The placenta functions as the lung and kidney of the fetus and produces hormones to maintain the pregnancy1,2. Derived from the trophectoderm layer of blastocyst, trophoblasts (TBs) are the main cell types that form the outer layer of placenta3. Among the three TB subtypes, proliferating villous cytotrophoblasts (VCTs) form the germative layer that gives rise to the other two types, extravillous trophoblasts (EVTs) and syncytiotrophoblasts (SCTs)3. EVTs invade the decidua to anchor the placenta to uterus and modify maternal vasculature1, whereas SCTs form the interface between the mother and fetus, where nutrient/waste exchange takes place1, and secrete a variety of hormones necessary for pregnancy, such as human chorionic gonadotropin (hCG), human placental lactogen, human placental growth hormone, progesterone, and estradiol4. While transcriptional regulation in TB differentiation has been extensively studied5,6,7, little is known about post-transcriptional control of gene expression in the process.

Almost all eukaryotic messenger RNAs (mRNAs) require cleavage and polyadenylation for 3ʹ end maturation8,9. Over 70% of mammalian genes harbor multiple polyadenylation sites (PASs), leading to expression of alternative polyadenylation (APA) isoforms10,11. Most APA sites are located in the 3ʹ most exon, resulting in isoforms with different 3ʹUTR sizes11. 3ʹUTR APA can play a substantial role in gene expression through regulation of sequence or structure motifs that impact aspects of mRNA metabolism, such as translation, stability and localization12,13,14. In addition, about 20% of human genes have APA sites located in introns, which additionally change coding sequences when used11,15,16.

The relative abundance of an APA isoform can vary widely across tissue types17. For example, brain tissues tend to express long 3ʹUTR isoforms, whereas blood and testis show the opposite trend16,17,18,19. In addition, increased cell proliferation rate has been associated with shortening of 3ʹUTRs through APA20, while 3ʹUTRs lengthen during cell differentiation and development21,22. Here, we report the unexpected finding of widespread transcript shortening through APA, including 3ʹUTR shortening and activation of intronic polyadenylation (IPA), during differentiation of SCTs. We examine several in vitro models as well as single placental cells in vivo. We further generalize this finding to B-cell differentiation.


Trophoblast differentiation displays a distinct APA profile

To study APA regulation in different cell differentiation lineages, we analyzed RNA-seq data previously generated by Xie et al.23, involving differentiation of human embryonic stem cells (hESCs, H1 line) into neuronal projector cells, mesenchymal stem cells, mesendoderm, and TBs (illustrated in Fig. 1a)23. By comparing RNA-seq reads covering different portions of 3ʹUTR that were subject to APA regulation (illustrated in Fig. 1b and see “Methods” for details), we found that, consistent with previous reports24,25, differentiation of hESCs into neuronal projector cells, mesenchymal stem cells, or mesendoderm led to 3ʹUTR lengthening to variable degrees (Fig. 1c). By contrast, surprisingly, differentiation of hESCs into TBs showed 3ʹUTR shortening (Fig. 1c).

Fig. 1: Trophoblast differentiation displays a unique APA profile.
figure 1

a Schematic showing differentiation of human embryonic stem cells (hESCs, H1 line) into four lineages (a study by Xie et al.23). b Schematic of 3ʹUTR APA analysis using RNA-seq data. RNA-seq reads in 3ʹUTRs are divided into constitutive UTR (cUTR) and alternative UTR (aUTR) groups based on the first and last PASs, as indicated. 3ʹUTR length change for a gene is indicated by relative expression difference (RED) value between two samples or sample groups, as shown in the graph. RED is calculated as indicated. c Median REDs of four differentiation lineages as compared to hESC. A total of 8,712 genes were used for analysis. d Boxplots showing expression changes of proliferation genes (left; 350 genes; defined by Sandberg et al.20) and development genes (right; 74 genes; defined by Ji et al.21) in TB vs. hESC. P-values (Wilcoxon test) based on comparisons with other genes are indicated.

Increased cell proliferation could trigger 3ʹUTR shortening20. Interestingly, the proliferation genes that were previously found to be negatively correlated with 3ʹUTR size20 were in fact globally downregulated during TB differentiation (Fig. 1d, left). Note that the same genes did indicate decreased cell proliferation in differentiation of mouse myoblast cell line C2C12 (Supplementary Fig. 1a), a model we have used to study 3ʹUTR lengthening in cell differentiation (Supplementary Fig. 1b)21,26. We previously identified a gene set whose expression levels positively correlated with 3ʹUTR size in mouse embryonic development21. While expression of this development gene set significantly increased in C2C12 differentiation (Supplementary Fig. 1a), they were also upregulated in hESC to TB differentiation (Fig. 1d, right), again indicating that 3ʹUTR size regulation in TB differentiation is different than previously studied differentiation and development systems.

Widespread transcript shortening in TB differentiation

To examine APA in TB differentiation in greater detail, we differentiated hESCs (H9 line) to TBs through the BMP4 induction protocol27, and subjected cellular RNA to 3ʹ Region Extraction and Deep Sequencing (3ʹREADS), a deep-sequencing method we previously developed to specifically interrogate 3ʹ ends of poly(A) + RNAs11 (Fig. 2a). Based on the top two most abundant 3ʹUTR APA isoforms of each gene (Fig. 2b), we found that genes showing increased relative expression of short 3ʹUTR isoform greatly outnumbered those showing increased relative expression of long 3ʹUTR isoform by 11.4-fold in TBs vs. hESCs (1070 vs. 94, Fig. 2c), indicating a substantial and global trend of 3ʹUTR shortening. Based on the size of the 3ʹUTR sequence that is different between the two isoforms, named alternative UTR or aUTR (illustrated in Fig. 2b), we divided genes into five bins (Fig. 2d). Using relative expression difference (RED) to represent the degree of 3ʹUTR APA regulation (described in Fig. 2b), we found that genes tended to display a greater extent of 3ʹUTR shortening as aUTR size increased (Fig. 2d). For example, genes with an aUTR size >1836 nucleotides (nt, bin 5) showed much more conspicuous 3ʹUTR shortening than genes with an aUTR size <152 nt (bin 1, P = 3.4 × 10−37, Wilcoxon test, Fig. 2d).

