Single-cell sequencing reveals the optimal time window for anti-inflammatory treatment in spinal cord injury

doi:10.21203/rs.3.rs-2604037/v1

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Single-cell sequencing reveals the optimal time window for anti-inflammatory treatment in spinal cord injury

https://doi.org/10.21203/rs.3.rs-2604037/v1

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Neuroinflammation is a necessary response to spinal cord injury (SCI) as it helps to clear antigens and promote tissue repair. However, excessive inflammation can result in the death of cells and axon dieback. The efficacy of anti-inflammatory medication in clinical treatment is still up for debate due to inappropriate therapeutic scheduling that does not align with the biological process of immune response. A better understanding of the immune process is crucial for effective anti-inflammatory therapeutics, but it is challenged by cellular heterogeneity and complex cellular functions. To address this, we conducted a single-cell RNA sequencing study and profiled tissue proximity to the injury site at various time points after SCI. Based on our analysis of single-cell data and histochemistry observations, we recommend an appropriate time window of 1-3 days post-injury for anti-inflammatory medication treatment. We also verified the mechanism of MPSS, a typical anti-inflammatory medication, which was found to inhibit the activation of cells with pro-inflammatory phenotypes by downregulating pathways such as TNF, IL2, and MIF. These pathways could be potential targets for anti-inflammatory treatment. In summary, we recommend a therapeutic schedule of 1-3 days post-injury to argue against classical early pulse therapy and provide potential pathways for target therapy in the future.

Single-cell sequence

Spinal cord injury

Neuroinflammation

Excessive inflammation

Secondary injury

Clarification of dynamic changes of inflammation cells during the sub-acute phase of spinal cord injury (SCI).
Optimization of the administration time window for anti-inflammatory treatment of SCI.
Illumination of the mechanism of anti-inflammatory function using standard medicine MPSS.

Severe spinal cord injury (SCI) leads to devastating irreversible neurological disabilities. A profound neuroinflammatory response is unleashed to remove the tissue debris and prepare a spinal cord repair after SCI. However, accumulated evidence indicates that excessive neuroinflammation in SCI is associated with neurological dysfunction and worse functional locomotor recovery, known as secondary injury^1–3. In recent clinical treatments, anti-inflammatory drugs such as riluzole (RLZ) and methylprednisolone sodium succinate (MPSS) have been proposed as neuroprotective strategies at the early stage after SCI with a function of remodeling the toxic environment in SCI⁴. However, the clinical use remains debatable owing to the controversial results according to different injury types and administration time points. For example, in a previous study, although the neuroprotective function of MPSS was reported and regarded as the “standard of care” for acute SCI at times, different MPSS treatments existing in 24- or 48-h protocols present significant differences and the functional gain remains questionable⁵. Furthermore, the use of RLZ is not fully effective in preventing early neuronal loss (3 h) after SCI and might confer benefits at a later time⁶. Altogether, a reasonable time window for clinical trials should be determined depending on the biological process of SCI and the mechanism of the medicine.

Inflammatory response activated after injury protects from pathogens and promotes tissue recovery to maintain the homeostasis of cells. However, excessive inflammation can cause a pathological mark in the immune system, resulting in the dysregulation of cells and chronic diseases^7,8. The dual role of inflammation response also reflects on the varied function of immune cells, such as microglia, macrophages, and astrocytes. Typically, activated microglia and macrophages are shown to play a dual role in both pro- and anti-inflammation. In a study, the activation of microglia secreted pro-inflammatory cytokines, resulting in further damage, and diminished microglia activation markedly reduced pathological tissue size⁹. However, depletion of microglia at the early phase (1 day) exacerbated the secretion of inflammatory mediators and caused neuronal death¹⁰. Macrophages are divided into two major subtypes with pro- (M1) or anti- (M2) functions in immunity reaction¹¹. Upon injury, monocyte-derived macrophages adopt a pro-inflammatory phenotype that secretes cytokines such as tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukin-1β (IL-1β). On the other hand, anti-inflammatory molecules such as transforming growth factor-β (TGF-β) were secreted by a more pro-regenerative polarized phenotype in the following immunity process¹². Likewise, reactive astrocytes express inflammation factors like IL-1β, which cause further cell damage¹³. However, they also take part in scar formation and micro-environment remodeling at a sub-acute phase and play a vital role in neuroprotection¹⁴. Based on recent research, inflammation takes on a combination of positive and negative roles depending on the various functions of immune cells at different injury phases. We hypothesized that an appropriate time window for anti-inflammatory treatment should focus on inhibiting excessive inflammation and avoiding the interruption of the neuroprotection function of the immune system. According to this hypothesis, the debated effect of recent treatment by MPSS or RLZ may be attributed to the mismatching of administration junction to excessive inflammation time point.

The objective of this study was to investigate the inflammatory process in spinal cord injury (SCI) and identify an optimal time for anti-inflammatory treatment. Single-cell sequencing allowed us to distinguish subpopulations of cells and reveal their functions at different differentiation phases through gene expression levels. We profiled the tissue proximity to the injury site using single-cell data and observed varying cell type compositions and chromatin profiles based on different days post-injury (dpi), allowing us to explore the relationship between cells. To investigate the appropriate time and mechanism of anti-inflammatory treatment in SCI, we used MPSS as a typical medication. Our findings revealed that administering MPSS at 3 dpi reduced pathological tissue area and alleviated excessive inflammation. Interestingly, we found that the anti-inflammatory effect of MPSS acted by inhibiting the number of pro-inflammatory cells, including neutrophils, T cells, microglia, and a subpopulation of macrophages, through pathways such as IL2, MIF, and TNF, but had no influence on astrocyte scar formation. Based on our results, we propose that the optimal time window for anti-inflammatory treatment is 1–3 dpi and that the IL2, MIF, and TNF pathways are attractive therapeutic targets for treating excessive neuroinflammation.

