Sample Submission Guidelines
Build a DNA/RNA QC evidence package for ABE and CBE projects
Base editors are powerful tools for creating precise nucleotide changes without introducing double-strand breaks. But an ABE or CBE project should not be reviewed only by asking whether the intended base changed at the target site. Your team may also need to understand the editing window, bystander edits, candidate DNA off-target sites, RNA-level editing signals, and how these results differ across editors, sgRNAs, doses, timepoints, or sample groups.
Our base editing safety assessment solution helps you organize these questions into a sequencing-supported QC evidence package. We use "safety assessment" in a research-service sense: the goal is to generate and organize data for project review, not to make clinical or regulatory conclusions about an editor.
What this solution helps you answer
- Did the intended A-to-G or C-to-T conversion occur at the target site?
- Are there bystander edits within or near the editing window?
- Are candidate DNA off-target sites edited?
- Is RNA-level review needed for this editor, sample type, or study stage?
- Should WGS, RNA-seq, targeted sequencing, or a hybrid strategy be used?
- Can multiple editors, sgRNAs, treatment groups, or timepoints be compared?
- What tables, figures, and QC notes are needed for internal project review?
The goal is to help your team move from raw sequencing results to a clear editing profile that can be reviewed by molecular biology, bioinformatics, and program teams.
Why base editing needs more than target-site validation
Target-site validation is the starting point. It tells you whether the expected base conversion occurred and whether bystander edits are present in the target region. However, base editors can also raise DNA-level and RNA-level questions that are not fully answered by a single target-site assay.
For example, one project may need targeted candidate validation for sgRNA-dependent DNA off-targets. Another project may need RNA-seq-supported review when transcriptome-level editing is part of the concern. A high-value editor comparison study may need WGS, RNA-seq, targeted sequencing, and custom bioinformatics in one package.
We help you choose the QC depth that matches your research goal, while keeping the interpretation tied to sequencing evidence and project context.
Our service capabilities for base editing assessment
We support base editing assessment as an integrated sequencing and bioinformatics workflow. Depending on your project stage, our team can help plan on-target validation, bystander edit profiling, DNA off-target assessment, RNA-level review, and report-ready bioinformatics outputs.
On-target validation and bystander edit profiling
For ABE and CBE projects, on-target validation usually starts with the expected base conversion. ABE projects often focus on A-to-G conversion, while CBE projects often focus on C-to-T conversion. But the target window may contain multiple editable bases, so the result should include more than a yes/no answer.
- Target-site amplification or targeted sequencing
- Editing efficiency summary
- Intended conversion frequency
- Bystander edit profile
- Allele or read-level editing pattern
- Sample or group comparison
- Visualization of the editing window
DNA off-target candidate discovery and validation
DNA off-target review can be approached in different ways depending on the project. Some projects begin with predicted or experimentally identified candidate sites. Others may need broader WGS-supported review or base editing-specific discovery approaches when available and appropriate.
- Candidate site list review
- Targeted off-target validation
- Amplicon or targeted sequencing
- WGS-supported variant context
- Candidate editing frequency table
- Genomic annotation and gene proximity notes
- Control-vs-edited sample comparison
RNA-level review and transcriptome-supported assessment
RNA-level assessment is not required for every base editing project, but it may be useful when the editor type, project stage, or research question makes transcriptome-level review important. RNA-seq-supported analysis can help review RNA-level editing candidates and compare edited samples with controls.
- RNA sample QC review
- RNA-seq-supported transcriptome assessment
- Candidate RNA editing event summary
- Transcript annotation
- Frequency or read-support summary
- Control-vs-edited sample comparison
- Filtering notes and report-ready figures
Bioinformatics reporting for editing spectrum and QC evidence
The main value of this solution is not only sequencing. It is interpretation. We help organize on-target edits, bystander edits, DNA off-target candidates, RNA-level findings, variant annotation, sample comparison, and QC notes into tables and figures that your team can review.