Fig. 2: Widespread 3ʹUTR shortening and IPA activation in trophoblast differentiation.
figure 2

a Schematic of 3ʹREADS experiment using hESCs (H9 line) treated with BMP4 for TB differentiation. b Schematic of 3ʹUTR APA analysis using 3ʹREADS data. Two representative 3ʹUTR isoforms are selected, named proximal and distal PAS (pPAS and dPAS) isoforms, respectively. RED is calculated as indicated. c Scatter plot showing 3ʹUTR APA changes. Each dot is a gene with two selected 3ʹUTR isoforms. The numbers of genes showing significantly lengthened 3ʹUTRs (red) or shortened 3ʹUTRs (blue) are indicated, and so is the ratio of these two numbers. Significance here and other panels of this figure is based on P < 0.05 (Fisher’s exact test) and > 20% change of expression ratio of the two isoforms. d Relationship between aUTR size and degree of 3ʹUTR size regulation (represented by RED). Genes are grouped into five similarly sized bins (about 500 genes each) based on their aUTR length. aUTR size range for each bin is shown in a table. Data are presented as median value ± SEM. P-value (Wilcoxon test) comparing Bins 1 and 5 is indicated. e Schematic of intronic polyadenylation (IPA) isoforms and 3ʹ terminal exon polyadenylation (TPA) isoforms. f Scatter plot showing IPA changes. Each dot is a gene with expression of both IPA and TPA isoforms. The numbers of genes with significant IPA suppression (red) or IPA activation (blue) are indicated, and so is their ratio.

A sizable fraction of APA sites are located in introns11, leading to IPA isoforms that have coding sequence changes in addition to 3ʹUTR alternations (illustrated in Fig. 2e). Using isoforms containing PASs in the 3ʹ terminal exons as reference, we found that IPA isoforms were generally upregulated in TBs vs. hESCs, with 1.7-fold more genes showing IPA activation than those showing suppression (172 vs. 100, Fig. 2f). Gene Ontology (GO) analysis indicated that the most significant biological processes associated with genes showing 3ʹUTR shortening appeared to be related to protein and RNA metabolism (Table 1), such as “regulation of catabolic process”, “post-transcriptional gene regulation”, and “protein localization to organelle”. For genes showing IPA activation (Table 2), “regulation of mRNA catabolic process,” “cellular response to acid chemical”, and “cytoplasmic translational initiation” were the top enriched biological processes. Altogether, the 3ʹREADS result corroborated our initial finding of global 3ʹUTR shortening in TB differentiation, and additionally revealed IPA activation. These APA events could potentially impact a diverse array of biological processes.

Table 1 Top biological processes enriched for genes that display 3ʹUTR shortening in TB differentiation from hESCs.
Table 2 Top biological processes enriched for genes that display upregulation of IPA isoforms in TB differentiation from hESCs.

APA regulation correlates with syncytiotrophoblast formation

As there are three TB subtypes, namely, VCT, EVT, and SCT, we next asked which TB subtype was responsible for APA regulation when differentiated from hESCs. Using gene expression datasets from four TB subtype studies28,29,30,31, we constructed a panel of 69 genes as biomarkers for three TB subtypes (Fig. 3a, and “Methods” for detail), with each subtype having one third of the genes (see Supplementary Table 1 for the full list). Using these marker genes, we found that differentiation of hESCs to TBs by BMP4 treatment was biased toward the SCT lineage (Fig. 3b), suggesting that SCT differentiation might be chiefly responsible for the observed APA regulation.

Fig. 3: Syncytialization of trophoblasts elicits 3ʹUTR shortening.
figure 3

a Venn diagram showing TB subtype marker genes based on four indicated publications. A panel of 69 marker genes were selected. Subtype marker genes were defined by at least two studies (gene number shown in bold) and had no conflicts across all four studies. b Violin plot showing expression changes of TB subtype marker genes in differentiation of hESCs into TBs by BMP4 treatment (Fig. 2a). Significance of difference between gene sets (Wilcoxon test) is indicated. c Schematic showing experimental design of the Yabe et al.32 study, in which TBs differentiated from hESCs (H1 line) by BAP (BMP4, A83-01, and PD173074) were fractionated into different size groups by cell strainers. d Median 3ʹUTR APA REDs for different TB cell size groups vs. hESCs. Data are presented as mean value+/− SD of three replicates. Significance of difference (t-test) between cell groups is indicated. A total of 8216 genes were used for analysis. e Schematic showing experimental design of the Azar et al.34 study, in which BeWo cells were induced to syncytialize by forskolin (Fsk). f Median 3ʹUTR APA REDs for different days of Fsk treatment vs. day 0. Data are presented as mean value+/− SD based on bootstrapped data (20 times). Significance of difference (t-test) with day 0 is indicated. A total of 7682 genes were used for analysis. g Scatter plot comparing 3ʹUTR APA REDs of hESC and BeWo RNA-seq data. Pearson correlation coefficients are indicated for genes showing 3ʹUTR shortening in the 3ʹREADS data (blue dots, 1070 genes from Fig. 2c) and other genes displaying 3ʹUTR APA (gray dots, 7133 genes). h UCSC Genome Browser tracks showing data for the gene DNAJC3. 3ʹREADS data for the hESC model (Fig. 2a), RNA-seq data for the hESC model (>70 μm TB cells vs. hESCs, Fig. 3c), and RNA-seq data for BeWo syncytialization (day 3 vs. day 0, Fig. 3e) are shown. The two APA sites and 3ʹUTR sizes for their corresponding isoforms are indicated. Sequence conservation based on 100 vertebrates is shown. REDs and P-values are indicated.

A previous study by Yabe et al.32 separated hESC-derived TBs (induced by BMP4/A83-01/PD173073 or BAP) into three populations based on cell size, namely, < 40 μm, between 40 μm and 70 μm, and >70 μm (illustrated in Fig. 3c). As such, larger cells were more likely to be syncytialized SCTs than smaller cells. Indeed, using the RNA-seq data from the study and our TB subtype marker genes, we confirmed this notion (Supplementary Fig. 2a) and, importantly, found that 3ʹUTR sizes decreased as cell size increased (Fig. 3d). The largest cells (>70 μm) displayed significantly shorter 3ʹUTRs than did the smallest cells (< 40 μm) (P < 0.05, t-test, Fig. 3d), confirming that differentiation of SCTs elicits 3ʹUTR shortening. Note that while the three hESC to TB differentiation datasets used here involved different hESC lines and induction protocols, leading to differences in gene expression changes (Supplementary Fig. 2b), all three converged on global 3ʹUTR shortening, highlighting the robustness of our finding.

