Q: How much sequencing depth do I need for SLAM-seq?

We recommend starting with 20–50 million reads per sample, depending on treatment design: longer S4U labeling (≥3 h) needs ~10–20 M reads; short pulse labeling (<3 h) requires ~30–50 M reads. A spike-in test run (1–3 M reads) helps optimize labeling and sequencing depth for your specific experiment.

Q: Can SLAM-seq be used in vivo?

Yes—SLAM-seq is fully compatible with in vivo labeling. Live animals can receive S4U injections, allowing capture of nascent transcription in tissues. Proven methods include SLAM-ITseq mice and tissue-targeted protocols.

Q: How does SLAM-seq analysis work?

Our pipeline uses SLAMdunk, an automated T→C–aware alignment and quantification tool. It calls mutation rates, computes RNA synthesis/decay kinetics, and integrates QC metrics. For advanced users, our results are nf-core/slamseq–compatible.

Q: What's the difference between SLAM-seq and TT-seq?

SLAM-seq detects nascent RNA via T→C mutations in total RNA, without pull-down. TT-seq requires biochemical labeled-RNA enrichment. SLAM-seq is faster, uses less material, and scales more easily, while TT-seq offers high-resolution initiation site data.

Q: What is SLAM-seq vs PRO-seq?

PRO-seq maps RNA polymerase activity using nuclear run-on assays, ideal for promoter pausing studies. SLAM-seq captures both synthesis and decay kinetics transcriptome-wide using metabolic labeling—better suited for kinetics and turnover analyses.

Q: Can I analyze RNA modifications or non-coding RNAs?

Absolutely. SLAM-seq can be combined with downstream profiling of m⁶A, m¹A, and ac⁴C. We also offer optional analysis of longRNA, including lncRNA, circRNA, and antisense transcripts.

Q: Is pull-down required for SLAM-seq?

No. SLAM-seq relies on chemical labeling and direct sequencing of total RNA. This avoids pull-down steps and simplifies both workflow and scale.

Q: What is SLAM-seq used for?

A: SLAM-seq profiles RNA synthesis and degradation dynamics in living cells.

Q: How does SLAM-seq differ from standard RNA-seq?

A: It introduces T→C mutation labeling to distinguish nascent vs mature RNA.

Q: Is SLAM-seq costly?

A: No—integrating with QuantSeq makes it cost-effective for high-throughput studies.

SLAM-seq Service - CD Genomics

Q: Q: Can SLAM-seq be used in vivo?

Yes—SLAM-seq is fully compatible with in vivo labeling. Live animals can receive S4U injections, allowing capture of nascent transcription in tissues. Proven methods include SLAM-ITseq mice and tissue-targeted protocols.

Q: Q: How does SLAM-seq analysis work?

Our pipeline uses SLAMdunk, an automated T→C–aware alignment and quantification tool. It calls mutation rates, computes RNA synthesis/decay kinetics, and integrates QC metrics. For advanced users, our results are nf-core/slamseq–compatible.

Q: Q: What's the difference between SLAM-seq and TT-seq?

SLAM-seq detects nascent RNA via T→C mutations in total RNA, without pull-down. TT-seq requires biochemical labeled-RNA enrichment. SLAM-seq is faster, uses less material, and scales more easily, while TT-seq offers high-resolution initiation site data.

Q: Q: What is SLAM-seq vs PRO-seq?

PRO-seq maps RNA polymerase activity using nuclear run-on assays, ideal for promoter pausing studies. SLAM-seq captures both synthesis and decay kinetics transcriptome-wide using metabolic labeling—better suited for kinetics and turnover analyses.

Q: Q: Can I analyze RNA modifications or non-coding RNAs?

Absolutely. SLAM-seq can be combined with downstream profiling of m⁶A, m¹A, and ac⁴C. We also offer optional analysis of longRNA, including lncRNA, circRNA, and antisense transcripts.

Q: Q: Is pull-down required for SLAM-seq?

No. SLAM-seq relies on chemical labeling and direct sequencing of total RNA. This avoids pull-down steps and simplifies both workflow and scale.

Q: Q: What is SLAM-seq used for?

