Fatty acids promote megakaryocyte biased differentiation

Fatty acids promote megakaryocyte biased differentiation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fatty acids promote megakaryocyte biased differentiation Weihua Huang, Ruoru Wang, Yan Zang, Yingwen Zhang, Shanshan Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6359690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract At present, the clinical demand for platelet transfusion is increasing, so new ways to promote the production of platelets in vivo and in vitro are urgently needed. However, the mechanism of megakaryocyte (MK) differentiation and platelet formation is still ambiguous and is also a major obstruction to artificial platelet production. In our study, we found that platelet counts were correlated with hyperlipidemia in humans. During human induced pluripotent stem cell (hiPSC) differentiation in vitro, human plasma (HP) promoted the differentiation of CD34 + hematopoietic cells into megakaryocyte progenitors (MKPs) at days 8–14. Fatty acid (FA) is the active component that promotes hiPSC-derived MK and proplatelet formation, and FA activates MK and platelet generation through the PPAR signaling pathway. Our data expand the theory of platelet differentiation and provide a technique for promoting platelet generation in vitro. platelets human plasma (HP) megakaryocyte progenitors fatty acid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights 1. Increased platelet count was correlated with hyperlipidemia in vivo. 2. Human plasma (HP) mainly promotes MK biased differentiation from HPCs at days 8–14 during platelet generation from iPSCs in vitro. 3. Fatty acid (FA) is the active component that promotes hiPSC-derived MK and proplatelet formation. 4. FA activated MK and platelet generation though the PPAR signaling pathway. Introduction Clinically, platelet (PLT) transfusion is the main measure for the prevention and treatment of thrombocytopenia [ 1 ] . However, the short room temperature storage time and total dependence of the supply on blood donors lead to an imbalance between the supply and demand of clinical PLT transfusions at present [ 2 ] . Moreover, the technology for the in vitro generation of large-scale PLTs has been greatly limited by the lack of clarity regarding the mechanism of platelet differentiation. The BM consists of hematopoietic islands and adipocytes surrounded by vascular sinuses [ 3 ] . Studies have shown that BM adipocytes can affect normal BM hematopoietic processes by regulating the generation of hematopoietic stem progenitor cells (HSPCs) [ 4 , 5 ] and support the differentiation of CD34 + HSPCs into myeloid and lymphoid immune cells [ 6 ] . Moreover, myelogenesis is positively correlated with increased lipogenesis and decreased osteoblast generation [ 7 ] . In diet-induced obese mice, enhancement of hematopoietic and lymphopoietic BM cell populations was associated with increased BM adiposity [ 7 ] . Lipolysis in the BM plays a crucial role in exercise-stimulated osteogenesis and lymphogenesis [ 8 ] . Considering these key influences, some studies have focused on whether BM obesity with increased fat cells affects BM megakaryogenesis [ 9 ] . Coculture of adipocytes and bone marrow hematopoietic progenitor cells in vitro revealed that a decrease in adipocytes directly supported MK maturation by promoting polyploidy, expanding the boundary membrane system and promoting the formation of proplatelets [ 9 ] . The effect of a high-fat environment on platelet generation in vitro remains unclear. In this study, we analyzed the clinical data of PLT counts and plasma lipid levels and detected the effects of plasma, hyperlipidemia and fatty acids on HSCs, HPCs, MKPs and platelets via an in vitro human induced stem cell (hiPSC)-derived platelet platform. We found that lipids in plasma promoted the differentiation of HPCs into megakaryocyte-biased cells and thus promoted the generation of platelets in vitro via the PPAR pathway, providing new evidence for the theory of platelet differentiation bias and new culture conditions for platelet generation in vitro. Materials and methods Data Collection Subjects All patient information and clinical data were obtained from the First Affiliated Hospital of Second Military Medical University from September 2010 to May 2023; 400 healthy people or individually diagnosed hyperlipidemia patients were selected from the health examination center. The median age (interquartile range) was 58 (17–75) years, among whom 282 (70.5%) were male. The detailed clinical information is listed in Table 1. The study inclusion and exclusion criteria are presented in Table 2. Importantly, participants must not have used antibiotics or thrombolytic, anticoagulant or platelet inhibitory drugs before blood collection. Written informed consent was obtained from all participants. All the participants were given routine blood tests on the day of admission or the next day. The basic data of the patients, such as age, sex, history (hypertension, diabetes), and laboratory test results (PLT count, TG, TC, etc.), were collected for statistical analysis. Blood production, cell culture and manipulation Blood production Human plasma (HP) and hyperlipidemic plasma (HLP) are obtained from healthy blood donors. hiPSC Culture hiPSCs are obtained via the reprogramming of MEFs. A total of 5-10×10 4 hiPSCs were cultured in Matrigel (BD Biosciences)-pretreated six-well plates with PSCeasy ® II medium (Cellapy). After reaching 70%~80% confluency, the cells were digested with 0.05 mM EDTA (Invitrogen), passaged at a 1:6 split ratio, and then incubated at 37°C in 5% CO 2 . Differentiation of hiPSCs into MKs and proplatelets We performed hematopoietic differentiation of hiPSCs via the EB-based protocol as previously described [10] . First, hiPSC colonies were digested with TrypLE™ Express (Gibco) into single cells and resuspended in STEMdiff™ APEL™2 medium (APEL, STEMCELL Technologies) supplemented with ROCK inhibitor (Y27632, 10 μM, STEMCELL Technologies), BMP4 (10 ng/ml, R&D Systems) and bFGF (10 ng/ml, Peprotech). The cells (3500 per well) were seeded into untreated round-bottom 96-well plates (Costar 3788), centrifuged at 300 × g at room temperature for 5 min and cultured at 37°C with 5% CO 2 . From day 2 to day 14, the cells were cultured in APEL containing BMP4 (10 ng/ml), bFGF (10 ng/ml), VEGF (10 ng/ml, PeproTech) and SCF (50 ng/ml, PeproTech). TPO (20 ng/ml, PeproTech) was added to the medium on day 11. On day 14, the suspended cells secreted by the EBs were collected and then filtered through 100-μm cell filters (BD Biosciences) to remove the EBs. The cells were transferred into 6-well plates for culture. The culture medium was APEL (2 ml per well) containing SCF (20 ng/ml), IL-11 (10 ng/ml, PeproTech) and TPO (50 ng/ml), and the mixture was cultured for 5 days to collect megakaryocytes and proplatelets. The cells were cultured with or without HP or HLP at different time points. Fatty acids (FA; product F7050, Sigma Aldrich), palmitic acid (PA; product P0500, Sigma Aldrich) and sulfosuccinimidyl oleate sodium (SSO; product ab145039, Abcam) were tested at 0.25 ml/L, 10 μM and 200 μM, respectively. Flow Cytometry Analysis The cells were collected, washed once with PBS, and stained with antibodies or isotype controls for 30 minutes at 4°C in flow cytometry buffer. The samples were analyzed on a FACS Calibur (BD Canto Plus), and the data were analyzed via FlowJo v10 (FlowJo, LLC). The cells were labeled with the following antibodies: CD34-PE (4H11, IgG1 κ-PE), CD45-APC-Cyanine 7 (2D1, IgG1 κ-APC-Cyanine7) and CD41-APC (MEM-06, IgG1 κ-APC) from Invitrogen; and CD42a-eFluor ® 450 (GR-P, IgG1 κ-eFluor ® 450), CD42b-PE-Cyanine7 (HIP1, IgG1 κ-PE-Cyanine7) and CD62P (P-Selectin)-PE (Psel.KO2.3, IgG1 κ-PE) from eBioscience. Morphological analysis The cells collected on days 14 and 19 were cytospun onto glass slides via a Cytospin 4 centrifuge (Thermo Scientific), stained with Wright‒Giemsa (product G1020, Solarbio Life Sciences), and observed with a Leica inverted contrast microscope fitted with a camera (DFC420, Leica Camera). Laser confocal microscopy On day 19 of culture, the cells were analyzed via fluorescence microscopy. The cells were spun onto glass slides, subsequently fixed with 4% paraformaldehyde and permeabilized with 0.3% Triton X-100. The glass slides were then stained with an anti-vWF antibody (Proteintech), an anti-CD42b antibody (eBioscience) and 4′6-diamidino-2-phenylindole (DAPI). Images were captured via a Leica TCS 166 SP8 laser confocal microscope. DNA ploidy analysis DNA ploidy analysis was performed by FACS on all samples as previously described [10] . Megakaryocyte ploidy was analyzed on day 19 of culture. The cells were labeled with an APC-conjugated anti-CD41 antibody, incubated for 20 minutes on ice, fixed with precooled 70% ethanol for 2 h at room temperature and then washed with PBS. Next, the cells were treated with 20 mg/ml propidium iodide (PI; Sigma‒Aldrich) and 100 mg/ml RNase A (Thermo Fisher Scientific) for 30 min in the dark at 37°C. The cellular DNA content was subsequently analyzed via a FACS Calibur (BD Canto Plus). Protein extracts The cells were harvested and suspended in whole-cell lysis buffer (50 mM KCl, 1% NP-40, 25 mM HEPES (pH 7.8), 10 μg/ml leupeptin, 20 μg/ml aprotinin, 125 μM DTT, 1 mM PMSF, and 1 mM Na 3 VO 4 ). The remaining debris was removed by centrifugation at 12,000 g at 4 °C for 5 min. Finally, the supernatant was collected, and the protein concentration was determined with a BCA kit according to the manufacturer’s instructions. Western blot The cell protein extracts were subjected to western blotting as previously described [10] . Briefly, protein samples were separated on an 8% SDS‒PAGE gel and transferred to a nitrocellulose membrane. After being blocked in 5% nonfat milk buffer containing 0.05% Tween-20, the membrane was incubated with the primary antibody. The antibodies used were purchased from Cell Signaling Technology (AKT antibody: #4685; pAKT antibody: #9267; p44/42 MAPK (Erk1/2) antibody: #9102; and GAPDH antibody: #5174) and Abcam (Erk1/2 antibody: # ab184699). GAPDH was used as a protein loading control. The signal was visualized on a film after exposure to chemiluminescence and analyzed for optical density at each band via an image-processing and analysis system. Total