Single Cell Transcriptome Research in Human Placenta
11 Single Cell Transcriptome Research in Human Placenta
Hui Li , Qianhui Huang , Yu Liu , Lana X Garmire
1, $ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA $Corresponding author: [email protected]
Abstract
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-Seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.
Keywords: single cell; sequencing; scRNA-seq; placenta; genomics; computation; heterogeneity
Introduction
The placenta is the interface between the mother and the fetus, which mediates the exchange of gas, nutrients, waste, and produces hormones and growth factors that support fetal development and ensure a healthy pregnancy. Much of the understanding of early stages of human implantation and placental development is based on histological analyses of specimens of the Boyd Collection and Carnegie Institution of Washington (Hertig 1956; Hamilton et al .1960), as well as anatomical studies of species closest to humans (Enders 2007). In addition, in vitro fertilization technology (Deglincerti et al . 2016; Shahbazi et al . 2016) and pre-implantation in the mouse (Cockburn et al . 2010) have contributed to our knowledge of pre-implantation events in humans. The development of the human placenta starts from the formation of the trophectoderm and the inner cell mass. At this developmental stage, the pre-implantation embryo is referred to as a blastocyst. Implantation starts around day 7 post conception (p.c.) when the blastocyst attaches and adheres to the uterine epithelium (Hertig 1956). How this is achieved is not clear.
Two groups cultured human embryos in vitro for 12–13 days p.c. and unveiled the self-organizing abilities in vitro attached human embryos (Deglincerti et al . 2016; Shahbazi et al . 2016). The presence of cell adhesion molecules including integrin, E-cadherin and L-selectin, on human oocytes, early embryos, and blastocysts, suggests that these molecules may play a role in embryo attachment and adhesion (
Campbell et al . 1995) . Following blastocyst attachment and adhesion, trophoblast cells undergo cell fusion to form the multinucleated syncytiotrophoblast (SCT), which invades the maternal uterine stroma. The mechanisms underlying the transition of cytotrophoblasts into SCTs remains largely unknown. The blastocyst eventually embeds itself into the stromal vasculature of the uterine lining (Norwitz et al . 2001; Boyd et al . 1970). With the embryo implanted in the uterus, epiblast and endoderm cells cavitate to form the amniotic cavity and yolk sac, respectively (Enders et al . 1970). Fetal blood vessels begin to form in the villi core by day 20 p.c., marking the formation of tertiary villi, the first generation of which are the mesenchymal villi. These stages of the development of new villi are repeated throughout pregnancy. From this time onwards, placental villi are tertiary villi consisting of a vascular network, mesenchyme, cytotrophoblasts and SCTs. These 4 constituents together form the placental barrier (Boyd et al . 1970). Around the 5th week p.c., mesenchymal villi begin to differentiate into immature intermediate villi with increased villous diameter and appearance of stromal channels, and later into stem villi by means of central stromal fibrosis (Castellucci et al . 1990). From around the 23th week p.c. until term, the mesenchymal villi differentiate into mature intermediate villi, from which highly capillarized terminal villi arise. These terminal villi, which begin to appear at around the 25th week p.c. and account for nearly 40% of villous volume of the placenta at term, are the most effective structures for fetal-maternal diffusion exchange (Castellucci et al, 2000). Morphometric observations have shown that, although the villous growth slows down in late pregnancy, it continues to grow towards term (Boyed, 1984). If the maternal environment becomes unfavorable, the villous will continue branching past term. Structurally, the placenta is a complex and heterogeneous organ consisting of multiple different cell types that carry out varied functions. The functional unit of the placenta is the chorionic villus that consists of a stromal core, an inner layer of villous cytotrophoblasts (VCT) and an outer layer of multinucleated SCTs that cover the surface of the villous tree. The stromal core contains a range of cells including macrophages (also called Hofbauer cells), mesenchymal stromal cells, fibroblasts, and fetal endothelial cells (Figure 1). Hofbauer cells are placental villous macrophages of fetal origin. scRNA-seq of the first-trimester placenta shows there are at least two subtypes of Hofbauer cells (Liu et al . 2018), which express genes involved in maintaining host defense, placental morphogenesis and homeostasis (Seval et al. et al . 2018). Two populations of mesenchymal stromal cells were also identified from the single cell transcriptional profiling of first-trimester placental cells (Liu et al . 2018). One cell population likely plays a role in the regulation of cell adhesion and migration, whereas the other population in the development of blood vessels and the mesenchyme (Liu et al . 2018). Three fibroblast subtypes are identified by scRNA-seq analysis from the first-trimester placenta. Genes highly expressed in these cells are involved in endocrine signaling, endothelial cell migration and angiogenesis, and sodium and fluid homeostasis (Suryawanshi et al . 