Sample Submission Guidelines
Separate sample preparations connect the same research question to different RNA readouts.
Choose the Right Profiling Approach
The three approaches answer different questions. Choose the smallest combination that can resolve your primary comparison, then allocate material before RNA extraction, dissociation or sectioning makes another route unavailable.
| Your research question | Approach | Material to plan | Main result | Interpretation boundary |
|---|---|---|---|---|
| Which genes and pathways differ between conditions? | Bulk RNA-seq | Organoid material for RNA extraction, or suitable extracted RNA | Sample-level expression and differential analysis | A change can reflect expression within cells, a shift in composition, or both |
| Which populations are present, and which states change? | Single-cell RNA-seq | Material suitable for the selected cell preparation and capture workflow | Cell-by-gene matrix, annotations and sample-aware comparisons | Recovered cells may not represent every population in the original culture |
| Where is an expression program located? | Spatial transcriptomics | Organoid blocks or sections compatible with the selected assay | Expression mapped to sampled section coordinates | Effective resolution and gene coverage depend on chemistry and analysis |
| Does a bulk difference arise from a particular population or region? | Matched RNA profiling | Separately allocated material from a coordinated design | Cross-method comparison with stated assumptions | Measurements from separate aliquots are not measurements of the same individual cell |
Bulk is often a suitable starting point for replicated expression comparisons. When the question depends on a rare population or a specific tissue region, an average expression profile may be insufficient. The broader organoid sequencing and analysis services page connects these RNA approaches with other sequencing options.
Bulk RNA Sequencing for Organoid Expression Comparisons
Bulk RNA-seq provides an expression profile for each submitted sample. It supports comparisons between culture conditions, organoid lines, developmental stages or defined experimental exposures when the design includes suitable controls and biological replication.
Match library design to the RNA question
For gene-level coding-transcript comparisons, an mRNA-focused library may be appropriate. Broader RNA questions can require a different enrichment strategy; RNA integrity, the transcripts of interest and compatibility with existing datasets should guide that decision. Our mRNA sequencing service provides additional background on coding-transcript profiling.
A gene-counting design does not automatically support detailed isoform or splice-junction analysis. If transcript structure is central to the study, define that requirement before selecting library chemistry, read layout and depth. Increasing depth after an unsuitable library has been generated cannot recover information that was never captured.
Compare conditions with the model history attached
Record the donor or source line, passage, culture medium, matrix, sampling point and experimental condition for every sample. These details allow the analysis to distinguish the intended comparison from differences introduced during model handling.
Bulk deliverables can include a gene expression matrix, sample similarity plots, differential-expression tables and pathway summaries. Replicate-based count models, such as those described for DESeq2, support estimates of condition-associated expression differences; the report should retain effect sizes and adjusted significance rather than reduce the result to a list of highlighted genes. [3]
For mixed-cell organoids, interpret a bulk change alongside the composition of the model. A larger signal for a lineage marker may result from more cells of that lineage, more expression per cell, or a combination. Single-cell profiling can help examine those alternatives when the samples and study design permit it.
Single-Cell RNA Sequencing for Cell Composition and State
Single-cell RNA-seq separates expression measurements by captured cell. It helps examine whether expected lineages are recovered, whether a population contains several transcriptional states, and how those states differ across organoid conditions.
Plan recovery before choosing a cell target
Organoid size, matrix carryover, cell adhesion and cell fragility can affect preparation. We review the available material and preparation route before capture, including whether dissociation should occur before shipment or after receipt under the agreed handling plan.
The analysis should assess low-quality profiles, background RNA and likely multiplets alongside gene detection and sample representation. A large recovered-cell count is not sufficient by itself: missing fragile cells or preferentially recovered populations can change the biological picture. The report should make those limitations visible.
Our single-cell RNA sequencing services support method selection. A single-nucleus route may be worth evaluating for some frozen or difficult-to-dissociate materials, but it requires a separate feasibility decision and is not automatically interchangeable with whole-cell profiling.
Make cell annotations reviewable
Cell clusters are starting points for interpretation. Annotation combines marker expression, model context and an appropriate reference where available. Ambiguous or weakly supported assignments should remain qualified rather than be given an overly specific cell identity.
Useful outputs include a cell-by-gene matrix, cell metadata, a cluster map, marker summaries and per-sample composition tables. For condition comparisons within a cell type, retain biological sample identities throughout analysis. Treating thousands of cells from one preparation as thousands of independent biological replicates can produce misleading statistical confidence. [2]
Observed population proportions describe the cells recovered and retained after QC. They should not be presented as an exact census of the intact organoid. Likewise, a trajectory inferred from expression similarity is a model of cell-state relationships; it does not establish lineage history without additional evidence.
