Skip to main content
NCBI home page
Regenerative Therapy logoLink to Regenerative Therapy
. 2020 May 18;14:299–305. doi: 10.1016/j.reth.2020.03.007

Imaging of lysophosphatidylcholine in an induced pluripotent stem cell-derived endothelial cell network

Yasuo Shimizu a,∗, Yusuke Nakamura a, Yasuhiro Horibata b, Mio Fujimaki a, Keitaro Hayashi c, Nobuhiko Uchida a, Hiroko Morita a, Ryo Arai a, Kazuyuki Chibana a, Akihiro Takemasa a, Hiroyuki Sugimoto b
PMCID: PMC7240204  PMID: 32462058

Abstract

Introduction

Vascular endothelial cell disorders are closely related to cardiovascular disease (CVD) and pulmonary diseases. Abnormal lipid metabolism in the endothelium leads to changes in cell signalling, and the expression of genes related to immunity and inflammation. It is therefore important to investigate the pathophysiology of vascular endothelial disorders in terms of lipid metabolism, using a disease model of endothelium.

Methods

Human induced pluripotent stem cell-derived endothelial cells (iECs) were cultured on a matrigel to form an iEC network. Lipids in the iEC network were investigated by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) analysis. Ion fragments obtained by mass spectrometry were analysed using an infusion method, involving precursor ion scanning with fragment ion.

Results

The MALDI TOF IMS analysis revealed co-localized intensity of peaks at m/z 592.1 and 593.1 in the iEC network. Tandem mass spectrometry (MS/MS) analysis by MALDI-imaging, in conjunction with precursor ion scanning using an infusion method with lipid extracts, identified that these precursor ions were lysophosphatidylcholine (LPC) (22:5) and its isotype.

Conclusion

The MALDI-imaging analysis showed that LPC (22:5) was abundant in an iEC network. As an in vitro test model for disease and potential therapy, present analysis methods using MALDI-imaging combined with, for example, mesenchymal stem cells (MSC) to a disease derived iEC network may be useful in revealing the changes in the amount and distribution of lipids under various stimuli.

Keywords: Endothelial, Imaging, Pluripotent, Lysophosphatidylcholine, MALDI, Stem

1. Introduction

Lipids are involved in various cellular functions and play a role in homeostasis and disease states [[1], [2], [3]]. Several lipid species in lipid classes such as fatty acids (FAs), phospholipids (PLs), and ceramides (CMs) have been proposed as useful biomarkers in early identification of disease [4,5]. Endothelial cells (ECs) are recognized as multifunctional cells involved in: regulating the transfer of oxygen; nutrients; coagulation; vascular tone; angiogenesis; stimulating smooth muscle cells and fibroblasts, and the vasculature composition of normal and/or tumor microenvironments [[6], [7], [8]]. External stresses, such as oxidative, glycative or lipoxidative, can alter lipid profiles in ECs compared to the homeostatic state [6,9]. Lipidomics has become a field of research as a result of technological advances in analytical tools of mass spectrometry (MS), nuclear magnetic resonance (NMR), high-performance of liquid chromatography (HPLC), and imaging mass spectrometry (IMS) [10,11]. These techniques identify unique lipid species that indicate potential adverse cardiovascular events. Specifically, phosphatidylcholine (PC) (32:0) and sphingomyelin (SM) (16:0) have been associated with mortality in cardiovascular disease (CVD) [12], and lysophosphatidylcholine (LPC) (18:1) and (18:2) has been shown to be associated with incidents of CVD [13]. The dysfunction of ECs can result in atherosclerosis [14] and IMS has revealed increased levels of LPC, phosphatidic acid (PA), PC and SM in aortic tissue [15,16].

Induced pluripotent stem cell (iPSC)-derived differentiated cells have been used for drug discovery, drug repositioning and toxicological screening of drugs [17]. Currently, iPSC-derived cell therapy is being used to investigate age-related macular degeneration (AMD), Parkinson's disease (PD), fibro dysplasia ossificans progressiva (FOP) and spinal cord injury (SCI) [[18], [19], [20], [21]]. In rats, disease models of idiopathic or heritable pulmonary hypertension (IPAH/HPAH), X-linked adrenoleukodystrophy (X-ALD), lipopolysaccharide-induced acute lung injury using induced pluripotent stem cell-derived endothelial cells (iECs) have been reported [[22], [23], [24]]. The investigation of IPAH/HPAH in this model revealed a reduction in the expression of several genes that may play a role in the effectiveness of treatment by affecting drug repositioning [22].

The first report investigating lipid accumulation was conducted in a blood brain barrier (BBB) model consisting of human brain microvascular iECs derived from childhood cerebral-ALD (cc-ALD) ECs. This study showed increased lipid accumulation as evaluated by Oil-Red-O staining in the BBB [23]. The specific lipid classes or clinical meanings of an increase in lipids in iECs remains, however, unknown.

Lipids are closely related to clinical information in disease states, therefore disease models using iECs may be useful in investigating the role of lipids in disease states to provide patients with more effective therapeutic treatments.

