scholarly journals Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

2020 ◽  
Author(s):  
Nadia M. V. Sampaio ◽  
Caroline M. Blassick ◽  
Jean-Baptiste Lugagne ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Single Cell ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Heterogeneity ◽  
Temporal Expression ◽  
Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

scholarly journals Escherichia coli can survive stress by noisy growth modulation

2018 ◽  
Author(s):  
Om Patange ◽  
Christian Schwall ◽  
Matt Jones ◽  
Douglas Griffith ◽  
Andrew Phillips ◽  
...  
Keyword(s):  
Gene Expression ◽  
Response Regulator ◽  
Single Cells ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Survival Strategies ◽  
Growth Modulation ◽  
Bacterial Populations ◽  
E Coli

Gene expression can be noisy1,2, as can the growth of single cells3,4. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations5–7. However, it remains unclear how single cells couple gene expression with growth to implement these survival strategies. Here we show how noisy expression of a key stress response regulator, rpoS8, allows E. coli to modulate its growth dynamics to survive future adverse environments. First, we demonstrate that rpoS has a long-tailed distribution of expression in an unstressed population of cells. We next reveal how a dynamic positive feedback loop between rpoS and growth rate produces multi-generation rpoS pulses, which are responsible for the rpoS heterogeneity. We do so experimentally with single-cell, time-lapse microscopy9 and microfluidics10 and theoretically with a stochastic model11,22. Finally, we demonstrate the function of the coupling of heterogeneous rpoS activity and growth. It enables E. coli to survive oxidative attack by causing prolonged periods of slow growth. This dynamic phenotype is captured by the rpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

scholarly journals Development of single-cell-level microfluidic technology for long-term growth visualization of living cultures of Mycobacterium smegmatis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Han Wang ◽  
Gloria M. Conover ◽  
Song-I Han ◽  
James C. Sacchettini ◽  
Arum Han
Keyword(s):  
Drug Treatment ◽  
Single Cell ◽  
Mycobacterium Smegmatis ◽  
Culture Media ◽  
Low Cost ◽  
Bacterial Pathogen ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Phenotype

AbstractAnalysis of growth and death kinetics at single-cell resolution is a key step in understanding the complexity of the nonreplicating growth phenotype of the bacterial pathogen Mycobacterium tuberculosis. Here, we developed a single-cell-resolution microfluidic mycobacterial culture device that allows time-lapse microscopy-based long-term phenotypic visualization of the live replication dynamics of mycobacteria. This technology was successfully applied to monitor the real-time growth dynamics of the fast-growing model strain Mycobacterium smegmatis (M. smegmatis) while subjected to drug treatment regimens during continuous culture for 48 h inside the microfluidic device. A clear morphological change leading to significant swelling at the poles of the bacterial membrane was observed during drug treatment. In addition, a small subpopulation of cells surviving treatment by frontline antibiotics was observed to recover and achieve robust replicative growth once regular culture media was provided, suggesting the possibility of identifying and isolating nonreplicative mycobacteria. This device is a simple, easy-to-use, and low-cost solution for studying the single-cell phenotype and growth dynamics of mycobacteria, especially during drug treatment.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

2019 ◽  
Vol 17 (06) ◽  
pp. 1950035
Author(s):  
Huiqing Wang ◽  
Yuanyuan Lian ◽  
Chun Li ◽  
Yue Ma ◽  
Zhiliang Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Knockout ◽  
Cell Heterogeneity ◽  
State Information ◽  
Gene Regulatory ◽  
Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

scholarly journals Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Blood ◽  
2017 ◽  
Vol 129 (17) ◽  
pp. 2384-2394 ◽  
Author(s):  
Rebecca Warfvinge ◽  
Linda Geironson ◽  
Mikael N. E. Sommarin ◽  
Stefan Lang ◽  
Christine Karlsson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Single Cell ◽  
Gene Expression Analysis ◽  
Therapy Response ◽  
Cell Heterogeneity ◽  
Key Points ◽  
Cell Gene Expression ◽  
Cell Subpopulations ◽  
Cell Gene

