Impact of Oxygen Concentration on Metabolic Profile of Human

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Impact of oxygen concentration on metabolic profile of human placenta-derived mesenchymal stem cells as determined by chemical isotope labeling LC–MS Dan Wang, Deying Chen, Jiong Yu, Jingqi Liu, Xiaowei Shi, Yanni Sun, Qiaoling Pan, Xian Luo, Jinfeng Yang, Yang Li, Hongcui Cao, Liang Li, and Lanjuan Li J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00887 • Publication Date (Web): 19 Apr 2018 Downloaded from http://pubs.acs.org on April 20, 2018

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Journal of Proteome Research 1

Impact of oxygen concentration on metabolic profile of human placenta-derived mesenchymal stem cells as determined by chemical isotope labeling LC–MS Dan Wang†1, Deying Chen†1, Jiong Yu1, Jingqi Liu1, Xiaowei Shi3, Yanni Sun1, Qiaoling Pan1, Xian Luo2, Jinfeng Yang1, Yang Li4, Hongcui Cao1*, Liang Li2, Lanjuan Li1 1 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, 79 Qingchun Rd., Hangzhou City 310003, China 2 Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2, Canada 3 Chu Kochen Honors College, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China 4 Obstetrical department, the First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Rd., Hangzhou City 310003, China

Author information *

Corresponding author:

Hongcui Cao, E-mail: [email protected] Tel: 86-571-87236451; Fax: 86-571-87236459 †

These authors contributed equally to this work.

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Abstract The placenta resides in a physiologically low oxygen microenvironment of the body. Hypoxia induces a wide range of stem cell cellular activities. Here, we report a workflow for exploring the role of physiological (hypoxic, 5% oxygen) and original cell culture (normoxic, 21% oxygen) oxygen concentrations in regulating the metabolic status of human placenta-derived mesenchymal stem cells (hPMSCs). The general biological characteristics of hPMSCs were assessed via a variety of approaches such as cell counts, flow cytometry and differentiation study. A sensitive 13

C/12C-dansyl labeling liquid chromatography mass spectrometry (LC–MS) method

targeting the amine/phenol submetabolome was used for metabolic profiling of the cell and corresponding culture supernatant. Multivariate and univariate statistical analyses were used to analyze the metabolomics data. hPMSCs cultured in hypoxia display smaller size, higher proliferation, greater differentiation ability and no difference in immunophenotype. Overall, 2987 and 2860 peak pairs or metabolites were detected and quantified in hPMSCs and culture supernatant, respectively. Approximately 86.0% of cellular metabolites and 84.3% of culture supernatant peak pairs were identified using a dansyl standard library or matched to metabolite structures using accurate mass search against human metabolome libraries. The orthogonal partial least squares discriminant analysis (OPLS-DA) showed a clear separation between the hypoxic group and the normoxic group. Ten metabolites from cells and six metabolites from culture supernatant were identified as potential biomarkers of hypoxia. This study demonstrated that chemical isotope labeling


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LC–MS can be used to reveal the role of oxygen in the regulation of hPMSC metabolism, whereby physiological oxygen concentrations may promote arginine and proline metabolism, pantothenate and coenzyme A (CoA) biosynthesis, and alanine, aspartate and glutamate metabolism. Keywords mesenchymal stem cells, metabolomics, hypoxia, normoxia, chemical isotope labeling, liquid chromatography−mass spectrometry Abbreviations： MSCs: mesenchymal stem cells, hPMSCs: human placental mesenchymal stem cells, O2: oxygen, QC: Quality control, CIL LC−MS: chemical isotope labeling liquid chromatography−mass spectrometry, PCA: principal component analysis, OPLS-DA: orthogonal partial least squares discriminant analysis, HMDB: human metabolome database, EML：Evidence-based Metabolome Library, Met PA: Metabolic pathway analysis, MeOH: methyl alcohol, ACN: acetonitrile, FA: formic acid, RT: retention time.



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1 Introduction Mesenchymal stem cells (MSCs) have become the most attractive candidates for regenerative medicine, and they can be obtained from diverse tissues, such as bone marrow, adipose tissue, placenta, umbilical cord blood, et al.

1, 2

. Human

placenta-derived mesenchymal stem cells (hPMSCs) are preeminent candidates for cell therapies because they are non-invasively accessible, abundant and have low immunogenicity 3, 4. A major challenge with using these cells is how to maintain their physiological properties in vitro before they are applied in clinical treatment. Therefore, it is necessary to optimize culture conditions suitable for clinical use in order to achieve better therapeutic safety and efficacy. Early pioneers in cell culture paid adequate attention to nutritional balance, growth factors, and buffer power of hydrogen (pH) values, but they rarely cared about the effects of oxygen (O2) concentrations. The placenta resides in a uterine environment (1–5% O2) in vivo 5; therefore, hPMSCs also experience low oxygen tension. Numerous studies have demonstrated that hypoxia has a significant effect on cell physiological status, signaling pathways, and stress responses

6-9

. Currently, studies

have indicated that hypoxia can affect the proliferation, differentiation, apoptosis, and other biological characteristics of MSC

10-12

. Additionally, our previous studies

showed that hPMSCs exhibited higher proliferative capability and higher hypoxia-inducible factor-2 alpha (HIF-2α) expression in hypoxic cultures (5% O2) 13. In contrast, the metabolic state of cells is rarely explored, which motivated us to study the impact of oxygen tension on the metabolism of hPMSCs.


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Metabolomics has become an important technique for investigating biological questions because it provides valuable and complementary information to the results obtained

from

genomics,

transcriptomics

and

proteomics.

Liquid

chromatography–mass spectrometry (LC–MS) has emerged among various analytical platforms as a powerful tool for addressing cellular biochemistry due to its relatively high sensitivity for metabolite detection. However, LC–MS still has some challenges, such as ion suppression, matrix effects and instrument drift, which can limit the number of quantifiable metabolites. Recently, we demonstrated that high-performance chemical isotope labeling (CIL) LC–MS can overcome the above drawbacks of conventional LC–MS

14, 15

. CIL LC–MS combines a sample normalization strategy

and differential isotope labeling with rationally designed labeling reagents to improve detection sensitivity and metabolomic coverage along with quantification precision and accuracy. For instance, differential

12

C-/13C-dansylation labeling LC–MS has

been shown to be a powerful method for analyzing amine- and phenol-containing metabolites or the amine/phenol submetabolome

16

. Dansylation LC–MS has been

applied to analyze many different types of biological samples, such as cells, plasma, cerebrospinal fluid, and fecal matter, with relatively high submetabolome coverage (i.e., thousands of metabolites) 17-20. In this work, a 5% oxygen concentration was used to mimic the physiological (hypoxic) oxygen concentration of hPMSCs, and a 21% oxygen concentration was used as regular culture conditions for hPMSCs. The effects of oxygen on the general biological characteristics of cells, such as morphology, immunophenotype,



