Introduction

Pluripotent embryonic stem cells have the ability to self-renew indefinitely and differentiate into all types of adult cells, thus holding promising prospects in regenerative medicine applications such as tissue replacement therapy1,2,3. Human embryonic stem cells (hESCs) are derived from the inner cell mass of blastocyst and can maintain their pluripotent state in vitro under certain cultural conditions for extended periods4. The transcription factors Nanog, Pou5f1, and Sox2 have long been central to the pluripotency network of hESCs; their interaction with each other, as well as many other pluripotency factors, play a crucial role in maintaining the pluripotency5,6. At the same time, the expressions of these key transcription factors endow hESCs with unique cell cycle and proliferation characteristics. Previous study has shown that Nanog can regulate the cell cycle independently via the ERas/phosphatidylinositol 3-kinase/Akt pathway without affecting pluripotency7. Therefore, the understanding of the key molecular mechanisms involved in regulating the self-renewal network are required.

Cell metabolism plays a critical role in regulating the self-renewal, quiescence, and differentiation potential of stem cells8. Previous studies have shown that embryonic stem cells (ESCs) possess a unique metabolic pattern distinct from differentiated cells9,10. Metabolic pathways regulate stem cell manners by generating energy and metabolites11. For example, ESCs enhance glycolysis and glutamine oxidation to provide intermediates, promoting macromolecular biosynthesis to support the self-renewal12. Therefore, a more comprehensive understanding of stem cell metabolism can allow us to improve cultural conditions, thereby increasing stem cell expansion and functionality.

Lipid metabolism is needed by cells for membrane biogenesis, energy production and protein modification13. Fatty acid β-oxidation (FAO) results in the shortening of fatty acids by two carbons per cycle, generating acetyl-CoA, which is a very efficient source for de novo synthesis of lipids14. FAO is not only a key pathway for energy generation but is also closely linked to multiple metabolic regulatory mechanisms. Studies have shown that FAO enhances ATP production by providing acetyl-CoA to mitochondria, supporting cellular metabolic activities under high energy demand conditions. For pluripotent stem cells, especially hESCs, FAO serves not only as a tool for energy provision but is also closely associated with signaling pathways that regulate cell growth, metabolic balance, and metabolism-related epigenetic modifications. Specifically, the acetyl-CoA produced by FAO can not only enter the tricarboxylic acid cycle to provide energy but also participate in histone acetylation reactions, regulating chromatin openness and thereby influencing the transcriptional activity of pluripotency-related genes. For example, FAO may promote the formation of histone modifications such as H3K27ac by increasing the supply of acetyl-CoA, which has been shown to be closely related to the expression of pluripotency-maintaining factors like Nanog, Oct4, and Sox2. Moreover, increased FAO activity has been observed in proliferating naïve-state hESCs, but the molecular mechanisms of FAO’s role in hESCs proliferation remain to be elucidated.

Chlorogenic acid (CGA), a vital bioactive dietary polyphenol, is abundantly present in various plants such as Eucommia, honeysuckle, coffee, potatoes, apples, and tea15. It exhibits a wide range of pharmacological effects, including antioxidant, antibacterial, antiviral, anticancer, lipid-lowering, blood sugar reduction, and immunomodulatory actions16,17,18,19,20,21,22. Previous studies have explored the crucial role of CGA in FAO23,24,25. However, the specific roles and mechanisms by which CGA regulates fatty acid metabolism in hESCs remain unclear, despite previous investigations into the important role of de novo lipid synthesis as a common transcriptional characteristic of human pluripotent stem cells (hPSCs) cultured in E8 and pre-implantation human epiblast in vivo26. In this study, we observed that CGA enhances the proliferation capacity and de novo lipid synthesis of H9 by promoting FAO and Nanog expression levels.

In addition, we further explored the critical role of CGA-activated key metabolic pathways in hESCs in regulating pluripotency of hESCs. Previous studies have shown that CGA plays a significant role in maintaining normal metabolism by impacting carbohydrate metabolism, lipid synthesis and breakdown, as well as amino acid metabolism27,28. In hESCs, glycolysis is considered a key pathway for maintaining pluripotency and proliferation, while mitochondrial function further supports the cell’s metabolic needs through the synergistic action of FAO and glycolysis. Specifically, CGA may regulate energy balance in hESCs by promoting the interaction between FAO and the glycolytic pathway, thereby supporting self-renewal and maintaining pluripotency. CGA may provide the necessary metabolic support for hESCs by facilitating the interaction between these pathways, enhancing their proliferative capacity.

