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Title Page 2

Abstract 5

Contents 8

Technical Terms and Abbreviation 12

1. Introduction 14

2. Understanding Xenopus mucociliary epithelium development using scRNA-seq 18

2.1. Introduction 18

2.2. Materials 20

2.2.1. Sample preparation 20

2.2.2. Single cell transcriptome analysis 20

2.2.3. Gene Ontology (GO) enrichment analysis 20

2.2.4. Gene correlation network analysis with Cytoscape 20

2.2.5. Xenopus embryo immunostaining 20

2.2.6. Cross species single cell transcriptome data 21

2.3. Results 22

2.3.1. Clustering and cell population change of developing MCE scRNA-seq 22

2.3.2. Differentially expressed gene analysis of each cell type at NF stage 27 data 24

2.3.3. Integrated data show clustering of same cell type from different stage 26

2.3.4. Gene correlation analysis with Xenopus laevis scRNA-seq data 28

2.3.5. Time course immunostaining using Xenopus laevis embryo can verify differentiation of SSC 30

2.3.6. Cross species analysis with human and mouse airway MCE show conserved transcriptomic signatures 31

2.4. Discussion 33

3. Convergent differentiation of multiciliated cells 34

3.1. Introduction 34

3.2. Materials and Methods 36

3.2.1. Collection of data used for the analysis 36

3.2.2. Preprocessing and normalization 36

3.2.3. Separation of MCCs from tissue data 36

3.2.4. Differentially expressed gene (DEG) analysis 36

3.2.5. Analysis for associated biological function with the DEGs 37

3.2.6. Analysis of pseudotime trajectory in MCC differentiation 37

3.2.7. Cell integration analysis for batch effect removal 37

3.3. Results 39

3.3.1. MCCs were distinctly identified in both mouse and human tissues using well-known cilia marker genes 39

3.3.2. Different expression patterns of MCC by their tissue and species origin 41

3.3.3. MCCs show similar expression of ciliogenesis or ciliary function-related genes 43

3.3.4. MCCs from distinct tissues show similar expression with motile ciliopathy genes 45

3.3.5. Marker genes of MCC progenitor cells in each tissue are expressed in the MCCs 48

3.3.6. Precursor cells from different tissues convergently differentiated into MCCs 50

3.4. Discussion 55

4. Roles of microglia subtypes during Xenopus spinal cord regeneration 57

4.1. Introduction 57

4.2. Materials and Methods 59

4.2.1. Preprocessing of scRNA-seqdata 59

4.2.2. Differentially expressed gene (DEG) analysis 59

4.2.3. Analysis for associated biological function with DEGs 59

4.2.4. Tail amputation of Xenopus tadpole 59

4.2.5. HCR staining 60

4.2.6. Removing batch effect by integration 60

4.3. Results 61

4.3.1. Microglia population increased during Xenopus tropicalis tail regeneration 61

4.3.2. Microglia subtypes 1 express genes related to tail regeneration 64

4.3.3. Integration analysis of Xenopus tropicalis and Xenopus laevis 66

4.3.4. Fluorescent imaging to verify different population change of microglia subtype 69

4.4. Discussion 71

5. Conclusion 72

6. References 75

Curriculum Vitae (2024.05.22) 84

List of Tables 11

Table 3.1. The list of scRNA-seq data used in this study 38

List of Figures 10

Figure 2.1. Single cell clustering of developing Xenopus MCE 23

Figure 2.2. Comparison of highly expressed genes in each cell type and their function at stage... 25

Figure 2.3. Integrating UMAP cluster of stage 24 and stage 27 data 27

Figure 2.4. Gene expression correlation network across the MCE development 29

Figure 2.5. Small secretory cell immunostaining during Xenopus MCE development 30

Figure 2.6. Gene expression heatmap of 1039 DEGs from human, mouse, and Xenopus MCE 32

Figure 3.1. Identification of MCCs in the airway, ependyma, and oviduct tissues 40

Figure 3.2. MCCs are clustered based on their species and tissue origins 42

Figure 3.3. Differences among MCCs by their tissue origin diminish when focusing solely... 44

Figure 3.4. Gene expression profiles related to known motile ciliopathies and the function... 47

Figure 3.5. Dot plot and heatmap of tissue specifically expressed genes 49

Figure 3.6. Pseudotime analysis was conducted to investigate the differentiation of MCCs in the… 52

Figure 3.7. Integration of MCCs and the precursor cells from human ALI culture and fetal spinal... 54

Figure 4.1. Clustering of Xenopus tropicalis time course spinal cord regeneration data 62

Figure 4.2. Marker gene plot to annotate each cell type 63

Figure 4.3. Distinctive gene expression of two microglia subtypes during spinal cord regeneration 65

Figure 4.4. Integrative analysis of Xenopus laevis and tropicalis spinal cord regeneration data 67

Figure 4.5. Tracking Xenopus laevis cells by sample origin to see the population change 68

Figure 4.6. Increasing Microglia subpopulation during Xenopus tropicalis spinal cord... 70

초록보기

 Since the widespread use of single-cell RNA-sequencing (scRNA-seq) technology, researchers have conducted sequencing with various species and conditions of tissues, leading to an accumulation of diverse scRNA-seq datasets. However, integrating and comparing these data poses challenges due to differences in experimental conditions of each data. Combining multiple datasets for comparison is crucial in single-cell analysis to make them comparable, including normalization and batch effect removal using consistent criteria. Particularly for multi-species data, aligning transcriptomic annotations across species is crucial because of the genomic and transcriptomic differences of each species.

This dissertation focuses on addressing these challenges to compare scRNA-seq data of similar cell types across various conditions. Initially, I analyzed the temporal changes in cell composition during the development of mucociliary epithelium (MCE) in Xenopus laevis, comparing them over time and compared the gene expression with MCEs from human and mouse airways. These findings demonstrate the similarities in cell composition between mammalian airways and Xenopus MCE tissue, as well as the gene expression in individual cell types.

Next, I compared the transcriptomes of multiciliated cells (MCCs), a common cell type found in various organs, including the airway, oviduct, and ependyma. To ensure a fair comparison, scRNA-seq data of each tissue from human and mouse were subjected to unsupervised comparison, revealing shared core MCC marker genes across different organs while tissue-specific gene expression differences still exist, particularly in precursor cell markers. Further comparison using developmental time course data from human airway and ependyma confirmed convergent differentiation of MCCs despite differences in precursor cell populations during development.

Lastly, I examined the changes occurring during spinal cord regeneration in Xenopus tropicalis tadpoles and compared them with data from the closely related species Xenopus laevis. This comparison revealed the emergence of two microglia subtypes during spinal cord regeneration, with a significant increase in the population of one subtype. By gene ontology analysis using marker genes for each subtype, I observed that the subtype involved in regeneration exhibited pronounced expression of genes related to morphogenesis. Validation through HCR staining of subtype marker genes confirmed distinct population changes of subtypes in Xenopus tropicalis tadpoles.

In summary, this study highlights the importance of normalization, batch effect removal, and cross-species transcriptomic annotation alignment in comparing scRNA-seq data, providing insights into the similarities and differences in cell composition and differentiation processes across various conditions and species.

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