Single-cell RNA-seq of myasthenia gravis reveals transcriptional heterogenity and dysfunction of immune cell populations

doi:10.21203/rs.3.rs-4918125/v1

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Single-cell RNA-seq of myasthenia gravis reveals transcriptional heterogenity and dysfunction of immune cell populations

https://doi.org/10.21203/rs.3.rs-4918125/v1

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The immune system imbalance and immune cell dysfunction of myasthenia gravis (MG) have been thoroughly studied, but the composition and function of immune cell subsets at single cell level in thymus and peripheral blood remain unclear. Here, we performed single-cell RNA sequencing with 9701 and 23846 cells respectively originated from the peripheral blood and thymus samples of MG patients, and 6 930 cells from the peripheral blood of healthy controls, and identified 4 major cell populations of T cells, B cells, myeloid cells, and NK cells, as well as their 15 cell subpopulations. We found an absolute predominance of T cells in the thymus and peripheral blood of MG patients, and the proportions of memory B cells in both plasma and thymus are significantly increased while the number of naïve B cells is significantly reduced in MG patients compared to healthy controls. Besides, the plasma cells in the peripheral blood of MG patients had the strongest interactions with other cells, while monocytes in the thymic tissue had the strongest interactions with other cells. On the whole, our research clarify the cellular heterogeneity in the pathogenesis of MG, and characterize the immune microenvironment of thymic tissues in MG patients.

Biological sciences/Immunology/Gene regulation in immune cells

Biological sciences/Genetics/Immunogenetics

Myasthenia gravis

Single-cell RNA sequencing

Immune cell

Thymus

Peripheral blood

Myasthenia gravis (MG) is an autoimmune disease that damages neuromuscular transmission mediated by pathogenic antibodies, with a global incidence of 150–250 per million people^[1]. The main clinical manifestations of MG are partial or systemic skeletal muscle weakness and fatigue, which can be manifested as ptosis, dysphagia, weakness in speech, and even respiratory failure caused by respiratory muscle involvement, which has a great impact on the quality of life and even life span of patients^[2–3].

At present, it is generally believed that the cause of MG is in the thymus, in which about 80% MG patients with thymic hyperplasia, about 20% of patients with thymic tumors^[4–5]. After thymectomy, the symptoms of MG patients were significantly improved and the titer of acetylcholine receptor (AChR) antibody decreased significantly^[6]. From the perspective of thymic pathology, it can be found that compared with normal thymus, the thymic medulla of early-onset MG is filled with a large number of B cells, lymphoid follicles, activated T cells and high endothelial vein dilatation, showing a state of inflammation^[7]. In the presence of MG, the immune function of the thymus with abnormal structure will inevitably change, the normal immune dynamic balance of the human body will be out of balance, and MG patients will be accompanied by other autoimmune diseases, such as Graves disease, Hashimoto thyroiditis and so on^[8].

Although the main types of immune cells involved in the pathogenesis of MG have been elucidated^[9], the characteristics and interactions of these cells at the single cell level in thymus and peripheral blood are still lacking. In addition, although AChR antibodies are essential for myasthenia gravis, levels of AChR antibodies do not correlate with the severity of the disease or the effectiveness of treatment, indicating that we still lack understanding of the underlying mechanisms of the disease.

Single-cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities to uncover the cellular heterogeneity at high resolution. Previous study constructed a comprehensive map of thymoma by scRNA-seq, and identified the ectopic expression of neuromuscular molecules in MG type thymoma^[10]. There are also studies on the function of immune cells in peripheral blood of patients with MG by single cell transcriptome technique^[11–12]. In our current study, we performed scRNA to profile immune cell subsets from peripheral blood mononuclear cells (PBMCs) and thymic tissues of MG patients, as well as gender- and age-matched healthy controls. Our study depicted the transcriptional profiles of immune cells of thymus tissue and PBMCs from MG patients at the single-cell level, characterized the cytopathic mechanisms through the differences in the composition of immune cell subpopulations, cellular differentiation and development, inter-cellular communication networks, and cellular functions, and found that there is significant transcriptional heterogeneity in the immune microenvironment of MG patients, which will provide support for the elucidation of MG pathogenesis, and the search for new diagnostic and therapeutic targets.

