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Transcriptome-aligned metabolic profiling by SERSome reflects biological changes following mesenchymal stem cells expansion

Stem Cell Research & Therapy volume 15, Article number: 467 (2024) Cite this article

Abstract

Background

Mesenchymal stem cells (MSCs) are widely applied in the treatment of various clinical diseases and in the field of medical aesthetics. However, MSCs exhibit greater heterogeneity limited stability, and more complex molecular and mechanistic characteristics compared to conventional drugs, making rapid and precise monitoring more challenging.

Methods

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive, tractable and low-cost fingerprinting technique capable of identifying a wide range of molecules related to biological processes. Here, we employed SERS for reproducible quantification of ultralow concentrations of molecules and utilized spectral sets, termed SERSomes, for robust and comprehensive intracellular multi-metabolite profiling.

Results

We revealed that with increasing passage number, there is a gradual decline in cell expansion efficiency, accompanied by significant changes in intracellular amino acids, purines, and pyrimidines. By integrating these metabolic features detected by SERS with transcriptomic data, we established a correlation between SERS signals and biological changes, as well as differentially expressed genes.

Conclusion

In this study, we explore the application of SERS technique to provide robust metabolic characteristics of MSCs across different passages and donors. These results demonstrate the effectiveness of SERSome in reflecting biological characteristics. Due to its sensitivity, adaptability, low cost, and feasibility for miniaturized instrumentation throughout pretreatment, measurement, and analysis, the label-free SERSome technique is suitable for monitoring MSC expansion and offers significant advantages for large-scale MSC manufacturing.

Background

Mesenchymal stem cells (MSCs) are a type of multipotent stem cell and a key focus in regenerative medicine and tissue engineering [1, 2]. Their differentiation and immunomodulatory abilities allow MSCs to address conditions such as heart disease, bone injuries, cartilage damage, and autoimmune diseases [3, 4]. MSC therapy is a promising area in regenerative medicine, leveraging the unique properties of MSCs for tissue repair and immune modulation, and is now the most extensively studied experimental cell therapy worldwide [5]. However, as living entities, cellular therapy products inherently exhibit greater heterogeneity, limited stability, and more complex molecular and mechanistic features than conventional drugs [6]. Simply confirming cell identity, quantity, and viability does not guarantee the functionality of the cell therapy product. Therefore, employing rapid and precise detection methods that accurately reflect the internal state of cells is crucial for MSC manufacture.

Changes in gene expression and biological processes can directly reflect the state of cells and predict trends in their condition. mRNA sequencing (mRNA-seq) is a high-throughput technique used to analyze the transcriptome of cells or tissues, capturing all mRNA molecules present at a given time[7]. This method offers a comprehensive snapshot of gene expression and helps researchers identify biological functions and processes associated with altered genes. However, RNA-seq analysis involves numerous steps, from raw data processing to interpreting gene expression and its biological significance, making it both time-consuming and labor-intensive.

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive fingerprinting technique which can identify a wide range of molecules simply based on their vibrational signatures [8]. Particularly, trace number of molecules (even down to single-molecule level) can be confidently detected in the electromagnetic hotspots on the SERS substrates. Such label-free SERS detection approaches have been widely explored in the untargeted profiling of various biological samples such as serum, urine, sweat, cell culture medium, cell lysates, etc. [9,10,11,12,13] In our previous work, we were the first to propose using SERS spectral sets, termed SERSomes, for metabolic profiling [14]. To ensure the reproducibility of SERSome-based metabolic profiling, we leveraged the Wasserstein distance, a statistical indicator of variability between two distributions [15]. This approach enabled robust and comprehensive metabolic phenotyping, which showed significant improvements in revealing cellular responses to drug treatments and detecting pathological variations in patients [14].

Metabolites, rich in physiological values, have been seen mounting research interest [16, 17], showing high potentials in clinical diagnosis and basic research on biological mechanisms based on label-free SERS detection. There has been a notable rise in using SERS for metabolic profiling, revealing vibrational evidence for the potential biomarkers and facilitating disease diagnosis (including subtypes and stages). With advances in data acquisition and analysis methods, using SERSome to gain insight into the underlying biological nature based on the metabolic information is increasingly feasible. Integrative analysis of multi-omics data such as transcriptomics and metabolic profiling can enable the link between transcript changes and biological activities and promote comprehensive understanding of the regulation cascade [18, 19].

In our study, SERSome was employed to analyze the intracellular molecular fingerprint characteristics of human umbilical cord-derived MSCs (hUC-MSCs) across different cell passages and donors. As the passage number increases, both cell activity and expansion efficiency progressively decline, accompanied by significant changes in amino acids, purines, and pyrimidines levels within the cells. By integrating these SERSome-detected metabolic signatures with transcriptomic data, we correlated them with biological changes and differential genes, demonstrating the validity of SERS in detecting biological characteristics. As SERS is sensitive, tractable, low-cost and realizable on miniaturized instrumentations throughout pretreatment, measurement and analysis procedures, the label-free SERSome technique is favorable for monitoring MSC expansion state and is advantageous for large-scale MSC manufacturing.