Spatial Transcriptomics for Expression Within Organoid Sections
Spatial transcriptomics links RNA measurements to locations in a sampled section. For organoids with recognizable layers, lumens or regional organization, this adds context that dissociated-cell data cannot directly retain.
Preserve the structure relevant to the question
The project begins with a review of organoid dimensions, embedding or fixation history, orientation and the region to be sampled. A section that misses the relevant structure cannot answer a regional question, even if its sequencing metrics are acceptable.
Fresh-frozen and fixed material require different compatibility checks. Depending on the selected assay, expression may be measured by direct RNA capture or by a defined probe design. Gene coverage, species support and the interpretation of unmeasured genes must therefore be specified for that route.
We select the spatial approach around the sample and requested result. Our spatial multi-omics sequencing services describe related options. Sequencing-based spatial assays and imaging-based in situ assays should be identified separately in the project plan because their raw data and analysis products differ.
Interpret location at the supported scale
Spatial outputs can include aligned tissue images, expression matrices, coordinates, regional annotations and gene-expression overlays. Review tissue coverage, background outside the specimen and the relationship between molecular signals and the sampled morphology before interpreting a region.
A small capture bin is not automatically a reliably segmented single cell. Depending on the platform, bins or capture areas can contain mixed signals, and interpretation may require cell segmentation or reference-informed deconvolution. Single-cell references can help estimate the distribution of annotated populations, but these estimates remain model-dependent. [4,5]
One section is a two-dimensional sample of a three-dimensional organoid. Additional sections or replicates may be needed to test whether an apparent boundary or expression zone is representative. Spatial proximity and predicted communication are useful hypotheses, not proof that two cells exchanged a functional signal.
Design Matched Samples Before Running Separate Assays
Joint analysis becomes more informative when the experiments share a planned comparison. Decide which sources, passages and conditions should be represented in every assay before dividing material, and record any differences that cannot be avoided.
| Design item | What to agree before processing | Why it matters |
|---|---|---|
| Primary contrast | The condition, time point or model comparison the study must answer | Keeps secondary analyses from replacing the original question |
| Biological unit | Source line, donor, independently grown culture or other justified replicate | Defines what constitutes independent evidence |
| Material allocation | Which aliquot supplies RNA, cells or sections | Preserves compatibility with each selected preparation |
| Culture history | Passage, medium, matrix and collection context | Helps identify culture-associated differences |
| Batch distribution | How groups are distributed across preparation and sequencing runs | Reduces avoidable confounding of condition with batch |
| Reference material | Whether source tissue, a reference model or public data is available | Determines the strength of model-to-reference comparisons |
Multiple organoids pooled from one culture may supply material without creating additional independent replicates. Conversely, splitting one specimen across several assays provides complementary measurements but does not increase the number of biological sources. These distinctions should be reflected in the comparison plan.
If model generation or expansion is needed first, organoid model development services can be coordinated with the sampling plan. For pre-sequencing structural or marker assessment, use organoid characterization services to define the evidence required before committing the remaining material.
From Organoid Samples to Sequencing Data
A coordinated workflow keeps every dataset tied to its starting material and intended comparison. QC is reviewed at the points where a problem can affect the next step, so unexpected sample behavior can be addressed before interpretation.
- Review the study. Define the models, experimental groups, replication and required outputs. Agree which assays contribute to the primary question.
- Review and prepare the samples. Check input condition and identifiers. Apply the chosen RNA extraction, cell preparation or spatial section workflow, with route-specific suitability checks.
- Prepare libraries. Select chemistry and library configuration for the measured RNA targets. Review library quantity and fragment characteristics to assess suitability for sequencing.
- Sequence and assess data quality. Examine read quality, alignment or assignment, sample representation and assay-specific metrics. Keep excluded data and filtering decisions traceable.
- Analyze and deliver. Generate the agreed matrices, annotations and comparisons, then provide the analysis records needed to interpret and reuse the results.
Samples follow the preparation appropriate to their selected assay.