Page 6 of 45 6

proliferation, and differentiation, were confirmed in this study. We report our investigation of using dansylation LC–MS to study whether oxygen concentrations affect the metabolism of hPMSCs. In addition to our metabolic profiling analysis of cells, we also analyzed metabolites in the culture supernatant. The combined results of metabolic profiling analysis provided a more comprehensive and in-depth understanding of hPMSC metabolism under varying oxygen concentrations 21, 22. The specific objectives of this study were (1) to present the metabolome profile of hPMSCs; and (2) to examine how oxygen concentration affects the cell metabolome. The ultimate goals were to determine the hypoxia-related biomarker that can cause significant alteration of the cell metabolome; (3) to provide a unique insight into the internal metabolic pathways that were perturbed or altered by varied oxygen conditions; and (4) to guide the rational design of culture methods and develop novel media for culturing hPMSCs. 2 Materials and Methods 2.1 Cell culture Placentas were obtained from donors at the First Affiliated Hospital, School of Medicine, Zhejiang University. The protocols for handling human tissues and cells were approved by the Research Ethics Committee of the hospital (Reference No. 2013–272). hPMSC culture was performed using previously reported protocols 4. Briefly, hPMSCs were cultured in T25-cm2 culture flasks (Nunc Flasks Nunclon™Δ with FilterCap, Denmark) at a density of 2×105 cells/flask with special medium (MesenCult® Human Basal Medium plus Mesen Cult® Human Supplement,


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STEMCELL Technologies Inc., Vancouver, Canada). The replacement of medium was done every 3 days. Trypsinization of the adherent cells were carried out using trypsin/ethylene diamine tetraacetic acid (EDTA) (Invitrogen, Carlsbad, CA, USA) and the cells were passaged when they reached approximately 70-80% confluence. After hPMSCs were stabilized, the hypoxic group cells were then placed in a humidified incubator (Forma™ Series II Water–Jacketed carbon dioxide (CO2) Incubators with Oxygen Control, Thermo Fisher Scientific Inc.) with 5% O2 and 5% CO2, and the normoxic group continued to be incubated in a standard humidified incubator (HERAcell®150, Thermo Fisher Scientific Inc.) with 21% O2 and 5% CO2. Passages 3 to 5 hPMSCs were used for the experiments. 2.2 Flow cytometry Cell size analysis. The size (forward scatter/side scatter) of unmarked hPMSCs was analyzed using flow cytometry (BeamCyte-1026, Beamdiag, Changzhou, China). After 60 h of incubation, cells in each group were harvested, washed, centrifuged and resuspended. Forward scatter parameters (FSC) were used for cell size measurements. All the experiments were carried out in triplicate. Cell surface markers. hPMSCs were placed in 21% O2 or 5% O2 atmosphere for 60 h to perform surface marker expression analysis. Subsequently, hPMSCs were harvested, washed with phosphate-buffered saline (PBS) solution (Hangzhou Gino Bio-pharmaceutical Technology Co. Ltd., Zhejiang, China), supplemented with 0.5% bovine serum albumin (BSA) (Sangon Biotech Corp., Shanghai, China) and stained with PE-conjugated antibodies against human CD105, CD90, CD73, CD45, CD34,



Page 8 of 45 8

CD19, CD11b, and HLA-DR (1 μl per 106 cells, e Bioscience Inc., San Diego, CA, USA) for 30 min at room temperature in the dark. Cells were then resuspended in 0.5% (v/v) BSA/PBS and analyzed by flow cytometry (BeamCyte-1026). Unstained samples of hPMSCs were selected to determine the surface antigens of their stained counterparts. The experiments were carried out in triplicate in each group. Cell cycle analysis. For cell cycle assay, hPMSCs at 70–80% confluence cultured in hypoxia or normoxia were harvested. Cells were then washed and fixed in 7/3 (v/v) ethanol/water (H2O) overnight at 4 °C. Fixed cells were washed and stained with PI/RNase (BD Biosciences) at room temperature for 30 min. The samples were assessed by using the Beckman Coulter Cytomics FC 500 MPL flow cytometry (Beckman Coulter, Inc., Palo Alto, CA, USA), and analysis was performed using ModFit LT 4.0 (Verity Software House, USA). 2.3 Cell growth curve Approximately 1.7×105 hPMSCs were inoculated in every T25-cm2 cell culture flasks. Three bottles of cells from hypoxic or normoxic atmosphere were taken out every 12 h to count and calculate the mean at successive time points. All cell counts were performed in triplicate. Growth curves were plotted, and growth equations were automatically fitted using GraphPad Prism (GraphPad Software, Inc. USA) with the culture time as the horizontal axis and the number of cells (logarithmic) as the vertical axis. 2.4 Differentiation studies of hPMSCs Osteogenic differentiation. hPMSCs were seeded in six-well culture plates


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(Nunclon™Δ Surface, Denmark) at a density of 2×103/well and cultured with osteogenic medium (OriCell™ hMSC Osteogenic Differentiation Medium, Cyagen Biosciences, Guangzhou, China) to induce osteogenic differentiation under different oxygen concentrations. The medium was changed twice per week. After 3 weeks, the cells were washed with PBS, then fixed in 4% paraformaldehyde (Sigma-Aldrich, St. Louis, MO, USA), and stained using Alizarin Red S (Cyagen Biosciences) for 30 min to confirm osteogenic differentiation. Adipogenic differentiation. Cells were cultivated in adipogenic medium (OriCell™ hMSC Adipogenic Differentiation Medium, Cyagen Biosciences) as per the manufacturer's protocol. Briefly, hPMSCs were cultured in adipogenic differentiation medium A for 3 days and expanded in differentiation medium B for 1 day. After 3 weeks, adipogenesis was assessed by oil red O staining (Cyagen Biosciences). Reverse transcription and quantitative real-time polymerase chain reaction (RT-qPCR). Total ribonucleic acid (RNA) in each group was extracted at 0, 7, 14 and 21 days of osteogenesis induction or adipogenic differentiation using Trizol reagent (Thermo Fisher Scientific Inc.). The concentration and purity of extracted RNA were measured using the SMA1000 ultraviolet (UV) spectrophotometer (Merinton Instrument Inc., Ann Arbor, MI, USA). Reverse transcription was performed using Prime Script™ RT Master Mix (TAKARA Biotechnology (Dalian) Co., Ltd., Liaoning, China) in an Eppendorf Mastercycler® Nexus GX2 (Hamburg, Germany). Complementary deoxyribonucleic acid (cDNA) was mixed with the primer and SYBR® Premix Ex Taq™ II (TAKARA Biotechnology) and subjected to real-time



Page 10 of 45 10

PCR on an Applied Biosystems® 7500 Real-Time PCR System (Thermo Fisher Scientific Inc.). RNA expression levels were compared after normalization with glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The relative expression of target genes was calculated using fold induction (2-ΔΔCT), and the primer sequences are listed in Supporting Information Table S1. 2.5 Metabolite extraction hPMSCs were adjusted to 5 × 105 cells and inoculated in the T75-cm2 cell culture flask (Nunc Flasks Nunclon™Δ with FilterCap) in a total of 84 bottles. After the hPMSCs were stabilized, replacement of fresh media marked the starting point of the experiment and was considered time = 0 h. Samples, including cells and media, were then collected every twelve h for 3 days. Metabolite extraction was carried out based on the protocols described by Luo and Lorenz

17, 23

with some modifications. The flasks were subjected to precooling PBS

(4 °C) 2–3 times, rocked quickly, and aspirated completely before quenching. Then, cells were quenched in 1/1 (v/v) methanol (MeOH)/H2O (-20 °C, 1 ml, Fisher Chemical, Waltham, MA, USA) and scraped with a cell lifter (Orange Scientific, B–1420 Braine–l’Alleud, Belgium). The solution was then transferred into the 1.5-ml centrifuge tube (Thermo Scientific, Waltham, MA, USA), removing 900 µl of supernatant into a new tube after centrifugation at 1200 rpm at 4 °C for 4 min (Beckman Coulter Microfuge 22R centrifuge, Brea, CA, USA). Subsequently, 0.5 ml of 1.0-mm-diameter glass beads (Biospec Products, USA) was added to perform cell lysis. hPMSC lysis was achieved using five periods of 1 min bead–beating at 2500


