Single-Cell Atlas of Fetal Immune Development Across Lung, Spleen, and Umbilical Cord Blood in Nonhuman Primates

Single-Cell Atlas of Fetal Immune Development Across Lung, Spleen, and Umbilical Cord Blood in Nonhuman Primates | 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 Single-Cell Atlas of Fetal Immune Development Across Lung, Spleen, and Umbilical Cord Blood in Nonhuman Primates Ilhem Messaoudi, Brianna Doratt, Sheridan Wagner, Katelyn Keen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734095/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 The fetal immune system develops within a tightly regulated environment that balances immune tolerance with readiness for postnatal antigen exposure. However, limited access to fetal tissues has constrained our understanding of immune ontogeny across distinct anatomical compartments. Here, we present a high-resolution, multi-tissue single-cell transcriptional atlas of the late-gestation (GD130–135) rhesus macaque ( Macaca mulatta ) fetal immune system, profiling leukocytes from lung, spleen, and umbilical cord blood mononuclear cell (UCBMC) compartments spanning myeloid, lymphoid, innate lymphoid, and hematopoietic stem cell (HSPC) lineages. The fetal lung was enriched in myeloid populations and ILC2 cells while fetal spleen was comprised primarily of T- and B-cells and UCBMC were dominated by T-cells. Despite reduced overall intercellular communication in lung compared to spleen and UCBMC, lung immune networks showed proinflammatory bias, suggesting preparation for postnatal environmental exposure. Splenic B cells showed strong transcriptional signatures associated with V(D)J recombination and isotype switching, while CD4 T cells displayed increased activation, and increased Tregs, consistent with the spleen's role as a secondary lymphoid organ which integrates antigen monitoring with immune tolerance to prevent overactivation. UCBMC showed a predominantly regulatory immune landscape. Together, this atlas provides a foundational resource defining tissue-specific immune specialization and intercellular communication in the late-gestation primate fetus. Biological sciences/Immunology/Immunogenetics Biological sciences/Immunology Biological sciences/Immunology/Gene regulation in immune cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION The development of the human fetal immune system is a tightly orchestrated process that primes the neonate for protective responses against antigenic encounters after birth. Hematopoietic stem cells (HSC) emerge in the yolk sac as early as four weeks of gestation ( 1 ). By six weeks, macrophages rapidly develop in the yolk sac and begin to colonize fetal tissues ( 2 , 3 ), coinciding with the fetal liver becoming the predominant site of hematopoiesis. The liver establishes a self-renewing pool of hematopoietic stem and progenitor cells (HSPC) and serves as a key site for macrophage development between weeks 9 and 13 ( 1 , 4 ). After gestational week 11, HSC begin colonizing the fetal bone marrow, giving rise to the full hematopoietic lineage ( 5 , 6 ), which continues to mature throughout gestation as lymphocyte compartments are progressively defined ( 4 , 7 ). Accumulating evidence demonstrates that the prenatal environment directly impacts neonatal health and shapes disease susceptibility into adulthood ( 8 ). The maternal inflammatory milieu and antigen exposure can mold fetal immunity; for example, maternal infection, even without vertical transmission, can alter neonatal immunity by impairing hematopoiesis ( 9 – 12 ). Human studies investigating immune perturbations in utero are limited by the inaccessibility of fetal tissues throughout pregnancy, often relying instead on postnatal peripheral samples, in vitro approaches, or small animal models ( 13 – 16 ). These strategies cannot fully capture the dynamic trajectory of human fetal immune system development. In contrast, the rhesus macaque ( Macaca mulatta ), with its phylogenetic proximity to humans, similar placental morphology and developmental trajectory, and gestational timeline ( 17 – 19 ), provides a valuable model for investigating prenatal immune development. Here, we present a single-cell atlas of the immune transcriptional landscapes of fetal lung, spleen, and umbilical cord blood (UCB) in late-gestational (gestational day (GD) 130) rhesus macaques. UCB was analyzed in place of fetal blood given easier access and greater volume. The spleen was selected as a key secondary lymphoid organ with a microenvironment essential for immune development, and the lung as a non-hematopoietic tissue seeded early in gestation that directly encounters antigens at birth. By leveraging single-cell RNA sequencing (scRNA-seq), we identified diverse immune cell populations across compartments and uncovered putative regulatory circuits underpinning fetal immune development. This atlas provides a foundational framework to investigate molecular mechanisms shaping fetal immunity across gestation. RESULTS Immune cell frequencies are tissue-dependent in fetal lung, spleen, and umbilical cord. Leukocytes isolated from fetal rhesus macaque spleen, lung, and UCB were analyzed via scRNA-seq to generate a fetal immune cell atlas. Using canonical immune markers, we identified 19 distinct leukocyte clusters, with substantial contribution from all three tissue sources (Fig. 1 A,B). Within the myeloid compartment, we identified monocytes ( CD14 , MAMU-DRA , S100A9 ), macrophages ( CD14 , MAMU-DRA, MRC1 ), FLT3 positive plasmacytoid dendritic cells (pDC; CCR7 , MAMU-DRA, TCF4 , FLT3 ), and myeloid dendritic cells (mDC; MAMU-DRA, CD1C ) (Fig. 1 A,C). B-cells expressing MS4A1 (CD20) were subdivided into three groups according to the differential expression of PAX5 , EBF1 , and CD79A : Bcell_1 ( PAX5 low, EBF1 low, CD79A high), Bcell_2 ( PAX5 mid, EBF1 mid, CD79A mid), and Bcell_3 ( PAX5 high, EBF1 high, CD79A low) (Fig. 1 A,C). Two natural killer (NK) cell clusters were identified based on the expression of KLRB1 and NKG7 and further distinguished by FCGR3 (CD16) expression: CD16 + NK ( KLRB1 +, NKG7 +, FCGR3 +) and CD16- NK ( KLRB1 +, NKG7 +, FCGR3 -) (Fig. 1 A,C). A cluster of natural killer T (NKT) cells was defined by positive ZBTB16 expression, and a cluster of T regulatory (Treg) cells was identified through positive CTLA4 expression (Fig. 1 A,C). A cluster of type two innate lymphoid cells (ILC2) was identified by the absence of canonical myeloid and adaptive cell markers and the expression of GATA3 and IL1RL1 (Fig. 1 A,C). T-cells were identified by expression of CD3E (Fig. 1 A,C). Additional T-cell clusters were delineated into CD4 Naive ( CD8A- , CD28 mid, CCR7+ , IL7R +, LEF1 +), CD4 central memory (CM; CD8A- , CD28 low, CCR7+ , IL7R +, LEF1 low), CD4 CAMK4 high (CAMK4; CD8A- , CD28 high, CCR7 low, IL7R low, CAMK4 high), CD4 cytotoxic effector memory (EM_CTL; CD8A -, CCR7- , KLRB1+ , NKG7+ , IL7R low) and CD8 ( CD8A+ , CCR7+ , IL7R +) cells (Fig. 1 A,C). A proliferating T-cell cluster was identified by high MKI67 expression (Fig. 1 A,C ) . Finally, a small cluster of hematopoietic stem/progenitor cells (HSPC) was distinguished by high CD34 expression, particularly in the spleen, representing extramedullary hematopoiesis (Fig. 1 A,C). Differences in cell cluster abundance were observed between the three tissue compartments. As expected, macrophages were detected only in lung and spleen, while ILC2 cells were found almost exclusively in the lung (Fig. 1 B,D). Leukocyte populations in the lung were dominated by myeloid cells (50.3%), followed by T-cells (17.2%) and B-cells (10.8%) (Fig. 1 D). The spleen was comprised primarily of T- and B-cell clusters (42.0% and 38.8% respectively), whereas UCBMCs were dominated by T-cells (83.0%) (Fig. 1 D). Increased B-cell maturation within the fetal spleen. We first performed Gene Ontology (GO) enrichment on the marker genes for each B-cell cluster to delineate subset-specific differences, independent of tissue of origin (Fig. 2 A). Marker genes in the Bcell_1 cluster mapped to B-cell receptor (BCR) signaling and immunoglobulin binding while the Bcell_2 cluster enriched for cellular homeostasis and nuclear transport, and the Bcell_3 cluster enriched for response to virus, response to type II interferon, and cellular respiration (Fig. 2 A). These enrichments suggest that the Bcell_1 cluster, which was augmented in the fetal lung as indicated by normalization of B cell cluster frequencies (Fig. 2 B), exhibited enhanced effector functionality compared to the homeostatic Bcell_2 cluster, while the Bcell_3 cluster enriched in the spleen and UCBMC (Fig. 2 B) appeared primed for antiviral responses. We next evaluated the tissue-specific transcriptional profile of the combined B cell clusters using gene set enrichment analysis (GSEA). Spleen B cell subsets were characterized by increased expression of genes important for B cell isotype switching, differentiation, and antigen processing and presentation compared to B cells in the lung and UCBMC, in line with the role of the spleen as a secondary lymphoid organ in the initiation of antimicrobial responses (Fig. 2 C). In contrast, isotype switching and differentiation were significantly downregulated in the UCBMC while differentiation and proliferation were significantly downregulated in the lung (Fig. 2 C). To identify specific differences in transcriptional profiles of B-cells across fetal tissues, we performed DEG analysis comparing gene expression in one cluster from one tissue to the same cell cluster of the other two tissues and retaining only the genes with a positive fold change. Venn diagrams of upregulated genes were generated to identify DEGs unique to each tissue. While most DEGs were tissue-specific, 137 shared upregulated DEGs were detected in both Spleen vs. Lung+UCBMC and UCBMC vs. Lung+Spleen (Fig. 2 D). DEG unique to the lung exclusively enriched to processes associated with oxidative phosphorylation ( COX7C, ATP5PF ) and oxidoreductase activity ( GSTP1 , ND4L ) (Fig. 2 E,F). Additional DEGs upregulated in the lung Bcell_1 cluster included the pro-apoptotic gene IL27L2 and the antiviral gene ISG20 (Fig. 2 F). DEG unique to spleen enriched to B cell activation ( PIK3CD , BLNK , BANK1 ), positive regulation of cell programmed death ( JUN , LTB , BCL2 ), regulation of cell-cell adhesion ( RUNX1 , CD47 , DOCK8 ), immunoglobulin recombination ( PAX5 , IKZF3 , IL27RA ), and cytokine production ( NFKBIA , HIF1A , LTB ) (Fig. 2 E,F). DEG shared between spleen and UCBMC also enriched to B cell activation ( PRKCB , PTPRJ ), positive regulation of cell programmed death ( FAF1 , FOXO1 ), and stem cell population maintenance ( FOXO1 ) (Fig. 2 E,F). DEG unique to UCBMC enriched to regulation of cell-cell adhesion ( CD44 , LYN ) and stem cell population maintenance ( BRAF , MED21 ) (Fig. 2 E,F). Next, we examined the Bcell_3 cluster, which was more abundant in the spleen and UCBMC compared to the lung (Fig. 2 B). Only six upregulated DEGs were unique to the lung, four of which were associated with mitochondrial functions ( COX1 , COX2 , ND4 , ND4L ) (Fig. 2 G,I, Sup. Table 2 ). Compared to UCBMC, Bcell_3 clusters in both lung and spleen exhibited significant upregulation of MEF2C , which limits leukocyte adhesion and migration (Fig. 2 I). Although there were no shared upregulated DEGs between spleen and UCBMC, the unique genes in these tissues enriched to similar functions, including histone modification, transcription factor binding, and regulation of cell cycle process (Spleen: BCL11A , HIST1H2AC ; UCBMC: KDM7A , JUND , NFKB1 ) (Fig. 2 H,I). Spleen-unique GO terms included B-cell activation ( CD19 , CD22 , CR2 ), tumor necrosis factor production ( TRAF3IP3 ), and immunoglobulin recombination ( BCL11A ) (Fig. 2 H,I). Other spleen-unique upregulated DEGs play a role in B-cell migration ( ITGA4 ) as well as antigen presentation ( MAMU-DBR1 ) (Fig. 2 H,I). UCBMC-unique DEG were important for RNA splicing and receptor internalization ( ITCH ), B-cell adhesion (PECAM1), and inflammatory response ( S100A6 and S100A10 ) (Fig. 2 H,I). T-cell activation and migration were prominent in the fetal spleen. This section will focus on the CD4 T‑cell compartment as our analysis of T‑cell subsets revealed that CD4 T cells exhibited the most pronounced alterations across tissue. Enrichment of CD4_Naive and CD4_CM cluster marker genes indicated that CD4_Naive represents an activated subset, as evidenced by higher expression of genes involved in T-cell activation, antigen receptor signaling, and positive regulation of IL-2 production ( CD4 , CD28 , RHOH , CASP8 , TRAC , ICOS , STAT5B ) (Fig. 3 A, Sup. Table 1 ). In contrast, CD4_CM markers enriched to GO terms such as cellular respiration ( ATP5F1C , COX7C ) and TNF-α signaling ( RPL6 , RPL8 , RPL30 , RPS13 ) suggesting heightened metabolic and pro-inflammatory capacity (Fig. 3 A, Sup. Table 1 ). Comparisons of T cell cluster frequencies indicated the CD4_Naive population was most abundant in UCBMC and least abundant in the lung (Fig. 3 B). In contrast, the proportion of CD4_CM T-cells was highest in the lung and lowest in the spleen (Fig. 3 B). The spleen contained higher proportions of both CD4_EM_CTL and Treg populations compared to lung and UCBMC while the lung had the lowest CD4_CAMK4 percentage (Fig. 3 B). We also performed GSEA to identify broad functional programs of the two CCR7 + CD4 clusters (CD4_Naive & CD4_CM) within each tissue. In both the spleen and UCBMC, transcriptional signatures of CD4 CCR7 + cells showed increased T-cell proliferation, anergy, positive selection, and V(D)J recombination (Fig. 3 C). Conversely, T-cell proliferation, anergy, positive selection, and V(D)J recombination were decreased in the lung (Fig. 