Sample Requirements by Profiling Route
There is no single DNA or RNA input rule that applies to all three approaches. Submit the material description and available QC information first; input quantity, preservation, transport and acceptance criteria are confirmed for the selected assay before shipment.
| Material | Potential route | Information to provide | Checks before acceptance |
|---|---|---|---|
| Extracted RNA | Bulk RNA-seq | Amount, concentration, extraction method and integrity report | Quantity, integrity, contaminants and library compatibility |
| Organoid pellets or cultures intended for extraction | Bulk RNA-seq | Model identity, matrix, harvest and storage history | Extraction feasibility and adequate RNA recovery |
| Fresh organoids or an agreed cell preparation | Single-cell RNA-seq | Handling history, preparation state and available cell assessment | Cell condition, aggregates, debris and capture compatibility |
| Frozen material proposed for nuclei isolation | A separately evaluated single-nucleus route | Freezing method, storage history and model type | Nuclear integrity, isolation feasibility and assay compatibility |
| Frozen or fixed organoid blocks/sections | Spatial transcriptomics | Embedding/fixation, orientation, dimensions and available images | Section integrity, RNA suitability and platform-specific requirements |
| Existing sequencing data | Analysis or reference integration | Raw/processed files, reference build, sample metadata and prior filtering | Format completeness, method compatibility and identifiable comparisons |
Avoid applying a bulk RNA preservation workflow to material reserved for viable-cell capture. Similarly, extracting RNA removes the spatial information required for a section-based experiment. Keep each planned sample route identifiable from collection onward.
Include a sample manifest linking each tube, block or file to its source, passage, treatment and replicate. For paired studies, explicitly identify the pairing. If material is limited, discuss the primary question before dividing it across assays that may each receive inadequate input.
Integrated Organoid Data Analysis
Integrated analysis links complementary measurements while preserving what each assay actually measured. We begin with QC and interpretation within each dataset, then compare supported findings across the matched study design.
Relate sample-level changes to cell populations
Bulk and single-cell results can be compared at the level of shared genes, pathways or annotated cell populations. Where appropriate, single-cell profiles can inform estimates of composition in bulk samples. Reference mismatch, missing populations and culture-specific states can limit those estimates, so they should accompany the measured data rather than replace it.
Within-cell-type differential analysis should preserve the biological sample as the unit of replication, using an appropriate sample-aware method. The integration report should identify the compared populations, samples retained after QC and design factors used in the model. [2]
Connect cell identities with spatial regions
A compatible single-cell reference can help interpret mixed spatial measurements. Methods such as cell2location estimate cell-type distributions, while approaches such as GraphST address spatial clustering, integration and deconvolution. The appropriate method depends on the assay and reference, rather than a fixed software list promised for every project. [4,5]
Joint displays should distinguish measured spatial expression from inferred cell-type abundance. Batch alignment also requires scrutiny: a visually well-mixed embedding does not establish that a genuine condition effect has been preserved. Where the evidence is insufficient, report the unresolved comparison explicitly.
Interpret complementary assays together while retaining measured and inferred results separately.
Sequencing Data and Reports You Receive
The deliverable list is agreed for the assays and analysis modules in your project. Shared identifiers and method records allow your team to follow a result back to the relevant sample, filtering decision and reference.
| Component | Typical deliverable | How your team can use it |
|---|---|---|
| Sequencing and QC | FASTQ files from selected sequencing assays, sample manifest and QC summaries | Reprocess reads and review whether each sample supports the comparison |
| Bulk expression | Gene count matrix, normalized expression summaries, differential tables and pathway results | Compare conditions and prioritize expression programs for follow-up |
| Single-cell analysis | Expression matrix, cell metadata, annotations, markers and sample-level composition summaries | Inspect recovered populations and evaluate annotation support |
| Spatial analysis | Expression matrix, section images, coordinates and region/gene overlays | Review where expression was measured within sampled sections |
| Agreed integration | Matched comparison tables, cross-method plots and inference notes | Connect compatible findings while keeping assumptions visible |
| Reuse records | Reference/annotation versions, method descriptions and a file guide | Reproduce the scope of analysis and reuse the dataset |
Matrix formats and analysis objects are confirmed before delivery so they fit your downstream workflow. Advanced trajectory analysis, reference mapping, deconvolution or custom integration is scoped according to the data and study objective; these analyses are not implied by sequencing alone.
Additional Sequencing Options
The RNA-focused project can connect with additional molecular assays when the biological question requires them. Each adds a distinct type of evidence and may need separately prepared material.
- Genomic variation: Organoid whole exome sequencing addresses coding-region comparisons. Whole genome sequencing can be considered for broader genomic questions; RNA-derived signals do not replace a dedicated DNA assay.
- Chromatin and methylation: Organoid epigenetic profiling connects expression findings with appropriate CUT&Tag, DNA methylation or accessibility measurements.
- Defined perturbations: Organoid drug response transcriptomics focuses on expression comparisons across compounds, doses or time points, including suitable high-throughput options.
- RNA kinetics: Organoid RNA stability analysis addresses synthesis/decay questions requiring a labeling or time-course design beyond a routine expression snapshot.