In the present study, an iEC network culture model was generated from human normal iECs. The two-dimensional lipid distribution was examined in the iEC network by IMS. A co-culture model with lung tissue from human pulmonary mesenchymal stem cells (HPMSC) and the iEC network was also analyzed. The findings from this present study indicate that IMS analysis of lipids in a disease-derived iEC network model may be beneficial in identifying patients at risk of CVD and providing more effective treatment for these patients.

2. Materials and methods

2.1. Cell culture of iPSC-derived endothelial cell network on indium/tin oxide-coated (ITO) glass slides

Human iPSC derived endothelial cells (iECs) were obtained from Cellular Dynamics Inc (iCell®, Wisconsin, U.S.A). The iECs were cultured in medium of VascuLife® VEGF Comp Kit (Lifeline Cell Technology, CA, U.S.A) as previously [25]. For IMS analysis, iECs were cultured in Matrigel® (Corning, NY, U.S.A) on indium/tin oxide-coated (ITO) glass slides (Sigma–Aldrich, U.S.A) in Quadri-PERM® 4-Compartment Culture Dish (Greiner Bio One, Austria). Briefly, the culture dish was kept at 4 °C , and the ITO glass slide was placed in the culture dish. The matrigel was then placed onto the cold ITO glass, and extended thinly by swing on ice. The ITO glass slide was then incubated in CO2 at 37 °C for 30 min. Following this, 80% confluent cultured iECs were washed three times with phosphate buffered saline (PBS) at room temperature (RT), then treated with 0.025% phenol free trypsin (NAKARAI, Japan) for 2–3 min, and harvested by cell scraper. The collected iECs were centrifuged at 1000 rpm for 5 min, and stored in 1.5 × 105/ml in culture medium at 37 °C. A total of 500 μl of 1.5 × 105/ml iECs were placed onto matrigel-mounted ITO glass slides, and incubated in CO2 at 37 °C for 1 h. After 1 h incubation, 6 ml of 37 °C culture medium was added to the culture dish to cover the surface of cells on the ITO glass slide. After 48 h culture (CO2 at 37 °C), the ITO glass slides were removed from the culture dish and washed three times by cold saline (Otsuka Pharmaceutical, Japan). The iEC network was identified by microscopic examination (BZ-X, KEYENCE, Japan) and the glass slide snap frozen at −80 °C with desiccant of silica until use. Control samples of matrigel on ITO glass slides were prepared in a similar manner, minus iECs. The HPMSCs were cultured in mesenchymal stem cell medium (MSCM) in accordance with the manufacturer's instructions (ScienCells Research laboratory, CA, U.S.A). For co-culture with the iEC network and HPMSC (ScienCells Research laboratory, CA, U.S.A), the iEC network formation was prepared prior to adding HPMSCs. The HPMSCs were labelled with CellLight™ –mitochondria GFP, BacMam 2.0 (Fischer Scinentific, U.S.A) for 45 min according to the manufacturer's instructions, and treated with 0.125% trypsin–EDTA (Invitrogen), and harvested. Following centrifugation at 1000 rpm for 5 min, HPMSCs were transferred into the iEC network on the ITO glass slide at the concentration of 1 × 104 with 50%/50% volume culture medium of VascuLife®/MSCM and incubated for 48 h. After 48 h of culture, the ITO glass slides were removed from the culture dish and washed three times with cold saline. The contact areas between the iEC network and green fluorescent protein (GFP)-HPMSCs were identified using a fluorescence microscope (BZ-X, KEYENCE).

2.2. MALDI TOF IMS

The MALDI TOF IMS analysis was performed with an iMScopeTRIO® (SHIMADZU, Japan) equipped with a 355 nm Nd:YAG laser, as previously described [26]. The ITO glass slide stored at −80 °C, was left at RT to air dry. The iEC network was traced on the screen monitor using a microscope equipped with iMScopeTRIO® (SHIMADZU, Japan). The inside of the traced area was then analyzed with MALDI-imaging analysis. The ITO glass slide was then homogenously coated with a matrix (2-hydroxy-5-methoxybenzoic acid: DHB) using an iMLayer® (SHIMADZU, Japan). The MS data were acquired in positive ion mode in the mass range m/z 500 to 1000. A laser beam diameter was set at 5 μm, and mass resolving power was 10,000 at m/z 1000 used in MS and tandem MS/MS scans. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) of image patterns was performed as previously described [26]. The target m/z was determined after comparison with the m/z peaks derived from the control matrigel without iECs. A sample ITO glass slide without iECs but coated with matrigel was prepared in a similar manner and used as a control.

2.3. Extraction of lipids in ECs network for LC-MS and database research

The ITO glass slide was washed three times in cold saline, and the matrigel, including the iEC network, was treated with cell recovery solution according to the manufacturer's instructions (Corning NY, U.S.A.). The iECs were then collected. After sonication of the cells, lipids were extracted by the Bligh-Dyer method as previously described [26]. Samples were stored at −80 °C after sealing under nitrogen. To identify the unknown lipids co-localized in the iEC network, lipid extracts were subjected to the infusion method, using a high sensitivity precursor scanning with fragment ion [27], referencing fragment ions from tandem MS/MS analysis by MALDI-imaging. The LipidView™ database with QTRAP5500 mass spectrometer (Sciex Inc., Framingham, MA USA) was used to determine specific ions.

