Sample Submission Guidelines
Distinguish RNA production from RNA persistence in an organoid model.
Steady-State RNA Cannot Reveal Turnover on Its Own
An ordinary RNA-seq experiment measures the RNA present when a sample is collected. That abundance reflects both the rate at which molecules enter the pool and the rate at which they leave it. A transcript can therefore remain unchanged at steady state while its synthesis and decay both accelerate. Conversely, a higher count can arise from increased production, slower removal, or a mixture of the two.
SLAM-seq adds a time dimension by metabolically labeling RNA synthesized in living material. After chemical conversion and sequencing, labeled and pre-existing RNA contribute different T-to-C mismatch patterns. Those patterns can be modeled across genes and time points to estimate new-RNA fractions and, in suitable designs, decay parameters.[1,2] The assay does not simply sort every read with certainty; background mismatches, labeling efficiency, coverage, and model assumptions all influence inference.
| Question behind the project | Why total RNA alone is incomplete | Kinetic readout to consider |
|---|---|---|
| Did a perturbation rapidly activate transcription? | A later total-RNA change combines production and decay | New-RNA fraction or production-associated comparison during a defined pulse |
| Did an RNA-binding regulator change transcript persistence? | Steady-state abundance may hide compensating synthesis | Pulse–chase or chase design with transcript-level decay fitting |
| Are two organoid states maintained by different RNA turnover programs? | Similar total abundance does not mean similar kinetics | Matched new-to-total fractions and stability estimates across states |
| Which response appears before the total transcriptome shifts? | Conventional sampling may miss early production changes | Short, model-qualified labeling windows with matched controls |
This service is for RNA metabolism in living organoid models. It is not a formulation-stability study, an RNA storage test, or a substitute for RNA integrity assessment. If the research question is simply which genes differ at a collection time, our organoid sequencing services or conventional mRNA sequencing service may be the more direct route.
Define the Kinetic Question Before Labeling
The experiment should start with the parameter your team needs, not with a borrowed labeling schedule. A new-RNA comparison, a relative turnover comparison, and a transcript half-life estimate require different timing, controls, and analytical assumptions. We convert the biological question into an estimable contrast before organoid material is committed.
For an early-response study, a pulse may be designed to capture RNA synthesized within a selected interval. For a decay study, labeled RNA must be followed after label removal or replacement, and the collection times must span the expected decay behavior. For a condition comparison, each state needs matched background and labeling controls so that a change in conversion efficiency is not mistaken for altered RNA kinetics.
The design review records the organoid source, passage or maturation state, culture matrix, medium, perturbation, replicate structure, intended labeling route, collection windows, and primary comparison. It also distinguishes independent biological preparations from repeated wells or technical measurements. More time points do not compensate for a missing biological replicate, and deeper sequencing does not fix a treatment that is completely confounded with a processing batch.
Best for: mechanistic studies of transcription, RNA-binding proteins, RNA modification, stress adaptation, or perturbation-associated turnover in label-compatible living organoids. Not for: purified RNA that was never metabolically labeled, a single stored sample with no time information, or a request to infer transcript half-lives from standard RNA-seq counts alone.
How SLAM-seq Separates New and Pre-existing RNA
SLAM-seq uses 4-thiouridine (4sU), a uridine analogue that living cells can incorporate into newly synthesized RNA. After RNA extraction, thiol-linked alkylation changes the reverse-transcription behavior of incorporated 4sU. At labeled positions, the resulting sequencing data contain an elevated T-to-C conversion signal.[1]
Use conversion-aware modeling to estimate new and pre-existing RNA fractions.
The signal is interpreted statistically. Unlabeled samples establish background mismatches, while labeled samples provide the combined contribution of incorporation, chemical conversion, and sequencing. A transcript with few informative positions or limited coverage may have wide uncertainty even when it is detected in total RNA. GRAND-SLAM demonstrated how mixture modeling can estimate new and old RNA proportions while carrying uncertainty into downstream kinetic estimates.[2] Slamdunk provides another established framework for quantifying experimentally induced nucleotide conversions.[3]
This is why conversion-aware QC is part of the service, not an optional plot added after expression analysis. We review global and sample-level conversion behavior, background rates, sequence composition, mapping quality, and usable coverage before reporting kinetic parameters. Known or suspected sequence variants and condition-specific technical effects may need additional handling because a persistent mismatch is not evidence of metabolic labeling by itself.
