Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

Abstract

The human placenta is a dynamic and heterogeneous organ critical in the establishment of the fetomaternal interface and the maintenance of gestational well-being. It is also the major source of cell-free fetal nucleic acids in the maternal circulation. Placental dysfunction contributes to significant complications, such as preeclampsia, a potentially lethal hypertensive disorder during pregnancy. Previous studies have identified significant changes in the expression profiles of preeclamptic placentas using whole-tissue analysis. Moreover, studies have shown increased levels of targeted RNA transcripts, overall and placental contributions in maternal cell-free nucleic acids during pregnancy progression and gestational complications, but it remains infeasible to noninvasively delineate placental cellular dynamics and dysfunction at the cellular level using maternal cell-free nucleic acid analysis. In this study, we addressed this issue by first dissecting the cellular heterogeneity of the human placenta and defined individual cell-type-specific gene signatures by analyzing more than 24,000 nonmarker selected cells from full-term and early preeclamptic placentas using large-scale microfluidic single-cell transcriptomic technology. Our dataset identified diverse cellular subtypes in the human placenta and enabled reconstruction of the trophoblast differentiation trajectory. Through integrative analysis with maternal plasma cell-free RNA, we resolved the longitudinal cellular dynamics of hematopoietic and placental cells in pregnancy progression. Furthermore, we were able to noninvasively uncover the cellular dysfunction of extravillous trophoblasts in early preeclamptic placentas. Our work showed the potential of integrating transcriptomic information derived from single cells into the interpretation of cell-free plasma RNA, enabling the noninvasive elucidation of cellular dynamics in complex pathological conditions.

Keywords: cell-free RNA; noninvasive prenatal testing; placenta; preeclampsia; single-cell transcriptomics.

Fig. 1.

Single-cell transcriptomic profiling and the dissection of the cellular heterogeneity of human placenta. (A) Schematic diagram showing the experimental design in this study. Cellular heterogeneity of the human placenta is dissected by large-scale droplet-based single-cell transcriptomic profiling. Cell-type–specific signatures of different types of placental cells are identified and used to obtain information of cellular dynamics in the maternal plasma RNA profiles in pregnancy and PE. (B) Biaxial scatter plot showing single-cell transcriptomic clustering of placental cells from human term placentas by t-SNE analysis. Cells were further grouped into specific subgroups (P1–P12) and colored individually based on expression patterns of specific marker genes and spatial proximity in the biaxial plot. (C) Column chart comparing the fraction of maternal or fetal cells in each cellular subgroup. (D) Column chart indicating the percentage of cells expressing Y chromosome-encoded genes in each cellular subgroup. (E) Biaxial scatter plots showing the expression pattern of specific genes among different subgroups of placental cells.

Fig. S1.

Dissection of the cellular heterogeneity and annotation of cellular identity in the human placenta. (A) Biaxial scatter plot showing the distribution of cells of predicted fetal/maternal origin in the original t-SNE clustering distribution as in Fig. 1B. Data from PN2 libraries have not been plotted, as no genotyping information was available for fetomaternal origin prediction. (B) The four major cellular groups in the human placenta: decidual (P1; green), stromal (P2–P4; olive), hematopoietic (P5–P9; red), and trophoblastic (P10–P12; turquoise). (C) Expression pattern of stromal (COL1A1, COL3A1, THY1, and VIM) and myeloid (CSF1R, CD14, AIF1, and CD53) markers in P5–P7 subgroups. (D) t-SNE analysis showed clustering of P5 cells (gray) with artificial P4/P7 duplets (light green) generated in silico, suggesting that P5 cells are likely multiplets. (E) Biaxial scatter plots showing the expression pattern of genes encoding for HLAs among different subgroups of placental cells. (F) Cellular subgroup composition heterogeneity in different single-cell transcriptomic datasets. PN3P/PN3C and PN4P/PN4C represent paired biopsies taken proximal to the umbilical cord insertion sites (PN3C/PN4C) and distal at the periphery of the placental disk (PN3P/PN4P). (G) Table summarizing the annotated nature of each cellular subgroup.

Fig. 2.

Reconstruction of the developmental relationship of trophoblast cells by pseudotime analysis. (A) Developmental trajectory visualization in biaxial scatter plot. The trophoblastic cell subgroups (P10–P12) were selected for trajectory reconstruction (Inset i). Trophoblastic cells were reclustered based on highly variable genes only by t-SNE analysis, and smaller trophoblastic subgroups (P10A–C, P11A–D, and P12A and -B colored individually) could be visualized in the biaxial plot (Inset ii). Pseudotime reconstruction revealed a bifurcating trajectory of trophoblast development. The coloring of individual cell along the optimized embedding path corresponded to that in Inset ii. (B) Biaxial scatter plots showing the distribution patterns of cells expressing specific marker genes in the pseudotemporal trajectories. For clear visualization, only cells with nonzero expression were plotted.

Fig. S2.