Results

CGA elevates NANOG expression and proliferation of H9

To examine the effect of CGA on hESCs, we introduced CGA into E8 medium and cultured H9 hESCs for either 24–48 h at varying concentrations. Our findings indicate that CGA at a concentration of 100 µg/ml had no significant impact on cell viability, as determined by the CCK8 assay (Fig. 1A). However, at a concentration of 1000 µg/ml, CGA significantly decreased H9 cell viability (Fig. 1B), demonstrating a low level of toxicity. Based on these results, we selected the addition of 100 µg/ml CGA for a duration of 24 h for subsequent analysis.

Fig. 1
figure 1

Effect of CGA on the pluripotency of H9. (A) CCK-8 assays were conducted to test the cell viability of H9 cells treated with different concentrations of CGA for 24 h and 48 h. Each group had five replicates. (B) Representative images of H9 clones in the vehicle group and the group treated with 1000 µg/ml CGA for 24 h. Vehicle group added an equal amount of 50% ethanol as the solvent control in H9 cell line. (C) Alkaline phosphatase staining was performed, and the diameters of H9 clones in the vehicle group and the group treated with 100 µg/ml CGA were measured. Statistically significant differences were observed (**p < 0.01, Student’s t-test). (D–F) RT-qPCR was used to analyze the expression of POU5F1 (D), SOX2 (E), and NANOG (F) in the vehicle group and the group treated with 100 µg/ml CGA. The data represent means ± SEM from three independent experiments. No statistically significant differences were observed when compared to the vehicle group (n.s. > 0.05), except for NANOG expression where a significant difference was observed (*p < 0.05). (G) Representative confocal images of H9 cells stained with NANOG antibody in the vehicle group and the group treated with 100 µg/ml CGA. The scale bar represents 100 μm. Quantification of NANOG fluorescence intensity showed a highly significant difference between the two groups (****p < 0.0001, Student’s t-test).

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Interestingly, the addition of CGA resulted in an increase in the colony size of H9 hESCs (Fig. 1C), suggesting a positive effect on their proliferation. We subsequently investigated the impact of CGA on the pluripotency of hESCs by examining the expression of pluripotency markers POU5F1, SOX2, and NANOG in H9 cells. We observed a significant upregulation of NANOG, but no change in POU5F1 and SOX2 expression (Fig. 1D-F). This elevation in NANOG expression was further confirmed through immunofluorescence analysis (Fig. 1G). Our results indicate that CGA has the potential to enhance NANOG expression and promote the proliferation of hESCs.

CGA enhances lipid synthesis and proliferation of H9 by activation of FAO

To investigate the mechanism underlying the effects of CGA on pluripotency in hESCs, we conducted RNA sequencing (RNA-seq) of CGA-treated H9 cells. Differential gene analysis revealed significant changes in the transcriptome, with 1,044 genes upregulated and 273 genes downregulated (Fig. 2A; Supplementary Table S1; Fold change > 1.4, Fold change < -1.4, p < 0.05). Gene Ontology (GO) analysis demonstrated that upregulated genes were enriched in pathways related to the inactivation of MAPK, cell cycle regulation, and fatty acid metabolism (Fig. 2B). Conversely, downregulated genes were enriched in pathways related to MAPK and transcription regulation (Fig. 2C). Notably, gene set enrichment analysis (GSEA) indicated that many of the genes upregulated by CGA treatment were associated with fatty acid metabolism (Fig. 2D). These findings suggest that CGA may enhance FAO in hESCs.

