Fertilization & Implantation Systems

Spring 2020 – Systems Biology of Reproduction Discussion Outline – Fertilization & Implantation Systems Michael K. Skinner – Biol 475/575 CUE 418, 10:35-11:50 am, Tuesday & Thursday April 16, 2020 Week 14

Primary Papers:

1. Teperek, et al. (2016) Genome Research 26:1034. 2. Brosens, et al. (2014) Scientific Reports 4:3894. 3. Vento-Tormo, et al. (2018) Nature 563(7731):347-353.

Discussion

Student 9: Reference 1 above • What was the experimental design and objectives? • What impact on the developing embryo was observed? • Can sperm epigenetic alterations modify the embryo?

Student 10: Reference 2 above • How do abnormal embryos influence the endometrial cells? • What transcriptional influences were observed? • How do component embryos promote a supportive uterine environment?

Student 11: Reference 3 above • What was the experimental design and technology? • What maternal-fetal interface interactions were identified? • What new insights for maternal-fetal interface were found to be critical for placentation and reproductive success?

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Research Sperm is epigenetically programmed to regulate gene transcription in embryos

Marta Teperek,1,2,6 Angela Simeone,1,2,6 Vincent Gaggioli,1,2 Kei Miyamoto,1,2 George E. Allen,1 Serap Erkek,3,4,7 Taejoon Kwon,5 Edward M. Marcotte,5 Philip Zegerman,1,2 Charles R. Bradshaw,1 Antoine H.F.M. Peters,3,4 John B. Gurdon,1,2 and Jerome Jullien1,2 1Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, United Kingdom; 2Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom; 3Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; 4Faculty of Sciences, University of Basel, 4001 Basel, Switzerland; 5Department of Molecular Bioscience, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas 78712, USA

For a long time, it has been assumed that the only role of sperm at fertilization is to introduce the male genome into the egg. Recently, ideas have emerged that the epigenetic state of the sperm nucleus could influence transcription in the embryo. However, conflicting reports have challenged the existence of epigenetic marks on sperm genes, and there are no functional tests supporting the role of sperm epigenetic marking on embryonic gene expression. Here, we show that sperm is epigenetically programmed to regulate embryonic gene expression. By comparing the development of sperm- and spermatid-derived frog embryos, we show that the programming of sperm for successful development relates to its ability to regulate transcription of a set of developmentally important genes. During spermatid maturation into sperm, these genes lose H3K4me2/3 and retain H3K27me3 marks. Experimental removal of these epigenetic marks at fertilization de-regulates gene expression in the resulting embryos in a paternal chromatin-dependent manner. This demonstrates that epigenetic instructions delivered by the sperm at fertilization are required for correct regulation of gene expression in the future embryos. The epigenetic mechanisms of developmental programming revealed here are likely to relate to the mechanisms involved in transgenerational transmission of acquired traits. Understanding how parental experience can influence development of the progeny has broad potential for improving human health. [Supplemental material is available for this article.]

Embryos obtained by fertilization develop to adulthood more fre- In this work, we compared the developmental potential of quently than those obtained by other methods, such as nuclear sperm to that of spermatids in order to understand the nature of transfer (Gurdon et al. 1958; Kimura and Yanagimachi 1995), sug- sperm programming for development in Xenopus laevis. The use gesting that sperm is specially programmed to support normal em- of spermatids, immediate precursors of sperm, suits such compar- bryonic development. Several hypotheses were proposed to isons because (1) spermatids have completed meiosis and have the explain the nature of this programming, including the idea that same DNA content as sperm, and (2) spermatid chromatin struc- sperm is programmed for efficient replication after fertilization ture resembles that of somatic cells (Gaucher et al. 2010). (Lemaitre et al. 2005) or for supporting proper embryonic transcrip- Furthermore, in the mouse, embryos derived from injection of tion (Suzuki et al. 2007; Hammoud et al. 2009; Zheng et al. 2012). spermatids into unfertilized oocytes develop to adulthood less fre- The latter hypothesis was proposed following the observation that quently than embryos derived from injection of sperm, suggesting promoters of developmentally important genes escape global re- that developmentally important information is acquired during placement of histones by protamines in mature sperm. In fact, spermatid to sperm maturation (Kimura and Yanagimachi 1995; these promoters retain post-translationally modified histones, sug- Kishigami et al. 2004). Here, we demonstrate that sperm is epige- gesting that epigenetic marks on sperm chromatin may be trans- netically programmed to regulate transcription of several develop- mitted to the embryo at fertilization and could subsequently mentally important embryonic genes. pattern transcription of embryonic genes (Suzuki et al. 2007; Hammoud et al. 2009; Brykczynska et al. 2010; Wu et al. 2011; Results Paradowska et al. 2012; Zheng et al. 2012; Ihara et al. 2014; Siklenka et al. 2015). However, the validity of this hypothesis was Sperm-derived embryos develop better than recently questioned (Carone et al. 2014; Samans et al. 2014). spermatid-derived embryos We first compared the development of embryos obtained by transplanting somatic cell nuclei (SCNT) with the development of 6 These authors contributed equally to this work. sperm-derived embryos. To minimize the experimental difference 7Present address: EMBL Heidelberg, 69117 Heidelberg, Germany Corresponding authors: [email protected], [email protected] Article published online before print. Article, supplemental material, and publi- © 2016 Teperek et al. This article, published in Genome Research, is available cation date are at http://www.genome.org/cgi/doi/10.1101/gr.201541.115. under a Creative Commons License (Attribution 4.0 International), as described Freely available online through the Genome Research Open Access option. at http://creativecommons.org/licenses/by/4.0/.

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Sperm is programmed for embryonic transcription in the way the embryos were generated, both types of embryos of any potential interference from the maternal nucleus, we used were generated by nuclear injection: cloned embryos were ob- haploid, paternally derived embryos generated by injection of per- tained by transplanting nuclei of embryonic cells to enucleated meabilized sperm or spermatids into enucleated eggs (Fig. 3A; eggs (Fig. 1A), and sperm-derived embryos were produced by in- Supplemental Fig. S2; Narbonne et al. 2011). We first confirmed tra-cytoplasmic injection of sperm (ICSI) to eggs (Fig. 1C). We ob- that haploid, sperm-derived embryos developed significantly bet- served that cloned embryos developed less efficiently to the ter to the swimming tadpole stage than haploid, spermatid-de- swimming tadpole stage than sperm-derived embryos (Fig. 1B). rived embryos (P-value < 0.05) (Fig. 3B), recapitulating the results This illustrates the better developmental potential of sperm from diploid embryos (Fig. 1E). Previous mammalian experiments over that of a somatic cell. In this experimental set-up, however, comparing developmental potential of sperm and spermatids used the way the embryos are generated is quite different: the maternal diploid biparental embryos (Kimura and Yanagimachi 1995; genome is present in sperm-derived embryos, whereas it has been Kishigami et al. 2004). Our results with paternal haploid embryos removed in the SCNT embryos. In order to better assess the devel- directly demonstrate that the sperm nucleus supports better developmental potential of sperm, we aimed to compare embryos opment than the spermatid nucleus, with or without the maternal produced in the same way. For that purpose, we generated embryos genome. Furthermore, this experimental set-up allows a specific by injecting permeabilized purified sperm or spermatids (Supple- assessment of transcription originating exclusively from sperm- mental Fig. S1) to the cytoplasm of unfertilized eggs (ICSI) (Fig. or spermatid-derived chromatin at the time of embryonic gene 1C). In that way, both types of embryos are obtained in the same activation. manner, and their development can be compared. The two types of embryos reached the gastrula stage with a similar frequency. Developmentally important genes are misregulated However, sperm-derived embryos developed significantly better in spermatid-derived embryos to the swimming tadpole stage than spermatid-derived ones (P- value < 0.05) (Fig. 1D,E). This is in agreement with observations Since experiments in the mouse suggested that sperm might be made previously in mouse (Kimura and Yanagimachi 1995; Kishi- better than other cell types at supporting mRNA transcription gami et al. 2004). Spermatid-derived embryos and cloned embryos (Ziyyat and Lefevre 2001; Vassena et al. 2007; Ihara et al. 2014), exhibit a similar reduction in developmental potential when com- we tested this hypothesis using haploid, sperm- and spermatid-de- pared to sperm-derived embryos (Fig. 1B–E), suggesting that the rived embryos. Embryos were rigorously staged (see Supplemental spermatid is as severely impaired to support development as a Experimental Procedures) and collected at gastrulation, before the somatic cell. onset of developmental defects. Gene expression was then ana- In conclusion, embryos can be generated in the same way lyzed by RNA-seq, using a set of 34,373 transcripts (provided as from sperm or spermatids, and spermatids show reduced develop- Supplemental Gene Annotation). Out of 18,340 expressed genes, mental potential compared to sperm. Therefore, the comparison 255 were differentially expressed in spermatid-derived embryos of sperm and spermatids was subsequently used to investigate compared to sperm-derived embryos (FDR < 0.05) (Supplemental why sperm support better development than other cell types. Table S1). One hundred of these transcripts showed consistent changes in at least six out of seven experiments, and we will refer Spermatids replicate their DNA as efficiently as sperm to them as “misregulated” (Fig. 3C; Supplemental Table S1). The majority (82/100) were up-regulated in spermatid-derived embry- Since it has been shown that sperm, as opposed to other cells, repos, and they include transcriptional regulators (e.g., gata2, gata3, licate DNA more efficiently (Lemaitre et al. 2005), we hypothesized hes1, and fos) as well as morphogens (e.g., bmp2, bmp7,ordhh) es- that the poor embryonic development of spermatid-derived em- sential for embryonic development. Accordingly, gene enrich- bryos is due to inefficient DNA replication. Egg extracts from ment analysis revealed that several development-related terms Xenopus have been widely used to investigate mechanisms of rep- were significantly enriched in the list of misregulated genes (P-val- lication (Hutchison et al. 1989; Lemaitre et al. 2001). By incubat- ue < 0.05) (Fig. 3D). ing nuclei in egg extracts, one can mimic replication events that The fact that most of the misregulated genes were up-regulat- occur prior to the first embryonic cell division. We measured ed in spermatid-derived embryos raised the possibility that these DNA replication efficiency in sperm and spermatids incubated in genes were actively transcribed in spermatids and continued to egg extracts (Hutchison et al. 1989) by molecular combing analysis be transcribed in embryos. Indeed, the spermatid, as opposed to (Gaggioli et al. 2013). In this assay, the egg extract is supplemented the sperm, is a transcriptionally active cell, and this difference with modified nucleotide precursors that will be incorporated into might explain why genes are up-regulated in embryos originating replicating DNA (Fig. 2A). Following replication, DNA is stretched from spermatids rather than sperm. To test this possibility, we on a slide, and both the total (green) and replicated (red) DNA fi- compared the expression level of genes in spermatids and in sper- bers are fluorescently labeled (Fig. 2B). By measuring the extent matid-derived embryos. We did not observe any correlation be- of replication on several hundreds of DNA fibers, we observed tween the expression of misregulated genes in spermatid-derived equally efficient DNA replication in both sperm and spermatids embryos and their expression in spermatids (r = −0.17, P-value < (Fig. 2C). We concluded from this analysis that the nature of sperm 0.05) (Fig. 3E). This suggests that the up-regulation of these programming is not related to replication. We then tested whether genes in spermatid-derived embryos is not the result of transcript spermatid-derived embryos are capable of correctly initiating em- carry-over (or ongoing transcription) from spermatid chromatin. bryonic transcription. Because permeabilized spermatids used to generate embryos are likely to contain additional RNAs, we performed an additional Haploid sperm-derived embryos develop better than haploid control for the potential effect of spermatid-derived RNAs on em- spermatid-derived embryos bryonic development. We purified total RNA from testis and in- To rigorously assess the developmental potential and transcrip- jected a quantity corresponding to the amount found in one tional ability of sperm and spermatids, and to eliminate the risk spermatid (50 pg) to embryos generated with sperm. TRIzol was

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Teperek et al.

Figure 1. Xenopus sperm is better at supporting development than a spermatid or a somatic cell. (A) Experimental design for the generation of cloned embryos. The somatic nucleus of a gastrula cell is transplanted to a UV-enucleated egg. The resulting embryos are scored at the gastrulation and tadpole stage. (B) Scoring of embryos as % of gastrulae and as % of swimming tadpoles to the total number of cleaved embryos. Average of n = 6 independent experiments (sperm ICSI), and n = 3 independent experiments (embryo cell NT). The total number of embryos analyzed is shown above the graph. Error bars: SEM. (∗) P-value < 0.05 (χ2 test). (C) Experimental design for the generation of sperm- and spermatid-derived embryos. Permeabilized sperm or spermatids are injected to the cytoplasm (ICSI) of an unfertilized egg. The resulting embryos are scored at the gastrulation and tadpole stage. (D) Representative images of sperm- and spermatid-embryos. Scale bars = 1 mm. (E) Scoring of embryos as % of gastrula and as % of swimming tadpoles to the total number of cleaved embryos (average of n = 6 independent experiments). The total number of embryos analyzed is shown above the graph. Error bars: SEM. (∗) P-value < 0.05 (χ2 test).

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Sperm is programmed for embryonic transcription

28S rRNAs are equally well produced in sperm- and spermatid-derived embryos (Supplemental Fig. S4). We conclude that the developmental failure of spermatid-derived embryos is not associated with carried over spermatid RNAs or with defects in rRNA expression. Rather, we observe a correlation between developmental defects and misexpression of a set of developmentally important genes in spermatid-derived embryos. We hypothesized that differences in gene expression between sperm- and spermatid-derived embryos might result from epigenetic differences of sperm/spermatid chromatin.

Epigenetic differences between sperm and spermatid chromatin To investigate potential links between the epigenetic marking of paternal chromatin and embryonic gene expression, we carried out epigenetic profiling of mononucleosomal chromatin from sperm and spermatid using an extensive MNase digestion protocol applied by others to probe for histones stably associated with chromatin in mature sperm in mouse and human (Supplemental Tables S2, S3; Supplemental Fig. S5; Hammoud et al. 2009; Brykczynska et al. 2010). In Xenopus, the transition from spermatid to sperm is characterized by histone H3 and H4 retention and partial loss of H2A and H2B (Risley and Eckhardt 1981). We first compared nucleosome occupancy profiles in sperm and spermatids. Similarly to what was observed in other vertebrates (Hammoud et al. 2009; Brykczynska et al. 2010), we observed higher nucleosomal occupancy (MNase-seq) (Fig. 4A) around TSSs (transcriptional start sites) in sperm when compared to spermatids. In this context, the positioned nucleosomes show a similar distribution in sperm and spermatids (Supplemental Fig. S6). Secondly, we analyzed DNA methylation profiles in sperm and spermatids by MBD-seq. DNA methylation was higher around sperm TSSs than spermatid TSSs (Fig. 4B). These differences were observed at the genome-wide level between sperm and spermatids, as well as on the set of misregulated genes (Supplemental Figs. S6, S7). A lower level of nucleosome occupancy and DNA methylation in spermatids compared to sperm could therefore explain the up-regulation of genes in spermatid- compared to sperm-derived embryos. Figure 2. Spermatids are as good as sperm at DNA replication. (A) To further characterize sperm and spermatid chromatin, we Sperm and spermatids are separately incubated with egg extracts supple- performed ChIP-seq analysis of histone marks associated with mented with biotin-dUTP. Subsequently, DNA fibers are isolated and sub- activation (H3K4me2, H3K4me3) or repression (H3K27me3, jected to molecular combing, which reveals replication on single DNA H3K9me3) of transcription. We looked for peaks (Fig. 4C; Supple- fibers. (B) Examples of DNA fibers after immunostaining procedure. Antibody staining against DNA reveals the total length of the fiber (green) mental Table S4) as well as for the overall methylation levels (Fig. and antibody staining against biotin reveals the replicated DNA (red). The 4D; Supplemental Fig. S8; Supplemental Table S5) around TSSs bottom panels show representative examples of replication staining from for each of these modifications. When compared to all genes, sperm and from spermatids incubated in egg extracts. (C) Replication ex- the set of misregulated genes was significantly enriched for tent measured as the proportion of DNA that incorporated biotin-dUTP to the total fiber length. Results are from at least 125 independent DNA fibers H3K27me3 in both sperm and spermatids (Fig. 4C). However, (22,000 kb of DNA for each sample). Error bars: SEM. Samples were not since H3K27me3 was present in both sperm and spermatids, it significantly different (P-value = 0.41, KS-test). suggests that this repressive mark alone cannot explain the difference in gene expression observed in sperm- and spermatid-derived embryos. used to isolate RNA as it allows the recovery of RNA in a broad Interestingly, histone marks associated with active transcrip- range of sizes (El-Khoury et al. 2016). All embryos generated in tion (H3K4me2/3) showed an enrichment at promoters of mis- that way developed normally, indicating that testicular RNAs are regulated genes in spermatids but not in sperm (Fig. 4C,D), not detrimental to embryonic development (Supplemental Fig. providing a plausible explanation for the up-regulation of these S3). Lastly, we have verified that the synthesis of rRNAs is not af- genes in spermatid-derived embryos. fected in spermatid-derived embryos. Indeed, in mouse, defect in rRNA synthesis has been proposed to explain the developmental Coexistence of H3K4me2/3 and H3K27me3 in spermatids defect of nuclear transfer embryos (Suzuki et al. 2007; Zheng et al. 2012) and could explain the difference in the bulk of RNA correlates with embryonic gene up-regulation synthesis observed between sperm- and spermatid-derived embry- The epigenetic features analyzed (histone marks, DNA methyla- os (Bui et al. 2011). We observed that newly synthesized 18S and tion, and nucleosome occupancy) can individually provide a

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Figure 3. Transcription of developmentally important genes is misregulated in spermatid-derived embryos compared to sperm-derived embryos. (A) Schematic representation of paternally derived haploid embryos generated by UV enucleation of eggs followed by intra-cytoplasmic sperm injection (ICSI). (B) Developmental advantage of sperm over spermatid is maintained in haploid embryos. Embryos were scored as the % of embryos reaching a gastrula stage and a swimming tadpole stage to the total number of cleaved embryos (average of n = 3 independent experiments). Numbers of embryos analyzed are indicated above the bars. Error bars: SEM. (∗) P-value < 0.05 (χ2 test). (C) Genes important for development are misregulated (mostly up-regulated) in spermatid-derived embryos. Heat map representing log fold-change in expression levels of the 100 genes (rows) misregulated in spermatid versus sperm gastrula embryos (FDR < 0.05; red: up-regulated; blue: down-regulated in spermatid) across seven independent experiments (columns). (D) Developmentally important gene ontology terms enriched in the list of misregulated genes (P-value < 0.05). (E) Up-regulation of genes in spermatid-derived embryos does not correlate with their transcription in spermatid. Density scatter plot showing gene expression in spermatid-derived embryos versus that in spermatids. No correlation is observed between the two parameters for all genes (r = 0.06) as well as for the misregulated genes (red dots, r = −0.17).

