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Human ageing is associated with loss of function and regenerative capacity. Human bone marrow derived mesenchymal stromal cells (hMSCs) are a potential cell source for cell based therapy since they are capable of differentiating in the osteogenic lineage and are widely studied for cell-based regeneration therapy. Due to ageing of our population, these therapies will mainly be used for elderly. Ageing is thought to influence therapeutic efficacy, therefore more insight in the process of ageing of hMSCs is of high interest. In addition, tissue maintenance and regeneration is dependent on stem cells and declining with age. Therefore, we hypothesized we must be able to detect signs of ageing in hMSCs. In order to find markers of donor age, early passage hMSCs were isolated from bone marrow of 61 donors, with ages varying from 17-84, and clinical parameters, in vitro characteristics and microarray analysis were assessed. Clinical parameters and in vitro performance could not be used as markers for ageing. Although large donor variations were present, genome-wide microarray analysis resulted in a considerable list of genes correlation with age. The gene signature presented here could be a useful tool for drug testing to rejuvenate hMSCs or selection of younger, more potent, hMSCs for cell-based therapy.
List of abbreviations:
Human ageing is associated with disease, loss of regenerative capacity and loss of function. Cell based regenerative therapies, with stem cells as potential sources, are widely investigated to repair, restore or reduce these issues. Among these stem cells are embryonic stem cells and adult stem cells such as neural stem cells, hematopoietic stem cells and mesenchymal stem cells (MSCs; also referred to as mesenchymal stromal cells or multipotent stromal cells) [1,2]. The latter is an interesting cell source, since these cells can be isolated from relatively easy accessible tissues such as bone marrow, they can be expanded in vitro and they can differentiate in the chondrogenic, adipogenic, myogenic, neurogenic and osteogenic lineage . Recently, it has been demonstrated that hMSCs secrete trophic factors and immunomodulatory agents, giving them therapeutic qualities and making them appropriate for allogenic transplantation . Furthermore, hMSCs have established their value in clinical trials . Tissue maintenance and regeneration are dependent on stem cells. Therefore, any loss in number or functionality due to ageing will have a profound effect on our regenerative capacity . In current literature there is little agreement on the effect of age on the performance of hMSCs . Age-related changes in pool size [8,9], proliferation rate [10,11] and differentiation capacity [10,12,13] have been reported and at least part of the human life span is expected to be determined by the loss of self-renewal and differentiation capacity of hMSCs . These conflicting results could be explained by the use of different species and different isolation and characterization techniques. In addition, research is restricted by large donor variation, making existing results less explicit . Since the microenvironment, where stem cells reside in, is most likely derived from these same stem cells, both intrinsic and extrinsic ageing should be considered [6,7]. Recently, Zhuo et al. found an equal contribution of donor age and recipient age to the efficacy of rat MSC based therapy with an overall decline in efficacy with age of the donor and the recipient, suggesting stem cells are indeed influenced by the process of ageing [7,16]. Since the target group of cell based therapy will comprise of elderly, more insight into the process of ageing is of high interest. Different species age at different rates and possess distinct maximum lifespan, suggesting at least part of the process of ageing is controlled by gene expression. Human lifespan, determined by genetics and external factors such as injuries and lifestyle, is inheritable for up to 25% . Lifespan in centenarians has an even larger genetic component [18,19]. With microarray techniques improving in specificity and accuracy, extensive gene studies become of high interest in the search for markers for ageing and the understanding of the genetics behind the process. Given that individuals age at different rates, markers of ageing should correlate to physiological function rather than chronological age. The research on ageing is hampered by the limited ability to manipulate this physiological age. Currently, caloric restriction is the only reliable method known to increase maximum life span in a variety of species . Therefore, several researchers include other characteristics indirectly linked to ageing, such as telomere length and the expression of cell cycle genes such as p16INK4a and p21WAF1, in their studies [21,22]. However, the role of in vitro senescence in in vivo ageing remains unclear. A gene profile that correlated with both chronological and physiological age could be established using a genome wide analysis of gene expression changes in skeletal muscle, as this tissue has specific age-related changes in physiology .
