Cross-species comparison of single-cell landscapes reveals conservation and innovation in chicken immune systems

Cross-species comparison of single-cell landscapes reveals conservation and innovation in chicken immune systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cross-species comparison of single-cell landscapes reveals conservation and innovation in chicken immune systems Yu Jiang, Fei Wang, Jilong Ren, Yaqi Zhou, Wuqiang Huo, Huichao Liu, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6164369/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Molecular characterization of chicken cells is essential for understanding avian physiology and vertebrate evolution, yet an organism-wide single-cell atlas in chicken is still lacking. Here we describe a comprehensive reference atlas of the chicken, encompassing 1.57 million cells across 157 cell types from 36 tissues, along with a spatial transcriptomic map of the embryo. By integrating it with 0.23 newly generated and 0.97 million single cells from 14 and 32 tissues in turtle and humans, respectively, we systematically explored the evolutionary rates of various cell types, particularly immune cells. The rapid evolution of chicken cells was generally characterized by changes in their gene regulatory networks and subsequent functional adaptations. In chicken, follicular dendritic cells emerge at the early development stage and exhibit myeloid rather than stromal origins, unlike in mammals. These cells share a regulatory network with mononuclear phagocytes and promote B cell proliferation and migration in the chicken-specific bursa of Fabricius. The observed variation in subtypes and proportions of γδ T cells across the three species reflected the evolution of pathogen recognition and signaling mechanisms among amniotes. These findings were further supported by generating 21,798 single cells from 3 tissues in ducks. Overall, we provides an invaluable resource to study chicken cell biology and evolution, as well as shines light on the evolutionary and cellular characteristics of immune cells across amniotes. Biological sciences/Zoology Biological sciences/Evolution Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology/Gene regulation in immune cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Birds and mammals diverged approximately 310 million years ago 1 , and birds have since developed several cells and tissues with specialized functions and physiological characteristics, including the gizzard and the bursa of Fabricius. As the most species-rich class of amniotes and the closest phylogenetic relatives to mammals, birds represent a critical branch in the study of vertebrate evolution. Several bird species, particularly chicken ( Gallus gallus ), have been widely used as models for studying avian biology and evolution. In addition, chicken is well-recognized as a model for human biology and diseases, such as neuroscience, immunology, and development, due to the ease of accessing and manipulating living embryos 2 . For example, HOXD11 has been identified as essential for limb bud development using chick embryos 3 . Moreover, birds are the major host of zoonotic pathogens such as avian influenza virus 4 and Salmonella 5 , which are transmitted globally and deleterious to public health. A comprehensive characterization of avian cells, particularly immune cells, and their unique functions is thus a prerequisite for developing effective and sustainable strategies to control these infectious diseases. Studies in birds have revealed notable differences in cellular function and composition compared to other animals, such as the ability of avian erythrocytes to recognize pathogens 6 , the presence of bursa-specific secretory dendritic cells (BSDCs) 7 , and a higher frequency of γδ T cells in peripheral blood 8 . Yet, investigating functions in avian cells using conventional cell-sorting approaches remains challenging due to their distinct cell receptors and the lack of reliable antibodies against them. Single-cell RNA sequencing(scRNA-seq) technology provided an unprecedented opportunity for characterizing cell composition, interactions, heterogeneity, and functions at the whole-transcriptome resolution without the need for species-specific antibodies. Single-cell atlases have been established in many species, including humans 9 , Macaca fascicularis 10 , and pigs 11 . However, previous single-cell studies in chickens were severely limited by cell numbers, tissue types, and developmental stages 12–15 . For example, scRNA-seq has been used to characterize the leukocyte profile in chicken peripheral blood 12 , but circulating immune cells represent only a subset of the entire immune cell landscape and have not been compared with other amniotes. A comprehensive chicken cell atlas is thus essential to assess the avian cellular evolution and conservation by comparing it with those of other amniotes. Here, we present a spatial transcriptomic map of chicken embryos and a comprehensive single-cell transcriptomic atlas using scRNA-seq, comprising over 1,576,581 cells from 36 tissues of chicken embryos and adults. To further explore the cellular evolution of amniotes and the unique characteristics of birds, we performed scRNA-seq on 232,761 cells from 14 tissues of adult turtles ( Trachemys scripta elegans ). By integrating publicly available 968,068 human cells from 32 tissues, we systematically studied the transcriptional evolution and conservation at single cell resolution across the three amniotes (Fig. 1 A). Additionally, to validate the observed evolution of immune cells is avian-specific rather than chicken-specific, we newly generated single-cell RNA-seq data of 21,798 cells from three immune tissues of ducks ( Anas platyrhynchos , i.e., spleen, bursa of Fabricius and intestine) to investigate the characteristics of the avian immune system. Altogether, the newly generated single-cell datasets from chickens, ducks and turtles provide a foundational resource for studying avian biology and vertebrate evolution at single cell resolution. Results Construction of single-cell and spatial transcriptome atlas for three amniotes To construct the chicken cell landscape, we performed sc/snRNA-seq across 10 and 35 tissues from three embryo and three adult animals, respectively, including immune-related tissues such as the thymus, caecal tonsil, bursa of Fabricius, spleen, and peripheral blood mononuclear cells (PBMCs) (Fig. 1 B). Most tissues were profiled by scRNA-seq, but for some tissues which contain cells with large diameters we used snRNA-seq (Extended Data Fig. 1 A). The gizzard was profiled using both scRNA-seq and snRNA-seq for comparison. After filtering out low-quality cells, we obtained a total of 1,576,581 single cells. The average number of cells and nuclei obtained from each tissue ranged from 5,084 (yolk sac) to 100,524 (gizzard), representing 0.32 and 6.4% of the total cells, respectively (Extended Data Fig. 1 A). After dimensionality reduction and clustering of all the cells, 157 cell types were annotated based on the expression of cell canonical marker genes (Extended Data Fig. 1 B and Supplementary Table 1). On average, 19 distinct cell types were identified per tissue, ranging from 3 in the oviduct to 35 in the spleen (Extended Data Fig. 1 A). The scRNA-seq and snRNA-seq from the same gizzard demonstrated a strong concordance in cell cluster integration and annotation (Extended Data Fig. 1 C). In addition, the common cell types captured by both approaches exhibited similar cell type-specific markers (Extended Data Fig. 1 D). However, the relative proportion of each cell type varied between approaches (Extended Data Fig. 1 E), consistent with previous studies 10,11 . To study the spatial localization of cell populations, we generated spatial transcriptomics from the 9-day-old chicken embryo when the major organs appeared 16 . In total, we retrieved transcriptomic information for 76,154 bins, with an average of 19,281 captured genes per bin (Extended Data Fig. 2 A). Unsupervised spatially constrained clustering of these bins showed transcriptomic configurations matching the localization of primary tissues (e.g., heart, lung and liver), demonstrating high-quality spatial transcriptomics data and annotation (Fig. 1 C, Extended Data Fig. 2 B). The primary cell types exhibited a good match with spatial distribution in tissue anatomy (Fig. 1 D, Extended Data Fig. 2 C). For example, in the gizzard, pit cells were localized to the center, whereas the two types of smooth muscle cells and fibroblasts consistently mapped to the periphery (Extended Data Fig. 3 A-C). For the turtle cell landscape, we generated a total of 232,761 cells from 14 adult tissues, representing 59 distinct cell types (Fig. 1 B, Extended Data Fig. 4 A, B, and Supplementary Table 2). On average, 20 distinct cell types were identified per tissue, ranging from 6 in the pancreas to 20 in the liver (Extended Data Fig. 4 A). Ionocytes, which specifically express FOXI1 and are important for ion transport and fluid pH regulation 17 , were found in the turtle lung and constituted 1.02% of all lung cells (Extended Data Fig. 4 C, D). Out of 33,289 cells in the chicken lung, no ionocytes were identified. Whereas ionocytes were identified recently and comprised a low proportion of epithelial cells (0.01%) in human lung (~ 75,000 cells) 18 , indicating changes in cell type composition of amniotes from aquatic to terrestrial environments. For human single cell data, we downloaded human 968,068 sc/snRNA-seq cells from 32 adult tissues that were nearly equivalent to those in chickens (Fig. 1 B). We then integrated them with our turtle and chicken data using 12,546 one-to-one orthologous genes. To address potential disparities in cell numbers among different cell types, we performed downsampling analysis for each cell type. Most same cell types from the three species clustered together (Fig. 1 E, Extended Data Fig. 5 ). Unsupervised MetaNeighbor analysis further revealed that the same cell: types displayed high AUROC values and similar transcriptional profiles (Extended Data Fig. 6 , Supplementary Table 3), indicating a high degree of similarity in global gene expression patterns between chicken and turtle. Comparison of cell landscapes among chickens, turtles and humans Approximately 75% of orthologous genes were expressed in the same cell types across chickens, turtles, and humans (A gene was defined as expressed if it was detected in more than 25% of cells within a specific cell type and its expression level exceeded the third quartile for that cell type), although some genes were expressed exclusively in one species (Fig. 2 A and Extended Data Fig. 7 ). Overall, most cell types across species showed significant similarity (Fig. 2 A and Extended Data Fig. 7 ). Notably, the expression levels of orthologous genes in matched cell types were more similar between chickens and turtles than between chickens and humans (Fig. 2 B). Some cell types, such as erythrocytes, exhibited rapid evolution among amniotes. Genes related to interferon signaling and antigen presentation exhibited species-specific expression in chicken erythrocytes (Fig. 2 C, D), but were either lowly expressed or not expressed in turtles and humans, respectively. This was consistent with previous reports that nucleated erythrocytes in birds participate in immune responses and react to microbes and pathogens 19 . Erythrocytes are also nucleated in turtles. However, the changes in erythrocyte function from turtles to chickens may reflect the enhancement of cellular immune functions during the transition from ectothermy to endothermy. For cell types shared between chickens and humans, several adrenal cell types (e.g., chromaffin cell, capsular cells, and zona glomerulosa cell) showed lower correlations compared to other shared cell types (Fig. 2 E and Extended Data Fig. 7 A). Chromaffin cells are primarily associated with the synthesis and secretion of catecholamines 20 . In chickens, 1,468 genes exhibited up-regulated expression in chromaffin cells compared to humans (Supplementary Table 4). Many of these genes participated in the synthesis and secretion of catecholamines ( SYT1 and SNAP25 ) 21,22 and the response to hormone ( CHRM3 and GHR ) 23,24 (Fig. 2 E). Functional enrichment analysis revealed these genes were significantly enriched in receptor tyrosine kinases, hormone secretion and response (Fig. 2 E). For zona glomerulosa cells, 1,471 genes were upregulated in chickens compared to humans (Supplementary Table 4). These genes, such as ACACA , FASN , HSD11B2 , and HSD3B1 , were enriched in response to hormones, lipid biosynthetic process, and metabolism of steroids 25–28 (Fig. 2 E). This indicates that chicken chromaffin cells and zona glomerulosa cells may possess elevated capacities for catecholamine and aldosterone production and secretion. Adrenal hormones play a critical role in regulating metabolism and electrolyte balance, including the elevation of blood glucose levels 29 . The increase in hormone secretion levels may represent a gene regulation required during powered flight, an energetically demanding transport form. In addition to these two cell types, capsular cells up-regulated 1,392 genes in chickens compared to humans (Supplementary Table 4). These up-regulated genes were enriched in gland development, steroid hormone-mediated pathways, and NOTCH and WNT pathways (Supplementary Table 4). Capsular cells for protective outer layer of the adrenal gland, with NOTCH and WNT pathways supporting their proliferation and self-renewal 30,31 . In addition to significant differences in cellular transcription, substantial differences in adrenal cell type composition were also observed between chickens and humans. In humans, the adrenal cortex is composed of three functional cell types: zona glomerulosa cells (ZG), zona fasciculata cells (ZF), and zona reticularis cells (ZR), which produce specific aldosterone, cortisol, and adrenal androgens, respectively 32 . In contrast, only two subpopulations of adrenal cortex cells were identified in chickens (Fig. 2 F). One cluster was highly enriched for the ZG marker genes HSD3B1 and AGTR1 , and another cluster enriched for the ZF and ZR marker genes CYP11A1 AKR1B1 and CYB5A (Fig. 2 G). Cellular divergence driven by evolutionary lability in gene expression localization Mononuclear phagocytes and lymphocytes are critical immune cell populations. Compared with humans, peculiarities in the chicken immune cells have been identified, such as the unique population of bursal secretory dendritic cells (BSDCs) in the bursa of Fabricius and the higher proportion of peripheral γδ T cells 7,8 . In total, we obtained 56,286, 40,420 and 264,141 immune cells in the adult chicken, turtle and human, respectively. This large dataset allowed us to explore the evolution of immune cells in amniotes. In total, 30, 17, and 31 mononuclear phagocyte and lymphocyte cell types were identified in chickens, turtles and humans, respectively. PDCs were present in both chickens and humans, while DC2s, migratory DCs, and MAIT cells were found exclusively in humans. In addition, some shared immune cell types, such as B cells and pDCs, exhibited distinct tissue distribution patterns. Besides the discrepancy in immune cell composition and distribution, we next quantified the similarity among the average transcriptomes of the immune cell types. We first used pairwise unsupervised MetaNeighbor analysis and the mean AUROC score to quantify the similarity between cell-type pairs 33 . Although human FDCs are stromal-derived cells rather than myeloid-derived cells, we included them in the following analysis to assess their similarities to chicken FDCs in gene expression. Cell-type dendrogram clustered immune cells into three major categories: mononuclear phagocytes, B lymphocytes, and T/innate lymphocytes (Fig. 3 A). While most cell types were arranged in accordance with the categories, some immune cell subtypes exhibited deviations, especially plasmacytoid dendritic cells(pDCs) (Fig. 3 A). Human pDCs clustered with B cells, whereas chicken pDCs clustered with mononuclear phagocytes. Further investigation revealed that 1,695 genes exhibited significant expression divergence in pDCs between the two species, with 898 showing a higher expression in chickens and 797 in humans (Supplementary Table 5). These genes with explicit expression in chicken pDCs were significantly enriched in viral infection, protein tyrosine kinase activity, platelet activation and leukocyte migration (Supplementary Table 5). Evolutionary changes in gene expression suggested that many of these genes showed marked changes in cell-type localization of expression between chicken and humans (Fig. 3 B and C, respectively). For example, genes related to lipid and lipoprotein transport, and those involved in scavenging free heme for iron recycling, were expressed in chicken pDCs, but in human erythrophagocytic macrophages (Fig. 3 B, C). Additionally, genes related to leukocyte migration and adhesion were expressed in chicken pDCs, but in human migratory dendritic cells(migDCs) (Fig. 3 B, C). These findings suggested that pDCs in chickens and humans underwent relatively rapid cellular divergence, driven partially by change in gene expression modules. To systematically identify evolutionary changes in the expression of orthologous genes in immune cells of chickens and humans, we referred to a previous method and divided expression patterns into four types 34 : Type 0 ('conserved'), Type 1('expression gain/loss'), Type 2 ('expression expansion/contraction') and Type 3('expression switch'). Through comparing gene expression of the same cell types between chickens and humans, Type 0 denotes genes that are expressed in both species, whereas type 1 denotes genes that are expressed in only one. Type 2 changes involve the gain or loss of expression in additional cell types during evolution. Type 3 ('expression switch') changes involve a switch in expression from one cell type to another. Only 5.05% of expressed genes showed fully conserved patterns between species. Most genes underwent expression gain/loss (type 1, 46.8%) or expansion/contraction (type 2,40.8%) (Supplementary Table 6). Given the divergence in pDC transcription observed above, we further focused on evolutionary changes in gene expression within mononuclear phagocytes. Only 9.49% of expressed genes showed fully conserved patterns (Fig. 3 D and Supplementary Table 7), suggesting expression patterns of nearly all genes are evolutionarily labile. For example, Type 0 gene CR2 was only expressed in FDCs in chickens and humans (Fig. 3 E). In contrast, Type 1 gene CXCL14 was expressed only in FDCs in humans (Fig. 3 E). For type 2 and type 3 genes, two important examples were ITGB2 and LYZ . ITGB2 , a known regulator of phagocytosis 35 , was mainly expressed in chicken FDCs and DC1s but in monocytes, macrophages, and DC1s in humans. LYZ encoding the antibacterial enzyme was selectively expressed in pDCs and FDCs in chickens, but in monocytes, macrophages, and DC1s in humans (Fig. 3 E). Avian FDC contributed to the migration and development of both B cell progenitors and germinal centre B cells In the chicken bursa of Fabricius, bursal secretory dendritic cells (BSDCs) are thought to contribute to the microenvironment necessary for B-lymphocyte differentiation 35 . However, their specific characteristics and role within follicle buds remain poorly understood. Here we analyzed 34,861 single cells from the embryonic bursa of Fabricius. One distinct cell cluster demonstrated a high and specific expression of BSDCs marker genes ( CSF1R and CD74 ) 37 , but not macrophage-specific markers C1QA and C1QC (Fig. 4 A, B). Spatial transcriptomics on the embryonic bursa of Fabricius further revealed that this cluster was scattered within the follicle and colocalized with B cells (Fig. 4 C, D and Extended Data Fig. 8A), indicating that this cluster is BSDCs. We then compared the transcriptional patterns of BSDC with those of other chicken mononuclear phagocytes. BSDCs in chickens specifically and highly expressed ZNF366 , LAMP3 , SPIC , MAFB and LYZ (Fig. 4 E), which are involved in the maturation and activation of mammalian DCs 38,39 , the differentiation of macrophage and antibacterial activity 40,41 . Among them, SPIC exhibited significant regulatory effects (Extended Data Fig. 8B). Up-regulated genes in BSDCs were significantly enriched in phagocytosis, adhesion, and positive regulation of leukocyte cell-cell adhesion (Extended Data Fig. 8C). Interestingly, they also expressed mammalian FDC marker genes CXCL13 , CR2 and TNFSF13B and interacted with B cell progenitors, partly mediated through CXCL13 and TNFSF13B (Fig. 4 F). CXCL13 was the main chemokine secreted in BSDCs, and its receptor CXCR5 was expressed by B cell progenitors (Fig. 4 F). In mammals, CXCL13/CXCR5 interaction from FDCs to mature B cells promotes the migration of the latter 42,43 . Parallel to this process, CXCL13/CXCR5 interaction from BSDCs to B cell progenitors facilitates