Variation of cell subtypes and differential gene expression: pPROM vs. sPTL

A broad range of differences in cell subtypes, as characterized by snRNA-seq, was observed between pPROM and sPTL. UMAP showcased diverse cell types, including trophoblast cells: CTBs, VCTs, STBs, and EVTs; immune cells: decidual macrophages (dMs), decidual natural killer (dNK), Hofbauer, and mixed immune; endothelial cells: fetal, maternal, and lymphatic; as well as fibroblasts and perivascular (PV) cells (Fig. 1A–D). In a previous study, we identified three distinct types of EVT and two types of STB [23], but in this study, we observed different gene expression patterns of the identical cell type, such as EVT3 consisting of two unique clusters.

Fig. 1

Cell clusters characterized by snRNA-seq among sPTB placentas. UMAPs of cell clusters were annotated by snRNA-seq among groups of the control (A), premature preterm rupture of membranes (pPROM) (B), spontaneous preterm labor (sPTL) (C), and integrated A, B, and C conditions (D). Violin plots present gene expressions of the top five scored upregulated or downregulated genes for all cell clusters of syncytiotrophoblasts (STBs) and extravillous trophoblasts (EVTs). The upregulated genes are lined up in upper rows, and the downregulated genes are in lower rows in each group (E)

When the UMAP findings were split by the pPROM and sPTL conditions, we noticed key distributional differences between the preterm conditions compared to full-term control (Fig. 1A–C). While pPROM included various immune cells, such as dNK, dM, and mixed immune cells, these clusters exhibited different gene expression patterns compared to the Hofbauer cell cluster observed in sPTL, suggesting that immune cells infiltrating the pPROM placental environment may be replacing or masking the normally expected Hofbauer cells. Furthermore, there were notable distributional differences between pPROM and sPTL. Despite limited overlap, a clear pattern emerged in the EVT and STB cell types. In the pPROM condition, EVTs were prevalent, whereas most STB cell types were absent. In contrast, sPTL exhibited an abundance of various STB cell subtypes but lacked EVT cells. This difference is unlikely related to gestational age, as all samples were taken from pregnancies at approximately the same gestational week. Differential gene expression in STB and EVT clusters across the three groups further supports the observed differences, highlighting the presence of EVT clusters in pPROM and of STB clusters in sPTL (Fig. 1E). (For enlarged versions of all figures throughout the results, please refer to Supplementary Figures.)

Gene dynamics in sPTB through pathway and trajectory analysis

Functional differences between pPROM and sPTL were explored using KEGG pathway enrichment and trajectory analysis [28, 29]. The phosphatidylinositol 3-kinase/protein kinase B pathway, which is crucial for maternal metabolism, placental-fetal growth, morphology, and nutrient transport, was enriched in both pPROM and sPTL conditions. This pathway displayed consistent enrichment within PV (SGIP1), fibroblast (MEG3), endothelial_fetal (ITGA2), and endothelial (GUCY1A2) cells. The mitogen-activated protein kinase pathway, which plays a role in cell proliferation, differentiation, and apoptosis, was exclusively enriched in pPROM, particularly in dS3, EVT3 (LVRN), and mixed immune (RHEX) cell clusters. In contrast, the vascular smooth muscle contraction pathway was uniquely enriched in sPTL, with high expression in fibroblast (AF165147.1), endothelial_fetal (MEOX2), endothelial (GUCY1A2), EVT3 (LVRN), CTB (ASPM), and PV (SGIP1) cells (Fig. 2).

Fig. 2

Pathophysiological pathways. Pathophysiological pathways, which were identified to be statistically significant (adjusted p < 0.05, FDR correction) in the premature preterm rupture of membranes (pPROM) and spontaneous preterm labor (sPTL) groups, when compared to the control group, are shown with bubble plots. The horizontal axis represents the gene ratio, and the vertical axis represents the enriched pathways. The color scale shows the − log10(p-value), and the size of the bubble indicates the gene count for each pathway. The labels on the right of each bubble chart represent the KEGG subcategories: CP = cellular processes, EIP = environmental information processing, HD = human diseases, GIP = genetic information processing, and OS = organismal systems (A–C). Violin plots present expression levels for genes significantly associated with a uniquely enriched KEGG pathway, either split by clinical features (D) or by cell clusters (E). The phosphatidylinositol 3-kinase/ protein kinase B (PI3K-Akt) pathway is associated with pPROM and sPTL; the mitogen-activated protein kinase (MAPK) pathway, with pPROM; and the vascular smooth muscle contraction pathway, with sPTL

