Functional modules distinguish human induced pluripotent stem cells from embryonic stem cells

Functional Annotation of Caenorhabditis elegans Genes by Analysis of Gene Co-Expression Networks

Caenorhabditis elegans (C. elegans) is a well-characterized metazoan, whose transcriptome has been profiled in different tissues, development stages, or other conditions. Large-scale transcriptomes can be reused for gene function annotation through systematic analysis of gene co-expression relationships. We collected 2101 microarray data from National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO), and identified 48 modules of co-expressed genes that correspond to tissues, development stages, and other experimental conditions. These modules provide an overview of the transcriptional organizations that may work under different conditions. By analyzing higher-order module networks, we found that nucleus and plasma membrane modules are more connected than other intracellular modules. Module-based gene function annotation may help to extend the candidate cuticle gene list. A comparison with other published data validates the credibility of our result. Our findings provide a new source for future gene discovery in C. elegans.

Genetically unmatched human iPSC and ESC exhibit equivalent gene expression and neuronal differentiation potential

The potential uniformity between differentiation and therapeutic potential of human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) remains debatable. We studied the gene expression profiles, pathways analysis and the ability to differentiated into neural progenitor cells (NPCs) and motor neurons (MNs) of genetically unmatched integration-free hiPSC versus hESC to highlight possible differences/similarities between them at the molecular level. We also provided the functional information of the neurons derived from the different hESCs and hiPSCs lines using the Neural Muscular Junction (NMJ) Assay. The hiPSC line was generated by transfecting human epidermal fibroblasts (HEF) with episomal DNAs expressing Oct4, Sox2, Klf4, Nanog, L-Myc and shRNA against p53. For the hESCs line, we used the NIH-approved H9 cell line. Using unsupervised clustering both hESCs and hiPSCs were clustered together implying homogeneous genetic states. The genetic profiles of hiPSCs and hESCs were clearly similar but not identical. Collectively, our data indicate close molecular similarities between genetically unmatched hESCs and hiPS in term of gene expression, and signaling pathways. Moreover, both cell types exhibited similar cholinergic motor neurons differentiation potential with marked ability of the differentiated hESCs and hiPSCs-derived MNs to induce contraction of myotubes after 4 days of co-culture.

Characterization of transcriptional modules related to fibrosing-NAFLD progression

Based on the severity of liver fibrosis, low or high-risk profile of developing end-stage liver disease was present in nonalcoholic fatty liver disease (NAFLD). However, the mechanisms inducing transition from mild to advanced NAFLD are still elusive. We performed a system-level study on fibrosing-NAFLD by weighted gene co-expression network analysis (WGCNA) to identify significant modules in the network, and followed by functional and pathway enrichment analyses. Moreover, hub genes in the module were analyzed by network feature selection. As a result, fourteen distinct gene modules were identified, and seven modules showed significant associations with the status of NAFLD. Module preservation analysis confirmed that these modules can also be found in diverse independent datasets. After network feature analysis, the magenta module demonstrated a remarkably correlation with NAFLD fibrosis. The top hub genes with high connectivity or gene significance in the module were ultimately determined, including LUM, THBS2, FBN1 and EFEMP1. These genes were further verified in clinical samples. Finally, the potential regulators of magenta module were characterized. These findings highlighted a module and affiliated genes as playing important roles in the regulation of fibrosis in NAFLD, which may point to potential targets for therapeutic interventions.

Pluripotent stem cells: the last 10 years

Pluripotent stem cells (PSCs) can differentiate into virtually any cell type in the body, making them attractive for both regenerative medicine and drug discovery. Over the past 10 years, technological advances and innovative platforms have yielded first-in-man PSC-based clinical trials and opened up new approaches for disease modeling and drug development. Induced PSCs have become the foremost alternative to embryonic stem cells and accelerated the development of disease-in-a-dish models. Over the years and with each new discovery, PSCs have proven to be extremely versatile. This review article highlights key advancements in PSC research, from 2006 to 2016, and how they will guide the direction of the field over the next decade.