3 C). In UCBMC, CD4 T cell transcriptional profile was indicative of regulatory T cell differentiation (Fig. 3 C). Overall, these data indicate that CD4 T cells were more activated in the spleen. Next, we used the same DEG analysis strategy as described previously for the B cells. Within each cluster, we identified DEG upregulated in each tissue relative to the remaining two tissues. DEGs upregulated in the CD4_Naive cluster were largely unique to each tissue, with only six genes shared between Lung vs. Spleen+UCBMC and Spleen vs. Lung+UCBMC (Fig. 3 D). DEGs unique to the lung and spleen played a role in T-cell activation (Lung: CCR7 , CD3D , ICOS ; Spleen: CD2 , FOS , JUN ) (Fig. 3 E,F). CD4_Naive cells in the lung expressed high levels of the antiviral gene IFI16 and numerous T-cell developmental transcription factor genes ( IKZF1 , BACH2 , SOX4 , STAT4 ) (Fig. 3 F). DEGs unique to the spleen enriched to positive regulation of immune response and cytokine production ( IL16 , IL17RA , IL27RA , LTB ), key transcription factors ( BCL11B , ID3 , IRF2 , KLF6, JUN ), and facilitators of TCR activation ( CD40LG , TRAF3IP3 ) (Fig. 3 E,F). DEGs unique to UCBMC enriched to GO terms associated with protein degradation, epigenetics and cell cycle ( RELB, EIF2AK3 , CASP3 FBXO33 , HERC1 ) (Fig. 3 E,F). DEGs in CD4_CM cell population were also largely tissue-specific (Fig. 3 G). DEGs unique to the lung mapped to oxidative phosphorylation ( COX7B ), T-cell receptor signaling ( CD3D, CD7 ), and antiviral immunity ( IFI16 ) (Fig. 3 H,I). DEGs upregulated in the spleen and UCBMC enriched to chromatin binding (Spleen: FOS , IRF2 ; UCBMC: KDM2A, ARID1A, CREBBP ; Spleen/UCBMC: RUNX1 ) (Fig. 3 H,I). Genes uniquely upregulated in the spleen included those important for T-cell activation, signaling, and cytokine response ( CD38 , FOS , IL27RA , IL6ST , TGFBR2 , and TRAC) (Fig. 3 I). DEGs unique to UCBMC enriched to regulation of stem cell population maintenance ( FOXO1 , FOXP1 , CTNNB1 , MYC , RHOH ) (Fig. 3 H,I). NK cells had higher cytotoxic capacity within the fetal lung. Comparisons of the frequency of the innate, HSPC, and proliferating clusters revealed the lung harbored the largest proportion of CD16 + NK cells and ILC2 cells, while the spleen was home to the largest proportion of CD16- NK cells (Fig. 4 A). Finally, the NKT subset was most prominent in the UCBMC (Fig. 4 A). Marker genes of both CD16 + and CD16- NK clusters enriched to GO processes associated with oxidative phosphorylation and response to cytokine stimulus (Fig. 4 B). Given the role of CD16 + NK cells in ADCC, we noted the enrichment to NF-kB signal transduction, which is crucial for NK cell IFN-gamma production and cytotoxicity ( 27 , 28 ) (Fig. 4 B). Marker genes of CD16- NK cells uniquely enriched to positive regulation of cytokine production, consistent with their cytokine-producing function and reduced ADCC capability compared to CD16 + NK cells ( 29 ) (Fig. 4 B). GSEA analysis of the combined NK cell clusters showed upregulation of NK cell differentiation and antigen processing/presentation in the spleen and UCBMC (Fig. 4 C). On the other hand, leukocyte-mediated cytotoxicity and immunoglobulin-like receptor signaling pathways were upregulated in the lung (Fig. 4 C). For CD16 + NK cells, the lung displayed 165 unique upregulated DEGs while the majority of UCBMC defining DEG were shared with the spleen (Fig. 4 D). DEGs in the lung CD16 + NK cluster uniquely enriched to oxidative phosphorylation ( COX7C ) and NK cell-mediated cytotoxicity ( GZMB , KLRB1 , NCR3 , PRF1 ) (Fig. 4 E,F). Spleen-unique and spleen/UCBMC-shared genes were associated with antigen receptor-mediated signaling and cytokine production (Spleen: IFNG , ITGAM , NKG2D ; Spleen/UCBMC: IL2RB , JAK1 ) as well as nuclear receptor binding (FOXP1, DDX5, NCOR1) and phospholipid binding (RAPGEF2, ITPR2) (Fig. 4 E,F). Spleen-unique DEGs were also associated with regulation of inflammation and apoptosis ( TNFAIP3 , IRF1 ) (Fig. 4 F). DEG that define the CD16- NK cells were distinct in the spleen and lung while those that defined UCBMC were largely shared with the spleen (Fig. 4 G). Lung-unique DEGs enriched to NK cell-mediated cytotoxicity ( GZMB , NKG7 ), antiviral immunity ( IFI16 , IFI27L2 , BST2), and chemotaxis ( CCL3, VIM ) (Fig. 4 H,I). DEG unique to the spleen enriched to leukocyte activation ( BCL2 , FOXP1 , IKZF3 ), response to type II IFN ( IFNG ), antigen-receptor-mediated signaling pathway ( CD74 ), and epigenetic regulation ( HDAC9 , KDM2B , FOS , FOXO3 , IKZF3 , IRF1, JUN ) (Fig. 4 H,I). DEG shared between spleen and UCBMC enriched to antigen-receptor-mediated signaling pathway, epigenetic regulation, and intracellular protein transport (Fig. 4 H). DEGs specific to UCBMC suggested an anti-inflammatory phenotype as indicated by increased expression of TGFB1 and apoptosis-inducing receptor TNFRSF10A (Fig. 4 I). Lung monocytes are primed for anti-microbial response Macrophage and pDC clusters were exclusively detected in the lung and spleen with the spleen containing the largest frequency of mDCs, pDCs, and macrophages (Fig. 5 A). GSEA analysis of the monocyte population revealed that TLR and complement signaling were uniquely upregulated in lung monocytes (Fig. 5 B). On the other hand, antigen processing/presentation and phagocytosis terms were enriched in both spleen and UCBMC (Fig. 5 B). Finally, extravasation pathways were upregulated in both lung and spleen monocytes (Fig. 5 B). We observed a large overlap between upregulated DEGs defining the spleen and UCBMC monocytes while lung-defining DEG were distinct (Fig. 5 C). As expected, DEG from all three tissue types included genes associated with innate immunity (Lung: C5AR1, TLR4, ISG20 , NOD2; Spleen: C1QBP , IFNGR1 , IRF1 ; Spleen/UCBMC: IRF8 ; UCBMC: JAK1 ) (Fig. 5 D,E). DEGs unique to lung monocytes enriched to electron transport chain and cytokine activity ( CCL4L1 , CXCL3 , IL1B , TNFRSF1B ) (Fig. 5 D,E). DEGs restricted to spleen monocytes enriched to histone modifying activity ( HDAC9 , KDM2B ) and included upregulated genes encoding adhesion molecules ( ITGAL , SELL ) (Fig. 5 D,E). Notable DEGs shared between spleen and UCBMC monocytes were involved in activation ( FOS ), response to oxidative stress ( FOXO3 ), immune modulation ( ILRUN ), and immune tolerance ( IRAK3 ) (Fig. 5 E). Expression of genes associated with cytokine signaling ( IL1RAP , JAK1 , TGFB1 ) was increased in UCBMC monocytes (Fig. 5 E). Module scoring of the tissue-specific monocyte populations revealed lung monocytes had highest scores for inflammation while splenic monocytes had the highest score for wound healing, and UCBMC monocytes displayed significantly lower viral/bacterial response (Fig. 5 F). DEG analysis between lung and spleen macrophages (Fig. 5 G) indicated that genes upregulated in the lung are involved in proinflammatory response ( NLRP3 , TLR2, IL1B ), response to interferon ( IFI30 , IL1B ), chemotaxis ( CCL3 , CXCL3 ), phagosome activity ( VAMP8 ), and wound healing ( ANXA1 , AREG ), (Fig. 5 H,I,). Genes upregulated in the spleen mapped to macrophage activation ( CD163, CD68, PECAM1 ), regulation of tumor necrosis factor production ( PYCARD ), phagocytosis ( C1QB , CD163 ), and pattern recognition receptor signaling ( C1QB , FCGR3, IFNGR1 ) (Fig. 5 H,I). Inferred cell-cell communication is reduced within the lung; while UCBMC displays pronounced HSPC signaling regulation Next, we used CellChat to identify unique cell-cell communications occurring within each tissue ( 26 ). The fetal spleen had the highest number and strongest ligand-receptor interactions, followed by UCBMC, and then the lung (Fig. 6 A). Similarly, the incoming and outgoing signal strengths, based on the inferred probabilities of ligand-receptor interactions, were higher for all spleen clusters and most UCBMC clusters compared to the lung (Fig. 6 B). Interestingly, HSPCs within UCBMC exhibited nearly twice the incoming interaction strength compared to those in the spleen and lung (Fig. 6 B). Additionally, both the CD16 + and CD16- NK cell clusters had increased incoming and outgoing interaction strength in the spleen compared to both lung and UCBMC (Fig. 6 B). Next, we quantified the relative information flow, an aggregate measure of signal number and strength for the selected pathways to identify those that were shared across tissues or unique (Fig. 6 C). In the lung, signaling pathways involved in pathogen response (COMPLEMENT), immune cell chemotaxis (PLAU), and proinflammatory response (IL1) were prominent (Fig. 6 C). The COMPLEMENT pathway was primarily driven by macrophage signaling within the lung (Fig. 6 D). Lung PLAU signaling was predicted to be mediated by macrophages and pDC, while IL1 signaling was driven by all myeloid cells (Fig. 6 D). Signaling pathways that dominated in the spleen were involved in inflammation (TNF, IGF, PTPR, IFN-II), B-cell survival (BAFF, APRIL), chemotaxis (CXCL, CCL), T-cell activation (PD-L1, CD96) and hematopoiesis (FTL3, BAFF, PD-L1) (Fig. 6 C). We then evaluated the relative signaling strength of tissue specific cell clusters for each signaling pathway (Fig. 6 D). TNF, PTPR, FLT-3, and PD-L1 pathways were primarily driven by signaling from pDC, while the chemotaxis-specific pathways (CXCL and CCL) were predominantly originating from macrophages and CD16- NK cells (Fig. 6 D). Finally, BAFF and APRIL pathways were predominantly driven by signaling from the Bcell_2 cluster (Fig. 6 D). UCBMC pathways were dominated by Colony stimulating factor (CSF), SELL, CD30 and ANGPT signaling, with higher relative strength and contribution by HSPC (Fig. 6 C,D). SELL and ANGPT signaling pathways were absent from the lung, while CD30 had contribution from the lung CD4_CAMK4 cluster which was lessened in the spleen and UCBMC (Fig. 6 D). FLT3 signaling which contributes to progenitor cell growth and division was prominent in splenic pDCs and UCBMC mDCs (Fig. 6 D). We evaluated the overall CCL pathway signaling between clusters in each tissue and observed signaling from both splenic NK cell clusters targeting monocytes, macrophages, and mDCs (Fig. 7 A). In contrast, fetal lung CCL signaling exclusively targeted NKT cells, whereas UCBMC overall CCL signaling was predominantly directed from NK cells to monocytes (Fig. 7 A). In the fetal lung, IL1 signaling was prominent among Bcell_3, monocyte, macrophage, mDC, pDC, and Proliferating clusters (Fig. 7 B). In the spleen, IL1 signaling originated from monocyte, macrophage, and mDC clusters, targeting Treg, mDC, pDC, Proliferating, and HSPC clusters (Fig. 7 B). Both pathways are important regulators of HSPC function, with MIF preventing accumulation of HSPCs within the bone marrow and TGFB regulating commitment to cell lineage fates. Notably, MIF signaling in UCBMC originated from the lymphoid compartment and targeted HSPCs, while lung and spleen lacked MIF signaling targeting the HSPC cluster (Fig. 7 C). In contrast, MIF signaling in the spleen originated from HSPCs to B-cell and myeloid clusters (Fig. 7 C). TGFb signaling within the lung and spleen also lacked the targeting to the HSPC cluster which was present in UCBMC, particularly from the lymphoid clusters (Fig. 7 D). DISCUSSION This study aimed to fill gaps and serve as a reference for fetal immune cell heterogeneity using the rhesus macaque, a well-established animal model of human immunology ( 17 ). The study aimed to generate a foundational single-cell dataset from the late-gestational fetal rhesus macaque encompassing three key tissues (blood, spleen, lung) which can be later expanded to include further gestational timepoints and additional tissues. We performed differential gene expression, gene set enrichment, and cell-cell interaction analyses on the integrated dataset to demonstrate its applicability. The lung is a crucial immunological organ as it is continuously exposed to respiratory pathogens, environmental pollutants, and allergens after birth ( 32 ). While maintaining its central role in gas exchange, the lung harbors tissue-resident immune cells that are essential for host defense and must balance immunoregulatory and anti-inflammatory responses to prevent tissue damage. Prominent immune cell populations residing within the lung tissue include alveolar macrophages, interstitial macrophages, innate lymphoid cells (ILCs), and NK cells. Consistent with previous studies ( 33 , 34 ), we observed a prominent proportion of ILC2 in the fetal lung. These cells produce Th2 cytokines contributing to fetal lung development, the formation of lymphoid tissue, and modulation of the neonatal response to the external environment ( 35 – 37 ). Our transcriptional analysis demonstrated that the fetal lung tissue contained the highest proportion of myeloid cells compared to spleen and UCBMC. Monocytes within the fetal lung were metabolically active, with heightened bacterial responsiveness and patterns associated with molecular recognition signaling. Additionally, our data demonstrated that lung macrophages have enhanced chemotaxis and myeloid cell recruitment capacity. Interestingly, genes upregulated in fetal lung relative to spleen/UCBMC also enriched to immune tolerance, which potentially limits overactivation, and maintain tolerance to the exposure of inhaled antigens at birth. Fetal lung macrophages showed increased expression of ANXA1 , which is involved in positive regulation of macrophage efferocytosis and an anti-inflammatory phenotype ( 41 , 42 ). ANXA1 signaling has been shown to resolve inflammation and promote the resolution of infections ( 43 – 45 ), highlighting its potential role in limiting neonatal inflammatory responses to novel antigens encountered after delivery. Fetal lung macrophages also upregulated