Deconstructing transcriptional heterogeneity in pluripotent stem cells

SUMMARY Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to characterize transcriptional heterogeneity in PSCs by single-cell expression profiling under different chemical and genetic perturbations. Signaling factors and developmental regulators show highly variable expression, with expression states for some variable genes heritable through multiple cell divisions. Expression variability and population heterogeneity can be influenced by perturbation of signaling pathways and chromatin regulators. Strikingly, either removal of mature miRNAs or pharmacologic blockage of signaling pathways drives PSCs into a low-noise ground state characterized by a reconfigured pluripotency network, enhanced self-renewal, and a distinct chromatin state, an effect mediated by opposing miRNA families acting on the c-myc / Lin28 / let-7 axis. These data illuminate the nature of transcriptional heterogeneity in PSCs

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)