How many STR loci are required for human cell line authentication?

According to the ANSI/ATCC ASN-0002 standard and ICLAC guidelines, a minimum of 13 STR loci must be analysed for sufficient discrimination power.

What should be included in an NIH AKBR plan for cell lines?

The plan should describe when authentication is triggered (e.g., upon acquisition, before pivotal experiments), the method used (STR profiling), and how evidence like raw electropherograms and allele tables is recorded and stored.

NIH & Journal Requirements: Ensuring Your Cell Line Data Meets ANSI Standards

When cell lines are misidentified or contaminated, experiments derail, funds evaporate, and manuscripts stall. The "reproducibility crisis" may feel abstract until a reviewer asks for your authentication documents and you realize a key line doesn't match. That's why authentication has shifted from "good practice" to a non‑optional, routinely requested part of grant and manuscript packages. This guide explains what NIH and major journals actually expect, and how to apply recognized community standards for STR‑based authentication in day‑to‑day lab work so you can submit with confidence—no guarantees, just fewer surprises.

What NIH and Journals Typically Expect You to Provide

Funding agencies and publishers want two things: evidence that your human cell lines are what you say they are, and a traceable way to re‑evaluate that evidence later. For human cell lines, short tandem repeat (STR) profiling — conducted according to recognized community consensus standards and best practices — remains the benchmark. Below is what that looks like in NIH applications and in manuscripts.

NIH grant applications: what to include in an Authentication of Key Biological Resources plan

In NIH applications, the Authentication of Key Biological and/or Chemical Resources (AKBR) plan is a short, standalone attachment that explains how you'll ensure identity/validity for key resources (including cell lines) that may vary over time or across labs. NIH's guidance and application materials emphasize method transparency, timing, and documentation—not preliminary data.

When authentication is triggered. State the moments you will authenticate human cell lines, such as upon acquisition (before introducing into culture), before pivotal experiments, after major manipulations (e.g., gene editing), and after extended passaging. This aligns your plan with routine points of risk.
Which method is appropriate. For human lines, name STR profiling and indicate that it will be carried out to meet recognized consensus practices; note any additional checks you will use (e.g., species identification) when relevant.
How evidence is recorded. Commit to retaining raw electropherograms (e.g., .fsa/.hid), annotated images, allele tables, software outputs, and comparison reports, each labeled with traceable sample IDs and dates. Briefly state your storage location and retention period, and who can retrieve the data during review.

For context, NIH notes that AKBR plans are reviewed under Additional Review Considerations within the simplified peer review framework effective for most due dates on and after January 25, 2025. Reviewers and staff look for clarity and appropriateness of your plan, not its experimental "results." See NIH resources on rigor and AKBR planning in the grants policy hub and the simplified review framework pages for current handling.

According to the official policy hub, AKBR language should concisely describe methods, timing, and documentation practices for identity/validity of key resources; examples are provided in NIH reproducibility resources: see the NIH page on rigor and reproducibility guidance and resources.
Under NIH's simplified framework, AKBR considerations are handled without affecting the overall impact score directly; see NIH's Simplified Peer Review Framework and the accompanying notice (2024) for details.

Manuscripts: what editors and reviewers commonly look for

Editors and reviewers typically expect a "submission‑ready evidence pack" that includes:

Raw and/or annotated electropherograms with peak quality evident.
An allele table summarizing per‑locus calls and any flags.
A database comparison (e.g., against ATCC/DSMZ/JCRB profiles) with an explicit similarity score and threshold used, plus a narrative interpretation statement.
A brief method summary, timing (when validation was done relative to experiments), and identifiers enabling traceability.

Nature Portfolio requires transparent reporting for key biological resources (with RRIDs and authentication statements) and provides a Life Sciences Reporting Summary template many journals use; Cell Press requires STAR Methods and a Key Resources Table with RRIDs and explicit authentication details. Align your documentation to these expectations and make sure your evidence is easy to find in your submission package.

