in the same buffer used for fixation and sections collected into buffer in well plates. For TEM, tissue was dehydrated with ethanol and embedded with Spurr resin. Thin sections (70 nm) were mounted on mesh grids and stained with 8% uranyl acetate followed by Sato lead stain. Sections were imaged on a Jeol (JEM-1400) electron microscope and acquired with an AMT V601 digital camera (Washington University Center for Cellular Imaging).
3.1. Profiling satellite glial cells from humans, mice, and rats at the single-cell level
To define the similarities and differences between SGC across different species, we performed snRNAseq of L4 and L5 human DRG and scRNAseq of L4 and L5 mouse and rat DRG using the Chromium Single Cell Gene Expression Solution (10x Genomics) (Fig. 1A). We chose to perform scRNAseq in rodents because we previously showed that this method efficiently captures SGCs.1,2 We opted for snRNAseq in human DRG because the tissue was frozen and the large size of cells in human may limit their capture rate in the 10x platform. The number of sequenced human nuclei from 5 donors (donor information is in the methods section) was 19,865, with an average of 129,520 mean reads per cell, 1480 mean genes per cell, and a total of average 25,643 genes detected. The number of sequenced mouse cells from 2 independent biologically replicates (pooled DRG from 3 mice for each replicate) was 6343 with an average of 65,378 mean reads per cell, 1510 mean genes per cell, and a total of 18,130 genes detected. The number of sequenced rat cells from 2 biologically independent replicates (pooled DRG from 2 rats for each replicate) was 15,892, with an average of 41,594 mean reads per cell, 2132 mean genes per cell, and a total of 17,137 genes detected. Low quality cells and doublets were filtered out from downstream analysis (see filtering criteria in the methods). Human cells from different donors clustered together by the cell type, with the exception of donor #3, in which SGCs clustered separately from the other 4 donors (Fig. 1B). Batches from mice and rats demonstrated high similarities in cell clustering (Fig. 1B).
To identify cluster-specific genes, we calculated the expression difference of each gene between that cluster and the average in the rest of the clusters (analysis of variance fold change threshold >1.5). Examination of cluster-specific marker genes revealed major cellular subtypes including neurons, SGC, endothelial cells, Schwann cells, pericytes, smooth muscle cells, macrophages, mesenchymal cells, and connective tissue cells (Table 3 and Fig. 1A).1 Human-specific marker genes were used to classify cell populations: macrophages (CD163 and MRC1), mesenchymal cells (APOD and PDGFRA), endothelial cells (FLT1, PECAM, and CLDC5), connective tissue or mesenchymal cells (COL1A1 and DCN), myelinating Schwann cells (PRX, MAG, and PMP22), SGCs (FABP7 and CDH19), myelinating or nonmyelinating Schwann cells and SGCs (S100B), T-cells (Cd2, Cd3g, and Cd28), and smooth muscle cells (MYOCD, ACTA2, and DES) (Supplementary Fig. 1A, available at https://links.lww.com/PAIN/B599; and Table 3). Rodents had slightly different cell types and markers: macrophages (Cd68 and Aif1), pericytes (Kcnj8 and Pdgfrb), neurons (Tubb3, Gal, Tac, and Prph), SGC (Cdh19, Fabp7, and Kcnj10), Schwann cells (Prx, Mag, and Pmp2), mesenchymal cells (Apod and Pdgfra), smooth muscle cells (Myocd, Acta2, and Des), endothelial cells (Flt1, Pecam, and Cldn5), and connective tissue (Col1a1 and Dcn) (Supplementary Figs. 1B and C, available at https://links.lww.com/PAIN/B599, and Table 3). The cell clusters obtained from the mouse data set matched our previous data set1 (Supplementary Fig. 1D, available at https://links.lww.com/PAIN/B599).
Table 3 -
Top 10 differentially expressed genes in each cell population compared with all other cell types in the dorsal root ganglia (fold change threshold >1.5).
