QIAGEN Digital Insights (QDI), the bioinformatics business of QIAGEN, announced the launch of their new cell line database developed through its partnership with ATCC, a biological materials organization, developer and supplier of authenticated cell lines. This database offers manually curated sequencing data for standardized, authenticated and reproducible cell lines. It will allow biopharma researchers to access genomic information from the most popular ATCC cell lines and primary cell lines and tissues for use in their preclinical experiments without first resorting to lengthy and costly gene sequencing.
The ATCC Cell Line Land database enables researchers to select and order relevant cell lines – cell cultures used to study the effects of compounds in drug development – from ATCC’s biorepository within hours. Cell lines have revolutionized research by replacing primary cells from living tissue, but they do not always replicate accurately. If cell lines are not authenticated by a trusted source like ATCC, they could be contaminated and misidentified. This means researchers usually have to spend weeks or even months sequencing and characterizing cell lines before they can use them for preclinical research.
Unlike existing data repositories, ATCC Cell Line Land provides not only sequencing data on the cell lines, but also detailed information about actual lots of cell lines stored in ATCC’s biorepository, guaranteeing each sample’s intact provenance and its quality as ready-to-use. As a result, researchers can quickly identify and access credible, authenticated and characterized cell lines, which crucially also guarantee reproducible research results. ATCC Cell Line Land also has data on non-cancer cell lines and mouse cell lines, which can’t be found in public sequence data repositories.
“Every dataset we produce contains authenticated data that can be traced to a physical lot of cells in our biorepository,“ said Jonathan Jacobs, Senior Director of Bioinformatics, ATCC. “Having uniformly standardized data production, curation and analysis, we can ensure the highest level of scientific reproducibility – and document and guarantee end-to-end data provenance.”
QDI has proven its ability to provide biopharma with high-quality sequencing data through its OmicSoft Lands databases. These integrate disease-relevant sequencing datasets and their metadata, such as tissue type, biological origin and experimental parameters, from the largest public sequencing dataset repositories. OmicSoft applies controlled vocabularies and skilled manual curation to make these datasets easier to search, compare and explore, making it an ideal framework to also deliver researchers reliable cell line sequence data.
“QDI is proud to have partnered with ATCC to provide access to unique and valuable cell line data to help its customers answer research questions more quickly and efficiently,” said Dr. Jonathan Sheldon, Senior Vice President of QIAGEN Digital Insights. “QDI is committed to providing its customers the most comprehensive and manually curated collection of genomic datasets for biopharma researchers to use for biomarker and target discovery and drug development research.”
ATCC Cell Line Land launched on November 1, 2022, featuring 600 of the most commonly used human and mouse cell lines from ATCC’s collection – a further 250 will be added every quarter, including datasets requested by customers. Samples are prepared using ISO-compliant laboratory methods that include QIAGEN RNAeasy technology for RNA purification and ATCC-validated cell culture protocols. Sequence data is processed using standard pipelines to provide comparable datasets.
Every entry in ATCC Cell Line Land is human certified to ensure reliable, high-quality data. After reviewing data submissions, QDI’s expert curators ensure all metadata is absolutely consistent in referencing key characteristics like cell and tissue type, data provenance and cell culture metrics. This quality and accuracy enable the data to be reliably integrated and leveraged with artificial intelligence and machine learning tools.
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