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Disclosing the molecular profile of the human amniotic mesenchymal stromal cell secretome by filter-aided sample preparation proteomic characterization



The secretome of mesenchymal stromal cells isolated from the amniotic membrane (hAMSCs) has been extensively studied for its in vitro immunomodulatory activity as well as for the treatment of several preclinical models of immune-related disorders. The bioactive molecules within the hAMSCs secretome are capable of modulating the immune response and thus contribute to stimulating regenerative processes. At present, only a few studies have attempted to define the composition of the secretome, and several approaches, including multi-omics, are underway in an attempt to precisely define its composition and possibly identify key factors responsible for the therapeutic effect.


In this study, we characterized the protein composition of the hAMSCs secretome by a filter-aided sample preparation (FASP) digestion and liquid chromatography-high resolution mass spectrometry (LC–MS) approach. Data were processed for gene ontology classification and functional protein interaction analysis by bioinformatics tools.


Proteomic analysis of the hAMSCs secretome resulted in the identification of 1521 total proteins, including 662 unique elements. A number of 157 elements, corresponding to 23.7%, were found as repeatedly characterizing the hAMSCs secretome, and those that resulted as significantly over-represented were involved in immunomodulation, hemostasis, development and remodeling of the extracellular matrix molecular pathways.


Overall, our characterization enriches the landscape of hAMSCs with new information that could enable a better understanding of the mechanisms of action underlying the therapeutic efficacy of the hAMSCs secretome while also providing a basis for its therapeutic translation.


Mesenchymal stromal cells isolated from the amniotic membrane (hAMSCs) of human term placenta possess potent immunomodulatory activity. Unlike other MSC, these cells do not require priming to exert their action on the cells of the immune system [1]. Furthermore, hAMSCs are readily available from biological waste at the time of delivery without posing any risk to the donor, and a large number of cells can be obtained from human term placenta [2].

It has been widely demonstrated that hAMSCs modulate the immune response mediated by both innate and adaptive immune cells. Indeed, hAMSCs inhibit the proliferation and differentiation of T lymphocytes toward inflammatory and cytotoxic subsets while promoting polarization toward regulatory T cells [3,4,5]. Furthermore, hAMSCs also modulate the polarization of monocytes to antigen presenting cells (mature dendritic cells and inflammatory macrophages) by inducing the acquisition of features that are typical of M2 immunoregulatory macrophages [4,5,6]. In addition, hAMSCs suppress the proliferation and differentiation of B lymphocytes to plasma cells [7]. The in vitro immunomodulatory properties exhibited by hAMSCs are reflected in their therapeutic efficacy in various disease models characterized by impaired immune responses. In both in vitro and in vivo studies, the administration of conditioned medium collected from hAMSCs demonstrated comparable therapeutic efficacy to that of hAMSCs themselves [1, 4, 8,9,10,11,12]. These findings emphasize how the therapeutic actions of hAMSCs are primarily determined by their paracrine activity.

The mechanisms through which hAMSCs regulate the immune response and enable other cells to facilitate tissue repair during pathological processes are only partially understood. The factors produced by hAMSCs that orchestrate their beneficial properties currently remain an intriguing subject of investigation. To date, the characterization of the hAMSCs secretome is limited to a few studies that employ various methodological approaches. For example, a study using Enzyme-Linked Immunosorbent Assays (ELISA) quantified 200 soluble cytokines, receptors, chemokines, growth factors, and inflammatory factors, and recently demonstrated that the hAMSCs secretome contains molecules associated with the remodeling and homeostasis of the extracellular matrix (ECM) as well as in immunomodulation [13]. Another study, employing ELISA and quantitative PCR, demonstrated the presence of pro-angiogenic factors such as HGF, EGF, and bFGF in the hAMSCs secretome [14]. In addition, a proteomic analysis using in-solution digestion label-free mass spectrometry revealed that the hAMSCs secretome contains proteins associated with wound healing [15]. In particular, the hAMSCs secretome contained high levels of proteins related to angiogenesis, cellular differentiation, immune response, cell motility, and wound healing, such as collagen triple helix repeat-containing protein 1 (CTHRC1), lysyl oxidase homolog 2 (LOXL2), A disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1), galectin-1 (LGALS1), complement C3 (C3), and CCN family member 1 (CYR61). These proteins are known to promote keratinocyte migration and differentiation [15]. However, a comprehensive characterization of the hAMSCs secretome, capable of providing insights into the vast array of molecules secreted by hAMSCs and potentially implicated in their therapeutic properties, is still lacking. Such characterization would advance our understanding of the molecular mechanisms and the factors contributing to or responsible for the immunomodulatory properties of the hAMSCs secretome. This knowledge could also contribute to the development of new, cell-free therapeutic strategies and the engineering of medical devices for MSC secretome-based treatments in regenerative medicine.

In this study, we characterized the protein content of the hAMSCs secretome using a proteomic approach based on Filter-Aided Sample Preparation (FASP) digestion coupled with Liquid Chromatography-high resolution Mass Spectrometry (LC–MS) analysis. We identified, with high confidence, a protein pattern consisting of 157 elements that consistently characterized the hAMSCs secretome. These elements were further investigated for gene ontology analysis, pathway classification, and relative label-free quantification.


Isolation of mesenchymal stromal cells from human amniotic membrane (hAMSCs)

Human term placentas (N = 19) were collected after obtaining written informed consent from mothers after vaginal delivery or cesarean section. The study was conducted in accordance with the Declaration of Helsinki, and informed consent was obtained following the guidelines defined by the Brescia Provincial Ethics Committee (number NP 2243, 19/01/2016).

Amniotic membrane mesenchymal stromal cells (hAMSC) were isolated as previously described [16]. Membrane fragments were digested at 37 °C in dispase (2.5 U/mL, VWR, Radnor, PA, USA) for 9 min and then transferred to RPMI 1640 complete medium containing 10% heat-inactivated fetal bovine serum (FBS), 1% P/S, and 1% L-glutamine (all from Sigma-Aldrich, St. Louis, MO, USA) to block digestion. The fragments were subsequently incubated in collagenase 0.94 mg/mL and DNase I (both from Roche, Basel, Switzerland) for 2.5–3 h at 37 °C. After centrifugation at low g, the resulting supernatant was filtered through a 100-μm cell strainer (BD Falcon, Bedford, MA, USA), and the cells were harvested by centrifugation.