RNA extraction Total RNA was extracted from the tissues via TRIzol (Invitrogen) according to the manufacturer’s instructions. Approximately 1×106 cells were collected in a 2 mL tube, and 1 ml of TRIzol was added directly to the cell pellet (approximately 1×10 6 per 1 ml). The mixture was subsequently homogenized for 2 minutes and allowed to rest horizontally for 5 minutes. The mixture was subsequently centrifuged for 5 minutes at 12,000×g at 4°C, after which the supernatant was transferred into a new 2.0 EP tube with 0.2 mL of chloroform/isoamyl alcohol (24:1). The mixture was shaken vigorously for 15 s and then centrifuged at 12,000×g for 10 minutes at 4°C. After centrifugation, the upper aqueous phase containing the remaining RNA was transferred into a new tube with an equal volume of isopropyl alcohol and then centrifuged at 13,600 rpm for 20 minutes at 4°C. After the supernatant was removed, the RNA pellet was washed twice with 1 mL of 75% ethanol, after which the mixture was centrifuged at 13,600 rpm for 3 minutes at 4°C to collect residual ethanol, after which the pellet was allowed to air dry for 5–10 minutes in a biosafety cabinet. Finally, 25~100 µL of DEPC-treated water was added to dissolve the RNA. The total RNA was subsequently quantified via a NanoDrop system and an Agilent 2100 bioanalyzer (Thermo Fisher Scientific). Sequencing data analysis SOAPnuke (V1.5.2) was used to filter the sequencing data. Clean reads were mapped to the reference genome via HISAT2 (V2.0.4). Clean reads were aligned to the reference coding gene set via Bowtie2 (V2.2.5), and gene expression levels were subsequently calculated via RSEM (V1.2.12). A heatmap was drawn with PheATMap (V1.0.8) on the basis of gene expression in different samples. In essence, differential expression analysis was performed via DESeq2 (v1.4.5) with a Q value ≤0.05. To gain insight into phenotypic changes, KEGG (https://www.kegg.jp/) enrichment analysis of annotated differentially expressed genes was performed via Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) via a hypergeometric test. Statistical analysis All the experiments were performed in triplicate. The results were analyzed statistically via Graph Pad Prism software (version 9.5.1). Comparisons between two groups were assessed via the unpaired Student’s t test (2-tailed), whereas multigroup analysis was assessed via one-way analysis of variance (ANOVA) (2-tailed). The data are expressed as the means ± standard deviations (SDs), and the significance level was set at 0.05 (two-sided, * P <0.05, ** P <0.01, *** P <0.001). Differential expression analysis of two conditions/groups (three biological replicates per condition) was performed via the DESeq R package (1.10.1). The resulting P values were adjusted via Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P < 0.05 according to DESeq and a log2 fold change greater than ±1 were considered differentially expressed. Results Increased platelet count was correlated with hyperlipidemia in vivo Hyperlipidemia patients are prone to coagulation-related complications, often involving changes in platelet procoagulant activity [ 11 ] , but few studies have investigated whether the number of platelets changes. Therefore, we collected clinical data to study the relationship between hyperlipidemia and platelet count. First, we compared the platelet, red blood cell (RBC) and white blood cell (WBC) counts in populations with hypertriglyceridemia (Hyper-TG), hypercholesterolemia (Hyper-TC) and mixed hyperlipidemia (HPL) with those in the normal population. The results revealed that in individuals in all three hyperlipidemic groups, the platelet count was significantly greater than that in the normal group (Fig. 1 A; *** P < 0.001) and was the same as the WBC count (Fig. 1 C; *** P < 0.001, ** P < 0.01). With the exception of the TG group, the RBC count of the other two groups was significantly greater than that of the control group (Fig. 1 b; ** P < 0.01). More parameters are shown in Table 1. These results indicate that a hyperlipidemic environment can efficiently promote the proliferation of blood cells, including platelets, in vivo. Human plasma (HP) promoted CD34 + hematopoietic cell differentiation into MKs in vitro To study whether hyperlipidemic plasma (HLP) can promote megakaryocyte and platelet differentiation, we first studied the role of HP in megakaryocyte formation in vitro, as shown in the flow chart in Fig. 2 A [ 10 ] . We divided platelet generation from hiPSCs in vitro into three stages, HPC generation (days 0–8), CD41 + MKP generation (days 8–14), and MK maturation and proplatelet generation (days 14–19), to observe the effects of HP. On days 3–8, the hematopoietic marker CD34 was expressed in the EB cells and increased with number of days in culture. Compared with that in the HP-untreated group, CD34 + CD45 − HSC growth was not greater in any of the HP-treated medium groups; thus, the number of CD34 + CD45 + HPCs increased significantly in the HP-treated medium groups (Fig. 2 B-C, Supplemental Fig. 1A). These findings indicate that HP favors the differentiation of HPCs from HSCs. On day 14, CD34 + CD45 + HPC and CD41 + MKP generation first peaked in the 20% and 25% HP-treated groups (Fig. 2 D; ** P < 0.01; * P < 0.05, respectively), although the number of CD34 + CD45 - HSCs appeared to be no greater than that in the untreated control group (Fig. 2 D). On day 19, typical images of cells from the HP-untreated and HP-treated groups on days 14 and 19 under a light microscope are shown in Supplemental Fig. 1C. CD41 + CD42a + CD42b - , CD41 + CD42a + CD42b + MK and CD41 + MK growth was at the highest level when the concentration of HP was 5% (Fig. 2 E, Supplemental Fig. 2A, * P < 0.05). These findings suggest that the different stages of platelet generation in hiPSCs require different concentrations of HP. We further studied the addition stage at which HP exerts its best stimulation effect on MK generation. The cells were cultured for 0–14 days in APEL medium supplemented with 20% HP for 14–19 days supplemented with 5% HP and were divided into seven various treatment groups (days 0–4, 0–8, 0–14, 0–19, 8–14, 8–19, and 14–19 HP). The results revealed that the counts of total cells and CD41 + CD42a + CD42b + cells were significantly greater in the 8–14 HP addition group than in the untreated group (Fig. 2 F-G; *** P < 0.001), although the percentages of CD41 + CD42a + CD42b + MKs in the HP addition group were greater than those in the control group (Supplemental Fig. 1B; *** P < 0.001). These results suggested that HP functions mainly at the stage from HPC to MKP generation on days 8–14 to promote HPC and MK differentiation from hiPSCs. TG-human hyperlipidemic plasma (TG-HLP) promoted hiPSC-derived MK and proplatelet formation in vitro The clinical data revealed a positive correlation between the PLT and TG level in vivo (Fig. 3 C; P = 0.0014), and the hyperlipidemic plasma we used was collected from volunteers with increased TG (TG-HLP) (Fig. 3 D). We added 20% TG-HLP to APEL differentiation medium on days 8–14 to study the effects of TG-HLP on hematopoietic cell, MK and proplatelet formation. We used flow cytometry, Wright‒Giemsa staining, and immunofluorescence microscopy images to identify the types of cells at days 14 and 19 (Supplemental Fig. 2A and 3A‒3B). However, the percentage of CD41 + CD42a + CD42b + cells in the different groups was not different on day 19 (Supplemental Fig. 2B). The HLP addition increased not only the cell counts of CD34 + CD45 - HSCs, CD34 + CD45 + HPCs and CD41 + MKPs (Fig. 3 A, *** P < 0.001, **P < 0.01, respectively) on day 14 but also the total, CD 41 + and CD41 + CD42a + CD42b + cell counts on day 19 (Fig. 3 B, *** P < 0.001). The numbers of total cells and CD41 + CD42a + CD42b + cells produced from the TG-HLP-treated group were also significantly greater than those produced from the HP-treated group (Fig. 3 E-F; P < 0.05, P < 0.05, respectively). Compared with those from the HP-treated group, the CD41 + MKs from the TG-HLP-treated group presented greater DNA ploidy (4 N and ≥ 8 N) (Fig. 3 G; P < 0.01, P < 0.001, respectively). The above findings have shown that TG-HLP may promote the bias of HPC differentiation into MKs and platelets. To further verify that fatty acids play an important role in promoting proplatelet generation, we chose FA (0.25 ml/L) and PA (10 µM) instead of hyperlipidemic blood components and the fatty acid transport inhibitor SSO (200 µM), which inhibits fatty acid transfer via the fatty acid transposase CD36 [ 12 ], for treatment on days 8–14 from hiPSCs to platelet generation. On day 14, the total number of cells, CD34 + CD45 + cells and CD41 + cells produced by the PA-treated group or FA-treated group was greater than that produced by the control, and the total number of CD34 + CD45 + cells, CD34 + CD45 + cells and CD41 + cells produced by the SSO-treated group was significantly lower than that produced by the control group (Fig. 3 H; P < 0.05, P < 0.01, P < 0.001, respectively). The number of CD34 + CD45 − cells produced by the FA-treated group was also greater than that produced by the untreated group (Fig. 3 H; P < 0.001). On day 19, the CD41 + cell and CD41 + CD42a + CD42b + cell counts were greater in the FA-treated group and PA-treated group than in the untreated group and were significantly lower in the SSO-treated group than in the control group (Fig. 3 I; P < 0.05, P < 0.01, P < 0.001, respectively). These findings indicate that fatty acids are the active component that promotes hiPSC-derived MK and proplatelet formation. We then added 250 µM ADP to the cells collected on day 19 from the TG-HLP-supplemented conditions and evaluated the CD62P expression levels in the resting and preactivated platelets via flow cytometric analysis. An increase in the expression of CD62P was observed (Supplemental Fig. 3C-D; P < 0.001). In addition, platelets can be visualized under an optical microscope (20×; Supplemental Fig. 3E). The individual cells tended to aggregate into clumps in the plate at days 14–19 (Supplemental Fig. 3F; P < 0.001), which also indicates the presence of platelets with aggregation ability. This result revealed that the platelets could be activated after 19 days of culture. HP and HLP promoted bias differentiation toward MK To confirm that the TG-HLP-treated group promoted the generation of