Fig. 2
figure 2

CGA regulates fatty acid synthesis and proliferation of H9 by FAO. (A) Volcano plot displaying the impact of CGA on the transcriptome of H9. (FC > 1.4, FC < -1.4, p < 0.05.) (B–C) GO analysis of differentially expressed genes in CGA-treated H9: red indicates pathways enriched in up-regulated genes (B), while blue indicates pathways enriched in down-regulated genes (C). (D) GSEA of fatty acid metabolism-associated genes showing preferential upregulation in CGA-treated H9. (E) Scheme of long chain fatty acids (LCFAs) into fatty acid oxidative (FAO) metabolism, showing the targets of TMZ. (F) Unsupervised clustering of transcriptomes in vehicle, CGA, and CGA + TMZ-treated groups. (G) Heatmap of fatty acid β-oxidation genes in H9 in the indicated groups. (H) Scatterplots of RNA-seq data of H9 in CGA-treated versus vehicle groups (x-axis) and CGA + TMZ versus CGA-treated groups (y-axis). Red dots and blue dots represent genes related to fatty acid synthesis and G1/S transition of mitotic cell cycle, respectively. (I) Prediction of long-chain fatty acid metabolites by COMPASS. (J) BODIPY 493/503 staining revealed increased fatty acids in CGA-treated H9 but rescued by TMZ. (K) Diameters of H9 clones in the indicated treatments. (**p < 0.01, Student’s t-test).

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To investigate the effect of CGA on the pluripotency of hESCs through the hyperactivation of FAO, we utilized trimetazidine (TMZ) as an inhibitor of the HADHA/B enzyme in the FAO pathway in the CGA-treated H9 cells (referred to as the CGA + TMZ-treated group; Fig. 2E). Subsequently, transcriptome sequencing data was generated for the CGA + TMZ-treated group. Unsupervised hierarchical clustering (MHC) analysis demonstrated a strong correlation between the CGA + TMZ-treated group and the vehicle group at the transcriptional level (Fig. 2F). Comparative gene expression analysis revealed the restoration of expression in CGA + TMZ-treated H9 cells for key genes involved in the FAO pathway, including CPT1C, CPT2, ACAA2, HADHA, and HADHB (Fig. 2G). Further analysis of RNA-seq data identified 72 genes associated with fatty acid synthesis and 27 genes linked to the G1/S transition of the mitotic cell cycle. Notably, these genes were found to be upregulated in the CGA-treated group and downregulated in the CGA + TMZ-treated group (Fig. 2H). This suggests that CGA-induced lipid synthesis and proliferation in hESCs may be attributed to the activation of FAO.

To validate the phenomenon, we initially utilized Compass to investigate the activity of fatty acid metabolism at the metabolic level. The metabolites related to fatty acid synthesis, such as palmitoleic acid, oleic acid, and stearic acid, were projected to increase in the CGA-treated group and decrease in the CGA + TMZ group (Fig. 2I). This was further confirmed by BODIPY 493/503 staining (Fig. 2J). Additionally, treatment of H9 cells with CGA + TMZ restored the size of colonies (Fig. 2K), indicating that the heightened FAO activation contributes to CGA-induced cell proliferation. In summary, these findings suggest that CGA has the ability to promote lipid synthesis and proliferation of hESCs through the activation of FAO.

CGA maintains H9 pluripotency

We conducted further investigation to determine if the impact of CGA on the pluripotency regulatory network is influenced. This was accomplished by analyzing the transcriptomics of vehicle, CGA, and CGA + TMZ-treated H9 cells. Our findings indicated that there were no significant changes in the transcription patterns of pluripotency genes, such as POU5F1, POU3F1, SOX2, SALL2, and SOX15, following treatment with CGA and TMZ. However, we did observe a significant upregulation in the expression of NANOG by CGA, which was not significantly downregulated in the CGA + TMZ-treated group. This suggests that the regulation of NANOG expression by CGA may not occur through FAO. Interestingly, we also observed that the expression of OTX2, which plays a role in cell proliferation during mitosis, was significantly upregulated by CGA and downregulated in the CGA + TMZ-treated group. This could potentially contribute to enhanced proliferation of hESCs (Fig. 3A). Furthermore, we utilized ScoreCard analysis to quantify the differentiation potential of hESCs, and interestingly, found no preferential differentiation in the CGA- and CGA + TMZ-treated H9 cells (Fig. 3B). These findings suggest that CGA does not have an effect on the pluripotency of hESCs.

Fig. 3
figure 3

CGA regulates the regulatory network of pluripotency in H9 through FAO. (A) Transcriptome analysis was conducted to determine the expression levels of pluripotency markers in hESCs under different treatment conditions: vehicle, CGA, and CGA + TMZ-treated H9 cells. (B) ScoreCard analysis was performed to assess the lineage tendencies of hESCs under the indicated treatments.

