Documentation

Cell lines

The cell line content page provides a summary over all cell lines available at DSMZCellDive. An overview shows the cell line, cell type, and species. Furthermore, the DSMZ ACC No. is given including a link to the DSMZ catalogue, where more detailed information is provided, and a link to the DSMZ webshop for purchasing the cell line of interest and to address appropriate customer support. The last row indicates if the cell line is featured in any RNA-seq data panel.

Search and filter

Cell lines can be searched by their name or DSMZ ACC No. When clicking on More filters, a popover with more filter options will appear. Here, cell lines can be filtered by species and cell types. Both fields feature auto suggestions when you start typing. Furthermore, only cell lines that belong to a certain RNA-seq panel can be selected here.

Integrated cell line data

When a single cell line is selected by clicking on its name, a cell line summary page is opened. On this page, all information on this particular cell line is shown, e.g. certain meta data, the STR profile or the COI DNA barcoding report, and RNA-seq projects.

RNA-seq summary

If the cell line is featured in one of the RNA-seq panels, charts on these data can be triggered by clicking on one of the buttons in the panel. After a short loading time, either a histogram or a bar chart with all normalised expression levels are shown.

List of genes

The genes content page lists all human genes and some meta data, e.g. the position in the human genome, database identifiers from Ensembl and Entrez and a short gene description extracted from Ensembl. Gene names correspond to GENCODE names (v38). The list of genes can be filtered by gene name, description, Ensembl-ID, or Entrez-ID. The search is always a contains-search in all of the fields.

Metabolic pathways

Associated metabolic pathways (BRENDA) to genes/enzymes can be searched for pathway names, EC numbers, or gene names. Furthermore, for visualising gene expression as heat map, genes can be selected belonging to a specific metabolic pathway (50 genes maximum). EC numbers are linked to a specific enyzme-catalyzed reaction. They do not specify unique proteins/enzymes encoded by a specific gene.
Please note, that the human genes might not be experimentally proven to be involved in the presented pathways.

Tissues

Cell lines were assigned to tissues and diseases according to the OncoTree cancer classification system published by Kundra et al., 2021, considering the two topmost levels. These tissues can be used as starting points for barplot visualisation of "All RNA-seq projects".
The category cell type refers to the DSMZ catalogue and is not finally harmonised yet.

RNA-seq data

Each RNA-seq panel has its own overview page that can be reached via the sidebar or the starting page. On this page, further information on this project are given, including literature to cite when using the data.
Please keep in mind, that raw count data should be taken for differential expression analysis (see DESeq2 paper Love et al., 2014).

Matrix description

Different to the description of the LL-100 paper (see below), RNA-seq data were quantified via Salmon and normalised via DESeq2. Following values are given:

• normalised values: calculated via DESeq2 within one RNA-seq project
• tpm: transcripts per million reads - a rough measure to compare between samples
• estimated counts: Salmon's estimate of counted reads per transcripts, corresponds to counted raw reads; limited comparability between samples but recommended as basis for further statistical analysis

Please note, that if all NGS projects are selected, TPM and count values are available only, since normalisation have been calculated for each project only. For specific statistical analyses please take the estimated counts of your samples of interest as starting point.

Gene selection

Genes can be selected via an intuitive selection tool. Start typing to get genes suggested based on their names and descriptions. Clicking on the × will remove the selected gene. You can clear the whole selection by clicking on clear below. For Bar charts, you can select up to 5 genes. For Heat maps, between 2 and 50 genes can be selected. Additionally, you may select genes, which are associated to BRENDA metabolic pathways.

You can also paste genes into this field (only gene names supported), if the genes are seperated by spaces, linebreaks, comma, or semicolon. The gene selection is saved as a cookie for your convenience.

Example bar chart

Bar charts

Bar charts are generated using the Plotly.js library. They are interactive, one can zoom in by selecting parts of the graph, pan by tagging the axes and hide/show single data sets by clicking on the legend. You can save the chart as PNG by clicking on the that appears after hovering the plot.

Bar charts can display multiple genes (up to 5). Grouping will be done by gene or cell line, depending on user selection.

Example heat map

Heat maps

Heat maps are generated using the Plotly.js library and the R package heatmaply. They can be zoomed and downloaded the same way as the bar charts.
For differential expression analysis raw count data are strongly recommended (see DESeq2 paper Love et al., 2014).

Heat maps can be clustered by genes (default) or cell lines. Clustering can also be disabled (none). The order of the genes is identical to the selection unless they are clustered. On top of the heat map is a color legend indicating the tumour entity. Hover over the labels to get more information.

Short tandem repeats for human cell lines

Short Tandem Repeats (STRs) are crucial for the authentication of human cell lines. Thus, we implemented a search engine comprising 17 STR loci from cell lines of different sources.

The STR Search

The search tool offers a form, where one can enter each STR value manually. If more alleles are needed, they can be added using the + button. Up to six alleles are possible. Click on Example to see an example. It is also possible to paste data from e.g. Excel files. To do this, please click on Use text input and follow the instructions in the popup.