2.4. Statistical analysis

The PCA was performed for the cumulative contribution ratio of PC > 80% in each image and the contribution of PC value in each group >mean PC value of each image. The HCA was performed in accordance with the Euclidean distance and the Ward Method. The PCA, HCA, and generation of dendrograms were performed using iMScopeTRIO® (SHIMADZU, Japan) software.

3. Results

3.1. MALDI-imaging analysis of iEC network

Formation of the iEC network or matrigel without iEC on an ITO glass slide was confirmed by microscopic examination before being stored at −80 °C (Fig. 1a–c). The ITO glass slides were air dried, and the iEC network traced using microscopy equipped with iMScopeTRIO® (SHIMADZU, Japan). The inside of the traced area was analyzed in regards to the iEC network (Fig. 1e) or in the matrigel without the iEC network (Fig. 1f). The MS analysis of the iEC network showed various intensities in m/z peaks including those derived from the matrigel and from the iEC network (Fig. 2a). High intensity of the peaks at m/z 545.0 and 585.0 were determined from DHB (Fig. 2b). The PCA analysis based on two–dimensional image patterns identified 24 groups (Supplemental data 1). PCA3 including peaks at m/z 592.1, 593.1, 632.3 and 728.1 were co-localized to the iEC network (Fig. 3a). The HCA analysis showed 13 clusters (Supplemental data 2). The following HCA measurements were co-localized with the iEC network: HCA10 of m/z 728.1; HCA11 of m/z 592.1 and 593.1; HCA12 of m/z 632.3, and HCA13 of m/z 672.4. Among the distribution of m/z 592.1, some spots were high in the iEC network as shown yellow to red dots (Fig. 3b). Furthermore, dendrogram of the HCA clusters demonstrated that these m/z peaks formed close trees (Fig. 4). These peaks of m/z were not observed on matrigel without the iEC network.

Fig. 1.

Fig. 1

Open in a new tab

Cell culture and preparation of slides for MALDI-imaging. Matrigel was mounted onto a sterilized ITO-coated glass slide and placed in a 4-compartment culture dish. Human iPSC-derived vascular endothelial cells (iECs) were cultured in matrigel for 2 days to form an iEC network (a). An ITO glass slide mounted with matrigel but without iECs underwent similar cell culture conditions (a). Microscopic analysis was performed on the iEC network (b) and the matrigel without the iEC network (c). The glass slide was washed 3 times with physiological saline, then stored at −80 °C with desiccant. On the day of MALDI-imaging analysis, the slide was air dried at RT (d). The iEC network (e) or the matrigel without the iEC (f) were again confirmed by microscope equipped with iMScopeTRIO® (SHIMADZU, Japan), and the area to irradiation determined by tracing technology. Inside of the blue line were irradiated in the iEC network (e), and inside of the yellow square were irradiated in matrigel without iEC (f). Scale bars in each panel indicate ranges from 200 to 400 μm.

Fig. 2.

Fig. 2

Open in a new tab

MS analysis of the iEC network and matrigel. The high peaks of m/z 545.0, 585.0 were from the matrix of DHB in both samples of the iEC network (a) and the matrigel (b).

Fig. 3.

Fig. 3

Open in a new tab

PCA for the iEC network. PCA analysis indicated the 5 components in the iEC network (a). A peak of m/z 592.1 was detected in the iEC network, and various intensities of this were also observed (b).

Fig. 4.

Fig. 4

Open in a new tab

Dendrogram of HCA cluster. HCA indicated m/z peaks in the iEC network (red square). These m/z peaks corresponded with the results of the PCA analysis.

3.2. Tandem MS/MS analysis of peaks of m/z co-localized to iEC network

To confirm the lipids co-localized with the iEC network, tandem MS/MS analysis was performed on peaks of m/z 592.1, 593.1, 632.3, 672.4 and 728.1 b y MALDI-imaging analysis. From the tandem MS/MS analysis on m/z peaks of 592.1 and 593.1 as precursor, a peak at m/z 184.1, a specific fragment of phosphoryl choline ion, was obtained (Fig. 5a and b). From other precursor m/z peaks at 632.3, 672.4 and 728.1, valuable fragments m/z could not be obtained (Supplemental data 3). To identify target lipids of m/z peaks at 592.1 and 593.1, lipid extract from the iEC network underwent analysis using the infusion method. Phosphoryl choline m/z 184 -containing phospholipids were determined via a database search of LipidView™ equipped in QTRAP5500. Peaks at m/z 592.1 and 593.1 were identified as LPC 22:5 (569.34) + Na (23) and the isotype. Other peaks at m/z 632.3, 672.4, and 728.1 could not be identified in the database. Tandem MS/MS analysis for the peak of ion precursor at m/z 728.1 showed a fragment at m/z 669.4, with the difference between 728.1 and 669.4 being 58.7. The neutral loss (NL) of 58.7 Da could be NL of 59 Da, and NL of 59 Da is a well known fragment from the phosphatidylcholine (PC) species of trimethylamine [N(CH3)3]. Therefore, m/z 728.14 might be PCs, however, no lipids matched the database.

Fig. 5.

Fig. 5

Open in a new tab

Tandem MS/MS analysis for peaks of m/z 592.1 and 593.1. Both m/z peaks of 592.1 (a) and 593.1 (b) showed the head group of phosphoryl choline, m/z 184.0.

