Introduction

The global food industry faces significant challenges in addressing the rising demand for meat while simultaneously ensuring environmental sustainability and addressing animal welfare concerns1. Additionally, there exists a growing disparity between the future demand for protein sources and the current capacity to supply meat2. To bridge this gap, alternative meat products—developed through cellular agriculture and plant-based technologies—have emerged as promising solutions3. However, concerns regarding the safety and nutritional profile of alternative meat compared to conventional meat remain under scrutiny. A key barrier to the commercialization of cultured meat is the lack of systematic and comprehensive risk assessments4,5. Metabolomics, a powerful tool for the comprehensive analysis of metabolites in biological systems, offers valuable insights for evaluating the biochemical implications of consuming cultured meat in comparison to traditional meat.

Metabolomics profiling has become a valuable tool for identifying key metabolic biomarkers following exposure to substances or pollutants, owing to its broad coverage, high sensitivity, and reproducibility6. This approach can detect subtle variations in the range of biological metabolites under different physiological or pathological conditions7. Additionally, metabolomics can uncover organismal responses to various internal and external environmental changes, as well as distinguishing phenotypic differences among individuals of the same species. Pathway enrichment analysis and metabolite-disease network analysis reveal functional impairments and highlight the connections between metabolites and related diseases8. These analyses help to elucidate the underlying biochemical pathways affected and the potential health implications associated with specific metabolic changes. Metabolomics has also shown significant promise in the field of safety assessment, especially in toxicology and medicine development9. Studies have demonstrated its capability to elucidate mechanisms of toxicity, identify early biomarkers, and improve preclinical safety evaluations. It provides a comprehensive overview of biochemical activity and cellular responses, helping to unravel adverse outcome pathways and improve the extrapolation of preclinical findings to humans. For example, the hepatotoxicity of triptolide, a compound from Tripterygium wilfordii, was investigated using LC-MS-based metabolomics, revealing significant changes in metabolites and identifying pathways related to liver injury medicine10. Similarly, the toxicity of Dioscorea bulbifera Rhizome was studied, identifying metabolites associated with liver toxicity and providing insights into the underlying mechanism.

In the context of a growing global commercial market and increasing safety concerns for consumers, this study aims to conduct a safety and nutritional assessment of lab-cultured meat using a metabolomics-based approach. Technological method allows for the detection of subtle biochemical changes and potential toxicological markers that might not be evident through traditional safety assessment methods9,10,11. We conducted a comparative metabolomic analysis of conventional chicken meat, muscle satellite cells, and myotube formed cells because there is currently a lack of research in this area. This study aims to fill the gap in knowledge by providing insights into the metabolic differences and similarities among these different cell types and conventional meat. By demonstrating a thorough understanding of the metabolic profile and ensuring the absence of harmful substances, producers can build trust and ensure the long-term safety of cultured meat products.

Method

To systematically analyze the metabolic profile of cultured meat in comparison to conventional chicken, we employed an untargeted metabolomics workflow. The overall experimental pipeline is summarized in Fig. 1.

Fig. 1
figure 1

Overview of the metabolomics workflow used in this study.

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Sample collection

For the comparative experiment, conventional meat, muscle satellite cells, and myotube formed cell. Conventional meats were purchased immediately after slaughter with the brisket of chickens raised in traditional farming (Harim, Iksan, Korea). To isolate chicken muscle satellite cells, leg muscles from 16-day-old chicken embryos were used. Briefly, the muscles were finely minced and digested with 0.1% pronase (Roche, Mannheim, Germany) at 37 °C for 80 min. After digestion, the supernatant was removed by centrifugation, and DMEM (Cytiva, Marlborough, USA) media supplemented with 10% FBS (Fetal bovine serum, Cytiva) and 1% P.S (Penicillin and streptomycin, Thermo Fisher Scientific, Waltham, USA) was added, followed by pipetting. The supernatant was then filtered through a 100 μm strainer and centrifuged. After removing the supernatant, growth medium [Ham’s F10 (Cytiva) + 20% FBS + 1% P.S + 5 ng/ml human FGF2 (fibroblast growth factor 2, Miltenyi, Auburn, USA)] was added to the remaining muscle satellite cells, which were then cultured at 37 °C. And then, to obtain myotube formed cells, DMEM media supplemented with 2% FBS and 1% P.S was added into muscle satellite cells and incubated at 37 °C.

