were identified in the integration and the curated marker lists. However, only SLC6A4 and SLC27A2 were specific markers of CTBs in the first trimester (Fig. 5c and Supplementary Table S8). All the other genes were either expressed at the same level in fusing-CTBs and/or EVTs or with a higher expression in fusing-CTBs. This demonstrates the difficulty of distinguishing CTBs from fusing-CTB using scRNAseq data in the first trimester placentas.

In both primary human trophoblasts from term placentas and BeWo cells, SLC6A4 (also known as SERT) was expressed more in STBs than in CTBs (Vachalova et al., 2022). Furthermore, SLC6A4 was also identified in STBs of term placentas using ISH (Bottalico et al., 2004). In human primary trophoblasts from term placentas, expression of SLC27A2 was significantly higher after syncytialization (Kallol et al., 2018) and was also expressed more in STBs than CTBs in second trimester placentas (Gormley et al., 2021). No literature regarding their expression in the first trimester placentas could be recovered. Therefore, it is likely that SLC6A4 and SLC27A2 are good markers for CTB using scRNAseq but only in the first trimester placentas.

Using spatial transcriptomics and snRNAseq, Arutyunyan et al. (2023) demonstrated that BCAM was only expressed in CTBs. In our integrated analysis, around 60% of the fusing-CTB expressed BCAM at a lower level than CTBs. This gene could probably also be considered as a good marker of CTB using scRNAseq. Furthermore, as it is expressed in >50% of the EVT in the integrated dataset, PARP1 was not selected but this expression is very low (<0.02), suggesting that PARP1 could also be a good marker for CTB in scRNAseq. This seems to be confirmed by the literature, as in healthy term placentas, PARP1, is specifically expressed by CTB (Longtine et al., 2012).

Taken together, only BCAM, SLC6A4, SLC27A2, and likely PARP1 can be used as specific markers of CTB in the first trimester placentas using scRNAseq.

## Fusing-CTB

In fusing-CTBs from the first trimester, 226 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d), among which 16 were previously used as marker for STBs in the first trimester placentas (Fig. 2 and Supplementary Table S4). When comparing the 226 DEGs with our annotated dataset comparison, 18 genes were common in at least three of the four studies. Altogether 34 genes were identified in the integration analysis and annotated datasets or curated marker genes analyses: LGALS16, SERPINB2, GREM2, EPS8L1, ERVV-1, LGALS13, KMO, GLDN, IGSF5, ADHFE1, PTPN21, HES2, NEURL1, PHYHIPL, HSD11B2, ENDOU, TCL1B, and LGALS14 were identified both in the integration and the annotated datasets; and CYP19A1, GDF15, ERVFRD-1, INSL4, CGA, SLC52A1, SLC13A4, ERVW-1, HSD3B1, SLC22A11, TFAP2A, LCMT1-AS2, PAGE4, KISS1, PSG6, and PSG2 were identified in the integration and the curated marker lists.

Interestingly, in contrast to the CTB, most of the genes identified in at least two analyses were mainly expressed in fusing-CTBs but only LGALS16, CYP19A1, SERPINB2, GREM2, EPS8L1, ERVV-1, and ERVFRD-1 were expressed in >50% of fusing-CTB (Supplementary Table S8).

Several studies using different methods (immunohistochemistry, immunofluorescence, and ISH) demonstrated that galectin family genes, such as LGALS16, are expressed in STB but not in CTB (Than et al., 2004, 2014; Huppertz et al., 2008). CYP19A1 (encoding the aromatase cytochrome P450) was exclusively observed in STB compared to underlying CTB and villous core using immunohistochemistry (Fournet-Dulguerov et al., 1987).

SERPINB2 protein was observed in the cytoplasm of STB but not in CTB or EVT (Feinberg et al., 1989). Similarly, GREM2 expression coincides with the activation of STB genes and hCG secretion (Sudheer et al., 2012). ERVV-1 was also highly expressed by differentiated trophoblast cultures compared to undifferentiated (Renaud et al., 2015). Using human trophoblast stem cells (hTSCs) and BeWo cells, ERVFRD-1 was shown to be nearly absent before differentiation and significantly upregulated after differentiation in STB (Renaud et al., 2015; West et al., 2022), suggesting that it is indeed a specific marker of STB that can be present in the early stages of the fusion process. No further information could be found to validate the use of EPS8L1 as marker of fusing-CTB in human placenta.

Using spatial transcriptomics and snRNAseq, Arutyunyan et al. (2023) also demonstrated that ERVV-1, ERVFRD-1, and GREM2 were markers of fusing-CTB and not STB, supporting the immature state of STB in the integrated analysis. ERVV-1, GLDN, LGALS13, and LGALS16 were also identified by HuBMAP and/or Cellmarker databases as STB markers. In the Descartes cell atlas (based on data from Cao et al., 2020), LGALS13, LGALS16, and ADHFE1 were associated with 'trophoblast giant cells' that were defined with pregnancy-specific glycoprotein (PSG) genes (PSG4, PSG6, and PSG9). LGALS13, LGALS16, and GDF15 were also used to define EVT or CTB in at least one of these databases.

Therefore, we concluded that LGALS16, SERPINB2, CYP19A1, GREM2, ERVV-1, and ERVFRD-1 can be used as specific markers of fusing-CTB in the first trimester placentas.

## Extravillous trophoblast

Altogether, 228 DEGs were identified in EVT (Supplementary Table S7 and Supplementary Fig. S2d), among which 19 genes were previously used as markers for EVT in the first trimester placentas (Fig. 2 and Supplementary Table S4). When comparing all DEGs with the gene list identified from annotated datasets, 10 genes were found in at least three of the four studies. Altogether, 29 genes were identified by at least two analyses, i.e. ASCL2, ABCA7, ACAN, IL1RAP, MGST1, KRT14, KRT17, KISS1R, FAT2, and SAA1 were identified in the integrated analysis and the list of annotated datasets; and HLA-G, DIO2, NOTUM, LAIR2, MFAP5, HTRA4, HPGD, ITGA5, SLCO4A1, CSH1, HSD3B1, MMP2, CGA, FN1, MMP12, FSTL1, HLA-C, PAPPA2, and PRG2 were identified in the integrated analysis and the curated marker list.

In the integrated data, HLA-G, DIO2, NOTUM, ASCL2, LAIR2, ABCA7, ACAN, IL1RAP, MGST1, MFAP5, HTRA4, HPGD, ITGA5, SLCO4A1, and CSH1 were specifically expressed in EVT (Supplementary Table S8).

HLA-G is the most used EVT signature gene (Supplementary Table S4) and is well known to be expressed only in EVT compared to other trophoblasts (McMaster et al., 1995; Hackmon et al., 2017b). In the first trimester placentas, DIO2 was expressed in CTB, STB, and CCC by immunofluorescence (Adu-Gyamfi et al., 2021). NOTUM was more expressed in EVT compared to CTB differentiated from hTSC (Jeyarajah et al., 2022) but was observed in all trophoblasts in the second trimester placentas by immunohistochemistry (Robinson et al., 2017). ASCL2 is a specific transcription factor for the EVT lineage with expression in the EVT columns in human 12 week-old placentas, and its deficiency results in increased STBs and reduced EVTs differentiation (Varberg et al., 2021, 2023). In addition, ASCL2 expression is decreased across differentiation in EVT and almost absent once the progenitor state is lost (Renaud et al., 2015). Using ISH and immunohistochemistry, LAIR2 was shown to be expressed only in the more distal portions of CCC and the invading EVT in the first
were identified in the integration and the curated marker lists. However, only SLC6A4 and SLC27A2 were specific markers of CTBs in the first trimester (Fig. 5c and Supplementary Table S8). All the other genes were either expressed at the same level in fusing-CTBs and/or EVTs or with a higher expression in fusing-CTBs. This demonstrates the difficulty of distinguishing CTBs from fusing-CTB using scRNAseq data in the first trimester placentas.

In both primary human trophoblasts from term placentas and BeWo cells, SLC6A4 (also known as SERT) was expressed more in STBs than in CTBs (Vachalova et al., 2022). Furthermore, SLC6A4 was also identified in STBs of term placentas using ISH (Bottalico et al., 2004). In human primary trophoblasts from term placentas, expression of SLC27A2 was significantly higher after syncytialization (Kallol et al., 2018) and was also expressed more in STBs than CTBs in second trimester placentas (Gormley et al., 2021). No literature regarding their expression in the first trimester placentas could be recovered. Therefore, it is likely that SLC6A4 and SLC27A2 are good markers for CTB using scRNAseq but only in the first trimester placentas.

Using spatial transcriptomics and snRNAseq, Arutyunyan et al. (2023) demonstrated that BCAM was only expressed in CTBs. In our integrated analysis, around 60% of the fusing-CTB expressed BCAM at a lower level than CTBs. This gene could probably also be considered as a good marker of CTB using scRNAseq. Furthermore, as it is expressed in >50% of the EVT in the integrated dataset, PARP1 was not selected but this expression is very low (<0.02), suggesting that PARP1 could also be a good marker for CTB in scRNAseq. This seems to be confirmed by the literature, as in healthy term placentas, PARP1, is specifically expressed by CTB (Longtine et al., 2012).

Taken together, only BCAM, SLC6A4, SLC27A2, and likely PARP1 can be used as specific markers of CTB in the first trimester placentas using scRNAseq.

## Fusing-CTB

In fusing-CTBs from the first trimester, 226 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d), among which 16 were previously used as marker for STBs in the first trimester placentas (Fig. 2 and Supplementary Table S4). When comparing the 226 DEGs with our annotated dataset comparison, 18 genes were common in at least three of the four studies. Altogether 34 genes were identified in the integration analysis and annotated datasets or curated marker genes analyses: LGALS16, SERPINB2, GREM2, EPS8L1, ERVV-1, LGALS13, KMO, GLDN, IGSF5, ADHFE1, PTPN21, HES2, NEURL1, PHYHIPL, HSD11B2, ENDOU, TCL1B, and LGALS14 were identified both in the integration and the annotated datasets; and CYP19A1, GDF15, ERVFRD-1, INSL4, CGA, SLC52A1, SLC13A4, ERVW-1, HSD3B1, SLC22A11, TFAP2A, LCMT1-AS2, PAGE4, KISS1, PSG6, and PSG2 were identified in the integration and the curated marker lists.

Interestingly, in contrast to the CTB, most of the genes identified in at least two analyses were mainly expressed in fusing-CTBs but only LGALS16, CYP19A1, SERPINB2, GREM2, EPS8L1, ERVV-1, and ERVFRD-1 were expressed in >50% of fusing-CTB (Supplementary Table S8).

Several studies using different methods (immunohistochemistry, immunofluorescence, and ISH) demonstrated that galectin family genes, such as LGALS16, are expressed in STB but not in CTB (Than et al., 2004, 2014; Huppertz et al., 2008). CYP19A1 (encoding the aromatase cytochrome P450) was exclusively observed in STB compared to underlying CTB and villous core using immunohistochemistry (Fournet-Dulguerov et al., 1987).

SERPINB2 protein was observed in the cytoplasm of STB but not in CTB or EVT (Feinberg et al., 1989). Similarly, GREM2 expression coincides with the activation of STB genes and hCG secretion (Sudheer et al., 2012). ERVV-1 was also highly expressed by differentiated trophoblast cultures compared to undifferentiated (Renaud et al., 2015). Using human trophoblast stem cells (hTSCs) and BeWo cells, ERVFRD-1 was shown to be nearly absent before differentiation and significantly upregulated after differentiation in STB (Renaud et al., 2015; West et al., 2022), suggesting that it is indeed a specific marker of STB that can be present in the early stages of the fusion process. No further information could be found to validate the use of EPS8L1 as marker of fusing-CTB in human placenta.

