scholarly journals The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Francisco Ferrezuelo ◽  
Neus Colomina ◽  
Alida Palmisano ◽  
Eloi Garí ◽  
Carme Gallego ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
Critical Size ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Heteroresistance at the Single-Cell Level: Adapting to Antibiotic Stress through a Population-Based Strategy and Growth-Controlled Interphenotypic Coordination

mBio ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Xiaorong Wang ◽  
Yu Kang ◽  
Chunxiong Luo ◽  
Tong Zhao ◽  
Lin Liu ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
Task Allocation ◽  
Bacterial Population ◽  
Population Based ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Bacterial Survival ◽  
Cell Level ◽  
Resistant Cells

ABSTRACT Heteroresistance refers to phenotypic heterogeneity of microbial clonal populations under antibiotic stress, and it has been thought to be an allocation of a subset of “resistant” cells for surviving in higher concentrations of antibiotic. The assumption fits the so-called bet-hedging strategy, where a bacterial population “hedges” its “bet” on different phenotypes to be selected by unpredicted environment stresses. To test this hypothesis, we constructed a heteroresistance model by introducing a bla CTX-M-14 gene (coding for a cephalosporin hydrolase) into a sensitive Escherichia coli strain. We confirmed heteroresistance in this clone and that a subset of the cells expressed more hydrolase and formed more colonies in the presence of ceftriaxone (exhibited stronger “resistance”). However, subsequent single-cell-level investigation by using a microfluidic device showed that a subset of cells with a distinguishable phenotype of slowed growth and intensified hydrolase expression emerged, and they were not positively selected but increased their proportion in the population with ascending antibiotic concentrations. Therefore, heteroresistance—the gradually decreased colony-forming capability in the presence of antibiotic—was a result of a decreased growth rate rather than of selection for resistant cells. Using a mock strain without the resistance gene, we further demonstrated the existence of two nested growth-centric feedback loops that control the expression of the hydrolase and maximize population growth in various antibiotic concentrations. In conclusion, phenotypic heterogeneity is a population-based strategy beneficial for bacterial survival and propagation through task allocation and interphenotypic collaboration, and the growth rate provides a critical control for the expression of stress-related genes and an essential mechanism in responding to environmental stresses. IMPORTANCE Heteroresistance is essentially phenotypic heterogeneity, where a population-based strategy is thought to be at work, being assumed to be variable cell-to-cell resistance to be selected under antibiotic stress. Exact mechanisms of heteroresistance and its roles in adaptation to antibiotic stress have yet to be fully understood at the molecular and single-cell levels. In our study, we have not been able to detect any apparent subset of “resistant” cells selected by antibiotics; on the contrary, cell populations differentiate into phenotypic subsets with variable growth statuses and hydrolase expression. The growth rate appears to be sensitive to stress intensity and plays a key role in controlling hydrolase expression at both the bulk population and single-cell levels. We have shown here, for the first time, that phenotypic heterogeneity can be beneficial to a growing bacterial population through task allocation and interphenotypic collaboration other than partitioning cells into different categories of selective advantage.

scholarly journals Bacterial Activity in the Rhizosphere Analyzed at the Single-Cell Level by Monitoring Ribosome Contents and Synthesis Rates

2000 ◽  
Vol 66 (2) ◽  
pp. 801-809 ◽  
Author(s):  
Cayo Ramos ◽  
Lars Mølbak ◽  
Søren Molin
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
Monitoring System ◽  
Fluorescent Protein ◽  
Transient Gene Expression ◽  
Single Cell Level ◽  
Cell Level ◽  
Gene Encoding ◽  
Rate Dependent ◽  
Green Fluorescent

ABSTRACT The growth activity of Pseudomonas putida cells colonizing the rhizosphere of barley seedlings was estimated at the single-cell level by monitoring ribosomal contents and synthesis rates. Ribosomal synthesis was monitored by using a system comprising a fusion of the ribosomal Escherichia coli rrnBP1 promoter to a gene encoding an unstable variant of the green fluorescent protein (Gfp). Gfp expression in a P. putida strain carrying this system inserted into the chromosome was strongly dependent on the growth phase and growth rate of the strain, and cells growing exponentially at rates of ≥0.17 h−1 emitted growth rate-dependent green fluorescence detectable at the single-cell level. The single-cell ribosomal contents were very heterogeneous, as determined by quantitative hybridization with fluorescently labeled rRNA probes in P. putida cells extracted from the rhizosphere of 1-day-old barley seedlings grown under sterile conditions. After this, cells extracted from the root system had ribosomal contents similar to those found in starved cells. There was a significant decrease in the ribosomal content of P. putida cells when bacteria were introduced into nonsterile bulk or rhizosphere soil, and the Gfp monitoring system was not induced in cells extracted from either of the two soil systems. The monitoring system used permitted nondestructive in situ detection of fast-growing bacterial microcolonies on the sloughing root sheath cells of 1- and 2-day-old barley seedlings grown under sterile conditions, which demonstrated that it may be possible to use the unstable Gfp marker for studies of transient gene expression in plant-microbe systems.

scholarly journals High-Throughput Growth Rate Determination of Bacteria Microcolonies on Single Cell Level

2012 ◽  
Vol 84 (8) ◽  
pp. 1336-1337
Author(s):  
A. Grünberger ◽  
C. Probst ◽  
W. Wiechert ◽  
D. Kohlheyer
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Insights on the Control of Yeast Single-Cell Growth Variability by Members of the Trehalose Phosphate Synthase (TPS) Complex

2021 ◽  
Vol 9 ◽  
Author(s):  
Sevan Arabaciyan ◽  
Michael Saint-Antoine ◽  
Cathy Maugis-Rabusseau ◽  
Jean-Marie François ◽  
Abhyudai Singh ◽  
...  
Keyword(s):  
Growth Rate ◽  
Cell Growth ◽  
Single Cell ◽  
Single Cells ◽  
Expression Level ◽  
Individual Growth ◽  
Single Cell Level ◽  
Cell Level ◽  
Growth Variability ◽  
Phosphate Synthase

Single-cell variability of growth is a biological phenomenon that has attracted growing interest in recent years. Important progress has been made in the knowledge of the origin of cell-to-cell heterogeneity of growth, especially in microbial cells. To better understand the origins of such heterogeneity at the single-cell level, we developed a new methodological pipeline that coupled cytometry-based cell sorting with automatized microscopy and image analysis to score the growth rate of thousands of single cells. This allowed investigating the influence of the initial amount of proteins of interest on the subsequent growth of the microcolony. As a preliminary step to validate this experimental setup, we referred to previous findings in yeast where the expression level of Tsl1, a member of the Trehalose Phosphate Synthase (TPS) complex, negatively correlated with cell division rate. We unfortunately could not find any influence of the initial TSL1 expression level on the growth rate of the microcolonies. We also analyzed the effect of the natural variations of trehalose-6-phosphate synthase (TPS1) expression on cell-to-cell growth heterogeneity, but we did not find any correlation. However, due to the already known altered growth of the tps1Δ mutants, we tested this strain at the single-cell level on a permissive carbon source. This mutant showed an outstanding lack of reproducibility of growth rate distributions as compared to the wild-type strain, with variable proportions of non-growing cells between cultivations and more heterogeneous microcolonies in terms of individual growth rates. Interestingly, this variable behavior at the single-cell level was reminiscent to the high variability that is also stochastically suffered at the population level when cultivating this tps1Δ strain, even when using controlled bioreactors.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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