Methods

Materials and instrumentations

Silver nitrate (AgNO3, AR, 99.8%), citrate trisodium (TSC, 98%), L-cysteine (99%), guanine (99%) and uracil (99%) were obtained from Aladdin (Shanghai, China). Adenine (HPLC, ≥ 99.5%) was purchased from Macklin (Shanghai, China). All materials were used as received without further purification. Ultrapure water (18.2 MΩ) was used for all experiments.

The hydrodynamic diameter of the Ag NPs was characterized by a Zetasizer Nano ZSP (Malvern, U.K.). The absorbance spectrum of the NPs was acquired on a UV1900 UV–Vis spectrophotometer (Aucybest, Shanghai, China).

Human samples and MSCs culture

Three fresh samples of independent human umbilical cord were obtained during surgery at the Department of Obstetrics and Gynecology, Ren Ji Hospital. All samples were collected with the informed consent of donors and approved by the Renji Hospital Ethics Committee (No. KY2021-027). Prior to obtaining the umbilical cord, all prenatal pathogen screenings and genetic screenings for the mother were confirmed to be normal.

After obtaining the umbilical cord, segments of Wharton's jelly are extracted and placed flat in a culture dish. Following 3 to 5 days of culture with TransStem®Serum-Free, Xeno-Free Human Mesenchymal Stromal Cell Medium (TransGene, MM102-01) at 37 °C in humidified 5% CO2 atmosphere, the Wharton's jelly fragments are removed, leaving the primary MSCs in the dish. Three replicates of each of the three MSC lines (C1, C2, and C3) were independently cultured in separate dishes. Subsequent cell cultures are conducted on these primary cells in the same conditions. After counting the cells, seed them at a cell density of 10,000 cells/cm2 in a T25 flask or 6,366 cells/cm2 in a 10 cm dish, and culture them under the conditions mentioned above. When cell confluence reaches 80%, subculture and count the cells. The cell expansion fold, passage number, population doubling number, and expansion efficiency were calculated based on the cell count and culture duration. Detailed cell culture data are shown in Supplementary Table 1.

RNA-seq and analyses

MSCs at different passages were harvested for RNA preparation. The complementary DNA (cDNA) libraries were prepared using the NEBNext TM Ultra Directional RNA Library Prep Kit, NEBNext Poly (A) mRNA Magnetic Isolation Module, NEBNext Multiplex Oligos according to the manufacturer’s instructions. The products were purified and enriched by PCR to create the final cDNA libraries and quantified by Agilent2200. The tagged cDNA libraries were pooled in equal ratio and used for 150 bp paired-end sequencing in a single lane of the Illumina HiSeqXTen. The raw sequencing data are evaluated by fastp (https://github.com/OpenGene/fastp) including quality distribution of nucleotides, position specific sequencing quality, GC content, the proportion of PCR duplication, kmer frequency etc. These evaluation metrics may help us understand the nature of data deeper before subsequent variant evaluation. Mapping of pair-end reads. Before read mapping, clean reads were obtained from the raw reads by removing the adaptor sequences, reads with > 5% ambiguous bases (noted as N) and low-quality reads containing more than 20 percent of bases with qualities of < 20. The clean reads were then aligned to mouse genome (version: GRCm38 NCBI) using the HISAT2 v2.2.1 (http://daehwankimlab.github.io/hisat2/)) [20]. HTseq was used to count gene counts [21] and RPKM method was used to determine the gene expression. RUVSeq was utilized to eliminate unwanted variation from RNA-Seq data [22]. We applied DESeq2 algorithm [23] to filter the differentially expressed genes. P-value and (False Discovery Rate) FDR analyses were subjected to the following criteria: i) Fold Change > 2 or < 0.5; ii) P-value < 0.05, FDR < 0.05.

Pathway enrichment analysis

The GO enrichment analysis was executed using clusterProfiler package (version: v.4.5.0)[24] through Hiplot Pro (https://hiplot.com.cn/), a comprehensive web service for biomedical data analysis and visualization. GO gene sets belonging to Homo Sapiens used in this article were integrated from AmiGO database (http://amigo.geneontology.org/amigo/landing). Pathway analysis was used to find out the significant pathway of the differentially expressed genes according to KEGG database. Fisher’s exact test was applied to identify significant GO categories and KEGG pathways, with a significance threshold set at P-value < 0.05 for both analyses[25].Gene expression data of mRNA was used to obtain NES over the active GO biological processes using GSEA software (v.4.1) (http://software.broadinstitute.org/gsea/index.jsp) [26].

Collection of cell lysates for metabolic profiling

MSCs were cultured and counted during each passage. Upon reaching specific passages (P2, P6, P10), three replicates of each of the MSC cell lines were harvested and washed with ice-cold PBS for metabolic profiling. The obtained cells were sonicated to release the intracellular molecules with a Q800R sonicator (QSonica, U.S.A.) at a 20 kHz frequency and 750 W power in a mode of 1 s working and 1 s interval on ice for a total duration of 3 min. The cell lysate (200 μL) was centrifugated at 200 g for 20 min to remove cell debris and then filtered with a 3 kDa-cutoff membrane filter (Millipore, Amicon Ultra-4, PLBC Ultracel-3) by centrifugation (14,500 rpm, 20 min) to extract the metabolite components.