IL1B , a notable proinflammatory cytokine. The production of IL-1b within the fetal lung by myeloid cells has been linked to epithelial development and lung tissue maturation ( 46 ). Additionally, CellChat analysis of intercellular communication revealed prominent IL1 signaling between lung cell clusters. While IL1 signaling contributes to antiviral immunity, overproduction can cause detrimental hyperinflammation, highlighting the importance of coupled anti-inflammatory signals such as ANXA1 ( 48 ). Collectively, our data indicate that the late-term fetal lung is capable of mounting an initial immune response while remaining highly regulated to maintain self-tolerance and anti-inflammatory capacity prior to delivery. The spleen is a prominent secondary lymphoid organ, which contains red pulp, that filters the circulating blood via phagocytosis of damaged erythrocytes by macrophages, and white pulp, that initiates defense against pathogens ( 49 ). Within white pulp is the periarteriolar lymphoid sheath where splenic T-cells are activated in response to blood-borne antigens and B-cell germinal centers, which elicit T-cell dependent antibody production ( 49 ). Consistent with its role as a prominent site of B-cell maturation, the fetal spleen exhibited the highest proportion of B-cells among the tissues studied ( 50 ). Critical B-cell signaling pathways BAFF and APRIL which influence B-cell survival and differentiation were solely observed within the spleen further aligning with the anticipated tissue functionality ( 51 , 52 ). Additionally, our DEG analysis showed an upregulation of immunoglobulin receptor recombination and isotype switching within splenic B-cells. T-cells were the second most prominent population within the fetal spleen. Although the frequency of CD4 populations was lower compared to UCBMC, splenic CD4 T cells were activated. The increased CD4 activation alongside the higher percentage of CD4 effector memory CTL aligns with the spleen’s function as a secondary lymphoid organ ( 53 ). The activation observed within the spleen could provide rationale for the higher proportion of splenic Treg cells. Although previous studies have mainly focused on the role of maternal Tregs in maintaining pregnancy, fetal Tregs also contribute to alloantigen tolerance ( 54 ). Our CellChat analysis showed the highest predicted signaling strength in the spleen, reflecting its role as a central site for peripheral immune activation and maturation ( 55 ). The strength of chemoattractant IL16 signaling to splenic T-cell clusters was notably higher, particularly Tregs, suggesting active recruitment into the spleen ( 56 , 57 ). Widespread CCL signaling was predicted via CellChat within the spleen between innate, CD4_EM_CTL, Bcell_3, HSPC, and proliferating clusters. Furthermore, CCL/CCR signaling is a key contributor to splenic tolerance and infection responses, as these ligand interactions drive myeloid cell recruitment and macrophage polarization ( 58 ). In particular, signaling through CCR1 directs macrophage migration and polarization toward an anti-inflammatory M2 phenotype ( 58 ). Blood mononuclear cells are key mediators of systemic immune responses, as they encompass mobile populations poised for activation and proliferation ( 60 ). UCB is often employed in clinical studies as a noninvasive substitute for neonatal blood due to its accessibility ( 61 , 62 ). Inflammatory perturbations within maternal circulation caused by diseases and disorders such as pre-eclampsia and SARS-CoV-2 infection are reflected in neonatal UCB ( 63 , 64 ). Fetal UCBMC contain immune cells predominantly in a state of tolerance to prevent immune overactivation prior to delivery ( 65 ). We found that UCBMC displayed heightened HSPC signaling strength and upregulation of genes important for stem cell population maintenance particularly among CD4 T cells. This finding aligns with previous studies showing that UCB is a rich source of HSPCs, contributing to fetal immune cell development ( 66 ). Notably, fetal spleen and UCB shared numerous upregulated DEGs in B-cell, NK, and monocyte clusters, which were absent in lung cells. Even in the absence of pathogens, UCBMC have shown heightened cytokine responsiveness following viral infection ( 67 ). The role of UCBMC and spleen cells in infection control was supported by our observations of increased expression of genes essential for activation and cytokine production. This study has several limitations. First, animals were not perfused, restricting our ability to distinguish between tissue resident leukocytes and the infiltrating peripheral blood cells. Second, our single-cell profiling was focused on only three fetal tissues. Expansion with publicly available fetal rhesus macaque transcriptional datasets will be needed to incorporate other prominent immunological tissues such as fetal bone marrow, liver, and thymus. Third, the study focused solely on transcriptomic data, constraining the interpretation of cellular functions and interactions. Finally, all fetal samples were derived from macaques at a single gestational age (approximately GD130), which restricts insights into developmental dynamics across gestation. These limitations highlight the need for future studies with larger, more diverse tissue sampling, integrated multi-omics, and functional validation across broader developmental time points. Nevertheless, this initial study provides the first in-depth immune single-cell atlas of the late gestational fetal Macaca mulatta across lung, spleen, and umbilical cord blood. Utilization of this atlas will allow for the cellular characterization of healthy fetal immunity in the Rhesus Macaque model. METHODS Animal studies. Eight healthy female rhesus macaques underwent time-mated breeding at the Oregon National Primate Research Center (ONPRC) resulting in ten fetal macaques (40% Female). All macaques in this study were managed according to the ONPRC animal care program, which is fully accredited by AAALAC International and is based on the laws, regulations, and guidelines set forth by the United States Department of Agriculture (e.g., the Animal Welfare Act and Animal Welfare Regulations, the Guide for the Care and Use of Laboratory Animals, 8th edition [Institute for Laboratory Animal Research]) and the Public Health Service Policy on Humane Care and Use of Laboratory Animals. Animals received ad libitum access to food (Purina 5000 Fiber-balanced Monkey Diet, Purina Mills, Richmond, IN, USA) and fresh water. Animals were fed a diet formulated according to National Research Council recommendations, supplemented with fruits and vegetables, provided through the Behavioral Services Unit’s environmental enrichment program. Animals were maintained in pair housing. Sample collection and processing. Fetal samples were obtained via scheduled cesarian section (C-section) between GD130 and GD135 as described to be representative of third trimester human development ( 20 , 21 ). UCB was collected at C-section in EDTA tubes and UCBMC and plasma were collected following centrifugation over a Ficoll gradient (Lymphoprep; STEMCELL). Final cell counts were obtained, and cells were cryopreserved in 10% DMSO/FBS. Fetal spleen was collected at necropsy and immediately placed on ice in RPMI supplemented with 10% fetal bovine serum (FBS), streptomycin/penicillin, and L-glutamine. Splenic leukocytes were isolated by mechanical disruption. Cells were centrifuged and red blood cells were lysed using 0.84% ammonium chloride pH 7.4, followed by several washes. Splenic leukocytes were cryopreserved in 10% DMSO/FBS. Lung tissue was collected and processed in RPMI-1640 media supplemented with 3% BSA, 1% Penicillin–Streptomycin, 1% L-glutamine, and 10 mM HEPES pH 7.4 (R3 medium). Lung tissue was subjected to enzymatic digestion using 120 mg collagenase II (Gibco), 2.5 mg elastase (Sigma-Aldrich), 40 mg DNase I (Sigma-Aldrich), and 12 mg hyaluronidase (Sigma-Aldrich) in R3 medium and supplemented with 80 µL of 1 M CaCl₂ for 1 hour at 37°C with gentle rotation. Remaining tissue was mechanically dissociated. Cells were pelleted and subjected to density separation using a discontinuous 60 − 30% Percoll gradient. The gradient was centrifuged at 2500 rpm for 30 minutes. Cells located at the interface between the 30% and 60% Percoll layers were collected and washed in R3 medium. Final cell counts were obtained, and cells were cryopreserved in CryoStor CS10 at a density of less than 20 × 10⁶ cells per vial. Single-cell RNA sequencing library generation. Leukocytes were thawed, washed in 2% FBS/DPBS, and incubated with Rhesus Fc Block (Invivogen). Each sample was labeled with a distinct TotalSeq Hashtag Oligo Antibody (HTO, Biolegend) and incubated per manufacturer’s instruction. Pellets were washed twice in 2% FBS/DPBS and pooled by tissue. Cell pools were filtered and counted in duplicate to confirm a viability greater than 80%. Samples with less than 80% viability (spleen and lung) were stained with anti-Rhesus CD45 BV650 (BD Biosciences) and propidium iodine before being sorted for live CD45 + leukocytes using a Sony SH800 Cell Sorter System. Cells were filtered and suspended in 2% FBS/DPBS to a final concentration of 1500–1600 cells/mL. Cell suspensions were then immediately loaded on the 10x Genomics Chromium X Controller Chip G with a target of 30,000 cells. Libraries were prepared using the V3.1 chemistry for gene expression and Single Cell 3ʹ Feature Barcode Library Kit per the manufacturer’s instructions (10x Genomics). Libraries were sequenced on Illumina NovaSeq X with a sequencing target of 30,000 gene expression reads and 5,000 feature barcoding reads per cell. Single-cell RNA-seq data analysis. Raw sequencing reads were aligned and HTOs were demultiplexed using Cell Ranger (v6.0.2, 10x Genomic) against the Macaca mulatta reference genome (mmul10) using the multi-option. Downstream processing of aligned reads was performed using Seurat (v5.1.0) ( 22 ). Droplets with ambient RNA or potential doublets ( 4000 detected genes) and inviable cells (> 20% total mitochondrial gene expression) were excluded during initial QC. Data objects from all libraries were integrated using Harmony ( 23 ). Data normalization and variance stabilization were performed on the integrated object using the NormalizeData and ScaleData functions in Seurat (v5.1.0), where a regularized negative binomial regression was corrected for differential effects of mitochondrial and ribosomal gene expression levels. Dimensionality reduction was performed using RunPCA function to obtain the first 30 principal components and clusters visualized using Seurat’s RunUMAP function. Cell types were assigned to individual clusters using FindAllMarkers function (Supplemental Table 1) with a log2 fold change ± 0.4, FDR < 0.05, and canonical scRNA markers for NHP leukocytes. Differential gene expression (DEG) analysis for each cluster was performed using the FindMarker function in Seurat comparing each tissue to the other two tissues and retaining only the genes with a positive fold change. For example, DEGs in the lung utilized the lung as ident.1 and both spleen and UCBMC as ident.2. Only statistically significant genes maintaining an FDR 0.585 were included in downstream analyses (Supplemental Table 2). Functional enrichment was performed using Metascape ( 24 ). Gene set enrichment analysis (GSEA) was performed using the irGSEA ( 25 ) package (v2). Briefly, the irGSEA.score function was used to calculate the enrichment score for each cell for genes sets in the MsigBD gene ontology (GO) GO0002376 Immune processes. The irGSEA.integrate function was then used to perform a Wilcox test of the enrichment score matrix comparing the tissues compartments. The heatmaps were visualized using the irGSEA.heatmap functions. The R CellChat ( 26 ) package was employed to infer probable intercellular communication networks. A CellChat object was generated from a Seurat v5 object with the createCellChat function. The CellChatDB.human database was utilized. Data was preprocessed with the function identifyOverExpressedGenes and identifyOverExpressedInteractions and communication probabilities between clusters were determined with computeCommunProb (truncatedMean, trim = 0.1, interaction.range = 250, contact.range = 100). Communications were filtered (filterCommunication) to a minimum number of 10 cell and signaling pathway probabilities were calculated (computeCommunProbPathway). Declarations All animal procedures were approved by the Institutional Animal Care and Use Committee of the Oregon National Primate Research Center, which is fully accredited by AAALAC International and is based on the laws, regulations, and guidelines set forth by the United States Department of Agriculture (e.g., the Animal Welfare Act and Animal Welfare Regulations, the Guide for the Care and Use of Laboratory Animals, 8th edition [Institute for Laboratory Animal Research]) and the Public Health Service Policy on Humane Care and Use of Laboratory Animals. Declaration of interests All authors declare no competing interests. Funding This study was funded by the National Institutes of Health grant number 7R01AI142841-04 (IM, OV) and P51 OD01192 for operation of the Oregon National Primate Research Center. Acknowledgments We thank CoreyAyne Singleton, Dr. Lauren Drew Martin, and Travis Hodge at the Oregon National Primate Center for their contributions to animal breeding and sample collection/processing along with Drs. Heather True and Delphine Malherbe for their valuable feedback during manuscript preparation. Data availability. The datasets supporting the conclusions of this article are available on NCBI’s Sequence Read Archive: single-cell RNA sequencing (PRJNA1247568). References Popescu DM, Botting RA, Stephenson E, Green K, Webb S, Jardine L et al (2019) Decoding human fetal liver haematopoiesis. Nature 574(7778):365–371 Ginhoux F, Guilliams M (2016) Tissue-Resident Macrophage Ontogeny and Homeostasis. Immunity 44(3):439–449 Hoeffel G, Ginhoux F (2018) Fetal monocytes and the origins of tissue-resident macrophages. Cell Immunol 330:5–15 Feyaerts D, Urbschat C, Gaudilliere B, Stelzer IA (2022) Establishment of tissue-resident immune populations in the fetus. 