Metabolomic Profiling of Equine Cord Blood Mesenchymal Stromal Cells in Both Early and Late Stages of Culture

doi:10.21203/rs.3.rs-1619662/v1

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Metabolomic Profiling of Equine Cord Blood Mesenchymal Stromal Cells in Both Early and Late Stages of Culture

https://doi.org/10.21203/rs.3.rs-1619662/v1

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Introduction: Equine Mesenchymal Stromal Cells (MSC) hold great potential as a future form of cellular therapy. Fetal Bovine Serum (FBS) is used as a supplement MSC culture, but challenges with its use have initiated a desire to create a defined media to expand these cells. MSCs have shown great variability in species, tissue source and passage number. Due to this, to effectively create a replacement for FBS we must better understand how it meets the metabolic needs for each type of MSC.

Objectives: The goal of this study is to determine key pathways and differences in pathways between the early and late passages of equine cord blood MSCs.

Methods: This study utilized metabolomics to give a snapshot of the metabolism in the long-term culture of equine cord blood MSCs (eCB-MSC) by comparing the profile of plain culture media to spend media.

Results: We found two significantly different metabolites between the early and late passage of eCB-MSC, alpha-ketoglutaric acid (p = 0.019) and creatine (p = 0.012). As well, the metabolic results allowed us to perform an enrichment analysis to assess which pathway(s) were most relevant.

Conclusion: The two metabolites suggest a difference in metabolism between the early and late passage reflecting different cellular priorities. Insights into the unique metabolism of these cells and how the requirement of the cell differs between the early and late passage may allow the formulation a serum free media tailored to the metabolic needs of eCB-MSC.

Mesenchymal Stromal Cells

Metabolomics

Metabolites

Regenerative Medicine

Long-Term Cell Culture

Cell-based therapy is considered one of the most promising disciplines in modern Science and Medicine, as it offers possibilities for understanding, researching, and treating a variety of diseases (Aly, 2020). In the veterinary field, several in-vitro and in-vivo studies have utilized stem cells to better understand their functionalities and combat numerous diseases such as musculoskeletal, cardiac, respiratory, and eye disease (Petchdee & Sompeewong, 2016; Saldinger et al., 2020; Shah et al., 2018).

Among different stem cell types, Mesenchymal Stromal Cells (MSCs) require simple isolation and culture techniques, with minimal ethical issues regarding their use. As such, they are more feasible for therapeutic purposes in the regenerative field (Voga, Adamic, Vengust, & Majdic, 2020). In the equine species, MSCs differentiation potential, self-renewal capability, and paracrine activities have made them a promising source of for various therapeutic applications (Caseiro et al., 2019).

MSCs can be isolated from a variety of adult tissues, the most common being bone marrow, adipose tissue, and more recently birth derived tissues such as cord-blood (CB) or cord-tissue (CT) (Amati et al., 2017; Kabat, Bobkov, Kumar, & Grumet, 2020; Stanko, Kaiserova, Altanerova, & Altaner, 2014). The main characteristics of MSCs are tissue differentiation, secretion of trophic factors and their immunoregulatory properties. These characteristics can be impacted by the species and tissue source of isolated cells (Fernandez & Fernandez, 2016; Hass, Kasper, Böhm, & Jacobs, 2011; Kozlowska et al., 2019; Lepage, Lee, & Koch, 2019; Uder et al. 2018). However, one of the most commonly studied factors influencing the characteristics of MSCs is the passage number, which can affect the cellular behavior and functionality (Izadpanah et al., 2008; Widowati et al., 2019; Yang, Ogando, Wang See, Chang, & Barabino, 2018, P. K. Wu et al., 2017; J. Yu et al., 2018).

Although MSCs are readily available in tissues, in order to get clinically significant numbers of cells, in-vitro expansion is required. The current standard of MSC culture involves media supplemented with Fetal Bovine Serum (FBS) to support cell growth. FBS has been used in cell culture for over 50 years as it provides factors required for cell growth and proliferation (Gstraunthaler, Lindl, & van der Valk, 2013). Although FBS provides the nutrition needed for these cells to grow, it must be noted that there is batch-to-batch variation between FBS lots due to biological, geographical, and seasonal differences (Gstraunthaler, Lindl, & van der Valk, 2013).

