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Uncovering the cellular and molecular changes in tendon stem/progenitor cells attributed to tendon aging and degeneration.

Kohler J, Popov C, Klotz B, Alberton P, Prall WC, Haasters F, Müller-Deubert S, Ebert R, Klein-Hitpass L, Jakob F, Schieker M, Docheva D - Aging Cell (2013)

Bottom Line: These analyses revealed an intriguing transcriptomal shift in A-TSPC, where the most differentially expressed probesets encode for genes regulating cell adhesion, migration, and actin cytoskeleton.Time-lapse analysis showed that A-TSPC exhibit decelerated motion and delayed wound closure concomitant to a higher actin stress fiber content and a slower turnover of actin filaments.Lastly, based on the expression analyses of microarray candidates, we suggest that dysregulated cell-matrix interactions and the ROCK kinase pathway might be key players in TSPC aging.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Experimental Surgery and Regenerative Medicine, Ludwig Maximilians University Munich, Nussbaumstr. 20, 80336, Munich, Germany.

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Genome-wide microarray and gene ontology analysis of differentially expressed probesets. (A) Venn diagram of global probesets changes. The number of significantly upregulated probesets in Y-TSPCand A-TSPC is shown. The intersection indicates the number of probesets that are not significantly regulated. Differentially expressed probesets were identified by pairwise comparison analysis (MAS5 algorithm) of experimental versus baseline array that displayed a signal log2 ratio of < −1 or > 1, containing a fold change (FC) lower than 0.5 and greater than 2. The detection p-value was ≤ 0.05, which was evaluated against certain cut-offs to determine the detection call (A, absent; M, marginal; P, present). The number of ‘present’ calls for a given probeset had to be greater than 66% in at least one of the donor groups. (B) Heatmap of the top 40 differentially expressed probesets. (C) Gene ontology (GO) analysis of all differentially expressed probesets identified significantly enriched ‘cellular component’ and ‘biological process’ GO clusters. (D) Cake diagram representing the literature-based categorization of the top 130 differentially expressed genes. Bar charts demonstrate for each category in (D) the exact number of upregulated genes in Y-TSPC (left) and A-TSPC (right). The data include three different donors per group.
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fig02: Genome-wide microarray and gene ontology analysis of differentially expressed probesets. (A) Venn diagram of global probesets changes. The number of significantly upregulated probesets in Y-TSPCand A-TSPC is shown. The intersection indicates the number of probesets that are not significantly regulated. Differentially expressed probesets were identified by pairwise comparison analysis (MAS5 algorithm) of experimental versus baseline array that displayed a signal log2 ratio of < −1 or > 1, containing a fold change (FC) lower than 0.5 and greater than 2. The detection p-value was ≤ 0.05, which was evaluated against certain cut-offs to determine the detection call (A, absent; M, marginal; P, present). The number of ‘present’ calls for a given probeset had to be greater than 66% in at least one of the donor groups. (B) Heatmap of the top 40 differentially expressed probesets. (C) Gene ontology (GO) analysis of all differentially expressed probesets identified significantly enriched ‘cellular component’ and ‘biological process’ GO clusters. (D) Cake diagram representing the literature-based categorization of the top 130 differentially expressed genes. Bar charts demonstrate for each category in (D) the exact number of upregulated genes in Y-TSPC (left) and A-TSPC (right). The data include three different donors per group.

Mentions: To identify molecular factors involved in tendon aging and degeneration, we performed microchip hybridization with RNA from three different donors per group. Comparative microarray analysis is summarized in a Venn diagram in Fig.2A, and the top 40 and 100 probesets are depicted in heatmaps in Fig.2B and Fig. S4, respectively (complete microarray data in Table S4). To identify a molecular pattern behind the observed transcriptomal shift, we performed gene ontology (GO) analysis with all differentially expressed probesets. In Fig.2C, exemplary entries from the ‘cellular component’ and ‘biological process’ gene clusters are shown. Finally, to spot the most relevant genes, we applied literature-based annotation approach as follows: using probesets with 2-fold changes, 130 known genes were identified and subjected to literature screening. Our investigation demonstrated that these genes distributed majorly in the following categories: (i) ‘cell–cell contact’, (ii) ‘cell adhesion’, (iii) ‘motility’, (iv) ‘migration’, (v) ‘cytoskeleton’, and (vi) ‘actin-related transcripts’ (Fig.2D and Table S5).