Trajectory analyses provided potential insights into the differences in disease progression as shown by pseudotime branching, which tracks the transcriptional status and differentiation of cell clusters across conditions [29]. In addition to the differentiation, various mechanisms, including cell turnover, can also be in effect. In pPROM, cell clusters dM2 (DNAJB1) and endothelial_fetal (RRM2) represented earlier pseudotime cell types, whereas endothelial_lymphatic (AL357507.1), mixed immune (TOP2A), and T (THEMIS) cells appeared at later pseudotime points. Unlike pPROM, sPTL exhibited clear branching points, especially from the earliest pseudotime cell type, CTB (MKI67), leading to two distinct branches: one involving the differentiation of CTB to VCT cells, and the other involving STB and endothelial cells, which were of the latest pseudotime (Fig. 3A–F).

Fig. 3

Trajectories across pseudotime between pPROM and sPTL. Pseudotime trajectory UMAPs represent placental cell differentiation in the control, premature preterm rupture of membranes (pPROM), and spontaneous preterm labor (sPTL) groups. The dark blue represents the original starting point of pseudotime, which is scaled as 0, and the yellow represents the tertiary point of pseudotime, which is scaled as 15 in the control group (A) and as 20 in the pPROM (B) and sPTL (C) groups. Boxplots represent variant subtypes of cell clusters (y-axis) that are arranged from bottom to top in ascending order of median pseudotime values (x-axis) in the control (D), pPROM (E), and sPTL (F) groups. The feature plots display all significant differentially expressed genes (adjusted p < 0.05, FDR correction) across pseudotime for the control (G), pPROM (H), and sPTL (I) groups

Significant changes in DEGs were observed across pseudotime. In the control group, ISG15 ubiquitin-like modifier, TTLL10 antisense RNA 1, stromal cell-derived factor 4, matrix remodeling–associated 8, and aurora kinase A–interacting protein 1 showed differential expression over pseudotime (Fig. 3G). In the pPROM group, genes such as tumor necrosis factor receptor superfamily members 18 and 14, potassium voltage-gated channel subfamily A regulatory beta subunit 2, matrix remodeling-associated 8, Rho guanine-nucleotide exchange factor 16, and ERBB receptor feedback inhibitor 1 were highly expressed in many early pseudotime clusters. These clusters included dM2 (DNAJB1), epithelial cells (RRM2), endothelial_fetal (ITGA2), and various trophoblasts such as EVT3 (LINC00511), EVT3 (GPR78), VCT (ATP13A4), EVT1 (FLT1), CTB (AC007368), and STB (MYH14) (Fig. 3H). Similar to pPROM, sPTL also displayed significant DEG with six key genes identified: protein kinase c zeta, pleckstrin homology and RhoGEF domain containing G5, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta, eukaryotic translation initiation factor 4 gamma 3, podoplanin, and phospholipase A2 group V. PRKCZ exhibited consistent expression, with its highest levels in later pseudotime STB, and endothelial (SPTLC3) and endothelial_fetal (ACVRL1) cells. In contrast, PLEKHG5, PIK3CD, and EIF4G3 displayed significant differential gene expression primarily in the dM2 (CR1) cell type (Fig. 3I).

Cell-to-cell communication signaling

We employed NMF to identify and categorize cell-to-cell communication signals within the pPROM and sPTL groups, comparing them to a control group. NMF reduces dimensionality and obtains cophenetic and silhouette indexes, which help assess clustering quality and consistency within RNA sequencing data [30]. In total, pPROM samples exhibited eight outgoing signals and nine incoming signals, whereas sPTL samples demonstrated nine outgoing and incoming signals. By contrast, the control group displayed eight outgoing and four incoming signaling patterns (Fig. 4A, D).

Fig. 4

Global cell-to-cell communications. For each group, control (A, D), premature preterm rupture of membranes (pPROM) (B, E), and spontaneous preterm labor (sPTL) (C, F), data involving the outgoing and incoming signaling were collected. The number of outgoing and incoming signals for the control, pPROM, and sPTL groups was determined by selecting the pattern number at the lowest measure score, excluding the final point, from the cophenetic and silhouette indexes, which, respectively, measure how well the clustering matches the original data, the consistency of clustering, and the separation between clusters [30] Arrows point to the value selected. A corresponding heatmap shows the cell and communication pathway patterns and their contributions and a Sankey diagram, a flow diagram in which the arrow width is proportional to the quantity (gene expression) to depict changes over time or hierarchy between nodes and presents the increase or the decrease of data elements in two or more time points [27], shows the communication patterns and signaling pathways of secreting cells and target cells

