Biology subjects

Genga, R. M. J.

Publications and source records attributed to Genga, R. M. J..

3 recordsLinked to original sources

bioRxiv2020

Systematic evaluation of chromosome conformation capture assays

Chromosome conformation capture (3C)-based assays are used to map chromatin interactions genome-wide. Quantitative analyses of chromatin interaction maps can lead to insights into the spatial organization of chromosomes and the mechanisms by which they fold. A number of protocols such as in situ Hi-C and Micro-C are now widely used and these differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of experimental parameters of 3C-based protocols. We find that different protocols capture different 3D genome features with different efficiencies. First, the use of cross-linkers such as DSG in addition to formaldehyde improves signal-to-noise allowing detection of thousands of additional loops and strengthens the compartment signal. Second, fragmenting chromatin to the level of nucleosomes using MNase allows detection of more loops. On the other hand, protocols that generate larger multi-kb fragments produce stronger compartmentalization signals. We confirmed our results for multiple cell types and cell cycle stages. We find that cell type-specific quantitative differences in chromosome folding are not detected or underestimated by some protocols. Based on these insights we developed Hi-C 3.0, a single protocol that can be used to both efficiently detect chromatin loops and to quantify compartmentalization. Finally, this study produced ultra-deeply sequenced reference interaction maps using conventional Hi-C, Micro-C and Hi-C 3.0 for commonly used cell lines in the 4D Nucleome Project.

genomics↗

bioRxiv2019

Multiple human adipocyte subtypes and mechanisms of their development

Human adipose tissue depots perform numerous diverse physiological functions, and are differentially linked to metabolic disease risk, yet only two major human adipocyte subtypes have been described, white and "brown/brite/beige." The diversity and lineages of adipocyte classes have been studied in mice using genetic methods that cannot be applied in humans. Here we circumvent this problem by studying the fate of single mesenchymal progenitor cells obtained from human adipose tissue. We report that a minimum of four human adipocyte subtypes can be distinguished by transcriptomic analysis, specialized for functionally distinct processes such as adipokine secretion and thermogenesis. Evidence for the presence of these adipocytes subtypes in adult humans is evidenced by differential expression of key adipokines leptin and adiponectin in isolated mature adipocytes. The human adipocytes most similar to the mouse "brite/beige" adipocytes are enriched in mechanisms that promote iron accumulation and protect from oxidative stress, and are derived from progenitors that express high levels of cytokines such as IL1B, IL8, IL11 and the IL6 family cytokine LIF, and low levels of the transcriptional repressors ID1 and ID3. Our finding of this adipocyte repertoire and its developmental mechanisms provides a high-resolution framework to analyze human adipose tissue architecture and its role in systemic metabolism and metabolic disease.

physiology↗

bioRxiv2017

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in T-cell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cells state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates time-series and genetic knock-out information with such transcriptome-based descriptions in order to describe and analyze the real-time evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cell-state-dependent growth and differentiation of thymic T-cell development. The model approximates a developmental potential function (Waddingtons landscape) and suggests that thymic T-cell development is biphasic and not strictly deterministic before beta-selection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.

bioinformatics↗