Single-cell analysis and histochemistry reveals cell types at different days post spinal cord injury

The experiment process of SCI and sample harvest was shown in Fig. 1A. Microglia, macrophage, and astrocyte was marked by Iba1, Cd68 and GFAP, then observed by confocal microscopy. From results of immunofluorescence, microglia marked by Iba1 kept increasing during 1–7 dpi along with a morphologic change from homeostatic to activated (Figs. 1B, B1–B4). The number of Cd68-positive macrophages reached the peak at 3 dpi and then reduced from 3 to 7 dpi (Figs. 1B, B5–B8). We performed single-cell sequencing on the first, third, and seventh day post-SCI (at 1, 3, and 7 dpi) to compare the changes in the gene expression profile. Our analysis revealed the presence of multiple cell clusters that could be fused into 10 main groups of cells. We mapped the cell clusters coming from distinct time points on UMAP plots (Fig. 1C). T cells, neutrophils, macrophages, microglia, dendritic cells, fibroblasts, astrocytes, oligodendrocytes, endothelial cells, and mural cells were identified (Figs. 1C, s1A). Neurons were excluded by our dissociation protocol. The highest differentially expressed genes (DEGs) of each cell type, which were identified using the CellMarker database, are presented in Fig. s1B. The cell number ratio of endothelial cells, microglia, and astrocytes decreased dramatically at 1 dpi, which was caused by cell death after SCI (Fig. 1D). In contrast, inflammation-related cells, such as macrophages, T cells, neutrophils, and dendritic cells increased at 1 dpi (Fig. 1D). Neutrophils, which are short-lived and provide an essential defense in acute inflammation, and dendritic cells, which present antigens to and induce the activation and differentiation of naive T lymphocytes, reached the peak at 1 dpi. Macrophages were not observed in the intact tissues and kept increasing from 1 to 3 dpi since they originated from a blood monocyte. Microglia, a type of small macrophage-like glial cells in the central nervous system (CNS), were found both in the intact and injured tissues and kept increasing from 1 to 7 dpi (Figs. 1C, 1D). We further verified some proteins that respond to SCI at distinct time points by performing western blotting. Proteins that promote the immune reaction, such as IL2, Ccl2, and TNF-α were highly expressed at 3 dpi, while anti-inflammatory markers IL33 and TGF-β barely existed at 3 dpi (Fig. 1E,F). From these results, we found that T cells, neutrophils, and dendritic cells primarily contributed in 1–3 dpi, while microglia and macrophage were the main cell types that led to the following 3–7 dpi immune reaction. Furthermore, 3 dpi was the most intense period of the immune response. To verify the cell functions on different days post-SCI, GO enrichment analysis was performed and analyzed in regard to three aspects: biological process, cellular component, and molecular function. We found that immune reaction-related leukocyte regulation, cell binding, and cytokine synthesis existed during 1–7 dpi, while at 3 and 7 dpi, functions were inclined to the regulation of cell death (Fig. s2).

Distinct macrophage and microglia subpopulation and differentiation trajectory assessed by DEGs and pseudo-time analysis

Macrophages are a crucial cell type during the inflammation process, with diverse cell morphology and functions in response to the microenvironment remodeling process. To further understand the functions of macrophages at different dpi, we explored the subpopulations and differentiation trajectory during the sub-acute phase of SCI. As a result, the subpopulation of macrophage was divided by DEGs into seven clusters, namely, M-interferon, M-anti-inflammation, M-MHC, M-pro-inflammation, M-complement, M-ROS, M-endocytosis (Figs. 2A, 2B). At 1 dpi, macrophage migration to the spinal cord was rapidly induced (including M1-like or M2-like). Mitochondrial and phosphorylation genes’ high-expression subpopulation, M-ROS, and interferon secretion subpopulation, M-interferon (Ifi205, Ifitm3, Ifit1, Ifi206, and Ifitm6), mainly existed at 1 dpi (Fig. s3A) and returned to baseline by 3 dpi. These macrophages were possibly involved in the regulation of activation, differentiation, transcription, and survival of immune cells¹². Specifically, the secretion of reactive oxygen species (ROS), nitric oxide (·NO) from M-ROS, and interferon from M-interferon should contribute to macrophage polarization and phagocytosis^15–17. M-anti-inflammation (Flor2, Cbr2, Mgl2, C4b, Igfbp4, Mrc1, and Cd163)^18,19 existed at 1 and 7 dpi but were rare at 3 dpi. Besides, M-pro-inflammatory express M1 markers (Ccl6, Clec4n, Inhba, and Cxcl3)^20–22 and M-MHC distinguished by MHC-related genes such as H2-DMA, H2-DMb1, H2-Eb1, and H2-Aa maintained up to 7 dpi could activate T cell and generate MHC during inflammation²³. At 3 dpi, subpopulation M-endocytosis with the function of cell endocytosis (Ctsb, Ctsd, and Pasp)^24–26 and cell attachment (Spp1 and Gpnmb)²⁷ maximally increased. M-complement subpopulation, which expresses genes such as C1qa, C1qb, C1qc, Trem2, and Ypel3^28,29, generated from 3 dpi and mainly existed at 7 dpi. It was the only subpopulation that kept a high level by 7 dpi, when other subpopulations returned to the pre-injury level.

To characterize the developmental insights of the macrophage post-SCI, we performed pseudo-time reconstruction using the Monocle package. We confirmed cells in the left branch as the origin of macrophage differentiation. One minor bifurcation, one large bifurcation, and three paths were observed in the analysis. Cells in M-pro-inflammation could be the first activated population during the pseudo-temporal ordering, sequentially followed by M-MHC, M-interferon, and M-ROS (Fig. 2C). Then, M-endocytosis acted in the main way for macrophages to clear antigen-positive cells and cell debris appearing in path 2 (Fig. 2C). M-complement were expressed at low levels at the starting site but had higher expression levels in path 3 to the end (Fig. 2C). Therefore, our results imply that during the sub-acute phase, macrophage first started from an activation and polarization program by 1 dpi, then went through foreign materials and phagocytized debris, and finally turned to be an immune regulation process by complement.

