A genome-wide CRISPR-Cas9 knockout screen identifies essential and growth-restricting genes in human trophoblast stem cells

Dong, Chen; Fu, Shuhua; Karvas, Rowan M.; Chew, Brian; Fischer, Laura A.; Xing, Xiaoyun; Harrison, Jessica K.; Popli, Pooja; Kommagani, Ramakrishna; Wang, Ting; Zhang, Bo; Theunissen, Thorold W.

doi:10.1038/s41467-022-30207-9

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Published: 10 May 2022

A genome-wide CRISPR-Cas9 knockout screen identifies essential and growth-restricting genes in human trophoblast stem cells

Nature Communications volume 13, Article number: 2548 (2022) Cite this article

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Abstract

The recent derivation of human trophoblast stem cells (hTSCs) provides a scalable in vitro model system of human placental development, but the molecular regulators of hTSC identity have not been systematically explored thus far. Here, we utilize a genome-wide CRISPR-Cas9 knockout screen to comprehensively identify essential and growth-restricting genes in hTSCs. By cross-referencing our data to those from similar genetic screens performed in other cell types, as well as gene expression data from early human embryos, we define hTSC-specific and -enriched regulators. These include both well-established and previously uncharacterized trophoblast regulators, such as ARID3A, GATA2, and TEAD1 (essential), and GCM1, PTPN14, and TET2 (growth-restricting). Integrated analysis of chromatin accessibility, gene expression, and genome-wide location data reveals that the transcription factor TEAD1 regulates the expression of many trophoblast regulators in hTSCs. In the absence of TEAD1, hTSCs fail to complete faithful differentiation into extravillous trophoblast (EVT) cells and instead show a bias towards syncytiotrophoblast (STB) differentiation, thus indicating that this transcription factor safeguards the bipotent lineage potential of hTSCs. Overall, our study provides a valuable resource for dissecting the molecular regulation of human placental development and diseases.

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Introduction

The placenta is a transient organ containing trophoblast cells derived from the trophectoderm (TE) of the early embryo¹. It performs several critical functions throughout gestation, including facilitating maternal-fetal exchanges, remodeling maternal spiral arteries, secreting pregnancy-related hormones, and acting as a protective barrier^2,3,4. Indeed, trophoblast defects can lead to pathologies such as miscarriage, preeclampsia, and intrauterine growth restrictions^5,6,7. However, the placenta also remains one of the least understood organs. Recently, a culture condition that allows for the derivation and maintenance of bona fide hTSCs from the blastocyst or first trimester placenta was developed⁸, which provides an invaluable tool to study human trophoblast development. Subsequent studies from our laboratory and others showed that hTSCs with similar biological and molecular properties can also be derived from naïve hPSCs or somatic cells via direct reprogramming^{9,10,11,12,13}. Nevertheless, efforts to systematically identify crucial regulators of hTSCs have so far been lacking.

Here, we employ a pooled genome-wide CRISPR-Cas9 knockout screen, an increasingly powerful tool to uncover and characterize genes controlling a variety of biological processes^{14,15,16,17,18,19,20,21,22}, to comprehensively determine the essential genes (EGs) and growth-restricting genes (GRGs) of hTSCs. Using a combination of chromatin accessibility, gene expression, and genome-wide location analyses, we further show that one such regulator, the transcription factor TEAD1, plays major role during progressive stages of human trophoblast development. Overall, this work provides important insights into the regulation of hTSCs and will be a valuable resource for future studies of trophoblast biology.

Results and discussion

A genome-wide CRISPR-Cas9 knockout screen identifies hTSC EGs

We transduced the blastocyst-derived BT5 hTSC line⁸ with the Brunello human CRISPR knockout pooled lentiviral library, which contains 76,441 single guide RNAs (sgRNAs) targeting 19,114 genes²³. Following puromycin selection, the cells were cultured and passaged for 18 days, with genomic DNA samples harvested for deep sequencing at days 0, 6, 12, and 18 (Fig. 1a). Two replicates with independent transductions showed good reproducibility (Supplementary Fig. 1a) and the identity of the hTSCs at day 18 was confirmed by homogeneous expression of the hTSC-specific cell surface markers EGFR and ITGA6 (Fig. 1b and Supplementary Fig. 1b)¹⁰. We analyzed the enrichment of sgRNAs targeting reference essential and non-essential genes and calculated a precision-recall curve, which indicated that the screen showed excellent performance (Fig. 1c and Supplementary Fig. 1c, d). Furthermore, analysis at intermediate timepoints revealed the lack of a bottlenecking effect (Supplementary Fig. 2a and Supplementary Data 1). In total, the screen identified 2139 EGs (Fig. 1d, Supplementary Fig. 1e, and Supplementary Data 1).

To confirm the specificity of our screen, we plotted the mean normalized sgRNA read counts of all EGs over time together with Bayes Factor (BF), a classifier of gene essentiality^24,25,26. As expected, the mean normalized sgRNA reads of all EGs steadily declined while their BF scores increased. In contrast, the gRNA enrichment and BF scores of neighboring genes remained unchanged (Fig. 1e). Amongst the hTSC EGs were several well-established trophoblast regulators, such as TFAP2C^{10,27,28,29,30,31} and TEAD4^32,33,34,35, as well as genes without a previously reported association with trophoblast, such as TCAF1, USP47, and TSC22D2 (Fig. 1e and Supplementary Fig. 3a). Biological processes and pathways enriched among the hTSC EGs included WNT, EGF, and histone acetylation (Fig. 1f and Supplementary Data 2), all of which are modulated by inhibitors or growth factors contained within hTSC medium⁸. We also detected enrichment in components of mTOR signaling and the pentose phosphate pathway (Supplementary Fig. 3b and Supplementary Data 2), which were previously implicated in mouse TE specification³⁶.

Consistent with their role in promoting hTSC fitness, EGs identified in our screen were expressed much more abundantly than nonessential genes in hTSCs (Supplementary Fig. 3c). We performed principal component analysis (PCA) using published RNA-sequencing (RNA-seq) data from naïve and primed hPSCs and various trophoblast cell types and found that hTSC EGs (n = 2139) and essential TFs (n = 124) accurately demarcated pluripotent and trophoblast identities, despite representing only minor fractions of all genes (Fig. 1g, Supplementary Fig. 3d, e, and Supplementary Data 1)¹⁰. We also compared the expression of hTSC EGs in different trophoblast cell types at 10 and 12 days post fertilization (d.p.f.) in 3D human embryos cultured through implantation stages³⁷. According to our prior analysis, these are the stages of early trophoblast development to which hTSCs most closely correspond¹⁰. At both timepoints, the hTSC EGs were statistically significantly upregulated in the cytotrophoblast (CTB) relative to the terminally differentiated extravillous trophoblasts (EVT) and syncytiotrophoblasts (STB) (Supplementary Fig. 3f, g). Similar expression patterns of hTSC EGs could be observed in trophoblast cells derived in vitro (Supplementary Fig. 3h). These transcriptional data further support the notion that hTSC EGs are central to the identity of hTSCs.

Identification and characterization of hTSC-specific EGs

To better define regulators specific to hTSCs, we referenced our hTSC EGs to core EGs that largely contain common housekeeping genes²⁶, as well as EGs identified through genetic screens in primed hPSCs (Fig. 2a)^19,20. Since hTSCs and primed hPSCs correspond to post-implantation CTB and epiblast (EPI), respectively^10,38, we reasoned that such a comparison may help identify essential regulators unique to these extraembryonic stem cells. While the hTSC EGs overlapped considerably with the core and especially primed hPSC EGs, we also identified 872 EGs specific to hTSCs (Fig. 2a and Supplementary Data 1), which were mainly localized to the nucleus and cytosol (Supplementary Fig. 4a). Pathways enriched in EGs shared between hTSCs and other cell types included housekeeping functions such as ribosome and spliceosome (Fig. 2b and Supplementary Data 2), while those unique to hTSC-specific EGs included HDAC and TNF signaling pathways (Fig. 2c and Supplementary Data 2)^8,39. We then sought to further distinguish hTSC-specific regulators based on gene expression in the 10 and 12 d.p.f. human embryo, since genes with increased expression in CTBs relative to other embryonic and extraembryonic lineages more likely represent critical regulators of the trophoblast lineage in vivo (Fig. 2d, Supplementary Fig. 4b, and Supplementary Data 1)^10,37. These genes included known trophoblast regulators such as ARID3A⁴⁰, CTNNB1⁸, GATA2^31,41, and SKP2⁴², as well as genes like ARID5B, TCAF1, and TEAD1, which have not previously been associated with trophoblast biology (Fig. 2e and Supplementary Fig. 4c). Screening data from intermediate timepoints showed that while core EGs became essential early in the screen, EGs more relevant to hTSC biology achieved essentiality at later time points (Supplementary Fig. 4d and Supplementary Data 1). We then validated the essentiality of SKP2, TEAD1, and TCAF1 via proliferation assays in hTSCs derived from H9 naïve hPSCs¹⁰ to ensure reproducibility in a different genetic background than the one used for the screen. hTSCs transduced with targeting sgRNAs displayed significantly fewer cells compared to cells transduced with non-targeting sgRNAs (Fig. 2f–h and Supplementary Fig. 4e, f). For most of these targets, the fraction of sgRNA-transduced cells that contain frameshift mutations declined over time, which indicates that they were outcompeted by cells that are wild-type or contain in-frame mutations. These results demonstrate that our screen has identified EGs that are functionally relevant for the maintenance of hTSCs.

**Fig. 2: Identification and analysis of hTSC-specific EGs.**

Investigating the role of TEAD1 in hTSC specification and maintenance

We were particularly intrigued by the hTSC-specific essential regulator TEAD1. While its paralog TEAD4 has been shown to be instrumental for mouse and human TE specification^32,34,35,43, TEAD1 was reportedly dispensable for mouse placentation³⁴. However, TEAD1 ranked more highly (#129) in our screen for hTSC EGs compared to TEAD4 (#1407), and its downregulation has been associated with recurrent spontaneous abortion⁴⁴, suggesting that it may have a human-specific role in trophoblast regulation. To explore this hypothesis, we utilized CRISPR-Cas9-mediated genome editing to generate stable TEAD1 homozygous knockout (KO) cell lines in the H9 primed hPSC background (Fig. 2i). We then reset wildtype (WT) control and TEAD1 KO primed hPSCs to the naïve state using our chemically defined conditions^45,46 (Fig. 2i). The removal of TEAD1 did not impair the ability to generate naïve hPSCs as evidenced by flow cytometry analysis for the naïve-specific cell surface markers CD75⁴⁷ and SUSD2⁴⁸ (Supplementary Fig. 5a). However, when we assayed the ability of these different genotypes to give rise to hTSCs (Fig. 2i), TEAD1 KO cells displayed significantly impaired proliferation (Supplementary Fig. 5b–c). Analysis of hTSC specification dynamics by flow cytometry revealed that TEAD1 KO cells adopt an hTSC identity with reduced kinetics compared to WT cells early in the derivation process (Supplementary Fig. 5d), although stable TEAD1 KO hTSC cell lines can still be obtained (Fig. 2j, k and Supplementary Fig. 5d). These findings indicate that TEAD1 promotes the efficient derivation of hTSCs from the naïve state.