The result page contains a table with your query and the best matching STR profiles. The first column shows the similarity. Read more about this score here. The next two columns give information on the cell line and the cell bank it was sourced from. The following columns contain the number of repeats for the specific STR-loci. Bold red fonts indicate mismatches from your query. The result can be downloaded as CSV file that can be opened with MS Excel.

The similarity score

The similarity score is calculated as recommended by ANSI/ATCC standard ASN-002 using the Tanabe formula:

Given that:

• Only loci at which query and reference have called alleles are considered
• Amelogenin is not an STR locus and excluded from similarity scoring
• Single allele peaks are counted only once
• A_query: Number of distinct alleles in query
• A_reference: Number of distinct alleles in reference
• A_shared: Number of distinct alleles shared by query and reference

Authentic or not authentic, a cruical question: STR profiling is the recommended approach for authentication testing of human cell lines. Categories of cell line relatedness are defined according to ASN-002 ANSI standard:

• ≤60 %: Different donor origin.
• >60 %: Unlikely to be from same donor. But could also be the result in microsatellite instable (MSI) cell lines that undergo substantial genetic drift. Additional analyses (like VNTR analysis) are needed to resolve identity questions.
• >80 %: Likely to be from the same donor when 13 or more STR loci are considered. If only eight STR loci are used, the cut-off score for a match should be 90%. Because of genetic drift, cell line samples might still differ genetically and show different phenotypes.

Read more about this topic here.

COI DNA barcoding for animal cell lines

While STR data are only used for human cell lines, the mitochondrial cytochrome c oxidase I (COI) gene is widely used to identify species of animal cell lines. DSMZCellDive offers COI DNA barcoding reports for all animal cell lines in the database. You can browse through them, view them in more detail and download each report as PDF.

If you are looking for an identification engine, we recommend to use BOLDSYSTEMS.

HLA typing data

Human leukocyte antigen (HLA) genes encode proteins in the major histocompatibility complex (MHC) which play a central role in discriminating self and non-self. MHC Class I proteins (HLA-A, -B and -C) bind to and present intracellular antigens on the cell surface for cytotoxic T-cells, which trigger apoptosis if non-selfpeptides are detected. MHC Class II proteins (including HLA-DPB1, -DQB1 and -DRB1), on the other hand, present extracellular proteins to helper T-cells which mediate the adaptive immune response.

The HLA gene cluster on chromosome 6 is highly polymorphic and suitable for cell line authentication. Furthermore, HLA typing is important for cancer research and for determination of tissue compatibility, in which tumour neoantigen binding to HLA surface proteins and rejection of specific HLA alleles play a role.

Here, HLA typing was determined on RNA-seq data via arcasHLA, an alignment-based tool (Orenbuch et al, 2020).

Protein expression was not determined.

Nomenclature based on Xie et al., 2010.

How to cite

If you found DSMZCellDive to be useful for your research, we will be happy if you cite us.

DSMZCellDive

Koblitz J., Dirks, W.G., Eberth, S., Nagel, S., Steenpass, L., & Pommerenke, C. (2022) DSMZCellDive: Diving into high-throughput cell line data. F1000Res, 11:420. DOI: 10.12688/f1000research.111175.2

The presented data

LL-100 RNA-seq data
Quentmeier, H., Pommerenke, C., Dirks, W. G., Eberth, S., Koeppel, M., MacLeod, R., Nagel, S., Steube, K., Uphoff, C. C., & Drexler, H. G. (2019). The LL-100 panel: 100 cell lines for blood cancer studies. Scientific reports, 9(1), 8218. DOI: 10.1038/s41598-019-44491-x

Breast Cancer RNA-seq data
Pommerenke C, Nagel S, Haake J, Koelz AL, Christgen M, Steenpass L, Eberth S. Molecular Characterization and Subtyping of Breast Cancer Cell Lines Provide Novel Insights into Cancer Relevant Genes. Cells. 2024; 13(4):301. DOI: 10.3390/cells13040301

Neuroblastoma RNA-seq data
Pommerenke C, Hauer V, Eberth S, Werr L, Kallnischkies H, Rand U, Nagel S, Bartenhagen C, Dirks WG, Fischer M, Steenpass L, Wang H. Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling. iScience. 2026; 29(7):116469. DOI: 10.1016/j.isci.2026.116469

STR profiling data
Dirks, W. G., MacLeod, R. A., Nakamura, Y., Kohara, A., Reid, Y., Milch, H., Drexler, H. G., & Mizusawa, H. (2010). Cell line cross-contamination initiative: an interactive reference database of STR profiles covering common cancer cell lines. International journal of cancer, 126(1), 303–304. DOI: 10.1002/ijc.24999

STR authentication and similarity score
Capes-Davis, A., Reid, Y. A., Kline, M. C., Storts, D. R., Strauss, E., Dirks, W. G., Drexler, H. G., MacLeod, R. A., Sykes, G., Kohara, A., Nakamura, Y., Elmore, E., Nims, R. W., Alston-Roberts, C., Barallon, R., Los, G. V., Nardone, R. M., Price, P. J., Steuer, A., Thomson, J., … Kerrigan, L. (2013). Match criteria for human cell line authentication: where do we draw the line?. International journal of cancer, 132(11), 2510–2519. DOI: 10.1002/ijc.27931