In addition to the hESC differentiation model, human choriocarcinoma cell line BeWo is a well-established system for SCT formation33,34. Using RNA-seq data generated by Azar et al.34, in which BeWo cells were induced to fuse by forskolin (Fsk)(illustrated in Fig. 3e), we found that, as expected, SCT marker genes were significantly upregulated after two days of Fsk treatment (Supplementary Fig. 3a). Importantly, 3ʹUTRs significantly shortened at the same time point (Fig. 3f). The extent of 3ʹUTR shortening in the BeWo model appeared similar to that in the hESC model, as indicated by their median 3ʹUTR APA REDs (–0.15 vs. –0.17, Fig. 3d, f) and RED distributions (Supplementary Fig. 3b). In addition, the RNA-seq data-derived 3ʹUTR APA REDs from the BeWo model and the hESC model were much better correlated for the genes that showed 3ʹUTR shortening in the 3ʹREADS data (r = 0.40, blue dots in Fig. 3g) than for other genes containing 3ʹUTR APA sites (r = 0.26, gray dots in Fig. 3g), further indicating consistency between the two SCT formation models. Both 3ʹREADS and RNA-seq data for an example gene DNAJC3 (encoding DNAJ heat shock protein family member C3) are shown in Fig. 3h, where REDs and P-values highlight its consistent and significant 3ʹUTR shortening in all the models.

Using Fsk-induced BeWo cell fusion model (Supplementary Fig. 3c), we confirmed upregulation of SCT marker genes, CGB and ERVFRD-1 (Supplementary Fig. 3d) by real-time quantitative PCR (RT-qPCR). In addition, using primer sets targeting different APA isoforms (illustrated in Supplementary Fig. 3e, top, and Supplementary Table 2), we confirmed 3ʹUTR shortening of a number of genes that displayed significant 3ʹUTR shortening in the RNA-seq data, such as DNAJC3, PLEKHA6, SPCS3, and TIMP2 (Supplementary Fig. 3e, bottom).

We also examined a mouse model of TB differentiation, in which ectopic expression of a constitutively active Hras mutant HrasQ61L in mouse ESCs led to formation of syncytial giant cells35. Using 3ʹREADS (three biological replicates, Supplementary Fig. 4a), we found that HrasQ61L expression in mouse ESCs elicited both global 3ʹUTR shortening (a 8.3-fold bias in gene number between shortened and lengthened genes, Supplementary Fig. 4b) and IPA activation (a 20.9-fold bias in gene number, Supplementary Fig. 4c). Note that while the mESC model did not involve upregulation of human SCT subtype marker genes (Supplementary Fig. 4d) or development genes (Supplementary Fig. 4e), cell proliferation genes were slightly downregulated (Supplementary Fig. 4e). These results indicate that despite many differences between human and mouse TB models, they both display global 3ʹUTR shortening and IPA activation.

Single-cell analysis in vivo corroborates in vitro findings

Several recent studies have generated single-cell RNA-seq (scRNA-seq) data from the placenta29,30,36, creating opportunities to interrogate APA in TBs in vivo. To address read paucity in single-cell data, which could lead to high noise levels for APA analysis25, we examined APA in different cell types using aggregated scRNA-seq data. This method, named single-cell significance analysis of APA (scSAAP, illustrated in Fig. 4a and see Methods for detail), first clustered cells based on their gene expression profiles; TB subtypes were identified using the TB subtype marker gene panel; RNA-seq reads from all cells of the same type were then combined for 3ʹUTR APA analysis.

Fig. 4: Single-cell analysis reveals short 3ʹUTRs in SCTs.
figure 4

a Schematic of the single cell significance analysis of alternative polyadenylation (scSAAP) method. Cells are clustered by the Seurat package based on all gene expression values. The result is presented by the t-distributed stochastic neighbor embedding (tSNE) method. TB clusters are identified and grouped using the TB subtype marker gene panel. Data for each subtype are aggregated and treated as bulk RNA-seq data for 3ʹUTR APA analysis. b scSAAP analysis of placental single-cell RNA-seq datasets from three indicated studies. 3ʹUTR APA REDs of each dataset were normalized to the mean of all samples. Statistical significance is based on the Student’s t-test. Data are presented as mean value+/– SD (two replicates in Tsang et al.29, four replicates in Vento-Tormo et al.30, and 20 randomly sampled data in Liu et al.36). c Cumulative distribution function (CDF) curves comparing 3ʹUTR APA REDs in SCTs vs. VCTs (from Vento-Tormo et al.30 data) for genes showing 3ʹUTR shortening in the hESC model (blue curve, 685 genes from Fig. 2c) and all 3ʹUTR APA genes (black curve, 2379 genes). P-value (K–S test) for significance of difference between the two gene sets is indicated. d UCSC Genome Browser tracks showing single-cell data for DNAJC3. scRNA-seq reads are based on data from the Vento-Tormo et al.30 study. e Pseudotime analysis of TB cells. The trajectories of TB cells, plotted by using DDRTree, are based on expression of TB subtype marker genes. Each lineage is divided into two halves, far and near, with similar cell numbers. Cells in each group are colored as indicated. The projected differentiation path from VCT far to SCT far is indicated. f 3ʹUTR APA REDs (4852 genes) for different TB groups, corresponding to those in e. 3ʹUTR APA REDs are normalized to the mean of all groups. Significance of RED difference (Wilcoxon test) between groups is indicated.

Applying scSAAP to three datasets corresponding to placental cells isolated at different stages of pregnancy, including the first30,36 and third29 trimesters, we found that SCTs consistently displayed shorter 3ʹUTRs than VCTs and EVTs (P < 0.05, t-test, Fig. 4b). Importantly, the genes showing 3ʹUTR shortening in the hESC model (based on 3ʹREADS data, Fig. 2c) displayed greater 3ʹUTR shortening in vivo (SCTs vs. VCTs) than other genes with 3ʹUTR APA sites (P = 4.0 × 10−11, Kolmogorov–Smirnov (K–S) test, Fig. 4c). The scRNA-seq data for DNAJC3 is shown in Fig. 4d, which matched well with bulk RNA-seq and 3ʹREADS data from in vitro models (Fig. 3h).

