Ligand/Receptor-mediated Cell Communication Alteration Causes the Lung Inflammation Microenvironment and the Redistribution of Inter-tissue Immune Cells in COVID-19 Patients

doi:10.21203/rs.3.rs-1442272/v1

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Ligand/Receptor-mediated Cell Communication Alteration Causes the Lung Inflammation Microenvironment and the Redistribution of Inter-tissue Immune Cells in COVID-19 Patients

https://doi.org/10.21203/rs.3.rs-1442272/v1

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How SARS-CoV-2 causes disturbances of the lung microenvironment and systemic immune response remains a mystery. Here, we first analyze detailedly paired single-cell transcriptome data of the lungs, blood and bone marrow of two patients who died of COVID-19. Second, our results demonstrate that SARS-CoV-2 infection significantly increases the cellular communication frequency between AT1/AT2 cells and highly inflammatory myeloid cells, and induces the pulmonary inflammation microenvironment, and drives the disorder of fibroblasts, club and ciliated cells, thereby causing the increase of pulmonary fibrosis and mucus accumulation. Third, our works reveal that the increase of the lung T cell infiltration is mainly recruited by myeloid cells through certain ligands/receptors (ANXA1/FPR1, C5AR1/RPS19 and CCL5/CCR1), rather than AT1/AT2. Fourth, we find that some ligands and receptors such as ANXA1/FPR1, CD74/COPA, CXCLs/CXCRs, ALOX5/ALOX5AP, CCL5/CCR1, are significantly activated and shared among patients’ lungs, blood and bone marrow, implying that dysregulated ligands and receptors may cause the migration, redistribution and the inflammatory storm of immune cells in different tissues. Overall, our study reveals a latent mechanism by which the disorders of ligands and receptors caused by SARS-CoV-2 infection drive cell communication alteration, the pulmonary inflammatory microenvironment and systemic immune responses across tissues in COVID-19 patients.

COVID-19

SARS-CoV-2

immune microenvironment

single cell transcriptome

cell communication

ligands and receptors

The SARS-CoV-2 has continued to infect more than 250 million people and caused more than 5 million deaths worldwide [1]. To date, the mechanism of SARS-CoV-2 infection causing critical illness and death in COVID-19 patients is still unclear. Recently, a large-scale autopsy reports have demonstrated that SARS-CoV-2 infection can cause damage to multiple organs, especially the lungs, digestive tract and immune system [2, 3]. However, it is still a mystery how SARS-CoV-2 causes systemic immune responses and multiple organ damages from local lungs. With the development of single-cell sequencing technology (scRNA-seq), many novel bioinformatics tools of integrating cellular and molecular information have been released one after another for analyzing the single-cell atlas of throat swabs, blood, alveolar lavage fluid and parenchymal lungs in COVID-19 patients [4–7]. Especially, these data have extended our understanding that in addition to the lungs and AT2 cells, SARS-CoV-2 had caused strong genetic changes in a variety of organs and immune cells [4–7]. However, how does SARS-CoV-2 infection cause the pulmonary microenvironment changes and the immune response of remote organs? as well as how do alveolar target cells with SARS-CoV-2 infection induce local and systemic inflammation storm, in particular the migration and redistribution of immune cells among distinct tissues? which are yet not clear.

Cellular communication refers to the transmission of information from one cell to another cell through a medium to produce a corresponding response [8–10]. The cell-cell communication mediated by the ligand/receptor complex plays a vital role in coordinating a variety of biological processes such as development, differentiation and inflammation [8–10]. Complex extracellular reactions originate from the binding of ligands to their cognate receptors and the activation of specific cell signaling pathways [8–10]. Therefore, localizing these ligand/receptor interactions is the important basis for understanding cell behavior and response to neighboring cells. Previous studies have applied cell communication to revealing the pathogenesis of COVID-19 patients. For example, some ligands and receptors such as ANXA1-FPR1 and S100A8/9-TLR4 have been identified as main mediators of cellular communication between lung epithelial cells and myeloid cells and these signals may cause an immune storm and systemic disorders in critically ill COVID-19 patients [7]. Even so, there are still much unknown about the changes of the detailed intercellular communication network in the lungs after SARS-CoV-2 infection to date.

More importantly, we want to know how do alveolar cells infected by SARS-CoV-2 induce immune responses in both neighboring cells and remote organs? as well as how do these infected alveolar target cells cause local and systemic inflammation and infiltration of inflammatory cells (e.g. neutrophils and monocytes/macrophages)? Of note, previous studies have shown that cell communication mediated by ligands and receptors plays a key role in the migration and circulation of immune cells. For example, the up-regulated CXCL9, CXL10 and CXCL1 ligands can respectively bind to their receptor CXCR3 to induce CD8 + cells in the lymph and blood to migrate to the infection site to kill the virus in the epicutaneous vaccinia virus infection model [11]. Especially, the C5AR1 receptor has been considered to be an important target for treatment COVID-19 patients [12, 13]. These above studies imply that the dysregulations of ligands and receptors may also alter cell communication to further causes the migration and the inflammatory storm of immune cells in different tissues in COVID-19 patients. In this study, our goal is to reveal how SARS-CoV-2 further alters the lung ecological microenvironment and causes the immune response of other organs after entering alveolar target cells through the single-cell communication network analysis.

Herein, we performed the scRNA-seq of lung, blood and bone marrow of autopsy specimens from the two COVID-19 patients in Wuhan Huoshenshan Hospital. We next carried out the unbiased analysis on these single-cell transcriptome data. In short, our current findings demonstrated that the receptor/ligand-mediated tissue or inter-tissue cell communication might be responsible for facilitating the pulmonary microenvironmental disturbance and systemic immune responses of patients with COVID-19, which will be also helpful for understanding on the pathogenesis of COVID-19.

2.1 Single-cell sequencing process and clinical characteristics of two COVID19 patients

We here used the scRNA-seq to characterize the changes at cellular and molecular levels in the three different organs of two died COVID19 patients. The specific process is as follows: the lung, blood and bone marrow of two COVID-19 patients were respectively dissociated into single-cell suspensions for single-cell transcriptome sequencing (Fig. 1A). Bioinformatics analysis was performed by integrating control data from public sources (Fig. 1A). After quality control of the cells, we obtained a total of 110,731 single cell including 21,773 bone marrow single cells (17,396 COVID19 and 4,377 healthy), 41,571 blood single cells (21,244 COVID19 and 20,327 healthy), and 47,397 lung single cells (4,241 COVID19 and 43,156 healthy). Remarkably, the multiple blood SARS-CoV-2-IgG and SARS-CoV-2-IgM test results of the two patients showed significant positive before death (Fig. 1B ~ 1C). This was consistent with the positive result of the SARS-CoV-2 virus in the lungs. The results of immunohistochemical staining (IHC) showed that SARS-CoV-2 virus was detected in the upper left lung and lower left lung samples of patient 1 (Fig. 1D ~ 1E). Multiple SARS-CoV-2 viruses were also detected in the right lower lung of patient 2 (Fig. 1F ~ 1G). Obviously, the viral load in the lungs of patient 1 is higher than that of patient 2, which might be related to the underlying disease sepsis in patient 1. Especially, the two patients had severe inflammation accompanied by the significant increases of C-reactive protein and interleukin 6 (Fig. 1H ~ 1I).

2.2 Single cell atlas and molecular changes of alveolar target cells

As the lungs is the main target organ of SARS-CoV-2 infection, we further explored lung cell compositions and changes in COVID19 patients and healthy people by using cell dimensionality reduction clustering and Uniform Manifold Approximation and Projection (UMAP) analysis [14]. We herein identified a variety of lung epithelial cells including type I alveolar cells (AT1) (AGER+), type II alveolar cells (AT2) (SFTPC+), club cells (SCGB3A2+), ciliated cells (TPPP3+), basal cells (PTTG1+) in the lungs (Fig. 2A ~ 2B). Of note, the types of immune cells in the lungs mainly include common neutrophils (Neu) (S100A8+), monocyte (MS4A6A+), T cells (CD3D+), natural killer cells (NK) (GNLY+), plasma cells (MZB1+), mast cells (TPSB2+) and macrophages (Macro) (CD68+) (Fig. 2A ~ 2B). Besides, endothelial cells (End) (CLDN5+) and fibroblasts (Fib) (DCN+) were also found in the lungs (Fig. 2A ~ 2B). Surprisingly, we discovered a cluster of MKI67 progenitor cells (MKI67+) with highly expressed MKI67 (Fig. 2A ~ 2B). Remarkably, the ratio of multiple cell types in the lungs is changed after infection, in particular the proportion of immune cells, including Neu, T, NK and Mono cells, is obviously increased (Fig. 2C), implying that the invasion of SARS-CoV-2 might lead to the enrichment of certain immune cells to the lungs. Additionally, the ratio of both Fib and club cells is respectively significantly increased (Fig. 2C), and their increases were usually related to lung fibrosis and increased lung mucus [15, 16]. In turn, we found that the ratio of AT1 and AT2 cells is drastically decreased (Fig. 2C), in particular many genes that maintain the integrity and homeostasis of alveolar cells, such as NKX2-1 as a key transcription factor, SFTP family members, IRX2, NPNT, SDC4, SHROOM3, TGFBR2 and TMED2, are significantly down-regulated after infection (Fig. 2D ~ E) [17–20]. Whilst it was clear from the results of hematoxylin-eosin staining (HE) that the alveolar cells of the left lower lung of patient 1 and the right lower lung of patient 2 are serious damage (Fig. 2F ~ G). Taken together, these above results seem to indicate that SARS-CoV-2 infection might destroy the integrity and homeostasis of alveolar cells.