To better understand the biological basis of the differences in microglia, we sought to determine the microglial subpopulations of individual clusters based on their DEGs. Interestingly, four clusters (G-immune cells, G-homeostatic, G-bifunctional, and G-neurotoxic) respectively existed on different days post-SCI and were identified and presented by UMAP (Figs. 2D, 2E). For example, we found that one cluster named G-homeostatic exhibited high expression of homeostatic genes (Tmem119, P2ry12, and Siglech)¹⁵ and constituted nearly 100% of microglia in the intact tissue. We also found a G-activated subpopulation with high expression of pro-inflammation effect (mt.Atp6, mt.Nd113, and Ccl12)^17,30 at 1 dpi and G-neurotoxic with the expression of neurotoxic molecules secretion, endocytosis, and axon injury gene (Ctsd16, Tmsb4x, and Rpl10a)^24,31,32 at 3 dpi, corresponding to cluster on the UMAP plot (Figs. 2D, s3B). At 7 dpi, a G-bifunctional cluster was found enriched with the expression of genes implicated in thymocyte proliferation and pro-inflammation (IL1b, IL1a) and cell death inhibition (Bcl2a1b, Bcl2a1d), consistent with a role of regulation including both neuron protection and chronic inflammation. Pseudo-time analysis was performed to define the differentiation orientation of microglia, and according to the result, the bottom branch was defined as the beginning (Fig. 2F). Differentiation started with the activation of microglia, then polarized into three phenotypes, namely, G-immune (path1), which participated in pro-inflammatory action and G-bifunctional and G-neurotoxic (path2) with the regulation of cell death and inflammation by cytokine secretion (Fig. 2F). Our analysis indicated that microglia at 1 and 3 dpi was mainly consistent with pro-inflammation and axon injury. However, at 7 dpi, microglia presented regulatory functions such as the inhibition of cell death, axon protection, and chronic inflammation.

Cell communication analysis reveals that signaling events anticipated in different cell types respond to SCI

Cellular communication can group signaling pathways by defining similarity measures and performing diverse learning from both functional and topological perspectives. Application of this analysis revealed five patterns of outgoing and incoming information (Fig. 3A). This result suggests that the majority of incoming and outgoing signals from macrophages are characterized by mode 1, representing multiple pathways, including but not limited to TGF-β, galectin, TWEAK, IL2, and CCL. TGF-β is known to be involved in the anti-inflammatory function of macrophages, and in contrast, IL2 promotes macrophage polarization to the M1 phase, a pro-inflammatory phenotype¹⁵. In addition, the TWEAK pathway is a member of the TNF group and is involved in apoptosis, fibrosis, and pro-inflammation³³. The galectin pathway has always played multiple roles, such as in T cell regulation, tissue repair, and oxidative inactivation³⁴.

Microglial release signals were characterized by three patterns (Fig. 3B), representing growth factor pathways (IGF), pro-inflammatory and apoptosis-related pathways (TNF and PROS), and macrophage colony-stimulating factor (CSF). These results suggest that macrophages and microglia simultaneously activate multiple signaling modalities and pathways, including pro- and anti-inflammatory pathways, apoptosis, and growth factor secretion, among others. At the same time, we analyzed the top 12 highly involved cellular pathways and found that the immune-related cellular pathways CCL, IL2, and IL1 are mainly composed of macrophages, dendritic cells, T cells, microglia, and monocytes (Fig. 3B). SPP1, which is also known for osteopontin (OPN), allowed neuron cells to respond to growth factors such as CNTF and BDNF³⁵ and built a cell chat network among all cell types. We found that the classic pro-inflammatory pathway, TNF, was mainly mediated by microglia, and the anti-inflammatory pathway, TGF-β connected macrophages, microglia, and dendritic cells. Other pathways such as CSF, MIF, TWEAK, and VISFATIN pathways were important linkages between macrophages, microglia, fibroblasts, endothelial cells, and astrocytes. These results also indicated that macrophages and microglia play an important role in the inflammation process during SCI.

Optimization of the time window for excessive immunity reaction inhibition mediated by MPSS

Since we noticed the process of immune reaction during 1–7 dpi, we then optimized the time window to mediate neuroinflammation for preventing excessive immunity reaction by assessment of the injury site area and immunohistochemical staining observation. MPSS or PBS solution was injected into the injury site at a series of time points, namely, 1, 3, and 7 dpi, and evaluated by immunofluorescence staining at 14 dpi. As a result, we found that animals injected at 1 and 3 dpi had a higher gray value of GFAP compared with animals injected at 7 dpi (Figs. 4B, 4C). Although injury site and cavity formation were observed at all time points after injection, the injury area at 3 dpi injection was significantly lower than other time points after injections (Figs. 4B, 4C), suggesting that injection early point may cause further tissue damage by interrupting the beneficial immunity reaction, preventing cell polarization and antigen clearance in the initial phase. In addition, higher Iba1-positive cells were found in animals injected at 7 dpi (Figs. 4B, 4C), which suggested that excessive activation of microglia might occur before 7 dpi, causing serious damage to neurons and spared axons. These results indicated that 3 dpi could be an optimal time window for MPSS to remodel the dysregulated inflammation and keep more spare tissue.

The mechanism for MPSS treatment in preventing excessive neuroinflammation

Above evidence has uncovered that the optimal treatment window during SCI was 3 dpi. To ascertain whether MPSS could alleviate excessive inflammatory response and protect spare tissue, we performed in situ injection with MPSS or PBS at 3 dpi. Immunohistochemical image and quantification data showed that MPSS injection resulted in a one-third injury site area the size of PBS injection, and Iba1-positive microglia number was nearly half of that in PBS injection (Figs. s4B, B3, B4). However, the GFAP-positive astrocyte scar at the margin of the injury site largely existed in both PBS and MPSS injection (Figs. s4B, B1, B2), indicating that MPSS could not prevent the activation of scar-forming astrocytes.