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Sperm is programmed for embryonic transcription

Figure 4. Genes that are misregulated in spermatid-derived embryos have different epigenetic features in sperm and spermatid. (A) Genome-wide average nucleosome occupancy at the TSS of sperm (blue) and spermatid (green) genes. (B) Boxplots showing genome-wide DNA methylation levels at the TSS ± 1 kb of sperm (blue) and spermatid (green) genes. Inset shows correlation between the DNA methylation levels of sperm and spermatid (R = 0.8, P- value < 0.05); red line: regression; dotted line: diagonal. (C) Percentage of genes harboring H3K27me3, H3K4me3, H3K4me2, or H3K9me3 peaks genome-wide (GW) and at misregulated genes (Mis). (∗) P-value < 0.05 (χ2 test). (D) Heat maps representing H3K27me3, H3K4me3, H3K4me2, and H3K9me3 overall levels (see Supplemental Material and Supplemental Fig. S8) at misregulated genes in sperm (first column) and spermatid (second column). Each map is sorted according to the signal in spermatid. Boxplots show the distribution of methylation levels across misregulated genes. (∗) P-value < 0.05 (KS-test) (Supplemental Table S7).

possible explanation for the observed differences in expression der to identify such interactions, we have performed a partial cor- in sperm- and spermatid-derived embryos. However, complex relation analysis. In this analysis, all the measured parameters interactions involving more than one epigenetic feature might are assessed simultaneously to produce maps indicating the way better explain this differential embryonic gene expression. In or- epigenetic features associate with each other and with gene

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Teperek et al. expression. The aim of such analysis is to extract more general thereby contributing to their up-regulation in spermatid-derived principles describing the paternal epigenetic program underlying embryos. gene expression and identify which epigenetic features are likely We checked whether the observed distribution of H3K4me2/ to have the strongest contribution to embryonic gene expression. 3 and H3K27me3 on the misregulated genes of Xenopus sperm was We applied the partial correlation analysis to identify links be- a conserved feature across species. For that purpose, we investigat- tween the measured epigenetic features in sperm and the expres- ed how these histone marks were distributed in human sperm sion of the misregulated genes in sperm-derived embryos (Fig. (Hammoud et al. 2009) on human orthologs of the Xenopus mis- 5A), or between the measured epigenetic features in spermatid regulated genes. We observed that, similarly to Xenopus sperm, and their expression in spermatid-derived embryos (Fig. 5B). We human sperm showed an enrichment for H3K27me3 on misregu- observed that sperm H3K4me2/3 and embryonic gene expression lated genes (Fig. 5C). Additionally, misregulated genes did not were positively associated, whereas sperm H3K27me3, H3K9me3, exhibit any enrichment over the genome-wide distribution for and DNA methylation are negatively linked to embryonic gene ex- H3K4me2 in both species. This indicates a conservation of sperm pression (Fig. 5A). Interestingly, activating H3K4me2/3 and repres- epigenetic features on these genes in the two species. sive H3K27me3 marks in spermatids were positively linked to gene We next tested if paternally derived H3K4me2/3 and expression in spermatid-derived embryos, and at the same time H3K27me3 were indeed involved in patterning embryonic gene they were also strongly associated with each other (Fig. 5B). expression. These associations were also observed when performing a similar analysis using an extended set of misregulated genes obtained Paternal H3K4me2/3 and H3K27me3 influence by relaxing the selection parameters from FDR ≤ 0.05 (255 genes) embryonic gene expression to FDR ≤ 0.4 and |logFC| ≥ 0.2 (1116 genes). The use of this extended set increased the predictive power of the analysis and To test the function of epigenetic marks from sperm or spermatid showed stronger links between the features tested within an overall chromatin on the regulation of embryonic transcription, we exper- similar network (Supplemental Fig. S9). Therefore, the difference imentally removed these marks in embryos (Fig. 6A; Supplemental between sperm- and spermatid-derived embryos is best explained Fig. S10). mRNAs encoding histone demethylases or control by the fact that, in contrast to sperm, where H3K27me3 is overrep- mRNAs were first injected into immature oocytes. After allowing resented at genes differentially expressed in haploid embryos, in 24 h for the enzymes to be expressed, the oocytes were in vitro–ma- spermatids H3K27me3 coexists with H3K4me2/3 on these genes, tured into eggs (IVM) and injected with sperm or spermatids

Figure 5. H3K27me3 target genes that lose H3K4me2/3 in sperm compared to spermatids are misregulated in spermatid-derived embryos. (A,B) Differential gene expression between sperm- and spermatid-derived embryos best correlates with differential H3K4me2/3 and H3K27me3 marking in sperm and spermatids. Partial correlation network between all tested epigenetic features of the paternal chromatin (A, sperm; B, spermatid) and gene expression in the corresponding embryos. Edges (lines) represent positive (red) or negative (blue) partial correlations. Edges thickness: strength of the partial correlations. (C) H3K4me2 and H3K27me3 marking on misregulated genes is conserved between Xenopus and human sperm. As compared to all orthologs, the misregulated orthologs are enriched for H3K27me3 marks over the genome-wide average in human sperm (χ2 test, [∗] P-value < 0.05). No statistical enrichment for H3K4me2 on misregulated genes as compared to the genome-wide average is observed in human sperm.

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Figure 6. Paternal genome marking by H3K4me2/3 and H3K27me3 is required for gene expression in the embryos. (A) Histone demethylase expression assay. (B) MA plot showing log fold-change (logFC, y-axis) in gene expression between Kdm5b (H3K4me2/3 demethylase)- versus control mRNA-injected embryos, against log counts per million (logCPM, x-axis). Red dots: genes differentially expressed (FDR < 0.05); N = 4 independent experiments. (C) Venn diagram of down-regulated genes upon KDM5B expression in sperm- (blue) and spermatid-derived (green) embryos. (D) Percentages of genes down-regulated upon KDM5B expression in embryos that show H3K4me2/3 and H3K27me3 promoter peaks in the paternal cell. (∗) P-value < 0.05 (χ2 test); ↑: overrepresented when compared to genome-wide distribution. (E) Proportion of misregulated genes affected in each demethylase expression assay. (∗) P-value <0.05(χ2 test). (F) Same as B for KDM6B (H3K27me3 demethylase) expression. (G) Same as C with genes up-regulated upon KDM6B expression. (H) Same as D for genes up-regulated upon KDM6B expression. (I) Model of epigenetic programming of sperm for the regulation of embryonic transcription.

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(ICSI). The resulting embryos were collected at the gastrulation Our analysis shows that, similar to what has been observed in stage for RNA-seq analysis. In this protocol, histones from both mouse (Kimura and Yanagimachi 1995; Kishigami et al. 2004), maternal and paternal chromatin are demethylated when the em- spermatids are not as good at supporting development as sperm. bryo is generated. By comparing embryos produced with different Second, we tested several hypotheses proposed to explain the paternal chromatin (sperm or spermatid), we can evaluate the developmental advantage of sperm over spermatids. We have effect of paternal epigenetic mark removal on embryonic gene ruled out the hypothesis that spermatids are less efficient than expression. sperm at supporting replication. Instead, we found evidence sup- We first expressed the H3K4me2/3 demethylase, KDM5B. As porting the hypothesis that sperm is programmed to support prop- expected, removal of the activating H3K4me2/3 marks led to gene er embryonic expression of genes encoding important embryonic down-regulation: 68% (1893 genes) and 80% (1392 genes) of all regulators (Fig. 3). Importantly, overexpression and knockdown differentially expressed (FDR < 0.05) genes were down-regulated studies of several of these genes have shown embryonic develop- in sperm- and spermatid-derived embryos, respectively (Fig. 6B; mental defects reminiscent of what is observed in spermatid-de- Supplemental Table S6). Importantly, genes down-regulated in rived embryos (sfrp2 [Lee et al. 2006]; tbx3 [Weidgang et al. sperm-derived embryos showed only limited overlap with those 2013]; foxa2 [Suri et al. 2004]; otx2 [Yasuoka et al. 2014]). These ob- down-regulated in spermatid-derived embryos (Fig. 6C). This indi- servations suggest that misexpression of this set of genes is the cates a paternal chromatin-dependent effect of H3K4me2/3 re- cause of the developmental defect observed in spermatid-derived moval on embryonic gene expression. Additionally, among the embryos. We also showed that the developmental advantage of genes affected by H3K4me2/3 removal in sperm- and spermatid- sperm over spermatids is maintained in haploid, paternally de- derived embryos, the misregulated genes are overrepresented, indi- rived embryos, indicating that the effect observed is independent cating that paternal H3K4me2/3 specifically regulates this set of of the presence of the maternal genome. To our knowledge, this is genes (Fig. 6E). Interestingly, the genes down-regulated in sperma- the first time that these two hypotheses, developmental advantage tid-derived embryos are enriched for H3K4me2/3 in spermatids related to ability to support replication versus transcription, have (Fig. 6D). These observations are in agreement with the hypothesis been rigorously tested. that the loss of H3K4me2/3 from H3K27me3 marked genes during These analyses allowed us to conclude that sperm is not mere- the spermatid to sperm maturation is necessary for their proper ex- ly a carrier of DNA, but that it also contributes epigenetic informa- pression in embryos. tion required for proper embryonic gene expression. We then To further validate this hypothesis, we tested the influence of focused our analysis on the sperm chromatin as it represents the paternal H3K27me3 by overexpressing the H3K27me3 demethy- most likely vector of such epigenetic information. lase KDM6B (Fig. 6A). In accordance with its repressive function, During spermiogenesis in Xenopus laevis, core histones H3 and removal of H3K27me3 at fertilization led to up-regulation of genes H4 are retained, whereas ∼90% of core histones H2A and H2B are at gastrulation in both sperm- and spermatid-derived embryos. lost (Risley and Eckhardt 1981). This leaves Xenopus sperm with Eighty-seven percent (487 genes) and 76% (173 genes) of differen- ∼10% of the amount of nucleosomal content of a spermatid. This tially expressed genes (FDR < 0.05) were up-regulated in sperm- level of histone retention in sperm is higher than that of mouse and spermatid-derived embryos, respectively (Fig. 6F; Supplemen- (∼1%) (Brykczynska et al. 2010), lower than that of zebrafish tal Table S6). Again, there was only a partial overlap between genes (∼100%) (Wu et al. 2011), and similar to that of human (∼10%) affected by KDM6B in sperm- and spermatid-derived embryos (Brykczynska et al. 2010). Nucleosome retention in vertebrates (Fig. 6G), indicating that this effect is paternal chromatin-depen- therefore seems to show a degree of variation among species. The dent. The affected genes were marked by H3K27me3 in the corre- epigenetic analysis of Xenopus sperm provided here extends the rep- sponding paternal cells (Fig. 6H). Additionally, upon H3K27me3 ertoire of characterized higher vertebrate sperm chromatin and demethylation, about five times more genes were specifically up- identifies conserved chromatin features relevant to developmental regulated in sperm- than in spermatid-derived embryos (402 ver- programming. In that respect, we observed that the programm- sus 88 genes). This suggests that the programming of genes for em- ing of sperm for embryonic gene expression entails a loss of bryonic expression in the paternal chromatin relies on the H3K4me2/3 marking at H3K27me3 target genes during spermatid establishment of an effective H3K27me3-mediated repression at to sperm maturation (Fig. 6I). We showed that the set of genes the spermatid to sperm transition. Lastly, the misregulated genes programmed for embryonic expression during Xenopus sperm are enriched among the genes affected by the H3K27me3 removal, maturation had similar epigenetic features in Xenopus and human indicating that paternal H3K27me3 specifically regulates this set of sperm (Figs. 4C, 5C). So, despite the existence of a hugely variable genes (Fig. 6E). degree of histone retention in sperm among species, this points toward the existence of universal mechanisms preparing sperm Discussion for participation in the normal development of vertebrate embryos. To functionally test the role of sperm epigenetic marks on em- Previous work characterizing the epigenetic features of sperm in bryonic gene expression, one would ideally like to erase these marks zebrafish, mouse, and human has revealed the presence of modi- from the sperm nucleus immediately prior to the generation of em- fied histones around genes involved in embryonic development bryos. Chromatin of mature sperm is highly condensed and inac- (Hammoud et al. 2009; Brykczynska et al. 2010; Wu et al. 2011). cessible, making enzymatic treatments to alter the epigenetic In these species, the presence of activating (H3K4me3) and repres- marks inefficient. Alternative strategies have been developed to sive (H3K27me3) histone marks in sperm correlated with gene ex- use such enzymes either during the process of spermiogenesis, pression in the early embryos (Hammoud et al. 2009; Brykczynska prior to full maturation of sperm (Siklenka et al. 2015), or at fertil- et al. 2010; Wu et al. 2011). In this work, we have used a compar- ization when the sperm chromatin becomes accessible again ison of sperm and its immediate precursor, the spermatid, to inves- (used in this study). In mouse, the former strategy has been used tigate the functional relationship between histone marks and gene to overexpress the H3K4/H3K9 demethylase KDM1A in germ expression. cells. Embryos generated with sperm from animals overexpressing

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Sperm is programmed for embryonic transcription

KDM1A exhibited developmental defects which were transmitted mM KCl, 2 mM CaCl2, 1 mM MgCl2, 5 mM HEPES, pH 7.4) using for several generations in the absence of exogenous KDM1A. This forceps and paper tissues. It is crucial to clean the testes well from analysis demonstrated the existence of epigenetic instruction de- any nontesticular tissues, as otherwise the cells released from the livered by sperm to the embryos and transmitted through several tissues may negatively affect the final purity of isolated cells. generations. However, the overexpression of KDM1A very early Subsequently, testes were torn into small pieces with forceps and – in the process of sperm differentiation leads to numerous ab- homogenized with 2 3 strokes of a Dounce homogenizer (tissue normalities in sperm—for example, the presence of additional from one testis at a time). The cell suspension was then filtered mRNAs (Siklenka et al. 2015). These abnormalities are indirect ef- to remove tissue debris and cell clumps (CellTrics, cat. 04-0042- 2317) and spun down at 800 rcf, 4°C, for 20 min. Supernatant fects of the overexpression of KDM1A early in the process of sperm was discarded and the cell pellet was resuspended in 12 mL of 1 × differentiation. For that reason, it has been difficult to link the dif- MMR. If any red blood cells were visible at the bottom of the pellet ference in gene expression and associated developmental defects to (a result of incomplete removal of blood vessels), only the uncon- particular epigenetic changes in sperm. The approach we describe taminated part of the pellet was recovered, taking extreme care here complements and extends previous analyses. First, by compar- not to disturb the red blood cells. Subsequently, step gradients of ing the epigenetic profiles of sperm and spermatids to differential iodixanol (Optiprep; Sigma, D1556; 60% iodixanol in water) in gene expression in sperm- and spermatid-derived embryos, we 1×MMR final were manually prepared in prechilled 14 mL ultra- identified H3K4me2/3 and H3K27me3 as candidate marks respon- clear centrifuge tubes (Beckman Coulter, #344060) in the following sible for the programming of genes. We then tested this hypothesis order from the bottom to the top of the tube: 4 mL of 30% iodixa- by demethylating the chromatin using KDM6B (H3K27 demethy- nol, 1 mL of 20% iodixanol, 5 mL of 12% iodixanol (all in 1 × lase) or KDM5B (H3K4 demethylase) in embryos generated with MMR), and 2 mL of cell suspension in 1 × MMR on top. Gradients sperm or spermatids. Importantly, in our experimental setup, were spun down in a prechilled SW40Ti rotor at 7500 rpm both sperm and spermatids used had been through a normal differ- (10,000g), 4°C, for 15 min, deceleration without brake (Beckman entiation process. Removal of H3K4me2/3 at fertilization affects Coulter Ultracentrifuge, Optima L-100XP). The top interface frac- different sets of genes in sperm- and spermatid-derived embryos. tion (between 1 × MMR and 12% iodixanol), containing sperma- Genes affected in sperm-derived embryos are enriched for tids, and the pelleted fraction, containing mature sperm, were H3K27me3, whereas genes affected in spermatid-derived embryos collected. Collected fractions were diluted six times with 1 × are enriched for H3K4me2/3. This indicates the importance of MMR and collected by spinning first at 805 rcf, 4°C, for 20 min H3K4me2/3 dynamics at the transition from spermatid to sperm and respinning at 3220 rcf, 4°C, for 20 min to pellet remaining cells. for patterning of the future embryonic gene expression. One hy- Pelleted cells were subjected to nuclei preparation (see below). pothesis to explain the sensitivity of sperm H3K27me3-marked genes to H3K4me2/3 removal would be that these genes acquire Sperm and spermatid nuclei preparation, intra-cytoplasmic H3K4 methylation following fertilization. Our analysis also sug- sperm injections to nonenucleated and to enucleated eggs gests a conserved role for these marks in Xenopus and mouse and embryo culture (Siklenka et al. 2015). Additionally, we also demonstrated that Sperm and spermatid nuclei were permeabilized as described be- removal of H3K27me3 at fertilization affects the embryonic expres- fore (Smith et al. 2006) and stored at −80°C. Injections were per- sion of genes that are marked by H3K27me3 in sperm/spermatids. formed using a Drummond Nanoject microinjector (NanojectII Recent reports probing histone modifications distribution in Auto Nanolitre Injector, Biohit, 3-00-206A) and glass capillaries mouse and human sperm suggested that these epigenetic marks oc- (Biohit, 3-00-203-G/XL) pulled using a Flaming-Brown micropi- curred mostly on repetitive regions of the genome rather than pette puller (settings: heat 700, pull 100, velocity 100, time 10). genes (Carone et al. 2014; Samans et al. 2014). These observations The cell suspension was sucked into the injection needle filled put into question the possibility that such marks would influence with mineral oil. Cells were injected in sperm dilution buffer gene expression in embryos. By providing a functional test of the (SDB) (Smith et al. 2006), and cell concentration was adjusted by need for histone modifications for embryonic gene expression, doing mock injections on a microscope slide to deliver one cell our analysis, together with that of Siklenka et al., clearly shows per 4.6 nL injection. The eggs were placed in batches of 20–25 that, regardless of their genomic location, sperm-delivered modi- on a blotting paper. If they were to be enucleated, they were placed fied histones are important regulators of expression in future em- with the animal pole facing upward, whereas if they were not sub- bryos (Siklenka et al. 2015). jected to enucleation, they were placed on a side (with the margin- Further investigations into the nature of sperm program- al zone upward). For enucleation, eggs were treated for 30 sec with ming, especially the requirement of other epigenetic marks and a UV mineralite lamp (Gurdon 1960) (this step was omitted for their cross-talk in the patterning of embryonic expression, will pro- nonenucleated eggs). Jelly was removed by a 5-sec Hanovia lamp treatment. The eggs were immediately injected with sperm or vide a better understanding of the transgenerational inheritance of spermatid solution and moved to 1 × MBS (Gurdon 1976) supple- epigenetic traits via gametes and could shed light on the mecha- mented with 0.2% bovine serum albumin (BSA). The cell sus- nisms underlying male infertility and other diseases in humans. pension in the needle was replaced every 20–25 batches of eggs injected. At the four-cell stage, embryos were sorted (all the non- Methods cleaved embryos or those with irregular cleavage furrows were discarded) and the culture media replaced with 0.1 × MBS, 0.2% All the experiments involving the use of animals were conducted BSA. Embryos were cultured in 0.1 × MBS, 0.2% BSA (changed dai- according to the regulatory standards of the funding bodies. ly) in a 16°C–18°C incubator. Assessment of developmental stages was performed according to Nieuwkoop and Faber (Nieuwkoop Separation of sperm and spermatids and Faber 1994). Using this table, matching gastrula embryos For each round of sperm and spermatid purification, testes from six from the various experimental groups were collected at stage adult Xenopus laevis males were isolated and manually cleaned 101/2–11 and processed for gene expression analysis (see Supple- from blood vessels and fat bodies in 1 × MMR (100 mM NaCl, 2 mental Data procedures for details).

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Teperek et al.