Pseudotemporal change of gene expression in trophoblasts. Genes were organized into two major groups by their similar pseudotemporal covariation of expression. Notable gene examples involved in trophoblast development were marked and enlarged.

Fig. S3.

Identification of cell-type–specific signature genes sets and noninvasive elucidation of placental cellular dynamic in maternal cell-free RNA. (A) Biaxial t-SNE plot showing the clustering pattern of PBMCs and placental cells. The PBMC data (donor B) from Zheng et al. (28) were plotted with the placental dataset. (B) Table summarizing the annotated nature of each cellular subgroup in the placenta/PBMC merged dataset. (C) Biaxial scatter plots showing the expression pattern of specific marker genes among different subgroups of placental cells and PBMCs. (D) Heat map showing the average expression of cell-type–specific signature genes in different PBMCs and placental cells clusters. The column side color corresponds to the cell cluster coloring in A. The row side color indicates the cell-type specificity of the gene. (E) Box plots comparing the expression levels of different cell-type–specific signature genes in human leukocytes, the liver, and the placenta. (F) Cell signature expression analysis of the maternal plasma RNA profiles of Koh et al. (20). Line plots showing the change of the average expression of individual cell-type–specific signature in different stages of pregnancy with respective to first trimester maternal plasma. The gray lines demarcate the range of the data. FPKM, fragments per kilobase of transcript per million mapped reads.

Fig. S3.

Identification of cell-type–specific signature genes sets and noninvasive elucidation of placental cellular dynamic in maternal cell-free RNA. (A) Biaxial t-SNE plot showing the clustering pattern of PBMCs and placental cells. The PBMC data (donor B) from Zheng et al. (28) were plotted with the placental dataset. (B) Table summarizing the annotated nature of each cellular subgroup in the placenta/PBMC merged dataset. (C) Biaxial scatter plots showing the expression pattern of specific marker genes among different subgroups of placental cells and PBMCs. (D) Heat map showing the average expression of cell-type–specific signature genes in different PBMCs and placental cells clusters. The column side color corresponds to the cell cluster coloring in A. The row side color indicates the cell-type specificity of the gene. (E) Box plots comparing the expression levels of different cell-type–specific signature genes in human leukocytes, the liver, and the placenta. (F) Cell signature expression analysis of the maternal plasma RNA profiles of Koh et al. (20). Line plots showing the change of the average expression of individual cell-type–specific signature in different stages of pregnancy with respective to first trimester maternal plasma. The gray lines demarcate the range of the data. FPKM, fragments per kilobase of transcript per million mapped reads.

Fig. 3.

Elucidation of placental cellular dynamic in maternal plasma RNA profiles during pregnancy. Line plots showing the change of the average cell signature expression of individual placental cell type in different gestational groups. The maternal plasma RNA profiles were retrieved from Tsui et al. (19). The gray lines demarcate the range of the data. I, early pregnancy (13–20 wk); II, mid/late pregnancy (24–30 wk); III, predelivery; NP, nonpregnant; PP, 24-h postpartum.

Fig. 4.

Uncovering placental cellular aberrations in early preeclamptic maternal plasma RNA profiles. (A) Box plot comparing the cell signature expression of different cell types in the maternal plasma RNA profiles of third trimester pregnancy (control) and early PE patients. Statistical testing was performed by two-tailed two-sample Wilcoxon signed rank test. (B) Biaxial scatter plot showing the average single-cell expression heterogeneity of different GO annotated gene sets in third trimester term and early preeclamptic placentas. Only data points with statistically significant differences are shown (P < 0.05). GO terms associated with cell proliferation, cell migration, apoptosis, antigen presentation, and DNA damages are colored and highlighted. (C) Violin plots comparing the expression level-corrected heterogeneity (DM values) and average expression levels of genes annotated in the GO term “Cell Death” between PE patients and normal controls.

Fig. S4.

Noninvasive detection of cellular aberrations in early preeclamptic placentas. (A) Box plot comparing the cell signature expression of decidual cells, placental endothelial cells, EVTB cells, and SCTB cells in the maternal plasma RNA profiles of third trimester pregnancy (control) and early preeclamptic patients (PE) from a separate small cohort assayed using the RNA library preparation method as described by Tsui et al. (19). (B) Biaxial scatter plots showing the trophoblast clusters in the merged PE/control placenta single-cell transcriptomic dataset and the expression of EVTB marker genes HLA-G, PAPPA2, TIMP1, CSH1, and ADAM12. The EVTB cells from PE/control placentas are colored in light green (control) and purple pink (PE). (C) Scatter plot showing the relationship of log-transformed CV² and log-transformed average expression levels of the cells in the EVTBs cluster: term placenta (Left) and PE (Right). The rolling medians across the expression range are plotted (orange line), and the expression level-corrected expression variability of a gene is calculated as the vertical distance of its CV² value from the rolling median regression line (DM). (D) Gene set enrichment analysis result (51) confirming enrichment of cell death-related genes expression in early preeclamptic EVTB cells.