the migration of these progenitors into the follicles of the bursa. In the bursa, only B cells not other immune cells, can migrate into the follicles 44 , likely attributed to the unique interactions between B cells and BSDCs. Besides, BAFF released by BSDCs interacted with BAFF-R in B cell progenitors (Fig. 4 F). This signaling promotes B cell proliferation and aids in maintaining the B cell population by preventing apoptosis 45 . Notably, in chickens, B cell lymphopoiesis is confined to the hematopoietic organs (such as the spleen) and the bursa of Fabricius, which was in line with the distribution of BSDC cells (Extended Data Fig. 9). The indispensable function and high tissue-specific distribution of BSDC partially explain why the bursa is the sole site for B cell development in chickens. Given that BSDCs exhibit high expression of FDC marker genes, we further explored the relationship between these two cell populations. In adult chickens, FDCs were identified using well-established marker genes CXCL13 , CR2 , and TNFSF13B . The distribution of FDCs aligned with that of GC B cells, predominantly found in the caecal tonsil, cecum, and bursa (Extended Data Fig. 9). The spatial transcriptomics data of the caecal tonsil showed that FDCs and GC B cells shared spatial neighbourhoods, indicating a preferential interaction between FDCs and B cells (Fig. 4 H). This interaction, mediated by CXCL13-CXCR5, VCAM1-ITGA4(VLA-4 subunit alpha) and TNFSF13B-TNFRSF13B signaling, is the same as the mechanism between human FDC and GC B cells (Extended Data Fig. 8D). These findings also support the accuracy of the annotation for chicken FDCs. When examining all mononuclear phagocytes, FDCs and BSDCs clustered together (Extended Data Fig. 8E). Correlation analysis revealed that the transcriptional patterns of the two cell types were highly similar (R = 0.92) (Fig. 4 G), indicating that BSDCs and FDCs are the same cell types at different developmental stages. Up-regulated genes in embryos were mainly enriched in protein transferring and processing, as well as histones/ribosome binding (Extended Data Fig. 8F). Conversely, up-regulated genes in adults showed enrichment for immune response and antigen presentation (Extended Data Fig. 8F). This transcriptional transition accompanies the maturation of immune functions analogous to the developmental process of mononuclear phagocytes in mammals 46 . Overall, chicken FDCs contribute to cellular migration and development for both embryonic B cell progenitors and adult germinal center B cells through similar mechanisms (Fig. 4 I). Human FDCs are differentiated from perivascular precursors of stromal cells, whereas chicken FDCs are differentiated from hematopoietic stem cells 7 . Pseudotime analysis also confirmed the myeloid origin of chicken FDCs (Extended Data Fig. 10A and Supplementary Table 8). To gain insight into the relationship between FDCs and other mononuclear phagocytes of amniotes, we integrated a total of 32,379 mononuclear phagocytes and FDCs from turtles (8,488; 0), chickens (11,517; 959) and humans (12,374; 203). The integrated cell map showed that monocytes, macrophages, DC1s and pDCs were distributed in the same cell clusters across the species (Fig. 4 J). By contrast, FDCs in chickens and humans were distributed in entirely different cell clusters. Human FDCs were concentrated in cluster 3, whereas chicken FDCs were in cluster 10 (Fig. 4 J). Moreover, chicken FDCs were closer to other mononuclear phagocytes, rather than human FDCs (Fig. 4 J). Pairwise cluster correlation analysis revealed that FDCs in chicken were more similar to myeloid cells than to human FDCs (Extended Data Fig. 10B). The above results indicated that, although FDCs in chicken and human expressed the same marker genes ( CXCL13 and CR2 ), their ontogeny and core regulatory complexes (CoRCs) were different. Human FDCs expressed marker genes TMEM119 and SOX9 47 , 48 (Fig. 4 J), whereas chicken FDC expressed ZNF366 , SPIC and MAFA (Fig. 4 K). In addition, by examining 17,383 cells from the duck spleen and bursa of Fabricius, we found that duck FDCs exhibited greater similarity to human monocytes than to FDCs (Extended Data Fig. 10C, D). In summary, the presence of distinct CoRCs enabled us to identify FDCs in both birds and mammals, which appear to have evolved similar functions through evolutionary convergence. The evolution of GC B cells and affinity maturation of antibodies in amniotes Germinal centers (GCs), present in birds and mammals, are key histological structures for generating high-affinity B cells. Reptiles lack visible GCs in their spleen and show limited affinity maturation and secondary antibody response 49 . However, sharks exhibit GC-like B cells and affinity maturation in splenic follicles 50 , prompting questions about the evolution of GCs and affinity maturation in vertebrates. Here we conducted a deep analysis and comparison on B cells in turtles (n = 13,686)) and chickens (n = 38,810). In adult chickens, we detected naive B, memory B, plasma B, and two populations of germinal center B cells (GC_B(I), GC_B(II)), consistent with mature and antigen-experienced B cell types in humans (Fig. 5 A). Naive B cells and memory B cells were characterized by differential expression of KLF2 , GPR183 , BCL2 and HHEX 51–54 (Extended Data Fig. 11A). In turtles, only four subtypes of B cells were identified (Fig. 5 A), including naive B, activated B, cycling B and plasma B cells. Naive B cells and activated B cells were distinguished by differential expression of KLF2 , IL16 and BCL6 (Extended Data Fig. 11A). To further investigate the transcriptional relationships between these populations, we compared B cells of turtles and chickens in detail (turtle:13,686; chicken:12,000, downsampled from the population of 38,810 B cells to make it comparable to the turtle dataset), due to the evolutionary proximity between them 55 . Pairwise correlation analysis confirmed that among all B cells, chicken GC_B(II) cells exhibited low similarity with turtle B cells and lacked a good counterpart in turtles. Integrative analysis also indicated that B cells from chicken co-clustered with their counterparts in turtle, and GC(I)_B of chicken co-clustered with cycling B cells of turtle (Extended Data Fig. 11B, C). Only GC(II)_B, concentrated in cluster 1, did not have a good counterpart in turtles. We also examined the expression of genes related to germinal center response in amniotes, including AICDA for somatic hypermutation and class-switch recombination 56 . The results showed that turtles exhibited lower levels of AICDA and BCL6 expression compared to those in chickens and humans (Fig. 5 C). This supports the Ag-receptor diversity of B cells in reptiles, but they may lack robust affinity maturation 57,58 . Interestingly, in amphibian Xenopus 59 , the expression of AICDA was comparable with that in turtles. However, MKI67 , a reliable marker of proliferating cells in Xenopus , was scarcely expressed, and no cluster of MKI67 -enriched proliferating B cells was observed (Fig. 5 C). Analogs of GC B cells in sharks suggested that the evolutionary foundation of GCs dates back to the jawed vertebrate ancestor. During animal evolution, the number of B cell populations has changed. GC_B(II) cell populations are lost in some amphibian and reptilian clades, such as Xenopus and turtle. Compared with humans, the proportion of GC B cells was remarkably increased in chickens, probably reflecting high germinal center activity (Fig. 5 D). The significant difference in proportions prompted us to explore the differences in the development of B cells between chickens and humans. We compared gene expression levels in the chicken cell populations with those of their orthologous genes in the corresponding human cell populations(12,000 chicken B cells and 9,022 human B cells). GC B(II) cells in the two species consistently exhibited low correlation, irrespective of the number of variable genes used (Fig. 5 E). Integrated analysis of chicken and human B cells also validated the lower similarity of GC(II)_B (Extended Data Fig. 11D, E). Gene expression analysis of GC(II)_B between chicken and human revealed 345 genes with species-biased expression patterns, including 121 up-regulated genes and 222 down-regulated genes in chicken (Fig. 5 F and Supplementary Table 9). Species-biased genes were enriched in lymphocyte activation and cell cycle categories, such as cell responses to stress, B cell receptor signaling pathway, and positive regulation of programmed cell death (Fig. 5 G). For example, MITF is a negative regulator of BCR signaling 60 . The voltage-gated proton channel HVCN1 promotes the production of ROS, which augments the proliferation of activated B cells and delays plasma cell differentiation 61–64 . In the germinal centre, B cells interact with FDCs and T follicular helper cells(Tfhs), which favours the survival of higher affinity B cells and forces others to undergo apoptosis by neglect 47 . Expression differences in genes related to BCR signal transduction and affinity selection pressures suggested potential differences in the production of high-affinity antibodies between chickens and humans. Cell type-specific CoRCs are the driver of cell type identity 65 . Transcriptional regulatory network analysis for GC(II)_B in chickens and humans showed that they shared transcription factors and regulatory mechanisms for GC B cell activation and survival, such as BCL6 and PAX5 (Fig. 5 H). Additionally, vital regulators of BCR signaling were unique in chickens, and genes encoding these regulators were highly expressed in chicken B cells. To validate whether these unique transcription patterns also exist in other birds, we compared the gene expression in ducks and humans' GC B(II) cells. Results showed that genes with high expression in chickens were also highly expressed in ducks (Fig. 5 I). This suggests that GC B(II) cells have undergone rapid molecular evolution in birds compared to other mature B cell populations. Divergent evolution of the γδ T lineage in amniotes Previous studies have revealed the T cells and innate lymphocyte subpopulations in chicken PBMCs using scRNA-seq 66 . Canonical marker genes of these cells are conserved between chickens and humans. In our comprehensive immune cells profiling, we identified 33 transcriptionally distinct cell populations in both embryonic and adult chickens (Fig. 6 A). Most cell types were consistent with those found in mammals, except for two unique clusters. One cell cluster, named CTSG + immune cells, was highly enriched for the lymphocyte marker genes such as CD3D and CD4 , as well as genes preferentially expressed in mammalian granulocytes or mast cells( CTSG , NDST2 , HDC , and CSF2RB ) (Fig. 6 B) 67 . This specific gene combinatorial code diverged from that of known conventional human immune cells. This cell type was detected in the spleen, intestine, and bursa of Fabricius in chickens (Extended Data Fig. 12A- C). The doublet formation rate, number of detected UMI and number of expressed genes of this cell type showed no significant difference to other cell clusters(Extended Data Fig. 12D-G). Up-regulated genes in CTSG + immune cells were enriched in leukocyte activation and differentiation (Fig. 6 C). Transcriptional pairwise cluster correlations between CTSG + immune cells and other immune cells revealed that it showed the strongest mutual correlations with ILC2 and heterophil, the avian equivalents of neutrophils (Fig. 6 D). To spatially resolve these cell populations, we performed subclustering and spatial gene expression analysis on transverse sections of the embryonic spleen. By mapping Stereo-seq data to our annotated single-cell RNA-seq dataset from the embryonic spleen, we identified various cell types involved in hematopoiesis, including hematopoietic progenitors and CTSG + immune cells (Extended Data Fig. 13A, B). Notably, CTSG + immune cells were found to be spatially localized near lymphoid cells (Extended Data Fig. 13C). Another cluster was enriched with genes involved in T cell activation, positive regulation of cytokine production and cytoskeleton regulation, but almost did not express CD4 and CD8A . This suggests that this cell population may be a non-canonical T cell subset with functions mediated through cytokines (Fig. 6 E). Notably, this cell cluster was only localized to adult caecal tonsil and spleen, indicating high tissue specificity (Extended Data Fig. 9). Compared to chickens and humans, only 13 T cell types/states were identified in turtles (Fig. 6 F), including major cell types such as ILC, NK, γδT and CD4+/CD8 + T cells. To examine T cells and innate lymphocytes with conserved or innovative gene expression profiles in adult turtles, chickens, and humans, we integrated 16,998, 29,800, and 28,258 cells in turtles, chickens and humans, respectively. Clustering of the integrated data yielded 37 cell clusters (Fig. 6 G-H). Generally, CD4+/CD8 + T cells and NK cells showed high similarity among amniotes (Extended Data Fig. 14A). For example, naïve T cells from these three species co-clustered and segregated into 4 clusters (C12, C37, C1, C35) (Fig. 6 I-J, Extended Data Fig. 14A). They shared expression of several transcription factors, including KLF2 , TCF7 , SATB1 and FOXP1 (Fig. 6 J), confirming that the CoRCs of naïve T cells were conserved across amniotes. Similarly, we also observed Tregs from these three species co-clustering in the same neighborhoods (cluster 7,11) (Fig. 6 I), which expressed CTLA4 at high levels (Fig. 6 J). Conversely, several integrated clusters included cells from chickens only, indicating that these cell types have unique gene expression profiles. For example, cluster 32 was mainly enriched in chicken γδ T_1 cells(Fig. 6 H). Consistent with this, the gene combinations specifically expressed in this cell type were not detected in either turtle or human (Fig. 6 J). Moreover, other γδ T cells from these three species did not co-cluster either, and the effector genes were also different (Fig. 6 H and Fig. 6 K, Extended Data Fig. 14A). For example, γδ T cells in humans with a distinctive expression of the cytotoxic effector molecul GZMA, co-clustered with effector CD8 + T cells. Although IL-17A producing γδ T cells exist in circulating T cells 68 , their low-frequency (1:2,762 T cells) may not be well represented in the human data we used. In turtles, γδ T cells mainly expressed the cytotoxic effector molecule GZMH. In chicken, γδ T_2 cells were committed to producing bacteriostatic or lytic molecules, such as IL17A and GNLY, and they co-clustered with Th17 cells. Weighted gene correlation network analysis also revealed that chicken γδ T_2 cells and Th17 cells shared a module and associated genes (Extended Data Fig. 14B, C). To explore if the main subpopulation of γδ T cells in other birds was consistent with those in chickens, we examined immune cell types and transcription patterns in the duck intestine. The results showed that duck γδ T cells were also enriched in IL17(Extended Data Fig. 14D, E). Taken together, these results indicated that the predominant γδ T cell subtypes differ across amniotes. Avian γδ T cell function and ontogeny Previous studies have shown that the frequency of chicken γδ T cells in peripheral blood is relatively high (20–50%) 8 , compared with that in humans (less than 5%) 69 . Therefore, chickens are referred to as “γδ T cell high” species, while humans belong to “γδ T cell low” species. Based on our data, proportions of γδ T cells in chickens increased by approximately 6.7% in all tissues compared to that in humans (Fig. 7 A). When focusing on PBMC, γδ T cells in chickens increased by approximately 10% compared to that in humans, respectively (Fig. 7 A). Apart from this, the integrated analysis above also indicated that γδ T cell subtypes and functions were significantly different between chicken and human. This substantive difference prompted us to explore the function and ontogeny of avian γδ T cells. In adult chickens, there were two γδ T cell subsets in peripheral tissue. Chicken γδ T_1 cell up-regulated genes that encode γδ T effector programming transcription factors SOX13 and MAF , as well as cytokine receptors ( IL20RA , IL9R ), but did not show cytokine production (Fig. 7 G). SOX13 and MAF are essential for the establishment of γδ T cell identity and the commitment of IL-17-producing γδ T cells 70,71 , indicating that γδ T_1 cells were not a terminally differentiated population. The γδ T_2 subpopulation up-regulated genes encoding bactericidal molecules (GNLY) and cytokines (IL17A, IL17F, IL22) (Fig. 7 G), which are involved in protective immunity against extracellular bacteria and fungi. Pseudotime analysis predicted a trajectory from immature γδ T cells in the thymus through γδ T_1 to γδ T_2(Fig. 7 B, C and Supplementary Table 10). Additionally, γδ T_1 cells were present in the thymus, PBMC and other tissues. In contrast, γδ T_2 cells were not identified in the thymus or PBMC, but only in other peripheral tissues (Extended Data Fig. 9). This suggested that γδ T cells in chicken commit to effector cytokine production in peripheral organs, rather than in the thymus. In peripheral tissues, apart from the direct function of pathogen clearance, the cytokines released by γδ T cells can interplay with other immune cells 72 , epithelial cells and fibroblasts to exert an immunoregulatory effect. To gain insight into the potential function of γδ T cells, we examined spatial transcriptomics in the chicken caecal tonsil, where immune cells were concentrated. Spatial distribution showed γδ T cells localized close to B cells. Local hotspots of ligand-receptor pairs occurred at the interface and mediated the interaction between them. These interactions included CD40LG-CD40, ICOS-ICOSL and TNFSF8-TNFRSF8 interaction(Fig. 7 D), which support B cell activation, growth and differentiation 73 . In mammals, IL17-expressing Th17 cells can function as B-cell helpers by not only triggering B cell proliferation but also promoting class-switch recombination 74 . Interestingly, Th1/Th17 were approximately 23% lower in chicken intestine compared with those in humans (Fig. 7 A). Therefore, γδ T cells expressing IL-17 function similarly to Th17 cells to exert immune regulatory effects in chickens. Additionally, γδ T cells were also physically close to the enterocytes. IL-17 released by γδ T_2 cells was predicted to interact with IL-17 receptors in enterocytes (Fig. 7 D). This interaction could induce the production of proinflammatory cytokines and chemokines, thereby recruiting lymphocytes 75 . In summary, the high frequency of γδ T_1 cells in adult chicken peripheral tissues contributes to the effective supply of effector γδ T_2 cells, which possess powerful antibacterial properties and bridge innate and adaptive immunity (Fig. 7 E). It has been reported that mammalian γδ T cells exhibit heterogeneity across developmental stages and tissues 76 . Based on this, we explored the heterogeneity of chicken γδ T cells. In addition to adult γδ T cells, we identified three distinct γδ T cell subsets in embryos, all of which displayed transcriptional profiles closely resembling those of adult γδ T cells (Fig. 7 F, G). For example, embryonic γδ T_2 cells highly expressed chemokines and IL-17 signaling molecules. They shared transcription modules with adult γδ T_2 subpopulation (Fig. 7 G, Extended Data Fig. 15A). Gene expression comparison between them revealed that embryonic γδ T_2 cells up-regulated interferon-related genes (IRF8, IFIH1, IFIT5), while adult γδ T_2 cells up-regulated genes related to antigen processing and presentation ( GPR183 , CD82 , TNFRSF9 ) (Fig. 7 H, Extended Data Fig. 15B). This indicated that the adaptive immune function of adult γδ T cells was gradually refined. To comprehensively understand the heterogeneity across tissues, we focused on the adult γδ T cells, due to their widespread organ distribution. In adult chickens, γδ T_1 cells showed a high tissue heterogeneity (Extended Data Fig. 15C). Thymic γδ T_1 cells expressed gene rearrangement and lineage differentiation-related genes ( RAG1 , RAG2 , SOX13 and TARP ) (Fig. 7 I). PBMC γδ T_1 cells overexpressed interferon and antigen presentation related genes ( BF1 , IFI6 and IRF1 ) (Fig. 7 I). Splenic and intestinal γδ T_1 cells had higher expression of chemotaxis genes. In contrast, γδ T_1 in non-immune organs had higher expression of genes associated with T cell activation (Fig. 7 I). These findings suggested that the maturation and activation of γδ T_1 cell accompany their egress from the thymus to seed peripheral tissues, where the local microenvironment shapes them into populations with distinct effector functions. In contrast, γδ T_2 could be further subdivided into three subsets (Extended Data Fig. 15D), all of which are distributed simultaneously in peripheral tissues. Cluster 1showed high expression of genes associated with lymphocyte-mediated antibacterial activity (GNLY), cluster 2 γδ T cells exhibited abundant pro-inflammatory cytokines and chemokines, whereas