In order to explain the above issue, we further performed a functional enrichment analysis on all differentially expressed genes in AT1 and AT2 cells after infection. We found that these differentially expressed genes are significantly enriched in energy electron chains, microtubule and vascular bundle assembly, response to virus, virus life cycle, interferon response and neutrophil chemotaxis (Fig. 2H). Usually, both AT1 and AT2 as epithelial cells do not have the ability to kill viruses [21, 22], but AT1 and AT2 cells can release a large number of molecules to induce immune and antiviral signal response after SARS-CoV-2 infection based on the enrichment analysis (Fig. 2H). Herein, we used the cell communication analysis to further illustrate this finding. Our results demonstrated that the number of cell communications in the entire lung are significantly reduced after infection (COVID-19: 3,275 vs Health: 6,573) (Fig S1A ~ S1B), which may be caused by the death of a large number of lung cells after infection. Interestingly, the cell types with the highest communication frequency among AT2/AT1 target cells are mainly MKI67+, End and basal cells in the normal group, but the most frequent cell type of communication are mainly Macro, Neu and Mono cells in the infection group (Fig. 2I ~ 2J and Fig S1C ~ S1D). Especially, we find that many receptor and ligand genes altered significantly of AT1 and AT2 cells are obviously enriched in myeloid cell migration, granulocyte migration, monocyte migration and chemotaxis (Fig. 2K). Taken together, these results seemed to imply that these significantly changed receptor and ligand genes play a crucial role in inducing AT2 and AT1 cells to communicate with innate immune cells in the invasion of SARS-CoV-2.

2.3 Dysregulated ligands and receptors cause the disorder of inflammatory genes and immune cell migration

Why can SARS-CoV-2 infection obviously increase the cell communication frequency among AT1 and AT2 cells and immune cells? We herein speculated that this increased communication frequency might be responsible for recruiting immune cells to kill SARS-CoV-2. Remarkably, our above results have demonstrated significant increased myeloid cells and T cells in the lungs after SARS-CoV-2 infection (Fig. 2C), which is consistent with previous studies [7, 23, 24]. However, as the first site of SARS-CoV-2 infection, how the AT1 and AT2 cells after infection induce the recruitment of myeloid cells and T cells, which is still unclear. Interestingly, we here found that a group of ligand and receptor genes, such as ANXA1_FPR1, CD74_APP, CD74_COPA, CXCL1_CXCR1, CXCL2_CXCR2, might participate in the recruitment of myeloid cells, and mediate the cell communication between AT1/AT2 cells and myeloid cells (Neu, Mono, Macro) after SARS-CoV-2 infection through in-depth cell communication analysis (Fig. 3A). Of note, previous studies have indicated that the CXCLs_CXCRs family as known chemokines can induce the recruitment of Neu and Mono [25], and the ANXA1_FPR1 is related to the increase of myeloid cells of COVID-19 patients [7], as well as both CD74_COPA and CD74_APP serve as inflammatory recruitment signals, in particular the COPA gene is also involved in the occurrence of autoimmune interstitial lung, arthritis, kidney disease and lipopneumonia [26, 27]. These results implied that the dysregulations of some certain receptor and ligand genes might cause the disorder of their downstream genes associated with inflammation in myeloid cells after SARS-CoV-2 infection.

We thus further explored the expressions of inflammation-related genes. Surprisingly, we found that the myeloid cells recruited by both AT1 and AT2 cells have higher inflammatory activity accompanied by significantly increased inflammatory gene set scores in Neu and Mono/Macro cells (Fig. 3B ~ 3C). Especially, many inflammation-related genes, such as C5AR1, CD55, CSF3R, CXCL8, HIF1A, NAMPT and NFKB1A, are significantly higher expressed in the Neu cells (Fig. 3D), as well as C5AR1, CD14, CSF3R, CXCL8, HIF1A, IFNAR1, NAMPT and NFKB1A are obvious higher expressed in the Mono/Macro cells of the lungs after SARS-CoV-2 infection (Fig. 3E). Previous reports have shown that both the ligand ANXA1 and receptor FPRs can served as an upstream signal to promote the up-regulation of granulocyte macrophage colony stimulating factor (GM-CSF) and cause the maturation and migration of neutrophil cells and macrophages [28]. In particular CSF3R as an inflammation marker can act as a receptor for GM-CSF [29], as well as the FPR-1 and its ligand are also necessary for effectively recruiting neutrophils to the damaged lung tissue and the reduction of neutrophils are protected from pulmonary fibrosis [30]. Additionally, C5AR1 can act as an upstream signal of Toll-like pathways (NFKB1, TLR1, TLR2, etc.) [31–33], which can further activate the downstream inflammatory cytokines, such as TNFRA family members (TNFAIP6, TNFRSF1B, TNFSF10) and IL family members (IL18R1, IL1B, IL7R) [34, 35]. And that epithelial cells can release CXCLs to induce neutrophil infiltration and up-regulate the expression of the inflammatory gene NAMPT [36]. These studies indicate that the dysregulations of receptor and ligand genes may result in the disorders of inflammatory genes. Together, our study seemed to suggest that the inflammatory storm of different myeloid cells might be driven by the dysregulations of certain ligand and receptor genes after SARS-CoV-2 infection.

Herein, we also explored how the AT1 and AT2 cells recruit T cells into the lungs to kill SARS-CoV-2 after infection. We firstly found that there is almost no cell communication between AT1 and AT2 cells and T cells after infection (Fig. 3F), implying that the recruitment of T cells in the lung might be not directly related to AT1 and AT2 cells. Secondly, we found that T cells could communicate with myeloid cells in the lung, in particular their communication can be mainly mediated by some ligand and receptor genes such as ANXA1_FPR1, C5AR1_RPS19 and CCL5_CCR1 (Fig. 3F). Interestingly, studies have demonstrated that the combination of C5AR1 and RPS19 is one of the main reasons for the activation of the coagulation complement system [37, 38], and C5AR1 monoclonal antibody is also considered to be a effective drug for the treatment of coagulopathy caused by SARS-CoV-2 [13, 39], as well as the combination of CCL5 and CCR1 can induce the recruitment of T cells to inflammation and viral sites to inhibit the virus [40–42]. Taken together, these results seemed to indicate that myeloid cells have obvious duality in the lungs after SARS-CoV-2 infection. On the one hand, these myeloid cells induced by infected AT1/AT2 through inflammatory ligands and receptors may be the source of the lung inflammation storm [43]. On the other hand, these myeloid cells of the lungs can further induce T lymphocytes to migrate into the lungs and kill SARS-CoV-2 (Fig. 3G).

2.4 Lung inflammation drives the increase of fibrosis and mucus accumulation

In this study, the HE staining shows a significant fibrosis increase in the lung tissues of the two dead COVID-19 patients (Fig. 4A ~ 4B), and the apoptosis score of fibroblasts is also significantly reduced compared with the control (Fig. 4C), which implies that severe lung inflammation might lead to the increase of lung fibrosis in the two COVID-19 patients. Remarkably, under certain physiological and pathological conditions, epithelial cells can undergo phenotypic transformation and become myofibroblasts that can produce extracellular matrix, which is called epithelial-mesenchymal transition (EMT) [44]. In particular, the type II EMT can induce fibroblasts and other related cells for tissue repair after trauma and inflammatory injury, and the characteristic of tissue fibrosis is endless wound repair caused by continuous inflammation [45, 46]. Therefore, we here further investigate gene expressions in fibroblasts of the lungs. Our results demonstrated that differentially expressed genes in fibroblasts are significantly enriched in the biological processes of extracellular matrix decomposition, cell adhesion, and extracellular matrix assembly (Fig. 4D), indicating that the cytoskeleton and cell morphology had been altered after SARS-CoV-2 infection. Interestingly, we also found that collagen-related proteins (e.g. COL1A1, COL1A2, COL3A1), laminin proteins (e.g. LAMA2, LAMB1), inflammatory marker proteins (e.g. S100A8, S100A9), the matrix metalloproteinase MMP2, fibronectin FN1 and the key regulator ZEB1 of EMT were significantly upregulated in the fibroblasts of COVID-19 patients (Fig. 4E). Of note, these above genes are all related to the increase of EMT-mediated fibrosis [45–47]. As expected, fibrosis in COVID-19 patients can indeed be driven by EMT, with a significantly increased EMT score (Fig. 4F) and the decrease of MAL2, CLND4 and TJP1 of epithelial cell as well as the increase of VIM and CDH2 of mesenchymal cell in AT1/AT2 cells (Fig S2). Especially, previous studies have demonstrated that the activation of certain receptors and ligands (e.g. CD74_APP and CD74_COPA) signals affects downstream gene expressions and promotes inflammation and lung diseases [48–50], and the activation of COL1A2_a2b1, COL3A1_a2b1 and FN1_a3b1 signals drives the process of EMT, interstitial pneumonia and pulmonary fibrosis [10, 51, 52]. Together, our work suggested that some receptors and ligands, such as CD74_APP, CD74_COPA, COL1A2_a2b1, COL3A1_a2b1 and FN1_a3b1 not only mediate the cell communication between AT2 cells and fibroblasts but also facilitate the increase of lung fibrosis EMT-mediated by the lung inflammation (Fig. 4G).