To further understand the mechanism of MPSS in inflammation inhibition, we sought to examine the difference in gene expression between MPSS and PBS injection through single-cell sequencing. We were able to recognize similar clusters that were identified in the previous analysis depicted in Fig. 1. In terms of the cell number ratio, we found that the MPSS treatment resulted in a decrease in microglia, T cells, and neutrophils compared with the PBS group (Fig. 5A). In contrast to these immune cells, oligodendrocytes and astrocytes in the MPSS group increased, reflecting the anti-inflammatory and protective function of MPSS after SCI. This result is also consistent with the immunohistochemical observation, smaller injury site area, and fewer Iba1-positive microglia in MPSS-treated animals than in PBS control animals (Fig. 4B). However, for macrophage, we found almost no difference in the cell ratio number between MPSS and CTRL groups. Therefore, a further analysis was performed to verify the subpopulation and gene expression changes in macrophages between MPSS and PBS groups. According to the analysis, the macrophage subpopulation (cluster 0) with high expression of immune response regulation and interferon-secreting genes (Ifitm1, Ifitm2, Fth1, and Ftl1)^36,37 was inhibited by MPSS, while the subpopulation (cluster 2) high expressing complement-associated genes (C1qa, C1qb, C1qc) increased in the MPSS treatment. (Figs. s5 A, C). In contrast, we found no dramatic changes in microglia subpopulations by MPSS treatment (Figs. s5 B, D). We then screened out four important inflammation genes that largely changed in MPSS treatment. Gene IL2 promoted inflammation, and Tnfaip2 decreased, while anti-inflammatory gene IL33 increased in MPSS injection animals, which was also confirmed by protein expression level in western blotting (Figs. 5C, 5D). Moreover, the expression of pro-inflammation genes IL2 and Tnfaip2 started at 1 dpi and reached the peak at 3 dpi. This result also explained the reason why the optimal time point after injection should be 3 dpi (Fig. s6).

Cell communication between cells was also investigated, and the top 20 pathways were similar in the MPSS and CTRL groups except for the absence of TNF and CALCR in the latter group (Fig. 5E). TNF is a pro-inflammatory pathway regulated between microglia and other cells, as depicted in Fig. 3. The contributions of ligand and receptor also present a strong connection between MIF and Cd44 and Cd74 in PBS condition compared with the MPSS condition (Fig. 5F). This binding of MIF and CD74 induced the formation of a complex with CD44 and was found to be essential for initiation of a signaling cascade required for immune cell maintenance³⁸. According to these results, we found that the MPSS treatment could inhibit excessive inflammation and protect spared tissue by reducing active microglia number and regulating macrophage subpopulation. Furthermore, the anti-inflammatory function of MPSS could attribute to the down-regulation of immune pathways TNF, MIF, and IL2. These pathways were also screened as important pathways in cell communication between immunity cells, especially T cells, microglia, and macrophages in SCI tissues (Fig. 3A).

Microglia play critical roles in the inflammatory response, and their role in SCI is heavily debated^39,40. Our results suggest that they assume many distinctive subpopulations over time, and these subpopulation cells can exert either beneficial or detrimental effects depending on their states.⁴¹ As we observed an advanced protection function with a reduction of activated microglia in MPSS injection at 3 dpi, we speculate that an excessive inflammation occurred at 3 dpi and was probably led by neurotoxicity and phagocytosis subpopulation in microglia and macrophages. Early activated microglia exerting in the recruitment of monocyte-derived-macrophage were noted to have a beneficial effect in relieving a degradative macrophage phenotype in pathology tissues⁴². However, microglia with the function of chemokine signal amplifying exacerbated the pathology and worsened the neuron function. Interestingly, the clearance of delayed microglia was reported to improve functional recovery in SCI,⁴³ which also indicated that early intervention with anti-inflammatory treatment may cause detrimental resistance in tissue repair. Another important role for microglia is scar formation, by regulating astrocyte survival, and adhesion ⁴⁴. In our result, astrocyte scar formation was not influenced by MPSS treatment. Astrocyte scar was generally regarded as a barrier to axon regrowth, with evidence that axon regrowth failed in the presence of mature astrocytes⁴⁵. However, recent studies have shown that contrary to the widespread view, astrocyte scar formation aids tissue repair and CNS axon regeneration^39,46,47. It is difficult to ascertain whether astrocyte scarring is beneficial or not.

Different from microglia, the influence of MPSS in macrophages reflects on the regulation of subpopulations instead of the total cell number, pro-inflammation subpopulation was inhibited in MPSS treatment but an increase in C1q high-expressing subpopulation was observed. C1q acts as an opsonin in the immune system, driving autocatalytic complement cascade activation. C1q was reported to anticipate various cellular functions, including cellular adhesion, clearance of cell debris, neurodegenerative pathogenesis, chemotaxis, and cellular differentiation^48,49. However, the view on the complement function in SCI injury was not consistent. In CNS injury, complement plays a role in improving neurite outgrowth by interacting with myelin-associated glycoprotein, resulting in reduced activation of growth-inhibitory signaling in neuron⁴⁸. Also, C1q was essential for cell phagocytosis, and deficiency of C1q led to a failure in apoptotic cell debris clearance⁵⁰. However, the view that complement activation was detrimental to secondary injury was also proved, with evidence that the deficiency or inhibition of C1q could improve histological and functional locomotor recovery at the early stage of secondary injury⁵¹. This possibility of complement functions in SCI injury is worth investigating further.

According to our pathway analysis, down-regulated genes belonging to TNF, IL2, and MIF pathways expressed in MPSS treatment were regarded as a mechanism of MPSS anti-inflammatory treatment. Commonly, these pathways comprise elements of pro-inflammation⁵². Typically, the TNF pathway was well known for its pro-inflammatory function. Functional activity of immune cells like dendritic cells, monocytes, and macrophages can be markedly increased by signaling through the TNF family. In addition, improvements in inflammation and cell apoptosis are always consistent with the up-regulation of TNF in acute SCI⁵². Macrophage migration inhibitory factor (MIF), mainly secreted by monocytes/macrophages, is described as a kind of pro-inflammatory cytokine involved in inflammation-associated pathophysiology. It forms the CD44 complex by linking to the CD74 receptor, which was reported to up-regulate lymphocyte survival^53,54. IL2 was identified as a cytokine capable of driving the expansion of the activated T cell population and manipulating the differentiation of T cells⁵⁵. Based on the understanding of pathways, we demonstrated that the function of MPSS in excessive inflammation inhibition was mainly attributed to the reduction in cell activation, such as microglia, T cells, and macrophages. Therefore, we supposed that the subpopulations G-neurotoxin and M-endocytosis, which maximally existed at 3 dpi, should be the critical cell types that induce excessive inflammation.

In summary, our results argue against the usage of anti-inflammatory medicine at an early stage (several hours) of SCI. To the best of our knowledge, this is the first work to propose an appropriate time window for anti-inflammatory treatment depending on cell heterogeneity changes through the immunity process. The mechanism of typical anti-inflammation medicine MPSS was found that it attributes to the activation inhibition of cells with pro-inflammation phenotype through the down-regulation of pathways like TNF, IL2, and MIF. These pathways also could be provided as targets for anti-inflammation treatment and for target therapy in the future.