In our lab, we used microarray analysis to develop a molecular signature of bone forming hMSCs . In this study we have used the same bank of 61 donors to successfully identify markers for donor age. Although we could not demonstrate the involvement of in vitro senescence, our results prove hMSCs are influenced by ageing.
Correlation of biological characteristics and donor age
In order to specify markers for donor age, hMSCs were isolated from bone marrow aspirated from either the acetabulum or the iliac crest of 61 donors. The entire donor population comprised of 46 females and 15 males in the age of 17 to 84 years with an average of 55 years, distribution of age, gender and locations of aspiration can be found in . This donor population has previous been used and characterized by our lab to establish a marker for bone formation in vivo . The aspirates were put in culture and the cells were identified according to the set of standards proposed by the Mesenchymal and Tissue Stem Cell Committee of the ISCT . Cells were adherent and over 94% expressed CD73 and CD90, 60% expressed CD105, and less than 2% were CD45, CD34, CD11b, CD19 and HLA-DR positive, as determined by flow cytometry in cells from 3 different donors.
Distribution of the donor population. A) The distribution of gender with age and B) the distribution of site of aspiration with age.
Biological characteristics, such as population doublings, total yield and differentiation capacity, were analyzed and correlated to bone formation in vivo by Mentink et al. . Unfortunately, correlations between these biological characteristics and bone formation in vivo were not significant, mainly due to larger inter-donor variability. In this study the same biological characteristics, were used to determine a marker for donor age. The relation between these parameters and donor age were evaluated with Pearson correlations, similarly to in vivo bone formation, strong correlation could not be identified for donor age (). Proliferative characteristics, such as, the number of population doublings between day 0 and day 1 (C) did not correlate with donor age. The expression of the early osteogenic marker, alkaline phosphatase (ALP), in hMSCs cultured in basic medium was the only characteristic that showed a slight correlation with donor age, for both the mean expression (A) and the percentage of positive cells (B). However, the osteogenic differentiation capacity, determined by dexamethasone (dex) induced ALP expression did not correlate in any way.
Pearson correlation of biological characteristics with donor age. * represents p < 0.05.
Number of nucleated cells per ml
Population doublings, day 0-1
Population doublings, day 1-2
Percentage of bone compared to scaffold
Percentage of bone contact
* Mean ALP expression (control)
Mean ALP expression (dex)
Mean ALP expression (index)
* Percentage ALP positive cells (control)
Percentage ALP positive cells (dex)
Percentage ALP positive cells (index)
Cartilage formation (GAG/DNA )
R = -0.315
P < 0.05 A)
mean ALP expression control.jpg
R = -0.353
P < 0.05 B)
Percentage ALP positive cells control.jpg
R = -0.193
P = 0.135 C)
Population doublings day 0-1.jpg
Correlation between biological parameters and donor age. A correlation between age and clinical and biological labels could solely be determined for A) the mean ALP expression and B) the percentage ALP positive cells in basic medium. Other labels such as C) population doublings per day between day 0-1 and bone formation did not correlate with donor age.
Genetic markers for donor age
Since the biological characteristics we gathered did not lead to a general marker for donor age, we performed a genome-wide gene expression analysis. For this purpose, RNA was isolated from undifferentiated hMSCs (passage 2) and hybridized to Human Genome U133A 2.0 Arrays (Afflimetrix) comprising of 22,277 probe sets representing 18,400 transcripts and variants. After statistical processing, probe sets with sufficient difference in expression (std > 0.4) remained. P-values were determined for these 1653 probe sets by permuting F-test scores and false positive rates were calculated. The probe sets were ranked by significance and 8.07% showed correlations with a significance of p < 0.05, the top 50 is represented in .
The top 50 of genes correlating in hMSCs with donor age based on f-test statistics. The p-value indicates the significance of the Pearson correlation, fdr stands for false discovery rate. Gene number 50 in this list (LIF) has an fdr of 0.1055 meaning a little over 5 genes in this list are false positive.