Synthesis of Ag NPs

The citrate-reduced Ag NPs were synthesized according to Lee and Meisel’s method [27] with slight modifications. Briefly, 75 mL AgNO3 (0.12 mg/mL) was heated to boil under constant stirring, followed by a dropwise addition of 2 mL TSC (10.1 mg/mL) within 2 min. The mixture was kept boiling for another 1 h and then cooled to room temperature under stirring. The product was stored in 4 °C without light exposure. Before being used, the Ag NPs were brought back to room temperature and ultrasonicated for 1 min to avoid aggregation.

SERS measurement

For SERS measurement, the cell metabolite extractions and pure metabolite solutions (metabolite: cysteine, adenine, guanine and uracil; concentration: 0.1 mM) were mixed with the Ag NPs at a ratio of 1:1 vol/vol, ultra-sonicated for 20 s and then incubated for 1 h under ambient conditions. Prior to measurement, the mixture was sonicated for 3 s to avoid NP sedimentation. The metabolite-NP mixture (10 μL) was injected into a quartz capillary (inside diameter: 1 mm, outside diameter: 2 mm) and measured by a ML-100 High-Resolution Raman Molecular Analyzer (Shanghai MoleTech Ltd.) equipped with a 633 nm laser (power: 17 mW) and a 10 × objective lens (numerical aperture: 0.25). For each measurement, 200 spectra were acquired with 1 s of acquisition time per spectrum in a pointwise scanning mode (step size: 10 μm). Three independent measurements were carried out for each cell lysate.

SERS spectral analysis

Prior to spectral analysis, each spectrum was pretreated by spike removal and baseline correction by polynomial curve fitting (degree: 10, max point: 200).

For the pure metabolites (cysteine, guanine, uracil and adenine), the mean spectra were computed followed by normalization to [0,1]. Each spectrum obtained from cell lysates was decomposed with the mean spectra of the four metabolites based on non-negative least squares using MATLAB R2023b. The coefficients corresponding to each metabolite were consequently obtained, indicating the contribution of the metabolites to the spectra of cell lysates.

Statistical methods

To evaluate the spectral number for robust metabolic profiling, Pearson’s correlation coefficient (PCC) was computed from the mean SERS spectra from two independent and randomly selected subsets of spectra. PCC was also leveraged to access the similarity of the SERS patterns between two samples of different generation, the reproducibility of different cell origins, biological duplicates and technical replicates, as well as the correlation between RNA expressions and SERS spectral intensities (or SERS metabolite coefficient). Herein, the highly correlated RNA expression-SERS spectral band pairs and RNA expression-metabolite pairs were screened under |PCC|> 0.8 and the corresponding p value < 0.01.

Differences between the two groups were assessed using two-tailed unpaired Student’s t-tests. A significant difference was defined as P. value < 0.05. T-distribution stochastic neighbor embedding (t-SNE) was used to visualize the data distribution. For the spectral data, all individual spectra were used. For the metabolite coefficients, the mean coefficients from each biological duplicate were applied. While for the RNA expressions, the mean values from each different sample were adopted.

Results

Workflow of hUC-MSC metabolome-transcriptomic analysis

To explore the application of SERS in analyzing the biological characteristics of hUC-MSCs across different passages and donors, we employed SERSome alongside mRNA-seq to correlate metabolic profiling by SERS with the biological characteristics identified through mRNA-seq. In detail, the hUC-MSCs are collected with the biological indicators recorded along the expansion. Each cell sample is first lysed and pretreated for label-free metabolite detection using SERS and transcriptomic detection by RNA sequencing. Correlation analysis is then performed based on the SERSome metabolic profile and gene expressions. Key mechanisms of hUC-MSC expansion are revealed based on the significant transcriptome-metabolite correlation. The workflow for metabolome-transcriptomic analysis is demonstrated in Fig. 1a. Herein, to account for individual variability, MSCs should be collected from different donors, and replicates should be performed throughout the experiment for solidity. At certain generations, a portion of cells are harvested with the standard biological indicators carefully recorded and then separated into two portions for metabolic profiling and transcriptomic detection, respectively. For the former, the cell lysates are pretreated and mixed with the metallic nanoparticles (NPs) for SERS measurement in a label-free approach, obtaining a spectral set (typically consisting of 200 spectra) as a SERSome profile. The underlying metabolite contribution to the SERS spectra is obtained by characteristic peak assignment and spectral decomposition and analyzed for the metabolic variations along the expansion. On the other hand, the transcriptome profile of each sample is measured using RNA sequencing with another portion of cells. Bioinformatic methods are used to screen the most significantly changed gene expression and biological processes during expansion. As for the integrative analysis, the correlation between SERSomic and transcriptomic profiles is computed to establish connections between SERS signals and biological changes, as well as differentially expressed genes.