Therefore, there has been a search for a suitable replacement of FBS in MSCs culture not only due to its variability but also its xenogenicity and ethical concerns (van der Valk & Gstraunthaler, 2017). Logically, when attempting to replace FBS, the metabolic profiling of the specific cell type is needed to better understand the nutrients and metabolites required for cell growth and proliferation. This field is further along in Human Medicine, which has resulted in serum-free media tailored to human cells. However, using this formulation on equine MSCs provided inconsistent results, with decreased proliferation and changes in functionality (Clark et al. 2016; Schwarz et al., 2012; Schubert et al., 2018). With all the variation in MSCs at a variety of biological levels, it is important not to assume that the metabolism of one type of MSC is consistent across all MSCs. This further enforces the concept of species-specific differences and the need to have a solution tailored to the specific type of MSCs.

As such, in order to replace FBS in the culture media, it is first essential to identify the nutritional need of the specie-specific cells for long-term culture by assessing what the currently used media is providing for these cells. With the emergence of new opportunities, a more recent method of metabolomics can be utilized to better understand the requirements of the cell. This will allow a snapshot look into the metabolism of specific MSCs to better understand the unique profile based on species, tissue source, and passage number.

Since metabolomics offers vast data on the detected metabolites, it can provide comprehensive coverage of biological processes and metabolomic pathways.

This is the first study to use metabolomics to provide a broad view of metabolites and pathways associated with the long-term culture of equine CB-MSCs (eCB-MSCs), comparing between the early and late passage. This work will provide a better understanding of eCB-MSC’s metabolism and functionality. Which will provide a base of knowledge that will be utilized to design a species specific FBS alternative for equine MSC culture.

Cell culture

Equine CB-MSCs from three different cell lines at passage 3 (P3) were thawed in MSC expansion media consisting of DMEM low glucose (DMEM-LG) (Multicell, Wisent), 10% FBS (Gibco, Lot #: 2051543), 2 mM l-glutamine (Multicell, Wisent), and 100IU penicillin-streptomycin (Multicell, Wisent) and plated on T-25 flasks at 5000 cells/cm². The cells were then incubated at 38^oC (normal equine body temperature) at 5% CO2. To prevent any metabolic fluctuation due to FBS variability, the same batch of serum was used for the entirety of the study. Expansion media was changed every 2 days until the cells reached the confluency level of 80%, on which the adherent cells were dissociated by 0.25% trypsin-EDTA (Multicell, Wisent) and replated. Cells were cultured until passage 8 (P8). At each passage, cells were counted via hemocytometer. From here on, cells at P3 will be referred to as the early passage, and cells at P8 will be referred to as the late passage.

Sample Collection & Analysis

Upon cell passage, the spent media was collected for targeted metabolomics analysis via direct injection-liquid chromatography-mass spectrometry to identify up to 180 different endogenous metabolites, including biogenic amines, amino acids, acylcarnitines, phospholipids, and sphingolipids. Media was collected prior to passaging to ensure the collection of matured MSCs at each passage. Biological replicates from three different cell lines of CB-MSC were used, and samples collected from the early passage and late passage of eCB-MSCs with a control group of plain expansion media that has not been in contact with any cells (n = 3 for each group). All spent media samples were filtered using a 3 ml syringe (Covidien Monoject) with a sterile filter with a pore size of 0.22 µm (FroggaBio) to remove any cellular debris. Then, 1 ml of filtered samples were snap-frozen in liquid nitrogen and kept at -80 freezer until analysis. Once all the samples were ready for analysis using Direct Flow Injection Mass Spectrometry (DI-MS), they were prepared and shipped to the University of Alberta, Canada, as instructed by The Metabolomics Innovation Centre.

Calculation of Population Doubling Time

At each passage, eCB-MSCs were counted by using a hemocytometer. Growth rate (gr) was calculated by using the formula gr = ln(N(t)/N(0))/t, where N(t) is the number of cells at time t (in days), and N(0) is the number of cells at time 0. Population Doubling Time (PDT) was then calculated using the formula PDT = ln(2)/gr.

Statistical and Enrichment Analysis

Statistical analysis was performed using the IBM SPSS Software designed by IBM Corporation. One-way ANOVA (Analysis of Variance) repeated measures with Bonferroni post hoc test was performed to identify the significant metabolite change between the plain, early, and late passage of eCB-MSCs. Values were normalized and a p-value of less than 0.05 (P < 0.05) was considered statistically significant.

Quantitative Enrichment analysis identifies biologically meaningful patterns that are significantly enriched in quantitative metabolomics data. Therefore, this was done using the MetaboAnalysit 4.0 computational platform, which considers both counts (presence/absence of metabolites) and each metabolite's concentration amount. The protocol for using this software was adapted from Chong and colleagues (Chong, Wishart, & Xia, 2019). Undetected values or values lower than detection values were excluded from the analysis.

