Abstract

   Dental pulp stem cells hold significant prospects for tooth
   regeneration and repair. However, a comprehensive understanding of the
   molecular differences between dental pulp stem cells (DPSC, from
   permanent teeth) and stem cells from human exfoliated deciduous teeth
   (SHED, from deciduous teeth) remains elusive, which is crucial for
   optimizing their therapeutic potential. To address this gap, we
   employed a novel data-independent acquisition (DIA) proteomics approach
   to compare the protein expression profiles of DPSC and SHED. Based on
   nano-LC-MS/MS DIA proteomics, we identified over 7,000 proteins in both
   cell types. By comparing their expression levels, 209 differentially
   expressed proteins were identified. Subsequent Gene Ontology and Kyoto
   Encyclopedia of Genes and Genomes enrichment analyses, along with
   protein–protein interaction network construction, revealed significant
   metabolic differences and key regulatory nodes. DPSC exhibited
   significantly higher expression of proteins belonging to the NDUFB
   family, SMARC family, RPTOR and TLR3. These proteins are known to be
   involved in critical cellular processes such as mitochondrial energy
   metabolism, mTOR-related autophagy pathway, and innate immune response.
   Conversely, SHED displayed elevated expression of AKR1B family, which
   participated in glycerolipid metabolism and adipogenic differentiation,
   PRKG1, MGLL and UQCRB proteins associated with thermogenesis. These
   findings highlight the specific proteomic landscape of DPSC and SHED,
   suggesting their distinct biological roles and potential applications.

   Keywords: human dental pulp stem cells, DIA proteomics, protein
   profile, DPSC, SHED

1. Introduction

   Dental pulp stem cells (DPSC) and stem cells from human exfoliated
   deciduous teeth (SHED) represent the two primary subtypes of
   tooth-derived pulp stem cells [[41]1,[42]2]. Both cell types originate
   from the dental pulp, the soft tissue within the tooth, distinguishing
   them from periodontal stem cells such as periodontal ligament stem
   cells (PDLSC), gingival mesenchymal stem cells (GMSC), and stem cells
   from apical papilla (SCAP), which are isolated from the surrounding
   periodontal tissues [[43]3,[44]4]. Since their discovery and
   characterization, these dental-derived mesenchymal stem cells have
   attracted significant research interest due to their inherent
   mesenchymal stem cell properties, including self-renewal,
   multi-differentiation potential, and low immunogenicity [[45]5,[46]6].
   These properties make them highly promising candidates for applications
   in regenerative medicine and tissue engineering [[47]7,[48]8].

   However, due to the distinct origin in terms of host ages and tooth
   types (permanent and deciduous), DPSC and SHED exhibit significant
   differences in their biological characteristics and clinical
   applications. Research indicated that SHED possessed a higher
   concentration of anti-aging signals, suggesting a potential for
   enhanced rejuvenation of aging cells most due to their origin from
   youth tissue [[49]9,[50]10]. Comparative studies have revealed
   disparities in the proliferation rate, differentiation capability, and
   cytokine expression profiles between DPSC and SHED. It showed that SHED
   demonstrated a higher proliferation rate and differentiation capacity
   in vitro, along with superior mineralization potential compared to DPSC
   [[51]11]. Moreover, SHED exhibited a more pronounced expression of
   cytokines associated with immunomodulation, odontogenesis, and
   osteogenesis, while DPSC demonstrated elevated expression of
   angiogenesis-related cytokines [[52]12] and exhibited significant
   potential in bone tissue engineering and nerve tissue repairing
   [[53]13,[54]14].

   Despite these intriguing findings, a comprehensive understanding of the
   protein molecular basis underlying the functional differences between
   DPSC and SHED remains elusive. Proteins, as the ultimate products of
   gene expression, are fundamental to cellular function. They are
   integral to a wide range of essential cellular processes, including
   metabolic catalysis, signal transduction, substance transport, immune
   defense, and cell division. Understanding the proteomic landscape of
   these two stem cell types is critical for optimizing their therapeutic
   applications. While DPSC and SHED share similar biological
   characteristics, their differences at the protein level may impact
   their efficacy and safety in specific applications.