Freshly isolated cells were expanded to passage 1 (p1) by plating them at a density of 104 cells/cm2 in Chang Medium C (Irvine Scientific, Santa Ana, CA, USA) supplemented with 2 mM L-glutamine at 37 °C in a 5% CO2 incubator.

hAMSCs at p1 were phenotypically characterized as previously reported [16]. The hAMSCs used in this study met the minimal criteria for consideration as MSCs, namely, the expression of MSC markers CD13 (97.7 ± 1.6%; mean ± SD), CD73 (88.3 ± 6.4%), and CD90 (94.8 ± 7.4%), and the lack of hematopoietic markers such as CD45 (1.8 ± 1.0%), CD66b (0%), and the epithelial marker CD324 (1.7 ± 1.0%) [2, 17, 18].

Preparation of conditioned medium (CM), lyophilization and reconstitution

hAMSC at p1 were cultured for 5 days in 24-well plates (Corning, NY, USA) at a density of 5 × 105 cells/well in 0.5 mL of DMEM-F12 medium (Sigma-Aldrich) without serum, supplemented with 2 mM L-glutamine (Sigma-Aldrich) and 1% P/S as previously described [1]. After the incubation period, CM was collected, centrifuged at 300 × g, filtered through a 0.2-μm sterile filter (Sartorius Stedim, Florence, Italy) and stored at − 80 °C.

The frozen CM was then lyophilized as previously described [4] using a Lyophilizer Pilot MAX MX 8556 (Millrock Technology, USA), following the previously described procedure [4]. This process involved freezing the samples at − 40 °C per 4 h and then at − 45 °C under vacuum. A first dry cycle, consisting of 7 steps at increasing temperatures was performed for 13 h under vacuum. Subsequently, a second dry cycle was performed under vacuum at 30 °C for one hour. Lyophilization was complete when the product reached 25 °C for at least 1 h. Prior to use, the lyophilized CM was reconstituted with 2.5 mL of sterile water and filtered through a 0.2-μm sterile filter (Sartorius Stedim, Florence, Italy).

A total of 17 placentae were used to produce CM from hAMSC at p1. Each experiment was performed using a mix of CM derived from at least 3 different hAMSC donors, which were previously validated for their immunomodulatory activity, as reported in [4].

Proteomic analysis


All organic solvents were of LC–MS grade. Iodoacetamide (IAA), D,L-dithiothreitol (DTT), ammonium bicarbonate (AMBIC), bovine serum albumin were purchased from Sigma-Aldrich (St. Louis, MO, USA). Water and formic acid (FA) were obtained from Merck (Darmstadt, Germany). Trypsin (Gold MS Grade) was supplied from Promega (Madison, WI, USA), and acetonitrile (ACN) was supplied from Merck (Darmstadt, Germany).

Treatment of secretome samples and protein quantification

Four different batches lyophilized hAMSCs secretome (Pools 1–4), with a total volume of 2500 μL each, and three DMEMF12 culture medium samples, used as reference controls (CTRL 1–3), were solubilized in 250 μL of LC–MS grade water to obtain a 10 × concentration. The samples were gently vortexed to facilitate resolubilization. Total protein content was measured using the Bradford protein assay (Bio-Rad Laboratories, Hercules, CA, USA) with a UV–Vis spectrophotometer (8453 UV–Vis Supplies, Agilent Technologies, Waldbronn, Germany), using BSA as the protein of reference.

FASP protein digestion protocol

Filter-aided sample preparation (FASP) was employed using centrifugal Microcon filtration devices (Millipore) equipped with a 10 kDa molecular mass cut-off filter membrane for sample purification, concentration, and proteins digestion [19, 20] for a MS-based proteomic analysis. The FASP method provides a higher number of identifications in comparison to in-solution digestion. This makes it a formidable choice for sample preparation in proteomic analysis, especially when dealing with secretomes characterized by a low or diluted protein content. FASP devices, equipped with a molecular cut-off membrane filter, allow efficient protein purification and enzymatic digestion through a series of steps involving buffer exchange, reagent addition, centrifugation, and ultimately protein concentration. This approach proves advantageous for the analysis of conditioned medium.

A secretome volume corresponding to 50 µg of total protein content was mixed with 8 M urea in 0.1 M Tris/HCl buffer at pH 8.5 (Urea Buffer solution), transferred to the filter device, and centrifuged at 14,000 rpm for 15 min. The concentrated sample was diluted in the device with Urea Buffer solution and centrifuged once more. Subsequently, the supernatant was treated with 8 mM DTT in Urea Buffer solution (DTT solution) to reduce disulfide bridges. It was then incubated at 37 °C for 15 min and centrifuged again. Any excess DTT was eliminated through washings with Urea Buffer followed by centrifugations. The supernatant was then treated for thiols carboxamide methylation with 50 mM iodoacetamide (IAA) solution in Urea Buffer. The mixture was incubated in the dark at room temperature (RT) for 15 min, followed by centrifugation. Excess IAA was removed by incubating the sample with DTT solution at 37 °C for 15 min, followed by washes in Urea Buffer solution and then in ammonium bicarbonate for buffer exchange. Sample digestion was carried out overnight at 37 °C using trypsin 1 µg/µL in 1:100 (w/w) in ammonium bicarbonate buffer 50 mM. Enzymatic digestion was stopped by the addition of 1% FA (final concentration). The proteolytic peptides were collected by centrifugation, lyophilized, and then dissolved in 0.1% FA water solution (v/v) for LC–MS analysis.

Ultra-high-performance liquid chromatography-nanoESI mass spectrometry analysis (UHPLC-ESI–MS/MS)

UHPLC-ESI–MS/MS analyses were performed for each sample in triplicate on UltiMate 3000 RSLCnano System coupled to Orbitrap Elite MS detector with EASY-Spray nanoESI source (Thermo Fisher Scientific, Waltham, MA, USA). Instrumental operation and data acquisition were performed using Thermo Xcalibur 2.2 computer program (Thermo Fisher Scientific). Each sample was analyzed in replicate chromatographic runs (n = 3) to ensure data repeatability, robustness, and the proper operation of the instruments. The proteomic analysis of different batches of the hAMSCs secretome, each with replicate (n = 3) LC–MS analyses, along with the parameters applied to software data elaboration and filtering, ensured the repeatability and robustness of protein and peptide identification data.