Decoding ANSI/ATCC ASN‑0002: What the Standard Means in Practice

ANSI/ATCC ASN‑0002 (current edition: 2022) is the community consensus standard for authenticating human cell lines via STR profiling. The full document is paywalled, but its practical expectations are reflected across authoritative summaries and repository practices. Here's how to operationalize it in your lab.

Loci coverage: what your STR dataset should include

Your STR panel should cover a recognized core set of loci with sufficient discrimination power for database comparison. In practice, most compliant implementations use at least 13 autosomal STR loci plus a sex marker (Amelogenin), and many modern kits profile 16–24 loci.

Why this matters: Using a broader, standardized set of loci increases the power to detect misidentification and mixed samples and makes your results comparable to major repositories.
Applicability boundaries: Human STR panels don't authenticate non‑human lines. If your model involves potential non‑human cells (e.g., feeder layers, cross‑species co‑culture, or xenograft samples), you'll need species identification and, in some cases, quantitative assessments alongside human STR.

For readers who want the official provenance and a detailed practical guide, consult ANSI's listing for ASN‑0002‑2022 and the ICLAC Guide to Human Cell Line Authentication (2023), which references current practices for loci coverage and interpretation.

Match threshold: how "≥80%" similarity is typically interpreted

In community practice informed by ICLAC and widely used by repositories, a profile similarity of ≥80% across compared loci is commonly interpreted as consistent with the reference (allowing for minor drift and kit differences). Scores between about 55% and 80% warrant investigation (possible drift, tumor instability, or mixed samples), while low scores support misidentification or contamination.

How the score works: Percentage similarity is generally calculated as the proportion of shared allele calls across loci profiled in both datasets, accounting for the fact that most loci have up to two alleles in diploid human cells. Some analysis software implements rule‑based tie‑breaking and flags atypical allele counts.
Caveats to document: Genetic drift after extended passaging, tumor heterogeneity, allelic dropout due to low template, and mixtures can all produce partial matches. Your report should state how such cases were interpreted and what follow‑up testing (if any) you performed or recommend.
Reproducibility: Keep both the algorithm/software outputs and a narrative interpretation so other researchers or reviewers can re‑assess your calls in context.

For authoritative context, see the ICLAC human guide (2023) discussing similarity thresholds and practical interpretation, and the ATCC STR analysis pages that describe analysis and reporting conventions.

ICLAC (2023) provides concise, citable guidance on loci and match thresholds: "A minimum of **13 STR loci must be analysed." (ICLAC Guide to Human Cell Line Authentication, p.1) and "If the cell line name is identical and both STR profiles show matches of more than 80% …" (ICLAC, p.1). For AKBR handling under peer review, NIH states in the Simplified Peer Review Framework (NOT‑OD‑24‑010) under "Additional Review Considerations": "Authentication of Key Biological and/or Chemical Resources — For projects involving key biological and/or chemical resources, evaluate the brief plans proposed for identifying and ensuring the validity of those resources." See the ICLAC guide (2023 PDF) and NIH framework for verification.

Standardization checklist: what to ask to be documented (authentication of human cell lines standardization)

To make your package reproducible and review‑ready, confirm that each of the following appears in your records and reports:

Sample identifiers and chain‑of‑custody notes (as available).
Kit/panel name, platform (multiplex PCR + capillary electrophoresis), and key non‑proprietary parameters.
Controls/QC statements (ladder, positive/negative controls, peak quality thresholds).
Software/algorithm outputs or a rule‑based interpretation summary that supports consistent allele calling and auditable re‑analysis.

Choosing a partner that strictly follows these protocols is the first step in selecting a valid STR profiling service.

What a "Compliant" Report Looks Like (Build a Submission‑Ready Package)

A compliant STR report has three core evidence components plus method/traceability details. When these are standardized and bundled, you minimize back‑and‑forth with reviewers and editors.