1. Patients

The patients enrolled were among the inpatients of the department of thoracic surgery and the Medical Check-up Center of Beijing Hospital (Beijing, China), including 2 age- and sex-matched control individuals. MG was diagnosed according to the Chinese guidelines for diagnosis and treatment of MG (2020 version).

Participants were excluded if they had: i) any other neuromuscular conduction disorders, such as muscular dystrophy syndrome; ii) autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, etc.; iii) neuromuscular conduction disorders caused by long-term use of certain anticholinesterase drugs or antiarrhythmic drugs (such as β-blockers); iv) other malignant diseases, acute and chronic inflammation and/or hepatorenal dysfunction; or v) MG caused by other causes, such as congenital muscular dystrophy.

2. Sample collection and single-cell suspension preparation

Thymic tissue and venous blood were collected from 2 patients with MG within 24 hours after admission. After the thymus tissue specimens were cut up and digested by trypsin, the thymic tissue and venous blood of the above 2 patients with MG were centrifuged at 400 g for 5 min. Then 10 X red blood cell lysis buffer (Solarbio, Beijing, China) was added to the cell precipitation, which was incubated on ice for 10 min to remove red blood cells. The cell pellets of thymic tissue and venous blood were resuspended in Hank’s buffer before the mononuclear cells were isolated using Ficoll-Paque (TBD, Tianjin, China) according to the manufacturer’s instructions.

3. ScRNA-seq library preparation and sequencing

The ScRNA-seq library was prepared with the 10×Genomics Chromium single-cell protocol using the v3 reagent kit (10×Genomics, Pleasanton, CA, USA), according to the manufacturer' s instructions. 90 μL cellular suspension, 40 μL barcoded Single Cell 5' Gel Beads, and 270 μL Partitioning Oil were loaded onto a Chromium Chip A to generate single-cell gel bead-in-emulsions (GEMs). GEM-reverse transcriptions (GEM-RTs) were performed in a Veriti 96-well thermal cycler (Thermo Fisher Scientific, Massachusetts, USA) together with the RT master mix. After RT, cDNAs were amplified (98°C for 45 sec; 13-18 cycles of 98°C for 20 sec, 67°C for 30 sec, 72°C for 1 min; and 72°C for 1 min) and then used for 5' gene expression library construction with the Chromium Single Cell 5' Library Construction Kit (10×Genomics, Pleasanton, CA, USA). The single-cell RNA library was sequenced on an Illumina NovaSeq 6000 with a read length of 150 bp paired-end reads.

4. ScRNA-seq data analysis

4.1 Cell filtering and quality control

For scRNA-seq raw data, cell ranger single-cell software suite (10×Genomics, v 3.1.0) with default parameters was used to compare scRNA-seq reads to Homo_sapiens.GRCh38 (V3.0.0) reference genome. Then, the unique molecular identifiers (UMIs) were mapped to the genes, and the barcodes were mapped to the cells. These indexes were further analyzed by R (v3.6.3) package Seurat (v 3.1.2). Only those genes expressed in at least three cells were retained. Low-quality cells that meet one of the following thresholds were further excluded: 1) the number of expressed genes was less than 500 or larger than 3500; 2) more than 7% of the UMIs were mapped to mitochondrial or ribosomal genes.

4.2 Dimensional reduction and visualization

After quality control, variable genes for each sample were identified using the FindVariableGenes function in Seurat. Then, single-cell transcriptomes data were log-normalized and scaled for further analysis. Principal component analysis (PCA) for dimension reduction was performed based on Seurat (R packet version 3.0.0). Clustering was further performed with the Seurat FindClusters function, and the Uniform manifold approximation and projection (UMAP) was used for visualization.