In addition to the list based on Pearson correlations in , a top 50 based on non-parametric Spearman rho correlations were calculated (data not shown). This list was for 75% similar to the list in , indicating the majority of the correlations were linear. Therefore, Pearson correlations were used for further data analysis. To further verify our findings, we selected 7 genes in from and 5 associating genes. First, quantitative polymerase chain reaction (qPCR) was used to validate the gene expression in a selection of the total donor population. We used 10 male and 10 female donors, with 5 young and 5 old donors for each gender. Correlations were calculated and 5 genes showed a significant linear trend with donor age, all except one, zinc finger protein 395 (ZNF395), appeared in the top 50 (). ZNF395, however, did show a correlation based on the microarray results, but did not meet the requirement of a std > 0.4. Homeobox B7 (HOXB7) was on the list, but did not correlate with donor age based on the qPCR results, this could mean HOXB7 is a false positive.
Gene expression validated with qPCR in 20 donors. Except HOXB7, all genes that are depicted in the top 50 () gave significant, or near significant, correlations with qPCR as well. Therefore, HOXB7 might be a false positive. ZNF395 on the other hand, has a significant trend while it was not on the list. * represents p < 0.05.
The genes that had a significant, or near significant, correlation with age in 20 donors were confirmed by qPCR on RNA from all donors (). As expected from the microarray data, ZNF395 has a nearly flat correlation curve, especially when compared to collectin sub-family member 12 (COLEC12), which shows a 5-fold downregulation with age. The genes that were near significant in 20 donors, Fibroblast growth factor receptor 2 (FGFR2) and slit homolog 3 (SLIT3), proved to have a valid correlation in 61 donors. Multiple regression analysis was used to exclude gender and location of aspiration, as depicted in (), as confounding factors. Although slightly different trends were observed in hMSCs obtained from male and female, gene expression of COLEC12, collagen, type XIII, Î±1 (COL13A1), FGFR2, follistatin (FST), SLIT3 and ZNF395 solely correlated with donor age. The expression of jagged-1 (JAG1), on the other hand, had a better correlation with location, JAG1 expression was significantly higher in hMSCs obtained from the acetabulum compared to hMSCs from the iliac crest. Multiple regression was used to combine these 7 genes in a general model for donor age according to () and an R was reached of 0.673. When a small data set is used, the R2 has to be corrected, resulting in an adjusted R2. In this case the adjusted R2 is 0.381, in other words, 38.1% of the variance of donor age is explained by the combination of these 7 genetic markers.
R = -0.469
P < 0.001colec12.jpg
R = 0.356
P < 0.01col13a1.jpg
R = -0.460
P < 0.001fgfr2.jpg
R = 0.365
P < 0.01follistatin.jpg
R = 0.307
P < 0.05Jagged-1.jpg
R = -0.418
P < 0.001slit3.jpg
R = -0.391
P < 0.01znf395.jpg
qPCR validation on the whole donor population. Correlation curves were produced for the expression of A) COLEC12, B) COL13A1, C) FGFR2, D) FST, E) JAG1, F) SLIT3 and G) ZNF395 in the entire donor population and significant correlations could be confirmed.
To verify if the genetic markers we discovered for in vivo ageing, simultaneously are relevant for in vitro ageing, we have performed qPCR on RNA isolated from hMSCs (3 donors, passage 0-7). Unfortunately, consistent trends could not be determined (), suggesting in vivo and in vitro ageing, occur through distinct mechanisms.
Gene-expression correlated to in vitro ageing. The trend observed in relation to in vivo ageing could not be confirmed in hMSCs aged in vitro. Gene-expression was determined up to passage 7 in 3 donors, however a general trend could not be observed. For some genes the expression pattern was random, A) COL13A1 and D) ZNF395, and for other genes there seemed to be a trend in one of the donors, but this could not be confirmed in the other two, B) FST and D) JAG1.
* Gene expression in fetal MSCs
Our donor bank covers an age-range of 17-84 years old, therefore, lacking juvenile donors. To determine if the correlation curves could be extrapolated to prenatal age, MSCs were isolated from the femora of 14-18 week old human embryos. Gene expression of fetal MSCs was compared to the gene expression of 4 donors from our donor bank (age 21, 28, 38 and 57). Analysis with qPCR revealed a significant higher expression of COL13A1 and FST in fetal MSCs, opposite to our results in adult donors, were expression is increased with age. Expression of JAG1 and ZNF395 was not significantly different between fetal and adult MSCs ().