For related CD Genomics services, see our CRISPR Off-Target Validation Sequencing, CRISPR Editing Analysis with NGS, and RNA Sequencing pages.
Choose the right assessment strategy for ABE, CBE, and project stage
There is no single method that answers every base editing QC question. The right strategy depends on editor type, expected base conversion, sample type, control design, project stage, and whether your team needs DNA-level, RNA-level, or layered evidence.
| Strategy | Main Question | Best-Fit Project | Input | Output | Limitation |
|---|---|---|---|---|---|
| On-target amplicon sequencing | Was the intended base conversion achieved? | Early validation, clone screening, sample screening | gDNA or purified amplicon | Editing frequency, intended conversion, bystander profile | Focused on selected target region |
| Targeted off-target validation | Are candidate DNA off-target sites edited? | Candidate site confirmation | gDNA and candidate site list | Candidate editing frequency table and annotation | Requires candidate selection |
| WGS-supported review | Are broader genome-wide variant signals present? | Higher-value DNA-level QC review | High-quality gDNA | Genome-wide variant context, CNV/SNV/SV review where applicable | May not capture all low-frequency editing events |
| RNA-seq-supported review | Are RNA-level editing signals present? | RNA-level assessment and transcriptome review | High-quality total RNA | Transcript-level summary and candidate RNA editing events | Requires careful filtering, controls, and RNA quality |
| Base editing-specific discovery approach | Are editor-induced events discoverable beyond prediction? | Advanced discovery projects | Project-specific DNA/RNA inputs | Candidate discovery output | Method availability and suitability must be confirmed |
| Hybrid strategy | Do we need layered evidence? | Platform, preclinical, or editor comparison studies | DNA, RNA, controls, editor/sgRNA information | Integrated QC evidence package | More complex project design |
For broader DNA-level review, our Whole Genome Sequencing service may be considered. For focused target or candidate-site analysis, Targeted Region Sequencing can support validation. For custom analysis and visualization, our Bioinformatics team can support project-specific reporting.
Start with the editor type: ABE or CBE. Then define the expected base conversion, editing window, target sequence, sgRNA, sample type, and control design. If the first goal is target-site confirmation, amplicon or targeted sequencing may be enough. If the project needs DNA-level context, WGS or targeted off-target validation may be added. If RNA-level editing is part of the concern, RNA-seq-supported review may be included.
A hybrid strategy is often useful when teams need to compare multiple editors, sgRNAs, delivery conditions, or sample groups. In those cases, the project should be designed with clear controls and report outputs from the beginning.
Sample-to-report workflow with DNA/RNA QC checkpoints
Our workflow connects the technical assay with the service process. From project intake to report delivery, each step is designed to keep the base editing question, sample type, sequencing method, and analysis output aligned.
Step 1: Project intake and editor information review
We start by reviewing editor type, sgRNA sequence, target sequence, PAM, expected editing window, expected base conversion, sample type, treatment groups, control sample design, DNA-level and RNA-level QC goals, and desired tables, figures, and report format.
This step helps us determine whether the project should focus on target-site validation, candidate DNA off-target review, RNA-level assessment, WGS-supported review, or a combined package.
Step 2: Sample receipt and DNA/RNA QC
After sample receipt, we check sample identity, labeling, container format, and available QC data. For DNA-based analysis, we review genomic DNA amount, concentration, purity, and degradation status.
For RNA-level assessment, we review RNA integrity, purity, DNA-free status, and whether the sample quality fits RNA-seq-supported analysis.
Step 3: On-target, DNA off-target, and RNA-level sequencing strategy
The sequencing strategy is selected based on the project question. On-target validation may use amplicon or targeted sequencing. Candidate DNA off-target review may use targeted validation panels or broader DNA-level methods. RNA-level assessment may use RNA-seq-supported transcriptome review.
The goal is to generate data that can support the planned output: editing frequency, bystander edit profile, candidate off-target table, DNA/RNA-level annotation, or multi-sample comparison.