   In this study, we aim to conduct a comparative proteomic analysis of
   DPSC and SHED utilizing the innovative data-independent acquisition
   (DIA) proteomics technology [[55]15]. By dissecting the protein
   profiles of these two stem cell populations, we aim to uncover the
   protein molecular basis for their distinct biological properties and
   guide future research into their therapeutic applications in tooth
   regenerative and tissue engineering. This investigation will shed light
   on the protein molecular basis of their functional differences and
   provide valuable insights for optimizing their clinical applications.

2. Materials and methods

2.1. Subjects and cell culture

   This study involved the isolation and in vitro culture of DPSC and
   SHED. DPSC was harvested from impacted third molars or other extracted
   teeth of healthy human subjects aged 16–29 years. SHED was obtained
   from exfoliated deciduous teeth of children aged 5–8 years. Informed
   consent was obtained from all participants or their guardians prior to
   the collection of any samples. Teeth were collected from Suzhou
   Stomatological Hospital. All experiments were conducted at Soochow
   University, adhering to all relevant national regulations,
   institutional policies, and the tenets of the Helsinki Declaration.
   This research involving human subjects received ethical approval from
   the ethics committees of Suzhou Stomatological Hospital and Soochow
   University (Approval Number: SUDA20230727H08).

   The tooth samples were placed in PBS buffer after extraction and sent
   to the laboratory for processing within 12 h. Following the extraction
   of pulp tissue, primary dental stem cells were cultured using either an
   adherent culture method or enzymatic digestion with collagenase I
   (3 mg/mL)/dispase (4 mg/mL) (Sigma, USA) as described previously
   [[56]16]. Once the cells had grown into fibroblasts, they were
   subcultured in α-modification of Eagle’s medium (α-MEM, HyClone, USA)
   supplemented with 15% fetal bovine serum (Gibco, USA), 100 U/mL
   penicillin‒streptomycin (Beyotime, China), 100 mM l-ascorbic acid
   phosphate (Sigma, USA), and 2 mM l-glutamine (Gibco, USA). Cultures
   were maintained in a humidified incubator at 37°C with 5% CO[2].

   Informed consent: Informed consent has been obtained from all
   individuals included in this study.

   Ethical approval: The research related to human use has been complied
   with all the relevant national regulations, institutional policies and
   in accordance with the tenets of the Helsinki Declaration, and has been
   approved by the Ethics Committees of Suzhou Stomatological Hospital and
   Soochow University (Approval Number: SUDA20230727H08).

2.2. Cell proliferation and identification

   Cell proliferation was assessed using a cell counting method. Briefly,
   2 × 10^4 cells were seeded into individual wells of six-well plates and
   counted at Days 1, 2, 3, and 4. Cell numbers were recorded at each time
   point. The cell proliferation rate was calculated based on the cell
   number of the seeded time. Cell proliferation rate = (No[1–4] −
   20,000)/20,000 × 100%.

   Dental stem cell identification was performed via flow cytometry (BD
   Calibur, USA) based on surface marker expression. Cells were collected
   and stained with antibodies targeting CD29, CD44, CD73, CD90, CD105,
   CD146, and CD166 (BioLegend, USA). The positive expression rate was
   analyzed using flow cytometry. Additionally, CD34 and CD45 staining was
   conducted to exclude potential hematopoietic cell. Data were presented
   as mean ± SD and statistically analyzed using GraphPad Prism 8.0.

2.3. Protein sample preparation and DIA proteome detection

   This study employed next-generation label-free DIA quantitative
   proteomics technology. Six cell samples, including three DPSCs and
   three SHEDs from different individuals, were selected for DIA proteomic
   analysis. Cells were collected at passage 3, with a minimum of 5 × 10^6
   cells required for protein extraction. For protein sample preparation,
   the cell pellets were suspended in cell lysis buffer containing sodium
   dodecyl sulfate (SDS) and 1× cocktail (with EDTA), followed by
   ultrasonic dissociation. The lysate was then centrifuged at 25,000g for
   15 min at 4°C. The supernatant was incubated with 10 mM dithiothreitol
   at 37°C for 30 min, followed by the addition of 55 mM iodoacetamide and
   incubation in the dark for 45 min. Five volumes of pre-cooled acetone
   were added to the lysate and incubated at −20°C for 2 h. After
   centrifugation at 25,000g for 15 min at 4°C, the supernatant was
   discarded. The protein precipitate was resuspended in an appropriate
   amount of protein lysate without SDS and sonicated. The final protein
   sample was obtained after centrifugation at 25,000g for 15 min at 4°C.