4.3 Differential expression analysis

Cluster-specific marker genes for transcriptional subpopulations were identified by the FindAllMarkers Seurat function with Wilcoxon rank-sum test. For specific cluster comparisons between MG patients and HCs, the DESeq2 package (v1.26.0) was used to detect differentially expressed genes (DEGs). Significant DEGs were filtered by |log fold change| > 0.5 and P value < 0.05.

4.4 Gene functional annotation

To compare the functional profiles of different clusters, the clusterProfiler package (v 3.14.3) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of significant DEGs.

5. Pseudotime-trajectory analysis

Monocle3 was used to construct the pseudotime-trajectories for CD4⁺ cells and CD8⁺ T cells. The positive significant marker genes identified by FindAllMarkers Seurat function were used to sort cells in a temporal differentiation order. Dimension reduction was performed using DDRTree from Monocle3. A diffusion map was generated using the destiny (v3.0.1) R package.

6. RNA velocity analysis

Velcyto.py (v0.17, https://github.com/velocyto-team/velocyto.py) was used to quantify the spliced and unspliced reads of each 10×bam file generated by the cell range with the default parameters. Expressed repetitive elements in the GRCh38 reference genome were masked, and the bed file for the masked interval was generated with the UCSC genome browser (https://genome.ucsc.edu/). Loom files were then processed with velocyto.R (v0.6 https://github.com/velocyto-team/velocyto.R). B cells were divided into 20 cell groups for further analysis, and the final RNA velocity plots was drawn by Monocle DDRTree embedding method, and cell to cell distance was calculated with the first 50 PCs. Parameters were set to default unless stated otherwise.

7. Statistical analysis

The marker genes of transcriptional subpopulations were determined by FindAllMarkers Seurat function with Wilcoxon rank-sum test. One-way analysis of variance (ANOVA) followed by Newman–Keul post hoc test were used to analyze the differences among multiple groups. The differences between paired samples were analyzed by Wilcox signed-rank test. Nonparametric Spearman test was used for correlation analysis. Statistical analysis and graphic processing were carried out by R (v3.6.3) software. Unless otherwise stated, the data are considered significant in terms of *P < 0.05, **P < 0.01, and ***P < 0.001.

1. Composition and classification of immune cells in thymus tissue and peripheral blood of MG patients

To explore the immune pathogenesis and the immune microenvironment of MG, we performed scRNA-seq to study the transcriptomic profiles of thymus tissue and PBMCs ( MG-T and MG-B) in 2 patients with MG as well as PBMCs from 2 gender- and aged-matched healthy controls (HC-B). After quality control and cell filtering, 23 846 and 9 701 cells respectively originated from MG-T and MG-B, and 6 930 cells from HC-B were used for analysis. According to the classical genetic markers, all immune cells were divided into four categories: T cells, B cells, myeloid cells and NK cells (Fig. 1A, 1C), of which T cells accounted for the vast majority. Then, we compared the proportion of four immune cell groups in different samples. Since the difficulty of obtaining absolutely normal thymus tissue intraoperatively, we cited data from other studies in the literature on thymus tissue from healthy controls (HC-T) as a control group^[13]. Obviously, each sample has a different frequency of cell distribution (Fig. 1B). The proportion of T cells in MG-T group was much higher than that in other groups including HC-T group. Compared with HC-B group, the proportions of T cells and B cells were higher and the proportions of myeloid cells and NK cells were lower than those in MG-B group. It can be seen that most of the differentially expressed genes were involved in T cell activation and differentiation signal pathways in both MG-T VS MG-B group and MG-B VS HC-B group, which further indicated that T cells are not only absolutely dominant in terms of numbers in the diseased tissues of MG, but also play a non-negligible role in terms of functions (Fig. 1D). We further clarified whether there was cell-specific expression of these MG-associated genes, and we found that the human leukocyte antigen (HLA) gene was the strongest associated risk factor for MG, similar to previous studies. Moreover, HLA-DQB1, HLA-DRA, HLA-DRB1, and HLA-DQA1 were predominantly expressed in B-cells and myeloid cells, suggesting a pathogenic antigen presentation function. Genes with the function of regulating T-cell activation were most highly expressed in T cells (e.g., CTLA4 gene), while the expression levels of genes such as AP2B1, HSPA8, and AP1B1 were significantly different in the MG-T, MG-B, and HC-B groups (Fig. 1E).