The method produces transcriptome-scale evidence, but its practical resolution is determined by the library, annotation, read allocation, and conversion signal. We therefore describe outputs at the gene or transcript level supported by the agreed workflow rather than promising unqualified "single-base resolution" or a half-life for every detected feature.
Choose a Pulse, Chase, or Pulse–Chase Design
The labeling architecture determines what the study can estimate. We select it around the biological time scale and the behavior of the organoid model, then use pilot evidence where the feasible window is uncertain.
| Design route | What is observed | Useful for | Important limitation |
|---|---|---|---|
| Pulse labeling | RNA synthesized during a defined labeling interval | Early transcriptional response and new-RNA fraction comparisons | A single pulse does not by itself define a complete decay curve |
| Chase after pre-labeling | Loss of labeled RNA after label withdrawal or replacement | Relative decay and half-life estimation when the time series is informative | Residual label, delayed washout, or poorly spaced times can distort the fitted start |
| Pulse–chase | Entry of label followed by its decline | Connecting production and persistence in one planned series | Requires more material and tighter collection coordination |
| Matched pulse across conditions | New-RNA signal in control and perturbed organoids | Comparing production-associated responses | Condition-dependent uptake or viability can mimic biological differences |
Short-lived transcripts require early observations, while slowly changing RNAs need a longer window. A schedule optimized for one range may provide little information for another. Expected kinetics, organoid handling time, culture stability, and sample capacity therefore need to be balanced before a final time course is approved.
We do not impose a universal 4sU concentration or exposure duration. Published protocols provide starting context, but organoid size, cell accessibility, matrix composition, medium, and metabolic state can change uptake and washout. The agreed design documents which parameters came from a pilot, which came from prior model evidence, and which remain assumptions to be evaluated in the data.
Qualify the Organoid Model Before the Main Study
Metabolic labeling occurs in living cultures, so model compatibility is part of analytical validity. A concentration that creates a strong conversion signal but changes growth, morphology, viability, or baseline transcription may answer a different question from the one the project intended. The pilot should find a useful signal window without treating the organoid as an inert RNA source.
We review the model format, matrix or scaffold, size distribution, culture density, feeding schedule, maturation state, passage history, and any planned perturbation. Where suitable, pilot conditions compare labeled and unlabeled cultures using predefined model observations and RNA quality checks. The goal is not to prove that labeling has no biological effect; it is to identify a condition where the signal is interpretable and limitations are documented.
Three-dimensional matrices deserve particular attention. A 2026 organoid SLAM-seq dataset reported residual 4sU retention in a basement-membrane matrix and a delayed effective decay peak, leading the investigators to shift the starting point used for half-life fitting.[6] That example shows why nominal washout time is not automatically the biological decay origin. Matrix exchange, diffusion, organoid geometry, and collection logistics must be considered together.
Model qualification also defines what can be submitted. Existing purified RNA can enter a SLAM-seq workflow only if the living culture was labeled under a documented, compatible design before extraction. RNA from an ordinary sequencing experiment cannot be chemically converted after the fact to recreate metabolic history. Teams sending externally labeled RNA should provide the complete labeling, washout, collection, matrix, and storage record for feasibility review.
From Living Organoids to Conversion-Aware Data
The workflow keeps culture decisions, chemical conversion, sequencing, and kinetic modeling connected under one sample map. Each checkpoint has a purpose: detect a design or material problem before it propagates into an apparently precise but unsupported parameter.
Coordinate model qualification, time-course collection, sequencing and kinetic analysis.
- Frame the kinetic contrast. Define whether the study prioritizes new-RNA production, relative decay, or transcript half-life. Confirm conditions, biological replicates, labeling controls, and the expected kinetic range.