Fig. 1
figure 1

Source data are available in the Source Data file

Metabolic profiling of hUC-MSC lysates by SERS. a Workflow of hUC-MSC metabolome-transcriptomic analysis. b Bright-field images of hUC-MSCs (scale bar: 200 μm) and the SERS metabolic profiling of the corresponding cell lysates. For each lysate sample, a spectral set comprising 200 spectra were measured as displayed by the heatmaps. The mean spectra are provided for clarity. c The relative cell number, d The cell viability, e the passaging number, and f the expansion rate has been recorded along the 14-day culture. For each cell origin, the data are displayed by the mean values from three biological replicates. Error bar: standard deviation (n = 3). g Absorbance spectrum and h the hydrodynamic diameter of citrate-reduced Ag NPs. i Pearson’s correlation coefficients among the mean SERS spectra obtained from the C1-originated hUC-MSC lysates of different generations (P2, P6 and P10) and different biological replicates (S1, S2 and S3). j Pearson’s correlation coefficients among the mean SERS spectra obtained from the hUC-MSC lysates of different generations (P2, P6 and P10) and origins (C1, C2 and C3). k 2D t-SNE visualization of all the SERS spectra and l the highlighted t-SNE distribution of the SERS spectra obtained from the C1-originated hUC-MSC lysates.

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SERSome reveals consistent metabolic variation following MSC expansion

For realization, we isolated MSCs from three umbilical cord tissues obtained from three donors, hereinafter referred to as C1, C2 and C3. As depicted in Figure S1, nearly 100% primary MSCs isolated from C1, C2 and C3 exhibited all five positive MSC markers and none of the six negative MSC markers, indicating high purity of these cells. Three biological replicates of cells from each donor were monitored to record biological indicators including cell number, viability, doubling time and expansion rate during cell culture. Along the 14-day culture, the cells were passaged once cultured to 80% coverage of plate to avoid contact inhibition. As seen, there were no significant changes in cell morphology across the three passages. Generally consistent SERS peaks were detected at each passage, indicating the primarily common metabolite species (Fig. 1b). The biological indicators showed steadily increased cell number (Fig. 1c) and favorable cell viability (~ 100%) (Fig. 1d). Besides, the expansion rate slowly declined (Fig. 1e–f) with increasing cell passages.

In terms of metabolite detection, a small portion of cells (106 cells dispersed in 1 mL PBS) were harvested at passage 2, 6 and 10 respectively, then washed by PBS and lysed by sonication. It is worth mentioning that non-contact sonication was specifically chosen for cell lysis owing to the high lysing efficiency and prevention from contamination. The lysates were pretreated by centrifugation to remove cell debris and ultrafiltration at the cutoff value of 3 kDa to remove large molecules such as proteins which may impede the metabolite molecules from approaching the SERS electromagnetic hotspots via protein corona effect as reported in our previous work [10].

For label-free SERS measurement of the metabolites, citrate-reduced silver (Ag) NPs were used for enhancement, showing an absorbance peak at 442 nm absorbance peak (Fig. 1g) and a hydrodynamic diameter of 153 nm (polydisperse index: 0.315) (Fig. 1h), which is consistent with previous results [28, 29]. The filtered cell lysates were then mixed with the Ag NPs at the ratio of 1:1 vol/vol and injected into a quartz capillary for SERS measurement in a pointwise scanning mode. As seen from the successively acquired spectra in one sample, fluctuations were observed in intensities (Fig. 1b). In this sense, the number of spectra for each measurement was evaluated by Pearson’s correlation coefficient (PCC) in advance for robust profiling [10, 14] (see Methods section for detail), showing that the spectrum number should be larger than 22 for PCC to reach 0.995 (Figure S2). Therefore, in this work, 200 spectra were acquired in each measurement.

Technical repeatability was evaluated by three independent measurements of one sample, showing consistency among the mean spectra with PCC as high as 0.997 ± 0.003 (Figure S3). Biological similarity was assessed by PCC using independently cultured cells from the same donor. In spite of the random cellular variation, the biological replicates present adequately smaller disparity than different generations (Fig. 1i). In addition, though individual heterogeneity is apparent to some extent as seen from the mean spectra of the same generations yet from different donors, the distinction across different generations is much more prominent, suggesting the similar metabolic variation along the cell expansion even from different human origins (Fig. 1j). To this regard, we further visualized the distribution of the SERS spectral patterns by t-distributed stochastic neighbor embedding (t-SNE), an unsupervised method enabling the cellular dynamics tracing [30]. Similar tracks have been discovered by analyzing all the spectra from the three donors (Fig. 1k) and analyzing the spectra from individual donor at a time (Figure S4). Detailed variations are excavated by focusing on the data distribution from one individual donor in the overall plot (Fig. 1l). Specifically, the hUC-MSCs varied dramatically from P2 to P6 while relatively mildly from P6 to P10, in accordance with the ever-reducing expansion rate (Fig. 1f).