3.3. Co-culture model of the iEC network and HPMSC

After forming the iEC network, GFP-HPMSCs were added with culture medium. The GFP-HPMSCs were observed in the iEC network line or joint. A representative pattern of GFP-HPMSCs in the iEC network is shown (Supplemental data 4). MALDI-imaging analysis were done on co-culture model. Intensity of the peaks of m/z 592 and 672 co-localized to iEC network were identified but it was difficult to distinguish the metabolic differences between GFP-HPMSC and iEC, or a GFP-HPMSC effect on iEC network (Supplemental data 5 and 6).

4. Discussion

The MALDI-imaging analysis of the iEC network demonstrated differences in the relative intensity reflecting various peaks of m/z. Tandem MS/MS analysis by MALDI-imaging of the iEC network and infusion method analysis using lipid extract identified LPC (22:5), and it was abundant in iEC network. A proposed model of co-culture with an iEC network and HPMSCs was also demonstrated.

One of the lysophospholipids generated from glycerophospholipid metabolism cleaved by phospholipase A2 or A1 is LPC is [28,29]. It is well known that LPC plays an important role in cell signaling, migration, proliferation and the release of cytokines in various types of cells including: ECs; adipocytes; hepatocytes; immune cells, and neuronal cells [30]. It has been shown that ECs can be activated by the following: LPC; induced monocyte chemoattractant protein-1 (MCP-1); interleukin-8 (IL-8); regulated on activation, normal T cell expressed and secreted (RANTES) expression, cytotoxicity or apoptosis, and oxidative stress [[31], [32], [33]]. Interestingly, LPC has a potent property that can result in trans differentiation of human aortic ECs into innate immune cells via up-regulation of cytosolic calcium and mitochondrial reactive oxygen species (ROS) [34]. Furthermore, it has been reported that LPC may play a role in the induction of histone acetylation levels at H3K14 in 19 trained immunity genes in ECs. The acetylation status of H3K14 is currently considered a biomarker or therapeutic target of CVD, further supporting the need to consider LPC as a biomarker for CVD [35].

As a biomarker, LPC has been observed in the plasma, serum and other fluids in the following diseases: bronchoalveolar lavage fluid (BALF); lipid extraction of tissue in cystic fibrosis; idiopathic pulmonary fibrosis (IPF); chronic obstructive pulmonary disease (COPD) CVD; hepatic cancer; lung cancer patients, and asthma [[36], [37], [38], [39], [40], [41], [42]]. In a murine bleomycin-induced pulmonary fibrosis model, LPC was reported to be associated the pathophysiology of this disease [30,43]. In the present study, LPC (22:5) was identified in an iEC network. Several LPCs, including LPC (14:0), (16.0), (18:0), (18:2), (20:4), (22:3) and (22:6) have been reported [30], however, few reports have identified LPC (22:5) [29,40,44]. In patients with hepatocellular carcinoma (HCC) LPC (22:5) levels were decreased which may indicate poorly differentiated HCC [40]. In a murine lung fibrosis experimental model, an increase in LPC (22:5) was observed in the lung, however, there was no increase in systemic LPC [29]. In addition, LPC (22:5) was produced from phospholipase A activity of PCs, and also by autotaxin (ATX) dependently, which was also elevated in the lung in the murine lung fibrosis experimental model [29]. The relationship between ECs and LPC is complex. The stimulation by the combination of LPC and ATX treatment has been shown to increase the permeability of human umbilical vein EC (HUVEC), and immunohistochemistry (IHC) has revealed that the area of vascular leakage was co-localized with ATX protein expression in a murine model of renal fibrosis [44]. The findings from the present study show that LPC (22:5) was abundant in ECs, and that injury to ECs may cause increased levels of LPC at the site of injury. This increase in LPC levels may exacerbate the vascular metabolic state.

The MALDI-imaging analysis can obtain two-dimensional data. Studies have been conducted investigating LPC distribution in injured ischemic rat brain, ischemic porcine kidney and restenosis in injured rat carotid artery [[45], [46], [47]]. These studies reported an increase in LPC (18:1) at the injured site, and that the LPC levels were increased compared to the control by the time course (day 0 to day 14 of injury) [47]. In addition, LPC (20:4) and (22:6) have also been identified in murine normal airway by nanospray desorption electrospray ionization MS coupled to shear force microscopy [48], however, there have been no reports of LPC (22:5) by MS imaging. The present study focused on the expression of LPC in an iEC network, composed of an iEC network developed on matrigel by cell culture. The difference between the present study and previous reports are that previous studies focused on tissue sections including various cells, and on larger cell areas other than the iEC network, and different matrix or ion-modes were used for analysis of LPC expression.

The present study reports findings of LPC expression using a co-culture cell model with HPMSCs and an iEC network. Several studies have reported metabolic changes of stem cell transplant by IMS analysis [49,50]. IMS analysis has revealed that glioma stem cell transplants to the murine brain lowers PA, diacylglycerol (DAG) metabolism [49], and bone marrow derived mesenchymal stem cells (BM-MSC) in injured left soleus muscle of rat altered protein changes at the transplant site [50]. Limitations of this study are lack of some comparison data in iEC network regarding oxidative, glycative or lipoxidative stresses with or without HPMSC. To efficacy of HPMSC on iEC network, those various stimulations and the observational experiments with time-course during co-culture would be needed. Taken together, the co-culture model reported in the current study may be useful in the evaluation of the HPMSC effects on the metabolic changes of disease derived iEC network, by stimulating various compound or using disease-derived iEC network, however, further experiments are needed for practical use.