Required element 1: Electropherogram (raw and/or annotated)

What it demonstrates: peak quality, allele calls, stutter/bleed patterns, off‑ladder events, and potential mixture indicators (e.g., extra peaks, imbalanced peak heights). Include both raw files (e.g., .fsa/.hid) and an annotated figure with legible labels for each locus. If you re‑run any samples, retain all runs and explain which run informed the final calls and why.

Practical tip: Ensure dynamic range is appropriate to avoid saturation and that baseline noise is controlled. A short method note should state instrument type, kit name and lot (if relevant), injection time/voltage, and the allelic ladder used.

Required element 2: Allele table (locus‑by‑locus)

Minimum fields: locus name, allele calls (one or two per locus; more suggests mixtures), any flags/notes for uncertainty, sample ID, kit/panel, run date, and operator or instrument ID. Optional: peak height ratios and QC flags per locus. Store the canonical version in CSV with immutable sample IDs.

Worked example: If your reference profile (R) at locus D5S818 is 11,12 and your sample (S) calls 11,12, that locus contributes 2/2 shared alleles. If at D13S317 R=8,11 and S=8, then the locus contributes 1/2 (only one allele shared). Compute this across all loci profiled in both R and S; the similarity percentage is (total shared alleles ÷ total possible alleles compared) × 100. Report any loci with off‑ladder or tri‑allelic patterns in the notes column.

Required element 3: Database comparison + interpretation

What to include: the reference database/source used (e.g., ATCC STR database; DSMZ catalogue; JCRB), the similarity score and threshold (e.g., ≥80%) used for decision‑making, and a clear conclusion: match, partial, mismatch, or inconclusive. When inconclusive, recommend next steps such as re‑extraction, re‑profiling, or species testing.

Transparency note: When specific repositories are referenced, document the accession or version date of the external profile used for comparison. Save a copy or a report export in your records when license terms allow.

The significance of automated STR analysis

Micro-case — real, anonymized example: In one recent submission to a Cell Press–family journal (accepted 2024), the authors included an STR authentication evidence pack prepared before key functional assays. Verification was performed on receipt and immediately prior to pivotal experiments; comparison used a ≥13‑locus panel. Result: 92% similarity to the reference (threshold ≥80%). Submitted materials: raw .fsa electropherograms, annotated figures, CSV allele table, database comparison report (ATCC/DSMZ), and a one‑page interpretation statement. Anonymous reviewer note: "Authentication package is clear and sufficient for reproducibility." This example demonstrates a reproducible, submission‑ready workflow.

Automation matters because it enforces consistent allele calling rules, captures software thresholds and flags, and reduces operator variability—key for auditability. For review, retain raw inputs and software outputs so a re‑review is possible if a question arises. Standardizing these steps also simplifies the narrative methods text for manuscripts and grant plans. Used properly, automated STR analysis strengthens authentication of human cell lines standardization by making your pipeline repeatable across operators and time.

Disclosure: CD Genomics provides RUO STR profiling and reporting. If you need a sense of what deliverables look like in practice (electropherogram, allele table, and database comparison bundled in a traceable package), see the Cell Line Identification service overview at CD Genomics. For sample packaging and logistics, refer to our Sample Submission Guidelines (PDF), and for onboarding steps, see Order Online. All services are for research use only.

Beyond Basic Compliance: When STR Alone Isn't Enough

Complex models can sit outside the comfort zone of standard human STR panels. Here are scenarios where you should plan for add‑ons and nuanced interpretation.

Xenografts, chimerism, and mixed‑origin samples

Patient‑derived xenografts (PDX), co‑culture systems, and chimeric models blend human with non‑human cells or combine two human sources. Human STR alone won't detect or quantify non‑human contributions and may mask mixtures.