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rpm with the use of a Touch Mixer Model 232 (Fisher Scientific, USA), alternating with five 1-min incubation on ice-water. After hPMSC lysis, 900 µl of the supernatant was added and then centrifuged to remove cell debris. Cell culture supernatant was collected and filtered through a 0.22-µm syringe filter (Millipore Express® PES Membrane, USA). The culture supernatant was then mixed with precooling MeOH at a ratio 1:3 (200 µl of sample: 600 µl of MeOH). The mixture was vortexed for 30 seconds and centrifuged to precipitate protein. The lysates of cells or supernatants of media containing the metabolites were separately transferred to new vials and dried in a refrigerated CentriVap concentrator system (Labconco, Kansas City, MO, USA). Dried metabolite extracts were stored at −80 °C. 2.6 Dansylation labeling, normalization The dried metabolites from cells or culture supernatant were redissolved in water (LC-MS grade, Sigma-Aldrich).

12

C-dansyl chloride (light chain) purchased from

Sigma-Aldrich (St. Louis, MO) was used to label individual samples, and chloride

(heavy

chain)

(mcid.chem.ualberta.ca)

24

synthesized

in-house

as

previously

13

C-dansyl

described

was used to label the pooled sample. The pool was

prepared by mixing the equal mole amount of aliquots from each of the samples, and then labeled using 13C-dansyl chloride. For labeling reaction, 25 μl of the metabolite extract was aliquoted and mixed with 12.5 μl of 0.5 M sodium carbonate/sodium bicarbonate buffer (pH 9.5; Sigma-Aldrich) and 12.5 μl of acetonitrile (ACN) (Sigma-Aldrich). After the solution was spun down, 25 μl of freshly prepared 12

C-dansyl chloride solution at 18 mg/ml in ACN (Sigma-Aldrich) or


13

C-dansyl


Page 12 of 45 12

chloride solution at18 mg/ml in ACN was added. The reaction was carried out for 1 h at 40 °C. Afterwards, 5 μl of 250 mM sodium hydroxide solution (NaOH) (Sigma-Aldrich) was added, followed by incubation at 40 °C for 10 min to quench the remaining labeling reagent. Ultimately, 25 μl of 425 mM formic acid (FA) (Sigma-Aldrich) in ACN/H2O was added to the mixture to neutralize the solution. For sample normalization, the

12

C-labeled individual samples were separately

analyzed by using liquid chromatography−ultraviolet analysis (LC–UV) (Agilent 1290 LC) with 338 nm detection to measure the total concentration of labeled metabolites in each sample. The column used was a Waters ACQUITY UPLC BEH C18 column with a dimension of 2.1 mm × 5 cm (particle size: 1.7-μm; pore size: 130-Å). Solvent A for LC graident was 0.1% (v/v) FA in H2O, while solvent B was 0.1% (v/v) FA in ACN. The elution profile for fast step-gradient was: t = 0 min, 5% B; t = 1.00 min, 5% B; t = 1.01 min, 98% B; t = 2.00 min, 98% B; t = 2.50 min, 5% B; and t = 6.00 min, 5% B. The flow rate used for LC-UV was 500 μl/min with 1 μl sample injection. 2.7 LC–MS For LC-MS analysis, an Agilent electrospray ionization time-of-flight (ESI-TOF) mass spectrometer (Model 6230, Agilent, Palo Alto, CA) was connected to an Agilent 1290 UPLC with a Waters ACQUITY UPLC BEH C18 column (dimension: 2.1 mm × 10 cm; particle size: 1.7-μm; pore size: 130-Å) . The ion source and TOF were operated under the following conditions: nitrogen nebulizer gas =1.38 Bar; dry gas flow=5 l/min; dry temperature=325 ºC; capillary voltage=4000 V; end plate offset =120 V; mass range= was up to 1700 m/z; and spectra rate=1 Hz. The TOF resolving


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power was about 11,000 FWHM at m/z 622. The MS spectra were acquired in positive ion detection mode. Solvent A for LC-MS was 0.1% (v/v) FA in H2O, while solvent B was 0.1% (v/v) FA in ACN. The gradient used was: 0 min, 15% B; 0.3 min, 15% B; 3.5 min, 35% B; 18 min; 65% B; 21 min, 95% B; 26 min, 95% B; 27 min, 100% B; 32 min, 100% B; 33 min,15% B; and 40 min, 15% B. The flow rate was 250 μl/min. The sample injection volume was fixed at 10 µl except in the injection amount optimization experiment, where the injection volume varied. All individual samples were arranged in a random sequence during the LC–MS runs. Equal mole amounts of the 12C-labeled sample and the 13C-labeled pool were mixed for LC-MS analysis. To gauge the instrument performance, a quality control (QC) sample was injected every 10 sample runs and the QC sample was a 1:1 mix of 12C-labeled and 13C-labeled pool. 2.8 Data processing and analysis The metabolomic data were analyzed using SPSS 21.0 (version 21.0; IBM Corp., NY, USA). All variables were analyzed for a normal distribution and compared with Student’s t-test, then expressed as the mean ± standard deviation. p < 0.05 was considered statistically significant. IsoMS software

25

was used to process the LC-MS data to produce a

metabolite-intensity table in CSV. It involved picking the 12C-/13C-labeled metabolite peak pairs, filtering out the redundant peak pairs such as adduct ions and dimers, calculating the peak pair intensity ratios, aligning the same peak pairs detected from multiple LC-MS runs. In the metabolite-intensity table, there were missing ratio values in some peak pairs due to their low intensities. These peak pairs were not picked by the IsoMS program initially. Another software, zero–filling program, 26 was used to search for the missing values in the raw mass spectral data, and if they were found, a peak ratio was calculated from the mass spectrum and added to the peak ratio



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table.

Finally,

IsoMS–Quant

27

was

used

to

determine

the

chromatography–peak–intensity ratio of a 12C-/13C-pair. The final metabolite-intensity table containing metabolites that were detectable in more than 80% of the samples in one group was uploaded to the statistical software, SIMCA-P+ 12.0 (Umetrics, Umeå, Sweden) for multivariate analysis includingprinciple component analysis (PCA) and OPLS-DA. Positive metabolite identification was performed by matching both mass and retention time to those of the dansyl standard library using DnsID identification was performed by searching accurate masses against

28

. Putative

those of the

metabolites in the MyCompoundID libraries which consist of 8,021 known human endogenous metabolites in the human metabolome database (HMDB) ( and

375,809

predicted human metabolites with one reaction inthe Evidence-based Metabolome Library (EML) () using MyCompoundID

29

. The mass accuracy tolerance window

used for accurate mass search was 0.008 Da. 3 Results 3.1 Morphology and phenotype profiles of hPMSCs in normoxic and hypoxic environment hPMSCs cultured in the low oxygen group (5% O2) became relatively smaller, showing a naive spindle-shaped morphology (Figure 1A-D, G), which represents a more primitive stem cell state compared with their control state in normoxia. The surface antigens of hPMSCs were analyzed by flow cytometry, which revealed that the cells expressed typical immunophenotypic characteristics consistent with those of a mesenchymal lineage. Expectedly, hPMSCs that were grown in a hypoxic atmosphere showed no difference in immunophenotype from cells grown in a normoxic atmosphere, which were positive for CD105, CD90, and CD73 and negative