DRG, dorsal root ganglia; mySC, myelinating Schwann cell; nmSC, nonmyelinating Schwann cell; SGC, satellite glial cell
We previously showed that although the actual percentage of neuronal cells in the mouse DRG is about 12%, the number of neurons detected in our scRNAseq analysis was only about 1%,1,2 which might be a result of the dissociation protocol that is biased towards nonneuronal cells. Another possibility is neuronal damage during the tissue dissociation process or the fact that sensory neurons are relatively large cells and are less amenable for single-cell studies. In the rat samples, only 0.5% of cells were neurons, and no neuronal cells were detected in the human sample (Fig. 1A). Nevertheless, our protocol achieved recovery of SGCs from all species with 42% in humans, 55% in mice, and 74% in rats (Fig. 1A), allowing us to compare the molecular profile of SGC across species.
We recently described that fatty acid–binding protein 7 (Fabp7) is a specific marker gene for SGC and that the FABP7 protein is highly enriched in mouse SGC compared with other cells in the DRG.1 t-distributed stochastic neighbor embedding plots overlaid for Fabp7 demonstrated that Fabp7 is also enriched in human and rat SGC (Fig. 1C). To validate Fabp7 expression at the protein level, we performed immunostaining of DRG sections from mouse and human, which revealed specific FABP7 labelling of SGC surrounding sensory neurons in both species (Figs. 2A and B).
In adult animals, SGC tightly enwrap the soma of each sensory neuron.56,57,59 The gap between SGC and the neuronal surface is only about 20 nm, which is similar to that of the synaptic cleft. This close association between the 2 cell types is essential for efficient mutual neuron–SGC interactions.25 The detailed morphology of the human neuron–SGC unit has only been examined at the light microscopy level in humans.21 To further compare the SGC organization surrounding sensory neurons across species, we performed TEM of human and mouse DRG sections (Fig. 2C), which demonstrated the tight contact between SGCs and neurons in both mouse and human and the increased human sensory neuron soma size, which can be up to 5 times larger than mouse sensory neurons (Fig. 2C).21 Quantification of the number of SGCs surrounding sensory neurons revealed that human sensory neurons are surrounded by significantly more SGCs than mouse neurons (Fig. 2D), consistent with the observations that the number of SGC surrounding sensory neurons increases with increasing soma size in mammals.36,56,59
3.2. Expression of satellite glial cell–specific marker genes in rodents and humans
We next examined the expression of known SGC marker genes in rodents and humans. Cells in the clusters identified as SGCs were pooled together. We identified 7880 SGCs in humans, 3460 SGCs in mice, and 8428 SGCs in rats (Figs. 3A–C). t-distributed stochastic neighbor embedding plots overlaid with Fabp7 demonstrated that, in all species, Fabp7 is expressed at high levels in a majority of SGCs (mice 92%, rats 88%, and humans 96% (Figs. 3A–D). Cadherin19 (Cdh19) has been described as a unique SGC marker in rat Schwann cell precursors71 and in adult rat SGCs.20 We found that Cdh19 was expressed in most human SGCs (98%), whereas only half of SGCs in rodents expressed this gene (56% in mice and 48% in rats) (Figs. 3A–D). Glutamine synthetase (GS/Glul) has been suggested as an SGC-specific marker in rat46 and mouse DRG.30,31 Our previous finding indicated a nonspecific expression of Glul in almost all cells in the DRG at the transcript level.1 Our current analysis suggests differences in Glul expression between species with more than half of SGC expressing Glul in rodents (∼60%) and only around 10% in human (Figs. 3A–D).