Key Points Single-cell gene expression analysis reveals CML stem cell heterogeneity and changes imposed by TKI therapy. A subpopulation with primitive, quiescent signature and increased survival to therapy can be high-purity captured as CD45RA−cKIT−CD26+.

scholarly journals Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

scholarly journals Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma

2020 ◽  
Author(s):  
Karin D. Prummel ◽  
Helena L. Crowell ◽  
Susan Nieuwenhuize ◽  
Eline C. Brombacher ◽  
Stephan Daetwyler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
De Novo ◽  
Primordial Germ Cells ◽  
Gene Expression Signature ◽  
Time Lapse ◽  
Lineage Tracing ◽  
Genetic Lineage ◽  
Lateral Plate

AbstractThe mesothelium forms epithelial membranes that line the bodies cavities and surround the internal organs. Mesothelia widely contribute to organ homeostasis and regeneration, and their dysregulation can result in congenital anomalies of the viscera, ventral wall defects, and mesothelioma tumors. Nonetheless, the embryonic ontogeny and developmental regulation of mesothelium formation has remained uncharted. Here, we combine genetic lineage tracing, in toto live imaging, and single-cell transcriptomics in zebrafish to track mesothelial progenitor origins from the lateral plate mesoderm (LPM). Our single-cell analysis uncovers a post-gastrulation gene expression signature centered on hand2 that delineates distinct progenitor populations within the forming LPM. Combining gene expression analysis and imaging of transgenic reporter zebrafish embryos, we chart the origin of mesothelial progenitors to the lateral-most, hand2-expressing LPM and confirm evolutionary conservation in mouse. Our time-lapse imaging of transgenic hand2 reporter embryos captures zebrafish mesothelium formation, documenting the coordinated cell movements that form pericardium and visceral and parietal peritoneum. We establish that the primordial germ cells migrate associated with the forming mesothelium as ventral migration boundary. Functionally, hand2 mutants fail to close the ventral mesothelium due to perturbed migration of mesothelium progenitors. Analyzing mouse and human mesothelioma tumors hypothesized to emerge from transformed mesothelium, we find de novo expression of LPM-associated transcription factors, and in particular of Hand2, indicating the re-initiation of a developmental transcriptional program in mesothelioma. Taken together, our work outlines a genetic and developmental signature of mesothelial origins centered around Hand2, contributing to our understanding of mesothelial pathologies and mesothelioma.

scholarly journals Possible epigenetic influence on cellular differentiation and lineage segregation in Escherichia coli

2019 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Cell Growth ◽  
Single Cell ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Environmental Stressors ◽  
Epigenetic Markers ◽  
E Coli ◽  
Growth Chambers

Epigenetics provides the critical connection between environmental influence and gene expression, where environmental stressors could modulate expression of specific genes in particular scenarios using molecular markers etched at the genome level. Hence, epigenetics likely play important roles in potentiating the development of specific lineages, cell fate or cellular differentiation. For example, when specific environmental stressor is present, epigenetic markers in the genome receive a signal for either activating or deactivating expression of particular sets of genes, which may be linked to the developmental trajectory of the organism. Using Escherichia coli as model organism, a possible study may investigate the role of epigenetics in influencing cellular differentiation of the bacterium. Specifically, a single E. coli cell would be propagated into a consortium of 12 or more bacterial cells in a microfluidics growth chamber. Genetic material extracted would be sent for single cell genomics, transcriptomics, and chromatin immunoprecipitation sequencing (ChIP-seq). After profiling, the residual population would be diverted by microchannels to 6 different cell growth chambers, where they would be cultivated under identical conditions for understanding possible triggers to cell differentiation. At suitable time points of 2, 4, 6, 8, 10, 12 hours, single cell would be extracted from each growth chamber for profiling single cell genomics, transcriptomics, and epigenetics markers. Optical and confocal laser scanning microscopy would provide readout of cell morphologies. Comparison of the readout between the original clonal population and those of the different growth chambers may provide important points for correlating epigenetic markers with gene expression and phenotypic readout in cell lineage, fate and differentiation. In subsequent experiments, different environmental stressors such as pH, imbalance nutrient composition between carbon and nitrogen, nanoparticles or heavy metals, could be used as triggers for specific cell growth response guided by epigenetic programmes embedded within the epigenome of the bacterium. Collectively, epigenetics hold influence for cellular differentiation in view of specific environmental stressors, where epigenetic markers on the genome communicate specific environmental factor's effect on the organism through altering expression of particular sets of genes, that result in different cell fate, lineage and differentiation. Using modern single cell techniques at the genomics, transcriptomics and epigenomics level, the study hopes to elucidate epigenetic potentiators of cellular differentiation in E. coli with and without environmental stressors such as nutrient deprivation, pH and toxic metals.