cluster 3 expressed stemness-associated markers (e.g., CCR7, TCF7) (Extended Data Fig. 15E). Discussion Our study provides a comprehensive single-cell dataset from multiple chicken organs, spanning > 1,500,000 single-cell profiles from 36 tissues and identifying 149 cell types. Using spatial transcriptomics, we delineated tissue organization and cellular communication networks. Besides, we profiled 232,761 cells from 59 cell types in adult turtles, allowing us to systematically compare cell types across amniotes by integrating our findings with published human single-cell transcriptome data. Notably, we detailed the immune cell landscapes to characterize avian immune features and to investigate the evolution of immunity across amniotes. Amniote vertebrates (reptiles, birds and mammals) originated from a common ancestor about 310 million years ago 1 . Our findings revealed high similarities in cell types between turtles, chickens and humans, underscoring the conservation of gene expression patterns across amniotes. While similarities in cell types across these species are evident, their evolutionary rates vary due to distinct evolutionary pressures. Previous studies have revealed the evolution of neurons and testicular cells in terms of anatomical structures, cell types, and molecular patterns, as shown by single-cell sequencing of brains and testis 77–79 . Our systematic comparison of amniotic cell landscapes confirmed rapid evolution in certain cell types, such as Sertoli cells and neurons, while also identifying other rapidly evolving cell types, including erythrocytes and adrenal cortex cells. Sequence divergence in non-coding genome regions likely drives the emergence of species-specific traits 80 . Future research should explore the evolution of gene regulatory programs to better understand how genetic divergence contributes to species-specific phenotypes. Accumulating studies demonstrate that molecular conservation is foundational to innate and adaptive immunity across vertebrates 81 . Consistently, in this study, we identified conserved immune profiles among amniotes in terms of immune cell populations and core regulatory complex, including macrophages, CD4+/CD8 + T cells, NK and B cells. Interestingly, innate immunity, considered primitive during evolution, displays low evolutionary conservation. Innate immune cell types have diversified during the evolution of amniotes. This diversification may reflect species-specific differences in pathogen recognition and signaling mechanisms. Furthermore, avian immune cell lineages, such as pDCs, FDCs, and γδ T cells, exhibit distinct characteristics compared to their mammalian counterparts. FDCs, for example, are essential for lymphoid follicles organization and the germinal center reaction. In humans, FDCs differentiate from stromal cells with the assistance of mature B cells 82,83 . However, avian FDCs were of hematopoietic origin, enabling their presence during embryonic hematopoiesis without mature B cells support. This early presence may offer two primary advantages for birds. First, it can assist B cell progenitors in migrating to the bursa of Fabricius (Fig. 4 ). Second, it may enhance the efficiency of germinal center formation and antibody production in birds. After hatching, chicks are immediately exposed to environmental antigens, pathogens, and gut microbiota colonization, increasing their immune system demands. The GC reaction is critical for production, as FDCs retains antigen-antibody complexes via complement and Fc receptors, supporting GC B cells survival 84 . Therefore, developing the follicular dendritic cell (FDC) network influences the germinal center responses. Previous research showed that the first germinal centers during the chicken ontogeny appear as early as the fourth day after hatching 85 . By contrast, there were no FDCs within the lymphoid microarchitecture in 7-days-old mice 86 and primary follicles first appeared on day 12 in the mesenteric nodes of mice 87 . Therefore, the early emergence of avian FDCs may be crucial for initiating antibody responses post-hatch. Apart from FDCs, innate γδ T cells also exhibit more rapid divergency than other immune cells in amniotes, varying in both proportions and the predominant cell subtypes. The high frequency of γδ T cells in both chicken embryos and adult chickens suggests an important role in pathogens' defence. In chicken embryos or young chicks, adaptive immune functions are relatively underdeveloped, positioning γδ T cells as critical components of the host's defence. In adults, γδ T cells are widely distributed across tissues in a preactivated or primed state, serving as frontline defenders. For example, increased numbers of γδ T cells have been reported in chickens' peripheral blood and spleen shortly after exposure to Marek’s disease virus and Salmonella 88,89 . Contrary to αβ T cells, γδ T cells recognize a broad range of antigens without restriction by major histocompatibility complex molecules and are primed for rapid effector function 90 . This characteristic may be particularly beneficial in defending farm animals against diverse environmental pathogens. In conclusion, our single-cell atlases of chicken and turtle, combined with spatial transcriptomic data, provide valuable resources for future research into avian morphological features, vertebrate evolution and zoonotic disease mechanisms. These findings lay the groundwork for further studies into the evolutionary pathways that have shaped immune responses across species. Methods Ethics statement The experimental protocol was approved by the Institutional Animal Care and Use Committee of HUNAU (2024 − 159). All experimental procedures were conducted following the national and institutional guidelines for using experimental animals for research. Collection of chicken, duck and turtle tissues A total of three adult White Leghorn chickens ( Gallus gallus , approximately 14 weeks) and three adult Xiangjia Black Phoenix chickens (approximately 13 weeks), three chicken embryos (17–21 days), three adult Peking ducks ( Anas platyrhynchos , approximately 24 weeks) and three adult red-eared slider turtles ( Trachemys scripta elegans , approximately 5 years) were obtained from the farm. All animals used in our study were healthy. Chicken, duck and turtle tissues were isolated, rinsed with PBS and minced into small pieces by mechanical dissociation. Next, the fresh samples prepared for scRNA-seq were transferred to MACS® tissue storage solution (Miltenyi Biotec Technology & Trading, #130-100-008) and stored at 4℃ until they were dissociated within 48 hours. Samples for snRNA-seq were transferred to cryogenic vials and then frozen and stored in liquid nitrogen until nuclear extraction was performed. Peripheral blood mononuclear cells from heparinized venous blood were isolated using mononuclear cell isolation kit (Solarbio, P8910) according to the protocol. Then cells from the blood were resuspended in freezing medium composed of 90% FBS and 10% DMSO and frozen using a freezing container in a - 80°C freezer for 24 hours before being transferred to liquid nitrogen for long-term storage. Single-nucleus/cell suspension preparation Single-nucleus isolation was performed as previously described 10,11 . In brief, frozen tissues were transferred to homogenizer with 1 ml lysis buffer consisting of 250 mM sucrose (Ambion), 10 mg/mL bovine serum albumin (Ambion), 5 mM MgCl2 (Ambion), 0.12 U/µL RNasin Plus (Promega, #N2115), 0.12 U/µL RNasein (Promega, #N2115) and 1× cOmplete Protease Inhibitor Cocktail (Roche, #11697498001). After two additional rounds of homogenization, the mixture was filtered through a 40-µm cell strainer and centrifuged at 500g for 5 min at 4°C. The pellets were resuspended in 1 ml of buffer B containing 320 mM sucrose, 10 mg ml-1 BSA, 3 mM CaCl2, 2 mM magnesium acetate, 0.1 mM EDTA, 10 mM Tris-HCl, 1 mM DTT, 1× cOmplete Protease Inhibitor Cocktail and 0.12 U µl-1 RNaseIn. After centrifugation as described above, nuclei were resuspended with cell resuspension buffer at a concentration of 1,000 nuclei per µl for library preparation. For cell suspension preparation, the fresh samples were incubated for 1 h in 10 ml DS-LT buffer (0.2 mg/ml CaCl2, 5 µM MgCl2, 0.2% BSA and 0.2 mg/ml Liberase in HBSS) at 37°C. After this, the tissue digestion was stopped by adding FBS. Then, the suspension was filtrated through a 100 µm cell strainer and centrifuge. Cells from the spleen were obtained from fresh tissue by mechanical dissociation. Cells from PBMCs were obtained as described above. Samples were filtered through a 40 µm cell strainer and centrifuged. Pellets were resuspended in cell resuspension buffer at 1,000 cells per µl for library preparation. Single-cell library construction and sequencing. The DNBelab C Series Single-Cell Library Prep Set (MGI, 1000021082) or 10X Chromium system were employed for library preparation according to the previously established protocols 10,91 . Briefly, single-nucleus/cell suspensions were used for droplet generation, emulsion breakage, bead collection, reverse transcription and cDNA amplification to generate barcoded libraries. Indexed libraries were constructed according to the manufacturer’s protocol. After the measurement of cDNA concentrations, libraries were sequenced on DNBSEQ-T7 or Illumina PE150 platform. scRNA-seq and snRNA-seq data processing After the filtering of raw sequencing reads, reads were aligned to the chicken genome (GCF_016699485.2), duck genome(GCA_008746955.1) and turtle genome (GCF_013100865.1). Three files related to genes, barcodes and the raw UMI count were generated by the DNBelab C Series scRNA analysis software ( https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_scRNA-analysis software) or Cell Ranger software. Then, we created a Seurat object using the Seurat R package (v.4.4.0) 92 . For each sample, cells were retained for downstream analysis if the number of detected genes exceeded 500 but was below the 95th percentile threshold, and if the percentage of mitochondrial genes was less than 15%. Doublets in the data set were filtered out with DoubletFinder (v.2.0.4). After filtering, the raw count matrix was normalized using the NormalizeData function with the LogNormalize method and a scaling factor of 10,000. The top 2,000 most variable genes of each sample were identified using the “FindVariableFeatures” function and library size was corrected using the ScaleData function. Harmony(v.1.2.0) performed batch correction across replicate s 93 . PCA was performed to reduce the dimensionality with the RunPCA function in Seurat. Graph-based clustering was performed to cluster cells according to their gene expression profile using the FindClusters function. Cells were visualized using two-dimensional UMAP algorithms with the RunUMAP function. We used the FindAllMarkers function to identify marker genes for each cluster. Only genes with an adjusted P-value 0.25, and detected in at least 25% of the cells within a cluster were identified as marker genes. Finally, each cluster was annotated based on the expression of established marker genes. To distinguish between cycling and non-cycling cell types, we used the CellCycleScoring function to calculate cell cycle scores, predicting whether they were in the G2M, S, or G1 phase. Clusters with elevated cell-cycle scores and increased expression of cell cycling marker genes(TOP2A and MKI67) were classified as cycling cell types. Spatial transcriptomics sequencing and data analysis For chicken embryo, spatial transcriptomics was performed using Stereo-seq platform according to the standard protocol 94 . Tissue section was adhered to the Stereo-seq chip surface and fixed in methanol, followed by nucleic acid staining and imaging. After permeabilization, mRNAs captured by DNA nanoballs (DNBs) on the chip were reverse transcribed and amplified. DNBs were then loaded onto the patterned Nano arrays and sequenced using the MGI DNBSEQ-Tx sequencer. After raw data was processed, we treated the bin 100 as the analysis unit and performed unsupervised clustering. Data normalization, scaling, and bins clustering were processed using the R package Seurat 92 . Tissue identities of clusters were annotated using tissue-specific expression genes. For the spleen and gizzard, we extract the corresponding areas and performed spatial reclustering. Specifically, we treated bin30 of the spleen and bin50 of the gizzard as the analysis units.For bursa of Fabricius and caecal tonsil, spatial transcriptomics was carried out using the BMKMANU S1000 platform according to the protocol 95 . Tissue sections of the chicken embryo bursa of Fabricius and adult chicken caecal tonsil were placed on sequence slides. The sections were then fixed with cold methanol, followed by H&E staining and imaging before permeabilization, according to the user guide for the BMKMANU S1000 Tissue Optimization Kit (BMKMANU, ST03003). Permeabilization, reverse transcription and cDNA synthesis were also performed according to the user guide. Libraries were sequenced on the Illumina NavoSeq. After filtering out low-quality sequences, the remaining sequences were aligned to the chicken reference genome using BSTMatrix v2.3 with default parameters, and an expression profile matrix was generated. To determine the cellular composition within spatial transcriptomics spots, we employed deconvolution methods in combination with annotated scRNA-seq data. Specifically, for the gizzard in the chicken embryo, we utilized the Tangram 96 method to integrate annotated gizzard scRNA-seq data with spatial transcriptomics data. This integration leveraged marker genes associated with annotated cell types in the gizzard as training genes for downstream analysis. After calculating probability scores for each cell's mapping within each spatial spot, we visualized the probability scores of each cell type using the tg.plot_cell_annotation_sc() function provided by Tangram. Spatial transcriptomics data of other tissues were analyzed by integrating scRNA-seq using conditional autoregressive-based deconvolution (CARD) 97 . This method enabled the transfer of cell-type annotations from scRNA-seq to spatial transcriptomics. CARD was also used to infer correlations in cell-type proportions across spatial locations between pairs of cell types. After annotating cell type diversity within each spot, significant ligand-receptor pairs between neighbouring spots were visualized using ggplot2. Pseudotime trajectory analysis The cell lineage trajectory was inferred via Monocle2 or Slingshot 98,99 . The trajectory of chicken FDCs was constructed by Monocle2(v2.8.0). The count matrix corresponding to the identified cell types was imported into Monocle. Cells were ordered along the trajectory and visualized in a reduced dimensional space. MPP cells were considered as the root state. Genes that exhibited significant changes along pseudotime were identified using the differentialGeneTest function and subsequently visualized using the plot_pseudotime_heatmap function. Slingshot was used to define computationally imputed pseudotime trajectories of γδ T cells. UMAP reduction was used to determine dimensionality and unbiased lineage was constructed by specifying only a start cluster (Double negative T cells). Lineages and gene expression were visualized using a combination of Slingshot visualization tools, the ggplot2 R package (v3.3.2) and the pheatmap R package (v1.17.4). TF-target interaction inference To uncover cell type-specific regulation, GENIE3 100 and SCENIC 101 were applied to infer the gene regulatory network and calculate the TF activity scores. The regulons specifically active in a selected cluster (compared to other clusters) were identified using the “calcRSS” and “plotRSS” functions in the AUCell R package. After screening the target modules, the gene interaction network was visualized using cytoscape 102 and Gephi 103 . To identify common regulatory networks for GC B(II) cells between chickens and humans, SCENIC was employed to characterize the transcription factors (TFs) in each species. Subsequently, GENIE3 was used to infer putative regulatory circuits based on these TFs, and the target genes identified by SCENIC were compared. Common regulatory circuits shared between chickens and humans were retained for further analysis. For each TF, the top three immune genes (from the Immunome database) with the highest regulatory weights were selected for subsequent regulatory network construction. For species-specific regulatory networks, the MAST package was used to identify differentially expressed genes (DEGs) unique to each species. Differentially expressed TFs were retained based on the TF lists for chicken and human obtained from the AnimalTFDB v4.0 database. For each TF, the top five immune genes with the highest regulatory weights, as determined by GENIE3, were used to construct the species-specific regulatory networks. Identification of One-to-One Orthologous Genes between Chicken, Turtle, and Human For chicken, we downloaded the list of orthologous genes between chicken and human from Ensemble BioMart release 113 and retained only one-to-one homologous gene pairs for downstream analysis. For turtle, we used BLASTP to align different sets of protein-coding genes, with human as the reference, based on nearest-neighbor alignment principles. To be specific, we performed both forward and reverse alignments: in the forward alignment, the turtle gene set was aligned to the human reference, and in the reverse alignment, the human gene set was aligned to the turtle reference. Orthologous gene relationships were determined based on criteria including an e-value threshold 80, and minimum sequence identity > 50%. The reciprocal best matches were considered one-to-one orthologous genes between humans and turtles. To ensure the completeness of orthologous genes across species, we utilized EggNOG Mapper with the taxonomic scope set to "Vertebrata" to identify one-to-one orthologs among the three species. We then performed a union of the orthologous gene pairs obtained from Ensemble BioMart and BLASTP with those identified by EggNOG Mapper. For any orthologous gene pairs not matching between these sources, we retained the orthologs identified by Ensemble BioMart and BLASTP. For cross-species comparison, we first extracted the single-cell expression matrices for each species, replacing the gene names with human orthologs and the vertebrate orthologs identified by EggNOG. For humans, we retained only those orthologous genes present in our identified homolog list. Cell-cell interaction analysis The R package CellChat 104 was used to identify and visualize cellular cross-talk between different cell types of scRNA-seq data. The over-expressed ligands or receptors were identified by “identifyOverExpressedGenes” and “identifyOverExpressedInteractions” functions. Then, we used the “computeCommunProb” functions to compute communication probability and infer cellular communication networks. And we applied the “filterCommunication” function to retain receptor-ligand pairs with at least 10 expressing cells. Cell-cell communication at the signaling pathway level between cell types was inferred using the “computeCommunProbPathway” and “aggregateNet” functions. Similarity of cell types across amniotes For shared cell types between chickens and humans, or chickens and turtles, we binarized genes as either expressed or not based on each cell type's average expression profile 18 . A gene was considered expressed if it was present in more than 25% of cells within a specific cell type and its expression level was above the third quartile for that cell type. The ratio of the number of genes expressed in both species to the number of genes expressed in either species is defined as the proportion of orthologous genes expressed in both species. For cell type similarity, we calculated mean expression profiles for each cell type (scaled as ln(CPM + 1)) and pairwise Spearman correlations were computed. The significance of the correlation coefficients was assessed using a permutation test (refer to PMID: 29724907). Specifically, gene expression values were shuffled 1,000 times across cell types, and the corresponding Spearman correlation coefficient (rho) was recalculated. The p-value was calculated as the fraction of absolute values of the rho values that were greater than or equal to the absolute value of rho from the actual (i.e.non-shuffled) data. For immune cells, Unsupervised MetaNeighbor analysis was used to systematically assess the transcriptional similarity between cell types across species, with mean AUROC (Area Under the Receiver Operating Characteristic) scores quantifying the similarity of cell-type pairs 33 . Before conducting the MetaNeighbor analysis, we quantified expression levels as counts per million (CPM) for each cell in the atlases and then scaled these values using the natural logarithm transformation (ln(CPM + 1)). To address data sparsity in low-coverage sequencing datasets, we employed a pseudo-cell approach to aggregate data from multiple