Metabolite Grouping and Excretion/Depletion

The metabolomic grouping was done using the Metabolomic Innovation Center (https://www.metabolomicscentre.ca/). Additionally, The Human Metabolome Database (https://hmdb.ca/) was used for any missing metabolite’s group. The metabolites were categorized based on six primary groupings including; Amino Acid, Aclycarnitines, Biogenic Amine, Glycophospholipids, Hexanes, and Sphingolipids. If a metabolite did not fall exactly into one of the common groupings, the metabolite group was referred to as “Others.”

In order to calculate the fluctuation of metabolites, the excretion and depletion of metabolites were calculated by subtracting plain media metabolite values from the spent media values. A positive number would indicate that the metabolite was excreted from the cells to the media, while a negative number means that the metabolite is depleted from the media by the cells.

Population Doubling Time (PDT)

The PDT, was used as a quantitative method of measuring cell growth upon each passage to track the growth rate of the long-term culture of eCB-MSCs. Figure 1 represents the PDT in days for the culture of eCB-MSCs from passage 3 to passage 8. According to the results, the cumulative population doubling time continuously increased throughout the passage, confirming the idea that as the passage number increases the population doubling time also increases, meaning the cells growth rate decreases as the passage number increases.

Metabolomics Analysis

The result of one-way-ANOVA showed that out of the 130 metabolites detected, two metabolites were significantly different between the early and late passage of eCB-MSCs culture. Additionally, 34 metabolites were significantly different between the plain serum supplemented media and the early passage of eCB-MSCs culture. Lastly, 35 metabolites were significantly different between plain serum supplemented media and the late passage of eCB-MSCs culture. Supplementary table 2 and 3 outlines these metabolites and their respective p values in more detail.

The two metabolites that significantly (p < 0.05) differ between the early and late passage of eCB-MSCs are Alpha-Ketoglutaric Acid (AKG) with an early passage at 7.45 µM and the late passage at 11.00 µM, and Creatine with an early passage at 11.83 µM and the late passage at 14.57 µM (shown in Fig. 2). AKG is a keto acid and creatine is an amino acid. Both of these metabolites were not only significantly different between the early and the late passages (Table 1), but they were also significantly different in the plain versus early passage of eCB-MSCs culture, and plain versus late passage of eCB-MSCs culture.

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Table 1

Significantly different metabolites in the early and late passage of eCB-MSCs.
Early vs. Late¹
Metabolite Family	Metabolite’s Name	P-value	Cells Excreted²	Cells Depleted²
Others	alpha-ketoglutaric acid*	0.019	X
Amino Acids	Creatine*	0.012		X

* Same metabolites in all three groups (early vs. late, plain vs. early, and plain vs. late)

Enrichment Analysis

Although the enrichment analysis provided 65 pathways, the top 50 pathways for each enrichment analysis of early and late passage are shown in Fig. 3. During the early stage of eCB-MSC culture, seven out of 65 pathways were significantly active. These include the Purine Metabolism (p = 0.007), Phenylalanine and Tyrosine Metabolism (p = 0.023), Mitochondrial beta-oxidation of Short Chain Saturated Fatty Acids (p = 0.034), Fatty Acid Biosynthesis (p = 0.037), Catecholamine Biosynthesis (p = 0.042), Thyroid hormone synthesis (p = 0.042), and Propanoate Metabolism (p = 0.046).

In contrast, during the late passage of eCB-MSCs, 15 pathways are significantly activated, including Arginine and Proline Metabolism (p = 0.0039), Pyruvaldehyde Degradation (p = 0.011), Biotin Metabolism (p = 0.015), Phytanic Acid Peroxisomal Oxidation (p = 0.0169), Porphyrin Metabolism (p = 0.020), Tryptophan Metabolism (p = 0.020), Glucose-Alanine Cycle (p = 0.020), Pyruvate Metabolism (p = 0.024), Citric Acid Cycle (p = 0.028), Alanine Metabolism (p = 0.030), Methylhistidine Metabolism (p = 0.031), Lysine Degradation (p = 0.033), Glycine and Serine Metabolism (p = 0.035), Propanoate Metabolism (p = 0.046), and Phenylalanine and Tyrosine Metabolism (p = 0.049)

Figure 4 outlines the comparison between these two groups. The same 65 pathways are expressed between the two groups; however, each pathway's expression level varies. This suggests that the metabolomic networks for both the early and late passage of eCB-MSCs are the same, but their metabolomic machinery differs.