biased MKs from HPCs, RNA sequencing analysis was performed on 9 cell samples (control: n = 3; HP: n = 3; TG-HLP: n = 3) collected on day 14, resulting in 1422 differentially expressed genes (DEGs) (Fig. 4 A-B). The PCA score plots revealed that the gene expression in the HP and HLP addition groups was significantly different from that in the control group (Supplemental Fig. 4A-B). A total of 217 common DEGs were found in the HP and HLP addition groups compared with the control group (Fig. 4 C). The KEGG analysis of 217 DEGs indicated that hematopoietic cell lineage pathways were predominant (Fig. 4 D). We subsequently screened 14 marker genes of HSC, HPC and MK overlapping DEGs, including 8 marker genes of HSCs (CXCR4, CD38, PROM1, GFl1, TEK, NGFR, CD44, and ACE), 2 marker genes of HPCs (CD34 and KDR) and 4 marker genes of MKs (SRGN, GP5, PF4, and CD9). The heatmap of the 14 DEGs revealed that genes related to MKs were significantly upregulated in the HP and HLP addition groups compared with the control group, and genes related to HPCs were significantly downregulated in the HP and HLP addition groups compared with the control group (Fig. 4 E). These results indicated that the addition of HP or HLP promoted the biased differentiation of MK from HPCs. Fatty acids promoted MK differentiation via the PPAR signaling pathway To confirm that the signaling pathway involved in the TG-HLP-treated group promoted the generation of biased MKs from HPCs, we analyzed the DEGs between the HLP and HP addition groups. We identified 201 DEGs between the HLP and HLP addition groups (Fig. 5 A-B). The KEGG analysis of the differentially expressed molecular pathways revealed that the PPAR pathways were predominant (Fig. 5 C). The relative mRNA expression levels of the PPAR pathway genes PLNA2, CPT1A, and ANGPTL4 in the HLP-treated groups were significantly greater than those in the control and HP-treated groups on day 14, as determined by real-time PCR (Fig. 5 D). The PPAR signaling pathway eventually activates the AKT/ERK protein [ 13 ] . Next, we detected the protein expression of pAKT and pERK1/2 via western blot analysis (Fig. 5 E). We found that pAKT and pERK1/2 expression increased on day 14 but not on day 8 in the HLP-treated group compared with the control group (Fig. 5 E, full-length blots are presented in Supplementary Fig. 5A). Our results suggested that fatty acids promoted MK differentiation via the PPAR signaling pathway. Discussion We proved that fatty acids in plasma can promote MK-biased MKP generation, while MK maturation and proplatelet generation are no longer affected by blood lipid levels. This finding further confirms the theory of platelet bias generation and provides a guiding direction for optimizing the platform for differentiating platelets in vitro. Our results revealed that fatty acids play an important role in the differentiation of MKPs from HPCs at 8–14 days in an in vitro model and can promote the biased differentiation of MKs. In recent years, many studies have investigated the biased differentiation of MKs. Low iron biases the commitment of megakaryocytic (Mk)-erythroid progenitors (MEPs) toward the MK lineage in both humans and mice [ 14 ] . All the RUNX-1+/- lines presented decreased iPSC-derived MK yields and depletion of an MK-biased iPSC-derived HPC subpopulation [ 15 ] . THBS1 is an early marker for MK-biased embryonic endothelial cells [ 16 ] . HSCs with elevated expression of CD41 (CD41hi) are biased toward MKs, and treatment with interferon-α can further increase the frequency and percentage of CD41hi HSCs [ 17 ] . Therefore, we identified a new factor that induces biased differentiation of MKs. Our clinical data analysis revealed that hyper-TG, hyper-TC and mixed-HLP were correlated with increased platelet counts in humans. These findings suggest that a high fat content may promote the formation of platelets in vivo. Studies have shown that obesity induced by a high-fat diet (HFD) (in which the medulla fat increases) affects hematopoietic function by regulating hematopoietic stem cells and progenitor cells, lymphocyte generation and bone marrow generation [ 18 ] . In addition, coculture of adipocytes and BM-HPCs in vitro could support MK maturation by promoting polyploidy, expanding the boundary membrane system and promoting the formation of proplatelets [ 9 ] . Previous studies have shown that MKs can directly absorb fatty acids transferred from adipocytes through the fatty acid translocator CD36 [ 9 , 19 ] . Our results also revealed that TG-HLP, FA and PA promoted the differentiation of hiPSCs into hematopoietic MKs in vitro. These results support the hypothesis that fatty acid composition promotes MK maturation and proplatelet generation. In our study, fatty acids promoted megakaryocytic biased differentiation from HPCs by activating the PPAR pathway on days 8–14. PPARa is functionally coupled to p38 and AKT activation. There is a sequential relay of PPARa, p38, ROS production, and AKT during platelet activation. Fatty acids upregulate PPARa expression in Meg-01 cells through ROS and subsequent NF-kB signaling [ 13 ] . These findings suggest that the PPAR pathway is important in the differentiation of platelets from hiPSCs in vitro. In summary, our study revealed that a high fat content can influence the level of platelets in vivo and that plasma, especially hyperlipidemic plasma, could promote the biased generation of MKs from HPCs in vitro. Fatty acids FA and PA further accentuated this shift toward HPCs, suggesting that fatty acids might affect the biased MK and platelet generation from HPCs through the PPAR pathway. Conclusion Our findings suggest that a high fat content can influence the platelet count in vivo and that the administration of plasma, especially hyperlipidemic plasma, to the spin-EB hiPSC differentiation model has a promoting effect, resulting in biased generation of MKs from HPCs in vitro. The aforementioned effects were found to be achieved through the PPAR signaling pathway. Abbreviations FA: Fatty acid HP: Human plasma HFD: High-fat diet hiPSC: Human induced pluripotent stem cell HLP: Hyperlipidemic plasma HPL: Hyperlipidemia HSPCs: Hematopoietic stem progenitor cells Hyper-TC: Hypercholesterolemia Hyper-TG: Hypertriglyceridemia MEPs: Megakaryocytic-erythroid progenitors MK: Megakaryocyte MKPs: Megakaryocyte progenitors PA: Palmitic acid PLT: Platelet RBC: Red blood cell SSO : Sulfosuccinimidyl oleate sodium TG-HLP: TG-human hyperlipidemic plasma WBC: White blood cell Declarations Ethics approval and consent to participate The ethics committee of the Children’s Medical Center affiliated with Shanghai Jiao Tong University approved the induction experiment for iPS cells (Approval ID: SCMCIRBK2014050; Approval Date: December, 2014) and the ethics committee of the Naval Medical University approved the induction experiment for human data (Approval Project: The National Natural Science Foundation of China, No. 81570185; Approval Date: March, 2022). Plasma donation was performed after written informed consent was obtained from the donors. Consent for publication Not applicable. Availability of data and materials The RNA-seq data has been deposited in the National Omics Data Encyclopedia database (https://www.biosino.org/node/), with accession code OED824195, OED824196 and OED824197. All other experimental protocols and data obtained in this study are available from the corresponding authors on reasonable request. All data supporting the conclusions of this study are included in the article and supplementary data. Competing interests The authors declare that they have no competing interests. Funding This work was supported in part by the National Natural Science Foundation of China (No. 81570185 to B.Q., No. 32271007 and No. 81972341 to Y.L., No. 81970165 and No. 81400152 to H.G., No. 82200257 to W.H.); the Shanghai Municipal Commission of Science and Technology (201409002700) to Y. L.; and the Shanghai Natural Science Foundation (23ZR1441000) to Y. L.; and the Technology Innovation Leading Program of Shaanxi (No. 2019CGHJ-09) to B.Q. Author contributions H.W.H.: Investigation, methodology, formal analysis, data curation and writing - Original draft; G.H.H. and W.R.R.: Validation, data curation and writing - Review & Editing; L.J.Q., Z.Y.W., L.S.S., Z.Y. and Y.Y.: Investigation, resources; G.H.H., L.Y.X. and Q.B.H.: Writing - Review & Editing, conceptualization, supervision, project administration and funding acquisition. Acknowledgments The authors declare that they have not use AI-generated work in this manuscript. 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Critical role of peroxisome proliferator-activated receptor α in promoting platelet hyperreactivity and thrombosis under hyperlipidemia [J]. Haematologica. 2022;107(6):1358–73. XAVIER-FERRUCIO J, SCANLON V, LI X, et al. Low iron promotes megakaryocytic commitment of megakaryocytic-erythroid progenitors in humans and mice [J]. Blood. 2019;134(18):1547–57. ESTEVEZ B, BORST S. RUNX-1 haploinsufficiency causes a marked deficiency of megakaryocyte-biased hematopoietic progenitor cells [J]. Blood. 2021;137(19):2662–75. WANG H, HE J, XU C et al. Decoding Hum Megakaryocyte Dev [J] Cell Stem Cell, 2021, 28(3): 535 – 49.e8. RAO T N, HANSEN N, STETKA J, et al. JAK2-V617F and interferon-α induce megakaryocyte-biased stem cells characterized by decreased long-term functionality [J]. Blood. 2021;137(16):2139–51. CUMINETTI V. ARRANZ L. Bone Marrow Adipocytes: The Enigmatic Components of the Hematopoietic Stem Cell Niche [J]. J Clin Med, 2019, 8(5). IMAMURA N, OTA H, ABE K, et al. Expression of the thrombospondin receptor (CD36) on the cell surface in megakaryoblastic and promegakaryocytic leukemias: increment of the receptor by megakaryocyte differentiation in vitro [J]. AM J HEMATOL/5. 1994;45(2):181–4. [2016 Chinese guideline for the management of dyslipidemia in adults] [J]. Zhonghua Xin Xue Guan Bing Za Zhi, 2016, 44(10): 833 – 53. [Chinese guidelines for lipid management. (2023)] [J]. Zhonghua Xin Xue Guan Bing Za Zhi, 2023, 51(3): 221 – 55. Tables Tables 1 and 2 are available in the Supplementary Files section. Supplementary Files SupplementalFigures.