What is ISO-SLAM-seq and how can it benefit my transcriptome study?

ISO-SLAM-seq integrates metabolic labelling of newly synthesised RNA (via S4U in SLAM-seq) with full-length transcript sequencing (via ISO-seq on PacBio or Oxford Nanopore) to deliver both RNA turnover and isoform/structural data from one experiment.

How does ISO-SLAM-seq compare with SLAM-seq alone or ISO-seq alone?

While SLAM-seq tracks synthesis and decay of transcripts, and ISO-seq resolves full-length isoforms and splice variants, ISO-SLAM-seq combines both capabilities—yielding comprehensive insight into RNA metabolism + transcript structure in one workflow.

Can ISO-SLAM-seq be applied in in-vivo or clinical sample scenarios?

Yes—metabolic labelling methods like SLAM-seq have been validated in vivo and ISO-seq is designed for full-length transcript capture, enabling studies of rare or precious sample types and supporting translational research.

What kind of analyses will I get from ISO-SLAM-seq results?

Results include new isoform discovery, alternative splicing, transcript quantification, RNA half-life/stability metrics, and full-length isoform annotation. These facilitate biomarker discovery, therapeutic mechanism studies and multi-omic integration.

What are the sample requirements and species compatibility for the workflow?

Typical workflows support human, mouse and rat samples; other species may be evaluated. Required input includes sufficient cell or tissue quantities suitable for long-read sequencing and metabolic labelling. (Sample size specifics are outlined in our service description.)

How does ISO-SLAM-seq support downstream integration with multi-omics or CRO workflows?

Because it generates both kinetic and structural RNA data in a single run, ISO-SLAM-seq reduces sample use, harmonises datasets and simplifies downstream analysis—making it ideal for CROs, biotech and pharma research pipelines.

ISO-SLAM-seq Service – Integrated RNA Metabolism and Isoform Sequencing

CD Genomics presents our advanced ISO-SLAM-seq service, combining state-of-the-art SLAM-seq metabolic RNA labelling with ISO-seq long-read sequencing (PacBio or Oxford Nanopore). This unified workflow empowers research and development teams to measure RNA synthesis, decay, full-length transcript isoform diversity and alternative splicing—all in one experiment.

Our ISO-SLAM-seq solution is built for researchers, academic labs, CROs, biotech firms and pharma R&D that require high-resolution insight into RNA dynamics and structure. It enables you to:

Track RNA metabolism and half-life with precision.
Resolve full-length transcripts and isoforms, rather than fragmented reads.
Detect alternative splicing events at single-molecule level.
Consolidate multiple analyses into a single, sample-efficient run.

Who this is for:

If your project involves gene-expression regulation, RNA-therapeutics, biomarker development, or transcriptome complexity in clinical or pre-clinical settings, ISO-SLAM-seq offers a streamlined yet powerful pathway.

Submit Your Request Now

ISO-SLAM-seq workflow illustration showing integration of SLAM-seq and ISO-seq (PacBio/Oxford Nanopore) for RNA metabolism and isoform profiling.

Measure RNA synthesis and decay (SLAM-seq)
Capture full-length isoforms and splicing (ISO-seq)
Enable multi-omics and clinical-sample analysis

Why Study RNA Metabolism and Isoforms Together?

In many research workflows, transcriptome profiling focuses solely on steady-state RNA abundance. However, this approach overlooks two crucial dimensions: RNA turnover and transcript structure diversity.

1. The Hidden Layer: RNA Metabolism

RNA metabolism—the life cycle of RNA molecules from synthesis to degradation—is central to cellular regulation. Changes in RNA half-life or degradation rates can directly alter gene expression outcomes even when transcript levels appear constant.

2. The Structural Layer: Isoform Diversity

A single gene can produce multiple mRNA isoforms through alternative splicing, alternative promoter usage or polyadenylation. Such isoforms often encode proteins with different functions or regulatory properties. Moreover, recent work shows isoform usage shifts occur independently of overall gene‐expression changes.

3. Why Integrate Them?

A transcript might be abundant yet unstable—studying only levels would mask its rapid degradation.
A gene may express multiple isoforms under different conditions—short‐read quantitation misses full‐length structure.
By combining metabolism and structure, you gain dynamic insight + molecular resolution—crucial for biomarker discovery, drug development and mechanistic studies.