Materials and antibodies

MPSS was purchased from Aladdin (China), and A bicinchoninic acid protein assay kit was purchased from Solarbio (Beijing, China). Polyvinylidene difluoride membranes were purchased from Millipore (Bedford, MA). Chicken anti-GFAP [Abcam (ab134436), 1:500] and rabbit anti-Iba1[Abcam(ab178847),1:500] were purchased from Abcam (USA). Mouse anti-Cd68 [Invitrogen (MA5-13324), 1:500], rabbit anti-Ccl2 [Invitrogen (PA5-115555), 1:1000], rabbit anti-IL2 [Invitrogen (PA5-95173), 1:1000], rabbit anti-Tnf-α [Invitrogen (PA5-19810), 1:1000], rabbit anti-IL33 [Invitrogen (PA5-79499), 1:1000], and rabbit anti-Tgf-β [Invitrogen (PA5-19810), 1:1000] were obtained from Invitrogen (USA). Rabbit anti-GAPDH [Abclonal (A19056), 1:1000] and donkey anti-rabbit HRP [Abclonal (AS014), 1:10000] were purchased from Abclonal (China). Goat anti-chicken secondary antibody conjugated with Alexa Fluor 488 [Invitrogen (A-11039), 1:500], donkey anti-rabbit IgG (H + L) highly cross-adsorbed secondary antibody conjugated with Alexa Fluor 555 [Invitrogen (A-31572),1:500], and donkey anti-mouse secondary antibody conjugated with Alexa Fluor 647 [Invitrogen (A-31571),1:500] were purchased from Invitrogen (USA).

Animals and surgical procedure

Female Sprague–Dawley rats (~ 250 g) were obtained from the Zhejiang Academy of Medical Sciences. After the animals were anesthetized with pentobarbital sodium (0.5 mL/100 g, injected intraperitoneally), a dorsal laminectomy was performed at the 10th thoracic vertebral level (T10–T11) to expose the dorsal surface of the spinal cord. To perform a crush injury animal model, iridectomy scissors were used to transect ventrally and laterally. After the injury, the muscles and skin were sutured. Bladder care was provided twice daily until spontaneous voiding resumed. Samples for analysis of different time points were collected at 1, 3, and 7 dpi at the injury site. For drug injection, MPSS was dissolved in PBS solution at a concentration of 10 mg/ml. The solution was loaded in a micropipette and injected at a rate of 200 nl/min at five different sites (1 µL per site) and two different depths, 0.6 and 1.2 mm at 3 dpi. For control animals, we performed an injection with PBS. Animals were sacrificed for analysis two weeks post-injury.

Immunohistochemistry and western blotting

To obtain tissue section for immunohistochemistry, 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.2–7.4) was used in animal perfusion. Then spinal cords were dissected and fixed in 4% paraformaldehyde for one night at 4°C. The fixed spinal cords were immersed in 15% and 30% sucrose solutions for dehydration before embedding. Sagittal sections of spinal cord tissue (1-cm long), including the lesion site, were sectioned using a cryostat (Leica CM1950, Germany) and thaw-mounted onto Super Frost Plus slides (Fisher Scientific, USA). Next, sections were blocked with 5% donkey serum and 0.3% Triton X-100 in PBS buffer, followed by incubation of primary antibodies. After one night of incubation at 4°C, sections were washed with PBS thrice and incubated with the appropriate secondary antibodies for 3 h at room temperature. Sections were finally observed using a confocal laser scanning microscope (A1Ti, Nikon, Japan). Quantification was determined in terms of the gray value of cells per section stained by different antibodies. We defined a baseline at the epicenter of the injury site, and an area with 1 mm rostral and caudal to the baseline was used in quantification. Three randomly selected sections in one animal and three animals per condition were used in this analysis. To assess the inflammatory protein levels in the injury site, a segment of 2 mm rostral and caudal to the injury site was dissected, and total protein was extracted by homogenate in a lysis buffer and centrifuged at 12,000g, 4°C for 10 min. Forty microgram sample protein was loaded onto 10% polyacrylamide gel and went through a separation process by SDS-PAGE. Proteins were then transferred to polyvinylidene fluoride membranes and blocked in 5% skimmed milk in PBST for 1 h at room temperature. Primary antibodies were added to membranes overnight at 4°C. Secondary antibodies were used in membrane incubation after washing thrice with PBST. Finally, protein bands on membranes were observed using an ECL kit and quantified by the Image Lab Software provided by Bio-Rad.

Single-cell RNA sequencing (SC-RNA-seq) analysis

Animals (five animals for each condition) were anesthetized deeply with pentobarbital sodium, and from the center of the SCI site, a 5-mm section of the spinal cord was dissected. For extraction of RNA in cells, the tissue was cut and chopped into small pieces, then incubated in 2 ml of collagenase (1 mg/ml in PBS) for 30 min at 37°C and centrifuged at 300 g for 5 min at 4°C. Pelleted cells were collected in 2 ml of 0.25% trypsin–EDTA solution and incubated at 37°C for 5 min. Then, digestion by trypsin was stopped by FBS (10%), and the pellet collected via centrifugation was dispersed in PBS with 0.5% BSA. The dispersed cell pellet was strained through a 40-µm cell strainer twice, ensuring that no large cell cluster existed. To avoid the influence of blood cells, 10 ml of erythrocyte lysate was added to the cell for 10 min and the cell suspension was collected by centrifuging. Finally, single-cell suspension was resuspended in PBS for counting. Libraries were prepared according to 10X Genomics Chromium Single Cell 3′ Reagent Kit v3 (10X Genomics) and Single Cell 3’ Gel Bead Kit v2 instructions, and an Agilent 2100 Expert High Sensitivity DNA Assay was used to assess the quantity of cDNA. Finally, cDNA libraries were sequenced using an Illumina HiSeqTM 4000 system, and the outputs were mapped to the mouse GrCm38 genome using Cell Ranger v.3.0.2. All raw data and proceeded files were provide in GEO (GSE222082).