- Qualify labeling and washout. Review organoid compatibility and, where required, run a pilot to assess model condition, conversion signal, background, and the practical timing of label entry or removal.
- Execute the matched time course. Apply the agreed pulse, chase, or pulse–chase design and collect samples against a controlled clock. Preserve model, well, batch, treatment, label, and harvest metadata.
- Extract and chemically convert RNA. Assess recovered RNA for the selected workflow, perform the required thiol-linked conversion, and retain labeled and unlabeled control identities through library preparation.
- Sequence with the planned readout. Generate the agreed library type and read allocation. A focused 3′ counting route may suit gene-level kinetics, while broader RNA coverage may be considered for other defined questions; compatibility is confirmed during scoping.
- Quantify conversions and model kinetics. Review background and induced T-to-C patterns, estimate new-RNA fractions, fit the agreed kinetic model, filter unsupported estimates, and deliver parameters with QC and interpretation notes.
Projects may include culture and labeling execution or begin with properly documented labeled RNA. Scope is confirmed before samples are shipped. If a pilot shows that the model does not produce an interpretable labeling window, the study is revised rather than forcing a half-life analysis from inadequate data.
Build Controls and Time Points Into the Model
Controls define the two distributions that the analysis is trying to separate. Unlabeled organoids reveal background T-to-C mismatches and sequence-specific noise. Labeled controls reveal incorporation and conversion behavior. Perturbation controls establish the biological contrast. Missing any of these can turn a kinetic difference into an unresolved technical alternative.
| Design element | What it protects against | Planning decision |
|---|---|---|
| Unlabeled control in the relevant model state | Calling background mismatches "new RNA" | Which conditions need their own background estimate |
| Matched labeled control | Confusing altered label uptake with altered RNA production | Whether each medium, state, or perturbation changes labeling behavior |
| Independent biological replicates | Overinterpreting a single culture preparation | How organoid preparations, passages, or source models define independence |
| Early and later chase points | Fitting a decay curve outside the informative window | Which half-life range is scientifically important |
| Recorded matrix and medium exchanges | Ignoring delayed washout or label carryover | How the effective chase start will be evaluated |
| Balanced processing batches | Confounding condition with conversion or library batch | How samples are distributed through extraction and library preparation |
Time points should support the model rather than form a decorative series. For a first-order decay estimate, the observed labeled fraction must change enough to constrain the slope, and the chosen points must not all cluster before or after the informative phase. Alternative kinetic behavior may require a different model or a limited descriptive result. A straight line on a transformed plot is not sufficient evidence if the conversion signal is weak or residual label continues to enter RNA.
Replicates remain necessary because organoids vary in size, cellular composition, and culture state. Pooling several organoids can provide material and average local variability, but it does not create independent biological replicates. The sample manifest records pooling and culture origin so uncertainty is not understated.
Estimate New-RNA Fractions and RNA Half-Lives
Analysis begins with read and sample QC, alignment, and conversion-aware summaries. We compare expected mismatch channels, quantify background and induced T-to-C signals, and review whether sample relationships follow the experimental map. Only samples and features that meet the agreed evidence requirements proceed to kinetic interpretation.
New-to-total RNA fractions can be estimated using models that account for background error and the conversion process.[2] Depending on the design, these fractions support within-time comparisons, production-associated contrasts, or decay fitting. Differential analyses are constructed from the predefined biological comparisons; they do not treat every time point as an unrelated sample or collapse the entire series into one steady-state contrast.
Half-life is calculated from an estimated decay rate under the selected model. The deliverable includes the observations used in the fit, parameter estimate, fit-quality or uncertainty information, and a status showing whether the transcript passed the reporting criteria. Low counts, sparse informative sites, weak label separation, delayed washout, non-monotonic behavior, or an insufficient time window can make a transcript non-estimable. Reporting that boundary is more useful than filling every row with a number.