Long-read transcriptomics or immune-receptor profiling can be evaluated when a specific transcript or cell population makes them relevant. Protein markers can provide targeted supporting evidence where needed. These additions are selected around the RNA study, with feasibility and deliverables agreed separately.
Illustrative Sequencing Results
These schematics show how common outputs can answer different questions. They are illustrative examples, not experimental data or promised outcomes; the final report contains the results supported by the selected assays and samples.
Illustrative example: compare sample relationships, gene expression and pathway summaries.
Bulk Expression and Pathway Comparisons
Sample similarity, gene-level effects and pathway summaries provide different views of the same comparison. Review whether the samples support the intended grouping before interpreting highlighted genes. A pathway result helps organize follow-up questions but does not independently establish a mechanism.
Illustrative example: interpret cell clusters alongside markers and sample representation.
Cell Populations and Marker Support
A cell map summarizes transcriptomic relationships; marker displays support annotation; per-sample composition charts show how recovered populations vary. Keep these views linked so that an apparent cluster difference can be checked against sample representation and marker evidence.
Illustrative example: distinguish measured section expression from regional interpretation.
Spatial Expression and Regional Context
Expression overlays connect RNA signals to a sampled organoid section. Morphology and a defined region map help distinguish location from abundance, while the report identifies whether a displayed cell-type distribution was measured directly or inferred using a reference.
Organoid Sequencing FAQs
Can I submit an organoid model that was developed elsewhere?
Yes. Provide the model identity, source, culture history, available material and research question for sample review. An existing model can enter at the sequencing stage; the preparation route depends on the intended RNA assay and current material condition.
Do I need bulk, single-cell and spatial profiling in every project?
No. A replicated bulk study may answer a gene-expression question adequately. Add single-cell profiling when population identity or state matters, and spatial profiling when the location of an expression program is central. Combining assays is useful only when each contributes to a defined comparison.
Can all three methods use the same extracted RNA sample?
No. Extracted RNA can support compatible bulk workflows, but it no longer preserves intact cells or tissue coordinates. A joint study normally allocates separate material from a coordinated culture and sampling design before extraction or dissociation.
Can frozen organoids be used for single-cell RNA sequencing?
Suitability depends on how the material was preserved and the selected assay. A frozen pellet is not equivalent to a viable cryopreserved cell preparation. In some projects, a single-nucleus route may be a candidate; review it separately before treating frozen material as acceptable input.
Can FFPE organoid material support spatial transcriptomics?
It may support a compatible fixed-tissue assay after review of fixation, section quality and RNA suitability. Probe coverage, species compatibility and the desired analysis must also fit the selected platform. Do not assume a protocol for fresh-frozen sections applies unchanged to FFPE material.
Is matched source tissue required?
It is important when the objective is a direct model-to-source comparison. For a comparison between organoid culture conditions, an appropriate organoid control may instead address the primary question. Public references can add context but do not replace a matched sample or remove differences in preparation and model maturity.
Will single-cell sequencing recover immune and stromal cells?
Only cells present in the submitted material and retained through preparation and QC can contribute profiles. An epithelial organoid does not automatically contain a complete immune or stromal compartment. Co-culture composition and the intended cell populations should be documented before profiling.
Can you analyze existing single-cell or spatial data?
Existing datasets can be reviewed for analysis or integration. Share the raw or processed files, sample metadata, chemistry and reference information, plus any previous filtering. Compatibility and the requested comparison determine whether reprocessing, reference mapping or a more limited analysis is appropriate.
Case Study: Comparing Organoid Cell Composition with Source Tissue
Source: Wang, Mao, Wang and colleagues, Genome Biology (2022). This is an independent literature example, not a CD Genomics customer project. [1]
Background: The investigators examined how colorectal organoid culture reflected source-tissue expression and how culture conditions affected the models.
Methods: They used paired tissue and organoid samples from seven donors, single-cell RNA sequencing and complementary molecular assays. Figure 1 relates the experimental design to cell-type annotation and composition.
Results: The profiles distinguished epithelial, immune and mesenchymal populations. Immune cells enriched in tumor tissue were scarcely recovered in the corresponding tumor organoids, and culture conditions influenced model composition and expression features.
Conclusion: The study illustrates why model-to-source comparisons benefit from cell-level evidence and documented culture conditions. It does not establish that every organoid preserves the full composition of its source tissue.
Independent study figure: cell-type annotation and composition in paired tissues and organoid cultures.
Figure source: Wang et al., Figure 1, Genome Biology 23, 106 (2022). Reproduced under the Creative Commons Attribution 4.0 International license. Format converted; scientific content unchanged.
