Preterm labor and infections are the leading causes of neonatal deaths worldwide. During pregnancy, immunological cross talk between the mother and her fetus is critical for the maintenance of pregnancy and the delivery of an immunocompetent neonate. A precise understanding of healthy fetomaternal immunity is the important first step to identifying dysregulated immune mechanisms driving adverse maternal or neonatal outcomes. This study combined single-cell mass cytometry of paired peripheral and umbilical cord blood samples from mothers and their neonates with a graphical approach developed for the visualization of high-dimensional data to provide a high-resolution reference map of the cellular composition and functional organization of the healthy fetal and maternal immune systems at birth. The approach enabled mapping of known phenotypical and functional characteristics of fetal immunity (including the functional hyperresponsiveness of CD4+ and CD8+ T cells and the global blunting of innate immune responses). It also allowed discovery of new properties that distinguish the fetal and maternal immune systems. For example, examination of paired samples revealed differences in endogenous signaling tone that are unique to a mother and her offspring, including increased ERK1/2, MAPK-activated protein kinase 2, rpS6, and CREB phosphorylation in fetal Tbet+CD4+ T cells, CD8+ T cells, B cells, and CD56loCD16+ NK cells and decreased ERK1/2, MAPK-activated protein kinase 2, and STAT1 phosphorylation in fetal intermediate and nonclassical monocytes. This highly interactive functional map of healthy fetomaternal immunity builds the core reference for a growing data repository that will allow inferring deviations from normal associated with adverse maternal and neonatal outcomes.
This article is featured in In This Issue, p.4181
Of the 2.9 million neonatal deaths occurring worldwide each year, the leading causes are preterm birth, infections, and intrapartum-related complications (1, 2). Delivery of a healthy term newborn depends on finely tuned innate and adaptive immune mechanisms regulating the balance between fetomaternal tolerance and the development of an immunocompetent fetus. When dysregulated, these mechanisms have been implicated in the pathogenesis of preterm birth and linked to adverse neonatal outcomes, such as neonatal infections and sepsis (3–5). A precise understanding of normal fetomaternal immunity at term gestation is the essential first step to identify immunological deviations associated with pregnancy-related complications.
Contained within distinct but interdependent compartments, umbilical cord and maternal peripheral blood provide uniquely accessible substrates that enable the study of the cellular mechanisms underpinning fetomaternal immunity. Single-cell analyses of cell populations within these immune compartments have substantially advanced our understanding of fetomaternal immune cross talk during pregnancy (5, 6). However, the limited parameterization afforded by traditional single-cell technologies has to date precluded comprehensive representation or mapping of the cellular and functional organization of the fetomaternal immune system. Such standardized mapping would provide an organized and curated dataset of normal immunity at term gestation and serves as a critical point of reference to understand deviations from normal that are associated with pathological pregnancies.
The recent development and successful bedside application of mass cytometry (also known as cytometry by time-of-flight mass spectrometry), a high-dimensional flow cytometry platform, now enables the combined phenotypical and functional characterization of the entire circulating immune system at single-cell resolution (7–12). Novel visualization approaches make possible intuitive exploration of high-dimensional mass cytometry datasets when used in tandem with more traditional quantitative approaches. Scaffold is a graphical approach developed by Spitzer et al. (13), which enables intra- and cross-species comparison of immune cell phenotypes populating different compartments (peripheral blood, spleen, liver, lungs, etc.) and provides a reference onto which immune deviations related to genetic or environmental variations are mapped. In this study, we apply Scaffold to graphically represent the entire peripheral immune system of mothers and their neonates, in essence taking a snapshot of fetomaternal immunity at term.
Expanding on this analytical framework, we developed a mass cytometry assay to simultaneously examine the phenotype and intracellular signaling activities of all major immune cell subsets derived from fetal umbilical cord and maternal peripheral blood samples. Three sets of data were obtained from 10 mothers and their respective neonates: a first set to describe the distribution of immune cell subsets; a second set to describe the endogenous intracellular signaling activities of immune cell subsets close to the in vivo condition; and a third set to quantify the capacity of immune cell subsets to mount a signaling response to an immune challenge. Capacity was inferred by stimulating whole blood samples ex vivo with a panel of receptor-specific ligands that engage canonical signaling pathways essential for the differentiation, proliferation, or pathogen response of innate and adaptive immune cells. The major goals of the study were to: 1) construct a high-resolution map of the cellular and functional organization of the fetal and maternal peripheral immune systems at term gestation; and 2) provide a reference of normal fetomaternal immunity for future studies designed to identify deviations associated with pregnancy-related pathologies.