We proceeded to investigate the consequences of the loss of TEAD1 in hTSCs. TEAD1 KO hTSCs had slightly increased G0/G1 phase and decreased S phase populations compared to WT hTSCs based on cell cycle analysis (Supplementary Fig. 5e), although levels of Annexin V-positive apoptotic cells were similar between the two genotypes (Supplementary Fig. 5f). We then performed RNA-seq on WT and TEAD1 KO hTSCs to characterize their global gene expression changes. There were 162 genes significantly downregulated in TEAD1 KO hTSCs (p-adjust < 0.05) (Fig. 2l), including many trophoblast regulators such as FOS^49,50, FN1⁵¹, ITGB8⁵², MET⁵³, and LOXL2⁵⁴. Genes downregulated in TEAD1 KO hTSCs were enriched for GO terms such as integrin-mediated signaling pathway, regulation of TGF-β production, and regulation of cell motility (Fig. 2l, m). Interestingly, a large number of genes downregulated in TEAD1 KO hTSCs are known to promote EVT differentiation; in fact, the EVT marker HLA-G was the most downregulated gene (Fig. 2l). On the other hand, there were 251 genes significantly upregulated in TEAD1 KO hTSCs (p-adjust < 0.05), including the trophoblast regulators AMOTL2⁵⁵, TET2⁵⁶, and CITED2⁵⁷ (Fig. 2l). Many STB marker genes such as CGB2, CGB7, and PSG11 were also upregulated in TEAD1 KO hTSCs (Fig. 2l). GO analysis revealed that genes upregulated in KO were enriched for terms such as response to hormone, cell adhesion, and apoptosis (Fig. 2m). We also used the Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) to compare the chromatin accessibility landscape in TEAD1 WT and KO hTSCs. There were 3988 and 4116 peaks that significantly lost or gained chromatin accessibility upon the loss of TEAD1, respectively (p-adjust < 0.05). Peaks with decreased ATAC-seq signal were enriched for GO terms such as placenta development, activation of JUN kinase activity, and embryo implantation, while those with increased signal were enriched for cytokine regulation, transmembrane transport, and peptide hormone processing (Fig. 2n). A number of key trophoblast regulators displayed significant chromatin accessibility changes consistent with reduced or increased expression in TEAD1 KO hTSCs, including ZFP57⁵⁸, FN1⁵¹ (downregulated), and CBR1⁵⁹ (upregulated) (Fig. 2o).

TEAD1 transcripts were upregulated during the transition from naïve hPSCs into hTSCs (Fig. 3a) and immunofluorescence analysis indicated that TEAD1 acquires a nuclear localization in hTSCs, which is consistent with its well-established role as a transcriptional co-factor for the Hippo effectors YAP/TAZ⁶⁰ (Fig. 3b). To further investigate how this transcription factor regulates the hTSC state, we assayed its genome-wide DNA binding sites in H9 hTSCs via Cleavage Under Targets and Tagmentation (CUT&Tag)⁶¹. This analysis identified 3566 reproducible peaks in hTSCs, which were enriched for motifs such as TEAD1/4, ETS, and AP2, and GO terms related to placental development (Fig. 3c and Supplementary Fig. 6a–c). These peaks were associated with 2,568 genes, including a number of hTSC EGs (Supplementary Fig. 6d). We then intersected TEAD1 targets with open chromatin regions (OCRs) identified by ATAC-seq in naïve hPSCs and hTSCs¹⁰. Of the 2,272 TEAD1 peaks that overlapped with OCRs in either cell state, most were located at accessible chromatin regions that were specific to hTSCs (group 1) or shared between naïve hPSCs and hTSCs (group 2) (Fig. 3d). Top GO terms enriched at group 1 peaks included placental development and the WNT signaling pathway, whereas those enriched at group 2 peaks included Hippo signaling (Fig. 3e). While group 1 TEAD1 targets exhibited a high ATAC-seq signal in hTSCs (Fig. 3f), group 2 peaks showed a similar ATAC-seq signal in both cell types (Fig. 3f–h). The precocious opening in naïve hPSCs of such a large set of chromatin regions bound by TEAD1 in hTSCs may partly explain why naïve hPSCs display an enhanced potential for trophoblast differentiation^10,12,13,62.

**Fig. 3: Investigation of TEAD1 targets in hTSCs.**

The binding of TEAD1 was associated with enhanced gene expression in hTSCs, particularly when the transcriptional start site was located within 1 kb from a TEAD1 peak (Fig. 3i). Expression of TEAD1 target genes, especially those associated with group 1 peaks, was significantly elevated in hTSCs relative to naïve hPSCs or STBs, one of two specialized trophoblast cell types that can be derived from hTSCs in vitro^8,10 (Fig. 3j, k and Supplementary Fig. 6e). Similar trends were observed in vivo (Supplementary Fig. 6f, g), suggesting that TEAD1 helps to specify and regulate hTSCs by activating CTB-specific transcriptional programs. However, TEAD1 target genes were not biased toward up- or downregulation in EVTs versus hTSCs (Fig. 3l and Supplementary Fig. 6h), which may point to an additional role for TEAD1 during differentiation into the EVT lineage. Finally, to uncover the direct regulation of gene expression and chromatin accessibility by TEAD1, we referenced group 1 and 2 TEAD1 CUT&Tag targets to differentially expressed genes (DEGs) and differentially accessible regions (DARs) between WT and TEAD1 KO hTSCs. Integration with RNA-seq data indicated that 26 and 34 TEAD1 target genes were down- and upregulated in TEAD1 KO hTSCs, respectively (Supplementary Fig. 6i). For example, the direct TEAD1 targets FOS^49,50,63 and EFNA1^64,65 were downregulated in TEAD1 KO hTSCs, while AMOTL2⁵⁵ and DDIT3⁶⁶ were upregulated in TEAD1 KO hTSCs (Fig. 3m). Meanwhile, integration with ATAC-seq data indicated that 141 and 14 TEAD1 targets displayed reduced or increased chromatin accessibility in TEAD1 KO hTSCs, respectively (Supplementary Fig. 6j). For example, chromatin accessibility was reduced at TEAD1 binding sites within the POLE4, MYLK3, and SERPINB7 loci, and increased at a TEAD1 binding site within the NRARP locus⁶⁷ (Fig. 3n). Collectively, these data indicate that TEAD1 maintains hTSC identity by regulating proper cell cycling, modulating key trophoblast regulators, and suppressing the STB program via direct or indirect mechanisms.

Investigating the role of TEAD1 in EVT and STB differentiation

Given the differential RNA-seq and ATAC-seq signals of EVT- and STB-associated genes in WT and TEAD1 KO hTSCs (Fig. 2l–o) and the expression levels of TEAD1 and its target genes in different trophoblast cell types (Fig. 3a, j–l, and Supplementary Fig. 6f–h), we hypothesized that TEAD1 may promote the differentiation of hTSCs into the EVT lineage, but present a barrier to STB differentiation (Fig. 4a). To test this hypothesis, we first performed lineage-directed EVT differentiation on WT and TEAD1 KO hTSCs by treating the cells with Neuregulin (NRG1) and the TGF-β inhibitor A83-01 in the presence of Matrigel^8,10. Unlike WT cells, TEAD1 KO cells subjected to this EVT differentiation protocol failed to acquire the typical spindle-like EVT morphology and were far less invasive in a Matrigel-coated transwell assay (Fig. 4b, c). We profiled the transcriptomes of WT and TEAD1 KO samples by RNA-seq and all samples were clearly separated by cell type and genotype in the PCA (Fig. 4d). TEAD1 KO “EVTs” displayed significant downregulation of 1,715 genes and upregulation 1,328 genes compared to WT EVTs (p-adjust < 0.05) (Fig. 4e). Downregulated DEGs included key EVT markers such as ASCL2, ITGA5, and HLA-G (Fig. 4e), and were enriched for GO terms like cytoskeleton organization, cell projection organization, and cell migration (Fig. 4f). The expression of EVT-specific genes reported by the human embryo scRNA-seq data³⁷ was also significantly lower in TEAD1 KO compared to WT EVTs (Fig. 4g). On the contrary, DEGs that were upregulated in TEAD1 KO EVTs consisted of a large number of STB regulators such as CGA, CGB, and ERVW-1, and were enriched for GO terms such as organic acid transmembrane transport, neutral amino acid transport, and secretion, which are typically associated with the function of STBs in mediating maternal-fetal exchange (Fig. 4e, f). We also profiled the chromatin accessibility landscapes of WT and TEAD1 KO EVTs via ATAC-seq. All samples were again clearly separated by cell type and genotype in the PCA (Fig. 4h). There were 24,675 and 10,026 loci with decreased or increased ATAC-seq signal in TEAD1 KO EVTs, respectively. Based on GO term enrichment analysis, the collagen-activated signaling pathway was enriched among genes with reduced chromatin accessibility in TEAD1 KO cells, while terms including negative regulation of Notch signaling pathway⁶⁸, embryonic placenta development, and negative regulation of ERK1 and ERK2 cascade⁶⁹ were enriched among chromatin that gained accessibility (Fig. 4i). Similar to our observations in hTSCs, changes in ATAC-seq intensity in TEAD1 KO EVTs were linked to changes in gene expression for a number of trophoblast regulators, including ASCL2⁷⁰ and HLA-G (downregulated), and CGA and CGB2 (upregulated) (Fig. 4j). Finally, when we probed the ATAC-seq signal at EVT-specific open chromatin, we discovered that TEAD1 KO “EVTs” failed to properly open those regions (Fig. 4k). These data lead us to conclude that TEAD1 is critical for EVT differentiation, while potentially hindering STB differentiation, and that loss of TEAD1 dysregulates EVT-specific gene expression and accessible chromatin.

**Fig. 4: Investigating the role of TEAD1 in EVT differentiation.**

To determine whether TEAD1 KO hTSCs may have an enhanced potential for STB differentiation, we performed lineage-directed STB differentiation on WT and TEAD1 KO hTSCs by applying the cyclic AMP agonist Forskolin together with epidermal growth factor (EGF). Interestingly, TEAD1 KO STBs were more abundant than WT STBs towards the end of the differentiation time course (Supplementary Fig. 7a). RNA-seq analysis revealed 1039 and 880 DEGs that were significantly downregulated or upregulated in TEAD1 KO STBs, respectively (p-adjust < 0.05) (Supplementary Fig. 7b). Genes with significantly higher expression in TEAD1 KO cells included the STB markers CGB1, CGB2, and SLC38A3³⁷ (Supplementary Fig. 7b), and were enriched for GO terms such as ribonucleoside bisphosphate biosynthetic process, response to organic cyclic compound, and in utero embryonic development (Supplementary Fig. 7c). Conversely, genes significantly downregulated in TEAD1 KO STBs were enriched for GO terms related to cytokine response and regulation of cell death (Supplementary Fig. 7c). We also performed ATAC-seq on these cells, which revealed 19,629 and 29,025 DARs that became significantly more closed or open in TEAD1 KO STBs, respectively. Open DARs were enriched for glucose transport, and closed DARs were enriched for regulation of gonadotropin secretion (Supplementary Fig. 7d). We again found that DARs were correlated to gene expression changes for a number of key trophoblast regulators (Supplementary Fig. 7e). Genes that gained ATAC-seq and RNA-seq signals in TEAD1 KO STBs included STB-specific genes such as FIBCD1³⁷, while those that lost ATAC-seq and RNA-seq signals included hTSC-specific genes like ITGA2⁷¹, KRT17⁷², and IFITM2⁷³ (Supplementary Fig. 7e). These data suggest that TEAD1 indeed acts as a barrier for STB differentiation and that TEAD1 KO STBs are potentially more mature than WT STBs. Taken together, we postulate that TEAD1 performs a variety of functions during the induction, maintenance, and differentiation of hTSCs by modulating the expression and chromatin accessibility of target trophoblast regulators.