5. Conclusion

The findings of the present study indicate that a model of co-culture iEC network and HPMSC may be beneficial in determining the expression of lipids in a disease state. Furthermore, LPC (22:5) was abundant in the iEC network, and MALDI-imaging analysis was useful in identifying the distribution of lipids in the iEC network.

Declaration of competing interest

None of the authors have any conflict of interests.

Acknowledgements

This work was partly supported by a grant-in-aid for the JSPS KAKENHI Grant to Y.S, Japan (16K09553).

Footnotes

Peer review under responsibility of the Japanese Society for Regenerative Medicine.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.reth.2020.03.007.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1

Supplemental data 1: Images of PCA on m/z peaks from the iEC network identified 24 groups. The left panel for each group is a representative pattern for the group. High intensity of m/z co-localized to the iEC network was seen in PCA 3. Supplemental data 2: Images of HCA on m/z peaks from the iEC network identified 13 groups. Representative images for each cluster are shown, along with the number of images (n) belonging to each cluster. High intensity of m/z co-localized to the iEC network were seen in HCA 10, 11, 12 and 13. Supplemental data 3 (table): List of m/z peaks of target precursor ions and the fragment ions by tandem MS/MS analysis. Supplemental data 4: Representative co-culture model for iEC and GFP-HPMSC. After the iEC network was formed, GFP-HPMSCs were added to culture medium composed of v/v (VEGF comp kit ®/MSCM). The GFP-HPMSCs were observed on the iEC network line (a) or joint (b). The pattern diagram of GFP-HPMSCs on the iEC network (c). Scale bars indicate 150 μm. Supplemental data 5: MALDI-imaging analysis on joint of iEC network and HPMSC of co-culture model. GFP-HPMSC were observed on joint of iEC network (a). MS analysis was done as previously described elsewhere (b). PCA analysis identified 17 groups (c), and HCA analysis identified 5 clusters (d). PCA 4, PCA9 and HCA5 groups showed the intensity of m/z 592 and 672 co-localized to iEC network (e). Intensity of m/z 592 and 672 in PCA4, PCA9 and HCA5 have been already identified on iEC network as shown in experiments of Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5. It was difficult to distinguish the metabolic differences between GFP-HPMSC and iEC, or a GFP-HPMSC effect on iEC network. Supplemental data 6: MALDI-imaging analysis on line of iEC network and HPMSC of co-culture model. GFP-HPMSC were observed on line of iEC network (a). MS analysis was done as previously described elsewhere (b). PCA analysis identified 20 groups (c), and HCA analysis identified 4 clusters (d). PCA 4 and HCA4 groups showed the m/z 592 and 672 co-localized to iEC network (e). Intensity of m/z 592 and 672 in PCA4 and HCA4 have been already identified on iEC network as shown in experiments Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5. It was difficult to distinguish the metabolic differences between GFP-HPMSC and iEC, or a GFP-HPMSC effect on iEC network.

mmc1.pptx (5.1MB, pptx)