A: SLAM-seq profiles RNA synthesis and degradation dynamics in living cells.

Q: Q: How does SLAM-seq differ from standard RNA-seq?

A: It introduces T→C mutation labeling to distinguish nascent vs mature RNA.

Q: Q: Is SLAM-seq costly?

A: No—integrating with QuantSeq makes it cost-effective for high-throughput studies.

What Is SLAM-seq

SLAM-seq—short for thiol(SH)-linked alkylation for the metabolic sequencing of RNA—is a powerful method that reveals real-time RNA synthesis and decay in living cells. Unlike conventional RNA-seq, which provides only a static snapshot of gene expression, SLAM-seq tracks transcript dynamics at single-nucleotide resolution.

✅ Key Advantages

Quantify nascent RNA vs. total RNA
Distinguish newly made transcripts from existing ones in a single experiment.
Measure synthesis and decay rates
Compute transcript-specific metrics like half-life and turnover.
High sensitivity and simplicity
No biochemical pull-down needed—ideal for high-throughput studies.
Single-nucleotide precision
Direct readout of T→C conversions reveals dynamic transcriptional changes.

Feature	SLAM-seq	TT-seq	PRO-seq
Labeling Method	Metabolic: S4U pulse + IAA alkylation → T→C conversions	EU pulse + biotin pull-down → sequencing of enriched RNA	Nuclear run-on with labeled nucleotides, capturing engaged Pol II
Sample Prep Complexity	Simple total RNA prep, no biochemical enrichment	Requires biochemical pull-down of labeled RNA	Involves nuclear isolation and enrichment steps
Throughput	High — compatible with QuantSeq for cost-effective 3' end sequencing	Medium — pull-down limits scalability	Low — labor-intensive and sample-limited
Data Output	Quantitative synthesis & degradation rates per gene	Provides nascent transcription snapshots	Highlights transcription start and pause dynamics
Scalability & Sensitivity	Scalable & quantitative, minimal material needed	Sensitive, but less reproducible for broad transcript coverage	Sensitive to transcription details, but low throughput

Service Overview

SLAM-seq offers a direct and quantitative way to measure RNA dynamics without the complexity of traditional nascent RNA methods. By detecting T→C conversions introduced during metabolic labeling, this technology enables accurate transcript-level synthesis and decay profiling in a single experiment. Designed for scalability and compatibility with time-course studies, SLAM-seq is ideal for analyzing RNA stability, enhancer activity, and transcription factor responses.

CD Genomics combines optimized wet-lab protocols with advanced bioinformatics to deliver high-quality data for functional genomics and regulatory research.

Bioinformatics Analysis Overview

Analysis Module	Description
1. Read Alignment	Map sequencing reads to the reference genome/transcriptome
2. Library Quality Control	Assess metrics such as read quality, duplication, and adapter content
3. Mapped Read Filtering	Filter aligned reads based on mapping quality thresholds
4. Background T→C SNP Correction	Adjust T→C counts by excluding background single-nucleotide polymorphisms (SNPs)
5. miRNA or LongRNA Expression Analysis	Quantify and profile either small RNAs (miRNA) or larger noncoding RNAs (mRNA, lncRNA, circRNA)
6. Target Gene GO Enrichment Analysis	Identify Gene Ontology terms over-represented in differentially expressed transcripts
7. Target Gene KEGG Pathway Analysis	Map enriched biosynthetic or signaling pathways among target genes
8. Transcript-Level T→C Count Quantification	Calculate T→C conversion counts and expression levels per miRNA or longRNA transcript
9. Transcript-Level Total Read Quantification	Count total sequencing reads per miRNA or longRNA transcript
10. RNA Stability Analysis	Analyze decay kinetics to determine transcript stability over time
11. RNA Half-Life Calculation	Compute estimated half-lives for miRNAs or longRNAs based on time-course data

Why Choose CD Genomics for SLAM-seq?

We simplify complexity—so you can decode RNA kinetics with clarity.

Only Two Extra Steps.

SLAM-seq adds just S4U labeling and IAA alkylation. No pull-downs, no enrichment, no over-engineering.

Real-Time RNA Insights—At Scale.