Identification and characterization of hTSC GRGs

Since the failure to restrict cellular growth often leads to diseases such as cancer, identification of GRGs is equally important for understanding regulatory mechanisms in hTSCs, especially given their potential relevance to choriocarnicoma⁷⁴. Our screen identified 619 GRGs in hTSCs (Fig. 5a, Supplementary Fig. 8a, and Supplementary Data 3), which mainly showed nuclear and cytosolic localizations (Supplementary Fig. 8b). The mean normalized read counts and log2 fold changes of all sgRNAs targeting GRGs steadily increased throughout the screen, while those targeting neighboring genes remained unchanged (Fig. 5b). Among the GRGs in hTSCs were GCM1, which encodes a transcription factor involved in STB differentiation^75,76,77, and TGFBI, which is induced by TGFβ signaling and encodes a secreted extracellular matrix protein⁷⁸. Consistent with the growth-restricting function of TGFBI in hTSCs, pharmacological inhibition of TGFβ signaling is integral to hTSC derivation and maintenance⁸ (Fig. 5b). Other GRGs included the genes encoding the phosphatase PTPN14, the 5-methylcytosine hydroxylase TET2, and the orphan receptor NR6A1 (Supplementary Fig. 8c). hTSC GRGs were the most enriched in pathways including negative regulation of cyclin-dependent kinases and negative regulation of Hippo signaling (that prevent YAP/TAZ nuclear translocation), such as SAV1, LATS1, AMOTL2, and AJUBA (Fig. 5c and Supplementary Data 2). These findings highlight the crucial role of Hippo signaling in regulating hTSCs. We validated the growth-restricting function of two of the GRGs, PTPN14 and TET2, via proliferation assays in H9 hTSCs following sgRNA transduction (Fig. 5d and Supplementary Fig. 8d, e). As expected, the fraction of sgRNA-transduced cells bearing frameshift mutations in these target genes increased over time. It has been reported that PTPN14 directly inhibits YAP in cancer cells in a density-dependent manner^79,80,81. Interestingly, TET2 reportedly cooperates with TET1 to maintain mTSC self-renewal⁵⁶, suggesting a potentially disparate role in mouse versus human trophoblast.

We proceeded to uncover GRGs that have unique expression patterns in CTBs relative to other lineages in human embryos cultured through implantation stages³⁷. First, we identified GRGs uniquely upregulated in CTBs at 10 and 12 d.p.f., which represents the stage of in vivo trophoblast development most closely aligned with hTSCs (Fig. 5e, Supplementary Fig. 8f, and Supplementary Data 3). These included the genes encoding GCM1^75,76, the mTSC cell cycle modulators TET2 and E2F8^56,82, the negative Hippo regulator AJUBA, and the negative WNT regulator AXIN2 (Fig. 5f and Supplementary Fig. 8g). We also identified GRGs uniquely depleted in CTBs at 10 or 12 d.p.f. (Fig. 5g, Supplementary Fig. 8h, and Supplementary Data 3), such as NODAL, TBX3, and MAPKAPK3 (Fig. 5h). The protein products of these genes include important components of the pluripotency network^83,84,85, although TBX3 has also been implicated in human STB development⁸⁶. CTB-depleted GRGs also included genes encoding the negative Hippo regulators SAV1 and NF2 (Fig. 5h), which ensure ICM specification and prevent ectopic TE formation⁸⁷. We found that hTSC GRGs mostly belonged to three different categories. The first were primarily housekeeping genes, exemplified by the enrichment of cell-cycle related pathways (Fig. 5c). The second category of GRGs included genes such as AJUBA and AXIN2 that antagonize pathways essential for hTSCs. The third included GRGs that promote other cellular identities, for instance, GCM1 (STB) and NODAL (EPI). Hence, two key functions of hTSC GRGs are to prevent the over-proliferation of hTSCs and the ectopic activation of hTSC-specific transcriptional programs in other cell types. Overall, our analysis revealed that hTSC GRGs are a diverse group of genes that together ensure the balanced development of the early human embryo.

Analysis of hTSC EGs and GRGs implicated in mouse placentation

It is well-established that given the evolutionary diversity of the mammalian placenta, animal models inadequately recapitulate human placental development^88,89. Therefore, we anticipated that few hTSC EGs and GRGs would be conserved regulators of mouse placental development. To address this question, we compared hTSC EGs and GRGs with regulators of mouse placentation identified by a large-scale analysis of mouse gene knockouts that cause intrauterine lethality⁹⁰. Of the 61 genes that led to mouse placental defects⁹⁰, 18 were identified as EGs and 2 as GRGs in hTSCs in this study (Fig. 6a). We found that these 20 conserved placental regulators were expressed more highly in villous cytotrophoblast (VCT or CTB) than in other placental, decidual, or immune cell types in scRNA-seq data sampled from the human maternal-fetal interface⁹¹ (Fig. 6b, c and Supplementary Fig. 9a). These 20 conserved regulators were also expressed more highly than mouse-specific placental regulators (n = 41) in various human trophoblast cell types (Fig. 6d)⁹¹, while there was no significant expression difference in various mouse trophoblast cell types (Supplementary Fig. 9b)⁹². These data suggest that our screen has identified conserved placental regulators that are robustly expressed during human placental development. The conserved regulators include TRAF2 (Fig. 6c), which encodes a member of the TNF signaling pathway that promotes the activation of NF-κB and MAPK pathways downstream⁹³, as well as multiple mitochondrial genes such as TRUB2⁹⁴, TIMMDC1⁹⁵, CRLS1^96,97, and DHODH⁹⁸ (Fig. 6c and Supplementary Fig. 9a), highlighting the importance of mitochondrial function and oxidative phosphorylation in trophoblast development⁹⁹. Several other conserved regulators with strong enrichment in trophoblast cells include SQLE, SMG9, VIRMA, WRAP53, NHLRC2, and GPATCH1 (Fig. 6c and Supplementary Fig. 9a). We also confirmed our findings by comparing our hTSC regulators to an independent list of mouse placenta regulators curated by the Jackson Laboratory (Supplementary Fig. 9c–e)¹⁰⁰. Lastly, we observed a sizable overlap between hTSC EGs/GRGs and genes associated with pregnancy-related diseases (Supplementary Fig. 9f, g)^101,102, which suggests that hTSCs could provide a powerful cellular model system to study trophoblast-related pathologies.

**Fig. 6: Comparison of hTSC EGs and GRGs with genes required for mouse placentation.**

In conclusion, we present the findings from a genome-wide CRISPR-Cas9 knockout screen in hTSCs. By identifying and characterizing hTSC EGs and GRGs, we systematically uncovered potential regulators of the hTSC state. Genetic deletion studies indicated that the removal of one such regulator, TEAD1, impaired the generation and maintenance of hTSCs and prevented the faithful differentiation of hTSCs into the invasive EVT lineage. Furthermore, integrated analysis of ATAC-seq, CUT&Tag, and RNA-seq data demonstrated that TEAD1 regulates the gene expression and chromatin accessibility of many trophoblast regulators in hTSCs, suggesting that this transcription factor plays an important role during progressive stages of human trophoblast development. The hTSC EGs and GRGs also overlapped with pregnancy disease-associated genes and regulators of placentation during mouse development in vivo^90,100, which provides a framework for elucidating conserved and species-specific regulators of trophoblast development. Overall, our study provides a valuable resource for future research into the genetic circuitry controlling placental development and disease.

Methods

Ethics statement

All human embryonic stem cell experiments performed in this study have been approved by Embryonic Stem Cell Research Oversight Committee at Washington University School of Medicine.

Culture of hTSCs

hTSCs were cultured as previously described^8,10. Prior to seeding, 6-well plates were coated with 5 μg/mL Collagen IV (Corning, 354233) at 37 °C overnight. Cells were cultured in 2 mL hTSC medium [DMEM/F12 (Gibco, 11320) supplemented with 0.1 mM 2-mercaptoethanol (Millipore Sigma, 8.05740), 0.2% FBS (Millipore Sigma, ES-009-B), 0.5% Penicillin-Streptomycin (Gibco, 15140), 0.3% BSA (Gibco, 15260), 1% ITS-X (Gibco, 51500), 1.5 μg/ml L-ascorbic acid (Wako, 013-12061), 50 ng/ml EGF (Peprotech, AF-100-15), 2 μM CHIR99021 (Stemgent, 04-0004), 0.5 μM A83-01 (BioVision, 1725), 1 μM SB431542 (BioVision, 1674), 0.8 mM VPA (Sigma, P4543), and 5 μM Y-27632 (Stemgent, 04-0012)] and in 5% CO₂ and 20% O₂. Tissue culture media were filtered using a 0.22 μm filter. Media were changed every 2 days, and cells were passaged using TrypLE Express (Gibco, 12604). Unless otherwise specified, hTSCs within 30 passages were used for experiments. The cell culture is regularly tested and confirmed negative for mycoplasma contamination.

Culture of naïve and primed hPSCs

Primed hPSCs were cultured in mTeSR1 (STEMCELL Technologies, 85850) on Matrigel (Corning, 354277) coated wells and passaged using ReLeSR (STEMCELL Technologies, 05872) every 4–6 days. Primed hPSCs were cultured in 5% CO₂ and 20% O₂. Naïve hPSCs were cultured on mitomycin C-inactivated mouse embryonic fibroblast (MEF) feeder cells and were passaged by a brief PBS wash followed by single-cell dissociation using 5 min treatment with TrypLE Express. Naïve hPSCs used for ATAC-seq experiments were cultured in the 5i/L/A media as previously described^46,103. 500 mL of 5i/L/A was generated by supplementing N2B27 [240 mL DMEM/F12, 240 mL Neurobasal (Gibco, 21103), 5 mL N2 100X supplement (Gibco, 17502), 10 mL B27 50X supplement (Gibco, 17504), 1X GlutaMAX (Gibco, 35050), 1X MEM NEAA (Gibco, 11140), 0.1 mM β-mercaptoethanol, 1% penicillin-streptomycin, and 50 µg/ml BSA Fraction V] with the following small molecules and cytokines: 1 μM PD0325901 (Stemgent, 04-0006), 1 μM IM-12 (Enzo, BML-WN102), 0.5 μM SB590885 (Tocris, 2650), 1 μM WH4-023 (A Chemtek, H620061), 10 μM Y-27632, 10 μg recombinant human LIF (Peprotech, 300-05), and 10 ng/mL Activin A (Peprotech, 120-14). To increase the growth rate, control and TEAD1 KO naïve hPSCs were expanded in PXGLY medium [N2B27 supplemented with 1 μM PD0325901, 2 μM XAV939 (Selleckchem, S1180), 2 μM Gö6983 (Tocris, 2285), 10 μM Y-27632, and 10 ng/mL human LIF]¹⁰⁴ prior to hTSC derivation. Naïve hPSCs were cultured in 5% O₂, 5% CO₂. For primed-to-naïve hPSC conversion, 2.5 × 10⁵ single primed cells were seeded on a six-well plate with MEF feeder layer in 2 mL mTeSR1 supplemented with 10 μM Y-27632. Two days later, medium was switched to 5i/L/A. After 6 to 10 days from seeding, the cells were expanded polyclonally using TrypLE Express on a MEF feeder layer. Tissue culture media were filtered using a 0.22 μm filter. Media were changed every 1–2 days. Naïve hPSCs before passage 10 were used for experiments. The cell culture is regularly tested and confirmed negative for mycoplasma contamination.