In addition to pulmonary fibrosis, whether can the patient's respiratory failure be caused by lung obstruction? Interestingly, the HE staining revealed significant mucus increase in the lungs of these two COVID-19 patients (Fig. 4H ~ I), and the entire alveolar cell space filled by these thick mucus might cause dyspnea or failure in the patients. Remarkably, this phenotype from the HE staining was consistent with the increase of the proportion of club cells, a main cell type that produces mucus, in the two COVID19 patients (Fig. 2C). Particularly, we found that some mucus components (e.g. MUC5B and MUC4) and genes related to mucus viscosity (e.g. TGM2 and TFF3) were significantly up-regulated in the two COVID19 patients (Fig. 4J), and the mucus secretion score of club cells was accordingly increased significantly in COVID19 patients (Fig. 4K). These results indicated that SARS-CoV-2 infection improves the ratio of club cells and mucus production. Herein, we further performed functional and transcription factor enrichment analysis to explore the reason that drives the increase of club cell-based mucus secretion. Our results demonstrated that differentially expressed genes in club cells were mainly enriched in these processes such as hypoxia, C-type lectin, virus life cycle and interferon response (Fig. 4L). Especially, HSF1, IRF8, IRF3, IRF2 and IRF1 are the top 5 enriched transcription factors (Fig. 4M). Of note, previous studies have shown that HSF1, as a stress-induced and DNA-binding transcription factor, could participate in regulating HIV-1 transcriptional activation and latent escape [53, 54], and the IRF family members as interferon regulatory factors could play vital regulatory roles in the upstream of antiviral immunity and inflammation [55, 56]. Additionally, our results demonstrated that many disordered genes in ciliated cells for cleaning up mucus, were mainly enriched in these biological processes such as protein translation, transcription initiation, microtubule movement, cilia assembly and cilia structure (Fig. 4O), implying that the structure and movement of the cilia might be affected. Among them, some cilia production-related regulators (e.g. FOXJ1, RFX2, and RFX3) and genes related to cilia structure, cilia movement and microtubule bundle movement (e.g. DRC1, TEKT1, ARMC4, LRRC6, RSPH9) are significantly disordered after infection (Fig. 4P) [57–62]. Especially, ATP synthesis-related genes (e.g. ATP5F1A, ATP5F1B, ATP5F1C, ATP5F1D, ATP5F1E, ATP5PO, ATP5PF) are significantly increased in COVID-19 patients (Fig. 4P). Remarkably, under the stimulation with high levels of extracellular ATP, the frequency of cilia beating will be destroyed and the secretion of airway mucin can increase thousands of times [57, 63]. These results indicated that SARS-CoV-2 may severely affect the functional homeostasis of ciliated cells, thereby resulting in the impaired function for cleaning mucus. Taken together, our study revealed that the dysregulation of receptors and ligands causes lung inflammation and results in the expression dysregulation of many mucus secretion- and cleaning-related genes to further induce the increase of mucus production in club cells and the functional defect of cleaning mucus, thereby causing COVID-19 patient's respiratory failure.

2.5 Single cell transcriptome and communication analyses in the blood uncover the roles of receptors and ligands in immune responses and cell migration

Both integrating signals from different pathways and cell migrations are indispensable for diverse physiological processes such as cell survival, cell development and immune response as well as wound healing [64–66]. Especially, as a multifunctional cell type, immune cells can not only act as sensors to receive external signals and instruct cell migration as well as connect to distant tissues and organs for combating pathogens or viruses together, but also serve as regulators to control immune response and tolerance as well as homeostasis [64–66]. This implies that the pulmonary microenvironment and immune response disturbances caused by SARS-CoV-2 infection may be also associated with the disorder of other tissues and organs. To answer this issue, we herein further detected the single-cell atlas of blood from a few days before two COVID-19 patients died.

Based on the single-cell atlas of the blood of COVID-19 patients, we identified multiple kinds of myeloid cells including Neu (CSF3R+), CD14 monocyte (CD14+), CD16 monocyte (FCGR3A+), dendritic cells (DC) (CD1C+), plasmacytoid dendritic cells (pDC) (CLEC4C+) and mast cells (CPA3+) (Fig. 5A ~ 5B), and numerous immune cells including B cell (MS4A1+), plasma cell (MZB1+), CD4 T (CD3D+, IL7R+), CD8 T (CD3D+, CD8A+) and NK (Fig. 5A ~ 5B), as well as other cells such as granulocyte-macrophage progenitor (GMP) (ELANE+), megakaryocytes (Meg) (PF4+) and erythroid cells (Ery) (HBA1+) (Fig. 5A ~ 5B). Of note, the significantly increased cell type in the blood of COVID-19 patients is Neu cells (Fig. 5C), but the significantly decreased cell types are T cells and NK cells (Fig. 5C), which are agreement with the multiple blood test results from the two COVID-19 patients (Fig. 5D ~ 5E). After admission, the ratio of their Neu cells was significantly higher than the normal value (Fig. 5D), but the lymphocyte ratio was significantly lower (Fig. 5E). Remarkably, compared with the blood, the lymphocyte cells (e.g. T cells and NK cells) is obviously increased in the lungs after SARS-CoV-2 infection (Fig. 2C). The reason might be that SARS-CoV-2 infection prompts immune cells of the blood to migrate to the lungs and causes the enrichment of many certain immune cells in the lungs. Moreover, we found that the two COVID-19 patient's monocytes presented an abnormally disordered state (Fig. 5F), in particular both Neu and Mono cells in the blood of COVID-19 patients demonstrated a higher inflammatory state with a significantly increased inflammatory score (Fig. 5G ~ 5H). Besides, we also found that multiple ligand and receptor genes as well as inflammation-related genes, such as C5AR1, CD55, CSF3R, CXCL8, FPR1, TLR1, HIF1A, IFNAR1A, IRF1 and NFKB1A, are also significantly up-regulated in the immune cells of the blood of COVID-19 patients (Fig. 5I), which are very similar to the lungs (Fig. 3D ~ 3E). These results indicated that the dysfunctions of some receptor and ligand as well as inflammation-related genes caused an excessive inflammation in the blood of patients after SARS-CoV-2 infection, and facilitated immune cells of the blood to migrate to the lungs.

Remarkably, ACE2 and TMPRSS2 have been demonstrated to be extremely important for SARS-CoV-2 to enter cells [67]. To our surprise, we could hardly detect the expressions of ACE2 and TMPRSS2 in the blood of COVID-19 patients (Fig S3A ~ S3B). So why and how do immune responses and inflammation in the blood of COVID-19 patients be caused without the expressions of ACE2 and TMPRSS2? In this work, we further explored the issue. Interestingly, we found the highly expressed genes C5AR1 and FPR1 in myeloid cells of the patient blood (Fig. 5I), which have been proved to are cell communication molecules that can transmit inflammatory signals [37, 38, 68, 69]. The previous study urges us to investigate further which signal molecules could cause inflammation of the blood. We thus detailedly analyzed these significantly changed ligands and receptors in the blood after SARS-CoV-2 infection. As shown in Fig S3C, these significantly altered ligand and receptor genes in the blood are mainly enriched in the migration, proliferation and cell adhesion functions of leukocytes and myeloid cells, suggesting that these ligand and receptor genes could be responsible for inflammation and cell migrations. Especially, we found that some significantly changed ligand and receptor genes in the blood such as ALOX5_ALOX5AP, ANXA1_FPR1, ANXA1_FPR2, C5AR1_RPS19, CCL5_CCR1 and CD74_MIF (Fig. 5J), which are similar to that in lung (Fig. 3). Of note, many significantly changed ligand and receptor genes in blood have been demonstrated to be inflammation and cell migration. For example, arachidonate 5-lipoxygenase (ALOX5) and arachidonic acid 5-lipoxygenase activating protein (ALOX5AP) are key enzymes and membrane proteins for the synthesis of pro-inflammatory products leukotrienes that have been proven to be related to the respiratory system and the heart [70]. Patients with sickle cell disease have increased inflammation and circulating leukotrienes and a high incidence of hyperresponsiveness, which is related to the increase in ALOX5AP induced by the up-regulated placental growth factor PlGF [71]. In addition, ALOX5AP is highly upregulated in circulating monocytes in patients with severe sepsis and can predict the clinical prognosis of patients [72]. Besides, CXCLs released by astrocytes and neurons could induce inflammatory neutrophils to enter the blood-brain barrier to increase encephalitis after herpes simplex virus infection [73]. The lungs could recruit a large number of inflammatory neutrophils in the blood transfusion-related Balb/c mouse acute lung injury model, as well as combinating the selectin ligand PSGL-1 and CXCR2 could promote the vasculature migration of neutrophils and combine with activated platelets to cause pathogenic inflammation [74]. Especially lack of PSGL-1 receptor or inhibition of PSGL-1 can prevent liver damage during endotoxemia [75]. Additionally, studies have revealed that the up-regulated C5AR1 could also lead to excessive inflammation and coagulation complement system disorders [38], as well as the CD74 could be involved in MIF-mediated inflammation via interacting with MIF on the membrane surface of alveolar macrophages to activate the p44/p43 MAPK signaling pathway and induce the accumulation of neutrophils in the alveolar cavity [76].