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6359690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452870314,"identity":"a230a880-4c65-4984-9283-421ce224cdaf","order_by":0,"name":"Weihua Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Huang","suffix":""},{"id":452870315,"identity":"22a9c310-0415-460c-aa21-9274de5117e5","order_by":1,"name":"Ruoru Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruoru","middleName":"","lastName":"Wang","suffix":""},{"id":452870316,"identity":"cd0e508e-36d7-4f40-a4a4-95e8518dcf17","order_by":2,"name":"Yan Zang","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zang","suffix":""},{"id":452870317,"identity":"37ca1130-0f0e-4b3b-aef1-754817fdd188","order_by":3,"name":"Yingwen Zhang","email":"","orcid":"","institution":"Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine Department of Hematology and Oncology","correspondingAuthor":false,"prefix":"","firstName":"Yingwen","middleName":"","lastName":"Zhang","suffix":""},{"id":452870318,"identity":"9705c1c0-f9e8-42cd-8088-85a0315407a3","order_by":4,"name":"Shanshan Li","email":"","orcid":"","institution":"Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine Department of Hematology and Oncology","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Li","suffix":""},{"id":452870319,"identity":"c13f727d-433b-432a-96c7-8707083b5132","order_by":5,"name":"Yan Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":452870320,"identity":"93747564-cf66-4116-b29d-d602acc15f52","order_by":6,"name":"Yue Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yang","suffix":""},{"id":452870321,"identity":"2ff2b020-0626-44e5-a79d-4f8ac01469a8","order_by":7,"name":"Zhanshan Cha","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhanshan","middleName":"","lastName":"Cha","suffix":""},{"id":452870322,"identity":"f6276254-2423-4618-9115-3f53857c27dd","order_by":8,"name":"Baohua Qian","email":"","orcid":"","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Qian","suffix":""},{"id":452870323,"identity":"e4770be3-7a57-48fb-b385-910beffadf1e","order_by":9,"name":"Yanxin Li","email":"","orcid":"","institution":"Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine Institute for Pediatric Translational Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanxin","middleName":"","lastName":"Li","suffix":""},{"id":452870324,"identity":"c6fd13cd-41d8-4fe1-a889-5e0841fc8459","order_by":10,"name":"Haihui Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACAwbGNhDJwA8VYGwgWotkA/FaGNggjAPEajFnP9z2mKegNnHz+dOpm3kYbGQ3HGB+9gCfFsuexHZjHoPjidtu5G67zcOQZrzhAJu5AV6HHUhsk+YxOAbUwgvScjhxwwEeNgm8Ws4/hGjZ3H8WpOU/EVpugG2pSdzAAHbYAWK0PGyTnGNwwHgG0C835xgkG888zGZGwGHpzyTe/KmT7Qc67MabCjvZvuPNz/BqgYLDMBOAmJkI9UBQR5yyUTAKRsEoGJkAACxDTtSdxyHcAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-0026-6957","institution":"The First Affiliated Hospital of Naval Medical University: Changhai Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haihui","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2025-04-02 09:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6359690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6359690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82462921,"identity":"82033502-5e7c-4e6d-bb5e-281aa47eb9c5","added_by":"auto","created_at":"2025-05-11 15:07:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePlatelets were correlated with hyperlipidemia in vivo\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Platelet counts in the hypertriglyceridemia (hyper-TG), hypercholesterolemia (hyper-TC) and mixed hyperlipidemia (HPL) populations were compared with those in the normal population (NS). (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)\u003c/p\u003e\n\u003cp\u003e(B) RBC counts in the hyper-TG, hyper-TC and HPL populations compared with those in the NS population. (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e(C) WBC counts in the hyper-TG, hyper-TC and HPL populations compared with those in the NS population. (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/527bf69183a67c28eaafca8c.png"},{"id":82461886,"identity":"69243c40-d9f8-437b-b864-8a9c72a39c52","added_by":"auto","created_at":"2025-05-11 14:51:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":845742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHuman plasma promotes CD34\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e hematopoietic cell differentiation into MKs \u003c/em\u003ein vitro\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Flow chart of the EB-based method for generating MKs from hiPSCs.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e cell proportions in the control group and different\u0026nbsp;HP-treated groups (5%, 10%, 15%, 20%, 25%, 30% and 35%) on days 3--8.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e cell proportions in the control group and different\u0026nbsp;concentrations of HP (5%, 10%, 15%, 20%, 25%, 30% and 35%) on days 3--8.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Relative CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003e cell counts in the HP-treated (5%, 10%, 15%, 20%, 25%, 30% and 35%) and untreated groups on day 14. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; Relative CD41\u003csup\u003e+\u003c/sup\u003e, CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e-\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts in the HP-treated (5%, 10%, 15%, 20%, 25%, 30% and 35%) and untreated groups on day 14. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp;\u0026nbsp; Total cell counts\u0026nbsp;on day 19 in the untreated, 20% HP (days 0--4)-treated, 20% HP (days 0--8)-treated, 20% HP (days 0--14)-treated, 20% HP (days 0--19)-treated, 20% HP (days 8--14)-treated, 20% HP (days 8--19)-treated and 20% HP (days 14--19)-treated groups. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e(G)\u0026nbsp; Relative CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e-\u003c/sup\u003e cell counts\u0026nbsp;on day 19 in the untreated, 20% HP (day 0--4)-treated, 20% HP (day 0--8)-treated, 20% HP (day 0--14)-treated, 20% HP (day 0--19)-treated, 20% HP (day 8--14)-treated, 20% HP (day 8--19)-treated and 20% HP (day 14--19)-treated groups. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/c95f742ff1817c1516a4fadb.png"},{"id":82462923,"identity":"a4eafa0c-5392-412e-8847-a575cc64ee52","added_by":"auto","created_at":"2025-05-11 15:07:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":621214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHuman TG-hyperlipidemic plasma (TG-HLP) promotes hiPSC-derived MK and proplatelet formation \u003c/em\u003ein vitro\u003cem\u003e, and fatty acids constitutethe active component.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Relative total, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003e cell counts on day 14 in the control, HP-treated and HLP-treated groups. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Relative total, CD41\u003csup\u003e+\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts on day 19 in the control, HP-treated and HLP-treated groups. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; Correlation analysis of platelet count and TG level in vivo.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; TG levels in blood lipids detected in HPs and HLP.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; Total cell counts in the HP (days 8--14)-treated and HLP (days 8--14)-treated groups on day 19. (*\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp;\u0026nbsp; Relative CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts in the HP (days 8--14)-treated and HLP (days 8--14)-treated groups on day 19. (*\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e(G)\u0026nbsp; Representative results of DNA ploidy analysis of hiPSC-MKs in the HP (days 8--14)-treated and HLP (days 8--14)-treated groups on day 19. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e(H)\u0026nbsp; Relative CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003e cell counts in the PA-treated, FA-treated, SSO-treated and control groups on day 14. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e(I)\u0026nbsp;\u0026nbsp;\u0026nbsp; Relative total, CD41\u003csup\u003e+\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts in the PA-treated, FA-treated, SSO-treated and control groups on day 19. (***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/3710aca47816b9132d977218.png"},{"id":82462703,"identity":"0ca2a66f-cb83-48be-a308-8b8df308fe18","added_by":"auto","created_at":"2025-05-11 14:59:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":612524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHP and HLP promoted bias differentiation toward MK\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot diagram of the DEGs in the control and HLP-treated groups.\u003c/p\u003e\n\u003cp\u003e(B) Scatter plot diagram of the DEGs in the control and HP-treated groups.\u003c/p\u003e\n\u003cp\u003e(C) Venn diagram showing the number of shared and differentially upregulated genes in the HP vs control group and HLP vs control group (FDR \u0026lt; 0.05 and fold change \u0026gt; 2).\u003c/p\u003e\n\u003cp\u003e(D) KEGG analysis of differentially expressed molecular pathways from the 217 shared upregulated genes from the HP vs control and HLP vs control groups on day 14.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap of HSC/HPC/MK lineage regulator expression in the control, HP-treated and HLP-treated groups on day 14(FDR\u0026lt;0.05 and fold change\u0026gt;2).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/ffc1b72600a3f9dd4c20746e.png"},{"id":82461888,"identity":"bfd9cdbb-0fd5-4e18-95b0-ad9e1722b22a","added_by":"auto","created_at":"2025-05-11 14:51:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2215229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFatty acidspromoted MK differentiation via the PPAR signalingpathway\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Scatter plot diagram of the DEGs in the HP-treated and HLP-treated groups.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Heatmap of regulator expression in the HP-treated and HLP-treated groups on day 14.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; KEGG analysis of differentially expressed molecular pathways from the gene sets.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Relative mRNA expression of genes involved in the PPAR signaling pathway in the control, HP-treated and HLP-treated groups on day 14.