In a word, the SERSome metabolic profiles reveal consistent metabolic variation following the MSC expansion regardless of the donors. Further backed upon the reproducible results across replicates, it promises reliable analysis of the SERS metabolic profiles for expansion-related variation.

SERSome reflects reliable metabolic indicators of hUC-MSC expansion

To further unveil the specific metabolic variations behind the spectral patterns, we carefully matched the lysate spectral features with SERS characteristic peaks of cell lysate metabolites commonly reported in literatures [31,32,33]. As the result, the SERS peaks in cell lysates can generally accord with the peaks of cysteine, guanine, uracil and adenine (Figs. 2a and S5). In detail, the peaks at 660, 800, 908 and 1390 cm−1 can be assigned to the C-S stretching, COO− bending, C–C stretching, and COO− stretching mode of cysteine, respectively [34,35,36]. Guanine may primarily contribute the intensity at 656 cm−1 by the symmetric stretching vibration of the six-membered ring [37]. Other vibrational peaks such as 796 (ring breathing), 1045 (NH and CH deformation, CNC stretching) and 1397 cm−1 (NH deformation) can be deduced to uracil [38]. Adenine, featuring in 727 cm−1 for ring stretching and 1325 cm−1 for C-N stretching [39], has also present its signatures in the lysate spectra. Therefore, we tentatively conjectured that these metabolites may play an important role in the lysate SERS spectra. Interestingly, a linear superposition of the four molecules can almost recover the lysate spectral signatures (Fig. S6).

Fig. 2
figure 2

Source data are available in the Source Data file

Evaluation of SERS metabolic indicators for hUC-MSC expansion. a The mean SERS spectra of all hUC-MSC lysates (cell lysate) and the standard SERS spectra (STD) of pure metabolite solutions (cysteine, guanine, uracil and adenine). The characteristic peaks of the metabolites are exhibited by colored arrows. The coefficients of b cysteine, c uracil, d adenine and e guanine obtained by decomposing the lysate SERS spectra of C1 hUC-MSCs at different generations. The data are displayed by the mean value from three biological replicates (blue, yellow and red) with the error bar indicating the standard deviation (n = 3 technical replicates). f t-SNE visualization of the metabolic coefficients of C1 hUC-MSC lysates. For each cell generation, there are 9 datapoints obtained from three biological replicates, each being measured three times. The changes of SERS peak intensities at g 877 and h 1459 cm−1 along the expansion (from P2 to P10) of three replicates of C1 hUC-MSCs. The SERS bands of 877 and 1459 cm−1 are pointed out by black circles in panel a.

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With this idea, we applied non-negative least squares to decompose the spectra to obtain the potential contributions of the four metabolites to the spectra by the coefficients (Figs. 2b–e, and S7). As seen for the C1-originated hUC-MSC lysates, cysteine continuously decreased in coefficients from P2 to P10, indicating potential alterations in protein metabolism concurrent with the reduced expansion rate of MSCs [40]. Conversely, the concentration of uracil shows a sustained upregulation. In comparison, the variation of adenine and guanine is relatively more complicated, generally presenting an increase from P2 to P6 and then decrease from P6 to P10, indicating potential alterations in DNA and RNA metabolism. T-SNE visualization has also been implemented with the coefficients of the metabolites instead of the original spectra data as used above, while similar track has been obtained, presenting a prominent variation from P2 to P6 followed by a milder change from P6 to P10 (Fig. 2f). This suggests that these metabolites can reflect the primary spectral features, evidencing the reliable interpretation of the lysate spectra with these metabolites.

Nevertheless, some small peaks are still not fitted by the four metabolites including 877 and 1458 cm−1 (as indicated by circles in Fig. 2a) which may be attributed to lipids by C–C symmetric stretching [41] and CH2 deformation vibration [42] according to the literatures. The contribution of these two bands exhibited a comparable increment as the expansion (Fig. 2g–h). As for cells from different origins (Figure S7), though there preserve heterogeneity in the detailed metabolite contributions across origins, the similar variation trend may indicate a consistent expansion mechanism.

Transcriptomic analysis reveals biological variations consistent with the SERS data

To further characterize biological alteration following MSCs expansion, we next conducted global transcriptomics studies across different passages of MSCs from three donors. Transcriptomic analysis identified a total of 2,558 differentially expressed genes (FDR < 0.05, fold change > 2), including 1,142 downregulated genes (44.64%) and 1,416 upregulated genes (55.36%) in P6 MSCs compared to P2 MSCs (Fig. 3a–b). Additionally, 32 differential genes were identified (FDR < 0.05, fold change > 2) in P10 compared to P6 MSCs, with 22 downregulated genes (68.75%) and 10 upregulated genes (31.25%) (Fig. 3a–b). This result is consistent with the SERS data, which reveals a prominent variation from P2 to P6 and a milder change from P6 to P10. To further clarify the biological processes and pathways altered following MSC expansion, we implement enrichment analysis based on differential mRNAs. Gene Ontology (GO) enrichment analysis of the transcriptomic data revealed distinct alterations in pathways related to DNA replication and cell cycle (Figs. 3c and S8). Key signaling pathways, including TNF, NF-κB, MAPK, TGF-β, and PI3K-AKT, essential for DNA replication and for amino acid, purine, and pyrimidine metabolism [43,44,45,46,47], were identified according to KEGG database (Fig. 3d). Furthermore, we identified differential expressions of genes associated with these pathways, such as NR4A1, TGFB3, MAP3K8, MAPK10, TNF, NFKBIA, TNFAIP3, PIK3R1, EDN1, MAP2K6, LIF, NR4A1, and KDR (Fig. 3e). Notably, we also observed broad alterations in genes related to purine and pyrimidine metabolism as well as amino acid metabolism, according to KEGG database (Fig. 3f). These findings indicate alterations in multiple pathways following MSC expansion, contributing to the changes in metabolite profiles.

