scholarly journals Cell-Line Annotation on Europe PubMed Central

2014 ◽  
Author(s):  
Jee-Hyub Kim
Keyword(s):  
Cell Culture ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Biomedical Literature ◽  
Pubmed Central ◽  
Preliminary Results ◽  
Data Citation ◽  
A Cell

A cell line is a cell culture developed from a single cell and therefore consisting of cells with a uniform genetic make-up. A cell line has an important role as a research resource such as organisms, antibodies, constructs, knockdown reagents, etc. Unique identification of cell lines in the biomedical literature is important for the reproducibility of science. As data citation, resource citation is also important for resource re-use. In this paper, we mention the challenges of identifying cell lines and describe a system for cell line annotation with preliminary results.

scholarly journals Human Astrovirus MLB Replication In Vitro: Persistence in Extraintestinal Cell Lines

2019 ◽  
Vol 93 (13) ◽  
Author(s):  
Diem-Lan Vu ◽  
Albert Bosch ◽  
Rosa M. Pintó ◽  
Enric Ribes ◽  
Susana Guix
Keyword(s):  
Cell Culture ◽  
Cell Line ◽  
Cell Lines ◽  
Culture System ◽  
Unknown Etiology ◽  
Cell Culture System ◽  
Persistent Infections ◽  
A Cell ◽  
Replicative Cycle

ABSTRACT MLB astroviruses were identified 10 years ago in feces from children with gastroenteritis of unknown etiology and have been unexpectedly detected in severe cases of meningitis/encephalitis, febrile illness of unknown etiology, and respiratory syndromes. The aim of this study was to establish a cell culture system supporting MLB astrovirus replication. We used two clinical strains to infect several cell lines, an MLB1 strain from a gastroenteritis case, and an MLB2 strain associated with a neurologic infection. Efforts to propagate the viruses in the Caco-2 cell line were unsuccessful. In contrast, we identified two human nonintestinal cell lines, Huh-7 and A549, permissive for both genotypes. After serial passages in the Huh-7.5 cell line, the adapted strains were able to establish persistent infections in the Huh-7.5, Huh-7AI, and A549 cell lines, with high viral loads (up to 10 log10 genome copies/ml) detected by quantitative reverse transcription-PCR (RT-qPCR) in the culture supernatant. Immunofluorescence assays demonstrated infection in about 10% of cells in persistently infected cultures. Electron microscopy revealed particles of 32 to 33 nm in diameter after negative staining of cell supernatants and capsid arrays in ultrathin sections with a particularly high production in Huh-7.5 cells. Interferon (IFN) expression by infected cells and effect of exogenous IFN varied depending on the type of infection and the cell line. The availability of a cell culture system to propagate MLB astroviruses represents a key step to better understand their replicative cycle, as well as a source of viruses to conduct a wide variety of basic virologic studies. IMPORTANCE MLB astroviruses are emerging viruses infecting humans. More studies are required to determine their exact epidemiology, but several reports have already identified them as the cause of unexpected clinical diseases, including severe neurologic diseases. Our study provides the first description of a cell culture system for the propagation of MLB astroviruses, enabling the study of their replicative cycle. Moreover, we demonstrated the unknown capacity of MLB astrovirus to establish persistent infections in cell culture. Whether these persistent infections are also established in vivo remains unknown, but the clinical consequences would be of high interest if persistence was confirmed in vivo. Finally, our analysis of IFN expression provides some trails to understand the mechanism by which MLB astroviruses can cause persistent infections in the assayed cultures.