Within the control group, many clusters of the same cell type exhibited the same cell signaling pattern. Among the outgoing signals, EVT cell types followed pattern 1, with pathways such as angiopoietin-like (ANGPTL) and pleiotrophin (PTN), whereas STBs exhibited pattern 2, involving activin, periostin, follicle-stimulating hormone (FSH), and thyroid-stimulating hormone (TSH). Pattern 3 was associated with immune cells (dMs, mixed immune cells) that are associated with galectin, CD30, B-cell activating factor (BAFF), and C-X-C motif chemokine ligand 1 (CXCL) (Fig. 4B, C. For incoming signals, STB clusters exhibited pattern 4, involving pathways such as colony-stimulating factor 3 and FSH. Immune cell clusters followed pattern 2, including but not limited to glucocorticoid-induced TNFR-related protein–ligand, BAFF, IL-16, and galectin (Fig. 4E, F).

In contrast to the control group, the pPROM group did not display the same pattern across cell types. In outgoing secreting cells, multiple trophoblast cell types—CTB, STB, VCT, and EVT—were associated with pattern 1, whereas only immune cells were associated with pattern 2. Patterns 5, 6, and 7 also involved immune cells but shared the same patterns with other cell types such as fibroblasts, CTB, and STB. These patterns consisted of many immune-related pathways, including osteopontin (SPP1), ANGPTL, CD30, CD70, and class 3 semaphorin (SEMA3) (Fig. 4B, C). Similar observations were made in incoming target cells. Pattern 1 comprised a mixture of EVT, STB, and fibroblast cell types. Aside from pattern 2, which was specific to immune cells, patterns 6 and 7 were shared with other cells, including CTB and STB and highlighted a mix of immune and cellular pathways including galectin and chemokine ligand (CCL) (Fig. 4E, F).

In the sPTL group, aside from pattern 2 (specific to STB1 cells), most trophoblast cell types exhibited unique patterns. Among the few immune cell types available, mixed immune cells and endothelial cells of maternal origin shared pattern 9, whereas other immune cells, such as dMs and Hofbauer cells, followed pattern 4, involving immune-related pathways such as SPP1, IL-10, and CXCL. We noticed similar trends in the incoming signals, whereby individual trophoblast clusters either exhibited their pattern or were grouped within patterns of the same cell type, such as in patterns 1, 2, 4, and 5. Pattern 3 consisted solely of immune cell clusters, which showed strong contributions from the IL-16 pathway (Fig. 4E, F). Overall, the global cell-to-cell analysis highlighted that incoming and outgoing signals are cell- and condition-specific.

Further analysis focused on fibroblast cell clusters (MEG3), in which clear differences in signaling strengths were observed across the control, pPROM, and sPTL groups (Fig. 5A–C). In the control group, outgoing fibroblast signals were dominated by vascular endothelial growth factor (VEGF) and growth arrest–specific (GAS) signaling, suggesting a baseline for maintaining the fetal membrane integrity, angiogenesis, and cell migration [31, 32]. In pPROM, there was an upregulation in VEGF, visfatin, transforming growth factor beta (TGFβ), ANGPTL, interleukin 6 (IL6), platelet-derived growth factor (PDGF), leukemia inhibitory factor receptor, neurotrophin, TSH, FSH, implying active signaling processes associated with tissue repair in response to inflammation and immune response, and hormone regulation [33,34,35,36,37,38,39]. In sPTL, neuregulin, GAS, IL6, prolactin (PRL), erythropoietin (EPO), and macrophage migration inhibitory factor signaling were found at high levels with absent VEGF signaling. Like the pPROM condition, the sPTL condition shows upregulated outgoing signaling associated with immune and tissue modulation and hormones [40,41,42,43,44,45] (Fig. 5D).