cells of the same cell type. Specifically, we selected 30 cells from each cell type within each species to construct pseudo-cells. The dendrogram of immune cell types was then generated by hierarchical clustering using the Ward.D2 method, with distances based on mean AUROC scores. Joint CCA embedding of immune cell data from amniotes For cross-species comparisons, we used previously published comprehensive immune cell datasets from humans 105 . We then integrated the immune cells of three species using Seurat’s SCTransform workflow. Each cell class (Mononuclear phagocytic cells, B cells, and T cells and innate lymphocytes) was integrated separately. Only one-to-one ortholog genes between these three species, identified using EggNOG mapper and Ensembl, were retained. Integration features were selected by the “SelectIntegrationFeature” function. Integration anchors were identified based on the first 40 canonical components (“FindIntegrationAnchors” function, reduction= “CCA” and normalization.method = “SCT”) 106 . The first 18 principal components of the integrated data were used to visualize the data by a UMAP embedding and to construct a neighbourhood graph. For integrated T cells and innate lymphocyte cells, we obtained mean expression profiles for each cluster. The cluster distance matrix was computed as Spearman correlation and used for hierarchical clustering with the Ward.D2 method to generate dendrograms. Dendrograms were coloured according to the proportion of cells from each species within the integrated cluster. Chicken/duck and human gene alignment For the comparison of transcriptome profiles across species, the gene expression matrices were collapsed into homologous genes to enable direct comparison. We calculated mean expression profiles for each cell type (scaled as ln(CPM + 1)) and pairwise Spearman correlation coefficients using the “cor” function 18 . Species-enriched gene expression was defined as genes enriched 2-fold in either direction (chicken/duck > human or human > chicken/duck) with a p-value less than 0.05 (calculated by “MAST”). Correlations and cell type-specific genes were obtained in the same manner using all cells from BSDCs and FDCs. Weighted Gene Co-expression Network Analysis Weighted gene co-expression network analysis (WGCNA) was performed with functions in the WGCNA R package 107 . To attenuate the effects of noise and outliers, we aggregated data from 11 to 50 cells within the same cluster to create pseudo-cells for each cell type. High variable genes among the cells of interest were calculated. The top 2,500 variable genes determined in this way were used for analysis. The adjacency matrix was constructed by setting the soft power parameter to 10 using “pickSoftThreshold” function. From this adjacency matrix, a topological overlap matrix (TOM) was calculated using the “TOMsimilarityFromExpr” function and the TOM dissimilarity measure (1- TOM) was then used to cluster genes. Modules were identified using the dynamic tree-cutting algorithm with the “cutreeDynamic” function, and module eigengenes were defined using the “moduleEigengene” function. Gene expression pattern We categorized expression patterns into four types based on a previously established method 34 . Briefly, genes were classified as either expressed (1) or not expressed (0) based on the average expression profiles of each mononuclear phagocyte subtype in chickens and humans. A gene was considered "expressed" in a cell type if the median of its non-zero expression values across the constituent cells was greater than the median of non-zero expression values for all other genes, adjusted by adding or subtracting two standard deviations. Additionally, the percentage of cells within the cell type showing non-zero expression for the gene had to exceed the median percentage of non-zero expression for all other genes, similarly adjusted by adding or subtracting two standard deviations. We then aligned these gene vectors to match homologous cell types between species, and combined them into a single vector for each gene (V = (a-b) + 2ab, where a represents the ordered human vector and b the ordered chicken vector). This vector indicated for each cell type whether: both chicken and human expressed the gene (2), only human expressed it (1), only chicken expressed it (-1), or neither expressed it (0). We then classified genes based on the following criteria: conserved if any element of V equaled 2 and all other elements were 0; type 2 if any element equaled 2 and any other equaled 1 or -1; not expressed if all elements were 0; type 3 if both positive and negative elements were present; and type 1 if elements were either positive or negative and 0. Both type 0 and type 1 involve comparisons of gene expression changes within the same cell type between chickens and humans. Type 0 represents genes expressed in both species, while Type 1 indicates a simple gain or loss of expression between the species. Type 2 changes involve the gain or loss of expression in additional cell types during evolution. Type 3 refers to a change in expression from one cell type to another. Enrichment analysis for GO and KEGG Gene Ontology (GO) and KEGG pathways analyses were performed using David ( https://david.ncifcrf.gov/summary.jsp ) and Metascape ( https://metascape.org ). GO terms and KEGG pathways with p < 0.05 were considered significantly enriched. Declarations Data availability The datasets analyzed in this study are available from the Gene Expression Omnibus (GEO) repository under the following accession numbers: PRJNA1125639 and PRJNA1128021. Human scRNA-seq datasets were collected from the published articles and database (GSE134355; E-MTAB-11536; Gut Cell Atlas: https://www.gutcellatlas.org/). Acknowledgments This work was supported by the National Key R&D Program of China (2022YFF1000100 to Y.J.), China Agriculture Research System of MOF and MARA (CARS-41-Z08 to H.Z.), the National Natural Science Foundation of China (32302733 to F.W.). We thank the High-Performance Computing platform of Northwest A&F University. Author contributions Y.J., X.F., F.W. and Y.J. conceived and designed the project. Y.J. and X.F. supervised the work. F.W., J.R., Y.Z., H.L. and W.H. collected tissue samples and performed data analyses. Y.J., M.Q., T.S., H.S., H.T., H.W. and X.G. helped with bioinformatic analyses. Y.J., X.H., X.F., and Z.H. provided funding. J.M., Z.Y., L.F., Y.J., and H.Z. provided relevant advice. F.W., J.R. and Y.Z. wrote the manuscript. Y.J., L.F., J.S., H.Z., M.F., S.E.D., L.L. and Y.L. reviewed the manuscript. All authors contributed to the work. All authors read and approved the manuscript for submission. Competing interests The authors declare no competing interests. References Benton, M. J. & Donoghue, P. C. J., Paleontological evidence to date the tree of life. MOL BIOL EVOL 24 26 (2007). Brown, W. R. A., Hubbard, S. J., Tickle, C. & Wilson, S. A., The chicken as a model for large-scale analysis of vertebrate gene function. NAT REV GENET 4 87 (2003). Morgan, B. A., Izpisúa-Belmonte, J. C., Duboule, D. & Tabin, C. J., Targeted misexpression of Hox-4.6 in the avian limb bud causes apparent homeotic transformations. NATURE 358 236 (1992). 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Fang","email":"","orcid":"https://orcid.org/0000-0001-7061-3337","institution":"BGI-shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Fang","suffix":""},{"id":430597930,"identity":"c1314f0b-3ee1-4b33-90a6-4ebdd8aab0f2","order_by":24,"name":"Haihan Zhang","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haihan","middleName":"","lastName":"Zhang","suffix":""},{"id":430597931,"identity":"d62c2fb6-97ba-41de-a6ab-5de5c7dc74e6","order_by":25,"name":"Xi He","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"He","suffix":""},{"id":430597932,"identity":"2947dddd-4f64-488e-9a98-fd37e9ba877c","order_by":26,"name":"Lingzhao Fang","email":"","orcid":"https://orcid.org/0000-0003-1103-3679","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Lingzhao","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2025-03-05 16:25:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6164369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6164369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78873748,"identity":"9686272f-f89b-4ed1-b9c8-e091ec3b213e","added_by":"auto","created_at":"2025-03-20 06:48:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":715158,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the amniote cell atlas and spatial transcriptomic map. \u003cstrong\u003eA,\u003c/strong\u003e Illustration of the experimental workflow. \u003cstrong\u003eB,\u003c/strong\u003e Schematic representation of the chicken, turtle and human tissues analyzed in this study. Superscripts “a” and “b” represent the tissue analyzed in chicken embryos and adult chickens respectively. \u003cstrong\u003eC,\u003c/strong\u003e Top: Hematoxylin and Eosin (H\u0026amp;E) staining of chicken embryo sections from a 9-day-old embryo. Bottom: Unsupervised clustering of chicken embryo identified anatomic regions. \u003cstrong\u003eD, \u003c/strong\u003eSpatial visualization of the indicated cell types annotated using deconvolution. \u003cstrong\u003eE, \u003c/strong\u003eUMAP representations of integrated transcriptomes from turtles, chickens and humans(left), also shown by species of origin(right). The numbers in parentheses represent the number of cells within each cell category for each species: turtle, chicken, and human\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/19cf183a121426ccc73af436.png"},{"id":78873753,"identity":"75566f06-5f93-4386-8a44-421a48aa14a7","added_by":"auto","created_at":"2025-03-20 06:48:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":668640,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of amniotic cell landscapes. \u003cstrong\u003eA,\u003c/strong\u003e Transcriptional similarity of cell types in chicken compared to human. Left: The proportion of orthologous genes expressed in both species and a particular species. Right: Transcriptional similarity of the same cell type; ns indicated no statistically significant correlations and dot indicated statistically significant correlations. Only the top cell types with the lowest similarity are shown here. The similarity comparisons for all cell types are shown in the Extended Data Fig. 6. \u003cstrong\u003eB, \u003c/strong\u003eComparison of transcriptional similarity of shared cell types between chickens and humans and between chickens and turtles. \u003cstrong\u003eC, \u003c/strong\u003eScatter plot showing DEGs of erythrocytes across amniotes. \u003cstrong\u003eD,\u003c/strong\u003e Dot plot showing the expression of interferon and antigen presentation-related genes in turtle, chicken and human erythrocytes (Top). Gene ontology analysis of up-regulated genes in chicken erythrocytes compared to those of turtles and humans (Bottom). \u003cstrong\u003eE, \u003c/strong\u003eGene ontology analysis of up-regulated genes in chicken chromaffin cell and zona glomerulosa cell compared to those of humans. Violin plot showing the expression of genes related to hormone secretion and lipid metabolism in chicken and human chromaffin and zona glomerulosa cells. \u003cstrong\u003eF, \u003c/strong\u003eUMAP visualization of all clusters in the chicken adrenal gland, colored by major cell types. \u003cstrong\u003eG,\u003c/strong\u003e Feature plot showing the expression of humanadrenal cortex cell marker genes in chicken adrenal cell types.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/b793ff8a4a1da469c42d6bfd.png"},{"id":78874798,"identity":"52631380-7d49-44c0-a2de-811d14b33d35","added_by":"auto","created_at":"2025-03-20 06:56:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":554179,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary divergence of immune cell types and expression patterns. \u003cstrong\u003eA, \u003c/strong\u003eDendrogram of immune cell types in turtle, chicken and human based on the AUROC scores acquired from MetaNeighbor analyses within and between species. \u003cstrong\u003eB-C,\u003c/strong\u003e Heatmaps showing different expressions of genes involved in cell metabolism and migration in chicken and human immune cell types respectively. \u003cstrong\u003eD,\u003c/strong\u003e Doughnut plots showing the proportion of each scenario (type 0, 1, 2, 3) for the evolution of cellular expression patterns in chicken and human mononuclear phagocytes. \u003cstrong\u003eE,\u003c/strong\u003e Dot plots of expression of homologous genes implicated in chicken and human mononuclear phagocytic cell types exemplifying the four observed scenarios (Type 0,1,2,3) for the evolution of cellular expression patterns.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/24448bb95f4973df6c96f3d6.png"},{"id":78874799,"identity":"1a3c3cc3-a5ce-4ae1-a5c1-b676c65bf895","added_by":"auto","created_at":"2025-03-20 06:56:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":601548,"visible":true,"origin":"","legend":"\u003cp\u003eChicken FDC regulated both B cellprogenitors and germinal centre B cells. \u003cstrong\u003eA,\u003c/strong\u003e UMAP visualization of major cell types in the embryonic bursa of Fabricius in chicken. \u003cstrong\u003eB,\u003c/strong\u003e Dot plot for expression of marker genes of the identified cell populations in the embryonic bursa of Fabricius in chicken. \u003cstrong\u003eC,\u003c/strong\u003e Spatial visualization of B cells and BSDCs in the bursa of Fabricius in chicken embryo. \u003cstrong\u003eD,\u003c/strong\u003e Heatmap showing spatial proximity enrichment of cell-type pairs and cell-cell interactions in the bursa of Fabricius in the chicken embryo. \u003cstrong\u003eE,\u003c/strong\u003e Violin plot showing the expression of BSDCs enriched genes in all chicken mononuclear phagocytes. \u003cstrong\u003eF, \u003c/strong\u003eHierarchical plot showing the inferred intercellular communication network in the embryonic bursa in chicken. \u003cstrong\u003eG,\u003c/strong\u003e Scatter plot showing average expression levels (ln(CPM+1)) in BSDC of embryo and adult. R: Spearman correlation coefficient. Red dots indicated divergent genes expressed 2-fold higher in either species, p\u0026lt;0.05 (‘MAST’ differential gene expression test). \u003cstrong\u003eH,\u003c/strong\u003e Spatial visualization of FDCs and B cell populations in the caecal tonsil of the adult chicken. \u003cstrong\u003eI,\u003c/strong\u003e Diagram of the interaction mechanism between chicken FDCs and B cell progenitors and mature B cells. \u003cstrong\u003eJ,\u003c/strong\u003e Integrated analysis of mononuclear phagocytes across three species. Left: UMAP plot after integration of scRNA-seq data from mononuclear phagocytes of turtle (this study), chicken (this study) and human (public datasets). Right: percentage of cells from the original species-specific clusters (rows) in the integrated clusters (columns). \u003cstrong\u003eK,\u003c/strong\u003e Dot plot showing the expression of marker genes of FDCs in turtle, chicken and human mononuclear phagocytes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/760707a4163344ca3b41a4cb.png"},{"id":78873755,"identity":"c7aa85ac-94d0-44f6-93bc-37e0ca5ae8fb","added_by":"auto","created_at":"2025-03-20 06:48:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":593122,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of B cells across amniotes. \u003cstrong\u003eA,\u003c/strong\u003eUMAP visualization of all clusters coloured by B cell types in chicken and turtle. \u003cstrong\u003eB,\u003c/strong\u003e Pairwise correlations of B cell populations betweenchicken and turtle, calculated from highly variable genes. \u003cstrong\u003eC, \u003c/strong\u003eExpression of genes related to somatic hypermutation and class-switch recombination in \u003cem\u003eXenopus\u003c/em\u003e,turtle, chicken and human. L and S represent the L homeolog and Shomeolog in \u003cem\u003eXenopus\u003c/em\u003e. \u003cstrong\u003eD,\u003c/strong\u003eStacked bar plots showing the proportion of B cell populations in the gut of chicken and human, respectively. \u003cstrong\u003eE,\u003c/strong\u003e Line graph showing correlations of B cell populations between chicken and humans with different numbers of highly variable genes. X-axis: number of highly variable genes(nDEgene); Y-axis: spearman correlation coefficient. \u003cstrong\u003eF,\u003c/strong\u003e Scatter plot comparing average expression levels of individual genes (dots) in GC_B(Ⅱ) between humans and chickens. Red dots denote divergent genes expressed 2-fold higher in either species. p\u0026lt;0.05 (‘MAST’ differential gene expression test). Scale: log(CPM+1). \u003cstrong\u003eG,\u003c/strong\u003e GO analysis of up-regulated genes in chicken. \u003cstrong\u003eH,\u003c/strong\u003e Transcriptional regulatory network of GC_B(Ⅱ) in chickens and humans. Yellow nodes: human-specific regulons; green nodes: common regulons of chicken and human; red nodes: chicken-specific regulons. \u003cstrong\u003eI, \u003c/strong\u003eTop: Dot plot for expression of marker genes of the identified B cell populations in duck. Bottom : Scatter plot comparing average expression levels of genes(red dots) highly expressed in chicken GC_B(Ⅱ) between duck and human.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/1ac340151257bd6eaafd0019.png"},{"id":78874802,"identity":"f3dc6c56-544f-4ce1-8e7f-9fe2e0fed412","added_by":"auto","created_at":"2025-03-20 06:56:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1189383,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of T cells and innate lymphoid cells across amniotes. \u003cstrong\u003eA,\u003c/strong\u003e UMAP visualization of all clusters coloured by T cell types in chicken. \u003cstrong\u003eB, \u003c/strong\u003eUMAP visualization of marker genes of CTSG\u003csup\u003e+ \u003c/sup\u003eimmune cells. \u003cstrong\u003eC, \u003c/strong\u003eGO analysis of up-regulated genes in CTSG\u003csup\u003e+ \u003c/sup\u003eimmune cells. \u003cstrong\u003eD,\u003c/strong\u003e Pairwise correlations of chicken T cells and innate lymphoid cells calculated from highly variable genes. \u003cstrong\u003eE,\u003c/strong\u003e GO analysis of up-regulated genes of non-canonical T cells. \u003cstrong\u003eF, \u003c/strong\u003eUMAP visualization of all clusters coloured by T cell types in turtle. \u003cstrong\u003eG,\u003c/strong\u003e Integration of scRNA-seq data of T cells and innate lymphoid cells from adult turtle, chicken and human. Left: UMAP of the integrated data with dots colored by species mixture; dot size indicates cluster size. Right: UMAP plots of the integrated dataset showing cells from each species highlighted in black. \u003cstrong\u003eH,\u003c/strong\u003e Hierarchical clustering of the average expression profiles of integrated clusters; branches were colored by species mixture.\u003cstrong\u003e I,\u003c/strong\u003e Top: hierarchical clustering of average expression profiles of integrated clusters; branches are colored by species mixture. Bottom: percentage of cells from the original species-specific clusters (rows) in the integrated clusters (columns). \u003cstrong\u003eJ, \u003c/strong\u003eDotplot showing the expression of differentiation markers and transcription factors in integrated clusters (consistent with I) split by species. \u003cstrong\u003eK,\u003c/strong\u003e UMAP plots colored by the expression of effector genes of γδ T cells in across amniotes.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/421576ce597f52f7884ec805.png"},{"id":78874800,"identity":"59a1ec64-e21c-4b34-bcb7-1875419f1c48","added_by":"auto","created_at":"2025-03-20 06:56:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":572043,"visible":true,"origin":"","legend":"\u003cp\u003eChicken γδ T cell variation across time and tissues.\u003cstrong\u003e A,\u003c/strong\u003e Percentage frequency of T cell and innate lymphoid cell populations in the adult chicken and human.\u003cstrong\u003e B\u003c/strong\u003e, Pseudotime analysis of γδ T subsets in adult chicken. Top: UMAP visualization of adult γδ T subtypes. Bottom: Pseudotime prediction of adult γδ T differentiation trajectories from DN to γδ T_2. \u003cstrong\u003eC,\u003c/strong\u003e Top: Ridge plots showing the distribution of cell types along the pseudotime axis. The x-axis represents pseudo-temporal ordering, while the y-axis represents the density of cells at each pseudotime point. Bottom: Heatmaps of genes differentially expressed along the trajectories of the γδ T cells. \u003cstrong\u003eD,\u003c/strong\u003e Spatial visualization of cell populations and specific distribution patterns of receptor-ligand pairs in the caecal tonsil of the adult chicken. \u003cstrong\u003eE,\u003c/strong\u003e Proposed model of γδ T cell development.