Taken together, our study seemed to imply that certain receptor and ligand genes in the blood of patients can act as sensors and cell communication molecules to respond to inflammatory signals from the lung, subsequently induce immune responses and promote immune cells (e.g. T cells) to migrate to the lungs for fighting on SARS-CoV-2 together. On the one hand, thereby their dysregulations further lead to an increase in inflammatory cells and a decrease in immune cells (e.g. lymphocytes) in the blood. On the other hand, they can serve as regulators and inflammation-related genes to participate in the regulations of immune response, tolerance and homeostasis, thus their dysfunctions result in an excessive inflammation in the blood of COVID-19 patients.

2.6 The decrease of lymphocytes in bone marrow is responsible for immune clearance failure in the blood and lungs

Our above works have demonstrated that SARS-CoV-2 infection caused the decrease of lymphocytes (especially T cells) in the blood, which is agreement with previous studies [77, 78]. In fact, studies have also revealed a significant reduction of lymphocytes of patient's blood after coronaviruses SARS-CoV or MERS-CoV infections [79, 80]. On the contrary, lymphocytes are significantly increased in the blood after other virus infections (e.g. Influenza virus, Epstein-Barr virus, Cytomegalovirus, Measles virus) [81–83]. The reasons for the decrease of lymphocytes in the blood after SARS-CoV-2 infection have be suggested to be the circulating migration to the peripheral target organs for destroying SARS-CoV-2 or be due to apoptosis and autophagy lead to lymphocyte failure [84]. Another reason is thought to be impairment of the bone marrow's function to produce lymphocytes, thereby promoting the decrease of lymphocytes in the blood of patients after SARS-COV-2 infection [85]. To further reveal whether the bone marrow is involved in response to SARS-CoV-2 infection and is responsible for the decline of lymphocytes, we here systematically analyzed the single cell data of the bone marrow from the two COVID-19 patients.

Herein, we identified common progenitor cells and stem cells in bone marrow including erythroid progenitor cells (Eryp) (GYPA+), Ery (HBA1+), GMP (CTSG+), hematopoietic stem cells (HSC) (MYB+), mesenchymal stem cells (MSC) (CD34+) and so on, and many myeloid cells such as Neu (CSF3R+), monocyte-derived dendritic cells (MDCs) (CST3+), Mono (LYZ+), pDC (CLEC4C+) and so on, as well as T cells (CD3D+), NK cells (GNLY+), B cells (CD79A+), plasma cells (MZB1+) and other immune cells (Fig. 6A ~ 6B). Furthermore, we identified Meg (PLEK+) and a small number of End cells (PDK4+) (Fig. 6A ~ 6B). Of note, among all these cell types, the increased proportion of Neu, GMP and Plasma cells is highest after SARS-CoV-2 infection (Fig. 6C). Remarkably, the proportion of Neu in bone marrow of COVID-19 patients is also increased significantly (Fig. 6C), which is similar with the blood and lungs (Fig. 5C and Fig. 2C), whilst the proportion of T cells and Mono in the bone marrow of COVID-19 patients was significantly reduced (Fig. 6C), which is also similar with the blood (Fig. 5C). These results indicate that the bone marrow does respond to SARS-CoV-2 infection.

We then evaluated the inflammatory scores of Neu and Mono cells in the bone marrow, finding that the inflammatory scores COVID-19 patients are significantly higher than those of normal people (Fig. 6D ~ 6E). Particularly, inflammation genes such as C5AR1, CD55, CSF3R, CXCL8, FPR1, KLF6 and NFKB1A were significantly up-regulated in Neu and Mono cells of the bone marrow of COVID-19 patients (Fig. 6F). It is worth noting that the inflammatory scores in the bone marrow area are not as strong as those in the lungs and blood because the number of inflammatory genes in the bone marrow are obvious lower that the lungs and blood in the COVID-19 patients (Fig. 3, Fig. 5 and Fig. 6). We further found that the bone marrow of COVID-19 patients almost detected the expressions of ACE2 and TMPRSS2 (Fig S4A ~ S4B), suggesting that the inflammatory response of the bone marrow myeloid cells might be caused by signal transmission between distinct cells or tissues, rather than SARS-CoV-2 directly infect the bone marrow [67]. Interestingly, we performed the functional enrichment analysis of ligands and receptors and found that many signaling molecules of the bone marrow cells are widely enriched in the leukocyte and myeloid cell migration, activation, proliferation and chemotaxis (Fig S4C). Therefore, we next focused on the significantly changed ligands and receptors in the bone marrow of COVID-19 patients. We surprisingly found that ALOX5_ALOX5AP, ANXA1_FPR1, CCL5_RPS19, CCL5_CCR1, CD74_COPA, etc. (Fig. 6G) also exist in both the lung and blood (Fig. 5 and Fig. 3), implying that the excessive inflammatory response of myeloid cells in three different tissues and the recruitment of T cells are mediated by a set of similar signaling molecules.

Interestingly, many studies have urged that immune cells from bone marrow and lymph can migrate to peripheral organs to perform a variety of specific functions. For example, the large number of neutrophils could be quickly infiltrated the heart to promote tissue healing and damage in the mouse model of cardiac infarction [86]. Remarkably, the up-regulated expressed CXCL9, CXL10 and CXCL1 can respectively function as CXCR3 ligands on T cells to ensure CD8 + T cells migration and location to the specifically peripheral organs or tissues to further kill virus-containing cells [11]. Especially, CXCR3 can act as a lymph node memory molecule to induce migration of CD8 + T cells to the infected peripheral tissues during the secondary viral infection [87], as well as the up-regulation expression of CXCR3 allows T cells with circulating effectors to home to inflamed lymph nodes [87]. Additionally, the high expressions of CXCL10 and CCL2 were observed in SARS-CoV infected patients, especially their up-regulation can inhibit the development of hematopoietic precursor cells and cause lymphopenia in patients infected with SASR-CoV [88–91]. Of note, our present results revealed that the multiple members of CXCLs and CXCRs families are also obviously up-regulated in the lungs, blood and bone marrow of COVID-19 patients. Together, our study seemed to indicate that after the lungs was infected by SARS-CoV-2, ligands and receptors of the blood and bone marrow tissues quickly respond to the inflammatory signaling from the lung and promote the activation of immune cells and migrate them to the lungs for fighting SARS-CoV-2. However, the abnormal expressions of many ligands and receptors further disrupt the normal movement and circulation of T cells among the lungs, blood and bone marrow tissues, thereby causing the increase of T cells and inflammatory storm in the lung, while the decrease of T cells in the blood and bone marrow of COVID-19 patients.

In this work, we next further explored the effects of apoptosis, autophagy and cell proliferation on changes of T cells in the bone marrow, blood and lungs of COVID-19 patients. We firstly demonstrated that after SARS-CoV-2 infection, the apoptosis scores of T cells were significantly increased in the bone marrow, blood and lungs of COVID-19 patients (Fig. 6H). In particular, T cells in the lungs have the highest apoptosis score (Fig. 6H), suggesting that the high viral load might exacerbate the apoptosis of T cells in the lungs. Secondly, we found that the proliferation scores of bone marrow and lungs significant increase after SARS-CoV-2 infection, but the blood is obviously decreased (Fig. 6I). Remarkably, the lung cell proliferation score and apoptosis score are significant increasing at the same time, implying that the high virus environment in the lungs not only induces the continuous proliferation of T cells, but continuously promotes the apoptosis T cells, which seems to be caused by continuous struggle between T cells and SARS-CoV-2 in the lungs. Thirdly, we discovered that the autophagy score of bone marrow is significantly increased after SARS-CoV-2 infection and is also the highest in COVID-19 patients, but the autophagy scores of both blood and lungs are significantly reduced (Fig. 6J). Of note, we observed changes in the order of T cell depletion in different tissues in the normal and COVID-19 groups suggesting SARS-CoV-2 infection reshaped the distribution pattern of T cell apoptosis, proliferation and autophagy in different tissues. For example, T cells in the bone marrow had the lowest apoptosis score in the healthy group, but the blood T cells in the infected group (Fig. 6H). Overall, our work revealed that the depletion patterns of T cells from three different tissues are different, i.e. the lungs are mainly through the pattern of increasing apoptosis, the blood is mainly through the patterns of increasing apoptosis and decreasing proliferation, as well as the bone marrow is mainly through the pattern of increasing apoptosis and autophagy.

