scholarly journals Detection of uncoupled circadian rhythms in individual cells of Lemna minor using a dual-color bioluminescence monitoring system

2020 ◽  
Author(s):  
Emiri Watanabe ◽  
Minako Isoda ◽  
Tomoaki Muranaka ◽  
Shogo Ito ◽  
Tokitaka Oyama
Keyword(s):  
Circadian Rhythms ◽  
Single Cell ◽  
Monitoring System ◽  
Lemna Minor ◽  
Single Cell Level ◽  
Cell Level ◽  
Dual Color ◽  
Stochastic Phenomena ◽  
Circadian Behavior ◽  
Bioluminescent Reporters

SummaryThe plant circadian oscillation system is based on the circadian clock of individual cells and coordinates the circadian behavior of the plant body. To observe the cellular circadian behavior of both the oscillator and its output in plants, we developed the dual-color bioluminescence monitoring system that automatically measured the luminescence of two luciferase reporters simultaneously at a single-cell level. We selected a yellow-green-emitting firefly luciferase (LUC+) and a red-emitting luciferase (PtRLUC) that is a mutant form of Brazilian click beetle ELUC. We used AtCCA1::LUC+ and CaMV35S::PtRLUC to observe the cellular behavior of the oscillator and output, respectively. These bioluminescent reporters were introduced into the cells of a duckweed, Lemna minor, by particle bombardment. Time series of the bioluminescence of individual cells in a frond were obtained using a dual-color bioluminescence monitoring system with a green-pass- and red-pass filter. Luminescence intensities from the LUC+ and PtRLUC of each cell were calculated from the filtered luminescence intensities. We succeeded in reconstructing the bioluminescence behaviors of AtCCA1::LUC+ and CaMV35S::PtRLUC in the same cells. Under prolonged constant light conditions, AtCCA1::LUC+ showed a robust circadian rhythm in individual cells in an asynchronous state in the frond, as previously reported in studies using other plants. In contrast, CaMV35S::PtRLUC stochastically showed circadian rhythms in a synchronous state. Thus, we clearly demonstrated the uncoupling between the oscillator and output in individual cells. This dual-color bioluminescence monitoring system is a powerful tool to analyze various stochastic phenomena accompanying large cell-to-cell variation in gene expression.Significance statementWe succeeded in establishing the world’s first dual-color bioluminescence monitoring system at a single-cell level that enables simultaneous measurement of the luminescence activities of two reporter genes in plants. This system is a strong tool to analyze stochastic phenomena, and we clearly demonstrated the uncoupling of rhythmic behavior between two bioluminescent reporters in individual cells that stochastically occurred in the same plant.

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 Detection of uncoupled circadian rhythms in individual cells of Lemna minor using a dual-color bioluminescence monitoring system

2021 ◽  
Author(s):  
Emiri Watanabe ◽  
Minako Isoda ◽  
Tomoaki Muranaka ◽  
Shogo Ito ◽  
Tokitaka Oyama
Keyword(s):  
Circadian Rhythms ◽  
Monitoring System ◽  
Lemna Minor ◽  
Large Cell ◽  
Firefly Luciferase ◽  
Circadian Oscillation ◽  
Mutant Form ◽  
Oscillation System ◽  
Pass Filter ◽  
Dual Color

Abstract The plant circadian oscillation system is based on the circadian clock of individual cells. Circadian behavior of cells has been observed by monitoring the circadian reporter activity such as bioluminescence of AtCCA1::LUC+. To deeply analyze different circadian behaviors in individual cells, we developed the dual-color bioluminescence monitoring system that automatically measured the luminescence of two luciferase reporters simultaneously at a single-cell level. We selected a yellow-green-emitting firefly luciferase (LUC+) and a red-emitting luciferase (PtRLUC) that is a mutant form of Brazilian click beetle ELUC. We used AtCCA1::LUC+ and CaMV35S::PtRLUC. CaMV35S::LUC+ was previously reported as a circadian reporter with a low amplitude rhythm. These bioluminescent reporters were introduced into the cells of a duckweed, Lemna minor, by particle bombardment. Time series of the bioluminescence of individual cells in a frond were obtained using a dual-color bioluminescence monitoring system with a green-pass- and red-pass filter. Luminescence intensities from the LUC+ and PtRLUC of each cell were calculated from the filtered luminescence intensities. We succeeded in reconstructing the bioluminescence behaviors of AtCCA1::LUC+ and CaMV35S::PtRLUC in the same cells. Under prolonged constant light conditions, AtCCA1::LUC+ showed a robust circadian rhythm in individual cells in an asynchronous state in the frond, as previously reported. In contrast, CaMV35S::PtRLUC stochastically showed circadian rhythms in a synchronous state. These results strongly suggested the uncoupling of cellular behavior between these circadian reporters. This dual-color bioluminescence monitoring system is a powerful tool to analyze various stochastic phenomena accompanying large cell-to-cell variation in gene expression.

Characterization of the Cytokine Network at a Single Cell Level in Mice with Collagen-Induced Arthritis Using a Dual Color ELISPOT Assay

2000 ◽  
Vol 20 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Yoshihiro Okamoto ◽  
Yoshimitsu Gotoh ◽  
Hirotaka Tokui ◽  
Akira Mizuno ◽  
Yoshiharu Kobayashi ◽  
...  
Keyword(s):  
Single Cell ◽  
Elispot Assay ◽  
Single Cell Level ◽  
Cell Level ◽  
Collagen Induced Arthritis ◽  
Cytokine Network ◽  
Dual Color

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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