A human haploid gene trap collection to study lncRNAs with unusual RNA biology

Many thousand long non-coding (lnc) RNAs are mapped in the human genome. Time consuming studies using reverse genetic approaches by post-transcriptional knock-down or genetic modification of the locus demonstrated diverse biological functions for a few of these transcripts. The Human Gene Trap Mutant Collection in haploid KBM7 cells is a ready-to-use tool for studying protein-coding gene function. As lncRNAs show remarkable differences in RNA biology compared to protein-coding genes, it is unclear if this gene trap collection is useful for functional analysis of lncRNAs. Here we use the uncharacterized LOC100288798 lncRNA as a model to answer this question. Using public RNA-seq data we show that LOC100288798 is ubiquitously expressed, but inefficiently spliced. The minor spliced LOC100288798 isoforms are exported to the cytoplasm, whereas the major unspliced isoform is nuclear localized. This shows that LOC100288798 RNA biology differs markedly from typical mRNAs. De novo assembly from RNA-seq data suggests that LOC100288798 extends 289kb beyond its annotated 3' end and overlaps the downstream SLC38A4 gene. Three cell lines with independent gene trap insertions in LOC100288798 were available from the KBM7 gene trap collection. RT-qPCR and RNA-seq confirmed successful lncRNA truncation and its extended length. Expression analysis from RNA-seq data shows significant deregulation of 41 protein-coding genes upon LOC100288798 truncation. Our data shows that gene trap collections in human haploid cell lines are useful tools to study lncRNAs, and identifies the previously uncharacterized LOC100288798 as a potential gene regulator.

Quantitative Identification of Compound-Dependent On-Modules and Differential Allosteric Modules From Homologous Ischemic Networks

Module‐based methods have made much progress in deconstructing biological networks. However, it is a great challenge to quantitatively compare the topological structural variations of modules (allosteric modules [AMs]) under different situations. A total of 23, 42, and 15 coexpression modules were identified in baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) in a global anti‐ischemic mice network, respectively. Then, we integrated the methods of module‐based consensus ratio (MCR) and modified Z_summary module statistic to validate 12 BA, 22 JA, and 8 UA on‐modules based on comparing with vehicle. The MCRs for pairwise comparisons were 1.55% (BA vs. JA), 1.45% (BA vs. UA), and 1.27% (JA vs. UA), respectively. Five conserved allosteric modules (CAMs) and 17 unique allosteric modules (UAMs) were identified among these groups. In conclusion, module‐centric analysis may provide us a unique approach to understand multiple pharmacological mechanisms associated with differential phenotypes in the era of modular pharmacology.

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Module-based network analysis of diverse pharmacological mechanisms is critical to systematically understand combination therapies and disease outcomes. We first constructed drug-target ischemic networks in baicalin, jasminoidin, ursodeoxycholic acid, and their combinations baicalin and jasminoidin as well as jasminoidin and ursodeoxycholic acid groups and identified modules using the entropy-based clustering algorithm. The modules 11, 7, 4, 8 and 3 were identified as baicalin, jasminoidin, ursodeoxycholic acid, baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid-emerged responsive modules, while 12, 8, 15, 17 and 9 were identified as disappeared responsive modules based on variation of topological similarity, respectively. No overlapping differential biological processes were enriched between baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid pure emerged responsive modules, but two were enriched by their co-disappeared responsive modules including nucleotide-excision repair and epithelial structure maintenance. We found an additive effect of baicalin and jasminoidin in a divergent pattern and a synergistic effect of jasminoidin and ursodeoxycholic acid in a convergent pattern on "central hit strategy" of regulating inflammation against cerebral ischemia. The proposed module-based approach may provide us a holistic view to understand multiple pharmacological mechanisms associated with differential phenotypes from the standpoint of modular pharmacology.

Differential variability and correlation of gene expression identifies key genes involved in neuronal differentiation

Background

Understanding the dynamics of stem cell differentiation processes at the molecular level is a central challenge in developmental biology and regenerative medicine. Although the dynamic behaviors of differentiation regulators have been partially characterized, the architecture regulating the underlying molecular systems remains unclear.

Result

System-level analysis of transcriptional data was performed to characterize the dynamics of molecular networks in neural differentiation of stem cells. Expression of a network module of genes tightly co-expressed in mouse embryonic stem (ES) cells fluctuated greatly among cell populations before differentiation, but became stable following neural differentiation. During the neural differentiation process, genes exhibiting both differential variance and differential correlation between undifferentiated and differentiating states were related to developmental functions such as body axis development, neuronal movement, and transcriptional regulation. Furthermore, these genes were genetically associated with neuronal differentiation, providing support for the idea they are not only differentiation markers but could also play important roles in neural differentiation. Comparisons with transcriptional data from human induced pluripotent stem (iPS) cells revealed that the system of genes dynamically regulated during neural differentiation is conserved between mouse and human.