Derivation of hTSCs from naïve hPSCs

hTSCs were derived from naïve hPSCs according to our recently described methodology^10,105. Briefly, naïve hPSCs were single-cell dissociated by TrypLE Express, and 0.25 × 10⁶ cells were seeded on a six-well plate with MEF feeder layer and cultured in 2 mL naïve media for 1 day in 5% CO₂ and 5% O₂. On the following day, the cells were cultured in hTSC medium in 5% CO₂ and 20% O₂, and media were replaced every 2 days. Upon reaching 80–100% confluency, the cells were passaged at a ratio of 1:2 to 1:4 to six-well plates pre-coated with 5 μg/mL Collagen IV, and cultured from this point on as regular hTSCs. Cell numbers were quantified at every passage to obtain the cumulative cell count for the TEAD1 knockout experiment. Trypan Blue (Invitrogen, T10282) was used to exclude dead cells.

EVT and STB differentiation from hTSCs

Differentiation of hTSCs into terminal cell types was performed as previously described⁸, with minor modifications. Prior to differentiation, hTSCs were grown to about 80% confluency, and then single-cell dissociated using TrypLE Express. For EVT differentiation, six-well plates were coated with 1 μg/mL Collagen IV overnight. On day 0, 0.75 × 10⁵ hTSCs were seeded per well in 2 mL EVT basal medium [DMEM/F12 supplemented with 0.1 mM β-mercaptoethanol, 0.5% penicillin-streptomycin, 0.3% BSA, 1% ITS-X, 7.5 μM A83-01, 2.5 μM Y27632] supplemented with 4% KSR (Gibco, 10828) and 100 ng/mL NRG1 (Cell signaling, 5218SC). Matrigel was added to a 2% final concentration shortly after resuspending hTSCs in the medium. At day 3, the media were replaced with 2 mL EVT basal medium supplemented with 4% KSR, and Matrigel was added to a 0.5% final concentration. At day 6, the media were replaced with 2 mL EVT basal medium, and Matrigel was added to a 0.5% final concentration. At day 8, the cells were ready for downstream analysis.

For STB differentiation, 2.5 × 10⁵ hTSCs were seeded per well in 3 mL 3D STB medium [DMEM/F12 supplemented with 0.1 mM β-mercaptoethanol, 0.5% penicillin-streptomycin, 0.3% BSA, 1% ITS-X, 2.5 μM Y-27632, 50 ng/ml EGF, 2 μM Forskolin (Sigma-Aldrich, F3917), and 4% KSR] in an ultra-low attachment six-well plate. At day 3, another 3 mL of 3D STB medium was added per well. At day 6, the cells were transferred to a 15 mL tube and gravity sedimented for 15 min, and the cells at the bottom of the tube were collected and used for downstream analysis.

TEAD1 CRISPR targeting

Guide RNAs (gRNAs) aimed at introducing out-of-frame indels to trigger nonsense-mediated decay of TEAD1 were designed and validated in K562 cells by the Genome Engineering and iPSC center (GEIC) at Washington University. In short, synthetic gRNAs were complexed with recombinant Cas9 protein and nucleofected into K562 cells. Transfected cells were harvested and lysed 48–72 h post-nucleofection. Each target region was PCR amplified, indexed, and analyzed on a MiSeq for insertion-deletion (indel) rate, indicative of cleavage activity. The most efficient gRNA sequence (TAGCCAGATACATCAAACTCNGG) was selected for nucleofection into primed hPSCs. 1.5 × 10⁶ primed H9 hPSCs were transfected with 0.5 µg pmaxGFP control vector, 300 pmol gRNA, and 192 pmol Cas9 protein. gRNA complexes targeting TEAD1 were transfected into primed hPSCs by nucleofection using the Amaxa P3 Primary Cell 4D-Nucleofector X Kit and 4D-Nucleofector device with program CA-137 (Lonza). 3 days after nucleofection, the cells were single cell dissociated and sorted for GFP-expressing (~5%) cells using a MoFlo flow cytometry system. Clonal lines were analyzed by NGS for presence of indels around the cut site. Samples that exhibited a mixed indel ratio were discarded. Primed TEAD1^+/+ (WT) and TEAD1^−/− hPSC clones were selected for subsequent analysis.

Cloning

Individual sgRNA lentiviral expression vector cloning was performed as previously described¹⁷. Briefly, LentiCRISPR v2 (Addgene, #52961) plasmid was digested with Esp3I restriction enzyme (Thermo Fisher, FD0454), dephosphorylated with FastAP (Thermo Fisher, EF0651), and gel purified. sgRNA oligos (sequence taken from the Brunello library and listed below) were phosphorylated with T4 PNK (NEB, M0201S) and annealed. The digested plasmid and annealed oligos were then ligated using Quick Ligase (NEB, M2200S) and transformed into One Shot StbI3 Chemically Competent cells (Thermo Scientific, C737303). Single colonies were then picked, and the identities of the clones were verified using Sanger sequencing.

TEAD1 CRISPR KO gRNA 1 F	CACCGACTGCCATTCATAACAAGCT
TEAD1 CRISPR KO gRNA 1 R	AAACAGCTTGTTATGAATGGCAGTC
TEAD1 CRISPR KO gRNA 2 F	CACCGAGATACATCAAACTCAGGAC
TEAD1 CRISPR KO gRNA 2 R	AAACGTCCTGAGTTTGATGTATCTC
SKP2 CRISPR KO gRNA 1 F	CACCGAAGACTTTGTGATTGTCCGC
SKP2 CRISPR KO gRNA 1 R	AAACGCGGACAATCACAAAGTCTTC
SKP2 CRISPR KO gRNA 2 F	CACCGTGGCAGACCTTAGACCTCAC
SKP2 CRISPR KO gRNA 2 R	AAACGTGAGGTCTAAGGTCTGCCAC
TCAF1 CRISPR KO gRNA 1 F	CACCGAGCACCGGTTAATTACGAGT
TCAF1 CRISPR KO gRNA 1 R	AAACACTCGTAATTAACCGGTGCTC
TCAF1 CRISPR KO gRNA 2 F	CACCGCCTTTGGCCAAAATCCTCGA
TCAF1 CRISPR KO gRNA 2 R	AAACTCGAGGATTTTGGCCAAAGGC
PTPN14 CRISPR KO gRNA 1 F	CACCGATTACGATGTACATTGGACC
PTPN14 CRISPR KO gRNA 1 R	AAACGGTCCAATGTACATCGTAATC
PTPN14 CRISPR KO gRNA 2 F	CACCGTGTGCTTACCGTGTGAAAGA
PTPN14 CRISPR KO gRNA 2 R	AAACTCTTTCACACGGTAAGCACAC
TET2 CRISPR KO gRNA 1 F	CACCGGATTCCGCTTGGTGAAAACG
TET2 CRISPR KO gRNA 1 R	AAACCGTTTTCACCAAGCGGAATCC
TET2 CRISPR KO gRNA 2 F	CACCGGTTCCAAAAACCCTCACACC
TET2 CRISPR KO gRNA 2 R	AAACGGTGTGAGGGTTTTTGGAACC

Lentivirus production

HEK293T cells were expanded in fibroblast medium [DMEM (Millipore Sigma, #SLM-021-B) supplemented with 10% FBS, 1X GlutaMAX (Gibco, 35050), and 1% penicillin-streptomycin]. 1 day prior to transfection, 1/10 of the cells in a confluent T150 flask were plated into a 10 cm cell culture dish. Transfection was performed using FuGENE 6 (Promega, E2691) according to manufacturer’s protocol. Briefly, 30 μL FuGENE 6 was added to 400 μL DMEM/F12, which was then mixed with psPAX2 (Addgene, #12260, 3.75 μg), pMD2.G (Addgene, #12259, 1.25 μg), and the donor vector (5 μg). The mixture was incubated at room temperature for 20 min, during which time fresh fibroblast medium without penicillin-streptomycin was changed on the HEK293T cells. Following incubation, the mixture was added dropwise to the HEK293T cells and mixed. The cells were then incubated at 37 °C in 5% CO₂ and 20% O₂ overnight. The morning of day 1 after transfection, the media were replaced with fibroblast medium with penicillin-streptomycin. Lentivirus was harvested on the mornings of day 2 and 3 after transfection.

CRISPR screen

Human Brunello CRISPR knockout pooled library lentiviral prep was a gift from David Root and John Doench (Addgene, #73179-LV). Prior to the screen, the transduction efficiency of the lentiviral prep was experimentally determined using BT5 hTSCs. A puromycin (Sigma, P8833) kill curve was also performed on BT5 hTSCs, and it was determined that 2 μg/mL was the minimal concentration required to kill all the cells. Two biological replicates of BT5 hTSCs were separately transduced with the Brunello pooled library lentivirus at low transduction efficiency (<50%) in the presence of 0.5 μg/mL polybrene (Millipore Sigma, TR-1003-G). Enough cells were transduced to achieve a representation of at least 500 cells per sgRNA, taking into account the transduction efficiency. One day after transduction, the media were replaced with puromycin-containing fresh hTSC medium. Two days after puromycin selection, the cells were dissociated and counted. Some of these cells were frozen in liquid nitrogen for genomic DNA isolation to serve as the day 0 (control) timepoint, while others were seeded to begin the screen, each maintaining a representation of at least 500 cells per sgRNA. From this point on, the media were changed every 2 days, and the cells were passaged every 3 days, with cell counts taken at every passage to maintain sufficient representation. Cells at the day 6, 12, and 18 timepoints were also frozen in liquid nitrogen for genomic DNA isolation, again maintaining sufficient representation of 500 cells per sgRNA. Some cells at the day 18 timepoint (endpoint) were also subject to flow cytometry analysis to confirm their hTSC identity.

Genomic DNA isolation and sequencing

Genomic DNA (gDNA) was isolated using the QIAamp DNA Blood Maxi Kit (Qiagen, 51192) per the manufacturer’s instructions. The gDNA concentrations were quantitated by Nanodrop. For PCR amplification, 252 μg of DNA was amplified per sample using the MyTaq Red Mix (Bioline, 25043). Reactions were set up in 384-well plates, using 200 ng of gDNA in 12 μL of reaction mix per well. Primers used for gDNA amplification were Fwd 5′ tgtggaaaggacgaaacacc 3′ and Rev 5′ cggactagccttattttaacttgc 3′. PCR cycling condition is as follows: an initial 2 min at 94 °C; followed by 15 s at 94 °C, 15 s at 56 °C, 30 s at 72 °C, for 30 cycles; and a final 5 min extension at 72 °C. P5/P7 primers were synthesized at Integrated DNA Technologies (IDT). PCR products were purified using the Agencourt AMPure XP SPRI beads (Beckman Coulter, A63880) according to the manufacturer’s instructions. Samples were sequenced on a NovaSeq S4 2 × 150 platform (Illumina).

Reads were counted by first searching for the CACCG sequence in the primary read file that appears in the lentiviral vector 5′ to all sgRNA inserts. The next 20 nucleotides are the sgRNA insert, which was then mapped to a reference file of all possible sgRNAs present in the library¹⁰⁶. The read was then assigned to a specific sample according to the barcode included in the P7 primer.