2018). Endothelial cells play an important physiological role in maintaining a vasodilated state in the fetoplacental vessels through the release of vasoactive substances, including nitric oxide. The villous cytotrophoblast (VCT) has been considered the proliferative stem-like cells. They either fuse to form SCTs during placental villous formation or penetrate the SCT to form extravillous trophoblast (EVT) columns in anchoring villi. scRNA-seq of the first-trimester placenta shows that one population of VCT cells express high levels of potentially important fusion genes, indicating that they may be the fusion-competent VCT cells (Liu et al . 2018). Single cell gene expression analysis of the first- and third trimester VCT cells indicates the continuous process of VCT to EVT differentiation throughout the pregnancy (Tsang et al . (2017; Suryawanshi et al . 2018). Invasion of the spiral arteries by EVTs leads to placental vascular remodeling in the early stages of the implantation process. The mechanisms involved in this evasion are still largely unknown. Recent scRNA-seq studies have shown that both the first- and second-trimester EVTs expressed genes involved in invasion and immunomodulation, consistent with a likely role for EVTs in cellular invasion and anti-inflammatory in the early stage (Liu et al . 2018; Suryawanshi et al . 2018; Vento-Tormo et al . 2018). The SCTs cover the entire surface of villous trees and line in the intervillous space, where the maternal-fetal exchange of gas and nutrients occurs. scRNA-seq profiling revealed that these cells expressed genes involved in the production of gestational hormones, which support the fetal and placental development (Tsang et al . (2017; Suryawanshi et al . 2018). Furthermore, the SCTs are involved in the immune tolerance of the fetus by the maternal immune system. Other cells may also exist at the interfacing of maternal-fetal surface. Uterine natural killer cells (uNK) constitute a major lymphocyte population in the uterus during early pregnancy. Their number increases around the time of implantation and remains high during the first trimester of pregnancy. uNK cells play a role in protecting the conceptus from maternal immune rejection and have been implicated in placental vascular remodeling (Sojka 2019). Decidual cells are a morphologically distinct cell class observed in the endometrium during implantation and early pregnancy. Functions of decidual cells are thought to regulate the trophoblast invasion and provide nutrition to the embryo (Kearns et al. 1983). The placenta plays a pivotal role in maintaining the health of both the fetus and the mother. Normal growth and development of placental structural components ensures a healthy pregnancy outcome, whereas alterations to placental structural components are associated with various pregnancy complications such as intrauterine growth restriction (IUGR), preeclampsia and preterm birth. These pregnancy complications and syndromes are associated with increased fetal and neonatal morbidity and have long-term negative impacts on the health of newborns. IUGR refers to a condition in which the fetus does not achieve the expected in utero growth potential. Fetal growth is a complex process that involves genetic and environmental factors, nutrient availability from the mother, as well as hormones and growth factors. The complications of IUGR include perinatal asphyxia, hypoglycemia, hypothermia, cerebral palsy, and pulmonary hypertension of the newborn (Mandruzzato et al. et al . 2010). While the etiology of preeclampsia is not fully understood, risk factors are proposed including hypertension and kidney disease, diabetes, obesity, a history of preeclampsia, aberrant placental implantation, and environmental and genetic factors. Preeclampsia has been linked to IUGR, as both conditions are assumed to be related to the similar placental disorder due to reduced trophoblast invasion (Kaufman et al . 2003). In fact, IUGR has been included among diagnostic criteria for severe preeclampsia (Tranquilli et al . 2014). The only effective treatment for preeclampsia is delivery of the fetus and placenta, leaving this disease one of the leading causes of preterm birth. Since preeclampsia is clinically and molecularly heterogeneous, advances in omics technologies, such as transcriptomics, epigenomics, proteomics and metabolomics, may help capture the complexity of the molecular mechanism of preeclampsia (Ching et al. et al. and pulmonary and neurological disorders. The possible causes of preterm birth include maternal infections, decidual hemorrhage, multiple pregnancies, and chronic conditions such as hypertension and diabetes. It is important to know, that phenotypes such as IUGR, preeclampsia and preterm birth share comorbidities. Special efforts should be thus be paid to control for confounding in data analysis, in order to accurately ping-point the genomics-phenotype associations are truly due to the condition of interest, rather than other confounding factors. Although the etiology and pathogenesis of these above mentioned diseases are still not clear, the placental dysfunction is thought to be one of the main causes (Burton et al . 2018). Normal placental function is dependent on appropriate growth and development of its cells, which are heterogeneous, dynamic, and are determined by the precise regulation of gene expression. Consequently, alterations to placental gene expression are thought to be a major contributor to pregnancy pathologies. Previously, studies investigating the variation in bulk gene expression in placental tissues have been conducted in order to elucidate the molecular basis of placental development and pregnancy pathologies (Chappell et al . 2006; Sood et al. Single-cell RNA sequencing technologies
Since the first scRNA-seq method was published in 2009 (Tang et al. et al. et al. et al. in situ , which offers the opportunity to restrict expression analysis to spatially defined subareas within the tissue.
