scholarly journals Visualization of phage DNA degradation by a type I CRISPR-Cas system at the single-cell level

2017 ◽  
Vol 5 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Jingwen Guan ◽  
Xu Shi ◽  
Roberto Burgos ◽  
Lanying Zeng
Keyword(s):  
Single Cell ◽  
Dna Degradation ◽  
Type I ◽  
Single Cell Level ◽  
Cell Level ◽  
Phage Dna

scholarly journals Tracking Single Cells Motility on Different Substrates

2020 ◽  
Vol 3 (3) ◽  
pp. 56
Author(s):  
Pooja Sharma ◽  
Van K. Lam ◽  
Christopher B. Raub ◽  
Byung Min Chung
Keyword(s):  
Cell Motility ◽  
Single Cell ◽  
Cancer Metastasis ◽  
Type I Collagen ◽  
Single Cells ◽  
Cost Effective ◽  
Type I ◽  
Single Cell Level ◽  
Cell Level ◽  
Average Cell

Motility is a key property of a cell, required for several physiological processes, including embryonic development, axon guidance, tissue regeneration, gastrulation, immune response, and cancer metastasis. Therefore, the ability to examine cell motility, especially at a single cell level, is important for understanding various biological processes. Several different assays are currently available to examine cell motility. However, studying cell motility at a single cell level can be costly and/or challenging. Here, we describe a method of tracking random cell motility on different substrates such as glass, tissue-culture polystyrene, and type I collagen hydrogels, which can be modified to generate different collagen network microstructures. In this study we tracked MDA-MB-231 breast cancer cells using The CytoSMARTTM System (Lonza Group, Basel, Switzerland) for live cell imaging and assessed the average cell migration speed using ImageJ and wrMTrck plugin. Our cost-effective and easy-to-use method allows studying cell motility at a single cell level on different substrates with varying degrees of stiffness and varied compositions. This procedure can be successfully performed in a highly accessible manner with a simple setup.

Persistent RNA virus infection is short-lived at the single-cell level but leaves transcriptomic footprints

2021 ◽  
Vol 218 (10) ◽  
Author(s):  
Peter Reuther ◽  
Katrin Martin ◽  
Mario Kreutzfeldt ◽  
Matias Ciancaglini ◽  
Florian Geier ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Persistent Infection ◽  
Rna Virus ◽  
Intrinsic Clearance ◽  
Viral Clearance ◽  
Type I ◽  
Matrix Remodeling ◽  
Single Cell Level ◽  
Cell Level

Several RNA viruses can establish life-long persistent infection in mammalian hosts, but the fate of individual virus-infected cells remains undefined. Here we used Cre recombinase–encoding lymphocytic choriomeningitis virus to establish persistent infection in fluorescent cell fate reporter mice. Virus-infected hepatocytes underwent spontaneous noncytolytic viral clearance independently of type I or type II interferon signaling or adaptive immunity. Viral clearance was accompanied by persistent transcriptomic footprints related to proliferation and extracellular matrix remodeling, immune responses, and metabolism. Substantial overlap with persistent epigenetic alterations in HCV-cured patients suggested a universal RNA virus-induced transcriptomic footprint. Cell-intrinsic clearance occurred in cell culture, too, with sequential infection, reinfection cycles separated by a period of relative refractoriness to infection. Our study reveals that systemic persistence of a prototypic noncytolytic RNA virus depends on continuous spread and reinfection. Yet undefined cell-intrinsic mechanisms prevent viral persistence at the single-cell level but give way to profound transcriptomic alterations in virus-cleared cells.

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.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

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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