CRISPR screen analysis

Reads of every sgRNA in the Human CRISPR Knockout Pooled Library (Brunello)²³ were calculated by using the MAGeCK count module (v0.5.9.2)¹⁰⁷ as previously reported¹⁰⁸. The sgRNAs reads counts of all genes were inputted into the BAGEL package (v.91)²⁵ and “BAGEL-calc_foldchange.py” script was used to calculate the fold change (FC) at day 6, day 12, and day 18, against the baseline at day 0. Mean of the log2 Bayes Factors (BF), recall, precision, and FDR were further calculated by following the manual instruction of the “BAGEL.py pr” module of the BAGEL2 package¹⁰⁹. The core essential and nonessential gene lists required for BAGEL were obtained from previous studies^24,26. EGs in CRISPR screening were defined as the depleted genes at day 18 compared to the day 0 baseline, with FDR < 0.05. 2139 genes with reliable levels of expression (RPKM > 0) in BT5 hTSCs¹⁰ were considered as EGs in this study. GRGs were identified as described previously²¹. Briefly, read counts of each gene were deduced by summing up the read counts of corresponding sgRNAs. DESeq2 was used to identify the GRGs as those with enriched sgRNA read counts at day 18 compared to the day 0 baseline, with p_adj < 0.01 and log₂FC > 1. 619 genes with reliable levels of expression (RPKM > 0) in BT5 hTSCs¹⁰ were considered as GRGs in this study. GENCODE human gene annotation (v27, hg38) was used to determine the genomic coordinates of EGs and GRGs in the human genome, and to identify their upstream and downstream neighboring genes.

Individual candidate gene validation

A puromycin kill curve was performed on hTSCs derived from H9 naïve hPSCs, and it was determined that 1 μg/mL was the minimal concentration required to kill all the cells. hTSCs were transduced with lentivirus expressing Cas9 and a single targeting (cloned as described above) or control (Addgene, #107402) sgRNA. One day after transduction, the media were replaced with puromycin-containing fresh hTSC medium. Two days after puromycin selection, the cells were dissociated and counted. For each condition, 50,000 cells were seeded per well in 6 wells. This is considered day 0 of the validation experiment, and media were replaced every 2 days. The total number of live cells in each well were counted on day 4 and/or day 6 of the experiment, with 3 wells (biological triplicates) counted at each timepoint. Trypan Blue was used to exclude dead cells. Two-tailed student’s t-test was used for statistical analysis. Additionally, cells were analyzed by NGS for presence of indels around the cut site at day 0, 6, 12, and 18 of the validation experiments.

Gene Ontology, pathway, and cellular compartment analysis

The Gene Ontology (GO) analysis and pathway analysis of EGs and GRGs were performed by using ToppFun¹¹⁰. Protein localization data from Subcellular Localization Database (http://compartments.jensenlab.org/About)¹¹¹ were used to determine the subcellular localization/compartment of identified EGs and GRGs. Gene-compartment assignment was determined by using the maximal confidence score method as described previously²². Genes that have the same maximal confidence scores in multiple compartments were assigned to each compartment independently. Hypergeometric test was further used to calculate the statistical significance of the enrichment/depletion of EGs and GRGs in each cellular compartment.

Re-analysis of published transcriptomic data

Gene-level expression data (FPKM) of the CTB, EVT, PrE, EPI, and STB were obtained from a published study of human embryos cultured through implantation stages in vitro (GEO accession: GSE136447)³⁷. The expression of select EGs and GRGs was obtained to generate the expression heatmaps and dot-plots in distinct embryonic and extraembryonic cell types at different developmental stages.

Gene-level RNA-seq expression data (reads counts) of cultured STB, EVT, hTSC, and naïve hPSC were downloaded from our prior study (accession: GSE138688)¹⁰. DEGs (hTSC vs naïve hPSC, hTSC vs STB, hTSC vs EVT) were identified using DESeq2 with criteria as absolute log₂FC > 1, p_adj < 0.01, and CPM > 1 of libraries of either condition. To generate PCA plots, the genes with low expression (CPM < 1) in all libraries were removed to ensure high specificity. The transcription factors in EGs were confirmed by using the human TF list retrieved from Animal TFDB 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/)¹¹².

Gene-level single-cell RNA-seq data (FPKM) of human first trimester maternal-fetal interface were obtained through EMBL-EBI ArrayExpress (accession: EMTAB6678 and EMTAB6701)⁹¹. UMAP visualization of processed scRNA-seq data was generated with the coordinates of cell barcodes provided by the previous study⁹¹.

Gene-level single nuclei RNA-seq expression data (reads counts) of mouse placental labyrinth development from all nuclei or subclustered trophoblast nuclei were downloaded from a previous study (accession: GSE152248). The expression of selected hTSC EGs and GRGs was obtained to generate boxplots and violin plots across distinct cell types.

Flow cytometry

Cells were single-cell dissociated using TrypLE Express and washed once in FACS buffer [PBS supplemented with 5% FBS]. The cells were then resuspended in 100 μL fresh FACS buffer, and incubated with antibodies for 30 min on ice. The following antibodies were used: anti-SUSD2-PE, 1:100 (BioLegend, 327406); anti-CD75-eFluor 660, 1:100 (Thermo Fisher, 50-0759-42); anti-ITGA6-FITC, 1:100 (Miltenyi, 130-097-245); anti-EGFR-APC, 1:20 (BioLegend, 352905); anti-Annexin V-FITC, 1:10 (Thermo Fisher, BMS147FI). Following antibody incubation, the cells were washed once with FACS buffer, resuspended in fresh FACS buffer, and passed through a cell strainer. Unstained cells that have undergone the same procedures were used as controls. Cell cycle analysis was performed using the Vybrant DyeCycle Violet Ready Flow Reagent (Thermo Fisher, R37172) according to the manufacturer’s instructions. 100 µM Verapamil (Sigma-Aldrich, V4629) was added during incubation to prevent dye efflux. Flow cytometry was performed using a BD LSRFortessa X-20 and the data were analyzed using the FACSDiva v9.0 or FlowJo v10.8.1 software.

Immunofluorescence staining

Immunostaining was performed directly in the wells. Cells were fixed with 4% paraformaldehyde for 20 min at room temperature, then washed with PBS 3 times. The cells were permeabilized with 0.1% Triton X-100 (Sigma, T8787) in PBS for 5 min, then blocked with blocking buffer [PBS supplemented with 0.5% BSA and 0.1% Triton X-100] for 1 h. Cells were incubated with the primary antibody diluted in the blocking buffer overnight at 4 °C. The following primary antibody was used: anti-TEAD1, 1:100 (Cell Signaling Technology, 12292S). The cells were washed 3 times in PBS, then incubated with the secondary antibody diluted in blocking buffer for 1 h at room temperature. The following secondary antibody was used: donkey anti-rabbit-Alexa 647, 1:500 (Invitrogen, A-31573). The nuclei were stained with Hoechst 33258 (Thermo Fisher, H3569). Cells were washed 3 times in PBS, then imaged with a Leica DMi-8 fluorescence microscope. Some images were globally adjusted for brightness and/or contrast.

Western blotting

Cells were harvested and resuspended in radioimmunoprecipitation assay (RIPA) buffer, then incubated on ice for 20 min. Whole cell extract concentration was measured by Bradford assay (Pierce, 23236). Proteins were balanced and subject to SDS-PAGE analysis. Primary antibodies used were TEAD1 (1:1000, Cell Signaling Technology, 12292S) and beta-actin (1:1000, Cell Signaling Technology, 4970). The membrane was imaged using the Thermo Fisher iBright Imaging System.

Quantitative real-time PCR

Total RNA was isolated using the E.Z.N.A. total RNA kit I (Omega, D6834), and cDNA synthesis was performed from total RNA using the high capacity cDNA reverse transcription kit (Applied Biosystems, 4368814). Real-time PCR was performed using PowerUp SYBR green master mix (Applied Biosystems, A25743) on the StepOnePlus Real-Time PCR System (Applied Biosystems). All analyses were done in triplicate. Gene expression was normalized to RPLP0. Error bars represent the standard error (SE) of the mean of triplicate reactions. Two-tailed student’s t-test was performed for statistical analysis. Primer sequences are included in the following primer table:

Gene	Primer sequence (5′− 3′)
RPLP0-F	GCTTCCTGGAGGGTGTCC
RPLP0-R	GGACTCGTTTGTACCCGTTG
SKP2-F	ATGCCCCAATCTTGTCCATCT
SKP2-R	CACCGACTGAGTGATAGGTGT
TEAD1-F	CCTGGCTATCTATCCACCATGTG
TEAD1-R	TTCTGGTCCTCGTCTTGCCTGT
TCAF1-F	TTGCCCACAGAAAATGTTGA
TCAF1-R	CAGATAGGCCAGGCTGGTAG
PTPN14-F	CGACTTCTGGCAGATGGTGT
PTPN14-R	GTGGCTTTTGGTTCGTCCAC
TET2-F	CAGCACATTGGTATGCACTC
TET2-R	TTTCCTTTGTCGGCAAGTCT

Matrigel invasion assay

In vitro invasion assay was performed in Matrigel-coated transwell inserts with 8.0 μm pores (Corning, 354480). EVTs were single cell dissociated, and seeded at a density of 2 × 10⁵ cells per well into the upper chamber of Matrigel-coated transwells in 200 μL EVT basal medium. The lower chamber was filled with 800 μL of the same type of medium containing 20% FBS. Cells were cultured at 37 °C in 5% CO₂ and 20% O₂. After 36 h, cells in the upper chamber were carefully removed with a cotton swab. The lower chamber was fixed with 4% paraformaldehyde, washed with PBS, and then stained with crystal violet. Invaded cells were imaged on a Leica DMi1 microscope. Thereafter, the stained cells from five random fields were counted to calculate the relative fold change in the number of invading cells. One-tailed student’s t-test was performed for statistical analysis.

RNA-seq

Total RNA was isolated using the E.Z.N.A. total RNA kit I. Library construction was performed using the SMARTer Directional cDNA Library Construction Kit (Clontech, 634933). Libraries were sequenced on an Illumina NovaSeq S4 2 × 150 platform. The data were processed as previously described¹⁰. Raw reads of RNA-seq libraries were processed using the RNA-seq pipeline, which consists of data processing, quality control, integrative analysis, and data visualization. In this pipeline, RNA-seq reads were aligned to the human genome hg38 with STAR version 2.5.4b¹¹³. Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount (version 1.4.6)¹¹⁴, with hg38 gene annotation ENCODE V27¹¹⁵. Unwanted variables among gene counts were removed using RUVr function of RUVSeq normalization package¹¹⁶, with the estimated number of factors of unwanted variation k as 1. Then the normalized gene counts were imported into the R/Bioconductor package DESeq2¹¹⁷. Genes with CPM > 1.0 were converted into a DESeq2 dataset and then regularized log-transformed using the rlog function from the DESeq2 package. Adjusted p-values for DEGs were determined by DESeq2 using the R stats function p.adjust with the Benjamini and Hochberg correction to determine the false discovery rate. Absolute log2(fold change of expression change) >0.5 and adjusted p-value< 0.05 were required to consider a gene as differentially expressed. Lists of WT vs. TEAD1 KO DEGs could be found in Supplementary Data 4.