Gene expression in fetal MSCs compared to adult MSCs. COL13A1 and FST were significantly upregulated in fetal MSCs, this is contradictory to the results found in adult MSCs. JAG1 and ZNF395 were not significant. * represents p < 0.05
Correlation of gene expression and age in rat MSCs
To determine if these genetic markers might be markers for ageing inter-species, we isolated MSCs from femora of young (1 month) and old (12 and 24 months) Wistar rats. We have performed qPCR for four genes that were selected from our marker set. The age-related expression of COL13A1, JAG1 and ZNF395 could not be verified in rat MSCs. However, for FST a significant upregulation was observed between MSCs from young rats and old rats (), consistent with our finding in human MSCs.
Rat MSC P0_bewerkt.jpg
Validation of gene-expression in rat MSCs. To assess if age-related gene-expression was similar inter-species, MSCs were isolated from 1, 12 and 24 month old rats. The correlation between the expression of follistatin and age could be verified in rat MSCs, suggesting this is possibly a marker for several species. For the other three genes no correlation was found.
Influence of FST on ALP expression
FST expression correlated with donor age in hMSCs and was significantly upregulated in MSCs from old rats compared to young rats. MSCs are a potential cell source for bone regeneration therapies. FST has been associated with bone formation, both as an inhibiter in murine MSCs  and a stimulator in human MSCs . To conclude if FST has a positive or negative effect on bone formation, hMSCs were cultured under the influence of dex, FST, and activin A, or combinations of these, for 5 days and ALP expression was determined with qPCR. As depicted in , the addition of FST alone did not increase the expression of ALP, an early marker for bone formation. However, the combination of FST and dex increased ALP expression. Additionally, this effect was counteracted by the addition of activin A, of which FST is an antagonist.
Follistatin, in combination with dex, increased ALP expression. To determine the influence of follistatin on ALP expression, cellcultures were supplemented with dex, follistatin and activinA, or a combination of these. ALP expression increased further in cultures supplemented with dex and follistatin, compared to dex alone. This effect was counteracted by addition of activinA. *p < 0.05, **p < 0.01.
* Gene expression normalized to telomere length
The set of markers we have identified are correlating to chronological age. Since not all individuals age at the same pace, a marker, or marker set, for biological age, rather than chronological age, would be extremely useful. We used telomere length as a measure of biological age to determine if our marker set would correlate with biological age. hMSCs were treated with bFGF, which is assumed to select for cells with longer telomeres . Therefore, fresh bone marrow aspirates were cultured with or without bFGF, up to passage 2 (approximately 4 weeks), DNA and RNA were isolated simultaneously and telomere lengths were determined by qPCR. Unfortunately, hMSCs cultured in the presence of bFGF did not contain longer telomeres compared to hMSCs in the control situation. Nevertheless, gene expression of COL13A1, FST, JAG1 and ZNF395 was determined for hMSCs of passage 0-2 and correlations to telomere length were calculated. However, correlations between telomere length and gene expression could not be verified.
The regenerative capacity of the human body is mainly dependent on stem cell activity and declines with age. Therefore, we hypothesized we would be able to indicate age-related changes in hMSCs. Indeed, we were able to identify a number of genetic markers for donor age in early passage hMSCs.
hMSCs are widely studied as potential cell source for regenerative therapies and have been entered in clinical trials . They can be harvested from relatively easy accessible tissues, can be expanded in vitro and differentiate in multiple tissue types. Experiments show, however, that the performance of hMSCs varies severely from donor to donor . In current literature, there is little agreement on the effect of age on stem cells and on the performance of hMSCs in particular. This is partly due to the lack of sufficient markers to characterize the stem cell population, resulting in heterogeneity of the population . Another explanation is the use of animal models to study the effect of ageing, disregarding inter-species discrepancies . We set out to identify markers for donor age in hMSCs. Biological characteristics such as proliferation rate and differentiation capacity proved insufficient as markers for age. Therefore, a large population of 61 donors, with ages ranging from 17-84 years, was used to analyze genome-wide expression profiles, resulting in a list of genes with age-related changes in expression levels. So far, this approach has proved successful in other tissues . Wagner et al. performed a similar study on a limited donor population (young, middle aged, old; n=4), providing possibly less accurate results . Their analysis resulted in a list of 184 negatively of positively correlating genes, of which solely HOXB7, S100 calcium binding protein A4 (S100A4) and short stature homeobox 2 (SHOX2) are present on our list. The expression of one of which, HOXB7, could not be verified by qPCR, suggesting this is a false positive. Interestingly, our top 50 contains 10 genes that have never been related to age or ageing in any species.