Chromatographic separations were performed on a PepMap C18 (2 µm particles, 100 Å pore size) EASY-Spray column with a length of 15 cm and an internal diameter (ID) of 50 µm (Thermo Fisher Scientific). This column was coupled to an Acclaim PepMap100 nano-trap cartridge (C18, 5 µm, 100 Å, 300 µm, ID × 5 mm) (Thermo Fisher Scientific). The separation was performed at 40 °C using gradient elution. Eluent A consisted of 0.1% FA, while eluent B was an ACN/FA solution (99.9:0.1, v/v). The gradient elution protocol was as follows: (i) 5% B for 7 min, (ii) 5% to 35% B for 113 min, (iii) 35% B to 99% for 2 min, (iv) 99% B for 3 min, (v) 99% to 1.6% B for 2 min, (vi) 1.6% B for 3 min, (vii) 1.6% to 78% B for 3 min, (viii) 78% B for 3 min, (ix) 78% to 1.6% B for 3 min, (x) 1.6% B for 3 min, (xi) 1.6% to 78% B for 3 min, (xii) 78% B for 3 min, (xiii) 78% B to 5% B for 2 min, and (xiv) 5% B for 20 min. The mobile phase flow rate was 0.3 µL/min. The injection volume was 5 µL. The Orbitrap Elite instrument operated in positive ionization mode with a 60,000 full scan resolution, in 350–2000 m/z acquisition range. MS/MS fragmentation was obtained using collision-induced dissociation (CID) with a normalized collision energy of 35%. The instrument used a Data-Dependent Scan (DDS) mode to perform MS/MS on the 20 most intense signals from each MS spectrum. The minimum signal threshold was set to 500.0, and an isolation width of 2 m/z was applied, with a default charge state to + 2. MS/MS spectra acquisition was performed in the linear ion trap at a normal scan rate.

Data analysis

LC–MS and MS/MS raw data were processed using two software tools: the HPLC–MS apparatus management software (Xcalibur 2.0.7 SP1, Thermo Fisher Scientific), and the Proteome Discoverer 1.4 software (version, Thermo Fisher Scientific) that was used for protein identification based on the SEQUEST HT cluster as search engine against the Homo Sapiens (UniProtKB/Swiss-Prot protein knowledgebase released in 2021_4), and Bos taurus (UniProtKB/Swiss-Prot protein knowledgebase released in 2022_02). The signal to Noise (S/N) threshold was set to 1.5. Trypsin was used for cleavage with a maximum of 2 missed cleavage sites, and the minimum and maximum peptide length were set to 6 and 144 residues, respectively. Tolerance settings for the analysis included a mass tolerance 10 ppm, fragment mass tolerance of 0.5 Da and 0.02 Da; both “use average precursor mass” and “use average fragment mass” were set to “False”. Methionine oxidation (+ 15.99 Da) and N-Terminal acetylation (+ 42.011 Da) were set as dynamic modifications, while carbamidomethylation of cysteine (+ 57.02 Da) was set as a static modification. Validation of protein and peptide identifications was performed through a decoy database search and calculation of the False Discovery Rate (FDR) statistical value using the Percolator node in Proteome Discoverer workflow. We set strict and relaxed FDR target values at 0.01 and 0.05, respectively. The FDR measures the confidence in the identification by estimating the number of false positive identifications among all the identifications found by a peptide identification search.

The protein identification results from each secretome pool (Pools 1–4) were analyzed as a multireport data file, combining data from three analytical replicates (runs 1–3). The data were filtered to ensure high confidence peptide identification, requiring a minimum of at least 2 peptides per protein, each with a minimum peptide length of 9 amino acids and peptide rank 1, according to the Human Proteome Project (HPP) Mass Spectrometry Data Interpretation Guidelines [21]. Gene ontology (GO) analysis and classification were conducted using Reactome ( [22] and Protein Analysis Through Evolutionary Relationships (PANTHER, by applying the Fisher’s Exact test type with false discovery rate (FDR) correction for statistical test of over-representation [23]. The protein expression data were sourced from The Human Protein Atlas ( [24, 25]. Functional protein interaction networks were analyzed using the STRING tool [26] with the highest confidence level (0.900), and Cytoscape ( Sample data grouping analysis was performed using the Venn diagram tool (


The proteomic profiles of four different hAMSCs-CM samples obtained from serum-free cell cultures were characterized using LC–MS analysis, after Filter-Aided Sample Preparation (FASP) digestion with trypsin. To identify common protein elements in comparison to a cell-free DMEMF12 culture medium used as a control, the data obtained for each pool were subjected to grouping analysis. The resulting list of proteins, which consistently characterized the hAMSCs secretome, and could thus be associated with its biological activities, was obtained by Proteome Discoverer data elaboration after validation by the Percolator node and FDR statistical value calculation and filtered for high confidence identification according to the Human Proteome Project Mass Spectrometry Data Interpretation Guidelines [21]. Bioinformatics tools were applied to investigate the gene ontology classification, pathway over-representation, and protein functional interactions of the identified proteins.

Proteomic characterization of the hAMSCs secretome

The total protein content of each hAMSC-CM pool was determined using the Bradford assay. The mean value for the 4 pools was 0.49 ± 0.23 mg/ml. This measurement was obtained after re-dissolving the lyophilized pools to a 10 × concentration in water. For proteomic analysis, an aliquot of the each pool, equivalent to 50 μg of total protein, underwent FASP digestion and LC–MS analysis in triplicate runs for proteomic characterization.

The Proteome Discoverer software processed the LC–MS data and identified a total of 1521 proteins in the hAMSCs secretome with a high level of confidence. These proteins were distributed differently among the analyzed pools, with 355, 436, 242, and 488 proteins identified in pools 1 through 4, respectively (protein identification data in Additional file 1: Table S1). Grouping analysis revealed a total of 662 unique elements with 157 proteins shared by all the hAMSCs-CM pools (Fig. 1 and Table 1). These 157 proteins, corresponding to 23.7%, of the total unique elements, characterize the hAMSCs secretome and thus may be associated to the biological activities exerted by hAMSCs-CM.