What to add: species identification assays to confirm and quantify non‑human material; mixture assessment strategies (e.g., quantitative STR or orthogonal markers); model‑specific interpretation rules documented in your methods.
Practical advice: Separate upstream steps (e.g., species ID before STR) and report both human identity and species composition to avoid confusion at review.

For complex oncology models, check our guide on Chimerism and Xenograft analysis.

Where to Put Authentication in a Manuscript (and Sample Language)

Reviewers aren't hunting for your evidence; they expect it where editorial policies direct them. Use this placement guide and adapt the sample text to your journal.

Methods/STAR Methods. Describe the STR profiling protocol, kit/panel, instrument, allelic ladder, and software used. Include timing (e.g., "authenticated upon receipt and before key functional assays; repeated after 20 passages"). State how similarity was calculated and your threshold.
Key Resources Table. List each cell line with RRID, source, and authentication status plus date. If a profile is archived publicly, include a reference or accession as allowed by policy.
Supplementary Information. Place the annotated electropherogram(s), allele table CSV/PDF, and database comparison report, with cross‑references from the main text.

Sample language (adapt as needed): "Human cell lines were authenticated by STR profiling (ANSI/ATCC ASN‑0002) using the [kit name] panel (≥13 autosomal loci + Amelogenin) on a [instrument]; similarity to reference profiles was ≥80% for all lines used in functional assays. Electropherograms (.fsa) and allele tables are provided in Supplementary Data, and comparison reports reference ATCC/DSMZ entries (accessed [month year])."

Mini‑SOP: From gDNA, Cell Pellets, and FFPE to a Clean STR Profile

These steps summarize common lab practices that support authentication of human cell lines standardization. Always adapt to your lab's quality system.

Genomic DNA (gDNA). Aim for ≥50 ng/µL and ≥20 µL per sample; A260/280 ≈ 1.8–2.0; minimal shearing. If concentration is low, concentrate and re‑QC. Avoid inhibitors (phenol, ethanol carryover). Re‑run extraction if inhibition or allele dropout is suspected (e.g., many loci missing, inconsistent peak heights).
Cell pellets. Target ≥5×10^5 cells. Wash to remove medium/serum components, then extract with a kit validated for STR. QC DNA as above. If bacterial or mycoplasma contamination is suspected, decontaminate or subculture prior to extraction; contamination can distort peak morphology.
FFPE curls/sections. Use deparaffinization and crosslink reversal; expect fragmentation. Choose STR kits tolerant of degraded DNA and reduce amplicon size where possible. Use replicate amplifications and combine consensus calls; flag loci with recurrent dropout. If too few loci amplify, report limitations and consider orthogonal identity checks.

QC acceptance and re‑run triggers: Require a minimum number of loci passing quality thresholds (lab‑defined; commonly ≥13 autosomal loci + Amelogenin with robust peak heights and ladder alignment). Re‑run if: (1) <80% of expected loci pass; (2) multiple loci show off‑ladder peaks without explanation; (3) peak height imbalances suggest mixtures; (4) electropherogram saturation or baseline noise precludes reliable calling.

Troubleshooting Partial Matches and Suspected Contamination

Ambiguous similarity scores happen; what matters is how you respond. Use this narrative decision path to standardize responses and documentation.

70–79% similarity (borderline). Re‑extract and re‑profile from an independent aliquot; check passage number and culture history. Review kit compatibility with the database profile (older vs newer panel differences). If repeat profiles converge ≥80% with consistent locus calls, document drift as the likely cause; otherwise, treat as partial and proceed to next checks.
Mixed peaks at multiple loci. Examine peak height ratios and presence of three or more alleles per locus. If consistent with mixtures, quantify contribution where possible (e.g., quantitative STR) and decide on deconvolution vs culture purification. Document actions and outcomes.
Low‑template or inhibition indicators. If many loci drop out or show imbalanced peaks, increase template within kit limits, clean up DNA, and re‑run. Note all changes in the method summary.
Cross‑species flags. If morphology or context suggests non‑human material (e.g., PDX), perform species ID before interpreting human STR results. Report species composition alongside identity conclusions.