Step 4: Variant calling, editing spectrum analysis, and filtering
Sequencing data are processed and filtered according to the selected method. Analysis may include read QC, alignment, target-window review, editing frequency calculation, bystander edit profiling, candidate off-target review, variant annotation, RNA-level filtering, and comparison against controls.
At this stage, we focus on making the result interpretable, not only producing variant lists.
Step 5: Bioinformatics reporting and review-ready deliverables
The final deliverable may include raw data, clean data where applicable, QC summaries, editing spectrum plots, bystander edit tables, DNA off-target candidate tables, RNA-level event summaries, sample comparison figures, and report notes.
Your team can use these outputs to support internal research review, method comparison, and next-step project planning.
Sample requirements for base editing safety assessment projects
Sample requirements depend on whether your project includes on-target validation, DNA off-target assessment, WGS-supported review, RNA-level assessment, or a combined package. The table below uses CD Genomics sample submission guidance as the baseline for nucleic acid and cell submissions. Exact requirements should be confirmed during project review.
| Input Type | Recommended Material | Quality Check | Container or Format | Shipping or Submission | Notes |
|---|---|---|---|---|---|
| Edited cells / cell pellets | For general cell submission, 1×106 cells are recommended | Sample identity, cell type, treatment group, DNA/RNA yield after extraction | Cryovial or approved frozen-cell tube | Dry ice | Useful when DNA/RNA extraction is included |
| Extracted gDNA for targeted validation | Assay-specific; WES/WGS-style short-read inputs commonly start from ≥500 ng gDNA | OD260/280 close to 1.8-2.0; RNase-treated; no obvious degradation or contamination | DNase-free tube; DNase-free water, elution buffer, or 10 mM Tris pH 8.0 | Ice packs | Used for on-target, bystander, or targeted off-target validation |
| Extracted gDNA for WGS-supported review | WGS: recommended ≥500 ng, minimum 200 ng, minimum 10 ng/µL; PCR-free WGS: recommended ≥1 µg, minimum 500 ng, minimum 20 ng/µL | Concentration by fluorometry when possible; if Nanodrop is used, higher input may be needed | DNase-free tube | Ice packs | Used for broader DNA-level context |
| Purified amplicon for focused validation | Amplicon sequencing: recommended ≥1 µg, minimum 500 ng, minimum 20 ng/µL | Single expected product if applicable; concentration and purity check | Low-bind or DNase-free tube | Ice packs | Useful for target-site or candidate-site validation |
| Extracted total RNA for RNA-level review | mRNA sequencing: recommended ≥500 ng, minimum 200 ng, minimum 20 ng/µL; whole transcriptome sequencing: recommended ≥3 µg, minimum 1 µg, minimum 20 ng/µL | DNA-free total RNA; A260/A280 ≥1.8; A260/230 ≥1.8; RIN ≥6 | RNase-free tube; RNase-free water, RNA stabilization reagent, or 10 mM Tris pH 8.0 | Dry ice | Used for RNA-seq-supported transcriptome review |
| Editor and target information | Editor type, ABE/CBE, sgRNA, target sequence, PAM, expected edit, control design | Sequence accuracy and group consistency | FASTA, GenBank, spreadsheet, or project sheet | Electronic submission | Required before method review |
CD Genomics asks customers to submit a completed sample submission form, ensure sample names match the labels on the tubes, and provide electronic QC data when available. DNA samples dissolved in H2O or TE buffer should be transported with ice packs, while RNA, cells, bacteria, and frozen tissue samples should be quickly frozen and transported with dry ice. The general cell submission baseline is 1×106 cells, and fresh frozen tissue guidance lists 10 mg with dry ice shipment. These values are from CD Genomics' sample submission guidance.
Bioinformatics analysis and deliverables
Bioinformatics is central to this solution. Base editing assessment only becomes useful when sequencing data are converted into editing spectrum, frequency summaries, candidate off-target tables, DNA/RNA-level annotations, and report-ready QC notes.