Using spatial transcriptomics and snRNAseq, Arutyunyan et al. (2023) also demonstrated that ERVV-1, ERVFRD-1, and GREM2 were markers of fusing-CTB and not STB, supporting the immature state of STB in the integrated analysis. ERVV-1, GLDN, LGALS13, and LGALS16 were also identified by HuBMAP and/or Cellmarker databases as STB markers. In the Descartes cell atlas (based on data from Cao et al., 2020), LGALS13, LGALS16, and ADHFE1 were associated with 'trophoblast giant cells' that were defined with pregnancy-specific glycoprotein (PSG) genes (PSG4, PSG6, and PSG9). LGALS13, LGALS16, and GDF15 were also used to define EVT or CTB in at least one of these databases.

Therefore, we concluded that LGALS16, SERPINB2, CYP19A1, GREM2, ERVV-1, and ERVFRD-1 can be used as specific markers of fusing-CTB in the first trimester placentas.

## Extravillous trophoblast

Altogether, 228 DEGs were identified in EVT (Supplementary Table S7 and Supplementary Fig. S2d), among which 19 genes were previously used as markers for EVT in the first trimester placentas (Fig. 2 and Supplementary Table S4). When comparing all DEGs with the gene list identified from annotated datasets, 10 genes were found in at least three of the four studies. Altogether, 29 genes were identified by at least two analyses, i.e. ASCL2, ABCA7, ACAN, IL1RAP, MGST1, KRT14, KRT17, KISS1R, FAT2, and SAA1 were identified in the integrated analysis and the list of annotated datasets; and HLA-G, DIO2, NOTUM, LAIR2, MFAP5, HTRA4, HPGD, ITGA5, SLCO4A1, CSH1, HSD3B1, MMP2, CGA, FN1, MMP12, FSTL1, HLA-C, PAPPA2, and PRG2 were identified in the integrated analysis and the curated marker list.

In the integrated data, HLA-G, DIO2, NOTUM, ASCL2, LAIR2, ABCA7, ACAN, IL1RAP, MGST1, MFAP5, HTRA4, HPGD, ITGA5, SLCO4A1, and CSH1 were specifically expressed in EVT (Supplementary Table S8).

HLA-G is the most used EVT signature gene (Supplementary Table S4) and is well known to be expressed only in EVT compared to other trophoblasts (McMaster et al., 1995; Hackmon et al., 2017b). In the first trimester placentas, DIO2 was expressed in CTB, STB, and CCC by immunofluorescence (Adu-Gyamfi et al., 2021). NOTUM was more expressed in EVT compared to CTB differentiated from hTSC (Jeyarajah et al., 2022) but was observed in all trophoblasts in the second trimester placentas by immunohistochemistry (Robinson et al., 2017). ASCL2 is a specific transcription factor for the EVT lineage with expression in the EVT columns in human 12 week-old placentas, and its deficiency results in increased STBs and reduced EVTs differentiation (Varberg et al., 2021, 2023). In addition, ASCL2 expression is decreased across differentiation in EVT and almost absent once the progenitor state is lost (Renaud et al., 2015). Using ISH and immunohistochemistry, LAIR2 was shown to be expressed only in the more distal portions of CCC and the invading EVT in the first
trimester placentas, while no expression was observed in any other fetal cells (Foundes et al., 2010). From the microarray profiling of CTB and EVT extracted from the first trimester placentas, ACAN was shown to be overexpressed in EVT compared to CTB (Oravecz et al., 2022). In contrast, HTRA4 expression was shown to increase during syncytialization of BeWo cells (Mansilla et al., 2021) and in term placentas, HTRA4 protein localizes mainly to STB using immunohistochemistry (Liu et al., 2018a). HPGD was shown to be primarily expressed by EVT, with a weak expression in STB in the first trimester and term placentas (de Assis et al., 2022). Using PCR array on CTB and EVT isolated from the first trimester placentas, ITGA5 was shown to be upregulated in EVT compared to CTB (DaSilva-Arnold et al., 2015). SLCO4A1 was expressed by both CTB and STB after differentiation from primary tissue of first trimester placentas (Berveiller et al., 2015). CSH1 was shown to be expressed both in STB and EVT from placental in vitro models (Takao et al., 2011). No further information could be found to validate the use of ABCA7, IL1RAP, MGST1, and MFAP5 as markers of EVT in human placenta.

In conclusion, HLA-G, NOTUM, ASCL2, LAIR2, ACAN, HPGD, and ITGA5 were shown to be good markers for EVT of the first trimester placentas.

## Non-trophoblast cells

Altogether, 302 DEGs were related to non-trophoblast cells (Supplementary Table S7 and Supplementary Fig. S2c) with four (HLA-A, HLA-B, HLA-C, and VIM) previously used to define them in first trimester placentas. HLA-B presented a scaled expression <0.5 in endothelial cells and HLA-C was highly expressed in EVT (Fig. 5d and Supplementary Table S8). Therefore, only HLA-A and VIM met the criteria to be specific markers of non-trophoblasts. Owing to this shared expression between non-trophoblast cell types, none of these genes appeared to be specific markers of only one non-trophoblast type.

In the integrated data, stromal cells are the second largest cluster (19% of total placental cells) and the most abundant (40%) among the non-trophoblast cells followed by HCs (30%) (Supplementary Table S6). Unlike other studies on first trimester placentas, our integrated analysis also identified perivascular cells in addition to stromal, endothelial, and HCs.

## Stromal and perivascular cells

Altogether, 392 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d) with 15 genes previously used as markers for stromal cells in the first trimester placentas: COL1A1, PITX2, COL6A2, COL3A1, COL1A2, EGFL6, DLK1, PDGFRA, ACTA2, ANGPTL2, VIM, CTGF, ANGPTL1, THY1, and ANGPTL4 (Fig. 2 and Supplementary Table S4). However, most of them (i.e. EGFL6, COL1A1, COL1A2, COL3A1, COL6A2, and DLK1) were also expressed in the perivascular cells, demonstrating the difficulties of identifying them in the first trimester placentas (Supplementary Table S8). Only PDGFRA was not expressed in perivascular cells and, therefore, met all criteria to be a specific marker of stromal cells in the first trimester placentas.

In perivascular cells, 329 DEGs were identified (Supplementary Table S7 and Fig. 2d), with NDUFA4L2, ACTA2, and TAGLN being used to identify perivascular cells in term placentas (no available marker from first trimester studies as perivascular cells were not defined). After this analysis, NDUFA4L2 is the only good marker to define perivascular cells (Supplementary Table S8).

## Endothelial cells

Altogether, 278 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d) with nine genes previously used as

markers for endothelial cells in the first trimester placentas: CD93, CD34, VIM, ITGA2, CDH5, KIT, NRN1, ZEB1, and PECAM1 (Supplementary Fig. S2 and Supplementary Table S4). Except for VIM and NRN1, these genes were specifically expressed in endothelial cells (Supplementary Table S8). However, only CD93, CD34, and CDH5 were expressed in >50% of endothelial cells, allowing them to be considered as good markers of this cell type in first trimester (Fig. 5d and Supplementary Table S7).

## Hofbauer cells

Altogether, 220 DEGs were identified (Supplementary Table S6 and Supplementary Fig. S2d) with 11 genes previously used as markers for HCs in the first trimester placentas: AIF1, CD68, CD14, CCL4, CXCL8, LYVE1, CSF1R, CD163, POU2F2, MAF, and PTPRC (Supplementary Fig. S2 and Supplementary Table S4). AIF1, CD68, CD14, CCL4, CXCL8, LYVE1, CD163, and MAF met the criteria (Supplementary Table S8), allowing them to be considered as specific markers of HCs in first trimester placentas.

## Term placentas

Only three raw datasets were available for integration (Lu-Culligan et al., 2021; Yang et al., 2021; Zhang et al., 2021a; Zhou et al., 2022; Campbell et al., 2023). All datasets presented alignment score (>0.15) with all other studies (Supplementary Fig. S3a). From the three datasets (GSE182381, GSE171381, and GSE173193, which included nine individuals), 23 378 cells remained after filtering 72 702 available cells (i.e. 25 972 cells failed the quality control and 23 352 were maternal immune cells) (Fig. 6a and Supplementary Table S6). Unlike the first trimester placentas, half of the cells in the available raw datasets were maternal immune cells. As human placenta bathes in maternal blood after the first trimester, such a high quantity of maternal immune cells likely comes from maternal immune cell infiltration into term placentas. Therefore, only 571 cells were used for the integration analysis from the total 4368 cells that passed the quality control in Campbell et al. (2023) (Supplementary Table S6).

The same seven clusters of cell types as those in the first trimester were identified in term placentas, including all trophoblast and non-trophoblast cells identified in previous scRNAseq analyses (Fig. 6b). Cells from the three datasets consisted of all clusters (Supplementary Fig. S3b and Supplementary Table S6). We first determined the top 10 DEGs for each cell type (Supplementary Fig. S3) and then cross-compared the genes with the annotated datasets and curated marker lists (Fig. 6 and Supplementary Fig. S2).

## Trophoblast cells

Altogether, 313 DEGs were obtained in trophoblasts versus non-trophoblast cells (Supplementary Table S8) with four genes already used as markers of trophoblast cells in term placentas: KRT8, KRT7, PERP, and CYP19A1. Only KRT8 was expressed in all trophoblast types but it was also expressed in 50% of the HCs. Therefore, none of them met the criteria to be specific markers.

Trophoblasts represented only 43.2% of the cells, almost 10% less than those in the first trimester placentas. As for the first trimester, CTBs were the most abundant type (32.6% of total cells and more than 75% of trophoblast cells), while STBs and EVTs also represent <10% of all cells and, respectively, 5.8% and 18.7% of the trophoblasts (Supplementary Table S6). Similar to the first trimester, it is unlikely that the cells identified as STB in scRNAseq were properly fused STB. Accordingly, we decided to name them fusing-CTB in this meta-analysis.
trimester placentas, while no expression was observed in any other fetal cells (Foundes et al., 2010). From the microarray profiling of CTB and EVT extracted from the first trimester placentas, ACAN was shown to be overexpressed in EVT compared to CTB (Oravecz et al., 2022). In contrast, HTRA4 expression was shown to increase during syncytialization of BeWo cells (Mansilla et al., 2021) and in term placentas, HTRA4 protein localizes mainly to STB using immunohistochemistry (Liu et al., 2018a). HPGD was shown to be primarily expressed by EVT, with a weak expression in STB in the first trimester and term placentas (de Assis et al., 2022). Using PCR array on CTB and EVT isolated from the first trimester placentas, ITGA5 was shown to be upregulated in EVT compared to CTB (DaSilva-Arnold et al., 2015). SLCO4A1 was expressed by both CTB and STB after differentiation from primary tissue of first trimester placentas (Berveiller et al., 2015). CSH1 was shown to be expressed both in STB and EVT from placental in vitro models (Takao et al., 2011). No further information could be found to validate the use of ABCA7, IL1RAP, MGST1, and MFAP5 as markers of EVT in human placenta.

In conclusion, HLA-G, NOTUM, ASCL2, LAIR2, ACAN, HPGD, and ITGA5 were shown to be good markers for EVT of the first trimester placentas.

## Non-trophoblast cells

Altogether, 302 DEGs were related to non-trophoblast cells (Supplementary Table S7 and Supplementary Fig. S2c) with four (HLA-A, HLA-B, HLA-C, and VIM) previously used to define them in first trimester placentas. HLA-B presented a scaled expression <0.5 in endothelial cells and HLA-C was highly expressed in EVT (Fig. 5d and Supplementary Table S8). Therefore, only HLA-A and VIM met the criteria to be specific markers of non-trophoblasts. Owing to this shared expression between non-trophoblast cell types, none of these genes appeared to be specific markers of only one non-trophoblast type.