The single-cell transcriptome data could also be used to decipher relationships between cells at different differentiation stages37. Using TB subtype marker genes, we divided cells of each TB subtype into two portions, “near” and “far”, based on distance to the converged point of all subtypes (Fig. 4e). As such, cells in the far group of each subtype had higher expression levels of the corresponding marker genes, and thus could be considered more differentiated as compared to those in the near group. Interestingly, using the first trimester placental cell data from Vento-Tormo et al.30, we found that the far group in the SCT lineage had significantly shorter 3ʹUTRs as compared to the near group (P < 2.2 × 10−16, Wilcoxon test, Fig. 4f). By contrast, the far group of VCT lineage displayed longer 3ʹUTRs than its near group (P < 2.2 × 10−16, Wilcoxon test, Fig. 4f). Since the path from VCT far cells to SCT far cells could represent the full SCT differentiation lineage in placenta, this result supports the notion that SCT differentiation in vivo, like in in vitro models, involves progressive 3ʹUTR shortening. This notion is also supported by the third trimester placental cell data by Tsang et al.29 (Supplementary Fig. 5a), in which SCT far cells showed shortest 3ʹUTRs among all cell groups (Supplementary Fig. 5b and see Supplementary Fig. 5c for DNAJC3 example). Interestingly, the difference in 3ʹUTR size between VCT near and far cells was distinct from that in first trimester samples (Fig. 4f vs. Supplementary Fig. 5b). Altogether, single-cell transcriptomic analysis of placental cells confirmed short 3ʹUTR expression in SCTs in vivo.

APA changes are coupled to secretion gene expression

While our analyses indicated 3ʹUTR shortening during SCT differentiation both in vitro and in vivo, intriguingly, we did not observe significant 3ʹUTR size changes during in vitro syncytialization of primary human trophoblast (PHT) cells, based on studies by Azar et al.34 and Yabe et al.32 (Fig. 5a). This is despite the fact that these cells underwent successful cell fusion32,34 and upregulation of SCT marker genes (Supplementary Fig. 6), suggesting that syncytialization process per se could be uncoupled from APA regulation. This prompted us to explore other biological processes that might accompany APA changes in SCT differentiation.

Fig. 5: Integrative analysis connects 3ʹUTR size regulation to protein secretion.
figure 5

a 3ʹUTR APA analysis of in vitro syncytialization of primary human trophoblast (PHT) cells. PHTu and PHTd are mononucleated PHT cells and syncytialized PHT cells, respectively. 3ʹUTR APA REDs are normalized to mean of all samples. Error bars are standard deviation based on two samples. n.s., P ≥ 0.05 (t-test). b Significantly regulated genes in hESC and BeWo models. Significance of regulation is based on fold change > 1.2 and FDR = 0.05 (DESeq) in both models. The hESC model data correspond to > 70 μm TB cells vs. hESCs (Fig. 3c) and the BeWo model data correspond to Fsk + vs. Fsk- (Fig. 3e). Commonly regulated genes are highlighted. Red for upregulation and blue for downregulation. Pearson correlation coefficient (r) comparing gene regulation in the two models is indicated. c Bubble plot showing top GO terms (Biological Processes only) enriched for genes upregulated in both hESC and BeWo models. Each GO term is represented by a circle, whose size reflects the number of genes associated with the term, and also by a dot, whose size reflects the number of upregulated genes associated with the term. All sizes are relative to other circles in the plot. For example, the term “response to endoplasmic reticulum stress” is associated with 273 genes, of which 62 are upregulated. Definition of USPS (3ʹUTR size-related protein secretion) genes (157 in total) is indicated. d CDF curves for USPS gene expression changes in four datasets, including two in vitro syncytialization models shown in a and two single-cell datasets (SCT far vs. VCT far, Fig. 4). A total of 131 USPS genes with detectable expression in all datasets are used for analysis. P-values (K–S test) for significance of difference between gene sets are indicated. e, f Scatter plots showing correlation between 3ʹUTR REDs and USPS gene expression levels for the hESC model (e) and the BeWo model (f). 3ʹUTR APA REDs are normalized to median of all samples. a.u., arbitrary value.

We found that, while similar in APA regulation, the hESC and BeWo models had substantial differences in gene expression changes, as indicated by a modest correlation coefficient (r = 0.26, Pearson Correlation, Fig. 5b). We thus reasoned that consistently regulated genes between these two divergent models might provide clues about some common underlying biological processes related to APA regulation. To this end, we identified 1071 upregulated genes and 521 downregulated genes common to both models (fold change > 1.2 and false discovery rate = 0.05, DESeq, Fig. 5b). GO analysis found several biological processes that were significantly enriched for upregulated genes (Fig. 5c). Notably, the top three biological processes appeared to be substantially more significant than others (all with P-value < 1.0 × 10−9, Fisher’s exact test, Fig. 5c), including “response to endoplasmic reticulum stress”, “Golgi vesicle transport”, and “endomembrane system organization”. By contrast, Biological processes enriched for commonly downregulated genes were much less significant based on P-values (Supplementary Fig. 7).

We next isolated 157 genes that were commonly upregulated in both hESC and BeWo models and were also associated with the top three GO processes. As both models involved 3ʹUTR shortening and the top three processes were all related to expression or transport of secreted proteins38,39, we named these genes 3ʹUTR Size-related Protein Secretion (USPS) genes (listed in Supplementary Table 3). Supporting the connection between 3ʹUTR shortening and protein secretion, we found that USPS genes were significantly upregulated in placental SCT far cells vs. VCT far cells (both first trimester and third trimester data), but were not so during in vitro syncytialization of PHT cells (P < 2.2 × 10−16, K–S test, Fig. 5d). In fact, in the Azar et al.34 data, which showed a mild 3ʹUTR lengthening (Fig. 5a), USPS genes were downregulated (Fig. 5d). Moreover, USPS gene expression levels were well correlated negatively with 3ʹUTR APA REDs in both the hESC model (Fig. 5e) and the BeWo cell model (Fig. 5f). Taken together, these data indicate that upregulation of genes involved in protein secretion functions, not cell syncytialization, is associated with 3ʹUTR shortening in SCT differentiation. We thus named this mechanism secretion-coupled APA (SCAP).