One of the characteristics of SGCs, which is similar to astrocytes, is the ability to control the microenvironment by expression of transporters and ion channels.26 The potassium channel Kir4.1/Kcnj10 is a known marker of SGCs. Kir4.1/Kcnj10 is expressed in rat SGCs and influences the level of neuronal excitability, which has been associated with neuropathic pain conditions.79 Our analysis demonstrates that Kcnj10 is expressed in almost half of mouse SGCs (42%), whereas it is expressed in fewer SGCs in rats (6%) and humans (17%) (Figs. 3A–D). Interestingly, we found that the potassium channel Kir3.1/Kcnj3 is widely expressed in human (82%) but not in rodent SGCs (Fig. 3D). The diversity in potassium channel expression in SGCs might suggest a special role for this channel in defining the physiological characteristics of SGC across species. Another potassium channel that has been shown to be expressed specifically in rat SGC is SK3/Kcnn3.79 Our analysis suggests that only a small subset of rat SGCs (12%) and human SGCs (6%) expresses Kcnn3 (Fig. 3D), whereas mouse SGCs do not express Kcnn3 (Fig. 3D).
Another main property of SGCs that is shared with astrocytes is functional coupling by gap junctions, with SGCs surrounding the same neuron connected by gap junctions.25,29 Gap junction protein alpha 1 (CX43/Gja1) is the most abundant connexin (Cx) and was shown to modulate pain responses.69 We found that Gja1 is expressed in a majority of human SCGs (70%) but less prevalent in mice (30%) and rats (40%) (Fig. 3D). Other known gap junctions proteins expressed in SGC include Cx32, followed by Cx30.2, Cx37, Cx26, Cx30, Cx45, and Cx36.25 Although these gap junction genes were reported to be expressed in SGC, we found that less than 5% of SGCs expressed them across all species, except for Cx30.2/Gjd3 which was expressed in 35% of mouse SGCs, and Cx45/Gjc1 that is expressed in 30% of human SGCs (Fig. 3D).
Membrane channels related to gap junctions are pannexins (Panx), which do not form cell-to-cell channels but are highly permeable to adenosine triphosphate (ATP).28 Pannexin1 (Panx1) was reported to be expressed in sensory ganglia where it is increased in pain models,69 and there is evidence that Panx1-mediated ATP release is implicated in nociception.25 Our analysis demonstrated moderate expression of Panx1 in human SGCs (30%) with lower expression in mice and rats (Fig. 3D). These observations indicate variability in gap junction and pannexin gene expression between rodents and humans, which may suggest functional differences in SGC communication and function in nociception. Satellite glial cells also express GFAP, and similar to astrocytes, GFAP expression is increased under pathological conditions, which can have a protective function.82 In mouse, Gfap is one of the top upregulated genes in SGCs on nerve injury,1,2,10,17 but it is not expressed in all SGCs.2,47 In uninjured SGCs, the distribution of Gfap was relatively low, with ∼20% expression in rat, 1.5% in mouse, and undetectable levels in human DRG (Fig. 3D).
Although SGCs do not typically myelinate neuronal soma, except in the spiral ganglion,62 some myelin-associated genes such as Mpz, Mbp, and Plp1 are highly expressed in SGCs.1 Proteolipid protein (Plp1) is the major myelin protein in the central and peripheral nervous system. Plp1 is expressed in all rodent SGCs and in more than half of human SGCs (Fig. 3D). Another gene that is expressed in all rodent and human SGCs is apolipoprotein E (ApoE) (Fig. 3D). Apolipoprotein E is a multifunctional protein, mainly involved in lipid synthesis and transport. High levels of APOE production occurs in the brain, where it is primarily synthesized by astrocytes.18 We recently found that one of the genes enriched in mouse SGCs is fatty acid synthase (Fasn),1 which controls the committed step in endogenous fatty acid synthesis.14 Examination of Fasn transcript expression revealed high distribution in rat SGCs (70%) and lower in mouse (40%) and human (33%) SGCs (Fig. 3D).