In the integrated data, stromal cells are the second largest cluster (19% of total placental cells) and the most abundant (40%) among the non-trophoblast cells followed by HCs (30%) (Supplementary Table S6). Unlike other studies on first trimester placentas, our integrated analysis also identified perivascular cells in addition to stromal, endothelial, and HCs.

## Stromal and perivascular cells

Altogether, 392 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d) with 15 genes previously used as markers for stromal cells in the first trimester placentas: COL1A1, PITX2, COL6A2, COL3A1, COL1A2, EGFL6, DLK1, PDGFRA, ACTA2, ANGPTL2, VIM, CTGF, ANGPTL1, THY1, and ANGPTL4 (Fig. 2 and Supplementary Table S4). However, most of them (i.e. EGFL6, COL1A1, COL1A2, COL3A1, COL6A2, and DLK1) were also expressed in the perivascular cells, demonstrating the difficulties of identifying them in the first trimester placentas (Supplementary Table S8). Only PDGFRA was not expressed in perivascular cells and, therefore, met all criteria to be a specific marker of stromal cells in the first trimester placentas.

In perivascular cells, 329 DEGs were identified (Supplementary Table S7 and Fig. 2d), with NDUFA4L2, ACTA2, and TAGLN being used to identify perivascular cells in term placentas (no available marker from first trimester studies as perivascular cells were not defined). After this analysis, NDUFA4L2 is the only good marker to define perivascular cells (Supplementary Table S8).

## Endothelial cells

Altogether, 278 DEGs were identified (Supplementary Table S7 and Supplementary Fig. S2d) with nine genes previously used as

markers for endothelial cells in the first trimester placentas: CD93, CD34, VIM, ITGA2, CDH5, KIT, NRN1, ZEB1, and PECAM1 (Supplementary Fig. S2 and Supplementary Table S4). Except for VIM and NRN1, these genes were specifically expressed in endothelial cells (Supplementary Table S8). However, only CD93, CD34, and CDH5 were expressed in >50% of endothelial cells, allowing them to be considered as good markers of this cell type in first trimester (Fig. 5d and Supplementary Table S7).

## Hofbauer cells

Altogether, 220 DEGs were identified (Supplementary Table S6 and Supplementary Fig. S2d) with 11 genes previously used as markers for HCs in the first trimester placentas: AIF1, CD68, CD14, CCL4, CXCL8, LYVE1, CSF1R, CD163, POU2F2, MAF, and PTPRC (Supplementary Fig. S2 and Supplementary Table S4). AIF1, CD68, CD14, CCL4, CXCL8, LYVE1, CD163, and MAF met the criteria (Supplementary Table S8), allowing them to be considered as specific markers of HCs in first trimester placentas.

## Term placentas

Only three raw datasets were available for integration (Lu-Culligan et al., 2021; Yang et al., 2021; Zhang et al., 2021a; Zhou et al., 2022; Campbell et al., 2023). All datasets presented alignment score (>0.15) with all other studies (Supplementary Fig. S3a). From the three datasets (GSE182381, GSE171381, and GSE173193, which included nine individuals), 23 378 cells remained after filtering 72 702 available cells (i.e. 25 972 cells failed the quality control and 23 352 were maternal immune cells) (Fig. 6a and Supplementary Table S6). Unlike the first trimester placentas, half of the cells in the available raw datasets were maternal immune cells. As human placenta bathes in maternal blood after the first trimester, such a high quantity of maternal immune cells likely comes from maternal immune cell infiltration into term placentas. Therefore, only 571 cells were used for the integration analysis from the total 4368 cells that passed the quality control in Campbell et al. (2023) (Supplementary Table S6).

The same seven clusters of cell types as those in the first trimester were identified in term placentas, including all trophoblast and non-trophoblast cells identified in previous scRNAseq analyses (Fig. 6b). Cells from the three datasets consisted of all clusters (Supplementary Fig. S3b and Supplementary Table S6). We first determined the top 10 DEGs for each cell type (Supplementary Fig. S3) and then cross-compared the genes with the annotated datasets and curated marker lists (Fig. 6 and Supplementary Fig. S2).

## Trophoblast cells

Altogether, 313 DEGs were obtained in trophoblasts versus non-trophoblast cells (Supplementary Table S8) with four genes already used as markers of trophoblast cells in term placentas: KRT8, KRT7, PERP, and CYP19A1. Only KRT8 was expressed in all trophoblast types but it was also expressed in 50% of the HCs. Therefore, none of them met the criteria to be specific markers.

Trophoblasts represented only 43.2% of the cells, almost 10% less than those in the first trimester placentas. As for the first trimester, CTBs were the most abundant type (32.6% of total cells and more than 75% of trophoblast cells), while STBs and EVTs also represent <10% of all cells and, respectively, 5.8% and 18.7% of the trophoblasts (Supplementary Table S6). Similar to the first trimester, it is unlikely that the cells identified as STB in scRNAseq were properly fused STB. Accordingly, we decided to name them fusing-CTB in this meta-analysis.
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Results of integration of raw data from term human placentas. (a) Representation of the process for the integration. (b) UMAP of the integration. Dotplots of the genes identified using the comparison of already annotated datasets, the curation of markers genes, and integration for trophoblasts (c) and non-trophoblasts (d). CTB: cytotrophoblast; EVT: extravillous trophoblast; UMAP: Uniform Manifold Approximation and Projection.</figcaption></figure>

## Cytotrophoblast

Altogether, 307 DEGs were identified (Supplementary Table S9) with 10 genes being identified by the three analyses (the integration, comparison of already annotated datasets, and curated marker genes): PAGE4, PHLDA2, IFI6, TINAGL1, SPINT1, FXYD3, MGST3, KRT18, PARP1, and SLC27A2. Furthermore, five genes (INSL4, EGFR, KRT7, GATA3, and CDH1) previously identified in the curated marker list were in the DEG list of the integrated analysis.

As for the first trimester analysis, most of the genes here were also expressed in fusing-CTB (Supplementary Table S8). However, SPINT1, FXYD3, MGST3, KRT18, and PARP1 met all the criteria (scaled expression in CTB >0.8, no other cell type with scaled expression >0.8, expression in at least 50% of CTB) and could be considered as specific markers of CTB.

Using immunohistochemistry, SPINT1 was increased throughout the pregnancy in CTBs with a clear specific staining in CTBs of term placentas (Mori et al., 2007). As for the first trimester
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Results of integration of raw data from term human placentas. (a) Representation of the process for the integration. (b) UMAP of the integration. Dotplots of the genes identified using the comparison of already annotated datasets, the curation of markers genes, and integration for trophoblasts (c) and non-trophoblasts (d). CTB: cytotrophoblast; EVT: extravillous trophoblast; UMAP: Uniform Manifold Approximation and Projection.</figcaption></figure>

## Cytotrophoblast

Altogether, 307 DEGs were identified (Supplementary Table S9) with 10 genes being identified by the three analyses (the integration, comparison of already annotated datasets, and curated marker genes): PAGE4, PHLDA2, IFI6, TINAGL1, SPINT1, FXYD3, MGST3, KRT18, PARP1, and SLC27A2. Furthermore, five genes (INSL4, EGFR, KRT7, GATA3, and CDH1) previously identified in the curated marker list were in the DEG list of the integrated analysis.

As for the first trimester analysis, most of the genes here were also expressed in fusing-CTB (Supplementary Table S8). However, SPINT1, FXYD3, MGST3, KRT18, and PARP1 met all the criteria (scaled expression in CTB >0.8, no other cell type with scaled expression >0.8, expression in at least 50% of CTB) and could be considered as specific markers of CTB.

Using immunohistochemistry, SPINT1 was increased throughout the pregnancy in CTBs with a clear specific staining in CTBs of term placentas (Mori et al., 2007). As for the first trimester
placentas, PARP1 was specifically expressed by CTB in healthy term placentas. However, it was also expressed in STBs in damaged regions in the placentas of pre-eclampsia or intra-uterine growth restriction (Longtine et al., 2012). The expression of KRT18 was detected in all trophoblasts differentiated from human KRT7+ induced placentas stem cells with bone morphogenetic protein 4 treatment (Tsuchida et al., 2020). No further information could be found to validate FXYD3 and MGST3 as marker of CTB in human term placentas. Taken together, only SPINT1 was confirmed as a specific marker of CTBs in term placentas.

## Fusing-CTB

For fusing-CTBs, 119 DEGs were identified (Supplementary Table S8) with six genes (CSH2, KISS1, PSG3, LGALS14, CGA, and CYP19A1) identified by the three analyses. Furthermore, 12 genes were common in the curated marker list and the integrated analysis: CSHL1, PSG1, PSG2, PSG5, PSG4, GH2, PSG11, PSG9, PSG6, LGALS13, HPGD, and SEMA3B. Most of these genes (CSHL1, PSG1, PSG2, PSG4, GH2, PSG11, PSG9, PSG6, LGALS13, CSH2, KISS1, PSG3, LGALS14, and CYP19A1) were identified as specific markers in term placentas.

CSHL1, CSH2, and GH2 exhibited almost 30, 40, and 10 times higher expression in STB, respectively, compared to those in the undifferentiated primary CTBs in healthy term placentas (Jiang et al., 2019). PSG1, PSG3, and PSG9 transcripts increased after differentiation into STBs from both JEG-3 cells and primary CTBs in term placentas (Camolotto et al., 2010). Furthermore, using one monoclonal antibody specific to PSG proteins, their specific expression in STBs was confirmed in both first trimester and term placentas using immunohistochemistry (Zhou et al., 1997). Therefore, all PSGs can probably be considered as good markers of fusing-CTB. Furthermore, hTSCs with MSX2 depletion led to elevated CSH2 and CYP19A1 expression owing to differentiation into STBs (Hornbachner et al., 2021). CYP19A1 was exclusively observed in STBs compared to the underlying CTBs and villous core using immunohistochemistry (Fournet-Dulguerov et al., 1987). In the first trimester placentas, KISS1 expression was specific in STBs using both ISH and immunohistochemistry (Bilban et al., 2004). However, more recent studies demonstrated that CTBs also expressed KISS1 (Park et al., 2011; Wu et al., 2014). Various methods (immunohistochemistry, immunofluorescence, and ISH) were used to demonstrate that galectin family genes (LGALS13, LGALS14, and LGALS16) were expressed in STBs but not in CTBs (Than et al., 2004, 2014; Huppertz et al., 2008). However, quantitative PCR (qPCR) assay indicated that EVTs also expressed LGALS13, although the expression was lower compared to STB (Ogushi et al., 2023).

Therefore, we concluded that CSHL1, PSG1, PSG2, PSG4, GH2, PSG11, PSG9, PSG6, CSH2, PSG3, LGALS14, and CYP19A1 can be used as specific markers of fusing-CTB in term placentas.

## Extravillous trophoblast

Altogether, 222 DEGs were identified in EVTs (Supplementary Table S9) with 31 genes observed by the three analyses: FN1, PRG2, NOTUM, AOC1, PAPPA2, TNFSF10, HPGD, FLT1, FSTL3, EBI3, KRT19, DIO2, HLA-G, HTRA1, TPM1, CST6, PLAC8, KRT7, COL17A1, HTRA4, TUBB2A, EGLN3, PTPRF, MMP11, MCAM, CORO6, ITGA5, EGFR-AS1, IL1RAP, ENG, and AIF1L. All these genes were highly expressed in EVT in term placentas but only KCNK12, DAPP1, IL1R2, PRG2, NOTUM, AOC1, PAPPA2, FSTL3, DIO2, HLA-G, CST6, PLAC8, COL17A1, HTRA4, TUBB2A, EGLN3, PTPRF, MMP11, CORO6, ITGA5, EGFR-AS1, IL1RAP, and AIF1L were specifically expressed in EVTs. Furthermore, five genes, i.e. TAC3, KCNK12, DAPP1, IL1R2, and PROCR, were also identified as curated markers from the integrated analysis.