   Following tryptic digestion and high pH reversed-phase separation, the
   peptide samples were analyzed by nano-liquid chromatography coupled
   with tandem mass spectrometry (nano-LC‒MS/MS) for data-dependent
   acquisition (DDA) library construction and DIA quantitative detection.
   This process was outsourced and completed at BGI, China. For DDA
   library construction, a pooled sample containing an equal amount of all
   six samples was subjected to LC–MS/MS for DDA library construction. For
   DIA analysis, each individual sample was separated using a 120 min
   high-performance liquid chromatography gradient prior to MS analysis.
   The gradient was as follows: 0–5 min, 5% mobile phase B (98%
   acetonitrile, 0.1% formic acid); 5–90 min, linear increase of mobile
   phase B from 5 to 25%; 90–100 min, increase of mobile phase B from 25
   to 35%; 100–105 min, increase of mobile phase B from 35 to 80%;
   105–115 min, 80% mobile phase B; 115–120 min, 5% mobile phase B. The
   LC-separated peptides were ionized by nano-electrospray ionization and
   analyzed using a Q-Exactive HF tandem mass spectrometer (Thermo Fisher
   Scientific, San Jose, CA) in DIA mode. The main MS settings were ion
   source voltage 1.9 kV, MS scan range 400–1,250 m/z, MS resolution
   120,000, and maximum injection time (MIT) 50 ms. The MS scan range was
   divided into 45 continuous windows for DIA–MS/MS analysis. MS/MS
   fragmentation was performed using higher-energy collision dissociation
   with automatic MIT. Fragment ions were detected in the Orbitrap with a
   resolution of 30,000. The collision energy was set to a distributed
   mode of 22.5, 25, and 27.5, with an automatic gain control target of
   1E6.

2.4. Data analysis and differential protein identification

   The data analysis consisted of three primary stages: spectral library
   construction from DDA data, large-scale DIA data acquisition, and
   subsequent data analysis. Spectral library construction was achieved
   using DDA data identified by the Andromeda search engine within
   MaxQuant. These identified results served as the foundation for
   constructing a comprehensive spectral library. For large-scale DIA
   data, mProphet algorithm was applied for analytical quality control,
   ensuring the reliability of the vast quantitative data generated.

   MSstats version 2.0 software package was used for quantitative and
   differential protein analysis [[57]17]. The selection of the protein
   database is crucial for accurate protein identification. In this study,
   we chose the non-redundant proteome database, RefSeq, obtained from the
   National Center for Biotechnology Information. The DIA data were
   further refined through retention time calibration using iRT peptides.
   To mitigate false positives, a target-decoy model, commonly used in
   SWATH-MS analysis, was implemented, achieving a 1% false discovery rate
   (FDR). This stringent filtering resulted in highly significant
   quantitative findings. These data were then preprocessed based on the
   defined comparison groups (SHED vs DPSC). Subsequent statistical
   significance testing, conducted using a specific model, identified
   differentially expressed proteins (DEPs) between DPSC and SHED. A
   stringent filtering process was applied, utilizing a fold change
   threshold of greater than 2 and a p-value less than 0.05, to obtain
   statistically significant DEPs.

   Hierarchical clustering was employed to group these DEPs, providing a
   visual representation of DEP expression at the sample level.
   Additionally, enrichment analysis was conducted on the identified DEPs
   to further explore their biological significance. Figure S1 displays
   the process of DIA proteomics.

2.5. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway enrichment analysis

   To delve into the functional implications of the identified DEPs, we
   conducted GO and KEGG pathway enrichment analyses. The GO analysis,
   facilitated by the GO plot package within the MSstats software, aimed
   to elucidate the enrichment of GO terms related to cellular components
   (CC), biological processes, and molecular functions associated with the
   differential protein expression.

   Furthermore, KEGG pathway enrichment analysis was performed to identify
   significantly over-represented pathways in the dataset. The
   classification and bubble chart visualization of KEGG pathways provided
   a comprehensive overview of pathway enrichment. The Phyper function
   within R software was utilized for enrichment analysis, calculating the
   p-value for each pathway. Subsequently, the p-value was corrected for
   multiple comparisons using the FDR to obtain the Q-value. A Q-value
   threshold of 0.05 was employed to define statistically significant
   enrichment.

