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Single-cell sequencing of the vermiform appendix during development identifies transcriptional relationships with appendicitis in preschool children

BMC Medicine volume 22, Article number: 383 (2024) Cite this article

Abstract

Background

The development of the human vermiform appendix at the cellular level, as well as its function, is not well understood. Appendicitis in preschool children, although uncommon, is associated with a high perforation rate and increased morbidity.

Methods

We performed single-cell RNA sequencing (scRNA-seq) on the human appendix during fetal and pediatric stages as well as preschool-age inflammatory appendices. Transcriptional features of each cell compartment were discussed in the developing appendix. Cellular interactions and differentiation trajectories were also investigated. We compared scRNA-seq profiles from preschool appendicitis to those of matched healthy controls to reveal disease-associated changes. Bulk transcriptomic data, immunohistochemistry, and real-time quantitative PCR were used to validate the findings.

Results

Our analysis identified 76 cell types in total and described the cellular atlas of the developing appendix. We discovered the potential role of the BMP signaling pathway in appendiceal epithelium development and identified HOXC8 and PITX2 as the specific regulons of appendix goblet cells. Higher pericyte coverage, endothelial angiogenesis, and goblet mucus scores together with lower epithelial and endothelial tight junction scores were found in the preschool appendix, which possibly contribute to the clinical features of preschool appendicitis. Preschool appendicitis scRNA-seq profiles revealed that the interleukin-17 signaling pathway may participate in the inflammation process.

Conclusions

Our study provides new insights into the development of the appendix and deepens the understanding of appendicitis in preschool children.

Graphical Abstract

Peer Review reports

Background

The appendix, or vermiform appendix, is a narrow, tube-shaped sac arising from the posteromedial cecal border near the ileocecal valve, originating from the midgut during embryonic development. By week 6 of gestation, the cecal diverticulum emerges as the precursor to the cecum and appendix [1]. By the eighth week, the appendix becomes visible [2]. As the colon elongates, the cecum and appendix rotate medially with the midgut and descend into the right lower abdomen.

The histological structure of the appendix closely resembles that of the colon but possesses a unique feature not found in the colon—a dense aggregation of lymphoid tissue. This lymphoid tissue, which begins to develop in the appendix mucosa during weeks 14 and 15 of gestation [2], is structurally reminiscent of lymph nodes, with B cells forming follicles and T cells residing in the interfollicular region [3]. Compared to other intestinal lymphoid tissues such as Peyer’s patches and colonic patches, appendix lymphoid tissue exhibits distinct differences in both structural and cellular composition [3,4,5]. Functionally, the appendix acts as a reservoir for beneficial gut bacteria, aiding in the repopulation of the gut microbiome following infection [3]. Additionally, the gut-associated lymphoid tissue in the appendix is believed to play a significant role in overall gut immunity, contributing to the maturation of B lymphocytes and the production of immunoglobulin (Ig)A antibodies [6, 7]. These features underscore the uniqueness of the appendiceal lymphoid tissue and the appendix itself. Despite these unique characteristics, research on the molecular and cellular development of the human appendix remains limited, adding to the organ’s mystery. Consequently, deciphering its structure, cellular composition, and function is crucial for a deeper understanding of its role in the immune system.

Acute appendicitis is a common surgical emergency in children, accounting for nearly 33% of pediatric admissions for abdominal pain [8,9,10]. However, it is relatively uncommon in preschool children, accounting for just 5% of cases [11]. Despite this low incidence, preschool patients often present with higher rates of complications such as perforation, abscess formation, peritonitis, and sepsis [12,13,14]. Perforation may already be present in younger children (affecting around 30%), at the time of diagnosis [15]. Some studies suggested that delayed diagnosis in preschool children leads to complications due to factors such as their inability to communicate, other associated illnesses, difficult physical examinations, and the lack of a specific clinical presentation [8, 10, 13, 16]. However, other studies propose that complicated appendicitis may have different pathogenesis compared to uncomplicated appendicitis [10, 17, 18]. These factors highlight the unique and severe nature of appendicitis in preschool-aged children.

Despite the availability of advanced diagnostic imaging, diagnosing acute appendicitis in young children remains challenging. In addition, in preschool-aged children, luminal obstruction, is considered to be a common feature in acute appendicitis, with fecalith being a less common cause [14]. These observations lead us to hypothesize that the unique anatomical and histological features of the preschool appendix during development may contribute to these clinical characteristics. Understanding the timeline of appendiceal maturation could provide valuable insights into the pathophysiology of acute appendicitis in this age group.

The developmental trajectory of the appendix at the transcriptomic level has not yet been fully elucidated. In this study, we performed single-cell RNA sequencing (scRNA-seq) on an early human embryo and fetal appendix samples (7–18 gestational weeks (GW)), as well as pediatric appendices (3 days to 13 years old). To explore the transcriptomic characteristics of preschool-age appendicitis, we additionally profiled inflammatory appendices and blood samples of this group. In total, 37 fetal and 31 pediatric specimens were included in this study. Using these data, as well as scRNA-seq profiles of pediatric colon data and bulk RNA-seq data of pediatric acute appendicitis from other studies, we aim to construct a comprehensive cellular atlas of the developing human appendix from the fetal stage to adolescence at the transcriptomic level. This study seeks to trace the differentiation dynamics and features of cells in the appendix, providing insights into the links between these cellular processes and preschool-age appendicitis.

Methods

Human specimens

A total of 37 fetal specimens (1 embryo and 36 fetal appendices), 31 pediatric samples (17 normal and 14 inflamed appendices), and 8 colonic mucosal biopsy samples (normal segments of Hirschsprung’s disease) were included in this study. Epithelial cell clusters were extracted from the embryo sequencing data for epithelium development. Colonic samples and data were utilized solely for comparative analyses with appendiceal epithelium. The fetal samples were obtained from elective terminations of pregnancy at different GW. Normal pediatric appendices were electively resected during the intra-abdominal surgery for a non-inflammatory condition, such as gastrointestinal perforation, intussusception, intraperitoneal foreign bodies, ileocecal mass, and ovarian cyst (Additional file 1: Table S1). The inflammatory samples were obtained from patients diagnosed with complicated acute appendicitis. The details are listed in Table S1.

Histology and immunostaining

After specimen collection, a portion was set aside for histological experiments. They were fixed in 10% buffered formalin for 48 h, dehydrated, embedded in paraffin, and sectioned at 5 μm. Hematoxylin and eosin (H&E) staining was performed with a staining kit (G1120; Solarbio) based on the manufacturer’s instructions. Normal immunohistochemistry (IHC) was performed using a DAB kit (GK500710, Gene Tech). Multi-color immunofluorescence was carried out using a fluorescence IHC kit (TSA-RM-275, Panovue) according to the protocol of the manufacturer. Briefly, antigen was retrieved by 10 × Tris–EDTA (pH9.0) and boiled in the microwave oven for 20 min. After blocking with goat serum, samples were incubated overnight at 4 °C with primary antibodies (Additional file 1: Table S2). A secondary horseradish peroxidase-conjugated antibody was added and incubated at room temperature for 1 h. Then samples were incubated with fluorescent dye at room temperature for 15 min. H&E and IHC images were acquired with a Leica light microscope (DM750). The immunofluorescent slides were imaged using a confocal microscope (Leica) and analyzed using Leica imaging software.

Single-cell RNA sequencing and analyses

Detailed methods for single-cell preparation, sequencing, and data analyses are provided in Additional file 1 (Supplementary Methods, Table S3).

