Dataset Information

Authentication of Primary Murine Cell Lines by a Microfluidics-Based Lab-On-Chip System.

ABSTRACT: The reliable authentication of cell lines is a prerequisite for the reproducibility and replicability of experiments. A common method of cell line authentication is the fragment length analysis (FLA) of short-tandem repeats (STR) by capillary electrophoresis. However, this technique is not always accessible and is often costly. Using a microfluidic electrophoresis system, we analyzed the quality and integrity of different murine cell lines by STR profiling. As a proof of concept, we isolated and immortalized hematopoietic progenitor cells (HPC) of various genotypes through retroviral transduction of the fusion of the estrogen receptor hormone-binding domain with the coding sequence of HoxB8. Cell lines were maintained in the HPC state with Flt3 ligand (FL) and estrogen treatment and could be characterized upon differentiation. In a validation cohort, we applied this technique on primary mutant Kras-driven pancreatic cancer cell lines, which again allowed for clear discrimination. In summary, our study provides evidence that FLA of STR-amplicons by microfluidic electrophoresis allows for stringent quality control and the tracking of cross-contaminations in both genetically stable HPC lines and cancer cell lines, making it a simple and cost-efficient alternative to traditional capillary electrophoresis.

SUBMITTER: Hong Y

PROVIDER: S-EPMC7763653 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Authentication of Primary Murine Cell Lines by a Microfluidics-Based Lab-On-Chip System.

Hong Yingfen Y Singh Nikita N Bamopoulos Stefanos S Gjerga Enio E Schmalbrock Laura K LK Balabanian Karl K Schick Markus M Keller Ulrich U Wirth Matthias M

Biomedicines 20201209 12

The reliable authentication of cell lines is a prerequisite for the reproducibility and replicability of experiments. A common method of cell line authentication is the fragment length analysis (FLA) of short-tandem repeats (STR) by capillary electrophoresis. However, this technique is not always accessible and is often costly. Using a microfluidic electrophoresis system, we analyzed the quality and integrity of different murine cell lines by STR profiling. As a proof of concept, we isolated and ...[more]

PMID: 33317212

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Project description:Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Project description:The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of colistin-resistant organisms. This work demonstrates a rapid response for detecting the most recently reported mcr gene, mcr-9, using a portable and affordable lab-on-a-chip (LoC) platform, offering a promising alternative to conventional laboratory-based instruments such as real-time PCR (qPCR). The platform combines semiconductor technology, for non-optical real-time DNA sensing, with a smartphone application for data acquisition, visualization and cloud connectivity. This technology is enabled by using loop-mediated isothermal amplification (LAMP) as the chemistry for targeted DNA detection, by virtue of its high sensitivity, specificity, yield, and manageable temperature requirements. Here, we have developed the first LAMP assay for mcr-9 - showing high sensitivity (down to 100 genomic copies/reaction) and high specificity (no cross-reactivity with other mcr variants). This assay is demonstrated through supporting a hospital investigation where we analyzed nucleic acids extracted from 128 carbapenemase-producing bacteria isolated from clinical and screening samples and found that 41 carried mcr-9 (validated using whole genome sequencing). Average positive detection times were 6.58 ± 0.42 min when performing the experiments on a conventional qPCR instrument (n = 41). For validating the translation of the LAMP assay onto a LoC platform, a subset of the samples were tested (n = 20), showing average detection times of 6.83 ± 0.92 min for positive isolates (n = 14). All experiments detected mcr-9 in under 10 min, and both platforms showed no statistically significant difference (p-value > 0.05). When sample preparation and throughput capabilities are integrated within this LoC platform, the adoption of this technology for the rapid detection and surveillance of antimicrobial resistance genes will decrease the turnaround time for DNA detection and resistotyping, improving diagnostic capabilities, patient outcomes, and the management of infectious diseases.

Dataset Information

Authentication of Primary Murine Cell Lines by a Microfluidics-Based Lab-On-Chip System.

Publications

Authentication of Primary Murine Cell Lines by a Microfluidics-Based Lab-On-Chip System.

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