Best practices for naming, receiving, and managing cells in culture

A SARS-CoV-2 minimum data standard to support national serology reporting

BACKGROUND

Healthcare laboratory systems produce and capture a vast array of information, yet do not always report all of this to the national infrastructure within the United Kingdom. The global COVID-19 pandemic brought about a much greater need for detailed healthcare data, one such instance being laboratory testing data. The reporting of qualitative laboratory test results (e.g. positive, negative or indeterminate) provides a basic understanding of levels of seropositivity. However, to better understand and interpret seropositivity, how it is determined and other factors that affect its calculation (i.e. levels of antibodies), quantitative laboratory test data are needed.

METHOD

36 data attributes were collected from 3 NHS laboratories and 29 CO-CONNECT project partner organisations. These were assessed against the need for a minimum dataset to determine data attribute importance. An NHS laboratory feasibility study was undertaken to assess the minimum data standard, together with a literature review of national and international data standards and healthcare reports.

RESULTS

A COVID serology minimum data standard (CSMDS) comprising 12 data attributes was created and verified by 3 NHS laboratories to allow national granular reporting of COVID serology results. To support this, a standardised set of vocabulary terms was developed to represent laboratory analyser systems and laboratory information management systems.

CONCLUSIONS

This paper puts forward a minimum viable standard for COVID-19 serology data attributes to enhance its granularity and augment the national reporting of COVID-19 serology laboratory results, with implications for future pandemics.

Misspellings or "miscellings"-Non-verifiable and unknown cell lines in cancer research publications

Reproducible laboratory research relies on correctly identified reagents. We have previously described gene research papers with wrongly identified nucleotide sequence(s), including papers studying miR-145. Manually verifying reagent identities in 36 recent miR-145 papers found that 56% and 17% of papers described misidentified nucleotide sequences and cell lines, respectively. We also found 5 cell line identifiers in miR-145 papers with misidentified nucleotide sequences and cell lines, and 18 cell line identifiers published elsewhere, that did not represent indexed human cell lines. These 23 identifiers were described as non-verifiable (NV), as their identities were unclear. Studying 420 papers that mentioned 8 NV identifier(s) found 235 papers (56%) that referred to 7 identifiers (BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803, and MGC-823) as independent cell lines. We could not find any publications describing how these cell lines were established. Six cell lines were sourced from cell line repositories with externally accessible online catalogs, but these cell lines were not indexed as claimed. Some papers also stated that short tandem repeat (STR) profiles had been generated for three cell lines, yet no STR profiles could be identified. In summary, as NV cell lines represent new challenges to research integrity and reproducibility, further investigations are required to clarify their status and identities.

Detection of viral infection in cell lines using ViralCellDetector

Cell lines are commonly used in research to study biology, including gene expression regulation, cancer progression, and drug responses. However, cross-contaminations with bacteria, mycoplasma, and viruses are common issues in cell line experiments. Detection of bacteria and mycoplasma infections in cell lines is relatively easy but identifying viral infections in cell lines is difficult. Currently, there are no established methods or tools available for detecting viral infections in cell lines. To address this challenge, we developed a tool called ViralCellDetector that detects viruses through mapping RNA-seq data to a library of virus genome. Using this tool, we observed that around 10% of experiments with the MCF7 cell line were likely infected with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral infection, we identified the differentially expressed genes involved in viral infection from two different cell lines and used these genes in a machine learning approach to classify infected samples based on the host response gene expression biomarkers. Our model reclassifies the infected and non-infected samples with an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping- and marker-based approaches can detect viral infections in any cell line simply based on readily accessible RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.

Major changes of cell function and toxicant sensitivity in cultured cells undergoing mild, quasi-natural genetic drift

Genomic drift affects the functional properties of cell lines, and the reproducibility of data from in vitro studies. While chromosomal aberrations and mutations in single pivotal genes are well explored, little is known about effects of minor, possibly pleiotropic, genome changes. We addressed this question for the human dopaminergic neuronal precursor cell line LUHMES by comparing two subpopulations (SP) maintained either at the American-Type-Culture-Collection (ATCC) or by the original provider (UKN). Drastic differences in susceptibility towards the specific dopaminergic toxicant 1-methyl-4-phenylpyridinium (MPP+) were observed. Whole-genome sequencing was performed to identify underlying genetic differences. While both SP had normal chromosome structures, they displayed about 70 differences on the level of amino acid changing events. Some of these differences were confirmed biochemically, but none offered a direct explanation for the altered toxicant sensitivity pattern. As second approach, markers known to be relevant for the intended use of the cells were specifically tested. The “ATCC” cells rapidly down-regulated the dopamine-transporter and tyrosine-hydroxylase after differentiation, while “UKN” cells maintained functional levels. As the respective genes were not altered themselves, we conclude that polygenic complex upstream changes can have drastic effects on biochemical features and toxicological responses of relatively similar SP of cells.

HuH-7 reference genome profile: complex karyotype composed of massive loss of heterozygosity

Human cell lines represent a valuable resource as in vitro experimental models. A hepatoma cell line, HuH-7 (JCRB0403), has been used extensively in various research fields and a number of studies using this line have been published continuously since it was established in 1982. However, an accurate genome profile, which can be served as a reliable reference, has not been available. In this study, we performed M-FISH, SNP microarray and amplicon sequencing to characterize the cell line. Single cell analysis of metaphases revealed a high level of heterogeneity with a mode of 60 chromosomes. Cytogenetic results demonstrated chromosome abnormalities involving every chromosome in addition to a massive loss of heterozygosity, which accounts for 55.3% of the genome, consistent with the homozygous variants seen in the sequence analysis. We provide empirical data that the HuH-7 cell line is composed of highly heterogeneous cell populations, suggesting that besides cell line authentication, the quality of cell lines needs to be taken into consideration in the future use of tumor cell lines.

Screening for 15 pathogenic viruses in human cell lines registered at the JCRB Cell Bank: characterization of in vitro human cells by viral infection

Human cell lines have been used in a variety of research fields as an in vitro model. These cells are all derived from human tissue samples, thus there is a possibility of virus infection. Virus tests are routinely performed in clinical practice, but are limited in cell lines. In this study, we investigated 15 kinds of viruses in 844 human cell lines registered at the Japanese Collection of Research Bioresources (JCRB) Cell Bank. Our real-time PCR analysis revealed that six viruses, EBV, HTLV-1, HBV, B19V, HHV-6 and HHV-7, were detected in 43 cell lines. Of them, 20 cell lines were transformed by intentional infection in vitro with EBV or HTLV-1. Viruses in the other 23 cell lines and one EBV transformed cell line are derived from an in vivo infection, including five de novo identifications of EBV, B19V or HHV-7 carriers. Among them, 17 cell lines were established from patients diagnosed with virus-associated diseases. However, the other seven cell lines originated from in vivo cells unrelated to disease or cellular tropism. Our approach to screen for a set of 15 viruses in each cell line has worked efficiently to identify these rare cases. Virus tests in cell lines contribute not only to safety assessments but also to investigation of in vivo viral infection which can be a characteristic feature of cell lines.