References

  • 1.Spiegel S., Ntambi J. Editorial for BBRC lipidomics special issue. Biochem Biophys Res Commun. 2018;504:iii. doi: 10.1016/j.bbrc.2018.09.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Nakamura Y., Shimizu Y. Induced pluripotent stem cells for regenerative medicine: quality control based on evaluation of lipid composition. Adv Exp Med Biol. 2020;1212:49–56. doi: 10.1007/5584_2019_394. [DOI] [PubMed] [Google Scholar]
  • 3.Johnson A.A., Stolzing A. The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell. 2019;18:1–26. doi: 10.1111/acel.13048. e13048. [Review] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Kohno S., Keenan A.L., Ntambi J.M., Miyazaki M. Lipidomic insight into cardiovascular diseases. Biochem Biophys Res Commun. 2018;504:590–595. doi: 10.1016/j.bbrc.2018.04.106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Matanes F., Twal W.O., Hammad S.M. Sphingolipids as biomarkers of disease. Adv Exp Med Biol. 2019;1159:109–138. doi: 10.1007/978-3-030-21162-2_7. [Review] [DOI] [PubMed] [Google Scholar]
  • 6.Bierhansl L., Conradi L.C., Treps L., Dewerchin M., Carmeliet P. Central role of metabolism in endothelial cell function and vascular disease. Physiology. 2017;32:126–140. doi: 10.1152/physiol.00031.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Datta M., Coussens L.M., Nishikawa H., Hodi F.S., Jain R.K. Reprogramming the tumor microenvironment to improve immunotherapy: emerging strategies and combination therapies. Am Soc Clin Oncol Educ Book. 2019;39:165–174. doi: 10.1200/EDBK_237987. [Review] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Martin J.D., Seano G., Jain R.K. Normalizing function of tumor vessels: progress, opportunities, and challenges. Annu Rev Physiol. 2019;81:505–534. doi: 10.1146/annurev-physiol-020518-114700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Colombo S., Melo T., Martínez-López M., Carrasco M.J., Domingues M.R., Pérez-Sala D. Phospholipidome of endothelial cells shows a different adaptation response upon oxidative, glycative and lipoxidative stress. Sci Rep. 2018;8:12365. doi: 10.1038/s41598-018-30695-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lee H.C., Yokomizo T. Applications of mass spectrometry-based targeted and non-targeted lipidomics. Biochem Biophys Res Commun. 2018;504:576–581. doi: 10.1016/j.bbrc.2018.03.081. [DOI] [PubMed] [Google Scholar]
  • 11.Nampei M., Horikawa M., Ishizu K., Yamazaki F., Yamada H., Kahyo T. Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter. Sci Rep. 2019;9:13213. doi: 10.1038/s41598-019-49819-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Sigruener A., Kleber M.E., Heimerl S., Liebisch G., Schmitz G., Maerz W. Glycerophospholipid and sphingolipid species and mortality: the Ludwigshafen risk and cardiovascular health (LURIC) study. PloS One. 2014;9:1–8. doi: 10.1371/journal.pone.0085724. e85724. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ganna A., Salihovic S., Sundstrom J., Broeckling C.D., Hedman A.K., Magnusson P.K. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet. 2014;10 doi: 10.1371/journal.pgen.1004801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Gimbrone M.A., Jr., García-Cardeña G. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circ Res. 2016;118:620–636. doi: 10.1161/CIRCRESAHA.115.306301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Martin-Lorenzo M., Balluff B., Maroto A.S., Carreira R.J., van Zeijl R.J., Gonzalez-Calero L. Lipid and protein maps defining arterial layers in atherosclerotic aorta. Data Brief. 2015;4:328–331. doi: 10.1016/j.dib.2015.06.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Meisenbichler C., Doppler C., Bernhard D., Müller T. Improved matrix coating for positive- and negative-ion-mode MALDI-TOF imaging of lipids in blood vessel tissues. Anal Bioanal Chem. 2019;411:3221–3227. doi: 10.1007/s00216-019-01826-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Karagiannis P., Takahashi K., Saito M., Yoshida Y., Okita K., Watanabe A. Induced pluripotent stem cells and their use in human models of disease and development. Physiol Rev. 2019;99:79–114. doi: 10.1152/physrev.00039.2017. [Review] [DOI] [PubMed] [Google Scholar]
  • 18.Mandai M., Kurimoto Y., Takahashi M. Autologous induced stem-cell-derived retinal cells for macular degeneration. N Engl J Med. 2017;377:792–793. doi: 10.1056/NEJMc1706274. [DOI] [PubMed] [Google Scholar]
  • 19.Kikuchi T., Morizane A., Doi D., Magotani H., Onoe H., Hayashi T. Human iPS cell-derived dopaminergic neurons function in a primate Parkinson's disease model. Nature. 2017;548:592–596. doi: 10.1038/nature23664. [DOI] [PubMed] [Google Scholar]
  • 20.Nakajima T., Ikeya M. Insights into the biology of fibrodysplasia ossificans progressiva using patient-derived induced pluripotent stem cells. Regen Ther. 2019;11:25–30. doi: 10.1016/j.reth.2019.04.004. [Review] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Nagoshi N., Tsuji O., Nakamura M., Okano H. Cell therapy for spinal cord injury using induced pluripotent stem cells. Regen Ther. 2019;11:75–80. doi: 10.1016/j.reth.2019.05.006. [Review] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Sa S., Gu M., Chappell J., Shao N.Y., Ameen M., Elliott K.A. Induced pluripotent stem cell model of pulmonary arterial hypertension reveals novel gene expression and patient specificity. Am J Respir Crit Care Med. 2017;195:930–941. doi: 10.1164/rccm.201606-1200OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lee C.A.A., Seo H.S., Armien A.G., Bates F.S., Tolar J., Azarin S.M. Modeling and rescue of defective blood-brain barrier function of induced brain microvascular endothelial cells from childhood cerebral adrenoleukodystrophy patients. Fluids Barriers CNS. 2018;15:9. doi: 10.1186/s12987-018-0094-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Xing D., Wells J.M., Giordano S.S., Feng W., Gaggar A., Yan J. Induced pluripotent stem cell-derived endothelial cells attenuate lipopolysaccharide-induced acute lung injury. J Appl Physiol. 2019;127:444–456. doi: 10.1152/japplphysiol.00587.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Nakamura Y., Shimizu Y., Horibata Y., Tei R., Koike R., Masawa M. Changes of plasmalogen phospholipid levels during differentiation of induced pluripotent stem cells 409B2 to endothelial phenotype cells. Sci Rep. 2017;7:9377. doi: 10.1038/s41598-017-09980-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Shimizu Y., Satou M., Hayashi K., Nakamura Y., Fujimaki M., Horibata Y. Matrix-assisted laser desorption/ionization imaging mass spectrometry reveals changes of phospholipid distribution in induced pluripotent stem cell colony differentiation. Anal Bioanal Chem. 2017;409:1007–1016. doi: 10.1007/s00216-016-0015-x. [DOI] [PubMed] [Google Scholar]
  • 27.Houjou T., Yamatani K., Nakanishi H., Imagawa M., Shimizu T., Taguchi R. Rapid and selective identification of molecular species in phosphatidylcholine and sphingomyelin by conditional neutral loss scanning and MS3. Rapid Commun Mass Spectrom. 2004;18:3123–3130. doi: 10.1002/rcm.1737. [DOI] [PubMed] [Google Scholar]