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for CD45, CD34, CD19, CD11b and HLA-DR (Figure 1K-L). In general, these data confirmed that a physiological oxygen concentration (5% O2) had no effect on immunophenotype, but it contributed to a more primitive morphology. 3.2 Analysis of the proliferation ability of hPMSCs cultured under different oxygen concentrations The exponential growth profile assays showed that the doubling time of hPMSCs at a physiological O2 concentration (19.6 h) was significantly earlier than that of cells grown at an atmospheric O2 concentration (23.8 h) (Figure 1I-J). The growth rate was calculated using the following equation: gr  ln( N (t ) / N (0)) / t

30

(N (t) = the

number of cells at time t, N (0) = the number of cells at time = 0), and significant differences were found between 5% O2 (0.034±0.005 h–1) and 21% O2 (0.029±0.003 h–1). For cell cycle analysis, 62.2% of hypoxia-treated cells were in G0/G1 phase, 30.7% in S phase, and 7.1% in G2/M phase compared to original cultured hPMSCs (G0/G1, 68.0%; S, 24.6%; and G2/M, 7.4%) (Figure 1E-F, H). This increase of hPMSCs in the S phase confirmed the positive effect of low oxygen concentrations on cell proliferation. Taken together, these results suggested that low oxygen tensions favored higher proliferation ability. 3.3 Evaluation of differentiation potential of hPMSCs submitted to different oxygen tensions The effect of oxygen concentration on the osteogenesis of hPMSCs was examined by culturing cells under varying oxygen conditions for 21 days and staining them with Alizarin Red S staining to detect their mineralization status. hPMSCs grown in a hypoxic atmosphere showed an increase in calcium deposition in comparison to hPMSCs grown in normoxia based on their osteogenic induction (Figure 1M, N). The relative mRNA expression levels of osteogenic-related markers OGN and RUNX2 in



Page 16 of 45 16

both groups gradually increased on day 21, and the levels of those genes were higher in the hypoxic group than in the normoxic group (Figure 1O, P). We also investigated the effect of hypoxia on hPMSC adipogenesis. hPMSCs exposed to low oxygen tension showed a significant increase in lipid droplets, which was identified by oil red O staining (Figure 1Q, R). The relative mRNA expression levels of CFD and PPAR-γ, adipogenic-related markers, were higher in the hypoxic group than in the normoxic group (Figure 1S, T). The above results indicate that low oxygen concentration promotes hPMSC differentiation ability. 3.4 Metabolomic analysis of hPMSCs and culture medium Using the workflow shown in Supporting Information Figure S1, experimental analyses of 42 cell and 42 media samples were carried out. These samples were composed of 14 groups of cells (3 samples per group) and 14 groups of media (3 samples per group) that were separately collected at multiple time points from the hypoxic group or normoxic group. The average concentrations of

12

C-labeled

metabolites for different groups were almost the same, ranging from 2.07 to 2.37 mM. However, the concentration of individual samples varied by more than 2.5-fold (i.e., between the lowest and the highest), indicating the importance of performing sample amount normalization before quantitative metabolomic analysis. In this work, sample normalization was done by mixing equal mole amounts of

12

C-labeled individual

samples and the 13C-labeled pool for LC–MS analysis. In the replicate analysis of the 1:1 12C-/13C-labeled mixture, we were able to detect 2987 and 2860

12

C/13C peak pairs from hPMSCs and the culture supernatant,

respectively. Identification of amine/phenol-containing metabolites was carried out based on mass and retention time (RT) matches with a mass tolerance of 0.008 Da and a RT tolerance of 30 s to a dansyl standard library containing 275 labeled standards.


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We also used MyCompoundID to search the accurate mass of each peak pair with a mass tolerance of 15 ppm against the HMDB and EML compound libraries. For a total of 2987 peak pairs detected in all the cell samples, 63 metabolites were positively identified using the dansyl library (see online Supporting Information Table S2A), 945 peak pairs were matched using an accurate mass search in the HMDB library (see online Supporting Information Table S2B) and 2568 peak pairs were matched to the EML library (see online Supporting Information Table S2C). Thus, 2568 peak pairs out of the 2987 pairs detected in cells (86.0%) could be either positively identified or mass-matched to metabolite structures. Similarly, for a total of 2860 peak pairs detected in all the medium samples, 52 metabolites were positively identified using the dansyl library (see online Supporting Information Table S3A), 795 peak pairs were matched using an accurate mass search in the HMDB library (see online Supporting Information Table S3B) and 2411 peak pairs matched to the EML library(see online Supporting Information Table S3C). Thus, 2411 peak pairs out of the 2860 pairs detected in media (84.3%) could be either positively identified or mass-matched

to

metabolite

structures.

The

above

results

indicate

that

12

C-/13C-dansylation LC–MS can detect and quantify a large number of metabolites in

cells or culture supernatant with relatively high submetabolome coverage (i.e., thousands of metabolites). 3.5 Metabolic profiling analysis of hPMSCs Multivariate statistical analysis was applied using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to evaluate whether oxygen concentrations affected the metabolic profile of hPMSCs. The QC samples from cells or supernatant clustered close together, demonstrating good



Page 18 of 45 18

instrument stability throughout dansylation LC–MS analysis. As shown in Figure 2A, PCA plots (first principal components (PCs): 31% and second PCs: 19.9%) showed that the hypoxic and normoxic groups of hPMSCs had poor separation, whereas the two groups were clearly separated in OPLS-DA analysis (first T score: 10.8% and orthogonal T score: 11.6%, Figure 2B), indicating that oxygen could alter cellular metabolomics. As expected, there was no clear separation of the medium groups in PCA score scatter plots (first PCs: 33% and second PCs: 16.1%, Figure 2C), but they still showed a separation in OPLS-DA analysis (first T score: 3.5% and orthogonal T score: 17.4%, Figure 2D), illustrating that hypoxia could change the metabolomic profiles of the medium. Importantly, a small percentage of variances (3.5%) was captured by first T scores in the supernatant in comparison to hPMSCs, which suggests that the extent of the medium metabolome change is smaller than that of the cell itself. This observation is supported by the volcano plots (Figure 2E-F). In total, 126 and 13 peak pairs were up–regulated in the cells and medium, respectively, and 414 cell and 32 culture medium metabolites were down–regulated in response to oxygen concentration changes. Clearly, the number of changes in the medium is far less than the number of changes in cell metabolites. Based on the growth profile assays of hPMSCs (Figure 1I-J), we reclassified cell samples into the lag phase, log phase, and stationary phase. The OPLS-DA plots displayed separation of the three different phases under hypoxia or normoxic conditions (Figure 2G-H). During exponential growth, the cells were in optimal growth conditions for protein synthesis with strong metabolic activity and were stable