Metabolomics sample preparation

The samples were suspended in lysis buffer (50 mM ammonium bicarbonate), followed by sonication for 12 min at 15 °C using Covaris S2 Focused-Ultrasonicator (Covaris, Woburn, MA, USA). The concentration of protein was quantified using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). Metabolites were extracted from each 100 µg of protein sample by incubation with 4 volumes of cold methanol solution at – 20 °C for 2 h. After centrifugation at 14,000×g for 10 min, the supernatant was transferred to a new 1.5 mL tube and completely dried using a speed-vac centrifugal vacuum concentrator(Vision Scientific, Daejeon, Korea). Dried metabolite contents were reconstituted in 100 µL of 0.1% formic acid in water. Reconstituted samples were transferred to autosampler vials and then subjected to LC-MS/MS analysis.

LC-MS/MS analysis

LC-MS/MS analysis for metabolomics was performed using a Q-Exactive Orbitrap hybrid mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) along with an Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA). A 5 µL sample solution was injected into an Eclipse Plus C18 RRHD column (50 × 2.1 mm; id. 1.8 μm; Agilent, CA, USA) at 40 °C. The flow rate of the mobile phase was 0.2 mL/min. Analytes were eluted from the column under a gradient (solvent A, 0.1% formic acid in water; solvent B, 0.1% formic acid in 80% acetonitrile). The elution gradient was as follows: 2.5% B for 2 min, 2–12% B over 2–11 min, 12–28% B over 11–15 min; 28–60% B over 15–22 min; 96% B over 22–26 min; returned to 2.5% B for 5 min. Solvent A was run every sample as a blank solution. Mass spectrometer parameters were as follows: Full-MS scans, 100 to 1,000 (Positive mode) and 50 to 500 (Negative mode) m/z of scan range, 70,000 of resolution, 1 × 106 of AGC target, and maximum IT of 100 ms; For MS2 scans, the following parameters were used: 17,500 of resolution, 1 × 105 of AGC target, maximum IT was 200 ms, ± 2 m/z of isolation width, and NCE for dd‐MS2 of 30; sheath gas flow rate was 19 (Positive mode) and 5 (Negative mode); aux gas flow rate was 1; spray voltage was 3.80 kV(Positive mode) and − 3.40 kV(Negative mode); capillary temp was 320 °C; S-lens RF level was 60.0.

Metabolomics and chemometrics analysis

LC-MS/MS raw files were analyzed with Compound Discoverer 3.3™ (Thermo Fisher Scientific, Waltham, MA, USA). Analysis is performed in the ‘Untargeted Metabolomics with Statistics Detect Unknowns with ID using online Data Base and mzlogic’ mode. Compound identification is carried out using mzCloud (ddMS2, https://www.mzcloud.org/) and ChemSpider (formula or exact mass, http://www.chemspider.com), followed by ddMS2 data similarity searches for all compounds using mzCloud. According to the Metabolomics Standards Initiative (MSI) criteria, compounds are filtered at Level 2 (< 10 ppm, mzcloud score > 80) and Level 1 (ChemSpider < 5 ppm)12. Metabolites with CV values of area exceeding 30% in QC (Pooled samples) are excluded, and duplicate metabolites are removed based on average area intensity. Based on the qualitative and quantitative analysis results of identified metabolites, statistical analysis is performed using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca)13.

Results

Clustering and correlation analysis

In the positive mode, which is particularly suited for identifying compounds with basic functional groups, such as amines, and molecules that readily accept protons, the samples were basically clustered together according to the type of cultivation (Fig. 2A). The Venn diagram showed the number of common and unique metabolites in the two cultured groups (Fig. 2B). Compared to conventional meat, 45 common different metabolites existed in the two groups, which included diethanolamine, paracetamol, and acetylcholine, etc. Figure 2C displays clusters of different metabolites and their expression levels across each group. As well, this figure consists of 45 common different metabolites among the groups, and they exhibit similar expression trends. Figure 2D presents a correlation heat map utilized to screen for differential metabolites.

Fig. 2
figure 2

Clustering and correlation analysis in positive mode. (A) The clustering dendrogram of cell type. (B) The number in different metabolites in cultured cell relative to the conventional meat (The filter conditions of differential metabolites are p < 0.05 and VIP > 1). (C) Heat map and clustering of the common different metabolites existed in the muscle satellite cells and myotube formed cell. (D) Correlation analysis and clustering heat map of all different metabolites, differential metabolites have been shown in bold.

