Single-cell characterization of self-renewing primary trophoblast organoids as modeling of EVT differentiation and interactions with decidual natural killer cells

Abstract

Background: Extravillous trophoblast cell (EVT) differentiation and its communication with maternal decidua especially the leading immune cell type natural killer (NK) cell are critical events for placentation. However, appropriate in vitro modelling system and regulatory programs of these two events are still lacking. Recent trophoblast organoid (TO) has advanced the molecular and mechanistic research in placentation. Here, we firstly generated the self-renewing TO from human placental villous and differentiated it into EVTs (EVT-TO) for investigating the differentiation events. We then co-cultured EVT-TO with freshly isolated decidual NKs for further study of cell communication. TO modelling of EVT differentiation as well as EVT interaction with dNK might cast new aspect for placentation research.

Results: Single-cell RNA sequencing (scRNA-seq) was applied for comprehensive characterization and molecular exploration of TOs modelling of EVT differentiation and interaction with dNKs. Multiple distinct trophoblast states and dNK subpopulations were identified, representing CTB, STB, EVT, dNK1/2/3 and dNKp. Lineage trajectory and Seurat mapping analysis identified the close resemblance of TO and EVT-TO with the human placenta characteristic. Transcription factors regulatory network analysis revealed the cell-type specific essential TFs for controlling EVT differentiation. CellphoneDB analysis predicted the ligand-receptor complexes in dNK-EVT-TO co-cultures, which relate to cytokines, immunomodulation and angiogenesis. EVT was known to affect the immune properties of dNK. Our study found out that on the other way around, dNKs could exert effects on EVT causing expression changes which are functionally important.

Conclusion: Our study documented a single-cell atlas for TO and its applications on EVT differentiation and communications with dNKs, and thus provide methodology and novel research cues for future study of human placentation.

Keywords: Extravillous trophoblast; Placentation; Pregnancy; Trophoblast differentiation; Trophoblast organoid.

Fig. 1

Experiment design of the study. (A) Schematic illustration for the formation of the trophoblast organoid, EVT differentiation and co-culture with decidual natural killer cells. (B) Image of the different cultured statuses of trophoblast organoids. Left, TO culture; middle, generation of EVTs from TO; right, EVT-TO co-culture with dNKs

Fig. 2

Validation of the trophoblast organoids. (A) Images of hematoxylin staining for the cross-section of TO (Left) and first-trimester human placenta (Right). The red arrow indicates the intercellular lacunae. (B) Over-the-counter pregnancy test denoting “positive” for the culture medium containing TO growth before next passage. (C) Immunofluorescence staining images of GATA3, TFAP2A and TFAP2C in TO. (D) Quantification of the relative mRNA expression for ITGA6, VGLL1 and TP63 in TO, compared to first-trimester human placenta and decidua. (E) Quantification of the relative mRNA expression for HLA-A, -B, -C and -G in TO, compared to first-trimester human placenta and decidua. (F) qRT-PCR analysis on the expression of trophoblast specific microRNAs: miR517-5p, miR-526b-3p and miR525-3p in TO and first-trimester human placenta. Decidua serves as a positive control. (G) Immunofluorescence staining images of HLA-G in EVT differentiation stages of TO. (H) Quantification of the relative mRNA expression for PRG2, ITGA5 and MMP2 in EVT-TO, compared to TO. **P < 0.01; ***P < 0.001; ns, not significant. N = 3 in triplicate

Fig. 3

Single-cell characterization of trophoblast organoids (TO). (A) Cell clusters for TO sample and EVT-TO sample from 10× Genomics scRNA-seq analysis visualized by UMAP. Colors indicate cell type or state. p, proliferative; CCC, cytotrophoblast cell column. (B) Feature plots showing the expression of canonical marker genes for the defined cell types. (C) Dot plots showing the expression of known lineage-specific genes for different EVT subtypes. (D) GO analysis of the DEGs for the three different subtypes of EVTs. (E) Proportion of TO system in each scRNA-seq-defined cluster in the undifferentiation TO state and differentiation EVT-TO state

Fig. 4

Trophoblasts differentiation trajectories identify a similar pattern of in vivo placentation. (A) Annotations query of the trophoblast clusters in the TO system by Seurat mapping analysis. Reference database: the in vivo single-cell dataset of placenta (E-MTAB-6701). (B) Pseudotime ordering of the trophoblast clusters indicate the EVT pathways in TO system. (C) Pseudotime kinetics of specific representative genes from the root of the trajectory to EVT (solid line) and STB (dashed line). (D) Functional enrichment annotations of the clustering branched genes. Left, heatmap showing the genes in trajectory from root to EVT or STB. Middle and Right, Bar plots showing the top annotated GO terms and KEGG terms in four hierarchically clustering genes sets, respectively

Fig. 5

Transcriptional regulators analysis for TO system. (A) EVTs-specific regulon activity analysis. Left, Rank for regulons based on regulon specificity score (RSS); Middle, EVT clusters are higjlighted in the t-SNE map (colors dots); Right, Binarized regulon activity score (RAS) for regulons in the t-SNE map (purple dots). (B) Binary activity matrix for cell types-specific regulons. Regulons were determined to be active (black) if they exceeded a manually adjusted AUC regulon-specific threshold, or inactive under this threshold (white). (C) Identified regulon modules based on regulon connection specificity index (CSI) matrix, along with representative transcription factors, corresponding binding motifs, and associated cell types. (D) Average module activity scores mapped on t-SNE. (E) KEGG terms for the enrichment of downstream targets of each module regulators

Fig. 6

Single-cell characterization of dNKs-EVT-TO co-cultures. (A) Cell clusters for dNKs-EVT-TO sample from 10× Genomics scRNA-seq analysis visualized by UMAP. Colors indicate cell type or state. (B) Feature plots showing the expression of canonical marker genes for the defined cell types. (C) GO analysis enriched different terms on the DEGs for the three different subtypes of EVTs. (D) GO analysis enriched shared terms in EVT2 and EVT3. (E) Pseudotime ordering of the trophoblast clusters indicate the dNKs pathways in dNKs-EVT-TO co-cultures. (F) Annotations query of the trophoblast clusters in the dNKs-EVT-TO co-cultures by Seurat mapping analysis. Reference database: the in vivo single-cell dataset of placenta (E-MTAB-6701)

Fig. 7

Cell-cell interactions in dNKs-EVT-TO co-cultures. (A) Putative ligand and receptor-based intercellular communication between dNKs and EVT-TO cell types. Left, circus plot. Color lines indicate ligands broadcast by the cell population of the same color and connect to cell populations where cognate receptors are expressed. The line thickness is proportional to the number of ligands where cognate receptors are present in the recipient cell population. Loops indicate autocrine circuits. Map quantifies potential communication, but does not account for anatomic position or boundaries of cell populations. Right, Heatmap for counts of predicted pairs. (B) Overview of HLA molecules-representative ligand-receptor interactions. P-values < 0.05 indicated by circle size. The means of the average expression level of cell-cell pairs indicated by colors. (C) Overview of selected ligand-receptor interactions. Immuno., immune checkpoints. (D) Diagram of the main receptors and ligands expressed on dNKs subsets and EVTs clusters that are involved in cytokines, chemokines and immune checkpoints. (E) Same as D but for angiogenesis, growth factors and signaling

Fig. 8

Effects on EVTs exerted by dNKs in the co-culture system. (A) Volcano plot showing DEGs for the three different subtypes of EVTs. (B) Simplified GO terms enrichments for DEGs as A