Real-time quantitative PCR (Rt-qPCR)

Total RNA was extracted using TRIzol reagent (T9108, Takara); then, 1 µg RNA was transcribed into cDNA using a reverse-transcription kit (AK4601, Takara). PCR amplification kit (AKA606, Takara) was used to perform the real-time qPCR. Additional file 1 Table S4 lists primer sequences for each gene, and each sample was analyzed in duplicate. Relative RNA expression levels were calculated using the 2–ΔΔCt method, which normalized to GAPDH [19]. Wilcoxon tests were used to compare the significance between two groups. P < 0.05 was considered statistically significant.

Results

Depicting the cell atlas of the developing human vermiform appendix

Given the limited research on the development of the human appendix, we aimed to create a comprehensive cell atlas across different developmental stages. We obtained samples of human fetal and pediatric appendices and performed histological and molecular analyses. We divided the samples according to age, including fetal (before birth), neonatal (0–1 month), infant and toddler (< 3 years), preschool (3–6 years), and school-age (≥ 7 years) groups. Intuitively, the rapid growth in girth (and in length as well [1]), formation of villi and crypts, increased volume of lymphoid tissue, and incrassation of smooth muscle layers were the major physical changes (Fig. 1A, Additional file 1: Fig. S1A).

Fig. 1
figure 1

scRNA-seq profiling of fetal and pediatric appendix. A Representative H&E staining of fetal (n = 6) and pediatric (n = 4) appendices. B General workflow of experimental design for scRNA-seq. Lines on the arrow mark the age of the samples. Black lines represent normal samples (n = 13), and red lines represent the appendicitis samples (n = 2). GW gestational week, D day, Y year. C UMAP visualization of merged normal appendix profiles colored by cell types. D Dot plots displaying marker genes for each cell type. Color represents the average expression within each cell cluster, and size indicates the proportion of cells expressing each marker gene. E Bar plots showing the proportion of each cell type in the merged cells colored by cell types. Each bar represents an individual sample, with samples grouped by age into five categories. GW gestational week, D day, Y year. F Violin plot with embedded box plot representing the transcriptional similarities in different cell types of the pediatric dataset. The embedded box plot represents the median and interquartile range of the similarities scores

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To further explore the whole cellular dynamics throughout the development of the human vermiform appendix, we analyzed the single-cell transcriptomic profiles of prenatal (8–18 GW) and pediatric (0–13 years) appendices, including gut endoderm cells from a 7 GW embryo (Fig. 1B). Over 91,000 high-quality cells were identified, revealing major clusters of T/NK (CD3D, NKG7), B (CD79A, MS4A1), myeloid (CD68, CD14), neural (HAND2, TUBB2B), epithelial (EPCAM, FABP1), mesenchymal (COL1A1, COL3A1), and endothelial (CLDN5, VWF) cells (Fig. 1C, D, Additional file 1: Fig. S1B, S1C). Fetal appendiceal samples were enriched in mesenchymal cells, while proportions of T/NK cells and B cells were highest in postnatal samples (Fig. 1E). Throughout development, the transcriptional similarities of B cells and T/NK cells were relatively greater than those of other cell types, rendering lymphoid cells had the least transcriptional diversity across the landscape of appendix development (Fig. 1F, Additional file 1: Fig. S1D). After further sub-clustering, we identified 76 cell types, and we discuss each compartment in the below section.

Deciphering mesenchymal components in appendix development

Mesenchymal cells proliferate and differentiate dramatically. For example, smooth muscle cells (SMCs) increased rapidly during development. SMCs were hard to distinguish in H&E sections at 8 GW but gradually thickened around 10 GW (Fig. 2A).

Fig. 2
figure 2

Development of mesenchymal compartment cell. A. Representative H&E staining of fetal appendices at 8 and 10 GW. GW gestational week. B UMAP visualization of merged mesenchymal cells in developing dataset colored by cell types. C Multicolor IHC identifying the location of S2 (PDGFRA+) and epithelial cells (CDH1+). D Enriched GO terms of the DEGs of ARID5B+ SMCs, TCF21+ fibroblasts, and PITX1+ fibroblasts. E Bar plots showing the proportion of each cell type in the mesenchymal compartment. F Multicolor IHC identifying C7+ S3 in appendices at the age of 8 GW and 8 years. Bar plots showing the relative number of C7+ S3 in postnatal (n = 3) and prenatal (n = 4) appendices. Error bars indicate the standard deviation (SD). Statistical analysis was performed using Student’s t-test. *p < 0.05; Y year. G Violin plots showing the scores of mesenchymal development and differentiation pathways among age groups. The embedded dot and stick represent the mean and standard deviation range, respectively. The dashed line showed the trend of change. H Intercellular communication between epithelial cells and other cell types. Numbers indicate the number of ligand–receptor pairs for each intercellular link. I Circos plot visualizing putative cross talk between epithelial cells and S2 cells. J Multicolor IHC identifying pericytes and endothelial cells in different age groups. Bar plots showing the ratio of pericytes to endothelial cells in infant and toddler (n = 3), preschool (n = 3), and school-age (n = 3) groups. Error bars indicate the standard deviation (SD). Statistical analysis was performed using Student’s t-test. *p < 0.05; **p < 0.01. K Violin plot with embedded box plot representing the vascular transport score in different age groups of endothelial cells. The embedded box plot represents the median and interquartile range of the scores. Wilcoxon test was performed. ns not significant; *p < 0.05; ***p < 0.001

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We sub-grouped the cells into 13 subsets (Fig. 2B) based on canonical marker genes and differentially expressed genes (DEGs) (Additional file 1: Fig. S2A). Unlike the composition of mesenchymal cells in adult colons [20], stromal 3 (S3) (ASPN, OGN) cells constitute a larger proportion compared to stromal 1 (S1) (ADAMDEC1, ADAM28) cells in developing appendices. S1 cells were reported to be distributed throughout the lamina propria [20], where the appendix contains a large number of lymphoid follicles, probably resulting in a small quantity of S1 cells. We observed heterogeneity with S3 cells and categorized them into three clusters separately. HAND1+ S3 cells express embryonic development markers (HAND1, HAND2, DLK1, PITX2) [21], while C7+ S3 express mature adult hallmark S3 markers (C7, THY1) [22]. Meanwhile, the expression of FBLN1+ S3 cells was akin to the transitional S3 in the previous report [22], whose state was between S3 progenitors (NR2F1, FNDC1) and mature C7+ S3 cells. Stromal 2 (S2) cells, marked by F3, NPY, and CH25H, are important in intestinal epithelial barrier function [23, 24]. The expression of PDGFRA in S2 was similar to the villus-based telocytes which support the epithelium [25] (Fig. 2C) and contribute to intestinal development and renewal [26]. We also identified two other fibroblast clusters: PITX1+ fibroblasts and TCF21+ fibroblasts. According to the top DEGs, most of them are essential for cell differentiation and development in multiple organs [27,28,29,30,31,32], and similarly, ARID5B+ SMCs also have differentiation characteristics [33] (Fig. 2D), suggesting that these cells may play roles in mesenchyme development. Mesenchymal lymphoid tissue organizers (mLTo), defined by CCL19, CCL21, and CXCL13 as well as adhesion and NF-κB pathway molecules [34], were believed to be associated with the formation of lymphoid organs and observed at 10 GW in our dataset. Pericytes were identified by KCNJ8 and RGS5.

Fetal samples showed greater cell type diversity than pediatric samples (Fig. 2E, Additional file 1: Fig. S2B). The proportion of PITX1+ fibroblasts was almost decreasing with age and existed in the fetal period. Likewise, transitional (FBLN1+) and development-associated (HAND1+) S3 were also enriched before the neonatal period and so were the cycling fibroblasts and S2. On the contrary, the proportion of mature (C7+) S3 cells, which emerged at around 8 GW, increased after birth (Fig. 2F). Thus, it is not difficult to speculate that the development and differentiation of mesenchymal cells trends with age, which was confirmed by scoring related pathways (Fig. 2G).