ATAC-seq

ATAC-seq was performed as previously described with minor modifications¹¹⁸. Cells were harvested by TrypLE Express dissociation and centrifuged at 500 × g for 5 min at 4 °C. The supernatant was aspirated. Cells were washed once with cold PBS containing 0.04% BSA. Cell pellets of hTSC and EVT were resuspended in 300 μL DNase I (Thermo Fisher, EN0521) solution [20 mM Tris pH 7.4, 150 mM NaCl, 1X reaction buffer with MgCl₂, 0.1 U/μL DNase I] on ice for 5 min. After DNase I treatment, 1 mL PBS containing 0.04% BSA was added and cells were centrifuged at 500 × g for 5 min at 4 °C. Another two washes were performed. Cell pellets of DNase I treated hTSC, EVT, and non-DNase I treated STB were then lysed in 100 µL ATAC-seq RSB [10 mM Tris pH 7.4, 10 mM NaCl, 3 mM MgCl₂] containing 0.1% NP40, 0.1% Tween-20, and 0.01% Digitonin by pipetting up and down and incubating on ice for 3 min (hTSC and EVT) or 10 min (STB). 1 mL of ATAC-seq RSB containing 0.1% Tween-20 was added and mixed with the lysis reaction. The STB cells were then filtered through a 30 μm cell strainer. Nuclei were then pelleted by centrifuging at 600 × g for 5 min at 4 °C. Supernatant was removed, and the nuclear pellets were resuspended in 20 µL 2× TD buffer [20 mM Tris pH 7.6, 10 mM MgCl₂, 20% Dimethyl Formamide]. Nuclei were counted, and 50,000 counted nuclei were then transferred to a tube with 2× TD buffer filled up to 25 µL. 25 µL of transposition mix [2.5 µL Transposase (100 nM final) (Illumina, 20034197, 16.5 µL PBS, 0.5 µL 1% Digitonin, 0.5 µL 10% Tween-20, 5 µL H2O) was then added. Transposition reactions were mixed and incubated at 37 °C for 30 min with gentle tapping every 10 min. Reactions were cleaned up with the Zymo DNA Clean and Concentrator-5 kit (Zymo Research, D4014). The ATAC-seq library was prepared by amplifying for 9 cycles on a PCR machine. The PCR reaction was purified with Ampure XP beads using double size selection following the manufacturer’s protocol, in which 27.5 µL beads (0.55X sample volume) and 50 µL beads (1.55X sample volume) were used based on 50 µL PCR reaction. The ATAC-seq libraries were quantitated by Qubit assays and sequenced on an Illumina NextSeq 500 platform. Data analysis was performed on data generated in this study and ATAC-seq data that we previously generated from hTSCs derived from naïve hPSCs¹⁰ (GSE138761). The data were processed as previously described¹¹⁹. Raw reads of ATAC-seq libraries were aligned to human reference genome (hg38) and further processed by using AIAP¹¹⁹, which consists of four steps: data processing, quality control, integrative analysis, and data visualization. The narrow peak files of all ATAC-seq libraries were further merged by using the merge function of BEDTools suite¹²⁰, and the read counts of each ATAC-seq peak were quantified by using BEDTools coverage command. hTSC-specific and hPSC-specific DARs were identified by using DESeq2¹¹⁷ with the cutoff as p_adj < 1e−5 and absolute log2(fold change)>2 as previously described¹⁰. Batch effects among read counts under ATAC-seq peaks of the WT and TEAD1 KO hTSC, EVT, and STB samples were removed using RUVr function of the RUVseq R package, with the estimated number of factors of unwanted variation k as 1. Cell type-specific DARs were identified by DESeq2 with the cutoff as p_adj < 0.01 and absolute log2(fold change)>1. TEAD1 KO-specific and WT-specific DARs were identified among hTSCs, EVTs and STBs using DESeq2 with the cutoff as p_adj < 0.05 and absolute log2(fold change) > 0.5. GREAT tool (v4.0.4) was used to identify the enriched GO terms and pathways of the genes around TEAD1 KO-specific and WT-specific DARs.

CUT&Tag

CUT&Tag was performed as previously described⁶¹. Briefly, 200,000 cells per sample were washed in Wash Buffer [1 mL 1 M HEPES pH 7.5 (Sigma Aldrich, H3375), 1.5 mL 5 M NaCl (Sigma Aldrich, S5150), 12.5 μL 2 M Spermidine (Sigma Aldrich, S2501), 1 Roche Complete Protease Inhibitor EDTA-Free tablet (Sigma Aldrich, 5056489001), and bring the final volume to 50 mL with dH₂O], captured with Concanavalin A beads (Bangs Laboratories, BP531), and incubated with primary antibody at 4 °C overnight. The cells were then incubated with secondary antibody at room temperature for an hour. The following antibodies were used: TEAD1 (1:100, Cell Signaling Technology, 12292S); guinea pig-anti-rabbit (1:100, Antibodies online, ABIN101961). After washing off the unbound antibodies with Dig-Wash Buffer [mix 400 μL 5% Digitonin (EMD Millipore, 300410) with 40 mL Wash Buffer], the pA-Tn5 adapter complex (kindly provided by Dr. Steve Henikoff) was added at 1:200 and incubated at room temperature for an hour. The cells were washed again with Dig-300 Buffer [1 mL 1 M HEPES pH 7.5, 3 mL 5 M NaCl, 12.5 μL 2 M Spermidine, 100 μL 5% Digitonin, 1 Roche Complete Protease Inhibitor EDTA-Free tablet, and bring the final volume to 50 mL with dH₂O], and incubated in 300 μL Tagmentation Buffer [5 mL Dig-300 Buffer and 50 μL 1 M MgCl₂ (Sigma Aldrich, M8266)] per sample at 37 °C for an hour. The reaction was stopped and the DNA solubilized by adding 10 μL 0.5 M EDTA (Research Organics, 3002E), 3 μL 10% SDS (Sigma Aldrich, L4509), and 2.5 μL Proteinase K (Thermo Fisher, EO0492) per sample, and incubated at 50 °C for an hour. The DNA was then extracted, excess RNA digested with RNase A (Thermo Fisher, EN0531), and PCR amplified with i5 and i7 indexing primers. The PCR product was cleaned up with AMPure XP beads, and the size distribution and concentration were confirmed using Tapestation. The libraries were then sequenced on an Illumina NovaSeq S4 2 × 150 platform.

TEAD1 CUT&Tag raw data of each biological replicate were processed with the human reference genome hg38 and further processed by using AIAP(v1.1)¹¹⁹, which consists of four steps: data processing, quality control, integrative analysis, and data visualization. The enriched TEAD1 peaks were identified by using MACS2 peak calling function with the q-value cutoff 1e−5. The peak files of all replicates were merged by using the merge function of BEDTools suite. Only the TEAD1 peaks identified in at least 2 replicates were considered as high-quality binding sites and were further assigned to their nearest neighboring genes for the downstream analysis.

HOMER (v4.11.1)¹²¹ was used to calculate the motif enrichment (Supplementary Data 5) and genomic enrichment under the high-quality TEAD1 binding regions. GREAT tool (v4.0.4)¹²² was used to identify the enriched GO terms and pathways of the genes around TEAD1 binding peaks in human hTSCs.

ATAC-seq signals on TEAD1 binding regions were calculated by using deepTools¹²³ with parameter detailed as “computeMatrix reference-point—referencePoint center -a 5000 -b 5000 -bs 100 –missingDataAsZero”, and the averaged ATAC-seq signals were subsequently plotted by using plotHeatmap in deepTools package.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support this study are available from the corresponding authors upon reasonable request. The CRISPR screen sequencing data, TEAD1 CUT&Tag data, naïve hPSC ATAC-seq data, and the WT and TEAD1 KO hTSC, EVT, and STB RNA-seq and ATAC-seq data generated in this study are available under the GEO accession number GSE172329. The WT primed hPSC, naïve hPSC, hTSC, EVT, and STB RNA-seq data and the WT hTSC ATAC-seq data were retrieved from GSE138762; the human embryo scRNA-seq data was retrieved from GSE136447; the human maternal-fetal interface scRNA-seq data was retrieved from E-MTAB-6701 and E-MTAB-6678; the mouse placenta snRNA-seq data was retrieved from GSE152248; the human reference genome was retrieved from GENCODE v27. Source data are provided with this paper.

Code availability

All custom scripts used in this study are deposited in https://github.com/Zhang-lab/CRISPR/.