\u003cstrong\u003e F,\u003c/strong\u003e UMAP visualization of major γδ T cell subsets in the embryo and adult. \u003cstrong\u003eG,\u003c/strong\u003e Dot plot showing the expression of effector genes in γδ T subtypes. \u003cstrong\u003eH, \u003c/strong\u003eScatter plot showing average expression levels (ln(CPM+1)) in γδ T_2 of embryo and adult. R: Spearman correlation coefficient. Red dots indicate divergent genes expressed 2-fold higher in either species, p\u0026lt;0.05 (‘MAST’ differential gene expression test). \u003cstrong\u003eI,\u003c/strong\u003e Mean expression of a selection of differentially expressed genes between γδ T cells from thymus and other organs.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/b5eca44858e1eb21650d6487.png"},{"id":78876001,"identity":"6c721cb0-ac6c-44c3-8749-e0cd3b827385","added_by":"auto","created_at":"2025-03-20 07:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6522736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/a1ee920e-367a-49e5-bebf-7eeb160f43ea.pdf"},{"id":78873767,"identity":"218b5a12-019c-4e60-906d-c15f48f91cb4","added_by":"auto","created_at":"2025-03-20 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06:48:11","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":454601,"visible":true,"origin":"","legend":"Supplementary Dataset 10","description":"","filename":"SupplementaryTable10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6164369/v1/cdfe3d8631e070dbfe69ef61.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cross-species comparison of single-cell landscapes reveals conservation and innovation in chicken immune systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBirds and mammals diverged approximately 310\u0026nbsp;million years ago\u003csup\u003e1\u003c/sup\u003e, and birds have since developed several cells and tissues with specialized functions and physiological characteristics, including the gizzard and the bursa of Fabricius. As the most species-rich class of amniotes and the closest phylogenetic relatives to mammals, birds represent a critical branch in the study of vertebrate evolution. Several bird species, particularly chicken (\u003cem\u003eGallus gallus\u003c/em\u003e), have been widely used as models for studying avian biology and evolution. In addition, chicken is well-recognized as a model for human biology and diseases, such as neuroscience, immunology, and development, due to the ease of accessing and manipulating living embryos\u003csup\u003e2\u003c/sup\u003e. For example, HOXD11 has been identified as essential for limb bud development using chick embryos\u003csup\u003e3\u003c/sup\u003e. Moreover, birds are the major host of zoonotic pathogens such as avian influenza virus\u003csup\u003e4\u003c/sup\u003e and \u003cem\u003eSalmonella\u003c/em\u003e\u003csup\u003e5\u003c/sup\u003e, which are transmitted globally and deleterious to public health. A comprehensive characterization of avian cells, particularly immune cells, and their unique functions is thus a prerequisite for developing effective and sustainable strategies to control these infectious diseases.\u003c/p\u003e \u003cp\u003eStudies in birds have revealed notable differences in cellular function and composition compared to other animals, such as the ability of avian erythrocytes to recognize pathogens\u003csup\u003e6\u003c/sup\u003e, the presence of bursa-specific secretory dendritic cells (BSDCs)\u003csup\u003e7\u003c/sup\u003e, and a higher frequency of γδ T cells in peripheral blood\u003csup\u003e8\u003c/sup\u003e. Yet, investigating functions in avian cells using conventional cell-sorting approaches remains challenging due to their distinct cell receptors and the lack of reliable antibodies against them. Single-cell RNA sequencing(scRNA-seq) technology provided an unprecedented opportunity for characterizing cell composition, interactions, heterogeneity, and functions at the whole-transcriptome resolution without the need for species-specific antibodies. Single-cell atlases have been established in many species, including humans\u003csup\u003e9\u003c/sup\u003e, \u003cem\u003eMacaca fascicularis\u003c/em\u003e\u003csup\u003e10\u003c/sup\u003e, and pigs\u003csup\u003e11\u003c/sup\u003e. However, previous single-cell studies in chickens were severely limited by cell numbers, tissue types, and developmental stages\u003csup\u003e12\u0026ndash;15\u003c/sup\u003e. For example, scRNA-seq has been used to characterize the leukocyte profile in chicken peripheral blood\u003csup\u003e12\u003c/sup\u003e, but circulating immune cells represent only a subset of the entire immune cell landscape and have not been compared with other amniotes. A comprehensive chicken cell atlas is thus essential to assess the avian cellular evolution and conservation by comparing it with those of other amniotes.\u003c/p\u003e \u003cp\u003eHere, we present a spatial transcriptomic map of chicken embryos and a comprehensive single-cell transcriptomic atlas using scRNA-seq, comprising over 1,576,581 cells from 36 tissues of chicken embryos and adults. To further explore the cellular evolution of amniotes and the unique characteristics of birds, we performed scRNA-seq on 232,761 cells from 14 tissues of adult turtles (\u003cem\u003eTrachemys scripta elegans\u003c/em\u003e). By integrating publicly available 968,068 human cells from 32 tissues, we systematically studied the transcriptional evolution and conservation at single cell resolution across the three amniotes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Additionally, to validate the observed evolution of immune cells is avian-specific rather than chicken-specific, we newly generated single-cell RNA-seq data of 21,798 cells from three immune tissues of ducks (\u003cem\u003eAnas platyrhynchos\u003c/em\u003e, i.e., spleen, bursa of Fabricius and intestine) to investigate the characteristics of the avian immune system. Altogether, the newly generated single-cell datasets from chickens, ducks and turtles provide a foundational resource for studying avian biology and vertebrate evolution at single cell resolution.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of single-cell and spatial transcriptome atlas for three amniotes\u003c/h2\u003e \u003cp\u003eTo construct the chicken cell landscape, we performed sc/snRNA-seq across 10 and 35 tissues from three embryo and three adult animals, respectively, including immune-related tissues such as the thymus, caecal tonsil, bursa of Fabricius, spleen, and peripheral blood mononuclear cells (PBMCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Most tissues were profiled by scRNA-seq, but for some tissues which contain cells with large diameters we used snRNA-seq (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The gizzard was profiled using both scRNA-seq and snRNA-seq for comparison. After filtering out low-quality cells, we obtained a total of 1,576,581 single cells. The average number of cells and nuclei obtained from each tissue ranged from 5,084 (yolk sac) to 100,524 (gizzard), representing 0.32 and 6.4% of the total cells, respectively (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After dimensionality reduction and clustering of all the cells, 157 cell types were annotated based on the expression of cell canonical marker genes (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Supplementary Table\u0026nbsp;1). On average, 19 distinct cell types were identified per tissue, ranging from 3 in the oviduct to 35 in the spleen (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The scRNA-seq and snRNA-seq from the same gizzard demonstrated a strong concordance in cell cluster integration and annotation (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In addition, the common cell types captured by both approaches exhibited similar cell type-specific markers (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). However, the relative proportion of each cell type varied between approaches (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), consistent with previous studies\u003csup\u003e10,11\u003c/sup\u003e. To study the spatial localization of cell populations, we generated spatial transcriptomics from the 9-day-old chicken embryo when the major organs appeared\u003csup\u003e16\u003c/sup\u003e. In total, we retrieved transcriptomic information for 76,154 bins, with an average of 19,281 captured genes per bin (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Unsupervised spatially constrained clustering of these bins showed transcriptomic configurations matching the localization of primary tissues (e.g., heart, lung and liver), demonstrating high-quality spatial transcriptomics data and annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The primary cell types exhibited a good match with spatial distribution in tissue anatomy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). For example, in the gizzard, pit cells were localized to the center, whereas the two types of smooth muscle cells and fibroblasts consistently mapped to the periphery (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003eFor the turtle cell landscape, we generated a total of 232,761 cells from 14 adult tissues, representing 59 distinct cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, and Supplementary Table\u0026nbsp;2). On average, 20 distinct cell types were identified per tissue, ranging from 6 in the pancreas to 20 in the liver (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Ionocytes, which specifically express \u003cem\u003eFOXI1\u003c/em\u003e and are important for ion transport and fluid pH regulation\u003csup\u003e17\u003c/sup\u003e, were found in the turtle lung and constituted 1.02% of all lung cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Out of 33,289 cells in the chicken lung, no ionocytes were identified. Whereas ionocytes were identified recently and comprised a low proportion of epithelial cells (0.01%) in human lung (~\u0026thinsp;75,000 cells)\u003csup\u003e18\u003c/sup\u003e, indicating changes in cell type composition of amniotes from aquatic to terrestrial environments.\u003c/p\u003e \u003cp\u003eFor human single cell data, we downloaded human 968,068 sc/snRNA-seq cells from 32 adult tissues that were nearly equivalent to those in chickens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We then integrated them with our turtle and chicken data using 12,546 one-to-one orthologous genes. To address potential disparities in cell numbers among different cell types, we performed downsampling analysis for each cell type. Most same cell types from the three species clustered together (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Unsupervised MetaNeighbor analysis further revealed that the same cell: types displayed high AUROC values and similar transcriptional profiles (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Supplementary Table\u0026nbsp;3), indicating a high degree of similarity in global gene expression patterns between chicken and turtle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of cell landscapes among chickens, turtles and humans\u003c/h3\u003e\n\u003cp\u003eApproximately 75% of orthologous genes were expressed in the same cell types across chickens, turtles, and humans (A gene was defined as expressed if it was detected in more than 25% of cells within a specific cell type and its expression level exceeded the third quartile for that cell type), although some genes were expressed exclusively in one species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Overall, most cell types across species showed significant similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, the expression levels of orthologous genes in matched cell types were more similar between chickens and turtles than between chickens and humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Some cell types, such as erythrocytes, exhibited rapid evolution among amniotes. Genes related to interferon signaling and antigen presentation exhibited species-specific expression in chicken erythrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D), but were either lowly expressed or not expressed in turtles and humans, respectively. This was consistent with previous reports that nucleated erythrocytes in birds participate in immune responses and react to microbes and pathogens\u003csup\u003e19\u003c/sup\u003e. Erythrocytes are also nucleated in turtles. However, the changes in erythrocyte function from turtles to chickens may reflect the enhancement of cellular immune functions during the transition from ectothermy to endothermy.\u003c/p\u003e \u003cp\u003eFor cell types shared between chickens and humans, several adrenal cell types (e.g., chromaffin cell, capsular cells, and zona glomerulosa cell) showed lower correlations compared to other shared cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Chromaffin cells are primarily associated with the synthesis and secretion of catecholamines\u003csup\u003e20\u003c/sup\u003e. In chickens, 1,468 genes exhibited up-regulated expression in chromaffin cells compared to humans (Supplementary Table\u0026nbsp;4). Many of these genes participated in the synthesis and secretion of catecholamines (\u003cem\u003eSYT1\u003c/em\u003e and \u003cem\u003eSNAP25\u003c/em\u003e)\u003csup\u003e21,22\u003c/sup\u003e and the response to hormone (\u003cem\u003eCHRM3\u003c/em\u003e and \u003cem\u003eGHR\u003c/em\u003e)\u003csup\u003e23,24\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Functional enrichment analysis revealed these genes were significantly enriched in receptor tyrosine kinases, hormone secretion and response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). For zona glomerulosa cells, 1,471 genes were upregulated in chickens compared to humans (Supplementary Table\u0026nbsp;4). These genes, such as \u003cem\u003eACACA\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e, \u003cem\u003eHSD11B2\u003c/em\u003e, and \u003cem\u003eHSD3B1\u003c/em\u003e, were enriched in response to hormones, lipid biosynthetic process, and metabolism of steroids\u003csup\u003e25\u0026ndash;28\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). This indicates that chicken chromaffin cells and zona glomerulosa cells may possess elevated capacities for catecholamine and aldosterone production and secretion. Adrenal hormones play a critical role in regulating metabolism and electrolyte balance, including the elevation of blood glucose levels\u003csup\u003e29\u003c/sup\u003e. The increase in hormone secretion levels may represent a gene regulation required during powered flight, an energetically demanding transport form. In addition to these two cell types, capsular cells up-regulated 1,392 genes in chickens compared to humans (Supplementary Table\u0026nbsp;4). These up-regulated genes were enriched in gland development, steroid hormone-mediated pathways, and NOTCH and WNT pathways (Supplementary Table\u0026nbsp;4). Capsular cells for protective outer layer of the adrenal gland, with NOTCH and WNT pathways supporting their proliferation and self-renewal\u003csup\u003e30,31\u003c/sup\u003e. In addition to significant differences in cellular transcription, substantial differences in adrenal cell type composition were also observed between chickens and humans. In humans, the adrenal cortex is composed of three functional cell types: zona glomerulosa cells (ZG), zona fasciculata cells (ZF), and zona reticularis cells (ZR), which produce specific aldosterone, cortisol, and adrenal androgens, respectively\u003csup\u003e32\u003c/sup\u003e. In contrast, only two subpopulations of adrenal cortex cells were identified in chickens (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). One cluster was highly enriched for the ZG marker genes \u003cem\u003eHSD3B1\u003c/em\u003e and \u003cem\u003eAGTR1\u003c/em\u003e, and another cluster enriched for the ZF and ZR marker genes \u003cem\u003eCYP11A1 AKR1B1\u003c/em\u003e and \u003cem\u003eCYB5A\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCellular divergence driven by evolutionary lability in gene expression localization\u003c/h3\u003e\n\u003cp\u003eMononuclear phagocytes and lymphocytes are critical immune cell populations. Compared with humans, peculiarities in the chicken immune cells have been identified, such as the unique population of bursal secretory dendritic cells (BSDCs) in the bursa of Fabricius and the higher proportion of peripheral γδ T cells\u003csup\u003e7,8\u003c/sup\u003e. In total, we obtained 56,286, 40,420 and 264,141 immune cells in the adult chicken, turtle and human, respectively. This large dataset allowed us to explore the evolution of immune cells in amniotes. In total, 30, 17, and 31 mononuclear phagocyte and lymphocyte cell types were identified in chickens, turtles and humans, respectively. PDCs were present in both chickens and humans, while DC2s, migratory DCs, and MAIT cells were found exclusively in humans. In addition, some shared immune cell types, such as B cells and pDCs, exhibited distinct tissue distribution patterns. Besides the discrepancy in immune cell composition and distribution, we next quantified the similarity among the average transcriptomes of the immune cell types. We first used pairwise unsupervised MetaNeighbor analysis and the mean AUROC score to quantify the similarity between cell-type pairs\u003csup\u003e33\u003c/sup\u003e. Although human FDCs are stromal-derived cells rather than myeloid-derived cells, we included them in the following analysis to assess their similarities to chicken FDCs in gene expression. Cell-type dendrogram clustered immune cells into three major categories: mononuclear phagocytes, B lymphocytes, and T/innate lymphocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). While most cell types were arranged in accordance with the categories, some immune cell subtypes exhibited deviations, especially plasmacytoid dendritic cells(pDCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Human pDCs clustered with B cells, whereas chicken pDCs clustered with mononuclear phagocytes. Further investigation revealed that 1,695 genes exhibited significant expression divergence in pDCs between the two species, with 898 showing a higher expression in chickens and 797 in humans (Supplementary Table\u0026nbsp;5). These genes with explicit expression in chicken pDCs were significantly enriched in viral infection, protein tyrosine kinase activity, platelet activation and leukocyte migration (Supplementary Table\u0026nbsp;5). Evolutionary changes in gene expression suggested that many of these genes showed marked changes in cell-type localization of expression between chicken and humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C, respectively). For example, genes related to lipid and lipoprotein transport, and those involved in scavenging free heme for iron recycling, were expressed in chicken pDCs, but in human erythrophagocytic macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). Additionally, genes related to leukocyte migration and adhesion were expressed in chicken pDCs, but in human migratory dendritic cells(migDCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). These findings suggested that pDCs in chickens and humans underwent relatively rapid cellular divergence, driven partially by change in gene expression modules.\u003c/p\u003e \u003cp\u003eTo systematically identify evolutionary changes in the expression of orthologous genes in immune cells of chickens and humans, we referred to a previous method and divided expression patterns into four types\u003csup\u003e34\u003c/sup\u003e: Type 0 ('conserved'), Type 1('expression gain/loss'), Type 2 ('expression expansion/contraction') and Type 3('expression switch'). Through comparing gene expression of the same cell types between chickens and humans, Type 0 denotes genes that are expressed in both species, whereas type 1 denotes genes that are expressed in only one. Type 2 changes involve the gain or loss of expression in additional cell types during evolution. Type 3 ('expression switch') changes involve a switch in expression from one cell type to another. Only 5.05% of expressed genes showed fully conserved patterns between species. Most genes underwent expression gain/loss (type 1, 46.8%) or expansion/contraction (type 2,40.8%) (Supplementary Table\u0026nbsp;6). Given the divergence in pDC transcription observed above, we further focused on evolutionary changes in gene expression within mononuclear phagocytes. Only 9.49% of expressed genes showed fully conserved patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Supplementary Table\u0026nbsp;7), suggesting expression patterns of nearly all genes are evolutionarily labile. For example, Type 0 gene \u003cem\u003eCR2\u003c/em\u003e was only expressed in FDCs in chickens and humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In contrast, Type 1 gene \u003cem\u003eCXCL14\u003c/em\u003e was expressed only in FDCs in humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). For type 2 and type 3 genes, two important examples were \u003cem\u003eITGB2\u003c/em\u003e and \u003cem\u003eLYZ\u003c/em\u003e. \u003cem\u003eITGB2\u003c/em\u003e, a known regulator of phagocytosis\u003csup\u003e35\u003c/sup\u003e, was mainly expressed in chicken FDCs and DC1s but in monocytes, macrophages, and DC1s in humans. \u003cem\u003eLYZ\u003c/em\u003e encoding the antibacterial enzyme was selectively expressed in pDCs and FDCs in chickens, but in monocytes, macrophages, and DC1s in humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAvian FDC contributed to the migration and development of both B cell progenitors and germinal centre B cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the chicken bursa of Fabricius, bursal secretory dendritic cells (BSDCs) are thought to contribute to the microenvironment necessary for B-lymphocyte differentiation\u003csup\u003e35\u003c/sup\u003e. However, their specific characteristics and role within follicle buds remain poorly understood. Here we analyzed 34,861 single cells from the embryonic bursa of Fabricius. One distinct cell cluster demonstrated a high and specific expression of BSDCs marker genes (\u003cem\u003eCSF1R\u003c/em\u003e and \u003cem\u003eCD74\u003c/em\u003e)\u003csup\u003e37\u003c/sup\u003e, but not macrophage-specific markers \u003cem\u003eC1QA\u003c/em\u003e and \u003cem\u003eC1QC\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Spatial transcriptomics on the embryonic bursa of Fabricius further revealed that this cluster was scattered within the follicle and colocalized with B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D and Extended Data Fig.