Uncovering the cellular and molecular changes in tendon stem/progenitor cells attributed to tendon aging and degeneration.

Kohler J, Popov C, Klotz B, Alberton P, Prall WC, Haasters F, Müller-Deubert S, Ebert R, Klein-Hitpass L, Jakob F, Schieker M, Docheva D - Aging Cell (2013)

Genome-wide microarray and gene ontology analysis of differentially expressed probesets. (A) Venn diagram of global probesets changes. The number of significantly upregulated probesets in Y-TSPCand A-TSPC is shown. The intersection indicates the number of probesets that are not significantly regulated. Differentially expressed probesets were identified by pairwise comparison analysis (MAS5 algorithm) of experimental versus baseline array that displayed a signal log2 ratio of < −1 or > 1, containing a fold change (FC) lower than 0.5 and greater than 2. The detection p-value was ≤ 0.05, which was evaluated against certain cut-offs to determine the detection call (A, absent; M, marginal; P, present). The number of ‘present’ calls for a given probeset had to be greater than 66% in at least one of the donor groups. (B) Heatmap of the top 40 differentially expressed probesets. (C) Gene ontology (GO) analysis of all differentially expressed probesets identified significantly enriched ‘cellular component’ and ‘biological process’ GO clusters. (D) Cake diagram representing the literature-based categorization of the top 130 differentially expressed genes. Bar charts demonstrate for each category in (D) the exact number of upregulated genes in Y-TSPC (left) and A-TSPC (right). The data include three different donors per group.
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Related In: Results  -  Collection

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fig02: Genome-wide microarray and gene ontology analysis of differentially expressed probesets. (A) Venn diagram of global probesets changes. The number of significantly upregulated probesets in Y-TSPCand A-TSPC is shown. The intersection indicates the number of probesets that are not significantly regulated. Differentially expressed probesets were identified by pairwise comparison analysis (MAS5 algorithm) of experimental versus baseline array that displayed a signal log2 ratio of < −1 or > 1, containing a fold change (FC) lower than 0.5 and greater than 2. The detection p-value was ≤ 0.05, which was evaluated against certain cut-offs to determine the detection call (A, absent; M, marginal; P, present). The number of ‘present’ calls for a given probeset had to be greater than 66% in at least one of the donor groups. (B) Heatmap of the top 40 differentially expressed probesets. (C) Gene ontology (GO) analysis of all differentially expressed probesets identified significantly enriched ‘cellular component’ and ‘biological process’ GO clusters. (D) Cake diagram representing the literature-based categorization of the top 130 differentially expressed genes. Bar charts demonstrate for each category in (D) the exact number of upregulated genes in Y-TSPC (left) and A-TSPC (right). The data include three different donors per group.
Mentions: To identify molecular factors involved in tendon aging and degeneration, we performed microchip hybridization with RNA from three different donors per group. Comparative microarray analysis is summarized in a Venn diagram in Fig.2A, and the top 40 and 100 probesets are depicted in heatmaps in Fig.2B and Fig. S4, respectively (complete microarray data in Table S4). To identify a molecular pattern behind the observed transcriptomal shift, we performed gene ontology (GO) analysis with all differentially expressed probesets. In Fig.2C, exemplary entries from the ‘cellular component’ and ‘biological process’ gene clusters are shown. Finally, to spot the most relevant genes, we applied literature-based annotation approach as follows: using probesets with 2-fold changes, 130 known genes were identified and subjected to literature screening. Our investigation demonstrated that these genes distributed majorly in the following categories: (i) ‘cell–cell contact’, (ii) ‘cell adhesion’, (iii) ‘motility’, (iv) ‘migration’, (v) ‘cytoskeleton’, and (vi) ‘actin-related transcripts’ (Fig.2D and Table S5).

Bottom Line: These analyses revealed an intriguing transcriptomal shift in A-TSPC, where the most differentially expressed probesets encode for genes regulating cell adhesion, migration, and actin cytoskeleton.Time-lapse analysis showed that A-TSPC exhibit decelerated motion and delayed wound closure concomitant to a higher actin stress fiber content and a slower turnover of actin filaments.Lastly, based on the expression analyses of microarray candidates, we suggest that dysregulated cell-matrix interactions and the ROCK kinase pathway might be key players in TSPC aging.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Experimental Surgery and Regenerative Medicine, Ludwig Maximilians University Munich, Nussbaumstr. 20, 80336, Munich, Germany.

Show MeSH
Related in: MedlinePlus