Interphase egg extract preparation PBS, 0.1% Tween (Sigma, #P5927) (5 min for each wash) and blocked in 3% BSA in PBS (1 h, RT). All antibodies were diluted Eggs were collected in 1 × MMR, de-jellied with 0.2 × MBS, 2% cys- in PBS, 3% BSA, 0.1% Tween. Total DNA was detected simultane- teine (pH 7.8–7.9) (Sigma, #W326305) and washed with 0.2 × ously with replicated DNA with primary antibodies: anti-DNA an- MMR. Subsequently, eggs were activated for 3 min at room temper- tibody (Millipore, #MAB3034) 1:300 dilution, and streptavidin- ature (RT) with 0.2 × MMR supplemented with 0.2 µg/mL calcium Alexa 594 antibody 1:50 to detect biotin (Invitrogen, #S-11227) ionophore (Sigma, #C7522). Eggs were rinsed with 0.2 × MMR, for 30 min at 37°C. Primary antibodies were washed away with and subsequently all abnormal or not activated eggs were re- PBS, 0.1% Tween (four washes) and detected with secondary anti- moved. Eggs were washed with 50 mL of ice-cold extraction buffer bodies diluted 1:50: chicken anti-mouse Alexa 488 (Invitrogen, (EB) (5 mM KCl, 0.5 mM MgCl , 0.2 mM DTT, 5 mM HEPES, 2 #A-21200) and biotinylated antibody anti-streptavidin (Vector pH 7.5) supplemented with protease inhibitors (PI) (Roche, Labs, #BA-0500) for 30 min, 37°C. After four washes in PBS, #11873580001), transferred into centrifugation tubes (Thinwall, 0.1% Tween, a tertiary detection was performed with antibodies Ultra-Clear, 5 mL, 13 × 51-mm tubes, Beckman, #344057), sup- diluted 1:50: goat anti-chicken Alexa 488 (Invitrogen, #A-11039) plemented with 1 mL of EB buffer with PI and 100 µg/mL of and streptavidin-Alexa 594 for 30 min, 37°C. The coverslip was cytochalasin B (Sigma, #C2743), and placed on ice for 10 min. washed three times with PBS, 0.1% Tween, three times in PBS, Subsequently, eggs were spun briefly at 350g for 1 min at 4°C mounted on a microscope slide with a mounting medium (50% (SW55Ti rotor, Beckman Coulter Ultracentrifuge, Optima L- glycerol in PBS), and sealed with nail polish. Images were acquired 100XP), and excess buffer was discarded. Eggs were then spun at with a Zeiss 510 META confocal LSM microscope. Image analysis 18,000g for 10 min at 1°C, the extract was collected with a needle, was performed in ImageJ; the amount of replicated DNA and total transferred to a fresh, prechilled tube, supplemented with PI and DNA was measured individually on single DNA fibers. 10 µg/mL of cytochalasin B, and respun using the same conditions. Extract was collected with a needle and used fresh for the replication assay (see below). RNA extraction and preparation of cDNA library for sequencing Spermatid (1 million) or a pool of five stage 10.5–11.5 embryos Replication in egg extracts and sample preparation were collected and frozen at −80°C. RNA extractions were per- for analysis of DNA fibers formed using a Qiagen RNeasy Mini Kit according to the manufacturer’s protocol. RNA was eluted in 50 µL of DEPC H2O and used to Replication on single DNA fibers was performed as described generate cDNA sequencing libraries using an Illumina TruSeq Kit before (Gaggioli et al. 2013) with slight modifications. Freshly pre- (#RS-122-2001), according to the manufacturer’s protocol. pared egg extracts were supplemented with 20× energy regenera- tion mix: 2 mg/mL creatine kinase (Roche, #10127566001), 150 mM creatine phosphate (Roche, #10621714001), 20 mM ATP mRNA injection to one-cell embryos (Roche, #10519979001), 2 mM EGTA, 20 mM MgCl2, and with Mouse KDM6B (aa1025–1642) or KDM5B (aa1–770) were cloned 20 µM biotin-16-dUTP (Roche, #11093070910). Permeabilized using the p-Entry cloning system (Invitrogen, #K2400-20 and cells were added to a final concentration of 200 nuclei/µL of ex- 11791-020) into a pCS2+ plasmid with a C-terminal HA-tag and tract and incubated at RT for 2 h (tapping every 10 min). The reac- NLS-tag. mRNA was synthesized in vitro using a MEGAscript tion was stopped by adding 10 volumes of ice-cold 1 × PBS SP6 Kit (Ambion, AM1330M) following the manufacturer’s in- (phosphate buffer saline: 137 mM NaCl, 2.7 mM KCl, 10 mM structions. Eggs were in vitro–fertilized and de-jellied using a 2% Na2HPO4 ×2H2O, 2 mM KH2PO4), and cells were spun down at cysteine solution in 0.1 × MMR. Injections into one-cell stage em- 1000g, 4°C, for 7 min. Cells were resuspended in 50 µL of 1 × PBS bryos were performed in injection solution (Smith et al. 2006) us- and mixed immediately with 50 µL of melted (at 65°C) 2% low ing a Drummond Nanoject microinjector, delivering 9.2 ng of melting point agarose (Invitrogen, #16520050) in 1 × PBS. After mRNA per injection (mRNA at 1 mg/mL in DEPC H2O). Embryos solidification, the agarose plug was incubated overnight (O/N) at were cultured at 18°C and collected for Western blot analysis at 50°C with 1 mL 0.5M EDTA, pH 8, 100 µL 10% sarkosyl (Sigma, stage 21 (Nieuwkoop and Faber 1994). Western blot analyses #L5125), 1 mg/mL Proteinase K (New England Biolabs, were performed on 12% polyacrylamide gels using antibodies #P8102S), followed by three washes in TE pH 6.5. Subsequently, against H3K27me3 (Cell Signalling, #9733), H3K9me2/3 (Cell the plug was incubated twice in TE supplemented with 0.1 mM Signalling, #5327), H3K4me2/3 (Abcam, #8580), H4 (Abcam, PMSF (Sigma, #93482) for 30 min at 50°C and washed four times #31830), and against H3 (Abcam, #18521) with 1 mL of 50 mM MES (Sigma, #69889), pH 6.35, 1 mM EDTA (1 h at RT each wash). Then, the solution was removed; Preparation of ChIP-seq samples the plug was melted in 400 µL of MES pH 6.35, 1 mM EDTA at 68°C for 20 min, and the agarose was digested with 2 units of β- Sperm and spermatids were separated as described above. agarase (New England Biolabs, #M0392S) O/N at 42°C. Chromatin fractionation and chromatin immunoprecipitation (ChIP) were performed as described before (Erkek et al. 2013; Hisano et al. 2013) with slight modifications. Pretreatment of Analysis of replication on single DNA fibers sperm cells with DTT was omitted, and chromatin was digested Silanized coverslips were prepared as described before (Labit et al. with 2.5 U of MNase/1 million of cells (Roche, #12533700) 2008). Thirty microliters of replicated DNA solution was pipetted for 30 min at 37°C. The following antibodies against histone onto a silanized coverslip, covered with a nonsilanized coverslip, marks were used in the study: anti-H3K4me2 (Millipore, and incubated for 5 min at RT. Subsequently, the top coverslip #07-030), anti-H3K4me3 (Abcam, #ab8580), anti-H3K4me3 was slid away to stretch DNA fibers and the silanized coverslip (Millipore, #CS200580), anti-H3K27me3 (Millipore, #07-449), with stretched fibers was fixed in a 3:1 solution of methanol:g anti-H3K27me3 (kind gift from Dr. Thomas Jenuwein), and anti- lacial acetic acid for 10 min, RT. The fibers were then denatured H3K9me3 (Abcam, #ab8898). Before ChIP, primary antibodies with 2.5 M HCl (1 h, RT) and dehydrated by washes in 70% etha- were bound to magnetic beads conjugated with secondary anti- nol, 90% ethanol, and 100% ethanol (1 min for each wash). body (Invitrogen, #11204D) according to the manufacturer’spro- Subsequently, the coverslip was dried, washed three times in tocol, and all wash steps in the protocol were carried out with a

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Sperm is programmed for embryonic transcription magnet, instead of centrifugation. Bound DNA was isolated, sepa- and RNA-seq. K.M., V.G., A.S., P.Z., and J.B.G. helped with the ex- rated by electrophoresis, and mononucleosomal bands from perimental design and paper writing. J.J. and J.B.G. supervised the sperm and spermatids were excised and subjected to library prepa- research. ration with a TruSeq DNA Kit (Illumina, #FC-121-2001). For the generation of the input sample, 5%–10% of the MNase-digested chromatin was collected, and the same purification scheme References was followed as with the immunoprecipitated chromatin prior Brykczynska U, Hisano M, Erkek S, Ramos L, Oakeley EJ, Roloff TC, Beisel C, to library preparation with a TruSeq DNA Kit (Illumina, #FC-121- Schubeler D, Stadler MB, Peters AH. 2010. Repressive and active histone 2001). methylation mark distinct promoters in human and mouse spermatozoa. Nat Struct Mol Biol 17: 679–687. 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Sperm is epigenetically programmed to regulate gene transcription in embryos

Marta Teperek, Angela Simeone, Vincent Gaggioli, et al.

Genome Res. 2016 26: 1034-1046 originally published online March 31, 2016 Access the most recent version at doi:10.1101/gr.201541.115

Supplemental http://genome.cshlp.org/content/suppl/2016/07/10/gr.201541.115.DC1 Material

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SUBJECT AREAS: Implantation REPRODUCTIVE BIOLOGY Jan J. Brosens1, Madhuri S. Salker1,2, Gijs Teklenburg3, Jaya Nautiyal2, Scarlett Salter1, Emma S. Lucas1, EMBRYOLOGY Jennifer H. Steel2, Mark Christian1, Yi-Wah Chan4, Carolien M. Boomsma3, Jonathan D. Moore4, Geraldine M. Hartshorne1, Sandra Sˇuc´urovic´5, Biserka Mulac-Jericevic5, Cobi J. Heijnen3, 1 6 6 1 Received Siobhan Quenby , Marian J. Groot Koerkamp , Frank C. P. Holstege , Anatoly Shmygol 3,7 2 October 2013 & Nick S. Macklon Accepted 8 January 2014 1Division of Reproductive Health, Warwick Medical School, Clinical Sciences Research Laboratories, University Hospital, Coventry CV2 2DX, UK, 2Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London Published W12 ONN, UK, 3Department for Reproductive Medicine and Gynecology, University Medical Center Utrecht, PO Box 85500, 6 February 2014 3508 GA, Utrecht, The Netherlands, 4Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK, 5Department of Physiology and Immunology, Medical School, University of Rijeka, Brac´e Branchetta 20, 51000 Rijeka, Croatia, 6Molecular Cancer Research, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands, 7Division of Correspondence and Developmental Origins of Adult Diseases (DOHaD), University of Southampton, Coxford Road, Southampton SO16 5YA, UK. requests for materials should be addressed to Human embryos frequently harbor large-scale complex chromosomal errors that impede normal J.J.B. (J.J.Brosens@ development. Affected embryos may fail to implant although many first breach the endometrial epithelium warwick.ac.uk) and embed in the decidualizing stroma before being rejected via mechanisms that are poorly understood. Here we show that developmentally impaired human embryos elicit an endoplasmic stress response in human decidual cells. A stress response was also evident upon in vivo exposure of mouse uteri to culture medium conditioned by low-quality human embryos. By contrast, signals emanating from developmentally competent embryos activated a focused gene network enriched in metabolic enzymes and implantation factors. We further show that trypsin, a serine protease released by pre-implantation embryos, elicits Ca21 signaling in endometrial epithelial cells. Competent human embryos triggered short-lived oscillatory Ca21 fluxes whereas low-quality embryos caused a heightened and prolonged Ca21 response. Thus, distinct positive and negative mechanisms contribute to active selection of human embryos at implantation.

eproduction in humans is marred by early pregnancy failure. Approximately 15% of clinically recognized pregnancies miscarry. When combined with pre-clinical losses, the true incidence is closer to 50%, ren- R dering miscarriage by far the most common complication of pregnancy1,2. This exceptional attrition rate is attributed to the intrinsic invasiveness of human embryos and the high prevalence of chromosomal errors. Based on genome-wide screening of individual blastomeres, in excess of 70% of high-quality cleavage-stage IVF embryos reportedly harbor cells with complex large-scale structural chromosomal imbalances, some caused by meiotic aneuploidies but most by mitotic non-disjunction3–5. The incidence of aneuploidy in human embryos is estimated to be an order of magnitude higher than in other mammalian species. Further, a vast array of chromosomal errors has been detected in human embryos throughout all stages of pre-implantation development. Many of the chromosomal abnormalities observed in blastocysts have never been recorded in clinical miscarriage samples3, suggesting that these embryos either fail to implant or are rejected soon after breaching the endometrial luminal epithelium2,6–8. Evidence from several mammalian species indicates that the endometrium is intrinsically capable of mounting an implantation response that is tailored to individual embryos. For example, microarray analysis of bovine endometrium has identified gene signatures that are dependent on the origins (e.g. somatic cell nuclear transfer, IVF, or artificial insemination) and developmental potential of the attaching embryo9. Using a co-culture system, we reported previously that human endometrial stromal cells (HESCs) become sensitive to embryonic signals upon differentiation into decidual cells and respond selectively to low-quality human embryos by inhibiting the secretion of key implantation factors, including interleukin-1 beta, heparin-binding EGF-like growth factor, and leukemia inhibitory factor10. Furthermore, aberrant decidualization of HESCs and lack of embryo sensoring are strongly associated with recurrent pregnancy loss11–14. These observations led to the hypothesis that active embryo selection

SCIENTIFIC REPORTS | 4 : 3894 | DOI: 10.1038/srep03894 1 www.nature.com/scientificreports at implantation is essential for reproductive success11–14,althoughthe genome-wide expression profiling using DNA microarrays. Surpri- underlying mechanisms remain as yet poorly characterized. singly, only 15 decidual genes were found to respond significantly A major obstacle is that human implantation events cannot be (P , 0.01) to signals emanating from DCEs. In contrast, 449 studied directly. In culture, the developmental potential of human maternal genes were perturbed in response to medium conditioned embryos can only be assessed indirectly, foremost on morphological by DIEs (Fig. 1A; Table S1). Gene ontology annotation categorized criteria, and over a legally restricted period. To overcome these hur- half of these maternal genes into three broad biological processes: dles, we prospectively collected conditioned medium of individually transport (20%), translation (17%), and cell cycle (13%) (Fig. 1B). cultured human pre-implantation embryos and then characterized To investigate further the response of decidual cells to compro- the maternal response, in vitro as well as in a heterologous in vivo mised embryos, we focused on HSPA8, the most downregulated gene model, to soluble factors produced by low-quality human embryos in the array analysis (Table S2). This gene encodes HSC70, a ubiqui- and embryos of proven developmental competence. We report that tously and constitutively expressed member of the heat shock protein human embryos presage their developmental competence even prior 70 family of molecular chaperones involved in the assembly of multi- to implantation. The spectrum of endometrial responses to cues from protein complexes, transport of nascent polypeptides and regulation different embryos ranges widely, from enhanced expression of key of protein folding16,17. HSC70 levels increased in primary HESCs implantation factors to overt endoplasmic reticulum (ER) stress. We decidualized for 4 or more days (Fig. 1C). Small interfering also provide evidence that embryo-derived serine proteases are (si)RNA-mediated knockdown of this molecular chaperone reduced involved in eliciting a maternal response tailored to the devel- the secretion of prolactin (PRL) and insulin-like growth factor-bind- opmental potential of the conceptus. ing protein 1 (IGFBP1) (Fig. 1D and E), two highly sensitive differentiation markers18. Cell viability was not affected significantly Results (Fig. 1F). 19,20 Developmentally impaired human embryos induce ER stress Because of its role in protein homeostasis , we postulated that response in decidualizing HESCs. We systematically collected the loss of HSC70 causes ER stress in decidual cells. In fact, decidualizing conditioned medium of day 4 human IVF embryos, which had been HESCs mount a physiological unfolded protein response (UPR) cultured individually for 72 h in microdroplets overlaid with mineral associated with ER expansion and acquisition of a secretory pheno- oil. Next, we incubated primary decidualizing HESCs with pooled type21. This UPR was characterized by synchronous induction of culture supernatants from developmentally impaired embryos various chaperones, including protein disulfide isomerase (PDI), (DIEs), which were deemed unsuitable for transfer15, and from BIP (GRP78), endoplasmic oxidoreductin-1a (ERO1a), and cal- embryos that resulted in ongoing pregnancies after single embryo nexin (Fig. 2A). In addition, differentiating HESCs upregulate the transfer (developmentally competent embryos, DCEs; Table S1). expression of the three key ER signaling proteins, the serine/threo- Control cultures consisted of decidualizing HESCs incubated with nine kinase inositol-requiring enzyme 1a (IRE1a), PKR-like unconditioned embryo culture medium (ECM). Incubation of ER-localized eIF2a kinase (PERK), and the protease-activated tran- primary cultures was repeated three times with separate pools of scription factor ATF6, which collectively determine the cellular res- conditioned media from DCEs and DIEs. RNA was isolated from ponse to ER stress signals21. Interestingly, HSC70 knockdown had decidualizing HESCs after 12 h of incubation and analyzed by little or no effect on the expression of other ER chaperones or ATF6

Figure 1 | Decidualizing endometrial cells are biosensors of embryo quality. (a) Venn diagram presenting the number of transcripts regulated in decidualizing HESCs significantly (P , 0.01) regulated in response to signals from developmentally competent embryos (DCE) and developmentally impaired embryos (DIE). (b) Gene Ontology classification of decidual genes regulated in response to soluble factors secreted by DIE. (c) Western blot analysis demonstrating the kinetics of HSC70 induction in primary HESC cultures decidualized with cAMP and MPA in a time-course lasting 8 d. b- ACTIN served as a loading control. A representative result from three different primary cultures is shown. Full length images are presented as Supplementary Information. (d) Primary HESCs were transfected with non-targeting (NT) siRNA or siRNA targeting HSC70, decidualized for 5 d, and then immunoblotted for HSC70. b-ACTIN served as a loading control. Full length images are presented as Supplementary Information. (e) HSC70 knockdown inhibits the secretion of decidual markers, PRL and IGFBP1, in primary HESC cultures differentiated in vitro for 5 d. The data represent mean (6SD) of triplicate experiments. * indicates P , 0.05, and *** P , 0.001. (f) The percentage of viable HESCs, transfected first with non-targeting (NT) siRNA or HSC70 targeting siRNA and then decidualized for 5 d, is presented relative to the number of viable cells in mock-transfected, undifferentiated cells (dotted line). The data represent the mean (6SD) of three biological repeat experiments.

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Figure 2 | HSC70 knockdown induces ER stress in decidualizing stromal cells. (a) Total cell lysates from primary HESC cultures, transfected first with non-targeting (NT) siRNA or siRNA targeting HSC70 and then decidualized with cAMP and MPA for 5 d, were immunoprobed for various proteins involved in UPR, ER stress, and autophagy as indicated. b-ACTIN served as a loading control. Full length images are presented as Supplementary Information. (b) HSC70 knockdown in HESCs promotes autophagosome formation. Primary cells cultured on chamber slides were transfected with either non-targeting (NT) or HSC70 targeting siRNA, decidualized for 5 d, stained for LC3B expression (green) and subjected to confocal microscopy. The nuclei were visualized with DAPI (blue). (c) Primary cultures were co-transfected with pcDNA3/XBP1-luc, pRL-sv40 and either siRNA targeting HSC70 or NT siRNA. The cells were left untreated or differentiated for 5 d before measuring luciferase activity. The results show the normalized mean firefly luciferase activity (6SD), expressed in relative light units (RLU), of four biological repeat experiments. *** indicates P , 0.001. (d) Confluent cultures were transfected as described in (c), left untreated (control) or decidualized for 5 d, and then exposed to 30 ml of unconditioned embryo culture medium (ECM) or media conditioned by DCEs or DIEs for 12 h. The results show normalized mean luciferase activity (6SD), expressed in relative light units (RLU), of three biological repeat experiments. *** indicates P , 0.001. but further enhanced the induction of IRE1a and PERK. In addition, expression of the downstream luciferase gene. Under ER stress con- silencing of this molecular chaperone protein in decidualizing cells ditions, however, activated IRE1a splices the XBP1-luc transcript, markedly upregulated the levels of X-box binding protein 1 (XBP1) allowing translation of the luciferase cDNA. As shown in Fig. 2C, and C/EBP-homologous protein (CHOP), downstream transcrip- decidualization of HESCs transfected with pcDNA3/XBP1-luc only tion factors that couple ER stress to translational inhibition, induc- marginally enhanced luciferase levels whereas simultaneous HSC70 tion of ER chaperones, cell cycle arrest and death21. We speculated knockdown elicited a 5-fold induction. We then reasoned that decid- that lack of overt cell death upon HSC70 knockdown could be ualizing HESCs transfected with pcDNA3/XBP1-luc could serve as a accounted for by augmented autophagy21. In keeping with this bioassay to monitor ER stress responses induced by limited amounts notion, the abundance of the microtubule-associated protein light of ECM. Primary cells seeded in 96-well plates were first transfected chain 3B (LC3B), a marker of autophagic activity22, increased mark- with the reporter construct, decidualized for 5 d, and then exposed edly upon HSC70 knockdown in decidualizing cells (Fig. 2A). for 12 h to either unconditioned ECM or pooled spent medium from Confocal microscopy showed that the pattern of LC3B staining chan- DCEs or DIEs. In keeping with the array findings, soluble factors ged from being punctate and finely granular in undifferentiated and derived from DIEs strongly induced luciferase expression whereas decidualizing cells to immunoreactive aggregates upon HSC70 silen- this response was entirely absent upon incubation with uncon- cing (Fig. 2B). ditioned ECM or medium conditioned by DCEs (Fig. 2D). To test further the assertion that decidualizing HESCs mount an ER stress response upon HSC70 knockdown, we transfected primary Developmentally competent human embryos signal to promote cultures with pcDNA3/XBP1-luc, a plasmid in which human XBP1 implantation. We next explored if the developmental potential of cDNA is fused upstream of luciferase cDNA23. Under non-ER stress human embryos impacts on the expression of uterine implantation conditions, the presence of an in-frame stop codon prevents the genes in vivo. Female C57BL/6 mice were hormonally stimulated to

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Figure 3 | Competent pre-implantation human embryos actively induce a supportive uterine environment. (a) Venn diagram presenting the number of maternal genes significantly (P , 0.01) altered 24 h after exposure of mouse uterus to unconditioned embryo culture medium or medium conditioned by either developmentally competent or impaired human embryos (DCE and DIE, respectively). (b) Tryptic activity was measured in 16 cultures containing a total of 163 human embryos between day 4 to 6 of development. Activity in unconditioned medium was below dotted line. (c) Application of 21 21 embryo-conditioned medium induces [Ca ]i oscillations in Ishikawa cells. Black traces show [Ca ]i recordings from individual cells in response to application of 1520 diluted culture medium obtained from DCE (top panel) or DIE (bottom panel). Red traces represent average of all individual traces in each panel. (d) Total protein lysates obtained from Ishikawa cells 24 h after treatment with trypsin (10 nM) for 5 min were subjected to western blot analysis and immunoprobed for COX2. b-ACTIN served as a loading control. Full length images are presented as Supplementary Information. induce uterine receptivity and the lumen flushed with 50 mlof of a stress response (Fig. S2). This was confirmed by Western blot unconditioned ECM or pooled conditioned culture medium from analysis demonstrating strong uterine expression of Xbp1, Chop and human DCEs or DIEs. The animals were sacrificed 24 h later and the Lc3b in response to DIE signals (Fig. S3). Notably, a modest stress- uterine transcriptome sequenced. As in the experiments with like response was also apparent upon flushing of the uterine lumen primary HESCs, many more genes (,63) were altered in response with medium conditioned by DCEs, which may point towards the to exposure of the uterine lumen to DIE versus DCE conditioned induction of a decidual response and associated UPR. medium (Fig. 3A). However, the extent and amplitude of the transcriptional response to DCE was much more pronounced in The role of embryo-derived trypsin in maternal embryo recogni- vivo when compared to decidualizing primary HESC cultures. tion. A surprising observation was the induction of Prss28 (68-fold) Strikingly, 27 of 90 (30%) uterine genes solely responsive to DCE and Prss29 (6-fold) mRNAs in the mouse uterus in response to DCE signals code factors that have already been implicated in the signals (Table S3). Prss28 and Prss29 are implantation-specific serine implantation process (Table S3), including COX-2 (Ptgs2), proteases that exhibit trypsin-like substrate specificity30,31. Embryo- Cytochrome P450 26a1 (Cyp26a1), and osteopontin (Spp1)24–26.In nic tryptases activate Ca21 signaling and upregulate COX-2 levels in addition, exposure to DCE signals strongly upregulated a group of murine endometrial epithelial cells (EECs), leading to prostaglandin metabolic genes, which included Fabp4, Plin1, Cidec, Adipoq, Retn, production required for implantation26. Prss28 and Prss29 have no and Car3 (Fig. S1). Fatty acid binding protein 4 (Fabp4) is a widely functional homologs in humans32, suggesting a compensatory role used marker of differentiating adipocytes27. Cell death-inducing for other serine proteases or, perhaps, that tryptic activity is no longer DFF45-like effector c (Cidec) promotes lipid droplet expansion involved in embryo-EEC signaling. Comparative analysis of gene whereas perilipin 1 (Plin1) prevents lipase-dependent breakdown expression data showed that (Fig. S4 & S5), unlike their murine of lipid droplets under basal conditions28. Further, adiponectin counterparts, developing human embryos up- and down-regulate (Adipoq) and resistin (Retn) are key adipokines involved in regulat- the expression of TMPRSS15 and AMBP coding for the trypsi- ing energy intake and expenditure as well as insulin sensitivity29. nogen activator (enterokinase) and trypsin inhibitor (trypstatin/ Taken together, the data point towards the existence of an evolu- bikunin precursor), respectively (Fig. S5). Trypsin activity was tionarily conserved network of maternal genes that is responsive to detectable in culture medium conditioned by human embryos embryonic signals and contributes to post-implantation develop- (Fig. 3B). Furthermore, exposure of Ishikawa cells (a cell line ment. By contrast, the response to DIE signals had the hallmarks model for human EECs) to spent embryo medium induced