  • 28.Bou Khalil M., Hou W., Zhou H., Elisma F., Swayne L.A., Blanchard A.P. Lipidomics era: accomplishments and challenges. Mass Spectrom Rev. 2010;29:877–929. doi: 10.1002/mas.20294. [DOI] [PubMed] [Google Scholar]
  • 29.Black K.E., Berdyshev E., Bain G., Castelino F.V., Shea B.S., Probst C.K. Autotaxin activity increases locally following lung injury, but is not required for pulmonary lysophosphatidic acid production or fibrosis. Faseb J. 2016;30:2435–2450. doi: 10.1096/fj.201500197R. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Law S.H., Chan M.L., Marathe G.K., Parveen F., Chen C.H., Ke L.Y. An updated review of lysophosphatidylcholine metabolism in human diseases. Int J Mol Sci. 2019;20:1–24. doi: 10.3390/ijms20051149. pii: E1149 [Review] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Takahara N., Kashiwagi A., Maegawa H., Shigeta Y. Phsophosphatidylcholine stimulates the expression and production of MCP-1 by human vascular endothelial cells. Metabolism. 1996;45:559–564. doi: 10.1016/s0026-0495(96)90024-4. [DOI] [PubMed] [Google Scholar]
  • 32.G1 Murugesan, Sandhya Rani M.R., Gerber C.E., Mukhopadhyay C., Ransohoff R.M., Chisolm G.M. Lysophosphatidylcholine regulates human microvascular endothelial cell expression of chemokines. J Mol Cell Cardiol. 2003;35:1375–1384. doi: 10.1016/j.yjmcc.2003.08.004. [DOI] [PubMed] [Google Scholar]
  • 33.Chang M.C., Lee J.J., Chen Y.J., Lin S.I., Lin L.D., Jein-Wen Liou E. Lysophosphatidylcholine induces cytotoxicity/apoptosis and IL-8 production of human endothelial cells: related mechanisms. Oncotarget. 2017;8:106177–106189. doi: 10.18632/oncotarget.22425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Li X., Wang L., Fang P., Sun Y., Jiang X., Wang H. Lysophospholipids induce innate immune transdifferentiation of endothelial cells, resulting in prolonged endothelial activation. J Biol Chem. 2018;293:11033–11045. doi: 10.1074/jbc.RA118.002752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Lu Y., Sun Y., Drummer C., 4th, Nanayakkara G.K., Shao Y., Saaoud F. Increased acetylation of H3K14 in the genomic regions that encode trained immunity enzymes in lysophosphatidylcholine-activated human aortic endothelial cells - novel qualification markers for chronic disease risk factors and conditional DAMPs. Redox Biol. 2019;24:101221. doi: 10.1016/j.redox.2019.101221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Ollero M., Astarita G., Guerrera I.C., Sermet-Gaudelus I., Trudel S., Piomelli D. Plasma lipidomics reveals potential prognostic signatures within a cohort of cystic fibrosis patients. J Lipid Res. 2011;52:1011–1022. doi: 10.1194/jlr.P013722. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rindlisbacher B., Schmid C., Geiser T., Bovet C., Funke-Chambour M. Serum metabolic profiling identified a distinct metabolic signature in patients with idiopathic pulmonary fibrosis - a potential biomarker role for LysoPC. Respir Res. 2018;19:7. doi: 10.1186/s12931-018-0714-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Deeb R.S., Walters M.S., Strulovici-Barel Y., Chen Q., Gross S.S., Crystal R.G. Smoking-associated disordering of the airway basal stem/progenitor cell metabotype. Am J Respir Cell Mol Biol. 2016;54:231–240. doi: 10.1165/rcmb.2015-0055OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Bandu R., Mok H.J., Kim K.P. Phospholipids as cancer biomarkers: mass spectrometry-based analysis. Mass Spectrom Rev. 2018;37:107–138. doi: 10.1002/mas.21510. [DOI] [PubMed] [Google Scholar]
  • 40.Zhang L., Huang Y., Lian M., Fan Z., Tian Y., Wang Y. Metabolic profiling of hepatitis B virus-related hepatocellular carcinoma with diverse differentiation grades. Oncol Lett. 2017;13:1204–1210. doi: 10.3892/ol.2017.5596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Tan Y., Yin P., Tang L., Xing W., Huang Q., Cao D. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteomics. 2012;11:M111. doi: 10.1074/mcp.M111.010694. 010694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Kang Y.P., Lee W.J., Hong J.Y., Lee S.B., Park J.H., Kim D. Novel approach for analysis of bronchoalveolar lavage fluid (BALF) using HPLC-QTOF-MS-based lipidomics: lipid levels in asthmatics and corticosteroid-treated asthmatic patients. J Proteome Res. 2014;13:3919–3929. doi: 10.1021/pr5002059. [DOI] [PubMed] [Google Scholar]
  • 43.Kulkarni Y.M., Dutta S., Iyer A.K.V., Wright C.A., Ramesh V., Kaushik V. A lipidomics approach to identifying key lipid species involved in vegf-inhibitor mediated attenuation of bleomycin-induced pulmonary fibrosis. Proteonomics Clin Appl. 2018;12 doi: 10.1002/prca.201700086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Sakai N., Bain G., Furuichi K., Iwata Y., Nakamura M., Hara A. The involvement of autotaxin in renal interstitial fibrosis through regulation of fibroblast functions and induction of vascular leakage. Sci Rep. 2019;9:7414. doi: 10.1038/s41598-019-43576-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Koizumi S., Yamamoto S., Hayasaka T., Konishi Y., Yamaguchi-Okada M., Goto-Inoue N. Imaging mass spectrometry revealed the production of lyso-phosphatidylcholine in the injured ischemic rat brain. Neuroscience. 2010;168:219–225. doi: 10.1016/j.neuroscience.2010.03.056. [DOI] [PubMed] [Google Scholar]
  • 46.van Smaalen T.C., Ellis S.R., Mascini N.E., Siegel T.P., Cillero-Pastor B., Hillen L.M. Rapid identification of ischemic injury in renal tissue by mass-spectrometry imaging. Anal Chem. 2019;91:3575–3581. doi: 10.1021/acs.analchem.8b05521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Shi Y., Johnson J., Wang B., Chen B., Fisher G.L., Urabe G. Mass spectrometric imaging reveals temporal and spatial dynamics of bioactive lipids in arteries undergoing restenosis. J Proteome Res. 2019;18:1669–1678. doi: 10.1021/acs.jproteome.8b00941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Nguyen S.N., Sontag R.L., Carson J.P., Corley R.A., Ansong C., Laskin J. Towards high-resolution tissue imaging using nanospray desorption electrospray ionization mass spectrometry coupled to shear force microscopy. J Am Soc Mass Spectrom. 2018;29:316–322. doi: 10.1007/s13361-017-1750-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Wildburger N.C., Wood P.L.1, Gumin J., Lichti C.F.2, Emmett M.R.2, Lang F.F. ESI-MS/MS and MALDI-IMS localization reveal alterations in phosphatidic acid, diacylglycerol, and DHA in glioma stem cell xenografts. J Proteome Res. 2015;14:2511–2519. doi: 10.1021/acs.jproteome.5b00076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Klein O., Strohschein K., Nebrich G., Fuchs M., Thiele H., Giavalisco P. Unraveling local tissue changes within severely injured skeletal muscles in response to MSC-based intervention using MALDI Imaging mass spectrometry. Sci Rep. 2018;8:12677. doi: 10.1038/s41598-018-30990-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1