This study is the first to ascertain the metabolic profiling of the culture media that supported the propagation of eCB-MSCs using metabolomics. We were not only able to identify the depleted or excreted metabolites in MSCs, but we were also able to identify pathways that were expressed in both the early and late stages of MSCs growth.

We found two metabolites to be significantly different between the early and the late passage of eCB-MSCs culture: AKG and creatine.

AKG levels were significantly increased as the passage number increased; the AKG levels were also much higher in both the early and late passage as compared to the plain culture media. This suggests that cells are overproducing AKG and excreting excess into the media. AKG is also known as oxoglutaric acid, or alpha-ketoglutarate, which is an organic acid, and more specifically a keto acid. AKG has shown to have a key role in multiple cellular and metabolic pathways. This includes its involvement in the Tricarboxylic Acid (TCA) cycle, its effect on cell growth and proliferation, and lastly its antioxidant effects.

The primary role of AKG in cells is its involvement in the eight-step process of the TCA, also known as the Kreb Cycle, or the citric acid cycle. The TCA cycle's primary purpose is to produce NADH and FADH, electron carriers, which go through oxidative phosphorylation to generate ATP (Berg, Tymoczko, & Stryer, 2002). In addition to the TCA cycle, AKG can be generated anaplerotically from glutamate via glutamate dehydrogenase, a process called oxidative deamination (Smith, Li, Stanley, & Smith, 2019; N. Wu et al., 2016). In addition to partaking in energy metabolism, AKG has shown to enhance cell growth and proliferation. For instance, an increased level of AKG in the culture media of fibroblast and chondrocyte cells resulted in an increase in cells’ proliferation rate (Singh, Vishnoi, & Kumar, 2013). One explanation as to how AKG could enhance cell growth and proliferation can be seen from the pancreatic progenitor-like cells, which enhanced the proliferation rate via upregulation of Ten-Eleven Translocation enzymes; however, such results were dose-dependent (Song et al., 2018). Another explanation can be due to the activation of h G-protein coupled receptors through AKG, which ultimately increases cell metabolism and signaling (W. He et al., 2004).

In addition to AKG’s role in energy metabolism and cell proliferation, its antioxidant effects are a vital process in cells. The antioxidant effect of AKG can be categorized in two ways, being direct and indirect effects. For instance, the direct impact of AKG is to react with hydrogen peroxide, which can be produced by oxidative phosphorylation or as a by-product of cellular metabolism, to produce succinate (Lennicke, Rahn, Lichtenfels, Wessjohann, & Seliger, 2015; Long & Halliwell, 2011). Indirectly, AKG has shown to act as a scavenger for ammonia, minimizing cells' exposure to potential accumulated toxic ammonia in the cell culture, consequently enabling cells to proliferate for a more extended period (Singh et al., 2013).

As evident, AKG has a variety of roles that are vital in proper cell growth and proliferation. This study presented that AKG level throughout the long-term culture continuously rose in the spent media, thus suggesting that these levels are also increased in the cells throughout the long-term culture. Therefore, it can be proposed that these cells naturally produce AKG to facilitate the functions stated above; energy production, cellular proliferation, and antioxidant defense. However, we do not know which functions are activated more frequently at the early or late passage of eCB-MSCs. Therefore, further examination must be done in order to produce a concrete answer.

Although our study provides insight into which metabolites play an important role in the long term cell growth of equine CB-MSCs, it is unclear as to which pathway the AKG is being produced or what its main effects are. However, it can be suggested that higher proliferation rate in the early passage combined with a higher production level of AKG could be due to the high demand of cell the cell growth and proliferation. In comparison, the lower proliferation rate in the late passage combined with a much higher level of AKG can suggest that cells may be dealing with some form of toxicity, and thus require more antioxidant effects. In order to be sure, further analysis must be done to verify the suggested results.

