scholarly journals Watching antibiotics in action: Exploiting time-lapse microfluidic microscopy as a tool for target-drug interaction studies inMycobacterium

2018 ◽  
Author(s):  
Damian Trojanowski ◽  
Marta Kołodziej ◽  
Joanna Hołówka ◽  
Rolf Müller ◽  
Jolanta Zakrzewska-Czerwińska

AbstractSpreading resistance to antibiotics and the emergence of multidrug-resistant strains have become frequent in many bacterial species, including mycobacteria. The genusMycobacteriumencompasses both human and animal pathogens that cause severe diseases and have profound impacts on global health and the world economy. Here, we used a novel system of microfluidics, fluorescence microscopy and target-tagged fluorescent reporter strains ofM.smegmatisto perform real-time monitoring of replisome and chromosome dynamics following the addition of replication-altering drugs (novobiocin, nalidixic acid and griselimycin) at the single-cell level. We found that novobiocin stalled replication forks and caused relaxation of the nucleoid, nalidixic acid triggered rapid replisome collapse and compaction of the nucleoid, and griselimycin caused replisome instability with subsequent over-initiation of chromosome replication and over-relaxation of the nucleoid. This work is an example of using a microscopy-based approach to evaluate the activity of potential replication inhibitors and provides mechanistic insights into their modes of action. Our system also enabled us to observe how the tested antibiotics affected the physiology of mycobacterial cells (i.e., growth, chromosome segregation, etc.). Because proteins involved in the DNA replication are well conserved among bacteria (including mycobacterial species), the properties of various replication inhibitors observed here in fast-growingM. smegmatismay be easily extrapolated to slow-growing pathogenic tubercle bacilli, such asM. tuberculosis.SignificanceThe growing problem of bacterial resistance to antibiotics and the emergence of new strains that are resistant to multiple drugs raise the need to explore new antibiotics and re-evaluate the existing options. Here, we present a system that allows the action of antibiotics to be monitored at the single-cell level. Such studies are important in the light of bacterial heterogeneity, which may be enhanced in unfavorable conditions, such as under antibiotic treatment. Moreover, our studies provide mechanistic insights into the action modes of the tested compounds. As combined therapies have recently gained increased interest, it is also notable that our described system may help researchers identify the best combination of antimicrobials for use against infections caused by a variety of bacteria.

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Nicolas Lemus-Diaz ◽  
Kai O. Böker ◽  
Ignacio Rodriguez-Polo ◽  
Michael Mitter ◽  
Jasmin Preis ◽  
...  

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  

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.


RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl

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.


2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.


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