KCNK12 and ITGA5 expression were higher in human primary placental EVTs than CTBs from the first and second trimester (Apps et al., 2011; Telugu et al., 2013). IL1R2 was demonstrated to be expressed by a small population of primary CTBs and HTR8/SVneo cells. However, once differentiated into STB or EVT, these cells no longer expressed IL1R2 (Takao et al., 2011). PRG2 was detected only in EVTs that invade venous and lymphatic vessels in first trimester placentas by immunofluorescence. In cell culture assays, PRG2 was observed only in differentiated EVTs and not in differentiated STBs from primary CTBs from first trimester placentas (Windsperger et al., 2017). Similarly, NOTUM was expressed by all trophoblast types in the second trimester placentas by immunohistochemistry (Robinson et al., 2017). AOC1, which encodes the D-amino acid oxidase protein, was demonstrated by immunofluorescence to be expressed only in invasive EVTs and was only secreted by primary EVTs from first trimester placentas compared to other trophoblasts (Velicky et al., 2018; Haider et al., 2022). PAPPA2 appeared to be controversial as its expression was reported in all trophoblasts (Windsperger et al., 2017) and only in invasive EVTs in both first and term placentas (Haider et al., 2022). FSTL3 was recently found in all trophoblast types, regardless of GA (Wu et al., 2023). DIO2 was expressed in CTBs, STBs, and CCCs by immunofluorescence (Adu-Gyamfi et al., 2021), but no information is available for term placentas. Expression of HLA-G in EVTs was detected with qPCR, immunohistochemistry, and western blot throughout pregnancy (Hackmon et al., 2017a). HLA-G was also the most used EVT signature gene in term placentas (Fig. 2 and Supplementary Table S4) and was expressed only in EVTs compared to other trophoblasts (McMaster et al., 1995; Hackmon et al., 2017b). However, CST6 was expressed in other trophoblast types, especially in differentiated STBs. PTPRF was decreased once primary CTBs were differentiated into STBs in healthy term placentas (Jiang et al., 2019). PLAC8 was detected in CCCs and EVTs throughout the pregnancy and specifically in EVTs in term placentas by immunofluorescence and ISH (Chang et al., 2018). Similarly, COL17A1 was expressed mainly by early ITGA2⁺ CCCs and in some EVTs (Lee et al., 2018). In contrast, HTRA4 expression increased during syngelialization of BeWo cells (Mansilla et al., 2021) and HTRA4 protein localized mainly in STB using immunohistochemistry in term placentas (Liu et al., 2018a). Also using immunochemistry, MMP11 was expressed in decidual stromal cells and EVTs in first trimester placentas (Anacker et al., 2011) but information on term placenta is missing.

No further information could be found to validate the use of DAPP1, TUBB2A, EGLN3, CORO6, EGFR-AS1, IL1RAP, and AIF1L as markers of EVT in human placenta. In conclusion, KCNK12, PRG2, NOTUM, AOC1, HLA-G, PLAC8, COL17A1, and ITGA5 could be reliable markers for EVT in term placentas.

## Non-trophoblast cells

A total of 434 DEGs were related to non-trophoblast cells, with only HLA-A previously used as a marker in term placentas. HLA-A met all the criteria and can, therefore, be considered as a specific marker for non-trophoblasts in term placentas.

As for the first trimester, stromal cells were the second largest cluster, accounting for 27.8% of total placental cells, and were the most abundant (49%) among the non-trophoblast cells followed by HCs (43.6%) (Supplementary Table S5).

## Stromal and perivascular cells

In total, 280 DEGs were identified (Supplementary Table S9 and Fig. 6) with 25 genes previously used as markers for stromal cells in term placentas: DCN, COL3A1, IGFBP3, COL1A1, HGF, CXCL14,
placentas, PARP1 was specifically expressed by CTB in healthy term placentas. However, it was also expressed in STBs in damaged regions in the placentas of pre-eclampsia or intra-uterine growth restriction (Longtine et al., 2012). The expression of KRT18 was detected in all trophoblasts differentiated from human KRT7+ induced placentas stem cells with bone morphogenetic protein 4 treatment (Tsuchida et al., 2020). No further information could be found to validate FXYD3 and MGST3 as marker of CTB in human term placentas. Taken together, only SPINT1 was confirmed as a specific marker of CTBs in term placentas.

## Fusing-CTB

For fusing-CTBs, 119 DEGs were identified (Supplementary Table S8) with six genes (CSH2, KISS1, PSG3, LGALS14, CGA, and CYP19A1) identified by the three analyses. Furthermore, 12 genes were common in the curated marker list and the integrated analysis: CSHL1, PSG1, PSG2, PSG5, PSG4, GH2, PSG11, PSG9, PSG6, LGALS13, HPGD, and SEMA3B. Most of these genes (CSHL1, PSG1, PSG2, PSG4, GH2, PSG11, PSG9, PSG6, LGALS13, CSH2, KISS1, PSG3, LGALS14, and CYP19A1) were identified as specific markers in term placentas.

CSHL1, CSH2, and GH2 exhibited almost 30, 40, and 10 times higher expression in STB, respectively, compared to those in the undifferentiated primary CTBs in healthy term placentas (Jiang et al., 2019). PSG1, PSG3, and PSG9 transcripts increased after differentiation into STBs from both JEG-3 cells and primary CTBs in term placentas (Camolotto et al., 2010). Furthermore, using one monoclonal antibody specific to PSG proteins, their specific expression in STBs was confirmed in both first trimester and term placentas using immunohistochemistry (Zhou et al., 1997). Therefore, all PSGs can probably be considered as good markers of fusing-CTB. Furthermore, hTSCs with MSX2 depletion led to elevated CSH2 and CYP19A1 expression owing to differentiation into STBs (Hornbachner et al., 2021). CYP19A1 was exclusively observed in STBs compared to the underlying CTBs and villous core using immunohistochemistry (Fournet-Dulguerov et al., 1987). In the first trimester placentas, KISS1 expression was specific in STBs using both ISH and immunohistochemistry (Bilban et al., 2004). However, more recent studies demonstrated that CTBs also expressed KISS1 (Park et al., 2011; Wu et al., 2014). Various methods (immunohistochemistry, immunofluorescence, and ISH) were used to demonstrate that galectin family genes (LGALS13, LGALS14, and LGALS16) were expressed in STBs but not in CTBs (Than et al., 2004, 2014; Huppertz et al., 2008). However, quantitative PCR (qPCR) assay indicated that EVTs also expressed LGALS13, although the expression was lower compared to STB (Ogushi et al., 2023).

Therefore, we concluded that CSHL1, PSG1, PSG2, PSG4, GH2, PSG11, PSG9, PSG6, CSH2, PSG3, LGALS14, and CYP19A1 can be used as specific markers of fusing-CTB in term placentas.

## Extravillous trophoblast

Altogether, 222 DEGs were identified in EVTs (Supplementary Table S9) with 31 genes observed by the three analyses: FN1, PRG2, NOTUM, AOC1, PAPPA2, TNFSF10, HPGD, FLT1, FSTL3, EBI3, KRT19, DIO2, HLA-G, HTRA1, TPM1, CST6, PLAC8, KRT7, COL17A1, HTRA4, TUBB2A, EGLN3, PTPRF, MMP11, MCAM, CORO6, ITGA5, EGFR-AS1, IL1RAP, ENG, and AIF1L. All these genes were highly expressed in EVT in term placentas but only KCNK12, DAPP1, IL1R2, PRG2, NOTUM, AOC1, PAPPA2, FSTL3, DIO2, HLA-G, CST6, PLAC8, COL17A1, HTRA4, TUBB2A, EGLN3, PTPRF, MMP11, CORO6, ITGA5, EGFR-AS1, IL1RAP, and AIF1L were specifically expressed in EVTs. Furthermore, five genes, i.e. TAC3, KCNK12, DAPP1, IL1R2, and PROCR, were also identified as curated markers from the integrated analysis.

KCNK12 and ITGA5 expression were higher in human primary placental EVTs than CTBs from the first and second trimester (Apps et al., 2011; Telugu et al., 2013). IL1R2 was demonstrated to be expressed by a small population of primary CTBs and HTR8/SVneo cells. However, once differentiated into STB or EVT, these cells no longer expressed IL1R2 (Takao et al., 2011). PRG2 was detected only in EVTs that invade venous and lymphatic vessels in first trimester placentas by immunofluorescence. In cell culture assays, PRG2 was observed only in differentiated EVTs and not in differentiated STBs from primary CTBs from first trimester placentas (Windsperger et al., 2017). Similarly, NOTUM was expressed by all trophoblast types in the second trimester placentas by immunohistochemistry (Robinson et al., 2017). AOC1, which encodes the D-amino acid oxidase protein, was demonstrated by immunofluorescence to be expressed only in invasive EVTs and was only secreted by primary EVTs from first trimester placentas compared to other trophoblasts (Velicky et al., 2018; Haider et al., 2022). PAPPA2 appeared to be controversial as its expression was reported in all trophoblasts (Windsperger et al., 2017) and only in invasive EVTs in both first and term placentas (Haider et al., 2022). FSTL3 was recently found in all trophoblast types, regardless of GA (Wu et al., 2023). DIO2 was expressed in CTBs, STBs, and CCCs by immunofluorescence (Adu-Gyamfi et al., 2021), but no information is available for term placentas. Expression of HLA-G in EVTs was detected with qPCR, immunohistochemistry, and western blot throughout pregnancy (Hackmon et al., 2017a). HLA-G was also the most used EVT signature gene in term placentas (Fig. 2 and Supplementary Table S4) and was expressed only in EVTs compared to other trophoblasts (McMaster et al., 1995; Hackmon et al., 2017b). However, CST6 was expressed in other trophoblast types, especially in differentiated STBs. PTPRF was decreased once primary CTBs were differentiated into STBs in healthy term placentas (Jiang et al., 2019). PLAC8 was detected in CCCs and EVTs throughout the pregnancy and specifically in EVTs in term placentas by immunofluorescence and ISH (Chang et al., 2018). Similarly, COL17A1 was expressed mainly by early ITGA2⁺ CCCs and in some EVTs (Lee et al., 2018). In contrast, HTRA4 expression increased during syntecytalization of BeWo cells (Mansilla et al., 2021) and HTRA4 protein localized mainly in STB using immunohistochemistry in term placentas (Liu et al., 2018a). Also using immunochemistry, MMP11 was expressed in decidual stromal cells and EVTs in first trimester placentas (Anacker et al., 2011) but information on term placenta is missing.

No further information could be found to validate the use of DAPP1, TUBB2A, EGLN3, CORO6, EGFR-AS1, IL1RAP, and AIF1L as markers of EVT in human placenta. In conclusion, KCNK12, PRG2, NOTUM, AOC1, HLA-G, PLAC8, COL17A1, and ITGA5 could be reliable markers for EVT in term placentas.

## Non-trophoblast cells

A total of 434 DEGs were related to non-trophoblast cells, with only HLA-A previously used as a marker in term placentas. HLA-A met all the criteria and can, therefore, be considered as a specific marker for non-trophoblasts in term placentas.

As for the first trimester, stromal cells were the second largest cluster, accounting for 27.8% of total placental cells, and were the most abundant (49%) among the non-trophoblast cells followed by HCs (43.6%) (Supplementary Table S5).