3ʹUTR shortening is associated with increased RNA abundance

We next wanted to understand the consequences of SCAP in SCT formation. We found that in both hESC and BeWo cell models, as well as in SCT far cells vs. VCT far cells in vivo, upregulated genes tended to show greater extent of 3ʹUTR shortening than genes without expression changes (P < 1 × 10−12, K–S test, Fig. 6a). Consistently, USPS genes in general displayed significant 3ʹUTR shortening in these conditions (Supplementary Fig. 8). On the other hand, downregulated genes displayed opposite trends (P < 1 × 10−13, K–S test, Fig. 6a). Note that we used RNA-seq reads in coding regions for gene expression analysis and those in 3ʹUTRs for APA analysis, avoiding confounding calculation (i.e., the same reads used for both expression and APA analyses). This result indicates that 3ʹUTR size regulation is related to gene expression changes. Consistently, the genes that showed shortened 3ʹUTRs in TB differentiation as defined by the 3ʹREADS data (Fig. 2c) were significantly upregulated in both the hESC and BeWo cell models, as well as in SCT far cells vs. VCT far cells (P < 1 × 10−3, K–S test, Fig. 6b).

Fig. 6: 3ʹUTR shortening is associated with increased mRNA abundance.
figure 6

a CDF curves of 3ʹUTR APA REDs for upregulated (red line), downregulated (blue line), and no change (black line) genes. Left, hESC model (>70 μm); middle, BeWo model (day 3); right, SCT far cells vs. VCT far cells (Vento-Tormo et al. and Tsang et al. data combined). Number of genes in each gene set is indicated. Regulated genes for the hESC and BeWo models are those with fold change > 1.2 and P-value < 0.05 (DESeq), and regulated genes in SCT far cells vs. VCT far cells are top and bottom 10% genes based on fold change. P-values (K–S test) for significance of difference between red or blue genes and black genes are indicated. b CDF curves of gene expression changes for genes showing 3ʹUTR shortening in the 3ʹREADS data (Fig. 2c, blue line) and all genes (black line) in three datasets (same as in a). P-values are based on the K–S test comparing blue and black genes. c Schematic of mRNA stability analysis of BeWo cells. mRNA stability of each transcript is represented by a Stability Score, as indicated. d Scatter plot comparing Stability Score of short 3ʹUTR isoform (x-axis) with that of long 3ʹUTR isoform (y-axis). Genes whose isoforms have significantly different Stability Scores (P < 0.05, DEXSeq) are highlighted in color. e Relationship between aUTR size and Stability Score difference between long and short isoforms. Genes are grouped into five similarly sized bins (about 550 genes each) based on their aUTR length. P-value (Wilcoxon test) comparing Bins 1 and 5 is indicated. Data are presented as median value ± SEM. f UCSC Genome Browser tracks showing 3ʹREADS data for DNAJC3 in total and 4sU-labeled RNA samples. g Tetramer enrichment scores for aUTRs of genes with shortened 3ʹUTRs (Fig. 2c) vs. aUTRs of other genes (x-axis) are compared to enrichment scores for 3ʹUTRs of stable vs. unstable transcripts (y-axis).

One way 3ʹUTRs could impact gene expression is through control of mRNA stability40. To explore this, we examined RNA stability of APA isoforms in BeWo cells. We metabolically labeled RNA with 4-thiouridine (4sU) for 1 h and compared the abundance of labeled RNAs, which represented newly made RNAs, with that of total cellular RNAs, which represented steady state RNAs (illustrated in Fig. 6c). As such, the ratio of abundance (steady state vs. newly made), named Stability Score, reflected transcript stability (Fig. 6c).

We found that long 3ʹUTR isoforms were generally less stable than short 3ʹUTR isoforms in BeWo cells by 9.4-fold (262 genes showing a long 3ʹUTR isoform being significantly less stable than a short isoform, and 28 genes showing the opposite trend, FDR = 0.05, DESeq, Fig. 6d). Importantly, as aUTR size increased, the stability difference between short and long 3ʹUTR isoforms became more evident (Fig. 6e). For example, for genes with an aUTR size >1836 nt (bin 5 in Fig. 6e), the median Stability Score difference was −0.61, whereas the difference was −0.16 for genes with an aUTR size <152 nt (P < 2.2 × 10−16 between these two groups test, Wilcoxon test, Fig. 6e). For DNAJC3, a USPS gene in bin 5, its long 3ʹUTR isoform was substantially less stable than its short 3ʹUTR isoform (Stability Scores = –1.7 vs. 1.5, Fig. 6f).

In contrast to 3ʹUTR shortening genes, the genes that displayed IPA activation showed only modest differences in gene expression as compared to all genes (Supplementary Fig. 9a). In addition, IPA isoforms were generally less stable than isoforms whose PASs were in the 3ʹ terminal exon (Supplementary Fig. 9b).

Using tetramer enrichment analysis, we found that several U-rich motifs were enriched in the aUTRs that were removed by APA in SCT differentiation (Supplementary Fig. 10a). Interestingly, these motifs were also associated with 3ʹUTRs of unstable transcripts in BeWo cells (Supplementary Fig. 10b). A general negative correlation could be discerned between tetramer enrichments in regulated aUTRs and in 3ʹUTRs for stability (Fig. 6g), indicating that removal of destabilizing motifs in aUTRs can potentially lead to increased expression for genes displaying 3ʹUTR shortening in SCT differentiation.

SCAP in differentiation of B cells to plasma cells

We next asked whether SCAP took place in other systems besides SCT differentiation. Differentiation of B cells to plasma cells was the first biological condition in which APA regulation was discovered41,42. Increasing protein secretion capacity from B cells to plasma cells is crucial for antibody secretion. We analyzed an RNA-seq dataset generated by Shi et al.43, which included four types of B cells, namely, B1 cells, marginal zone B cells, germinal center (GC) B cells, and follicular B cells, as well as three types of plasma cells, namely, spleen plasmablasts (PBs), spleen plasma cells, and bone marrow plasma cells. Among these cell types, spleen PBs and marginal zone B cells displayed the shortest and longest 3ʹUTRs, respectively, based on 3ʹUTR APA REDs (Fig. 7a, y-axis). Significantly, 3ʹUTR sizes correlated well with USPS gene expression across these cell types (r = −0.82, Pearson correlation, Fig. 7a). By contrast, proliferation gene expression levels were not correlated with 3ʹUTR sizes (r = –0.05, Pearson correlation, Fig. 7b).

