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 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 High-throughput Sequencing for Species Authentication and Contamination Detection of 63 Cell Lines

2021 ◽  
Author(s):  
Oliver Lung ◽  
Rebecca Candlish ◽  
Michelle Nebroski ◽  
Peter Kruckiewicz ◽  
Cody Buchanan ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Classical Swine Fever ◽  
Virus Genome ◽  
Cox1 Gene ◽  
Species Identity ◽  
Cell Line Authentication ◽  
Dna And Rna

Abstract Cell lines are widely used in research and for diagnostic tests and are often shared between laboratories. Lack of cell line authentication can result in the use of contaminated or misidentified cell lines, potentially affecting the results from research and diagnostic activities. Cell line authentication and contamination detection based on metagenomic high-throughput sequencing (HTS) was tested on DNA and RNA from 63 cell lines available at the Canadian Food Inspection Agency’s National Centre for Foreign Animal Disease. Through sequence comparison of the cytochrome c oxidase subunit 1 (COX1) gene, the species identity of 53 cell lines was confirmed, and eight cell lines were found to show a greater pairwise nucleotide identity in the COX1 sequence of a different species within the same expected genus. Two cell lines, LFBK-αvβ6 and SCP-HS, were determined to be composed of cells from a different species and genus. Mycoplasma contamination was not detected in any cell lines. However, several expected and unexpected viral sequences were detected, including part of the classical swine fever virus genome in the IB-RS-2 Clone D10 cell line. Metagenomics-based HTS is a useful laboratory QA tool for cell line authentication and contamination detection that should be conducted regularly.

scholarly journals Characterization of a novel embryonic stem cell line from an ICSI-derived blastocyst in the African green monkey

Reproduction ◽  
2010 ◽  
Vol 139 (3) ◽  
pp. 565-573 ◽  
Author(s):  
Nobuhiro Shimozawa ◽  
Shinichiro Nakamura ◽  
Ichiro Takahashi ◽  
Masanori Hatori ◽  
Tadashi Sankai
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Biomedical Research ◽  
Es Cells ◽  
Cell Monolayer ◽  
Embryonic Stem ◽  
African Green Monkey ◽  
Embryoid Bodies ◽  
Es Cell ◽  
Green Monkey

Several cell types from the African green monkey (Cercopithecus aethiops), such as red blood cells, primary culture cells from kidney, and the Vero cell line, are valuable sources for biomedical research and testing. Embryonic stem (ES) cells that are established from blastocysts have pluripotency to differentiate into these and other types of cells. We examined an in vitro culture system of zygotes produced by ICSI in African green monkeys and attempted to establish ES cells. Culturing with and without a mouse embryonic fibroblast (MEF) cell monolayer resulted in the development of ICSI-derived zygotes to the blastocyst stage, while culturing with a buffalo rat liver cell monolayer yielded no development (3/14, 21.4% and 6/31, 19.4% vs 0/23, 0% respectively; P<0.05). One of the nine blastocysts, which had been one of the zygotes co-cultured with MEF cells, formed flat colonies consisting of cells with large nuclei, similar to other primate ES cell lines. The African green monkey ES (AgMES) cells expressed pluripotency markers, formed teratomas consisting of three embryonic germ layer tissues, and had a normal chromosome number. Furthermore, expression of the germ cell markers CD9 and DPPA3 (STELLA) was detected in the embryoid bodies, suggesting that AgMES cells might have the potential ability to differentiate into germ cells. The results suggested that MEF cells greatly affected the quality of the inner cell mass of the blastocysts. In addition, AgMES cells would be a precious resource for biomedical research such as other primate ES cell lines.

Traceability Methods for Cell Line Authentication and Mycoplasma Detection

2021 ◽  
pp. 247263032110302
Author(s):  
Kate Dennert ◽  
Rajeev Kumar
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Research Community ◽  
Surveillance Strategy ◽  
Cell Line Authentication ◽  
Mycoplasma Detection ◽  
Established Cell Lines ◽  
Downstream Effects ◽  
Potential Problems ◽  
Established Cell

Many laboratories struggle with mycoplasma contamination and cell line misidentification when growing cells in culture. These well-documented issues affect the scientific research community and have detrimental downstream effects. Research published with suspect cultures can produce misleading results. There is increasing pressure to verify the integrity of experimental and established cell lines before publishing. Therefore, laboratories need to define how and when to perform these critical tests, analyze the results, and determine action plans if disparities exist. Our laboratory is committed to producing cell lines of the highest quality for use in experiments; thus, we created a surveillance strategy for these potential problems. We developed processes for both testing and tracing cell line authentication and mycoplasma detection data. Using these methods, we can protect the integrity of our patient and commercial cell lines, maintaining reliable cultures for our research.

scholarly journals Digital Karyotyping for Rapid Authentication of Cell Lines

2019 ◽  
Author(s):  
Ahmed Ibrahim Samir Khalil ◽  
Anupam Chattopadhyay ◽  
Amartya Sanyal
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
De Novo ◽  
Simulated Data ◽  
Read Depth ◽  
Cancer Cell Lines ◽  
Scientific Data ◽  
Cell Line Authentication ◽  
A Cell