Fig. 3
figure 3

Source data are available in the Source Data file

Transcriptomics analysis of MSCs. a Heat map of the altered mRNAs between P6 (n = 3) and P2 (n = 3) MSCs, as well as between P10 (n = 3) and P6 (n = 3) MSCs. b Volcano plot of gene alterations across different passages of MSCs. Significantly differential mRNAs (adjusted P. value < 0.05 and fold change > 1.5) are colored in red (up-regulated) and blue (down-regulated). c Significantly altered GO pathways. d Significantly altered KEGG pathways. e Relative mRNA levels of indicated genes. f Heat map of relative mRNAs levels of indicated genes. Data are represented as mean ± SEM.

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Then, we integrated transcriptomic datasets across three passages of MSCs to determine the consistent alterations in cellular processes from P2 to P10. By adopting gene set enrichment analysis (GSEA) pathway enrichment, we found 384 of 391 GO pathways were downregulated across three passages of MSCs (both P2 to P6 and P6 to P10) (Fig. 4a), including pathways related to DNA/RNA metabolism and protein modification (Fig. 4b). At gene level, 19 genes were altered between P6 and P2 MSCs, as well as between P10 and P6 MSCs (Fig. 4c), including genes involved in DNA/RNA metabolism such as NIR4A1, NIR4A3 and MIDEAS, as well as genes relate to protein modification such as NOCT, LIF and ID3, which could serve as potential biomarkers for MSC passaging. Together, transcriptomic analysis revealed consistent alterations in gene expression and multiple pathways that potentially contribute to observed variations in the abundance of purine, piperidine and amino acid detected by SERS.

Fig. 4
figure 4

Source data are available in the Source Data file

Consistent alterations in cellular processes. a GSEA analysis of up-regulated and down-regulated pathways in P6 MSCs compared to P2 MSCs (x-axis) and in P10 MSCs compared to P6 (y-axis), based on GO subset in AmiGO database. Normalized enrichment scores (NES) of GO terms are plotted. b Quantitative analysis of altered pathways across different passages of MSCs based on GSEA analysis. (c) Venn diagram and heat map showing dis-regulated genes across different passages of MSCs.

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SERSome metabolic variation aligns biological changes across different passages of MSCs

An overall visualization of the genetic expression was further conducted by t-SNE based on the nineteen most significant genes as mentioned above, presenting similar expansion track as the metabolic profile (Fig. 5a), probably evidencing the consistent biological variation as the metabolic profile. In this sense, we implemented the correlation analysis between the genetic expression and the metabolic profile to further excavate the regulation process for stem cell expansion. Pearson’s correlation analysis was conducted between the genetic expression and the metabolite coefficients as well as the spectral intensities over the expansion process (Fig. 5b). Correlations with high confidence (|PCC|> 0.8, p < 0.01) have been identified for the four metabolites while with distinctive genes (as indicated by the thick black boxes in Fig. 5b). Some of the characteristic peaks can be found presenting accordant correlation with the coefficient of the corresponding metabolites (as indicated by the colored arrows on the leftmost side in Fig. 5b), underpinning the results computed with the coefficients.

Fig. 5
figure 5

Source data are available in the Source Data file

Relation between SERS metabolic profiling and RNA expression. a t-SNE visualization of the 19 most significantly changed genes. b Pearson’s correlation coefficient between the RNA expression and the SERS spectral intensities (left) as well as the metabolite coefficients (right). The mean spectrum of all lysate SERS spectra is displayed with the characteristic peaks of the metabolites pointed by colored arrows for clarity. The correlation between RNA expressions and metabolite coefficients with |PCC|> 0.8 and p < 0.01 are exhibited by thick black boxes and c displayed in detail by scatter plots with the different cell generations labeled by dashed circles.