To validate the expression of unique SGC marker genes across species at the protein level, immunostaining for selected markers was performed in human and mouse DRG sections. Immunostaining for FASN revealed its specific expression in SGC surrounding neurons (stained for TUJ1) in both human and mouse DRG tissue (Fig. 4A). These results suggest that the expression of genes related to lipid metabolism and transport in SGCs, such as Fasn, is conserved between rodents and human. Gfap was detected only at low levels in our scRNAseq analysis, and immunostaining with GFAP further demonstrated expression only in very few SGCs in both mice and humans (Fig. 4B). The SGC marker Glul was highly expressed in mouse SGCs and less in human SGCs at the RNA level (Fig. 3D). Staining for GLUL also revealed higher expression around most mouse SGCs, with lower detection in human SGCs (Fig. 4C). Another striking difference between human and mouse SGCs is the expression of the potassium channel Kcnj3 (Kir3.1), which is highly expressed in human SGCs but not in rodent SGCs (Fig. 3D). Immunostaining for Kcnj3 (Kir3.1) further confirmed expression in human SGCs, with almost undetectable expression in mouse SGCs (Fig. 4D). Quantification of the percent of neurons associated with SGCs expressing FASN, GFAP, GLUL, or KCNJ3 (Fig. 4E) confirms the important similarities and differences in ion channels and gap junction genes between human and mouse SGCs that might affect their function in controlling neuronal activity and nociceptive thresholds.
3.3. Satellite glial cells in rodents and humans share functional properties
To further examine the biological properties of SGCs across species, we calculated the top differentially expressed genes in SGCs in humans (2070 genes), mice (1622 genes), and rats (993 genes) (fold change >1.5, significant differences across groups, and P < 0.05 compared with average gene expression in all other populations in the DRG in the same species). This analysis might be influenced by the fact that the representation of cell population differs in each data set (Fig. 1A). Nonetheless, this analysis allowed us to compare the 3 gene sets, which revealed 193 genes shared between SGCs in humans and rodents (Fig. 5A; Supplementary Table 1, available at https://links.lww.com/PAIN/B600). The common genes included Fabp7, ApoE, Fasn, Kcnj10, and Gja1, suggesting conserved roles of SGCs related to lipid metabolism (Fabp7, ApoE, and Fasn), physiological properties (Kcnj10), and cell–cell communication (Gja1). Many of the shared genes were also expressed in astrocytes (Supplementary Fig. 2A, available at https://links.lww.com/PAIN/B599), consistent with our previous findings that 10% of top enriched genes in mouse SGCs were shared with brain astrocytes.1 We next examined if human SGCs also share unique expressed genes with human mature and fetal astrocytes.84 We found that human SGCs shared 152 genes with human mature astrocytes, 107 genes with human fetal astrocytes, and 260 genes were shared with both mature and fetal astrocytes (Supplementary Fig. 2A, available at https://links.lww.com/PAIN/B599; and Supplementary Table 2, available at https://links.lww.com/PAIN/B600). Despite the diversity in morphology and signaling mechanisms between astrocytes and SGC, these results support that important parallels between these 2 cell types are conserved between mice and humans.
We next compared human SGCs with Schwann cells, the other main types of glial cells in the peripheral nervous system.81 Schwann cells are divided to 2 types: myelinating (mySC) and nonmyelinating (nmSC). We identified both types of Schwann cells in our human scRNAseq data set and compared their similarity with SGCs. We found that human SGCs share 453 genes with nmSC, 350 genes with mySC, and 260 genes with both mySC and nmSC (Supplementary Fig. 2B, available at https://links.lww.com/PAIN/B599, and Supplementary Table 3, available at https://links.lww.com/PAIN/B600). Comparison of Schwann cell types between mice81 and humans showed more similarity between mouse mySC and human nmSC (Supplementary Fig. 2C, available at https://links.lww.com/PAIN/B599, and Supplementary Table 4, available at https://links.lww.com/PAIN/B600). Our recent work also identified that mouse SGCs do not represent a uniform cell population, and at least 4 subtypes exist.2 One of the subcluster (mSGC3) we defined showed the highest similarity to astrocytes, and another subcluster (mSGC4) resembled the most to mySC.2 We found that human SGCs expressed a unique gene set that showed the highest similarity to the mSGC3 (Supplementary Fig. 2D, available at https://links.lww.com/PAIN/B599, and Supplementary Table 5, available at https://links.lww.com/PAIN/B600). These results further support the high similarity at the gene expression level between SGCs and astrocytes and extend this similarity to human tissue.