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Cytotechnology 67(3):387–396 Goncalves J, Melro M, Alenquer M, Araujo C, Castro-Neves J, Amaral-Silva D et al (2023) Balance between maternal antiviral response and placental transfer of protection in gestational SARS-CoV-2 infection. JCI Insight. ;8(17) Additional Declarations There is NO Competing Interest. Supplementary Files DorattFetalAtlasSupTable1.pdf Supplemental Table 1 DorattFetalAtlasSupTable2.pdf Supplemental Table 2 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8734095","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586895667,"identity":"a159180e-a490-4348-8ad5-24441e2feb50","order_by":0,"name":"Ilhem Messaoudi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACNnYeFK6EHANYgA2PFmY0LcYEtTCgaWFIbCCkhY+Z9+CjGwzb5OT7Dz98zFNmkb7hzBkDhg9lh/E4jC/ZOIfhtrHBjTRjY55zErkbzvYYMM44h08Lj5k0UEviBgkGM2neNqCW8zwGzLxteLWY/wZqqZ/ff/wbSEu6AUjLX/xazJiBWhIYDuSAbUkwADqMmRG/FmPpHIPbhhtu5BQbzjknYTjzzLGCgz3n0nFqkW/vMfycU3FbXr7/+MYHb8rq5PnOJG988KPMGqcWCDBA4XEYHCCgHgOwPyBVxygYBaNgFAxvAACFm0y6l5rjZgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3203-2405","institution":"University of Kentucky College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ilhem","middleName":"","lastName":"Messaoudi","suffix":""},{"id":586895668,"identity":"e8818815-3076-41e5-aeb5-06d166a6b4bd","order_by":1,"name":"Brianna Doratt","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Brianna","middleName":"","lastName":"Doratt","suffix":""},{"id":586895669,"identity":"5a9296c4-7c09-4b67-80a4-641e9eccfb35","order_by":2,"name":"Sheridan Wagner","email":"","orcid":"","institution":"University of Kentucky College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sheridan","middleName":"","lastName":"Wagner","suffix":""},{"id":586895670,"identity":"eaf3f942-cac8-4358-a8af-539705f6fc94","order_by":3,"name":"Katelyn Keen","email":"","orcid":"","institution":"University of Kentucky College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Katelyn","middleName":"","lastName":"Keen","suffix":""},{"id":586895671,"identity":"d6d6acc2-731f-4d02-aa59-617de59d29de","order_by":4,"name":"Uriel Avila","email":"","orcid":"","institution":"Oregon National Primate Research Center","correspondingAuthor":false,"prefix":"","firstName":"Uriel","middleName":"","lastName":"Avila","suffix":""},{"id":586895672,"identity":"5fb71a62-d938-4fe4-8e31-1dcbe54470b3","order_by":5,"name":"Oleg Varlamov","email":"","orcid":"","institution":"Oregon Health \u0026 Science University","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Varlamov","suffix":""}],"badges":[],"createdAt":"2026-01-29 17:35:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8734095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8734095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102396541,"identity":"54d6c6d7-52dc-4539-806a-e6796343bb27","added_by":"auto","created_at":"2026-02-11 09:46:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":947255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell analysis of fetal immune cells. \u003c/strong\u003e(A) UMAP projection of 102,245 cells from the fetal lung, spleen, and UCBMC leukocytes of ten fetal rhesus macaques. (B) UMAP separated by tissue type. (C) Violin plots of marker gene expression used for cluster identification. (D) Stacked bar plots of cluster frequencies within the lung, spleen, and UCBMC. Statistical tests were performed using two-way ANOVA using Tukey correction in Prism (v10). Error bars represent standard error of the mean. ⁕=p\u0026lt;0.05, ⁕⁕=p\u0026lt;0.01, ⁕⁕⁕=p\u0026lt;0.001, ⁕⁕⁕⁕=p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/6df58b1eedf54460d7d5248a.jpg"},{"id":102396548,"identity":"894bd959-fecc-4ed3-8899-33731b6636ce","added_by":"auto","created_at":"2026-02-11 09:46:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1440225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of B-cell populations across fetal tissues. \u003c/strong\u003e(A) Bubble plot of GO terms to which marker genes for the three B-cell clusters enriched. Size of the bubble indicates the number of genes present in each GO term. The color of bubble indicates -log(q-value). (B) Bar plot of the relative frequencies of B-cell populations within sample types. (C) Heatmap of selected gene sets from the GSEA for Bcell_1. Blue indicates downregulation and red indicates upregulation compared to all other cell types. (D,G) Venn diagrams showing overlap of upregulated DEGs for the indicated clusters. (E,H) Bubble plots showing GO terms to which upregulated DEGs in the indicated clusters enriched. Bubble size indicates the number of genes associated with each GO term, and color corresponds to -log(q-value). (F,I) Violin plots depicting the expression levels of select DEGs within the indicated clusters. Arrows denote the tissue type with which each DEG is associated.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/4c504433e73a7f4aadbde174.jpg"},{"id":102398497,"identity":"c053bcb6-68fe-44c8-aaa3-ccebf5fdce84","added_by":"auto","created_at":"2026-02-11 10:23:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1121909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of T-cell populations across fetal tissues. \u003c/strong\u003e(A) Bubble plot of GO terms to which marker genes for the two CD4 T-cell clusters enriched. The size of bubble indicates the number of genes present in each GO term. The color of bubble indicates -log(q-value). (B) Bar plot of the relative frequencies of T-cell populations within tissue types. (C) Heatmap of selected gene sets from the GSEA for CD4_1. Blue indicates downregulation and red indicates upregulation compared to all other cell types. (D,G) Venn diagrams showing overlap of upregulated DEGs for the indicated clusters. (E,H) Bubble plots showing GO terms to which upregulated DEGs in the indicated clusters enriched. Bubble size indicates the number of genes associated with each GO term, and color corresponds to -log(q-value). (F,I) Violin plots depicting the expression levels of select DEGs within the indicated clusters. Arrows denote the tissue type with which each DEG is associated.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/dc6ede45240f1402f40cc1d2.jpg"},{"id":102396544,"identity":"a6fe1f1f-5790-4ecc-9726-50fee7d9b0d0","added_by":"auto","created_at":"2026-02-11 09:46:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1361625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of NK cell populations across fetal tissues. \u003c/strong\u003e(A) Bar plot of the relative frequencies of innate lymphocyte, Proliferating (\u003cem\u003eMKI67\u003c/em\u003e+), and HSPC populations within tissue types. (B) Bubble plot of GO terms to which marker genes for the two NK cell clusters enriched. The size of bubble indicates the number of genes present in each GO term. The color of bubble indicates -log(q-value). (C) Heatmap of selected gene sets from the GSEA for CD16+ NK cells. Blue indicates downregulation and red indicated upregulation compared to all other cell types. (D,G) Venn diagrams showing overlap of upregulated DEGs for the indicated clusters. (E,H) Bubble plots showing GO terms to which upregulated DEGs in the indicated clusters enriched. Bubble size indicates the number of genes associated with each GO term, and color corresponds to -log(q-value). (F,I) Violin plots depicting the expression levels of select DEGs within the indicated clusters. Arrows denote the tissue type with which each DEG is associated.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/5ceda85d608275764cd7d222.jpg"},{"id":102398244,"identity":"995c2f4d-e632-4983-8688-a24ff8034bba","added_by":"auto","created_at":"2026-02-11 10:21:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1313448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of myeloid populations across fetal tissues. \u003c/strong\u003e\u0026nbsp;(A) Bar plot of the relative frequencies for myeloid cell populations within each cell type. (B) Heatmap of selected gene sets from the GSEA for Monocytes. Blue indicates downregulation and red indicated upregulation compared to all other cell types. (C) Venn diagram showing overlap of upregulated DEGs for monocytes in each tissue. (D) Bubble plot showing GO terms to which upregulated DEGs for monocyte populations enriched. Bubble size indicates the number of genes associated with each GO term, and color corresponds to -log(q-value). (E) Violin plots depicting the expression levels of select DEGs within the Monocyte clusters. Arrows denote the tissue type with which each DEG is associated. (F) Box plots of module scoring for wound healing, inflammation, and viral/bacterial response in Monocytes between tissue types. Statistics were performed via Kruskal-Wallis test. Error bars represent standard error of the mean. ⁕=p\u0026lt;0.05, ⁕⁕=p\u0026lt;0.01, ⁕⁕⁕=p\u0026lt;0.001, ⁕⁕⁕⁕=p\u0026lt;0.0001. (G) Volcano plot of upregulated DEGs for Macrophages in lung and spleen. (H) Bubble plot showing GO terms to which upregulated DEGs for Macrophage populations enriched. Bubble size indicates the number of genes associated with each GO term, and color corresponds to -log(q-value). (I) Violin plots depicting the expression levels of select DEGs within the Monocyte clusters.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/e6df73d3dff4effe060b2ce5.jpg"},{"id":102396547,"identity":"62427947-b3ab-4951-90dc-dba6d61a35d4","added_by":"auto","created_at":"2026-02-11 09:46:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":650842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteractome analysis across cell and tissue types.\u003c/strong\u003e (A) Bar plot showing the number of inferred interactions (left) and interaction strengths (right) for each tissue type. (B) Scatterplots of outgoing and incoming interaction strengths for each immune cell cluster in the lung, spleen, and UCBMC samples. The dot size is proportional to the number of significant ligand-receptor pairs. (C) Bar plots of specific signaling pathways ranked by relative information flow. Bar colors designate pathways as dominant in lung (green), spleen (red), or UCBMC (blue). (D) Heatmaps of the overall signaling strength of pathways for each separate cluster in lung, spleen, and UCBMC samples. The color bar plot on top displays total signaling strength of each cell cluster across all pathways. The gray bar plot on the right displays the total signaling strength of each pathway across all cell clusters.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/b60cbb4a1454a44e1eeb2d29.jpg"},{"id":102396549,"identity":"4883c84b-5c4b-4c7e-a21f-d7f42d95b89e","added_by":"auto","created_at":"2026-02-11 09:46:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3617943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway interactions across cell and tissue types\u003c/strong\u003e. (A-D) Network signaling diagram depicting predicted communication between cell subsets within the indicated tissue across the specific pathways. The color of each line represents the source of the signal.\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/2247b25f87d4c466173f9ffa.jpg"},{"id":102400184,"identity":"2e994f79-37ac-4a0d-8e99-8136eb41d2c1","added_by":"auto","created_at":"2026-02-11 10:38:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11462778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/1a8521ed-11de-43ef-acf9-8ad65175f691.pdf"},{"id":102396543,"identity":"68661951-7f8e-4935-84a3-caf77978cee8","added_by":"auto","created_at":"2026-02-11 09:46:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1944013,"visible":true,"origin":"","legend":"Supplemental Table 1","description":"","filename":"DorattFetalAtlasSupTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/9cbdc765165a0bccb959cfce.pdf"},{"id":102396546,"identity":"e21391c7-75dc-46d4-b657-12d979024122","added_by":"auto","created_at":"2026-02-11 09:46:23","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10059217,"visible":true,"origin":"","legend":"Supplemental Table 2","description":"","filename":"DorattFetalAtlasSupTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734095/v1/630abfbff720ccc6ebf1c11b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-Cell Atlas of Fetal Immune Development Across Lung, Spleen, and Umbilical Cord Blood in Nonhuman Primates","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe development of the human fetal immune system is a tightly orchestrated process that primes the neonate for protective responses against antigenic encounters after birth. Hematopoietic stem cells (HSC) emerge in the yolk sac as early as four weeks of gestation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). By six weeks, macrophages rapidly develop in the yolk sac and begin to colonize fetal tissues (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), coinciding with the fetal liver becoming the predominant site of hematopoiesis. The liver establishes a self-renewing pool of hematopoietic stem and progenitor cells (HSPC) and serves as a key site for macrophage development between weeks 9 and 13 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). After gestational week 11, HSC begin colonizing the fetal bone marrow, giving rise to the full hematopoietic lineage (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which continues to mature throughout gestation as lymphocyte compartments are progressively defined (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccumulating evidence demonstrates that the prenatal environment directly impacts neonatal health and shapes disease susceptibility into adulthood (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The maternal inflammatory milieu and antigen exposure can mold fetal immunity; for example, maternal infection, even without vertical transmission, can alter neonatal immunity by impairing hematopoiesis (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Human studies investigating immune perturbations in utero are limited by the inaccessibility of fetal tissues throughout pregnancy, often relying instead on postnatal peripheral samples, in vitro approaches, or small animal models (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These strategies cannot fully capture the dynamic trajectory of human fetal immune system development. In contrast, the rhesus macaque (\u003cem\u003eMacaca mulatta\u003c/em\u003e), with its phylogenetic proximity to humans, similar placental morphology and developmental trajectory, and gestational timeline (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), provides a valuable model for investigating prenatal immune development.