Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells.

Steering the differentiation of induced pluripotent stem cells (iPSCs) toward specific cell types is crucial for patient-specific disease modeling and drug testing. This effort requires the capacity to predict and control when and how multipotent progenitor cells commit to the desired cell fate. Cell fate commitment represents a critical state transition or tipping point at which complex systems undergo a sudden qualitative shift. To characterize such transitions during iPSC to cardiomyocyte differentiation, we analyzed the gene expression patterns of 96 developmental genes at single-cell resolution. We identified a bifurcation event early in the trajectory when a primitive streak-like cell population segregated into the mesodermal and endodermal lineages. Before this branching point, we could detect the signature of an imminent critical transition: increase in cell heterogeneity and coordination of gene expression. Correlation analysis of gene expression profiles at the tipping point indicates transcription factors that drive the state transition toward each alternative cell fate and their relationships with specific phenotypic readouts. The latter helps us to facilitate small molecule screening for differentiation efficiency. To this end, we set up an analysis of cell population structure at the tipping point after systematic variation of the protocol to bias the differentiation toward mesodermal or endodermal cell lineage. We were able to predict the proportion of cardiomyocytes many days before cells manifest the differentiated phenotype. The analysis of cell populations undergoing a critical state transition thus affords a tool to forecast cell fate outcomes and can be used to optimize differentiation protocols to obtain desired cell populations

Consensus miRNA expression profiles derived from interplatform normalization of microarray data

Eukaryotic gene expression is controlled at the post-transcriptional level by small noncoding RNAs called microRNAs (miRNA). miRNAs play important roles during early development and participate in gene regulatory circuits in the cell. Different high-throughput expression analysis methods including microarrays, bead-based detection, and small RNA cloning have been applied to quantitatively detect miRNAs in various tissues, cell types, and biological conditions. High-throughput expression data was collected from public repositories and processed to create a database of miRNA expression profiles. Several commonly used normalization methods were compared to identify suitable methods for cross-platform comparison of high-throughput miRNA expression data. The database provides interlaboratory and interplatform validated reference expression levels for miRNAs. The normalized expression profiles were validated by querying for well-established features of miRNA expression. Firstly, expression profiles of several tissue-specific miRNAs showed good agreement between the database and previously reported profiles. We have also identified a set of miRNAs that are constitutively expressed across mammalian tissues. Secondly, we used the database to compare the expression patterns of miRNAs belonging to the let-7 family, where the divergence in expression patterns implies that they may have diversified functionally. Lastly, we compared expression profiles of intronic and clustered miRNAs. Expression profiles of intronic miRNAs and clustered miRNAs showed either very good, or in certain cases, very poor correlation with the host gene. Interplatform comparison of miRNA expression profiles thus provides a resource of consensus expression profiles that can be used in the future for studying miRNA function and regulation

Taking Systems Medicine to Heart.

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine

Abrogation of Rb Tumor Suppression Initiates GBM in Differentiated Astrocytes by Driving a Progenitor Cell Program.

Glioblastoma (GBM) remains lethal with no effective treatments. Despite the comprehensive identification of commonly perturbed molecular pathways, little is known about the disease\u27s etiology, particularly in early stages. Several studies indicate that GBM is initiated in neural progenitor and/or stem cells. Here, we report that differentiated astrocytes are susceptible to GBM development when initiated by perturbation of the RB pathway, which induces a progenitor phenotype. In vitro and in vivo inactivation of Rb tumor suppression (TS) induces cortical astrocytes to proliferate rapidly, express progenitor markers, repress differentiation markers, and form self-renewing neurospheres that are susceptible to multi-lineage differentiation. This phenotype is sufficient to cause grade II astrocytomas which stochastically progress to GBM. Together with previous findings, these results demonstrate that cell susceptibility to GBM depends on the initiating driver

Identification of Novel Targets for miR-29a Using miRNA Proteomics

<div><p>MicroRNAs (miRNAs) are short regulatory RNA molecules that interfere with the expression of target mRNA by binding to complementary sequences. Currently, the most common method for identification of targets of miRNAs is computational prediction based on free energy change calculations, target site accessibility and conservation. Such algorithms predict hundreds of targets for each miRNA, necessitating tedious experimentation to identify the few functional targets. Here we explore the utility of miRNA-proteomics as an approach to identifying functional miRNA targets. We used Stable Isotope Labeling by amino acids in cell culture (SILAC) based proteomics to detect differences in protein expression induced by the over-expression of miR-34a and miR-29a. Over-expression of miR-29a, a miRNA expressed in the brain and in cells of the blood lineage, resulted in the differential expression of a set of proteins. Gene Ontology based classification showed that a significant sub-set of these targets, including Voltage Dependent Anion Channel 1 and 2 (VDAC1 and VDAC2) and ATP synthetase, were mitochondrial proteins involved in apoptosis. Using reporter assays, we established that miR-29a targets the 3′ Untranslated Regions (3′ UTR) of VDAC1 and VDAC2. However, due to the limited number of proteins identified using this approach and the inability to differentiate between primary and secondary effects we conclude that miRNA-proteomics is of limited utility as a high-throughput alternative for sensitive and unbiased miRNA target identification. However, this approach was valuable for rapid assessment of the impact of the miRNAs on the cellular proteome and its biological role in apoptosis.</p> </div

Experimental schema for miRNA-proteomics.

<p>Cells were transfected with either miRNA expressing vector or LNA modified oligonucleotide against miRNA. The lysate mix was analyzed on LTQ-ORBITRAP mass spectrometer. The data was analyzed as mentioned in materials and methods. The quantification data was then compared with other miRNA-target identification methods.</p

Gene Ontology (GO) classification of miR-29a Upregulated and Downregulated Targets.

<p>Gene Ontology (GO) classification of miR-29a Upregulated and Downregulated Targets.</p

Differential Expression of VDAC1 by miR-29a.

<p>HEK293T cells were transfected with mock LNA, LNA modified anti-miR-29a, Vector or plasmid over-expressing miR-29a. (A) Immunoblotting was performed as mentioned in materials and methods and B) Band intensities quantified and normalized to loading control (GAPDH).</p

miR-29a targets 3′ untranslated region of VDAC1, 2 and 3.

<p>Binding pattern of miR-29a with the 3′UTR of the VDAC1(A),VDAC2 (B) AND VDAC3 (C). Free energies of the interactions are mentioned in Kcal/mol. (D,E,F) Luciferase activity for VDAC1, VDAC2 and VDAC3 was determined 24hours after co-transfection with Luciferase-VDAC-3′ UTR fusion and hsa-miR-29a overexpression constructs. Relative luciferase activity is shown after normalization to Firefly Luciferase. Error bars represent standard deviation from three independent experiments.</p