References

Shahbazi, M. N. & Zernicka-Goetz, M. Deconstructing and reconstructing the mouse and human early embryo. Nat. Cell Biol. 20, 878–887 (2018).
Article CAS PubMed Google Scholar
James, J. L., Carter, A. M. & Chamley, L. W. Human placentation from nidation to 5 weeks of gestation. Part I: What do we know about formative placental development following implantation? Placenta 33, 327–334 (2012).
Article CAS PubMed Google Scholar
Burton Graham, J., & Fowden Abigail, L. The placenta: A multifaceted, transient organ. Philos. Trans. R. Soc. B: Biol. Sci. 370, 20140066 (2015).
Article CAS Google Scholar
Burton, G. J., Woods, A. W., Jauniaux, E. & Kingdom, J. C. Rheological and physiological consequences of conversion of the maternal spiral arteries for uteroplacental blood flow during human pregnancy. Placenta 30, 473–482 (2009).
Article CAS PubMed PubMed Central Google Scholar
Norwitz, E. R. Defective implantation and placentation: Laying the blueprint for pregnancy complications. Reprod. BioMed. Online 13, 591–599 (2006).
Article CAS PubMed Google Scholar
Moffett, A. & Loke, C. Immunology of placentation in eutherian mammals. Nat. Rev. Immunol. 6, 584–594 (2006).
Article CAS PubMed Google Scholar
Koot, Y. E., Teklenburg, G., Salker, M. S., Brosens, J. J. & Macklon, N. S. Molecular aspects of implantation failure. Biochim. Biophys. Acta 1822, 1943–1950 (2012).
Article CAS PubMed Google Scholar
Okae, H. et al. Derivation of human trophoblast stem cells. Cell Stem Cell 22, 50–63.e6 (2018).
Article CAS PubMed Google Scholar
Io, S et al. Capturing human trophoblast development with naive pluripotent stem cells in vitro. Cell Stem Cell 28, 1023–1039.e13 (2021).
Dong, C et al. Derivation of trophoblast stem cells from naive human pluripotent stem cells. Elife 9, e52504 (2020).
Liu, X. et al. Reprogramming roadmap reveals route to human induced trophoblast stem cells. Nature 586, 101–107 (2020).
Article ADS CAS PubMed Google Scholar
Cinkornpumin, J. K. et al. Naive human embryonic stem cells can give rise to cells with a trophoblast-like transcriptome and methylome. Stem Cell Rep. 15, 198–213 (2020).
Article CAS Google Scholar
Castel, G. et al. Induction of human trophoblast stem cells from somatic cells and pluripotent stem cells. Cell Rep. 33, 108419 (2020).
Article CAS PubMed Google Scholar
Koike-Yusa, H., Li, Y., Tan, E. P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).
Article CAS PubMed Google Scholar
Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).
Article CAS PubMed PubMed Central Google Scholar
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Article CAS PubMed Google Scholar
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Article ADS CAS PubMed Google Scholar
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).
Article ADS CAS PubMed Google Scholar
Ihry, R. J. et al. Genome-scale CRISPR screens identify human pluripotency-specific genes. Cell Rep. 27, 616–630. e6 (2019).
Article CAS PubMed Google Scholar
Mair, B. et al. Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Rep. 27, 599–615 e12 (2019).
Article CAS PubMed Google Scholar
Yilmaz, A., Peretz, M., Aharony, A., Sagi, I. & Benvenisty, N. Defining essential genes for human pluripotent stem cells by CRISPR-Cas9 screening in haploid cells. Nat. Cell Biol. 20, 610–619 (2018).
Article CAS PubMed Google Scholar
Yilmaz, A., Braverman-Gross, C., Bialer-Tsypin, A., Peretz, M. & Benvenisty, N. Mapping gene circuits essential for germ layer differentiation via loss-of-function screens in haploid human embryonic stem cells. Cell Stem Cell 27, 679–691.e6 (2020).
Article CAS PubMed Google Scholar
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Article CAS PubMed PubMed Central Google Scholar
Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733 (2014).
Article PubMed PubMed Central Google Scholar
Hart, T. & Moffat, J. BAGEL: A computational framework for identifying essential genes from pooled library screens. BMC Bioinform. 17, 164 (2016).
Article CAS Google Scholar
Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3: Genes|Genomes|Genet. 7, 2719–2727 (2017).
Article CAS PubMed PubMed Central Google Scholar
Auman, H. J. et al. Transcription factor AP-2γ is essential in the extra-embryonic lineages for early postimplantation development. Development 129, 2733–2747 (2002).
Article CAS PubMed Google Scholar
Werling, U. & Schorle, H. Transcription factor gene AP-2γ essential for early murine development. Mol. Cell. Biol. 22, 3149–3156 (2002).
Article CAS PubMed PubMed Central Google Scholar
Biadasiewicz, K. et al. Transcription factor AP-2α promotes EGF-dependent invasion of human trophoblast. Endocrinology 152, 1458–1469 (2011).
Article CAS PubMed Google Scholar
Kuckenberg, P., Kubaczka, C. & Schorle, H. The role of transcription factor Tcfap2c/TFAP2C in trophectoderm development. Reprod. BioMed. Online 25, 12–20 (2012).
Article CAS PubMed Google Scholar
Krendl, C. et al. GATA2/3-TFAP2A/C transcription factor network couples human pluripotent stem cell differentiation to trophectoderm with repression of pluripotency. Proc. Natl Acad. Sci. USA 114, E9579–E9588 (2017).
Article CAS PubMed PubMed Central Google Scholar
Yagi, R. et al. Transcription factor TEAD4 specifies the trophectoderm lineage at the beginning of mammalian development. Development 134, 3827–3836 (2007).
Article CAS PubMed Google Scholar
Nishioka, N. et al. The Hippo signaling pathway components Lats and Yap pattern Tead4 activity to distinguish mouse trophectoderm from inner cell mass. Dev. Cell 16, 398–410 (2009).
Article CAS PubMed Google Scholar
Nishioka, N. et al. Tead4 is required for specification of trophectoderm in pre-implantation mouse embryos. Mech. Dev. 125, 270–283 (2008).
Article CAS PubMed Google Scholar
Saha, B. et al. TEAD4 ensures postimplantation development by promoting trophoblast self-renewal: An implication in early human pregnancy loss. Proc. Natl Acad. Sci. USA 117, 17864–17875 (2020).
Article CAS PubMed PubMed Central Google Scholar
Chi, F., Sharpley, M. S., Nagaraj, R., Roy, S. S. & Banerjee, U. Glycolysis-independent glucose metabolism distinguishes TE from ICM fate during mammalian embryogenesis. Dev. Cell 53, 9–26.e4 (2020).
Article CAS PubMed PubMed Central Google Scholar
Xiang, L. et al. A developmental landscape of 3D-cultured human pre-gastrulation embryos. Nature 577, 537–542 (2020).
Article CAS PubMed Google Scholar
Nichols, J. & Smith, A. Naive and primed pluripotent states. Cell Stem Cell 4, 487–492 (2009).
Article CAS PubMed Google Scholar
Knöfler, M., Mösl, B., Bauer, S., Griesinger, G. & Husslein, P. TNF-α/TNFRI in primary and immortalized first trimester cytotrophoblasts. Placenta 21, 525–535 (2000).
Article PubMed Google Scholar
Rhee, C. et al. ARID3A is required for mammalian placenta development. Dev. Biol. 422, 83–91 (2017).
Article CAS PubMed Google Scholar
Home, P. et al. Genetic redundancy of GATA factors in the extraembryonic trophoblast lineage ensures the progression of preimplantation and postimplantation mammalian development. Development 144, 876–888 (2017).
CAS PubMed PubMed Central Google Scholar
Yamauchi, Y. et al. Skp2 contributes to cell cycle progression in trophoblast stem cells and to placental development. Genes Cells 25, 427–438 (2020).
Article CAS PubMed Google Scholar
Gerri, C et al. Initiation of a conserved trophectoderm program in human, cow, and mouse embryos. Nature 587, 443–447 (2020).
Wang, Y. et al. [Expression of lethal gene mRNA on placenta villi in patients with spontaneous abortion]. Zhonghua Fu Chan Ke Za Zhi 37, 542–545 (2002).
ADS PubMed Google Scholar
Theunissen, T. W. et al. Molecular criteria for defining the naive human pluripotent state. Cell Stem Cell 19, 502–515 (2016).
Article CAS PubMed PubMed Central Google Scholar
Theunissen, T. W. et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 15, 471–487 (2014).
Article CAS PubMed PubMed Central Google Scholar
Collier, A. J. et al. Comprehensive cell surface protein profiling identifies specific markers of human naive and primed pluripotent states. Cell Stem Cell 20, 874–890. e7 (2017).
Article CAS PubMed PubMed Central Google Scholar
Bredenkamp, N., Stirparo, G. G., Nichols, J., Smith, A. & Guo, G. The cell-surface marker Sushi Containing Domain 2 facilitates establishment of human naive pluripotent stem cells. Stem Cell Rep. 12, 1212–1222 (2019).
Doneda, L., Bulfamante, G., Grimoldi, M. G., Volpi, L. & Larizza, L. Localization of fos, jun, kit and SCF mRNA in human placenta throughout gestation using in situ RT-PCR. Early Pregnancy 3, 265–271 (1997).
CAS PubMed Google Scholar
Renaud, S. J., Kubota, K., Rumi, M. A. & Soares, M. J. The FOS transcription factor family differentially controls trophoblast migration and invasion. J. Biol. Chem. 289, 5025–5039 (2014).
Article CAS PubMed Google Scholar
Ji, J. et al. Fibronectin 1 inhibits the apoptosis of human trophoblasts by activating the PI3K/Akt signaling pathway. Int. J. Mol. Med. 46, 1908–1922 (2020).
CAS PubMed PubMed Central Google Scholar
Tilburgs, T. et al. Human HLA-G+ extravillous trophoblasts: Immune-activating cells that interact with decidual leukocytes. Proc. Natl Acad. Sci. USA 112, 7219–7224 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Ueno, M. et al. c-Met-dependent multipotent labyrinth trophoblast progenitors establish placental exchange interface. Dev. Cell 27, 373–386 (2013).
Article CAS PubMed PubMed Central Google Scholar
Xu, X.-H. et al. Downregulation of lysyl oxidase and lysyl oxidase-like protein 2 suppressed the migration and invasion of trophoblasts by activating the TGF-β/collagen pathway in preeclampsia. Exp. Mol. Med. 51, 1–12 (2019).
PubMed Google Scholar
Zhao, B. et al. Angiomotin is a novel Hippo pathway component that inhibits YAP oncoprotein. Genes Dev. 25, 51–63 (2011).
Article PubMed PubMed Central CAS Google Scholar
Chrysanthou, S. et al. A critical role of TET1/2 proteins in cell-cycle progression of trophoblast stem cells. Stem Cell Rep. 10, 1355–1368 (2018).
Article CAS Google Scholar
Imakawa, K. et al. CITED2 modulation of trophoblast cell differentiation: Insights from global transcriptome analysis. Reproduction 151, 509–516 (2016).
Article CAS PubMed PubMed Central Google Scholar
Hanna, C. W. & Kelsey, G. The specification of imprints in mammals. Heredity 113, 176–183 (2014).
Article CAS PubMed PubMed Central Google Scholar
Waclawik, A. & Ziecik, A. J. Differential expression of prostaglandin (PG) synthesis enzymes in conceptus during peri-implantation period and endometrial expression of carbonyl reductase/PG 9-ketoreductase in the pig. J. Endocrinol. 194, 499–510 (2007).
Article CAS PubMed Google Scholar
Lin, K. C., Park, H. W. & Guan, K. L. Regulation of the Hippo pathway transcription factor TEAD. Trends Biochem. Sci. 42, 862–872 (2017).
Article CAS PubMed PubMed Central Google Scholar
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).
Article ADS PubMed PubMed Central CAS Google Scholar
Guo, G. et al. Human naïve epiblast cells possess unrestricted lineage potential. Cell Stem Cell 28, 1040–1056.e6 (2021).
Kubota, K., Kent, L. N., Rumi, M. A. K., Roby, K. F. & Soares, M. J. Dynamic regulation of AP-1 transcriptional complexes directs trophoblast differentiation. Mol. Cell. Biol. 35, 3163–3177 (2015).
Article CAS PubMed PubMed Central Google Scholar
Goldman-Wohl, D. et al. Eph and ephrin expression in normal placental development and preeclampsia. Placenta 25, 623–630 (2004).
Article CAS PubMed Google Scholar
Fujiwara, H. et al. Eph-ephrin A system regulates human choriocarcinoma-derived JEG-3 cell invasion. Int. J. Gynecol. Cancer 23, 576–582 (2013).
Article PubMed Google Scholar
Yung, H. W. et al. Evidence of placental translation inhibition and endoplasmic reticulum stress in the etiology of human intrauterine growth restriction. Am. J. Pathol. 173, 451–462 (2008).
Article CAS PubMed PubMed Central Google Scholar
Lamar, E. et al. Nrarp is a novel intracellular component of the Notch signaling pathway. Genes Dev. 15, 1885–1899 (2001).
Article CAS PubMed PubMed Central Google Scholar
Haider, S., Pollheimer, J. & Knöfler, M. Notch signalling in placental development and gestational diseases. Placenta 56, 65–72 (2017).
Article CAS PubMed Google Scholar
Yang, W., Li, Q. & Pan, Z. Sphingosine-1-phosphate promotes extravillous trophoblast cell invasion by activating MEK/ERK/MMP-2 signaling pathways via S1P/S1PR1 axis activation. PLoS One 9, e106725 (2014).
Article ADS PubMed PubMed Central CAS Google Scholar
Varberg, K. M. et al. ASCL2 reciprocally controls key trophoblast lineage decisions during hemochorial placenta development. Proc. Natl Acad. Sci. USA 118, e2016517118 (2021).
Lee, C. Q. E. et al. Integrin α2 marks a niche of trophoblast progenitor cells in first trimester human placenta. Development 145, dev162305 (2018).
Azar, C. et al. RNA-Seq identifies genes whose proteins are transformative in the differentiation of cytotrophoblast to syncytiotrophoblast, in human primary villous and BeWo trophoblasts. Sci. Rep. 8, 5142 (2018).
Article ADS PubMed PubMed Central CAS Google Scholar
Zani, A. et al. Interferon-induced transmembrane proteins inhibit cell fusion mediated by trophoblast syncytins. J. Biol. Chem. 294, 19844–19851 (2019).
Article CAS PubMed PubMed Central Google Scholar
Cheung, A. N., Zhang, H. J., Xue, W. C. & Siu, M. K. Pathogenesis of choriocarcinoma: Clinical, genetic and stem cell perspectives. Future Oncol. 5, 217–231 (2009).
Article CAS PubMed Google Scholar
Mi, S. et al. Syncytin is a captive retroviral envelope protein involved in human placental morphogenesis. Nature 403, 785–789 (2000).
Article ADS CAS PubMed Google Scholar
Yu, C. et al. GCMa regulates the syncytin-mediated trophoblastic fusion. J. Biol. Chem. 277, 50062–50068 (2002).
Article CAS PubMed Google Scholar
Hughes, M. et al. The Hand1, Stra13, and Gcm1 transcription factors override FGF signaling to promote terminal differentiation of trophoblast stem cells. Dev. Biol. 271, 26–37 (2004).
Article CAS PubMed Google Scholar
Skonier, J. et al. cDNA cloning and sequence analysis of beta ig-h3, a novel gene induced in a human adenocarcinoma cell line after treatment with transforming growth factor-beta. DNA Cell Biol. 11, 511–522 (1992).
Article CAS PubMed Google Scholar
Wang, W. et al. PTPN14 is required for the density-dependent control of YAP1. Genes Dev. 26, 1959–1971 (2012).
Article CAS PubMed PubMed Central Google Scholar
Huang, J. M. et al. YAP modifies cancer cell sensitivity to EGFR and survivin inhibitors and is negatively regulated by the non-receptor type protein tyrosine phosphatase 14. Oncogene 32, 2220–2229 (2013).
Article CAS PubMed Google Scholar
Liu, X. et al. PTPN14 interacts with and negatively regulates the oncogenic function of YAP. Oncogene 32, 1266–1273 (2013).
Article PubMed CAS Google Scholar
Ouseph, MadhuM. et al. Atypical E2F repressors and activators coordinate placental development. Dev. Cell 22, 849–862 (2012).
Article CAS PubMed PubMed Central Google Scholar
Vallier, L., Alexander, M. & Pedersen, R. A. Activin/Nodal and FGF pathways cooperate to maintain pluripotency of human embryonic stem cells. J. Cell Sci. 118, 4495–4509 (2005).
Article CAS PubMed Google Scholar
Russell, R. et al. A dynamic role of TBX3 in the pluripotency circuitry. Stem Cell Rep. 5, 1155–1170 (2015).
Article CAS Google Scholar
Niwa, H., Ogawa, K., Shimosato, D. & Adachi, K. A parallel circuit of LIF signalling pathways maintains pluripotency of mouse ES cells. Nature 460, 118–122 (2009).
Article ADS CAS PubMed Google Scholar
Lv, B. et al. Single-cell RNA sequencing reveals regulatory mechanism for trophoblast cell-fate divergence in human peri-implantation conceptuses. PLoS Biol. 17, e3000187–e3000187 (2019).
Article CAS PubMed PubMed Central Google Scholar
Cockburn, K., Biechele, S., Garner, J. & Rossant, J. The Hippo pathway member Nf2 is required for inner cell mass specification. Curr. Biol. 23, 1195–1201 (2013).
Article CAS PubMed Google Scholar
Carter, A. M. Animal models of human placentation—a review. Placenta 28, S41–S47 (2007).
Article PubMed Google Scholar
Roberts, R. M., Green, J. A. & Schulz, L. C. The evolution of the placenta. Reproduction 152, R179–R189 (2016).
Article CAS PubMed PubMed Central Google Scholar
Perez-Garcia, V. et al. Placentation defects are highly prevalent in embryonic lethal mouse mutants. Nature 555, 463–468 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).
Article ADS CAS PubMed Google Scholar
Marsh, B. & Blelloch, R. Single nuclei RNA-seq of mouse placental labyrinth development. Elife 9, e60266 (2020).
Shi, J.-H. & Sun, S.-C. Tumor necrosis factor receptor-associated factor regulation of nuclear factor κB and mitogen-activated protein kinase pathways. Front. Immunol. 9, 1849 (2018).
Antonicka, H. et al. A pseudouridine synthase module is essential for mitochondrial protein synthesis and cell viability. EMBO Rep. 18, 28–38 (2017).
Article CAS PubMed Google Scholar
Guarani, V. et al. TIMMDC1/C3orf1 functions as a membrane-embedded mitochondrial complex I assembly factor through association with the MCIA complex. Mol. Cell Biol. 34, 847–861 (2014).
Article PubMed PubMed Central CAS Google Scholar
Lee, R. G. et al. Cardiolipin is required for membrane docking of mitochondrial ribosomes and protein synthesis. J. Cell Sci. 133, jcs240374 (2020).
Kasahara, T. et al. Cardiolipin is essential for early embryonic viability and mitochondrial integrity of neurons in mammals. Faseb J. 34, 1465–1480 (2020).
Article CAS PubMed Google Scholar
Boukalova, S. et al. Dihydroorotate dehydrogenase in oxidative phosphorylation and cancer. Biochim. Biophys. Acta Mol. Basis Dis. 1866, 165759 (2020).
Article CAS PubMed Google Scholar
Houghton, F. D. Energy metabolism of the inner cell mass and trophectoderm of the mouse blastocyst. Differentiation 74, 11–18 (2006).
Article CAS PubMed Google Scholar
Smith, C. L. & Eppig, J. T. The mammalian phenotype ontology: Enabling robust annotation and comparative analysis. Wiley Interdiscip. Rev. Syst. Biol. Med. 1, 390–399 (2009).
Article CAS PubMed PubMed Central Google Scholar
Köhler, S. et al. Expansion of the human phenotype ontology (HPO) knowledge base and resources. Nucleic Acids Res. 47, D1018–D1027 (2018).
Article PubMed Central CAS Google Scholar
Wu, G., Bazer, F. W., Wallace, J. M. & Spencer, T. E. BOARD-INVITED REVIEW: Intrauterine growth retardation: Implications for the animal sciences1. J. Anim. Sci. 84, 2316–2337 (2006).
Article CAS PubMed Google Scholar
Fischer, L. A., Khan, S. A. & Theunissen, T. W. Induction of human naive pluripotency using 5i/L/A medium. Methods Mol. Biol. 2416, 13–28 (2022).
Article PubMed Google Scholar
Bredenkamp, N. et al. Wnt inhibition facilitates RNA-mediated reprogramming of human somatic cells to naive pluripotency. Stem Cell Rep. 13, 1083–1098 (2019).
Dong, C. & Theunissen, T. W. Generating trophoblast stem cells from human naive pluripotent stem cells. Methods Mol. Biol. 2416, 91–104 (2022).
Article PubMed Google Scholar
Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
Article PubMed PubMed Central CAS Google Scholar
Li, W. et al. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 16, 281 (2015).
Article CAS PubMed PubMed Central Google Scholar
Kim, E. & Hart, T. Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier. Genome Med. 13, 2 (2021).
Article CAS PubMed PubMed Central Google Scholar
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).
Article CAS PubMed PubMed Central Google Scholar
Binder, J. X. et al. COMPARTMENTS: Unification and visualization of protein subcellular localization evidence. Database 2014, bau012 (2014).
Hu, H. et al. AnimalTFDB 3.0: A comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 47, D33–d38 (2019).
Article CAS PubMed Google Scholar
Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2012).
Article PubMed PubMed Central CAS Google Scholar
Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: Fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
Article PubMed PubMed Central CAS Google Scholar
Harrow, J. et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Article CAS PubMed PubMed Central Google Scholar
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).
Article CAS PubMed PubMed Central Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central CAS Google Scholar
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
Article CAS PubMed PubMed Central Google Scholar
Liu, S. et al. AIAP: A quality control and integrative analysis package to improve ATAC-seq data analysis. Genomics, Proteomics & Bioinformatics 19, 641–651 (2021).
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Article CAS PubMed PubMed Central Google Scholar
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Article CAS PubMed PubMed Central Google Scholar
Ramírez, F. et al. deepTools2: A next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Article PubMed PubMed Central CAS Google Scholar