S2 cells were reported to support epithelial homeostasis, which could be reflected in most receptors–ligands interactions with epithelial cells within the mesenchymal compartment (Fig. 2H) by ERBB3 and NRG1 (Fig. 2I). NRG1 was reported to enhance differentiation in developing intestine enteroid cultures [35]. Notch signaling was also found between the epithelial–mesenchymal interactions, which was considered as an essential role in intestinal development [36, 37]. However, the limited presence of S2 cells in preschoolers reduces epithelial barrier support, as they express key basement membrane components (Fig. 2E, Additional file 1: Fig. S2B). On the other hand, the proportion of pericytes was largest in the preschool group among all stages, and the ratio of pericytes to endothelial cells in the preschool group reached 1:2.4, which was the highest ratio recorded postnatally (Fig. 2J, Additional file 1: Fig. S2C). Greater pericyte coverage may accelerate the transportation of cytokines in the microcirculation during appendicitis, which probably accounts for the more severe inflammatory response in the preschool group [38] (Fig. 2K). SMCs and ARID5B+ SMCs compose the muscular layer of the appendix wall, and their proportions were limited in the preschool group (Additional file 1: Fig. S2D). Appendix wall is thinner in preschool than in school-age children, which may contribute to early perforation during appendicitis (Additional file 1: Fig. S2E).

Charting the map of endothelial cells in developing appendices

We divided the endothelial cells into nine groups (Fig. 3A) based on vessel types and size using reference marker genes (Additional file 1: Fig. S3A). The composition varied with age groups (Fig. 3B, Additional file 1: Fig. S3B). To be noticed, small-vessel endothelial cells transitioned to large-vessel types through development (Fig. 3B–C), with venous capillary endothelial cells decreasing and large-vessel cells increasing. We performed pseudotime analysis by RNA velocity and scored the cells with arteriovenous marker genes [39]. Then, we discovered the gradual changes of the arteriovenous features along the pseudotime, whose respective paths were almost opposite (Fig. 3D).

Fig. 3
figure 3

Endothelial and neural compartments in developing appendix. A UMAP visualization of merged endothelial cells in developing dataset colored by cell types. CP capillary, L large size, M medium size. B Bar plots showing the proportion of each cell type in the endothelial compartment. C Multicolor IHC identifying endothelial cells in appendices at the age of 18 GW and 8 years. GW gestational week, Y year. D Line plots showing the gam-smoothed fit curves of arterial (red) and venous (blue) scores for cells along the velocity-predicted pseudotime. E Bar plots showing the proportion of endothelial cells of all cells among different age groups. F Eclipse plots displaying the average (dot) and distribution range (oval shape) of arteriovenous scores for cells in each age group. G Violin plot with embedded box plot representing the scores of angiogenesis signature genes among age groups. The embedded box plot represents the median and interquartile range of the scores. Wilcoxon test was performed. ns not significant, *p < 0.05; ****p < 0.0001. H Box plots showing the scores of cellular junction signature genes among age groups. Wilcoxon test was performed. ns not significant; *p < 0.05; **p < 0.01; ****p < 0.0001. I IHC sections of S100B in fetal appendix samples at the age of 8 and 9 GW. J UMAP visualization of merged neural cells in developing dataset colored by cell types. K Bar plots showing the proportion of each cell type in the neural compartment. L Partition-based graph abstraction of neural cells development. M Dot plots showing the top highly expressed ligand-receptor interactions of Glial_3 cells and endothelial cells and myeloid cells

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Generally, the preschool group had the smallest proportion of endothelial cells (Fig. 3E) and exhibited lower arterial and venous scores, suggesting a more naïve state (Fig. 3F). On the contrary, fetal groups not only possessed higher arteriovenous features but also showed a distinct ability of angiogenesis as well (Fig. 3G), which is considered to be beneficial for development. However, the preschool group received higher scores in angiogenesis. The vascular endothelium plays a critical role in the regulation of vessel walls, whose integrity maintains the fundamental barrier function. Among the elements involved in vascular structure and permeability, the intercellular junction is the most important one [40]. The scores of three types of junctions, especially tight junctions, in the preschool group, were the lowest among all groups (Fig. 3H). Downregulated genes in this group were also enriched in cell junctions (Additional file 1: Fig. S3C, S3D), including MARCKS [41], EFNB2 [42], CFL1 [43], and CDH5 [44]. Above all, these findings indicate the weakness of the endothelium (low proportion and vascular junction) in preschool-age appendices, whose relative dysfunction may accelerate the disease process when inflammation occurs.

Mapping the heterogeneity of neural cells in appendices

During early development (4–7 GW), neural crest–derived precursor cells have been found to colonize and migrate the human gut, then form into the neurons and glial cells [45]. We identified distinct neural cells at 10 GW (the earliest time point) in our data, and glial cells at 8–9 GW (Fig. 3I) on IHC sections. Neural cells were categorized into cycling (MKI67, CENPF, TOP2A), neuronal (ELAVL4, CHRNA3, GAP43), and glial (S100B, SOX10, PLP1) types (Fig. 3J). According to the DEGs, sub-populations were identified respectively (Additional file 1: Fig. S3E). Cycling neural cells took a small proportion in fetal samples and early postnatal appendices, while most postnatal neural cells were glial—specifically lymphoid-associated glial cells (Gial_3) (FGL2, GFRA3, RXRG) (Fig. 3K, Additional file 1: Fig. S3F). It is reasonable to speculate that the size of neuronal cells is too large to capture due to technical defects. In the meantime, the proportion of submucosal glial cells (Glial_2) (CEBPD, SOCS3, ZFP36) increased before birth, which may be associated with the development of epithelium. We delineated the relationships using partition-based graph abstraction (Fig. 3L) and found that neuronal and glial formed separate branches from cycling neural cells. On the other hand, lymphoid-associated glial cells shared the most receptor–ligands pairs with endothelial cells among all types of neural cells (Additional file 1: Fig. S3G). Among those pairs with high mean expression levels between endothelial cells and Glial_3, endothelial cells expressing Notch ligands activated Notch receptors on glial cells (Fig. 3M). The NOTCH signaling involved in interactions was reported to be a powerful means of determining cell fate in the nervous systems and gliogenesis [46]. Complementarily, glial cells interacted with endothelial cells by expressing PDGFC, VEGFB, and PGF to promote angiogenesis and vessel maturation (Fig. 3M). Especially, PDGFC can provide protection to neurons and blood vessels. Therefore, neurovascular cross talk was accomplished to maintain tissue homeostasis and function [47]. On the other hand, MDK binding with PTPRZ1 was reported to promote neuronal migration and embryonic neuron survival, which plays a vital role in enteric neuronal development [48]. We also found that Glial_3 had the most interactions with myeloid cells among immune cells by tumor necrosis factor-related pairs. Enteric glia can produce pro-inflammatory cytokines and influence disease processes by regulating either innate or adaptive immune responses [49, 50]. In addition, neural cells were reported to cause neurogenic appendicitis by overactivation of neuropeptides [51]. Thus, the increase of Glial_3 in the preschool group may have correlations with the appendicitis process.

Epithelial cell development in human appendices

At the very beginning, the appendix epithelium was thick and pseudostratified during 8–12 GW. At approximately 13 GW, it continuously warped and a single-cell layer of villus and crypt structures was generated, while in the meantime, the vacuole-like structure of goblet cells was observed (Fig. 4A).