2.6. Protein–protein interaction (PPI) network analysis

   Proteins often interact with each other to form functional complexes
   and execute cellular processes. To investigate these interactions, we
   conducted PPI network analysis by comparing our dataset with the STRING
   database. This analysis resulted in a network interaction diagram
   depicting the relationships between DEPs, focusing on the top 100
   interactions with the highest confidence levels. This combined
   approach, encompassing GO and KEGG pathway enrichment analysis
   alongside PPI network analysis, provided a multifaceted understanding
   of the functional consequences of differential protein expression
   between SHED and DPSC groups.

3. Results

3.1. Primary stem cell culture and identification

   Primary stem cells were isolated using a combined approach of enzymatic
   digestion and tissue adherence. Cells migrated out of the tissue
   explants within 7–10 days of culture and were subsequently sub-cultured
   into 60 mm dishes after 14–20 days. [58]Figure 1a demonstrates the
   morphology of the cultured cells. Once cells reached confluence, they
   exhibited a relatively uniform, spindle-shaped, fibroblast-like
   morphology and displayed characteristic colony formation. Cell
   proliferation analysis revealed no significant differences in growth
   rate between DPSC and SHED ([59]Figure 1b), which might be related to
   the small sample size.

Figure 1.

   [60]Figure 1
   [61]Open in a new tab

   Cell morphology, proliferation, and identification. (a) Microscopic
   morphology of the cells. (b) Cell proliferation of the cells in
   different time points. (c) Representative FCM chart illustrating
   surface marker expression on the cells. (d) Statistical histogram of
   the expression of markers in DPSC and SHED.

   To confirm the stem cell nature of the cultured cells, surface marker
   expression was assessed by flow cytometry. Both DPSC and SHED exhibited
   high expression of mesenchymal stem cell markers, including CD29, CD44,
   CD73, CD90, CD105, CD146, and CD166, while lacking expression of
   hematopoietic markers CD34 and CD45 ([62]Figure 1c and d). These
   findings indicate that the cultured cells were indeed dental-derived
   mesenchymal stem cells.

3.2. Differential protein analysis and GO annotation reveals the subcellular
protein localization

   Following successful culture of primary dental pulp tissue into stable
   cell lines, a comparative proteomics study using DIA was performed on
   six individual DPSC and SHED cell lines (three replicates per group). A
   total of 8,509 proteins were identified across all cells, with 7,662
   proteins exhibiting comparable expression levels between DPSC and SHED
   (data uploaded in public database iProX: IPX0009667000). Database and
   software analysis revealed an average of 7,114 ± 184 proteins in DPSC
   and 7,136 ± 138 proteins in SHED. Figure S2 shows the CV value and PCA
   analysis of DIA quality control between DPSC and SHED. For clinical
   human samples, due to the individual differences between samples, the
   experiment is considered stable and reliable when the median CV value
   is below 30%. In this study, the median CV values of DPSC and SHED
   groups were between 15 and 25%, indicating that the experiment was
   stable and reliable.

   Differential expression analysis identified 107 proteins upregulated
   and 102 proteins downregulated in SHED compared to DPSC, with 7,453
   proteins showing no significant changes (Supplementary File 1 with 209
   DEPs). The volcano plot in [63]Figure 2a visually represents the DEPs
   between DPSC and SHED. [64]Figure 2b depicts the clustering analysis of
   these DEPs.

Figure 2.

   [65]Figure 2
   [66]Open in a new tab

   DEP analysis. (a) Volcano plot of the up- and down-regulated proteins
   between SHED and DPSC. (b) Clustering heatmap of the DEPs. (c) GO
   analysis of the CC for the differential proteins.

   GO analysis of the DEPs revealed that a significant portion (42
   proteins) were localized to the plasma membrane, with a notable number
   also found in the Golgi apparatus (32 proteins) or the extracellular
   space (22 proteins). The remaining DEPs were localized to other
   cellular compartments ([67]Figure 2c).

3.3. KEGG classification and pathway enrichment highlights metabolic
differences

   The DEPs were subjected to KEGG pathway classification, revealing
   significant enrichment across diverse functional categories ([68]Figure
   3). Notably, a substantial number of proteins (57) were associated with
   human diseases, indicating potential implications for cellular
   dysfunction and disease pathogenesis. Further investigation revealed a
   prominent role of metabolism, with 28 proteins enriched in metabolic
   pathways.

Figure 3.