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Acknowledgements

This work was supported by the NIH Director’s New Innovator Award (DP2 GM137418) and grants from the Shipley Foundation Program for Innovation in Stem Cell Science, the Edward Mallinckrodt, Jr. Foundation, and the Washington University Children’s Discovery Institute to T.W.T. Federal NIH/NIGMS funds were not used to develop integrated 3D models of human embryonic development. This work was also supported by the NIH Maximizing Investigators’ Research Award (R35 GM142917) and Chan Zuckerberg Initiative to B.Z. We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant# UL1 TR000448 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCRR or NIH. We are grateful to Dr. Steven Henikoff for kindly providing the pA-Tn5 enzyme, Dr. William Pastor, Dr. Hiroaki Okae, and Dr. Takahiro Arima for sharing the BT5 hTSC line, and Dr. Kristen Mengwasser, Dr. Monica Sentmanat, and Dr. William Buchser for advice on CRISPR screen design and analysis. We appreciate Dalyn S. Werner’s help with data analysis. We also thank Dr. Sarah Teichmann and Dr. Mirjana Efremova for providing the UMAP coordinates of EMTAB6678 and EMTAB6701.

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These authors contributed equally: Chen Dong, Shuhua Fu.
These authors jointly supervised this work: Bo Zhang, Thorold W. Theunissen.

Authors and Affiliations

Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
Chen Dong, Shuhua Fu, Rowan M. Karvas, Brian Chew, Laura A. Fischer, Bo Zhang & Thorold W. Theunissen
Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
Chen Dong, Shuhua Fu, Rowan M. Karvas, Brian Chew, Laura A. Fischer, Xiaoyun Xing, Jessica K. Harrison, Ting Wang, Bo Zhang & Thorold W. Theunissen
Department of Genetics, Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
Xiaoyun Xing, Jessica K. Harrison & Ting Wang
Department of Obstetrics and Gynecology, Center for Reproductive Health Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA
Pooja Popli & Ramakrishna Kommagani

Authors

Chen Dong
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Shuhua Fu
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Rowan M. Karvas
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Brian Chew
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Laura A. Fischer
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Xiaoyun Xing
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Jessica K. Harrison
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Pooja Popli
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Ramakrishna Kommagani
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Ting Wang
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Bo Zhang
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Thorold W. Theunissen
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Contributions

C.D. and T.W.T. conceived of and designed the study. C.D. performed the experiments and collected the data. R.M.K., B.C., L.A.F., X.X., J.K.H., and P.P. contributed to experiments. C.D. and S.F. performed the data analysis. C.D. wrote the paper with input from T.W.T. T.W.T., B.Z., T.W., and R.K. supervised the study. T.W.T. and B.Z. acquired funding for the study.

Corresponding authors

Correspondence to Bo Zhang or Thorold W. Theunissen.

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Nature Communications thanks Takahiro Arima and Marta Shahbazi for their contribution to the peer review of this work. This article has been peer reviewed as part of Springer Nature’s Guided Open Access initiative.

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Dong, C., Fu, S., Karvas, R.M. et al. A genome-wide CRISPR-Cas9 knockout screen identifies essential and growth-restricting genes in human trophoblast stem cells. Nat Commun 13, 2548 (2022). https://doi.org/10.1038/s41467-022-30207-9

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Received: 10 September 2021
Accepted: 21 April 2022
Published: 10 May 2022
DOI: https://doi.org/10.1038/s41467-022-30207-9

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