\u0026nbsp;8A), indicating that this cluster is BSDCs. We then compared the transcriptional patterns of BSDC with those of other chicken mononuclear phagocytes. BSDCs in chickens specifically and highly expressed \u003cem\u003eZNF366\u003c/em\u003e, \u003cem\u003eLAMP3\u003c/em\u003e, \u003cem\u003eSPIC\u003c/em\u003e, \u003cem\u003eMAFB\u003c/em\u003e and \u003cem\u003eLYZ\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), which are involved in the maturation and activation of mammalian DCs\u003csup\u003e38,39\u003c/sup\u003e, the differentiation of macrophage and antibacterial activity\u003csup\u003e40,41\u003c/sup\u003e. Among them, \u003cem\u003eSPIC\u003c/em\u003e exhibited significant regulatory effects (Extended Data Fig.\u0026nbsp;8B). Up-regulated genes in BSDCs were significantly enriched in phagocytosis, adhesion, and positive regulation of leukocyte cell-cell adhesion (Extended Data Fig.\u0026nbsp;8C). Interestingly, they also expressed mammalian FDC marker genes \u003cem\u003eCXCL13\u003c/em\u003e, \u003cem\u003eCR2\u003c/em\u003e and \u003cem\u003eTNFSF13B\u003c/em\u003e and interacted with B cell progenitors, partly mediated through \u003cem\u003eCXCL13\u003c/em\u003e and \u003cem\u003eTNFSF13B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). CXCL13 was the main chemokine secreted in BSDCs, and its receptor CXCR5 was expressed by B cell progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). In mammals, CXCL13/CXCR5 interaction from FDCs to mature B cells promotes the migration of the latter\u003csup\u003e42,43\u003c/sup\u003e. Parallel to this process, CXCL13/CXCR5 interaction from BSDCs to B cell progenitors facilitates the migration of these progenitors into the follicles of the bursa. In the bursa, only B cells not other immune cells, can migrate into the follicles\u003csup\u003e44\u003c/sup\u003e, likely attributed to the unique interactions between B cells and BSDCs. Besides, BAFF released by BSDCs interacted with BAFF-R in B cell progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This signaling promotes B cell proliferation and aids in maintaining the B cell population by preventing apoptosis\u003csup\u003e45\u003c/sup\u003e. Notably, in chickens, B cell lymphopoiesis is confined to the hematopoietic organs (such as the spleen) and the bursa of Fabricius, which was in line with the distribution of BSDC cells (Extended Data Fig.\u0026nbsp;9). The indispensable function and high tissue-specific distribution of BSDC partially explain why the bursa is the sole site for B cell development in chickens.\u003c/p\u003e \u003cp\u003eGiven that BSDCs exhibit high expression of FDC marker genes, we further explored the relationship between these two cell populations. In adult chickens, FDCs were identified using well-established marker genes \u003cem\u003eCXCL13\u003c/em\u003e, \u003cem\u003eCR2\u003c/em\u003e, and \u003cem\u003eTNFSF13B\u003c/em\u003e. The distribution of FDCs aligned with that of GC B cells, predominantly found in the caecal tonsil, cecum, and bursa (Extended Data Fig.\u0026nbsp;9). The spatial transcriptomics data of the caecal tonsil showed that FDCs and GC B cells shared spatial neighbourhoods, indicating a preferential interaction between FDCs and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). This interaction, mediated by CXCL13-CXCR5, VCAM1-ITGA4(VLA-4 subunit alpha) and TNFSF13B-TNFRSF13B signaling, is the same as the mechanism between human FDC and GC B cells (Extended Data Fig.\u0026nbsp;8D). These findings also support the accuracy of the annotation for chicken FDCs. When examining all mononuclear phagocytes, FDCs and BSDCs clustered together (Extended Data Fig.\u0026nbsp;8E). Correlation analysis revealed that the transcriptional patterns of the two cell types were highly similar (R\u0026thinsp;=\u0026thinsp;0.92) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG), indicating that BSDCs and FDCs are the same cell types at different developmental stages. Up-regulated genes in embryos were mainly enriched in protein transferring and processing, as well as histones/ribosome binding (Extended Data Fig.\u0026nbsp;8F). Conversely, up-regulated genes in adults showed enrichment for immune response and antigen presentation (Extended Data Fig.\u0026nbsp;8F). This transcriptional transition accompanies the maturation of immune functions analogous to the developmental process of mononuclear phagocytes in mammals\u003csup\u003e46\u003c/sup\u003e. Overall, chicken FDCs contribute to cellular migration and development for both embryonic B cell progenitors and adult germinal center B cells through similar mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003eHuman FDCs are differentiated from perivascular precursors of stromal cells, whereas chicken FDCs are differentiated from hematopoietic stem cells\u003csup\u003e7\u003c/sup\u003e. Pseudotime analysis also confirmed the myeloid origin of chicken FDCs (Extended Data Fig.\u0026nbsp;10A and Supplementary Table\u0026nbsp;8). To gain insight into the relationship between FDCs and other mononuclear phagocytes of amniotes, we integrated a total of 32,379 mononuclear phagocytes and FDCs from turtles (8,488; 0), chickens (11,517; 959) and humans (12,374; 203). The integrated cell map showed that monocytes, macrophages, DC1s and pDCs were distributed in the same cell clusters across the species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). By contrast, FDCs in chickens and humans were distributed in entirely different cell clusters. Human FDCs were concentrated in cluster 3, whereas chicken FDCs were in cluster 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Moreover, chicken FDCs were closer to other mononuclear phagocytes, rather than human FDCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Pairwise cluster correlation analysis revealed that FDCs in chicken were more similar to myeloid cells than to human FDCs (Extended Data Fig.\u0026nbsp;10B). The above results indicated that, although FDCs in chicken and human expressed the same marker genes (\u003cem\u003eCXCL13\u003c/em\u003e and \u003cem\u003eCR2\u003c/em\u003e), their ontogeny and core regulatory complexes (CoRCs) were different. Human FDCs expressed marker genes \u003cem\u003eTMEM119\u003c/em\u003e and \u003cem\u003eSOX9\u003c/em\u003e\u003csup\u003e47\u003c/sup\u003e,\u003csup\u003e48\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ), whereas chicken FDC expressed \u003cem\u003eZNF366\u003c/em\u003e, \u003cem\u003eSPIC\u003c/em\u003e and \u003cem\u003eMAFA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). In addition, by examining 17,383 cells from the duck spleen and bursa of Fabricius, we found that duck FDCs exhibited greater similarity to human monocytes than to FDCs (Extended Data Fig.\u0026nbsp;10C, D). In summary, the presence of distinct CoRCs enabled us to identify FDCs in both birds and mammals, which appear to have evolved similar functions through evolutionary convergence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThe evolution of GC B cells and affinity maturation of antibodies in amniotes\u003c/h3\u003e\n\u003cp\u003eGerminal centers (GCs), present in birds and mammals, are key histological structures for generating high-affinity B cells. Reptiles lack visible GCs in their spleen and show limited affinity maturation and secondary antibody response\u003csup\u003e49\u003c/sup\u003e. However, sharks exhibit GC-like B cells and affinity maturation in splenic follicles\u003csup\u003e50\u003c/sup\u003e, prompting questions about the evolution of GCs and affinity maturation in vertebrates. Here we conducted a deep analysis and comparison on B cells in turtles (n\u0026thinsp;=\u0026thinsp;13,686)) and chickens (n\u0026thinsp;=\u0026thinsp;38,810). In adult chickens, we detected naive B, memory B, plasma B, and two populations of germinal center B cells (GC_B(I), GC_B(II)), consistent with mature and antigen-experienced B cell types in humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Naive B cells and memory B cells were characterized by differential expression of \u003cem\u003eKLF2\u003c/em\u003e, \u003cem\u003eGPR183\u003c/em\u003e, \u003cem\u003eBCL2\u003c/em\u003e and \u003cem\u003eHHEX\u003c/em\u003e\u003csup\u003e51\u0026ndash;54\u003c/sup\u003e(Extended Data Fig.\u0026nbsp;11A). In turtles, only four subtypes of B cells were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), including naive B, activated B, cycling B and plasma B cells. Naive B cells and activated B cells were distinguished by differential expression of \u003cem\u003eKLF2\u003c/em\u003e, \u003cem\u003eIL16\u003c/em\u003e and \u003cem\u003eBCL6\u003c/em\u003e(Extended Data Fig.\u0026nbsp;11A). To further investigate the transcriptional relationships between these populations, we compared B cells of turtles and chickens in detail (turtle:13,686; chicken:12,000, downsampled from the population of 38,810 B cells to make it comparable to the turtle dataset), due to the evolutionary proximity between them\u003csup\u003e55\u003c/sup\u003e. Pairwise correlation analysis confirmed that among all B cells, chicken GC_B(II) cells exhibited low similarity with turtle B cells and lacked a good counterpart in turtles. Integrative analysis also indicated that B cells from chicken co-clustered with their counterparts in turtle, and GC(I)_B of chicken co-clustered with cycling B cells of turtle (Extended Data Fig.\u0026nbsp;11B, C). Only GC(II)_B, concentrated in cluster 1, did not have a good counterpart in turtles. We also examined the expression of genes related to germinal center response in amniotes, including \u003cem\u003eAICDA\u003c/em\u003e for somatic hypermutation and class-switch recombination\u003csup\u003e56\u003c/sup\u003e. The results showed that turtles exhibited lower levels of \u003cem\u003eAICDA\u003c/em\u003e and \u003cem\u003eBCL6\u003c/em\u003e expression compared to those in chickens and humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This supports the Ag-receptor diversity of B cells in reptiles, but they may lack robust affinity maturation\u003csup\u003e57,58\u003c/sup\u003e. Interestingly, in amphibian \u003cem\u003eXenopus\u003c/em\u003e\u003csup\u003e59\u003c/sup\u003e, the expression of \u003cem\u003eAICDA\u003c/em\u003e was comparable with that in turtles. However, \u003cem\u003eMKI67\u003c/em\u003e, a reliable marker of proliferating cells in \u003cem\u003eXenopus\u003c/em\u003e, was scarcely expressed, and no cluster of \u003cem\u003eMKI67\u003c/em\u003e-enriched proliferating B cells was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Analogs of GC B cells in sharks suggested that the evolutionary foundation of GCs dates back to the jawed vertebrate ancestor. During animal evolution, the number of B cell populations has changed. GC_B(II) cell populations are lost in some amphibian and reptilian clades, such as \u003cem\u003eXenopus\u003c/em\u003e and turtle.\u003c/p\u003e \u003cp\u003eCompared with humans, the proportion of GC B cells was remarkably increased in chickens, probably reflecting high germinal center activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The significant difference in proportions prompted us to explore the differences in the development of B cells between chickens and humans. We compared gene expression levels in the chicken cell populations with those of their orthologous genes in the corresponding human cell populations(12,000 chicken B cells and 9,022 human B cells). GC B(II) cells in the two species consistently exhibited low correlation, irrespective of the number of variable genes used (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Integrated analysis of chicken and human B cells also validated the lower similarity of GC(II)_B (Extended Data Fig.\u0026nbsp;11D, E). Gene expression analysis of GC(II)_B between chicken and human revealed 345 genes with species-biased expression patterns, including 121 up-regulated genes and 222 down-regulated genes in chicken (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF and Supplementary Table\u0026nbsp;9). Species-biased genes were enriched in lymphocyte activation and cell cycle categories, such as cell responses to stress, B cell receptor signaling pathway, and positive regulation of programmed cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). For example, \u003cem\u003eMITF\u003c/em\u003e is a negative regulator of BCR signaling\u003csup\u003e60\u003c/sup\u003e. The voltage-gated proton channel \u003cem\u003eHVCN1\u003c/em\u003e promotes the production of ROS, which augments the proliferation of activated B cells and delays plasma cell differentiation\u003csup\u003e61\u0026ndash;64\u003c/sup\u003e. In the germinal centre, B cells interact with FDCs and T follicular helper cells(Tfhs), which favours the survival of higher affinity B cells and forces others to undergo apoptosis by neglect\u003csup\u003e47\u003c/sup\u003e. Expression differences in genes related to BCR signal transduction and affinity selection pressures suggested potential differences in the production of high-affinity antibodies between chickens and humans. Cell type-specific CoRCs are the driver of cell type identity\u003csup\u003e65\u003c/sup\u003e. Transcriptional regulatory network analysis for GC(II)_B in chickens and humans showed that they shared transcription factors and regulatory mechanisms for GC B cell activation and survival, such as BCL6 and PAX5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Additionally, vital regulators of BCR signaling were unique in chickens, and genes encoding these regulators were highly expressed in chicken B cells. To validate whether these unique transcription patterns also exist in other birds, we compared the gene expression in ducks and humans' GC B(II) cells. Results showed that genes with high expression in chickens were also highly expressed in ducks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). This suggests that GC B(II) cells have undergone rapid molecular evolution in birds compared to other mature B cell populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDivergent evolution of the γδ T lineage in amniotes\u003c/h3\u003e\n\u003cp\u003ePrevious studies have revealed the T cells and innate lymphocyte subpopulations in chicken PBMCs using scRNA-seq\u003csup\u003e66\u003c/sup\u003e. Canonical marker genes of these cells are conserved between chickens and humans. In our comprehensive immune cells profiling, we identified 33 transcriptionally distinct cell populations in both embryonic and adult chickens (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Most cell types were consistent with those found in mammals, except for two unique clusters. One cell cluster, named CTSG\u0026thinsp;+\u0026thinsp;immune cells, was highly enriched for the lymphocyte marker genes such as \u003cem\u003eCD3D\u003c/em\u003e and \u003cem\u003eCD4\u003c/em\u003e, as well as genes preferentially expressed in mammalian granulocytes or mast cells(\u003cem\u003eCTSG\u003c/em\u003e, \u003cem\u003eNDST2\u003c/em\u003e, \u003cem\u003eHDC\u003c/em\u003e, and \u003cem\u003eCSF2RB\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB)\u003csup\u003e67\u003c/sup\u003e. This specific gene combinatorial code diverged from that of known conventional human immune cells. This cell type was detected in the spleen, intestine, and bursa of Fabricius in chickens (Extended Data Fig.\u0026nbsp;12A- C). The doublet formation rate, number of detected UMI and number of expressed genes of this cell type showed no significant difference to other cell clusters(Extended Data Fig.\u0026nbsp;12D-G). Up-regulated genes in CTSG\u0026thinsp;+\u0026thinsp;immune cells were enriched in leukocyte activation and differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Transcriptional pairwise cluster correlations between \u003cem\u003eCTSG\u003c/em\u003e\u0026thinsp;+\u0026thinsp;immune cells and other immune cells revealed that it showed the strongest mutual correlations with ILC2 and heterophil, the avian equivalents of neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To spatially resolve these cell populations, we performed subclustering and spatial gene expression analysis on transverse sections of the embryonic spleen. By mapping Stereo-seq data to our annotated single-cell RNA-seq dataset from the embryonic spleen, we identified various cell types involved in hematopoiesis, including hematopoietic progenitors and CTSG\u0026thinsp;+\u0026thinsp;immune cells (Extended Data Fig.\u0026nbsp;13A, B). Notably, CTSG\u0026thinsp;+\u0026thinsp;immune cells were found to be spatially localized near lymphoid cells (Extended Data Fig.\u0026nbsp;13C). Another cluster was enriched with genes involved in T cell activation, positive regulation of cytokine production and cytoskeleton regulation, but almost did not express \u003cem\u003eCD4\u003c/em\u003e and \u003cem\u003eCD8A\u003c/em\u003e. This suggests that this cell population may be a non-canonical T cell subset with functions mediated through cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Notably, this cell cluster was only localized to adult caecal tonsil and spleen, indicating high tissue specificity (Extended Data Fig.\u0026nbsp;9). Compared to chickens and humans, only 13 T cell types/states were identified in turtles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), including major cell types such as ILC, NK, γδT and CD4+/CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e \u003cp\u003eTo examine T cells and innate lymphocytes with conserved or innovative gene expression profiles in adult turtles, chickens, and humans, we integrated 16,998, 29,800, and 28,258 cells in turtles, chickens and humans, respectively. Clustering of the integrated data yielded 37 cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-H). Generally, CD4+/CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells showed high similarity among amniotes (Extended Data Fig.\u0026nbsp;14A). For example, na\u0026iuml;ve T cells from these three species co-clustered and segregated into 4 clusters (C12, C37, C1, C35) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI-J, Extended Data Fig.\u0026nbsp;14A). They shared expression of several transcription factors, including \u003cem\u003eKLF2\u003c/em\u003e, \u003cem\u003eTCF7\u003c/em\u003e, \u003cem\u003eSATB1\u003c/em\u003e and \u003cem\u003eFOXP1\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ), confirming that the CoRCs of na\u0026iuml;ve T cells were conserved across amniotes. Similarly, we also observed Tregs from these three species co-clustering in the same neighborhoods (cluster 7,11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI), which expressed \u003cem\u003eCTLA4\u003c/em\u003e at high levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Conversely, several integrated clusters included cells from chickens only, indicating that these cell types have unique gene expression profiles. For example, cluster 32 was mainly enriched in chicken γδ T_1 cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Consistent with this, the gene combinations specifically expressed in this cell type were not detected in either turtle or human (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Moreover, other γδ T cells from these three species did not co-cluster either, and the effector genes were also different (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK, Extended Data Fig.\u0026nbsp;14A). For example, γδ T cells in humans with a distinctive expression of the cytotoxic effector molecul GZMA, co-clustered with effector CD8\u0026thinsp;+\u0026thinsp;T cells. Although IL-17A producing γδ T cells exist in circulating T cells\u003csup\u003e68\u003c/sup\u003e, their low-frequency (1:2,762 T cells) may not be well represented in the human data we used. In turtles, γδ T cells mainly expressed the cytotoxic effector molecule GZMH. In chicken, γδ T_2 cells were committed to producing bacteriostatic or lytic molecules, such as IL17A and GNLY, and they co-clustered with Th17 cells. Weighted gene correlation network analysis also revealed that chicken γδ T_2 cells and Th17 cells shared a module and associated genes (Extended Data Fig.\u0026nbsp;14B, C). To explore if the main subpopulation of γδ T cells in other birds was consistent with those in chickens, we examined immune cell types and transcription patterns in the duck intestine. The results showed that duck γδ T cells were also enriched in IL17(Extended Data Fig.\u0026nbsp;14D, E). Taken together, these results indicated that the predominant γδ T cell subtypes differ across amniotes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAvian γδ T cell function and ontogeny\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that the frequency of chicken γδ T cells in peripheral blood is relatively high (20\u0026ndash;50%)\u003csup\u003e8\u003c/sup\u003e, compared with that in humans (less than 5%)\u003csup\u003e69\u003c/sup\u003e. Therefore, chickens are referred to as \u0026ldquo;γδ T cell high\u0026rdquo; species, while humans belong to \u0026ldquo;γδ T cell low\u0026rdquo; species. Based on our data, proportions of γδ T cells in chickens increased by approximately 6.7% in all tissues compared to that in humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). When focusing on PBMC, γδ T cells in chickens increased by approximately 10% compared to that in humans, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Apart from this, the integrated analysis above also indicated that γδ T cell subtypes and functions were significantly different between chicken and human. This substantive difference prompted us to explore the function and ontogeny of avian γδ T cells.\u003c/p\u003e \u003cp\u003eIn adult chickens, there were two γδ T cell subsets in peripheral tissue. Chicken γδ T_1 cell up-regulated genes that encode γδ T effector programming transcription factors \u003cem\u003eSOX13\u003c/em\u003e and \u003cem\u003eMAF\u003c/em\u003e, as well as cytokine receptors (\u003cem\u003eIL20RA\u003c/em\u003e, \u003cem\u003eIL9R\u003c/em\u003e), but did not show cytokine production (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). \u003cem\u003eSOX13\u003c/em\u003e and \u003cem\u003eMAF\u003c/em\u003e are essential for the establishment of γδ T cell identity and the commitment of IL-17-producing γδ T cells\u003csup\u003e70,71\u003c/sup\u003e, indicating that γδ T_1 cells were not a terminally differentiated population. The γδ T_2 subpopulation up-regulated genes encoding bactericidal molecules (GNLY) and cytokines (IL17A, IL17F, IL22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG), which are involved in protective immunity against extracellular bacteria and fungi. Pseudotime analysis predicted a trajectory from immature γδ T cells in the thymus through γδ T_1 to γδ T_2(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C and Supplementary Table\u0026nbsp;10). Additionally, γδ T_1 cells were present in the thymus, PBMC and other tissues. In contrast, γδ T_2 cells were not identified in the thymus or PBMC, but only in other peripheral tissues (Extended Data Fig.\u0026nbsp;9). This suggested that γδ T cells in chicken commit to effector cytokine production in peripheral organs, rather than in the thymus. In peripheral tissues, apart from the direct function of pathogen clearance, the cytokines released by γδ T cells can interplay with other immune cells\u003csup\u003e72\u003c/sup\u003e, epithelial cells and fibroblasts to exert an immunoregulatory effect. To gain insight into the potential function of γδ T cells, we examined spatial transcriptomics in the chicken caecal tonsil, where immune cells were concentrated. Spatial distribution showed γδ T cells localized close to B cells. Local hotspots of ligand-receptor pairs occurred at the interface and mediated the interaction between them. These interactions included CD40LG-CD40, ICOS-ICOSL and TNFSF8-TNFRSF8 interaction(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), which support B cell activation, growth and differentiation\u003csup\u003e73\u003c/sup\u003e. In mammals, IL17-expressing Th17 cells can function as B-cell helpers by not only triggering B cell proliferation but also promoting class-switch recombination\u003csup\u003e74\u003c/sup\u003e. Interestingly, Th1/Th17 were approximately 23% lower in chicken intestine compared with those in humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Therefore, γδ T cells expressing IL-17 function similarly to Th17 cells to exert immune regulatory effects in chickens. Additionally, γδ T cells were also physically close to the enterocytes. IL-17 released by γδ T_2 cells was predicted to interact with IL-17 receptors in enterocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). This interaction could induce the production of proinflammatory cytokines and chemokines, thereby recruiting lymphocytes\u003csup\u003e75\u003c/sup\u003e. In summary, the high frequency of γδ T_1 cells in adult chicken peripheral tissues contributes to the effective supply of effector γδ T_2 cells, which possess powerful antibacterial properties and bridge innate and adaptive immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eIt has been reported that mammalian γδ T cells exhibit heterogeneity across developmental stages and tissues\u003csup\u003e76\u003c/sup\u003e. Based on this, we explored the heterogeneity of chicken γδ T cells. In addition to adult γδ T cells, we identified three distinct γδ T cell subsets in embryos, all of which displayed transcriptional profiles closely resembling those of adult γδ T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, G). For example, embryonic γδ T_2 cells highly expressed chemokines and IL-17 signaling molecules. They shared transcription modules with adult γδ T_2 subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, Extended Data Fig.\u0026nbsp;15A). Gene expression comparison between them revealed that embryonic γδ T_2 cells up-regulated interferon-related genes (IRF8, IFIH1, IFIT5), while adult γδ T_2 cells up-regulated genes related to antigen processing and presentation (\u003cem\u003eGPR183\u003c/em\u003e, \u003cem\u003eCD82\u003c/em\u003e, \u003cem\u003eTNFRSF9\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH, Extended Data Fig.\u0026nbsp;15B). This indicated that the adaptive immune function of adult γδ T cells was gradually refined. To comprehensively understand the heterogeneity across tissues, we focused on the adult γδ T cells, due to their widespread organ distribution. In adult chickens, γδ T_1 cells showed a high tissue heterogeneity (Extended Data Fig.\u0026nbsp;15C). Thymic γδ T_1 cells expressed gene rearrangement and lineage differentiation-related genes (\u003cem\u003eRAG1\u003c/em\u003e, \u003cem\u003eRAG2\u003c/em\u003e, \u003cem\u003eSOX13\u003c/em\u003e and \u003cem\u003eTARP\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). PBMC γδ T_1 cells overexpressed interferon and antigen presentation related genes (\u003cem\u003eBF1\u003c/em\u003e, \u003cem\u003eIFI6\u003c/em\u003e and \u003cem\u003eIRF1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). Splenic and intestinal γδ T_1 cells had higher expression of chemotaxis genes. In contrast, γδ T_1 in non-immune organs had higher expression of genes associated with T cell activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). These findings suggested that the maturation and activation of γδ T_1 cell accompany their egress from the thymus to seed peripheral tissues, where the local microenvironment shapes them into populations with distinct effector functions. In contrast, γδ T_2 could be further subdivided into three subsets (Extended Data Fig.\u0026nbsp;15D), all of which are distributed simultaneously in peripheral tissues. Cluster 1showed high expression of genes associated with lymphocyte-mediated antibacterial activity (GNLY), cluster 2 γδ T cells exhibited abundant pro-inflammatory cytokines and chemokines, whereas cluster 3 expressed stemness-associated markers (e.g., CCR7, TCF7) (Extended Data Fig.\u0026nbsp;15E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides a comprehensive single-cell dataset from multiple chicken organs, spanning\u0026thinsp;\u0026gt;\u0026thinsp;1,500,000 single-cell profiles from 36 tissues and identifying 149 cell types. Using spatial transcriptomics, we delineated tissue organization and cellular communication networks. Besides, we profiled 232,761 cells from 59 cell types in adult turtles, allowing us to systematically compare cell types across amniotes by integrating our findings with published human single-cell transcriptome data. Notably, we detailed the immune cell landscapes to characterize avian immune features and to investigate the evolution of immunity across amniotes.\u003c/p\u003e \u003cp\u003eAmniote vertebrates (reptiles, birds and mammals) originated from a common ancestor about 310\u0026nbsp;million years ago\u003csup\u003e1\u003c/sup\u003e. Our findings revealed high similarities in cell types between turtles, chickens and humans, underscoring the conservation of gene expression patterns across amniotes. While similarities in cell types across these species are evident, their evolutionary rates vary due to distinct evolutionary pressures. Previous studies have revealed the evolution of neurons and testicular cells in terms of anatomical structures, cell types, and molecular patterns, as shown by single-cell sequencing of brains and testis\u003csup\u003e77\u0026ndash;79\u003c/sup\u003e. Our systematic comparison of amniotic cell landscapes confirmed rapid evolution in certain cell types, such as Sertoli cells and neurons, while also identifying other rapidly evolving cell types, including erythrocytes and adrenal cortex cells. Sequence divergence in non-coding genome regions likely drives the emergence of species-specific traits\u003csup\u003e80\u003c/sup\u003e. Future research should explore the evolution of gene regulatory programs to better understand how genetic divergence contributes to species-specific phenotypes.\u003c/p\u003e \u003cp\u003eAccumulating studies demonstrate that molecular conservation is foundational to innate and adaptive immunity across vertebrates\u003csup\u003e81\u003c/sup\u003e. Consistently, in this study, we identified conserved immune profiles among amniotes in terms of immune cell populations and core regulatory complex, including macrophages, CD4+/CD8\u0026thinsp;+\u0026thinsp;T cells, NK and B cells. Interestingly, innate immunity, considered primitive during evolution, displays low evolutionary conservation. Innate immune cell types have diversified during the evolution of amniotes. This diversification may reflect species-specific differences in pathogen recognition and signaling mechanisms. Furthermore, avian immune cell lineages, such as pDCs, FDCs, and γδ T cells, exhibit distinct characteristics compared to their mammalian counterparts.\u003c/p\u003e \u003cp\u003eFDCs, for example, are essential for lymphoid follicles organization and the germinal center reaction. In humans, FDCs differentiate from stromal cells with the assistance of mature B cells\u003csup\u003e82,83\u003c/sup\u003e. However, avian FDCs were of hematopoietic origin, enabling their presence during embryonic hematopoiesis without mature B cells support. This early presence may offer two primary advantages for birds. First, it can assist B cell progenitors in migrating to the bursa of Fabricius (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Second, it may enhance the efficiency of germinal center formation and antibody production in birds. After hatching, chicks are immediately exposed to environmental antigens, pathogens, and gut microbiota colonization, increasing their immune system demands. The GC reaction is critical for production, as FDCs retains antigen-antibody complexes via complement and Fc receptors, supporting GC B cells survival\u003csup\u003e84\u003c/sup\u003e. Therefore, developing the follicular dendritic cell (FDC) network influences the germinal center responses. Previous research showed that the first germinal centers during the chicken ontogeny appear as early as the fourth day after hatching\u003csup\u003e85\u003c/sup\u003e. By contrast, there were no FDCs within the lymphoid microarchitecture in 7-days-old mice\u003csup\u003e86\u003c/sup\u003e and primary follicles first appeared on day 12 in the mesenteric nodes of mice\u003csup\u003e87\u003c/sup\u003e. Therefore, the early emergence of avian FDCs may be crucial for initiating antibody responses post-hatch.\u003c/p\u003e \u003cp\u003eApart from FDCs, innate γδ T cells also exhibit more rapid divergency than other immune cells in amniotes, varying in both proportions and the predominant cell subtypes. The high frequency of γδ T cells in both chicken embryos and adult chickens suggests an important role in pathogens' defence. In chicken embryos or young chicks, adaptive immune functions are relatively underdeveloped, positioning γδ T cells as critical components of the host's defence. In adults, γδ T cells are widely distributed across tissues in a preactivated or primed state, serving as frontline defenders. For example, increased numbers of γδ T cells have been reported in chickens' peripheral blood and spleen shortly after exposure to Marek\u0026rsquo;s disease virus and \u003cem\u003eSalmonella\u003c/em\u003e\u003csup\u003e88,89\u003c/sup\u003e. Contrary to αβ T cells, γδ T cells recognize a broad range of antigens without restriction by major histocompatibility complex molecules and are primed for rapid effector function\u003csup\u003e90\u003c/sup\u003e. This characteristic may be particularly beneficial in defending farm animals against diverse environmental pathogens.\u003c/p\u003e \u003cp\u003eIn conclusion, our single-cell atlases of chicken and turtle, combined with spatial transcriptomic data, provide valuable resources for future research into avian morphological features, vertebrate evolution and zoonotic disease mechanisms. These findings lay the groundwork for further studies into the evolutionary pathways that have shaped immune responses across species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The experimental protocol was approved by the Institutional Animal Care and Use Committee of HUNAU (2024\u0026thinsp;\u0026minus;\u0026thinsp;159). All experimental procedures were conducted following the national and institutional guidelines for using experimental animals for research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCollection of chicken, duck and turtle tissues\u003c/h2\u003e \u003cp\u003eA total of three adult White Leghorn chickens (\u003cem\u003eGallus gallus\u003c/em\u003e, approximately 14 weeks) and three adult Xiangjia Black Phoenix chickens (approximately 13 weeks), three chicken embryos (17\u0026ndash;21 days), three adult Peking ducks (\u003cem\u003eAnas platyrhynchos\u003c/em\u003e, approximately 24 weeks) and three adult red-eared slider turtles (\u003cem\u003eTrachemys scripta elegans\u003c/em\u003e, approximately 5 years) were obtained from the farm. All animals used in our study were healthy. Chicken, duck and turtle tissues were isolated, rinsed with PBS and minced into small pieces by mechanical dissociation. Next, the fresh samples prepared for scRNA-seq were transferred to MACS\u0026reg; tissue storage solution (Miltenyi Biotec Technology \u0026amp; Trading, #130-100-008) and stored at 4℃ until they were dissociated within 48 hours. Samples for snRNA-seq were transferred to cryogenic vials and then frozen and stored in liquid nitrogen until nuclear extraction was performed. Peripheral blood mononuclear cells from heparinized venous blood were isolated using mononuclear cell isolation kit (Solarbio, P8910) according to the protocol. Then cells from the blood were resuspended in freezing medium composed of 90% FBS and 10% DMSO and frozen using a freezing container in a \u003cb\u003e-\u003c/b\u003e80\u0026deg;C freezer for 24 hours before being transferred to liquid nitrogen for long-term storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSingle-nucleus/cell suspension preparation\u003c/h2\u003e \u003cp\u003eSingle-nucleus isolation was performed as previously described\u003csup\u003e10,11\u003c/sup\u003e. In brief, frozen tissues were transferred to homogenizer with 1 ml lysis buffer consisting of 250 mM sucrose (Ambion), 10 mg/mL bovine serum albumin (Ambion), 5 mM MgCl2 (Ambion), 0.12 U/\u0026micro;L RNasin Plus (Promega, #N2115), 0.12 U/\u0026micro;L RNasein (Promega, #N2115) and 1\u0026times; cOmplete Protease Inhibitor Cocktail (Roche, #11697498001). After two additional rounds of homogenization, the mixture was filtered through a 40-\u0026micro;m cell strainer and centrifuged at 500g for 5 min at 4\u0026deg;C. The pellets were resuspended in 1 ml of buffer B containing 320 mM sucrose, 10 mg ml-1 BSA, 3 mM CaCl2, 2 mM magnesium acetate, 0.1 mM EDTA, 10 mM Tris-HCl, 1 mM DTT, 1\u0026times; cOmplete Protease Inhibitor Cocktail and 0.12 U \u0026micro;l-1 RNaseIn. After centrifugation as described above, nuclei were resuspended with cell resuspension buffer at a concentration of 1,000 nuclei per \u0026micro;l for library preparation.\u003c/p\u003e \u003cp\u003eFor cell suspension preparation, the fresh samples were incubated for 1 h in 10 ml DS-LT buffer (0.2 mg/ml CaCl2, 5 \u0026micro;M MgCl2, 0.2% BSA and 0.2 mg/ml Liberase in HBSS) at 37\u0026deg;C. After this, the tissue digestion was stopped by adding FBS. Then, the suspension was filtrated through a 100 \u0026micro;m cell strainer and centrifuge. Cells from the spleen were obtained from fresh tissue by mechanical dissociation. Cells from PBMCs were obtained as described above. Samples were filtered through a 40 \u0026micro;m cell strainer and centrifuged. Pellets were resuspended in cell resuspension buffer at 1,000 cells per \u0026micro;l for library preparation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell library construction and sequencing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe DNBelab C Series Single-Cell Library Prep Set (MGI, 1000021082) or 10X Chromium system were employed for library preparation according to the previously established protocols\u003csup\u003e10,91\u003c/sup\u003e. Briefly, single-nucleus/cell suspensions were used for droplet generation, emulsion breakage, bead collection, reverse transcription and cDNA amplification to generate barcoded libraries. Indexed libraries were constructed according to the manufacturer\u0026rsquo;s protocol. After the measurement of cDNA concentrations, libraries were sequenced on DNBSEQ-T7 or Illumina PE150 platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq and snRNA-seq data processing\u003c/h2\u003e \u003cp\u003eAfter the filtering of raw sequencing reads, reads were aligned to the chicken genome (GCF_016699485.2), duck genome(GCA_008746955.1) and turtle genome (GCF_013100865.1). Three files related to genes, barcodes and the raw UMI count were generated by the DNBelab C Series scRNA analysis software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_scRNA-analysis\u003c/span\u003e\u003cspan address=\"https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_scRNA-analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e software) or Cell Ranger software. Then, we created a Seurat object using the Seurat R package (v.4.4.0) \u003csup\u003e\u003cb\u003e92\u003c/b\u003e\u003c/sup\u003e. For each sample, cells were retained for downstream analysis if the number of detected genes exceeded 500 but was below the 95th percentile threshold, and if the percentage of mitochondrial genes was less than 15%. Doublets in the data set were filtered out with DoubletFinder (v.2.0.4). After filtering, the raw count matrix was normalized using the NormalizeData function with the LogNormalize method and a scaling factor of 10,000. The top 2,000 most variable genes of each sample were identified using the \u0026ldquo;FindVariableFeatures\u0026rdquo; function and library size was corrected using the ScaleData function. Harmony(v.1.2.0) performed batch correction across replicate\u003cb\u003es\u003c/b\u003e\u003csup\u003e\u003cb\u003e93\u003c/b\u003e\u003c/sup\u003e. PCA was performed to reduce the dimensionality with the RunPCA function in Seurat. Graph-based clustering was performed to cluster cells according to their gene expression profile using the FindClusters function. Cells were visualized using two-dimensional UMAP algorithms with the RunUMAP function. We used the FindAllMarkers function to identify marker genes for each cluster. Only genes with an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, an absolute log fold change (LogFC)\u0026thinsp;\u0026gt;\u0026thinsp;0.25, and detected in at least 25% of the cells within a cluster were identified as marker genes. Finally, each cluster was annotated based on the expression of established marker genes. To distinguish between cycling and non-cycling cell types, we used the CellCycleScoring function to calculate cell cycle scores, predicting whether they were in the G2M, S, or G1 phase. Clusters with elevated cell-cycle scores and increased expression of cell cycling marker genes(TOP2A and MKI67) were classified as cycling cell types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSpatial transcriptomics sequencing and data analysis\u003c/h2\u003e \u003cp\u003eFor chicken embryo, spatial transcriptomics was performed using Stereo-seq platform according to the standard protocol\u003csup\u003e94\u003c/sup\u003e. Tissue section was adhered to the Stereo-seq chip surface and fixed in methanol, followed by nucleic acid staining and imaging. After permeabilization, mRNAs captured by DNA nanoballs (DNBs) on the chip were reverse transcribed and amplified. DNBs were then loaded onto the patterned Nano arrays and sequenced using the MGI DNBSEQ-Tx sequencer. After raw data was processed, we treated the bin 100 as the analysis unit and performed unsupervised clustering. Data normalization, scaling, and bins clustering were processed using the R package Seurat\u003csup\u003e92\u003c/sup\u003e. Tissue identities of clusters were annotated using tissue-specific expression genes. For the spleen and gizzard, we extract the corresponding areas and performed spatial reclustering. Specifically, we treated bin30 of the spleen and bin50 of the gizzard as the analysis units.For bursa of Fabricius and caecal tonsil, spatial transcriptomics was carried out using the BMKMANU S1000 platform according to the protocol\u003csup\u003e95\u003c/sup\u003e. Tissue sections of the chicken embryo bursa of Fabricius and adult chicken caecal tonsil were placed on sequence slides. The sections were then fixed with cold methanol, followed by H\u0026amp;E staining and imaging before permeabilization, according to the user guide for the BMKMANU S1000 Tissue Optimization Kit (BMKMANU, ST03003). Permeabilization, reverse transcription and cDNA synthesis were also performed according to the user guide. Libraries were sequenced on the Illumina NavoSeq.