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The negative ion mode, known for its effectiveness in analyzing acidic compounds such as carboxylic acids, phenols, and other molecules with acidic functional groups, did not reveal any clustering among the samples in this study (Fig. 3A). No significant differences in metabolites between the cultured groups. This suggests that the metabolite profiles of the test groups are similar with little to no variation. The Venn diagram showed the number of common and unique metabolites in the two cultured groups (Fig. 3B). Among them, 7 common different metabolites existed in the two groups, which included creatine, carnosine, and adenosine, etc. Figure 3C illustrated the clusters of different metabolites and their expression level. Additionally, this figure consists of 7 common different metabolites among the groups, and they show basically the same expression trends. Figure 3D is a correlation heat map used to screen for differential metabolites.

Fig. 3
figure 3

Clustering and correlation analysis in negative mode. (A) The clustering dendrogram of cell type. (B) The number in different metabolites in cultured cell relative to the conventional meat (The filter conditions of differential metabolites are p < 0.05 and VIP > 1). (C) Heat map and clustering of the common different metabolites existed in the muscle satellite cells and myotube formed cell. (D) Correlation analysis and clustering heat map of all different metabolites, differential metabolites have been shown in bold.

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Metabolomics analysis

We established criteria to identify differential metabolites using thresholds of p < 0.05 and VIP > 1 (Table 1). In the positive ion mode, compared to conventional meat, the muscle satellite cells, and myotube-formed cells groups exhibited 66 and 69 different metabolites, respectively, with a fold change (FC) difference of 2 or more. In the negative ion mode, the muscle satellite cells, and myotube-formed cells groups showed 13 and 11 different metabolites, respectively, compared to conventional meat.

Table 1 Filtered differential metabolites by cultured type.
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In muscle satellite cells, compared to conventional meat, several metabolites were found to be upregulated or downregulated. As a result, 32 upregulated and 34 downregulated metabolites were identified in the muscle satellite cell and visualized in a volcano plot (Fig. 4A). Representatively, metabolites including phosphoethanolamine, cytarabine, choline, and isocytosine were upregulated in the muscle satellite cell. Creatine and inosine-5’-monophosphate hypoxanthine were downregulated in the muscle satellite cell.

Fig. 4
figure 4

Volcano plot of muscle satellite cells versus conventional meat. (A) positive mode (B) negative mode. Selected metabolites are annotated in the graphs. Dotted horizontal line indicates threshold for p value of 0.01 and the dotted vertical lines indicate thresholds for ± 2-fold changes.

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During the analysis in negative mode, 7 upregulated and 6 downregulated metabolites were identified in the muscle satellite cells and visualized in a volcano plot (Fig. 4B). Cytarabine (an anticancer drug), Monobutyl phthalate (a potentially endocrine-disrupting chemical), and Methyldopa (an anti-hypertensive drug) were detected.

In myotube-formed cells, metabolites were analyzed for up/down-regulation compared to conventional meat. As a result, 34 upregulated and 35 downregulated metabolites were identified in the myotube-formed cells and visualized in a volcano plot (Fig. 5A). Metabolites include levofloxacin, cytarabine, MTIC (5-(3-Methyl-1-triazeno) imidazole-4-carboxamide), diethanolamine, and furfural. Some metabolites are reported to cause tingling and corrosion due to acidity but are also naturally present in the body or consumed in food. In negative mode analysis of metabolites, 6 upregulated and 5 downregulated metabolites were identified in the myotube-formed cells and visualized in a volcano plot (Fig. 5B). Metabolites related to nucleic acids were predominantly detected. Cytarabine, UMP, and creatine were also detected in myotube-formed cells, just as it was in muscle satellite cells.

Fig. 5
figure 5

Volcano plot of myotube formed cells vs. conventional meat. (A) positive mode (B) negative mode. Selected metabolites are annotated in the graphs. Dotted horizontal line indicates threshold for p value of 0.01 and the dotted vertical lines indicate thresholds for ± 2-fold changes.

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We analyzed the metabolites present in muscle satellite cells and myotube-formed cells and compared them to those of conventional meat using the HMDB classification (Table 2). The total metabolites were composed of amino alcohols (n = 2), amino acids and derivatives (n = 15), fatty acids and derivatives (n = 10), aromatic compounds and benzenoids (n = 9), steroids and related compounds (n = 2), nucleotide and related compound (n = 14), phosphate and phosphoesters (n = 5), carboxylic acid and derivatives (n = 6), heterocyclic compounds (n = 8), quaternary ammonium compounds (n = 2), organosulfur compounds (n = 1), antibiotics (n = 1), peptide and related compound (n = 3), alcohols (n = 2), and miscellaneous compounds (n = 2). Among them, amino acids, peptide, organic chemicals, fatty acid, steroids, and alcohols, which are known to have no impact on safety. Aromatic compounds and benzenoids are environmental pollutants that can potentially trigger allergic reactions in certain individuals, depending on their sensitivity. However, these substances are also used as food additives, where they have been proven safe and are approved for use14. Additionally, there are nucleotide, phosphate, carboxylic acid, and ammonium compounds, which are known to be involved in maintaining physiology or nutrient15,16,17.

Table 2 Metabolites clustered into chemical classes according to HmDB classification.
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Metabolic pathway enrichment

The enrichment analysis of metabolic pathways for muscle satellite cells of filtered differential metabolites was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database18 (Fig. 6A and C) and the Small Molecule Pathway Database (SMPDB) (Fig. 6B and D).

Fig. 6
figure 6

Metabolic pathway enrichment analysis for filtered differential metabolites between conventional group and muscle satellite cell group. Dot chart in positive mode (A) and negative mode (B) by KEGG database; Dot chart in positive mode (C) and negative mode (D) by SMPDB database.

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Figure 6A shows the top 25 enriched metabolic pathways identified using the KEGG database18 under positive ion mode. Larger and darker red dots indicate pathways with higher statistical significance (smaller p-values). Key pathways identified include beta-alanine metabolism, histidine metabolism, and purine metabolism, reflecting significant differences between the groups. Highlights enriched pathways identified using the KEGG database18 under negative ion mode was shown in Fig. 6B. Pathways such as purine metabolism, histidine metabolism, and glycine, serine, and threonine metabolism are prominent. Metabolic pathways analyzed using the SMPDB database under positive ion mode was displayed in Fig. 5C. Enrichment analysis identifies significant pathways similar to those in Fig. 6A, including beta-alanine metabolism, histidine metabolism, and purine metabolism. Figure 6D focuses on pathways enriched in the SMPDB database under negative ion mode. Pathways such as purine metabolism, betaine metabolism, and selenometabolism are emphasized.

Metabolic pathway enrichment analysis for filtered differential metabolites between the conventional meat and myotube-formed cells are shown in Fig. 7. This figure presents the results of metabolic pathway enrichment analysis, visualized as dot charts, to identify significant metabolic pathways in positive and negative ion modes using the KEGG18 and SMPDB databases. Positive ion mode results using the KEGG database18. Pathways such as histidine metabolism, purine metabolism, and beta-alanine metabolism show significant enrichment, with larger dots representing higher enrichment ratios and smaller p-values indicating greater statistical significance (Fig. 7A). Notable pathways identified in the negative ion mode using the KEGG database18 include purine, hypotaurine, taurine glycine, serine, and threonine metabolism (Fig. 7B). Positive ion mode results using the SMPDB database (Fig. 7C). Enriched pathways include histidine metabolism, purine metabolism, and beta-alanine metabolism, consistent with KEGG18 analysis. The results using the SMPDB database, taurine and hypotaurine metabolism, purine metabolism, and selenometabolism emerge as significantly enriched pathways in the negative ion mode (Fig. 7D).

Fig. 7
figure 7

Metabolic pathway enrichment analysis for filtered differential metabolites between conventional group and myotube formed cell group. Dot chart in positive mode (A) and negative mode (B) by KEGG database(Kanehisa et al., 2025); Dot chart in positive mode (C) and negative mode (D) by SMPDB database.

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According to KEGG18, significant enrichments were observed in pathways including sphingolipid metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, beta-alanine metabolism, and histidine metabolism. SMPDB highlighted pathways such as arginine and proline metabolism, glycine, serine and threonine metabolism, pyrimidine metabolism, pantothenate and CoA biosynthesis, purine metabolism, and beta-alanine metabolism (Table 3). For the myotube-formed cells, according to KEGG18, significant enrichments were observed in pathways including primary bile acid biosysthesis, beta-alanine metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism. SMPDB highlighted pathways such as taurine and hypotaurin metabolism, bile acid biosysnthesis, methionine metabolism.