## Stromal and perivascular cells

In total, 280 DEGs were identified (Supplementary Table S9 and Fig. 6) with 25 genes previously used as markers for stromal cells in term placentas: DCN, COL3A1, IGFBP3, COL1A1, HGF, CXCL14,
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Specific markers of human first trimester and term placental villous cells and their evolution across pregnancy. (a) Representation of human placenta from first trimester with specific markers identified in this review. (b) Representation of human placenta at term with specific markers identified in this review. (c) Dotplot of specific markers that particularly highlight the maturation of extravillous trophoblast. (d) Dotplot of specific markers that particularly highlight the maturation of syncytiotrophoblasts. CTB: cytotrophoblast.</figcaption></figure>

APOD, COL6A2, DLK1, VCAN, SPARC, CALD1, MYL9, SERPINF1, TGFBI, TXNIP, SOD3, TAGLN, IFITM3, LGALS1, ECM1, NNMT, ACTA2, FMOD, and PDGFRB (Fig. 2 and Supplementary Table S4). Like the first trimester placentas, most of these genes were also expressed in perivascular cells. Only PDGFRB, CXCL14, and APOD were specific to stromal cells and, therefore, met all criteria to be specific markers of stromal cells in term placentas.

In perivascular cells, 146 DEGs were identified (Supplementary Table S9 and Fig. 6) with MYH11, NDUFA4L2, ACTA2, and TAGLN being previously used. Here, all of them were specific markers of perivascular cells (Supplementary Table S8) and were, therefore, reliable markers to define them.

## Endothelial cells

Altogether, 227 DEGs were identified in endothelial cells (Supplementary Table S9 and Fig. 6) with six genes previously used as markers in term placentas: PECAM1, IFI27, CDH5, CD34, VWF, and PLVAP (Supplementary Fig. S2 and Supplementary Table S4). Except for PECAM1 and PLVAP, all these genes met the criteria to be specific markers (Supplementary Table S9 and Fig. 6).

## Hofbauer cells

Altogether, 342 DEGs were identified (Supplementary Table S9, Fig. 6) with only 17 genes previously used as markers for HCs in term placentas: C1QB, LYVE1, RNASE1, CD14, PLTP, LGMN, FOLR2, CD163, AIF1, CSF1R, MAF, CD68, NPC2, CD53, CD209, PCDH12, and OLFML2B (Supplementary Fig. S2, Supplementary Table S4). All

these genes were highly expressed but only C1QB, LYVE1, CD14, FOLR2, CD163, AIF1, CD68, CD53, CD209, PCDH12, and OLFML2B were not expressed in other cell types and were thus specific markers of HCs (Supplementary Table S9, Fig. 6).

## Dynamics of marker gene expression across gestation

Our meta-analysis provided a list of specific marker genes for each placental cell type in the first trimester and at term (Fig. 7). Many genes in several cell types, however, did not overlap between the two GA, preventing them to be used as markers for both (Supplementary Table S10).

None of the markers of all trophoblasts were usable in term placentas and only HLA-A for non-trophoblasts could be used regardless of the GA (Supplementary Table S10 and Fig. 7). No CTB marker and only CYP19A1 for STB could be used at both timepoints, while for EVT, ABCA7, ASCL2, DIO2, EGLN3, FSTL3, HLA-G, HTRA4, IL1RAP, ITGA5, LAIR2, NOTUM, PLAC8, and PTPRF were specifically expressed at both timepoints (Supplementary Table S10 and Fig. 7). In non-trophoblasts, CXCL14 and PDGFRA for stroma and NDUFA4L2 for perivascular cells were specifically expressed in both timepoints. Endothelial markers CD34 and CDH5 and most of the HC markers (AIF1, C1QB, CCL4, CD14, CD163, CD53, CD68, CXCL8, and FOLR2) were also specific at both timepoints. These results highlight the dynamic gene expression across gestation.

Interestingly, in EVT most of the genes (AOC1, COL17A1, CORO6, CST6, DAPP1, EGFR-AS1, IL1R2, KCNK12, MMP11, PAPPA2,
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Specific markers of human first trimester and term placental villous cells and their evolution across pregnancy. (a) Representation of human placenta from first trimester with specific markers identified in this review. (b) Representation of human placenta at term with specific markers identified in this review. (c) Dotplot of specific markers that particularly highlight the maturation of extravillous trophoblast. (d) Dotplot of specific markers that particularly highlight the maturation of syncytiotrophoblasts. CTB: cytotrophoblast.</figcaption></figure>

APOD, COL6A2, DLK1, VCAN, SPARC, CALD1, MYL9, SERPINF1, TGFBI, TXNIP, SOD3, TAGLN, IFITM3, LGALS1, ECM1, NNMT, ACTA2, FMOD, and PDGFRB (Fig. 2 and Supplementary Table S4). Like the first trimester placentas, most of these genes were also expressed in perivascular cells. Only PDGFRB, CXCL14, and APOD were specific to stromal cells and, therefore, met all criteria to be specific markers of stromal cells in term placentas.

In perivascular cells, 146 DEGs were identified (Supplementary Table S9 and Fig. 6) with MYH11, NDUFA4L2, ACTA2, and TAGLN being previously used. Here, all of them were specific markers of perivascular cells (Supplementary Table S8) and were, therefore, reliable markers to define them.

## Endothelial cells

Altogether, 227 DEGs were identified in endothelial cells (Supplementary Table S9 and Fig. 6) with six genes previously used as markers in term placentas: PECAM1, IFI27, CDH5, CD34, VWF, and PLVAP (Supplementary Fig. S2 and Supplementary Table S4). Except for PECAM1 and PLVAP, all these genes met the criteria to be specific markers (Supplementary Table S9 and Fig. 6).

## Hofbauer cells

Altogether, 342 DEGs were identified (Supplementary Table S9, Fig. 6) with only 17 genes previously used as markers for HCs in term placentas: C1QB, LYVE1, RNASE1, CD14, PLTP, LGMN, FOLR2, CD163, AIF1, CSF1R, MAF, CD68, NPC2, CD53, CD209, PCDH12, and OLFML2B (Supplementary Fig. S2, Supplementary Table S4). All

these genes were highly expressed but only C1QB, LYVE1, CD14, FOLR2, CD163, AIF1, CD68, CD53, CD209, PCDH12, and OLFML2B were not expressed in other cell types and were thus specific markers of HCs (Supplementary Table S9, Fig. 6).

## Dynamics of marker gene expression across gestation

Our meta-analysis provided a list of specific marker genes for each placental cell type in the first trimester and at term (Fig. 7). Many genes in several cell types, however, did not overlap between the two GA, preventing them to be used as markers for both (Supplementary Table S10).

None of the markers of all trophoblasts were usable in term placentas and only HLA-A for non-trophoblasts could be used regardless of the GA (Supplementary Table S10 and Fig. 7). No CTB marker and only CYP19A1 for STB could be used at both timepoints, while for EVT, ABCA7, ASCL2, DIO2, EGLN3, FSTL3, HLA-G, HTRA4, IL1RAP, ITGA5, LAIR2, NOTUM, PLAC8, and PTPRF were specifically expressed at both timepoints (Supplementary Table S10 and Fig. 7). In non-trophoblasts, CXCL14 and PDGFRA for stroma and NDUFA4L2 for perivascular cells were specifically expressed in both timepoints. Endothelial markers CD34 and CDH5 and most of the HC markers (AIF1, C1QB, CCL4, CD14, CD163, CD53, CD68, CXCL8, and FOLR2) were also specific at both timepoints. These results highlight the dynamic gene expression across gestation.

Interestingly, in EVT most of the genes (AOC1, COL17A1, CORO6, CST6, DAPP1, EGFR-AS1, IL1R2, KCNK12, MMP11, PAPPA2,
PRG2, and TUBB2A) with a different expression profile between first trimester and term presented a low expression and were expressed by <50% of the EVTs in first trimester placentas (Fig. 7). Moreover, ACAN, MGST1, and SLCO4A1 presented the opposite profile with low expression and by <50% of the EVT in term placentas. These genes could therefore represent markers of EVT maturation across gestation.

Similarly, CSH2, CSHL1, GH2, KISS1, LGALS13, LGALS14, PSG1, PSG11, PSG2, PSG3, PSG4, PSG6, and PSG9 showed low expression and were expressed in <50% of fusing-CTBs in the first trimester placentas, demonstrating the progression to a secretory phenotype in term placentas. Furthermore, fusing-CTB in term placentas did not express ERVFRD-1, ERVV-1, GREM2, and LGALS16 but around 15% of CTB did. These results suggested that fusing-CTB defined in the first trimester clustered with CTB in term placentas, and that more mature STBs were produced in term placentas by scRNAseq. Despite the more mature phenotype, owing to the dissociation method and the placental structure, it is unlikely that these cells in term placentas are completely fused STB, and more research is needed to confirm this.

Furthermore, CSH1 was shown to be a specific marker of EVT in the first trimester data (Fig. 5). However, even if more than 50% of EVT still expressed this marker at term, the expression was low (Fig. 7). On the contrary, all the fusing-CTB in term placentas expressed this gene at a very high level indicating that placental lactogen is mainly produced by EVT in the first trimester but by STB at term (Fig. 7). These comparisons demonstrated that trophoblast cells may particularly evolve throughout gestation, highlighting the importance of adapting the specific markers to GA when annotating cell types in a scRNAseq study.

# Discussion

## Contribution of single-cell RNAseq of the maternal-fetal interface to pathologies

To date, placentas from women with recurrent pregnancy loss (RPL), pre-term birth, advanced maternal age, pre-eclampsia, gestational diabetes mellitus (GDM), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have all been compared to healthy placentas using scRNAseq (Table 1).

### Recurrent pregnancy loss

RPL has been studied with scRNAseq analysis primarily using decidual tissue (Saha et al., 2020; Chen et al., 2021; Du et al., 2021; Guo et al., 2021; Wang et al., 2021). The first study focused on the expression of the TEAD4 pathway in progenitor CTBs in two normal 6- to 8-week placentas and showed a strong reduction of TEAD4 expression in villous CTBs and CCCs in RPL placentas, confirming the essential role of TEAD4 in the establishment of pregnancy and its likely involvement in RPL (Saha et al., 2020). Interestingly, all studies that focused on decidual immune cells have demonstrated that a subset of natural killer cells supporting fetal growth is dramatically diminished, while one other subset with a cytotoxic and immune-active signature is increased in RPL (Chen et al., 2021; Guo et al., 2021; Wang et al., 2021), showing an alteration of the placental immune microenvironment in RPL, which provides clues for future treatments. The only study considering all cell types in the decidua observed aberrant decidualization with alteration of decidual cell subtype proportions, and obstructed communication of decidual cells with other cells, possibly linked to the observed over-activation of the tumor necrosis factor superfamily of genes (Du et al., 2021).

### Gestational age

Only one study has investigated the effect of GA on placenta and compared preterm (33-35 weeks of gestation) and term placentas (38-40 weeks of gestation) collected with and without labor (n = 3/group). The greatest numbers of DEGs were found in CTBs, EVTs, and maternal macrophages. Furthermore, bulk RNA sequencing of maternal blood at different gestational stages enabled the identification of non-invasive markers for monitoring preterm labor risk (Pique-Regi et al., 2019).

### Maternal aging

Analysis of three term placentas collected from women >35 years of age versus those from younger mothers showed no changes in cell type distribution. However, EVT function, especially in one subtype playing a role in cell invasion, was affected by maternal age. Extracellular matrix receptor interaction and cell invasion-related genes were also altered in placentas from older women (Zhang et al., 2022). These results demonstrated that maternal age affects the function of specific placental cell types in an apparently normal pregnancy. These effects could be similar, but to a lesser extent, in the placentas involved in adverse pregnancy outcomes owing to maternal aging.

