The Role of Heterogeneity During Development and Stem Cell Differentiation

Heterogeneity is an integral property of many biological responses from molecular scales to members of a population of one species. While several recent studies have demonstrated the extent of the underlying heterogeneity on different scales, its origin, regulation, and consequences have not been thoroughly understood. In this dissertation, first we draw a general picture of how heterogeneity accompanies most biological responses from molecular to tissue scale, then we uncover the factors that can cause or contribute to non-uniformity. We explore the regulation of variation of biological systems as well as its consequences and illustrate the pivotal role that molecular, cellular and tissue heterogeneity plays in survival of an organism. First, we seek to elucidate the role of macromolecular crowding in transcription and translation. It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population. Recent experimental observations by Tan et al. (Nature nanotechnology. 2013 Aug;8(8):602) have improved our understanding of the functional role of macromolecular crowding. It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression, resulting in a more homogeneous cell population. We introduce a spatial stochastic model to provide insight into this process. Our results show that macromolecular crowding reduces noise (as measured by the kurtosis of the mRNA distribution) in a cell population by limiting the diffusion of transcription factors (i.e. removing the unstable intermediate states), and that crowding by large molecules reduces noise more efficiently than crowding by small molecules. Finally, our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population. Next we incorporate three-dimensional (3D) conformation of chromosome (Hi-C) and single-cell RNA sequencing data together with discrete stochastic simulation, to explore the role of chromatin reorganization in determining gene expression heterogeneity during development. While previous research has emphasized the importance of chromatin architecture on activation and suppression of certain regulatory genes and gene networks, our study demonstrates how chromatin remodeling can dictate gene expression distribution by folding into distinct topological domains. We hypothesize that the local DNA density during differentiation accentuates transcriptional bursting due to the crowding effect of chromatin. This phenomenon yields a heterogeneous cell population, thereby increasing the potential of differentiation of the stem cells. Finally, we depict the interplay between microRNAs and mRNAs and how this network can regulate human fetal brain development. microRNAs (miRNAs) regulate many cellular events during brain development by interacting with hundreds of mRNA transcripts. However, miRNAs operate non-uniformly upon the transcriptional profile with an as yet unknown logic. Shortcomings in defining miRNA-mRNA networks are limited knowledge of in vivo miRNA targets, and their abundance in single cells. By combining multiple complementary approaches: AGO2-HITS-CLIP, single-cell profiling, and innovative computational analyses using bipartite and co-expression networks, we show that miRNA- mRNA interactions operate as functional modules that often correspond to cell-type identities and undergo dynamic transitions during brain development. These networks are highly dynamic during development and over the course of evolution. One such interaction is between radial glia-enriched ORC4 and miR-2115, a great ape specific miRNA, which appears to control radial glia proliferation rates during human brain development

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute