scholarly journals Analysis of SOS-Induced Spontaneous Prophage Induction in Corynebacterium glutamicum at the Single-Cell Level

2013 ◽  
Vol 196 (1) ◽  
pp. 180-188 ◽  
Author(s):  
A. M. Nanda ◽  
A. Heyer ◽  
C. Kramer ◽  
A. Grunberger ◽  
D. Kohlheyer ◽  
...  
Keyword(s):  
Single Cell ◽  
Corynebacterium Glutamicum ◽  
Single Cell Level ◽  
Cell Level ◽  
Prophage Induction

scholarly journals Growth response and recovery of Corynebacterium glutamicum colonies on single-cell level upon defined pH stress pulses

2021 ◽  
Author(s):  
Sarah Täuber ◽  
Luisa Blöbaum ◽  
Volker F. Wendisch ◽  
Alexander Grünberger
Keyword(s):  
Single Cell ◽  
Corynebacterium Glutamicum ◽  
Intracellular Ph ◽  
Growth Response ◽  
Small Range ◽  
Single Cell Level ◽  
Ph Homeostasis ◽  
Cell Level ◽  
Ph Changes ◽  
Ph Stress

Bacteria respond to pH changes in their environment via pH homeostasis to keep the intracellular pH as constant as possible within a small range. A change of the intracellular pH value influences e.g., the enzyme activity, protein stability, solubility of trace elements and the proton motive force. Here, the species Corynebacterium glutamicum has been chosen as a neutralophilic and moderately alkali-tolerant bacterium capable of maintaining an internal pH of 7.5 ± 0.5 in environments with an external pH between 5.5 and 9. In the recent years, the phenotypic response of C. glutamicum to pH changes has been systematically investigated at the bulk population level. A detailed understanding of the C. glutamicum cell responding to defined short-term pH perturbations/pulses is missing. In this study, dynamic microfluidic single-cell cultivation (dMSCC) was applied to analyse the physiological growth response of C. glutamicum upon precise pH stress pulses at a single-cell level. Analysis of the growth behaviour at the colony level by dMSCC exposed to single pH stress pulses (pH = 4, 5, 10, 11) revealed a decrease in the viability with increasing stress duration. Colony regrowth was possible after increasing lag phases when stress durations were increased from 5 min to 9 h for all tested pH values. Furthermore, the single-cell analysis revealed heterogeneous regrowth of cells after pH stress, which can be distinguished into two distinct behaviours: firstly, cells continue to grow without interruption after the pH stress, and secondly, some cells rest for several hours after the pH stress before they start to grow again after this lag phase. This study provides the first insights into the single-cell response to acidic and alkaline pH stress adaptation of C. glutamicum.

scholarly journals Growth Response and Recovery of Corynebacterium glutamicum Colonies on Single-Cell Level Upon Defined pH Stress Pulses

2021 ◽  
Vol 12 ◽  
Author(s):  
Sarah Täuber ◽  
Luisa Blöbaum ◽  
Volker F. Wendisch ◽  
Alexander Grünberger
Keyword(s):  
Single Cell ◽  
Corynebacterium Glutamicum ◽  
Intracellular Ph ◽  
Growth Response ◽  
Physiological State ◽  
Single Cell Level ◽  
Cell Level ◽  
Stress Pulse ◽  
Ph Values ◽  
Ph Stress