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and uniform, which facilitated comparison of the experimental results. Therefore, only data from the exponential growth phase (time = 24 h, 36 h, 48 h, and 60 h) were used for the following metabolomics analysis. 3.6 Hypoxia-related biomarker analysis of the hPMSC and culture medium In cell biology, a biomarker or biological marker, is defined as a "biochemical or molecular alteration in cells". We prefer to use the term biomarkers for monitoring measurable indicators of certain biological states or conditions and identifying major metabolites that may cause significant changes in metabolomics. First, we used volcano plots with thresholds of ≥ 1.2-fold change and p ≤ 0.05 to investigate how metabolites change over the log phase. In this study, the cell and medium samples were analyzed at four time points (time = 24 h, 36 h, 48 h, and 60 h), whereas the data at time = 0 h were used as the comparison baseline. The binary comparison of cell groups is shown in Supporting Information Figure S2A-B, and the comparison of medium groups is seen in Supporting Information Figure S2C-D. Based on the list of significant metabolites found in each comparison, the metabolites that changed at three or four consecutive time points were determined by Venn diagram analysis. Figure 2I-J shows the Venn diagram of cell groups and Figure 2K-L shows that of the medium groups. Since we were particularly interested in metabolites with varying concentrations of oxygen, we compared the metabolites that exhibited statistically significant temporal changes in the normoxic and hypoxic groups (Figure 2M-N). We found that there were

85

hypoxia-specific

biomarkers,

17

common


metabolites,

and

19


Page 20 of 45 20

normoxia-specific candidates from the cell samples (see online Supporting Information Table S4A-C). Similarly, 40 hypoxia-specific biomarkers, 41 common metabolites and 14 normoxia-specific candidates were found in the medium samples (see online Supporting Information Table S4A-C). In our analysis, if the fold changes did not vary over the time course (24 h to 60 h), the metabolites were not considered to be good candidates of hypoxia-related biomarkers. On the other hand, if the metabolites displayed a pattern of level changes, the metabolites were considered to be useful biomarkers. Therefore, the changes of these 102 cell metabolites and 81 culture medium metabolites were deemed to be caused by varying oxygen concentrations, and thus, these metabolites may be used as potential hypoxia-related biomarker candidates. Two major patterns were adopted to identify hypoxia-related biomarkers, and metabolites that did not meet these particular patterns of change were discarded. The first pattern was a gradual increase or decrease of metabolite levels in the hypoxic and normoxic groups as a function of time, and the second pattern showed changes in mass spectral peak ratios (increase or decrease) in one group while the metabolite concentration of the other group remained unchanged. Among the hypoxia-related biomarker candidates of hPMSCs, only 3 followed pattern 1, and 7 conformed to pattern 2. We narrowed the 81 culture medium metabolites down to 6 correlated markers (pattern 1: 2 and pattern 2: 4). Table 1 provides a summary of the biomarkers. Out of the 10 potential biomarkers of hPMSCs, seven (L-arginine, citrulline, L-proline,

threonine,

5'-methylthioadenosine,


pantothenic

acid,

and

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gamma-aminobutyric acid) were positively identified using the library of dansyl standards, and three (farnesyl cysteine, canavaninosuccinate and cysteine glutathione disulfide) were putatively identified using HMDB. Out of the 6 biomarkers for the medium, half (alanine, glycyl-l-leucine and glycyl-phenylalanine) were positively identified, and three (cystine, 3-dehydroxycarnitine and hydroxyphenylacetylglycine) were putatively identified. 3.7 Metabolic pathways MetaboAnalyst 3.0 was used to apply the 7 positively identified metabolites of cells and 3 positive biomarkers of culture supernatant in an examination of metabolic pathways that were significantly altered by low oxygen concentrations. As shown in Figure 3, the overview of pathway analysis for the cells and media samples was based on p values from pathway enrichment analysis and pathway impact values from pathway topological analysis. Table 2 provides a summary of the metabolic pathway analysis (MetPA) results, including a total of 16 biologically relevant metabolic pathways (cell: 11 and medium: 6). A higher -log(p) and higher impact value reflect more relevant pathways in hypoxia, which mainly mapped to 3 significantly affected pathways in hPMSCs containing arginine and proline metabolism (arginine, citrulline, proline and gamma-aminobutyric acid), pantothenate and coenzyme A (CoA) biosynthesis (pantothenic acid), and alanine, aspartate and glutamate metabolism (gamma-aminobutyric acid). For medium, only one hit, alanine, was found in alanine, aspartate and glutamate metabolism.



Page 22 of 45 22

Figure 4 provides a schematic illustration of the hPMSC and media metabolic pathways. In particular, we observed gradually decreasing levels of proline, citrulline, arginine and increasing levels of downstream metabolites gamma-aminobutyric acid (GABA). In pantothenate and CoA biosynthesis, a gradual decrease in pantothenic acid was also observed. Interestingly, in alanine, aspartic acid and glutamic acid metabolism in the medium, an increase in alanine means that the hPMSCs submitted to hypoxia secreted more alanine. Figure 5 shows the changing patterns of 6 biomarkers (arginine, citrulline, proline, GABA, pantothenic acid and alanine), and the level changes (i.e., peak pair intensity ratio relative to the pool) of the other 10 biomarkers are shown in Supporting Information Figure S3. In summary, an overview of the pathway analysis suggested that the physiological oxygen concentration did cause significant changes in the metabolic networks of hPMSCs. 4 Discussion Oxygen concentration affects cell behavior and function, serving as a key regulator of cellular bioenergetics

31

. hPMSCs are exposed to low oxygen tension in vivo.

Currently, research on the influence of hypoxia on MSCs is mainly focused on detecting immunophenotypes, pluripotency, renewal ability, differentiation ability, migration, and viability

32-35

. In our work, we found that hPMSCs subjected to low

oxygen concentrations had smaller sizes, higher proliferation, greater differentiated ability, and no difference in immunophenotype. We were particularly interested in metabolic changes caused by oxygen concentration differences. Metabolic homeostasis is controlled by cellular demand or the supply of metabolites from


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culture supernatant. It is necessary to perform metabolic uptake and conversion analysis because culture supernatant metabolite analysis provides information on metabolite utilization and production during culturing, which can provide useful insights into cell metabolism and cellular pathways used for growth

21, 22

. Therefore,

in this work, we developed and applied a quantitative workflow based on CIL LC–MS to examine the impact of oxygen on the metabolic profiling of hPMSCs. Unlike conventional LC–MS, dansylation LC–MS allows high-coverage detection of the amine/phenol submetabolome, which provides 10– to 1000–fold signal enhancement

16

. From the 84 sample runs, 2987 and 2860 peak pairs or metabolites

were detected in cells and culture supernatant, respectively, and 63 hPMSCs and 52 media metabolites were positively identified. We used PCA/OPLS-DA to provide an overview of the hypoxic groups and normoxic groups and combined analyses of volcano plots, Venn diagrams and time-dependent intensity ratio plots to determine potential biomarkers. For metabolic profile analysis, the metabolomic data generated from hPMSCs showed a clear separation between the hypoxic group and the normoxic group in OPLS plots. Our metabolic

analysis generated from culture

supernatant also displayed a separation in OPLS analysis. Volcano plots indicated that the metabolite change of the supernatant was less than that of the cells (45 vs 550). Pair-wise comparisons at different time points of 5% O2 or 21% O2 (time = 24 h to 60 h vs time = 0 h) revealed that many metabolites were significantly altered during the exponential growth phase. It was noted that the up-regulation or down-regulation of these differential metabolites in the culture supernatant reflected the production or



