Detection and quantification of endoplasmic reticulum stress in living cells using the fluorescent compound, Thioflavin T

In vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate, in real time, the processes occurring in living cells. At present, there are fluorescence, protein-based, sensory systems for detecting various substances in living cells (for example, hydrogen peroxide, ATP, Ca2+ etc.,) or for detecting processes such as endoplasmic reticulum stress. Such systems help to study the mechanisms underlying the pathogenic processes and diseases and to screen for potential therapeutic compounds. It is also necessary to develop new tools for the processing and analysis of obtained microimages. Here, we present our web-application CellCountCV for automation of microscopic cell images analysis, which is based on fully convolutional deep neural networks. This approach can efficiently deal with non-convex overlapping objects, that are virtually inseparable with conventional image processing methods. The cell counts predicted with CellCountCV were very close to expert estimates (the average error rate was < 4%). CellCountCV was used to analyze large series of microscopic images obtained in experimental studies and it was able to demonstrate endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin.

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X-Ray fluorescence microscopy reveals that rhenium(i) tricarbonyl isonitrile complexes remain intact in vitro

Chemical Communications ◽

10.1039/d0cc02451a ◽

2020 ◽

Vol 56 (48) ◽

pp. 6515-6518 ◽

Cited By ~ 3

Author(s):

Chilaluck C. Konkankit ◽

James Lovett ◽

Hugh H. Harris ◽

Justin J. Wilson

Keyword(s):

Endoplasmic Reticulum ◽

Endoplasmic Reticulum Stress ◽

Fluorescence Microscopy ◽

Living Cells ◽

Axial Ligand ◽

X Ray

An endoplasmic reticulum stress-inducing rhenium isonitrile complex was investigated for its axial ligand stability in living cells using X-ray fluorescence microscopy.

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Protein-AMPylierungs-Identifikation in lebenden Zellen

BIOspektrum ◽

10.1007/s12268-020-1491-2 ◽

2020 ◽

Vol 26 (7) ◽

pp. 743-746

Author(s):

Tobias Becker ◽

Pavel Kielkowski

Keyword(s):

Endoplasmic Reticulum ◽

Endoplasmic Reticulum Stress ◽

Neural Development ◽

Click Reaction ◽

Living Cells ◽

Fluorescence Labeling ◽

Post Translational Modification ◽

Design Synthesis ◽

Stress Regulation

AbstractProtein AMPylation is a prevalent protein post-translational modification in human cells involved in endoplasmic reticulum stress regulation and neural development. In this article we describe the design, synthesis and application of a pronucleotide probe suitable for in situ fluorescence imaging and chemical protemics profiling of AMPylated proteins. Our probe utilizes straightforward strain-promoted azidealkyne click reaction for fluorescence labeling in living cells.

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Senescent Changes and Endoplasmic Reticulum Stress May Be Involved in the Pathogenesis of Missed Miscarriage

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.656549 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yunhui Tang ◽

Xinyan Zhang ◽

Yi Zhang ◽

Hua Feng ◽

Jing Gao ◽

...

Keyword(s):

Oxidative Stress ◽

Endoplasmic Reticulum ◽

Endoplasmic Reticulum Stress ◽

First Trimester ◽

Explant Culture ◽

Misfolded Proteins ◽

Ros Production ◽

Oxygen Species ◽

Fluorescent Compound ◽

Explant Cultures

BackgroundSenescence is involved in many complications of pregnancy. However, whether senescent changes are also associated with missed miscarriage has not been fully investigated.MethodsThe levels of p16, p21, and γH2AX, markers of senescence, were measured in placentas collected from women with missed miscarriage by immunohistochemistry and Western blotting. Levels of misfolded proteins in missed miscarriage placentas or normal first-trimester placenta that had been treated with H2O2 (100 μM) or extracellular vesicles (EVs) collected from missed miscarriage placental explant culture were measured by fluorescent compound, thioflavin-T. The production of reactive oxygen species (ROS) by missed miscarriage placentas was measured by CellROX® Deep Red.ResultsIncreased levels of p16, p21, and γH2AX were presented in missed miscarriage placentas compared to controls. Increased levels of misfolded proteins were shown in missed miscarriage placentas, but not in EVs that were collected from missed miscarriage placentas. The ROS production was significantly increased in missed miscarriage placental explant cultures. Increased levels of misfolded proteins were seen in the normal first-trimester placenta that had been treated with H2O2 compared to untreated.ConclusionOur data demonstrate that there are increases in senescence and endoplasmic reticulum stress and ROS production in missed miscarriage placenta. Oxidative stress and an accumulation of misfolded proteins in missed miscarriage placentas may contribute to the changes of senescence and endoplasmic reticulum stress seen in missed miscarriage placentas.

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CellCountCV – a web-application for accurate cell counting and automated batch processing of microscopy images using fully-convolutional neural networks

10.1101/867218 ◽

2019 ◽

Author(s):

Denis Antonets ◽

Nikolai Russkikh ◽

Antoine Sanchez ◽

Victoria Kovalenko ◽

Elvira Bairamova ◽

...

Keyword(s):

Neural Networks ◽

Endoplasmic Reticulum ◽

Endoplasmic Reticulum Stress ◽

Web Application ◽

Experimental Studies ◽

Living Cells ◽

Cell Counting ◽

Cellular Models ◽

Cell Counts ◽

Microscopy Images

ABSTRACTThe in vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate the processes occurring in living cells in real time. Today, there are fluorescence protein based sensory systems for detecting various substances in living cells (for example, hydrogen peroxide, ATP, Ca2+ etc.) or for detecting processes such as endoplasmic reticulum stress. Such systems help to study mechanisms underlying the pathogenic processes and diseases and for screening potential therapeutic compounds. It is also necessary to develop new tools for processing and analysis of obtained microimages. Here we present our web-application CellCountCV for automation of microscopy cell images analysis which is based on fully-convolutional deep neural networks. This approach can efficiently deal with non-convex overlapping objects, that are virtually inseparable with conventional image processing methods. The cell counts predicted with CellCountCV were very close to expert estimates (the average error rate was < 4%). CellCountCV was used to analyse large series of microscopy images obtained in experimental studies and it was able to demonstrate the endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin.

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