Pavličev et al. used LCM method to enrich SCTs from fresh-frozen sectioned human placentas and analyzed SCT-specific genes (Pavličev et al. et al. et al . 2018). MACS method has been used in isolating multiple cell types from first- and second-trimester placentas (Liu et al . 2018). Various microfluidic techniques are used for single-cell isolation. A widely used microfluidic technique is droplet-based microfluidics, such as Drop-seq and Chromium system from 10× Genomics. In the droplet-based systems, cells are individually encapsulated into a water-in-oil droplet, followed by the cDNA synthesis via reverse transcription, during which the cell barcode and the unique molecular identifiers (UMI) are incorporated into the cDNA molecule. The cDNAs from individual cells are amplified for library preparation and subsequently next-generation sequencing (Klein et al. et al. et al. et al . compared two droplet-based methods, Drop-seq and 10x Chromium platforms, for scRNA-seq analysis of human first-trimester placental cells. They found that although more genes were detected in the 10x Chromium method, there was a good correlation between the Drop-seq and 10x Chromium expression data (Suryawanshi et al . 2018). One limitation of droplet-based methods, however, is that it requires specific microfluidic devices and instruments for droplet sorting. The commercial droplet-based platforms have limited room for optimization or modification of cell capture conditions for specific samples. The scRNA-seq library preparation procedure involves several steps including the conversion of cellular RNA into first-strand cDNA by reverse transcription, second-strand cDNA synthesis by poly(A) tailing or by a template-switching approach, and cDNA amplification using PCR or by in vitro transcription (Chen et al. et al. et al . used Smart-seq2 method to investigate genes of the KIR family, which are polymorphic and highly homologous, in uNK cells from early pregnancy (6-14 weeks) human placentas, and successfully mapped the single-cell reads to the corresponding donor- specific reference of KIR alleles (Vento-Tormo et al . 2018). Other methods combined with UMI or barcodes only capture either the 5’-end or 3’-end of the transcripts (Islam et al. et al. There are a few next generation sequencing platforms. Illumina sequencers are by far the most popular, with a claimed more than 70% dominance of the market, and have produced the majority of the publicly available human sequencing data. In 2015, BGI launched the BGISEQ-500 as an alternative to existing sequencing platforms, with lower sequencing cost. Sequencing performance comparison between the Illumina platform and the BGISEQ-500 showed that the two platforms have comparable sensitivity and accuracy in bulk RNA seq, small noncoding RNA seq, whole genome DNA seq, and scRNA-seq (Natarajan et al . 2019). The sequencing system chosen for a scRNA-seq experiment depends largely on the biological question being asked. Considerations for a sequencing experiment include total number of reads per cell, paired vs single read and the estimated desirable number of single cells to yield in each experiment. For example, 10x genomics recommend 50,000 read pairs per cell, with a targeted population up to 10,000 cells per sample. Single-read sequencing involves sequencing from only one end of DNA fragments in a library, while paired-end sequencing involves sequencing both ends of DNA. Single-read sequencing is faster and cheaper than paired-end sequencing and is mostly used to assess the abundancy of gene expression. Paired-end sequencing improves the ability to detect structural rearrangements (Rizzetto et al. 2017). Typically, paired-end reads (2 x 75 bp or 2 x 100 bp) are required for novel transcriptome assembly and annotation. Compared to the bulk RNA-seq method, scRNA-seq protocols have significantly more technical variations. The technical replicates can be used to assess and correct batch effects, however this is expensive given the cost of scRNA-Seq. Alternatively, some protocols use a set of synthetic RNAs with various lengths and GC content as spike-in controls (Brennecke et al . 2013). UMIs, which tag individual transcripts within a cell and thus can theoretically enable the estimation of absolute molecular counts, have also been used to reduce amplification bias (Kivioja et al . 2011). However, spike-ins and UMIs are not available for every protocol. Spike-in RNAs can be used in protocols such as Smart-seq2 but are not compatible with droplet-based approaches, whereas UMIs are usually used in methods where only the 3’-ends of transcripts are sequenced, such as Drop-seq. Although the development of the experimental technologies and analytical methods have empowered the popularity of the single-cell RNA sequencing, one should be aware of the challenges. For example, scRNA-seq requires mechanical or enzymatic dissociation of the tissue, which could result in the loss of vulnerable cell types or even alter gene expression in cells. Thus, dissociation protocols must be optimized for the specific type of tissues of interest to minimize dissociation bias. A quality assessment of single cell preparation should be performed to ensure that cells are viable and fully dissociated into single cells before loading them onto the microfluidic chip. Two human placental scRNA-seq studies have shown that tissue dissociation may alter the frequencies of certain placental cell types (Liu et al . 2018; Suryawanshi et al . 2018). Variability in results may arise from both experimental and data analytical phases. Single Cell RNA-seq analysis pipelines
The development of novel computational tools and analytical frameworks are closely coupled with single cell genomics technologies, in order to extract biological information. It is thus worthy to overview the computational pipelines and workflows, in the context of placenta scRNA-seq analysis.