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CGA regulates H9 metabolic state

Previous studies have demonstrated the potential role of CGA in regulating cell metabolism29,30,31. To investigate the impact of CGA on metabolic reprogramming in hESCs, we initially assessed the expressions of genes associated with four key metabolic pathways: glycolysis, the TCA cycle, OXPHOS, and one-carbon metabolism. The expression levels of genes related to these pathways, such as GCK, PGK1, ENO2 for glycolysis; SDHC, ADH1, IDH2 for the TCA cycle; NDUFS1, COX4I2, COX7A1 for OXPHOS; and FPGS, MAT2A, MTHFD1 for one-carbon metabolism, were significantly increased in the CGA-treated group. Importantly, the expression levels of most genes were rescued after CGA + TMZ treatment. (Fig. 4A). Furthermore, the changes in metabolites, such as pyruvate, glucose 1-phosphate, fructose 6-phosphate for glycolysis; oxaloacetate, malate, citrate for the TCA cycle; and S-adenosyl-L-methionine (SAM), methionine, S-Adenosyl-L-homocysteine for one-carbon metabolism, were predicted by Compass (Fig. 4B).

Fig. 4
figure 4

Metabolic reprogramming by CGA treatment in H9 cells. (A) The heatmap illustrates gene expressions associated with metabolic pathways in the vehicle, CGA, and CGA + TMZ-treated H9 cells. (B) The heatmap visualizes changes in intermediate metabolites linked to metabolic pathways in the vehicle, CGA, and CGA + TMZ-treated H9 cells. (C) This schematic diagram depicts the one-carbon metabolism pathway, which provides methyl donors for DNA and histone methylation. (D–F) Transcriptome analysis reveals the expression levels of DNA methyltransferase genes (D), histone methyltransferase genes (E), and XIST (F) in the vehicle, CGA, and CGA + TMZ-treated H9 cells. (G) The violin plot demonstrates the expression levels of genes located on Chromosome X and Chromosome 1 in the vehicle, CGA, and CGA + TMZ-treated H9 cells.

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Among them, SAM from one-carbon metabolism was found to be the most dynamic. SAM plays a crucial role in chromatin reprogramming by providing one-carbon units for DNA and histone methylation32,33 (Fig. 4C). However, there were no significant changes observed in the transcription of DNA methyltransferases, such as DNMT1, DNMT3A, and DNMT3B (Fig. 4D). This suggests that the DNA methylation state of hESCs is not influenced by CGA. In terms of histone methylation, only EZH1, a component of a noncanonical polycomb repressive complex-2 (PRC2), was significantly upregulated and downregulated in the CGA and CGA + TMZ-treated H9, respectively (Fig. 4E). This may provide further specificity to PRC2 action following CGA treatment. To determine the effects of CGA on X-chromosome inactivation, we analyzed the X-chromosome states in vehicle, CGA, and CGA + TMZ-treated H9 using RNA-seq analysis. Interestingly, we observed no significant alterations in the expressions of XIST and X chromosome genes34 (Fig. 4F, G). This indicates that CGA has no effect on the activity of the X-chromosome. Taken together, these results suggest that CGA may regulate the metabolic state of hESCs without significantly affecting DNA and histone methylations.

CGA induces H3K27 hyperacetylation to promote lipid synthesis in H9

The results above demonstrate that CGA promotes FAO in H9 (Fig. 2G-I). FAO produces acetyl-CoA, which is not only an efficient source for lipid synthesis, but also serves as a substrate for enzymes involved in histone acetylation35,36,37. Based on this, we hypothesized that CGA treatment may lead to changes in histone acetylation (Fig. 5A). To test this hypothesis, we analyzed various acetylation marks on histone H3 (H3K18ac, H3K27ac, and H3K9ac) using publicly available data. Each of these marks has been shown to play a significant role in defining chromatin states and regulating gene expression. Among them, H3K27ac has the highest genome coverage (Fig. 5B), suggesting that it may be particularly important in maintaining the pluripotent state of hESCs.

Fig. 5
figure 5

CGA induces H3K27 hyperacetylation in H9. (A) Scheme illustrating the FAO production of acetyl CoA for de novo lipid synthesis and histone acetylation. (B) Distribution of H3K9ac, H3K18ac, and H3K27ac peaks in promoter, intron, exon, and intergenic regions of H9, expressed as percentages. (C) Representative confocal images of H9 cells treated with Vehicle, CGA, and CGA + TMZ, stained with H3K27ac antibody. Scale bar represents 20 μm. Quantification of H3K27ac fluorescence intensity (**** p < 0.0001, Student’s t-test). (D) MHC analysis of H3K27ac signals in H9 cells under the indicated treatments. (E) Normalized signal intensity of H3K27ac across the genome in H9 cells under the indicated treatments. (F) Violin plot showing significant upregulation of H3K27ac signals at transcriptional start sites (TSS) in the CGA-treated group compared to the vehicle and CGA + TMZ-treated groups. (G) Heatmap (left) displaying relative changes in H3K27ac signals among H9 cells under the indicated treatments. Heatmap (right) shows expression changes of genes between the indicated groups. (H) GO analysis of the genes in panel (G). (I) Pearson correlation analysis of RNA and H3K27ac enrichment at the promoters of genes involved in lipid biosynthesis. (J) Genome browser view of H3K27ac signals at the ELOVL1 locus in H9 cells under the indicated treatments.

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Immunofluorescence staining was conducted to assess H3K27ac expression, revealing a significant increase in H3K27ac levels with CGA treatment and a decrease with CGA + TMZ treatment (Fig. 5C). To further investigate the impact of H3K27ac in CGA-treated H9, the H3K27ac landscape was profiled using cleavage under targets and tagmentation (CUT&Tag) in the vehicle, CGA, and CGA + TMZ-treated cells. The three groups were clearly distinguished by MHC (Fig. 5D). Notably, CGA treatment led to a marked enhancement of H3K27ac signals, specifically at gene promoter regions, which were subsequently reversed upon TMZ supplementation (Fig. 5E, F). These findings suggest that CGA may modulate gene expression by influencing H3K27ac patterns.

To further elucidate the changes in gene expression mediated by CGA and H3K27ac, we conducted a joint analysis of RNA-seq and H3K27ac CUT&Tag data. Our findings reveal that the hyperacetylation of H3K27 in the promoter regions of certain genes (n = 1,689), induced by CGA, was partially restored by TMZ supplementation, consequently leading to a decrease in their expressions (Fig. 5G). Importantly, GO analysis demonstrated that the majority of these genes were closely associated with fatty acid metabolisms (Fig. 5H). Moreover, the expressions of 351 genes involved in fatty acid metabolism positively correlated with the enrichment of H3K27ac in the promoter regions (Fig. 5I), such as ELOVL1 (Fig. 5J), which is responsible for elongating long chain fatty acids38. Overall, these results suggest that the acetyl-CoA generated through CGA-mediated fatty acid oxidation not only serves as a substrate for lipid synthesis but also induces hyperacetylation of H3K27 to enhance the expressions of genes related to fatty acid metabolism in human embryonic stem cells.

Discussion

In this study, we investigated the impact of CGA on the metabolism of fatty acids in hESCs. With the use of the H9 model, we discovered that CGA actively stimulated cell proliferation by activating FAO. Furthermore, CGA also boosted the production of acetyl-CoA through enhanced FAO, which in turn promoted the synthesis of new lipids and the upregulation of genes associated with lipid metabolism. These effects were observed through the process of H3K27 hyperacetylation. Our findings suggest that CGA holds potential in providing beneficial effects for hESCs (Fig. 6).

Fig. 6
figure 6

Schematic diagram of CGA promotes FAO to increase hESCs proliferation and lipid synthesis.