### Pre-eclampsia

Most studies of placental pathologies have focused on pre-eclampsia (defined as hypertension after 20 weeks of gestation only (Zhou et al., 2022) or associated with high proteinuria as well (Tsang et al., 2017; Zhang et al., 2021a)) that resulted in preterm C-section deliveries. Tsang et al. (2017) showed that there is an up-regulation of genes involved in cell death in EVTs in placentas from pre-eclamptic compared to normal pregnancies, implying that EVTs are more affected than other trophoblast types. Moreover, the authors also correlated EVT dysfunction with an increased release of cell-free RNA by the EVTs in maternal blood, providing a potential non-invasive approach for early detection of pre-eclampsia. Through the integrated analysis of Tsang et al. and Pique-Regi et al. data, one study showed that EVTs were overrepresented while stromal cell types were less abundant (Campbell et al., 2023) and overexpression of FLT1, LEP, and ENG in bulk RNAseq analysis of pre-eclamptic placentas was mainly linked to differences in cell composition (Campbell et al., 2023). On the other hand, Zhang et al. identified up-regulation of endoplasmic reticulum-related genes in STBs of pre-eclampsia placentas (Zhang et al., 2021a). Moreover, the expression of genes involved in immune function was enriched in both in vCTBs and EVTs from pre-eclamptic placentas, suggesting altered placental immune function. Interestingly, Zhou et al. showed alterations in gene profiles related to immunity, similar to Zhang et al. (2021a) and Tsang et al. (2017), apoptosis in EVT was also altered in pre-eclamptic samples: CEBPB and GTF2B, regulating apoptosis and cell invasion respectively, were novel transcription factors identified with reduced activity in pre-eclamptic samples (Zhou et al., 2022). In addition to this, they also observed an enrichment in hormone secretion function in EVTs (Zhou et al., 2022).

### Gestational diabetes mellitus

The comparison of two placentas from healthy women and two from women with GDM after cesarean delivery highlighted 136 genes up-regulated in GDM trophoblast cells related to estrogen signaling as well as antigen processing and presentation, while the down-regulated genes were mostly related to the inflammatory IL-17 pathway (Yang et al., 2021). In granulocytes and myelocytes, genes involved in the inflammatory response
PRG2, and TUBB2A) with a different expression profile between first trimester and term presented a low expression and were expressed by <50% of the EVTs in first trimester placentas (Fig. 7). Moreover, ACAN, MGST1, and SLCO4A1 presented the opposite profile with low expression and by <50% of the EVT in term placentas. These genes could therefore represent markers of EVT maturation across gestation.

Similarly, CSH2, CSHL1, GH2, KISS1, LGALS13, LGALS14, PSG1, PSG11, PSG2, PSG3, PSG4, PSG6, and PSG9 showed low expression and were expressed in <50% of fusing-CTBs in the first trimester placentas, demonstrating the progression to a secretory phenotype in term placentas. Furthermore, fusing-CTB in term placentas did not express ERVFRD-1, ERVV-1, GREM2, and LGALS16 but around 15% of CTB did. These results suggested that fusing-CTB defined in the first trimester clustered with CTB in term placentas, and that more mature STBs were produced in term placentas by scRNAseq. Despite the more mature phenotype, owing to the dissociation method and the placental structure, it is unlikely that these cells in term placentas are completely fused STB, and more research is needed to confirm this.

Furthermore, CSH1 was shown to be a specific marker of EVT in the first trimester data (Fig. 5). However, even if more than 50% of EVT still expressed this marker at term, the expression was low (Fig. 7). On the contrary, all the fusing-CTB in term placentas expressed this gene at a very high level indicating that placental lactogen is mainly produced by EVT in the first trimester but by STB at term (Fig. 7). These comparisons demonstrated that trophoblast cells may particularly evolve throughout gestation, highlighting the importance of adapting the specific markers to GA when annotating cell types in a scRNAseq study.

# Discussion

## Contribution of single-cell RNAseq of the maternal-fetal interface to pathologies

To date, placentas from women with recurrent pregnancy loss (RPL), pre-term birth, advanced maternal age, pre-eclampsia, gestational diabetes mellitus (GDM), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have all been compared to healthy placentas using scRNAseq (Table 1).

### Recurrent pregnancy loss

RPL has been studied with scRNAseq analysis primarily using decidual tissue (Saha et al., 2020; Chen et al., 2021; Du et al., 2021; Guo et al., 2021; Wang et al., 2021). The first study focused on the expression of the TEAD4 pathway in progenitor CTBs in two normal 6- to 8-week placentas and showed a strong reduction of TEAD4 expression in villous CTBs and CCCs in RPL placentas, confirming the essential role of TEAD4 in the establishment of pregnancy and its likely involvement in RPL (Saha et al., 2020). Interestingly, all studies that focused on decidual immune cells have demonstrated that a subset of natural killer cells supporting fetal growth is dramatically diminished, while one other subset with a cytotoxic and immune-active signature is increased in RPL (Chen et al., 2021; Guo et al., 2021; Wang et al., 2021), showing an alteration of the placental immune microenvironment in RPL, which provides clues for future treatments. The only study considering all cell types in the decidua observed aberrant decidualization with alteration of decidual cell subtype proportions, and obstructed communication of decidual cells with other cells, possibly linked to the observed over-activation of the tumor necrosis factor superfamily of genes (Du et al., 2021).

### Gestational age

Only one study has investigated the effect of GA on placenta and compared preterm (33-35 weeks of gestation) and term placentas (38-40 weeks of gestation) collected with and without labor (n = 3/group). The greatest numbers of DEGs were found in CTBs, EVTs, and maternal macrophages. Furthermore, bulk RNA sequencing of maternal blood at different gestational stages enabled the identification of non-invasive markers for monitoring preterm labor risk (Pique-Regi et al., 2019).

### Maternal aging

Analysis of three term placentas collected from women >35 years of age versus those from younger mothers showed no changes in cell type distribution. However, EVT function, especially in one subtype playing a role in cell invasion, was affected by maternal age. Extracellular matrix receptor interaction and cell invasion-related genes were also altered in placentas from older women (Zhang et al., 2022). These results demonstrated that maternal age affects the function of specific placental cell types in an apparently normal pregnancy. These effects could be similar, but to a lesser extent, in the placentas involved in adverse pregnancy outcomes owing to maternal aging.

### Pre-eclampsia

Most studies of placental pathologies have focused on pre-eclampsia (defined as hypertension after 20 weeks of gestation only (Zhou et al., 2022) or associated with high proteinuria as well (Tsang et al., 2017; Zhang et al., 2021a)) that resulted in preterm C-section deliveries. Tsang et al. (2017) showed that there is an up-regulation of genes involved in cell death in EVTs in placentas from pre-eclamptic compared to normal pregnancies, implying that EVTs are more affected than other trophoblast types. Moreover, the authors also correlated EVT dysfunction with an increased release of cell-free RNA by the EVTs in maternal blood, providing a potential non-invasive approach for early detection of pre-eclampsia. Through the integrated analysis of Tsang et al. and Pique-Regi et al. data, one study showed that EVTs were overrepresented while stromal cell types were less abundant (Campbell et al., 2023) and overexpression of FLT1, LEP, and ENG in bulk RNAseq analysis of pre-eclamptic placentas was mainly linked to differences in cell composition (Campbell et al., 2023). On the other hand, Zhang et al. identified up-regulation of endoplasmic reticulum-related genes in STBs of pre-eclampsia placentas (Zhang et al., 2021a). Moreover, the expression of genes involved in immune function was enriched in both in vCTBs and EVTs from pre-eclamptic placentas, suggesting altered placental immune function. Interestingly, Zhou et al. showed alterations in gene profiles related to immunity, similar to Zhang et al. (2021a) and Tsang et al. (2017), apoptosis in EVT was also altered in pre-eclamptic samples: CEBPB and GTF2B, regulating apoptosis and cell invasion respectively, were novel transcription factors identified with reduced activity in pre-eclamptic samples (Zhou et al., 2022). In addition to this, they also observed an enrichment in hormone secretion function in EVTs (Zhou et al., 2022).

### Gestational diabetes mellitus

The comparison of two placentas from healthy women and two from women with GDM after cesarean delivery highlighted 136 genes up-regulated in GDM trophoblast cells related to estrogen signaling as well as antigen processing and presentation, while the down-regulated genes were mostly related to the inflammatory IL-17 pathway (Yang et al., 2021). In granulocytes and myelocytes, genes involved in the inflammatory response
were up-regulated and those involved in actin binding were
down-regulated. Of particular interest, an increase in natural
killer cells and anti-inflammatory polarized macrophages was
observed in GDM placentas compared to controls. Altogether,
these results suggest that the most important transcriptomic
changes in GDM are related to immune system regulation in the
placental microenvironment (Yang et al., 2021).

Infectious diseases

Four studies applied scRNAseq to analyze infectious diseases. The first study analyzed placentas from non-infected women at three different GAs with both scRNAseq and snRNAseq (Pique-Regi et al., 2020). They demonstrated that ACE2 and TMPRSS2 (canonical cell entry mediators for SARS-CoV-2) had a negligible co-expression in the different placental cells, indicating that this is unlikely to be the entrance pathway of SARS-CoV2 in case of vertical transmission. Further investigations by the same group and others confirmed that SARS-CoV2 infection during pregnancy mainly induced unique inflammatory responses at the maternal-fetal interface (Lu-Culligan et al., 2021; Chen et al., 2022; Garcia-Flores et al., 2022) and that SARS-CoV2 was not detected in placental tissues (Garcia-Flores et al., 2022). However, receptors for Zika and cytomegalovirus, both responsible for congenital malformations, were shown to be expressed by decidual and stromal cells of the placenta, validating their effects at the feto-maternal interface (Pique-Regi et al., 2020).

**Limitation of the meta-analyses**

One limitation of both meta-analyses is the number of studies
that were included as most studies have not deposited annotated
or raw datasets. However, the analytical pipeline provided here
can be easily applied in future when more datasets will be avail-
able. To help with the annotation of most placental cell types, we
provide a list of specific markers, which can be used in further
annotation of scRNAseq studies. However, it remains to be deter-
mined if these markers will work equally well for snRNAseq data.
Furthermore, integrated analysis yielded several hundred DEGs
for each cell type. Genes were further selected if they overlapped
with our annotated datasets and/or the curated markers.
Therefore, more genes could probably be identified as specific
markers. Particularly, the validation of markers for fusing-CTB in
the literature was challenging as this cell type is transitory. Here,
we referred to published STB markers to validate their use as
markers and more work is expected to confirm their utility.

Limitations and perspectives of single-cell
RNAseq use in placenta research

Overall, usage of scRNAseq for the analysis of placental tran-
scriptomes in various pathologies has led to new insights and un-
derstanding of placental diseases. Furthermore, the use of single-
cell analysis in trophoblast organoids has yielded novel informa-
tion regarding trophoblast differentiation (Shannon et al., 2022;
Arutyunyan et al., 2023).

As demonstrated in this review, however, the characterization
of cell types in the human placenta is not straightforward and
has not been performed optimally in some published scRNAseq
studies, which can limit the impact of the results. Indeed, mature
STBs were likely missed using scRNAseq and precursors of STBs
at the initial step of the cell fusion (fusing-CTB) were captured,
limiting the annotation of this placental-specific cell type.
Furthermore, cell types from the inner core of the villi, which are
particularly rich in stromal cells, that may have important impli-
cations for disease pathogenesis were understudied.

Owing to the high cost of scRNAseq studies, most investiga-
tions of placental gene expression rely on a limited number of
individuals (typically three/group), which poses challenges for ro-
bust statistical analysis. This limitation is compounded by the
difficulty of obtaining fresh, high-quality cells for analysis. As a
result, studies are often unable to consider covariables for more
detailed analysis, which could provide valuable insights into the
identification of cell types and their potential association with
adverse pregnancy outcomes.

