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 Generation and attenuation of variability in a bacterial signaling network revealed by single-cell FRET

2017 ◽  
Author(s):  
J.M. Keegstra ◽  
K. Kamino ◽  
F. Anquez ◽  
M.D. Lazova ◽  
T. Emonet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Gene Expression Noise ◽  
Switching Regime ◽  
Molecular Noise ◽  
Entire Cell ◽  
Chemotaxis System ◽  
Cell To Cell Variability

AbstractWe presentin vivosingle-cell FRET measurements in theEscherichia colichemotaxis system that reveal pervasive signaling variability, both across cells in isogenic populations and within individual cells over time. We quantify cell-to-cell variability of adaptation, ligand response, as well as steady-state output level, and analyze the role of network design in shaping this diversity from gene expression noise. In the absence of changes in gene expression, we find that single cells demonstrate strong temporal fluctuations. We provide evidence that such signaling noise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) receptor-kinase dynamics in the absence of adaptation. We demonstrate that under certain conditions, (ii) can generate giant fluctuations that drive signaling activity of the entire cell into a stochastic two-state switching regime. Our findings underscore the importance of molecular noise, arising not only in gene expression but also in protein networks.

scholarly journals Phenotypic diversity and temporal variability in a bacterial signaling network revealed by single-cell FRET

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Johannes M Keegstra ◽  
Keita Kamino ◽  
François Anquez ◽  
Milena D Lazova ◽  
Thierry Emonet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Gene Expression Noise ◽  
Switching Regime ◽  
Molecular Noise ◽  
Entire Cell ◽  
Chemotaxis System

We present in vivo single-cell FRET measurements in the Escherichia coli chemotaxis system that reveal pervasive signaling variability, both across cells in isogenic populations and within individual cells over time. We quantify cell-to-cell variability of adaptation, ligand response, as well as steady-state output level, and analyze the role of network design in shaping this diversity from gene expression noise. In the absence of changes in gene expression, we find that single cells demonstrate strong temporal fluctuations. We provide evidence that such signaling noise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) receptor-kinase dynamics in the absence of adaptation. We demonstrate that under certain conditions, (ii) can generate giant fluctuations that drive signaling activity of the entire cell into a stochastic two-state switching regime. Our findings underscore the importance of molecular noise, arising not only in gene expression but also in protein networks.

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.

scholarly journals Biphasic Metabolism and Host Interaction of a Chlamydial Symbiont

mSystems ◽  
2017 ◽  
Vol 2 (3) ◽  
Author(s):  
Lena König ◽  
Alexander Siegl ◽  
Thomas Penz ◽  
Susanne Haider ◽  
Cecilia Wentrup ◽  
...  
Keyword(s):  
Gene Expression ◽  
Host Cell ◽  
Type Iii Secretion ◽  
Acanthamoeba Castellanii ◽  
Secretion System ◽  
Developmental Cycle ◽  
Type Iii ◽  
Gene Expression Dynamics ◽  
First Time

ABSTRACT Chlamydiae are known as major bacterial pathogens of humans, causing the ancient disease trachoma, but they are also frequently found in the environment where they infect ubiquitous protists such as amoebae. All known chlamydiae require a eukaryotic host cell to thrive. Using the environmental chlamydia Protochlamydia amoebophila within its natural host, Acanthamoeba castellanii, we investigated gene expression dynamics in vivo and throughout the complete chlamydial developmental cycle for the first time. This allowed us to infer how a major virulence mechanism, the type III secretion system, is regulated and employed, and we show that the physiology of chlamydiae undergoes a complete shift regarding carbon metabolism and energy generation. This study provides comprehensive insights into the infection strategy of chlamydiae and reveals a unique adaptation to life within a eukaryotic host cell. Chlamydiae are obligate intracellular bacteria comprising well-known human pathogens and ubiquitous symbionts of protists, which are characterized by a unique developmental cycle. Here we comprehensively analyzed gene expression dynamics of Protochlamydia amoebophila during infection of its Acanthamoeba host by RNA sequencing. This revealed a highly dynamic transcriptional landscape, where major transcriptional shifts are conserved among chlamydial symbionts and pathogens. Our data served to propose a time-resolved model for type III protein secretion during the developmental cycle, and we provide evidence for a biphasic metabolism of P. amoebophila during infection, which involves energy parasitism and amino acids as the carbon source during initial stages and a postreplicative switch to endogenous glucose-based ATP production. This fits well with major transcriptional changes in the amoeba host, where upregulation of complex sugar breakdown precedes the P. amoebophila metabolic switch. The biphasic chlamydial metabolism represents a unique adaptation to exploit eukaryotic host cells, which likely contributed to the evolutionary success of this group of microbes. IMPORTANCE Chlamydiae are known as major bacterial pathogens of humans, causing the ancient disease trachoma, but they are also frequently found in the environment where they infect ubiquitous protists such as amoebae. All known chlamydiae require a eukaryotic host cell to thrive. Using the environmental chlamydia Protochlamydia amoebophila within its natural host, Acanthamoeba castellanii, we investigated gene expression dynamics in vivo and throughout the complete chlamydial developmental cycle for the first time. This allowed us to infer how a major virulence mechanism, the type III secretion system, is regulated and employed, and we show that the physiology of chlamydiae undergoes a complete shift regarding carbon metabolism and energy generation. This study provides comprehensive insights into the infection strategy of chlamydiae and reveals a unique adaptation to life within a eukaryotic host cell.