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ESCs have a high demand for synthetic metabolic precursors, which are crucial for replicating their genome, cytoplasm, and organelles. In this study, we have shown the regulatory role of CGA in promoting FAO in (hESCs. Previous research has demonstrated that FAO is essential for the self-renewal of adult stem cells and is also involved in sustaining the self-renewal of breast cancer stem cells and supporting tumor survival under metabolic stress conditions39. Similarly, our findings indicate that CGA-induced FAO promotes the proliferation of hESCs. Ground state ESCs heavily rely on elevated FAO for oxidative phosphorylation and energy production. When FAO/oxidative phosphorylation is inhibited, naïve hESCs enter a state of pluripotent quiescence, characterized by small, dome-shaped colonies and sustained expression of pluripotency markers40. We observed that CGA-activated FAO increased the expression of Nanog, but not Pou5F1 and Sox2, highlighting the role of Nanog in regulating hESCs proliferation. The increase in Nanog expression after FAO activation may indicate an enhanced self-renewal ability of hESCs through the FAO signaling pathway. Specifically, fatty acid oxidation might indirectly influence the metabolic environment required for stem cell self-renewal by improving mitochondrial function and energy metabolism. Further experiments can investigate whether FAO activation regulates the expression of self-renewal-related genes by upregulating Nanog. Additionally, there is a complex balance between Nanog expression levels and differentiation potential. For instance, moderate Nanog expression may promote certain types of differentiation, while Nanog overexpression might maintain an undifferentiated state and inhibit differentiation. Thus, Nanog upregulation caused by FAO activation may have a bidirectional effect on hESCs differentiation potential, depending on the intensity of FAO activation and its specific regulation of cellular metabolism.

The active FAO can also promote de novo lipid synthesis by supplying acetyl-CoA. Lipids are vital for maintaining cellular homeostasis, as they provide macromolecules for membrane biosynthesis during growth and proliferation41. Cell proliferation is closely linked to the de novo synthesis of lipids. Therefore, we attribute the enhanced proliferation of hESCs to the lipid synthesis induced by CGA-mediated FAO. FAO can oxidize medium and long-chain fatty acids in order to produce acetyl-CoA42. Acetyl-CoA is a key precursor in lipid synthesis and also serves as an acetyl donor for histone acetylation36,43. In this study, we observed that CGA promotes H3K27ac through FAO. The promoter regions of lipid synthesis-associated genes showed hyperacetylation of H3K27, thereby enhancing their expression. Thus, a positive feedback loop occurs for FAO-mediated lipid synthesis induced by CGA in hESCs.

In summary, our study revealed that CGA enhances the proliferation and lipid synthesis of hESCs by promoting FAO. Mechanistically, CGA-induced FAO generates acetyl-CoA, which in turn facilitates de novo lipid synthesis. Additionally, CGA hyperacetylates H3K27 at the promoter regions of genes associated with lipid synthesis, leading to enhanced gene expression. These findings demonstrate the potential beneficial effects of CGA on hESCs. Furthermore, our study provides an avenue for optimizing the in vitro culture of hESCs.

CGA-induced FAO promotes cell proliferation and de novo lipid synthesis in H9 cells. Additionally, CGA enhances FAO, leading to the production of acetyl-CoA, which further promotes de novo lipid synthesis and increases the expression of genes associated with lipid synthesis through H3K27 hyperacetylation.

Materials and methods

Cell culture

The H9 cell line of hESCs was generously provided by Professor Ling Shuai from Nankai University. In accordance with the WiCell Feeder Independent Pluripotent Stem Cell Protocols from the University of Wisconsin, the hESCs are cultured on Matrigel-coated dishes using E8 medium. To initiate the culture, a mixture of 200 µl Matrigel and 12 ml DMEM/F-12 medium is prepared and 1 ml of this solution is added per well in a 6-well plate. The plate is then incubated at 37 °C in a cell culture incubator for 90 min, after which any excess Matrigel is removed prior to cell inoculation. The E8 hESCs are passed every 3–4 days using an EDTA dissociation buffer containing 0.5 mM EDTA in DPBS. The cells are first washed twice with DPBS, followed by incubation with the EDTA dissociation buffer at 37 °C for 5 min. After removing the EDTA, 1 ml of E8 medium is added to the culture dish. Gently agitating the dish facilitates the formation of high-quality small clones, which are subsequently passaged at a ratio of 1:10 to 1:20.

Cell viability assays

The cells were seeded in 96-well plates with five duplicate wells in each group. Around the sample wells, 100 µl of DPBS was added to prevent liquid evaporation. After the cells adhered to the wall, different concentrations of drug-treated medium were replaced, and the blank vehicle groups were set. The IC50 assay was performed using the CCK8 kit (Dojindo, Kyushu, Japan), and cell proliferation was determined at specified times following the manufacturer’s protocol. Cell viability was measured by determining the absorbance at 450 nm. The formula used for calculation was as follows: cell viability (%) = [A (treat) - A (blank)] / [A (vehicle) - A (blank)] × 100.