To overcome these issues, snRNAseq can be applied, as well
as the recently introduced scRNAseq analysis of formalin-fixed
paraffin-embedded samples by 10× Genomics, both simplifying
the experimental planning and logistics. Unlike scRNAseq,
snRNAseq can be performed on frozen tissue, which vastly sim-
plifies experimental planning and offers the additional advan-
tage of increasing recovery of STBs and decidual GCs. In addition,
placental RNA integrity is rapidly and easily affected by sampling
and storage methods (Fajardy et al., 2009; Huang et al., 2013;
Reiman et al., 2017). As high-quality input of single cells (or nu-
clei) is a key variable for successful scRNAseq analyses, correct
handling and sampling is crucial. Notably, in a recent study ana-
lyzing post-mortem brain snRNAseq, RNA integrity was only
weakly correlated with the final number of recovered cells, and
had little effect on the number of genes or UMI detected (Maitra
et al., 2021), suggesting that for samples with suboptimal quality
and RNA integrity, the main consequence may be simply that
more cells fail quality control, leading to less cells recovered
overall rather than the preferential loss of specific cell types.
Importantly, this assumes that the RNA degradation is evenly
distributed across all cell types.

Conclusions

In this review, we have highlighted how scRNAseq has enabled
the identification of previously unknown cell subtypes in the hu-
man placenta and provided insight into the molecular mecha-
nisms underlying the differentiation of placental cell types.
Despite these advances, there is still a lack of consensus regard-
ing cell type annotations and the genes used to identify them.
This also suggests the fallibility of the clustering algorithms for
developing tissues when comparing to fully developed tissues, as
cells do not have a fixed but a continuous signature throughout
development. To assist future studies, here we have outlined spe-
cific marker genes using different meta-analyses and the avail-
able literature for both first trimester and term placentas (Fig. 7).
While application of the single-cell technique to study placentas
has traditionally been affected by sampling and lack of consider-
ation for potential confounding factors, such as fetal sex, the use
of scRNAseq, snRNAseq, or spatial transcriptomics holds strong
promise for enhancing our understanding of human placentas
and pregnancy-related disorders in future studies.

**Supplementary data**

Supplementary data are available at Human Reproduction
Update online.

Data availability

The datasets were derived from sources in the public domain
[Gene Expression Omnibus (GEO): GSE89497, GSE174481,
GSE173193; BioProject ID: PRJNA492324; ArrayExpress: E-MTAB-
6701; dbGaP: phs001886.v1.p1]. The data underlying this article
were up-regulated and those involved in actin binding were
down-regulated. Of particular interest, an increase in natural
killer cells and anti-inflammatory polarized macrophages was
observed in GDM placentas compared to controls. Altogether,
these results suggest that the most important transcriptomic
changes in GDM are related to immune system regulation in the
placental microenvironment (Yang et al., 2021).

Infectious diseases

Four studies applied scRNAseq to analyze infectious diseases. The first study analyzed placentas from non-infected women at three different GAs with both scRNAseq and snRNAseq (Pique-Regi et al., 2020). They demonstrated that ACE2 and TMPRSS2 (canonical cell entry mediators for SARS-CoV-2) had a negligible co-expression in the different placental cells, indicating that this is unlikely to be the entrance pathway of SARS-CoV2 in case of vertical transmission. Further investigations by the same group and others confirmed that SARS-CoV2 infection during pregnancy mainly induced unique inflammatory responses at the maternal-fetal interface (Lu-Culligan et al., 2021; Chen et al., 2022; Garcia-Flores et al., 2022) and that SARS-CoV2 was not detected in placental tissues (Garcia-Flores et al., 2022). However, receptors for Zika and cytomegalovirus, both responsible for congenital malformations, were shown to be expressed by decidual and stromal cells of the placenta, validating their effects at the feto-maternal interface (Pique-Regi et al., 2020).

Limitation of the meta-analyses

One limitation of both meta-analyses is the number of studies
that were included as most studies have not deposited annotated
or raw datasets. However, the analytical pipeline provided here
can be easily applied in future when more datasets will be avail-
able. To help with the annotation of most placental cell types, we
provide a list of specific markers, which can be used in further
annotation of scRNAseq studies. However, it remains to be deter-
mined if these markers will work equally well for snRNAseq data.
Furthermore, integrated analysis yielded several hundred DEGs
for each cell type. Genes were further selected if they overlapped
with our annotated datasets and/or the curated markers.
Therefore, more genes could probably be identified as specific
markers. Particularly, the validation of markers for fusing-CTB in
the literature was challenging as this cell type is transitory. Here,
we referred to published STB markers to validate their use as
markers and more work is expected to confirm their utility.

Limitations and perspectives of single-cell
RNAseq use in placenta research

Overall, usage of scRNAseq for the analysis of placental tran-
scriptomes in various pathologies has led to new insights and un-
derstanding of placental diseases. Furthermore, the use of single-
cell analysis in trophoblast organoids has yielded novel informa-
tion regarding trophoblast differentiation (Shannon et al., 2022;
Arutyunyan et al., 2023).

As demonstrated in this review, however, the characterization
of cell types in the human placenta is not straightforward and
has not been performed optimally in some published scRNAseq
studies, which can limit the impact of the results. Indeed, mature
STBs were likely missed using scRNAseq and precursors of STBs
at the initial step of the cell fusion (fusing-CTB) were captured,
limiting the annotation of this placental-specific cell type.
Furthermore, cell types from the inner core of the villi, which are
particularly rich in stromal cells, that may have important impli-
cations for disease pathogenesis were understudied.

Owing to the high cost of scRNAseq studies, most investiga-
tions of placental gene expression rely on a limited number of
individuals (typically three/group), which poses challenges for ro-
bust statistical analysis. This limitation is compounded by the
difficulty of obtaining fresh, high-quality cells for analysis. As a
result, studies are often unable to consider covariables for more
detailed analysis, which could provide valuable insights into the
identification of cell types and their potential association with
adverse pregnancy outcomes.

To overcome these issues, snRNAseq can be applied, as well
as the recently introduced scRNAseq analysis of formalin-fixed
paraffin-embedded samples by 10× Genomics, both simplifying
the experimental planning and logistics. Unlike scRNAseq,
snRNAseq can be performed on frozen tissue, which vastly sim-
plifies experimental planning and offers the additional advan-
tage of increasing recovery of STBs and decidual GCs. In addition,
placental RNA integrity is rapidly and easily affected by sampling
and storage methods (Fajardy et al., 2009; Huang et al., 2013;
Reiman et al., 2017). As high-quality input of single cells (or nu-
clei) is a key variable for successful scRNAseq analyses, correct
handling and sampling is crucial. Notably, in a recent study ana-
lyzing post-mortem brain snRNAseq, RNA integrity was only
weakly correlated with the final number of recovered cells, and
had little effect on the number of genes or UMI detected (Maitra
et al., 2021), suggesting that for samples with suboptimal quality
and RNA integrity, the main consequence may be simply that
more cells fail quality control, leading to less cells recovered
overall rather than the preferential loss of specific cell types.
Importantly, this assumes that the RNA degradation is evenly
distributed across all cell types.

Conclusions

In this review, we have highlighted how scRNAseq has enabled
the identification of previously unknown cell subtypes in the hu-
man placenta and provided insight into the molecular mecha-
nisms underlying the differentiation of placental cell types.
Despite these advances, there is still a lack of consensus regard-
ing cell type annotations and the genes used to identify them.
This also suggests the fallibility of the clustering algorithms for
developing tissues when comparing to fully developed tissues, as
cells do not have a fixed but a continuous signature throughout
development. To assist future studies, here we have outlined spe-
cific marker genes using different meta-analyses and the avail-
able literature for both first trimester and term placentas (Fig. 7).
While application of the single-cell technique to study placentas
has traditionally been affected by sampling and lack of consider-
ation for potential confounding factors, such as fetal sex, the use
of scRNAseq, snRNAseq, or spatial transcriptomics holds strong
promise for enhancing our understanding of human placentas
and pregnancy-related disorders in future studies.

Supplementary data

Supplementary data are available at Human Reproduction
Update online.

Data availability

The datasets were derived from sources in the public domain
[Gene Expression Omnibus (GEO): GSE89497, GSE174481,
GSE173193; BioProject ID: PRJNA492324; ArrayExpress: E-MTAB-
6701; dbGaP: phs001886.v1.p1]. The data underlying this article
are available in the article and in its online supplementary material.

## Authors' roles

E.D., H.J., P.C.-P., and Q.D. conceived and designed the review and meta-analysis. E.D. curated the studies. H.J. performed the analysis to obtain DEGs from available data. E.D. and H.J. wrote the original draft. A.Z., P.C.P., and Q.D. revised the manuscript. All authors approved the final version and are accountable for all aspects of the work.

## Funding

This work was supported by the Wallenberg Academy Fellow Grant, Swedish Medical Research Council (no.2018-02557 and 2020-00253), Åke Wibergs Stiftelse, the Karolinska Institute, and the PHASE (Physiologie animale et Systèmes d'Elevage) Department of INRAE.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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are available in the article and in its online supplementary material.

## Authors' roles

E.D., H.J., P.C.-P., and Q.D. conceived and designed the review and meta-analysis. E.D. curated the studies. H.J. performed the analysis to obtain DEGs from available data. E.D. and H.J. wrote the original draft. A.Z., P.C.P., and Q.D. revised the manuscript. All authors approved the final version and are accountable for all aspects of the work.

## Funding

This work was supported by the Wallenberg Academy Fellow Grant, Swedish Medical Research Council (no.2018-02557 and 2020-00253), Åke Wibergs Stiftelse, the Karolinska Institute, and the PHASE (Physiologie animale et Systèmes d'Elevage) Department of INRAE.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## References

Adu-Gyamfi EA, Lamptey J, Chen X-M, Li F-F, Li C, Ruan L-L, Yang X-N, Liu T-H, Wang Y-X, Ding Y-B *et al*. Iodothyronine deiodinase 2 (DiO2) regulates trophoblast cell line cycle, invasion and apoptosis; and its downregulation is associated with early recurrent miscarriage. *Placenta* 2021; **111**:54–68.

Al-Lamki RS, Skepper JN, Burton GJ. Are human placental bed giant cells merely aggregates of small mononuclear trophoblast cells? An ultrastructural and immunocytochemical study. *Hum Reprod* 1999; **14**:496–504.

Anacker J, Segerer SE, Hagemann C, Feix S, Kapp M, Bausch R, Kämmerer U. Human decidua and invasive trophoblasts are rich sources of nearly all human matrix metalloproteinases. *Mol Hum Reprod* 2011; **17**:637–652.

Aplin JD. Developmental cell biology of human villous trophoblast: current research problems. *Int J Dev Biol* 2010; **54**:323–329.

Aplin JD, Haigh T, Vicovac L, Church HJ, Jones CJP. Anchorage in the developing placenta: an overlooked determinant of pregnancy outcome? *Hum Fertil (Camb)* 1998; **1**:75–79.

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Mayhew TM, Leach L, McGee R, Ismail WW, Myklebust R, Lammiman MJ. Proliferation, differentiation and apoptosis in vil- lous trophoblast at 13–41 weeks of gestation (including observa- tions on annulate lamellae and nuclear pore complexes). *Placenta* 1999;**20**:407–422.

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Pique-Regi R, Romero R, Tarca AL, Sendler ED, Xu Y, Garcia-Flores V, Leng Y, Luca F, Hassan SS, Gomez-Lopez N et al. Single cell tran- scriptional signatures of the human placenta in term and pre- term parturition. *Elife* 2019;**8:e52004**.

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Renaud SJ, Chakraborty D, Mason CW, Rumi MAK, Vivian JL, Soares MJ. OVO-like 1 regulates progenitor cell fate in human tropho- blast development. *Proc Natl Acad Sci U S A* 2015; **112**:E6175–E6184.

Reyes L, Golos TG. Hofbauer Cells: Their Role in Healthy and Complicated Pregnancy. *Front Immunol* 2018;**9**:2628.

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Sudheer S, Bhushan R, Fauler B, Lehrach H, Adjaye J. FGF inhibition directs BMP4-mediated differentiation of human embryonic stem cells to syncytiotrophoblast. *Stem Cells Dev* 2012; **21**:2987–3000.