\u0026nbsp;After filtering out low-quality sequences, the remaining sequences were aligned to the chicken reference genome using BSTMatrix v2.3 with default parameters, and an expression profile matrix was generated.\u003c/p\u003e \u003cp\u003eTo determine the cellular composition within spatial transcriptomics spots, we employed deconvolution methods in combination with annotated scRNA-seq data. Specifically, for the gizzard in the chicken embryo, we utilized the Tangram\u003csup\u003e96\u003c/sup\u003e method to integrate annotated gizzard scRNA-seq data with spatial transcriptomics data. This integration leveraged marker genes associated with annotated cell types in the gizzard as training genes for downstream analysis. After calculating probability scores for each cell's mapping within each spatial spot, we visualized the probability scores of each cell type using the tg.plot_cell_annotation_sc() function provided by Tangram.\u003c/p\u003e \u003cp\u003eSpatial transcriptomics data of other tissues were analyzed by integrating scRNA-seq using conditional autoregressive-based deconvolution (CARD)\u003csup\u003e97\u003c/sup\u003e. This method enabled the transfer of cell-type annotations from scRNA-seq to spatial transcriptomics. CARD was also used to infer correlations in cell-type proportions across spatial locations between pairs of cell types. After annotating cell type diversity within each spot, significant ligand-receptor pairs between neighbouring spots were visualized using ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime trajectory analysis\u003c/h2\u003e \u003cp\u003eThe cell lineage trajectory was inferred via Monocle2 or Slingshot\u003csup\u003e98,99\u003c/sup\u003e. The trajectory of chicken FDCs was constructed by Monocle2(v2.8.0). The count matrix corresponding to the identified cell types was imported into Monocle. Cells were ordered along the trajectory and visualized in a reduced dimensional space. MPP cells were considered as the root state. Genes that exhibited significant changes along pseudotime were identified using the differentialGeneTest function and subsequently visualized using the plot_pseudotime_heatmap function. Slingshot was used to define computationally imputed pseudotime trajectories of γδ T cells. UMAP reduction was used to determine dimensionality and unbiased lineage was constructed by specifying only a start cluster (Double negative T cells). Lineages and gene expression were visualized using a combination of Slingshot visualization tools, the ggplot2 R package (v3.3.2) and the pheatmap R package (v1.17.4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTF-target interaction inference\u003c/h2\u003e \u003cp\u003eTo uncover cell type-specific regulation, GENIE3\u003csup\u003e100\u003c/sup\u003e and SCENIC\u003csup\u003e101\u003c/sup\u003e were applied to infer the gene regulatory network and calculate the TF activity scores. The regulons specifically active in a selected cluster (compared to other clusters) were identified using the \u0026ldquo;calcRSS\u0026rdquo; and \u0026ldquo;plotRSS\u0026rdquo; functions in the AUCell R package. After screening the target modules, the gene interaction network was visualized using cytoscape\u003csup\u003e102\u003c/sup\u003e and Gephi\u003csup\u003e103\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo identify common regulatory networks for GC B(II) cells between chickens and humans, SCENIC was employed to characterize the transcription factors (TFs) in each species. Subsequently, GENIE3 was used to infer putative regulatory circuits based on these TFs, and the target genes identified by SCENIC were compared. Common regulatory circuits shared between chickens and humans were retained for further analysis. For each TF, the top three immune genes (from the Immunome database) with the highest regulatory weights were selected for subsequent regulatory network construction. For species-specific regulatory networks, the MAST package was used to identify differentially expressed genes (DEGs) unique to each species. Differentially expressed TFs were retained based on the TF lists for chicken and human obtained from the AnimalTFDB v4.0 database. For each TF, the top five immune genes with the highest regulatory weights, as determined by GENIE3, were used to construct the species-specific regulatory networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of One-to-One Orthologous Genes between Chicken, Turtle, and Human\u003c/h2\u003e \u003cp\u003eFor chicken, we downloaded the list of orthologous genes between chicken and human from Ensemble BioMart release 113 and retained only one-to-one homologous gene pairs for downstream analysis. For turtle, we used BLASTP to align different sets of protein-coding genes, with human as the reference, based on nearest-neighbor alignment principles. To be specific, we performed both forward and reverse alignments: in the forward alignment, the turtle gene set was aligned to the human reference, and in the reverse alignment, the human gene set was aligned to the turtle reference. Orthologous gene relationships were determined based on criteria including an e-value threshold\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, alignment score\u0026thinsp;\u0026gt;\u0026thinsp;80, and minimum sequence identity\u0026thinsp;\u0026gt;\u0026thinsp;50%. The reciprocal best matches were considered one-to-one orthologous genes between humans and turtles. To ensure the completeness of orthologous genes across species, we utilized EggNOG Mapper with the taxonomic scope set to \"Vertebrata\" to identify one-to-one orthologs among the three species. We then performed a union of the orthologous gene pairs obtained from Ensemble BioMart and BLASTP with those identified by EggNOG Mapper. For any orthologous gene pairs not matching between these sources, we retained the orthologs identified by Ensemble BioMart and BLASTP.\u003c/p\u003e \u003cp\u003eFor cross-species comparison, we first extracted the single-cell expression matrices for each species, replacing the gene names with human orthologs and the vertebrate orthologs identified by EggNOG. For humans, we retained only those orthologous genes present in our identified homolog list.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell interaction analysis\u003c/h2\u003e \u003cp\u003eThe R package CellChat\u003csup\u003e104\u003c/sup\u003e was used to identify and visualize cellular cross-talk between different cell types of scRNA-seq data. The over-expressed ligands or receptors were identified by \u0026ldquo;identifyOverExpressedGenes\u0026rdquo; and \u0026ldquo;identifyOverExpressedInteractions\u0026rdquo; functions. Then, we used the \u0026ldquo;computeCommunProb\u0026rdquo; functions to compute communication probability and infer cellular communication networks. And we applied the \u0026ldquo;filterCommunication\u0026rdquo; function to retain receptor-ligand pairs with at least 10 expressing cells. Cell-cell communication at the signaling pathway level between cell types was inferred using the \u0026ldquo;computeCommunProbPathway\u0026rdquo; and \u0026ldquo;aggregateNet\u0026rdquo; functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSimilarity of cell types across amniotes\u003c/h2\u003e \u003cp\u003eFor shared cell types between chickens and humans, or chickens and turtles, we binarized genes as either expressed or not based on each cell type's average expression profile\u003csup\u003e18\u003c/sup\u003e. A gene was considered expressed if it was present in more than 25% of cells within a specific cell type and its expression level was above the third quartile for that cell type. The ratio of the number of genes expressed in both species to the number of genes expressed in either species is defined as the proportion of orthologous genes expressed in both species. For cell type similarity, we calculated mean expression profiles for each cell type (scaled as ln(CPM\u0026thinsp;+\u0026thinsp;1)) and pairwise Spearman correlations were computed. The significance of the correlation coefficients was assessed using a permutation test (refer to PMID: 29724907). Specifically, gene expression values were shuffled 1,000 times across cell types, and the corresponding Spearman correlation coefficient (rho) was recalculated. The p-value was calculated as the fraction of absolute values of the rho values that were greater than or equal to the absolute value of rho from the actual (i.e.non-shuffled) data.\u003c/p\u003e \u003cp\u003eFor immune cells, Unsupervised MetaNeighbor analysis was used to systematically assess the transcriptional similarity between cell types across species, with mean AUROC (Area Under the Receiver Operating Characteristic) scores quantifying the similarity of cell-type pairs \u003csup\u003e33\u003c/sup\u003e. Before conducting the MetaNeighbor analysis, we quantified expression levels as counts per million (CPM) for each cell in the atlases and then scaled these values using the natural logarithm transformation (ln(CPM\u0026thinsp;+\u0026thinsp;1)). To address data sparsity in low-coverage sequencing datasets, we employed a pseudo-cell approach to aggregate data from multiple cells of the same cell type. Specifically, we selected 30 cells from each cell type within each species to construct pseudo-cells. The dendrogram of immune cell types was then generated by hierarchical clustering using the Ward.D2 method, with distances based on mean AUROC scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eJoint CCA embedding of immune cell data from amniotes\u003c/h2\u003e \u003cp\u003eFor cross-species comparisons, we used previously published comprehensive immune cell datasets from humans\u003csup\u003e105\u003c/sup\u003e. We then integrated the immune cells of three species using Seurat\u0026rsquo;s SCTransform workflow. Each cell class (Mononuclear phagocytic cells, B cells, and T cells and innate lymphocytes) was integrated separately. Only one-to-one ortholog genes between these three species, identified using EggNOG mapper and Ensembl, were retained. Integration features were selected by the \u0026ldquo;SelectIntegrationFeature\u0026rdquo; function. Integration anchors were identified based on the first 40 canonical components (\u0026ldquo;FindIntegrationAnchors\u0026rdquo; function, reduction= \u0026ldquo;CCA\u0026rdquo; and normalization.method = \u0026ldquo;SCT\u0026rdquo;)\u003csup\u003e106\u003c/sup\u003e. The first 18 principal components of the integrated data were used to visualize the data by a UMAP embedding and to construct a neighbourhood graph. For integrated T cells and innate lymphocyte cells, we obtained mean expression profiles for each cluster. The cluster distance matrix was computed as Spearman correlation and used for hierarchical clustering with the Ward.D2 method to generate dendrograms. Dendrograms were coloured according to the proportion of cells from each species within the integrated cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eChicken/duck and human gene alignment\u003c/h2\u003e \u003cp\u003eFor the comparison of transcriptome profiles across species, the gene expression matrices were collapsed into homologous genes to enable direct comparison. We calculated mean expression profiles for each cell type (scaled as ln(CPM\u0026thinsp;+\u0026thinsp;1)) and pairwise Spearman correlation coefficients using the \u0026ldquo;cor\u0026rdquo; function\u003csup\u003e18\u003c/sup\u003e. Species-enriched gene expression was defined as genes enriched 2-fold in either direction (chicken/duck\u0026thinsp;\u0026gt;\u0026thinsp;human or human\u0026thinsp;\u0026gt;\u0026thinsp;chicken/duck) with a p-value less than 0.05 (calculated by \u0026ldquo;MAST\u0026rdquo;). Correlations and cell type-specific genes were obtained in the same manner using all cells from BSDCs and FDCs.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis\u003c/h2\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) was performed with functions in the WGCNA R package\u003csup\u003e\u003cb\u003e107\u003c/b\u003e\u003c/sup\u003e. To attenuate the effects of noise and outliers, we aggregated data from 11 to 50 cells within the same cluster to create pseudo-cells for each cell type. High variable genes among the cells of interest were calculated. The top 2,500 variable genes determined in this way were used for analysis. The adjacency matrix was constructed by setting the soft power parameter to 10 using \u0026ldquo;pickSoftThreshold\u0026rdquo; function. From this adjacency matrix, a topological overlap matrix (TOM) was calculated using the \u0026ldquo;TOMsimilarityFromExpr\u0026rdquo; function and the TOM dissimilarity measure (1- TOM) was then used to cluster genes. Modules were identified using the dynamic tree-cutting algorithm with the \u0026ldquo;cutreeDynamic\u0026rdquo; function, and module eigengenes were defined using the \u0026ldquo;moduleEigengene\u0026rdquo; function.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGene expression pattern\u003c/h2\u003e \u003cp\u003eWe categorized expression patterns into four types based on a previously established method \u003csup\u003e34\u003c/sup\u003e. Briefly, genes were classified as either expressed (1) or not expressed (0) based on the average expression profiles of each mononuclear phagocyte subtype in chickens and humans. A gene was considered \"expressed\" in a cell type if the median of its non-zero expression values across the constituent cells was greater than the median of non-zero expression values for all other genes, adjusted by adding or subtracting two standard deviations. Additionally, the percentage of cells within the cell type showing non-zero expression for the gene had to exceed the median percentage of non-zero expression for all other genes, similarly adjusted by adding or subtracting two standard deviations. We then aligned these gene vectors to match homologous cell types between species, and combined them into a single vector for each gene (V = (a-b)\u0026thinsp;+\u0026thinsp;2ab, where a represents the ordered human vector and b the ordered chicken vector). This vector indicated for each cell type whether: both chicken and human expressed the gene (2), only human expressed it (1), only chicken expressed it (-1), or neither expressed it (0). We then classified genes based on the following criteria: conserved if any element of V equaled 2 and all other elements were 0; type 2 if any element equaled 2 and any other equaled 1 or -1; not expressed if all elements were 0; type 3 if both positive and negative elements were present; and type 1 if elements were either positive or negative and 0. Both type 0 and type 1 involve comparisons of gene expression changes within the same cell type between chickens and humans. Type 0 represents genes expressed in both species, while Type 1 indicates a simple gain or loss of expression between the species. Type 2 changes involve the gain or loss of expression in additional cell types during evolution. Type 3 refers to a change in expression from one cell type to another.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eEnrichment analysis for GO and KEGG\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) and KEGG pathways analyses were performed using David (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org\u003c/span\u003e\u003cspan address=\"https://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GO terms and KEGG pathways with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are available from the Gene Expression Omnibus (GEO) repository under the following accession numbers: PRJNA1125639 and PRJNA1128021.\u003c/p\u003e\n\u003cp\u003eHuman scRNA-seq datasets were collected from the published articles and database (GSE134355; E-MTAB-11536; Gut Cell Atlas: https://www.gutcellatlas.org/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2022YFF1000100 to Y.J.), China Agriculture Research System of MOF and MARA (CARS-41-Z08 to H.Z.), the National Natural Science Foundation of China (32302733 to F.W.). We thank the High-Performance Computing platform of Northwest A\u0026amp;F University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.J., X.F., F.W. and Y.J. conceived and designed the project. Y.J. and X.F. supervised the work. F.W., J.R., Y.Z., H.L. and W.H. collected tissue samples and performed data analyses. Y.J., M.Q., T.S., H.S., H.T., H.W. and X.G. helped with bioinformatic analyses. Y.J., X.H., X.F., and Z.H. provided funding. J.M., Z.Y., L.F., Y.J., and H.Z. provided relevant advice. F.W., J.R. and Y.Z. wrote the manuscript. Y.J., L.F., J.S., H.Z., M.F., S.E.D., L.L. and Y.L. reviewed the manuscript. All authors contributed to the work. 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Here we describe a comprehensive reference atlas of the chicken, encompassing 1.57\u0026nbsp;million cells across 157 cell types from 36 tissues, along with a spatial transcriptomic map of the embryo. By integrating it with 0.23 newly generated and 0.97\u0026nbsp;million single cells from 14 and 32 tissues in turtle and humans, respectively, we systematically explored the evolutionary rates of various cell types, particularly immune cells. The rapid evolution of chicken cells was generally characterized by changes in their gene regulatory networks and subsequent functional adaptations. In chicken, follicular dendritic cells emerge at the early development stage and exhibit myeloid rather than stromal origins, unlike in mammals. These cells share a regulatory network with mononuclear phagocytes and promote B cell proliferation and migration in the chicken-specific bursa of Fabricius. The observed variation in subtypes and proportions of γδ T cells across the three species reflected the evolution of pathogen recognition and signaling mechanisms among amniotes. These findings were further supported by generating 21,798 single cells from 3 tissues in ducks. Overall, we provides an invaluable resource to study chicken cell biology and evolution, as well as shines light on the evolutionary and cellular characteristics of immune cells across amniotes.\u003c/p\u003e","manuscriptTitle":"Cross-species comparison of single-cell landscapes reveals conservation and innovation in chicken immune systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 06:48:05","doi":"10.21203/rs.3.rs-6164369/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6deacec3-1f59-4da2-b4f8-6c00c701cf58","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45861637,"name":"Biological sciences/Zoology"},{"id":45861638,"name":"Biological sciences/Evolution"},{"id":45861639,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":45861640,"name":"Biological sciences/Immunology/Gene regulation in immune cells"}],"tags":[],"updatedAt":"2026-03-30T07:21:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 06:48:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6164369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6164369","identity":"rs-6164369","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}