There is a growing list of new computational tools and pipelines, which were reviewed in detail elsewhere (Luecken et al . 2019; Vieth et al. et al. molecular identifiers (UMIs) were included in the library preparation protocol. This step consists of quality control, demultiplexing, alignments and quantification. Many benchmark studies have compared different aspects of the data preprocessing step and the effect of the choice of tools to downstream analysis (Vieth et al. et al. et al . 2016; McCarthy et al. et al. et al. et al. et al. et al. et al. et al. ever possible. To address the variations due to analytical methods, one approach is to enforce the same standard pipeline and re-analyze, re-annotate the samples, which will be time consuming. Alternatively, methods of data integration could be used to analyze datasets from multiple experiments on the same tissue type. A series of single-cell data integration methods have been proposed to deal with such problems, such as LIGER, Harmony, scAlign (Welch et al . 2019; Korsunsky et al . 2019; Johansen and Quon 2019). Through data integration, we could enable an increase in sample size and standardize analytical frameworks across different datasets/studies which help us generalize observations (Lähnemann et al . 2020). In the context of placenta study, this will enable the meta atlas for placenta tissues, benefiting the subsequent experiments especially in cell type classification and rare cell identification. Human placental single cell RNA-seq studies
During pregnancy, the placenta gene expression continuously changes in order to regulate the fetal growth, maintain immunological tolerance and to modulate metabolism according to pregnancy requirement. scRNA-seq has been used to reveal heterogeneity among various cell types in the human placenta, and to elucidate molecular interactions within a population of cells in different states. Thus far, seven scRNA-seq studies have been reported on placenta, with the samples ranging from as early as 6 th week gestation to full term, in normal, preeclampsia or preterm birth conditions (Table 1). In the first-trimester, human placenta and decidua undergo dynamic changes to enable the establishment of a successful pregnancy. Four scRNA-seq studies so far have investigated the gene expression in human placentas from early pregnancy (Suryawanshi et al . 2018; Liu et al. et al. et al. responses, and metabolism, consistent with the findings from microarray study (Sitras et al . 2012). In addition, scRNA-seq gene profiling mapped the transcripts to the individual cells. These scRNA-seq studies also identified new cell subtypes and predicted interactions between cell types through the analysis of cell-to-cell gene expression variability. Suryawanshi et al . analyzed the gene expression in more than 20,000 cells from human placental villous and decidual tissues from early pregnancy (6-11 weeks). In order to differentiate fetal and maternal origin, they only included male fetus samples. 20 distinct cell populations (9 from villi and 11 from decidua) and uncovered previously unknown subtypes of the fibroblast-like (FB) cells were identified using unsupervised clustering and differential gene expression analyses. Gene profiling showed that all cell types identified in the villi sample expressed Y chromosome–encoded genes, indicating fetal origin of these villous cells, whereas female-specific gene XIST was only expressed in cell types identified in the decidua samples, indicative of their maternal origin. SCTs expressed genes involved in the production of gestational hormones, including
CGA, CYP19A, HSD3B1 , and
GH2 , that support the fetal and placental development. EVTs specifically express ECM genes, consistent with their role in invasion and mobility. Three villous FB subtypes were identified, which specifically express the genes associated with endothelial cell migration, angiogenesis, and the regulation of fluid homeostasis. Two decidual FB subtypes were also identified, which expressed the genes involved in cell adhesion and lipid metabolism. These observations indicate the influence of the microenvironment on FB cell fate determination. In addition, a proliferating and a resting NK subpopulation were differentiated through gene expression and gene ontology enrichment analyses. Receptor-ligand interaction analysis between and within villi and decidua showed abundantly expressed receptors and ligands for each cell type of villi and decidua, providing insights into the cell-cell interactions in the fetomaternal microenvironment. Vento-Tormo et al . investigated the transcriptome of more than 70,000 cells from the maternal–fetal interface between 6–14 weeks of gestation, and matched maternal peripheral blood cells from early pregnancy (6-14 weeks). Alignment of scRNA-seq reads with overlapping single nucleotide polymorphisms called from fetal and maternal genomic DNA showed that placental samples contained mostly fetal cells with some maternal macrophages, whereas most cells from decidual samples except a few EVTs were of maternal origin. Unsupervised clustering and differential gene expression analyses identified two clusters of decidual fibroblast cells (PV1 and PV2), three clusters of decidual stromal cells (dS1, dS2 and dS3), and three subsets of decidual NK cells (dNK1, dNK2 and dNK3). The localization of decidual fibroblast cells and stromal cells was verified by immunohistochemistry as well as multiplexed single-molecule fluorescent in situ hybridization, and the presence of decidual NK cells was confirmed by flow cytometry. PV1 cells expressed high levels of genes involved in cell adhesion, while genes associated in tissue remodeling are higher in PV2 cells. All subsets of decidual NK cells express genes encoding proteins involved in anti-inflammatory roles, which suggests a ential role of these cells in regulating the extent of the trophoblast invasion. Expression analysis of the KIR gene family using the KIRid method indicates a likely function of dNK1 in the recognition and response to EVT. Trajectory modeling and psudotemporal ordering of cells predicted two distinct trophoblast differentiation pathways, with the decidual EVTs being at the end of the trajectory and expressing high levels of HLA-G . In addition, they developed a statistical tool named CellPhone to identify the specific interactions between decidual NK cells and invading fetal EVTs, maternal immune and stromal cells. Using this tool, they predicted the ligand- receptor interactions that may control the differentiation processes of trophoblast cells to SCTs or EVTs (Vento-Tormo et al. et al . 