   [69]Figure 3
   [70]Open in a new tab

   KEGG pathway classification of the differential proteins between SHED
   and DPSC. The X-axis represents the numbers of proteins associated with
   each pathway, while the Y-axis displays the enriched pathway names.

   Subsequent pathway enrichment analysis ([71]Figure 4a) revealed
   distinct metabolic pathways that characterize the differences of DPSC
   and SHED. The bubble chart visualization highlighted the most
   significantly enriched pathways, with thermogenesis, glycerolipid
   metabolism, folate biosynthesis, and pentose/glucuronate
   interconversions emerging as key contributors to the observed metabolic
   divergence between these cell types.

Figure 4.

   [72]Figure 4
   [73]Open in a new tab

   Bubble chart of KEGG pathway enrichment and clustering of
   representative pathways. (a) Bubble chart illustrating the significant
   KEGG pathway enrichment. (b) Clustering of the three most significantly
   enriched pathways.

   Protein-pathway associations were as follows: eight proteins enriched
   in thermogenesis pathway, including NDUFB9, NDUFB5, SMARCC2, SMARCA2,
   RPTOR, PRKG1, MGLL, and UQCRB. Four proteins, AGPAT4, MGLL, AKR1B10,
   and AKR1B1, exhibited enrichment in glycerolipid metabolism pathway.
   Folate biosynthesis pathway encompassed three proteins: AKR1B10,
   AKR1B1, and PCBD1. AKR1B10 and AKR1B1 were also involved in
   pentose/glucuronate interconversions ([74]Figure 4b).

   [75]Table 1 provides a detailed description of these proteins,
   highlighting differential expression patterns. Eight proteins (NDUFB
   family, SMARC, RPTOR, etc.) showed elevated expression in DPSC, while
   six proteins (AKR1B family, PRKG1, MGLL, etc.) were upregulated in
   SHED.

Table 1.

   List of the protein names and descriptions of the KEGG differential
   proteins
   Protein name Description Ratio (SHED/DPSC)
   NDUFB9 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 9
   0.300
   NDUFB5 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 5
   0.474
   SMARCC2 SWI/SNF related, matrix associated, actin dependent regulator
   of chromatin subfamily c member 2 0.371
   SMARCA2 SWI/SNF related, matrix associated, actin dependent regulator
   of chromatin subfamily a member 2 0.494
   RPTOR Regulatory-associated protein of mTOR 0.350
   AGPAT4 1-Acylglycerol-3-phosphate acyltransferase 4 0.162
   TLR3 Toll-like receptor 3 0.304
   SRRM2 Serine/arginine repetitive matrix protein 2 0.12
   PRKG1 Protein kinase cGMP-dependent 1 2.662
   MGLL Monoglyceride lipase 2.183
   UQCRB Ubiquinol-cytochrome c reductase binding protein 2.088
   AKR1B10 Aldo-keto reductase family 1 member B10 5.790
   AKR1B1 Aldo-keto reductase family 1 member B1 6.653
   PCBD1 Pterin-4-alpha-carbinolamine dehydratase 1 2.271
   [76]Open in a new tab

3.4. PPI network analysis unveils key regulatory nodes

   The PPI network analysis ([77]Figure 5) revealed a complex interplay of
   protein interactions, highlighting four distinct modules enriched in
   specific pathways and proteins. These modules converged around four
   central proteins – RPTOR, MGLL, SRRM2, and TLR3 – acting as pivotal
   regulatory nodes within the network. RPTOR, with its extensive
   connections to seven proteins or pathways, emerges as a key player in
   stem cell functions. Its involvement in the mTOR-related autophagy
   pathway warrants particular attention. MGLL, linked to four proteins or
   pathways, participates in crucial metabolic processes, including
   thermogenesis and glycerolipid metabolism. Further investigation into
   the roles and interactions of these proteins within the PPI network is
   essential to comprehensively understand their contributions to the
   observed metabolic differences between DPSC and SHED.

Figure 5.

   [78]Figure 5
   [79]Open in a new tab

   PPI network of the differential proteins. (a) PPI network of the
   differential proteins between DPSC and SHED. Red nodes represent
   upregulated proteins, while blue nodes represent downregulated
   proteins. The size of each nodes reflects the degree of relationship
   density. (b) Clustering heatmap depicting the four most significant
   protein interaction networks.