Materials and Methods
Based on the premise that umbilical cord and maternal blood provide a unique immunological window into the fetomaternal peripheral immune system in term pregnancies, a 46-parameter mass cytometry assay was developed to assess the cellular and functional organization of fetal and maternal peripheral immune compartments with single-cell resolution. Studying pairs of mothers and their respective neonates provided a unique opportunity to detect differences that are distinct for each pair. To avoid the effect of stress resulting from active labor on the maternal or fetal immune system, only mothers delivering by scheduled Cesarean (C)-section were enrolled.
This study was conducted at Lucile Packard Children’s Hospital (Stanford, CA); it was approved by the Institutional Review Board of Stanford University School of Medicine, and all participants gave informed consent. Ten healthy pregnant women scheduled for planned C-section at term gestation (38.69 ± 1.14 wk gestational age) were enrolled in the study. All C-sections were elective procedures that were scheduled at term gestation. The majority of C-sections (70%) were indicated due to maternal history of a prior C-section with no other fetomaternal pathology. One C-section was scheduled for a history of marginal placenta previa, one for a history of myomectomy, and one for suspected macrosomia (weight at birth was 4.1 kg). There were no reported pregnancy- or birth-related complications. Maternal demographic characteristics included the following: age (34.6 ± 4.0 y [range: 27–40]); gravidity (3 ± 2); parity (1 ± 1); and maternal body mass index (32.1 ± 12.4). Five participants were Caucasian, three were Asian, one was Hispanic, and one reported “other.” Neonatal demographics were as follows: six males and four females with a mean birth weight of 3.50 ± 0.37 kg and Apgar scores at 1 min ranging from 8 to 10.
Maternal peripheral blood was collected by venipuncture prior to C-section into heparinized tubes (sodium heparin; BD Biosciences, San Jose, CA). A dedicated clinical research nurse was present at all deliveries. Prior to C-sections, the obstetrician was instructed to clamp the umbilical cord at the cut end and at the junction of the cord and placenta to prevent retrograde blood flow into the placenta postsection. Umbilical cord blood was collected from the umbilical vein into a syringe using an 18-gauge needle and transferred into the requisite collection tubes within minutes of delivery. Throughout the manuscript we refer to immune cells derived from umbilical cord blood samples as fetal immune cells with the understanding that post–C-section umbilical cord blood was briefly exposed to different environmental influences than native fetal blood.
To ensure comparability between acquired samples, all blood samples were processed using a standardized protocol for stimulation, fixation, storage, and processing of whole-blood samples for mass cytometry analysis (10–12). To examine the function of fetal and maternal immune cells, whole-blood samples were stimulated with a control vehicle to assess endogenous cellular function (unstimulated condition), the TLR4 agonist LPS (LPS condition), or a combination of extracellular cytokines eliciting receptor-specific intracellular responses (IL-2, IL-6, GM-CSF, and IFN-α2A). The integrity of evoked intracellular responses was assessed at the single-cell level by measuring sentinel signaling events, including the phosphorylation of key kinases (p-P38, p-ERK1/2, p-MAPK-activated protein kinase 2 [MAPKAPK2], and p-S6K) and transcription factors (p-STAT1, p-STAT3, p-STAT5, p-CREB, and p-NF-κB) or total protein levels of regulatory factors (IκB). Stimulation conditions were chosen based on the ability to activate canonical intracellular signaling pathways essential for the differentiation, proliferation, or pathogen response of innate and adaptive immune cells. In the absence of single-cell cytokine expression data (not measured in this study), cytokine stimulations analyzed in combination with expression of specific phenotypic and functional markers were used to emphasize aspects of CD4+ T cell differentiation, including Th1 (IFN-α2A, Tbet, p-STAT1), Th2 (IL-2 and p-STAT5), Th17 (IL-6 and p-STAT3), and T regulatory cells (Tregs; IL-2, FOXP3, and p-STAT5).
Within 30 min of sample collection, 1.0 ml whole-blood aliquots were stimulated with either 1 μg/ml LPS (E&K Scientific, Santa Clara, CA), a mixture of 100 ng/ml IL-2 (BD Biosciences), 100 ng/ml IL-6 (BD Biosciences), 100 ng/ml GM-CSF (BD Biosciences), and 100 ng/ml IFN-α2A (Sigma-Aldrich, St. Louis, MO), or left unstimulated. Samples were incubated at 37°C for 15 min, fixed with a proteomic stabilization buffer (Smart Tube, San Carlos, CA) for 10 min at room temperature, and then frozen at −80°C for storage until further processing. Fixation of whole-blood samples before long-time storage—in contrast to freezing and freeze-thawing of live cells—ensures that surface and intracellular epitopes are minimally affected by the freeze-thaw process.