Fig. 4
figure 4

Epithelial cell development in human appendices. A Representative H&E staining of fetal appendices at 8 and 13 GW. GW gestational week. B UMAP visualization of merged epithelial cells in developing dataset colored by cell types. C Violin plot with embedded box plot showing the scores of crypt–villus axis markers among different cell types. The embedded box plot represents the median and interquartile range of the scores. D scVelo graphs displaying epithelial cells with overlaid arrows colored by cell types. E Box plots showing the scores of cellular junction signature genes among age groups. Wilcoxon test was performed. ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001. F Heatmap displaying the relative expression of mucus-related genes in different age groups. G Violin plot with embedded box plot showing the scores of mucus-related genes in goblet cells from the developing appendix dataset (left) and the dataset integrated with colonic cells (right). Wilcoxon test was performed. ns, not significant; **p < 0.01; ****p < 0.0001. H Box plots showing relative expression of the indicated genes between colon (n = 8) and appendix samples (n = 8) from qPCR analysis. Wilcoxon test was performed. *p < 0.05; ***p < 0.001. I DEGs analysis showing up- and down-regulated genes across all 14 clusters. J Multicolor IHC identifying SPINK4+ goblet cells in colon and appendix. K Violin plots showing the relative expression of the BMP-off gene (SPINK4) and BMP target gene (ID1) in the colon and appendix group. Wilcoxon test was performed. ****p < 0.0001. L Violin plots showing the scores of BMP pathway in colon and appendix group. Wilcoxon test was performed. ****p < 0.0001. M Rank for regulons in colon and appendix goblet cells based on the regulon specificity score (RSS). N Box plots showing relative expression of the indicated genes between colon (n = 8) and appendix samples (n = 8) from qPCR analysis. Wilcoxon test was performed. *p < 0.05; **p < 0.01; ***p < 0.001. O Differential regulons associated with types of goblet cells, ranked by correlation with appendiceal goblet scores using the appendix signature genes. P Violin plots showing the relative expression of DDIT3 and XBP1 in the colon and appendix group. Wilcoxon test was performed. ****p < 0.0001

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We extracted appendiceal epithelial cells and combined the gut endoderm epithelial cells of an embryo (7 GW) for further investigation. We identified 10 cell types (Fig. 4B) according to the marker genes (Additional file 1: Fig. S4A). These clusters included cycling epithelial cells (MKI67, UBE2C), stem cells (LGR5, ASCL2), secretory (MAFB, HEXIM1), and absorptive progenitors (HMGCS2, FXYD3), goblet cells (MUC2, SPDEF), enterocytes (APOB, FABP6), enteroendocrine cells (CHGA, NEUROD1), tuft cells (LRMP, PTGS1), and microfold cells (CCL20, CCL23). Cycling and stem cells were dominant in the embryo gut epithelium (Additional file 1: Fig. S4B, S4C). With advancing age, the proportion of secretory cells increases, and that of absorptive cells decreases, proving the inferior absorptive function of appendices. Besides, no Paneth cells were detected in healthy appendices, and a cluster of BEST4+ enterocytes was observed in the first trimester of development (13 GW in our dataset), similar to in other reports, and they were considered pH-sensing cells [52, 53]. We scored the cells with selected crypt–villus axis markers to define the position of epithelial cell subsets along the axis [53]. Cycling cells and stem cells are localized to the crypt bottom, while differentiated enterocytes are localized to the top (Fig. 4C). Pseudotime analysis provided a two-direction trajectory, starting with stem cells and cycling cells, and then diverging to absorptive and secretory lineages (Fig. 4D). The pseudotime was generally consistent with the actual age groups, while epithelial cells were slightly less mature in the preschool group (Additional file 1: Fig. S4D).

The intestinal epithelial layers are the primary barrier between the body and the external environment, which are constituted by physical and biochemical barriers [54]. The physical barrier is constructed by the tight junctions between adjacent epithelial cells, which participate in regulating intestinal permeability and guarding against toxins or infections, and are crucial for epithelial barrier integrity [55]. Mucus secreted by goblet cells dilutes toxins and traps bacteria, then quickly washes away to prevent their penetration [56]. Moreover, mucins together with various other molecules (FCGBP, CLCA1, TFF3, ZG16, DMBT1, IgA, defensins, lysozyme) construct a thick chemical layer, which provides a diffusion barrier for a more effective immune response [57, 58]. To evaluate the functions of the barriers, we calculated the scores of tight junction- and mucus-related genes [57, 59] in all epithelial cells and goblet cells. The score of tight junctions gradually increased and reached the top in the infant and toddler group before decreasing in the preschool- and school-age groups (Fig. 4E), while the mucus score of goblet cells was slightly higher in the preschool group (Fig. 4 F, G). The scores of inflammasomes, reactive oxygen species, autophagy, endocytosis, and exocytosis, which are considered to modulate mucus secretions of goblet cells [59, 60], were similarly increased. Conversely, endoplasmic reticulum stress, which was proven to limit mucus secretion [61], exhibited lower scores in the preschool group (Additional file 1: Fig. S4E). Expressions of the mucus-related genes validated by rt-qPCR were also relatively higher (Additional file 1: Fig. S4F) in the preschool group. In the preschool group, the loss of tight junctions may provide more opportunities for pathogen invasion. Thicker mucus continuously secreted by epithelial cells may raise the possibility of luminal obstruction and result in increased intra-luminal pressure and appendix distension.

Since colonic and appendiceal epithelium are alike, we combined scRNA-seq data of colonic samples from He et al. [62] and epithelial cells from our data to compare differences (Additional file 1: Fig. S4G, S4H). The appendices had fewer secretory cell types, especially goblet cells (Additional file 1: Fig. S4I), such that they expressed lower mucus-related genes, as validated by qPCR (Fig. 4G, H). The position of the appendiceal epithelium along the crypt–villus axis was relatively greater, consistent with its higher BMP signaling score and lower WNT signaling score (Additional file 1: Fig. S4J). Focusing on all goblet cells, we noticed that cluster 7 was mostly in colonic epithelium (Additional file 1: Fig. S4K, S4L), which highly expresses SPINK4 (Fig. 4I). We performed immunofluorescent staining and confirmed that SPINK4+ goblet cells are mainly located in the colon (Fig. 4J). SPINK4+ goblet cells were documented when culturing intestinal organoids under BMP-off differentiation conditions (with BMP inhibitor), which is a standard differentiation condition in vitro, and colonic goblet cells expressed higher BMP-off genes [63] (Fig. 4K, Additional file 1: Fig. S4M). Meanwhile, BMP target genes (ID1, ID2, ID3, CREB3L1, KLF10, PRDX2) were significantly upregulated in appendiceal goblet cells [64] (Fig. 4K, Additional file 1: Fig. S4M). Thus, it is reasonable to presume that activation of BMP-related pathways may play roles in the development of appendiceal epithelium (Fig. 4L).

We next applied SCENIC analysis to further analyze their differences at the transcription factor (TF) level. In the appendix goblet cells, HOXC8 and PITX2 were identified as top specific regulons, while SMARCA4 and XBP1 were listed as top regulons of colonic goblet cells (Fig. 4M, Additional file 1: Fig. S4N). Rt-qPCR was performed to validate their expression, respectively (Fig. 4N). We next examined regulons correlated with appendix signatures that may be associated with organ development. We estimated the correlation between the top 50 signatures of the appendix and all identified regulons (Fig. 4O). Then, we found that appendiceal goblet cells upregulated TFs involved in stimulating BMPs (DDIT3 [65], EGR1 [66]) and inhibiting Wnt pathways (CEBPB [67]). On the other hand, in colonic goblet cells, the top regulons were relevant to epithelial cell proliferation (ARNT, XBP1, EHF) and development (SPDEF) (Additional file 1: Fig. S4O, S4P). Moreover, colonic epithelial signatures scored slightly higher in the preschool group (Additional file 1: Fig. S4Q).

