3D-Image analysis platform monitoring relocation of pluripotency genes during reprogramming

Nuclear organization of chromatin is an important level of genome regulation with positional changes of genes occurring during reprogramming. Inherent variability of biological specimens, wide variety of sample preparation and imaging conditions, though pose significant challenges to data analysis and comparison. Here, we describe the development of a computational image analysis toolbox overcoming biological variability hurdles by a novel single cell randomizing normalization. We performed a comparative analysis of the relationship between spatial positioning of pluripotency genes with their genomic activity and determined the degree of similarity between fibroblasts, induced pluripotent stem cells and embryonic stem cells. Our analysis revealed a preferred positioning of actively transcribed Sox2, Oct4 and Nanog away from the nuclear periphery, but not from pericentric heterochromatin. Moreover, in the silent state, we found no common nuclear localization for any of the genes. Our results suggest that the surrounding gene density hinders relocation from an internal nuclear position. Altogether, our data do not support the hypothesis that the nuclear periphery acts as a general transcriptional silencer, rather suggesting that internal nuclear localization is compatible with expression in pluripotent cells but not sufficient for expression in mouse embryonic fibroblasts. Thus, our computational approach enables comparative analysis of topological relationships in spite of stark morphological variability typical of biological data sets

Determining protein structures using deep mutagenesis

Determining the three-dimensional structures of macromolecules is a major goal of biological research, because of the close relationship between structure and function; however, thousands of protein domains still have unknown structures. Structure determination usually relies on physical techniques including X-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional backbone structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides an alternative experimental strategy for structure determination, with the potential to reveal functional and in vivo structures.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy, Industry and Competitiveness (MEIC; BFU2017-89488-P), the AXA Research Fund, the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322), the EMBL-CRG Systems Biology Program and the CERCA Program/Generalitat de Catalunya. J.M.S. was supported by an EMBO Long-Term Fellowship (ALTF 857-2016). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 752809 (J.M.S.). We acknowledge support from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa

Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify because of the mechanistic coupling of noise to mean expression. To independently quantify the effects of changes in mean expression and noise we determine the fitness landscapes in mean-noise expression space for 33 genes in yeast. For most genes, short-lived (noise) deviations away from the expression optimum are nearly as detrimental as sustained (mean) deviations. Fitness landscapes can be classified by a combination of each gene's sensitivity to protein shortage or surplus. We use this classification to explore evolutionary scenarios for gene expression and find that certain landscape topologies can break the mechanistic coupling of mean and noise, thus promoting independent optimization of both properties. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.This work was supported by a European Research Council Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2011–26206 and SEV-2012–0208), the AXA Research Fund, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, 2014SGR831), FP7 project 4DCellFate (277899), the EMBL-CRG Systems Biology Program (all to B.L.), an EMBO Long-Term Fellowship (ALTF 857–2016), the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No 752809) (both to J.M.S.) an AGAUR grant (2014SGR0974) and a MINECO grant (BFU2015–68351-P) (both to L.B.C.). The authors acknowledge support from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme / Generalitat de Catalunya

Systematic analysis of the determinants of gene expression noise in embryonic stem cells

Isogenic cells in a common environment show substantial cell-to-cell variation in gene expression, often referred to as "expression noise." Here, we use multiple single-cell RNA-sequencing datasets to identify features associated with high or low expression noise in mouse embryonic stem cells. These include the core promoter architecture of a gene, with CpG island promoters and a TATA box associated with low and high noise, respectively. High noise is also associated with "conflicting" chromatin states-the absence of transcription-associated histone modifications or the presence of repressive ones in active genes. Genes regulated by pluripotency factors through super-enhancers show high and correlated expression variability, consistent with fluctuations in the pluripotent state. Together, our results provide an integrated view of how core promoters, chromatin, regulation, and pluripotency fluctuations contribute to the variability of gene expression across individual stem cells.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2011-26206 and “Centro de Excelencia Severo Ochoa 2013-2017” SEV-2012-0208), the AXA Research Fund, the Bettencourt Schueller Foundation, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), FP7 project 4DCellFate (277899), and the EMBL-CRG Systems Biology Program. AF was supported by a MINECO post-doctoral grant (FPDI-2013-17783

Mapping the energetic and allosteric landscapes of protein binding domains.