Table 3 Enriched pathways by two different databases for MSC and MFC.
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Network analysis

For the muscle satellite cell, the inclusion of energy-related molecules like adenosine triphosphate (ATP), adenosine monophosphate (AMP), and creatine underscores the diagram’s focus on energy metabolism, with phosphorylation processes being evident through the connections to ribose-1-phosphate and guanosine diphosphate (Fig. 8A and B). The diagram also shows interactions with cofactors like sodium and magnesium, which are crucial for stabilizing nucleotide structures and facilitating enzymatic reactions. The presence of specialized metabolites such as methyldopa and 6-hydroxydopamine suggests links to neurotransmitter metabolism or specific drug metabolism pathways, further reflecting the complexity of the interconnected biochemical processes in the network. In the negative mode, the network illustrates the complex interplay between energy production, amino acid metabolism, and nucleotide synthesis, showcasing how these processes are tightly integrated to maintain cellular function (Fig. 8B).

Fig. 8
figure 8

Network analysis for connection between metabolites. MetaboAnalyst 6.0 was applied to generate connection maps. Connection map of muscle satellite cells in positive mode (A) and negative mode (B); connection map of myotube formed cells in positive mode (C) and negative mode (D).

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Figure 8C and D shows a metabolic network diagram highlighting the interactions between various metabolites with a focus on energy metabolism, amino acid metabolism, and nucleotide metabolism in the myotube foamed cells. The larger, red nodes like ATP, AMP, and guanosine monophosphate (GMP) appear to be central metabolites. These molecules play crucial roles in energy transfer, signaling, and nucleotide synthesis in cellular metabolism (Fig. 8C). The connections around AMP and ATP suggest a focus on nucleotide metabolism, which is crucial for processes such as DNA and RNA synthesis, as well as energy metabolism. The presence of amino acids like L-aspartic acid, L-isoleucine, and others like creatine and choline, shows the network’s inclusion of both structural components of proteins and molecules involved in cellular signaling and membrane structure. The lines connecting metabolites like citric acid, which is a key component of the citric acid cycle (Krebs cycle), to other compounds highlight the interconnected nature of metabolic pathways. This cycle is essential for energy production in the form of ATP. Metabolites such as NADP and FAD, which are coenzymes, are essential for redox reactions, indicating that this network might also highlight oxidative metabolism. Central metabolites such as ATP and AMP are prominent, reflecting their critical roles in cellular energy transfer and signaling processes. Magnesium and L-lysine are also central, playing essential roles in stabilizing ATP and participating in protein synthesis.

Discussion

Metabolomics, focusing on the comprehensive analysis of metabolites in biological systems, has become a critical tool in evaluating food safety19,20. Recent developments for non-targeted food authenticity such as metabolomics have significantly advanced the capabilities of food metabolomics, enhancing its role in verifying the safety of food products21. These emerging trends and applications provide deeper insights into metabolic pathways and their implications for food safety22. By leveraging advanced analytical techniques such as Liquid Chromatography-Mass Spectrometry (LC-MS/MS), researchers can uncover detailed biochemical profiles in food products23,24. For example, LC-MS/MS is known for its sensitivity and broad metabolite coverage, plays a pivotal role in profiling complex matrices like fermented cereals and irradiated meats, offering insights into food safety and quality25,26. Additionally, spatial metabolomics using LC-MS has revealed significant metabolic differences in red and white muscles of rabbit meat, contributing to the understanding of muscle-specific metabolic functions27. This approach not only supports regulatory frameworks but also contributes to the development of safer, high-quality food products by providing reliable data for safety assessments11,28. Similarly, the integration of mass spectrometry-based metabolomics in food science assures food traceability, thereby enhancing consumer protection6,29. Consequently, metabolomics can elucidate microbial metabolic pathways, supporting food safety by identifying its metabolic consequences7,30. This overall process in metabolomics offers significant benefits in ensuring food safety and quality9,10,31, highlighting its broader applications in the evaluation of cultured meat safety.

The results of this study provide crucial insights into the metabolic profiles of muscle satellite cells and myotube formed cells, revealing how different cultivation conditions influence these profiles and the potential implications for cellular function and safety. The clustering and correlation analysis in positive ion mode demonstrates that the samples cluster well according to cultivation type, indicating distinct metabolic profiles for each group. This finding suggests that the cultivation environment significantly impacts the metabolic state of the cells. The identification of 45 common metabolites, such as diethanolamine, paracetamol, and acetylcholine, highlights key metabolic changes associated with cultivation. These metabolites are likely involved in critical cellular processes such as neurotransmission and cellular signaling, which may be influenced by the cell culture environment. The consistent expression trends observed across these metabolites further support the robustness of the metabolic shifts induced by cultivation conditions.

In contrast, the negative ion mode did not show significant clustering, indicating that the metabolite profiles across the test groups are relatively similar. This lack of differentiation suggests that the metabolic alterations observed in the positive mode may be more specific to certain pathways or conditions that are not as pronounced in the negative mode.