Sample processing and RBC lysis
Fixed samples were thawed for 10 min at 4°C and then for 10 min in a water bath kept at room temperature. Samples were then filtered using a 100-μm membrane into a hypotonic lysis solution (Smart Tube) and incubated for 5 min at room temperature. Samples were centrifuged and resuspended in lysis solution for 5 min and washed twice with cell-staining media (CSM; PBS with 0.5% BSA, 0.02% NaN3).
Mass-tag cellular barcoding
To minimize experimental variability, samples were barcoded, as previously described (14, 15). Briefly, isothiocyanobenzyl-EDTA/palladium–based reagents were prepared for mass-tag barcoding. Twenty-well barcode plates were prepared with each well containing a unique combination of three palladium isotopes (102, 104, 105, 106, 108, or 110) at a concentration of 200 nM in DMSO. After sample thawing and RBC lysis, cells were washed with CSM, then with PBS, and finally with 0.02% saponin in PBS. Barcoding plates were thawed and resuspended in 1 ml 0.02% saponin in PBS. Barcoding reagent was added to each sample, and each sample was incubated with shaking at room temperature for 15 min. Samples were subsequently washed twice with CSM and pooled into a single tube for Ab staining. Three barcoded plates were used to process samples from the entire study. To maximize the sensitivity of the assay for detection of differences unique to fetomaternal pairs, all samples from a fetomaternal pair were barcoded and then stained together, and run simultaneously.
Abs were either obtained preconjugated from the manufacturer (Fluidigm, South San Francisco, CA) or conjugated in-house with the appropriate metal isotopes. Purified unconjugated Abs in protein-free PBS carrier were labeled using the MaxPAR Ab conjugation kit (Fluidigm), according to the manufacturer’s instructions. Conjugated Abs were stored at a concentration of 0.2 mg/ml (based on percent yield calculated from measured absorbance at 280 nm) in Candor PBS Ab Stabilization Solution (Candor Biosciences, Wangen, Germany) at 4°C. All Abs conjugated in-house as well as those obtained preconjugated were titrated and validated on samples that were processed identically to the samples used in the study. Abs were tested for specificity and sensitivity across a range of concentrations (typically, 0.25–4 μg/ml), as described in Gaudilliere et al. (10). All but one Ab (GATA3) produced consistent Ag-specific staining above background levels. The anti-GATA3 Ab was thus not used in the gating of immune cell subsets included in the analysis. Abs were used at concentrations listed in Supplemental Table I.
Pooled barcoded samples were incubated with a mixture of 25 Abs against surface Ag (Supplemental Table I) for 30 min with shaking at room temperature. Samples were washed with CSM, permeabilized with methanol for 10 min at 4°C, and washed twice with CSM. Samples were subsequently incubated with a mixture of 13 Abs against intracellular proteins (Supplemental Table I) for 30 min with shaking at room temperature, and then washed with CSM. Samples were incubated with an iridium intercalator (Fluidigm) with 1.6% paraformaldehyde in PBS overnight at 4°C.
Intercalated samples were washed once with CSM and twice with double-distilled water, and then resuspended in a solution of normalization beads (Fluidigm). Samples were filtered prior to mass cytometry analysis through a 35-μm membrane and analyzed at a flow rate of ∼500 cells/s. Samples were normalized and debarcoded, as described previously (15, 16).
Scaffold maps were constructed with minor modifications, as described by Spitzer et al. (13). Twenty-eight surface markers and intracellular molecules (phenotypic markers: CD235, CD61, CD45, CD66, CD7, CD19, CD45RA, CD11b, CD4, CD8a, CD11c, CD123, CXCR3, CD161, TCR-γδ, CD33, Tbet, FOXP3, CD16, GATA3, CCR9, CD25, CD3, IgM, CD127, HLA-DR, CD14, and CD56) were simultaneously analyzed to phenotype innate and adaptive immune cells using an unsupervised clustering algorithm. Cells were grouped into 100 clusters. The Scaffold map was built by relating derived cell clusters (Fig. 1, gray/red nodes proportionally sized to cell frequency) to manually gated landmark cell populations that provided the basic structure of the Scaffold (Fig. 1, black nodes; Supplemental Fig. 1, landmark cell populations highlighted in red). The following populations were used: granulocytes, B cells, CD4+ T cells, CD8+ T cells, NK cells, classical monocytes (cMCs), and classical dendritic cells (cDCs). Graphs were constructed using edge weights based on cosine similarity of the phenotypic expression vectors between clusters and landmarks. A strength of this method is that the distance measured between these nodes can be calculated from data generated from independent experiments allowing for comparability across studies (13). A force-directed algorithm produced a layout indicating phenotypic similarities by the proximity between cell clusters and landmark cell populations. Analyses and visualizations were performed using the Scaffold package in R and the accompanying user interface. Unsupervised clusters were sized proportionally by the frequency of immune cells contained within each cluster. The resulting dataset was uploaded onto an interactive and freely accessible interface (http://188.8.131.52:8080).