4. Research and CRO Implications

For investigators in academia, biotech or pharma:

Investigating drug response: seeing how treatments affect RNA half‐life and isoform switch can illuminate mechanisms.
Benchmarking biomarkers: isoform‐specific stability changes may prove more robust indicators than total RNA.
Maximising clinical samples: using a workflow that captures both metabolism and structure reduces sample and cost burden.

In summary, studying RNA metabolism and isoforms together delivers a deeper, integrated view of transcriptome regulation—one that conventional methods cannot match.

For detailed methods in RNA metabolic labelling, see our dedicated SLAM-seq service page.

How ISO-SLAM-seq Works

Principle of the Method

The workflow begins by incorporating 4-thiouridine (S4U) into nascent RNA transcripts. During reverse transcription, the S4U leads to T→C mismatches, enabling identification of newly synthesised RNA. Next, full-length transcripts—including alternative isoforms—are sequenced using long-read platforms such as PacBio or Oxford Nanopore. The result: one streamlined assay that captures both RNA kinetics and transcript structure in the same sample.

ISO-SLAM-seq principle diagram showing S4U incorporation into nascent RNA, T-to-C mismatch during reverse transcription, and full-length isoform sequencing using PacBio or Oxford Nanopore.

Key Benefits

Combines metabolic labelling and structural transcriptomics into a single workflow.
Multiparametric output: RNA synthesis, decay (half-life), isoform diversity, and splicing events.
Suitable for human, mouse and rat models with moderate input and high efficiency.
Compatible with ISO-seq analysis pipeline and SLAM-seq quantification methods.

Typical Applications

Quantify newly generated versus stable RNA pools in response to treatment.
Resolve full-length isoforms and detect alternative splicing under experimental conditions.
Determine RNA half-life and stability changes in disease or drug-response studies.
Integrate RNA metabolism and transcript-structure insights for biomarker or therapeutic research.

"For details on long-read sequencing of full-length transcripts, see our ISO-seq service."

Technology Comparison – ISO-SLAM-seq vs SLAM-seq vs ISO-seq vs TT-seq

Feature / Parameter	ISO-SLAM-seq	SLAM-seq	ISO-seq (PacBio/ONT)	TT-seq
Core Principle	Combines S4U metabolic labelling (as in SLAM-seq) with long-read sequencing (ISO-seq) to capture both RNA dynamics and full-length transcript structure.	Uses 4-thiouridine (S4U) incorporation and T→C conversion to distinguish newly synthesised RNA from existing RNA.	Uses third-generation long-read platforms (PacBio/ONT) to sequence full-length cDNAs from 5′ to poly-A, avoiding assembly.	Labels nascent RNA (e.g., via 4sU) and captures RNA produced over short pulses to map transcription initiation, elongation and processing.
Primary Goal	Integrate RNA metabolism (synthesis + decay) and isoform/structure profiling in one workflow.	Quantify RNA synthesis and decay (half-life) of transcripts.	Define full-length transcript isoforms, alternative splicing, and novel transcripts.	Map nascent transcription units and estimate RNA production rates, not necessarily full isoform structure.
Output Data	RNA metabolism metrics (new vs old transcripts) + full-length isoform structures + alternative splicing events.	RNA turnover rates, synthesis/decay kinetics, new vs total RNA fraction.	Comprehensive isoform sets, fusion transcripts, full-length sequences.	Nascent RNA profiles, transcription dynamics, but limited isoform resolution.
Sequencing Platform	Long-read sequencing (PacBio Sequel II or Oxford Nanopore) combined with metabolic labelling.	Short-read sequencing (e.g., Illumina) of total RNA with T→C conversions.	Long-read platforms (PacBio/ONT) generating full-length reads.	Short-read sequencing of nascent RNA after enrichment/purification.
Optimal Use Case	When you need both RNA dynamics (synthesis + degradation) and transcript structure (isoforms/splicing) in the same sample.	Studies focused on RNA stability, turnover, and kinetics without deep isoform detail.	Studies focused on transcriptome reconstruction, isoform diversity, novel splicing, gene annotation.	Studies focused on immediate transcriptional response, nascent RNA mapping, promoter activity.
Main Limitation	Requires long-read sequencing infrastructure and more complex data analysis (combined kinetics + structure).	Cannot resolve full-length transcript structure or isoforms well due to short reads.	Does not provide direct measurement of RNA synthesis/decay kinetics (unless combined with labelling).	Limited in isoform resolution and long-read structure; often higher sample requirement or enrichment steps.