Fig. 7: Additional systems supporting SCAP.
figure 7

a Correlation between 3ʹUTR APA REDs and USPS gene expression levels across different types of B cells and plasma cells (a study by Shi et al.43). 3ʹUTR APA REDs are normalized to the median of all samples. Pearson correlation coefficient (r) is indicated. There are two samples for each cell type except for spleen plasmablasts (1 sample), spleen plasma cells (3 samples), and bone marrow plasma cells (1 sample). Data are presented as mean value+/− SD. a.u., arbitrary unit. b As in a, except that correlation between 3ʹUTR APA REDs and proliferation gene expression level is shown. c 3ʹUTR APA REDs for different types of B cells in the germinal center (GC) and GC-derived plasmablast (PB)(a study by Ise et al.44). 3ʹUTR APA REDs are normalized to the median of all samples. Data are presented as mean value+/− SD of three replicates. Significance of difference (t-test) between cell groups is indicated. Plasma cell fate B cells in the light zone are Bcl6low and CD69high cells, and recycling cell fate B cells are Bcl6high and CD69high cells. d CDF curves of gene expression changes for USPS (red) and proliferation (orange) genes in GC-derived PBs vs. dark zone B cells. Number of genes in each set is indicated. P-values (K–S test) for significance of difference between each gene set and all genes are indicated. e CDF curves of gene expression changes for USPS genes (red, 145), proliferation genes (orange, 298), and all genes in AtT-20 cells with overexpression of indicated gene(s) vs. control cells (a study by Khetchoumian et al.45). P-values (K–S test) for significance of difference between gene sets are indicated. f Bar plot of median 3ʹUTR APA REDs for the samples in e. Data are presented as mean value+/– SD based on two (Creb3l2) or three (XBP1 and Creb3l2/XBP1) replicates. Significance of difference (t-test) vs. control or between cell groups is indicated.

The difference between GC B cells and spleen PBs is quite striking, with the former having the highest proliferation gene expression levels (Fig. 7b, x-axis) and the latter having the highest USPS gene expression levels (Fig. 7a, axis). Importantly, consistent with the SCAP model, GC B cells had longer 3ʹUTRs than PBs. To corroborate this finding, we analyzed a dataset by Ise et al.44, in which different types of GC B cells were isolated, including plasma cell fate B cells in the light zone, recycling cell fate B cells in light zone, and dark zone B cells. In addition, GC-derived PBs were used as a control. In agreement with the data by Shi et al.43, PBs had shorter 3ʹUTRs than all GC B cells (Fig. 7c). Importantly, compared to dark zone B cells, which had the longest 3ʹUTRs among the three GC B-cell types (Fig. 7c), PBs had significantly higher expression of USPS genes and lower expression of cell proliferation genes (P = 2.2 × 10−12 and = 1.3 × 10−6, respectively, K–S test, Fig. 7d). This result confirms that USPS gene expression is a good indicator of 3ʹUTR size in B-cell differentiation to plasma cells.

We next took an exploratory approach to identify genes whose expression changes were related to 3ʹUTR size differences across the B cells and plasma cells analyzed in the Shi et al.43 study. Interestingly, using Pearson Correlation analysis (example cases shown in Supplementary Fig. 11a), we found an enrichment of genes with negative correlation coefficients (note the uneven data distribution in Supplementary Fig. 11b). GO analysis of the genes with the most negative correlation coefficients (r < –0.79, bottom 5% of all genes) revealed that these genes were highly enriched with functions in protein secretion, including “negative regulation of response to endoplasmic reticulum stress” and “protein exit from endoplasmic reticulum” (Supplementary Fig. 11c), further supporting the SCAP mechanism in B-cell differentiation.

Perturbation of cell secretion further confirms SCAP

We next wondered whether perturbation of cellular secretion capacity could lead to 3ʹUTR size changes. To this end, we analyzed an RNA-seq dataset involving overexpression (OE) of active forms of XBP1 (spliced form) and Creb3l2 (cleaved form) in the mouse pituitary tumor cell line AtT-2045. OEs of these factors individually or jointly led to enhancement of secretory capacity to various degrees45. As expected, Creb3l2 OE, which was reported to control expression of translation factors45, had only a modest impact on USPS gene expression (median log2Ratio = 0.03, Fig. 7e, left). By contrast, XBP1 OE, which upregulates endoplasmic reticulum (ER) biogenesis45, led to significant upregulation of USPS genes (median log2Ratio = 0.15, Fig. 7e, middle). Interestingly, co-OE of both factors led to the greatest upregulation of USPS genes (median log2Ratio = 0.22, Fig. 7e, right), indicating synergy between these two factors. Consistent with the SCAP model, 3ʹUTR shortening was significant in cells with XBP1 OE and co-OE of XBP1 and Creb3l2 (Fig. 7f), with the latter being greater in magnitude than the former. By contrast, Creb3l2 OE did not elicit significant 3ʹUTR shortening (Fig. 7f). Notable also is that proliferation genes were slightly downregulated in all OE samples (orange lines in Fig. 7e), arguing against the possibility that the observed 3ʹUTR shortening was due to increased cell proliferation. Therefore, forced enhancement of secretion capacity through XBP1 or XBP1 + Creb3l2 OEs could also elicit 3ʹUTR shortening, lending a strong support to the SCAP mechanism.


In this study, we reveal secretion-coupled APA, or SCAP (illustrated in Fig. 8), involving global 3ʹUTR shortening and IPA activation in SCT differentiation. This mechanism is also in play during differentiation of B cells to plasma cells, indicating its generality in secretory cell formation. We further show that 3ʹUTR shortening is coupled with upregulation of gene expression, due likely to prolonged transcript half-life through removal of destabilizing sequences in 3ʹUTRs. We thus advocate that SCAP is an integral part of gene expression program in secretory cells, tailoring their transcriptome for enhanced protein production and secretion.

Fig. 8: Secretion-coupled APA (SCAP) in differentiation of secretory cells.
figure 8

SCAP mechanism in SCT differentiation and B-cell differentiation. IPA activation is not shown in the diagram.