We next analyzed the enriched biological pathways using KEGG 2021 (Kyoto Encyclopedia of Genes and Genomes) and GO. We found that human and rodent SGCs show enriched molecular functions (GO MF) related to glutamtae receptor activity and transporter activity (Figs. 5B–D). This further supports the important role SGCs may play in human DRG to control neuronal excitability and pain thresholds. Both rodent and human SGCs shared some cellular components (GO CC), including involvement membrane and synapse organization (Figs. 5B–D). The biological process enriched pathways (GO BP) in human and rodents were particularly similar, with enrichment for mainly metabolic pathways of lipids and cholesterol, as well as processes related to nervous system development (Figs. 5B–D). We previously revealed that fatty acid synthesis and PPARα signaling pathway were enriched in mouse SGCs, and those pathways were also upregulated after peripheral nerve injury in SGCs1 but not after central axon injury.2 We demonstrated that PPARα activity downstream of fatty acid synthesis in SGC contributes to promote axon regeneration in adult peripheral nerves and that the Food and Drug Administration–approved PPARα agonist fenofibrate increased axon regeneration in the dorsal root, a model of poor sensory axon regeneration.1,2 Pathway analysis using KEGG demonstrated lipid metabolic pathways such as fatty acid and steroid metabolism along with the PPAR signaling pathway in the top enriched pathways in all species (Figs. 5B–D). These observations further confirm the similarity between human and rodent SGCs and suggest that PPARα activation is a promising therapeutic for nerve injuries and other pathological conditions of peripheral nerves.
3.4. Human satellite glial cells express a greater variety of ion channels and receptors compared with rodent satellite glial cells
Astrocytes influence neural activity, in part, by controlling the neuronal microenvironment through maintaining homeostasis of neurotransmitters, potassium buffering, and synaptic transmission. Satellite glial cells express potassium channels and glutamate transporters, suggesting that they perform similar functions in the PNS. However, the exact composition of ion channels and receptors in human SGCs is not well characterized. Pathway analysis of the SGC gene set in human SGCs demonstrated enrichment in post-synaptic organization, glutamate receptor activity and transporter activity (Fig. 5B). We observed differences at the level of the classical SGC marker Kcnj10 in mice, which shows much lower expression in humans, where we note substantially higher relative expression of Kcnj3 (Fig. 3D). Within the top enriched genes is SGCs with channels and receptors functions, we detected 34 unique ion channel and receptors expressed in human SGCs, with 14 of them in the top expressed genes in SGCs (>100,000 normalized total counts, Table 4), 31 genes in mice, and 17 genes in rats (Fig. 6A, Supplementary Table 6, available at https://links.lww.com/PAIN/B600). A majority of genes were related to potassium channels and glutamate receptors in both humans and rodents (Fig. 6B). All species shared 5 genes: Kcnj10, Gja1, Grid2 (glutamate ionotropic receptor delta type subunit 2), Cacng4 (calcium voltage-gated channel auxiliary subunit gamma 4), and Aqp4 (aquaporin-4) (Fig. 6A). Although the potassium channel Kcnj10 and the Gap junction Gja1 have been previously reported as rodent SGC marker genes, Grid2, Cacng4, and Aqp4 were not. Interestingly, the glutamate receptor Grid2 and the calcium voltage-gated channel Cacng4 were enriched in fetal human astrocytes but not in mature astrocytes (Supplementary Table 2, available at https://links.lww.com/PAIN/B600).84 Aquaporin-4 is a water channel predominantly found in astrocytes in the central nervous system and is believed to play a critical role in the formation and maintenance of the blood–brain barrier and in water secretion from the brain,51 further highlighting that the similarity between SGCs and astrocytes is conserved in humans.