The metabolomics analysis reveals a dynamic metabolic landscape in both muscle satellite cells and myotube-formed cells, with numerous metabolites showing significant upregulation or downregulation. In muscle satellite cells, the upregulation of metabolites like phosphoethanolamine and cytarabine suggests heightened activity in pathways related to membrane synthesis and nucleotide metabolism, which are crucial for rapid cell division. The downregulation of creatine and inosine-5’-monophosphate, on the other hand, may reflect a shift away from energy storage and towards immediate energy consumption, a characteristic of proliferating cells. The identification of potentially hazardous metabolites, such as Levofloxacin and 5-Nitro-o-toluidine, in myotube-formed cells raises concerns about the safety of cultured cells under certain conditions. These findings underscore the importance of rigorous safety assessments, particularly when considering cultured cells for food production or therapeutic use. The detection of cytarabine and guanosine in the negative ion mode further highlights the need for careful monitoring of metabolites that could pose health risks.

The pathway enrichment analysis underscores the complexity of the metabolic changes associated with cell differentiation. The enrichment of pathways such as sphingolipid metabolism and glycerophospholipid metabolism indicates significant alterations in membrane composition and function, which are essential for maintaining cellular integrity during differentiation. The involvement of amino acid metabolism pathways, including glycine, serine, and threonine metabolism, suggests a shift towards biosynthetic activities necessary for protein synthesis and cellular growth. These findings highlight the intricate balance between energy production, biosynthesis, and cellular maintenance in differentiated cells. The observed enrichments in pathways like purine metabolism and pantothenate and CoA biosynthesis further emphasize the centrality of nucleotide and coenzyme metabolism in supporting the energetic and biosynthetic demands of differentiating cells.

The network analysis provides a comprehensive view of the interconnected metabolic processes in muscle satellite cells and myotube-formed cells. The centrality of metabolites like ATP, AMP, and GMP underscores their pivotal roles in energy metabolism, signaling, and nucleotide synthesis. These metabolites serve as hubs in the network, connecting various metabolic pathways and facilitating the coordination of cellular activities. The presence of amino acids like L-aspartic acid and L-isoleucine, alongside energy-related molecules like creatine and choline, reflects the network’s focus on supporting both the structural and functional demands of cells. The identification of specialized metabolites such as methyldopa and 6-hydroxydopamine suggests that differentiation may involve unique metabolic pathways related to neurotransmitter synthesis or drug metabolism, adding another layer of complexity to the cellular metabolic landscape.

One limitation of this study is the relatively small sample size, which may affect the generalizability of the findings. However, as this is the first study to investigate the fundamental metabolic changes in cultured meat, our primary aim was to explore the baseline metabolic alterations rather than to derive definitive conclusions requiring a larger sample size. Future studies with larger sample sizes are warranted to further confirm and expand upon our findings, providing a more comprehensive understanding of the metabolic changes involved in cultured meat production.

In addition, consumer acceptance of cultured meat may be influenced by both perceived safety and nutritional benefits. Any metabolic differences identified in our study could raise questions regarding equivalence to conventional meat, making it essential to clearly communicate metabolomic data in a transparent and accessible manner. However, it is important to recognize that metabolic differences do not necessarily indicate a safety risk, as variations in metabolite composition are expected due to differences in the cultivation process. As long as safety is thoroughly assessed and validated, these metabolic variations should not pose a concern for consumer health or regulatory approval.

To address these concerns, additional studies—including larger-scale clinical trials, long-term feeding studies, and expanded metabolomics analyses with diverse meat types—would be beneficial to further validate safety and nutritional equivalence. Continued research, along with engagement with regulatory bodies and clear public communication, will be key factors in ensuring successful adoption of this novel food product. Providing robust scientific evidence on the safety and quality of cultured meat is essential for both regulatory approval and public acceptance.

Conclusion

The metabolomic analysis revealed differences between cultured cells and conventional meat, suggesting variations in their metabolic profiles. However, these differences do not seem to pose toxicity concerns. Although antibiotics were detected in some cases, this is likely due to components in the cell culture medium rather than an inherent issue with the cultured cells themselves. The impact on amino acid, peptide, and fatty acid metabolism indicates that the nutrient content in the final cultured product might differ from that of conventional meat. These findings suggest that while cultured meat may offer a viable alternative, its nutritional composition could vary, necessitating further optimization to match or enhance the nutritional value of traditional meat products.