Human mesenchymal stem cells retain multilineage differentiation capacity including neural marker expression after extended in vitro expansion

The suitability of human mesenchymal stem cells (hMSCs) in regenerative medicine relies on retention of their proliferative expansion potential in conjunction with the ability to differentiate toward multiple lineages. Successful utilisation of these cells in clinical applications linked to tissue regeneration requires consideration of biomarker expression, time in culture and donor age, as well as their ability to differentiate towards mesenchymal (bone, cartilage, fat) or non-mesenchymal (e.g., neural) lineages. To identify potential therapeutic suitability we examined hMSCs after extended expansion including morphological changes, potency (stemness) and multilineage potential. Commercially available hMSC populations were expanded in vitro for > 20 passages, equating to > 60 days and > 50 population doublings. Distinct growth phases (A-C) were observed during serial passaging and cells were characterised for stemness and lineage markers at representative stages (Phase A: P+5, approximately 13 days in culture; Phase B: P+7, approximately 20 days in culture; and Phase C: P+13, approximately 43 days in culture). Cell surface markers, stem cell markers and lineage-specific markers were characterised by FACS, ICC and Q-PCR revealing MSCs maintained their multilineage potential, including neural lineages throughout expansion. Co-expression of multiple lineage markers along with continued CD45 expression in MSCs did not affect completion of osteogenic and adipogenic specification or the formation of neurospheres. Improved standardised isolation and characterisation of MSCs may facilitate the identification of biomarkers to improve therapeutic efficacy to ensure increased reproducibility and routine production of MSCs for therapeutic applications including neural repair.

Methylenetetrahydrofolate reductase CpG islands: Epigenotyping

Background Determination of the differential DNA methylation patterns of methylenetetrahydrofolate reductase (MTHFR) that are associated with differential MTHFR activity is important to understand the pathogenesis of ischemic stroke. However, to date, no data are available on the differential DNA methylation profiles of Kelantanese Malays. Therefore, we developed a rapid and efficient serial pyrosequencing assay to determine differential DNA methylation profiles of MTHFR, which help to further our understanding of the pathogenesis of ischemic stroke. The developed assay also served as the validation platform for our previous computational epigenetic research on MTHFR. Methods Polymerase chain reaction primers were designed and validated to specifically amplify the cytosine that is followed by guanine residues (CpGs) A and B regions. Prior epigenotyping on 110 Kelantanese Malays, the serial pyrosequencing assays for the CpGs A and B regions were validated using five validation controls. The mean values of the DNA methylation profiles of CpGs A and B were calculated. Results The mean DNA methylation levels for CpGs A and B were 0.984 ± 0.582 and 2.456 ± 1.406, respectively. The CpGs 8 and 20 showed the highest (5.581 ± 4.497) and the lowest (0.414 ± 2.814) levels of DNA methylation at a single-base resolution. Conclusion We have successfully developed and validated a pyrosequencing assay that is fast and can yield high-quality pyrograms for DNA methylation analysis and is therefore applicable to high throughput study. Using this newly developed pyrosequencing assay, the MTHFR DNA methylation profiles of 110 Kelantanese Malays were successfully determined. It also validated our computational epigenetic research on MTHFR.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.