4. Discussion

   Proteomics, a fundamental technique for protein screening and
   functional analysis, has attained significant prominence within the
   scientific community. DIA mass spectrometry offers a distinct advantage
   over DDA by systematically fragmenting all detectable ions within a
   wide m/z range, regardless of their intensity. This comprehensive
   approach results in a wider dynamic range, enhanced reproducibility,
   improved sensitivity, greater accuracy in quantification, and
   potentially increased proteome coverage [[80]18]. DIA has proven to be
   a widely adopted methodology in quantitative proteomic studies,
   particularly in plasma/serum proteomics, facilitating the
   identification of potential biomarkers across a diverse spectrum of
   diseases such as in 3P medicine (predictive, preventive, and
   personalized), cancer, etc. [[81]19,[82]20].

   This study presents the first application of DIA proteomic technology
   to elucidate the proteomic landscape of DPSC and SHED. The
   comprehensive analysis identified over 7,000 proteins and 209 DEPs
   between DPSC and SHED, revealing distinct metabolic profiles between
   these two stem cell types. KEGG pathway and PPI network analysis
   revealed significant enrichment of proteins involved in metabolic
   pathways, particularly thermogenesis, glycerolipid metabolism, and
   folate biosynthesis. Notably, we identified the NDUFB protein family,
   SMARC family, RPTOR, and TLR3 proteins as prominent proteins in DPSC,
   while the AKR1B protein family, MGLL, and UQCRB were significantly
   enriched in SHED. Furthermore, RPTOR and MGLL emerged as central nodes
   in the PPI network, indicating their critical roles in cellular
   function. Importantly, these proteins were not previously reported in
   other dental stem cell proteomic studies.

   Previous research on dental-related stem cells, such as DPSC, PDLSC,
   and SCAP, primarily relied on traditional two-dimensional gel
   electrophoresis proteomics. These studies primarily identified
   high-abundance proteins such as cytoskeletal proteins and cell adhesion
   molecules [[83]21,[84]22]. While subsequent gel-free high-throughput
   proteomic methods, like iTRAQ, tandem mass tags (TMT), and other
   shotgun approaches, have significantly expanded the repertoire of
   identified proteins and their associated functions and pathways, these
   studies mainly focused on comparisons between DPSC and other stem
   cells, such as PDLSC, SCAP, etc. [[85]23–[86]25], leaving the
   differential proteomic landscape between DPSC and SHED largely
   unexplored. A study investigating odontogenic differentiation from
   human DPSC using TMT-based proteomic analysis [[87]23] identified 223
   DEPs involved in cellular processes, metabolic processes, and
   biological regulation, and the protein expression levels of FBN1 and
   TGF-β2 validated by WB were consistent with the proteomic analysis. Lei
   et al. [[88]24,[89]25] employed TMT proteomics to compare the protein
   profiles of DPSC with those of PDLSC/SCAP. They identified high-scoring
   sub-networks in DPSC enriched with neural-related molecules, encoding
   cell vesicle transport and mitochondrial energy transfer to regulate
   cell proliferation and secretion factors. Additionally, a significant
   number of cell adhesion molecules were identified among the highly
   expressed molecules in PDLSC, indicating their potential role in
   providing cell attachment functions. While SCAP was characterized by a
   high expression of Niemann-Pick C1, a protein implicated in immune
   stability of SCAP.

   Regarding the study between DPSC and SHED, Zainol Abidin et al.
   conducted a label-free quantitative proteomic analysis to investigate
   the differential expression of osteoblast-related proteins during
   osteogenic differentiation in DPSC and SHED. Their findings revealed
   significant differences in the expression of three proteins
   (PSMD11/RPN11, PLS3, and CLIC1) in stem cells from SHED, while a single
   protein (SYNCRIP) exhibited differential expression in DPSC [[90]26].
   However, a comprehensive proteomic analysis of undifferentiated DPSC
   and SHED has remained elusive until now. This research addressed this
   significant gap in the current area.