1983). Incomplete EVT invasion and failure of spiral artery transformation by this time has been reported in preeclampsia (Brosens et al . 1972), indicating that preeclampsia may be related to shallow EVT invasion. Liu et al . investigated the transcriptomic profile of sorted single cells from both first- (8 weeks of gestation) and second-trimester (24 weeks of gestation) placentas. Unsupervised clustering analysis and differential gene expression analysis of maker genes revealed 14 subtypes of placental cells which vary with the different gestational stages. Specifically, they identified three CTB subtypes (CTB_8W_1, CTB_8W_2 and CTB_8W_3) from the first trimester placenta. These CTB subtypes have various degrees of proliferative capacity, which may serve as the pool of CTBs and progenitor cells of the STBs, respectively. CTB_8W_1 cells, with the least proliferative capacity, expressed high levels of potentially important fusion genes, indicating that they may be the fusion-competent CTB cells. They also identified five EVT subtypes (three EVT subtypes from 8-week placentas, EVT_8W, and two EVT subtypes from 24-week placentas, EVT_24W). Gene ontology analysis and immunostaining revealed that EVT_8W cells with proliferative potential were localized at the proximal end of the placental cell column, while EVT_8W cells involved in receptor activity regulation and the immune response were found to be at distal end of the placental cell column. Gene ontology analysis of EVT_24W cells showed that one subtype may be associated with the response to wounding, digestion and immunomodulation, while the other subtype related to growth regulation, gonadotropin secretion and pregnancy. A pseudotime analysis of CTB_8W, EVT_8W and EVT_24W cells predicted a differentiation pathway of CTB_8W_2 cells to CTB_8W_3 cells and then to EVT_8W and EVT_24W cells, which is consistent with the study by Vento-Tormo et al . (Vento-Tormo et al. et al . 2017; Suryawanshi et al . 2018). Surprisingly, both the first- and second-trimester EVTs also expressed many hormone genes, indicating a role for these hormones in maintaining the fetal growth and the maternal adaptations to normal pregnancy (Liu et al . 2018). Recently, Sun et al . analyzed the gene expression in more than 7,000 cells from human placental villous and decidual tissues at 6-11 weeks of gestation. Using unsupervised clustering and differential gene expression analysis, they identified 5 major cell types (trophoblasts, stromal cells, hofbauer cells, antigen-presenting cells and endothelial cells) with unique crosstalk at the fetal-maternal interface. Gene ontology enrichment analysis showed that the top enriched processes include cell proliferation and adhesion in trophoblasts, cell differentiation and signal transduction in stromal fibroblasts, inflammatory responses in Hofbauer cells, and cell migration and angiogenesis in endothelial cells. Cell-type specific upstream regulators were predicted by Ingenuity pathway analysis. In addition, a number of chemokine genes were found to have sex-differential expression in trophoblasts and Hofbauer cells, suggesting a sexual dimorphism of immune responses in the placenta (Sun et al . 2019). Although both microarray and bulk RNA-seq studies have shown sex-biased gene expression in human placentas at first-trimester and term (Sood, et al . 2006; Buckberry et al . 2014; Gonzalez et al . 2018), this is the first report by far that has investigated the sex-specific differences in single cell transcriptome of the placental cells. Given the vast differences in the types of cells and cell subtypes identified in these studies, it will be of interest to perform meta-analysis and comparative studies, to confirm the cell types that are either claimed novel, as well as the degrees of consistency among common cell types. Three studies have examined the human placental transcriptome during the third trimester. Pavli č ev et al . analyzed the gene expression profiles of full-term placental cells. They compared the single-cell transcriptomes with the tissue-level data and showed that scRNA-seq analysis recovered more than 4000 genes that were not detected in the tissue-level transcriptome, consistent with the view that scRNA-seq may reveal the expression profiles of rare cells. They identified five distinct clusters belonging to different types of decidual and fetal cells of utero-placental interface using hierarchical clustering and marker gene expression analysis. Among the five trophoblast clusters, the large clusters were identified as intravillous cytotrophoblast (iVCT), and the smaller cluster as EVT. These trophoblast clusters likely reflect the continuous differentiation process of cytotrophoblasts. The expression profiles of iVCT cells suggest that they are a very heterogeneous group and that the differentiation proceeds by individual cells in a different order. In addition to the identification of cell clusters, they inferred the cell-cell communication network at the maternal-fetal interface using public databases of ligand-receptor relationships based on the mRNA expression. A large number of interactions were found to be between decidual cells and the SCT cells. Further, they showed that the cell-type specificity of G-protein-coupled receptor (GPCR) profiles may serve to biological characterize the cell type identity. Tsang et al . analyzed single-cell transcriptomes of more than 24,000 cells isolated from full-term and second trimester preeclamptic placentas. Clustering analysis by t-SNE identified diverse placental cell types and comparison of the ratio of transcripts carrying fetal-specific SNP alleles with those carrying maternal-specific SNP alleles showed that most clusters were of fetal origin. Gene profiling analysis showed that SCTs specifically expressed genes involved in the production of important gestational hormones, and EVTs expressed HLAs associated with maternal immune tolerance of the fetus with uNKs. In addition, they predicted the differentiation relationship of trophoblast cells using reclustering and pseudotime analyses. They showed that SCTs expressed high levels of the genes involved in cell fusion and identified several genes that could serve as regulators in SCT development. Analysis of the EVT developmental pathway revealed up-regulation of various extracellular enzymes associated with invasion and migration. Importantly, comparison of transcriptomic profiling of biopsies taken from different sites of the placenta showed spatial heterogeneity, consistent with previous bulk tissue transcriptomic profiling (Sood et al . 