We show that SCAP is different from cell proliferation-elicited APA, which has been implicated in 3ʹUTR shortening in T-cell activation20 and cancer cell development46, or development-associated APA, which is executed in embryonic development and many cell differentiation lineages21. These conclusions are based largely on expression patterns of USPS genes, proliferation genes, and development genes. However, during the course of this study, we also noticed that gene expression levels of USPS genes and proliferation genes could be correlated in some systems. For example, in T-cell activation, where there is global 3ʹUTR shortening20 (Supplementary Fig. 12a), both USPS genes and proliferation genes are activated (Supplementary Fig. 12b). A similar observation was made using data from a set of NCI-60 human tumor cell lines47 (Supplementary Figs. 12c, d). Therefore, in most cells, secretion capacity (represented by USPS genes) appears to be coupled with cell proliferation. An open question that requires further investigation, therefore, is whether it is cell proliferation or protein secretion that is the primary driver for APA regulation in these non-secretory cells.

While 3ʹUTR shortening is widespread in secretory cell differentiation, it appears to impact secretion genes to a greater extent than other genes, such as those involved in ER stress response, ER organization, and secretory pathways. One ramification of SCAP, therefore, is amplification of transcriptional activation of secretion genes through stabilization of transcripts, leading to rapid accumulation of their RNAs in secretory cell differentiation. A case in point is DNAJC3, whose protein product (also known as P58IPK) plays critical roles in mitigating ER stress, including prevention of translational inhibition caused by stress-induced eIF2α phosphorylation48 and function as a co-chaperone to protect stressed ER49. Mutations of DNAJC3 have been implicated in diabetes and neurodegeneration50. Here, we show significant 3ʹUTR shortening of DNAJC3 in SCT differentiation, and its two 3ʹUTR isoforms are conspicuously different in size and stability. It is thus conceivable that APA could augment DNAJC3 protein expression to preemptively address potential ER stress when differentiating secretory cells ramp up their secretion capacity.

Another possible impact of 3ʹUTR shortening is alternation of mRNA localization, such as the association with ER51 or other organelles52. This may lead to modulation of localized translation. The ER interaction may deserve special attention, given our observation that overexpression of XBP1, an activator of ER biogenesis, is sufficient to cause global 3ʹUTR shortening in the pituitary tumor cell line AtT-20. In this vein, it would be worthwhile to test how perturbation of APA might alter ER metabolism in secretory cells.

It is also worth noting that the genes encoding secreted proteins do not necessarily display APA. For example, many hormone-encoding genes, such as hCG genes (the major hormones secreted by SCTs), contain only one PAS in their 3ʹUTR (Supplementary Table 4). Therefore, 3ʹUTR shortening in general functions to facilitate the growth of secretory capacity in the cell rather than to help the production of individual secreted proteins.

Our result also suggests that some genes may employ APA to express protein isoforms through intronic PAS usage, which is globally activated during SCT differentiation. Notably, intronic PAS activation of the immunoglobulin M heavy chain gene in B-cell differentiation is one of the first reported APA events41,42. This regulation switches expression of a membrane-associated protein isoform in B cells to a secreted isoform in plasma cells. How widespread is protein isoform switch in SCT differentiation and in other secretory systems needs to be further delineated.

Global 3ʹUTR shortening could be caused by activated usage of proximal PASs during pre-mRNA processing (processing-driven) or by enhanced degradation of long 3ʹUTR isoforms (degradation-driven). We think the former is more likely in SCT differentiation because of two reasons. First, genes that display shortened 3ʹUTRs tend to have increased RNA abundance, making the processing-driven model more plausible. If the degradation-driven mechanism was the underlying cause, the RNA abundance increase could only be explained by a non-parsimonious scenario where enhanced RNA degradation is countered by transcriptional activation. Second, more importantly, we observed increased expression of IPA isoforms during SCT differentiation, which is in line with the notion that 3ʹ end processing activity is activated. Note that IPA isoforms are typically less stable than isoforms using 3ʹ terminal exon PASs, further lending a support to the processing-driven model.

A growing number of factors have been implicated in global regulation of APA12, including core cleavage and polyadenylation factors53, U1 snRNP54, transcriptional elongation55, nuclear export56, etc. How these mechanisms are in play in secretory cell differentiation is an open question. Notably, upregulation of CstF64, one of the core 3ʹ end processing factors, was previously found to be regulate APA in B-cell differentiation57. Whether different secretory cells share the same APA regulatory mechanism needs to be explored in the future.


Cell culture

Human embryonic stem cells (hESCs) were differentiated into the trophectoderm lineage by using the BMP4 method27. BeWo choriocarcinoma cell line was a gift from the Sergei Kotenko lab at Rutgers University. BeWo cells were cultured in Dulbecco’s modified Eagle’s medium/F12 (DMEM/12) supplemented with 10% fetal bovine serum (FBS). Syncytialization of BeWo cells was induced by treating cells with 50 μM Forskolin (Fsk, Sigma-Aldrich) for 72 h. Mouse embryonic stem cells with doxycycline-inducible Hras mutant HrasQ61L were cultured on a tissue culture dish precoated with 0.5% gelatin in media containing leukemia inhibitory factor (LIF)35. At the time of induction with doxycycline (24 h after LIF removal), culture media was replaced with Dulbecco’s modified Eagle’s medium/F12 (DMEM/F12) containing 10% FBS. All culture media also contained 100 IU/ml penicillin and 100 μg/ml streptomycin.


Cells grown on glass coverslips were fixed by using 4% paraformaldehyde in PBS for 10 min and permeabilized with PBS containing 0.1% Triton X-100 for 10 min. After blocking with 1% bovine serum albumin in PBST (PBS + 0.1% Tween 20) for 30 min, samples were immunostained with rabbit anti-e-cadherin (Cell Signaling, #3195, 1:200 in PBST) overnight. FITC-conjugated goat anti-rabbit antibody (Jackson ImmunoResearch, 111-095-144, 1:500 in PBST) was applied for 60 min. Cells were mounted on glass slides in SlowFade Gold Antifade reagent (Thermo Fisher) with DAPI. Fluorescence images were collected by using the EVOS FL Auto Cell Imaging System (Thermo Fisher).

Real-time quantitative PCR (RT-qPCR)

Total RNA was extracted by using TRIzol (Thermo Fisher) and treated with Turbo DNase (Thermo Fisher). Complementary DNA (cDNA) was synthesized by using oligo(dT) and M-MLV reverse transcriptase with 1.5–2 µg of RNA. cDNA was then mixed with real-time PCR primers and Luna qPCR master mix (NEB). qPCR was run on an Applied Biosciences StepOne Plus Real-Time PCR system.