Alkaline phosphatase staining

Alkaline phosphatase staining was performed using the Alkaline Phosphatase Staining Kit II (00–0055, Stemgent, Hamburg, Germany) according to the manufacturer’s instructions. The residual culture medium was washed away with 1×PBS, and this step was repeated twice. Subsequently, the cells were fixed with 4% PFA for 10 min. A 1×PBST solution was prepared by adding 5 µl of Tween 20 to 10 ml of 1×PBS, resulting in a final concentration of 0.05%. The fixation solution was discarded, and the residual polyformaldehyde was washed away with 1×PBST, repeating this step twice. To prepare the AP substrate solution, 500 µl of Solution A was mixed with 500 µl of Solution B for each well, and the mixture was left to stand at room temperature for 2 min. Then, the required volume of Solution C (500 µl) was added to each well, and the working solution was mixed. The 1×PBST was discarded, and 1.5 ml of the working solution was added. The cells were incubated in darkness at room temperature for 10 min. Afterward, the excess staining solution was washed off with 1×PBST, and the cells were air-dried. Finally, the cell colonies were counted to evaluate the proliferation ability of hESCs.

BODIPY

Prepare cell slides in advance in a 24-well cell culture plate, add 500 µl of Martrigel to the 24-well plate with prepared slides to ensure close contact between the slides and the bottom of the culture dish, and incubate at 37 °C for 90 min. After preparation, inoculate cells of suitable density according to routine procedures for wall attachment growth. Stain when cell confluence reaches 70%. Wash 3 times with DPBS, then fix for 15 min with 4% paraformaldehyde (PFA). Remove the fixative and wash 3 times with DPBS. Incubate the fixed cells with 1 µg/ml BODIPY 493/503 (molecular probe) in DPBS for 30 min, and observe imaging under a confocal laser microscope.

RT-qPCR

Total RNA was extracted using the Arcturus PicoPure RNA Isolation Kit (12204-01; Ambion, Austin, TX, USA) according to the manufacturer’s instructions, and reverse transcription was performed to generate cDNA using the PrimeScript RT Master Mix (RR036; Takara, Kusatsu, Japan). The RNase-Free DNase Set (79254; Qiagen, Hilden, Germany) was used to ensure that there was no DNA contamination. We performed RT-qPCR using the TB Green Premix Ex Taq II (RR820; Takara) and CFX96 Real-Time System (Bio-Rad, Hercules, CA, USA). The reaction parameters were as follows: 95 °C for 30 s, followed by 40 two-step cycles of 95 °C for 5 s and 60 °C for 34 s. GAPDH was used as a reference gene. Ct values were calculated using the BioRadCFXManager software (version2.2 https://en.freedownloadmanager.org/Windows-PC/Bio-Rad-CFX-Manager.html), and the target sequences were normalized to the reference sequence using the 2−△△Ct method.

Immunofluorescence staining

After removing the zona pellucida with acidic operating fluid, cells were fixed for 15 min at room temperature in 4% PFA, followed by permeabilization in 10% Triton X-100 for 40 min at 37°C. Cells were then blocked-in blocking solution consisting of 1% bovine serum albumin (BSA) in phosphate-buffered saline (PBS) for 1 h at 37°C after three washes in washing solution (0.1% Tween-20, 0.01% Triton X-100 in PBS). Incubations were performed overnight at 4 °C using the following antibodies and dilutions in blocking solution: anti-NANOG (1:100; ab21624; Abcam, Cambridge, UK), anti-H3K27ac (1:100; ab4729; Abcam). Then, the cells were washed three times in washing solution and incubated with secondary antibodies: Alexa Flor 546-conjugated donkey anti-rabbit IgG (1:500; A10040; Invitrogen, Carlsbad, CA, USA) for 1 h at 37°C. Samples were mounted onto slides with antifade medium containing DAPI (C1005; Beyotime, Shanghai, China) and examined under TCS SP8 inverted confocal microscope.

RNA-seq library preparation

RNA-seq libraries were prepared as previously described44. Briefly, every 50 cells from a dish were pooled for one group. All groups were amplified to obtain cDNA by the SMART-Seq2 protocol (N712; Vazyme, Nanjing, China). cDNA was quantified by Qubit 3.0 (Thermo Fisher) and 5 ng cDNA was used for DNA library construction with TruePrep DNA Library Prep Kit V2 for Illumina (TD502, Vazyme) following the manufacturer’s instructions.