Data normalization and dimensional reduction

Initial processing of peripheral cells was performed using the Seurat R Package (v4.0.1). Individual cells were filtered for the total number of genes expressed and the percentage of mitochondrial reads. This filtering was set to retain cells with 200–2500 genes and percent mitochondrial reads less than 5%. Individual cells were then normalized using log normalization with a scale factor of 10,000. After processing, clustering was performed using the Seurat package. To identify the shared and unique cell types in different samples, an integration workflow from the Seurat package was used. In brief, canonical correlation analysis was first performed between two samples to find anchors by FindIntegrationAnchors() function. Then, depending on the consistency of anchors between cells, a gene expression matrix could be generated. To verify the largest variation in gene expression, dimensional reduction was performed. The top 10 calculated principal components were selected using a jackstraw analysis to quantify p-value distributions. The FindCluster() function was used to define cell types based on similar gene expressions. For visualization, the UMAP cluster was utilized to present cell clusters based on examining the standard deviations of the top 10 principal components and running. The differential markers between the clusters were isolated by comparing significantly up-regulated genes as defined as adjusted p-values < 0.05. We distinguished and named cell types by DEGs based on the CellMarker database.

Cell–cell communication

To infer the interaction between several cell types, we performed a ligand–receptor analysis. The transmission between cells is based on the interaction of ligands and receptors on the cell surface and cytokines produced by cells. Cell Chat is a package that provides a database including ligands, receptors, cofactors, and polymers. Cell–cell communication was performed using Cell chat (v0.5.5) (https://github.com/sqjin/CellChat). In brief, a Cell Chat DB.mouse database with ligand–receptor was added as a reference list. Clustered cell data from upstream analysis using Seurat was used to construct the cell chat object. The highly expressed gene was screened by identifyOverExpressedGenes () function, and the corresponding pathway was identified based on these genes. With significant differences in gene expression, the possibility of ligand–receptor relationship pairs between cells was calculated by the computeCommunprob() function. Cells with cell communication possibilities below 20% were removed by a parameter of cut-off. Finally, results were compared with the imported ligand–receptor database, and an alluvial and circle plot was built for visualization.

Pseudo-time analysis

Cell trajectory and pseudo-time analysis were performed using the Monocle R package (v2.18.0) and the reverse-graph embedding machine learning algorithm. First, genes at different differentiation phases were selected for ordering. In our case, samples at distinct time points were used in the analysis. Therefore, DEGs at different time points were selected. These genes were ordered by the orderCells() function after principal component analysis. Each cell in a high-dimensional space was represented as a point corresponding to the expression level of ordered genes. Then, Monocle 2 constructed a differentiation tree based on the selected data, and the algorithm moved the cell to the nearest vertex in the tree and updated the position to fit the cell well. Therefore, a new spanning tree was constructed after the iteratively continued process performed until all cells converged. In the whole process, ordering and dimension were both conducted, and once the tree was constructed, Monocle selected a tip as the “root.” The geodesic distance from each cell to the root was calculated to automatically assign its branches. Differential gene testing for the pseudo-time analysis was based on the previously identified cell clusters in the Seurat object and a cut-off for significance p-value < 0.01.

Statistical analysis

All statistical analyses were performed using the GraphPad Prism software (GraphPad Software Inc.). Error bars in all figures represent as mean ± standard error of the mean (SEM). One- or two-way ANOVA was used in statistical analysis, as indicated in figure legends. Post-ANOVA comparisons were made using the Turkey analysis. A p-value of less than 0.05 was considered significant.

Acknowledgments

We acknowledge the excellent technical staff of the Imaging Facility, Core Facility of Zhejiang University School of Medicine, Zhejiang University for their assistance with confocal laser scanning microscope.

Ethical Approval and Consent to participate

All experiments were approved by the Zhejiang University Animal Experimentation Committee and were in complete compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (ZJU20220394).

Consent for publication

All authors agreed to submit the manuscript for publication.

Availability of data and materials

All single-cell data was available in GEO database (GSE222082).

Competing interests

Authors have no competing interests in this paper.

Funding

This study was supported by the National Natural Science Foundation of China (81902187), Zhejiang Medical and Health Science and Technology Project (2022493383), and the Science and Technology program of Jinhua Science and Technology Bureau (Grant No. 2022-3-046).

Authors' contributions

Jingjia Ye wrote the main manuscript text, Wei Wei and Bingbing Wu helped modifing the manunscript. Jingjia Ye, Zhengfa Wen, Tianxin Wu, Linchao Sheng, Chenhuan Wang, contribute in figures perparing and experiments. Liangliang Chen helped analysing data. Chong Teng, Jian Xu and Wei Wei is the administrater of this project. All authors reviewed the manuscript.