Conclusions

The results of this study provide a systematic analytical framework for identifying key genes involved in neural differentiation by detecting their dynamical behaviors, as well as a basis for understanding the dynamic molecular mechanisms underlying the processes of neural differentiation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0231-6) contains supplementary material, which is available to authorized users.

Computational Models for Transplant Biomarker Discovery

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

Systematically dissecting the global mechanism of miRNA functions in mouse pluripotent stem cells

Background

MicroRNAs (miRNAs) critically modulate stem cell properties like pluripotency, but the fundamental mechanism remains largely unknown.

Method

This study systematically analyzes multiple-omics data and builds a systems physical network including genome-wide interactions between miRNAs and their targets to reveal the systems mechanism of miRNA functions in mouse pluripotent stem cells.

Results

Globally, miRNAs directly repress the pluripotent core factors during differentiation state. Surprisingly, during the pluripotent state, the top important miRNAs do not directly regulate the pluripotent core factors as previously thought, but they only directly target the pluripotent signal pathways and directly repress developmental processes. Furthermore, at the pluripotent state miRNAs predominately repress DNA methyltransferases, the core enzymes for DNA methylation. The decreasing methylation repressed by miRNAs in turn activates the top miRNAs and pluripotent core factors, creating an active circuit system to modulate pluripotency.

Conclusion

MiRNAs vary their functions with stem cell states. While miRNAs directly repress pluripotent core factors to facilitate differentiation during the differentiation state, they also help stem cells to maintain pluripotency by activating pluripotent cores through directly repressing DNA methylation systems and primarily inhibiting development in the pluripotent state.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1706-y) contains supplementary material, which is available to authorized users.

Dynamics of social network structure for Alzheimer and Lymphoma scientific communities

Abstract

It is generally assumed that sociology affects scientific progress but specific examples of this assumption are hard to find. We examined this hypothesis by comparing the social network structure and its dynamics over the last 16 years, for two common human diseases; Alzheimer’s disease, for which there has been very little therapeutic progress, and Lymphoma, were there has been significant therapeutic progress. We found that the Alzheimer’s research community is more interlinked (‘dense’) and more ‘cliquish’ than that of Lymphoma and suggest that this could affect its scientific progress.

Reviewers

This article was reviewed by Vladimir Kuznetsov and Anthony Almudevar

Discovery of consensus gene signature and intermodular connectivity defining self-renewal of human embryonic stem cells

Molecular markers defining self-renewing pluripotent embryonic stem cells (ESCs) have been identified by relative comparisons between undifferentiated and differentiated cells. Most of analysis has been done under a specific differentiation condition that may present significantly different molecular changes over others. Therefore, it is currently unclear if there are true consensus markers defining undifferentiated human ESCs (hESCs). To identify a set of key genes consistently altered during differentiation of hESCs regardless of differentiation conditions, we have performed microarray analysis on undifferentiated hESCs (H1 and H9) and differentiated EBs and validated our results using publicly available expression array datasets. We constructed consensus modules by Weighted Gene Coexpression Network Analysis and discovered novel markers that are consistently present in undifferentiated hESCs under various differentiation conditions. We have validated top markers (downregulated: LCK, KLKB1, and SLC7A3; upregulated: RhoJ, Zeb2, and Adam12) upon differentiation. Functional validation analysis of LCK in self-renewal of hESCs using LCK inhibitor or gene silencing with siLCK resulted in a loss of undifferentiation characteristics-morphological change, reduced alkaline phosphatase activity, and pluripotency gene expression, demonstrating a potential functional role of LCK in self-renewal of hESCs. We have designated hESC markers to interactive networks in the genome, identifying possible interacting partners and showing how new markers relate to each other. Furthermore, comparison of these datasets with available datasets from induced pluripotent stem cells (iPSCs) revealed that the level of these newly identified markers was correlated to the establishment of iPSCs, which may imply a potential role of these markers in gaining of cellular potency.

Induced Pluripotent Stem Cells Derived from Alzheimer's Disease Patients: The Promise, the Hope and the Path Ahead

The future hope of generated induced pluripotent stem cells (iPS cells) from Alzheimer’s disease patients is multifold. Firstly, they may help to uncover novel mechanisms of the disease, which could lead to the development of new and unprecedented drugs for patients and secondly, they could also be directly used for screening and testing of potential new compounds for drug discovery. In addition, in the case of familial known mutations, these cells could be targeted by use of advanced gene-editing techniques to correct the mutation and be used for future cell transplantation therapies. This review summarizes the work so far in regards to production and characterization of iPS cell lines from both sporadic and familial Alzheimer’s patients and from other iPS cell lines that may help to model the disease. It provides a detailed comparison between published reports and states the present hurdles we face with this new technology. The promise of new gene-editing techniques and accelerated aging models also aim to move this field further by providing better control cell lines for comparisons and potentially better phenotypes, respectively.