Minimum deliverables
- Project and sample information summary
- Sequencing QC summary
- On-target editing frequency
- Intended conversion profile
- Bystander edit summary
- Candidate DNA off-target table where included
- Variant annotation and genomic context where included
- RNA-level editing summary where RNA-seq is included
- Sample comparison tables
- Report notes and visualization files
Optional add-ons
- ABE/CBE-specific editing window analysis
- sgRNA-dependent candidate site annotation
- WGS-supported genome-wide variant review
- RNA-seq-supported transcriptome-level editing review
- Candidate site deep validation
- Control-vs-edited sample comparison
- Multi-editor or multi-sgRNA comparison
- Custom figure-ready visualization
- Pipeline parameter record
A useful report should help your team compare samples, not just archive sequencing files. Outputs may include base conversion plots, bystander edit tables, DNA off-target candidate summaries, RNA-level event summaries, annotation files, and final report notes.
This helps molecular biology, bioinformatics, and program teams discuss the same evidence package.
Application scenarios for base editing QC
These application scenarios show how sequencing-supported base editing QC can help teams compare editor candidates, organize DNA/RNA evidence, and plan next-step research.
ABE candidate comparison
ABE projects may require comparison of editing efficiency, bystander edits, candidate DNA off-targets, and RNA-level signals across editor versions, sgRNAs, or delivery conditions. A structured QC package helps your team compare candidates using consistent outputs.
CBE candidate comparison
CBE projects may need careful review of C-to-T editing patterns, target-window behavior, bystander edits, and DNA/RNA-level profiles. Sequencing-supported QC helps organize these results into interpretable tables and figures.
Cell and gene therapy research programs
Cell and gene therapy research teams may need layered evidence before choosing a base editor candidate for further study. A combined strategy can include target-site validation, DNA off-target review, RNA-level assessment, sample comparison, and bioinformatics reporting.
Preclinical and translational base editor studies
For preclinical or translational research, a more complete evidence package may be helpful. This can include WGS-supported DNA-level review, RNA-seq-supported transcriptome review, candidate validation, and report-ready figures. The results support research review and next-step planning; they do not represent a clinical or regulatory safety conclusion.
Demo results: what a base editing QC package may include
Demo results help your team understand how base editing data can be organized. The examples below show common result formats that may be included when they match the study design.
Demo 1: On-target editing spectrum and bystander edit profile
A target-site editing spectrum can show the intended base conversion, editing frequency, base position, read support, and bystander edits within the editing window. This helps your team see whether the observed edit pattern matches the design goal.
A typical visualization may include a base-position plot, allele or read-level table, and sample comparison chart.
Demo 2: DNA off-target candidate table and annotation
A DNA off-target candidate table can show candidate site sequence, genomic coordinate, editing frequency, nearby gene, genomic feature, and sample group comparison. This format helps your team review candidate sites in biological context rather than reading a raw variant list.
Where suitable, candidate sites can be prioritized for deeper validation or follow-up analysis.
Demo 3: RNA-level editing summary and transcriptome review
When RNA-level assessment is included, the report may show candidate RNA editing events, transcript annotation, read support, filtering notes, and control-vs-edited sample comparison.
A typical output may include a transcript-level table, heatmap, or summary plot showing RNA-level editing candidates across samples.
FAQ: planning a base editing safety assessment project
1. Is ABE assessment different from CBE assessment?
Yes. ABE and CBE projects involve different expected base conversions, editing windows, and possible editing profiles. The assessment strategy should start with the editor type, target sequence, expected edit, and project goal.
2. Is on-target validation enough for a base editing project?
Sometimes it may be enough for early screening. For higher-value projects, your team may also need bystander edit profiling, DNA off-target candidate review, WGS-supported review, RNA-level assessment, or a hybrid QC package.
3. What are bystander edits?
Bystander edits are additional base changes that occur near the intended edit, often within or near the editor's activity window. They may matter if they affect the final sequence, protein coding region, regulatory region, or project interpretation.
4. Should DNA off-target and RNA-level profiles both be reviewed?
Not always. The need depends on editor type, sample type, research stage, and project concern. DNA off-target review may focus on candidate genomic sites or WGS-supported context. RNA-level review may be added when transcriptome-level editing is relevant.
