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Quantitative methods for assessing differentiative potency of adipose-derived stem/stromal cells may lead to improved clinical application of this multipotent stem cell, by advancing our understanding of specific processes such as adipogenic differentiation. Conventional cell staining methods are used to determine the formation of adipose areas during adipogenesis as a qualitative representation of adipogenic potency. Staining methods such as oil-red-O are quantifiable using absorbance measurements, but these assays are time and material consuming. Detection methods for cell characteristics using advanced image analysis by machine learning are emerging. Here, live-cell imaging was combined with a deep learning-based detection tool to quantify the presence of adipose areas and lipid droplet formation during adipogenic differentiation of adipose-derived stem/stromal cells. Different detection masks quantified adipose area and lipid droplet formation at different time points indicating kinetics of adipogenesis and showed differences between individual donors. Whereas CEBPA and PPARG expression seems to precede the increase in adipose area and lipid droplets, it might be able to predict expression of ADIPOQ. The applied method is a proof of concept, demonstrating that deep learning methods can be used to investigate adipogenic differentiation and kinetics in vitro using specific detection masks based on algorithm produced from annotation of image data. [ABSTRACT FROM AUTHOR]

Future studies in this area are anticipated and will hopefully determine whether rare CD34 SP + sp stem cell clones with lymphoma mutations exists in patients with lymphoma, and whether they have an impact on clinical course and treatment response. Patients were included if they had a diagnosis of lymphoma, were intended to undergo ASCT, had a sufficient stem cell harvest and had at least one additional vial of their stem cell product available for research purposes. However, another possibility is that the methods used in this report (detection limit of VAF >2%, hence able to identify mutations in four out of a 100 CD34 SP + sp cells) are not sensitive enough to detect possible B-cell lymphoma CD34 SP + sp stem cells. [Extracted from the article]

Several single nucleotide polymorphisms (SNPs) have been identified in the β2-adrenergic receptor gene (ADRB2). By the use of five SNPs (G46A, C79G, C491T, C523A, G1053C) for identification of ADRB2 haplotypes in 814 Danish Caucasians, we investigated whether ADRB2 haplotypes are associated with body mass index (BMI). The SNPs showed organization into 13 distinct haplotypes and 41 haplotype pairs. The study identified four common haplotypes: ACCCC (10.1 ± 0.3 %), ACCCG (27.9 ± 0.3 %), GCCAC (10.8 ± 0.1 %) and GGCCG (41.0 ± 0.2 %) (frequencies (SD), seen in 91 % of the population. In the total population (mean age ± SD: 50 ± 16 years), BMI was not related to haplotype pairs, individual SNPs or allelic haplotypes. However, in subjects < 50 years ( N = 356, 36 ± 8 years) BMI levels varied significantly between pairs of major haplotype groups ( p = 0.014) but were not related to individual SNPs. In subjects < 37 years, the haplotype pair homozygote for the Gly16 and Glu27 amino acid variants (GGCCG/GGCCG) had a higher frequency of lean subjects (BMI ≤ 25 kg/m2) compared with the GCCAC/GGCCG pair (73% versus 35%, odds ratio with 95% confidence interval: 4.95 (1.50–16.38). In conclusion, the haplotype analysis clearly revealed the prevalence of four major ADRB2 haplotypes in Caucasians. The results suggest that unique interactions in specific haplotype pairs rather than individual SNPs may affect BMI and that this effect of ADRB2 haplotypes is blunted by age-related factors. [ABSTRACT FROM AUTHOR]