2. Characterization of T cells derived from scRNA-seq data

We regrouped the T cells from MG-T, MG-B, and HC-B into four populations, including CD4⁺T cells, CD8⁺T cells, double negative T cells (DNT) and double positive T cells (DPT). On this basis, we focus on the analysis of CD4⁺T cells and CD8⁺T cells, in which CD4⁺T cells can be further divided into naïve, Th1/17, Treg, and Tfh cells, CD8⁺T cells can be divided into naïve, cytotoxic and exhausted CD8⁺T cells (Fig. 2A).

The proportions of T cell subsets in MG-B and HC-B were very similar, but the proportions of T cell subsets in MG-T were significantly different from those of MG-B and HC-T groups. In particular, the proportions of naïve CD8⁺T cells and DPT in MG-T were much higher than those in MG-B and HC-T groups, indicating that there were a large number of immature T cells in the thymus of patients with MG, which were the immune reserve of the human body (Fig. 2B).

Figure 2C demonstrated that most of the marker genes were highly expressed only in their corresponding cluster. For instance, FOXP3 was a recognized marker gene in Treg cells, which was only highly expressed in CD4⁺Treg cells, but low expressed in other clusters, which further proves the accuracy of cell clustering.

Then, we used the Monocle 3 method to construct the differentiation trajectory of CD4⁺ T cells and CD8⁺ T cells. With the development of the differentiation process, CD4⁺CD8^- T cells gradually developed from naïve state to Th1/17, Treg, and Tfh states. However, almost all the CD4⁺CD8^- T cells in MG-T remain naïve state, resulting in disruption of immune cell activation and differentiation processes (Fig. 2D).

In CD4^-CD8⁺ T cells, trajectory analysis revealed that naïve CD8⁺ T cells differentiated into cytotoxic CD8⁺ T cells in MG-B, while in MG-T, a small number of naïve CD8⁺ T cells differentiated into exhausted CD8⁺ T cells. This may contribute to the imbalance of thymic immune function in MG patients. In addition, the KEGG enrichment analysis of up-regulated and down-regulated genes of MG-T VS MG-B showed CD4⁺ was highly correlated with NOD-like receptor signaling pathway and primary immunodeficiency, and CD8⁺ was highly correlated with NOD-like receptor signaling pathway and Fc gamma R-mediated phagocytosis, indicating that MG-T affected MG through many immune-related pathways (Fig. 2E, 2F)

3. Characterization of B cells derived from scRNA-seq data

Similarly, we subdivided B cells into 8 subgroups and named them according to the reported marker genes. These included 4 memory B cell clusters (0, 1, 2, 3), 3 naïve B cell clusters (4, 5, 6), and 2 plasma cell clusters (7, 8) (Fig. 3A, C).

Compared with HC-T, the proportion of memory B cells in MG-T increased significantly, while the number of naïve B cells decreased significantly. Similarly, compared with HC-B, the proportion of memory B cells in MG-B increased significantly, while the number of naïve B cells decreased significantly (Fig. 3B). Meanwhile, compared with HC-B, we identified 460 up-regulated and 399 down-regulated genes of B cells in MG-B (Fig. 3D). Furthermore, RNA velocity analysis of B cells across various samples showed that the differentiation velocity of cluster 8 in MG-T and MG-B was faster than that of HC-B (Fig. 3E).

4. Myeloid cell clustering analysis of MG patients and healthy controls

Next, myeloid cells were sub-grouped into 7 clusters, including three classical monocytes (clusters 0, 1, and 3), nonclassical monocytes (clusters 2), intermediate monocytes (clusters 5), cDC (clusters 4), and pDC (clusters 6) (Fig. 4A). And, there were significantly fewer myeloid cells cells in MG-T. The proportion of myeloid cell subtypes shows significant heterogeneity (Fig. 4B). The number of classical monocytes was significantly lower and the number of non-classical monocytes was significantly higher in MG-B compared to HC-B. DCs were predominant in HC-T, whereas monocytes and DCs each accounted for 50% of the total in MG-T.