Anwar, M. A., Al Shehabi, T. S. & Eid, A. H. Inflammogenesis of Secondary Spinal Cord Injury. Front Cell Neurosci 10, 98, doi:10.3389/fncel.2016.00098 (2016).
Ahuja, C. S. et al. Traumatic spinal cord injury. Nat Rev Dis Primers 3, 17018, doi:10.1038/nrdp.2017.18 (2017).
Alizadeh, A., Dyck, S. M. & Karimi-Abdolrezaee, S. Traumatic Spinal Cord Injury: An Overview of Pathophysiology, Models and Acute Injury Mechanisms. Front Neurol 10, 282, doi:10.3389/fneur.2019.00282 (2019).
Slotkin, J. R. et al. Sustained Local Release of Methylprednisolone From a Thiol-Acrylate Poly(Ethylene Glycol) Hydrogel for Treating Chronic Compressive Radicular Pain. Spine 41, E441-E448, doi:10.1097/brs.0000000000001309 (2016).
Bydon, M., Lin, J., Macki, M., Gokaslan, Z. L. & Bydon, A. The current role of steroids in acute spinal cord injury. World Neurosurg 82, 848–854, doi:10.1016/j.wneu.2013.02.062 (2014).
Samano, C. & Nistri, A. Mechanism of Neuroprotection Against Experimental Spinal Cord Injury by Riluzole or Methylprednisolone. Neurochem Res 44, 200–213, doi:10.1007/s11064-017-2459-6 (2019).
Bennett, J. M., Reeves, G., Billman, G. E. & Sturmberg, J. P. Inflammation-Nature's Way to Efficiently Respond to All Types of Challenges: Implications for Understanding and Managing "the Epidemic" of Chronic Diseases. Front Med (Lausanne) 5, 316, doi:10.3389/fmed.2018.00316 (2018).
Han, V. X., Patel, S., Jones, H. F. & Dale, R. C. Maternal immune activation and neuroinflammation in human neurodevelopmental disorders. Nature Reviews Neurology 17, 564–579, doi:10.1038/s41582-021-00530-8 (2021).
Fu, S. P. et al. Advances in the research of the role of macrophage/microglia polarization-mediated inflammatory response in spinal cord injury. Front Immunol 13, 1014013, doi:10.3389/fimmu.2022.1014013 (2022).
Jayaraj, R. L., Azimullah, S., Beiram, R., Jalal, F. Y. & Rosenberg, G. A. Neuroinflammation: friend and foe for ischemic stroke. J Neuroinflammation 16, 142, doi:10.1186/s12974-019-1516-2 (2019).
Watanabe, S., Alexander, M., Misharin, A. V. & Budinger, G. R. S. The role of macrophages in the resolution of inflammation. J Clin Invest 129, 2619–2628, doi:10.1172/JCI124615 (2019).
Van Broeckhoven, J., Sommer, D., Dooley, D., Hendrix, S. & Franssen, A. Macrophage phagocytosis after spinal cord injury: when friends become foes. Brain 144, 2933–2945, doi:10.1093/brain/awab250 (2021).
Hennessy, E., Griffin, E. W. & Cunningham, C. Astrocytes Are Primed by Chronic Neurodegeneration to Produce Exaggerated Chemokine and Cell Infiltration Responses to Acute Stimulation with the Cytokines IL-1beta and TNF-alpha. J Neurosci 35, 8411–8422, doi:10.1523/JNEUROSCI.2745-14.2015 (2015).
Brandebura, A. N., Paumier, A., Onur, T. S. & Allen, N. J. Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders. Nat Rev Neurosci, doi:10.1038/s41583-022-00641-1 (2022).
Piotrowska, A. et al. Pharmacological Blockade of Spinal CXCL3/CXCR2 Signaling by NVP CXCR2 20, a Selective CXCR2 Antagonist, Reduces Neuropathic Pain Following Peripheral Nerve Injury. Front Immunol 10, 2198, doi:10.3389/fimmu.2019.02198 (2019).
Choi, K. et al. An inflammatory gene signature distinguishes neurofibroma Schwann cells and macrophages from cells in the normal peripheral nervous system. Sci Rep 7, 43315, doi:10.1038/srep43315 (2017).
Louis, C. et al. NK cell-derived GM-CSF potentiates inflammatory arthritis and is negatively regulated by CIS. J Exp Med 217, doi:10.1084/jem.20191421 (2020).
Junttila, I. S. Tuning the Cytokine Responses: An Update on Interleukin (IL)-4 and IL-13 Receptor Complexes. Front Immunol 9, 888, doi:10.3389/fimmu.2018.00888 (2018).
Roszer, T. Understanding the Mysterious M2 Macrophage through Activation Markers and Effector Mechanisms. Mediators Inflamm 2015, 816460, doi:10.1155/2015/816460 (2015).
Frodermann, V. & Nahrendorf, M. Macrophages and Cardiovascular Health. Physiol Rev 98, 2523–2569, doi:10.1152/physrev.00068.2017 (2018).
Angajala, A. et al. Diverse Roles of Mitochondria in Immune Responses: Novel Insights Into Immuno-Metabolism. Front Immunol 9, 1605, doi:10.3389/fimmu.2018.01605 (2018).
Koo, S. J., Chowdhury, I. H., Szczesny, B., Wan, X. & Garg, N. J. Macrophages Promote Oxidative Metabolism To Drive Nitric Oxide Generation in Response to Trypanosoma cruzi. Infect Immun 84, 3527–3541, doi:10.1128/IAI.00809-16 (2016).
David, S. & Kroner, A. Repertoire of microglial and macrophage responses after spinal cord injury. Nat Rev Neurosci 12, 388–399, doi:10.1038/nrn3053 (2011).
Marques, A. R. A. et al. Enzyme replacement therapy with recombinant pro-CTSD (cathepsin D) corrects defective proteolysis and autophagy in neuronal ceroid lipofuscinosis. Autophagy 16, 811–825, doi:10.1080/15548627.2019.1637200 (2020).
Sendler, M. et al. Cathepsin B-Mediated Activation of Trypsinogen in Endocytosing Macrophages Increases Severity of Pancreatitis in Mice. Gastroenterology 154, 704–718 e710, doi:10.1053/j.gastro.2017.10.018 (2018).
Mendsaikhan, A. et al. Characterization of lysosomal proteins Progranulin and Prosaposin and their interactions in Alzheimer's disease and aged brains: increased levels correlate with neuropathology. Acta Neuropathol Commun 7, 215, doi:10.1186/s40478-019-0862-8 (2019).
Katayama, A. et al. Beneficial impact of Gpnmb and its significance as a biomarker in nonalcoholic steatohepatitis. Sci Rep 5, 16920, doi:10.1038/srep16920 (2015).
Fonseca, M. I. et al. Cell-specific deletion of C1qa identifies microglia as the dominant source of C1q in mouse brain. J Neuroinflammation 14, 48, doi:10.1186/s12974-017-0814-9 (2017).
Spivia, W., Magno, P. S., Le, P. & Fraser, D. A. Complement protein C1q promotes macrophage anti-inflammatory M2-like polarization during the clearance of atherogenic lipoproteins. Inflamm Res 63, 885–893, doi:10.1007/s00011-014-0762-0 (2014).
Kanno, M. et al. Functional expression of CCL6 by rat microglia: a possible role of CCL6 in cell-cell communication. J Neuroimmunol 167, 72–80, doi:10.1016/j.jneuroim.2005.06.028 (2005).