Page 24 of 45 24

consumption of metabolites, providing information on metabolite utilization and production in hPMSCs. Subsequently, a comparison between hypoxic groups and normoxic controls indicated that a number of metabolites were significantly altered by physiological oxygen concentrations (5% O2). Among them, 10 metabolites of cells and 6 metabolites of media were selected for in-depth characterization as potential biomarkers of hypoxia. These positively identified biomarkers from cell and culture supernatant were used to perform metabolic pathways analysis, indicating a perturbation in arginine and proline metabolism, pantothenate and CoA biosynthesis and alanine, aspartate, glutamate metabolism. Amino acids are fundamental factors in the synthesis of biomass components, such as, DNA, RNA, and proteins

36

; their

levels are closely related to cell development, proliferation, differentiation and self-renewal 37-39. Analysis of the arginine and proline pathway and alanine, aspartate, glutamate metabolism in hypoxia-treated hPMSCs revealed a significant decrease in proline, citrulline, and arginine and an obvious increase in GABA. Arginine is downstream of citrulline in the arginine and proline pathway. It seems likely that a decreased level of arginine is not due to an enzyme (arginosuccinate synthase/arginosuccinate lysase) activity change; rather, it is only due to the down-regulation of citrulline and proline. GABA is the downstream product of 4-guanidinobutanoic acid in alanine, aspartate, glutamate metabolism, and it can be also obtained from arginine degradation. In conclusion, hPMSCs appear to synthesize more GABA using proline, citrulline and arginine under hypoxic conditions. Consistent with our findings, Carla António

40

used gas chromatography mass


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spectrometry (GC–MS) to study the metabolite profiling of soybean roots, suggesting that both alanine metabolism and GABA shunting were activated during hypoxia. GABA is widely known as the chief inhibitory neurotransmitter in the mammalian central nervous system 41. This molecule is also used as a common substance in hard tissue regeneration. Additionally, GABA is a precursor of succinic acid, and consequently, it enters the citric acid cycle as a usable source of energy. However, Rocha et al.

42

demonstrated that the reaction from GABA to succinic acid required

NAD+, which became limited under a hypoxic atmosphere. Other researchers have indicated that the production of GABA can contribute to stabilizing cytosolic pH at different stress conditions, such as hypoxia 43. A study on the biological characteristics of gamma-aminobutyric acid/lactam analogs on ovine mesenchymal stem cells showed that they can stimulate MSC proliferation

44

. Another work indicated that

active GABA secretion by MSC increased MSC-mediated immunosuppression 45. In general, during the exponential growth phase, stem cells tend to utilize the glycolytic pathway to obtain more ATP for cell proliferation at physiological hypoxia, whereas synthetic pathways such as alanine-glucose synthesis (gluconeogenesis) are relatively inhibited

46-48

. It is well documented that stem cells such as MSCs can

secrete more alanine than differentiated cells

49

. Our results indicated that alanine

content (in hPMSCs and in supernatant) was elevated when hPMSCs were cultured under hypoxia (Figure S4). In the exponential growth phase, the proliferation rate of hPMSCs in hypoxia is higher than in normoxia, which requires the consumption of a large amount of energy. The glycolytic pathway is predominated to provide ATP, and



Page 26 of 45 26

the alanine-glucose pathway (gluconeogesis) is relatively inhibited, resulting in the accumulation of alanine and lactate. When hPMSCs almost reached the stationary phase, the alanine-glucose pathway is gradually becoming dominated, leading to no significant difference in alanine concentration under different oxygen conditions (Figure S5). Similar to our results, Griffin et al found that hypoxia-cultured brain tissues also presented higher alanine content 50. Our results demonstrated that aspartate and glutamate levels in hPMSCs, asparagine and glutamine levels in culture supernatant, of the hypoxia-cultured groups were significantly lower than those of normoxia-cultured groups (Figure S6). Asparagine and glutamine were degraded to aspartate and glutamate after being transported from the medium into the cytoplasm. This indicates that hPMSCs utilizes more glutamate and aspartate in hypoxia. Figure S5A shows that aspartate, glutamate and malate aspartate shuttle (MAS) are closely related, increased aspartate or glutamine consumption under hypoxia may play a role to increase the activity of MAS. The speculation is that aspartate and glutamate maximize the energy produced by glycolysis through MAS. At present, our dansylation labeling LC–MS mainly detects amines and phenols, new optimized labeling reagents are needed for detection of malate, oxaloacetate, α-ketoglutarate in the MAS in the future. During myocardial ischemia (hypoxia), MAS played a key role in the inhibition of mitochondrial respiration and prevention of further myocardium damage

51

. So our results are in

general agreement with previous evidence. We have also identified pantothenic acid (positively) and L-cystine (putatively) as


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being differentially expressed in the pantothenate and CoA biosynthesis pathway during hPMSCs exposure to 5% O2. It is worth noting that pantothenic acid is down–regulated and that the concentration of L-cystine in the growth medium decreases. Pantothenic acid is a precursor of coenzyme A and is mainly derived from the metabolism of alanine, pyruvate (a glycolytic product) and cysteine

52, 53

.

L-cystine is rapidly degraded to cysteine after it is transported from the growth medium into the cytoplasm. Cysteine is also involved in CoA synthesis 53. In general, it seems that the cell is trying to synthesize more CoA in hypoxic conditions. Coenzyme A plays an important role in the oxidation and synthesis of fatty acids and the oxidation of pyruvate in the citric acid cycle, which is associated with energy metabolism

54

. Currently, there is growing evidence that coenzyme A is directly

involved in gene expression control, influencing the pluripotency of stem cells 55-57. Collectively, our studies and observations underscore the importance of physiological oxygen concentrations as a metabolic regulator of placenta-derived mesenchymal stem cells by enhancing the metabolism of the above three metabolic pathways. 5 Conclusions Maintenance of the physiological properties of hPMSCs in vitro culture is an important requirement for cell-based therapies. During cultivation, metabolic activity appears to change when hPMSCs are exposed to original cell culture oxygen concentrations, including changes in arginine and proline metabolism, pantothenate and CoA biosynthesis, and alanine, aspartate and glutamate metabolism. Oxygen was found to significantly impact hPMSC morphology, proliferation, differentiation and



Page 28 of 45 28

metabolism (amine/phenol submetabolome). However, the submetabolomes of other chemical groups, such as acids, aldehydes, ketones and hydroxyls, remain to be further clarified using new reagents. Nevertheless, our current results provide an invaluable resource for understanding hPMSC metabolism and can be used to guide the rational design of culture methods and the development of novel culture media.

Funding sources This work was supported by the National Key Research and Development Program of China (No. 2016YFA0101001), National Natural Science Foundation of China (No. 81471794), and Chinese High Tech Research & Development (863) Program (No. 2013AA020102).

Acknowledgments We thank the staff and the patients of the First Affiliated Hospital, College of Medicine, Zhejiang University for providing the placenta tissue.

Conflict of interest No competing financial interests exist regarding the subject matter or materials discussed in the presented work.