Suppressed functioning of myeloid cells in the preschool group

We collected 2129 myeloid cells and divided them into 10 groups (Fig. 5A). Among dendritic cells (DCs), we obtained four different types: DC1 (CLEC9A), DC2 (CD1C), DC3 (LAMP3), and plasmacytoid DC (pDC) (JCHAIN). We designated two types of macrophages, which differed in their expressions of LYVE1 and SPP1 (Additional file 1: Fig. S5A). Through developmental time, we observed a decreased proportion of LYVE1+ macrophages (Fig. 5B, Additional file 1: Fig. S5B). Notably, we identified LYVE1+ macrophages in our earliest sample (8 GW) (Fig. 5C). It was reported in newborn mice that LYVE1hi macrophages may originate from embryonic progenitors [68]. LYVE1+ macrophages were considered as M2-like and may play a crucial part in angiogenesis and extracellular matrix remodeling [69, 70] during the fetal period. On the contrary, the proportion of LAMP3+ DCs, which are considered mature DCs [71], increased over time (Additional file 1: Fig. S5B). DC3 also expressed CCR7 and FSCN1, both of which positively directed the migration of mature DCs into secondary lymphoid nodes [72,73,74]. We scored the myeloid cells with several gene sets from MSIGDB. The developmental pathways scored higher in the fetal group (Fig. 5D), which also had the greatest proportion of cell-cycling myeloid cells (Additional file 1: Fig. S5B). The response to oxidative stress accumulated through pseudotime (Fig. 5E). Interestingly, the state of myeloid cells seemed to be relatively quiescent in the preschool group when comparing multiple activation signatures (Fig. 5F). Besides, we found that cytokine production, neutrophil chemotaxis, and migration were markedly suppressed in myeloid cells of the preschool group (Fig. 5G). In addition, myeloid cells from preschool group appendices exhibited significantly decreased expression of phagocytosis genes (MERTK, IL1B) and scavenger genes (MARCO, CD5L) (Fig. 5H). In total, the functions of activation, cytokine production, chemotaxis and migration, phagocytosis, and scavenger in myeloid cells from the preschool group were suppressed, which may attenuate the initial innate immune function in some ways, exacerbating the inflammation process during appendicitis.

Fig. 5
figure 5

Suppressed function of myeloid cells in developing dataset. A UMAP visualization of merged myeloid cells in the developing dataset colored by cell types. B Bar plot showing the proportion of each cell type in the myeloid compartment. C Multicolor IHC identifying LYVE1+ goblet cells in the fetal appendix at age of 8 GW. GW gestational week. D Violin plots showing the scores of myeloid development and differentiation pathways among age groups. The embedded dot and stick represent the mean and standard deviation range, respectively. The dashed line showed the trend of change. E Scatterplots displaying correlations of velocity-predicted pseudotime with myeloid development, differentiation, and oxidative stress response score. F Box plots showing the scores of myeloid activation-related pathways in different age groups. Wilcoxon test was performed. **p < 0.01; ***p < 0.001; ****p < 0.0001; ns not significant. G Box plots showing the scores of myeloid function-related pathways in different age groups. Wilcoxon test was performed. **p < 0.01; ***p < 0.001; ****p < 0.0001. H Heatmap displaying the relative expression of phagocytosis and scavenger-related genes in different age groups

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Compositions of lymphoid cells in developing appendices

The cell composition evolves over time from fetal to pediatric appendices (Fig. 1E). Lymphoid cells were observed first at 10 GW from our dataset, composing 0.13% of all cells. With advancing age, the proportion of lymphoid cells increased to 5.7% at 18 GW, then sharply increased after birth (74.89%) and topped off (98.44%) at the preschool stage, indicating the maturity of the appendiceal immune structure. Oppositely, myeloid cells dominated in the early stage of gestation but gradually decreased (Additional file 1: Fig. S6A).

Histologically, T cells and B cells were first observed at around 12–13 GW, accumulating and congregating further at 16–17 GW (Fig. 6A). However, the lymphatic nodules with germinal centers were only observed in the postnatal appendix and not obvious in the 3-day-old appendix (Fig. 1B).

Fig. 6
figure 6

B cells and T cells in the appendix during development. A IHC sections of CD3 and CD79a in fetal appendix samples. B UMAP visualization of merged B cells in developing dataset colored by cell types. C Bar plots showing the proportion of each cell type in the B cell compartment. D Representative IHC staining of BCL6 of the fetal appendix at the age of 17 GW. GW gestational week. E Violin plots showing the scores of B cell development and differentiation pathways among age groups. The embedded dot and stick represent the mean and standard deviation range, respectively. The dashed line showed the trend of change. F Heatmap displaying the relative expression of genes from indicated pathways in different types of B cells. G UMAP visualization of merged T cells in developing dataset colored by cell types. H Bar plots showing the proportion of each cell type in the T cell compartment. I Box plots showing the scores of naïve, cytotoxic, resident, and exhaust-related signatures in different age groups. Wilcoxon test was performed. **p < 0.01; ****p < 0.0001. J scVelo graphs displaying T cells with overlaid arrows colored by cell types. K Scatterplots displaying correlations of velocity-predicted pseudotime with T-cell-mediated immunity, cytotoxicity, naïve signatures, and proliferation score. L Identification of the top 300 genes contributing to the development over pseudotime. M Circos plot visualizing the number of interactions between ILC3, ILC2, and LYVE1+ macrophages, monocytes, DC2, S2, and mLTo. N Dot plots showing the top highly expressed ligand–receptor interactions of ILC2 and monocytes, ILC3 and LYVE1+ macrophages, and ILC3 and mLTo

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Focusing on B cells, we obtained 19,894 cells in total (Fig. 6B, Additional file 1: Fig. S6B). The pro-B cluster accounted for a relatively larger proportion of cells in fetal groups (Fig. 6C). Germinal-center B cells first appeared at 13 GW in our data, earlier than previously reported [2] (Fig. 6C), and were observed microscopically at 17 GW (Fig. 6D). The only plasma cell type that first existed in the fetal appendices was IgM plasma cells at 14 GW, which possibly suggests primary antigen stimulation at a very early fetal stage. After birth, IgA and IgG plasma cells emerged. Similarly, we scored B cells with several gene sets. The fetal group had higher scores in B cell developmental and differentiation pathways (Fig. 6E). DEGs of PLCG2+ B cells were associated with pathways like B cell differentiation (PTK2B, PLCL2, ITFG2), and regulation of B-cell–mediated immunity (IL4, FOXP3, TGFB1), as PLCG2 was required for many aspects of BCR-mediated signaling (VAV3, SYK, BLK) (Fig. 6F, Additional file 1: Fig. S6C), impacting B cell development and function [75, 76]. Additionally, the B cell activation score was lower in the preschool group (Additional file 1: Fig. S6D).