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21 21 oscillatory increases in intracellular Ca ([Ca ]i). Interestingly, proteases implicated in ENaC activation, although the pattern of 21 [Ca ]i oscillations were discrete, lasting approximately 5 min, in expression of individual genes during pre-implantation development response to DCE medium (Fig. 3C, upper panel). This contrasted frequently differs between mouse and human embryos (Fig. S4 & S5 to more pronounced and much longer oscillations when cells were and data not shown). Remarkably, cues from competent human 21 exposed to DIE signals (Fig. 3C, lower panel). The [Ca ]i fluxes embryos strongly induced two non-conserved implantation-specific 21 induced by embryonic cues bore a striking similarity to [Ca ]i serine proteinases, Prss28 (also known as implantation serine prote- oscillations induced upon application of trypsin (Fig. S6A and D). ase 1 or ISP1) and Prss29 (ISP2), in the mouse uterus. These prote- 21 30,31 Soybean trypsin inhibitor dramatically decreased [Ca ]i signals ases are also co-expressed in pre-implantation murine embryos . induced by spent embryo medium (Fig. S6B and D). Conversely, ISP1 and 2 heterodimerize and form an enzymatic complex essential 21 embryo-induced [Ca ]i oscillations greatly attenuated the for blastocyst hatching, outgrowth and implantation in mice. Taken 21 subsequent [Ca ]i responses to trypsin (Fig. S6C and D). Finally, together, these observations suggest that endometrial protease pro- short exposure to trypsin (5 min) was sufficient to upregulate COX-2 duction accelerates as the embryo approaches the surface epithelium, expression in Ishikawa cells (Fig. 3D). These data indicate that EECs perhaps aligning hatching of the blastocyst with implantation. By serve to amplify discrete embryonic protease signals that induce a contrast, low-quality human embryos triggered prolonged and dis- 21 supportive maternal environment. organized [Ca ]i oscillations in EECs and an uterine stress response in vivo. The mechanism that couples these events warrants further investigations, although it is likely to involve illicit, excessive or Discussion unopposed activation of embryonic proteases, leading to proteotoxic Conflict between parent and offspring is thought to drive reproduct- stress in both the conceptus and surrounding maternal cells. This ive evolution and innovation33. This hypothesis predicts that the conjecture is supported by the observation that DCE but not DIE embryonic genome evolves to extract as much as possible from the signals strongly enhance uterine expression of Spink3, which codes mother to ensure its propagation whereas maternal genes will adapt for the secreted serine protease inhibitor Kazal type 3 (SPINK3). The to safeguard the success of current as well as future offspring. Thus, human homolog SPINK1 critically protects the pancreas from auto- reproductive success in different species depends on balancing evol- digestion by preventing premature protease activation43,44. ving embryonic and maternal traits34–36. A singular feature of the In summary, reproductive success in humans depends on sus- reproductive cycle in humans, shared with very few other mam- tained maternal investment in one - occasionally two - implanting malian species, is menstruation, a process triggered by ‘spontaneous’ embryos. Genomic instability, giving rise to a vast array of chromo- decidualization of the endometrium in an embryo-independent somal errors of variable complexity3, is prevalent in human embryos manner36,37. When placed in the context of fetal-maternal con- throughout all stages of pre-implantation development. This flict33,38, our findings indicate that cyclic decidualization coupled to engrained diversity in embryo quality poses an obvious maternal menstruation emerged as a strategy for early detection and active challenge. Our observations show that both positive and negative rejection of developmentally abnormal embryos that have breached selection mechanisms govern implantation (Fig. 4), rendering this the luminal epithelium. We show that decidual cells mount an extra- process intrinsically dynamic and adaptable to individual embryos. ordinarily polarized transcriptional response to embryonic signals, ranging from being exceptionally discrete in case of a competent Methods embryo to extensive and complex in the presence of a low-quality Experimental ethics policy. This study was approved by the Medical Review Ethics embryo. We further demonstrate that HSPA8 is particularly sensitive Committee of the University Medical Center Utrecht, the Central Committee for to signals from DIEs. Down-regulation of this molecular chaperone Research on Human Subjects in The Netherlands (NL 12481.000.06), and the in decidual cells converts the differentiation-associated UPR into an Hammersmith and Queen Charlotte’s & Chelsea Research Ethics Committee (1997/ overt ER stress response, which in turn compromises secretion of 5065). Written informed consent was obtained from all participating subjects. decidual factors, including PRL and IGFBP1, essential for placental Primary cultures. Endometrial samples were obtained during the secretory phase at formation and fetal development. the time of hysterectomy for benign indications or as an outpatient procedure using Conversely, this study shows that competent pre-implantation using a Wallach EndocellTM sampler (Wallach, USA) under ultrasound guidance. HESC cultures were established, passaged once and decidualized with 0.5 mM 8- human embryos have retained the ability to actively enhance the Bromo-cAMP (Sigma, UK) and 1026 M medroxyprogesterone acetate (MPA; Sigma, uterine environment for implantation. This response to DCE signals UK) as previously described45. is characterized by the induction of 29 known implantation factors, including COX-2, as well as various metabolic enzymes involved in Embryo conditioned media. All patients underwent ovarian stimulation with lipid accumulation, glucose uptake, and energy expenditure (Table recombinant FSH and final oocyte maturation was triggered with hCG. Human embryos were cultured in microdroplets (30 ml) of Human Tubal Fluid medium, S3). Interestingly, several of these metabolic genes (e.g. Cidec, Plin1, supplemented with 5% GPO (40 g/l pasteurized plasma protein, containing 95% Adipoq, Retn and Fabp4) are transcriptionally regulated by peroxi- albumin) under mineral oil from the second day after oocyte retrieval until day 4 some proliferator-activated receptor gamma (PPARc) in adipo- (morula stage). The supernatants were collected from individually cultured embryo cytes39–41, suggesting that embryonic signals activate this nuclear that resulted in pregnancy after transfer of a single fresh embryo (n 5 40) and from embryos deemed of poor quality (n 5 49), and unsuitable for embryo transfer based receptor in the endometrium, perhaps indirectly via induction of on standard morphological criteria15. In parallel, microdroplets not containing 42 COX-2-dependent prostaglandin production . In mice, initiation embryos were collected for control experiments. of implantation requires release of embryonic serine proteases, which in turn activate epithelial Na1 channel (ENaC) in EECs, triggering Microarray analysis of primary HESC cultures and gene ontology. Primary HESCs 21 26 were plated in 48-well tissue culture-grade plates, decidualized with 8-Bromo-cAMP Ca influx and induction of COX-2 . Several lines of evidence and MPA for 5 d, and then incubated for 12 h with 100 ml of separately pooled indicate that this implantation pathway is not only conserved in supernatants, each derived from 10 individually cultured embryos. Three pools of humans but also important for embryo sensoring. We found that conditioned media used were from DCEs and three from DIEs. Total RNA from tryptic activity is detectable in medium conditioned by human HESCs in individual wells was subjected to microarray analysis. Human 70-mer oligos (Operon, Human V2 AROS) spotted onto Codelink Activated slides embryos even before hatching. Incubation of EECs with ECM eli- (Surmodics USA) were used for genome-wide expression profiling. RNA 21 cited [Ca ]i oscillations, a response markedly blunted by soybean amplifications, labeling and hybridizations were performed as described46. Briefly, trypsin inhibitor and recapitulated upon treatment of cells with low 500 ng of each amplified cRNA was coupled to Cy3 or Cy5 fluorophores (Amersham, concentrations of trypsin. Further, brief exposure of EECs to trypsin UK) and subsequently hybridized on a Tecan HS4800PRO and scanned on an Agilent G2565BA microarray scanner. After data extraction using Imagene 8.0 was sufficient to induce COX-2 expression. In silico analysis showed (BioDiscovery), print-tip Loess normalization was performed on mean spot- that progression to the blastocyst stage is associated with gene intensities without background subtraction 30. Data were analyzed using ANOVA (R changes predictive of increased expression and activation of various version 2.2.1/MAANOVA version 0.98-7) (http://www.r-project.org/). Genes with a

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Figure 4 | Positive and negative mechanisms contribute to active selection of human embryos at implantation. (A) Developmentally competent human embryos secrete evolutionary conserved serine proteases that activate epithelial Na1 channel (ENaC) expressed on luminal epithelial cells26, triggering Ca21 signalling and, ultimately, induction of genes involved in implantation and post-implantation embryo development. In concert, the decidualizing endometrium secretes serine protease inhibitors, such as murine SPINK3 and the human homolog SPINK1, to limit embryo-derived proteolytic activity. Note that acquisition of a secretory phenotype upon decidualization depends on massive expansion of the ER in HESCs. (B) By contrast, excessive protease activity emanating from developmentally compromised embryos that have breached the luminal epithelium down-regulates the expression of molecular chaperones in surrounding decidual cells, leading to accumulation of misfolded proteins and ER stress. This in turn compromises decidual cell functions and triggers tissue breakdown and early maternal rejection.

P , 0.01 after false discovery rate correction were considered significant. In addition, 2000 platform. The sequence reads that passed quality filters were analyzed with the a 1.2 fold-change cutoff was applied and the resulting gene lists used for gene ontology following methods: Bowtie Alignment followed by TopHat (splice junctions mapper) (GO) analysis. Regulated genes were mapped to GO-slim categories according to the and Cufflinks (transcript abundance). Sequence data have been submitted to GEO Gene Ontology Consortium: (http://www.geneontology.org/GO_slims/goslim_ (GSE47019). generic.obo). Microarray data have been submitted to ArrayExpress under accession number E-TABM-1064. Transfections of primary endometrial cells. Primary HESCs at 80% confluency Animal experiments. C57BL/6 mice were purchased from Charles River Ltd were transfected with DNA vectors or small interfering RNA (siRNA) (Margate, UK) and all experiments were carried out in accordance with the UK Home oligonucleotides by the calcium phosphate co-precipitation method using the Office Project Licence (PPL70/6867). Immature female (3-week old) mice received a ProFection mammalian transfection kit (Promega, Madison, WI) according to the single dose of 1 mg progesterone and 10 mg/kg/day b-estradiol (Sigma) for a total of manufacturer’s instructions. Reporter assays were done in 96-well plates. The X-box 3 d to prime the uterus for embryo transfer. The uterine horns of control and study binding protein 1 (pcDNA3/XBP1-luc) reporter construct was a kind gift from Dr. mice were injected with an equal volume (50 ml) of either unconditioned embryo Etsu Tashiro (Keio University, Tokyo, Japan). A constitutively active renilla culture medium (ECM), serving as controls, or pooled conditioned media from DCE expression vector (pRL-sv40) served as an internal transfection control. The plates (n 5 9) or DIE embryos (n 5 18). The cervix was not clamped. Then, the incision was were washed twice in phosphate-buffered saline (PBS) and firefly and Renilla closed to allow recovery of the mice. The control and treatment groups each consisted activities were measured using the Luclite luciferase reporter assay system (Luclite, of three animals. The mice were sacrificed 24 h later and uteri either fixed in formalin PerkinElmer, Boston, MA) and the luminescence was measured on a Victor II plate or snap-frozen and stored at 280uC for RNAseq and protein analyses. Both uterine reader (PerkinElmer). For gene-silencing studies, HESCs were cultured in 6-well horns of each animal were analyzed individually. plates until 80% confluency and transiently transfected with 100 nM of the following siRNA reagents (Dharmacon, Lafayette, CO): siCONTROL non-targeting (NT) RNAseq analysis of mouse uteri. Uterine mRNA profiles of 25-day old wild-type siRNA pool and HSPA8 siGENOME SMARTpool. All experiments were performed (WT) mice were generated by deep sequencing, in triplicate, using Illumina HiSeq on three or more primary cultures from different endometrial biopsies.

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Western blot analysis. Protein extracts were prepared by lysing cells in RIPA buffer. 4. Mertzanidou, A. et al. Microarray analysis reveals abnormal chromosomal Protein yield was quantified using the Bio-Rad DC protein assay kit (Bio-Rad, USA). complements in over 70% of 14 normally developing human embryos. Hum Equal amounts of protein were separated by 10% SDS-Polyacrylamide Gel Reprod 28, 256–264, doi:10.1093/humrep/des362 (2013). Electrophoresis (SDS-PAGE) before wet-transfer onto PVDF membrane (Amersham 5. Vanneste, E. et al. Chromosome instability is common in human cleavage-stage Biosciences, UK). Nonspecific binding sites were blocked by overnight incubation embryos. Nat Med 15, 577–583 (2009). with 5% nonfat dry milk in Tris-buffered saline with 1% Tween (TBS-T; 130 mmol/L 6. Quenby, S., Vince, G., Farquharson, R. & Aplin, J. Recurrent miscarriage: a defect NaCl, 20 mmol/L Tris, pH7.6 and 1% Tween). Primary antibodies used were anti- in nature’s quality control? Hum Reprod 17, 1959–1963 (2002). HSC70 (Abcam, UK), anti-BiP, anti-Calnexin, anti-ERo1a, anti-CHOP, anti-PERK, 7. Rajcan-Separovic, E. et al. Identification of copy number variants in miscarriages anti-PDI, anti-LC3B, anti-COX2 (Cell Signaling, USA) and b-ACTIN (Abcam, UK) from couples with idiopathic recurrent pregnancy loss. Hum Reprod 25, which was used as a loading control. All primary antibodies were diluted 151000; 2913–2922, doi:10.1093/humrep/deq202 (2010). except for the anti-b-ACTIN, which was diluted 15100,000. Full length scans are 8. Stephenson, M. D., Awartani, K. A. & Robinson, W. P. Cytogenetic analysis of presented as supplementary information. Note that some images were reflected for miscarriages from couples with recurrent miscarriage: a case-control study. Hum consistency in the sequence of presentation. Reprod 17, 446–451 (2002). 9. Mansouri-Attia, N. et al. Endometrium as an early sensor of in vitro embryo PRL, IGFBP1, and trypsin activity measurements. PRL and IGFBP-1 levels in the manipulation technologies. Proc Natl Acad Sci U S A 106, 5687–5692 (2009). HESC culture media were determined using an amplified two-step sandwich-type 10. Teklenburg, G. et al. Natural selection of human embryos: decidualizing immunoassay (R&D Systems, UK) according to the manufacturer’s protocol. The endometrial stromal cells serve as sensors of embryo quality upon implantation. Trypsin Activity Assay Kit (ABCAM) was used according to the manufacturer’s PLoS One 5, e10258 (2010). instructions to measure trypsin activity in undiluted ECM and unconditioned culture 11. Salker, M. et al. Natural selection of human embryos: impaired decidualization of medium (control). Trypsin activity in ECM was measured between day 4–6 of the endometrium disables embryo-maternal interactieons and causes recurrent embryo development in 16 cultures (containing a total of 163 embryos). The ECM pregnant loss. 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SCIENTIFIC REPORTS | 4 : 3894 | DOI: 10.1038/srep03894 8 Article https://doi.org/10.1038/s41586-018-0698-6

Single-cell reconstruction of the early maternal–fetal interface in humans roser Vento-tormo1,2,13, Mirjana efremova1,13, rachel A. Botting3, Margherita Y. turco2,4,5, Miquel Vento-tormo6, Kerstin B. Meyer1, Jong-eun Park1, emily Stephenson3, Krzysztof Polański1, Angela Goncalves1,7, lucy Gardner2,4, Staffan Holmqvist8, Johan Henriksson1, Angela Zou1, Andrew M. Sharkey2,4, Ben Millar3, Barbara innes3, laura Wood1, Anna Wilbrey-clark1, rebecca P. Payne3, Martin A. ivarsson4, Steve lisgo9, Andrew Filby3, David H. rowitch8, Judith N. Bulmer3, Gavin J. Wright1, Michael J. t. Stubbington1, Muzlifah Haniffa1,3,10,14*, Ashley Moffett2,4,14* & Sarah A. teichmann1,11,12,14*

During early human pregnancy the uterine mucosa transforms into the decidua, into which the fetal placenta implants and where placental trophoblast cells intermingle and communicate with maternal cells. Trophoblast–decidual interactions underlie common diseases of pregnancy, including pre-eclampsia and stillbirth. Here we profile the transcriptomes of about 70,000 single cells from first-trimester placentas with matched maternal blood and decidual cells. The cellular composition of human decidua reveals subsets of perivascular and stromal cells that are located in distinct decidual layers. There are three major subsets of decidual natural killer cells that have distinctive immunomodulatory and chemokine profiles. We develop a repository of ligand–receptor complexes and a statistical tool to predict the cell-type specificity of cell–cell communication via these molecular interactions. Our data identify many regulatory interactions that prevent harmful innate or adaptive immune responses in this environment. Our single-cell atlas of the maternal–fetal interface reveals the cellular organization of the decidua and placenta, and the interactions that are critical for placentation and reproductive success.