Examination of the gene ontology (GO) terms revealed SLIT3, COL13A1, JAG1, empty spiracles homeobox 2 (EMX2), HOXB7 and frizzled homolog 1 (FZD1) are involved in multi-cellular organismal development, which is the most occurring biological process in the list. Other, frequently occurring, GO terms are of a more general nature, such as protein binding, and located to the nucleus. Some genes occur more often in the list, making them more powerful indicators for donor age. It is important to keep in mind that gene expression is a dynamic process, therefore there might be genes that correlate with age by chance, with p < 0.05 less than 5% of the probes should correlate with age if it was a random event, in this study 8.07% of the probes correlated with donor age. Moreover, false positive rates were calculated, showing our top 50 contains less than 5 false positive genes. qPCR validation proved microarray analysis is a reliable method for gene expression studies. Moreover, gene expression in rat MSCs indicated FST as a possible inter-species marker for age. Unfortunately, the genetic markers do not apply to in vitro senescence, suggesting ageing and senescence occur through distinct genetic mechanisms.
* The expression of genetic markers could not be extrapolated to fetal MSCs. A possible explanation is that specific genes are active during prenatal development, while these genes are dormant in adults. Our donor population does not include juvenile donors, as a result these markers might be relevant for fetal MSC and MSCs from very young donors.
* To verify if these genetic markers correlate with chronological and biological age, fresh bone marrow aspirates were treated with bFGF in order to select for cells with longer telomeres. Unfortunately, telomere lengths of hMSCs treated with bFGF were not significantly longer compared to hMSCs in basic medium. They were kept in culture up to the second passage (approximately 28 days). According to Bianchi et al. , the largest effect of bFGF treatment is reached after 16-18 population doublings. Possibly, our culture period, or the interval, was not sufficient. In conclusion, we could not verify these genetics marker apply to biological donor age in addition to chronological donor age.
A marker set as presented here could be very valuable in drug discovery. At present, there are no reliable markers for ageing. Therefore, it is difficult to interpret the influence of supposedly rejuvenating compounds. However, our genetic markers could be used to select for drugs with the desired effects. In addition, these markers could be used to select younger, and therefore more potent, cells from a heterogeneous population. One must consider these markers correlate with chronological age, rather than biological age and while the genes we identified are markers for donor age, these genes are not intended as targets for ageing. Further research is required to identify targets for ageing within our list of marker. In addition, more insight in biological ageing will allow us to further specify our set of markers.
Since FST had a significant correlation with age for human and rat MSCs, we decided to study the function of this gene. FST has been associated with bone formation, both as an inhibiter  and a stimulator  of mineralization. Our results showed a significant upregulation of ALP expression in cells treated with a combination of dex and FST, this effect was counteracted by addition of activin A of which FST is a known antagonist. Since bone formation is reduced in elderly people , one would expect FST to go down with age. Our data imply the opposite, possibly FST is overproduced to compensate for an impaired bone forming mechanism.
In conclusion, we were able to indentify genes in hMSCs with significant correlations to donor age, indicating stem cells are influenced by ageing. Further research, in other donor populations, will have to confirm the impact of each of our markers. In the meantime, a selection of markers identified in this study can be combined to create a powerful tool for research in the field of ageing.