Where total RNA profiles are included, production and persistence can be interpreted together. A gene with higher total abundance and unchanged stability points to a different hypothesis from a gene whose total abundance rises while decay slows. These are research interpretations within the tested organoid system, not proof of a direct molecular interaction. Follow-up may require an RNA-binding assay, genetic perturbation, reporter, or other orthogonal evidence.
Receive a Kinetic Data Package with Clear Boundaries
Deliverables are defined during scoping, because the useful package depends on whether the project asks for new-RNA fractions, relative turnover, or fitted half-lives. The core output preserves the connection among sample metadata, conversion evidence, model settings, and reported parameters.
| Package component | How it supports review and reuse |
|---|---|
| Sample manifest with model, condition, label, time, replicate, matrix, and batch fields | Reconstructs the time course and distinguishes biological from technical structure |
| Sequence data and agreed read-level QC | Supports archive, reprocessing, and review of library performance |
| Background and induced-conversion summaries | Shows whether the labeled signal separates from model- and sample-specific noise |
| Gene- or transcript-level total and new-RNA estimates, when supported | Enables production-associated comparisons and downstream kinetic modeling |
| Half-life or decay-rate table for estimable features | Reports parameters together with fit, uncertainty, and filtering information |
| Non-estimable feature status and reasons | Prevents absent evidence from being mistaken for biological stability |
| Contrast tables and pathway summaries included in scope | Connects kinetic changes with the predefined perturbation or model question |
| Methods, software settings, design decisions, and limitations | Makes the result auditable and guides a focused follow-up experiment |
The report separates directly measured quantities, model-derived estimates, and biological hypotheses. Raw data alone cannot communicate that hierarchy, while a figure-only report cannot support reanalysis. Providing both allows experimental teams to review the biological story and computational teams to examine how it was constructed.
Compare RNA Stability Analysis with Standard RNA-seq
SLAM-seq is not automatically better than standard RNA-seq; it answers a narrower kinetic question and requires living-model intervention. The most efficient project uses the least complex method that resolves the decision.
| Approach | Primary question | Requires living labeling? | Typical strength | Does not establish on its own |
|---|---|---|---|---|
| Standard mRNA-seq | Which polyadenylated RNAs differ at collection? | No | Broad steady-state expression comparison | Whether change came from synthesis or decay |
| Total RNA-seq | Which coding and selected non-coding RNA classes differ? | No | Broader RNA-class coverage with the chosen depletion strategy | RNA age or transcript half-life |
| Pulse SLAM-seq | Which RNAs were produced during a defined window? | Yes | Time-windowed new-RNA fraction and early response | A complete decay curve from one time point |
| Chase or pulse–chase SLAM-seq | How does labeled RNA decline over time? | Yes | Relative turnover and half-life estimation where supported | A guaranteed parameter for every detected transcript |
Standard total RNA sequencing may be paired with kinetic measurements when broader steady-state context is important. High-throughput perturbation projects may use the DRUG-seq service to screen expression responses first, then reserve SLAM-seq for a focused mechanistic subset. That staged design avoids placing a labor-intensive time course around every condition before the key comparison is known.
Illustrative RNA Kinetics Results
The following examples show how a project may organize its outputs. They are conceptual illustrations, not customer data, acceptance thresholds, or promised results. Actual figures depend on the agreed design, signal quality, and features that pass reporting criteria.
Illustrative example: review conversion separation before kinetic interpretation.
Conversion Signal and Background
A sample-level view compares background T-to-C rates in unlabeled controls with the induced signal in labeled organoids. The purpose is to show separation and consistency before kinetic estimates are interpreted. A strong global average does not rescue an individual transcript with inadequate coverage.
Illustrative example: compare new-RNA fraction trajectories with replicate and uncertainty context.
New-RNA Fraction Across Time
Time-course trajectories show how estimated new or labeled RNA fractions change for selected transcripts or groups. Replicates and uncertainty remain visible. Differences in curve shape can guide follow-up, but they are interpreted with the label-entry and washout behavior observed in the model.
Illustrative example: report half-life parameters only when the evidence supports the fit.