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For better metabolome-transcriptome analysis, the detail correlation between the metabolites and the gene expressions with high significance (p < 0.05) are provided in Fig. 5c. Here, nine genes have been identified with high correlation with the metabolite contribution, including MIDEAS, FERMT1, GPRC5A, LYPD1, PRSS35, AKR1B1, C15orf48, GNA14 and LOXL4. These genes are involved in various biological processes, including DNA replication, mRNA transcription, protein metabolism, cytoskeleton formation and signal transduction, all of which are crucial for maintaining normal cell metabolism and expansion stages. Specifically, MIDEAS [48], FERMT1 [49], GPRC5A [50], LYPD1 [51], AKR1B1 [52] encode cell signaling factors that are involved in the regulation of cell cycling and metabolic processes. Additionally, PRSS35 [53], C15orf48 [54] and LOXL4 [55] encode a serine protease, a cytochrome C oxidase and a lysyl oxidase, respectively, contributing to protein, redox and amino acid metabolism. Lastly, GNA14 [56] encodes a guanine nucleotide binding protein playing a role in guanine nucleotide metabolism and cell signaling transduction. These results indicate the potential capability of SERSome in reflecting gene expression and biological characteristics by providing profound metabolic variations.

Discussion

In this work, we leveraged the SERSome technique to investigate metabolic changes during MSC passaging, introducing a sensitive, adaptable, and low-cost approach that is suitable for monitoring MSC expansion. In large-scale MSC production, small aliquots of cells can be extracted during passaging and harvesting for SERS analysis, offering a rapid and precise detection method for quality control. Compared to conventional cell growth monitoring methods that rely on cell counting, the SERS-based approach offers a more direct and sensitive assessment of the cells' intrinsic growth state. The application of rapid, sensitive, and high-throughput SERS technique will be highly beneficial for enforcing stringent quality control standards and optimizing cell culture conditions. With further practical application, this method would enable direct analysis of low-abundance metabolic molecules in the culture supernatant, making quality control more convenient and efficient. However, widespread adoption remains challenging, primarily due to the technical complexity of the equipment and the specialized expertise required for operation and data analysis.

This integration of SERSome with transcriptomics represents a novel approach to exploring biological nature. Replication is critical for accurate and reliable statistics and interpretations [57], so we have carried out a comprehensive study design to compare the distinctions among both technical and biological replicates. Technical reproducibility ensured the data reliability while biological replicates were performed in aim to reliably extract the passage-related metabolic changes regardless of individual differences. Consequently, our subsequent step involves further investigation of the biological indicators based on the SERS detection system to establish the range of SERS indicators during both normal and abnormal MSC expansion processes, thus improving the monitoring efficiency of large-scale MSC manufacturing.

For the SERS measurement, we utilized citrate-reduced Ag NPs due to its accessibility to a wide range of metabolite molecules. This kind of Ag NPs is also relatively easy to fabricate and remain stable in various biological samples, suitable for mass production and application. SERSome, namely SERS spectral sets, was applied specifically to address the disparities in metabolite composition and enhancement capability across probed volumes. This improved the comprehensiveness and robustness of metabolic profiling. To take a step further, SERSomic profiles can achieve accurate and sensitive quantification of metabolites if incorporated with more advanced techniques such as digital colloid-enhanced Raman spectroscopy[29, 58], and can be leveraged for broader scientific investigations even in the single-cell level[59]. In comparison with other currently established techniques for metabolite detection such as mass spectrometry and nuclear magnetic resonance spectroscopy, SERS exhibits higher sensitivity even down to single-molecule level as well as rapidness, tractability and low cost in the practical sense. This may promise broader applications of SERSome in general metabolic profiling and multi-omic investigations.

As for the integrated study of metabolic data and transcriptomic data, the overall correlation and the corresponding significance derived from the original spectral data was reduced to some extent compared with using the metabolite coefficients probably due to the mitigated spectral intensity variation caused by the overlapping signatures of reversely varied metabolites. This may suggest the importance of spectral decomposition for more profound analysis. Furthermore, metabolite biomarkers were primarily identified based on existing literature, but targeted detection is necessary to validate these screened biomarkers. In addition, a more comprehensive knowledge on the metabolite SERS fingerprints may facilitate the identification and analysis of more metabolite species. Consequently, our next step involves conducting further biological experiments to elucidate the relationship between significantly altered genes and metabolic biomarkers.

Our study revealed dysregulated levels of three purine/pyrimidine and cysteine in MSC during passaging (both P2 to P6 and P6 to P10). DNA replication requires the four deoxyribonucleotides (A, G, C and T). Thus, the rise in adenine and guanine at P6, followed by a decline at P10 in MSCs, likely reflects an initially active, then relative inactive, DNA replication process. Cysteine and uracil participate in protein and RNA synthesis, respectively. The decreased cysteine and increased uracil levels suggest downregulated amino acid metabolism and an elevated demand for protein translation in MSCs. Therefore, based on metabolic profiling by SERSome, MSCs with relatively low levels of adenine, guanine and cysteine, alongside high uracil level, can be considered “young” cells with lower passage numbers. Conversely, MSCs with relatively high levels of adenine, guanine and cysteine, along with low uracil level, can be considered “old” cells with higher passage numbers. Consequently, we identified 19 genes that altered in MSC during passaging (both P2 to P6 and P6 to P10). These genes provide a useful panel for assessing MSC passages. Specifically, MSCs with low expression levels of PRSS35, AKR1B1, C15orf48, GNA14, LOXL4, and LINC00968, alongside high expression levels of FERMT1, GPRC5A, and LYPD1, can be considered “young” cells with lower passage numbers, characterized by higher biological activity and proliferative capacity. Conversely, MSCs exhibiting high expression of PRSS35, AKR1B1, C15orf48, GNA14, LOXL4, and LINC00968, and low expression of FERMT1, GPRC5A, and LYPD1, can be characterized as “old” cells with higher passage numbers, characterized by lower biological activity and proliferative capacity. Additionally, MSCs that express high levels of NR4A1, KLF10, EGR2, CSRNP1, NR4A3, NOCT, LIF, ID3, NTRK3, and MIDEAS can be considered to represent an intermediate passage stage, characterized by moderate biological activity and proliferative capacity. The broad alterations in these genes lead to dysregulated metabolic processes, underlying the observed metabolic shifts. Further validation of these gene expression profiles, along with the quantification of related metabolite abundances in a larger sample cohort, will enhance the assessment of MSC passages.