During early pregnancy, the uterine mucosal lining—the endometrium— associated with pregnancy disorders such as pre-eclampsia, in which is transformed into the decidua under the influence of progesterone. trophoblast invasion is deficient12. However, detailed understanding Decidualization results from a complex and well-orchestrated differ- of the cellular interactions in the decidua that support early pregnancy entiation program that involves all cellular elements of the mucosa: is lacking. stromal, glandular and immune cells, the last of which include the In this study, we used single-cell transcriptomics to comprehen- distinctive decidual natural killer (dNK) cells1,2. The blastocyst sively resolve the cell states that are involved in maternal–fetal com- implants into the decidua, and initially—before arterial connections munication in the decidua, during early pregnancy when the placenta are established—uterine glands are the source of histotrophic nutrition is established. We then used a computational framework to predict in the placenta3,4. After implantation, placental extravillous trophoblast cell-type-specific ligand–receptor complexes and present a new data- cells (EVT) invade through the decidua and move towards the spiral base of the curated complexes (www.CellPhoneDB.org/). By inte- arteries, where they destroy the smooth muscle media and transform grating these predictions with spatial in situ analysis, we construct a the arteries into high conductance vessels5. Balanced regulation of EVT detailed molecular and cellular map of the human decidual–placental invasion is critical to pregnancy success: to ensure correct allocation of interface. resources to mother and baby, arteries must be sufficiently transformed but excessive invasion must be prevented6. The pivotal regulatory role Maternal and fetal cells in early pregnancy of the decidua is obvious from the life-threatening, uncontrolled troph- We combined droplet-based encapsulation (using the 10x Genomics oblast invasion that occurs when the decidua is absent, as when the Chromium system)13 and plate-based Smart-seq214 single-cell tran- placenta implants on a previous Caesarean section scar7. scriptome profiles from the maternal–fetal interface (11 deciduas and EVT have a unique human leukocyte antigen (HLA) profile: they 5 placentas from 6–14 gestational weeks) and six matched periph- do not express the dominant T cell ligands, class I HLA-A and HLA-B, eral blood mononuclear cells (Fig. 1a, b, Supplementary Tables 1, 2, or class II molecules8,9 but do express HLA-G and HLA-E and poly- Extended Data Fig. 1). After computational quality control and integra- morphic HLA-C class I molecules. These trophoblast HLA ligands tion of transcriptomes from both technologies, we performed graph- have receptors that are expressed by the dominant decidual immune based clustering (see Methods) of the combined dataset and used cells (that is, dNKs), including maternal killer immunoglobulin-like cluster-specific marker genes to annotate the clusters (Fig. 1c, Extended receptors (KIRs) some of which bind to HLA-C molecules10,11. Certain Data Figs. 2, 3a–d, Supplementary Table 2). We studied T cell com- combinations of maternal KIRs and fetal HLA-C genetic variants are position and clonal expansion using full-length transcriptomes from

1Wellcome Sanger Institute, Cambridge, UK. 2Centre for Trophoblast Research, University of Cambridge, Cambridge, UK. 3Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK. 4Department of Pathology, University of Cambridge, Cambridge, UK. 5Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK. 6YDEVS software development, Valencia, Spain. 7German Cancer Research Center (DKFZ), Heidelberg, Germany. 8Department of Paediatrics, Wellcome - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK. 9Human Developmental Biology Resource, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK. 10Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. 11Theory of Condensed Matter Group, The Cavendish Laboratory, University of Cambridge, Cambridge, UK. 12European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. 13These authors contributed equally: Roser Vento-Tormo, 14 Mirjana Efremova. These authors jointly supervised this work: Muzlifah Haniffa, Ashley Moffett, Sarah A. Teichmann. *e-mail: [email protected]; [email protected]; [email protected]

a b d Decidua Early pregnancy: Tissue Cell staining Placenta Maternal Single-cell RNA-seq TCR proling expanded blood • placenta (5) dissociation and sorting HB Clusters 1, 4, 8, 10, 15 • decidua (11) Droplets Blood F • blood (6) CD8 expanded Capillaries VCT CD8c/γδ γδ Immune CD4 SCT CD45+ Non-immune CD45– Treg CD4 MAIT SS2 MAIT/γδ Droplets CD8 EVT SS2 14 panel UMAP2 Decidua sorting UMAP1 c Location Immune Non-immune e Placenta dM EVT 5 dNK1 14.1 PV1 dNK DC 12 20 2 dNK2 14.2 PV2 1 11 dNK3 0 dS1 16 33 24 dNK p 3 dS2 14.1 25.1 13 32 ILC3 21 dS3 PV 30 9 31 NK 12 F1 T cell 14.2 25.2 18 17 NK CD16+ 33 F2 dS 22 8 T cells 19.1 Endo m 0 26 24 Maternal 6 10 T cells 19.2 Endo l blood Placenta Decidua Blood 4 T cells 19.3 Endo f Epi 7 5 Genotype 3 2 15 T cells 25.1 Epi 1 PV 21 29 6 dM2 25.2 Epi 2 28 23 8 7 dM1 1 VCT 27 32 11 Endo 27 DC1 9 VCT p Decidua 15 19.2 31 22 M3 20 SCT 10 18 HB 13 EVT 19.3 19.1 4 17 28 DC2 16 EVT p 23 Monocytes Decidua Blood 29 Monocytes UMAP2

UMAP1 30 Granulocytes UMAP2 UMAP1 Plasma cells 26 Fetal Maternal Unassigned Fig. 1 | Identification of cell types at the maternal–fetal interface. from 10x Genomics and Smart-seq2 (SS2) scRNA-seq analysis visualized a, Diagram illustrating the decidual–placental interface in early pregnancy. by UMAP. Colours indicate cell type or state. n = 11 deciduas, n = 5 DC, dendritic cells; dM, decidual macrophages; dS, decidual stromal cells; placentas and n = 6 blood samples. f, fetal; ILC, innate lymphocyte cells; Endo, endothelial cells; Epi, epithelial glandular cells; F, fibroblasts; HB, l, lymphatic; m, maternal; p, proliferative; M3, maternal macrophages. Hofbauer cells; PV, perivascular cells; SCT, syncytiotrophoblast; VCT, d, UMAP visualization of T cell clonal expansion and clusters by villous cytotrophoblast; EVT, extravillous trophoblast. b, Workflow for integrating Smart-seq2 and 10x Genomics T cell data from clusters 4, 8, 10 single-cell transcriptome profiling of decidua, placenta and maternal and 15 from c. TCR, T cell receptor. MAIT, mucosal-associated invariant peripheral blood mononuclear cells. Numbers in parentheses indicate T cell. e, Origin of droplet cells in c by tissue (above) or genotype (below). number of individuals analysed. c, Placental and decidual cell clusters Purple circle, maternal cells in placenta; green circle, fetal cells in decidua.

Smart-seq2 and reconstructed the T cell receptor sequences from this Trophoblast differentiation by scRNA-seq data, which showed expansion of CD8 T cells in the decidua (Fig. 1d). To investigate maternal–fetal interactions at the decidual–placental interface, we first analysed fetal trophoblast cells isolated from placen- We aligned single-cell RNA-sequencing (scRNA-seq) reads from tal and decidual samples: the latter contain invasive EVT (Extended each cell with overlapping single nucleotide polymorphisms called Data Fig. 5a, b). Consistent with previous results16,17, we resolved two from maternal and fetal genomic DNA to assign cells as fetal or mater- distinct trophoblast differentiation pathways (Fig. 2a). As expected, nal (Fig. 1e, Extended Data Fig. 3e). As expected, decidual samples decidual EVT are at the end of the trajectory, have high levels of expres- contained mostly maternal cells with a few fetal HLA-G+ EVT. Fetal sion of HLA-G and no longer express cell-cycle genes (Extended Data cells dominate the placental samples, with the exception of maternal Fig. 5c). For villous cytotrophoblast cells, CellPhoneDB predicts inter- macrophages (M3 cluster) that express CD14, S100A9, CD163, CD68 actions of receptors involved in cellular proliferation and differentiation and CSF1R (Extended Data Fig. 3f). These are probably derived from (EGFR, NRP2 and MET) with their corresponding ligands expressed by blood monocytes incorporated into the syncytium15. other cells in the placenta. HBEGF, potentially interacting with EGFR, is expressed by Hofbauer cells, and PGF and HGF—the respective lig- Cell communication predicted by CellPhoneDB ands of NRP2 and MET—are expressed by different placental fibroblast To systematically study the interactions between fetal and maternal cells subsets (Fig. 2b, Supplementary Table 5). in the decidual–placental interface, we developed a repository (www. By contrast, during EVT differentiation there is upregulation of CellPhoneDB.org) of ligand–receptor interacting pairs that accounts receptors involved in immunomodulation, cellular adhesion and inva- for their subunit architecture, representing heteromeric complexes sion, the ligands of which are expressed by decidual cells (Fig. 2b). For accurately (Extended Data Fig. 4a). Both secreted and cell-surface mol- example, ACKR2 is a decoy receptor for inflammatory cytokines that ecules are considered; the repository therefore encompasses ligand– are produced by maternal immune cells18 and CXCR6 is a chemokine receptor interactions mediated by the diffusion of secreted molecules. receptor that binds to CXCL16 expressed by the maternal macrophages. Our repository forms the basis of a computational approach to iden- Expression of TGFB1—the function of which is to suppress immune tify biologically relevant ligand–receptor complexes. We consider the responses19 and activate epithelial–mesenchymal transitions—and its expression levels of ligands and receptors within each cell type, and use receptor increases as EVT differentiate. Components involved in the empirical shuffling to calculate which ligand–receptor pairs display epithelial–mesenchymal-transition program are upregulated at the significant cell-type specificity (Extended Data Fig. 4b, see Methods). end of the trajectory20 (Extended Data Fig. 5d); these include PAPPA This predicts molecular interactions between cell populations via spe- and PAPPA2, which encode metalloproteinases that are known to be cific protein complexes, and generates a potential cell–cell communi- involved in cellular invasion. In pregnancy, a decreased level of PAPPA cation network in the decidua and placenta (Extended Data Fig. 4c–e, is a biomarker for pre-eclampsia and fetal growth restriction, which are Supplementary Tables 3, 4). associated with defective EVT invasion21.

a b EGFR HBEGF ACKR2 CCL5 3 2 EVT path SCT 3 2 4 path 2 2 1 1 1 0 0 0 0 NRP2 PGF CXCR6 CXCL16 3 3 4 3 VCT 2 2 2 1 1 2 1 0 0 0 0 MET HGF TGFB1 TGFBR2 EVT 4 3 3 2 3 2 2 1 2 1 1 1 0 0 0 0 log-transformed normalized counts log-transformed normalized counts TGFBR1 Decidua EVTVCTSCT HB M3 Endo F1 F2 EVTVCTSCT 3 SCT Placenta 2 Placenta 1 0 Complex

Epi PV dS dM dNKEVT Endo T cell Decidua Fig. 2 | Ligand–receptor expression during EVT differentiation. ligand–receptor pairs that change during pseudotime and are predicted to a, Pseudotime ordering of trophoblast cells reveals EVT and SCT be significant by CellPhoneDB (EGFR, HBEGF, NRP2, PGF, MET, HGF, pathways. Enriched EPCAM+ and HLA-G+ cells on placental and decidual ACKR2, CCL5, CXCR6, CXCL16, TGFB1, TGFBR2 and TGFBR1). Cells isolates are included. n = 11 deciduas and n = 5 placentas. b, Violin plots from Fig. 1c are used for the violin plots. showing log-transformed, normalized expression levels for selected

Stromal cells in the two decidual layers PV2, and lacks expression of the classical decidual markers prolactin EVT initially invade through the surface epithelium into the decidua (PRL) and IGFBP1. By contrast, dS2 and dS3 express IGFBP1, IGFBP2 compacta. Beneath this is the decidua spongiosa that contains hyper- and IGFBP6 and share markers with two subsets of decidualized stromal secretory glands, which provide histotrophic nutrition to the early cells that have recently been described in vitro22. The dS3 subset conceptus. Markers that distinguish the different decidual fibroblast expresses PRL as well as genes involved in steroid biosynthesis (for populations identify two clusters of perivascular cells (referred to as example, CYP11A1) (Extended Data Fig. 6a). PV1 and PV2) that share expression of the smooth muscle marker To locate the different perivascular and stromal populations in situ, (MGP) and are distinguished by different levels of MCAM, which we used immunohistochemistry as well as multiplexed single-mole- is higher in PV1, and MMP11, which is higher in PV2 (Fig. 3a, cule fluorescent in situ hybridization (smFISH) for selected markers Supplementary Table 6). There are three clusters of stromal cells on serial sections of decidua parietalis. These experiments confirm (labelled dS1, dS2 and dS3), all of which express the WNT inhibitor that cells that express ACTA2 and MCAM are present in the smooth DKK1. dS1 shares the expression of ACTA2 and TAGLN with PV1 and muscle media of the spiral arteries23 and show that MMP11 is also

a b ACTA2 d ACTA2, PRL, CD34 PV1, PV2, MCAM MMP11 IGFBP1, DAPI ACTA2 PRL IGFBP1 z-score PV1 PV2 dS1 dS2 dS3 Smooth muscle MGP Endo dS1 PV1 PV2 SUSD2 1 Cell proliferation TPPP3 and development 0 NOTCH3 MCAM

–1 Perivascular RGS5 Compacta REN PDGFRB CSRP2 Cell signalling EPS8 c CSF1 ACTA2 MMP11 PV1, PV2, dS1 Remodelling CHI3L1 lumen 1 CTSK Uterine Stromal

DKK1 Spongiosa Myobroblasts ACTA2 1 TAGLN Transcription factor ID2 e Around Around Growth regulation IGF1 dS Stroma Partner 1 Partner 2 Stroma z-score arteries arteries z-score IGF2 2 PDGFRA Compacta 2 ANGPT1 TEK 3 1 2 IL15 ANGPT2 TEK Cell signalling COL4A1 ITGB1 1 FOXO1 0 0 –1 COL4A1 ITGA1 –1 SCARA5 NGFR NGF LEFTY2 NOTCH1 JAG1 PLAGL1 dS NOTCH1 DLK1 Growth regulation IGFBP2 Epi VEGFA KDR IGFBP6 Epi 3 VEGFA NRP1 IGFBP1 IGF2 IGF1R IL15 IL2RB Remodelling TIMP3 3

Decidualization IL15RA IL2RG CXCL14 Spongiosa Immunomodulation Epi LGALS9 TIM3 IL1RL1 CLEC2D KLRB1 GLRX PRL PRLR Hormones PRL CYP11A1

Cell cycle ING1 trium Myome- Endo PV1 PV2 EVT dS1 dS2 dS3 Epi dNK1 dNK2 dNK3 dM1 dM2 PV1 PV2 dS1 dS2 dS3

Fig. 3 | Stromal distribution in the two distinct decidual layers. biological replicates). Right panels are a higher magnification of the a, Heat map showing relative expression (z-score) of selected genes respectively numbered inset. Scale bar, 50 µm. d, Multiplexed smFISH of for perivascular and decidual stromal cells (n = 11 deciduas; adjusted decidua parietalis showing two decidual layers. ACTA2+ dS1 in decidua P value < 0.1; Wilcoxon rank-sum test with Bonferroni correction). spongiosa (40× objective); IGBP1+ and PRL+ dS2 and dS3 confined b, Immunohistochemistry of a spiral artery in serial sections of the to decidua compacta (20× objective) (n = 2 biological replicates). decidua, stained for CD34 (endothelial cells), ACTA2 (PV cells and dS1 e, Heat map shows selected significant ligand–receptor interactions cells), MCAM (PV1 cells) and MMP11 (PV2 cells) (n = 2 biological (n = 6 deciduas, P value < 0.05, permutation test, see Methods) replicates). Scale bar, 100 µm. c, Immunohistochemistry of decidual between PV cells and dS cells (left) and decidual cells (right) (n = 11 sections stained for ACTA2, which distinguishes between ACTA2+ dS1 in deciduas). Assays were carried out at the mRNA level, but are extrapolated decidua spongiosa and ACTA2− dS2 and dS3 in decidua compacta (n = 3 to protein interactions.

z-score SPINK2 a dNK1 dNK2 dNK3 b d e g Fructose-1,6-bisP 1 CD39 ??? Gated on live, single cells, z-score z-score LIN–CD56+CD16– ALDOA 0 CYP26A1 1 KIR genes dNK1 GZMB TPI1 –1 2.5 5 5 5 0 B4GALNT1 10 + 10 10 GZMA KIR haplotype + IPD-KIR CD103 dNK3 Glyceraldehyde-3-P CSF1 4 4 4 –1 0.5 10 10 10 GNLY GAPDH EPAS1 PRF1 3 3 dNK2 3 CDKN1A 10 10 10 GAPDHS –1.0

STAT3 Individual A Individual B 0 0 0

dNK1 dNK2 PKM dNK3 1,3-bisphosphoglycerate 3 5 3 5 ID3 0104 0 10 10 0 10 10 f PGK1 CD2 Gated on live, CD103 CD39 ITGB2 CD49a ITGB2 CD103 ITGB2 single cells, 3-phosphoglycerate c z-score – + + ANXA1 1 LIN CD56 NKG2A , PGAM1 NKG2E KIR– versus KIR+ CD27 0 NKG2C 2-phosphoglycerate GZMH –1 104 * NKG2A ENO1 dNK1 dNK2 dNK3 103 ** 5 5 5 ** ** LILRB1 10 86.2 2.01 10 0.31 25.9 10 08.47 102 ENO2 CXCR4 KIR2DS4 104 104 104 101 CCL5 Phosphoenolpyruvate KIR2DS1 10 CD160 3 3 3 – – – – KIR2DL3 10 10 10 + + + + PKM HLA-C HLA-G HLA-E KIR KIR KIR KIR KIR2DL2 0 0 0 KIR KIR KIR KIR

TIGIT 11.4 0.36 0.45 73.4 0 91.5 Pyruvate

AMZG BMZG 1FRP KIR2DL1 YLNG

01103 105 0103 105 0 03 105 LDHA

3KNd 2KNd RGS2 1KNd KIR2DL1 KIR2DL1 Lactate KIR2DL1

ITGB2 ITGB2 ITGB2 dNK1 dNK2 dNK3 Fig. 4 | Three dNK populations. a, Heat map showing relative expression (representative sample from n = 6 individuals; Supplementary Table 9). (z-score) of markers defining the three dNK subsets (n = 11 deciduas; e, z-scores of expression of granule molecules PRF1, GNL1, GZMA and percentage 1 > 10%, percentage 2 < 60%; refers to the percentage of GZMB in dNK subsets (n = 11 individuals). f, Flow cytometry to compare cells with expression above 0 in the corresponding cluster and all other staining of granule components in NKG2A+KIR+ versus NKG2A+KIR− clusters; P value < 0.1 after Bonferroni correction, Wilcoxon rank-sum dNK cells (PRF1 n = 9 individuals; GNLY n = 7 individuals; GZMA n = 8 test). b, Workflow for KIRid method (see https://github.com/Teichlab/ individuals; GZMB n = 10 individuals; Supplementary Table 9). Non- KIRid). IPD-KIR, database for human KIR (available at https://www.ebi. parametric paired Wilcoxon test. *P < 0.05, **P < 0.01. g, Right, z-scores ac.uk/ipd/kir/). c, z-scores of KIR receptors (mean expression levels). of glycolysis enzymes (mean mRNA expression). Left, only differentially Expression values were generated using Smart-seq2 data and the KIRid expressed enzymes are shown in the glycolysis pathway (n = 11 deciduas; approach (n = 5 deciduas). d, FACS gating strategy to identify dNK subsets P value < 0.1 after Bonferroni correction, Wilcoxon rank-sum test). present, which demonstrates that both PV1 and PV2 are perivascular results predict a likely function of dNK1 in the recognition and (Fig. 3b). ACTA2+ dS1 cells are present between glands in the decidua response to EVT. spongiosa, whereas IGFBP1+ and PRL+ dS2 and dS3 cells are located To investigate these three dNK populations further, we analysed in decidua compacta (Fig. 3c, d, Extended Data Fig. 7). CYP11A1 is six decidual samples by flow cytometry using CD49a (expressed by also expressed more abundantly in decidua compacta than in decidua resident dNKs), combined with markers for each dNK subset predicted spongiosa (Extended Data Fig. 6b). from our transcriptomics data (CD39, ITGB2, CD103 and KIR2DL1) Our CellPhoneDB tool predicts that the cognate receptors for (Fig. 4d, Extended Data Fig. 8b). We confirmed the presence of the angiogenic factors that are expressed by PV1 and PV2 (for example, three dNK populations by flow cytometry and the preferential expres- ANGPT1 and VEGFA) are located in the endothelium (Fig. 3e). EVT sion of KIR2DL1 in dNK1 (Fig. 4d, Supplementary Table 9). We first invade the decidua compacta, where dS2 and dS3 express high analysed the morphology of dNK subsets by Giemsa staining of cells levels of LGALS9 and CLEC2D. These molecules could interact with isolated by flow cytometric sorting (Extended Data Fig. 8c). dNK1 their respective inhibitory receptors TIM3 (also known as HAVCR2) contains more cytoplasmic granules than dNK2 and dNK3, which is and KLRB1—which are expressed by subsets of dNKs—enabling the consistent with our scRNA-seq data that show higher levels of expres- stroma to suppress inflammatory reactions in the decidua. sion of PRF1, GNLY, GZMA and GZMB RNA in this subset (Fig. 4e). Higher levels of expression of the granule proteins (PRF1, GNLY, Three decidual NK cell states GZMA and GZMB) are found in KIR+ compared to KIR− dNK cells We identified three main dNK subsets (dNK1, dNK2 and dNK3), by flow cytometry (Fig. 4f). dNK1 cells also express high levels of which all co-express the tissue-resident markers CD49A (also known enzymes involved in glycolysis (Fig. 4g). Thus, dNK1 cells are charac- as ITGA1) and CD9 (Extended Data Fig. 8a). dNK1 cells express CD39 terized by active glycolytic metabolism, and show higher expression of (also known as ENTPD1), CYP26A1 and B4GALNT1, whereas the KIR genes (KIR2DS1, KIR2DS4, KIR2DL1, KIR2DL2 and KIR2DL3), defining markers of dNK2 cells are ANXA1 and ITGB2; the latter is LILRB1 and cytoplasmic granule proteins, suggesting that it is dNK1 shared with dNK3 cells (Fig. 4a, Supplementary Table 7). dNK3 cells cells that particularly interact with EVT. express CD160, KLRB1 and CD103 (also known as ITGAE), but not the First pregnancies are associated with lower proportions of dNK cells innate lymphocyte cell marker CD127 (also known as IL7R) (Extended that express LILRB124, lower birth weights and increased occurrence Data Fig. 8a). of disorders such as pre-eclampsia25. Metabolomic programming of Genes of the KIR family are polymorphic and highly homologous, mature ‘memory’ natural killer cells also occurs in chronic human which makes the quantification of mRNA expression of individual cytomegalovirus infection26. Together, these findings are consistent KIR genes challenging12. We therefore developed ‘KIRid’, a method with the ‘priming’ of dNK1 cells during a first pregnancy so they can that uses full-length transcript Smart-seq2 data to map the single-cell respond more effectively to the implanting placenta in subsequent reads of each donor to the corresponding donor-specific reference of pregnancies. KIR alleles (Fig. 4b, see Methods). We find that dNK1 cells express higher levels of KIRs that can bind to HLA-C molecules: inhibi- Immunomodulation during early pregnancy tory KIR2DL1, KIR2DL2 and KIR2DL3 and activating KIR2DS1 and We next used CellPhoneDB to identify the expression of cytokines KIR2DS4 (Fig. 4c, Supplementary Table 8). LILRB1, the receptor and chemokines by dNKs, and to predict their interactions with other with high affinity for the dimeric form of HLA-G molecules, is cells at the maternal–fetal interface (Fig. 5a, Extended Data Fig. 9a). expressed only by the dNK1 subset. Both dNK1 and dNK2—but However, contrary to previous studies24,27, we find no evidence for not dNK3—express activating NKG2C (also known as KLRC2) and substantial VEGFA or IFNG expression by dNKs in vivo—probably NKG2E (also known as KLRC3) as well as inhibitory NKG2A (also because these studies used dNK cells cultured with IL-2 or IL-15 known as KLRC1) receptors for HLA-E molecules (Fig. 4c). These in vitro.