Figure 4C demonstrated that the marker genes of monocytes and DCs were specifically highly expressed in the corresponding clusters. Compared with HC-B, we identified 854 up-regulated and 640 down-regulated genes of myeloid cells in MG-B. Moreover, the dys-regulated genes were mainly involved in the secretion of cytokines (Fig. 4D). The dys-regulated genes of MG-T VS MG-B were mainly involved in antigen presentation and monocyte differentiation (Fig. 4E). Furthermore, we analyzed the expression level of the MG-related genes in myeloid cell subpopulations and found that there was also cellular subpopulation heterogeneity (Fig. 4F). Genes such as HLA-DQA1, HLA-DQB1, HLA-DRA, and HLA-DRB1, whose expression was significantly up-regulated in MG-T, were predominantly expressed in intermediate monocytes, and genes such as AP1S3 and AP1G1, which were significantly over-expressed in the patients compared to controls, were predominantly distributed in plasmacytoid dendritic cells.

5. Cell-to-cell communication network analysis among samples

In addition to intracellular information, scRNA-seq can also explore interactions between cells by integrating ligand and receptor information. We analyzed the possible interactions between the major immune cells in the MG patient group and the control group and found that plasma cells interacted most strongly with other cells in the PBMC of MG patients (Fig. 5A). Whereas in MG-T samples, monocytes showed stronger interactions with DCs (Fig. 5B). Compared to HC-B, CXCL, GAS and CD30 signaling pathways were more enriched in MG-B (Fig. 5C). While, COMPLEMENT, XCR and IFN-II signaling pathways were more enriched in MG-T (Fig. 5D). Finally, we focused on analyzing the role of MG-related signaling pathways CD40 and Interleukin-16 (IL-16) pathway among various cell subpopulations. Compared with HC-B, Th1/17 cells and Treg cells interacted more strongly with NK cells and monocytes through CD40 signaling pathway, and this interaction of Treg cells was also reflected in MG-T. In MG-B and HC-B, the IL-16 signaling pathway is ubiquitous across cell populations, while in MG-T, this pathway communicates more strongly between T cells and B cells (Fig. 5E).

MG is a T-cell-dependent autoimmune disease mediated by acetylcholine receptor antibodies^[14]. In the present study, we are the first to provided a detailed molecular description of immune cells in MG patients at the single-cell level, charting differences in proportion and molecular state of immune cells in thymic tissue and blood.

During the period of embryonic development, the lymphatic progenitor cells experienced the stage of differentiation from DNT to DPT in the thymus, and eventually differentiate into mature T cells with immune function, which only expressed CD4⁺ or CD8^+[15–16]. Then they leave the thymus and migrate to the surrounding lymphoid organs, and exert the functions of cellular immunity and immune regulation^[17]. We found the proportions of naïve CD8⁺ T cells and DPT in MG-T were much higher than those in MG-B and HC-T groups. In addition, the differentiation trajectory demonstrated that almost all the CD4⁺CD8⁻ T cells in MG-T remain naïve state. The naïve CD4⁺ T cells, naïve CD8⁺ T cells and DPT cells are mature T cells that have never been stimulated by antigens. If the proportion of naïve cells is too large, it can not play an effective role in cellular immunity and immune regulation, which may be one of the pathogenesis of MG.

However, the differentiation of CD8⁺ T cells was slightly different from that of CD4 cells. Naïve CD8⁺ T cells differentiate into cytotoxic CD8⁺T cells by short-term antigen stimulation, and then enter the peripheral blood to kill immune disorder cells, providing long-term protection for the human body^[18]. On the other hand, the naïve CD8⁺T cells in the thymus gradually enter a state of dysfunction due to long-term exposure to antigens and inflammation, which is mainly characterized by the loss of killing function. Therefore, T cell exhaustion is a common feature of MG and will become a major target in immunotherapy.