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; The expression levels of p-AKT, AKT, p-ERK1/2 and ERK1/2 in CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+ \u003c/sup\u003ecells\u003csup\u003e \u003c/sup\u003ewere determined by Western blotting in the control, HP-treated and HLP-treated groups on days 8 and 14.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/c5abfb9a9f589ec6a28fbc31.png"},{"id":89615720,"identity":"0112d1bc-5269-4f1b-b153-30f2df0431b0","added_by":"auto","created_at":"2025-08-22 02:46:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5461800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/73854e53-4531-4be9-8b51-d765f438fa6d.pdf"},{"id":82461881,"identity":"8bfd71de-ef2d-4f22-8e7e-1b6816787c06","added_by":"auto","created_at":"2025-05-11 14:51:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1436995,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/792fac0b5c365c99869c5206.docx"},{"id":82462699,"identity":"ad85926c-a973-47d1-b815-8c4feac533d5","added_by":"auto","created_at":"2025-05-11 14:59:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19548,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6359690/v1/8e5d6c9bc147411ddf57147a.docx"}],"financialInterests":"","formattedTitle":"Fatty acids promote megakaryocyte biased differentiation","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. Increased platelet count was correlated with hyperlipidemia in vivo.\u003c/p\u003e\u003cp\u003e2. Human plasma (HP) mainly promotes MK biased differentiation from HPCs at days 8\u0026ndash;14 during platelet generation from iPSCs in vitro.\u003c/p\u003e\u003cp\u003e3. Fatty acid (FA) is the active component that promotes hiPSC-derived MK and proplatelet formation.\u003c/p\u003e\u003cp\u003e4. FA activated MK and platelet generation though the PPAR signaling pathway.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eClinically, platelet (PLT) transfusion is the main measure for the prevention and treatment of thrombocytopenia\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, the short room temperature storage time and total dependence of the supply on blood donors lead to an imbalance between the supply and demand of clinical PLT transfusions at present \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Moreover, the technology for the in vitro generation of large-scale PLTs has been greatly limited by the lack of clarity regarding the mechanism of platelet differentiation.\u003c/p\u003e \u003cp\u003eThe BM consists of hematopoietic islands and adipocytes surrounded by vascular sinuses \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that BM adipocytes can affect normal BM hematopoietic processes by regulating the generation of hematopoietic stem progenitor cells (HSPCs)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e and support the differentiation of CD34\u003csup\u003e+\u003c/sup\u003e HSPCs into myeloid and lymphoid immune cells\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Moreover, myelogenesis is positively correlated with increased lipogenesis and decreased osteoblast generation \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In diet-induced obese mice, enhancement of hematopoietic and lymphopoietic BM cell populations was associated with increased BM adiposity \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Lipolysis in the BM plays a crucial role in exercise-stimulated osteogenesis and lymphogenesis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Considering these key influences, some studies have focused on whether BM obesity with increased fat cells affects BM megakaryogenesis \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Coculture of adipocytes and bone marrow hematopoietic progenitor cells in vitro revealed that a decrease in adipocytes directly supported MK maturation by promoting polyploidy, expanding the boundary membrane system and promoting the formation of proplatelets \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The effect of a high-fat environment on platelet generation in vitro remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the clinical data of PLT counts and plasma lipid levels and detected the effects of plasma, hyperlipidemia and fatty acids on HSCs, HPCs, MKPs and platelets via an in vitro human induced stem cell (hiPSC)-derived platelet platform. We found that lipids in plasma promoted the differentiation of HPCs into megakaryocyte-biased cells and thus promoted the generation of platelets in vitro via the PPAR pathway, providing new evidence for the theory of platelet differentiation bias and new culture conditions for platelet generation in vitro.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patient information and clinical data were obtained from the First Affiliated Hospital of Second Military Medical University from September 2010 to May 2023; 400 healthy people or individually diagnosed hyperlipidemia patients were selected from the health examination center. The median age (interquartile range) was 58 (17–75) years, among whom 282 (70.5%) were male. \u003cstrong\u003eThe\u0026nbsp;detailed\u0026nbsp;clinical\u0026nbsp;information\u0026nbsp;is listed\u0026nbsp;in\u0026nbsp;Table\u0026nbsp;1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe study inclusion and exclusion criteria are presented in Table 2.\u003c/strong\u003e Importantly, participants must not have used antibiotics or thrombolytic, anticoagulant or platelet inhibitory drugs before blood collection. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eAll the participants were given routine blood tests on the day of admission or the next day. The basic data of the patients, such as age, sex, history (hypertension, diabetes), and laboratory test results (PLT count, TG, TC, etc.), were collected for statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood production, cell culture and manipulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBlood production\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman plasma (HP)\u0026nbsp;and hyperlipidemic plasma (HLP) are obtained from healthy blood donors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ehiPSC Culture\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehiPSCs are obtained via the reprogramming of MEFs. A total of 5-10×10\u003csup\u003e4\u003c/sup\u003e hiPSCs were cultured in Matrigel (BD Biosciences)-pretreated six-well plates with PSCeasy\u003csup\u003e®\u003c/sup\u003e II medium (Cellapy). After reaching 70%~80% confluency, the cells were digested with 0.05 mM EDTA (Invitrogen), passaged at a 1:6 split ratio, and then\u0026nbsp;incubated at 37°C in 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferentiation of hiPSCs into MKs and proplatelets\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed hematopoietic differentiation of hiPSCs via the EB-based protocol as previously described\u003csup\u003e[10]\u003c/sup\u003e. First, hiPSC colonies were digested with TrypLE™ Express\u0026nbsp;(Gibco) into single cells and resuspended in STEMdiff™ APEL™2 medium (APEL, STEMCELL Technologies) supplemented with ROCK inhibitor (Y27632, 10 μM, STEMCELL Technologies), BMP4 (10 ng/ml, R\u0026amp;D Systems) and bFGF (10 ng/ml, Peprotech). The cells (3500 per well) were seeded into untreated round-bottom 96-well plates (Costar 3788), centrifuged at 300 × g at room temperature for 5 min and cultured at 37°C with 5% CO\u003csub\u003e2\u003c/sub\u003e. From day 2 to day 14, the cells were cultured in APEL containing BMP4 (10 ng/ml), bFGF (10 ng/ml), VEGF (10 ng/ml, PeproTech) and SCF (50 ng/ml, PeproTech).\u0026nbsp;TPO (20 ng/ml, PeproTech) was added to the medium on day 11. On day 14, the suspended cells secreted by the EBs were collected and then filtered through 100-μm cell filters (BD Biosciences) to remove the EBs. The cells were transferred into 6-well plates for culture. The culture medium was APEL (2 ml per well) containing SCF (20 ng/ml), IL-11 (10 ng/ml, PeproTech) and TPO (50 ng/ml), and the mixture was cultured for 5 days to collect megakaryocytes and proplatelets. The cells were cultured with or without HP or HLP at different time points. Fatty acids (FA; product F7050, Sigma Aldrich), palmitic acid (PA; product P0500, Sigma Aldrich) and sulfosuccinimidyl oleate sodium (SSO; product ab145039, Abcam) were tested at 0.25 ml/L, 10 μM and 200 μM, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFlow Cytometry Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cells were collected, washed once with PBS, and stained with antibodies or isotype controls for 30 minutes at 4°C in flow cytometry buffer. The samples were analyzed on a FACS Calibur (BD Canto Plus), and the data were analyzed via FlowJo v10 (FlowJo, LLC). The cells were labeled with the following antibodies: CD34-PE (4H11, IgG1 κ-PE), CD45-APC-Cyanine 7 (2D1, IgG1 κ-APC-Cyanine7) and CD41-APC (MEM-06, IgG1 κ-APC) from Invitrogen; and CD42a-eFluor\u003csup\u003e®\u0026nbsp;\u003c/sup\u003e450 (GR-P, IgG1 κ-eFluor\u003csup\u003e®\u0026nbsp;\u003c/sup\u003e450), CD42b-PE-Cyanine7 (HIP1, IgG1 κ-PE-Cyanine7) and CD62P (P-Selectin)-PE (Psel.KO2.3, IgG1 κ-PE) from eBioscience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMorphological analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cells collected on days 14 and 19 were cytospun onto glass slides via a Cytospin 4 centrifuge (Thermo Scientific), stained with Wright‒Giemsa (product G1020, Solarbio Life Sciences), and observed with a Leica inverted contrast microscope fitted with a camera (DFC420, Leica Camera).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLaser confocal microscopy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn day 19 of culture, the cells were analyzed via fluorescence microscopy. The cells were spun onto glass slides, subsequently fixed with 4% paraformaldehyde and permeabilized with 0.3% Triton X-100. The glass slides were then stained with an anti-vWF antibody (Proteintech), an anti-CD42b antibody (eBioscience) and 4′6-diamidino-2-phenylindole (DAPI). Images were captured via a Leica TCS 166 SP8 laser confocal microscope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA ploidy analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA ploidy analysis was performed by FACS on all samples as previously described \u003csup\u003e[10]\u003c/sup\u003e.