Defining differentially methylated regions specific for the acquisition of pluripotency and maintenance in human pluripotent stem cells via microarray

Background

Epigenetic regulation is critical for the maintenance of human pluripotent stem cells. It has been shown that pluripotent stem cells, such as embryonic stem cells and induced pluripotent stem cells, appear to have a hypermethylated status compared with differentiated cells. However, the epigenetic differences in genes that maintain stemness and regulate reprogramming between embryonic stem cells and induced pluripotent stem cells remain unclear. Additionally, differential methylation patterns of induced pluripotent stem cells generated using diverse methods require further study.

Methodology

Here, we determined the DNA methylation profiles of 10 human cell lines, including 2 ESC lines, 4 virally derived iPSC lines, 2 episomally derived iPSC lines, and the 2 parental cell lines from which the iPSCs were derived using Illumina's Infinium HumanMethylation450 BeadChip. The iPSCs exhibited a hypermethylation status similar to that of ESCs but with distinct differences from the parental cells. Genes with a common methylation pattern between iPSCs and ESCs were classified as critical factors for stemness, whereas differences between iPSCs and ESCs suggested that iPSCs partly retained the parental characteristics and gained de novo methylation aberrances during cellular reprogramming. No significant differences were identified between virally and episomally derived iPSCs. This study determined in detail the de novo differential methylation signatures of particular stem cell lines.

Conclusions

This study describes the DNA methylation profiles of human iPSCs generated using both viral and episomal methods, the corresponding somatic cells, and hESCs. Series of ss-DMRs and ES-iPS-DMRs were defined with high resolution. Knowledge of this type of epigenetic information could be used as a signature for stemness and self-renewal and provides a potential method for selecting optimal pluripotent stem cells for human regenerative medicine.

Genetic background drives transcriptional variation in human induced pluripotent stem cells

Human iPS cells have been generated using a diverse range of tissues from a variety of donors using different reprogramming vectors. However, these cell lines are heterogeneous, which presents a limitation for their use in disease modeling and personalized medicine. To explore the basis of this heterogeneity we generated 25 iPS cell lines under normalised conditions from the same set of somatic tissues across a number of donors. RNA-seq data sets from each cell line were compared to identify the majority contributors to transcriptional heterogeneity. We found that genetic differences between individual donors were the major cause of transcriptional variation between lines. In contrast, residual signatures from the somatic cell of origin, so called epigenetic memory, contributed relatively little to transcriptional variation. Thus, underlying genetic background variation is responsible for most heterogeneity between human iPS cell lines. We conclude that epigenetic effects in hIPSCs are minimal, and that hIPSCs are a stable, robust and powerful platform for large-scale studies of the function of genetic differences between individuals. Our data also suggest that future studies using hIPSCs as a model system should focus most effort on collection of large numbers of donors, rather than generating large numbers of lines from the same donor.

Human induced pluripotent stem (hiPS) cells are a potentially powerful model system for studying human disease and development, and a resource for personalized medicine. However, it has been reported that hiPS cells exhibit substantial heterogeneity which could limit their use as model systems. Clearly, knowledge of the source of heterogeneity is key for deeper understanding of the use of human iPS cells for basic and therapeutic applications. One source of this heterogeneity has been presumed to be “memory” of the adult somatic cell from which the hIPS cells were derived, but the evidence to support this view is scant. We have generated a set of human iPS cells from a set of somatic cell types from different donors. Our study shows that cell lines from different somatic sources but from the same donor (i.e. with the same genome) are more similar than cell lines isolated from the same tissue type but from different donors. Once genetic changes are accounted for, all aspects of gene expression, including mRNA levels, splicing and imprinting are highly similar between iPS cells derived from different human tissues. Thus, most of the previously described transcriptional variation between cell lines is likely to be genetic in origin.

Gene co-expression analysis identifies common modules related to prognosis and drug resistance in cancer cell lines

To discover a common gene co-expression network in cancer cell, we applied weighted gene co-expression network analysis to transcriptional profiles of 917 cancer cell lines. Fourteen biologically meaningful modules were identified, including cytoskeleton, cell cycle, RNA splicing, signaling pathway, transcription, translation and others. These modules were robust in an independent human cancer microarray dataset. Furthermore, we collected 11 independent cancer microarray datasets, and correlated these modules with clinical outcome. Most of these modules could predict patient survival in one or more cancer types. Some modules were predictive of relapse, metastasis and drug resistance. Novel regulatory mechanisms were also implicated. In summary, our findings, for the first time, provide a modular map for cancer cell lines, new targets for therapy and modules for regulatory mechanism of cancer development and drug resistance.

