Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies

Science ◽  
2020 ◽  
Vol 367 (6482) ◽  
pp. 1151-1156 ◽  
Author(s):  
Nico Battich ◽  
Joep Beumer ◽  
Buys de Barbanson ◽  
Lenno Krenning ◽  
Chloé S. Baron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Messenger Rna ◽  
Mammalian Cells ◽  
Dynamic Range ◽  
Single Cells ◽  
Regulatory Strategies ◽  
Degradation Rates ◽  
Gene Expression Dynamics ◽  
Homogeneous Cell ◽  
Synthesis And Degradation

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates using relatively homogeneous cell populations. However, to determine these rates during complex dynamical processes, for instance during cellular differentiation, single-cell resolution is required. Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory strategies. These strategies have distinct consequences for controlling the dynamic range and precision of gene expression. These findings advance our understanding of how individual cells in heterogeneous populations shape their gene expression dynamics.

scholarly journals SHERRY2: A method for rapid and sensitive single cell RNA-seq

2021 ◽  
Author(s):  
Lin Di ◽  
Bo Liu ◽  
Yuzhu Lyu ◽  
Shihui Zhao ◽  
Yuhong Pang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Dynamic Range ◽  
Single Cells ◽  
Rna Seq ◽  
Wide Dynamic Range ◽  
Uniform Coverage ◽  
Optimized Protocol ◽  
Tn5 Transposase ◽  
Higher Sensitivity

Many single cell RNA-seq applications aim to probe a wide dynamic range of gene expression, but most of them are still challenging to accurately quantify low-aboundance transcripts. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, an optimized protocol for sequencing transcriptomes of single cells or single nuclei. SHERRY2 is robust and scalable, and it has higher sensitivity and more uniform coverage in comparison with prevalent scRNA-seq methods. With throughput of a few thousand cells per batch, SHERRY2 can reveal the subtle transcriptomic differences between cells and facilitate important biological discoveries.

scholarly journals Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

2015 ◽  
Author(s):  
Andrzej Jerzy Rzepiela ◽  
Arnau Vina-Vilaseca ◽  
Jeremie Breda ◽  
Souvik Ghosh ◽  
Afzal P Syed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Human Embryonic Kidney Cells ◽  
Mirna Targets ◽  
Wide Range ◽  
Gene Expression Dynamics ◽  
Cell To Cell Variability ◽  
First Time

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each other's expression as 'competing RNAs' (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

scholarly journals Unusual Stability of Messenger RNA in Snake Venom Reveals Gene Expression Dynamics of Venom Replenishment

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e41888 ◽  
Author(s):  
Rachel B. Currier ◽  
Juan J. Calvete ◽  
Libia Sanz ◽  
Robert A. Harrison ◽  
Paul D. Rowley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Snake Venom ◽  
Messenger Rna ◽  
Gene Expression Dynamics

scholarly journals High-performance chemical and light-inducible recombinases in mammalian cells and mice

2019 ◽  
Author(s):  
Benjamin H. Weinberg ◽  
Jang Hwan Cho ◽  
Yash Agarwal ◽  
N. T. Hang Pham ◽  
Leidy D. Caraballo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
High Performance ◽  
Genome Engineering ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Genetic Circuits ◽  
Control Of Gene Expression ◽  
Gene Expression Control ◽  
High Performance Systems

ABSTRACTSite-specific DNA recombinases are some of the most powerful genome engineering tools in biology. Chemical and light-inducible recombinases, in particular, enable spatiotemporal control of gene expression. However, the availability of inducible recombinases is scarce due to the challenge of engineering high performance systems with low basal activity and sufficient dynamic range. This limitation constrains the sophistication of genetic circuits and animal models that can be created. To expand the number of available inducible recombinases, here we present a library of >20 orthogonal split recombinases that can be inducibly dimerized and activated by various small molecules, light, and temperature in mammalian cells and mice.Furthermore, we have engineered inducible split Cre systems with better performance than existing inducible Cre systems. Using our orthogonal inducible recombinases, we created a “genetic switchboard” that can independently regulate the expression of 3 different cytokines in the same cell. To demonstrate novel capability with our split recombinases, we created a tripartite inducible Flp and a 4-Input AND gate. We have performed extensive quantitative characterization of the inducible recombinases for benchmarking their performances, including computation of distinguishability of outputs in terms of signal-to-noise ratio (SNR). To facilitate sharing of this set of reagents, we have deposited our library to Addgene. This library thus significantly expands capabilities for precise and multiplexed mammalian gene expression control.