In all cases, preserve raw and processed data from each attempt, and state clearly which dataset supports your final interpretation.

Data Retention, Versioning, and Auditability for NIH and Journals

Your authentication package isn't complete unless it's findable and auditable months later. Build light‑weight governance into your workflow.

Retention. Keep raw electropherograms, processed reports, allele tables, and software outputs in a versioned repository with access control and backups. Define a retention period that covers the grant/manuscript lifecycle and institutional policy.
Versioning and traceability. Use immutable sample IDs and record kit lots, instrument IDs, and run metadata. Store a readme with the software version and calling rules used.
Retrieval plan. In your AKBR plan and manuscript cover letter, state where the files live and who can provide them on request. If journal policy encourages public deposition, provide de‑identified data in supplements or a repository consistent with license terms.

These practices help reviewers validate your calls and reinforce that your lab treats authentication as a repeatable quality process.

Practical Submission Checklist (NIH + Journal)

Copy this checklist into your internal SOPs or use it verbatim in your grant/manuscript prep notes.

STR profile with sufficient loci coverage (state the kit/panel; commonly ≥13 autosomal loci plus Amelogenin).
Similarity score + threshold (e.g., ≥80%) and the rule you used to interpret match/partial/mismatch.
Electropherogram included (raw files retained and annotated image provided).
Allele table included (locus‑by‑locus; flags for uncertainty if any).
Database comparison documented (reference source, version/date, accession if applicable).
Sample traceability (immutable IDs) + method summary (kit/panel, instrument, key parameters, controls/QC).
Add‑ons for special cases (e.g., xenografts/mixed samples: species ID, mixture assessment, model‑specific notes).

Conclusion

Here's the deal: if you assemble a traceable, standardized evidence pack aligned with ASN‑0002 expectations—and you make it easy for reviewers to see your methods, timing, and interpretation—you reduce back‑and‑forth and keep your project moving. Authentication is not a one‑time checkbox; re‑confirm identity at planned milestones and retain raw data so your work remains auditable months or years later. That is how authentication of human cell lines standardization supports rigor without slowing the science.

Author

Yang H. — Senior Scientist, CD Genomics; University of Florida.

Yang is a genomics researcher with over 10 years of research experience in genetics, molecular and cellular biology, sequencing workflows, and bioinformatic analysis. Skilled in both laboratory techniques and data interpretation, Yang supports RUO study design and NGS‑based projects.

Related Services

References:

NIH — Rigor & Reproducibility: Authentication guidance and resources (accessed 2026). See the NIH hub for policy guidance on AKBR plans and examples: NIH Rigor & Reproducibility guidance and resources and NIH resources page with examples.
NIH — Simplified Peer Review Framework (effective 2025), explaining handling of AKBR under Additional Review Considerations: NIH Simplified Peer Review Framework and the 2024 notice NOT‑OD‑24‑010.
ANSI/ATCC — Consensus standard for human cell line authentication via STR: ANSI/ATCC ASN‑0002‑2022 listing and ATCC product page.
ICLAC — Guide to Human Cell Line Authentication (2023), practical interpretation of loci coverage and similarity thresholds: ICLAC Guide to Human Cell Line Authentication (PDF, 2023).
Nature Portfolio — Reporting standards and Life Sciences Reporting Summary template: Nature reporting standards hub and Life Sciences Reporting Summary (PDF).
Cell Press — Author Guidelines hub and STAR Methods background: Cell Press author guidelines and "STAR Methods: Democratizing Science," Cell (2016), doi:10.1016/j.cell.2016.09.038.
ATCC — STR analysis and database comparison overview for human cell lines: ATCC STR profiling analysis overview.
NCI — Best Practices for Biospecimen Resources (2016) for data retention and stewardship ideas: NCI Best Practices PDF.

For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.