XCR1–XCL1 –log (P value) XCR1–XCL2 10 EPHB2–EFNA5 0 CSF1R–CSF1 1 CCR1–CCL5 2 PLXNB1–SEMA4D ≥3 recruitment

Adhesion and FGFR1–CD56 CXCL12–CXCR4 FLT1–VEGFB FLT1–VEGFA Angio. KDR–VEGFA EGFR–AREG OSMR–OSM 4 factors Growth HLA-G–LILRB1 ACKR2–CCL5 2 ACKR2–CCL3 0 ACKR2–CCL4 –2 PVR–TIGIT CLEC2D–KLRB1 PDL1–PD1 (mean(molecule 1, molecule 2))

ADORA2B–CD39 2 ADORA3–CD39 LGALS9–TIM3 log

Immunomodulation FPR1–ANXA1 IL10 receptor–IL10 1 reg reg reg reg reg T T T T Interacting T dM1 dM2 dM1 dM2 dM1 dM2 dM1 dM2 dM1 dM2 1 DC1 CD8 CD4 DC1 CD8 CD4 DC1 CD8 CD4 DC1 CD8 CD4 DC1 CD8 CD4 molecules 2 dNK1 dNK2 dNK3 dNK1 dNK2 dNK3 dNK1 dNK2 dNK3 dNK1 dNK2 dNK3 dNK1 dNK2 dNK3 EVT Endothelial Stromal Macrophages (dM2) Dendritic cells (DC1) 2 Interacting cells b Fetal EPHB2 XCR1 CCR1 Fetal c EVT CSF1R ACKR2 EVT PVR HLA-C HLA-G CLEC2D CD73 Adenosine KIRs EFNA5 cAMP KIRs low XCL1 TIGIT ATP LILRB1 KLRB1 dNK1 dNK2 dNK3 CD39 dNK1 dNK2 dNK3 CCL5 CSF1 XCL1 TIM3 KLRB1 TIGIT CXCR4 TIM3 low KLRB1 CCR1 CXCL12 low ADORA3 LGALS9 PVR dM DC1 dM Stromal CSF1R XCR1 Maternal dM2 dS and dM dS and dM dS and dM CD8+ T cells Maternal expanded PD1 Fig. 5 | Multiple regulatory immune responses at the site of Angio., angiogenesis. Assays were carried out at the mRNA level, but are placentation. a, Overview of selected ligand–receptor interactions; extrapolated to protein interactions. b, Diagram of the main receptors P values indicated by circle size, scale on right (permutation test, and ligands expressed on the three dNK subsets that are involved in see Methods). The means of the average expression level of interacting adhesion and cellular recruitment. c, Diagram of the main receptors molecule 1 in cluster 1 and interacting molecule 2 in cluster 2 are and ligands expressed on the three dNK subsets that are involved in indicated by colour. Only droplet data were used (n = 6 deciduas). immunomodulation.

dNK1 cells express higher levels of CSF1, the receptor of which express immunomodulatory molecules such as IL10, the receptor of (CSF1R) is expressed by EVT and macrophages (Fig. 5a, b). Secretion which is expressed by EVT and by maternal endothelial, stromal and of CSF1 by dNK cells and interaction with the CSF1R on EVT have myeloid cells. dNK1 cells express high levels of SPINK2, and dNK2 previously been described28,29, and we now pinpoint this interaction and dNK3 cells express high levels of ANXA1. Both of these genes specifically to the dNK1 subset. By contrast, dNK2 and dNK3 express encode proteins that have anti-inflammatory roles, such as inhibiting high levels of XCL1, and CCL5 is highly expressed by dNK3 (Fig. 5a, b, kallikreins32. The dNK1 subset expresses CD39 (which is encoded by Extended Data Fig. 9b). CCR1, the receptor for CCL5, is expressed by ENTPD1), which—together with CD73 (which is encoded by NT5E)— EVT, which suggests a role for dNK3 in regulating EVT invasion30. converts ATP to adenosine to prevent immune activation33 (Fig. 5c, The expression pattern of the XCL1–XCR1 ligand–receptor complex Extended Data Fig. 9b). Expression of CD73 is high in epithelial glands suggests functional interactions between dNK2 and dNK3 and both and EVT, and the adenosine receptor (ADORA3) is present in mac- EVT and conventional DC1 (labelled as DC1). DC1 recruitment, rophages (Fig. 5c, Extended Data Fig. 9b). KIR2DL1+ dNK1 cells are which is mediated by natural killer cells, occurs in tumour microen- in close physical contact with HLA-G+ EVT (Extended Data Fig. 9d), vironments31. We find an increased proportion of DC1 compared to which suggests that together they could convert extracellular ATP—an DC2—which possibly leads to the expansion of decidual CD8+ T cells inflammatory signal released upon cell death—to adenosine34. (Fig. 1d)—but co-expression of PD1 (also known as PDCD1) suggests that local T cell activation is limited. Discussion Our results collectively suggest that in the decidua microenviron- Reproductive success depends on events that occur during placentation ment all damaging maternal T or natural killer cell responses to fetal in the first-trimester decidua35. Other scRNA-seq studies of uterine trophoblast cells are prevented. There is high expression of PDL1 cells in pregnancy have analysed cells at the end of gestation16,36 or (also known as CD274) in EVT, which we confirmed in situ by using are restricted to fetal placental populations17. To our knowledge, our immuno histochemistry on serial sections of decidua basalis (the site study is the first comprehensive single-cell transcriptomics atlas of the of trophoblast invasion) stained for PDL1 and HLA-G (Extended Data maternal–fetal interface between 6–14 weeks of gestation (Extended Fig. 9c). We also identified putative inhibitory interactions between Data Fig. 10). Similar to previous scRNA-seq analyses36–39, we pre- dNKs and EVT, in addition to the previously discussed receptor–ligand dict possible ligand–receptor interactions; we have developed an open complexes between KIR2DL1, KIR2DL2 or KIR2DL3 and HLA-C. repository for this purpose (www.CellPhoneDB.org/). This database These include KLRB1 and TIGIT, which are highly expressed by dNK3 accounts for the multimeric nature of ligands and receptors and is cells, potentially binding CLEC2D and PVR, which are expressed by integrated with a statistical framework that predicts enriched cellular EVT (Fig. 5a). interactions between two cell types. We predict that the immune microenvironment of the decidua pre- We show the differentiation trajectory of trophoblast cells to either vents inflammatory responses that could potentially be triggered by villous syncytiotrophoblast (which is involved in nutrient exchange) or trophoblast invasion and destruction of the smooth muscle media of the EVT (which invade and remodel the spiral arteries), and predict the spiral arteries by trophoblast (Fig. 5c). Subsets of decidual macrophages ligand–receptor interactions that are likely to control these processes.

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FET-OPEN grant (MRG-GRAMMAR no. 664918) and Wellcome Sanger core K.P., M.H., A.M. and S.A.T.; R.V.-T., A.M. and S.A.T. wrote the manuscript with funding (no. WT206194). R.V.-T. is supported by an EMBO and HFSP Long- contributions from M.H., M.E., K.B.M. and M.Y.T.; M.H., A.M. and S.A.T. co-directed Term Fellowship and J.-E.P. by an EMBO Long-Term Fellowship; M.Y.T. holds the study. All authors read and accepted the manuscript. a Royal Society Dorothy Hodgkin Fellowship and A.M. has a Wellcome Trust Investigator award. The human embryonic and fetal material was provided by Competing interests The authors declare no competing interests. the Joint MRC/Wellcome Trust (MR/R006237/1) HDBR. Additional information Extended data is available for this paper at https://doi.org/10.1038/s41586- thanks B. Treutlein and the other anonymous Reviewer information Nature 018-0698-6. reviewer(s) for their contribution to the peer review of this work. Supplementary information is available for this paper at https://doi.org/ 10.1038/s41586-018-0698-6. Author contributions R.V.-T. and S.A.T. conceived the study. Sample and library Reprints and permissions information is available at http://www.nature.com/ preparation was performed by R.V.-T. with contributions from M.Y.T., J.-E.P., reprints. E.S. and S.L.; FACS experiments were performed by R.V.-T., R.A.B., A.F., A.M.S., Correspondence and requests for materials should be addressed to M.H., A.M. R.P.P. and M.A.I.; histology staining was performed by J.N.B., L.G., R.V.-T., M.Y.T., or S.A.T. B.M., B.I., S.H., D.H.R. and A.W.-C.; M.E. and R.V.-T. analysed and interpreted the Publisher’s note: Springer Nature remains neutral with regard to jurisdictional data with contributions from M.V.-T., M.J.T.S., L.W., G.J.W., A.G., A.Z., J.H., K.B.M., claims in published maps and institutional affiliations.

MEthodS populations in the decidua (Extended Data Fig. 1). B cells (CD19+ or CD20+) No statistical methods were used to predetermine sample size. The experiments were excluded from our analysis, owing to their absence in decidua46. Single cells were not randomized and investigators were not blinded to allocation during were sorted into 96-well full-skirted Eppendorf plates chilled to 4 °C, prepared experiments and outcome assessment. with lysis buffer consisting of 10 µl of TCL buffer (Qiagen) supplemented with Patient samples. All tissue samples used for this study were obtained with written 1% β-mercaptoethanol. Single-cell lysates were sealed, vortexed, spun down at informed consent from all participants in accordance with the guidelines in The 300g at 4 °C for 1 min, immediately placed on dry ice and transferred for storage Declaration of Helsinki 2000 from multiple centres. at −80 °C. The Smart-seq2 protocol was performed on single cells as previously Human embryo, fetal and decidual samples were obtained from the MRC and described11,47, with some modifications48. Libraries were sequenced, aiming at an Wellcome-funded Human Developmental Biology Resource (HDBR43, http:// average depth of 1 million reads per cell, on an Illumina HiSeq 2000 with version 4 www.hdbr.org), with appropriate maternal written consent and approval from the chemistry (paired-end, 75-bp reads). Newcastle and North Tyneside NHS Health Authority Joint Ethics Committee For the droplet scRNA-seq methods, blood and decidual cells were sorted (08/H0906/21+5). The HDBR is regulated by the UK Human Tissue Authority into immune (CD45+) and non-immune (CD45−) fractions. B cells (CD19+ or (HTA; www.hta.gov.uk) and operates in accordance with the relevant HTA Codes CD20+) were excluded from blood analysis, owing to their absence in decidua46. of Practice. Decidual tissue for smFISH (Extended Data Fig. 7c) was also covered Only viable cells were considered. Placental cells were stained for DAPI and only by this ethics protocol. viable cells were sorted. To improve trophoblast trajectories, an additional enrich- Peripheral blood from women undergoing elective terminations was collected ment of EPCAM+ and HLA-G+ was performed for selected samples (Fig. 2 only). under appropriate maternal written consent and with approvals from the Newcastle Cells were sorted into an Eppendorf tube containing PBS with 0.04% BSA. Cells Academic Health Partners (reference NAHPB-093) and HRA NHS Research Ethics were immediately counted using a Neubauer haemocytometer and loaded in the committee North-East-Newcastle North Tyneside 1 (REC reference 12/NE/0395) 10x-Genomics Chromium. The 10x-Genomics v2 libraries were prepared as per Decidual tissue for immunohistochemistry (Fig. 3b, c, Extended Data Figs. 7a, the manufacturer’s instructions. Libraries were sequenced, aiming at a minimum 9c, d) and flow cytometry staining for granule proteins was obtained from elec- coverage of 50,000 raw reads per cell, on an Illumina HiSeq 4000 (paired-end; read tive terminations of normal pregnancies at Addenbrooke’s Hospital (Cambridge) 1: 26 cycles; i7 index: 8 cycles, i5 index: 0 cycles; read 2: 98 cycles). between 6 and 12 weeks gestation, under ethical approval from the Cambridge Flow cytometry staining for granule proteins. For intracellular staining of gran- Local Research Ethics Committee (04/Q0108/23). ule proteins, dNKs were surface-stained for 30 min in FACS buffer with anti- Decidual tissue for smFISH (Fig. 3d, Extended Data Fig. 6b, 7b) was obtained bodies (listed in Supplementary Table 10). Cells were washed with FACS buffer from the Newcastle Uteroplacental Tissue Bank. Ethics numbers are: Newcastle and followed by staining with dead cell marker (DCM Aqua) and streptavidin Qdot605. North Tyneside Research Ethics Committee 1 Ref:10/H0906/71 and 16/NE/0167. dNKs were then treated with FIX & PERM (Thermo Fisher Scientific) and stained Isolation of decidual, placental and blood cells. Decidual and placental tissue for granule proteins. Samples were run on an LSRFortessa FACS analyser (BD was washed in Ham’s F12 medium, macroscopically separated and then washed Biosciences) and data analysed using FlowJo (Tree Star). dNKs were gated as for at least 10 min in RPMI or Ham’s F12 medium, respectively, before processing. CD3−CD14−CD19− live cells; CD56+NKG2A+ and then KIR+ and KIR− subsets Decidual tissues were chopped using scalpels into approximately 0.2-mm3 cubes were generated using Boolean functions with the gates for all the different KIRs and enzymatically digested in 15 ml 0.4 mg/ml collagenase V (Sigma, C-9263) stained (KIR+), and their inverse gates (KIR−). Wilcoxon test was used to compare solution in RPMI 1640 medium (Thermo Fisher Scientific, 21875-034)/10% FCS granule protein staining between paired dNK subsets from the same donor. A (Biosfera, FB-1001) at 37 °C for 45 min. The supernatant was diluted with medium P value < 0.05 was considered to be statistically significant. and passed through a 100-µm cell sieve (Corning, 431752) and then a 40-µm cell Immunohistochemistry. Four-micrometre tissue sections from formalin-fixed, sieve (Corning, 431750). The flow-through was centrifuged and resuspended in 5 paraffin-wax-embedded human decidual and placental tissues were dewaxed with ml of red blood cell lysis buffer (Invitrogen, 00-4300) for 10 min. Histoclear, cleared in 100% ethanol and rehydrated through gradients of ethanol Each first-trimester placenta was placed in a Petri dish and the placental villi to PBS. Sections were blocked with 2% serum (of species in which the secondary were scraped from the chorionic membrane using a scalpel. The stripped mem- antibody was made) in PBS, incubated with primary antibody overnight at 4 °C brane was discarded and the resultant villous tissue was enzymatically digested in and slides were washed in PBS. Biotinylated horse anti-mouse or goat anti-rab- 70 ml 0.2% trypsin 250 (Pan Biotech P10-025100P)/0.02% EDTA (Sigma E9884) in bit secondary antibodies were used, followed by Vectastain ABC–HRP reagent PBS with stirring at 37 °C for 9 min. The disaggregated cell suspension was passed (Vector, PK-6100) and developed with di-aminobenzidine (DAB) substrate (Sigma, through sterile muslin gauze (Winware food grade) and washed through with D4168). Sections were counterstained with Carazzi’s haematoxylin and mounted Ham’s F12 medium (Biosera SM-H0096) containing 20% FBS (Biosera FB-1001). in glycerol and gelatin mounting medium (Sigma, GG1-10). Primary antibody Cells were pelleted from the filtrate by centrifugation and resuspended in Ham’s was replaced with equivalent concentrations of mouse or rabbit IgG for negative F12. The undigested gelatinous tissue remnant was retrieved from the gauze and controls. See Supplementary Table 10 for antibody information. Tissue sections further digested with 10–15 ml collagenase V at 1.0 mg/ml (Sigma C9263) in Ham’s were imaged using a Zeiss Axiovert Z1 microscope and Axiovision imaging soft- F12 medium/10% FBS with gentle shaking at 37 °C for 10 min. The disaggregated ware SE64 version 4.8. cell suspension from collagenase digestion was passed through sterile muslin gauze smFISH. Samples were fixed in 10% NBF, dehydrated through an ethanol series and the cells pelleted from the filtrate as before. Cells obtained from both enzyme and embedded in paraffin wax. Five-millimetre samples were cut, baked at digests were pooled together and passed through a 100-µm cell sieve (Corning, 60 °C for 1 h and processed using standard pre-treatment conditions, as per the 431752) and washed in Ham’s F12. The flow-through was centrifuged and resus- RNAScope multiplex fluorescent reagent kit version 2 assay protocol (manual) pended in 5 ml of red blood cell lysis buffer (Invitrogen, 00-4300) for 10 min. or the RNAScope 2.5 LS fluorescent multiplex assay (automated). TSA-plus Blood samples were carefully layered onto a Ficoll–Paque gradient (Amersham) fluorescein, Cy3 and Cy5 fluorophores were used at 1:1,500 dilution for the manual and centrifuged at 2,000 r.p.m. for 30 min without breaks. Peripheral blood mono- assay or 1:300 dilution for the automated assay. Slides were imaged on different nuclear cells from the interface between the plasma and the Ficoll–Paque gradient microscopes: Hamamatsu Nanozoomer S60 (Extended Data Fig. 7c). Zeiss Cell were collected and washed in ice-cold phosphate-buffered saline (PBS), followed Discoverer 7 (Fig. 4d, Extended Data Figs. 6, 7c). Filter details were as follows. by centrifugation at 2,000 r.p.m. for 5 min. The pellet was resuspended in 5 ml of DAPI: excitation 370–400, BS 394, emission 460–500; FITC: excitation 450–488, red blood cell lysis buffer (Invitrogen, 00-4300) for 10 min. BS 490, emission 500–55; Cy3: excitation 540–570, BS 573, emission 540–570; Cy5: Assignment of fetal developmental stage. Up to eight post-conception weeks, excitation 615–648, BS 691, emission 662–756. The camera used was a Hamamatsu embryos are staged using the Carnegie staging method44. At fetal stages beyond ORCA-Flash4.0 V3 sCMOS camera. eight post-conception weeks, age was estimated from measurements of foot length Whole-genome sequencing. Tissue DNA and RNA were extracted from fresh- and heel-to-knee length. These were compared with a standard growth chart45. frozen samples using the AllPrep DNA/RNA/miRNA kit (Qiagen), following Flow cytometry staining, cell sorting and single-cell RNA-seq. Decidual and the manufacturer’s instructions. Short insert (500-bp) genomic libraries were blood cells were incubated at 4 °C with 2.5 µl of antibodies in 1% FBS in DPBS constructed, flowcells were prepared and 150-bp paired-end sequencing clusters without calcium and magnesium (Thermo Fisher Scientific, 14190136). DAPI was generated on the Illumina HiSeq X platform, according to Illumina no-PCR library used for live versus dead discrimination. We used an antibody panel designed to protocols, to an average of 30× coverage. Genotype information is provided in enrich for certain populations for single-cell sorting and scRNA-seq. Cells were Supplementary Table 1. sorted using a Becton Dickinson (BD) FACS Aria Fusion with 5 excitation lasers Single cell RNA-seq data analysis. Droplet-based sequencing data were aligned (355 nm, 405 nm, 488 nm, 561 nm and 635 nm red), and 18 fluorescent detectors, and quantified using the Cell Ranger Single-Cell Software Suite (version 2.0, 10x plus forward and side scatter. The sorter was controlled using BD FACS DIVA Genomics)13 against the GRCh38 human reference genome provided by Cell software (version 7). The antibodies used are listed in Supplementary Table 10. Ranger. Cells with fewer than 500 detected genes and for which the total mito- For single-cell RNA-seq using the plate-based Smart-seq2 protocol, we cre- chondrial gene expression exceeded 20% were removed. Mitochondrial genes and ated overlapping gates that comprehensively and evenly sampled all immune-cell genes that were expressed in fewer than three cells were also removed.

