Transcriptional Profile of Cytokines, Regulatory Mediators and TLR in Mesenchymal Stromal Cells after Inflammatory Signaling and Cell-Passaging

Abstract

Adult human subcutaneous adipose tissue (AT) harbors a rich population of mesenchymal stromal cells (MSCs) that are of interest for tissue repair. For this purpose, it is of utmost importance to determine the response of AT-MSCs to proliferative and inflammatory signals within the damaged tissue. We have characterized the transcriptional profile of cytokines, regulatory mediators and Toll-like receptors (TLR) relevant to the response of MSCs. AT-MSCs constitutively present a distinct profile for each gene and differentially responded to inflammation and cell-passaging. Inflammation leads to an upregulation of IL-6, IL-8, IL-1β, TNFα and CCL5 cytokine expression. Inflammation and cell-passaging increased the expression of HGF, IDO1, PTGS1, PTGS2 and TGFβ. The expression of the TLR pattern was differentially modulated with TLR 1, 2, 3, 4, 9 and 10 being increased, whereas TLR 5 and 6 downregulated. Functional enrichment analysis demonstrated a complex interplay between cytokines, TLR and regulatory mediators central for tissue repair. This profiling highlights that following a combination of inflammatory and proliferative signals, the sensitivity and responsive capacity of AT-MSCs may be significantly modified. Understanding these transcriptional changes may help the development of novel therapeutic approaches.

Keywords: TLR; cell-passage; cytokines; inflammation; mesenchymal stromal cells; regulatory mediators; transcriptional profile.

Figure 1

The culture characteristics of adipose MSCs. MSCs are derived from liposuction waste material. The cells are obtained by Gradient Ficoll Centrifugation and cultured by classical adherent method. (A) The image is representative for the morphology of MSCs. The cells are observed by light microscopy. Scale bar = 500 µm. (B) The cumulative cell number of MSCs at each passage as a function of culture time. (C) The percentage of SA β-Gal positive MSCs during passage expansion.

Figure 2

The expression profile of CD34, CD105 and HLA-Dr during cell expansion of MSCs. These markers were analyzed by flow cytometry. The results are presented in the graphic as the mean ± SEM percentage of each marker expression. Representative FACS histograms are also shown for basic (black curve) and inflammation conditions (red curve) during PM, early and late passage cultures. The grey curve represents the antibody control.

Figure 2

The expression profile of CD34, CD105 and HLA-Dr during cell expansion of MSCs. These markers were analyzed by flow cytometry. The results are presented in the graphic as the mean ± SEM percentage of each marker expression. Representative FACS histograms are also shown for basic (black curve) and inflammation conditions (red curve) during PM, early and late passage cultures. The grey curve represents the antibody control.

Figure 3

The differentiation potential of adipose MSCs is confirmed by light microscopy. The osteogenic and adipogenic differentiation of MSCs are carried out in specific induction media and confirmed by using lineage-staining techniques. The images are representative for MSCs differentiated into osteoblast (A) and adipocytes (B), being confirmed by using Alizarin Red (calcium deposits) and Oil red O (Lipid vacuoles) staining, respectively. Scale bar = 100 µm.

Figure 4

The expression of cytokines is upregulated in MSCs. The gene expression level of each cytokine is determined by qPCR analysis from basic MSCs and inflamed MSCs (INFL) during the primo culture (PM), early (EARLY) and late (LATE) passage (P). The values are expressed as mean ± SEM compared to the expression of the housekeeping gene (ACTB and GAPDH). *, **, *** Significant increase of expression of inflamed MSCs (INFL) versus basic MSCs (p-value: p ≤ 0.05, p ≤ 0.01, p ≤ 0.001, respectively).

Figure 5

The expression of regulatory mediators is upregulated in MSCs. The gene expression level of each cytokine is determined by qPCR analysis from basic MSCs and inflamed MSCs (INFL) during the primo culture (PM), early (EARLY) and late (LATE) passage (P). The values are expressed as mean ± SEM compared to the expression of the housekeeping gene (ACTB and GAPDH). **, *** Significant increase of expression of inflamed MSCs (INFL) versus basic MSCs (p-value: p ≤ 0.01, p ≤ 0.001, respectively). ^{$, $$,} Significant decrease of expression of inflamed MSCs (INFL) versus basic MSCs (p-value: p ≤ 0.05, p ≤ 0.01, respectively). ^{#, ##} Significantly lower expression compared to the consecutive passage (p-value: p ≤ 0.05, p ≤ 0.01, respectively).

Figure 6

Distinct expression of Toll-like receptor (TLRs) and differential regulation in MSCs. The gene expression level of each cytokine is determined by qPCR analysis from basic MSCs and inflamed MSCs (INFL) during the primo culture (PM), early (EARLY) and late (LATE) passage (P). The values are expressed as mean ± SEM compared to the expression of the housekeeping gene (ACTB and GAPDH). *, **, *** Significant increase of expression of inflamed MSCs (INFL) versus basic MSCs (p-value: p ≤ 0.05, p ≤ 0.01, p ≤ 0.001, respectively). ^{$, $$$} Significant decrease of expression of inflamed MSCs (INFL) versus basic MSCs (p-value: p ≤ 0.05, p ≤ 0.001, respectively). ^{§, §§§} Significantly higher expression compared to the consecutive passage (p-value: p ≤ 0.05, p ≤ 0.001, respectively).

Figure 7

STRING 11.0 analysis of known and predicted TLR-protein interactions. The interactions include direct (physical) and indirect (functional) associations. Network nodes represent proteins and edges represent protein-protein associations (specific and meaningful). The lines represent the existence of the several types of evidence used in predicting the associations (high confidence score 0.9). The interactions are shown in different colors: black is co-expression; dark blue is co-occurrence; purple is experimental evidence; light green is text mining.

Figure 8

Functional enrichments and Statistics of TLR networks interactions and association. Gene Ontology (GO) is used to perform enrichment analysis for biological process, molecular functions and cellular components.