scholarly journals Single-cell phenotyping and RNA sequencing reveal novel patterns of gene expression heterogeneity and regulation during growth and stress adaptation in a unicellular eukaryote

2018 ◽  
Author(s):  
Malika Saint ◽  
François Bertaux ◽  
Wenhao Tang ◽  
Xi-Ming Sun ◽  
Laurence Game ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Size ◽  
Single Cells ◽  
Yeast Cells ◽  
Stress Adaptation ◽  
Unicellular Eukaryote ◽  
Gene Expression Heterogeneity ◽  
Cell To Cell Variability

Cell-to-cell variability is central for microbial populations and contributes to cell function, stress adaptation and drug resistance. Gene-expression heterogeneity underpins this variability, but has been challenging to study genome-wide. Here, we report an integrated approach for imaging of individual fission yeast cells followed by single-cell RNA sequencing (scRNA-seq) and novel Bayesian normalisation. We analyse >2000 single cells and >700 matching RNA controls in various environmental conditions and identify sets of highly variable genes. Combining scRNA-seq with cell-size measurements provides unique insights into genes regulated during cell growth and division in single cells, including genes whose expression does not scale with cell size. We further analyse the heterogeneity and dynamics of gene expression during adaptive and acute responses to changing environments. Entry into stationary phase is preceded by a gradual, synchronised adaptation in gene regulation, followed by highly variable gene expression when growth decreases. Conversely, a sudden and acute heat-shock leads to a stronger and coordinated response and adaptation across cells. This analysis reveals that the extent and dynamics of global gene-expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote. In summary, this works illustrates the potential of combined transcriptomics and imaging analysis in single cells to provide comprehensive and unbiased mechanistic understanding of cell-to-cell variability in microbial communities.

scholarly journals Autophagy Deficiency by Atg4B Loss Leads to Metabolomic Alterations in Mice

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 481
Author(s):  
Gemma G. Martínez-García ◽  
Raúl F. Pérez ◽  
Álvaro F. Fernández ◽  
Sylvere Durand ◽  
Guido Kroemer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Amino Acids ◽  
Mammalian Cells ◽  
Tissue Homeostasis ◽  
In Vivo Studies ◽  
Protective Mechanism ◽  
Cardiac Damage ◽  
Wide Range ◽  
First Time

Autophagy is an essential protective mechanism that allows mammalian cells to cope with a variety of stressors and contributes to maintaining cellular and tissue homeostasis. Due to these crucial roles and also to the fact that autophagy malfunction has been described in a wide range of pathologies, an increasing number of in vivo studies involving animal models targeting autophagy genes have been developed. In mammals, total autophagy inactivation is lethal, and constitutive knockout models lacking effectors of this route are not viable, which has hindered so far the analysis of the consequences of a systemic autophagy decline. Here, we take advantage of atg4b−/− mice, an autophagy-deficient model with only partial disruption of the process, to assess the effects of systemic reduction of autophagy on the metabolome. We describe for the first time the metabolic footprint of systemic autophagy decline, showing that impaired autophagy results in highly tissue-dependent alterations that are more accentuated in the skeletal muscle and plasma. These changes, which include changes in the levels of amino-acids, lipids, or nucleosides, sometimes resemble those that are frequently described in conditions like aging, obesity, or cardiac damage. We also discuss different hypotheses on how impaired autophagy may affect the metabolism of several tissues in mammals.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy

2014 ◽  
Vol 25 (22) ◽  
pp. 3699-3708 ◽  
Author(s):  
Anyimilehidi Mazo-Vargas ◽  
Heungwon Park ◽  
Mert Aydin ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Time Lapse ◽  
Photon Flux ◽  
Luminescence Microscopy ◽  
Cell Cycle Genes ◽  
Light Excitation ◽  
Better Than

Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15–20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.

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