scholarly journals IraL Is an RssB Anti-adaptor That Stabilizes RpoS during Logarithmic Phase Growth in Escherichia coli and Shigella

mBio ◽  
2014 ◽  
Vol 5 (3) ◽  
Author(s):  
Andrew J. Hryckowian ◽  
Aurelia Battesti ◽  
Justin J. Lemke ◽  
Zachary C. Meyer ◽  
Rodney A. Welch
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Sigma Factor ◽  
Logarithmic Phase ◽  
Phase Growth ◽  
Content Type ◽  
General Stress Response ◽  
E Coli ◽  
K 12

ABSTRACTRpoS (σS), the general stress response sigma factor, directs the expression of genes under a variety of stressful conditions. Control of the cellular σSconcentration is critical for appropriately scaled σS-dependent gene expression. One way to maintain appropriate levels of σSis to regulate its stability. Indeed, σSdegradation is catalyzed by the ClpXP protease and the recognition of σSby ClpXP depends on the adaptor protein RssB. Three anti-adaptors (IraD, IraM, and IraP) exist inEscherichia coliK-12; each interacts with RssB andinhibitsRssBactivity under different stress conditions, thereby stabilizing σS. Unlike K-12, someE. coliisolates, including uropathogenicE. colistrain CFT073, show comparable cellular levels of σSduring the logarithmic and stationary growth phases, suggesting that there are differences in the regulation of σSlevels amongE. colistrains. Here, we describe IraL, an RssB anti-adaptor that stabilizes σSduring logarithmic phase growth in CFT073 and otherE. coliandShigellastrains. By immunoblot analyses, we show that IraL affects the levels and stability of σSduring logarithmic phase growth. By computational and PCR-based analyses, we reveal thatiraLis found in manyE. colipathotypes but not in laboratory-adapted strains. Finally, by bacterial two-hybrid and copurification analyses, we demonstrate that IraL interacts with RssB by a mechanism distinct from that used by other characterized anti-adaptors. We introduce a fourth RssB anti-adaptor found inE. colispecies and suggest that differences in the regulation of σSlevels may contribute to host and niche specificity in pathogenic and nonpathogenicE. colistrains.IMPORTANCEBacteria must cope with a variety of environmental conditions in order to survive. RpoS (σS), the general stress response sigma factor, directs the expression of many genes under stressful conditions in both pathogenic and nonpathogenicEscherichia colistrains. The regulation of σSlevels and activity allows appropriately scaled σS-dependent gene expression. Here, we describe IraL, an RssB anti-adaptor that, unlike previously described anti-adaptors, stabilizes σSduring the logarithmic growth phase in the absence of additional stress. We also demonstrate thatiraLis found in a large number ofE. coliandShigellaisolates. These data suggest that strains containingiraLare able to initiate σS-dependent gene expression under conditions under which strains withoutiraLcannot. Therefore, IraL-mediated σSstabilization may contribute to host and niche specificity inE. coli.

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