The second metabolite that changed significantly during the early and the late passage is creatine, an amino acid. Cells depleted creatine from the media in both the early and late passage; with more depletion occurring in early passage as compared to the later passage. The role of creatine has been extensively studied in muscle and cardiac cells (Chilibeck, Kaviani, Candow, & Zello, 2017; Farshidfar, Pinder, & Myrie, 2017; Gaddi, Galuppo, & Yang, 2017; Zervou, J. Whittington, J. Russell, & A. Lygate, 2015). Creatine is known to be part of the phosphocreatine “shuttle” (transport) system hypothesis, which is a quicker way of producing energy regions under high metabolic demand (Guimarães-Ferreira, 2014; Wyss & Kaddurah-Daouk, 2000). Due to ATP's high level in the mitochondria, high-energy inorganic phosphate is transferred from ATP and is transferred to creatine via an enzyme called creatine kinase. The combination of the phosphate and the creatine creates phosphocreatine and ADP. Then, the phosphocreatine diffuses into the cytoplasm, undergoes a reversal change with the help of creatine kinase, and generates ATP and creatine. The ATP can be used for energy, and the creatine returns to the interior of mitochondria (Guimarães-Ferreira, 2014). Although the shuttle system is a widely known phenomenon, some criticize this hypothesis, suggesting that such a system may not be a one-fit-for-all cell type (Wyss & Kaddurah-Daouk, 2000). This suggests that cells require higher energy levels in both passages, but more so in the early passage, which can be due to the higher growth rate in the early as compared to the late passage.

Several studies have suggested that undifferentiated MSCs utilize a mixture of glycolysis and oxidative phosphorylation in order to produce the required energy for the cells (Barilani et al., 2019; C.-T. Chen, Shih, Kuo, Lee, & Wei, 2008; Hu et al., 2016). In particular, glycolysis contributes to more than 97% of ATP production; in comparison, oxidative phosphorylation contributes less than 3% of ATP production (Fillmore et al., 2015). This could be because glycolysis is much faster than oxidative phosphorylation (Jose, Bellance, & Rossignol, 2011). By observing the metabolite levels, the creatine level was shown to continuously decrease in the spent media, suggesting that these cells are utilizing creatine. From the metabolomic analysis and the enrichment analysis, we can suspect that eCB-MSCs do use both glycolysis in addition to oxidative phosphorylation. However, we suggest for the first time that creatine may play a role in ATP production in cultured eCB-MSCs. Whether creatine is needed for more efficient energy production via the shuttle system or another function is not clear, and further inspection is necessary.

Following the metabolomic results, during the early passage, the enrichment analysis provided seven significant pathways that were significantly active, whereas the late passage had 15 pathways. When looking at the early passage, we found that the significant-top pathway is purine metabolism. This pathway maintains adenosine and guanine cellular levels via synthesis and degradation of purine nucleotide (J. Yin et al., 2018). The synthesis of purine nucleotide in mammalian cells can be either via a complementary salvage pathway or the de novo biosynthetic pathway. However, the complementary salvage pathway generally accounts for most cellular purine needs. The de novo biosynthetic pathway provides an additional force when there is a high requirement for purine nucleotides (J. Yin et al., 2018). In the degradation process, the purines undergo several catabolic reactions to produce xanthine and, ultimately, uric acid (Moffatt & Ashihara, 2002; J. Yin et al., 2018). Comparing the expression level of the purine metabolism pathway from the early passage to the late passage, we found that this pathway was not significant in the late passage; it was the 50th pathway expressed. This is not surprising, as several studies have suggested that cells at an early passage tend to proliferate faster, requiring greater DNA synthesis, and this rate tends to decrease as the passage number increases (Kwist et al. 2016; Neumann et al. 2010; Yang et al. 2018). Such phenomena can be referred to as the replicative senescence, where cells undergo three stages of proliferation, including an initial vigorous proliferation, declining proliferation followed with quiescence, or no proliferation (Peterson, Tachiki, & Yamaguchi, 2004).

In contrast, the first highly expressed pathway in the late passage is the metabolism of amino acids arginine and proline from glutamate. Under normal conditions, the production of arginine endogenously is sufficient for the cell’s needs. However, under metabolomic stress, organ maturation and development of exogenous sources may be needed (Luiking & Deutz, 2007). Although arginine was not significant in the presented data analysis, we found a decrease in arginine levels in the spent media in both the early and the late passage of eCB-MSCs. This suggests that these cells took arginine up from the expansion media. The effect of arginine has been shown to be cell type-dependent (Greene et al., 2013; J. Y. Kim et al., 2011, Albina, Cui, Mateo, & Reichner, 1993; Okazaki et al., 1997). In MSCs, arginine has been shown to increase cell proliferation (Huh et al., 2014). In addition to enhancing cell proliferation, arginine degradation plays a vital role as an intermediate of the urea cycle to eliminate nitrogenous waste. When needed, arginine can also convert back to glutamate and, subsequently AKG for the TCA cycle (Albaugh & Barbul, 2017). Similarly, proline can convert to AKG and enter the TCA cycle and convert to ornithine to enter the urea cycle (Pandhare, Donald, Cooper, & Phang, 2009). In mammalian cells, under stressed conditions, proline biosynthesis is upregulated (Krishnan, Dickman, & Becker, 2008; Meena et al., 2019). It has been suggested that this upregulation is to combat the cellular stress via either directly scavenging for reactive oxygen species (ROS), activating glutathione synthesis, or invoking signaling pathways that upregulate the cellular antioxidant defenses (Krishnan et al., 2008). In this study, although not significant, the cells continuously excreted proline. This could either be used for energy production via the TCA cycle (ultimately oxidative phosphorylation) or as an antioxidant defense mechanism.