Materials and Methods
Isolation and culture of human mesenchymal stem cells
Bone marrow aspirates (5-20 ml) were obtained from donors with written informed consent, and hMSCs were isolated and proliferated as described previously . Briefly, aspirates were resuspended using a 20-gauge needle, plated at a density of 500,000 nucleated cells/cm2 and cultured in hMSC proliferation medium containing Î±-minimal essential medium (Î±-MEM; Gibco), 10% fetal bovine serum (FBS; Biowhittaker, Australia), 0.2 mM ascorbic acid (Sigma), 2 mM L-glutamine (Gibco), 100 U/ml penicillin with 100 Âµg/ml streptomycin (Gibco) and 1 ng/ml basic fibroblast growth factor (bFGF; Instruchemie, Delfzijl, The Netherlands). The serum batch was selected on proliferation rate and osteogenic differentiation potential and used for all experiments in this paper. Cells were grown at 37Â°C in a humid atmosphere with 5% CO2. Medium was first changed after 5 days to remove non-adherent cells and was further refreshed twice a week. Cells were used for further subculturing or cryopreservation upon reaching near confluence. hMSC basic/control medium (BM) was composed of hMSC proliferation medium without bFGF, hMSC osteogenic medium was composed of hMSC basic medium supplemented with 10-8 M dexamethasone (dex, Sigma). In order to examine to influence of follistatin on ALP expression, basic culture medium was supplemented with either dex (10-8 M), follistatin (100 ng/mL, R&D Systems) or activinA (50 ng/mL, R&D Systems), or combinations of these. Basic medium was used as a control. ALP expression was measure by qPCR.
Isolation and culture of rat mesenchymal stem cells
Rat MSCs were obtained as described previously . Femora of 1, 12 and 24 months old male rats (Male Crl:WI(Han), Charles River, n=3) were removed, thoroughly cleaned and submerged in PBS containing 250 Âµg/ml Fungizone (Invitrogen). After removal of the epiphyses, the bone marrow was flushed out with 10 ml of rat MSC proliferation medium containing Î±-MEM (Gibco), 15% FBS (Biowhittaker, Australia), 0.2 mM ascorbic acid (Sigma), 2 mM L-glutamine (Gibco), 100 U/ml penicillin with 100 Âµg/ml streptomycin (Gibco) and 1 ng/ml bFGF (Instruchemie, Delfzijl, The Netherlands) and cultured in T75 flasks. Cells were grown at 37Â°C in a humid atmosphere with 5% CO2. Medium was first changed after 3 days to remove non-adherent cells and was further refreshed three times a week. Animals were housed at the Central Laboratory for Animal Institute (Utrecht University, Utrecht, The Netherlands), and experiments were approved by the local animal care and use committee.
Isolation and culture of human fetal mesenchymal stem cells
Human fetal mesenchymal stem cells (fMSCs) were obtained from femora from 14-18 week old human embryos. Briefly, femora were removed from the embryos and cleaned thoroughly, bone marrow was flushed from femora and cultured in fMSCs proliferation medium containing M199 medium (Gibco), 10% FBS (Biowhittaker, Australia), 20 Âµg/mL bovine endothelial cell growth factor (bECGF, Roche), 8 U/mL heparin (Leo Pharma) and 100 U/ml penicillin with 100 Âµg/ml streptomycin (Gibco) and cultured in T75 flasks. Cells were grown at 37Â°C in a humid atmosphere with 5% CO2. Medium was first changed after 4 days and was further refreshed twice a week. Cells were cultured until reaching near confluence and gene expression was determined using qPCR.
To analyze the gene expression profile of hMSCs, cells were seeded at 1000 cells/cm2 and upon reaching near confluence RNA was isolated using an RNeasy mini kit (Qiagen) and DNase treated on column with 10U RNase free DNase I (Gibco) at 37 Â°C for 30 minutes. DNase was inactivated at 72 Â°C for 15 minutes. The quality and quantity of RNA was analyzed by gel electrophoresis and spectrophotometrically. The RNA was hybridized to the Human Genome U133A 2.0 Array (Affymetrix) and scanned with a GeneChip G3000 scanner (Affymetrix). The microarray experiments were performed in three batches. Although this was done at the same microarray facility using arrays from the same production batch, there were still noticeable batch effects. To normalize the measurements, we used a normalization method which removes hybridization, amplification and array location effects . Afterwards, probe sets which did not show significant differences in expression between arrays (std < 0.4) were removed. The remaining 1653 probe sets (out of 22,277 probe sets) were used for further analysis. To determine the most significant probe sets with respect to age, we determined a p-value for each gene by permuting F-test scores. In total, 105 permutations were performed for each gene. To adjust for multiple testing, we calculated a false discovery rate.