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Supporting Information

Figure S1：Workflow diagram Figure S2：Volcano plot showed how metabolites changed over log phase Figure S3：The hypoxia-related biomarkers of hPMSC and its corresponding culture medium Figure S4: Peak pair intensity ratio changes of cellular alanine as a function of time from 12h to 72h Figure S5: Malate aspartate shuttle (MAS) pathway and alanine pathway schematic Figure S6: Peak pair intensity ratio changes of aspartate, glutamate, asparagine and glutamine Table S1: Primer sequences used for qRT-PCR Table S2A: List of cellular metabolites identified by mass and retention time matches to the dansyl standards library Table S2B; List of cellular metabolites identified by mass match to the HMDB metabolite library Table S2C; List of cellular metabolites identified by mass match to the EML predicated metabolite library Table S3A: List of culture-supernatant metabolites identified by mass and retention time matches to the dansyl standards library Table S3B. List of culture-supernatant metabolites identified by mass match to the



Page 30 of 45 30

HMDB metabolite library Table S3C: List of culture-supernatant metabolites identified by mass match to the EML predicated metabolite library Table S4A: List of peak pairs deemed to be significantly changed in binary comparison of hypoxic group vs. normoxic group (hypoxia-specific biomarkers) Table S4B: List of peak pairs deemed to be significantly changed in binary comparison of hypoxic group vs. normoxic group (common metabolites) Table S4C: List of peak pairs deemed to be significantly changed in binary comparison of hypoxic group vs. normoxic group (normoxia-specific candidates)


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33. Annabi, B.; Lee, Y. T.; Turcotte, S.; Naud, E.; Desrosiers, R. R.; Champagne, M.; Eliopoulos, N.; Galipeau, J.; Beliveau, R., Hypoxia promotes murine bone-marrow-derived stromal cell migration and tube formation. Stem Cells 2003, 21, (3), 337-47. 34. Dionigi, B.; Ahmed, A.; Pennington, E. C.; Zurakowski, D.; Fauza, D. O., A comparative analysis of human mesenchymal stem cell response to hypoxia in vitro: Implications to translational strategies. J Pediatr Surg 2014, 49, (6), 915-8. 35. Yun, S. P.; Han, Y. S.; Lee, J. H.; Yoon, Y. M.; Yun, C. W.; Rhee, P.; Lee, S. H., Role of hypoxia mediated cellular prion protein functional change in stem cells and potential application in angiogenesis (Review). Mol Med Rep 2017, 16, (5), 5747-5751. 36. Epstein, C. J.; Smith, S. A., Amino acid uptake and protein synthesis in preimplanatation mouse embryos. Dev Biol 1973, 33, (1), 171-84. 37. Shyh-Chang, N.; Locasale, J. W.; Lyssiotis, C. A.; Zheng, Y.; Teo, R. Y.; Ratanasirintrawoot, S.; Zhang, J.; Onder, T.; Unternaehrer, J. J.; Zhu, H.; Asara, J. M.; Daley, G. Q.; Cantley, L. C., Influence of threonine metabolism on S-adenosylmethionine and histone methylation. Science 2013, 339, (6116), 222-6. 38. Tan, B.; Yin, Y.; Kong, X.; Li, P.; Li, X.; Gao, H.; Li, X.; Huang, R.; Wu, G., L-Arginine stimulates proliferation and prevents endotoxin-induced death of intestinal cells. Amino Acids 2010, 38, (4), 1227-35. 39. Chen, W.; Jia, W.; Wang, K.; Zhou, Q.; Leng, Y.; Duan, T.; Kang, J., Retinoic acid regulates germ cell differentiation in mouse embryonic stem cells through a Smad-dependent pathway. Biochem Biophys Res Commun 2012, 418, (3), 571-7. 40. Antonio, C.; Papke, C.; Rocha, M., Regulation of Primary Metabolism in Response to Low Oxygen Availability as Revealed by Carbon and Nitrogen Isotope Redistribution. Plant Physiol 2016, 170, (1), 43-56. 41. McCormick, D. A., GABA as an inhibitory neurotransmitter in human cerebral cortex. J Neurophysiol 1989, 62, (5), 1018-27. 42. Rocha, M.; Licausi, F.; Araujo, W. L.; Nunes-Nesi, A.; Sodek, L.; Fernie, A. R.; van Dongen, J. T., Glycolysis and the tricarboxylic acid cycle are linked by alanine aminotransferase during hypoxia induced by waterlogging of Lotus japonicus. Plant Physiol 2010, 152, (3), 1501-13. 43. Shelp, B. J.; Mullen, R. T.; Waller, J. C., Compartmentation of GABA metabolism raises intriguing questions. Trends Plant Sci 2012, 17, (2), 57-9. 44. Sauerbier, S.; Gutwald, R.; Wiedmann-Al-Ahmad, M.; Wolkewitz, M.; Haberstroh, J.; Obermeyer, J.; Kuenz, A.; Betz, H.; Wolter, F.; Duttenhoefer, F.; Schmelzeisen, R.; Nagursky, H.; Proksch, S.; Al-Ahmad, A., Effect of gabapentin-lactam and gamma-aminobutyric acid/lactam analogs on proliferation and phenotype of ovine mesenchymal stem cells. Int J Oral Maxillofac Implants 2013, 28, (5), e230-8. 45. Urrutia, M.; Fernandez, S.; Gonzalez, M.; Vilches, R.; Rojas, P.; Vasquez, M.; Kurte, M.; Vega-Letter, A. M.; Carrion, F.; Figueroa, F.; Rojas, P.; Irarrazabal, C.; Fuentealba, R. A., Overexpression of Glutamate Decarboxylase in Mesenchymal Stem Cells Enhances Their Immunosuppressive Properties and Increases GABA and Nitric Oxide Levels. PLoS One 2016, 11, (9), e0163735. 46. Ryall, J. G.; Cliff, T.; Dalton, S.; Sartorelli, V., Metabolic Reprogramming of Stem Cell Epigenetics. Cell Stem Cell 2015, 17, (6), 651-662. 47. Silva, M. M.; Rodrigues, A. F.; Correia, C.; Sousa, M. F.; Brito, C.; Coroadinha, A. S.; Serra, M.; Alves, P. M., Robust Expansion of Human Pluripotent Stem Cells: Integration of Bioprocess Design With Transcriptomic and Metabolomic Characterization. Stem Cells Transl Med 2015, 4, (7), 731-42.



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Figures with Legends Figure 1. Comparison of general biological characteristics between hypoxic and normoxic human placenta-derived mesenchymal stem cells (hPMSCs). (A-D) Morphologies (4×, scale bars, 100μm) and sizes (FSC vs. SSC assays) of the hypoxic and normoxic cells. (E) Cell cycle profile of normoxic hPMSCs. (F) Cell cycle profile of hypoxic hPMSCs. (G) Forward scatter parameter results showed that hPMSCs became comparatively smaller in the hypoxic conditions. FSC, forward scatter; SSC, side scatter. (H) Quantitative analyses of the data showed a decrease in G0/G1 phase and an increase in S phase when hPMSCs exposed to hypoxia. (I) Growth profile of hPMSCs cultured at 5% O2 and 21% O2. (J) The semi-log plot of hPMSCs under different oxygen concentration. (K) Analysis of immunophenotype of normoxic hPMSCs. (L) Analysis of immunophenotype of hypoxic hPMSCs. (M) Osteogenic differentiation of normoxic hPMSCs was identified by alizarin red staining (10×). (N) Hypoxic hPMSCs that had undergone osteogenic differentiation (10×). (O-P) The qRT-PCR analyses of osteogenic-related markers (OGN, RUNX2) of hPMSCs cultured in osteogenic medium at 0, 7, 14 and 21 days. (Q) Adipogenic differentiation of normoxic hPMSCs was identified by oil red-O staining (20×). (R) Hypoxic hPMSCs that had undergone adipogenic differentiation (20×). (S-T) The qRT-PCR analyses of adipogenic-related markers (CFD and PPAR-γ) of hPMSCs cultured in adipogenic medium at 0, 7, 14 and 21 days. MSC-H: Hypoxic hPMSCs, MSC-N: Normoxic hPMSCs. All tests were conducted in triplicate. The data are means±standard deviation and were statistically analyzed using Student’s t-test.