\u003c/p\u003e \u003cp\u003eHere, we present a single-cell atlas of the immune transcriptional landscapes of fetal lung, spleen, and umbilical cord blood (UCB) in late-gestational (gestational day (GD) 130) rhesus macaques. UCB was analyzed in place of fetal blood given easier access and greater volume. The spleen was selected as a key secondary lymphoid organ with a microenvironment essential for immune development, and the lung as a non-hematopoietic tissue seeded early in gestation that directly encounters antigens at birth. By leveraging single-cell RNA sequencing (scRNA-seq), we identified diverse immune cell populations across compartments and uncovered putative regulatory circuits underpinning fetal immune development. This atlas provides a foundational framework to investigate molecular mechanisms shaping fetal immunity across gestation.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eImmune cell frequencies are tissue-dependent in fetal lung, spleen, and umbilical cord.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLeukocytes isolated from fetal rhesus macaque spleen, lung, and UCB were analyzed via scRNA-seq to generate a fetal immune cell atlas. Using canonical immune markers, we identified 19 distinct leukocyte clusters, with substantial contribution from all three tissue sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B). Within the myeloid compartment, we identified monocytes (\u003cem\u003eCD14\u003c/em\u003e, \u003cem\u003eMAMU-DRA\u003c/em\u003e, \u003cem\u003eS100A9\u003c/em\u003e), macrophages (\u003cem\u003eCD14\u003c/em\u003e, \u003cem\u003eMAMU-DRA, MRC1\u003c/em\u003e), FLT3 positive plasmacytoid dendritic cells (pDC; \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003eMAMU-DRA, TCF4\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e), and myeloid dendritic cells (mDC; \u003cem\u003eMAMU-DRA, CD1C\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). B-cells expressing \u003cem\u003eMS4A1\u003c/em\u003e (CD20) were subdivided into three groups according to the differential expression of \u003cem\u003ePAX5\u003c/em\u003e, \u003cem\u003eEBF1\u003c/em\u003e, and \u003cem\u003eCD79A\u003c/em\u003e: Bcell_1 (\u003cem\u003ePAX5\u003c/em\u003elow, \u003cem\u003eEBF1\u003c/em\u003elow, \u003cem\u003eCD79A\u003c/em\u003ehigh), Bcell_2 (\u003cem\u003ePAX5\u003c/em\u003emid, \u003cem\u003eEBF1\u003c/em\u003emid, \u003cem\u003eCD79A\u003c/em\u003emid), and Bcell_3 (\u003cem\u003ePAX5\u003c/em\u003ehigh, \u003cem\u003eEBF1\u003c/em\u003ehigh, \u003cem\u003eCD79A\u003c/em\u003elow) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). Two natural killer (NK) cell clusters were identified based on the expression of \u003cem\u003eKLRB1\u003c/em\u003e and \u003cem\u003eNKG7\u003c/em\u003e and further distinguished by \u003cem\u003eFCGR3\u003c/em\u003e (CD16) expression: CD16\u0026thinsp;+\u0026thinsp;NK (\u003cem\u003eKLRB1\u003c/em\u003e+, \u003cem\u003eNKG7\u003c/em\u003e+, \u003cem\u003eFCGR3\u003c/em\u003e+) and CD16- NK (\u003cem\u003eKLRB1\u003c/em\u003e+, \u003cem\u003eNKG7\u003c/em\u003e+, \u003cem\u003eFCGR3\u003c/em\u003e-) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). A cluster of natural killer T (NKT) cells was defined by positive \u003cem\u003eZBTB16\u003c/em\u003e expression, and a cluster of T regulatory (Treg) cells was identified through positive \u003cem\u003eCTLA4\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). A cluster of type two innate lymphoid cells (ILC2) was identified by the absence of canonical myeloid and adaptive cell markers and the expression of \u003cem\u003eGATA3\u003c/em\u003e and \u003cem\u003eIL1RL1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). T-cells were identified by expression of \u003cem\u003eCD3E\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). Additional T-cell clusters were delineated into CD4 Naive (\u003cem\u003eCD8A-\u003c/em\u003e, \u003cem\u003eCD28\u003c/em\u003emid, \u003cem\u003eCCR7+\u003c/em\u003e, \u003cem\u003eIL7R\u003c/em\u003e+, \u003cem\u003eLEF1\u003c/em\u003e+), CD4 central memory (CM; \u003cem\u003eCD8A-\u003c/em\u003e, \u003cem\u003eCD28\u003c/em\u003elow, \u003cem\u003eCCR7+\u003c/em\u003e, \u003cem\u003eIL7R\u003c/em\u003e+, \u003cem\u003eLEF1\u003c/em\u003elow), CD4 \u003cem\u003eCAMK4\u003c/em\u003e high (CAMK4; \u003cem\u003eCD8A-\u003c/em\u003e, \u003cem\u003eCD28\u003c/em\u003ehigh, \u003cem\u003eCCR7\u003c/em\u003elow, \u003cem\u003eIL7R\u003c/em\u003elow, \u003cem\u003eCAMK4\u003c/em\u003ehigh), CD4 cytotoxic effector memory (EM_CTL; \u003cem\u003eCD8A\u003c/em\u003e-, \u003cem\u003eCCR7-\u003c/em\u003e, \u003cem\u003eKLRB1+\u003c/em\u003e, \u003cem\u003eNKG7+\u003c/em\u003e, \u003cem\u003eIL7R\u003c/em\u003elow) and CD8 (\u003cem\u003eCD8A+\u003c/em\u003e, \u003cem\u003eCCR7+\u003c/em\u003e, \u003cem\u003eIL7R\u003c/em\u003e+) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C). A proliferating T-cell cluster was identified by high \u003cem\u003eMKI67\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C\u003cb\u003e)\u003c/b\u003e. Finally, a small cluster of hematopoietic stem/progenitor cells (HSPC) was distinguished by high \u003cem\u003eCD34\u003c/em\u003e expression, particularly in the spleen, representing extramedullary hematopoiesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferences in cell cluster abundance were observed between the three tissue compartments. As expected, macrophages were detected only in lung and spleen, while ILC2 cells were found almost exclusively in the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB,D). Leukocyte populations in the lung were dominated by myeloid cells (50.3%), followed by T-cells (17.2%) and B-cells (10.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The spleen was comprised primarily of T- and B-cell clusters (42.0% and 38.8% respectively), whereas UCBMCs were dominated by T-cells (83.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncreased B-cell maturation within the fetal spleen.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe first performed Gene Ontology (GO) enrichment on the marker genes for each B-cell cluster to delineate subset-specific differences, independent of tissue of origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Marker genes in the Bcell_1 cluster mapped to B-cell receptor (BCR) signaling and immunoglobulin binding while the Bcell_2 cluster enriched for cellular homeostasis and nuclear transport, and the Bcell_3 cluster enriched for response to virus, response to type II interferon, and cellular respiration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These enrichments suggest that the Bcell_1 cluster, which was augmented in the fetal lung as indicated by normalization of B cell cluster frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), exhibited enhanced effector functionality compared to the homeostatic Bcell_2 cluster, while the Bcell_3 cluster enriched in the spleen and UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) appeared primed for antiviral responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next evaluated the tissue-specific transcriptional profile of the combined B cell clusters using gene set enrichment analysis (GSEA). Spleen B cell subsets were characterized by increased expression of genes important for B cell isotype switching, differentiation, and antigen processing and presentation compared to B cells in the lung and UCBMC, in line with the role of the spleen as a secondary lymphoid organ in the initiation of antimicrobial responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In contrast, isotype switching and differentiation were significantly downregulated in the UCBMC while differentiation and proliferation were significantly downregulated in the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo identify specific differences in transcriptional profiles of B-cells across fetal tissues, we performed DEG analysis comparing gene expression in one cluster from one tissue to the same cell cluster of the other two tissues and retaining only the genes with a positive fold change. Venn diagrams of upregulated genes were generated to identify DEGs unique to each tissue. While most DEGs were tissue-specific, 137 shared upregulated DEGs were detected in both Spleen vs. Lung+UCBMC and UCBMC vs. Lung+Spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). DEG unique to the lung exclusively enriched to processes associated with oxidative phosphorylation (\u003cem\u003eCOX7C, ATP5PF\u003c/em\u003e) and oxidoreductase activity (\u003cem\u003eGSTP1\u003c/em\u003e, \u003cem\u003eND4L\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE,F). Additional DEGs upregulated in the lung Bcell_1 cluster included the pro-apoptotic gene \u003cem\u003eIL27L2\u003c/em\u003e and the antiviral gene \u003cem\u003eISG20\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). DEG unique to spleen enriched to B cell activation (\u003cem\u003ePIK3CD\u003c/em\u003e, \u003cem\u003eBLNK\u003c/em\u003e, \u003cem\u003eBANK1\u003c/em\u003e), positive regulation of cell programmed death (\u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003eLTB\u003c/em\u003e, \u003cem\u003eBCL2\u003c/em\u003e), regulation of cell-cell adhesion (\u003cem\u003eRUNX1\u003c/em\u003e, \u003cem\u003eCD47\u003c/em\u003e, \u003cem\u003eDOCK8\u003c/em\u003e), immunoglobulin recombination (\u003cem\u003ePAX5\u003c/em\u003e, \u003cem\u003eIKZF3\u003c/em\u003e, \u003cem\u003eIL27RA\u003c/em\u003e), and cytokine production (\u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eLTB\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE,F). DEG shared between spleen and UCBMC also enriched to B cell activation (\u003cem\u003ePRKCB\u003c/em\u003e, \u003cem\u003ePTPRJ\u003c/em\u003e), positive regulation of cell programmed death (\u003cem\u003eFAF1\u003c/em\u003e, \u003cem\u003eFOXO1\u003c/em\u003e), and stem cell population maintenance (\u003cem\u003eFOXO1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE,F). DEG unique to UCBMC enriched to regulation of cell-cell adhesion (\u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eLYN\u003c/em\u003e) and stem cell population maintenance (\u003cem\u003eBRAF\u003c/em\u003e, \u003cem\u003eMED21\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE,F).\u003c/p\u003e \u003cp\u003eNext, we examined the Bcell_3 cluster, which was more abundant in the spleen and UCBMC compared to the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Only six upregulated DEGs were unique to the lung, four of which were associated with mitochondrial functions (\u003cem\u003eCOX1\u003c/em\u003e, \u003cem\u003eCOX2\u003c/em\u003e, \u003cem\u003eND4\u003c/em\u003e, \u003cem\u003eND4L\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG,I, \u003cb\u003eSup. Table\u0026nbsp;2\u003c/b\u003e). Compared to UCBMC, Bcell_3 clusters in both lung and spleen exhibited significant upregulation of \u003cem\u003eMEF2C\u003c/em\u003e, which limits leukocyte adhesion and migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Although there were no shared upregulated DEGs between spleen and UCBMC, the unique genes in these tissues enriched to similar functions, including histone modification, transcription factor binding, and regulation of cell cycle process (Spleen: \u003cem\u003eBCL11A\u003c/em\u003e, \u003cem\u003eHIST1H2AC\u003c/em\u003e; UCBMC: \u003cem\u003eKDM7A\u003c/em\u003e, \u003cem\u003eJUND\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH,I). Spleen-unique GO terms included B-cell activation (\u003cem\u003eCD19\u003c/em\u003e, \u003cem\u003eCD22\u003c/em\u003e, \u003cem\u003eCR2\u003c/em\u003e), tumor necrosis factor production (\u003cem\u003eTRAF3IP3\u003c/em\u003e), and immunoglobulin recombination (\u003cem\u003eBCL11A\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH,I). Other spleen-unique upregulated DEGs play a role in B-cell migration (\u003cem\u003eITGA4\u003c/em\u003e) as well as antigen presentation (\u003cem\u003eMAMU-DBR1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH,I). UCBMC-unique DEG were important for RNA splicing and receptor internalization (\u003cem\u003eITCH\u003c/em\u003e), B-cell adhesion (PECAM1), and inflammatory response (\u003cem\u003eS100A6\u003c/em\u003e and \u003cem\u003eS100A10\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH,I).\u003c/p\u003e \u003cp\u003e \u003cb\u003eT-cell activation and migration were prominent in the fetal spleen.