Compared to SLAM-seq or ISO-seq alone, ISO-SLAM-seq delivers both RNA metabolism and isoform insights in a unified pipeline.

CD Genomics Expertise and Workflow

At CD Genomics, we leverage over a decade of experience in RNA sequencing and long-read technologies to deliver the ISO-SLAM-seq service with precision and confidence. Our infrastructure and team are built to meet the needs of researchers, CROs and pharmaceutical clients alike.

Why Choose CD Genomics

State-of-the-art platforms including PacBio Sequel II and Oxford Nanopore PromethION for full-length transcript and long-read data.
Expert bioinformatics team, experienced in analysing both molecular kinetics (as in SLAM-seq) and full-length isoform data (as in ISO-seq).
Global reach: serving customers in over 50 countries with reproducible workflows and rigorous quality control.
Commitment to research use only (RUO) services, fully geared for translational and pre-clinical studies.

Workflow Overview: From Sample to Insight

S4U Labeling & Sample Preparation

Cells or tissues are incubated with 4-thiouridine (S4U) for metabolic labelling.
RNA is extracted and purified, preserving both labelled and unlabelled transcripts.

Library Construction for Long-read Sequencing

Libraries are prepared for either PacBio or Nanopore long-read platforms, optimised for full-length RNA and variant detection.
We ensure compatibility with metabolic labelling signals for downstream analysis.

Sequencing Runs

High-throughput long-read sequencing generates continuous reads spanning entire transcripts, ensuring isoform resolution.
Data include mismatches (T→C) derived from S4U incorporation for degradation/synthesis analysis.

Bioinformatics Pipeline

Our pipeline integrates SLAM-seq-style kinetics computation (new vs stable RNA, half-life estimation) with ISO-seq structure analysis (isoform detection, fusion genes, splicing events).
Outputs ready-to-use tables, visualisations and interpretation geared for drug-development, translational or basic research workflows.

Project Report & Consultation

Deliverables include an interactive report summarising discoveries: RNA metabolism metrics, isoform distributions, differential splicing, functional enrichment.
Our scientists are available for interpretation and collaborative discussion to maximise study impact.

ISO-SLAM-seq workflow diagram showing S4U RNA labeling, T-to-C mismatch detection, long-read sequencing with PacBio/ONT, and RNA metabolism plus isoform analysis.

Analysis Content

Category	Sub-analysis	Description
I. Isoform Analysis	1. Variable (alternative) splicing analysis	Detect and quantify different splice variants of transcripts (e.g., exon skipping, intron retention).
	2. Fusion gene identification	Identify transcripts that result from gene-fusion events, often relevant for disease and cancer research.
	3. SSR analysis	Analyse simple sequence repeats (SSR) within transcripts or genome-derived RNAs, useful for structural variation or marker studies.
	4. lncRNA analysis	Characterise long non-coding RNAs (lncRNAs): detection, quantification, isoform structure, and their potential regulatory roles.
	5. PolyA analysis	Analyse transcript 3′ end polyadenylation: polyA site usage, alternative polyadenylation events, tail length implications.
II. Isoform Combined Quantification & Expression	1. Transcript quantification	Quantitatively measure expression of each isoform (transcript variant) in the sample.
	2. Differential transcript expression analysis	Compare transcript/isoform expression levels between conditions, treatments or time points to find significant changes.
	3. AltTP (Alternative Transcript Processing) analysis	Investigate regulatory patterns of alternative transcript processing (splicing, polyadenylation, promoter usage) and their functional impact.
	4. Functional Diversity Analysis (FDA)	Assess functional consequences of isoform variation (e.g., changes in protein domains, localisation signals, binding motifs).
	5. Differential enrichment analysis	Perform gene-set/isoform-set enrichment analyses (e.g., GO, pathways) based on differentially expressed or processed transcripts.
III. Nascent (Newly Synthesised) mRNA Analysis	1. New mRNA identification	Detect transcripts newly synthesised (e.g., via metabolic labelling) and distinguish from stable existing RNAs.
	2. New mRNA half-life/decay analysis	Estimate the RNA half-life and decay kinetics of newly synthesised transcripts to understand stability dynamics.
	3. mRNA stability correlation analysis	Correlate mRNA stability (or decay rates) with functional or structural features (isoform, splicing, polyA site, etc.).
	4. Differential new mRNA analysis	Compare newly synthesised transcript levels across conditions (e.g., treatment vs control) to reveal altered transcription/turnover.
	5. New mRNA alternative splicing analysis	Analyse splice variant usage specifically among newly synthesised transcripts to examine how splicing and synthesis integrate.