Immune feature derivations
Cytometry by time-of-flight mass spectrometry data from each sample was manually gated into immune cell types of interest (Supplemental Fig. 1) using Cytobank software (Mountain View, CA). The following cell types were included in all analyses: granulocytes, B cells, CD4+ T cells, naive CD45RA+CD4+ T cells, memory CD45RA−CD4+ T cells, Tbet+CD4+ Th1 cells, Tregs, CD8+ T cells, naive CD45RA+CD8+ T cells, memory CD45RA−CD8+ T cells, γδT cells, CD56hiCD16− NK cells, CD56loCD16+ NK cells, cMCs, nonclassical monocytes, intermediate monocytes, lin−CD11b+CD14+HLA-DRlo myeloid-derived suppressor cells (MDSCs), cDCs, and plasmacytoid dendritic cells. Frequency of immune features was calculated from unstimulated conditions and expressed as a percentage of gated singlets in the case of granulocytes, and as a percentage of mononuclear cells (CD45+CD66−) in the case of all other cell types.
For signaling immune features (functional immune features), a transformation was applied to the gated data (arcsinh[data/five]). The following signaling proteins or phospho-proteins were used: p-STAT1, p-STAT3, p-STAT5, p-NF-κB, IκB, p-MAPKAPK2, p-P38, p-rpS6, p-ERK1/2, and p-CREB. Signaling immune features from unstimulated conditions were calculated as the median transformed value of each signaling protein in each of the gated cell types listed above. Immune signaling features from stimulated conditions (LPS and cytokines) were calculated by subtracting unstimulated from stimulated transformed data for each patient.
Analyses of significant differences between maternal and neonatal immune features
Differences between maternal and neonatal immune features were assessed using significance analysis of microarrays (SAM) with a false discovery rate (FDR) cutoff of 1% using the same R package (17). In all paired analyses, maternal–neonatal pairs were used. By analogy to the analysis of large genomic dataset, identified signaling immune features were further filtered by applying an effect-size threshold to the absolute value of observed differences, as previously described in Fragiadakis et al. (11). Thresholds were set based on either: 1) a visual breakpoint in the data (cytokine condition, arcsinh ratio >1.0), or 2) in the absence of a breakpoint, a visual slope change in the data (unstimulated condition, arcsinh ratio >0.3 and LPS condition, arcsinh ratio >0.1). A summary of SAM analyses used for each dataset is provided in Supplemental Table II.
Principal component analyses
Principal component analyses were performed on immune feature data from the three signaling conditions (LPS, cytokine, and unstimulated) for each sample (10 neonatal and 10 maternal samples). Samples were plotted based on their values for the first two principal components and subsequently identified as either maternal or neonatal. Calculations were performed in R.
Visualizations and data resources
All plots were created in R primarily using the ggplot2 package. Scaffold maps were created using the Scaffold R package user interface. The dataset utilized in this study, the code written to perform the analysis, and instructions for visualizing interactive Scaffold maps are publicly available at http://github.com/gfragiadakis/neonatals.
High-resolution organization of the fetomaternal immune landscape
Scaffold maps visualize and contrast the cellular organization of the maternal (Fig. 1A) and fetal (Fig. 1B) immune systems. Navigating across the Scaffold reference map by sequentially coloring cell clusters by phenotypic marker expression revealed striking differences in the organization of the fetal and maternal immune systems, which spanned all hematopoietic compartments. Expected shifts in the fetomaternal distribution of adaptive cells (18–21) (including naive and memory T cell subsets, and IgM+ B cells) were demarcated and visualized alongside noticeable changes in the distribution of innate cell subsets (granulocytes, CD56loCD16+ NK cells, and lin−CD11b+CD14+HLA-DRlo MDSCs) (Fig. 1A, Supplemental Fig. 2A–D). Further quantification of the differences visualized on the reference map with a SAM algorithm (17) in 19 manually gated cell subsets (Supplemental Fig. 1) identified 11 populations (∼60% of gated populations) whose frequency differed significantly between the fetal and maternal immune systems (FDR <0.01).