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis section will focus on the CD4 T‑cell compartment as our analysis of T‑cell subsets revealed that CD4 T cells exhibited the most pronounced alterations across tissue. Enrichment of CD4_Naive and CD4_CM cluster marker genes indicated that CD4_Naive represents an activated subset, as evidenced by higher expression of genes involved in T-cell activation, antigen receptor signaling, and positive regulation of IL-2 production (\u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003eCD28\u003c/em\u003e, \u003cem\u003eRHOH\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, \u003cem\u003eTRAC\u003c/em\u003e, \u003cem\u003eICOS\u003c/em\u003e, \u003cem\u003eSTAT5B\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eSup. Table\u0026nbsp;1\u003c/b\u003e). In contrast, CD4_CM markers enriched to GO terms such as cellular respiration (\u003cem\u003eATP5F1C\u003c/em\u003e, \u003cem\u003eCOX7C\u003c/em\u003e) and TNF-α signaling (\u003cem\u003eRPL6\u003c/em\u003e, \u003cem\u003eRPL8\u003c/em\u003e, \u003cem\u003eRPL30\u003c/em\u003e, \u003cem\u003eRPS13\u003c/em\u003e) suggesting heightened metabolic and pro-inflammatory capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eSup. Table\u0026nbsp;1\u003c/b\u003e). Comparisons of T cell cluster frequencies indicated the CD4_Naive population was most abundant in UCBMC and least abundant in the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In contrast, the proportion of CD4_CM T-cells was highest in the lung and lowest in the spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The spleen contained higher proportions of both CD4_EM_CTL and Treg populations compared to lung and UCBMC while the lung had the lowest CD4_CAMK4 percentage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We also performed GSEA to identify broad functional programs of the two \u003cem\u003eCCR7\u003c/em\u003e\u0026thinsp;+\u0026thinsp;CD4 clusters (CD4_Naive \u0026amp; CD4_CM) within each tissue. In both the spleen and UCBMC, transcriptional signatures of CD4 \u003cem\u003eCCR7\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells showed increased T-cell proliferation, anergy, positive selection, and V(D)J recombination (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Conversely, T-cell proliferation, anergy, positive selection, and V(D)J recombination were decreased in the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In UCBMC, CD4 T cell transcriptional profile was indicative of regulatory T cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Overall, these data indicate that CD4 T cells were more activated in the spleen.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we used the same DEG analysis strategy as described previously for the B cells. Within each cluster, we identified DEG upregulated in each tissue relative to the remaining two tissues. DEGs upregulated in the CD4_Naive cluster were largely unique to each tissue, with only six genes shared between Lung vs. Spleen+UCBMC and Spleen vs. Lung+UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). DEGs unique to the lung and spleen played a role in T-cell activation (Lung: \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eICOS\u003c/em\u003e; Spleen: \u003cem\u003eCD2\u003c/em\u003e, \u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F). CD4_Naive cells in the lung expressed high levels of the antiviral gene \u003cem\u003eIFI16\u003c/em\u003e and numerous T-cell developmental transcription factor genes (\u003cem\u003eIKZF1\u003c/em\u003e, \u003cem\u003eBACH2\u003c/em\u003e, \u003cem\u003eSOX4\u003c/em\u003e, \u003cem\u003eSTAT4\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). DEGs unique to the spleen enriched to positive regulation of immune response and cytokine production (\u003cem\u003eIL16\u003c/em\u003e, \u003cem\u003eIL17RA\u003c/em\u003e, \u003cem\u003eIL27RA\u003c/em\u003e, \u003cem\u003eLTB\u003c/em\u003e), key transcription factors (\u003cem\u003eBCL11B\u003c/em\u003e, \u003cem\u003eID3\u003c/em\u003e, \u003cem\u003eIRF2\u003c/em\u003e, \u003cem\u003eKLF6, JUN\u003c/em\u003e), and facilitators of TCR activation (\u003cem\u003eCD40LG\u003c/em\u003e, \u003cem\u003eTRAF3IP3\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F). DEGs unique to UCBMC enriched to GO terms associated with protein degradation, epigenetics and cell cycle (\u003cem\u003eRELB, EIF2AK3\u003c/em\u003e, \u003cem\u003eCASP3 FBXO33\u003c/em\u003e, \u003cem\u003eHERC1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F).\u003c/p\u003e \u003cp\u003eDEGs in CD4_CM cell population were also largely tissue-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). DEGs unique to the lung mapped to oxidative phosphorylation (\u003cem\u003eCOX7B\u003c/em\u003e), T-cell receptor signaling (\u003cem\u003eCD3D, CD7\u003c/em\u003e), and antiviral immunity (\u003cem\u003eIFI16\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH,I). DEGs upregulated in the spleen and UCBMC enriched to chromatin binding (Spleen: \u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eIRF2\u003c/em\u003e; UCBMC: \u003cem\u003eKDM2A, ARID1A, CREBBP\u003c/em\u003e; Spleen/UCBMC: \u003cem\u003eRUNX1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH,I). Genes uniquely upregulated in the spleen included those important for T-cell activation, signaling, and cytokine response (\u003cem\u003eCD38\u003c/em\u003e, \u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eIL27RA\u003c/em\u003e, \u003cem\u003eIL6ST\u003c/em\u003e, \u003cem\u003eTGFBR2\u003c/em\u003e, and \u003cem\u003eTRAC)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). DEGs unique to UCBMC enriched to regulation of stem cell population maintenance (\u003cem\u003eFOXO1\u003c/em\u003e, \u003cem\u003eFOXP1\u003c/em\u003e, \u003cem\u003eCTNNB1\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eRHOH\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH,I).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNK cells had higher cytotoxic capacity within the fetal lung.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eComparisons of the frequency of the innate, HSPC, and proliferating clusters revealed the lung harbored the largest proportion of CD16\u0026thinsp;+\u0026thinsp;NK cells and ILC2 cells, while the spleen was home to the largest proportion of CD16- NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Finally, the NKT subset was most prominent in the UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Marker genes of both CD16\u0026thinsp;+\u0026thinsp;and CD16- NK clusters enriched to GO processes associated with oxidative phosphorylation and response to cytokine stimulus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Given the role of CD16\u0026thinsp;+\u0026thinsp;NK cells in ADCC, we noted the enrichment to NF-kB signal transduction, which is crucial for NK cell IFN-gamma production and cytotoxicity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Marker genes of CD16- NK cells uniquely enriched to positive regulation of cytokine production, consistent with their cytokine-producing function and reduced ADCC capability compared to CD16\u0026thinsp;+\u0026thinsp;NK cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). GSEA analysis of the combined NK cell clusters showed upregulation of NK cell differentiation and antigen processing/presentation in the spleen and UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). On the other hand, leukocyte-mediated cytotoxicity and immunoglobulin-like receptor signaling pathways were upregulated in the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor CD16\u0026thinsp;+\u0026thinsp;NK cells, the lung displayed 165 unique upregulated DEGs while the majority of UCBMC defining DEG were shared with the spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). DEGs in the lung CD16\u0026thinsp;+\u0026thinsp;NK cluster uniquely enriched to oxidative phosphorylation (\u003cem\u003eCOX7C\u003c/em\u003e) and NK cell-mediated cytotoxicity (\u003cem\u003eGZMB\u003c/em\u003e, \u003cem\u003eKLRB1\u003c/em\u003e, \u003cem\u003eNCR3\u003c/em\u003e, \u003cem\u003ePRF1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE,F). Spleen-unique and spleen/UCBMC-shared genes were associated with antigen receptor-mediated signaling and cytokine production (Spleen: \u003cem\u003eIFNG\u003c/em\u003e, \u003cem\u003eITGAM\u003c/em\u003e, \u003cem\u003eNKG2D\u003c/em\u003e; Spleen/UCBMC: \u003cem\u003eIL2RB\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e) as well as nuclear receptor binding (FOXP1, DDX5, NCOR1) and phospholipid binding (RAPGEF2, ITPR2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE,F). Spleen-unique DEGs were also associated with regulation of inflammation and apoptosis (\u003cem\u003eTNFAIP3\u003c/em\u003e, \u003cem\u003eIRF1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eDEG that define the CD16- NK cells were distinct in the spleen and lung while those that defined UCBMC were largely shared with the spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Lung-unique DEGs enriched to NK cell-mediated cytotoxicity (\u003cem\u003eGZMB\u003c/em\u003e, \u003cem\u003eNKG7\u003c/em\u003e), antiviral immunity (\u003cem\u003eIFI16\u003c/em\u003e, \u003cem\u003eIFI27L2\u003c/em\u003e, BST2), and chemotaxis (\u003cem\u003eCCL3, VIM\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH,I). DEG unique to the spleen enriched to leukocyte activation (\u003cem\u003eBCL2\u003c/em\u003e, \u003cem\u003eFOXP1\u003c/em\u003e, \u003cem\u003eIKZF3\u003c/em\u003e), response to type II IFN (\u003cem\u003eIFNG\u003c/em\u003e), antigen-receptor-mediated signaling pathway (\u003cem\u003eCD74\u003c/em\u003e), and epigenetic regulation (\u003cem\u003eHDAC9\u003c/em\u003e, \u003cem\u003eKDM2B\u003c/em\u003e, \u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eFOXO3\u003c/em\u003e, \u003cem\u003eIKZF3\u003c/em\u003e, \u003cem\u003eIRF1, JUN\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH,I). DEG shared between spleen and UCBMC enriched to antigen-receptor-mediated signaling pathway, epigenetic regulation, and intracellular protein transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). DEGs specific to UCBMC suggested an anti-inflammatory phenotype as indicated by increased expression of \u003cem\u003eTGFB1\u003c/em\u003e and apoptosis-inducing receptor \u003cem\u003eTNFRSF10A\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLung monocytes are primed for anti-microbial response\u003c/h2\u003e \u003cp\u003eMacrophage and pDC clusters were exclusively detected in the lung and spleen with the spleen containing the largest frequency of mDCs, pDCs, and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). GSEA analysis of the monocyte population revealed that TLR and complement signaling were uniquely upregulated in lung monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). On the other hand, antigen processing/presentation and phagocytosis terms were enriched in both spleen and UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Finally, extravasation pathways were upregulated in both lung and spleen monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). We observed a large overlap between upregulated DEGs defining the spleen and UCBMC monocytes while lung-defining DEG were distinct (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). As expected, DEG from all three tissue types included genes associated with innate immunity (Lung: \u003cem\u003eC5AR1, TLR4, ISG20\u003c/em\u003e, \u003cem\u003eNOD2;\u003c/em\u003e Spleen: \u003cem\u003eC1QBP\u003c/em\u003e, \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIRF1\u003c/em\u003e; Spleen/UCBMC: \u003cem\u003eIRF8\u003c/em\u003e; UCBMC: \u003cem\u003eJAK1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD,E). DEGs unique to lung monocytes enriched to electron transport chain and cytokine activity (\u003cem\u003eCCL4L1\u003c/em\u003e, \u003cem\u003eCXCL3\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e, \u003cem\u003eTNFRSF1B\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD,E). DEGs restricted to spleen monocytes enriched to histone modifying activity (\u003cem\u003eHDAC9\u003c/em\u003e, \u003cem\u003eKDM2B\u003c/em\u003e) and included upregulated genes encoding adhesion molecules (\u003cem\u003eITGAL\u003c/em\u003e, \u003cem\u003eSELL\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD,E). Notable DEGs shared between spleen and UCBMC monocytes were involved in activation (\u003cem\u003eFOS\u003c/em\u003e), response to oxidative stress (\u003cem\u003eFOXO3\u003c/em\u003e), immune modulation (\u003cem\u003eILRUN\u003c/em\u003e), and immune tolerance (\u003cem\u003eIRAK3\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Expression of genes associated with cytokine signaling (\u003cem\u003eIL1RAP\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e) was increased in UCBMC monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Module scoring of the tissue-specific monocyte populations revealed lung monocytes had highest scores for inflammation while splenic monocytes had the highest score for wound healing, and UCBMC monocytes displayed significantly lower viral/bacterial response (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDEG analysis between lung and spleen macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) indicated that genes upregulated in the lung are involved in proinflammatory response (\u003cem\u003eNLRP3\u003c/em\u003e, \u003cem\u003eTLR2, IL1B\u003c/em\u003e), response to interferon (\u003cem\u003eIFI30\u003c/em\u003e, \u003cem\u003eIL1B\u003c/em\u003e), chemotaxis (\u003cem\u003eCCL3\u003c/em\u003e, \u003cem\u003eCXCL3\u003c/em\u003e), phagosome activity (\u003cem\u003eVAMP8\u003c/em\u003e), and wound healing (\u003cem\u003eANXA1\u003c/em\u003e, \u003cem\u003eAREG\u003c/em\u003e), (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH,I,). Genes upregulated in the spleen mapped to macrophage activation (\u003cem\u003eCD163, CD68, PECAM1\u003c/em\u003e), regulation of tumor necrosis factor production (\u003cem\u003ePYCARD\u003c/em\u003e), phagocytosis (\u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eCD163\u003c/em\u003e), and pattern recognition receptor signaling (\u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eFCGR3, IFNGR1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH,I).