Fig. 5

Signaling of cell-to-cell dialogues. Scatterplots of all cell types plotted, with the x-axis representing the signaling strength of outgoing cell-to-cell dialogues and the y-axis showing that of the incoming interaction of cell-to-cell dialogues which cross premature preterm rupture of membranes (pPROM) and spontaneous preterm labor (sPTL), when compared to the control group (A–C). The biological function of a specific cell to be incoming or outgoing signaling in a cell-to-cell dialogue may vary on the pathophysiological condition. For example, the circled fibroblast (MEG3) had an outgoing signal whose strength was at (x = 15, y = 8) approximately in the pPROM group, whereas it had an incoming signal strength at (x = 8, y = 17) in the sPTL group. Gene expression of various transcripts involved in cell-to-cell dialogues, among all cell clusters in the control, pPROM, and sPTL groups are presented with a heatmap (D)

For the control condition’s incoming signaling to the fibroblast cluster, patterns of higher displayed ANGPTL, protein S (PROS), and ectodysplasin A (EDA) suggested a baseline of angiogenesis, cell cycle regulation, lipid regulation, and ectoderm development [46,47,48]. In pPROM, we observed SPP1, ANGPTL, PDGF, oncostatin M, myostatin, CD40, EDA, and FSH signaling, which are involved in inflammation, trophoblast invasion, growth factor, and immune regulation [49,50,51,52]. Lastly, sPTL exhibited a diverse range of incoming signaling patterns, including SEMA3, SPP1, bone morphogenetic protein (BMP), ANGPTL, TGFβ, non-canonical WNT (ncWNT), PRL, IL2, midkine, periostin, PTN, EPO, EDA, PROS, resistin, and nerve growth factor, which suggests roles in inhibition of angiogenesis, axon guidance, tissue development, inflammation, trophoblast differentiation, implantation success, and cellular responses [33, 53,54,55,56,57,58,59] (Fig. 5D).

Cell dialogues: immunocyte to trophoblast in pPROM vs. trophoblast to immunocyte in sPTL

To explore cell-to-cell communication in sPTB, the interaction between dMs (SLC16A10) and the EVTs (LVRN) was assessed in the pPROM condition (Fig. 6A), whereas the interaction between STB2 (CACNA2D3) and fetal macrophages and Hofbauer cells (F13A1) was measured for the sPTL condition (Fig. 6B). We selected interactions involving EVTs in pPROM and STBs in sPTL due to the prominence of these specific cell types within each condition. In pPROM, the interaction from dMs to EVTs was chosen since the dot plot illustrating outgoing and incoming signal interaction strength, indicated strong outgoing signals from dMs (SLC16A10) and moderate but highly receptive incoming signals in EVTs (LVRN) (Fig. 5B). In the sPTL condition, we prioritized STB (CACNA2D3) because it exhibited the strongest outgoing signaling among STB clusters (Fig. 5C). Hofbauer cells (F13A1) were selected due to their essential role in supporting STB function within the chorionic villi.

Fig. 6

Cell interactions between immunocytes and trophoblasts. White boxes present cell-to-cell interactions of ligand-receptor pairs between immunocyte dM (SLC16A10), which functions as the ligand, and trophoblasts EVT3 (LVRN), which act as the receptor in the incoming signaling in the clinical condition of premature preterm rupture of membranes (pPROM) (A). Similarly, for the outgoing signaling of cell-to-cell interactions, trophoblasts STB2 (CACNA2D3) are the ligand, and immunocyte Hofbauer cells (F13A1) are the receptor in spontaneous preterm labor (sPTL) (B). The dot plots present the communication probability and p-values of specific ligand-receptor pairs involved, and the corresponding violin plots present the transcripts that have been differentially expressed in ligand-receptor (C, D)

The highest communication probabilities in the pPROM condition were observed in the VEGF, SPP1, nicotinamide phosphoribosyl transferase, TGFβ, CSF, CCL, and the epidermal growth factor signaling pathways (Fig. 6C). These pathways collectively support the intricate balance of tissue homeostasis, immune responses, and angiogenetic repair mechanisms, highlighting their critical roles in maintaining and restoring cellular and tissue integrity [26, 33, 60,61,62,63,64].

In contrast, only two pathways exhibited high communication probabilities in the sPTL condition: semaphorin 3C (SEMA3C) and growth differentiation factor (GDF) (Fig. 6D). GDF signaling, particularly via the GDF15-TGFBR2 interaction, is involved in cell growth and differentiation. SEMA3C signaling, facilitated by interactions such as SEMA3C-PLXND1 and SEMA3C-(NRP1-NRP2), plays a role in inhibiting VEGF-mediated endothelial cell survival, migration, and angiogenesis by altering cytoskeletal dynamics and disrupting endothelial cell adhesion [53, 65, 66]. This inhibition suggests that SEMA3C may regulate blood vessel development within the placental tissue in individuals with sPTL, emphasizing a distinct pathway profile compared to the angiogenic focus observed in pPROM.