Results of the SAM analyses of manually gated cell types aligned with the visual examination of the Scaffold reference maps and revealed the phenotypic organization of the fetomaternal immune landscape (Fig. 1C). Key findings are the proportional increase of naive relative to memory CD4+ and CD8+ T cells, and the scarcity of Tbet+CD4+ T cells in the fetal adaptive immune branch (Fig. 1C, Supplemental Fig. 2E), which reflects the relative immaturity of the developing adaptive immune system. It is notable that Tbet+CD4+ T cells were detectable in fetal cord blood samples, which is reminiscent of recently described populations of memory CD4+ T cells with Th1 function in fetal blood (22). Consistent with this report, in our study, fetal immune cells harboring a CD45RA− memory phenotype were highly represented within the Tbet+CD4+ T cell compartment (accounting for >52% of Tbet+CD4+ T cells; Supplemental Fig. 2F).
Within innate immune compartments, the increased frequency of fetal CD56loCD16+ NK cells points to the predominant role of the innate immune branch for protecting the neonate against newly encountered pathogens. Interestingly, suppressive innate cells, including MDSCs, were also overrepresented. These cells may be remnants of immunotolerant mechanisms established during pregnancy in the intrauterine environment.
Mapping functional differences in fetal and maternal immune cells at term
We visualized and examined the functional organization of the fetal and maternal immune system by projecting functional attributes (cell signaling activity) onto their respective Scaffold maps depicting the cellular organization. Color coding the signaling activity of each intracellular marker provided highly accessible qualitative information reflecting the functional state of immune cell clusters across the entire fetal and maternal immune systems (Fig. 2). Functional differences between fetal and maternal immune cell clusters were immediately apparent in large cell clusters representing major immune cell compartments as well as smaller clusters containing rarer cell populations. Examples shown in Fig. 2 are the differences in endogenous ERK1/2 signaling activity and evoked STAT1 and P38 signaling in response to ex vivo stimulation with cytokines and LPS, respectively. The endogenous p-ERK1/2 signal was strikingly higher in fetal T and B cells compared with those of their respective mothers (Fig. 2A, 2B). The evoked p-STAT1 signal was visibly decreased in fetal monocytes and CD56loCD16+ NK cells (Fig. 2C, 2D). Finally, the evoked p-P38 signal in granulocytes, monocytes, and cDCs was dramatically decreased in neonates (Fig. 2E, 2F).
Scaffold maps offer a unique platform to visually explore the entire cellular and functional fetomaternal peripheral immune system, and orient us toward differences in abundant as well as scarce innate and adaptive cell populations. These visually compelling findings then prompted further quantitative analyses of the functional differences that distinguish fetal from maternal immune cells. We performed such analyses in a subset of 19 manually gated cell types (Supplemental Fig. 1) that represented the majority of cell clusters revealed by unsupervised clustering analyses rather than all cell clusters to reduce the number of comparisons and increase statistical power.
Broad functional differences characterize the fetal and maternal peripheral immune systems
Principal component analysis of functional activities measured in each manually gated cell subset at baseline and in response to extracellular stimulations fully separated maternal from fetal blood samples (Fig. 3A). Further characterization of the functional differences between maternal and fetal immune cells using SAM identified 161 significant cell type–specific signaling features of 570 (Fig. 3B, Supplemental Table II; FDR <0.01). Of these signaling features, 18 were statistically significant only when related pairs of fetomaternal samples were examined (Fig. 3B, left, purple circles; SAM paired, FDR <0.01). Interestingly, all pair-specific differences were endogenous rather than evoking signaling events (Fig. 3C, purple circles). These results emphasize unique differences in the basal signaling tone of immune cells from related mother–fetus pairs that would have remained undetected if the dataset had been generated from unrelated populations.
Differences in endogenous signaling activity
Differences in endogenous signaling activity (features) were ranked according to effect size (mean difference between maternal and fetal signal intensities; Fig. 4A). The top 16 features (see 2Materials and Methods) split into 11 features that were elevated (Fig. 4B) and 5 features that were decreased in fetal samples (Fig. 4C). Remarkably, 10 of the 11 elevated features related to signaling activities in adaptive immune cell subsets, including p-ERK1/2, p-MAPKAPK2, p-rpS6, and p-CREB signals in Tbet+CD4+ Th1, CD8+ T, and B cells. Conversely, all of the 5 features that were decreased in fetal samples related to signaling activities in innate immune cells, including p-ERK1/2, p-MAPKAPK2, and p-STAT1 signals in intermediate and nonclassical monocytes. The finding that Tbet+CD4+ Th1 cells were the only cell type with increased endogenous signaling activities within CD4+ T cell compartment prompted a post hoc analysis of p-ERK1/2, p-MAPKAPK2, p-rpS6, and p-CREB signals in Tbet− naive and Tbet− memory CD4+ T cells. The analysis revealed that endogenous p-ERK1/2, p-MAPKAPK2, p-rpS6, and p-CREB signals were elevated in fetal Tbet−CD4+ T cell subsets (FDR <0.01). However, the magnitude of noted differences between fetal and maternal samples did not pass the significance threshold for effect size (arcsinh ratio <0.3; Fig. 4A).