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInferred cell-cell communication is reduced within the lung; while UCBMC displays pronounced HSPC signaling regulation\u003c/h3\u003e\n\u003cp\u003eNext, we used CellChat to identify unique cell-cell communications occurring within each tissue (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The fetal spleen had the highest number and strongest ligand-receptor interactions, followed by UCBMC, and then the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Similarly, the incoming and outgoing signal strengths, based on the inferred probabilities of ligand-receptor interactions, were higher for all spleen clusters and most UCBMC clusters compared to the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Interestingly, HSPCs within UCBMC exhibited nearly twice the incoming interaction strength compared to those in the spleen and lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Additionally, both the CD16\u0026thinsp;+\u0026thinsp;and CD16- NK cell clusters had increased incoming and outgoing interaction strength in the spleen compared to both lung and UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we quantified the relative information flow, an aggregate measure of signal number and strength for the selected pathways to identify those that were shared across tissues or unique (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the lung, signaling pathways involved in pathogen response (COMPLEMENT), immune cell chemotaxis (PLAU), and proinflammatory response (IL1) were prominent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The COMPLEMENT pathway was primarily driven by macrophage signaling within the lung (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Lung PLAU signaling was predicted to be mediated by macrophages and pDC, while IL1 signaling was driven by all myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eSignaling pathways that dominated in the spleen were involved in inflammation (TNF, IGF, PTPR, IFN-II), B-cell survival (BAFF, APRIL), chemotaxis (CXCL, CCL), T-cell activation (PD-L1, CD96) and hematopoiesis (FTL3, BAFF, PD-L1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). We then evaluated the relative signaling strength of tissue specific cell clusters for each signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). TNF, PTPR, FLT-3, and PD-L1 pathways were primarily driven by signaling from pDC, while the chemotaxis-specific pathways (CXCL and CCL) were predominantly originating from macrophages and CD16- NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Finally, BAFF and APRIL pathways were predominantly driven by signaling from the Bcell_2 cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). UCBMC pathways were dominated by Colony stimulating factor (CSF), SELL, CD30 and ANGPT signaling, with higher relative strength and contribution by HSPC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC,D). SELL and ANGPT signaling pathways were absent from the lung, while CD30 had contribution from the lung CD4_CAMK4 cluster which was lessened in the spleen and UCBMC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). FLT3 signaling which contributes to progenitor cell growth and division was prominent in splenic pDCs and UCBMC mDCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eWe evaluated the overall CCL pathway signaling between clusters in each tissue and observed signaling from both splenic NK cell clusters targeting monocytes, macrophages, and mDCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In contrast, fetal lung CCL signaling exclusively targeted NKT cells, whereas UCBMC overall CCL signaling was predominantly directed from NK cells to monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In the fetal lung, IL1 signaling was prominent among Bcell_3, monocyte, macrophage, mDC, pDC, and Proliferating clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In the spleen, IL1 signaling originated from monocyte, macrophage, and mDC clusters, targeting Treg, mDC, pDC, Proliferating, and HSPC clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Both pathways are important regulators of HSPC function, with MIF preventing accumulation of HSPCs within the bone marrow and TGFB regulating commitment to cell lineage fates. Notably, MIF signaling in UCBMC originated from the lymphoid compartment and targeted HSPCs, while lung and spleen lacked MIF signaling targeting the HSPC cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In contrast, MIF signaling in the spleen originated from HSPCs to B-cell and myeloid clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). TGFb signaling within the lung and spleen also lacked the targeting to the HSPC cluster which was present in UCBMC, particularly from the lymphoid clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to fill gaps and serve as a reference for fetal immune cell heterogeneity using the rhesus macaque, a well-established animal model of human immunology (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The study aimed to generate a foundational single-cell dataset from the late-gestational fetal rhesus macaque encompassing three key tissues (blood, spleen, lung) which can be later expanded to include further gestational timepoints and additional tissues. We performed differential gene expression, gene set enrichment, and cell-cell interaction analyses on the integrated dataset to demonstrate its applicability.\u003c/p\u003e \u003cp\u003eThe lung is a crucial immunological organ as it is continuously exposed to respiratory pathogens, environmental pollutants, and allergens after birth (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). While maintaining its central role in gas exchange, the lung harbors tissue-resident immune cells that are essential for host defense and must balance immunoregulatory and anti-inflammatory responses to prevent tissue damage. Prominent immune cell populations residing within the lung tissue include alveolar macrophages, interstitial macrophages, innate lymphoid cells (ILCs), and NK cells. Consistent with previous studies (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), we observed a prominent proportion of ILC2 in the fetal lung. These cells produce Th2 cytokines contributing to fetal lung development, the formation of lymphoid tissue, and modulation of the neonatal response to the external environment (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Our transcriptional analysis demonstrated that the fetal lung tissue contained the highest proportion of myeloid cells compared to spleen and UCBMC. Monocytes within the fetal lung were metabolically active, with heightened bacterial responsiveness and patterns associated with molecular recognition signaling. Additionally, our data demonstrated that lung macrophages have enhanced chemotaxis and myeloid cell recruitment capacity. Interestingly, genes upregulated in fetal lung relative to spleen/UCBMC also enriched to immune tolerance, which potentially limits overactivation, and maintain tolerance to the exposure of inhaled antigens at birth.\u003c/p\u003e \u003cp\u003eFetal lung macrophages showed increased expression of \u003cem\u003eANXA1\u003c/em\u003e, which is involved in positive regulation of macrophage efferocytosis and an anti-inflammatory phenotype (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). ANXA1 signaling has been shown to resolve inflammation and promote the resolution of infections (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), highlighting its potential role in limiting neonatal inflammatory responses to novel antigens encountered after delivery. Fetal lung macrophages also upregulated \u003cem\u003eIL1B\u003c/em\u003e, a notable proinflammatory cytokine. The production of IL-1b within the fetal lung by myeloid cells has been linked to epithelial development and lung tissue maturation (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Additionally, CellChat analysis of intercellular communication revealed prominent IL1 signaling between lung cell clusters. While IL1 signaling contributes to antiviral immunity, overproduction can cause detrimental hyperinflammation, highlighting the importance of coupled anti-inflammatory signals such as ANXA1 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Collectively, our data indicate that the late-term fetal lung is capable of mounting an initial immune response while remaining highly regulated to maintain self-tolerance and anti-inflammatory capacity prior to delivery.\u003c/p\u003e \u003cp\u003eThe spleen is a prominent secondary lymphoid organ, which contains red pulp, that filters the circulating blood via phagocytosis of damaged erythrocytes by macrophages, and white pulp, that initiates defense against pathogens (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Within white pulp is the periarteriolar lymphoid sheath where splenic T-cells are activated in response to blood-borne antigens and B-cell germinal centers, which elicit T-cell dependent antibody production (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Consistent with its role as a prominent site of B-cell maturation, the fetal spleen exhibited the highest proportion of B-cells among the tissues studied (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Critical B-cell signaling pathways BAFF and APRIL which influence B-cell survival and differentiation were solely observed within the spleen further aligning with the anticipated tissue functionality (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Additionally, our DEG analysis showed an upregulation of immunoglobulin receptor recombination and isotype switching within splenic B-cells. T-cells were the second most prominent population within the fetal spleen. Although the frequency of CD4 populations was lower compared to UCBMC, splenic CD4 T cells were activated. The increased CD4 activation alongside the higher percentage of CD4 effector memory CTL aligns with the spleen\u0026rsquo;s function as a secondary lymphoid organ (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The activation observed within the spleen could provide rationale for the higher proportion of splenic Treg cells. Although previous studies have mainly focused on the role of maternal Tregs in maintaining pregnancy, fetal Tregs also contribute to alloantigen tolerance (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Our CellChat analysis showed the highest predicted signaling strength in the spleen, reflecting its role as a central site for peripheral immune activation and maturation (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The strength of chemoattractant IL16 signaling to splenic T-cell clusters was notably higher, particularly Tregs, suggesting active recruitment into the spleen (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Widespread CCL signaling was predicted via CellChat within the spleen between innate, CD4_EM_CTL, Bcell_3, HSPC, and proliferating clusters. Furthermore, CCL/CCR signaling is a key contributor to splenic tolerance and infection responses, as these ligand interactions drive myeloid cell recruitment and macrophage polarization (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In particular, signaling through CCR1 directs macrophage migration and polarization toward an anti-inflammatory M2 phenotype (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlood mononuclear cells are key mediators of systemic immune responses, as they encompass mobile populations poised for activation and proliferation (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). UCB is often employed in clinical studies as a noninvasive substitute for neonatal blood due to its accessibility (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Inflammatory perturbations within maternal circulation caused by diseases and disorders such as pre-eclampsia and SARS-CoV-2 infection are reflected in neonatal UCB (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). Fetal UCBMC contain immune cells predominantly in a state of tolerance to prevent immune overactivation prior to delivery (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). We found that UCBMC displayed heightened HSPC signaling strength and upregulation of genes important for stem cell population maintenance particularly among CD4 T cells. This finding aligns with previous studies showing that UCB is a rich source of HSPCs, contributing to fetal immune cell development (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Notably, fetal spleen and UCB shared numerous upregulated DEGs in B-cell, NK, and monocyte clusters, which were absent in lung cells. Even in the absence of pathogens, UCBMC have shown heightened cytokine responsiveness following viral infection (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). The role of UCBMC and spleen cells in infection control was supported by our observations of increased expression of genes essential for activation and cytokine production.