Bacteria respond to pH changes in their environment and use pH homeostasis to keep the intracellular pH as constant as possible and within a small range. A change in intracellular pH influences enzyme activity, protein stability, trace element solubilities and proton motive force. Here, the species Corynebacterium glutamicum was chosen as a neutralophilic and moderately alkali-tolerant bacterium capable of maintaining an internal pH of 7.5 ± 0.5 in environments with external pH values ranging between 5.5 and 9. In recent years, the phenotypic response of C. glutamicum to pH changes has been systematically investigated at the bulk population level. A detailed understanding of the C. glutamicum cell response to defined short-term pH perturbations/pulses is missing. In this study, dynamic microfluidic single-cell cultivation (dMSCC) was applied to analyze the physiological growth response of C. glutamicum to precise pH stress pulses at the single-cell level. Analysis by dMSCC of the growth behavior of colonies exposed to single pH stress pulses (pH = 4, 5, 10, 11) revealed a decrease in viability with increasing stress duration w. Colony regrowth was possible for all tested pH values after increasing lag phases for which stress durations w were increased from 5 min to 9 h. Furthermore, single-cell analyses revealed heterogeneous regrowth of cells after pH stress, which can be categorized into three physiological states. Cells in the first physiological state continued to grow without interruption after pH stress pulse. Cells in the second physiological state rested for several hours after pH stress pulse before they started to grow again after this lag phase, and cells in the third physiological state did not divide after the pH stress pulse. This study provides the first insights into single-cell responses to acidic and alkaline pH stress by C. glutamicum.

scholarly journals Induction of Shiga Toxin-Encoding Prophage by Abiotic Environmental Stress in Food

2017 ◽  
Vol 83 (19) ◽  
Author(s):  
Yuan Fang ◽  
Ryan G. Mercer ◽  
Lynn M. McMullen ◽  
Michael G. Gänzle
Keyword(s):  
Gene Expression ◽  
Lactic Acid ◽  
Environmental Stress ◽  
Single Cell ◽  
Shiga Toxin ◽  
Sos Response ◽  
Single Cell Level ◽  
Cell Level ◽  
Prophage Induction ◽  
Cell Counts

ABSTRACT The prophage-encoded Shiga toxin is a major virulence factor in Stx-producing Escherichia coli (STEC). Toxin production and phage production are linked and occur after induction of the RecA-dependent SOS response. However, food-related stress and Stx-prophage induction have not been studied at the single-cell level. This study investigated the effects of abiotic environmental stress on stx expression by single-cell quantification of gene expression in STEC O104:H4 Δstx2::gfp::amp r . In addition, the effect of stress on production of phage particles was determined. The lethality of stressors, including heat, HCl, lactic acid, hydrogen peroxide, and high hydrostatic pressure, was selected to reduce cell counts by 1 to 2 log CFU/ml. The integrity of the bacterial membrane after exposure to stress was measured by propidium iodide (PI). The fluorescent signals of green fluorescent protein (GFP) and PI were quantified by flow cytometry. The mechanism of prophage induction by stress was evaluated by relative gene expression of recA and cell morphology. Acid (pH < 3.5) and H2O2 (2.5 mM) induced the expression of stx 2 in about 18% and 3% of the population, respectively. The mechanism of prophage induction by acid differs from that of induction by H2O2. H2O2 induction but not acid induction corresponded to production of infectious phage particles, upregulation of recA, and cell filamentation. Pressure (200 MPa) or heat did not induce the Stx2-encoding prophage (Stx2-prophage). Overall, the quantification method developed in this study allowed investigation of prophage induction and physiological properties at the single-cell level. H2O2 and acids mediate different pathways to induce Stx2-prophage. IMPORTANCE Induction of the Stx-prophage in STEC results in production of phage particles and Stx and thus relates to virulence as well as the transduction of virulence genes. This study developed a method for a detection of the induction of Stx-prophages at the single-cell level; membrane permeability and an indication of SOS response to environmental stress were additionally assessed. H2O2 and mitomycin C induced expression of the prophage and activated a SOS response. In contrast, HCl and lactic acid induced the Stx-prophage but not the SOS response. The lifestyle of STEC exposes the organism to intestinal and extraintestinal environments that impose oxidative and acid stress. A more thorough understanding of the influence of food processing-related stressors on Stx-prophage expression thus facilitates control of STEC in food systems by minimizing prophage induction during food production and storage.

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.

scholarly journals Quality control of direct cell–mineral adhesion measurements in air and liquid using inverse AFM imaging

RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
C Cell ◽  
Afm Imaging ◽  
Direct Cell ◽  
Mineral Interaction

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.

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