In this study, we have systematically analyzed the functional role of the ligand/receptor-mediated tissue and inter-tissue cell communication in SARS-CoV-2 infection for the first time, and uncovered the mechanism by which the ligand/receptor mediated cell communication alteration causes the inflammatory storm and the disorder of immune cell redistribution observed clinically in COVID-19 patients (Fig. 7). Namely, after SARS-CoV-2 enters the alveolar target cells AT1/AT2, AT1/AT2 cells are destroyed and release a large amount of inflammatory cytokines and specific ligands and receptors (e.g. ANXA1_FPRs, CXCLs_CXCRs, CD74_APP, CD74_COPA) (Fig. 7). Next, these inflammatory ligands further bind to their receptors on the free myeloid cells (Neu, Mono/Macro) in the lung tissue and rapidly induce them to be enriched into the infection site. On the one hand, the enriched Neu, Mono/Macro cells can destroy SARS-CoV-2 viruses and remove damaged cells and tissues. On the other hand, Neu, Mono/Macro cells release plenty of inflammatory genes to form the pulmonary inflammatory microenvironment, which in turn promotes pulmonary fibrosis and leads to lung obstruction and poor gas exchange by disrupting the cell dysfunction club and ciliated cells (Fig. 7). Whilst, activated myeloid cells further induce T cells in the blood and bone marrow to migrate into the infection site through certain ligand and receptor pairs (e.g. ALOX5_ALOX5AP, C5AR1_RPS19, CCL5_CCR1) to enhance the ability of killing SARS-CoV-2 (Fig. 7). Especially, multiple activated ligand and receptor pairs such as ANXA1_FPRs, CXCLs_CXCRs, C5AR1_RPS19, CD74_COPA and ALOX5_ALOX5AP are shared among the lungs, blood and bone marrow, which may induce immune cell migration among the lungs, blood and bone marrow tissues (Fig. 7), thereby eventually leading to the pulmonary inflammatory microenvironment and systemic immune responses of COVID-19 patients.

4.1 Sample information and tissue collection

This study was conducted in accordance with legitimate ethical principles. We obtained prior approval from the Ministry of Education of China, Southwest Hospital, Third Military Medical University (Army Medical University) and Jinling Hospital, Nanjing University School of Medicine. Lung, bone marrow, and blood tissues from COVID-19 patients were obtained with approval from the Research Ethics Committee of Huoshenshan Hospital. All samples used in this study obtained written informed consent from donors or their relatives.

We collected two bone marrow, two pbmc and four lung tissues from two patients who died of COVID-19. Patient 1 had a malignant tumor, type 2 diabetes, and he also developed klebsiella infection, fever and ground glass in the lungs. He died at the age of 77. Patient 2 suffered from hypertension, coronary heart disease, Alzheimer's disease, and he was also accompanied by unknown bacterial infections, headaches, weakness, and ground glass-like lung symptoms. He died at the age of 81. Fresh or formalin fixed, paraffin embedded (FFPE) postmortem specimens were used for viral RNA detection, tissue staining or morphological analysis.

4.2 Tissue staining

Hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining were performed according to the standard procedures previously described [2, 3]. The sections were incubated with the primary antibody (Table S1) overnight at 4°C. Pathological lesions and SARS-CoV-2 spike protein were independently quantified by two pathologists. For each patient, the calculation required at least 20 randomly selected 100x microscopic fields.

4.3 Serological testing

The blood cell ratio and count of the two COVID-19 patients were tested using Mindray's automatic blood cell analyzer (BC-5390CRP). The instrument can detect lymphocytes, monocytes and eosinophils in basophils and neutrophils through laser light scattering and flow cytometry in the DIFF laser channel. During operation, only 2ml of EDTA-K2 anticoagulated venous blood needed to be put into the sample holder for testing.

4.4 Tissue dissociation and single cell suspension preparation

Fresh tissue samples were washed with Hanks Balanced Salt Solution (HBSS) and minced into pieces. The pieces were digested in GEXSCOPE Tissue Dissociation Solution (Singleron Biotechnologies). A 40-micron sterile strainer was used to separate cells from cell debris and other impurities. The cells were centrifuged and cell pellets were resuspended in PBS (HyClone). To remove red blood cells, GEXSCOPE Red Blood Cell Lysis Buffer (Singleron Biotechnologies) was added to the cell suspension. The mixture was then centrifuged and resuspended in PBS. Cells were counted with TC20 automated cell counter (Bio-Rad).

4.5 Single cell RNA sequencing library preparation

The concentration of single-cell suspension was adjusted to 1×10⁵ cells/mL in PBS. Single cell suspension was then loaded onto a microfluidic chip (GEXSCOPE Single Cell RNAseq Kit, Singleron Biotechnologies) and scRNA-seq libraries were constructed according to the manufacturer’s instructions (Singleron Biotechnologies). The resulting scRNA-seq libraries were sequenced on an Illumina HiSeq X10 instrument with 150bp paired end reads.

4.6 Generation of single-cell gene expression matrices and quality control

Raw reads were processed to generate gene expression matrices by CeleScope (https://github.com/SingleronBio/CeleScope). We removed cells that had either lower than 200 or higher than 5,000 expressed genes. Furthermore, we discarded cells with more than 30,000 UMIs and mitochondria content higher than 50%.

4.7 Single cell data analysis

We used Seurat package (version 3) to integrate single cell data [92]. The CCA + anchor algorithm that comes with the Seurat package was used to remove batch effects [92, 93]. The top 2000 feature variables of single-cell data were used for subsequent dimensionality reduction and clustering. The AddModuleScore function from the Seurat package was used to evaluate the cell's gene set score [92, 93]. The gene set mainly came from msigdb database (http://www.gsea-msigdb.org/gsea/msigdb) and msigdb R package [94] including HALLMARK_INFLAMMATORY_RESPONSE, HALLMARK_APOPTOSIS, GO_T_CELL_PROLIFERATION (GO:0042098), GO_MUCUS_SECRETION (GO:0070254, GO:0070255, GO:0070257) and KEGG_REGULATION_OF_AUTOPHAGY (hsa04140). The Monocle2 R package was used to perform pseudo-chronological analysis to simulate the development and differentiation trajectory of cells [95]. The SCENIC R package was used to evaluate the transcription factors enriched in each single cell [96]. CellPhoneDB software was used to analyze the interactions between cells and discover important receptor ligand pairs [10]. Only ligand-receptor pairs with pvalue < 0.01 were included in subsequent analyses.

4.8 Differentially Expressed Gene Analysis

Using the Findmarker function of the Seurat package to find differentially expressed genes [92, 93]. Differential gene analysis (|logFC| > 0.25 & fdr < 0.01) was performed only when the number of cells between the two groups was more than 10. Using bonferroni method to correct the p value. The gene ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis of differential genes were completed by the clusterProfiler R package [97]. Using RcisTarget software to perform transcription factor enrichment analysis on differentially expressed genes (http://bioconductor.riken.jp/packages/3.8/bioc/html/RcisTarget.html).

4.9 Visualization and statistics

To assess whether cell type compositions are significantly different between patients and normal groups, we used R package ggpubr for the statistical testing and visualization (https://cran.r-project.org/web/packages/ggpubr/index.html). Wilcox test was used to calculate the significance of gene set scores and gene expression levels between groups. P values < 0.05 were considered statistically significance.

4.10 Data availability

We analyzed the single-cell transcriptome data of four lung tissues, two bone marrow and two blood sample derived from 2 patients with COVID-19. The raw sequencing data were deposited at GEO of GSE154395. In addition, 8 normal lung tissues (GSE122960) [98], 2 normal bone marrow (GSE116256) [99] and 6 normal blood tissues (GSE150728) [6] were also included in the analysis.

Acknowledgments

We thank other researchers and experiments for sharing the data. All the authors have agreed to the manuscript. This work was supported by the Key Foundation of Wuhan Huoshenshan Hospital (2020[18]), the Key Research & Development Program of Jiangsu Province (BE2018713), the Medical Innovation Project of Logistics Service (18JS005), Jiangsu Provincial Key Medical Talent (ZDRCA 2016097), 333 high-level talents of Jiangsu Province (BRA2019338), the Foundation of Jiangsu Population Association (JSPA2019017, JSPA2019020), the Jiangsu Provincial Association for Maternal and Child Health Studies Commissioned Research Project Funding (JSFY202005). Jiangsu Commission of Health Geriatric Health Research Project (LK2021025).

Author Contributions

Conceptualization, XWB, XYX, SJQ and PJ; Methodology, SJQ, CBW, XHY, WWL and WJX; Investigation, SJQ, CBW, XHY and YY; Resources, XWB, XYX, XHY and WWL; Writing, SJQ, CBW; Review & Editing, XWB, XYX, PJ, WWL, WJX and SHK; Supervision, XYX, PJ and XWB; Funding, XYX and XWB; Commentary, ZHG, YY, AFZ, BW, ZCH, JW, QYW, WJJ, YH, FW, ZHW, YHK, JZ, JYG and LQ.

Competing Interest

The authors declare no competing interests.

Data and materials availability

All data can be found in materials and methods or request the corresponding author XYX ([email protected]).