Thus, overall, there were two characteristics of T cells in the thymic tissues of MG patients: (1) A high percentage of naïve CD8⁺ T cells and double-positive T cells, and almost all CD4⁺ T cells remained naïve state, suggesting that most of the T cells in the thymic tissues of MG patients were in the juvenile stage. The immune homeostasis was disrupted for the interruption of immune cell activation and differentiation processes; (2) Some naïve CD8 ⁺ T cells directly differentiated into exhausted CD8⁺ T cells, which may also contribute to the imbalance of immune function. KEGG enrichment analysis also further indicated that the thymus of MG patients affected MG through the involvement of T cells in multiple immune-related pathways.

No matter MG-B compared with HC-B, or MG-T compared with HC-T, the proportion of memory B cells increased significantly, while the proportion of naïve B cells decreased significantly. It is suggested that more naïve B cells in thymus tissue and plasma of MG patients are stimulated by antigens and activated to differentiate into memory B cells, resulting in more rapid, more efficient and more specific humoral immunity^[19]. RNA velocity analysis showed that naïve B cells in thymus tissue and blood of MG patients had a stronger tendency to differentiate into plasma cell phenotype and higher degree of activation, which may be related to the production of more acetylcholine receptor antibodies in MG patients.

We found that the monocytes and DC cells in the thymus showed the greatest heterogeneity. First of all, the number of myeloid cells in the thymus was greatly reduced, which further proved that lymphocytes play a dominant role in the thymus. Secondly, DC accounted for the vast majority of HC-T, while monocytes and DC accounted for half of MG-T, in which the classical monocytes contributed to the host's pro-inflammatory defense mechanism^[20]. At the same time, we found that compared with HC-T, the most significant signal pathway changes in MG patients were inflammation-related signal pathways, such as positive regulation of cytokine production, tumor necrosis factor production, type I interferon signal pathway. Therefore, the role of inflammation in the pathogenesis of myasthenia gravis can not be ignored. The results of immune cell communication analysis also showed that plasma cells interacted most with other cells in peripheral blood of MG patients, and monocytes interacted most with other cells in thymus tissue of MG patients, which further proved the antibody secretion of plasma cells in peripheral blood and the role of monocytes in inflammatory pathways^[21–23].

There are also some limitations in our study. First, inadequate sample size of only 2 patients. The heterogeneities between thymus tissues and peripheral blood in MG patients is needed further verification with flow analysis and so on. Second, this study only conducted scRNA-seq, which did not perform experiments, such as flow cytometry or immunofluorescence, to verify the positive result of scRNA-seq. Third, lack of integrated analysis of transcriptome and proteomics.

Taken together, this preliminary study present a comprehensive transcriptome map of immune cells in diseased tissues and peripheral blood of patients with MG at the single cell level. Our study contributes to a better understanding of the underlying pathogenesis of MG, which is an interesting candidate for further studies addressing its potential role in the diagnosis and treatment of MG.

Funding information

This work was supported by the National High Level Hospital Clinical Research Fund (Grant BJ-2023-077).

Competing interests

The authors declare that they have no competing interests.
Ethics approval

The study was approved by the ethics committee of Beijing hospital (2023BJYYEC-222-02).
Consent to Participate

All participants provided written informed consent.
Consent to Publish

Obtained.
Data and/or Code availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Authors’ contribution statements

LH.Z. contributed to the design of the project. Y.T., HB.Y. and T.Z provided clinical samples and relevant information. YY.G. performed sequencing and data analysis. SY.Y. performed the validation of sequencing. QJ.W. performed experiments and data analysis, and wrote the manuscript.

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There is NO conflict of interest to disclose. The authors declare that they have no competing interests.

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Single-cell RNA-seq of myasthenia gravis reveals transcriptional heterogenity and dysfunction of immune cell populations

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