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.This work was funded by European Research Council (ERC) Advanced (883742) and Consolidator (616434) grants, the Spanish Ministry of Science and Innovation (PID2020-118723GB-I00, BFU2017-89488-P, EMBL Partnership, Severo Ochoa Centre of Excellence), the Bettencourt Schueller Foundation, the AXA Research Fund, Agencia de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322), and the CERCA Program/Generalitat de Catalunya. J.M.S. was supported by an EMBO Long-Term Fellowship (ALTF 857-2016) and a Marie Skłodowska-Curie Fellowship (752809, EU Commission Horizon 2020

Gene expression. MicroRNA control of protein expression noise

MicroRNAs (miRNAs) repress the expression of many genes in metazoans by accelerating messenger RNA degradation and inhibiting translation, thereby reducing the level of protein. However, miRNAs only slightly reduce the mean expression of most targeted proteins, leading to speculation about their role in the variability, or noise, of protein expression. We used mathematical modeling and single-cell reporter assays to show that miRNAs, in conjunction with increased transcription, decrease protein expression noise for lowly expressed genes but increase noise for highly expressed genes. Genes that are regulated by multiple miRNAs show more-pronounced noise reduction. We estimate that hundreds of (lowly expressed) genes in mouse embryonic stem cells have reduced noise due to substantial miRNA regulation. Our findings suggest that miRNAs confer precision to protein expression and thus offer plausible explanations for the commonly observed combinatorial targeting of endogenous genes by multiple miRNAs, as well as the preferential targeting of lowly expressed genes

MicroRNA control of protein expression noise

MicroRNAs (miRNAs) repress the expression of many genes in metazoans by accelerating messenger RNA degradation and inhibiting translation, thereby reducing the level of protein. However, miRNAs only slightly reduce the mean expression of most targeted proteins, leading to speculation about their role in the variability, or noise, of protein expression. We used mathematical modeling and single-cell reporter assays to show that miRNAs, in conjunction with increased transcription, decrease protein expression noise for lowly expressed genes but increase noise for highly expressed genes. Genes that are regulated by multiple miRNAs show more-pronounced noise reduction. We estimate that hundreds of (lowly expressed) genes in mouse embryonic stem cells have reduced noise due to substantial miRNA regulation. Our findings suggest that miRNAs confer precision to protein expression and thus offer plausible explanations for the commonly observed combinatorial targeting of endogenous genes by multiple miRNAs, as well as the preferential targeting of lowly expressed genes

3D-Image analysis platform monitoring relocation of pluripotency genes during reprogramming.

Nuclear organization of chromatin is an important level of genome regulation with positional changes of genes occurring during reprogramming. Inherent variability of biological specimens, wide variety of sample preparation and imaging conditions, though pose significant challenges to data analysis and comparison. Here, we describe the development of a computational image analysis toolbox overcoming biological variability hurdles by a novel single cell randomizing normalization. We performed a comparative analysis of the relationship between spatial positioning of pluripotency genes with their genomic activity and determined the degree of similarity between fibroblasts, induced pluripotent stem cells and embryonic stem cells. Our analysis revealed a preferred positioning of actively transcribed Sox2, Oct4 and Nanog away from the nuclear periphery, but not from pericentric heterochromatin. Moreover, in the silent state, we found no common nuclear localization for any of the genes. Our results suggest that the surrounding gene density hinders relocation from an internal nuclear position. Altogether, our data do not support the hypothesis that the nuclear periphery acts as a general transcriptional silencer, rather suggesting that internal nuclear localization is compatible with expression in pluripotent cells but not sufficient for expression in mouse embryonic fibroblasts. Thus, our computational approach enables comparative analysis of topological relationships in spite of stark morphological variability typical of biological data sets

DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs, and regulatory elements. Here, we present a customizable pipeline, DiMSum, that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322.), and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 752809 (J.M.S.)