Re-clustering T/NK cells obtained 16 populations, including NK (KLRF1, KLRD1), gdT (TRDC, TRGC1, TRGC2), two innate lymphoid cells (ILCs) (IL9R, NCR2), cycling T cells (MKI67), regulatory T cells (Treg) (FOXP3), two naïve T cell subsets (CD4-C1-CCR7, CD8-C1-LEF1), four clusters of memory T cells (CD4-C2-ANXA1, CD4-C3-GPR183, CD8-C2-GPR183, CD8-C3-NR4A1), cytotoxic T cells (CD8-C4-GZMK), mucosal-associated invariant T cells (MAITs) (CD8-C5-SLC4A10), and two groups of exhausted T cells (CD4-C4-CXCL13, CD8-C6-CTLA4) (Fig. 6G, Additional file 1: Fig. S6E). In the fetal group, the composition of T cells was more balanced than that of after birth. ILCs, MAITs, and gdT were more abundant prenatally, while naïve and memory T cells were more dominant in postnatal groups (Fig. 6H, Additional file 1: Fig. S6F). We used signature marker genes to define the resident, cytotoxic, exhausted, and naïve scores of T cells [77, 78]. The naïve and cytotoxic signatures were downregulated after birth, while the resident score was upregulated. The exhausted score presented an ascending trend over time. Older groups had higher exhaustion scores. The preschool group had higher scores in resident and cytotoxic signatures compared to the infant and toddler group and the school-age group, respectively, suggesting that the function of T cells may be relatively mature in the preschool group (Fig. 6I), and reflecting the stronger immune reserve capacity in preschool appendices.

To uncover transcriptional dynamics during T cell maturation, we performed RNA velocity analysis on the T cell subsets (Fig. 6J), finding RNA velocity-predicted pseudotime positively correlated with T cell-mediated immunity and cytotoxic pathways and negatively correlated with naïve signatures and proliferation pathways (Fig. 6K, Additional file 1: Fig. S6G). Ordering the cells along the pseudotime revealed a group of driver genes related to the development (fit likelihood > 0.165) (Fig. 6L). These genes were generally enriched in the T cell activation, proliferation, and differentiation pathways (Additional file 1: Fig. S6H). At an early stage of the predicted timeline, T cells required the capacity to express genes of naiveness (SELL, CCR7) and differentiation (PLAC8 [79], IFITM1 [80]). At late time points, T cell exhaustion-associated genes (PDCD1, CTLA4, and TIGIT [81]) were listed as diver genes (Fig. 6L).

Using CellPhoneDB, we found that the ILC2 displayed the highest number of interactions with myeloid cells (monocytes, LYVE1+ macrophages, DC2) (Fig. 6M). ILC2 can produce the type 2 cytokines interleukin (IL)-4, IL-5, and IL-13 and assist in tissue remodeling and repair [82]. SAA1 was reported to induce type 2 immune responses via FPR2 [83], and CD99 negatively regulated monocyte adhesion and transmigration through PILRα [84]. ILC3 had strong cross talks with mesenchymal cells (s2, mLTo). The interactions between mLTo and lymphoid tissue inducers (LTi) initiated secondary lymphoid organogenesis [82, 85]. LTi-like ILC3 subsets decreased with increasing GW and drastically dropped after birth. mLTo secreted CCL19 and CCL21 and received by LTi-like ILC3 through CCR7 to further promote IL-7 and RANKL expression, which resulted in LTo activation and maturation [85] (Fig. 6N).

Transcriptional characteristics in preschool-age appendicitis

Multiple features of the preschool group stood out in each compartment of developing appendices and may contribute to the clinical characteristics of appendicitis in preschool children. To study the transcriptomic changes and further investigate the molecular alterations in preschool appendicitis, we combined the scRNA-seq data of two samples from preschool patients with complicated appendicitis, their peripheral blood mononuclear cells (PBMCs), two normal age-matched appendices, and published healthy PBMC data. In total, we obtained 41,008 cells and divided them into 12 sub-groups (Fig. 7A). The proportions of neutrophils and other myeloid cells were higher in appendicitis; most of these cells were sourced from the blood, while T and B cells were sourced mostly from the tissue (Fig. 7B, Additional file 1: Fig. S7A). The similarity within myeloid cells among these samples was less than that of T or B cells (Additional file 1: Fig. S7B). It was reported that appendectomy is related to inflammatory bowel disease (IBD) [86], so we evaluated the risk of IBD by its high-risk gene sets [87] and found a slightly elevated risk in the appendicitis group (Fig. 7C). We compared the DEGs and performed Gene Set Enrichment Analysis (GSEA) in the KEGG database, determining that top-scored pathways were infection-related (Fig. 7D, E). In particular, the IL-17 signaling pathway was at the top. Rt-qPCR analysis confirmed the upregulation of IL-17 pathway genes in preschool appendicitis (Fig. 7F).

Fig. 7
figure 7

Transcriptional characteristics in preschool-age appendicitis. A UMAP visualization of merged cells colored by cell types. B Bar plots showing the proportion of each cell type in merged cells and colored by disease group. C Violin plots showing the scores of IBD-risk genes among normal and appendicitis groups. Wilcoxon test was performed. ****p < 0.0001. D Volcano plot showing the top 10 DEGs between the appendicitis group (red) and normal group (blue). Gray dots represent insignificant genes. E Enriched KEGG terms of the DEGs in the appendicitis group. F Box plots showing relative expression of the indicated genes among normal (n = 8), preschool (n = 6), and school-age (n = 6) appendicitis samples from qPCR analysis. Wilcoxon test was performed. *p < 0.05; **p < 0.01; ***p < 0.001; ns not significant. G GSEA enrichment plot of expression signatures of IL-17 signaling pathway in myeloid cells, T cells, and B cells of appendicitis group, respectively. H–J Violin plots showing the scores of the IL-17 signaling pathway among different types of myeloid cells, T cells, and B cells, respectively. Wilcoxon test was performed. **p < 0.01; ****p < 0.0001. K Jaccard similarities of myeloid, T, and B cell clusters in developing dataset with the signatures of IL1B+ macrophages, CD8-C3-NR4A1, and CD69+ B cell, respectively. L Bar plots showing the proportion of Cluster_3 IL1B+ macrophages, CD8-C3-NR4A1, and Cluster_10 CD69+ B cells in the developing dataset among different age groups. M Box plots displaying the scores of IL-17 signaling pathway, CD69+ B cell signatures, CD8-C3-NR4A1 signatures, and IL1B+ macrophage signatures in the developing dataset among different age groups. Wilcoxon test was performed. ns not significant; ****p < 0.0001. N Scatterplots displaying correlations of IL-17 signaling scores with CD69+ B cell signatures, CD8-C3-NR4A1 signatures, and IL1B+ macrophage signatures scores. O Enriched KEGG terms of the DEGs in appendicitis group of bulk RNA-seq. P Heatmap depicting clinical information, IL-17 signaling score of the bulk RNA-seq. Q Violin plots showing the scores of the IL-17 signaling pathway between different groups. Wilcoxon test was performed. *p < 0.05. R Bar plots showing the scores in different age groups. S Scatterplots displaying correlations of IL-17 signaling scores, WBC counts, and histological scores

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When inflammation happened, as the major effector cells, immune cells responded quickly. Neutrophils, CD4-C1-CCR7 T cells, and memory B cells took larger parts during appendicitis (Additional file 1: Fig. S7C–S7H). Firstly, DEGs of myeloid cells between the normal and appendicitis groups were enriched in the IL-17 signaling pathway and so were the DEGs of B cells and T cells (Fig. 7G, Additional file 1: Fig. S7I–S7K). After scoring myeloid cells with IL-17 pathway genes, IL1B + macrophages stood out with higher scores (Fig. 7H) and may be the responsible cells. To identify the IL1B+ macrophages in developing datasets, we extracted the signature genes of IL1B+ macrophages and found that cluster 3 of the developing datasets scored the highest (Fig. 7K, Additional file 1: Fig. S7L–S7N). In the T cell compartment, CD8-C3-NR4A1 T cells scored the highest in IL-17 signatures (Fig. 7I) and matched with CD8-C3-NR4A1 T cells from the developing datasets (Fig. 7K, Additional file 1: Fig. S7O). As for B cells, CD69+ B cells contributed the most to IL-17 scores (Fig. 7J). Likewise, it was cluster 10 that also had the greatest Jaccard similarities with CD69+ B cells and exhibited the greatest abundances of CD69+ B cells as predicted by CIBERSORTX (Fig. 7K, Additional file 1: Fig. S7P–S7R).

In total, IL1B+ macrophages, CD8-C3-NR4A1 T cells, and CD69+ B cells may be the main effector cells and promote inflammation during appendicitis. Interestingly, going through the developing datasets, cluster 3 as the IL1B+ macrophages, CD8-C3-NR4A1 T cells, and cluster 10 as the CD69+ B cells account for a significant percentage after birth in the preschool group (Fig. 7L). Moreover, the signatures of those cell types, along with IL-17 signaling pathways scored higher in the preschool group (Fig. 7M). Scores of IL1B+ macrophages signatures had the strongest correlation with IL-17 signaling pathways (Fig. 7N).

Next, we downloaded bulk RNA-seq data of appendicitis from Horwitz et al. [88] for external verification. According to the DEGs, the IL-17 pathway was also enriched at the top place (Fig. 7O, Additional file 1: Fig. S7S). We scored the samples with IL-17-related genes and found that the score was higher in patients with appendicitis, especially those without fecalith (Fig. 7P, Q). Comparing the clinical data, we found that histological scores from the article [88] (evaluated by H&E sections), WBC counts, and IL-17 scores were higher in the preschool group (Fig. 7R). IL-17 scores were also significantly positively correlated with WBC count and histological scores (Fig. 7S). To sum up, the IL-17 signaling pathway was upregulated in appendicitis, especially in preschool children.

Discussion

Our study is the first to construct a developing cell atlas of the normal human vermiform appendix at single-cell transcriptomic resolution, revealing dynamic changes in cellular components and differentiation state over time. Early prenatal samples showed various types of mesenchymal, endothelial, and neural cells, while postnatal samples showed a dominance of diverse immune cells.

Macroscopically, the structure of the appendix can be seen at 8 GW, and it grows rapidly in girth and length through time, mostly deriving benefits from the thickening muscularis layer and exploding lymphoid tissue. PITX1+ fibroblasts, a type of embryonic fibroblast that is mostly present in fetal appendices and declines with GW advancement, may be involved in muscle development of the fetal appendix. It was reported that PITX1+ in embryonic fibroblasts is an essential TF for inducing skeletal muscle progenitor cells [89] and plays a critical role in promoting chondrogenesis and myogenesis in the hindlimb [90]. The emergence of the lymphoid tissue started through the interaction between mLTo and ILCs during embryo development. It was reported that mLTo is derived from a group of perivascular myofibroblastic precursors. The precursors attracted the LTi cells and differentiated into mLTo by lymphotoxin LTα1β2 [91]. Then, activated mLTo secreted CCL2, CCL19, and CCL21 to further recruit and retain more LTi cells, thus forming a positive feed-forward loop. This interaction may initiate before 10 GW. Germinal-center B cells were observed at a very early stage, i.e., 13 GW in our data and 17 GW in the IHC section. Combining the observation of IgM plasma cells at 14 GW, we speculate that primary antigen stimulation in the appendix occurs at a very early fetal stage, consistent with the theory that the development of gut microbiota begins before birth [92]. The maternal microbiota travels to the fetus through the placenta in direct (microbes themselves) or indirect (microbiota-derived metabolites) manners [93].

When comparing the appendiceal and colonic epithelial cells, we found that BMP-related pathways may account for the differences. BMPs can control epithelial proliferation by blocking β-catenin activity, whose function is known to promote proliferation within intestinal crypts [94]. As daughter cells of stem cells move upwards from the base of the crypt, they encounter diminishing degrees of WNT signals and an increase in BMP signals [95]. We discovered an upregulated level of BMP target genes in appendiceal epithelial cells compared to colonic cells, indicating a likely poor state of proliferation in appendix epithelium. Regarding mucus secretion, appendiceal goblet cells exhibited lower scores than colonic goblet cells.

Appendicitis in preschool children is an uncommon surgical disease associated with a high perforation rate and increased morbidity. This was previously attributed to a delay in diagnosis owing to the atypical clinical symptoms. However, recent studies suggest that complicated appendicitis is likely a different disease from uncomplicated appendicitis. In other words, preschool children are more prone to developing complicated appendicitis. When analyzing the scRNA-seq profiles of the developing appendix by age group, we found various features of the preschool appendix that may explain the characteristics of preschool appendicitis. On the one hand, the rarity of preschool appendicitis could profit from its better microcirculation. Pericytes envelop the endothelial wall of the microcirculation. The ratio of pericytes to endothelial cells is the highest in the preschool group, which guarantees the blood vessel integrity and bloodstream [96], to avoid the colonization of pathogens and accumulation of inflammatory factors. The higher angiogenesis score of the endothelium also showed the capability to maintain general homeostasis, including in response to trauma [97]. Besides, the mucus secreted by the epithelium of preschool children was rich; to some extent, it can better prevent pathogen invasion.

On the other hand, preschool appendicitis progresses faster once the inflammation begins, possibly due to the thin-walled appendix. The lowest proportion of SMCs was present in this group, while school-age children exhibited higher proportions with less risk of perforation. Then, although higher pericyte coverage is good for vessels, it also appears to correlate positively with endothelial barrier selectivity and stringency [98]. During the inflammation process, pericytes may also potentiate inflammation by releasing inflammatory cytokines and chemokines and by overexpressing cell adhesion molecules [96]. Meanwhile, higher proportions of neural cells in this group may promote inflammation. It was reported that glia reactively proliferates and exhibits an increase in pro-inflammatory functions in acute colitis [49]. During acute inflammation, glial cells generate nitric oxide, IL-1β, tumor necrosis factor, and other pro-inflammatory mediators to recruit immune cells and enhance the response [50]. Moreover, the attenuated tight junctions within endothelial cells and epithelial cells facilitate greater permeability so that the inflammatory cytokines can more quickly enter the circulation and accelerate the response. Meanwhile, the thicker mucus and hyperplasic lymphoid tissue in the preschool group further lead to the obstruction of the appendiceal lumen and worsen the situation. Most importantly, preschool children exhibit the highest proportion of lymphoid cells, whose abundance may enhance immune surveillance and response [99, 100], and potentially explain many of the features we observed in this group. This lymphoid tissue grows rapidly during the toddler period, peaks in preschool-aged children, and plateaus during adolescence. During this period of rapid growth, other structures may respond correspondingly. For example, increased pericyte coverage and endothelial angiogenesis support vessel development and substance exchange, while goblet mucus secretion may be a response to the newly established gut flora. Conversely, the robust growth may weaken cell junctions as the appendix prepares for an increase in volume. To dig deeper, the gut microbiome may play a significant role in lymphoid tissue development, as bacterial translocation has been reported to parallel its growth [101]. However, these findings may also be influenced by heterogeneity between groups due to the small sample size. Further validation is needed to confirm our presumptions.

We discovered that the IL-17 signaling pathway was activated during the appendicitis process, especially in the preschool group. IL1B+ macrophages, CD8-C3-NR4A1 T cells, and CD69+ B cells, enriched in IL-17 signaling, maybe the responsible cell types and promote inflammation during appendicitis. Their higher proportion in normal preschool appendices may provide evidence for the quick inflammatory response. IL1B+ macrophage is one of the resident tissue macrophages with higher expression of IL1B and NLRP3, consistent with the role of NLRP3 inflammasome in IL-1β activation and regulation of intestinal homeostasis [102]. NR4A1 and CD69 were also tissue-resident genes, proving that those cell types were resident in the appendix and related to the development. Therefore, their larger proportion and greater devotion to IL-17 signaling probably are the characteristics of preschool appendicitis.