Dong E, Du H, Gardner L: An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious diseases 2020, 20(5):533-534.
Bian XW, Team C-P: Autopsy of COVID-19 patients in China. National science review 2020, 7(9):1414-1418.
Yao XH, Luo T, Shi Y, He ZC, Tang R, Zhang PP, Cai J, Zhou XD, Jiang DP, Fei XC et al: A cohort autopsy study defines COVID-19 systemic pathogenesis. Cell research 2021, 31(8):836-846.
Ziegler CGK, Miao VN, Owings AH, Navia AW, Tang Y, Bromley JD, Lotfy P, Sloan M, Laird H, Williams HB et al: Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19. Cell 2021.
Zhu L, Yang P, Zhao Y, Zhuang Z, Wang Z, Song R, Zhang J, Liu C, Gao Q, Xu Q et al: Single-Cell Sequencing of Peripheral Mononuclear Cells Reveals Distinct Immune Response Landscapes of COVID-19 and Influenza Patients. Immunity 2020, 53(3):685-696 e683.
Wilk AJ, Rustagi A, Zhao NQ, Roque J, Martinez-Colon GJ, McKechnie JL, Ivison GT, Ranganath T, Vergara R, Hollis T et al: A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nature medicine 2020, 26(7):1070-1076.
Ren X, Wen W, Fan X, Hou W, Su B, Cai P, Li J, Liu Y, Tang F, Zhang F et al: COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas. Cell 2021, 184(7):1895-1913 e1819.
Guerrero-Juarez CF, Dedhia PH, Jin S, Ruiz-Vega R, Ma D, Liu Y, Yamaga K, Shestova O, Gay DL, Yang Z et al: Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. Nat Commun 2019, 10(1):650.
Gupta K, Levinsohn J, Linderman G, Chen D, Sun TY, Dong D, Taketo MM, Bosenberg M, Kluger Y, Choate K et al: Single-Cell Analysis Reveals a Hair Follicle Dermal Niche Molecular Differentiation Trajectory that Begins Prior to Morphogenesis. (1878-1551 (Electronic)).
Efremova M, Vento-Tormo M, Teichmann SA-O, Vento-Tormo RA-O: CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. (1750-2799 (Electronic)).
Hickman HD, Reynoso GV, Ngudiankama BF, Cush SS, Gibbs J, Bennink JR, Yewdell JW: CXCR3 chemokine receptor enables local CD8(+) T cell migration for the destruction of virus-infected cells. Immunity 2015, 42(3):524-537.
Carvelli J, Demaria O, Vely F, Batista L, Chouaki Benmansour N, Fares J, Carpentier S, Thibult ML, Morel A, Remark R et al: Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis. Nature 2020, 588(7836):146-150.
Woodruff TM, Shukla AK: The Complement C5a-C5aR1 GPCR Axis in COVID-19 Therapeutics. Trends in immunology 2020, 41(11):965-967.
Becht E, McInnes LA-O, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux FA-O, Newell EA-OX: Dimensionality reduction for visualizing single-cell data using UMAP. LID - 10.1038/nbt.4314 [doi]. (1546-1696 (Electronic)).
Plikus MV, Wang X, Sinha S, Forte E, Thompson SM, Herzog EL, Driskell RR, Rosenthal N, Biernaskie J, Horsley V: Fibroblasts: Origins, definitions, and functions in health and disease. (1097-4172 (Electronic)).
Lewis BW, Patial S, Saini Y: Immunopathology of Airway Surface Liquid Dehydration Disease. Journal of immunology research 2019, 2019:2180409.
Little DR, Lynch AM, Yan Y, Akiyama H, Kimura S, Chen J: Differential chromatin binding of the lung lineage transcription factor NKX2-1 resolves opposing murine alveolar cell fates in vivo. Nat Commun 2021, 12(1):2509.
Selman M, Lin HM, Montano M, Jenkins AL, Estrada A, Lin Z, Wang G, DiAngelo SL, Guo X, Umstead TM et al: Surfactant protein A and B genetic variants predispose to idiopathic pulmonary fibrosis. Human genetics 2003, 113(6):542-550.
King SD, Chen SY: Recent progress on surfactant protein A: cellular function in lung and kidney disease development. American journal of physiology Cell physiology 2020, 319(2):C316-C320.
He J, Cai S, Feng H, Cai B, Lin L, Mai Y, Fan Y, Zhu A, Huang H, Shi J et al: Single-cell analysis reveals bronchoalveolar epithelial dysfunction in COVID-19 patients. Protein & cell 2020, 11(9):680-687.
Hekman RM, Hume AJ, Goel RK, Abo KM, Huang J, Blum BC, Werder RB, Suder EL, Paul I, Phanse S et al: Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2. Molecular cell 2020, 80(6):1104-1122 e1109.
Salahudeen AA, Choi SS, Rustagi A, Zhu J, van Unen V, de la OS, Flynn RA, Margalef-Catala M, Santos AJM, Ju J et al: Progenitor identification and SARS-CoV-2 infection in human distal lung organoids. Nature 2020, 588(7839):670-675.
Chen H, Liu W, Wang Y, Liu D, Zhao L, Yu J: SARS-CoV-2 activates lung epithelial cell proinflammatory signaling and leads to immune dysregulation in COVID-19 patients. EBioMedicine 2021, 70:103500.
Bost P, Giladi A, Liu Y, Bendjelal Y, Xu G, David E, Blecher-Gonen R, Cohen M, Medaglia C, Li H et al: Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients. Cell 2020, 181(7):1475-1488 e1412.
Khalil BA, Elemam NM, Maghazachi AA: Chemokines and chemokine receptors during COVID-19 infection. Computational and structural biotechnology journal 2021, 19:976-988.
Watkin LB, Jessen B, Wiszniewski W, Vece TJ, Jan M, Sha Y, Thamsen M, Santos-Cortez RL, Lee K, Gambin T et al: COPA mutations impair ER-Golgi transport and cause hereditary autoimmune-mediated lung disease and arthritis. Nature genetics 2015, 47(6):654-660.
Stray-Pedersen A, Sorte HS, Samarakoon P, Gambin T, Chinn IK, Coban Akdemir ZH, Erichsen HC, Forbes LR, Gu S, Yuan B et al: Primary immunodeficiency diseases: Genomic approaches delineate heterogeneous Mendelian disorders. The Journal of allergy and clinical immunology 2017, 139(1):232-245.
Schloer S, Hubel N, Masemann D, Pajonczyk D, Brunotte L, Ehrhardt C, Brandenburg LO, Ludwig S, Gerke V, Rescher U: The annexin A1/FPR2 signaling axis expands alveolar macrophages, limits viral replication, and attenuates pathogenesis in the murine influenza A virus infection model. Faseb J 2019, 33(11):12188-12199.
Jaillon S, Ponzetta A, Di Mitri D, Santoni A, Bonecchi R, Mantovani A: Neutrophil diversity and plasticity in tumour progression and therapy. Nature reviews Cancer 2020, 20(9):485-503.
Leslie J, Millar BJ, Del Carpio Pons A, Burgoyne RA, Frost JD, Barksby BS, Luli S, Scott J, Simpson AJ, Gauldie J et al: FPR-1 is an important regulator of neutrophil recruitment and a tissue-specific driver of pulmonary fibrosis. JCI insight 2020, 5(4).
Reichhardt MP, Meri S: Intracellular complement activation-An alarm raising mechanism? Seminars in immunology 2018, 38:54-62.
Modinger Y, Rapp A, Pazmandi J, Vikman A, Holzmann K, Haffner-Luntzer M, Huber-Lang M, Ignatius A: C5aR1 interacts with TLR2 in osteoblasts and stimulates the osteoclast-inducing chemokine CXCL10. Journal of cellular and molecular medicine 2018, 22(12):6002-6014.
Maekawa T, Krauss JL, Abe T, Jotwani R, Triantafilou M, Triantafilou K, Hashim A, Hoch S, Curtis MA, Nussbaum G et al: Porphyromonas gingivalis manipulates complement and TLR signaling to uncouple bacterial clearance from inflammation and promote dysbiosis. Cell host & microbe 2014, 15(6):768-778.
Zhao H, Wu L, Yan G, Chen Y, Zhou M, Wu Y, Li Y: Inflammation and tumor progression: signaling pathways and targeted intervention. Signal transduction and targeted therapy 2021, 6(1):263.
Mitchell S, Vargas JA-O, Hoffmann A: Signaling via the NFκB system. (1939-005X (Electronic)).
van der Vorst EP, Doring Y, Weber C: Chemokines and their receptors in Atherosclerosis. Journal of molecular medicine 2015, 93(9):963-971.
Revollo I, Nishiura H, Shibuya Y, Oda Y, Nishino N, Yamamoto T: Agonist and antagonist dual effect of the cross-linked S19 ribosomal protein dimer in the C5a receptor-mediated respiratory burst reaction of phagocytic leukocytes. Inflammation research : official journal of the European Histamine Research Society [et al] 2005, 54(2):82-90.
Wood AJT, Vassallo A, Summers C, Chilvers ER, Conway-Morris A: C5a anaphylatoxin and its role in critical illness-induced organ dysfunction. European journal of clinical investigation 2018, 48(12):e13028.
Posch W, Vosper J, Noureen A, Zaderer V, Witting C, Bertacchi G, Gstir R, Filipek PA, Bonn GK, Huber LA et al: C5aR inhibition of nonimmune cells suppresses inflammation and maintains epithelial integrity in SARS-CoV-2-infected primary human airway epithelia. The Journal of allergy and clinical immunology 2021, 147(6):2083-2097 e2086.
Choi SW, Hildebrandt GC, Olkiewicz KM, Hanauer DA, Chaudhary MN, Silva IA, Rogers CE, Deurloo DT, Fisher JM, Liu C et al: CCR1/CCL5 (RANTES) receptor-ligand interactions modulate allogeneic T-cell responses and graft-versus-host disease following stem-cell transplantation. Blood 2007, 110(9):3447-3455.
Sato K, Kawasaki H, Morimoto C, Yamashima N, Matsuyama T: An abortive ligand-induced activation of CCR1-mediated downstream signaling event and a deficiency of CCR5 expression are associated with the hyporesponsiveness of human naive CD4+ T cells to CCL3 and CCL5. Journal of immunology 2002, 168(12):6263-6272.
Owen JL, Criscitiello MF, Libreros S, Garcia-Areas R, Guthrie K, Torroella-Kouri M, Iragavarapu-Charyulu V: Expression of the inflammatory chemokines CCL2, CCL5 and CXCL2 and the receptors CCR1-3 and CXCR2 in T lymphocytes from mammary tumor-bearing mice. Cellular immunology 2011, 270(2):172-182.
Wang S, Yao X, Ma S, Ping YA-O, Fan Y, Sun S, He Z, Shi YA-OX, Sun L, Xiao S et al: A single-cell transcriptomic landscape of the lungs of patients with COVID-19. (1476-4679 (Electronic)).
Brabletz S, Schuhwerk H, Brabletz T, Stemmler MP: Dynamic EMT: a multi-tool for tumor progression. The EMBO journal 2021:e108647.
Marconi GA-O, Fonticoli L, Rajan TA-O, Pierdomenico SA-O, Trubiani OA-O, Pizzicannella J, Diomede FA-O: Epithelial-Mesenchymal Transition (EMT): The Type-2 EMT in Wound Healing, Tissue Regeneration and Organ Fibrosis. LID - 10.3390/cells10071587 [doi] LID - 1587. (2073-4409 (Electronic)).
Stepp MA, Menko AS: Immune responses to injury and their links to eye disease. (1878-1810 (Electronic)).
Paolillo M, Schinelli S: Extracellular Matrix Alterations in Metastatic Processes. LID - 10.3390/ijms20194947 [doi] LID - 4947. (1422-0067 (Electronic)).
Schoeps B, Eckfeld C, Flüter L, Keppler S, Mishra R, Knolle P, Bayerl F, Böttcher J, Hermann CD, Häußler D et al: Identification of invariant chain CD74 as a functional receptor of tissue inhibitor of metalloproteinases-1 (TIMP-1). (1083-351X (Electronic)).
Le Hiress M, Tu L, Ricard N, Phan C, Thuillet R, Fadel E, Dorfmuller P, Montani D, de Man F, Humbert M et al: Proinflammatory Signature of the Dysfunctional Endothelium in Pulmonary Hypertension. Role of the Macrophage Migration Inhibitory Factor/CD74 Complex. American journal of respiratory and critical care medicine 2015, 192(8):983-997.
Baumkotter F, Schmidt N, Vargas C, Schilling S, Weber R, Wagner K, Fiedler S, Klug W, Radzimanowski J, Nickolaus S et al: Amyloid precursor protein dimerization and synaptogenic function depend on copper binding to the growth factor-like domain. J Neurosci 2014, 34(33):11159-11172.
