Temporal dynamics and transcriptional control using single-cell gene expression analysis.
TL;DR: Compared to averaged cell populations, temporal single-cell expression profiling provides a much more powerful technique to probe for mechanistic insights underlying cellular differentiation, and will form the basis of novel strategies to study the regulation of transcription at a single- cell level.
Abstract: Background
Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event.
Citations
More filters
TL;DR: The authors comprehensively benchmark the accuracy, scalability, stability and usability of 45 single-cell trajectory inference methods and develop a set of guidelines to help users select the best method for their dataset.
Abstract: Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline ( https://benchmark.dynverse.org ) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.
928 citations
TL;DR: It is demonstrated that dynamic looping events are regulatory rather than structural in nature and widespread coordination of dynamic enhancer activity at preformed and acquired DNA loops is uncovered.
217 citations
Cites background from "Temporal dynamics and transcription..."
...Second, they exhibit high functional similarity to in vivo monocytes, including the ability to differentiate into extremely pure populations of macrophages, with over 95% of cells transitioning to macrophages following PMA treatment (Kouno et al., 2013; Lund et al., 2016)....
[...]
École normale supérieure de Lyon1, French Institute for Research in Computer Science and Automation2, University of Lyon3, Université de Montréal4, Lyon College5, Swiss Institute of Bioinformatics6, ETH Zurich7, French Institute of Health and Medical Research8, Centre for Life9, École Normale Supérieure10
TL;DR: When analyzed at the single cell level, the differentiation process looks very different from its classical population average view, and new observables can be computed, fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.
Abstract: In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a "simple" program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.
201 citations
TL;DR: Recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels are reviewed, and their applications in cancer research are discussed.
157 citations
Cites methods from "Temporal dynamics and transcription..."
...Coexpression networks have been applied to single-cell analysis of the mammalian embryonic development [19], hematopoiesis [14], neural stem cells [85], and leukemia [33,86]....
[...]
TL;DR: This review breaks down the strategies used by this novel class of methods, and organizes their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods.
Abstract: Recent developments in single-cell transcriptomics have opened new opportunities for studying dynamic processes in immunology in a high throughput and unbiased manner. Starting from a mixture of cells in different stages of a developmental process, unsupervised trajectory inference algorithms aim to automatically reconstruct the underlying developmental path that cells are following. In this review, we break down the strategies used by this novel class of methods, and organize their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods. We also give an overview of new insights these methods have already provided regarding the wiring and gene regulation of cell differentiation. As the trajectory inference field is still in its infancy, we propose several future developments that will ultimately lead to a global and data-driven way of studying immune cell differentiation.
156 citations
References
More filters
Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
"Temporal dynamics and transcription..." refers methods in this paper
...Multiple dimension scaling (MDS; Pearson correlated) and principal component analysis (PCA) were performed in R [44]....
[...]
TL;DR: The 2-Delta Delta C(T) method as mentioned in this paper was proposed to analyze the relative changes in gene expression from real-time quantitative PCR experiments, and it has been shown to be useful in the analysis of realtime, quantitative PCR data.
139,407 citations
TL;DR: Understanding how mesenchymal cells arise from an epithelial default status will also have a strong impact in unravelling the mechanisms that control fibrosis and cancer progression.
Abstract: Epithelial-mesenchymal transition is an indispensable mechanism during morphogenesis, as without mesenchymal cells, tissues and organs will never be formed. However, epithelial-cell plasticity, coupled to the transient or permanent formation of mesenchyme, goes far beyond the problem of cell-lineage segregation. Understanding how mesenchymal cells arise from an epithelial default status will also have a strong impact in unravelling the mechanisms that control fibrosis and cancer progression.
3,804 citations
"Temporal dynamics and transcription..." refers background in this paper
...This particular noise pattern substantiates the regulation of SNAIL superfamily of transcriptional repressors, SNAI1 and SNAI3, which regulate the changes in gene expression patterns that underlie components of the extracellular matrix, cell migration, and cell adhesion [26]....
[...]
TL;DR: Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others, including the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
2,471 citations
"Temporal dynamics and transcription..." refers background in this paper
...Factors such as gene regulatory signals, the abundance of polymerases and ribosomes, and the strength of their binding, availability of TFs, and cell cycle can inevitably change cell size and activity [41,42]....
[...]
TL;DR: Stochasticity in gene expression can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
Abstract: Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
2,381 citations
"Temporal dynamics and transcription..." refers background in this paper
...However, these approaches revealed a snapshot of the THP-1 regulatory network while the endogenous gene networks are highly dynamic [1,11]....
[...]
...It is a commonly held idea that negative-feedback provides a noise-reduction mechanism [1,11,32,33]....
[...]
...Although synthetic circuits can be engineered to operate in isolation, gene circuits in nature are highly interconnected [1,11]....
[...]
...Background Genetically identical cells exposed to the same environmental factors can elicit significant variation in gene expression and phenotype [1]....
[...]
...Coordinated and complex transcriptional responses generally involve multiple regulatory interactions organized into gene circuits [1,6,11]....
[...]