Fig. 1
figure 1

Venn diagram resulting from grouping analysis of the proteins identified in the hAMSCs-CM pools 1–4 (list of identifications per pool in Additional file 1: Table S1)

Table 1 List of the 157 proteins elements commonly identified in hAMSCs secretome pools 1–4

As shown in Table 1, the 157 proteins shared by all hAMSCs-CM pools exhibited a wide range of molecular masses, spanning from 5 to 504 kDa. Interestingly, despite the cut-off of the FASP membrane filter, thymosin beta 4 and metallothionein 2, both with molecular masses < 10 kDa, were identified. One possible explanation could be the existence of protein complexes or dimeric peptides in hAMSCs-CM. Such complexes or dimers could account for their retention by the filter. Indeed, several reports have demonstrated the presence of actin-profilin complexes involving thymosin beta 4, the main G-actin sequestering agent, as well as the oligomerization of metallothioneins [27, 28], which could support our hypothesis.

The same proteomic analysis was applied to three different samples of cell free DMEMF12 culture medium, used as reference control samples (CTRL 1–3), in order to evaluate the influence of the medium’s composition on the proteome composition of hAMSCs-CM. Specifically for CTRL samples, protein identification was obtained by elaborating the LC–MS data against both the Homo sapiens and the Bos taurus protein databases (protein identification data of CTRL samples 1–3 are in Additional file 2: Table S2A-F). Additional file 2: Table S2G lists the proteins consistently identified in all DMEMF12 samples analyzed, therefore characterizing with repeatability the culture medium matrix, and representing the putative contaminants to be excluded from the analysis of proteins originating from the secretome. This list includes common contaminants such as keratins and albumin, which are frequently detected in proteomic analyses. Keratins are often associated with dust/contact-related contaminants, while albumin can derive from reagents and materials during the sample preparation step [2931].

Since albumin was part of the 157 common protein elements of the hAMSCs secretome, we compared the pattern of the relative tryptic peptides identified either in the hAMSCs secretome or in DMEMF12, with both the Homo sapiens and Bos taurus databases (Additional file 2: Table S2H). The absence of peptides exclusively belonging to human albumin in the hAMSCs secretome suggests that the protein or its fragment peptides are probable contaminants rather than components of the secretome. Given the serum-free nature of the medium, it can be concluded that the contribution of the DMEMF12 to the proteome of the hAMSCs secretome is negligible.

Gene ontology analysis of the 157 proteins characterizing the hAMSCs secretome

Gene ontology classification and over-representation analysis of the molecular function, cellular component, and protein class of the 157 proteins which repeatedly characterize the hAMSCs secretome were performed using PANTHER (, accessed on August 23, 2022). Among the 157 proteins, different molecular functions were found to be significantly over- or under-represented (p value < 0.05, Fisher’s Exact statistical test type with FDR correction) when compared to the reference classification Homo sapiens (Table 2). The fold enrichment values revealed that the over-represented molecular functions with the highest values were disulfide isomerase activity, collagen and extracellular matrix binding, and disulfide oxidoreductase activity.

Table 2 List of the molecular functions with statistically significant over-/under-representation in the group of the 157 common proteins of hAMSCs secretome pools 1–4

Figure 2 shows the results of the cellular components overrepresentation analysis (p value < 0.05, Fisher’s Exact test type with FDR correction), conducted using the 157 proteins characterizing the hAMSCs secretome (blue histograms), in comparison with the Homo sapiens list of genes used as a reference (orange histograms). While statistically significant, the fold enrichment values for cellular components were not as high as those observed for molecular functions, actin cytoskeleton, extracellular matrix, external encapsulating structure, endoplasmic reticulum, extracellular space and extracellular region cellular components, which showed the highest values.

Fig. 2
figure 2

Protein cellular component over-representation analysis of the157 proteins commonly identified in hAMSC-CM using Homo sapiens database as reference (Over-representation Test PANTHER GO-Slim Cellular Component, FISHER’s Exact Test Type with FDR correction, P < 0.05)

Relative to the identified proteins in hAMSCs secretome pools, the highest % of genes belonged to extracellular matrix and structural proteins, chaperons, proteases, cytoskeletal proteins, and metalloproteases. This aligns with the biological activity of the hAMSCs secretome exerted during the proliferative and the remodeling phases of the healing process [32]. The aldolase protein class, followed by the Hsp90 family and Hsp70 family chaperones, as well as extracellular matrix proteins, showed the highest fold enrichment values. Conversely, the protein classes of gene-specific transcriptional regulator, transmembrane signal receptor, and DNA-binding transcription factor resulted instead under-represented (Fig. 3).

Fig. 3
figure 3

Protein class over-representation analysis of the157 proteins commonly identified in hAMSC-CM using Homo sapiens database as reference (Over-representation Test PANTHER GO-Slim, FISHER’s Exact Test Type with FDR correction, P < 0.05)

We finally investigated the predicted network of functional interactions between the 157 proteins common to all hAMSCs secretome pools using the STRING tool (, accessed on august 23, 2022) (Fig. 4). The network highlights functional and physical associations between the majority of the proteins and revealed 41 clusters significantly enriched, with the top ten listed in Table 3. It is noteworthy that some of these clusters are related to molecular processes involved in collagen formation and synthesis. Additionally, disease-gene associations inside the network revealed 21 significantly enriched diseases, that notably include different bone and cartilage related disorders (Table 4).

Fig. 4
figure 4

Protein–protein functional interaction network of the 157 proteins commonly identified in hAMSCs secretome (STRING tool analysis, highest confidence)

Table 3 Top ten clusters significantly enriched in the 157 proteins that were repeatedly identified in hAMSC-CM (Fig. 4)
Table 4 Disease-gene associations significantly enriched in the proteins functional network (Fig. 4) of the 157 hAMSC secretome pools 1–4 common proteins

Next, we investigated the classification/prediction of secreted proteins among the 157 commonly-identified proteins, using the Human Protein Atlas (HPA) Subcellular Section—Secreted proteins (2793 genes) as a reference (, accessed on October 13, 2022). Seventy-nine proteins (50.3%) were classified/predicted as secreted (Additional file 3: Table S3A). Among these, 27 were subclassified as “proteins secreted to extracellular matrix”, 27 as “proteins secreted to blood”, 20 as “intracellular and membrane proteins”, 3 as “proteins secreted in other tissues”, and 2 as “proteins secreted in female reproductive system” (data in Additional file 3: Table S3B).