Guerrero-Juarez CF, Dedhia PH, Jin S, Ruiz-Vega R, Ma D, Liu Y, Yamaga K, Shestova O, Gay DL, Yang Z et al: Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. (2041-1723 (Electronic)).
Marconi GD, Fonticoli L, Rajan TS, Pierdomenico SD, Trubiani O, Pizzicannella J, Diomede F: Epithelial-Mesenchymal Transition (EMT): The Type-2 EMT in Wound Healing, Tissue Regeneration and Organ Fibrosis. Cells 2021, 10(7).
Nekongo EE, Ponomarenko AI, Dewal MB, Butty VL, Browne EP, Shoulders MA-O: HSF1 Activation Can Restrict HIV Replication. (2373-8227 (Electronic)).
Rawat P, Mitra D: Cellular heat shock factor 1 positively regulates human immunodeficiency virus-1 gene expression and replication by two distinct pathways. (1362-4962 (Electronic)).
Chiang HS, Liu HM: The Molecular Basis of Viral Inhibition of IRF- and STAT-Dependent Immune Responses. Frontiers in immunology 2018, 9:3086.
Jefferies CA: Regulating IRFs in IFN Driven Disease. Frontiers in immunology 2019, 10:325.
Bustamante-Marin XM, Ostrowski LE: Cilia and Mucociliary Clearance. Cold Spring Harbor perspectives in biology 2017, 9(4).
Ryan R, Failler M, Reilly ML, Garfa-Traore M, Delous M, Filhol E, Reboul T, Bole-Feysot C, Nitschké P, Baudouin V et al: Functional characterization of tektin-1 in motile cilia and evidence for TEKT1 as a new candidate gene for motile ciliopathies. (1460-2083 (Electronic)).
Takeuchi KA-O, Xu YA-O, Kitano M, Chiyonobu K, Abo M, Ikegami K, Ogawa S, Ikejiri M, Kondo M, Gotoh S et al: Copy number variation in DRC1 is the major cause of primary ciliary dyskinesia in the Japanese population. (2324-9269 (Electronic)).
Onoufriadis A, Shoemark A Fau - Munye MM, Munye Mm Fau - James CT, James Ct Fau - Schmidts M, Schmidts M Fau - Patel M, Patel M Fau - Rosser EM, Rosser Em Fau - Bacchelli C, Bacchelli C Fau - Beales PL, Beales Pl Fau - Scambler PJ, Scambler Pj Fau - Hart SL et al: Combined exome and whole-genome sequencing identifies mutations in ARMC4 as a cause of primary ciliary dyskinesia with defects in the outer dynein arm. (1468-6244 (Electronic)).
Raidt J, Wallmeier J, Hjeij R, Onnebrink JG, Pennekamp P, Loges NT, Olbrich H, Haffner K, Dougherty GW, Omran H et al: Ciliary beat pattern and frequency in genetic variants of primary ciliary dyskinesia. Eur Respir J 2014, 44(6):1579-1588.
Zou W, Lv Y, Liu ZI, Xia P, Li H, Jiao J: Loss of Rsph9 causes neonatal hydrocephalus with abnormal development of motile cilia in mice. Scientific reports 2020, 10(1):12435.
Fahy JV, Dickey BF: Airway mucus function and dysfunction. The New England journal of medicine 2010, 363(23):2233-2247.
Yu LY, Tseng TJ, Lin HC, Hsu CL, Lu TX, Tsai CJ, Lin YC, Chu I, Peng CT, Chen HJ et al: Synthetic dysmobility screen unveils an integrated STK40-YAP-MAPK system driving cell migration. Science advances 2021, 7(31).
Yamada KM, Sixt M: Mechanisms of 3D cell migration. Nature reviews Molecular cell biology 2019, 20(12):738-752.
Cattenoz PB, Monticelli S, Pavlidaki A, Giangrande A: Toward a Consensus in the Repertoire of Hemocytes Identified in Drosophila. Frontiers in cell and developmental biology 2021, 9:643712.
Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, Schiergens TS, Herrler G, Wu NH, Nitsche A et al: SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. (1097-4172 (Electronic)).
Li Z, Li Y, Han J, Zhu Z, Li M, Liu Q, Wang Y, Shi FD: Formyl peptide receptor 1 signaling potentiates inflammatory brain injury. Science translational medicine 2021, 13(605).
Leslie J, Millar BJ, Del Carpio Pons A, Burgoyne RA, Frost JD, Barksby BS, Luli S, Scott J, Simpson AJ, Gauldie J et al: FPR-1 is an important regulator of neutrophil recruitment and a tissue-specific driver of pulmonary fibrosis. LID - 125937 [pii] LID - 10.1172/jci.insight.125937 [doi] LID - e125937. (2379-3708 (Electronic)).
Ferguson AD, McKeever BM, Xu S, Wisniewski D, Miller DK, Yamin TT, Spencer RH, Chu L, Ujjainwalla F, Cunningham BR et al: Crystal structure of inhibitor-bound human 5-lipoxygenase-activating protein. Science 2007, 317(5837):510-512.
Patel N, Gonsalves CS, Yang M, Malik P, Kalra VK: Placenta growth factor induces 5-lipoxygenase-activating protein to increase leukotriene formation in sickle cell disease. Blood 2009, 113(5):1129-1138.
Liepelt A, Hohlstein P, Gussen H, Xue J, Aschenbrenner AC, Ulas T, Buendgens L, Warzecha KT, Bartneck M, Luedde T et al: Differential Gene Expression in Circulating CD14(+) Monocytes Indicates the Prognosis of Critically Ill Patients with Sepsis. Journal of clinical medicine 2020, 9(1).
Michael BD, Bricio-Moreno L, Sorensen EW, Miyabe Y, Lian J, Solomon T, Kurt-Jones EA, Luster AD: Astrocyte- and Neuron-Derived CXCL1 Drives Neutrophil Transmigration and Blood-Brain Barrier Permeability in Viral Encephalitis. Cell reports 2020, 32(11):108150.
Yago T, Liu Z, Ahamed J, McEver RP: Cooperative PSGL-1 and CXCR2 signaling in neutrophils promotes deep vein thrombosis in mice. Blood 2018, 132(13):1426-1437.
Sreeramkumar V, Adrover JM, Ballesteros I, Cuartero MI, Rossaint J, Bilbao I, Nacher M, Pitaval C, Radovanovic I, Fukui Y et al: Neutrophils scan for activated platelets to initiate inflammation. Science 2014, 346(6214):1234-1238.
Takahashi K, Koga K, Linge HM, Zhang Y, Lin X, Metz CN, Al-Abed Y, Ojamaa K, Miller EJ: Macrophage CD74 contributes to MIF-induced pulmonary inflammation. Respiratory research 2009, 10:33.
Qin S, Li W, Shi X, Wu Y, Wang C, Shen J, Pang R, He B, Zhao J, Qiao Q et al: 3044 Cases reveal important prognosis signatures of COVID-19 patients. Computational and structural biotechnology journal 2021, 19:1163-1175.
Mahmoodpoor A, Hosseini M, Soltani-Zangbar S, Sanaie S, Aghebati-Maleki L, Saghaleini SH, Ostadi Z, Hajivalili M, Bayatmakoo Z, Haji-Fatahaliha M et al: Reduction and exhausted features of T lymphocytes under serological changes, and prognostic factors in COVID-19 progression. Molecular immunology 2021, 138:121-127.
Yan SA-O, Wu GA-O: Is lymphopenia different between SARS and COVID-19 patients? (1530-6860 (Electronic)).
Schoeman DA-O, Fielding BA-O: Human Coronaviruses: Counteracting the Damage by Storm. LID - 10.3390/v13081457 [doi] LID - 1457. (1999-4915 (Electronic)).
Morris G, Bortolasci CC, Puri BK, Marx W, O'Neil A, Athan E, Walder K, Berk M, Olive L, Carvalho AF et al: The cytokine storms of COVID-19, H1N1 influenza, CRS and MAS compared. Can one sized treatment fit all? (1096-0023 (Electronic)).
van den Heuvel D, Jansen MA, Dik WA, Bouallouch-Charif H, Zhao D, van Kester KA, Smits-te Nijenhuis MA, Kolijn-Couwenberg MJ, Jaddoe VW, Arens R et al: Cytomegalovirus- and Epstein-Barr Virus-Induced T-Cell Expansions in Young Children Do Not Impair Naive T-cell Populations or Vaccination Responses: The Generation R Study. (1537-6613 (Electronic)).
Griffin DE: The Immune Response in Measles: Virus Control, Clearance and Protective Immunity. Viruses 2016, 8(10).
Yang Y, Liangjian K, Li L, Yongjian W, Zhong B, Huang X: Distinct mitochondria-mediated T cells apoptosis responses in children and adults with COVID-19. The Journal of infectious diseases 2021.
Jafarzadeh A, Jafarzadeh S, Nozari P, Mokhtari P, Nemati M: Lymphopenia an important immunological abnormality in patients with COVID-19: Possible mechanisms. Scandinavian journal of immunology 2021, 93(2):e12967.
Vafadarnejad E, Rizzo G, Krampert L, Arampatzi P, Arias-Loza AP, Nazzal Y, Rizakou A, Knochenhauer T, Bandi SR, Nugroho VA et al: Dynamics of Cardiac Neutrophil Diversity in Murine Myocardial Infarction. Circulation research 2020, 127(9):e232-e249.
Kastenmuller W, Brandes M, Wang Z, Herz J, Egen JG, Germain RN: Peripheral prepositioning and local CXCL9 chemokine-mediated guidance orchestrate rapid memory CD8+ T cell responses in the lymph node. Immunity 2013, 38(3):502-513.
Cheung CY, Poon LL, Ng IH, Luk W, Sia SF, Wu MH, Chan KH, Yuen KY, Gordon S, Guan Y et al: Cytokine responses in severe acute respiratory syndrome coronavirus-infected macrophages in vitro: possible relevance to pathogenesis. Journal of virology 2005, 79(12):7819-7826.
Wong CK, Lam CW, Wu AK, Ip WK, Lee NL, Chan IH, Lit LC, Hui DS, Chan MH, Chung SS et al: Plasma inflammatory cytokines and chemokines in severe acute respiratory syndrome. Clinical and experimental immunology 2004, 136(1):95-103.
Broxmeyer HE, Sherry B, Cooper S, Lu L, Maze R, Beckmann MP, Cerami A, Ralph P: Comparative analysis of the human macrophage inflammatory protein family of cytokines (chemokines) on proliferation of human myeloid progenitor cells. Interacting effects involving suppression, synergistic suppression, and blocking of suppression. Journal of immunology 1993, 150(8 Pt 1):3448-3458.
Sarris AH, Broxmeyer HE, Wirthmueller U, Karasavvas N, Cooper S, Lu L, Krueger J, Ravetch JV: Human interferon-inducible protein 10: expression and purification of recombinant protein demonstrate inhibition of early human hematopoietic progenitors. The Journal of experimental medicine 1993, 178(3):1127-1132.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija RA-O: Integrating single-cell transcriptomic data across different conditions, technologies, and species. (1546-1696 (Electronic)).
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, 3rd, Hao Y, Stoeckius M, Smibert P, Satija R: Comprehensive Integration of Single-Cell Data. Cell 2019, 177(7):1888-1902 e1821.
Subramanian A, Tamayo P Fau - Mootha VK, Mootha Vk Fau - Mukherjee S, Mukherjee S Fau - Ebert BL, Ebert Bl Fau - Gillette MA, Gillette Ma Fau - Paulovich A, Paulovich A Fau - Pomeroy SL, Pomeroy Sl Fau - Golub TR, Golub Tr Fau - Lander ES, Lander Es Fau - Mesirov JP et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. (0027-8424 (Print)).
Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C: Single-cell mRNA quantification and differential analysis with Census. Nature methods 2017, 14(3):309-315.
Aibar SA-O, González-Blas CA-O, Moerman TA-O, Huynh-Thu VA, Imrichova HA-O, Hulselmans GA-O, Rambow F, Marine JC, Geurts P, Aerts JA-O et al: SCENIC: single-cell regulatory network inference and clustering. (1548-7105 (Electronic)).
Yu GC, Wang LG, Han YY, He QY: clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omics-a Journal of Integrative Biology 2012, 16(5):284-287.
Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, Fernandez R, Akbarpour M, Chen CI, Ren Z et al: Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis. American journal of respiratory and critical care medicine 2019, 199(12):1517-1536.
van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, Verga JA, Stephansky J, Pastika TJ, Lombardi Story J et al: Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Cell 2019, 176(6):1265-1281 e1224.