\u0026nbsp;Megakaryocyte ploidy was analyzed on day 19 of culture. The cells were labeled with an APC-conjugated anti-CD41 antibody, incubated for 20 minutes on ice, fixed with precooled 70% ethanol for 2 h at room temperature and then washed with PBS. Next, the cells were treated with 20 mg/ml propidium iodide (PI; Sigma‒Aldrich) and 100 mg/ml RNase A (Thermo Fisher Scientific) for 30 min in the dark at 37°C. The cellular DNA content was subsequently analyzed via a\u0026nbsp;FACS Calibur (BD Canto Plus).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProtein extracts\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cells were harvested and suspended in whole-cell lysis buffer (50 mM KCl, 1% NP-40, 25 mM HEPES (pH 7.8), 10 μg/ml leupeptin, 20 μg/ml aprotinin, 125 μM DTT, 1 mM PMSF, and 1 mM Na\u003csub\u003e3\u003c/sub\u003eVO\u003csub\u003e4\u003c/sub\u003e). The remaining debris was removed by centrifugation at 12,000 \u003cem\u003eg\u003c/em\u003e at 4 °C for 5 min. Finally, the supernatant was collected, and the protein concentration was determined with a BCA kit according to the manufacturer’s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWestern blot\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cell protein extracts were subjected to western blotting as previously described \u003csup\u003e[10]\u003c/sup\u003e. Briefly, protein samples were separated on an 8% SDS‒PAGE gel and transferred to a nitrocellulose membrane. After being blocked in 5% nonfat milk buffer containing 0.05% Tween-20, the membrane was incubated with the primary antibody. The antibodies used were purchased from Cell Signaling Technology (AKT antibody: #4685; pAKT antibody: #9267; p44/42 MAPK (Erk1/2) antibody: #9102; and GAPDH antibody: #5174) and Abcam (Erk1/2 antibody: # ab184699). GAPDH was used as a protein loading control. The signal was visualized on a film after exposure to chemiluminescence and analyzed for optical density at each band via an image-processing and analysis system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal RNA extraction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from the tissues via TRIzol (Invitrogen) according to the manufacturer’s instructions. Approximately 1×106 cells were collected in a 2 mL tube, and 1 ml of TRIzol was added directly to the cell pellet (approximately 1×10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003eper 1 ml). The mixture was subsequently homogenized for 2 minutes and allowed to rest horizontally for 5 minutes. The mixture was subsequently centrifuged for 5 minutes at 12,000×g at 4°C, after which the supernatant was transferred into a new 2.0 EP tube with 0.2 mL of chloroform/isoamyl alcohol (24:1). The mixture was shaken vigorously for 15 s and then centrifuged at 12,000×g for 10 minutes at 4°C. After centrifugation, the upper aqueous phase containing the remaining RNA was transferred into a new tube with an equal volume of isopropyl alcohol and then centrifuged at 13,600 rpm for 20 minutes at 4°C. After the supernatant was removed, the RNA pellet was washed twice with 1 mL of 75% ethanol, after which the mixture was centrifuged at 13,600 rpm for 3 minutes at 4°C to collect residual ethanol, after which the pellet was allowed to air dry for 5–10 minutes in a biosafety cabinet. Finally, 25~100 µL of DEPC-treated water was added to dissolve the RNA. The total RNA was subsequently quantified via a NanoDrop system and an Agilent 2100 bioanalyzer (Thermo Fisher Scientific).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSequencing data analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSOAPnuke (V1.5.2) was used to filter the sequencing data. Clean reads were mapped to the reference genome via HISAT2 (V2.0.4). Clean reads were aligned to the reference coding gene set via Bowtie2 (V2.2.5), and gene expression levels were subsequently calculated via RSEM (V1.2.12). A heatmap was drawn with PheATMap (V1.0.8) on the basis of gene expression in different samples. In essence, differential expression analysis was performed via DESeq2 (v1.4.5)\u0026nbsp;with a Q value\u0026nbsp;≤0.05. To gain insight into phenotypic changes, KEGG (https://www.kegg.jp/) enrichment analysis of annotated differentially expressed genes was performed via Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) via a hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the experiments were performed in triplicate. The results were analyzed statistically via Graph Pad Prism software (version 9.5.1). Comparisons between two groups were assessed via the unpaired Student’s \u003cem\u003et\u003c/em\u003e test (2-tailed), whereas multigroup analysis was assessed via one-way analysis of variance (ANOVA) (2-tailed). The data are expressed as the means ± standard deviations (SDs), and the significance level was set at 0.05 (two-sided, *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Differential expression analysis of two conditions/groups (three biological replicates per condition) was performed via the DESeq R package (1.10.1). The resulting \u003cem\u003eP\u003c/em\u003e values were adjusted via Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 according to DESeq and a log2 fold change greater than ±1 were considered differentially expressed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eIncreased platelet count was correlated with hyperlipidemia in vivo\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eHyperlipidemia patients are prone to coagulation-related complications, often involving changes in platelet procoagulant activity\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, but few studies have investigated whether the number of platelets changes. Therefore, we collected clinical data to study the relationship between hyperlipidemia and platelet count. First, we compared the platelet, red blood cell (RBC) and white blood cell (WBC) counts in populations with hypertriglyceridemia (Hyper-TG), hypercholesterolemia (Hyper-TC) and mixed hyperlipidemia (HPL) with those in the normal population. The results revealed that in individuals in all three hyperlipidemic groups, the platelet count was significantly greater than that in the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and was the same as the WBC count (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). With the exception of the TG group, the RBC count of the other two groups was significantly greater than that of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). More parameters are shown in Table\u0026nbsp;1. These results indicate that a hyperlipidemic environment can efficiently promote the proliferation of blood cells, including platelets, in vivo.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHuman plasma (HP) promoted CD34\u003csup\u003e+\u003c/sup\u003e hematopoietic cell differentiation into MKs in vitro\u003c/h2\u003e \u003cp\u003eTo study whether hyperlipidemic plasma (HLP) can promote megakaryocyte and platelet differentiation, we first studied the role of HP in megakaryocyte formation in vitro, as shown in the flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. We divided platelet generation from hiPSCs in vitro into three stages, HPC generation (days 0\u0026ndash;8), CD41\u003csup\u003e+\u003c/sup\u003e MKP generation (days 8\u0026ndash;14), and MK maturation and proplatelet generation (days 14\u0026ndash;19), to observe the effects of HP. On days 3\u0026ndash;8, the hematopoietic marker CD34 was expressed in the EB cells and increased with number of days in culture. Compared with that in the HP-untreated group, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e\u0026minus;\u003c/sup\u003e HSC growth was not greater in any of the HP-treated medium groups; thus, the number of CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e HPCs increased significantly in the HP-treated medium groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C, Supplemental Fig.\u0026nbsp;1A). These findings indicate that HP favors the differentiation of HPCs from HSCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn day 14, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e HPC and CD41\u003csup\u003e+\u003c/sup\u003e MKP generation first peaked in the 20% and 25% HP-treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively), although the number of CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e HSCs appeared to be no greater than that in the untreated control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). On day 19, typical images of cells from the HP-untreated and HP-treated groups on days 14 and 19 under a light microscope are shown in Supplemental Fig.\u0026nbsp;1C. CD41\u003csup\u003e+\u003c/sup\u003e CD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e-\u003c/sup\u003e, CD41\u003csup\u003e+\u003c/sup\u003e CD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e MK and CD41\u003csup\u003e+\u003c/sup\u003e MK growth was at the highest level when the concentration of HP was 5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, Supplemental Fig.\u0026nbsp;2A, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that the different stages of platelet generation in hiPSCs require different concentrations of HP.\u003c/p\u003e \u003cp\u003eWe further studied the addition stage at which HP exerts its best stimulation effect on MK generation. The cells were cultured for 0\u0026ndash;14 days in APEL medium supplemented with 20% HP for 14\u0026ndash;19 days supplemented with 5% HP and were divided into seven various treatment groups (days 0\u0026ndash;4, 0\u0026ndash;8, 0\u0026ndash;14, 0\u0026ndash;19, 8\u0026ndash;14, 8\u0026ndash;19, and 14\u0026ndash;19 HP). The results revealed that the counts of total cells and CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003eCD42b\u003csup\u003e+\u003c/sup\u003e cells were significantly greater in the 8\u0026ndash;14 HP addition group than in the untreated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G; *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although the percentages of CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003eCD42b\u003csup\u003e+\u003c/sup\u003e MKs in the HP addition group were greater than those in the control group (Supplemental Fig.