Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

BackgroundHuman-induced pluripotent stem cells (hiPSCs) show a great promise as a renewable source of cells with broad biomedical applications. Since insulin has been used in the maintenance of hiPSCs, in this study we explored the role of insulin in culture of these cells.MethodsWe report conditions for insulin starvation and stimulation of hiPSCs. Crystal violet staining was used to study the adhesion and proliferation of hiPSCs. Apoptosis and cell cycle assays were performed through flow cytometry. Protein arrays were used to confirm phosphorylation targets, and mRNA sequencing was used to evaluate the effect of transcriptome.ResultsInsulin improved the seeding and proliferation of hiPSCs. We also observed an altered cell cycle profile and increase in apoptosis in hiPSCs in the absence of insulin. Furthermore, we confirmed phosphorylation of key components of insulin signaling pathway in the presence of insulin and demonstrated the significant effect of insulin on regulation of the mRNA transcriptome of hiPSCs.ConclusionInsulin is a major regulator of seeding, proliferation, phosphorylation and mRNA transcriptome in hiPSCs. Collectively, our work furthers our understanding of human pluripotency and paves the way for future studies that use hiPSCs for modeling genetic ailments affecting insulin signaling pathways.

Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.

The study of complex cardiovascular disease (CVD) has been hampered by the lack of appropriate human cellular model systems. In response, the NHBLI sponsored the NextGen Consortium, which encompasses 9 independent efforts spanning the portfolio of NHLBI related phenotypes. The goals of the consortium include: 1. To develop and improve methods for large-scale production and characterization of induced pluripotent stem cell (iPSC) models for CVD; 2. To create a resource of iPSC lines from a large number of phenotypically and genotypically characterized individuals. Our GENESiPS project is focused on insulin resistance (IR), a condition that affects 25-33% of the US population with serious health consequences including risk of type II diabetes and CVD. Although much is known about the physiological changes occurring during IR, little is known about the molecular pathways that drive the appearance of IR. Certain mature cell types as adipocytes, endothelial cells and skeletal muscle cells have been associated with the origin, maintenance and progression of IR. IPSCs offer an unprecedented opportunity of modeling human disease in vitro. We have created iPSC lines on insulin resistant and insulin sensitive patient groups with prior GWAS genotyping. Differentiation of these iPSCs to relevant cell types is providing the opportunity to correlate insulin sensitivity and high-density genetic variation data with specific cell-based profiling. We will validate our in vitro model and study the molecular pathways that define IR and its relationship to endothelial dysfunction. Relevant to the larger scientific community the establishment of iPSC lines on over 150 individuals (3 to 6 clones per patient) that reflect the range of insulin resistance in the general population. The iPSC lines were created from erythroblasts using the non-integrative Sendai virus system, passaged to allow clearance of Sendai virus and growth in feeder free conditions. The lines have been extensively characterized for markers of pluripotency (Tra1-60), sample identity and genomic integrity. Through the NextGen consortium, these lines, as well as phenotypic and genome-wide genotyping data will be available to qualified investigators.

The derivation of pluripotent stem cells from somatic tissues has provided researchers with a source of patient-specific stem cells. The potential applications of this technology are truly momentous, and include cellular modeling of disease processes, drug discovery, and cell-based therapy. Here, we review the use of induced pluripotent stem cells (iPSCs) to study CNS disease. Since the iPSC field is still in its infancy, we also discuss some of the challenges that will need to be overcome before the potential of this technology to study and to treat neurological and psychiatric disorders can be fully harnessed.

The commitment of multi-potent cortical progenitors to a neuronal fate depends on the transient induction of the basic-helix-loop-helix (bHLH) family of transcription factors including Neurogenin 1 (Neurog1). Previous studies have focused on mechanisms that control the expression of these proteins while little is known about whether their pro-neural activities can be regulated by kinase signaling pathways. Using primary cultures and ex vivo slice cultures, here we report that both the transcriptional and pro-neural activities of Neurog1 are regulated by extracellular signal-regulated kinase (ERK) 5 signaling in cortical progenitors. Activation of ERK5 potentiated, while blocking ERK5 inhibited Neurog1-induced neurogenesis. Furthermore, endogenous ERK5 activity was required for Neurog1-initiated transcription. Interestingly, ERK5 activation was sufficient to induce Neurog1 phosphorylation and ERK5 directly phosphorylated Neurog1 in vitro. We identified S179/S208 as putative ERK5 phosphorylation sites in Neurog1. Mutations of S179/S208 to alanines inhibited the transcriptional and pro-neural activities of Neurog1. Our data identify ERK5 phosphorylation of Neurog1 as a novel mechanism regulating neuronal fate commitment of cortical progenitors.