The remaining 78 proteins, which were not classified/predicted as secreted proteins, were further investigated for their potential functional relationships and for enriched clusters of interactions using STRING (, accessed on August 23, 2022). Notably, clusters related to carbon metabolism were among the top ten most significantly enriched (Fig. 5, Table 5).

Fig. 5
figure 5

Protein–protein functional interaction network of the 78 out of the 157 commonly identified proteins in hAMSCs secretome which were not classified as secreted proteins (STRING tool analysis, highest confidence)

Table 5 List of the top ten clusters significantly enriched among the 78 proteins (Fig. 5) not classified as secreted proteins

Transcriptomics data revealed that 65% (n = 13,060) of all human proteins (n = 20,090) are expressed in placenta [33], and 286 of these have an elevated expression when compared to other tissues. It was therefore interesting to determine how many of the 157 proteins that characterized the hAMSCs secretome were classified as placental proteins. To do this, we referred to the HPA placenta proteome database (, accessed on October 13, 2022). The donut chart in Fig. 6 illustrates the distribution of the 157 proteins (outer blue ring) in relation to their classification as “elevated expression in placenta”, or “elevated in other tissues but expressed in placenta”, or “low tissue specificity but expressed in placenta”. The results demonstrate that 13 elements (8%) were classified to have an elevated expression in placenta, 57 (36%) with elevated expression in other tissues but expressed in placenta, and 84 elements (54%) expressed in the placenta but with low tissue specificity (data analysis in Additional file 4: Table S4). Only 3 out of the 157 proteins, namely keratins type I cytoskeletal 9, keratin type II cytoskeletal 2 epidermal, and complement C1r subcomponent, were not classified as genes expressed in placenta. Complement C1r subcomponent is involved in the assembling of complement C1, the first component of the classical pathway of the complement system of the innate immune system. On the other hand, both keratins were identified in the control medium (DMEMF12) and therefore they can be excluded from the components of the hAMSCs secretome.

Fig. 6
figure 6

Distribution of the 157 commonly identified proteins in all hAMSCs-CM based on placenta gene classification as i) elevated expression in placenta; ii) elevated expression in other tissues but expressed in placenta; iii) low tissue specificity but expression in placenta and iv) proteins identified in hAMSCs not classified as placenta proteins.The number of proteins and the relative percent value (versus total 157) are shown

It is noteworthy that the 13 genes with elevated expression in placenta were all classified as secretome proteins in the HPA database (Venn diagram grouping analysis in Additional file 4: Table S4). These genes include tissue factor pathway inhibitor 2, pappalysin-1, glia-derived nexin, collagen alpha -1(III) chain, collagen alpha-2(IV) chain, fibrillin-1, fibronectin, insulin-like growth factor-binding protein 3, laminin subunit gamma-1, nidogen-2, plasminogen activator inhibitor 1, SPARC, and transforming growth factor-beta-induced protein ig-h3. SPARC is a 32 kDa calcium-binding matricellular multifunctional glycoprotein [34], whose expression is closely associated with that of fibrillar collagens, such as type I collagen. This protein acts more as a regulator of cellular behavior rather than as a structural component of the ECM, and is involved in tissue remodeling, repair, development, and cellular turnover [35].

Pathway enrichment analysis in the secretome of hAMSCs cells

The molecular pathways over-representation analysis of the 157 commonly-identified proteins in the hAMSCs secretome was performed using Reactome (, accessed on September 21, 2022). Figure 7 shows the Voronoi diagram representation of the results obtained. The diagram highlights the over-represented hierarchical pathways, including the Immune system, Signal transduction, Gene expression (Transcription), Hemostasis, Developmental biology, DNA repair, Disease, Extracellular matrix organization, Cellular responses to stimuli, and Nephrin family interactions. Some of these pathways are extremely important in regenerative processes, thus we further investigated their protein elements.

Fig. 7
figure 7

Voronoi diagram representation of the hierarchical pathways overrepresentations analysis by Reactome of the 157 commonly-identified protein elements of hAMSC-CM s

The Immune system pathway plays a critical role in directing tissue repair and regeneration outcomes [36]. Sixty-three out of the 157 commonly-identified proteins were involved in this pathway (list in Table 6). The disease gene annotation performed using the STRING tool showed an enrichment of genes associated with various conditions, including Ehlers-Danlos syndrome, Amyloidosis, Connective tissue disease, Disease of anatomical entity, Autosomal dominant disease, Osteogenesis imperfecta type 1, Osteogenesis imperfecta type 4, and Otopalatodigital syndrome type.

Table 6 List of the 63 proteins involved in the Immune system pathway

Another over-represented pathway identified in the hAMSCs secretome is the Hemostasis pathway. This pathway plays a crucial role in the physiological response that ultimately leads to the arrest of bleeding from an injured vessel [37]. A total of 34 proteins resulted involved in this pathway (Table 7) and, among these, STRING analysis revealed 13 significantly enriched clusters. These clusters include “RHO GTPases Activate WASPs and WAVEs, and actin filament organization” (CL:17,048), “Mixed, incl. profilin binding, and ef-hand, ca insensitive”(CL:17,130), “RHO GTPases Activate WASPs and WAVEs, and actin filament organization” (CL:17,049), “Collagen formation, and Defective B3GALTL causes Peters-plus syndrome (PpS)”( CL:16,429), “EF-hand, Ca insensitive, and Vinculin”(CL:17,150), “Collagen formation, and Matrix metalloproteinases”(CL:16,430), “Mixed, incl. dissolution of fibrin clot, and negative regulation of metallopeptidase activity”(CL:16,571), “Collagen type i trimer, and lateral cystocele”(CL:16,514), “Mixed, incl. conjunctivochalasis, and metalloproteinase inhibitor 1”(CL:16,575), “Integrin alpha5-beta1 complex, and integrin alphav-beta6 complex”(CL:16,873), “Profilin conserved site, and Baraitser-Winter syndrome”(CL:17,132), “Mixed, incl. annexin a5, and transgelin-2”(CL:17,241), and “Dissolution of Fibrin Clot, and positive regulation of sterol import”(CL:16,595).

Table 7 List of the 34 proteins involved in the Hemostasis pathway

The pathway of Developmental biology includes several developmental processes such as the transcriptional regulation of pluripotent stem cells, gastrulation, and the activation of HOX genes during differentiation. A total of 31 proteins resulted to be involved in this important pathway (Table 8) and, among these, STRING analysis revealed16 significantly enriched clusters. The clusters included “Collagen formation, and Matrix metalloproteinases”, “Collagen biosynthesis and modifying enzymes”, “Proteasome”, “Keratin type II head”, “Proteasome subunit, and proteasome regulatory particle”, “Myosin ii complex, and rho gtpases activate rocks”, “Collagen biosynthesis and modifying enzymes”, “Bethlem myopathy, and NCAM1 interactions”, “EF-hand domain, and myosin II filament”, “Keratin”, “Mixed, incl. laminin-10 complex, and sprouting of injured axon”, “Protein complex involved in cell adhesion, and met activates ptk2”, “signalling”, “Fibrillar collagen, C-terminal, and Lateral cystocele”, “Mixed, incl. cystadenoma, and intrahepatic cholangiocarcinoma”, “Proteasome regulatory particle, and proteasome subunit”, and “Mixed, incl. profilin binding, and ef-hand, ca insensitive”. Some of these clusters are shared with the enriched clusters of proteins involved in the Hemostasis pathway.

Table 8 List of the 31 proteins involved in the Developmental biology pathway

The pathway of Extracellular matrix organization is involved in the regulation of cell differentiation processes, such as the establishment and maintenance of stem cell niches, branching morphogenesis, angiogenesis, bone remodeling, and wound repair [38]. A total of 37 proteins were found to be involved in this pathway (Table 9) and, among these, STRING analysis revealed the enrichment of clusters mostly related to collagen biosynthesis and degradation (Table 9). Among these, collagen is most abundant fibrous protein in the ECM that constitutes up to 30% of total proteins in multicellular animals. It provides tensile strength and it is associated with elastic fibers composed of elastin and fibrillin microfibrils, which give tissues the ability to recover after stretching. Other ECM proteins, such as fibronectin, laminins, and matricellular proteins, participate as connectors or linking proteins [38], (Table 9).

Table 9 List of the 37 proteins involved in the Extracellular matrix organization

The pathway of Cellular responses to stimuli is essential for normal development, maintenance of homeostasis in mature tissues, and effective defensive responses to potentially noxious agents [39]. A total of 30 proteins were found to be involved in this pathway (Table 10) and, among these, STRING analysis revealed 8 significantly enriched clusters. These clusters include “Photodynamic therapy-induced unfolded protein response, and protein disulfide isomerase activity”, “Photodynamic therapy-induced unfolded protein response, and Inhibition of PKR”, “Protein processing in endoplasmic reticulum, and Insertion of tail-anchored proteins into the endoplasmic reticulum membrane”, “Sequestering of calcium ion, and disulfide isomerase”, “Proteasome”, “Proteasome subunit, and proteasome regulatory particle”, “Chaperone complex, and chaperone cofactor-dependent protein refolding”, and “Fructose 1,6-bisphosphate metabolic process, and xylulose biosynthetic process”.

Table 10 List of the 30 proteins involved in the pathway of Cellular responses to stimuli

Evaluation of the most abundant proteins in the hAMSCs secretome

Finally, we identified the most abundant proteins among the 157 commonly-identified proteins in the hAMSCs secretome. We thus applied a filter to the protein area data obtained from the Proteome Discoverer analysis of the LC–MS raw files, specifically considering protein area values ≥ 5 × 107. This resulted in a total of 33 proteins that showed protein area values in the range 5 × 107–1 × 109 (Additional file 5: Table S5A and B). These proteins collectively constitute the most abundant proteins of the hAMSCs secretome. Figure 8 illustrates the label-free relative quantitation graph for these proteins across the four hAMSCs secretome pools. This quantitation is based on the average protein area values obtained from LC–MS analysis performed in triplicate. These data provide valuable insights into the proteins that significantly contribute to the overall protein profile of the hAMSC secretome.

Fig. 8
figure 8

Histogram of the relative quantitation of the most abundant proteins identified inside the group of 157 commonly-identified proteins in the hAMSCs secretome. The average protein area values and the stabndard deviation as resulting from analytical triplicate analysis are reported on the y-axis

This group of abundant proteins was further analysed using STRING and Cytoscape bioinformatic tools ( to discover specific functional relationships. Figure 9 illustrates the Cytoscape-STRING network, which represents interactions among the 33 most abundant proteins in the hAMSCs secretome. Additionally, it highlights the top 6 enriched terms, which are annotated in different colours at the nodes. The results demonstrate that each protein appears to be involved in multiple categories. Interestingly, a large number of the abundant proteins are classified as components of the Extracellular region, Extracellular space, matrix, and exosomes and vescicle categories.

Fig. 9
figure 9

Functional interaction network and biological pathways of the most abundant proteins of the hAMSC secretome


To enhance our understanding of the therapeutic mechanisms of mesenchymal stromal cells (MSC) in regenerative medicine and immune-related disorders, we conducted an in-depth analysis of the proteins secreted by human amniotic membrane-derived MSCs (hAMSCs). In the hAMSCs secretome, we identified 157 highly abundant proteins, 79 of which are reported as secreted in the Human Protein Atlas (HPA) database, and the remaining 78 have not been previously categorized as secreted proteins. Reactome analysis unveiled several pathways prominently represented in the hAMSCs secretome, including Immune system, Signal transduction, Gene expression (transcription), Hemostasis, Developmental biology, DNA repair, Disease, Extracellular matrix organization, Cellular responses to stimuli, and Nephrin family interactions. Some of these pathways, such as Extracellular matrix organization, Hemostasis, and Immune system, directly relate to processes like tissue remodeling, particularly evident in wound healing [4042]. The significant presence of elements associated with the Developmental biology pathway may stem from the fetal origin of hAMSCs [43, 44].