Table 4 -
Top normalized counts expression of ion channels and receptors genes in human satellite glial cells.
Examination of enriched molecular pathways (KEGG) of the enriched channels and receptor gene sets in SGCs for each species revealed that the top pathways were related to neuroactive ligand–receptor interaction, GnRH secretion, insulin secretion, and endocannabinoid signaling pathway (Fig. 6C). Mouse and human SGCs were also enriched for glutamatergic and dopaminergic synapse (Fig. 6C). The similarity in ion channel and receptor composition between human and rodent SGCs suggests conserved physiological roles of SGCs across species. However, the differences in subset of channels and receptors may inform future study design for therapeutic development targeting SGCs in pain conditions and other peripheral neuropathies.
3.5. Human satellite glial cells express severe acute respiratory syndrome coronavirus 2 –associated factors and receptors
Satellite glial cells have been implicated in pain conditions related to viral infection such as herpesvirus, varicella zoster virus, and also swine hemagglutinating encephalomyelitis virus, which is related to the coronavirus family.25 Current models suggest that SGC surrounding virally infected neurons may restrict the virus spread.25 A recent study suggested that sensory neurons could be potential targets for the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with SARS-CoV-2 gaining access to the nervous system through entry into nociceptor nerve endings in the skin and luminal organs.67 Coronavirus disease 2019, the disease caused by the SARS-CoV-2, can trigger many unexplained neurological effects including chronic pain. Price and colleagues found that a subset of human DRG neurons expresses the SARS-CoV-2 receptor angiotensin-converting enzyme 2 at the RNA and protein level.67 Dorsal root ganglion neurons also express SARS-CoV-2 coronavirus–associated factors and receptors, which were shown to be expressed in DRG at the lumbar and thoracic level as assessed by bulk RNA sequencing of human DRG tissue.67 Having the resolution of single cell in human DRG, we assessed the expression of Ace2 and SCARF genes specifically in SGCs. While the Ace2 receptor was lowly expressed in SGCs in all 3 species, the assembly or trafficking factors Rab10, Rab1a, Rab14, and RhoA and the restriction factors Ifitim3 and Ifitim2 were highly expressed (Table 5 and Figs. 7A and B). Further examination of these abundant genes in human SGCs revealed high similarities in expression across individual donors (Fig. 7B). Interferon‐induced transmembrane proteins (IFITMs) restrict infections by many viruses, but a subset of IFITMs can enhance infections by specific coronaviruses. Recently, it has been showed that human IFITM3 with mutations in its endocytic motif enhances SARS‐CoV‐2 spike‐mediated cell‐to‐cell fusion and, thus, raised the concept that polymorphisms in IFITM3 can positively or negatively influence coronavirus disease 2019 severity.66 Whether IFITM3 expression in SGCs enhances or limits viral infection in sensory ganglia remains to be determined. Together, these results suggest that SARS-CoV-2 may gain access to the nervous system through entry into sensory neurons at free nerve endings in organs and that SGC may attempt to restrict the local diffusion of the virus.25,40
Table 5 -
SCARF genes expression in human satellite glial cells.
||Gene product role
SCARF, SARS-CoV-2–associated factors and receptor; SGC, satellite glial cell.
The biology of SGCs has remained poorly characterized under normal or pathological condition. Most of the current knowledge on SGC function stems from studies in rodents. Here, we present a direct comparison of the transcriptional profile of SGCs in mice, rats, and humans at the single-cell level. Our findings suggest that key features of SGCs in rodent models, such as similarities with astrocytes and enrichment for lipid metabolism and PPARα signaling, are conserved in humans. However, notable differences exist in ion channels and receptors expression, which may suggest differences in SGC–neuron communication and functions in pain conditions and other peripheral neuropathies. Our study provides the potential to leverage rodent and human SGC properties and unravel novel mechanisms and potential targets for treating nerve injuries and other pathological conditions. Furthermore, depending on the specific target that is under study for therapeutic development, these parallels and differences should be considered in the study design.
Our previous study demonstrated that SGCs contribute to the nerve repair process in mice1 and that the Food and Drug Administration approved PPARα agonist fenofibrate, which is used in dyslipidemia treatment, can increase axon regeneration after dorsal root injury, a model of poor sensory axon growth.2 Fenofibrate was surprisingly shown in clinical trials to have neuroprotective effects in diabetic retinopathy5,48 and traumatic brain injury.8 Fenofibrate was also shown to exert analgesic and neuroprotective effects in rodent models of chronic neuropathic pain and inflammation as well as in some human studies.15,53,55 The neuroprotective role of fenofibrate was also recently observed in a paclitaxel chemotherapy-induced peripheral neuropathy.7 It is possible that this neuroprotection is mediated by an effect of PPARα activation in SGCs. Together, these studies further support a central role for SGCs in multiple pathological conditions affecting peripheral nerves. The observation that PPAR signaling is similarly enriched in human and rodent SGCs opens the potential pharmacological repurposing of fenofibrate and that manipulation of SGCs could lead to avenues to promote functional recovery after mechanical or chemical nervous system injuries.
Our study also highlights that the functional similarity of SGCs and astrocytes is conserved across species. Both cell types undergo major changes under pathological conditions, which can have a protective function, but can also contribute to disease, and chronic pain.26 One of the main functional similarities is buffering of extracellular potassium. In the central nervous system, glial buffering of extracellular potassium is performed by astrocytes and consists of potassium uptake by inwardly rectifying potassium (Kir) channels.35 Kir channels are key regulators of glial functions, which in turn determine neuronal excitability and axonal conduction.6 Functionally, Kir channels can be divided into different subtypes based on their biophysical properties. Kir4.1 is an ATP-dependent potassium channel, whereas Kir3.1 is a G protein–activated potassium channel.6 Most astrocytes express Kir4.1, but rat astrocytes and guinea pig Muller glia have been shown to express Kir3.1.54,60 Our studies revealed that human SGCs preferentially express Kir3.1, mice SGCs mainly express Kir4.1, and rat SGCs express low level of both Kir channels and more SK3. Nerve damage was shown to downregulate the expression of Kir4.1,72,73,79 and silencing Kir4.1 in the rat trigeminal ganglia leads to pain-like behavior.79 Similarly, gain or loss of Kir4.1 affects astrocyte ability to regulate neuronal activity.11 The diversity in potassium channel expression in SGCs might suggest that differences in signaling mechanisms related to ATP or G protein–coupled receptors define the physiological characteristics of SGCs across species.
We also observed enrichment for other types of channels in human SGCs, including sodium and water channels, chloride channels, and transient receptor potential channels. Transient receptor potential proteins consist of a superfamily of cation channels that have been involved in diverse physiological processes in the brain as well as in the pathogenesis of neurological disease. Transient receptor potential channels are widely expressed in the brain, including neurons and glial cells. Channels of transient receptor potential family have been shown to be involved in sensation and modulation of pain in peripheral ganglia, but their expression in SGCs have not been demonstrated. Transient receptor potential channels contribute to the transition of inflammation and immune responses from a defensive early response to chronic and pathological conditions. The expression of purinergic and glutamate receptor was also highly conserved between humans and mice, suggesting that the ATP and glutamate-dependent communication between neuron and glia in response to the neuronal activity and pathological conditions is largely conserved. Whether different channels are expressed in SGCs surrounding the different types of sensory neurons remains to be determined. In mice, we detected at least 4 SGC subtypes with no evidence that one of the subtypes is dedicated to 1 class of sensory neurons.2 Spatial transcriptomics approaches or in situ hybridization to molecularly characterize transcriptomes of DRG and their adjacent SGCs74 might shed light on the molecular properties of individual neuron–SGC units in humans.