Although pyruvaldehyde (also known as methylglyoxal) degeneration is the second expressed pathway in the late passage of eCB-MSCs, it is a critical pathway that must be covered in this paper. Methylglyoxal is a by-product of glycolysis, lipid, and protein metabolism. It has been shown to have a toxic role in cells, as it enhances ROS formation, DNA damage, cell apoptosis, and senescence (Allaman, Bélanger, & Magistretti, 2015; H. Li, Zheng, Chen, Liu, & Zhang, 2019; Okado et al., 1996). In order to avoid the toxic effect of methylglyoxal, cells have natural degradation mechanisms such as glyoxalase, aldose reductase, aldehyde dehydrogenase, and carbonyl reductase, with the glyoxalase system being the central detoxifying system for eliminating methylglyoxal (Allaman et al., 2015). Although the enrichment analysis provides us with an insight into the activation of the pyruvaldehyde degeneration pathway, it is not clear which specific mechanism is being activated. Therefore, this suggests that the level of methylglyoxal is much higher in the later passage, and thus may be a factor in ROS formation, DNA damage and/or cell apoptosis and senescence. Unfortunately, the levels of methylglyoxal in the metabolomic analysis were not present, and thus further analysis must be done. However, from this we can suggest that cells in the later passage are combating some form of toxicity, and its secondary effects may be the trigger for higher production of AKG in the later passage of the cells. In summary, methylglyoxal is correlated to oxidative stress and has already been shown to be a promising biomarker in measuring the stress level in plant cells and being a reliable biomarker for tumor growth (Angeloni, Zambonin, & Hrelia, 2014; Irigaray & Belpomme, 2020; Kaur, Singla-Pareek, & Sopory, 2014).

This study used metabolomics to provide a broad view of metabolites and pathways associated with the long-term culture of eCB-MSCs, comparing between the early and late passage. The above results suggest that cells at different passage numbers have different metabolomic profiling. These results provide important insights into the metabolism of eCB-MSCs and can help work towards formulating a chemically defined serum-free media that is tailored to these cells. Future studies should examine the impact of high-level AKG, creatine's role in energy production, and methylglyoxal as a marker of cellular stress in eCB-MSC. This study only looked at passages three and eight of the cell culture process. Future work should be completed on cells at varying passages in order to get complete understanding.

Funding: PM NSERC (Grant #400735) & Start Up Funds; TGK funding (Equine Guelph & Partners; & NSERC (Grant# 400968)

Both Saba & Cara were supported by the Ontario Veterinary College Graduate Scholarship.

Conflicts of interest/Competing interests: the authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of data and material: Data is available from authors upon reasonable request. Raw data outlining metabolite concentrations are included in the supplementary table 1.

Authors' contributions: SO, PM & TK conceived and designed the studies. SO conducted experimentation and analyzed data. SO & CP drafted the manuscript. All authors read and approved the manuscript.

Acknowledgements:

The authors would like to acknowledge contributions of Dr. Sarah Lepage, Dr. Monica Antenos and Dr. Jyoti Sharma for their intellectual contributions as well as help with some techniques. We would also like to thank Ed Reyes and Allison MacKay for help with ordering and procurement of research reagents. Finally, we would like to acknowledge The Metabolomics Innovation Center for their contribution to this work.

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Metabolomic Profiling of Equine Cord Blood Mesenchymal Stromal Cells in Both Early and Late Stages of Culture

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Sample Collection & Analysis

Calculation of Population Doubling Time

Statistical and Enrichment Analysis

Metabolite Grouping and Excretion/Depletion

Results

Population Doubling Time (PDT)

Metabolomics Analysis

A) (Dup: Abstract ?)

Enrichment Analysis

Discussion

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