\u0026nbsp;1B; ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results suggested that HP functions mainly at the stage from HPC to MKP generation on days 8\u0026ndash;14 to promote HPC and MK differentiation from hiPSCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTG-human hyperlipidemic plasma (TG-HLP) promoted hiPSC-derived MK and proplatelet formation in vitro\u003c/h2\u003e \u003cp\u003eThe clinical data revealed a positive correlation between the PLT and TG level in vivo (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014), and the hyperlipidemic plasma we used was collected from volunteers with increased TG (TG-HLP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We added 20% TG-HLP to APEL differentiation medium on days 8\u0026ndash;14 to study the effects of TG-HLP on hematopoietic cell, MK and proplatelet formation. We used flow cytometry, Wright‒Giemsa staining, and immunofluorescence microscopy images to identify the types of cells at days 14 and 19 (Supplemental Fig.\u0026nbsp;2A and 3A‒3B). However, the percentage of CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003eCD42b\u003csup\u003e+\u003c/sup\u003e cells in the different groups was not different on day 19 (Supplemental Fig.\u0026nbsp;2B). The HLP addition increased not only the cell counts of CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e-\u003c/sup\u003e HSCs, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e HPCs and CD41\u003csup\u003e+\u003c/sup\u003e MKPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003e**P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively) on day 14 but also the total, CD 41\u003csup\u003e+\u003c/sup\u003e and CD41\u003csup\u003e+\u003c/sup\u003e CD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts on day 19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The numbers of total cells and CD41\u003csup\u003e+\u003c/sup\u003eCD42a\u003csup\u003e+\u003c/sup\u003eCD42b\u003csup\u003e+\u003c/sup\u003e cells produced from the TG-HLP-treated group were also significantly greater than those produced from the HP-treated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Compared with those from the HP-treated group, the CD41\u003csup\u003e+\u003c/sup\u003e MKs from the TG-HLP-treated group presented greater DNA ploidy (4 N and \u0026ge;\u0026thinsp;8 N) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). The above findings have shown that TG-HLP may promote the bias of HPC differentiation into MKs and platelets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further verify that fatty acids play an important role in promoting proplatelet generation, we chose FA (0.25 ml/L) and PA (10 \u0026micro;M) instead of hyperlipidemic blood components and the fatty acid transport inhibitor SSO (200 \u0026micro;M), which inhibits fatty acid transfer via the fatty acid transposase CD36\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e],\u003c/sup\u003e for treatment on days 8\u0026ndash;14 from hiPSCs to platelet generation. On day 14, the total number of cells, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e cells and CD41\u003csup\u003e+\u003c/sup\u003e cells produced by the PA-treated group or FA-treated group was greater than that produced by the control, and the total number of CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e cells, CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e+\u003c/sup\u003e cells and CD41\u003csup\u003e+\u003c/sup\u003e cells produced by the SSO-treated group was significantly lower than that produced by the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). The number of CD34\u003csup\u003e+\u003c/sup\u003eCD45\u003csup\u003e\u0026minus;\u003c/sup\u003e cells produced by the FA-treated group was also greater than that produced by the untreated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On day 19, the CD41\u003csup\u003e+\u003c/sup\u003e cell and CD41\u003csup\u003e+\u003c/sup\u003e CD42a\u003csup\u003e+\u003c/sup\u003e CD42b\u003csup\u003e+\u003c/sup\u003e cell counts were greater in the FA-treated group and PA-treated group than in the untreated group and were significantly lower in the SSO-treated group than in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). These findings indicate that fatty acids are the active component that promotes hiPSC-derived MK and proplatelet formation.\u003c/p\u003e \u003cp\u003eWe then added 250 \u0026micro;M ADP to the cells collected on day 19 from the TG-HLP-supplemented conditions and evaluated the CD62P expression levels in the resting and preactivated platelets via flow cytometric analysis. An increase in the expression of CD62P was observed (Supplemental Fig.\u0026nbsp;3C-D; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, platelets can be visualized under an optical microscope (20\u0026times;; Supplemental Fig.\u0026nbsp;3E). The individual cells tended to aggregate into clumps in the plate at days 14\u0026ndash;19 (Supplemental Fig.\u0026nbsp;3F; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which also indicates the presence of platelets with aggregation ability. This result revealed that the platelets could be activated after 19 days of culture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHP and HLP promoted bias differentiation toward MK\u003c/h2\u003e \u003cp\u003eTo confirm that the TG-HLP-treated group promoted the generation of biased MKs from HPCs, RNA sequencing analysis was performed on 9 cell samples (control: n\u0026thinsp;=\u0026thinsp;3; HP: n\u0026thinsp;=\u0026thinsp;3; TG-HLP: n\u0026thinsp;=\u0026thinsp;3) collected on day 14, resulting in 1422 differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). The PCA score plots revealed that the gene expression in the HP and HLP addition groups was significantly different from that in the control group (Supplemental Fig.\u0026nbsp;4A-B). A total of 217 common DEGs were found in the HP and HLP addition groups compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The KEGG analysis of 217 DEGs indicated that hematopoietic cell lineage pathways were predominant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). We subsequently screened 14 marker genes of HSC, HPC and MK overlapping DEGs, including 8 marker genes of HSCs (CXCR4, CD38, PROM1, GFl1, TEK, NGFR, CD44, and ACE), 2 marker genes of HPCs (CD34 and KDR) and 4 marker genes of MKs (SRGN, GP5, PF4, and CD9). The heatmap of the 14 DEGs revealed that genes related to MKs were significantly upregulated in the HP and HLP addition groups compared with the control group, and genes related to HPCs were significantly downregulated in the HP and HLP addition groups compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). These results indicated that the addition of HP or HLP promoted the biased differentiation of MK from HPCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFatty acids promoted MK differentiation via the PPAR signaling pathway\u003c/h2\u003e \u003cp\u003eTo confirm that the signaling pathway involved in the TG-HLP-treated group promoted the generation of biased MKs from HPCs, we analyzed the DEGs between the HLP and HP addition groups. We identified 201 DEGs between the HLP and HLP addition groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). The KEGG analysis of the differentially expressed molecular pathways revealed that the PPAR pathways were predominant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The relative mRNA expression levels of the PPAR pathway genes PLNA2, CPT1A, and ANGPTL4 in the HLP-treated groups were significantly greater than those in the control and HP-treated groups on day 14, as determined by real-time PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The PPAR signaling pathway eventually activates the AKT/ERK protein \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Next, we detected the protein expression of pAKT and pERK1/2 via western blot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). We found that pAKT and pERK1/2 expression increased on day 14 but not on day 8 in the HLP-treated group compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, full-length blots are presented in Supplementary Fig.\u0026nbsp;5A). Our results suggested that fatty acids promoted MK differentiation via the PPAR signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe proved that fatty acids in plasma can promote MK-biased MKP generation, while MK maturation and proplatelet generation are no longer affected by blood lipid levels. This finding further confirms the theory of platelet bias generation and provides a guiding direction for optimizing the platform for differentiating platelets in vitro.\u003c/p\u003e \u003cp\u003eOur results revealed that fatty acids play an important role in the differentiation of MKPs from HPCs at 8\u0026ndash;14 days in an in vitro model and can promote the biased differentiation of MKs. In recent years, many studies have investigated the biased differentiation of MKs. Low iron biases the commitment of megakaryocytic (Mk)-erythroid progenitors (MEPs) toward the MK lineage in both humans and mice \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. All the RUNX-1+/- lines presented decreased iPSC-derived MK yields and depletion of an MK-biased iPSC-derived HPC subpopulation \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. THBS1 is an early marker for MK-biased embryonic endothelial cells \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. HSCs with elevated expression of CD41 (CD41hi) are biased toward MKs, and treatment with interferon-α can further increase the frequency and percentage of CD41hi HSCs\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Therefore, we identified a new factor that induces biased differentiation of MKs.\u003c/p\u003e \u003cp\u003eOur clinical data analysis revealed that hyper-TG, hyper-TC and mixed-HLP were correlated with increased platelet counts in humans. These findings suggest that a high fat content may promote the formation of platelets in vivo. Studies have shown that obesity induced by a high-fat diet (HFD) (in which the medulla fat increases) affects hematopoietic function by regulating hematopoietic stem cells and progenitor cells, lymphocyte generation and bone marrow generation \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In addition, coculture of adipocytes and BM-HPCs in vitro could support MK maturation by promoting polyploidy, expanding the boundary membrane system and promoting the formation of proplatelets\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that MKs can directly absorb fatty acids transferred from adipocytes through the fatty acid translocator CD36 \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our results also revealed that TG-HLP, FA and PA promoted the differentiation of hiPSCs into hematopoietic MKs in vitro. These results support the hypothesis that fatty acid composition promotes MK maturation and proplatelet generation.