Quantitative polymerase chain reaction (qPCR)
To investigate gene expression, hMSCs were seeded at 1000 cells/cm2 and freshly isolated rat MSCs were used, upon reaching near confluence RNA was isolated using an RNeasy mini kit (Qiagen) and DNase treated on column with 10U RNase free DNase I (Gibco) at 37 Â°C for 30 minutes. DNase was inactivated at 72 Â°C for 15 minutes. The quality and quantity of RNA was analyzed using gel electrophoresis and spectrophotometrically. The iScript cDNA synthesis kit (Bio-rad) was used according to the manufacturer's protocol to synthesize first strand complementary DNA (cDNA) from 1Âµg RNA. qPCR was carried out on a iQâ„¢5 Real-Time PCR Detection System (Bio-Rad) using 1Âµl of 1x, 10x or 100x diluted cDNA, 500nM forward primers, 500nM reverse primers, 2x iQ SYBR Green Supermix (Bio-rad). For the gene expression analysis of AUTS2, DLK1 and JAG2, the forward and reverse primers were substituted for 1Âµl gene-specific RTÂ² qPCR Primers (SABiosciences). The remaining primer sequences and PCR conditions are depicted in Error: Reference source not found, as a reference gene GAPDH was used for hMSCs and Î²-actin for rat MSCs. Primer efficiencies were determined and gene expression was calculated and normalized to the average gene expression according to the method of Pfaffl et al. .
hMSCS were cultured in the presence or absence of bFGF (1 ng/mL) up to passage 2 and telomere lengths were determined by qPCR as described by Cawthon et al. . Briefly, DNA and RNA were isolated at the same time using Allprep DNA/RNA mini kit (Qiagen). DNA concentrations were determined spectrophotometrically and 20 ng DNA with a total volume of 8 ÂµL was used to carry out qPCR. Per sample, with a final volume of 25 ÂµL, 5 ÂµL betaine (Sigma), 250 nM Telomere forward primers, 250 nM Telomere reverse primers, 250 nM Î²-globin forward primers, 250 nM Î²-globin reverse primers, 10 ÂµL Sybr Green Supermix (Bio-rad) were added. qPCR was performed on a iQâ„¢5 Real-Time PCR Detection System (Bio-Rad). The thermal cycling profile was Stage 1: 15 min at 95Â°C; Stage 2: 2 cycles of 15 s at 94Â°C, 15 s at 49Â°C; and Stage 3: 32 cycles of 15 s at 94Â°C, 10 s at 62Â°C, 15 s at 74Â°C with signal acquisition, 10 s at 84Â°C, 15 s at 88Â°C with signal acquisition. The primers were designed so that 74Â°C reads provided the Ct values for the amplification of the telomere template (in early cycles when the Î²-globin signal is still at baseline) and the 88Â°C reads provided the Ct values for the amplification of the Î²-globin template (at this temperature there is no signal from the telomere PCR product, because it is fully melted). Primer sequences can be found in Error: Reference source not found. Ct values of the samples were related to standard curves for telomere and the internal reference gene, serial dilutions (3-fold) in a range of 150-1.85 ng/ÂµL were prepared from a mixture of all DNA samples. Telomere to reference gene (T/S) ratios were calculated by dividing T, the number of nanograms of the Standard DNA that matches the experimental sample for copy number of the telomere template, by S, the number of nanograms of the standard DNA that matches the experimental sample for copy number of the reference gene Î²-globin.
Regression analysis and Pearson correlations were used to analyze correlations between donor age and biological parameters and gene expression. In order to exclude gender and location of aspiration as confounding factors () and to analyze if the combinations of multiple genes gave a more accurate description of the age of the donor (, standard multiple regressions were used. One-way ANOVA was used to determine significant differences in ALP expression after treatment with dex, follistatin or activinA, Tukey was used for post-hoc testing. A value of p < 0.05 was considered significant.