A web-server of cell type discrimination system

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.

A panel of CpG methylation sites distinguishes human embryonic stem cells and induced pluripotent stem cells

Whether human induced pluripotent stem cells (hiPSCs) are epigenetically identical to human embryonic stem cells (hESCs) has been debated in the stem cell field. In this study, we analyzed DNA methylation patterns in a large number of hiPSCs (n = 114) and hESCs (n = 155), and identified a panel of 82 CpG methylation sites that can distinguish hiPSCs from hESCs with high accuracy. We show that 12 out of the 82 CpG sites were subject to hypermethylation in part by DNMT3B. Notably, DNMT3B contributes directly to aberrant hypermethylation and silencing of the signature gene, TCERG1L. Overall, we conclude that DNMT3B is involved in a wave of de novo methylation during reprogramming, a portion of which contributes to the unique hiPSC methylation signature. These 82 CpG methylation sites may be useful as biomarkers to distinguish between hiPSCs and hESCs.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several “-omic” layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several "-omic" layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.

A quantitative system for discriminating induced pluripotent stem cells, embryonic stem cells and somatic cells

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.

A poor imitation of a natural process: a call to reconsider the iPSC engineering technique

Reprogramming somatic cells into a pluripotent state is expected to initiate a new era in medicine. Because the precise underlying mechanism of reprogramming remains unclear, many efforts have been made to optimize induced pluripotent stem cell (iPSC) engineering. However, satisfactory results have not yet been attained. In this review, we focus on recent roadblocks in iPSC reprogramming engineering, such as the inefficiency of the process, tumorigenicity and heterogeneity of the generation. We conclude that cell reprogramming is a naturally occurring phenomenon rather than a biological technique. We will only be able to mimic the natural process of reprogramming when we fully understand its underlying mechanism. Finally, we highlight the alternative method of direct conversion, which avoids the use of iPSCs to generate cell materials for patient-specific cell therapy.

Does transcription factor induced pluripotency accurately mimic embryo derived pluripotency?

When Takahashi and Yamanaka first demonstrated that just four transcription factors could reprogram a fibroblast to a pluripotent state, the first wave of data to emerge focused on how similar these induced pluripotent stem cells (iPSCs) were to embryo-derived pluripotent stem cells (ESCs) [1]. The next wave of data focused on determining the degree of difference between iPSCs and ESCs [2]. Now the focus is on tweaking the process to generate iPSCs that are more similar to ESCs [3,4]. Because transcription factor based reprogramming allows for nearly any type of cell to be created from any donor cell, there is obviously enormous interest in this technique as a tool for both basic developmental biology and for clinical applications. In this review, I will attempt to summarize the data that serve to distinguish these types of pluripotent stem cells and speculate on the ramifications of any differences.

Induced pluripotent stem cells as tools for disease modelling and drug discovery in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder that leads to a progressive decline in a person's memory and ability to communicate and carry out daily activities. The brain pathology in AD is characterized by extensive neuronal loss, particularly of cholinergic neurons, intracellular neurofibrillary tangles composed of the tau protein (NFTs) and extracellular deposition of plaques composed of β-amyloid (Aβ), a cleavage product of the amyloid precursor protein (APP). These two insoluble protein aggregates are accompanied by a chronic inflammatory response and extensive oxidative damage. Whereas dys-regulation of APP expression or processing appears to be important for the familial, early-onset form of AD, controversy exists between the "Baptists" (in favour of Aβ) and the "Tauists" (in favour of tau) as to which of these two protein dysfunctions occur at the earliest stages or are the most important contributors to the disease process in sporadic AD. However, more and more "non-amyloid" and "non-tau" causes have been proposed, including, glycation, inflammation, oxidative stress and dys-regulation of the cell cycle. However, to get an insight into the ultimate cause of AD, and to prove that any drug target is valuable in AD, disease-relevant models giving insight into the pathogenic processes in AD are urgently needed. In the absence of a good animal model for sporadic AD, we propose in this review that induced pluripotent stem cells, derived from dermal fibroblasts of AD patients, and differentiated into cholinergic neurons, might be a promising novel tool for disease modelling and drug discovery for the sporadic form of AD.