Cell Line Authentication Testing

Cell Line Authentication

Human cultured cell lines are used in a number of biomedical research and clinical applications, including cancer research, drug discovery, genetics and biobanking. However, misidentified human and animal cell lines have continued to be used even today despite multiple and repeated warnings, articles and letters by prominent scientists in the field calling for authentication.

The need is great for researchers to authenticate their human cell lines. The call for authentication has been made for over five decades now, yet still, many researches do not perform this vital quality assessment. Recently there have been more spotlights shining on this deficiency from National Public Radio (NPR) series last December, comments from Dr. Francis Collins of the NIH, Nature announcing changes to abstract submission and most recently, newly revised NIH grant application instructions. As a result, more institutions are coming around to offering, even requiring, human cell line authentication through their DNA core labs or service providers.

Cell line authentication is achieved by genetic profiling using polymorphic short tandem repeat (STR) loci. STR loci consist of repetitive sequence elements 3–7 base pairs in length. These highly discriminative markers can be utilized using rapid and inexpensive multiplex-PCR based method for the identification and detection of contaminating human cells.

Standards for Cell Line Authentications

To standardize STR analysis for human cell line authentication, the American Tissue Culture Collection (ATCC) Standards Development Organization Workgroup published ASN-0002-2011, which recommends the use of at least eight STR loci (TH01, TPOX, vWA, CSF1PO, D16S539, D7S820, D13S317 and D5S818) plus Amelogenin for gender identification for human cell line authentication. This comprehensive standard provides detailed information on preparing samples for analysis, general instructions for analyzing the PCR products using capillary electrophoresis, tips and recommendations on how to view and interpret the resulting DNA profiles and more.

Cell Line Authentication Matters

Misidentified or contaminated cell lines lead to invalidation of data and lost time, money and effort.

Authentication of human cell lines is required or strongly encouraged by many journalsjournals and funding agencies.

ANSI Standard ASN-0002-2011 recommends the use of STRs for human cell line authentication.

STR testingSTR testing is recommended when receiving a cell line from an unreliable source, after ten passages, when preparing a cell bank and when in doubt.

  1. Send cells or extracted DNA to an experienced STR profile testing service.
  2. Compare the STR profile to a reference sample and other known cell line STR profiles.
  3. Check the cell line name against the database of misidentified cell line.

Be sure to authenticate at key intervals in a project when you:

  1. Establish or acquire a new cell line.
    • At an early passage (within the first week of culture).
  2. Start a new series of experiments.
  3. Are routinely passaging cell lines.
  4. See inconsistent cell behavior or unexpected results.
    • Limit passage number because cell lines mutate over time.
  5. Prepare to publish.
  6. Freeze cell stocks to verify purity.

What are STRs?

Short tandem repeats or STRs are repetitive sequence elements 3–7 base pairs long scattered throughout the human genome. By amplifying and analyzing these polymorphic loci, then comparing the resulting STR profile to that of a reference sample, the origin of biological samples such as cells or tissues can be identified and verified. The more loci that are amplified, the higher the statistical power of discrimination due to the repeat variation among humans and the human cell lines derived from individuals.

Example of STRs
Trinucleotide: ---CTTCTTCTTCTTCTT---
Tetranucleotide: ----AATGAATGAATG----
Pentanucleotide: -----AAAGAAAAGAAAAGAAAAGAG-----

STR Analysis of Cell Lines

DNA Extraction, Multiplex PCR, STR Allele Separation and Sizing, Data Analysis, Database Comparison

The user is sent a simple text-based table and may also receive a copy of the electropherogram results from the STR analysis. The nomenclature is based on number of STR repeats for each loci tested.

Table 1: Example of text-based results

STR Locus Alleles
TH01 6, 9
D21S11 29, 30
D5S818 11, 12
D13S317 11, 12
D7S820 10, 10
D16S539 11, 12
CSF1PO 11, 12
Amelogenin X, X
vWA 17, 19
TPOX 8, 9

Figure 1. Example electropherogram results.

Example Electropherogram

Once you have your data table, here are some resources for interpreting the results.

Database Comparison

The American Tissue Culture Collection (ATCC) Standards Development Organization Workgroup published ASN-0002-2011, which recommends the use of at least eight STR loci (TH01, TPOX, vWA, CSF1PO, D16S539, D7S820, D13S317 and D5S818) plus Amelogenin for gender identification for human cell line authentication. The probability of two cell lines with identical STR profiles using the 8 core STR markers is 2 × 109.

Compare your cell lines using the ATCC STR (login required), DSMZ STR (login required) and NCBI BioSample.

Matching Percentage

The matching criteria is based on an algorithm comparing the number of shared alleles between two cell line samples, expressed as a percentage. A previously authenticated sample is selected as a 'reference' profile, while the sample undergoing authentication is the ‘test sample' profile. You can use the Match Criteria Worksheet to help.

  • First, combine total number of alleles observed in the Test Sample and Reference (TOTAL ALLELES)
  • Second, count the number of alleles shared by the Test Sample and the Reference Sample (SHARED ALLELES)
  • Third, use the Match Algorithm to calculate a percent match result for the two samples
  • Percent Match = SHARED ALLELES × 2
  • Authentic cell line: A match at ≥80% of alleles across the eight (8) core STR loci
  • Match: When two STR profiles show identical alleles. This is described as a percentage. Cell line samples matching at ≥80% of alleles across the eight (8) core loci are said to be related.
  • Unrelated STR profiles: When STR profiles match at <55% of alleles. STR profiles with alleles matching at 55–80% may be related and require further investigation.

This matching algorithm can be used to define cell lines as unique, authenticated, misidentified, or cross-contaminated. Additional information may be needed for these conclusions, including the presumed identity of the cell line and what is known about its history. In cases where a hybrid cell line is newly formed and its STR is generated for the first time, that profile should be compared first to the parental cell line’s profile. An already established hybrid with its profile entered in the database maybe treated like any other cell line.

In addition to applying the algorithm, the quality of the STR profile should be examined to determine if the sample is appropriate for interpretation (e.g., a degraded sample will be uninterpretable), or if a mixture is likely to be present.

The publications below all require some level of cell line authentication before accepting research articles.

AACR Journals

  • Cancer Discovery
  • Cancer Research
  • Clinical Cancer Research
  • Cancer Epidemiology, Biomarkers & Prevention
  • Molecular Cancer Research
  • Molecular Cancer Therapeutics
  • Cancer Prevention Research

Endocrine Society Journals

  • Endocrinology
  • Endocrine Reviews
  • Journal of Clinical Endocrinology & Metabolism
  • Molecular Endocrinology
  • Hormones and Cancer

Society for Endocrinology journals

  • Journal of Endocrinology
  • Journal of Molecular Endocrinology
  • Endocrine-Related Cancer


Cell Biochemistry and Biophysics

Cell Biology International

International Journal of Cancer

In Vitro Cellular & Developmental Biology – Animal

Journal of Molecular Biology

Journal of the National Cancer Institute

Molecular Vision

Nature Publishing Group

  • Nature Reviews Molecular Cell Biology
  • Nature
  • Nature Genetics
  • Nature Reviews Immunology
  • Nature Reviews Cancer
  • Nature Reviews Neuroscience
  • Nature Biotechnology
  • Nature Methods