   In the DEPs, DPSC exhibited significantly higher expression of the
   NDUFB (NADH dehydrogenase) family proteins, essential components of the
   mitochondrial electron transport chain responsible for energy
   production. This observation aligns with previous studies indicating
   the up-regulation of NDUF-related proteins during the differentiation
   of human GMSCs into neuron-like cells, suggesting a potential role for
   NDUFB in cell differentiation and metabolic reprogramming [[91]27].
   Furthermore, DPSC demonstrated elevated expression of RPTOR, a crucial
   component of the rapamycin target complex 1 (mTORC1), which plays a
   central role in the mTOR signaling pathway and governs essential
   cellular processes including body development, differentiation, and
   cell homeostasis [[92]28]. Studies in mice have implicated RPTOR in
   maintaining the spermatogonial stem cell pool [[93]29] and its deletion
   in hematopoietic stem/progenitor cells has been shown to lead to
   self-destructive innate immunity [[94]30]. The increased RPTOR
   expression in DPSC suggests a higher autophagy capacity and enhanced
   regulation of cellular processes through the mTOR pathway.

   Conversely, SHED displayed significantly higher expression of the AKR1B
   protein family, known for catalyzing the reduction of various
   aldehydes, including the aldehyde form of glucose [[95]31]. This role
   in glucose metabolism is particularly relevant in diabetic
   complications, where AKR1B reduces glucose to sorbitol. Studies have
   shown that AKR1B1 expression increases during the differentiation of
   human multipotent adipose-derived stem cells, paralleling PGF2α
   release, while PGF2α receptor levels decline in early differentiation
   [[96]32]. The elevated expression of AKR1B in SHED suggests a potential
   association with enhanced adipogenic differentiation ability. Other
   prominent proteins up-regulated in SHED include MGLL and UQCRB. MGLL,
   an enzyme responsible for hydrolyzing the endocannabinoid
   2-arachidonoylglycerol, plays a crucial role in various physiological
   processes, including pain and nociperception [[97]33]. UQCRB, a
   component of the mitochondrial respiratory chain, has been shown to
   play a pivotal role in hypoxic signaling-induced epithelial–mesenchymal
   transition (EMT), potentially serving as a therapeutic target for
   reversing EMT by inhibiting mitochondrial ROS production [[98]34].
   Additionally, N-acetyltransferase 10 has been demonstrated to repress
   Uqcr11 and Uqcrb, promoting heart regeneration by regulating their
   expression in mouse and human cardiomyocytes [[99]35], further
   emphasizing the importance of UQCRB in metabolic reprogramming.

   In conclusion, this study successfully established a DIA proteomics
   approach to analyze dental stem cells derived from permanent and
   deciduous teeth, leading to the identification of DEPs and enrichment
   of KEGG pathways. The findings revealed distinct metabolic profiles and
   key regulatory nodes between DPSC and SHED. Specifically, DPSC
   displayed higher expression of proteins belonging to the NDUFB family,
   SMARC family, RPTOR, and TLR3, implicated in pathways related to
   mitochondrial energy metabolism, mTOR-related autophagy, and immune
   signaling. In contrast, SHED exhibited higher expression of the AKR1B
   family, MGLL, and UQCRB, suggesting a stronger capacity for adipogenic
   differentiation and involvement in glycerolipid metabolism and
   thermogenesis pathways.

   The identification of key regulatory proteins such as NDUFB, RPTOR,
   AKR1B, and MGLL provides valuable insights into the underlying
   mechanisms governing the differences between DPSC and SHED, and opens
   avenues for further investigation into their roles in stem cell
   function and potential therapeutic applications. However, it is
   important to acknowledge the limitations inherent in the use of a
   limited sample size and the absence of validation. Further research
   with larger sample sizes is necessary to validate these findings and
   confirm the observed differences.

Supplementary Material

   Supplementary material

Footnotes

   Funding information: This research was supported by the Science and
   Technology Development Plan Project of Suzhou, China (SYSD2019067), the
   Jiangsu Province Preventive Medicine Research Project (Ym2023016), and
   the Construction Plan of Scientific Innovation Team of Suzhou
   Vocational Health College (SZWZYTD202202).

   Author contributions: All authors have contributed to this manuscript
   and have agreed to its submission to the journal. They have reviewed
   all results and approved the final version. S.Z., Y.L., J.W., and F.M.
   designed the study; S.Z. and Y.L. conducted all experiments; J.D.,
   Y.G., and D.Y. collected the tooth samples and assisted with cell
   culture experiments; M.J., L.C., and N.Y. analyzed the data; S.Z. and
   F.M. drafted and revised the manuscript.

   Conflict of interest: Authors state no conflict of interest.

   Data availability statement: The datasets generated during and/or
   analyzed during the current study are available from the corresponding
   author on reasonable request.

References