These data underscore the functional organization of the fetomaternal immune system at baseline and highlight its polarized structure, which is weighted toward an enhanced adaptive and dampened innate signaling tone of fetal immune cell subsets. Although a decreased signaling tone of all innate cells most likely reflects the global immaturity of the fetal innate immune system, the increased signaling tone of the adaptive cell types was surprising. This begged the question whether fetal and maternal adaptive cell types also differ in their capacity to mount a functional response when stimulated with an external ligand.
Differences in functional capacity
Differences in signaling responses to ex vivo stimulation with a combination of cytokines or LPS were identified with SAM and ranked according to the difference in signal intensities from the control condition (Figs. 5A, 6A).
In response to cytokine stimulation (Fig. 5), 10 immune features (of the top 32 features) were elevated, whereas 22 were decreased in fetal cells relative to maternal cells (Fig. 5B, 5C). Consistent with endogenous signaling activities, all of the 10 elevated features related to signaling responses in adaptive cell subsets, that is, p-STAT3 and p-STAT5 in B cells, γδ T cells, and naive and memory CD4+ and CD8+ T cells. Similarly, the majority (16) of decreased features related to signaling responses in innate immune cells, including the p-STAT1 and p-STAT3 signals in CD56loCD16+ NK cells, granulocytes, cDC, and monocyte subsets. The remaining six features that were decreased in fetal cells related to signaling responses in adaptive immune cells, that is, p-STAT1 in naive and memory CD4+ T cells, naive CD8+ T cells, γδ T cells, Tregs, and B cells. Given the respective roles of STAT1 and STAT5 in promoting Th1 or Th2 differentiation (23, 24), decreased STAT1 and increased STAT5 signaling responses in fetal CD4+ T cells may reflect the diminished capacity of CD4+ T cells to differentiate into functional Th1 cells. More surprising was the finding of signal-specific response patterns (STAT1, STAT3, STAT5) that was shared across fetal adaptive cells of different lineages. This suggests that common mechanisms regulate the activity and differentiation of multiple adaptive cell types of the fetal immune system during the later stage of pregnancy.
In response to LPS stimulation, all of the top 22 features were decreased in fetal compared with maternal blood (Fig. 6A, 6B). Specifically, a decrease in p-P38, p-ERK1/2, p-MAPKAPK2, p-rpS6, p-CREB, and p-NF-κB signals was observed across multiple innate fetal immune cell subtypes, including granulocytes, and monocyte and dendritic cell subsets (Fig. 6B). Taken together, the results suggest that signaling events downstream of TLR4, a critical pattern recognition receptor for orchestrating the innate immune response in sterile and infectious inflammation, are broadly blunted across fetal innate immune cells.
Contrasting the fetal and maternal functional immune landscape
The Scaffold reference was used to illustrate the functional balance between the fetal and maternal immune systems across all immune cell subsets (Fig. 6C). Results from the SAM analyses of endogenous and evoked signaling events were mapped on the Scaffold reference (Fig. 6C). Each cell cluster was colored according to the number of signaling features that differed significantly between fetal and maternal samples. The Scaffold map highlights the polarized functional organization of the fetomaternal peripheral immune system, generally characterized by elevated adaptive and diminished innate responsiveness of fetal immune cell subsets. However, there are relevant exceptions to this general pattern, as indicated by the p-STAT1 response across all examined adaptive immune cell subsets, which was consistently lower in fetal samples. Our findings thus suggest that, although a general pattern characterizes the functional balance between innate and adaptive immune cells at birth, distinct signaling in precisely phenotyped immune cell subsets requires careful examination.