What You Receive

Raw data files (FASTQ/BAM) from full-length sequencing runs and metabolic labelling.

Processed data tables including:

Newly synthesised vs stable RNA quantification (half-life estimates).
Full-length transcript isoform annotation and classification.
Alternative splicing event lists and differential isoform usage results.

Visual outputs: publication-ready charts such as isoform-type pie charts, expression heatmaps, splicing event histograms and functional diversity plots.

Functional and pathway analysis: enriched gene lists with Gene Ontology (GO) and pathway annotations for differentially expressed or newly synthesised transcripts.

Detailed report summarising experiment set-up, data quality metrics, results interpretation, and recommendations for follow-up.

Sample Requirements

Sample Type	Minimum Requirement	Notes
Cells	≥ 2.5 × 10⁵ cells per sample	Fresh or properly preserved cells are recommended; avoid freeze–thaw cycles to maintain RNA integrity.
Tissue	≥ 100 mg per sample	Snap-frozen or RNAlater-preserved tissue preferred; ensure sufficient RNA yield for long-read sequencing.
Species Supported	Human, Mouse, Rat	Other species may be evaluated on a case-by-case basis before project initiation.

Demo Results and Visualization

Representative ISO-SLAM-seq analysis results are shown below. These examples demonstrate how the integrated workflow captures new isoforms, alternative splicing, and transcript-level expression dynamics.

ISO-SLAM-seq analysis results infographic showing new isoform types, isoform distribution, alternative splicing, and novel isoform identification.

ISO-SLAM-seq analysis infographic showing gene, isoform, and CDS expression levels, functional diversity analysis (FDA), differential gene expression (DGE), and tappAS combined AltTP and DGE analysis.

New isoform type pie chart

New isoform type distribution

Alternative splicing analysis

Novel isoform identification

Gene, isoform, and CDS expression levels

Functional diversity analysis (FDA)

tappAS-based integrated analysis of differential gene expression and AltTP

FAQs

- What is ISO-SLAM-seq and how can it benefit my transcriptome study?
  - ISO-SLAM-seq integrates metabolic labelling of newly synthesised RNA (via S4U in SLAM-seq) with full-length transcript sequencing (via ISO-seq on PacBio or Oxford Nanopore) to deliver both RNA turnover and isoform/structural data from one experiment.
- How does ISO-SLAM-seq compare with SLAM-seq alone or ISO-seq alone?
  - While SLAM-seq tracks synthesis and decay of transcripts, and ISO-seq resolves full-length isoforms and splice variants, ISO-SLAM-seq combines both capabilities—yielding comprehensive insight into RNA metabolism + transcript structure in one workflow.
- Can ISO-SLAM-seq be applied in in-vivo or clinical sample scenarios?
  - Yes—metabolic labelling methods like SLAM-seq have been validated in vivo and ISO-seq is designed for full-length transcript capture, enabling studies of rare or precious sample types and supporting translational research.
- What kind of analyses will I get from ISO-SLAM-seq results?
  - Results include new isoform discovery, alternative splicing, transcript quantification, RNA half-life/stability metrics, and full-length isoform annotation. These facilitate biomarker discovery, therapeutic mechanism studies and multi-omic integration.
- What are the sample requirements and species compatibility for the workflow?
  - Typical workflows support human, mouse and rat samples; other species may be evaluated. Required input includes sufficient cell or tissue quantities suitable for long-read sequencing and metabolic labelling. (Sample size specifics are outlined in our service description.)
- How does ISO-SLAM-seq support downstream integration with multi-omics or CRO workflows?
  - Because it generates both kinetic and structural RNA data in a single run, ISO-SLAM-seq reduces sample use, harmonises datasets and simplifies downstream analysis—making it ideal for CROs, biotech and pharma research pipelines.

References:

Herzog, V., Reichholf, B., Neumann, T. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat Methods 14, 1198–1204 (2017).
Sarantopoulou, D., Brooks, T.G., Nayak, S. et al. Comparative evaluation of full-length isoform quantification from RNA-Seq. BMC Bioinformatics 22, 266 (2021).
Hu, Y., Fang, L., Chen, X. et al. LIQA: long-read isoform quantification and analysis. Genome Biol 22, 182 (2021).