Conclusion

In this work, we applied SERSome technique to robustly characterize the metabolic profile of MSCs across different passages and donors. Backed up by the transcriptomic detection, we succeeded in correlating the SERS metabolic variations with the biological changes as well as the differentially expressed genes. In this way, rather than the sole evidence provided by SERS signatures as primarily reported in previous literatures, our work, as an exploratory attempt, has provided reliable indications on the biological processes involved in maintaining normal cell metabolism and expansion stages, including DNA replication, mRNA transcription, protein metabolism, cytoskeleton formation and signal transduction. This illustrates the application potential of SERSome in general MSC monitoring and large-scale MSC manufacturing with the additional superiority in sensitivity, tractability as well as cost- and time-efficiency.

Availability of data and materials

Transcriptomics (RNA-seq) raw data have been deposited in the Gene Expression Omnibus (GEO) under accession number GEO: GSE275980 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE275980). All data are available from the corresponding author upon reasonable request.

Fresh samples of human umbilical cord were obtained during surgery at the Department of Obstetrics and Gynecology, Ren Ji Hospital. All samples were collected with the informed consent of donors and approved by the Renji Hospital Ethics Committee (No. KY2021-027). Prior to obtaining the umbilical cord, all prenatal pathogen screenings and genetic screenings for the mother were confirmed to be normal.

Abbreviations

SERS:

Surface-enhanced Raman spectroscopy

MSC:

Mesenchymal stem cells

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Acknowledgements

We would like to thank doctors and staff at the Renji Hospital who assisted with human umbilical cord collections. The authors declare that they have not used AI-generated work in this manuscript.

Funding

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Nos. 82203255 to H.R., 82272054 to J.Y., W2431055 to W-Q.G. and 623B2070 to X.B.), Major Projects of Special Development Fund in Shanghai Zhangjiang National Independent Innovation Demonstration Zone (No. ZJ2021-ZD-007 to W-Q.G.), Science and Technology Commission of Shanghai Municipality (No. 24DIPA00300 to J.Y.), Shanghai Key Laboratory of Gynecologic Oncology, Sichuan Science and Technology Program (No. 2024ZYD0112 to J.Y.), Shanghai Jiao Tong University (No. YG2024LC09 to J.Y.), Shanghai Jiao Tong University Trans-med Awards Research (STAR) Project (No. WF540162608 to W-Q.G. and B.M.), and Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai.

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Author notes

  1. Xinyuan Bi and Bin Ma have contributed equally to this work.

Authors and Affiliations

  1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

    Xinyuan Bi, Bin Ma, Wei Liu, Wei-Qiang Gao, Jian Ye & Hanyu Rao

  2. Clinical Stem Cell Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China

    Bin Ma, Wei-Qiang Gao & Hanyu Rao

  3. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Jian Ye

  4. Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, People’s Republic of China

    Jian Ye

  5. Shanghai Jiao Tong University Sichuan Research Institute, Chengdu, 610213, People’s Republic of China

    Xinyuan Bi & Jian Ye

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Contributions

J.Y., B.M., and H.R. conceived of and designed the study. X.B. and H.R. wrote the original draft. X.B. and H.R. performed the experiments and generated figures. W. L. isolated and cultured MSCs. J.Y. and H.R. guided the concept and data analysis. J.Y., B.M., and W.-Q.G. administrated the project and provided guidance on the methodology. All the authors wrote and revised the manuscript. X.B. and B.M. contributed equally to this work.

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Correspondence to Jian Ye or Hanyu Rao.

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This study was conducted at the School of Biomedical Engineering and Clinical Stem Cell Research Center, Shanghai Jiao Tong University. Approval was granted for a project titled “Cancer Immunotherapy Using Umbilical Cord Mesenchymal Stem Cells” in 2021 by the Renji Hospital Ethics Committee (No. KY2021-027). All participants provided informed consent.

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Bi, X., Ma, B., Liu, W. et al. Transcriptome-aligned metabolic profiling by SERSome reflects biological changes following mesenchymal stem cells expansion. Stem Cell Res Ther 15, 467 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13287-024-04109-0

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Stem Cell Research & Therapy

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