2006). Furthermore, they integrated the single-cell-derived placental signature genes with maternal plasma-circulating RNA analysis to analyze the fetal and maternal cellular dynamics during pregnancy progression and suggested that the cellular pathology in early preeclampsia can be identified noninvasively (Tsang et al. et al . carried out a comparative study to define individual cell-type–specific gene signatures during physiologic and preterm labor (Pique-Regi et al. gestation). Using differential gene expression analysis of marker genes, a new cluster of non-proliferative interstitial cytotrophoblasts was identified in the placental villi, and the basal plate which is adjacent to the placental villi. A new cluster of lymphatic endothelial decidual cells was also revealed in the chorioamniotic membranes using cell-type–specific gene signatures. Gene enrichment analysis using clusterProfiler indicated that these lymphatic endothelial decidual cells may mediate the influx of immune cells into the chorioamniotic membranes. In addition, significant differences in transcriptional profiles were identified among placental cell types and between the term and preterm labor, with the largest number of differentially expressed genes in the maternal macrophages between the term labor and term no labor groups. Further, an in silico analysis in public datasets showed that the single-cell signatures of macrophages, monocytes, activated T cells, and fibroblasts is modulated in the circulation of women with preterm labor compared to gestational age-matched controls. These results suggested the feasibility of using single-cell signatures as biomarkers to non-invasively monitor the complex fetal and maternal cellular dynamics for normal or pathological conditions in pregnancy. Yet, additional studies are needed to characterize the transcriptional profiles of the very preterm labor (less than 32 weeks of gestation). Perspectives and conclusions
Placenta is a very heterogeneous, blood-vessel rich organ with maternal and fetal origins and various cell types. The human placenta scRNA-seq studies reviewed here reveal that vast transcriptional differences exist among geolocations of placentas, between various cell types, gestational ages as well as in pregnancy diseases. These studies open up the possibilities to better understand cellular networks and pathways in cells of normal and complicated pregnancies, which could help to discover the underlying causes of adverse pregnancy outcomes. However, one needs to be cautious before attempting scRNA-Seq studies using human placenta. Access to normal human placenta before delivery is challenging due to ethical concerns, limiting the kind of studies that can be conducted. Upon delivery, various factors need to be taken in consideration including hospital procedures, mode of delivery and sample sizes. Placenta grows and constantly adapts to the maternal environment to ensure a healthy pregnancy, thus static views of placenta cell proportions and gene expression are incorrect. Temporal, spatial and between individual variability have to be considered. Fresh samples are limited to deliveries and planning is critical. This also comes with challenges regarding deconvoluting disease state vs. batch variations. To get around these issues, single-nucleus RNA sequencing (snRNA-seq) has arisen as an alternative to single-cell methods. snRNA-seq does not require enzymatic digestion, which reduces dissociation artifacts. In addition, unlike scRNA-seq which requires the preparation of a live single cell suspension from fresh tissue which inevitably introduces batch effect, snRNA-seq can be used for frozen tissues, enabling single-cell gene profiling of archived clinical samples simultaneously, reducing batch effect and increasing power of the study by allowing for more biological replicates (Habib et al . 2017). Despite these technological and practical challenges, in the coming years, we expect to see more studies that will reveal the normal placenta developments throughout normal pregnancy and provide a more complete picture for a dynamic placenta atlas, such as those in Pediatric Cell Atlas (PCA) (Taylor et al. scRNA-seq studies that address the effect of lifestyle choices, such as smoking and alcohol use in pregnancy, will also pingpoint the biological changes in placentas at single cell level. Additionally, with the development of single cell technologies, other types of single cell omics studies, such as single cell epigenetics studies, as well as multi-omics single cell studies (scRNA-seq, sc methylation and scATAC-Seq etc) will be very useful to delineate the coherent changes from epigenetic to transcriptomics level (Poirion et al. ; Ortega et al. in situ , is the spatial transcriptomics, where single cell (or close to single cell ) resolution of gene expression data can be spatially mapped back to the tissue at 2D level (Moncada et al. different datasets will be valuable to uncover the development trajectory of placenta through gestational ages. Methods that integrate single cell multi-omics and multi-modal data, such as those handling spatial transcriptomics data, will be much needed for deeper characterization of placenta tissue. It will be interesting to see if and how the multi-omics data integration methods developed for bulk cell analysis can be extended to single cell (Huang et al. et al. Author contributions
LXG envisioned and supervised the project. HL wrote the majority of the manuscript, with significant help from QH, YL and LXG.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this review.
