Top biomarker list.

<p>Left panel, top 10 biomarkers for discriminating SCs from PCs. Right panel, top 10 biomarkers for discriminating iPSCs from ESCs. Please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056095#pone.0056095.s002" target="_blank">Table S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056095#pone.0056095.s003" target="_blank">S3</a>, and S4 for complete list used in this study.</p

Overall methylation profiling of three cell types.

<p>All methylation sites measured by microarray were used to profile the overall methylation patterns of the three cell types, iPSCs, ESCs, and SCs. A, unsupervised clustering analysis revealed that SCs were separated from PCs (iPSCs and ESCs). In the PCs subgroup, most ESCs were separated from iPSCs. B, Correspondence analysis classified three cell types, SCs, iPSCs, and ESCs. SCs and PCs were separated in first component while most of ESCs and iPSCs were separated in second component. C, iPSCs and ESCs were further classified by correspondence analysis in 3D space. For visualization purposes, only one subset of data was shown here.</p

A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells

<div><p>Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.</p> </div

Our system works accurately with raw data.

<p>Our system reaches the similar discriminating power as that with normalized data.</p

The discriminating system performs precisely on converted data.

<p>A, a high correction relationship exists between methylation percentage measured from sequencing and the beta value measured from Illumina microarray. B, Our system discriminates the three cell types with high accuracy with converted data. For visualization purposes, only percentage of NNET was shown here due to its similarity with SVM and the high correlation between accuracy percentage and kappa. This practice was also applied to following figures in this study.</p

SZ34 inhibits the proteolytic cleavage of pVWF by rADAMTS13 under shear stress.

<p>(A) Purified pVWF (150 nM) was pre-incubated with SZ34 (0–200 µg/ml) for 30 min at 37°C, and then incubated with 50 nM rADAMTS13. After 5 min of vortexing at 2,500 rpm on a mini vortexer, the 350 kDa cleavage products were visualized by 5% SDS-PAGE under non-reducing conditions and Western blot analysis. 1C1E7 (an anti-VWF D'D3 mAb), SZ129 (an anti-VWF A1 mAb) and SZ123 (an anti-VWF A3 mAb) were used as negative controls. (B) Changes in the cleavage products detected relative to that observed in the absence of mAbs were determined under shear stress by densitometry. The extent of cleavage was analyzed by detection of the intensity of the 350 kDa cleavage products. Results represent the mean ± standard deviation of four independent experiments.</p

Kinetics of SZ34 and SZ29 interactions with native or denatured pVWF and various recombinant VWF fragments.

<p>Data represent mean ± SD.</p

SZ34 inhibits the proteolysis of VWF multimers by ADAMTS13 under shear stress.

<p>Purified pVWF (150 nM) was pre-incubated with SZ34 (0–200 µg/ml) for 30 min at 37°C, and then incubated with 50 nM rADAMTS13. After 5 min of vortexing at 2,500 rpm on a mini vortexer, VWF multimers were separated by 1.5% agarose gel electrophoresis and immunologic analysis. A representative image of 4 independent experiments is shown.</p

Summary of 8 mAbs to VWF and their effects on VWF proteolysis by ADAMTS13.

<p>Summary of 8 mAbs to VWF and their effects on VWF proteolysis by ADAMTS13.</p

Comparison of the binding activity of SZ34 to native and denatured pVWF using Western blot in combination with ELISA based on polystyrene microspheres.

<p>SZ34 (20 µg/ml) was coated on polystyrene microspheres, then incubated with native pVWF or denatured pVWF (100 nM). The denatured pVWF was obtained by thermal treatment (20 min at 80°C) or treatment with 1.5 M guanidine-HCl (2 h at 37°C) of native pVWF. Bound VWF was separated on 6% SDS-PAGE in reducing conditions, followed by Western blotting with SZ34. SZ129 (anti-VWF A1) was a control as a mAb with a linear epitope. The figure is representative of four separate experiments.</p