IL-17 signaling plays a vital role in protecting mammals against fungal and bacterial infections by enhancing host defense mechanisms [103]. It controls inflammation by regulating concerted actions of multiple inflammatory mediators and synergizing with other inflammatory signals [104]. IL-17 was also reported in embryonic development and tissue regeneration [105]. However, a disrupted IL-17 signaling pathway has been implicated in various inflammatory autoimmune disorders, including IBD, multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, psoriasis, and asthma [103]. The IBD-risk gene set scored higher in appendicitis, and it is reported that undergoing appendectomy before developing ulcerative colitis can reduce the risk of colectomy as well as ulcerative colitis–related hospital admissions [106]. In an experimental appendicitis model, appendectomy substantially inhibits Th17 recruitment, differentiation, activation, and interleukin expression in the distal colon, thereby significantly suppressing Th17-pathway-mediated immunopathological damage in IBD or experimental colitis [107]. As a microbial reservoir, appendix has shown a positive correlation between Proteobacteria and IL-17A production by appendiceal epithelial CD4+ T cells in pediatric appendicitis [108], further validating that gut microbes can participate in Th17 responses [109, 110]. These findings suggest that appendix could function as an immune organ, interacting with gut microbiota and potentially contributing to autoimmune diseases by regulating the IL-17 signaling pathway. Researchers have targeted IL-17 for therapy and achieved significant efficacy in treating psoriasis, ankylosing spondylitis, and other IL-17-imbalanced autoimmune diseases [111], either by directly targeting the IL-17 pathway to inhibit inflammation or by indirectly inhibiting the IL-17-mediated inflammatory response through regulating Th17 cell differentiation [112]. Similarly, antibody therapy for preschool appendicitis patients might be a future treatment option. It could be administered preoperatively to alleviate symptoms and reduce the risk of complications or postoperatively to accelerate recovery.

Several limitations should be noted. Firstly, the sample size in this study is small, with a limited number of subjects in each age group and within the appendicitis group. There is heterogeneity both within and between groups, which has impacted the consistency of the developmental atlas. Secondly, we used normal segments of Hirschsprung’s disease as relatively healthy controls in the epithelium compartment. While this approach provided some comparative insights, using authentic healthy colonic specimens for comparison would have provided a more accurate baseline. Thirdly, most of the patients in the preschool group had complicated appendicitis, so our results did not classify between complicated and uncomplicated cases. Including more cases of uncomplicated appendicitis in preschool children would provide a more comprehensive understanding of preschool appendicitis. Lastly, most results are based on bioinformatic analyses and lack experimental validation. Further experiments and larger sample sizes are required in future studies.

Conclusions

In summary, we provide a detailed single-cell atlas of the human appendix during development and explore the transcriptional characteristics of preschool appendicitis. Higher pericyte coverage, endothelial angiogenesis, and goblet mucus scores in healthy preschool appendices may contribute to the rarity of preschool appendicitis. Meanwhile, the abundance of lymphoid-associated glial cells and lymphoid cells, suppressed function in myeloid cells, and activation of the IL-17 pathway likely contribute to the severe inflammation observed in preschool appendicitis. IL1B+ macrophages, CD8-C3-NR4A1 T cells, and CD69+ B cells may be the key cell types involved in preschool appendicitis. Overall, our study provides a developmental perspective on the transcriptional landscape of preschool appendicitis, offering new insights for future research in this area.

Availability of data and materials

Healthy human PBMC data was downloaded from the 10 × Genomics websites (https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz). scRNA-seq data of the normal colon samples were gathered from the study of He et al. Bulk RNA-seq data of pediatric appendicitis were downloaded from the GSE9579 dataset. Raw sequencing data of fetal and pediatric appendices specimens in our study are available in the Genome Sequence Archive database under accession number HRA008187.

Abbreviations

DCs:

Dendritic cells

DEGs:

Differentially expressed genes

GSEA:

Gene set enrichment analysis

GW:

Gestational weeks

H&E:

Hematoxylin and eosin

IBD:

Inflammatory bowel disease

Ig:

Immunoglobulin

IHC:

Immunohistochemistry

IL:

Interleukin

ILCs:

Innate lymphoid cells

LTi:

Lymphoid tissue inducers

MAITs:

Mucosal-associated invariant T cells

mLTo:

Mesenchymal lymphoid tissue organizers

PBMCs:

Peripheral blood mononuclear cells

pDC:

Plasmacytoid dendritic cell

Rt-qPCR:

Real-time quantitative PCR

scRNA-seq:

Single-cell RNA sequencing

SMCs:

Smooth muscle cells

TF:

Transcription factor

Treg:

Regulatory T cells

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Acknowledgements

We wish to thank all the patients for their involvement in this study. Some of the icons and elements of diagrams were created with BioRender.com.

Funding

This study was supported by the Cyrus Tang Foundation, and Shanghai Hospital Development Center Foundation (SHDC 22022306).

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Author notes

  1. Lingdu Meng, Yifan Yang, and Shiwei He contributed equally to this work.

Authors and Affiliations

  1. Department of Pediatric Surgery, Shanghai Key Laboratory of Birth Defect, Children’s Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China

    Lingdu Meng, Yifan Yang, Huifen Chen, Yong Zhan, Ran Yang, Zifeng Li, Jiajie Zhu, Jin Zhou, Yi Li, Lulu Xie, Gong Chen, Shan Zheng & Rui Dong

  2. Family Planning Department, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

    Xiaoying Yao

  3. Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fujian, China

    Shiwei He

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  1. Lingdu Meng
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Contributions

LM, YY, and SH contributed to the study concept and design, data interpretation and analysis, drafting of the manuscript, and critical revision of the manuscript. HC participated in the study investigation. YZ, RY, and ZL contributed to data analysis. JZ1, JZ2, YL, LX, and GC collected the samples. SZ, XY, and RD supervised the study and critically revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shan Zheng, Xiaoying Yao or Rui Dong.

Ethics declarations

Ethics approval and consent to participate

This study was reviewed and approved by the ethics committees of the Children’s Hospital of Fudan University (protocol no. 2020(420)) and the Obstetrics and Gynecology Hospital of Fudan University (protocol no. 2018–069). Informed consent was obtained from each patient or the legal guardians of each participant if younger than 18 years old before the surgery and the termination of pregnancy, and the anonymity of all samples was strictly maintained throughout the study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

12916_2024_3611_MOESM1_ESM.docx

Additional file 1: Supplementary Methods, Table S1-S4, Fig. S1-S7. Table S1. Sample information; Table S2. Primary antibodies; Table S3. Gene sets used in cell scoring; Table S4. Primers used in qPCR assays. Fig. S1. Related to Fig. 1. Cell atlas of developing appendices; Fig. S2. Related to Fig. 2. Mesenchymal cells in different groups; Fig. S3. Related to Fig. 3. Endothelial and neural compartments in developing atlas; Fig. S4. Related to Fig. 4. Appendiceal epithelial cells in the development dataset and the differences between colonic epithelial cells; Fig. S5. Related to Fig. 5. Myeloid cells in developing appendices; Fig. S6. Related to Fig. 6. Lymphoid cells of appendices during the development; Fig. S7. Related to Fig. 7. Characteristics of preschool appendicitis.

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Meng, L., Yang, Y., He, S. et al. Single-cell sequencing of the vermiform appendix during development identifies transcriptional relationships with appendicitis in preschool children. BMC Med 22, 383 (2024). https://doi.org/10.1186/s12916-024-03611-9

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Keywords

BMC Medicine

ISSN: 1741-7015

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