Among the proteins in the Immune system cluster, we found those with immunoregulatory action such as metallothionein-2 (MT2), glutathione S-transferase Omega 1, elongation factor 2 (eEF-2 K), various immunoproteasome-related proteins, and complement proteins.

Metallothioneins (MT) have been reported to play an immunoregulatory role in autoimmune diseases, infections, and inflammatory bowel diseases [45, 46]. Among the MT, MT2 showed better outcomes in a mouse model of autoimmune encephalomyelitis, preserving myelin and reducing neuroinflammation compared to MT1 [47]. In an acute lung injury model, MT knockout (-/-) mice displayed increased edema, proinflammatory molecules, and NF-κB nuclear localization compared to wildtype mice [48]. MT also protected against inflammatory organ damage in a study with MT knockout mice, reducing prothrombin, C-reactive protein, and fibrinogen production after LPS exposure [49]. Furthermore, in vitro studies evidenced that MT can inhibit the proliferation of cytotoxic T lymphocytes, as also previously reported for macrophages and lymphocytes [50, 51], possibly by interfering with cell–cell interactions, resulting in immature T lymphocytes and reduced differentiation to effector CTLs [52]. eEF-2 K is another protein with immune-regulatory action identified in the hAMSCs secretome. CD8 lymphocytes from eEF-2 K KO mice show increased proliferation but reduced post-activation survival, likely due to premature induction of senescence via hyperactivation of the Akt-mTOR-S6K pathway [53]. In addition, several proteins in hAMSCs secretome identified in the Immune cluster have been shown to be crucial for the activation of immune responses. For example, some immunoproteasome-related proteins (Proteasome subunit beta type-1, Proteasome subunit alpha type-3, Proteasome subunit beta type-4, Proteasome subunit alpha type-1) which have been reported to be crucial for inflammatory T helper lymphocyte differentiation and implicated in autoimmune disease pathogenesis [54, 55]. The enzyme glutathione S-transferase omega 1 (GSTO1-1), which has been reported to play a pro-inflammatory role in response to LPS [56]. Furthermore, knockdown of GSTO1-1 in macrophage-like cells was shown to block NADPH oxidase 1 expression and ROS generation after LPS stimulation. Paradoxically, GSTO1-1-deficient mice exhibited a more severe inflammatory response and increased bacterial escape in a model of inflammatory bowel disease [57]. Other proteins identified in the hAMSCs secretome that were reported to be crucial for immune responses are complement protein C3 and their proteolytic products C3a, which has chemotactic properties, and C3b which stimulates the innate immune response by opsonizing pathogens. C3 combined with collagen type 1 has been shown to boost inflammation, collagen deposition, and wound healing [58]. Among the other proteins represented in the Immune cluster, are tissue inhibitor of metalloproteinases 1 and 2 (TIMP1 and TIMP2), which are reported to exert an immunoregulatory action and also play a major role in matrix remodeling. During wound healing, keratinocytes produce TIMP1 and TIMP2 to promote tissue remodeling and homeostasis, but dysregulated expression can lead to fibrosis [59]. Increased TIMP1 levels, for example, enhance wound healing and have an antiapoptotic effect in diabetic patients [60]. On the other hand, TIMP levels can sometimes decrease in diseased tendons [61], and TIMP1 can inhibit ECM degradation through MMP2 [62]. Interestingly, a key protein known to be involved in the immunomodulatory actions of MSCs, namely indoleamine 2,3 dioxygenase (IDO), which is known to inhibit lymphocyte responses, was not detected in our analysis. This absence of detection could be attributed to the stringent limitations of the analytical method we applied, which exceeded the protein's limit of detection.

Other highly represented proteins in the hAMSC secretome were clustered in the Hemostasis pathway and have also been reported to possess immunomodulatory properties. These proteins include transgelin-2, thymosin beta 4, thrombospondin 1, talin-1, filamin A (FlnA), and Galectin 3 binding protein. Transgelin-2 stabilizes cytoskeletal actin, facilitating T-cell synapse interactions with antigen-presenting cells [71]. Thrombospondin-1 (TSP-1) is transiently released in large quantities by neutrophils during the initial stages of acute inflammation and can exert a strong chemotactic action [63]. Its action is mainly carried out by inducing a strong inflammatory action that accelerates the repair process by facilitating the phagocytosis of damaged cells and the generation of T regulatory cells [64, 65]. Thus, TSP-1 could represent a compensatory mechanism for controlling the immune response and protecting tissues from excessive damage. Talin-1 has been shown to maintain T regulatory cell homeostasis since its absence in CD4 lymphocytes led to spontaneous activation due to decreased T regulatory cell levels [66]. FlnA, has been shown to affect T lymphocyte adhesion and infiltration into tissues, indirectly impacting their function [67]. Galectin 3 plays a multifaceted role in regulating the inflammatory response by influencing macrophage polarization [68], angiogenesis [69], and fibroblast-to-myofibroblast conversion [70], making it pivotal in wound healing processes [71]. Thymosin beta 4 participates in recruiting stem and progenitor cells, promoting cardiac repair after myocardial infarction [72, 73]. It also interferes with TNF-α-mediated NF-κB activation and IL-8 gene transcription, contributing to immunomodulation [74]. Other proteins identified in the hemostasis cluster, but without immunomodulatory actions, are mesencephalic astrocyte-derived neurotrophic factor (MANF) and secreted protein acidic and rich in cysteine (SPARC). MANF is a neurotrophic factor reported to exert protective actions in various central nervous system diseases [75]. For example, pretreatment with MANF before middle cerebral artery occlusion in rats has been shown to improve locomotor abilities and reduced neurological deficits [76]. MANF has also shown benefit in Parkinson's and Alzheimer's disease by protecting dopaminergic neurons and reducing intracellular α-synuclein aggregates in Parkinson's disease [77] and mitigating Aβ1-42-induced neuronal cell death in Alzheimer's disease [78]. SPARC, a matrix glycoprotein, with diverse functions which is highly expressed during tissue damage, inflammation, and in tumors [79], and was shown to inhibit angiogenesis by interfering with the binding of pro-angiogenic factors such as VEGF, PDGF, and bFGF to their receptors, thus countering blood vessel formation [80]. In regenerative medicine, exogenous SPARC was shown to accelerate the proliferation of limbal epithelial stem cells and promote their migration, expediting corneal wound healing in in vivo corneal damage models [81].