This study applied high-dimensional mass cytometry with single-cell resolution to map the cellular and functional organization of the fetal and maternal peripheral immune systems at term pregnancy. Interactive graphical maps (Scaffold) allowed navigating and interrogating the complex dataset with relative ease, and highlighted the global functional organization of the fetomaternal immune landscape. Scaffold maps and subsequent quantitative analyses revealed marked differences between the neonatal and maternal immune system with respect to the abundance of major immune cell subsets, their functional status, and their capacity to respond to an immune challenge. Thematically, in neonates endogenous signaling activity and functional capacity were broadly dampened across all innate immune cell subsets. A more complex picture emerged for the adaptive immune branch. In adaptive immune cells, differences in endogenous signaling activity and functional capacity were cell type and signaling pathway specific (i.e., were either higher or lower across different cell subsets). This most likely reflects complex and dynamic adaptations of the developing immune system that are time and context sensitive. Importantly, Scaffold maps and data presented in this work provide the necessary frame onto which future and larger datasets can be superimposed to further define normal fetomaternal immunity and infer deviations from normal that define pregnancy-related pathologies and perinatal complications.
Examining fetal cord blood to study the cellular basis of fetomaternal immunity has been the subject of decades of investigation. Our approach takes advantage of recent technological advances, interactive mapping tools, and novel computational algorithms to map existing and novel knowledge onto a common immune reference. This reference builds on prior efforts characterizing the phenotypic immune cell repertoire in fetal samples by integrating multiple functional dimensions (25, 26).
Several themes evolved that resonated with and extend prior work. First, we were able to recapitulate previously reported immune cell distribution characteristics of the neonatal immune system at birth (18–21). These included a preponderance of naive CD4+ and CD8+ T cell populations, B cells, NK cells, and MDSCs. With respect to adaptive cell function, our data integrate with established concepts of hyperresponsiveness and Treg bias of fetal CD4+ T cells (27, 28). For instance, STAT5 activity, a hallmark of Treg differentiation (28, 29), was higher in fetal naive CD4+ T than maternal naive CD4+ T cells in response to IL-2 stimulation. It is remarkable, however, that the increased capacity of fetal adaptive immune cells to cytokine stimulation was not restricted to CD4+ T cells, but was also observed in CD8+ T cell subsets, B cells, and γδ T cells. This novel finding suggests that common mechanisms regulate the activity and differentiation of multiple fetal adaptive cell types during the ultimate phase of gestation. Our results also underscore the global blunting of the innate immune branch in neonates. This is consistent with previous studies examining cord blood leukocytes and documenting the limited capacity of innate immune cells to produce proinflammatory cytokines (30, 31).
The multiplex analysis of paired fetal and maternal blood samples with mass cytometry enabled intriguing discoveries, particularly with regard to the endogenous signaling tone of the fetomaternal immune system. For example, endogenous p-ERK1/2, p-rpS6, p-MAPKAPK2, and p-CREB signals were increased in fetal Tbet+CD4+ Th1 cells relative to maternal Tbet+CD4+ Th1 cells. Although increased fetal signaling tone was also observed in Tbet−CD4+ T cell compartments, differences between fetal and maternal signaling tone were most pronounced in Tbet+CD4+ Th1 cells. This is a surprising finding given a large body of evidence suggesting that fetal CD4+ T cells have a markedly reduced capacity to differentiate into Th1 cells under ex vivo as well as in vivo conditions (18). A possible explanation is that increased endogenous CREB activation impedes Th1 differentiation downstream of Tbet activity. For instance, it has been shown for other cell types that activated CREB can inhibit the activity of NF-κB (32, 33), an important downstream target of Tbet in Tbet-induced Th1 differentiation (34–36). The combination of high endogenous signaling tone and blunted functional capacity for acquiring Th1 effector function may in turn promote their differentiation into anergic cells that are unable to carry out immune responses rather than competent effector cells (37). This functional response pattern has previously been reported in fetal CD4+ T cells, but also in adults for recent thymic emigrant CD4+ T cells (37, 38). Thus, observed functional features of fetal CD4+ T cells may reflect, at least in part, a high proportion of recent thymic emigrant T cells generated during the last weeks of pregnancy. These cells may confer immediate protection for the neonate, who will encounter a host of new foreign Ags after delivery. These explanations, which dovetail with recent models of lymphocyte ontogeny of the human fetus (27, 28), warrant further exploration.
Our results may also guide future studies investigating the mechanisms driving unique signaling properties of fetal immune cells. For example, we observed shared patterns of transcriptional activation across several adaptive cell populations in response to cytokine stimulation (e.g., the p-STAT1, p-STAT3, and p-STAT5 responses; Fig. 5). This observation suggests that a common mechanism—perhaps an exposure to an intrauterine environmental factor or the impact of rapid expansion of adaptive cell compartments as part of normal fetal growth—may drive these signaling phenotypes in fetal adaptive cell types. Similarly, the blunted signaling response to LPS in multiple fetal innate immune cells (Fig. 6) suggests that a common mechanism inhibits TLR4 signaling in these cell types. For example, the endogenous metabolite adenosine has been shown to inhibit TLR-dependent TNF-α production in fetal classical monocytes (39). It will be interesting to determine whether this endogenous mediator also modulates the TLR4 response of other innate immune cell subtypes that featured a blunted response to a TLR4 ligand in our study.