SmartSeq2 sequencing data were aligned with HISAT249, using the same tool demuxlet57. In brief, demuxlet can be used to deconvolve droplet-based scRNA- genome reference and annotation as the 10x Genomics data. Gene-specific read seq experiments in which cells are pooled from multiple genetically distinct indi- counts were calculated using HTSeq-count50. Cells with fewer than 1,000 detected viduals. Given a set of genotypes corresponding to these individuals, demuxlet genes and more than 20% mitochondrial gene expression content were removed. infers the most likely genetic identity of each droplet by estimating the likelihood of Furthermore, mitochondrial genes and genes expressed in fewer than three cells observing scRNA-seq reads from the droplet overlapping known single nucleotide were also removed. To remove batch effects due to background contamination of polymorphisms. Demuxlet inferred the identities of cells in this study by analysing cell free RNA, we also removed a set of genes that had a tendency to be expressed each Cell Ranger-aligned BAM file from decidua and placenta in conjunction with in ambient RNA (PAEP, HBG1, HBA1, HBA2, HBM, AHSP and HBG2). a VCF file, containing the high-quality whole-genome-sequence variant calls from Downstream analyses—such as normalization, shared nearest neighbour the corresponding mother and fetus. Each droplet was assigned to be maternal, graph-based clustering, differential expression analysis and visualization—were fetal or unknown in origin (ambiguous or a potential doublet), and these identities performed using the R package Seurat51 (version 2.3.3). Droplet-based and were then linked with the transcriptome-based cell clustering data to confirm the SmartSeq2 data were integrated using canonical correlation analysis, implemented maternal and fetal identity of each annotated cell type. in the Seurat alignment workflow52. Cells, the expression profile of which could not T cell receptor analysis by TraCeR. The T cell receptor sequences for each be well-explained by low-dimensional canonical correlation analysis compared to single T cell were assembled using TraCeR58, which allowed the reconstruction of low-dimensional principal component analysis, were discarded, as recommended the T cell receptors from scRNA-seq data and their expression abundance (tran- by the Seurat alignment tutorial. Clusters were identified using the community scripts per million), as well as identification of the size, diversity and lineage relation identification algorithm as implemented in the Seurat ‘FindClusters’ function. The of clonal subpopulations. In total, we obtained the T cell receptor sequences for shared nearest neighbour graph was constructed using between 5 and 40 canonical 1,482 T cells with at least one paired productive αβ or γδ chain. Cells for which correlation vectors as determined by the dataset variability; the resolution parame- more than two recombinants were identified for a particular locus were excluded ter to find the resulting number of clusters was tuned so that it produced a number from further analysis. of clusters large enough to capture most of the biological variability. UMAP analysis Whole-genome sequencing alignment and variant calling. Maternal and was performed using the RunUMAP function with default parameters. Differential fetal whole-genome sequencing data were mapped to the GRCh37.p13 refer- expression analysis was performed based on the Wilcoxon rank-sum test. The P ence genome using BWA-MEM version 0.7.1559. The SAMtools60 fixmate utility values were adjusted for multiple testing using the Bonferroni correction. Clusters (version 1.5) was used to update read-pairing information and mate-related flags. were annotated using canonical cell-type markers. Two clusters of peripheral blood Reads near known indels from the Mills61 and 1000G62 gold standard reference set monocytes represented the same cell type and were therefore merged. for hg19/GRCh37 were locally realigned using GATK IndelRealigner version 3.761. We further removed contaminating cells: (i) maternal stromal cells that were Base-calling assessment and base-quality scores were adjusted with GATK gathered in the placenta for one of the fetuses; (ii) a shared decidual–placental BaseRecalibrator and PrintReads version 3.760,63. PCR duplicates were identified cluster with fetal cells mainly present in two fetuses (which we think is likely to and removed using Picard MarkDuplicates version 2.14.163,64. Finally, bcftools be contaminating cells from other fetal tissues due to the surgical procedure). mpileup and call version 1.665 were used to produce genotype likelihoods and This can occur owing to the source of the tissue and the trauma of surgery. We output called variants at all known biallelic single nucleotide polymorphism sites also removed a cluster for which the top markers were genes associated with dis- that overlap protein-coding genes. For each sample, variants called with phred- sociation-induced effects53. Each of the remaining clusters contained cells from scale quality score ≥ 200, at least 20 supporting reads and mapping quality ≥ 60 multiple different fetuses, indicating that the cell types and states we observed are were retained as high-quality variants. not affected by batch effects. Quantification of KIR gene expression by KIRid. The KIR locus is highly We found further diversity within the T cell clusters, as well as the clusters of polymorphic in terms of both numbers of genes and alleles11. Including a single endothelial, epithelial and perivascular cells, which we then reanalysed and parti- reference sequence for each gene can lead to reference bias for donors that happen tioned separately, using the same alignment and clustering procedure. to better match the reference sequence. To address these issues, we used a The trophoblast clusters (clusters 1, 9, 20, 13 and 16 from Fig. 1d) were taken tailored approach in which we first built a total cDNA reference by concatenating from the initial analysis of all cells and merged with the enriched EPCAM+ and the Ensembl coding and non-coding transcript sequences, excluding transcripts HLA-G+ cells. The droplet-based and Smart-seq2 datasets were integrated and belonging to the KIR genes (GRCh38, version 90), and the full set of known KIR clustered using the same workflow as described above. Only cells that were iden- cDNAs sequences from the IPD-KIR database66 (release 2.7.0). For each donor, tified as trophoblast were considered for trajectory analysis. we removed transcript sequences for KIR genes determined to be absent in that Trajectory modelling and pseudotemporal ordering of cells was performed with individual, which decreases the extent of multi-mapping and quantification. The the monocle 2 R package54 (version 2.8.0). The most highly variable genes were single-cell reads of each donor were then mapped to the corresponding donor- used for ordering the cells. To account for the cell-cycle heterogeneity in the tropho- specific reference using Kallisto67 (version 0.43.0 with default options). Expression blast subpopulations, we performed hierarchical clustering of the highly variable levels were quantified using the multi-mapping deconvolution tool MMSEQ68, genes and removed the set of genes that cluster with known cell-cycle genes such and gene-level estimates were obtained by aggregating over different alleles for as CDK1. Genes which changed along the identified trajectory were identified by each KIR gene. performing a likelihood ratio test using the function differentialGeneTest in the Cell–cell communication analysis. To enable a systematic analysis of cell–cell monocle 2 package. communication molecules, we developed CellPhoneDB, a public repository of Network visualization was done using Cytoscape (version 3.5.1). The decidual ligands, receptors and their interactions. Our repository relies on the use of public network was created considering only edges with more than 30 interactions. The resources to annotate receptors and ligands. We include subunit architecture for networks layout was set to force-directed layout. both ligands and receptors, to accurately represent heteromeric complexes. KIR typing. Polymerase chain reaction sequence-specific primer was performed Ligand–receptor pairs are defined based on physical protein–protein inter- to amplify the genomic DNA for presence or absence of 12 KIR genes (KIR2DL1, actions (see sections of ‘CellPhoneDB annotations’). We provide CellPhoneDB KIR2DL2, KIR2DL3, KIR2DL5 (both KIR2DL5A and KIR2DL5B), KIR3DL1, with a user-friendly web interface at www.CellPhoneDB.org, where the user can KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5 and KIR3DS1) and the pseudo- search for ligand–receptor complexes and interrogate their own single-cell tran- gene KIR2DP1. KIR2DS4 alleles were also typed as being either full-length or scriptomics data. having the 22-bp deletion that prevents cell-surface expression. Two pairs of primers To assess cellular crosstalk between different cell types, we used our repository were used for each gene, selected to give relatively short amplicons of 100–800 bp, in a statistical framework for inferring cell–cell communication networks from as previously described55. Extra KIR primers were designed using sequence single-cell transcriptome data. We derived enriched receptor–ligand interactions information from the IPD-KIR database (release 2.4.0) to detect rare alleles of between two cell types based on expression of a receptor by one cell type and a KIR2DS5 and KIR2DL3 (KIR2DS5, 2DS5rev2: TCC AGA GGG TCA CTG GGA ligand by another cell type, using the droplet-based data. To identify the most and KIR2DL3, 2DL3rev3: AGA CTC TTG GTC CAT TAC CG)56. KIR haplotypes relevant interactions between cell types, we looked for the cell-type specific inter- were defined by matrix subtraction of gene copy numbers using previously charac- actions between ligands and receptors. Only receptors and ligands expressed in terized common and contracted KIR haplotypes using the KIR Haplotype Identifier more than 10% of the cells in the specific cluster were considered. software (www.bioinformatics.cimr.cam.ac.uk/haplotypes). We performed pairwise comparisons between all cell types. First, we randomly Inferring maternal or fetal origin of single cells from droplet-based scRNA-seq permuted the cluster labels of all cells 1,000 times and determined the mean of the using whole-genome sequencing variant calls. To match the processing of the average receptor expression level of a cluster and the average ligand expression level whole-genome sequencing datasets, droplet-based sequencing data from decidua of the interacting cluster. For each receptor–ligand pair in each pairwise comparison and placenta samples were realigned and quantified against the GRCh37 human between two cell types, this generated a null distribution. By calculating the propor- reference genome using the Cell Ranger Single-Cell Software Suite (version 2.0)13. tion of the means which are ‘as or more extreme’ than the actual mean, we obtained The fetal or maternal origin of each barcoded cell was then determined using the a P value for the likelihood of cell-type specificity of a given receptor–ligand

© 2018 Springer Nature Limited. All rights reserved. RESEARCH ARTICLE complex. We then prioritized interactions that are highly enriched between cell Linking Ensembl and Uniprot identification. We assigned to the custom-curated types based on the number of significant pairs, and manually selected biologically interaction list all the Ensembl gene identifications by matching information from relevant ones. For the multi-subunit heteromeric complexes, we required that all Uniprot and Ensembl by the gene name. subunits of the complex are expressed (using a threshold of 10%), and therefore Database structure. Information is stored in a PostgreSQL relational database we used the member of the complex with the minimum average expression to (www.postgresql.org). SQLAlchemy (www.sqlalchemy.org) and Python 3 were perform the random shuffling. used to build the database structure and the query logic. All the code is open source CellPhoneDB annotations of membrane, secreted and peripheral proteins. and uploaded to the webserver. Secreted proteins were downloaded from Uniprot using KW-0964 (secreted). Code availability. CellPhoneDB code is available in https://github.com/Teichlab/ Secreted proteins were annotated as cytokines (KW-0202), hormones (KW-0372), cellphonedb. The code can also be downloaded from https://cellphonedb.org/ growth factors (KW-0339) and immune-related using Uniprot keywords and man- downloads. KIRid can be downloaded from https://github.com/Teichlab/KIRid. ual annotation. Cytokines, hormones, growth factors and other immune-related Reporting summary. Further information on research design is available in proteins were annotated as ‘secreted highlight’ proteins in our lists. the Nature Research Reporting Summary linked to this paper. Plasma membrane proteins were downloaded from Uniprot using KW-1003 (cell membrane). Peripheral proteins from the plasma membrane were annotated Data availability using the Uniprot Keyword SL-9903, and the remaining proteins were annotated Our expression data for different tissues are also available for user-friendly inter- as transmembrane proteins. We completed our lists of plasma transmembrane pro- active browsing online at http://data.teichlab.org (maternal–fetal interface). The teins by doing an extensive manual curation using literature mining and Uniprot raw sequencing data, expression-count data with cell classifications and the whole- description of proteins with transmembrane and immunoglobulin-like domains. genome sequencing data are deposited at ArrayExpress, with experiment codes Plasma membrane proteins were annotated as receptors and transporters. E-MTAB-6701 (for droplet-based data), E-MTAB-6678 (for Smart-seq2 data) and Transporters were defined by the Uniprot keyword KW-0813. Receptors were defined E-MTAB-7304 (for the whole-genome sequencing data). Our CellPhoneDB repos- by the Uniprot keyword KW-0675. The list of receptors was extensively reviewed and itory is available at www.CellPhoneDB.org. new receptors were added based on Uniprot description and bibliography revision.

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HTSeq—a Python framework to work with membrane; (ii) ‘peripheral_add’, which indicates that the protein is manually anno- high-throughput sequencing data. Bioinformatics 31, 166–169 (2015). tated as a peripheral protein instead of plasma membrane; (iii) ‘secreted_add’, which 51. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction indicates that the protein is manually annotated as secreted; (iv) ‘secreted_high’, of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015). 52. Butler, A. & Satija, R. Integrated analysis of single cell transcriptomic data across which indicates that the protein is manually annotated as secreted highlight. For conditions, technologies, and species. Preprint at https://www.biorxiv.org/ cytokines, hormones, growth factors and other immune-related proteins; option content/early/2017/07/18/164889 (2017). (v) ‘receptor_add’ indicates that the protein is manually annotated as a receptor. 53. van den Brink, S. 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Bioinformatics annotated following the International Union of Pharmacology annotation69. Other 25, 2078–2079 (2009). 61. Mills, R. E. et al. Natural genetic variation caused by small insertions and groups of cell-surface proteins the interactions of which were manually reviewed deletions in the human genome. Genome Res. 21, 830–839 (2011). include the TGF family, integrins, lymphocyte receptors, semaphorins, ephrins, 62. 1000 Genomes Project Consortium. A map of human genome variation from Notch and TNF receptors. population-scale sequencing. Nature 467, 1061–1073 (2010). In addition, we considered interacting partners as: (i) binary interactions anno- 63. Van der Auwera, G. A. et al. From FastQ data to high confdence variant calls: the tated by IUPHAR (http://www.guidetopharmacology.org/) and (ii) cytokines, Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, hormones and growth factors interacting with receptors annotated by the iMEX 11.10.1–11.10.33 (2013). 70 64. Broad Institute. Picard tools https://broadinstitute.github.io/picard/ (Broad consortium (https://www.imexconsortium.org/) . Institute, 2018). We excluded from our analysis transporters and a curated list of proteins including: 65. Li, H. A statistical framework for SNP calling, mutation discovery, association (i) co-receptors; (ii) nerve-specific receptors such as those related to ear-binding, mapping and population genetical parameter estimation from sequencing olfactory receptors, taste receptors and salivary receptors, (iii) small molecule data. Bioinformatics 27, 2987–2993 (2011). receptors, (iv) immunoglobulin chains, (v) pseudogenes and (vi) viral and retro- 66. Robinson, J., Mistry, K., McWilliam, H., Lopez, R. & Marsh, S. G. E. IPD—the Immuno Polymorphism Database. Nucleic Acids Res. 38, D863–D869 (2010). viral proteins, pseudogenes, cancer antigens and photoreceptors. These proteins 67. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic are annotated as ‘others’ in the protein list. We also excluded from our analysis a RNA-seq quantifcation. Nat. Biotechnol. 34, 525–527 (2016). list of interacting partners not directly involved in cell–cell communication. The 68. Turro, E. et al. Haplotype and isoform specifc expression estimation using ‘remove_interactions’ list is available in https://www.cellphonedb.org/downloads. multi-mapping RNA-seq reads. Genome Biol. 12, R13 (2011). Lists of interacting protein chains are available from https://www.cellphonedb. 69. Bachelerie, F. et al. International Union of Basic and Clinical Pharmacology. LXXXIX. Update on the extended family of chemokine receptors and introducing org/downloads. The column labelled ‘source’ indicates the curation source. a new nomenclature for atypical chemokine receptors. Pharmacol. Rev. 66, Manually curated interactions are annotated as ‘curated’, and the bibliography used 1–79 (2013). to annotate the interaction is stored in ‘comments_interaction’. ‘Uniprot’ indicates 70. Orchard, S. et al. Protein interaction data curation: the International Molecular that the interaction has been annotated using UniProt descriptions. Exchange (IMEx) consortium. Nat. Methods 9, 345–350 (2012).

Extended Data Fig. 1 | Gating strategy for Smart-seq2 data. CD3−CD56−CD94− (labelled ‘all -’ on the figure); (vii) CD45+HLA-DR− a, Gating strategy for a panel of 14 antibodies to analyse immune cells in CD3−CD56+CD94−; (viii) autofluorescence; (ix) CD45+HLA-DR−CD3− decidual samples by Smart-seq2 (CD3, CD4, CD8, CD9, CD14, CD16, CD56+CD94+CD9−; (x) CD45+HLA-DR−CD3−CD56+CD94+CD9+; CD19, CD20, CD34, CD45, CD56, CD94, DAPI, HLA-DR and HLA-G). (xi) CD45−HLA-G+; (xii) CD45−HLA-G−. Sample F9 is shown as Cells isolated for Smart-seq2 data were gated on live; CD19- and an example. Cells from different gates were sorted in different plates: CD20-negative, singlets and the following cell types were sorted: (i) myeloid cells (gates (i) and (ii)); T cells (gates (iii), (iv) and (v)); natural CD45+CD14highHLA-DRhigh; (ii) CD45+HLA-DR+; (iii) CD45+HLA- killer cells (gates (vi), (vii), (viii), (ix) and (x)); CD45− (gates (xi) and DR−CD56−CD3+CD4+CD8−; (iv) CD45+HLA-DR−CD56−CD3+CD8+; (xii)). Antibody information is provided in Supplementary Table 10. (v) CD45+HLA-DR−CD56−CD3+CD4−CD8−; (vi) CD45+HLA-DR−

Extended Data Fig. 2 | Quality control of droplet and Smart-seq2 seq. Each row is a separate donor. d, Canonical correlation vectors (CC1 datasets. a, Histograms show the distribution of the cells from the Smart- and CC2) of integrated analysis of decidual and placental cells from the seq2 dataset ordered by number of detected genes and mitochondrial Smart-seq2 (n = 5 deciduas, n = 2 peripheral blood samples) and droplet- gene expression content. b, Histograms show the distribution of the cells based datasets (n = 5 placentas, n = 6 deciduas and n = 4 blood samples), from the droplet-based dataset ordered by number of detected genes and coloured on the basis of their assignment to clusters and the technology mitochondrial gene expression content. c, Total numbers of cells that that was used for scRNA-seq. passed the quality control, processed by Smart-seq2 and droplet scRNA-

Extended Data Fig. 3 | See next page for caption.

Extended Data Fig. 3 | Overview of droplet and Smart-seq2 datasets. set of genes, see Supplementary Table 2. Adjusted P value < 0.1; Wilcoxon a, UMAP plot showing the integration of the Smart-seq2 and droplet- rank-sum test with Bonferroni correction. NK, natural killer cells; NKp, based dataset and the log-transformed expression of MKI67 (which proliferating natural killer cells; MO, monocytes; Granulo, granulocytes; + marks proliferating cells). b, UMAP plots showing the separate and more- Treg, regulatory T cells; GD, γδ T cells; CD8c, cytotoxic CD8 T cells; detailed integration analysis of the cells from cluster 14 (perivascular Plasma, plasma cells. e, log-likelihood differences between assignment to cells), cluster 19 (endothelial cells) and cluster 25 (epithelial cells). Clusters fetal versus assignment to maternal origin of cells, on the basis of single are labelled as in Fig. 1c. c, UMAP visualization of T cell clusters obtained nucleotide polymorphism calling from the droplet RNA-seq data. Cells are by integrating Smart-seq2 and droplet-based T cells subpopulations coloured by their assignment as determined by demuxlet. For this figure, (clusters 4, 8, 10 and 15) from Fig. 1c. Cells are coloured by the tissue of we used n = 5 placentas, n = 6 deciduas and n = 4 blood individuals. f, origin (top) and the identified clusters (bottom). d, Heat map showing the UMAP visualization of the log-transformed, normalized expression of z-score of the mean log-transformed, normalized counts for each cluster selected marker genes of the M3 subpopulation. of selected marker genes used to annotate clusters. For a more extensive

Extended Data Fig. 4 | Cell–cell communication networks in the data were considered for the CellPhoneDB analysis (n = 6 deciduas). maternal–fetal interface using CellPhoneDB. a, Information aggregated d, Networks visualizing potential specific interactions in the placenta, in within www.CellPhoneDB.org. b, Statistical framework used to infer which nodes are clusters and edges represent the number of significant ligand–receptor complex specific to two cell types from single-cell ligand–receptor pairs. The network layout was set to force-directed layout. transcriptomics data. Predicted P values for a ligand–receptor complex Only droplet data were considered for the analysis (n = 5 placentas). e, An across two cell clusters are calculated using permutations, in which cells example of significant interactions identified by CellPhoneDB. Violin plots are randomly re-assigned to clusters (see Methods) c, Networks visualizing show log-transformed, normalized expression levels of the components of potential specific interactions in the decidua, in which nodes are clusters the IL6–IL6R complex in placental cells. IL6 expression is enriched in the (cell types) and edges represent the number of significant ligand–receptor fibroblast 2 cluster (F2; dark brown in d) and the two subunits of the IL6 pairs. The network was created for edges with more than 30 interactions receptors (IL6R and IL6ST) are co-expressed in Hofbauer cells. and the network layout was set to force-directed layout. Only droplet

Extended Data Fig. 5 | Trophoblast analysis. a, UMAP visualization of canonical trophoblast marker genes (n = 5 placentas). c, Visualization of the integrated analysis of the trophoblast subpopulations that were used log-transformed, normalized expression of HLA-G, MKI67 and LGALS13 for pseudotime analysis, including the enriched EPCAM+ and HLA-G+ across trophoblast differentiation. d, Heat map showing genes that are cells (see Methods). Cells that were excluded from the pseudotime involved in the epithelial–mesenchymal transition, identified as varying analysis are coloured in grey (n = 5 placentas, n = 11 deciduas). b, UMAP significantly as EVT differentiate (q value < 0.1, likelihood ratio test, visualization of the log-transformed, normalized expression of selected P values were adjusted for the false discovery rate).

Extended Data Fig. 6 | Steroid synthesis. a, Heat map showing relative an enrichment of CYP11A1 expression in the decidua compacta. Section expression of enzymes involved in cholesterol and steroid synthesis in stained by CYP11A1, LDLR and DAPI. Images are shown at 40× the three stromal subsets (n = 11 deciduas). b, Multiplexed smFISH in magnification. A high resolution is needed to detect differences between two decidua parietalis sections from two different individuals, showing the sections (n = 2 individuals).

Extended Data Fig. 7 | In situ staining for the different stromal cells. IGFBP1+. b, Multiplexed smFISH for a decidua parietalis section showing a, Immunohistochemistry of decidual serial sections stained for the two decidual layers. ACTA2, dS1 population confined to decidua cytokeratin (uterine glands), CD34 (endothelial cells), ACTA2 spongiosa; IGBP1 and PRL, dS2 and dS3 populations confined to decidua (perivascular populations and dS1) and IGFBP1 (stromal cells and compacta. Samples shown are from a different individual than samples glandular secretions) (n = 2 biological replicates). ACTA2+ stromal cells shown in Fig. 4d (n = 2 biological replicates). c, Multiplexed smFISH for a are confined to the stromal cells of the deeper decidua spongiosa, whereas decidua parietalis section showing the two decidual layers. DKK1, decidual stromal cells in the decidua compacta are ACTA2−. IGFBP1+ stromal cells stromal marker; ACTA2, dS1 population confined to decidua spongiosa; are enriched in the decidua compacta, whereas stromal cells around the PRL, dS3 population confined to decidua compacta (n = 1 biological glands in the decidua spongiosa are IGFBP1−. Glandular secretions are replicate).