Isolation of newly made RNAs

BeWo cells were cultured in medium supplemented with 50 μM of 4-thiouridine (4sU; Sigma) for 1 h before harvest. Total RNA (steady state) was extracted by using TRIzol. Newly made (4sU-labeled) RNAs were fractionated following the protocol described in ref. 58. Briefly, 100 μg of total RNA was biotinylated by using biotin-HPDP (1 μg/μl in Dimethylformamide; Thermo Fisher Scientific), and then extracted with chloroform three times and precipitated with ethanol. The biotinylated RNA was captured by Streptavidin C1 Dynabeads. After washing for six times, biotinylated RNA was eluted by DTT. Both newly made and steady state RNAs were precipitated with ethanol and then used for 3ʹREADS analysis.

3ʹREADS and data processing

The 3ʹREADS or its newer version 3ʹREADS + procedures were performed to sequence the 3ʹ end fragment of poly(A) + RNAs11,59. Libraries were sequenced on an Illumina GAIIx (1 × 72 nt) or HiSeq (1 × 150 nt) platform. 3ʹREADS/3ʹREADS + data were then processed to identify polyadenylation site-containing reads, or PAS reads59. Briefly, the sequence corresponding to 5ʹ adaptor was first removed from raw reads by using Cutadapt60. Reads with short inserts (< 23 nucleotides) were discarded. The remaining reads were then mapped to the genome (hg19 for human or mm9 for mouse) by using bowtie2 (local mode)61. The 5ʹ random nucleotides derived from the 3ʹ adaptor were removed before mapping. Reads with a mapping quality score (MAPQ) ≥ 10 were kept for further analysis. Reads with ≥2 non-genomic 5ʹ-Ts after alignment were called PAS reads. PASs within 24 nucleotides from each other were clustered11. The PAS read counts of genes were normalized by the median ratio method in the DESeq program62.

Analysis of 3ʹREADS data

For 3ʹUTR APA analysis, the two most abundant APA isoforms (based on PAS reads) whose PASs are in the 3ʹUTR of the last exon were selected. They were named proximal PAS (pPAS) and distal PAS (dPAS) isoforms. Significant APA events were those with relative abundance change > 5% and P-value < 0.05 (Fisher’s exact test or DEXSeq analysis) between samples. Relative Expression Difference (RED) was calculated as the difference in log2(RPM ratio) of the two APA isoforms between two samples. The aUTR size was the distance between the two APA sites in the 3ʹUTR. For Intronic APA analysis, both PAS reads in intronic regions (IPA isoforms) and PAS reads in the last exon (TPA isoforms) were respectively summed and compared. Significant APA events were those with relative abundance change > 5% and P-value < 0.05 (Fisher’s exact test or DEXSeq analysis) between samples.

Analysis of APA using RNA-seq data

3ʹUTR APA analysis using RNA-seq data were carried out by the SAAP-RS method25. Briefly, reads were aligned to the human genome with STAR v2.5.263 by using default settings. Raw bam files were further processed by using the R packages RSamtools (for processing bam files), GenomicAlignments (for counting reads), and GenomicFeatures (for defining genomic regions). Human PAS locations were obtained from PolyA_DB364. Relative expression difference (RED) was calculated as difference in log2(aUTR read number/cUTR read number) between two samples. Significance of difference was analyzed by either DEXSeq when there were replicates or the Fisher’s exact test when there were no replicates. When there were no replicates, standard deviation was obtained by sampling data with a bootstrapping method for 20 times53.

Gene sets used in this study

For the TB subtype marker gene panel, four trophoblast studies were used28,29,30,31. Each marker gene for a specific TB subtype was reported by at least two studies and had no conflict between studies. Proliferation gene set was based on the study by Sandberg et al.20. Development gene set contained the genes whose expression levels positively correlated with 3ʹUTR size in mouse embryonic development, as defined in the study by Ji et al.21 (called PCS in that study). The USPS gene set defined in this study contained genes whose expression levels increased in both hESC and BeWo models and were associated with the top three GO terms for upregulated genes in the two models.

Motif analysis

The frequencies of tetramers in specific regions were calculated and compared between different gene sets. Motif enrichment scores were calculated by –log10(P-values), where P-values were based on the Fisher’s exact test.

Gene expression analysis using single-cell RNA-seq data

Droplet-based single-cell sequencing data29,30 were processed by using the Cell Ranger Single-Cell Software Suite (v3.0.1, 10x Genomics). SMART-Seq2 data were processed as described by Tsang et al.36 Briefly, raw reads were assigned to cells based on cell-specific barcodes in read 2 of paired-end reads. Read 1 reads were trimmed by using CutAdapt60 to remove the template switch oligo sequence and the poly(A) tail sequence. All reads were aligned to hg19 genome by using STAR (v2.5.2)63.

Single-cell significance analysis of alternative polyadenylation (scSAAP)

Single-cell reads were mapped to the hg19 genome by using STAR63. Single cells were clustered with the Seurat package65. TB clusters were extracted and subtyped based on the TB marker gene panel. Reads from single cells in each cell cluster were summed, and each cell cluster was treated as a bulk RNA-seq sample for 3ʹUTR APA analysis. Pseudotime analysis was performed by using the monocle package37. The TB marker panel was used to order single cells along the TB subtype branches. The cells in each TB subtype branch were then divided equally in cell number into two groups (“near” and “far” based on distance from the center).

Gene ontology analysis

Gene ontology (GO) analysis was carried out by using the GOstats Bioconductor package66, where the hypergeometric test was performed without multiple comparison adjustment. Generic terms (associated with >1000 genes) were discarded. To reduce redundancy in reporting, any GO term with a greater than 75% gene overlap with a more significant term was discarded.

Statistics and reproducibility

The Student’s t-test was used to determine statistical significance between groups; all tests are two-sided. The Fisher’s exact test or DEXSeq were used to determine the significance of APA changes. The K–S test was used to compare distributions between gene sets. The Wilcoxon test was used to compare gene expression changes in different gene sets; all tests are two-sided. Boxplots were generated with the top bound defined as the third quartile, the bottom bound defined as the first quartile, and the center defined as the median. Where shown, the following symbols indicate statistical significance: n.s., P ≥ 0.05; *P < 0.05; **P < 0.01, ***P < 0.001.

Reporting summary

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