\u003c/p\u003e \u003cp\u003eIn our study, fatty acids promoted megakaryocytic biased differentiation from HPCs by activating the PPAR pathway on days 8\u0026ndash;14. PPARa is functionally coupled to p38 and AKT activation. There is a sequential relay of PPARa, p38, ROS production, and AKT during platelet activation. Fatty acids upregulate PPARa expression in Meg-01 cells through ROS and subsequent NF-kB signaling \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that the PPAR pathway is important in the differentiation of platelets from hiPSCs in vitro.\u003c/p\u003e \u003cp\u003eIn summary, our study revealed that a high fat content can influence the level of platelets in vivo and that plasma, especially hyperlipidemic plasma, could promote the biased generation of MKs from HPCs in vitro. Fatty acids FA and PA further accentuated this shift toward HPCs, suggesting that fatty acids might affect the biased MK and platelet generation from HPCs through the PPAR pathway.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that a high fat content can influence the platelet count in vivo and that the administration of plasma, especially hyperlipidemic plasma, to the spin-EB hiPSC differentiation model has a promoting effect, resulting in biased generation of MKs from HPCs in vitro. The aforementioned effects were found to be achieved through the PPAR signaling pathway.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFA: Fatty acid\u003c/p\u003e\n\u003cp\u003eHP: Human plasma\u003c/p\u003e\n\u003cp\u003eHFD: High-fat diet\u003c/p\u003e\n\u003cp\u003ehiPSC: Human induced pluripotent stem cell\u003c/p\u003e\n\u003cp\u003eHLP: Hyperlipidemic plasma\u003c/p\u003e\n\u003cp\u003eHPL: Hyperlipidemia\u003c/p\u003e\n\u003cp\u003eHSPCs: Hematopoietic stem progenitor cells\u003c/p\u003e\n\u003cp\u003eHyper-TC: Hypercholesterolemia\u003c/p\u003e\n\u003cp\u003eHyper-TG: Hypertriglyceridemia\u003c/p\u003e\n\u003cp\u003eMEPs: Megakaryocytic-erythroid progenitors\u003c/p\u003e\n\u003cp\u003eMK:\u0026nbsp; \u0026nbsp; \u0026nbsp; Megakaryocyte\u003c/p\u003e\n\u003cp\u003eMKPs: Megakaryocyte progenitors\u003c/p\u003e\n\u003cp\u003ePA: Palmitic acid\u003c/p\u003e\n\u003cp\u003ePLT: Platelet\u003c/p\u003e\n\u003cp\u003eRBC: Red blood cell\u003c/p\u003e\n\u003cp\u003eSSO\u0026nbsp; \u0026nbsp; \u0026nbsp;: Sulfosuccinimidyl oleate sodium\u003c/p\u003e\n\u003cp\u003eTG-HLP: TG-human hyperlipidemic plasma\u003c/p\u003e\n\u003cp\u003eWBC: White blood cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee of the Children\u0026rsquo;s Medical Center affiliated with Shanghai Jiao Tong University approved the induction experiment for iPS cells (Approval ID: SCMCIRBK2014050; Approval Date: December, 2014) and the ethics committee of the Naval Medical University approved the induction experiment for human data (Approval Project:\u0026nbsp;The National Natural Science Foundation of China, No. 81570185; Approval Date: March, 2022). Plasma donation was performed after written informed consent was obtained from the donors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq data has been deposited in the National Omics Data Encyclopedia database (https://www.biosino.org/node/), with accession code OED824195, OED824196 and OED824197. \u0026nbsp;All other experimental protocols and data obtained in this study are available from the corresponding authors on reasonable request. All data supporting the conclusions of this study are included in the article and supplementary data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the National Natural Science Foundation of China (No. 81570185 to B.Q., No. 32271007 and No. 81972341 to Y.L., No. 81970165 and No. 81400152 to H.G., No. 82200257 to W.H.); the Shanghai Municipal Commission of Science and Technology (201409002700) to Y. L.; and the Shanghai Natural Science Foundation (23ZR1441000) to Y. L.; and the Technology Innovation Leading Program of Shaanxi (No. 2019CGHJ-09) to B.Q.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.W.H.: Investigation, methodology, formal analysis, data curation and writing - Original draft; G.H.H. and W.R.R.: Validation, data curation and writing - Review \u0026amp; Editing; L.J.Q., Z.Y.W., L.S.S., Z.Y. and Y.Y.: Investigation, resources; G.H.H., L.Y.X. and Q.B.H.: Writing - Review \u0026amp; Editing, conceptualization, supervision, project administration and funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have not use AI-generated work in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor details\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Transfusion Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai 200433, China. \u003csup\u003e2\u003c/sup\u003eKey Laboratory of Pediatric Hematology \u0026amp; Oncology of China Ministry of Health, Department of Hematology \u0026amp; Oncology, Pediatric Translational Medicine Institute, Shanghai Children\u0026rsquo;s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China. \u003csup\u003e3\u003c/sup\u003eDepartment of Neurology, The\u0026nbsp;First\u0026nbsp;Affiliated\u0026nbsp;Hospital\u0026nbsp;of\u0026nbsp;Naval\u0026nbsp;Medical\u0026nbsp;University, Shanghai 200433, China.\u003csup\u003e4\u003c/sup\u003eDepartment of Hematology, The First Affiliated Hospital of Naval Medical University, Shanghai 200433, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWANG B, ZHENG J. Platelet generation in vivo and in vitro [J]. Springerplus. 2016;5(1):787.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG B, WU X. Large-scale generation of megakaryocytes from human embryonic stem cells using transgene-free and stepwise defined suspension culture conditions [J]. Cell Prolif. 2021;54(4):e13002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTRAVLOS GS. Normal structure, function, and histology of the bone marrow [J]. Toxicol Pathol. 2006;34(5):548\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eADLER B J, GREEN D E, PAGNOTTI G M, et al. High fat diet rapidly suppresses B lymphopoiesis by disrupting the supportive capacity of the bone marrow niche [J]. PLoS ONE. 2014;9(3):e90639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDO CARMO L S, ROGERO M M, PAREDES-GAMERO E J, et al. A high-fat diet increases interleukin-3 and granulocyte colony-stimulating factor production by bone marrow cells and triggers bone marrow hyperplasia and neutrophilia in Wistar rats [J]. Exp Biol Med (Maywood). 2013;238(4):375\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCORRE J, PLANAT-BENARD V, CORBERAND J X, et al. Human bone marrow adipocytes support complete myeloid and lymphoid differentiation from human CD34 cells [J]. Br J Haematol. 2004;127(3):344\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAARON N, ROSEN C COSTAS. The Implications of Bone Marrow Adipose Tissue on Inflammaging [J]. Front Endocrinol (Lausanne). 2022;13:853765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHU PENGH. A mechanosensitive lipolytic factor in the bone marrow promotes osteogenesis and lymphopoiesis [J]. Cell Metab. 2022;34(8):1168\u0026ndash;e826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVALET C, BATUT A, VAUCLARD A, et al. Adipocyte Fatty Acid Transfer Supports Megakaryocyte Maturation [J]. Cell Rep. 2020;32(1):107875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG W, GU H, ZHAN Z, et al. The plant hormone abscisic acid stimulates megakaryocyte differentiation from human iPSCs in vitro [J]. Platelets. 2022;33(3):462\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHENG T J, KOHS T C L, MUELLER PA, et al. Effect of antiplatelet agents and tyrosine kinase inhibitors on oxLDL-mediated procoagulant platelet activity [J]. Blood Adv; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKUDA O, PIETKA T A, DEMIANOVA Z, et al. 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AM J HEMATOL/5. 1994;45(2):181\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[2016 Chinese guideline for the management of dyslipidemia in adults] [J]. Zhonghua Xin Xue Guan Bing Za Zhi, 2016, 44(10): 833\u0026thinsp;\u0026ndash;\u0026thinsp;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[Chinese guidelines for lipid management. (2023)] [J]. Zhonghua Xin Xue Guan Bing Za Zhi, 2023, 51(3): 221\u0026thinsp;\u0026ndash;\u0026thinsp;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"platelets, human plasma (HP), megakaryocyte progenitors, fatty acid","lastPublishedDoi":"10.21203/rs.3.rs-6359690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6359690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAt present, the clinical demand for platelet transfusion is increasing, so new ways to promote the production of platelets in vivo and in vitro are urgently needed. However, the mechanism of megakaryocyte (MK) differentiation and platelet formation is still ambiguous and is also a major obstruction to artificial platelet production. In our study, we found that platelet counts were correlated with hyperlipidemia in humans. During human induced pluripotent stem cell (hiPSC) differentiation in vitro, human plasma (HP) promoted the differentiation of CD34\u0026thinsp;+\u0026thinsp;hematopoietic cells into megakaryocyte progenitors (MKPs) at days 8\u0026ndash;14. Fatty acid (FA) is the active component that promotes hiPSC-derived MK and proplatelet formation, and FA activates MK and platelet generation through the PPAR signaling pathway. Our data expand the theory of platelet differentiation and provide a technique for promoting platelet generation in vitro.\u003c/p\u003e","manuscriptTitle":"Fatty acids promote megakaryocyte biased differentiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-11 14:51:47","doi":"10.21203/rs.3.rs-6359690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7397952-ffc7-45fe-9967-c8edd1aeb78f","owner":[],"postedDate":"May 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-22T02:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-11 14:51:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6359690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6359690","identity":"rs-6359690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}