Extended Data Fig. 8 | Lymphocyte populations in the decidua. for the sample shown in Fig. 5 (n = 2 biological replicates). c, Morphology a, Heat map showing z-scores of the mean log-transformed, normalized of dNK1, dNK2 and dNK3 subsets by Giemsa–Wright stain after cytospin expression of selected genes in the lymphocyte populations. Proliferating (representative data from 1 of n = 2 biological replicates are shown). Scale dNK cells (dNKp) are excluded from the analysis (n = 11 deciduas). bar, 10 µm. b, FACS gating strategy in Fig. 5 applied in matched blood. Matched blood

Extended Data Fig. 9 | Expression of ligands and receptors at the shown the areas in white boxes in the top panels at higher power. maternal–fetal interface. a, Heat map showing z-scores of the mean HLA-G+ cells are only present at the site of placentation (decidua basalis) log-transformed, normalized expression of genes annotated as cytokines, and are absent elsewhere (decidua parietalis). SpA, spiral arteries. The growth factors, hormones and angiogenic factors with a log-mean > 0.1 EVT is strongly PDL1+. We show representative data from one individual in the selected decidual immune populations (n = 11 deciduas). b, Violin of n = 5 biological replicates. d, Immunohistochemistry images of decidual plots showing log-transformed, normalized expression levels of selected serial sections of the decidual implantation site (at 10 weeks of gestation), ligands expressed in the three dNK cells and their corresponding receptors stained for the trophoblast cell marker, cytokeratin-7 (red arrow) and the expressed on other decidual cells and EVT (CD39, CD73, ADORA3, CSF1, inhibitory receptor KIR2DL1 on a natural killer cell (black arrow). The CSF1R, CCL5, CCR1, XCL1 and XCR1; n = 11 deciduas, n = 5 placentas) asterisk marks the lumen of a spiral artery that supplies the conceptus. We c, Immunohistochemistry images of serial decidual sections stained for show representative data from one individual of n = 5 samples). the EVT marker HLA-G and the inhibitory ligand PDL1. Bottom panels

Extended Data Fig. 10 | Encyclopaedia of cells at the maternal–fetal interface. a, Summary of populations from our scRNa-seq data. Blue, fetal; red, maternal.

Corresponding author(s): Sarah Teichmann

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Software and code Policy information about availability of computer code Data collection Software used include: R, Python 3, SQLAlchemy, PostgreSQL, HISAT2, 10X Genomics’ Cell Ranger, Seurat, Monocle 2, Cytoscape, Demuxlet, HTSeq-count, Samtools, GATK, BWA, Picard, bcftools, TraCeR, Kallisto, MMSEQ, BD FACS DIVA software, Axiovision and ZEN software (zeiss), Graphpad, Photoshop, Illustrator. Detailed parameters of each of the methods are mentioned in relevant sections in Methods.

Data analysis Software used include: R, Python 3, SQLAlchemy, PostgreSQL, HISAT2, 10X Genomics’ Cell Ranger, Seurat, Monocle 2, Cytoscape, Demuxlet, HTSeq-count, Samtools, GATK, BWA, Picard, bcftools, TraCeR, Kallisto, MMSEQ, Graphpad, Axiovision and ZEN software (zeiss), FlowJo, Photoshop, Illustrator. Detailed parameters of each of the methods are mentioned in relevant sections in Methods.

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Data is uploaded to ArrayExpress: Experiment: E-MTAB-6701, E-MTAB-6678, E-MTAB-7304

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Life sciences study design All studies must disclose on these points even when the disclosure is negative. Sample size No statistical methods were used to predetermine sample size. We followed standards in the field and Human Cell Atlas criteria.

Data exclusions No exclusion was applied to the uploaded raw data in ArrayExpress. For the final count matrix, we excluded cells based on pre-established criteria for single-cells: we excluded low quality samples and contaminating cells (i.e. - cells with low number of detected genes and high mitochondria content) - exclusion criteria for each case are comprehensively detailed in the relevant Methods section.

Replication We did scRNAseq and flow cytometry analysis on 11 deciduas, 5 placentas and 6 matched peripheral blood from 6-14 gestational weeks. Additional samples were used for the validation experiments: (a) Immunohistochemistry: staining of perivascular populations was performed in two independent individuals (Fig. 3b); ACTA2 staining of decidual stromal cells was performed in three independent individuals (Fig. 3c; Extended Data Figure 7a), staining of PDL1 was performed in five independent individuals (Extended Data Figure 9c); (b) smFISH: staining of stromal populations was performed in two independent individuals (Fig. 3d, Extended Data Figure 6b, Extended Data Figure 7b), staining of stromal population in Extended Data Figure 7c was performed in one individual; (c) Flow cytometry: flow cytometry for Figure 4d was performed in 6 of the deciduas sequenced and dNK for two of them were isolated and stained with Giemsa-Wright stain after cytospin 8c ; seven to ten independent individuals were additionally used for flow cytometry analysis on Figure 4f. Any replication experiment was excluded.

Randomization Only healthy individuals were considered in our analysis. Therefore, any further randomization protocol was required.

Blinding Only healthy individuals were considered in our analysis. Therefore, no blinding was performed

Reporting for specific materials, systems and methods

Materials & experimental systems Methods n/a Involved in the study n/a Involved in the study Unique biological materials ChIP-seq Antibodies Flow cytometry Eukaryotic cell lines MRI-based neuroimaging Palaeontology Animals and other organisms April 2018 Human research participants

Antibodies Antibodies used Antibody Company Catalog number Clone Use Dilution (Supplementary table 10) CD45-BUV395 BD Bioscience 563791 Clone HI30 (RUO) Flow cytometry - index data; Fig. 5d 2.5ul:100ul CD94-BV421 BD Bioscience 743948 HP-3D9 (RUO) Flow cytometry - index data 2.5ul:100ul

2 CD16-BV500 BD Bioscience 561394 3GH Flow cytometry - index data; Fig. 5d 2.5ul:100ul

CD3-BV605 Biolegend 344836 SK7 Flow cytometry - index data; Fig. 5d 2.5ul:100ul nature research | reporting summary CD4-BV711 Biolegend 300558 RPA-T4 Flow cytometry - index data 2.5ul:100ul HLA-DR BV786 Biolegend 307642 L243 Flow cytometry - index data 2.5ul:100ul HLA-G FITC BioRad MCA2044 MEM-G/9 Flow cytometry - index data 2.5ul:100ul CD56-PE Miltenyi 130-100-622 REA196 Flow cytometry - index data; Fig 5d 2.5ul:100ul CD14-PECF594 BD Bioscience 562334 PE-CF594 Flow cytometry - index data 2.5ul:100ul CD9-APC BD Bioscience 341638 M-L13 Flow cytometry - index data 2.5ul:100ul CD8-AF700 Biolegend 300920 HIT8a Flow cytometry - index data 2.5ul:100ul CD19-APCCy7 BIoLegend 302217 HIB19 Flow cytometry - index data 2.5ul:100ul CD20-APCCy7 BIoLegend 302313 2H7 Flow cytometry - index data 2.5ul:100ul Cytokeratin 7 Dako MA5-11986 OV-TL 12/30 Immunochemistry Dilution:1:200; antigen retrieval buffer:Citrate PD-L1 Cell Signaling Technology 13684 E13LN Immunochemistry Dilution:1:400; antigen retrieval buffer:TRIS/EDTA HLA-G Biorad MCA2043 MEM-G1 Immunochemistry Dilution:1:25; antigen retrieval buffer:Citrate CD146 / MCAM Abcam ab75769 EPR3208 Immunochemistry Dilution:1:2500; antigen retrieval buffer:Citrate Smooth Muscle Actin Dako M0851 1A4 Immunochemistry Dilution:1:100 antigen retrieval buffer:TRIS/EDTA NKG2A - Viobright Miltenyi 130-105-646 REA110 Flow cytometry - Fig 5f Dilution: 1:100 in cocktail KIR2DL2/3/S2 - PE-Cy5.5 Beckman Coulter A66900 GL183 Flow cytometry - Fig 5f Dilution: 1:50 in cocktail KIR2DS4 - APC Miltenyi 130-114-773 REA860 Flow cytometry - Fig 5f Dilution: 1:50 in cocktail GzmB - AF700 BD Biosciences 560213 GB11 Flow cytometry - Fig 5f Dilution: 1:100 in intracellular cocktail KIR2DL1 - APC-Vio770 Miltenyi 130-103-937 REA284 Flow cytometry - Fig 5f Dilution: 1:25 in cocktail Perforin - BV421 Biolegend 308122 DG9 Flow cytometry - Fig 5f Dilution: 1:100 in intracellular cocktail CD3 - BV510 BioLegend 317332 OKT3 Flow cytometry - Fig 5f Dilution: 1:100 in cocktail CD14 - BV510 BioLegend 301842 M5E2 Flow cytometry - Fig 5f Dilution: 1:100 in cocktail CD19 – BV510 BioLegend 302242 HIB19 Flow cytometry - Fig 5f Dilution: 1:100 in cocktail LIVE/DEAD™ Fixable Aqua Dead Cell Stain Kit Thermofisher L34957 N/A Flow cytometry - Fig 5f Dilution: 1:100 in secondary cocktail (from 200ul dilution of stock powder) KIR2DL3 biotin - biotin Miltenyi 130-100-126 REA147 Flow cytometry - Fig 5f Dilution: 1:100 in cocktail streptavidin Qdot605 (detection of KIR2DL3)- Q605 Thermofisher Q10103MP N/A Flow cytometry - Fig 5f Dilution: 1:250 in secondary cocktail Granulysin - PE Biolegend 348003 DH2 Flow cytometry - Fig 5f Dilution: 1:200 in intracellular cocktail CD56-PE-Dazzle BioLegend 318348 HCD56 Flow cytometry - Fig 5f Dilution: 1:200 in intracellular cocktail GzmA - PE-Cy7 ebioscience/Thermo Fischer 25-9177-42 CB9 Flow cytometry - Fig 5f Dilution: 1:100 in intracellular cocktail CD49a-BV421 Bd Bioscience 742357 SR84 (RUO) Flow cytometry - Fig 5d 2.5ul:100ul CD14- BV605 Bd Bioscience 564054 M5E2 (RUO) Flow cytometry - Fig 5d 2.5ul:100ul CD19-BV605 Bd Bioscience 740394 HIB19 (RUO) Flow cytometry - Fig 5d 2.5ul:100ul CD20-BV605 Bd Bioscience 747736 2H7 (RUO) Flow cytometry - Fig 5d 2.5ul:100ul CD103-PerCP-Cy5.5 Biolegend 350225 Ber-ACT8 Flow cytometry - Fig 5d 2.5ul:100ul KIR2DL1 - PeVio770 MACS - Miltenyi Biotec 130-103-936 REA284 Flow cytometry - Fig 5d 2.5ul:100ul CD18 (ITGB2)- APC BioLegend 366307 CBR LFA-1/2 Flow cytometry - Fig 5d 2.5ul:100ul CD39 -APC Cy7 BioLegend 328225 A1 Flow cytometry - Fig 5d 2.5ul:100ul

Validation Antibody Validation (Supplementary table 10) CD45-BUV395 Flow cytometry (Routinely Tested) . Flow cytometric analysis of CD45 expression on human peripheral blood lymphocytes (website). CD94-BV421 Flow cytometry (Qualified) (website) CD16-BV500 Flow cytometry (Routinely Tested). Flow cytometric analysis of CD16 expression on human peripheral blood lymphocytes. (Website) CD3-BV605 Flow cytometry. Human peripheral blood lymphocytes were stained with CD3 (clone SK7) Brilliant Violet 605™ (website) CD4-BV711 Flow cytometry. Human peripheral blood lymphocytes were stained with CD4 (clone RPA-T4) Brilliant Violet 711™ (filled histogram) (website) HLA-DR BV786 Flow cytometry. Human peripheral blood lymphocytes were stained with HLA-DR (clone L243) Brilliant Violet 785™. (website) HLA-G FITC Evaluation of HLA-G expression on transfected cells by flow cytometry (website) CD56-PE REAfinity clone is tested against different known clones, in a blocking experiment, to identify whether they recognize completely overlapping (++), partially overlapping (+), or completely different epitopes (-). In a blocking experiment, cells are incubated with an excess of purified unconjugated REAfinity clone followed by staining with conjugated form of the other known clones (website). CD14-PECF594 Flow cytometry (Routinely Tested). Flow cytometric analysis of CD14 expression on human peripheral blood monocytes (webpage) CD9-APC Flow cytometry (Routinely Tested) (website). ImmunogenHuman C-ALL Cells. CD8-AF700 quality control tested by immunofluorescent staining with flow cytometric analysis (website) CD19-APCCy7 quality tested by flow cytometry (website) CD20-APCCy7 quality tested by flow cytometry (website) Cytokeratin 7 Tested in Immunocytochemistry (ICC), Immunofluorescence (IF), Immunohistochemistry (Paraffin) (IHC (P)) and April 2018 Western Blot (WB) PD-L1 Tested in Western Blotting, Immunoprecipitation, IHC-Leica® Bond™, Immunohistochemistry (Paraffin), Flow Cytometry (website) HLA-G Tested in Immunohistochemistry (Paraffin), Western Blotting (website) CD146 / MCAM Tested in immunofluorescence, immunohistochemestry, Western Blotting, Flow Cytometry (website) Smooth Muscle Actin Tested in Western Blotting, Immunofluorescence, ELISA, Immunohistochemestry (website) NKG2A - Viobright Flow cytometry. Human peripheral blood and decidual NK cells used for titration. KIR2DL2/3/S2 - PE-Cy5.5 Tested by FACS of cell line transfected with gene target KIR2DS4 - APC Flow cytometry. Human peripheral blood and decidual NK cells used for titration.

3 GzmB - AF700 Flow cytometry. Human peripheral blood and decidual NK cells used for titration.

KIR2DL1 - APC-Vio770 Tested by FACS of cell line transfected with gene target nature research | reporting summary Perforin - BV421 Flow cytometry. Human peripheral blood and decidual NK cells used for titration. CD3 - BV510 Flow cytometry. Human peripheral blood lymphocytes used for titration. CD14 - BV510 Flow cytometry. Human peripheral blood lymphocytes used for titration. CD19 – BV510 Flow cytometry. Human peripheral blood lymphocytes used for titration. Amine reactive dye Flow cytometry. Human peripheral blood lymphocytes used for titration of each batch since diluted aliquotes are stored frozen. KIR2DL3 biotin - biotin REAfinity clone is tested against different known clones, in a blocking experiment, to identify whether they recognize completely overlapping (++), partially overlapping (+), or completely different epitopes (-). In a blocking experiment, cells are incubated with an excess of purified unconjugated REAfinity clone followed by staining with conjugated form of the other known clones (website). streptavidin Qdot605 (detection of KIR2DL3)- Q605 Flow cytometry. Human peripheral blood lymphocytes used for titration. Granulysin - PE Flow cytometry. Human peripheral blood and decidual NK cells used for titration. CD56-PE-Dazzle Flow cytometry. Human peripheral blood and decidual NK cells used for titration. GzmA - PE-Cy7 Flow cytometry. Human peripheral blood and decidual NK cells used for titration. CD49a-BV421 Flow cytometry (Qualified) (website) CD14- BV605 Human (QC Testing). Flow cytometric analysis of CD14 expression on human peripheral blood monocytes (website) CD19-BV605 Human (Tested in Development) (website) CD20-BV605 Human (Tested in Development) (website) CD103-PerCP-Cy5.5 Flow cytometry (Quality tested) (website) KIR2DL1 - PeVio770 REAfinity clone is tested against different known clones, in a blocking experiment, to identify whether they recognize completely overlapping (++), partially overlapping (+), or completely different epitopes (-). In a blocking experiment, cells are incubated with an excess of purified unconjugated REAfinity clone followed by staining with conjugated form of the other known clones (website). CD18 (ITGB2)- APC Flow cytometry (Quality tested) (website) CD39 -APC Cy7 Flow cytometry (Quality tested) (website)

Human research participants Policy information about studies involving human research participants Population characteristics Fetal, decidual samples and peripheral blood samples were collected from woman undergoing elective terminations. All participants in the study were healthy pregnant woman (6-14 gestational weeks) without any exclusion.

Recruitment Samples were taken in UK. Ethnicity was not recorded but is expected to be primary caucasian. This is not expected to affect the composition of cells in the maternal–fetal interface during early pregnancy. All tissue samples used for this study were obtained with written informed consent from all participants in accordance with the guidelines in The Declaration of Helsinki 2000 from multiple centres. Human embryo, fetal and decidual samples were obtained from the MRC/WellcomeTrust funded Human Developmental Biology Resource (HDBR36, http://www.hdbr.org) with appropriate maternal written consent and approval from the Newcastle and North Tyneside NHS Health Authority Joint Ethics Committee (08/H0906/21+5). HDBR is regulated by the UK Human Tissue Authority (HTA; www.hta.gov.uk) and operates in accordance with the relevant HTA Codes of Practice. Decidual tissue for smFISH (Extended Data Figure 7c-d) was also covered by this ethics. Peripheral blood from woman undergoing elective terminations were under appropriate maternal written consent and approvals from the Newcastle Academic Health Partners (reference NAHPB-093) and HRA NHS Research Ethics committee North-East- Newcastle North Tyneside 1 (REC reference 12/NE/0395) Decidual tissue for immunohistochemistry (Figure 3b,c and Extended Data Figure 9c) were obtained from elective terminations of normal pregnancies at Addenbrooke’s Hospital between 6 and 12 weeks gestation, under ethical approval from the Cambridge Local Research Ethics Committee (04/Q0108/23). Decidual tissue for smFISH (Figure 3d and Extended Data Figure 6b and Extended Data Figure 7b) were obtained from the Newcastle Uteroplacental Tissue Bank and Ethics numbers are Newcastle and North Tyneside Research Ethics Committee 1 Ref:10/H0906/71 and 16/NE/0167.

Flow Cytometry Plots Confirm that: The axis labels state the marker and fluorochrome used (e.g. CD4-FITC). The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).

All plots are contour plots with outliers or pseudocolor plots. April 2018 A numerical value for number of cells or percentage (with statistics) is provided. Methodology Sample preparation Decidual and blood cells were incubated at 4°C with 2.5ul of antibodies in 1% FBS in DPBS without Calcium and Magnesium (ThermoFisher Scientific, 14190136). DAPI was used for live/dead discrimination. For intracellular staining of granule proteins, dNK were surface stained for 30 mins in FACS buffer with antibodies. Cells were

4 washed with FACS buffer followed by staining with dead cell marker (DCM Aqua) and strepavidinQ605. dNK were then treated

with FIX & PERM (Thermofisher) and stained for granule proteins. nature research | reporting summary

Instrument Becton Dickinson (BD) FACS Aria Fusion. For granule experiment we used LSRFortessa FACS analyser (BD Biosciences)

Software Becton Dickinson (BD) FACS Aria Fusion was controlled using BD FACS DIVA software (version 7) and FlowJo v10.3 was used for analysis.

Cell population abundance Describe the abundance of the relevant cell populations within post-sort fractions, providing details on the purity of the samples and how it was determined.

Gating strategy Gating strategy for a panel of 14 antibodies to analyse immune cells in decidual samples by SS2 (CD3, CD4, CD8, CD9, CD14, CD16, CD19, CD20, CD34, CD45, CD56, CD94, DAPI, HLA-DR, HLA-G). Cells isolated for SS2 data were gated on: live; CD19/20-ve, singlets, and the following cell types sorted: i) CD45+, HLA-DR++; ii) CD45+, HLA-DR+; iii) CD45+, HLA-DR-, CD56-, CD3+, CD4+; CD8- iv) CD45+, HLA-DR-, CD56-, CD3+, CD8+; v) CD45+, HLA-DR-, CD56-, CD3+, CD4-, CD8-; vi) CD45+, HLA-DR-, CD3-, CD56-, CD94- (all -); vii) CD45+, HLA-DR-, CD3-, CD56+, CD94-; viii) CD45+, HLA-DR-, CD3, CD56+, CD94+, CD9-; ix) CD45+, HLA-DR-, CD3, CD56+, CD94+, CD9+; x) CD45-, HLA-G+; xi) CD45-, HLA-G-. For the isolation of the three dNK subsets, dNK cells were gated on live, single-cell, LIN- (CD3, CD14, CD19, CD20), CD56+, CD16-, CD49a+. dNK3 were additionally gated on CD103 and ITGB2. dNK1 were additionally gated on CD39 and ITGB2. dNK2 were additionally gated on CD103 and ITGB2. For the detection of granule molecules dNK were gated as CD3-CD14-CD19-, live cells then CD56+NKG2A+ and then KIR+ and KIR- subsets generated using Boolean functions with the gates for all the different KIRs stained (KIR+), and its inverse gate (KIR-). Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information. April 2018