Funding References
Benny PA, Alakwaa FM, Schlueter RJ, Lassiter CB & Garmire LX 2020 A review of omics approaches to study preeclampsia.
Placenta The human placenta . Cambridge: Heffer. Boyd PA 1984 Quantitative structure of the normal human placenta from 10 weeks of gestation to term.
Early Human Development P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V,
Teichmann SA, Marioni JC et al . 2013 Accounting for technical noise in single-cell RNA-seq experiments.
Nature Methods Obstetrics and Gynecology Annual Molecular Human Reproduction American Journal of Obstetrics and Gynecology
S745–S761. Butler A, Hoffman P, Smibert P, Papalexi E & Satija R 2018 Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Nature Biotechnology Human Reproduction et al. Nature
Clinical Science
Human Reproduction Update Castellucci M, Schepe M, Scheffen I, Celona A & Kaufmann P 1990 The development of the human placental villous tree
Anatomy and Embryology
Frontiers in Genetics Clinical Epigenetics
21. Ching T, Song M-A, Tiirikainen M, Molnar J, Berry M, Towner D & Garmire LX 2014 Genome-wide hypermethylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset pre-eclampsia.
Molecular Human Reproduction Journal of Clinical Investigation
Science
Nature G3 (Bethesda) Nature
Science
American Journal of Anatomy
Placenta Current Protocols in Molecular Biology Bioinformatics Biocomputing Gonzalez TL, Sun T, Koeppel AF, Lee B, Wang ET, Farber CR, Rich SS, Sundheimer LW, Buttle RA, Chen YI et al . 2018 Sex differences in the late first trimester human placenta transcriptome. Biology of Sex Differences
4. Guo F, Li L, Li J, Wu X, Hu B, Zhu P, Wen L & Tang F 2017 Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells.
Cell Research et al . 2017 Massively parallel single-nucleus RNA-seq with DroNc-seq. Nature Methods Journal of Anatomy Cell Reports American Journal of Anatomy. Frontiers in Genetics
84. Iacono G, Massoni-Badosa R & Heyn H 2019 Single-cell transcriptomics unveils gene regulatory network plasticity.
Genome Biology Genome Research Nucleic Acids Research e117. Johansen N & Quon G 2019 scAlign: A Tool for Alignment, Integration, and Rare Cell Identification from scRNA-Seq Data Genome Biology Proceedings of the National Academy of Sciences of the United States of America Biology of Reproduction . Kearns M & Lala PK 1983 Life history of decidual cells: a review.
American Journal of Reproductive Immunology Nature
Methods Cell
Nature Methods : 1289–96. Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A et al . 2020 Eleven Grand Challenges in Single-Cell Data Science. Genome Biology
31. Liu Y, Fan X, Wang R, Lu X, Dang Y-L, Wang H, Lin H-Y, Zhu C, Ge H, Cross JC et al.
Cell Research Molecular Systems Biology e8746. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM et al. Cell
Journal of Perinatal Medicine Bioinformatics Nature Biotechnology et al . 2019 Comparative analysis of sequencing technologies for single-cell transcriptomics. Genome Biology
70. Norwitz ER, Schust DJ & Fisher SJ 2001 Implantation and the survival of early pregnancy.
New England Journal of Medicine Ortega MA, Poirion O, Zhu X, Huang S, Wolfgruber TK, Sebra R & Garmire LX 2017 Using single-cell multiple omics approaches to resolve tumor heterogeneity.
Clinical and Translational Medicine Pavličev M, Wagner GP, Chavan
AR, Owens K, Maziarz J, Dunn-Fletcher C, Kallapur SG, Muglia L & Jones H 2017 Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal-fetal interface.
Genome Research Nature Methods Placenta eLife Frontiers in Genetics Nature Communications Science Reports Placenta et al . 2016 Self-organization of the human embryo in the absence of maternal tissues. Nature Cell Biology :700-708. Sitras V, Fenton C, Paulssen R, Vårtun Å & Acharya G 2012 Differences in gene expression between first and third trimester human placenta: a microarray study. PLoS One e33294. Sojka DK, Yang L & Yokoyama WM 2019 Uterine Natural Killer Cells. Frontiers in Immunology Proceedings of the National Academy of Sciences of the United States of America
BMC Genomics Sun T, Gonzalez TL, Deng N, DiPentino R, Clark EL, Lee B, Tang J, Wang Y, Stripp BR, Yao C et al. bioRxiv , pp 641118. Suryawanshi, H, Morozov P, Straus A, Sahasrabudhe N, Max KEA, Garzia A, Kustagi M, Tuschl M & Williams Z 2018b A single-cell survey of the human first-trimester placenta and decidua.