FigS101.jpg
Fig S1. Supplementary images related to Fig 2.A~B: The frequency of cell communication between different cell types in the healthy group and the COVID-19 group. C~D: The frequency of cell communication between AT1 cells and other cell types in the healthy group and the COVID-19 group. The thicker the line, the higher the communication frequency, the thinner the line, the lower the communication frequency
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Fig S2. Supplementary images related to Fig 4.The expression changes of epithelial cell markers MAL2, CLDN4, TJP1 and mesenchymal cell marker VIM and CDH2 in healthy groups and COVID-19 patients.
FigS301.jpg
Fig S3. Supplementary images related to Fig 5.A: The expression level of ACE2 gene in blood. B: The expression level of TMPRSS2 gene in blood. C: The top 20 biological processes enriched by significantly changed receptor and ligand molecules in the blood. D: The enriched heat map shows the significantly changed ligand and receptor genes corresponding to the top 20 biological processes in the blood.
FigS401.jpg
Fig S4. Supplementary images related to Fig 6.A: The expression level of ACE2 gene in bone marrow. B: The expression level of TMPRSS2 gene in bone marrow. C: The top 20 biological processes enriched by significantly altered receptor and ligand molecules in the bone marrow. D: The enriched heat map shows the significantly changed ligand and receptor genes corresponding to the top 20 biological process in the bone marrow.
TableS1.xlsx
Table S1. Differences in cell communication in the blood.
TableS2.xlsx
Table S2. Differences in cell communication in the bone marrow.
TableS3.xlsx
Table S3. Primary antibodies used in immunohistochemical staining (IHC).

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Ligand/Receptor-mediated Cell Communication Alteration Causes the Lung Inflammation Microenvironment and the Redistribution of Inter-tissue Immune Cells in COVID-19 Patients

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Abstract

Figures

1 Introduction

2 Results and Discussion

2.1 Single-cell sequencing process and clinical characteristics of two COVID19 patients

2.2 Single cell atlas and molecular changes of alveolar target cells

2.3 Dysregulated ligands and receptors cause the disorder of inflammatory genes and immune cell migration

2.4 Lung inflammation drives the increase of fibrosis and mucus accumulation

3 Conclusion

4 Methods And Materials

4.1 Sample information and tissue collection

4.2 Tissue staining

4.3 Serological testing

4.4 Tissue dissociation and single cell suspension preparation

4.5 Single cell RNA sequencing library preparation

4.6 Generation of single-cell gene expression matrices and quality control

4.7 Single cell data analysis

4.8 Differentially Expressed Gene Analysis

4.9 Visualization and statistics

4.10 Data availability

Declarations

References

Supplementary Files

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