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.

lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning

2021 ◽  
Author(s):  
Yang Lin ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Subcellular Localization ◽  
Cell Line ◽  
Cell Lines ◽  
Short Term Memory ◽  
Computational Method ◽  
Language Models ◽  
Supplementary Information ◽  
Deep Model ◽  
A Cell ◽  
Non Coding Rnas

Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cell-line-specific predictor lncLocator 2.0, which trains an end-to-end deep model per cell line, for predicting lncRNA subcellular localization from sequences.We first construct benchmark datasets of lncRNA subcellular localizations for 15 cell lines. Then we learn word embeddings using natural language models, and these learned embeddings are fed into convolutional neural network, long short-term memory and multilayer perceptron to classify subcellular localizations. lncLocator 2.0 achieves varying effectiveness for different cell lines and demonstrates the necessity of training cell-line-specific models. Furthermore, we adopt Integrated Gradients to explain the proposed model in lncLocator 2.0, and find some potential patterns that determine the subcellular localizations of lncRNAs, suggesting that the subcellular localization of lncRNAs is linked to some specific nucleotides. Availability The lncLocator 2.0 is available at www.csbio.sjtu.edu.cn/bioinf/lncLocator2 and the source code can be found at https://github.com/Yang-J-LIN/lncLocator2. Supplementary information Supplementary data are available at Bioinformatics online.

Fish cell culture: Characteristics of a cell line from the silver perch,Bairdiella chrysura

In Vitro ◽  
1977 ◽  
Vol 13 (6) ◽  
pp. 389-397 ◽  
Author(s):  
J. H. Wharton ◽  
R. D. Ellender ◽  
B. L. Middlebrooks ◽  
P. K. Stocks ◽  
Adrian R. Lawler ◽  
...  
Keyword(s):  
Cell Culture ◽  
Cell Line ◽  
Fish Cell ◽  
Fish Cell Culture ◽  
Culture Characteristics ◽  
A Cell

Quantitative cell fusion: the fusion sensitivity (FS) potential

1981 ◽  
Vol 49 (1) ◽  
pp. 87-97
Author(s):  
D. Rohme
Keyword(s):  
Cell Line ◽  
Cell Fusion ◽  
Cell Lines ◽  
Dose Response ◽  
Sendai Virus ◽  
Biological Significance ◽  
Diploid Cell ◽  
Mammalian Cell Lines ◽  
Virus Dose ◽  
A Cell

The dose response of Sendai virus-induced cell fusion was studied in 10 mammalian cell lines, comprising 5 continuous and 5 diploid cell lines originating from 5 species. The extent of fusion was calculated using a parameter directly proportional to the number of fusion events (t-parameter). At lower levels of fusion the dose response was found to be based on the same simple kinetic rules in all cell lines and was defined by the formula: t = FS. FAU/(I + FS. FAU), where FS (fusion sensitivity) is a cell-specific constant of the fusion rate and FAU (fusion activity units) is the virus dose. The FS potential of a cell line was determined as the linear regression coefficient of the fusion index (t/(I - t)) on the virus dose. At higher levels of fusion, when the fusion extent reached cell-line-specific maximal levels, the dose response was not as uniform. In general, and particularly in the cases of the diploid cell lines, these maximal levels were directly proportional to the FS potentials. Thus, it was concluded that the FS potential is the basic quantitative feature, which expresses the cellular fusion efficiency. The fact that FS varied extensively between cell lines, but at the same time apparently followed certain patterns (being higher in continuous compared to diploid cell lines and being related to the species of origin of the cells), emphasizes it biological significance as well as its possible usefulness in studies of the efficiency of various molecular interactions in the cell membrane/cytoskeleton system.

The Cisplatin, 5-fluorouracil, Irinotecan, and Gemcitabine Treatment in Resistant 2D and 3D Model Triple Negative Breast Cancer Cell Line: ABCG2 Expression Data

2021 ◽  
Vol 21 ◽  
Author(s):  
Fatma Kubra Ata ◽  
Serap Yalcin
Keyword(s):  
Breast Cancer ◽  
Cell Culture ◽  
Cell Line ◽  
Cell Lines ◽  
Expression Profile ◽  
Three Dimensional ◽  
Parental Cell ◽  
Parental Cell Line ◽  
Xtt Assay ◽  
Gene And Protein Expression