Science Advances eaau4788. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A et al. Nature Methods et al. Developmental Cell Pregnancy Hypertension: An International Journal of Women’s Cardiovascular Health . et al. Proceedings of the National Academy of Sciences of the United States of America
E7786–E7795. Turco MY & Moffett A. 2019 Development of the human placenta.
Development dev163428. Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park J-E,
Stephenson E, Polański K, Goncalves A et al.
Nature et al . 2019 High-definition spatial transcriptomics for in situ tissue profiling
Nature Methods Nature Communications Genome Biology
15. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME, Mburu FM, Mantalas GL, Sim S, Clarke MF & Quake SR 2014 Quantitative assessment of single-cell RNA- sequencing methods. Nature Methods et al. Nature Communications Genome Medicine Figure Legend
Figure 1. Single cell RNA-sequencing on placenta cells. The heterogeneity of placenta gene expression can be revealed by single cell RNA-sequencing (scRNA-seq) technology. The placenta tissue is first dissociated into a single cell suspension and cells are then individually encapsulated into a water-in-oil droplet, followed by the cDNA synthesis via reverse transcription, during which the cell barcode and the unique molecular identifiers (UMI) are incorporated into the cDNA molecule. The cDNAs from individual cells are amplified for library preparation, followed by next-generation sequencing. The sequences are then mapped by alignment algorithms and counted for those mapped to the reference transcriptome. The cell origin of the mapped reads can be identified by the cell barcode within them. As a result, a sequence read count matrix is generated over thousands of genes (rows) and thousands of single cells (columns). This count matrix is then subject to preprocessing and downstream bioinformatics analysis, such as clustering and pseudo-time reconstruction among single cells, as shown in the figure. EVT, extravillous trophoblast; F, fibroblast; H, Hofbauer cell; SCT, syncytiotrophoblast; VCT, villous cytotrophoblast.
B FVCTSCTEVT able 1. Summary of current single cell RNA-seq studies on human placenta
Gestational age (weeks) Tissue Number of cells Number of placenta Single cell isolation Platform Average reads length (bp) Mean Pair-end reads Reference et al . (2018) 6-14 Placenta, blood 70,000 5 Enzyme, FACS 10x Smart-seq2 75 1x10 Vento-Tormo et al . (2018) 8, 24 Placenta 1,567 8 Enzyme, gradient centrifugation, MACS Smart-seq2 150 1x10 Liu et al . (2018) 11-13 Placenta 7245 10 Enzyme 10x N/A N/A Sun et al . (2019) Term Placenta 87 2 Enzyme, , gradient centrifugation Fluidigm 75 3.65x10 Pavličev et al . (2017) Term (preeclampsia or healthy Placenta, blood 24,000 8 Enzyme 10x 130 35800 Tsang et al . (2017) Term (with or without labor) preterm (33-35 weeks) Placenta 79,906 9 Enzyme
10x N/A N/A Pique-Regi et al. (2019) able 2. A list of platforms for single cell RNA-Seq analysis . Tool Platform URL Description Reference
Granatum Web http://granatum.dcmb.med.umich.edu:8102/?_state_id_=6e60464c6d7d66a3&tab=info A web-based comprehensive scRNA analysis framework with easy utilization Zhu et al . (2017) GranatumX Web http://35.222.3.147:34567/step/f6ba5588-93c1-4507-8013-196273fb055b Updated version of Granatum with customized analysis modules Zhu et al . (2018) ASAP Web https://asap.epfl.ch/ Web-based automatic single-cell analysis pipeline Gardeux et al . (2017) Seurat R https://satijalab.org/seurat/ A comprehensive scRNA-seq analysis framework, includes the anchor technology for data integration Butler et al . (2018) Monocle3 R https://cole-trapnell-lab.github.io/monocle3/ A comprehensive scRNA-seq analysis framework, has good performance on pseudotime cell trajectories construction Cao et al . (2019) bigSCale2 R https://github.com/iaconogi/bigSCale2 A comprehensive scRNA-seq analysis framework, especially appropriate for large datasets Iacono et al . (2019) Scater R https://bioconductor.org/packages/release/bioc/html/scater.html A comprehensive scRNA-seq analysis framework, with a focus on quality control and visualization McCarthy et al . (2017) Slingshot R https://bioconductor.org/packages/release/bioc/html/slingshot.html Designed to model developmental trajectories in single-cell RNA sequencing data Street et al . (2018) TSCAN R https://github.com/zji90/TSCAN Designed to model developmental trajectories in single-cell RNA sequencing data Ji et al . (2016) Scanpy Python https://icb-scanpy.readthedocs-hosted.com/en/stable/ A scalable scRNA-seq analysis toolkit, able to efficiently deal with datasets of more than one million cells, because of the Python-based implementation Wolf et alet al