5. When should WGS be added to base editing QC?
WGS may be useful when broader DNA-level context is needed, such as for candidate editor comparison, higher-value research samples, or projects that require genome-wide variant review. It should be combined with targeted validation when focused editing-frequency evidence is needed.
6. When should RNA-seq be added?
RNA-seq may be considered when RNA-level editing signals are part of the research question, when editor type raises transcriptome-level concerns, or when the project requires control-vs-edited transcriptome comparison.
7. What sample types can be used?
Projects may use edited cells, cell pellets, extracted gDNA, extracted RNA, purified amplicons, or submitted project files such as editor type, sgRNA, target sequence, PAM, and expected edit. The exact input depends on the selected analysis strategy.
8. What editor and sgRNA information should I provide?
Useful information includes editor type, ABE/CBE version if known, sgRNA sequence, target sequence, PAM, expected base conversion, editing window, sample groups, control design, and candidate off-target list if available.
9. Can you compare multiple editors, sgRNAs, or sample groups?
Yes. A comparison project can organize editing frequency, bystander profile, candidate DNA off-targets, RNA-level events, and QC metrics across groups. Clear control design is important.
10. What bioinformatics outputs can be included?
Outputs may include editing spectrum plots, bystander edit tables, candidate DNA off-target tables, RNA-level event summaries, WGS/RNA-seq QC summaries, sample comparison figures, annotation files, and report notes.
Case Study: RNA-level off-target editing can be missed without transcriptome-wide review
Public literature case
Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors
Journal: Nature
Published: 2019
DOI: 10.1038/s41586-019-1161-z
Background
This study addressed an important question in base editing research: can DNA base editors also create transcriptome-wide RNA editing signals? At the time, many base editing assessments focused heavily on DNA target sites. The study showed why RNA-level review may be important when transcriptome-wide off-target editing is part of the project concern.
This case is relevant to our base editing safety assessment solution because it supports the need for layered QC evidence. Target-site validation can confirm the expected DNA edit, but RNA-level review may reveal additional editing signals that require separate analysis.
Methods
The study evaluated CRISPR-guided DNA base editors using sequencing-supported analysis. The authors used RNA-seq to profile transcriptome-wide RNA editing events, WES to examine DNA-level variants, and targeted amplicon sequencing to validate selected sites.
The study compared edited samples with controls and examined RNA editing patterns associated with different base editor conditions. These methods support the same logic used in layered base editing QC: use the right sequencing approach for the specific evidence question.
Results
The study reported transcriptome-wide off-target RNA editing induced by DNA base editors. RNA-seq revealed RNA editing signals that would not be captured by DNA-only target-site validation. The article also provides data availability for RNA-seq, WES, and targeted amplicon sequencing, supporting the role of multiple sequencing layers in base editor review.
Figure 1 presents transcriptome-wide RNA SNV analysis after base editor expression and shows how RNA-seq can reveal RNA-level editing signals across samples.
Figure 1 from the public literature case shows how transcriptome-wide RNA analysis can reveal RNA-level editing signals that are not captured by DNA-only target-site validation.
Conclusion
This case supports a practical QC principle: base editing assessment should match the evidence question. On-target validation, bystander edit profiling, DNA off-target review, RNA-level assessment, WGS/RNA-seq options, and bioinformatics reporting can be combined when the project requires layered evidence.
Reference
Related customer publication
The publication below is relevant to base editing and RNA-level analysis. It is listed as a related customer publication, not as the figure-based case study.
| Publication | Journal / Year | Related Service Tag | Why It Is Relevant |
|---|---|---|---|
| In vivo base editing rescues ADPKD in a humanized mouse model | Nature Communications, 2025 | RNA-seq / RNA-seq Library Construction and Sequencing | The study involved in vivo ABE base editing and transcriptome-wide off-target RNA base editing analysis; the methods state that RNA-seq library construction and sequencing were performed by CD Genomics. |