Dynamics of Bacterial Gene Regulatory Networks

2018 ◽  
Vol 47 (1) ◽  
pp. 447-467 ◽  
Author(s):  
David L. Shis ◽  
Matthew R. Bennett, ◽  
Oleg A. Igoshin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Single Cells ◽  
Cell Physiology ◽  
Bacterial Cells ◽  
Bacterial Gene ◽  
Gene Expression Dynamics ◽  
Gene Regulatory

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

scholarly journals Repulsive expansion dynamics in colony growth and gene expression

2021 ◽  
Vol 17 (3) ◽  
pp. e1008168
Author(s):  
Yangxiaolu Cao ◽  
John Neu ◽  
Andrew E. Blanchard ◽  
Ting Lu ◽  
Lingchong You
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Colony Growth ◽  
Coarse Grained ◽  
Mathematical Framework ◽  
Spatial Expansion ◽  
Continuous Growth ◽  
Efficient Simulation ◽  
Gene Expression Dynamics ◽  
Peripheral Cells

Spatial expansion of a population of cells can arise from growth of microorganisms, plant cells, and mammalian cells. It underlies normal or dysfunctional tissue development, and it can be exploited as the foundation for programming spatial patterns. This expansion is often driven by continuous growth and division of cells within a colony, which in turn pushes the peripheral cells outward. This process generates a repulsion velocity field at each location within the colony. Here we show that this process can be approximated as coarse-grained repulsive-expansion kinetics. This framework enables accurate and efficient simulation of growth and gene expression dynamics in radially symmetric colonies with homogenous z-directional distribution. It is robust even if cells are not spherical and vary in size. The simplicity of the resulting mathematical framework also greatly facilitates generation of mechanistic insights.

scholarly journals Simultaneous quantification of protein-DNA contacts and transcriptomes in single cells

2019 ◽  
Author(s):  
Koos Rooijers ◽  
Corina M. Markodimitraki ◽  
Franka J. Rang ◽  
Sandra S. de Vries ◽  
Alex Chialastri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Single Cells ◽  
Critical Role ◽  
Nuclear Lamina ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Expression Variability ◽  
The Impact

AbstractThe epigenome plays a critical role in regulating gene expression in mammalian cells. However, understanding how cell-to-cell heterogeneity in the epigenome influences gene expression variability remains a major challenge. Here we report a novel method for simultaneous single-cell quantification of protein-DNA contacts with DamID and transcriptomics (scDamID&T). This method enables quantifying the impact of protein-DNA contacts on gene expression from the same cell. By profiling lamina-associated domains (LADs) in human cells, we reveal different dependencies between genome-nuclear lamina (NL) association and gene expression in single cells. In addition, we introduce the E. coli methyltransferase, Dam, as an in vivo marker of chromatin accessibility in single cells and show that scDamID&T can be utilized as a general technology to identify cell types in silico while simultaneously determining the underlying gene-regulatory landscape. With this strategy the effect of chromatin states, transcription factor binding, and genome organization on the acquisition of cell-type specific transcriptional programs can be quantified.

scholarly journals Engineering a circular riboregulator inEscherichia coli

2014 ◽  
Author(s):  
William Rostain ◽  
Shensi Shen ◽  
Teresa Cordero ◽  
Guillermo Rodrigo ◽  
Alfonso Jaramillo
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
Dynamic Range ◽  
Circular Rnas ◽  
Antibiotic Resistance Marker ◽  
Fluorescent Reporter ◽  
Group I ◽  
Circular Molecule ◽  
Self Splicing ◽  
Activate Gene

Circular RNAs have recently been shown to be important gene expression regulators in mammalian cells. However, their role in prokaryotes remains elusive. Here, we engineered a synthetic riboregulator that self-splice to produce a circular molecule, exploiting group I permuted intron-exon (PIE) sequences. We demonstrated that the resulting circular riboregulator can activate gene expression, showing increased dynamic range compared to the linear form. We characterized the system with a fluorescent reporter and with an antibiotic resistance marker. Thanks to the increased regulatory activity by higher stability, isolation due to self-splicing, and modularity of PIE, we envisage engineered circular riboregulators in further synthetic biology applications.

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

2021 ◽  
Author(s):  
Joshua Burton ◽  
Cerys S Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

2021 ◽  
Vol 18 (182) ◽  
Author(s):  
Joshua Burton ◽  
Cerys S. Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document