Hu, X. et al. Role of Pyroptosis in Traumatic Brain and Spinal Cord Injuries. Int J Biol Sci 16, 2042–2050, doi:10.7150/ijbs.45467 (2020).
Chen, H. R. et al. Monocytes promote acute neuroinflammation and become pathological microglia in neonatal hypoxic-ischemic brain injury. Theranostics 12, 512–529, doi:10.7150/thno.64033 (2022).
Yarar-Fisher, C. et al. Heightened TWEAK-NF-kappaB signaling and inflammation-associated fibrosis in paralyzed muscles of men with chronic spinal cord injury. Am J Physiol Endocrinol Metab 310, E754-761, doi:10.1152/ajpendo.00240.2015 (2016).
Brinchmann, M. F., Patel, D. M. & Iversen, M. H. The Role of Galectins as Modulators of Metabolism and Inflammation. Mediators Inflamm 2018, 9186940, doi:10.1155/2018/9186940 (2018).
Liu, Y. et al. A Sensitized IGF1 Treatment Restores Corticospinal Axon-Dependent Functions. Neuron 95, 817–833 e814, doi:10.1016/j.neuron.2017.07.037 (2017).
Kore RA, E. J., Jenkins SV, Jamshidi-Parsian A, Dings RPM, Reyna NS, Griffin RJ. Hypoxia-derived exosomes induce putative altered pathways in biosynthesis and ion regulatory channels in glioblastoma cells. Biochem Biophys Rep. 14, 104–113 (2018).
Park JE, D. B., Tse SW, Gupta N, Tan CF, Low JK, Yeoh KW, Kon OL, Tam JP, Sze SK. Hypoxia-induced tumor exosomes promote M2-like macrophage polarization of infiltrating myeloid cells and microRNA-mediated metabolic shift. Oncogene 38, 5158–5173 (2019).
David, K. et al. CD74 as a regulator of transcription in normal B cells. Cell Rep 41, 111572, doi:10.1016/j.celrep.2022.111572 (2022).
Bellver-Landete, V. et al. Microglia are an essential component of the neuroprotective scar that forms after spinal cord injury. Nat Commun 10, 518, doi:10.1038/s41467-019-08446-0 (2019).
Gerber, Y. N. et al. CSF1R Inhibition Reduces Microglia Proliferation, Promotes Tissue Preservation and Improves Motor Recovery After Spinal Cord Injury. Front Cell Neurosci 12, 368, doi:10.3389/fncel.2018.00368 (2018).
Tran, A. P., Warren, P. M. & Silver, J. The Biology of Regeneration Failure and Success After Spinal Cord Injury. Physiol Rev 98, 881–917, doi:10.1152/physrev.00017.2017 (2018).
Zhou, X. et al. Microglia and macrophages promote corralling, wound compaction and recovery after spinal cord injury via Plexin-B2. Nat Neurosci 23, 337–350, doi:10.1038/s41593-020-0597-7 (2020).
Li, Y. et al. Delayed microglial depletion after spinal cord injury reduces chronic inflammation and neurodegeneration in the brain and improves neurological recovery in male mice. Theranostics 10, 11376–11403, doi:10.7150/thno.49199 (2020).
Du, Y., Brennan, F. H., Popovich, P. G. & Zhou, M. Microglia maintain the normal structure and function of the hippocampal astrocyte network. Glia 70, 1359–1379, doi:10.1002/glia.24179 (2022).
Li, Y. et al. Microglia-organized scar-free spinal cord repair in neonatal mice. Nature 587, 613–618, doi:10.1038/s41586-020-2795-6 (2020).
Bylicky, M. A., Mueller, G. P. & Day, R. M. Mechanisms of Endogenous Neuroprotective Effects of Astrocytes in Brain Injury. Oxidative Medicine and Cellular Longevity 2018, 1–16, doi:10.1155/2018/6501031 (2018).
Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195–200, doi:10.1038/nature17623 (2016).
Peterson, S. L., Nguyen, H. X., Mendez, O. A. & Anderson, A. J. Complement protein C1q modulates neurite outgrowth in vitro and spinal cord axon regeneration in vivo. J Neurosci 35, 4332–4349, doi:10.1523/JNEUROSCI.4473-12.2015 (2015).
KBM., R. Complement Component C1q: Historical Perspective of a Functionally Versatile, and Structurally Unusual, Serum Protein. Front Immunol 9 (2018).
Galvan, M. D., Foreman, D. B., Zeng, E., Tan, J. C. & Bohlson, S. S. Complement component C1q regulates macrophage expression of Mer tyrosine kinase to promote clearance of apoptotic cells. J Immunol 188, 3716–3723, doi:10.4049/jimmunol.1102920 (2012).
Tei, R., Kaido, T., Nakase, H. & Sakaki, T. Protective effect of C1 esterase inhibitor on acute traumatic spinal cord injury in the rat. Neurol Res 30, 761–767, doi:10.1179/174313208X284241 (2008).
Kalliolias, G. D. & Ivashkiv, L. B. TNF biology, pathogenic mechanisms and emerging therapeutic strategies. Nature Reviews Rheumatology 12, 49–62, doi:10.1038/nrrheum.2015.169 (2015).
Yu Su, Y. W., Yue Zhou, Zhenjie Zhu, Qing Zhang, Xuejie Zhang, Wenjuan Wang, Xiaosong Gu, Aisong Guo and Yongjun Wang. Macrophage migration inhibitory factor activates inflammatory responses of astrocytes through interaction with CD74 receptor. Oncotarget 8, 2719–2730 (2017).
Gore, Y. et al. Macrophage migration inhibitory factor induces B cell survival by activation of a CD74-CD44 receptor complex. J Biol Chem 283, 2784–2792, doi:10.1074/jbc.M703265200 (2008).
Spolski R, L. P., Leonard WJ. Biology and regulation of IL-2: from molecular mechanisms to human therapy. Nat Rev Immunol 18, 648–659 (2018).

No competing interests reported.

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Single-cell sequencing reveals the optimal time window for anti-inflammatory treatment in spinal cord injury

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Abstract

Figures

Highlights

Introduction

Results

Single-cell analysis and histochemistry reveals cell types at different days post spinal cord injury

Distinct macrophage and microglia subpopulation and differentiation trajectory assessed by DEGs and pseudo-time analysis

Cell communication analysis reveals that signaling events anticipated in different cell types respond to SCI

Optimization of the time window for excessive immunity reaction inhibition mediated by MPSS

The mechanism for MPSS treatment in preventing excessive neuroinflammation

Discussion

Method

Materials and antibodies

Animals and surgical procedure

Immunohistochemistry and western blotting

Single-cell RNA sequencing (SC-RNA-seq) analysis

Data normalization and dimensional reduction

Cell–cell communication

Pseudo-time analysis

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

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