Background: Chemotherapeutics have been commonly used in cancer treatment. Objective: In this study, the effects of Cisplatin, 5-fluorouracil, Irinotecan, and Gemcitabine have been evaluated on two-dimensional (2D) (sensitive and resistance) cell lines and three dimensional (3D) spheroid structure of MDA-MB-231. The 2D cell culture lacks a natural tissue-like structural so, using 3D cell culture has an important role in the development of effective drug testing models. Furthermore, we analyzed the ATP Binding Cassette Subfamily G Member 2 (ABCG2) gene and protein expression profile in this study. We aimed to establish a 3D breast cancer model that can mimic the in vivo 3D breast cancer microenvironment. Methods: The 3D spheroid structures were multiplied (globally) using the three-dimensional hanging drop method. The cultures of the parental cell line MDA-MB-231 served as the controls. After adding the drugs in different amounts we observed a clear and well-differentiated spheroid formation for 24 h. The viability and proliferation capacity of 2D (sensitive and resistant) cell lines and 3D spheroid cell treatment were assessed by the XTT assay. Results: Cisplatin, Irinotecan, 5-Fu, and Gemcitabine-resistant MDA-MB-231 cells were observed to begin to disintegrate in a three-dimensional clustered structure at 24 hours. Additionally, RT-PCR and protein assay showed overexpression of ABCG2 when compared to the parental cell line. Moreover, MDA-MB-231 cells grown in 3D showed decreased sensitivity to chemotherapeutics treatment. Conclusion: More resistance to chemotherapeutics and altered gene expression profile was shown in 3D cell cultures when compared with the 2D cells. These results might play an important role to evaluate the efficacy of anticancer drugs, explore mechanisms of MDR in the 3D spheroid forms.

scholarly journals Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Zeljana Babic ◽  
Amanda Capes-Davis ◽  
Maryann E Martone ◽  
Amos Bairoch ◽  
I Burak Ozyurt ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Biomedical Research ◽  
Cell Line Authentication ◽  
Pubmed Central ◽  
International Cell ◽  
Unique Cell

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.

scholarly journals A Place for High-Throughput Electrophysiology in Cardiac Safety: Screening hERG Cell Lines and Novel Compounds with the Ion Works HTTM System

2005 ◽  
Vol 10 (8) ◽  
pp. 832-840 ◽  
Author(s):  
Heather Guthrie ◽  
Frederick S. Livingston ◽  
Ueli Gubler ◽  
Ralph Garippa
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Torsades De Pointes ◽  
Delayed Rectifier ◽  
Drug Candidates ◽  
Delayed Rectifier Potassium Channel ◽  
A Cell ◽  
Line Selection ◽  
Herg Channels ◽  
Cell Line Selection

Several commercially available pharmaceutical compounds have been shown to block the I Krcurrent of the cardiac action potential. This effect can cause a prolongation of the electrocardiogram QTinterval and a delay in ventricular repolarization. The Food and Drug Administration recommends that all new potential drug candidates be assessed for I Krblock to avoid a potentially lethal cardiac arrhythmia known as torsades de pointes. Direct compound interaction with the human ether-a-go-go– related gene (hERG) product, a delayed rectifier potassium channel, has been identified as a molecular mechanism of I Kr block. One strategy to identify compounds withh ERGliability is to monitor hERGcurrent inhibition using electrophysiology techniques. The authors describe the Ion Works HT ™instrument as a tool for screening cell lines expressing hERG channels. Based on current amplitude and stability criteria, a cell line was selected and used to perform a 300-compound screen. The screen was able to identify compounds with hERG activity within projects that spanned different therapeutic areas. The cell line selection and optimization, as well as the screening abilities of the Ion Works HT ™system, provide a powerful means of assessinghERGactive compounds early in the drug discovery pipeline.

scholarly journals Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Noemi Andor ◽  
Billy T Lau ◽  
Claudia Catalanotti ◽  
Anuja Sathe ◽  
Matthew Kubit ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Cancer Cell Lines ◽  
Gastric Cancer Cell Lines

Abstract Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

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