Supplemental data 1: Images of PCA on m/z peaks from the iEC network identified 24 groups. The left panel for each group is a representative pattern for the group. High intensity of m/z co-localized to the iEC network was seen in PCA 3. Supplemental data 2: Images of HCA on m/z peaks from the iEC network identified 13 groups. Representative images for each cluster are shown, along with the number of images (n) belonging to each cluster. High intensity of m/z co-localized to the iEC network were seen in HCA 10, 11, 12 and 13. Supplemental data 3 (table): List of m/z peaks of target precursor ions and the fragment ions by tandem MS/MS analysis. Supplemental data 4: Representative co-culture model for iEC and GFP-HPMSC. After the iEC network was formed, GFP-HPMSCs were added to culture medium composed of v/v (VEGF comp kit ®/MSCM). The GFP-HPMSCs were observed on the iEC network line (a) or joint (b). The pattern diagram of GFP-HPMSCs on the iEC network (c). Scale bars indicate 150 μm. Supplemental data 5: MALDI-imaging analysis on joint of iEC network and HPMSC of co-culture model. GFP-HPMSC were observed on joint of iEC network (a). MS analysis was done as previously described elsewhere (b). PCA analysis identified 17 groups (c), and HCA analysis identified 5 clusters (d). PCA 4, PCA9 and HCA5 groups showed the intensity of m/z 592 and 672 co-localized to iEC network (e). Intensity of m/z 592 and 672 in PCA4, PCA9 and HCA5 have been already identified on iEC network as shown in experiments of Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5. It was difficult to distinguish the metabolic differences between GFP-HPMSC and iEC, or a GFP-HPMSC effect on iEC network. Supplemental data 6: MALDI-imaging analysis on line of iEC network and HPMSC of co-culture model. GFP-HPMSC were observed on line of iEC network (a). MS analysis was done as previously described elsewhere (b). PCA analysis identified 20 groups (c), and HCA analysis identified 4 clusters (d). PCA 4 and HCA4 groups showed the m/z 592 and 672 co-localized to iEC network (e). Intensity of m/z 592 and 672 in PCA4 and HCA4 have been already identified on iEC network as shown in experiments Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5. It was difficult to distinguish the metabolic differences between GFP-HPMSC and iEC, or a GFP-HPMSC effect on iEC network.

mmc1.pptx (5.1MB, pptx)

Articles from Regenerative Therapy are provided here courtesy of Japanese Society for Regenerative Medicine

RESOURCES