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Thomas JR, Appios A, Calderbank EF, Yoshida N, Zhao X, Hamilton RS, Moffett A, Sharkey A, Laurenti E, Hanna CW et al. Primitive haematopoiesis in the human placenta gives rise to macrophages with epigenetically silenced HLA-DR. *Nat Commun* 2023; **14**:1764.

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Turco MY, Moffett A. Development of the human placenta. *Development* 2019; **146**:dev163428.

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Varberg KM, Iqbal K, Muto M, Simon ME, Scott RL, Kozai K, Choudhury RH, Aplin JD, Biswell R, Gibson M et al. ASCL2 reciprocally controls key trophoblast lineage decisions during hemochorial placenta development. *Proc Natl Acad Sci U S A* 2021; **118**:e2016517118.

Velicky P, Meinhardt G, Plessl K, Vondra S, Weiss T, Haslinger P, Lendl T, Aumayr K, Mairhofer M, Zhu X et al. Genome amplification and cellular senescence are hallmarks of human placenta development. *PLoS Genet* 2018; **14**:e1007698.

Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park J-E, Stephenson E, Polański K, Goncalves A et al Single-cell reconstruction of the early maternal-fetal interface in humans. *Nature* 2018; **563**:347–353.

Wallace JM, Bhattacharya S, Horgan GW. Gestational age, gender and parity specific centile charts for placental weight for singleton deliveries in Aberdeen, UK. *Placenta* 2013; **34**:269–274.

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Wu H, Luo C, Rong Y, Yu R, Zhao Y, Yan L, Wang H. Elucidating the dynamics of polypeptide hormones in the physiological and preeclampsic placental trophoblast cells across gestation at single-cell level. *Life Medicine* 2023; **2**:Inad003.

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Than NG, Pick E, Bellyei S, Szigeti A, Burger O, Berente Z, Janaky T, Boronkai A, Kliman H, Meiri H et al. Functional analyses of placental protein 13/galectin-13. *Eur J Biochem* 2004; **271**:1065-1078.

Than NG, Romero R, Xu Y, Erez O, Xu Z, Bhatti G, Leavitt R, Chung TH, El-Azzamy H, LaJeunesse C et al. Evolutionary origins of the placental expression of chromosome 19 cluster galectins and their complex dysregulation in preeclampsia. *Placenta* 2014; **35**:855-865.

The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. *Science* 2020; **369**:1318-1330.

Thomas JR, Appios A, Calderbank EF, Yoshida N, Zhao X, Hamilton RS, Moffett A, Sharkey A, Laurenti E, Hanna CW et al. Primitive haematopoiesis in the human placenta gives rise to macrophages with epigenetically silenced HLA-DR. *Nat Commun* 2023; **14**:1764.

Thornburg KL, Kolahi K, Pierce M, Valent A, Drake R, Louey S. Biological features of placental programming. *Placenta* 2016; **48** **Suppl 1**:S47-S53.

Tsang JCH, Vong JSL, Ji L, Poon LCY, Jiang P, Lui KO, Ni Y-B, To KF, Cheng YKY, Chiu RWK et al. Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics. *Proc Natl Acad Sci U S A* 2017; **114**:E7786-E7795.

Tsuchida N, Kojima J, Fukuda A, Oda M, Kawasaki T, Ito H, Kuji N, Isaka K, Nishi H, Umezawa A et al. Transcriptomic features of trophoblast lineage cells derived from human induced pluripotent stem cells treated with BMP 4. *Placenta* 2020; **89**:20-32.

Turco MY, Gardner L, Kay RG, Hamilton RS, Prater M, Hollinshead MS, McWhinnie A, Esposito L, Fernando R, Skelton H et al. Trophoblast organoids as a model for maternal-fetal interactions during human placentation. *Nature* 2018; **564**:263-267.

Turco MY, Moffett A. Development of the human placenta. *Development* 2019; **146**:dev163428.

Vachalova V, Karahoda R, Ottaviani M, Anandam KY, Abad C, Albrecht C, Staud F. Functional reorganization of monoamine transport systems during villous trophoblast differentiation:

evidence of distinct differences between primary human trophoblasts and BeWo cells. *Reprod Biol Endocrinol* 2022; **20**:112.

Varberg KM, Dominguez EM, Koseva B, Varberg JM, McNally RP, Moreno-Irusta A, Wesley ER, Iqbal K, Cheung WA, Schwendinger-Schreck C et al. Extravillous trophoblast cell lineage development is associated with active remodeling of the chromatin landscape. *Nat Commun* 2023; **14**:4826.

Varberg KM, Iqbal K, Muto M, Simon ME, Scott RL, Kozai K, Choudhury RH, Aplin JD, Biswell R, Gibson M et al. ASCL2 reciprocally controls key trophoblast lineage decisions during hemochorial placenta development. *Proc Natl Acad Sci U S A* 2021; **118**:e2016517118.

Velicky P, Meinhardt G, Plessl K, Vondra S, Weiss T, Haslinger P, Lendl T, Aumayr K, Mairhofer M, Zhu X et al. Genome amplification and cellular senescence are hallmarks of human placenta development. *PLoS Genet* 2018; **14**:e1007698.

Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park J-E, Stephenson E, Polański K, Goncalves A et al Single-cell reconstruction of the early maternal-fetal interface in humans. *Nature* 2018; **563**:347-353.

Wallace JM, Bhattacharya S, Horgan GW. Gestational age, gender and parity specific centile charts for placental weight for singleton deliveries in Aberdeen, UK. *Placenta* 2013; **34**:269-274.

Wang F, Jia W, Fan M, Shao X, Li Z, Liu Y, Ma Y, Li Y-X, Li R, Tu Q et al. Single-cell immune landscape of human recurrent miscarriage. *Genomics Proteomics Bioinformatics* 2021; **19**:208-222.

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West RC, Ezashi T, Schoolcraft W, Yuan Y. Beyond fusion: a novel role for ERVW-1 in trophoblast proliferation and type I interferon receptor expression. *Placenta* 2022; **126**:150-159.

Windsperger K, Dekan S, Pils S, Golletz C, Kunihs V, Fiala C, Kristiansen G, Knöfler M, Pollheimer J. Extravillous trophoblast invasion of venous as well as lymphatic vessels is altered in idio-pathic, recurrent, spontaneous abortions. *Hum Reprod* 2017; **32**:1208-1217.

Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. *Genome Biol* 2018; **19**:15.

Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. *Cell Syst* 2019; **8**:281-291.e9.

Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. *J Am Soc Nephrol* 2019; **30**:23-32.

Wu H, Luo C, Rong Y, Yu R, Zhao Y, Yan L, Wang H. Elucidating the dynamics of polypeptide hormones in the physiological and preeclampsic placental trophoblast cells across gestation at single-cell level. *Life Medicine* 2023; **2**:Inad003.

Wu S, Zhang H, Tian J, Liu L, Dong Y, Mao T. Expression of kisspeptin/GPR54 and PIBF/PR in the first trimester trophoblast and decidua of women with recurrent spontaneous abortion. *Pathol Res Pract* 2014; **210**:47-54.

Xiao Z, Yan L, Liang X, Wang H. Progress in deciphering trophoblast cell differentiation during human placentation. *Curr Opin Cell Biol* 2020; **67**:86-91.

Yang Y, Guo F, Peng Y, Chen R, Zhou W, Wang H, OuYang J, Yu B, Xu Z. Transcriptomic profiling of human placenta in gestational diabetes mellitus at the single-cell level. *Front Endocrinol* (Lausanne) 2021; **12**:679582.

Zaghlool A, Ameur A, Nyberg L, Halvardson J, Grabherr M, Cavelier L, Feuk L. Efficient cellular fractionation improves RNA
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Zhang B, Zhang F, Lu F, Wang J, Zhou W, Wang H, Yu B. Reduced cell invasion may be a characteristic of placental defects in pregnant women of advanced maternal age at single-cell level. *J Zhejiang Univ Sci B* 2022; **23**:747–759.

Zhang EG, Burton GJ, Smith SK, Charnock-Jones DS. Placental vessel adaptation during gestation and to high altitude: changes in diameter and perivascular cell coverage. *Placenta* 2002; **23**:751–762.

Zhang T, Bian Q, Chen Y, Wang X, Yu S, Liu S, Ji P, Li L, Shrestha M, Dong S et al. Dissecting human trophoblast cell transcriptional heterogeneity in preeclampsia using single-cell RNA sequencing. Mol Genet Genomic Med 2021a; **9:e1730**.

Zhang Z, Cui F, Wang C, Zhao L, Zou Q. Goals and approaches for each processing step for single-cell RNA sequencing data. *Brief Bioinform* 2021b; **22**:bbaa314.

Zheng Y, Pan J, Xia C, Chen H, Zhou H, Ju W, Wegiel J, Myatt L, Roberts JM, Guo X et al. Characterization of placental and decid- ual cell development in early pregnancy loss by single-cell RNA sequencing. Cell Biosci 2022; **12**:168.

Zhou GQ, Baranov V, Zimmermann W, Grunert F, Erhard B, Mincheva-Nilsson L, Hammarström S, Thompson J. Highly specific monoclonal antibody demonstrates that pregnancy-specific glycoprotein (PSG) is limited to syncytiotro-phoblast in human early and term placenta. *Placenta* 1997; **18**:491–501.

Zhou W, Wang H, Yang Y, Guo F, Yu B, Su Z. Trophoblast cell sub-
types and dysfunction in the placenta of individuals with pre-
eclampsia revealed by single-cell RNA sequencing. Mol Cells 2022;
**45**:317-328.
sequencing analysis of mature and nascent transcripts from hu-
man tissues. BMC Biotechnol 2013; **13**:99.

Zaghlool A, Niazi A, Björklund ÅK, Westholm JO, Ameur A, Feuk L. Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular dis- tribution of RNA transcripts. *Sci Rep* 2021; **11**:4076.

Zhang B, Zhang F, Lu F, Wang J, Zhou W, Wang H, Yu B. Reduced cell invasion may be a characteristic of placental defects in pregnant women of advanced maternal age at single-cell level. *J Zhejiang Univ Sci B* 2022; **23**:747–759.

Zhang EG, Burton GJ, Smith SK, Charnock-Jones DS. Placental vessel adaptation during gestation and to high altitude: changes in diameter and perivascular cell coverage. *Placenta* 2002; **23**:751–762.

Zhang T, Bian Q, Chen Y, Wang X, Yu S, Liu S, Ji P, Li L, Shrestha M, Dong S et al. Dissecting human trophoblast cell transcriptional heterogeneity in preeclampsia using single-cell RNA sequencing. *Mol Genet Genomic Med* 2021a; **9**:e1730.

Zhang Z, Cui F, Wang C, Zhao L, Zou Q. Goals and approaches for each processing step for single-cell RNA sequencing data. *Brief Bioinform* 2021b; **22**:bbaa314.

Zheng Y, Pan J, Xia C, Chen H, Zhou H, Ju W, Wegiel J, Myatt L, Roberts JM, Guo X et al. Characterization of placental and decid- ual cell development in early pregnancy loss by single-cell RNA sequencing. *Cell Biosci* 2022; **12**:168.

Zhou GQ, Baranov V, Zimmermann W, Grunert F, Erhard B, Mincheva-Nilsson L, Hammarström S, Thompson J. Highly specific monoclonal antibody demonstrates that pregnancy-specific glycoprotein (PSG) is limited to syncytiotro-phoblast in human early and term placenta. *Placenta* 1997; **18**:491–501.

Zhou W, Wang H, Yang Y, Guo F, Yu B, Su Z. Trophoblast cell sub-
types and dysfunction in the placenta of individuals with pre-
eclampsia revealed by single-cell RNA sequencing. *Mol Cells* 2022;
**45**:317–328.