Furthermore, proteins found in the Extracellular matrix organization cluster, such as laminin, collagen, and fibronectin, play crucial roles in regeneration processes. Laminin can facilitate cell interactions with other extracellular matrix components, such as collagen and heparin sulfate [82]. It can serve as a substrate for the migration of epithelial keratinocytes during re-epithelialization [83] and contribute to the formation and maturation of blood vessels, particularly in processes like neoangiogenesis [84]. Collagen, a fundamental extracellular matrix protein, is essential for wound healing [85]. It undergoes accelerated turnover during skin remodeling and healing. However, alterations in collagen can lead to functional changes in repairing tissue, potentially resulting in fibrosis [86]. Fibronectin can play a significant role in mediating hemostasis and the migration and recruitment of cell progenitors during wound healing processes [8789].


In conclusion, our analysis has revealed specific proteins within the hAMSCs secretome that fall into clusters with significantly enriched interactions. Nevertheless, it's important to acknowledge the existence of many other proteins whose functional roles in regenerative medicine remain undefined. What emerges from our study is the remarkable complexity of the hAMSCs secretome, whereby we observe proteins with distinct but also paradoxical actions. Yet, it's worth noting that this coexistence of proteins with seemingly contradictory roles may be essential to maintain a dynamic balance between inflammatory and immunoregulatory responses which, in turn, could play a pivotal role in promoting a pro-regenerative environment. On another front, proteins identified in clusters related to hemostasis and extracellular matrix organization hold critical importance in processes like endothelial cell recruitment and matrix remodeling during wound healing. These processes influence the matrix-cell interactions and recruit cell progenitors involved in re-epithelialization and angiogenesis. This holistic view, particularly focused on the protein phenotypes, offers valuable insights for understanding the functional impact of the hAMSCs secretome in regenerative medicine. It paves the way for potential breakthroughs in harnessing the therapeutic potential of hAMSC.

Availability of data and materials

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE ( [90] partner repository with the dataset identifier PXD041088.



Filter-aided sample preparation


Liquid chromatography-high resolution mass spectrometry


Human amniotic membrane mesenchymal stromal cells


Extracellular matrix


Enzyme-linked immunosorbent assays


Fetal bovine serum




D,L-dithiothreitol (DTT)


Ammonium bicarbonate


Formic acid


Ultra-high-performance liquid chromatography-electrospray mass spectrometry


Collision-induced dissociation


Data-dependent scan


Control of reference


False discovery rate


Human proteome project


Gene ontology


Conditioned medium


Human amniotic membrane mesenchymal stromal cells conditioned medium


Human protein atlas


Mesenchymal stromal cells


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The authors thank the physicians and midwives of the Department of Obstetrics and Gynecology of Fondazione Poliambulanza, Brescia, Italy, and all of the mothers who donated placenta. This work contributes to the COST Action CA17116 International Network for Translating Research on Perinatal Derivatives into Therapeutic Approaches (SPRINT), supported by COST (European Cooperation in Science and Technology).


This research was funded by PRIN 2017 program of Italian Ministry of Research and University (MIUR, Grant No. 2017RSAFK7), and Contributi per il funzionamento degli Enti privati che svolgono attività di ricerca—C.E.P.R. (2020–2021) of Italian Ministry of Research and University. This works was partially supported by “5 × 1000” tax income allocated to Università Cattolica del Sacro Cuore.

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Authors and Affiliations



OP and CD conceptualized, designed the experiments and supervised the work; AM, AP and CD were involved in methodology, performed formal analysis, data acquisition and bioinformatic data elaboration. All authors (AM, AP, FV, AV, WL, PR, AC, AS, OP, CD) have been involved in the investigation, results discussion and data interpretation. AM and CD wrote the original draft. All authors (AM, AP, FV, AV, WL, PR, AC, AS, OP, CD) reviewed and edited the manuscript. All authors (AM, AP, FV, AV, WL, PR, AC, AS, OP, CD) have read and approved the manuscript final version.

Corresponding authors

Correspondence to Ornella Parolini or Claudia Desiderio.

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Ethics approval and consent to participate

Human term placentas (N = 19) were collected after obtaining written informed consent from mothers after vaginal delivery or following cesarean section. The study was conducted in accordance with the Declaration of Helsinki, and informed consent was drafted following the guidelines defined by the Brescia Provincial Ethics Committee (number NP 2243, 19/01/2016 “Studio delle applicazioni dei tessuti placentari e delle cellule da questi isolate in medicina rigenerativa”).

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

: Table S1. Protein identification data for hAMSCs secretome pools 1-4

Additional file 2

Table S2. Proteins identification data for DMEMF12 samples (CTRL 1-3) analyzed against Homo sapiens and Bos Taurus databanks (A-F), proteins common to all samples (G) and summary of relative albumin fragments identification data (H)

Additional file 3

Table S3. Out of the 157 proteins common to secretome pools 1-4, these proteins have been classified as secreted proteins based on the grouping analysis against the Human Protein Atlas secretome database (B) and Sub-categories distribution of the proteins classified as secreted (C)

Additional file 4

Table S4. Classification of the 157 common proteins was carried out using the Human Protein Atlas placenta proteome database as a reference. The relative distribution is based on tissue expression. Furthermore, the grouping analysis shows that the proteins within this group are also classified as secreted proteins

Additional file 5

Table S5. Protein average area values of the 157 proteins common to the hAMSCs secretome pools 1-4 (A), and the list of the 33 most abundant proteins in the hAMSCs secretome (average area in the range1xE9-5xE7 in decreasing order) (B).

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Muntiu, A., Papait, A., Vincenzoni, F. et al. Disclosing the molecular profile of the human amniotic mesenchymal stromal cell secretome by filter-aided sample preparation proteomic characterization. Stem Cell Res Ther 14, 339 (2023).

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