All-trans retinoic acid (RA) is a differentiation factor in many tissues. However, its role in astrogliogenesis has not been extensively studied. Here, we investigated the effect of RA on the regulation of astrogliogenesis at different cortical developmental stages. We prepared rat cortical progenitor cells from embryonic day (E) 13 and E17, which correspond to the beginning of neurogenic and astrogliogenic periods, respectively. Surprisingly, RA promoted astrogliogenesis at E17 but inhibited astrogliogenesis induced by ciliary neurotrophic factor (CNTF) at E13. The inhibitory effect of RA on astrogliogenesis at E13 was not due to premature commitment of progenitors to a neuronal or oligodendroglial lineage. Rather, RA retained more progenitors in a proliferative state. Furthermore, RA inhibition of astrogliogenesis at E13 was independent of STAT3 signaling and required the function of the alpha and beta isoforms of the RA receptors (RAR). Moreover, the differential response of E13 and E17 progenitors to RA was due to differences in the intrinsic properties of these cells that are preserved in vitro. The inhibitory effect of RA on cytokine-induced astrogliogenesis at E13 may contribute to silencing of any potential precocious astrogliogenesis during the neurogenic period.

Multipotent cortical progenitor cells differentiate into neurons and glial cells during development; however, mechanisms governing the specification of progenitors to a neuronal fate are not well understood. Although both extrinsic and intrinsic factors regulate this process, little is known about kinase signaling mechanisms that direct neuronal fate. Here, we report that extracellular signal-regulated kinase (ERK) 5 is expressed and active in proliferating cortical progenitors. Lentiviral gene delivery of a dominant negative ERK5 or dominant negative MAP kinase kinase 5 reduced the number of neurons generated from rat cortical progenitor cells in culture, whereas constitutive activation of ERK5 increased the production of neurons. Furthermore, when cortical progenitor cells were treated with ciliary neurotrophic factor, which induces precocious glial differentiation, ERK5 activation still promoted neuronal fate while suppressing glial differentiation. Our data also indicate that ERK5 does not directly regulate proliferation or apoptosis of cultured cortical progenitors. We conclude that ERK5 is necessary and sufficient to stimulate the generation of neurons from cortical progenitors. These results suggest a previously uncharacterized function for ERK5 signaling during brain development and raise the interesting possibility that extrinsic factors may instruct cortical progenitors to become neurons by activating the ERK5 pathway.

Cyclic AMP is a positive regulator of synaptic plasticity and is required for several forms of hippocampus-dependent memory including recognition memory. The type I adenylyl cyclase, Adcy1 (also known as AC1), is crucial in memory formation because it couples Ca 2+ to cyclic AMP increases in the hippocampus. Because Adcy1 is neurospecific, it is a potential pharmacological target for increasing cAMP specifically in the brain and for improving memory. We have generated transgenic mice that overexpress Adcy1 in the forebrain using the Camk2a (also known as α- CaMKII ) promoter. These mice showed elevated long-term potentiation (LTP), increased memory for object recognition and slower rates of extinction for contextual memory. The increase in recognition memory and lower rates of contextual memory extinction may be due to enhanced extracellular signal–related kinase (ERK)/mitogen-activated protein kinase (MAPK) signaling, which is elevated in mice that overexpress Adcy1.

Combining genetic inheritance information, for both molecular profiles and complex traits, is a promising strategy not only for detecting quantitative trait loci (QTLs) for complex traits but for understanding which genes, pathways, and biological processes are also under the influence of a given QTL. As a primary step in determining the feasibility of such an approach in humans, we present the largest survey to date, to our knowledge, of the heritability of gene-expression traits in segregating human populations. In particular, we measured expression for 23,499 genes in lymphoblastoid cell lines for members of 15 Centre d'Etude du Polymorphisme Humain (CEPH) families. Of the total set of genes, 2,340 were found to be expressed, of which 31% had significant heritability when a false-discovery rate of 0.05 was used. QTLs were detected for 33 genes on the basis of at least one P value <.000005. Of these, 13 genes possessed a QTL within 5 Mb of their physical location. Hierarchical clustering was performed on the basis of both Pearson correlation of gene expression and genetic correlation. Both reflected biologically relevant activity taking place in the lymphoblastoid cell lines, with greater coherency represented in Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways than in Gene Ontology database pathways. However, more pathway coherence was observed in KEGG pathways when clustering was based on genetic correlation than when clustering was based on Pearson correlation. As more expression data in segregating populations are generated, viewing clusters or networks based on genetic correlation measures and shared QTLs will offer potentially novel insights into the relationship among genes that may underlie complex traits.

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using 'exon' and 'tiling' arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Gene expression profiling is rapidly becoming a mainstay of functional genomic studies. However, there have been relatively few studies of how the data from expression profiles integrate with more classic approaches to examine gene expression. This study used gene expression profiling: of a portion of the genome of Saccharomyces cerevisiae to explore the impact of blocks in the isoprenoid biosynthetic pathway on the expression of genes and the regulation of this pathway. Approximately 50% of the genes whose expression was altered by blocks in isoprenoid biosynthesis were genes previously known to participate in the pathway. In contrast to this simple correspondence, the regulatory patterns revealed by different blocks, and in particular by antifungal azoles, was complex in a manner not anticipated by earlier studies.