Another main feature common to astrocytes and SGCs and conserved across species is the enrichment for genes related to lipid metabolism and expression of ApoE. In astrocytes, lipid metabolism is critical for synapse development and function in vivo.3,76 ApoE is predominantly secreted by astrocytes in the brain and functions as a major transporter of lipoproteins between cells. Of the 3 ApoE alleles, the ApoE4 allele is associated with an increased risk for Alzheimer disease (AD).18,42 ApoE likely regulates AD risk in large part through effects on amyloid pathology.38 However, several studies revealed a role for ApoE in lipid delivery for axon growth.39,49,77,78 We previously found that ApoE expression is increased in SGCs by activation of the PPARα signaling after nerve injury.1 The pathophysiological changes in AD are believed to arise in part from defects in neuronal communication in the central nervous system.32,65 However, decline in different sensory modalities is suggested to be a primary first-tier pathology.12 In cultured sensory neurons, exogenously applied ApoE4 directly inhibits neurite outgrowth, whereas ApoE3 stimulates neurite outgrowth.50 These studies suggest that the ApoE4 risk factors in human SGCs may directly affect sensory neurons and potentially hearing dysfunction27,41,43,61,64,70,75 and postural instability,4,37 which have been associated with neurodegenerative disorders such as AD and age-related dementia in humans.
Our analysis of human SGCs complements prior studies on human sensory ganglia that have characterized DRG samples in bulk sequencing or focused on expression of specific markers in sensory neurons,13,52,63,74 revealing similarities and difference between mouse and human sensory neurons. Our study highlights the potential to leverage on rodent SGC properties and generate new knowledge that can be used to develop novel therapeutics to treat pain and other aspects of peripheral neuropathies.
Conflict of interest statement
The authors have no conflicts of interest to declare.
Appendix A. Supplemental digital content
Supplemental digital content associated with this article can be found online at https://links.lww.com/PAIN/B599 and https://links.lww.com/PAIN/B600.
This research was funded in part by a postdoctoral fellowship from The McDonnell Center for Cellular and Molecular Neurobiology to O. Avraham, by NIH grant NS042595 to R. W. Gereau, by The McDonnell Center for Cellular and Molecular Neurobiology to V. Cavalli, by a Pilot Project Award from the Hope Center for Neurological Disorders at Washington University to V. Cavalli and by NIH grants NS111719, NS122260, and NS115492 to V. Cavalli. The authors thank Greg Strout, Ross Kossina, and Dr James Fitzpatrick from the Washington University Center for Cellular Imaging, which is supported in part by the Washington University School of Medicine, The Children's Discovery Institute of Washington University, and St. Louis Children's Hospital (CDI-CORE-2015-505 and CDI-CORE-2019-813) and the Foundation for Barnes-Jewish Hospital (3770) for assistance in acquiring and interpreting transmission electron microscopy data. The authors thank the human tissue donor families for their generous donations to science, which made the human tissue work presented here possible. The authors thank Mid-America Transplant for providing access to donor tissue and their facilities and J. Lemen for his time and surgical skill in assisting with hDRG extractions.
Author contributions: O. Avraham and V. Cavalli designed research and wrote the article. O. Avraham performed mouse and rat single-cell sequencing, bioinformatic analyses, and immunofluorescence experiments and analyzed data. A. Chamessian and L. Yang performed human single-nucleus sequencing. R. Feng performed bioinformatic analyses and analyzed data. A. E. Halevi collected rat DRG for single-cell sequencing. A. M. Moore, R. W. Gereau, and V. Cavalli supervised the project. All authors edited and approved the article.
Data availability: The raw Fastq files and the processed filtered count matrix for scRNA sequencing were deposited at the NCBI GEO database under the accession number GSE158892 (mice) and GSE169301 (rats and humans). Data analysis and processing were performed using commercial code from Partek Flow package at https://www.partek.com/partek-flow/.
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