Half-Life Estimates with Fit Status
A results table or plot separates well-supported estimates from features that fail coverage, signal, or fit criteria. This prevents a single numerical column from implying equal confidence across all transcripts. Parameters are compared only within designs and conditions that support the same interpretation.
Organoid RNA Stability FAQs
Is RNA Stability Analysis the Same as Checking RNA Integrity?
No. RNA integrity evaluates the physical quality of an extracted sample. This service studies RNA metabolism in living organoids by labeling newly synthesized RNA and following its behavior. Integrity QC is part of sample assessment, but an integrity value does not measure biological transcript half-life.
Can You Measure RNA Half-Lives from One Standard RNA-seq Sample?
No. A conventional RNA-seq snapshot does not contain the time information needed to separate synthesis and decay. A single SLAM-seq pulse may support a new-RNA fraction under an appropriate model, but a robust decay or half-life estimate generally requires an informative temporal design and suitable controls.
Do All Detected Transcripts Receive a Half-Life Value?
No. Reporting depends on read support, informative uridines, conversion separation, the observed time range, and model fit. Features that fail the agreed criteria are retained with a non-estimable status or filtering reason rather than assigned an unsupported number.
Can We Submit Purified RNA from an Earlier Organoid Experiment?
Only if the living organoids were metabolically labeled under a documented, compatible design before RNA extraction. Ordinary purified RNA cannot be labeled afterward to reconstruct when each molecule was synthesized. We review labeling, matrix, collection, conversion, and storage records before accepting existing material.
How Many Time Points Do We Need?
There is no universal number. The schedule must cover the kinetic range the project aims to estimate and include enough information to assess model fit. A pilot may be the most efficient way to locate that range when the organoid model, matrix, or perturbation has not been tested previously.
Will 4sU Labeling Leave the Organoids Unchanged?
That cannot be assumed. Label concentration and duration can affect living systems, while too little signal limits analysis. Model qualification therefore balances conversion evidence with predefined observations of organoid condition and documents any remaining limitation.
Can SLAM-seq Identify the Direct Target of an RNA-Binding Protein?
Not by itself. It can show that RNA persistence changes after a perturbation. Demonstrating direct binding requires an appropriate orthogonal assay, and establishing causality may require additional genetic or molecular evidence. Kinetic results help prioritize those experiments.
Can We Compare Different Organoid Lines or Culture Conditions?
Yes, when the design can distinguish biological differences from labeling and culture effects. Each model state may require its own unlabeled and labeled controls. A different matrix or medium can alter uptake or washout, so identical nominal timing does not guarantee identical effective labeling.
Is This a Formulation or Storage Stability Service?
No. "RNA stability" here means biological RNA persistence and degradation in living organoid models. It does not evaluate RNA drug formulations, storage buffers, shipping conditions, freeze–thaw stability, or shelf life.
Case Study: RNA Half-Life Changes in a Colorectal Cancer Organoid Model
Source. Jiang and colleagues, Nature (2026).[6] This is an independent published study, not a CD Genomics customer project.
Background. The researchers asked how the RNA-binding regulator ZFP36L2 influences stress-associated transcripts in organoid models.
Methods. In a patient-derived colorectal cancer liver metastasis organoid model, the team compared control and ZFP36L2-depleted conditions using SLAM-seq with a planned labeling-and-chase series. Public data records document how matrix-associated label retention affected the effective decay timeline.
Results. Transcript-level half-life comparisons identified RNAs with prolonged persistence after ZFP36L2 depletion, and enrichment analysis organized the affected set into stress-related biological programs.
Conclusion. The study shows how organoid SLAM-seq can test a post-transcriptional mechanism when time-course design and model-specific washout behavior are considered. The result remains specific to the tested model and perturbation.
Independent study figure: transcript half-life changes after ZFP36L2 perturbation in an organoid model.
Figure 5 reproduced from Jiang Q, Raghavan MS, Rodriguez AM, et al.[6] under the Creative Commons Attribution 4.0 International license. Scientific content unchanged; file format converted for web delivery. Original figure: publisher figure page.