*P


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< 0.05, **P < 0.01, *** p < 0.001.


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Figure 2. Metabolome profiling analysis and Venn diagram for comparisons of numbers of significant peak pairs. (A) Principal component analysis (PCA) score plots and (B) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots for cells as a function of time from 0 h to 72 h. (C) PCA score plots and (D) OPLS-DA score plots for culture medium over a period of 3 days. MSC-H group: hPMSCs cultured under hypoxic conditions. MSC-N group: hPMSCs cultured under normoxic conditions. CM-H group: Culture medium collected from the MSC-H group. CM-N group: Culture medium collected from the MSC-N group. (E) Volcano plots for cells. (F) Volcano plots for culture supernatant. Volcano plot analyses were used to determine the significant metabolites that separated the normoxic and hypoxic groups over a period of 3 days. Data points with fold changes > 1.2 and p < 0.5 are labeled in red. (G) OPLS-DA plots of different culture stages for the MSC-H group. (H) OPLS-DA plots of different culture stages for the MSC-N group. A-H: the lag phase (0 h and 12 h) of the MSC-H group. B-H: the log phase (from 24 h to 60 h) of the MSC-H group. C-H: the stationary phase (72 h) of the MSC-H group. A-N: the lag phase of the MSC-N group. B-N: the log phase of the MSC-N group. C-N: the stationary phase of the MSC-N group. Venn diagram showing the number of metabolites that are significantly different (fold change > 1.2, p < 0.05) between the log phase (time = 24 h, 36 h, 48 h and 60 h) and lag phase (time = 0 h) samples in (I) cells exposed to normoxia, (J) cells exposed to hypoxia, (K) culture medium exposed to normoxia and (L) culture medium exposed to hypoxia. MSC-H: Hypoxic hPMSCs, MSC-N: Normoxic hPMSCs, CM-H: Hypoxic culture medium, CM-N: Normoxic



Page 38 of 45 38

culture medium. 24, 36, 48 and 60: the log phase time at 24, 36, 48, and 60 h, 0: the lag phase time at 0 h. A comparison of the metabolites that exhibit statistically significant changes at three or four consecutive time points between the normoxic and hypoxic groups of (M) hPMSCs and (N) culture supernatant.


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Figure 3. Metabolic pathway analysis results. (A) Overview of pathway analysis based on selected cell metabolites. (B) Overview of pathway analysis based on selected culture supernatant metabolites.



Page 40 of 45 40

Figure 4. Schematic illustration of three metabolic pathways (arginine and proline metabolism, pantothenate and CoA biosynthesis, and alanine, aspartate and glutamate metabolism). The metabolites in blue boxes display significant changes as a function of time from 24 h to 60 h (metabolite concentration that is consistently up or down). The metabolites in yellow boxes can be detected by dansylation LC–MS, but their changes did not follow a pattern of sustained increase or decrease. The pink boxes represent undetectable metabolites.


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Figure 5. The hypoxia-related biomarkers of hPMSCs and culture supernatant. Peak pair intensity ratio changes of six potential biomarkers over the time course (24 h to 60 h). The data are presented as the mean ± S.D. (error bars). *p < 0.05, **p < 0.01, ***p < 0.001.


Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

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Table 1. List of peak pairs deemed to be hypoxia-related biomarkers. X

rt

mz_light

mz_heavy

mz

distance

int_light

nCharge

nTag

HMDB

DNS

Biomarkers of cells 831

128.466442

408.1694696

410.1757243

174.1111496

2.006254414

1822117.908

1

1

L-Arginine

1049

159.7489962

409.1550322

411.1611717

175.0967122

2.006139621

39204.63715

1

1

Citrulline

1921

239.3005943

337.1242172

339.1302474

103.0658972

2.006030348

1494201.816

1

1

1463

198.6200344

353.1232804

355.1300007

119.0649604

2.006719753

9734079.331

1

1

L-Threonine

1847

232.2200385

531.1494087

533.1542053

297.0910887

2.004796578

96334.26022

1

1

5'-Methylthioadenosine

2140

264.0611868

453.1708025

455.1753587

219.1124825

2.004556243

63121.69175

1

1

Pantothenic acid

2530

304.2701368

349.1236352

351.1296482

115.0653152

2.006013402

26277721.42

1

1

L-Proline

4207

508.4812823

447.6054702

449.6109528

213.5471502

2.005482535

6745.228882

1

1

Cysteine glutathione disulfide

3307

383.8314875

559.2623058

561.26807

325.2039858

2.005763637

8882.964531

1

1

Farnesyl cysteine

3249

378.5981997

526.1639821

528.1694996

292.1056621

2.005517627

25983.11796

1

1

Canavaninosuccinate

Gamma-Aminobutyric acid (GABA)

Biomarkers of culture supernatant

1046

246.7230105

323.1063401

325.1136548

89.04802007

2.00731492

17348294.44

1

1

L-Alanine

1846

356.8674295

422.1753341

424.1827527

188.1170141

2.007418549

240012.1499

1

1

Glycyl-L-Leucine

1917

368.2766515

456.1581641

458.1661178

222.0998441

2.007953687

44725.9308

1

1

Glycyl-Phenylalanine

2692

514.8989213

354.0737948

356.0798798

240.0309495

2.006085519

684284.729

2

2

L-Cystine

3248

616.8957695

379.1671856

381.1751432

145.1088656

2.007957432

33527.10551

1

1

3-Dehydroxycarnitine

2186

409.3391436

443.1240079

445.1302676

209.0656879

2.006259669

167397.1675

1

1

Hydroxyphenylacetylglycine


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Table 2. Summary of pathway analysis results. Pathway name from cell data

Total

Hits

p

-log(p)

Impact

Arginine and proline metabolism

77

4

0.000031536

10.364

0.26765

Aminoacyl-t RNA biosynthesis

75

3

0.00092983

6.9805

0

Beta-Alanine metabolism

28

2

0.0026441

5.9354

0

D-Arginine and D-ornithine metabolism

8

1

0.023063

3.7695

0

Alanine, aspartate and glutamate metabolism

24

1

0.067825

2.6908

0.10256

Valine, leucine and isoleucine degradation

27

1

0.076019

2.5768

0

Pantothenate and CoA biosynthesis

27

1

0.076019

2.5768

0.18014

Butanoate metabolism

40

1

0.11082

2.1999

0.01067

Glycine, serine and threonine metabolism

48

1

0.13167

2.0275

0.09661

Cysteine and methionine metabolism

56

1

0.1521

1.8832

0.0478

Porphyrin and chlorophyll metabolism

104

1

0.26624

1.3234

0

Total

Hits

p

-log(p)

Impact

Taurine and hypotaurine metabolism

20

1

0.016553

4.1012

0.03237

Selenoamino acid metabolism

22

1

0.0182

4.0063

0

Alanine, aspartate and glutamate metabolism

24

1

0.019847

3.9197

0.05698

Cysteine and methionine metabolism

56

1

0.045999

3.0791

0

Aminoacyl-tRNA biosynthesis

75

1

0.06136

2.791

0

Pathway name from culture supernatant data



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For Table of Contents only


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Journal of Proteome Research

124x152mm (300 x 300 DPI)


Impact of Oxygen Concentration on Metabolic Profile of Human

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