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, animals were not perfused, restricting our ability to distinguish between tissue resident leukocytes and the infiltrating peripheral blood cells. Second, our single-cell profiling was focused on only three fetal tissues. Expansion with publicly available fetal rhesus macaque transcriptional datasets will be needed to incorporate other prominent immunological tissues such as fetal bone marrow, liver, and thymus. Third, the study focused solely on transcriptomic data, constraining the interpretation of cellular functions and interactions. Finally, all fetal samples were derived from macaques at a single gestational age (approximately GD130), which restricts insights into developmental dynamics across gestation. These limitations highlight the need for future studies with larger, more diverse tissue sampling, integrated multi-omics, and functional validation across broader developmental time points. Nevertheless, this initial study provides the first in-depth immune single-cell atlas of the late gestational fetal \u003cem\u003eMacaca mulatta\u003c/em\u003e across lung, spleen, and umbilical cord blood. Utilization of this atlas will allow for the cellular characterization of healthy fetal immunity in the Rhesus Macaque model.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003eAnimal studies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEight healthy female rhesus macaques underwent time-mated breeding at the Oregon National Primate Research Center (ONPRC) resulting in ten fetal macaques (40% Female). All macaques in this study were managed according to the ONPRC animal care program, which is fully accredited by AAALAC International and is based on the laws, regulations, and guidelines set forth by the United States Department of Agriculture (e.g., the Animal Welfare Act and Animal Welfare Regulations, the Guide for the Care and Use of Laboratory Animals, 8th edition [Institute for Laboratory Animal Research]) and the Public Health Service Policy on Humane Care and Use of Laboratory Animals. Animals received ad libitum access to food (Purina 5000 Fiber-balanced Monkey Diet, Purina Mills, Richmond, IN, USA) and fresh water. Animals were fed a diet formulated according to National Research Council recommendations, supplemented with fruits and vegetables, provided through the Behavioral Services Unit\u0026rsquo;s environmental enrichment program. Animals were maintained in pair housing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample collection and processing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFetal samples were obtained via scheduled cesarian section (C-section) between GD130 and GD135 as described to be representative of third trimester human development (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). UCB was collected at C-section in EDTA tubes and UCBMC and plasma were collected following centrifugation over a Ficoll gradient (Lymphoprep; STEMCELL). Final cell counts were obtained, and cells were cryopreserved in 10% DMSO/FBS. Fetal spleen was collected at necropsy and immediately placed on ice in RPMI supplemented with 10% fetal bovine serum (FBS), streptomycin/penicillin, and L-glutamine. Splenic leukocytes were isolated by mechanical disruption. Cells were centrifuged and red blood cells were lysed using 0.84% ammonium chloride pH 7.4, followed by several washes. Splenic leukocytes were cryopreserved in 10% DMSO/FBS. Lung tissue was collected and processed in RPMI-1640 media supplemented with 3% BSA, 1% Penicillin\u0026ndash;Streptomycin, 1% L-glutamine, and 10 mM HEPES pH 7.4 (R3 medium). Lung tissue was subjected to enzymatic digestion using 120 mg collagenase II (Gibco), 2.5 mg elastase (Sigma-Aldrich), 40 mg DNase I (Sigma-Aldrich), and 12 mg hyaluronidase (Sigma-Aldrich) in R3 medium and supplemented with 80 \u0026micro;L of 1 M CaCl₂ for 1 hour at 37\u0026deg;C with gentle rotation. Remaining tissue was mechanically dissociated. Cells were pelleted and subjected to density separation using a discontinuous 60\u0026thinsp;\u0026minus;\u0026thinsp;30% Percoll gradient. The gradient was centrifuged at 2500 rpm for 30 minutes. Cells located at the interface between the 30% and 60% Percoll layers were collected and washed in R3 medium. Final cell counts were obtained, and cells were cryopreserved in CryoStor CS10 at a density of less than 20 \u0026times; 10⁶ cells per vial.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell RNA sequencing library generation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLeukocytes were thawed, washed in 2% FBS/DPBS, and incubated with Rhesus Fc Block (Invivogen). Each sample was labeled with a distinct TotalSeq Hashtag Oligo Antibody (HTO, Biolegend) and incubated per manufacturer\u0026rsquo;s instruction. Pellets were washed twice in 2% FBS/DPBS and pooled by tissue. Cell pools were filtered and counted in duplicate to confirm a viability greater than 80%. Samples with less than 80% viability (spleen and lung) were stained with anti-Rhesus CD45 BV650 (BD Biosciences) and propidium iodine before being sorted for live CD45\u0026thinsp;+\u0026thinsp;leukocytes using a Sony SH800 Cell Sorter System. Cells were filtered and suspended in 2% FBS/DPBS to a final concentration of 1500\u0026ndash;1600 cells/mL. Cell suspensions were then immediately loaded on the 10x Genomics Chromium X Controller Chip G with a target of 30,000 cells. Libraries were prepared using the V3.1 chemistry for gene expression and Single Cell 3ʹ Feature Barcode Library Kit per the manufacturer\u0026rsquo;s instructions (10x Genomics). Libraries were sequenced on Illumina NovaSeq X with a sequencing target of 30,000 gene expression reads and 5,000 feature barcoding reads per cell.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell RNA-seq data analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRaw sequencing reads were aligned and HTOs were demultiplexed using Cell Ranger (v6.0.2, 10x Genomic) against the \u003cem\u003eMacaca mulatta\u003c/em\u003e reference genome (mmul10) using the multi-option. Downstream processing of aligned reads was performed using Seurat (v5.1.0) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Droplets with ambient RNA or potential doublets (\u0026lt;\u0026thinsp;400 or \u0026gt;\u0026thinsp;4000 detected genes) and inviable cells (\u0026gt;\u0026thinsp;20% total mitochondrial gene expression) were excluded during initial QC. Data objects from all libraries were integrated using Harmony (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Data normalization and variance stabilization were performed on the integrated object using the NormalizeData and ScaleData functions in Seurat (v5.1.0), where a regularized negative binomial regression was corrected for differential effects of mitochondrial and ribosomal gene expression levels. Dimensionality reduction was performed using RunPCA function to obtain the first 30 principal components and clusters visualized using Seurat\u0026rsquo;s RunUMAP function. Cell types were assigned to individual clusters using FindAllMarkers function (Supplemental Table\u0026nbsp;1) with a log2 fold change\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and canonical scRNA markers for NHP leukocytes.\u003c/p\u003e \u003cp\u003eDifferential gene expression (DEG) analysis for each cluster was performed using the FindMarker function in Seurat comparing each tissue to the other two tissues and retaining only the genes with a positive fold change. For example, DEGs in the lung utilized the lung as ident.1 and both spleen and UCBMC as ident.2. Only statistically significant genes maintaining an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.585 were included in downstream analyses (Supplemental Table\u0026nbsp;2). Functional enrichment was performed using Metascape (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) was performed using the irGSEA (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) package (v2). Briefly, the irGSEA.score function was used to calculate the enrichment score for each cell for genes sets in the MsigBD gene ontology (GO) GO0002376 Immune processes. The irGSEA.integrate function was then used to perform a Wilcox test of the enrichment score matrix comparing the tissues compartments. The heatmaps were visualized using the irGSEA.heatmap functions.\u003c/p\u003e \u003cp\u003eThe R CellChat (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) package was employed to infer probable intercellular communication networks. A CellChat object was generated from a Seurat v5 object with the createCellChat function. The CellChatDB.human database was utilized. Data was preprocessed with the function identifyOverExpressedGenes and identifyOverExpressedInteractions and communication probabilities between clusters were determined with computeCommunProb (truncatedMean, trim\u0026thinsp;=\u0026thinsp;0.1, interaction.range\u0026thinsp;=\u0026thinsp;250, contact.range\u0026thinsp;=\u0026thinsp;100). Communications were filtered (filterCommunication) to a minimum number of 10 cell and signaling pathway probabilities were calculated (computeCommunProbPathway).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll animal procedures were approved by the Institutional Animal Care and Use Committee of the Oregon National Primate Research Center, which is fully accredited by AAALAC International and is based on the laws, regulations, and guidelines set forth by the United States Department of Agriculture (e.g., the Animal Welfare Act and Animal Welfare Regulations, the Guide for the Care and Use of Laboratory Animals, 8th edition [Institute for Laboratory Animal Research]) and the Public Health Service Policy on Humane Care and Use of Laboratory Animals.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eDeclaration of interests\u003c/h2\u003e \u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the National Institutes of Health grant number 7R01AI142841-04 (IM, OV) and P51 OD01192 for operation of the Oregon National Primate Research Center.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank CoreyAyne Singleton, Dr. Lauren Drew Martin, and Travis Hodge at the Oregon National Primate Center for their contributions to animal breeding and sample collection/processing along with Drs. Heather True and Delphine Malherbe for their valuable feedback during manuscript preparation.\u003c/p\u003e\u003ch2\u003eData availability.\u003c/h2\u003e \u003cp\u003eThe datasets supporting the conclusions of this article are available on NCBI\u0026rsquo;s Sequence Read Archive: single-cell RNA sequencing (PRJNA1247568).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePopescu DM, Botting RA, Stephenson E, Green K, Webb S, Jardine L et al (2019) Decoding human fetal liver haematopoiesis. Nature 574(7778):365\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinhoux F, Guilliams M (2016) Tissue-Resident Macrophage Ontogeny and Homeostasis. Immunity 44(3):439\u0026ndash;449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoeffel G, Ginhoux F (2018) Fetal monocytes and the origins of tissue-resident macrophages. 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JCI Insight. ;8(17)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8734095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8734095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fetal immune system develops within a tightly regulated environment that balances immune tolerance with readiness for postnatal antigen exposure. However, limited access to fetal tissues has constrained our understanding of immune ontogeny across distinct anatomical compartments. Here, we present a high-resolution, multi-tissue single-cell transcriptional atlas of the late-gestation (GD130\u0026ndash;135) rhesus macaque (\u003cem\u003eMacaca mulatta\u003c/em\u003e) fetal immune system, profiling leukocytes from lung, spleen, and umbilical cord blood mononuclear cell (UCBMC) compartments spanning myeloid, lymphoid, innate lymphoid, and hematopoietic stem cell (HSPC) lineages. The fetal lung was enriched in myeloid populations and ILC2 cells while fetal spleen was comprised primarily of T- and B-cells and UCBMC were dominated by T-cells. Despite reduced overall intercellular communication in lung compared to spleen and UCBMC, lung immune networks showed proinflammatory bias, suggesting preparation for postnatal environmental exposure. Splenic B cells showed strong transcriptional signatures associated with V(D)J recombination and isotype switching, while CD4 T cells displayed increased activation, and increased Tregs, consistent with the spleen's role as a secondary lymphoid organ which integrates antigen monitoring with immune tolerance to prevent overactivation. UCBMC showed a predominantly regulatory immune landscape. Together, this atlas provides a foundational resource defining tissue-specific immune specialization and intercellular communication in the late-gestation primate fetus.\u003c/p\u003e","manuscriptTitle":"Single-Cell Atlas of Fetal Immune Development Across Lung, Spleen, and Umbilical Cord Blood in Nonhuman Primates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 09:46:17","doi":"10.21203/rs.3.rs-8734095/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":"ca03396a-b7f4-4e84-8364-f6169638bf43","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62445749,"name":"Biological sciences/Immunology/Immunogenetics"},{"id":62445750,"name":"Biological sciences/Immunology"},{"id":62445751,"name":"Biological sciences/Immunology/Gene regulation in immune cells"}],"tags":[],"updatedAt":"2026-03-13T01:25:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 09:46:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8734095","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8734095","identity":"rs-8734095","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}