Our study has limitations. First, enrolled women were a small group of homogeneous patient sample undergoing elective C-section, which may not be representative of the population at large. However, our approach and findings provide the analytical framework and foundational reference dataset that larger studies of fetomaternal immunity can build on and expand by including patients with diverse demographics, different socioeconomic backgrounds, or undergoing normal vaginal delivery. Second, it is difficult to perfectly control for clinical treatments at the time of delivery that may affect the fetal circulating immune system. Exposure of umbilical cord blood to indirect effects of preoperative antibiotics and neuraxial anesthesia during C-section, as well as the inflammatory and hormonal responses to the surgical intervention, are important limitations of using cord blood as a surrogate for fetal blood. However, we reasoned that environmental influences on cord blood immune cells would be minimized in the context of a C-section procedure (average time from incision to delivery of the fetus is 10–15 min) compared with the relatively long process of labor and normal vaginal delivery of the newborn. Third, the analysis—by design—was performed in blood samples collected at term pregnancy. The dynamic state of the maternal immune system is an important factor to consider when interpreting differences between fetal and maternal immune cells. For instance, properties intrinsic to the developing fetal immune system may only partially explain the dampened response pattern of fetal innate immune cells to an immune challenge. The proinflammatory state and shift toward heightened innate immune responses characteristic of the maternal immune system at term may also contribute to observed differences (40, 41). Future experiments mapping maternal immunity throughout and beyond pregnancy will be instrumental in elucidating pregnancy-specific maternal contributions to observed fetomaternal differences. Mapping the healthy nonpregnant adult peripheral immune system onto the current dataset will also provide valuable information regarding the impact of pregnancy on maternal immune profiles. Fourth, neither the set of tested stimulations nor the list of selected surface and intracellular markers was exhaustive. Although stimulation conditions were chosen to activate a broad range of canonical immune signaling mechanisms, they were not meant to fully reproduce physiological cellular responses. Expanding the analysis to other stimulation conditions and cellular parameters (including other cytokines, physiological TLR agonists, and TCR and BCR ligands) is an exciting prospect that will require input from the entire scientific community.
Term gestation is associated with profound shifts in fetal and maternal immunity driven by cellular mechanisms implicated in the breakdown of fetomaternal tolerance, the onset of labor, and the delivery of an immunocompetent neonate (3). When dysfunctional, these mechanisms reflect and contribute to pregnancy-related pathologies, including pre-eclampsia, chorioamnionitis, and preterm labor (4, 42). Our efforts are directed toward building a detailed and interactive data repository to characterize the normal cellular and functional organization of the fetal and maternal immune systems. We propose that the construction of such a data repository will yield a reference that will precisely map pathological deviations from normal that are associated with and possibly drive adverse fetal, neonatal, and maternal outcomes.
We thank Dr. Elena Hsieh (Departments of Immunology, Microbiology, and Pediatrics, University of Colorado, Children’s Hospital Colorado, Denver, CO) for providing guidance in the cytometry by time-of-flight mass spectrometry panel design for profiling fetal cord blood. We thank William Magruder and Christine Junge (Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA) for critically editing the manuscript.
This work was supported by the March of Dimes Prematurity Research Center at Stanford and the Bill and Melinda Gates Foundation (OPP 1017093, OPP1113682); additional funding was provided by the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University. G.K.F. was supported by a Stanford Bio-X graduate research fellowship (Stanford, CA) and National Institutes of Health Grant T32GM007276. B.L.G. was supported by National Institutes of Health Grant 1K23GM111657-02. This work was also supported in part by the Child Health Research Institute (to R.J.W., G.M.S., and D.K.S.); the Mary L. Johnson Research Fund (to R.J.W. and D.K.S.); the Christopher Hess Research Fund (to R.J.W. and D.K.S.); National Institutes of Health Grants 5R01AI10012104 (to D.B.L.), U19AI057229 (to G.P.N.), and 1U19AI100627 (to G.P.N.); and Food and Drug Administration Grant HHSF223201210194C (to G.P.N.).
The online version of this article contains supplemental material.
G.P.N. has a personal financial interest in the companies Fluidigm and BD Biosciences, the manufacturers that produce the reagents or instrumentation used in this manuscript.The other authors have no financial conflicts of interest.