RNA-seq data processing

For RNA-seq analysis of all data, we downloaded the human reference genome (genome assembly: GRCh38) from the Ensembl database and used the HISAT2 software (version 2.2.1, https://daehwankimlab.github.io/hisat2/) for read alignment with a default setting after removing adaptor sequences and low-quality reads by fastp (version 0.12.4, https://github.com/OpenGene/fastp). The gene-level quantification approach was used to aggregate raw counts of mapped reads using the featureCounts tool. The expression level of each gene was quantified in terms of the normalized fragments per kilobase of transcript per million mapped reads (FPKM). Total genes of chromosome X and chromosome 1 were extracted, and the geometric mean FPKM of chromosomal expressed genes was calculated for each sample. Each FPKM value was incremental by one (FPKM + 1) for the calculation of geometricmean FPKM. Next, we used the R package DESeq2 (version 1.46.0, https://bioconductor.org/packages/release/bioc/html/DESeq2.html) for differential gene expression analysis. GO analysis (online, https://www.geneontology.org/) of screened DEGs was performed using the R package clusterProfiler. Functional annotation analysis of the screened differentially expressed genes using KOBAS (online, http://bioinfo.org/kobas/annotate/).

CUT&Tag library preparation

CUT&Tag libraries were prepared as previously described. Briefly, every 1 × 103 cells from a dish were pooled for one group. All groups were used for H4K16ac CUT&Tag DNA library construction with Hyperactive Universal CUT&Tag Assay Kit for Illumina (TD903, Vazyme) following the manufacturer’s instructions. cDNA was quantified by Qubit 3.0.

CUT&Tag data analysis

The CUT&Tag paired-end reads were aligned to the human genome (hg38) using Bowtie 2 (version 2.2.5), which was downloaded from https://sourceforge.net/projects/bowtie-bio/files/bowtie2/2.2.5/. Prior to alignment, adaptor sequences and low-quality reads were removed using fastp (version 0.12.4, available at https://github.com/OpenGene/fastp). For the alignment of H3K27ac paired-end reads to the human reference genome GRCh38, Bowtie2 was employed with specific parameters: -local -very-sensitive -no-unal -nomixed -no-discordant -phred33 -I 10 -X 700.

Following alignment, PCR duplicates were removed using Picard software, which can be accessed at https://github.com/broadinstitute/picard. After assessing the correlation among biological replicates, the replicate reads from the two groups were merged using Samtools’ ‘merge’ function. The resulting BAM files were then converted to BED format using BEDTools. Peak calling was performed on the merged files using MACS2 software, with peaks having a false discovery rate (FDR) ≤ 5% being selected for downstream analysis.

Peak annotation was carried out using the R package ChIPseeker with default parameters. For peak comparison and overlap analysis, BEDTools was utilized. Quantitative analysis was based on the calculation of RPKM values from the merged BAM files, which was accomplished using the bamCoverage tool from the DeepTools software suite https://test-argparse-readoc.readthedocs.io/en/latest/. Finally, the H3K27ac CUT&Tag data were visualized using IGV, available at https://igv.org/.

Compass predictive analysis

Enter the matrix of routine transcriptome gene expression for calculation, convert reaction encoding into metabolic pathways based on the reaction penalties reflected in the results, and the Recon2 meta information, transform reaction penalties, and use Wilcoxon for differential analysis.

Statistics analysis

RNA-seq data were statistically analyzed using R software (version 4.2.3, https://www.r-project.org/). Differentially expressed genes were analyzed using the Wilcoxon rank-sum test, and p-values were adjusted using the Benjamini-Hochberg method. Data are presented as mean ± standard error of the mean (SEM). RT-qPCR data were evaluated for differences between means using a two-tailed Student’s t-test. A p-value of < 0.05 was considered statistically significant. Immunofluorescence intensity was statistically analyzed using ImageJ software (version 1.5, https://imagej.net/ij/) in combination with the two-tailed t-test of GraphPad Prism software (version 10.1.2, https://www.graphpad.com/updates/prism-10-1-2-release-notes). Experiments such as AP colony counting and cell viability were analyzed using Welch’s one-tailed t-test.