Abstract Background The widespread concern about genetic drift and cross-contamination of cell lines calls for a pressing need for their authentication. The current genetic techniques for authentication are time-consuming and require specific documentary standard and laboratory protocols. Given the fact that whole-genome sequencing (WGS) data are readily available, read depth (RD)-based computational analyses has allowed the estimation of genetic profiles of cell lines. Results We propose WGS-derived aneuploidy profiling as a prototype of digital karyotyping for authentication of cancer cell lines. Here, we describe a Python-based software AStra for de novo estimation of the genome-wide aneuploidy profile, the copy number of every genomic loci, from raw WGS reads. We demonstrated that aneuploidy profile offers a unique signature that can distinguish the clonal variants (strains) of a cell line. We evaluated our approach using simulated data and variety of cancer cell lines. We further showed that cell lines exhibit distinct aneuploidy patterns which corroborate well with the experimental observations. Conclusions AStra is a simple, user-friendly, and free tool that provides the elementary information about the chromosomal aneuploidy for cell line authentication. AStra provides an analytical and visualization platform for rapid and easy comparison between different cell lines/strains. We recommend AStra for rapid first-pass quality assessment of scientific data that employ cancer cell lines. AStra is an open source software and is available at https://github.com/AISKhalil/AStra.

scholarly journals Recent developments of the cell line integrated molecular authentication database

2016 ◽  
Author(s):  
Paolo D Romano ◽  
Paola Visconti ◽  
Barbara Parodi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Animal Cell ◽  
Cell Line Authentication ◽  
Molecular Authentication ◽  
Standard Data ◽  
Str Profile ◽  
Frequent Event ◽  
Recent Developments ◽  
Cell Banks

Cross-contamination of human and animal cell lines is a frequent event. For this reason, the results obtained with the same cell lines by different research groups are often not fully comparable, this leading to main reproducibility issues. The short tandem repeat (STR) profile has been proposed as a molecular method for cell line authentication. STR profile standard data sets for human cell lines were proposed by some of the leading cell banks worldwide which also have made the results of STR profiling of their cell lines available on-line. We have built the Cell Line Integrated Molecular Authentication Database (CLIMA) as a reference portal where authentication data are made available to the scientific community. This system, although already largely utilized by researchers from all over the world, presented some limitations and only included a limited amount of STR profiles. Here, we present its most recent developments: the inclusion of additional cell banks and profiles and the availability of a new identification tool.

scholarly journals Genetic Profile Evaluation of Human Cell Lines Treated with Anastatica hierochuntica Using Forensic DNA Fingerprinting Markers

2021 ◽  
Vol 3 (2) ◽  
pp. 231-244
Author(s):  
Saranya Rameshbabu ◽  
Mohammed S. Ali ◽  
Abrar B. Alsaleh ◽  
Anuradha Venkatraman ◽  
Safia A. Messaoudi
Keyword(s):  
Cell Line ◽  
Hepg2 Cell ◽  
Cell Lines ◽  
Tandem Repeats ◽  
Dna Analysis ◽  
Genetic Profile ◽  
Forensic Dna ◽  
Cell Line Authentication ◽  
Hepg2 Cell Lines ◽  
Mcf 7

Cell line authentication using Short Tandem Repeats (STRs) is necessary to ensure the integrity of the cell for its continuous culture and to identify misidentification and cross-contamination issues. This study investigates the changes in the genetic profile of MCF-7 and HepG2 cell lines caused by the methanolic leaf extract of Anastatica hierochuntica (AH) using human identification based STR markers. MCF-7 and HepG2 cell lines were treated with various concentrations of AH extracts for three different periods. The treated and control cells' DNA was extracted using a QIAamp® DNA Micro Kit, quantified using a Quantifiler Duo DNA Quantification Kit, and amplified using an AmpFlSTR Identifiler plus PCR Amplification Kit. The concentrations of the DNA extracted from control and MCF-7 and HepG2 cell lines treated with AH extract at 300 to 2400 µg/ml for 24hr and 150 to 2400 µg/ml for 48 and 72hrs were statistically significant (p<0.05). Microsatellite instability (MSI), loss of heterozygosity (LOH), insertion/deletions changes in the STRs profile were observed in treated cell lines at 1200 and 2400 µg/ml in MCF-7 cells for 48 and 72hrs and HepG2 cells for 24, 48, and 72hrs. We conclude that the highest concentration of AH extracts affected the genotype of the cell lines leading to misidentification. Therefore, cell line authentication by forensic DNA analysis techniques plays a decisive role for cells tested with a high concentration of chemical compounds and gives the forensic investigator an insight into these changes in the STR genotype of a victim/suspect who has been been under long term chemotherapeutic treatment.

scholarly journals An Automated Cell Line Authentication Method for AstraZeneca Global Cell Bank using Deep Neural Networks on Brightfield Images

2021 ◽  
Author(s):  
Lei Tong ◽  
Adam Corrigan ◽  
Navin Rathna Kumar ◽  
Kerry Hallbrook ◽  
Jonathon Orme ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cell Line ◽  
Cell Lines ◽  
Deep Neural Networks ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Great Success ◽  
Cell Bank ◽  
Cell Line Authentication ◽  
Gold Standard Method

Abstract Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines’ data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a sensitivity of 97.7% and specificity of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images.

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