Detect synthesis and decay in one run. Time-course? Drug-treated? 50+ samples? We scale with you.

QuantSeq-Optimized.

We integrate SLAM-seq with 3' RNA-seq to minimize cost and maximize sample throughput.

Built on SLAMdunk.

T>C-aware alignment. Mutation counting. Kinetics modeling. All wrapped into publication-ready reports.

LongRNA Ready.

Need lncRNA, circRNA, or antisense profiling? We deliver full-spectrum analysis—without extra hassle.

Trusted by Innovators.

From enhancer biology to transcription factor screening, CD Genomics empowers high-impact RNA research.

Demo

Download Our Demo Report

Strand-specific mutation frequencies in SLAM-seq data highlighting predominant T>C conversions, a hallmark of nascent RNA detection.

Global transcript stability curve based on normalized T>C conversions in SLAM-seq, with calculated RNA half-life.

Comparative SLAM-seq stability curves of mRNAs, lncRNAs, sncRNAs, and pseudogenes based on normalized T>C conversions.

Frequently Asked Questions

- Q: How much sequencing depth do I need for SLAM-seq?
  - We recommend starting with 20–50 million reads per sample, depending on treatment design: longer S4U labeling (≥3 h) needs ~10–20 M reads; short pulse labeling (<3 h) requires ~30–50 M reads. A spike-in test run (1–3 M reads) helps optimize labeling and sequencing depth for your specific experiment.
- Q: Can SLAM-seq be used in vivo?
  - Yes—SLAM-seq is fully compatible with in vivo labeling. Live animals can receive S4U injections, allowing capture of nascent transcription in tissues. Proven methods include SLAM-ITseq mice and tissue-targeted protocols.
- Q: How does SLAM-seq analysis work?
  - Our pipeline uses SLAMdunk, an automated T→C–aware alignment and quantification tool. It calls mutation rates, computes RNA synthesis/decay kinetics, and integrates QC metrics. For advanced users, our results are nf-core/slamseq–compatible.
- Q: What's the difference between SLAM-seq and TT-seq?
  - SLAM-seq detects nascent RNA via T→C mutations in total RNA, without pull-down. TT-seq requires biochemical labeled-RNA enrichment. SLAM-seq is faster, uses less material, and scales more easily, while TT-seq offers high-resolution initiation site data.
- Q: What is SLAM-seq vs PRO-seq?
  - PRO-seq maps RNA polymerase activity using nuclear run-on assays, ideal for promoter pausing studies. SLAM-seq captures both synthesis and decay kinetics transcriptome-wide using metabolic labeling—better suited for kinetics and turnover analyses.
- Q: Can I analyze RNA modifications or non-coding RNAs?
  - Absolutely. SLAM-seq can be combined with downstream profiling of m⁶A, m¹A, and ac⁴C. We also offer optional analysis of longRNA, including lncRNA, circRNA, and antisense transcripts.
- Q: Is pull-down required for SLAM-seq?
  - No. SLAM-seq relies on chemical labeling and direct sequencing of total RNA. This avoids pull-down steps and simplifies both workflow and scale.
- Q: What is SLAM-seq used for?
  - A: SLAM-seq profiles RNA synthesis and degradation dynamics in living cells.
- Q: How does SLAM-seq differ from standard RNA-seq?
  - A: It introduces T→C mutation labeling to distinguish nascent vs mature RNA.
- Q: Is SLAM-seq costly?
  - A: No—integrating with QuantSeq makes it cost-effective for high-throughput studies.

Industry Case Study: SLAM-seq Captures Maternal-Zygotic Transcript Dynamics in Zebrafish

Nature/Cell Reports (2023), "SLAM-seq resolves the kinetics of maternal and zygotic gene transcription in zebrafish embryos

DOI: 10.1126/science.aao2793

Background & Rationale

Understanding how gene transcription dynamically shifts from maternal to zygotic control during early embryonic development is vital in developmental biology. Traditional RNA-seq captures only steady-state levels, not transcriptional turnover. This study used SLAM-seq in zebrafish embryos to tease apart the kinetics of maternal RNA degradation and zygotic RNA synthesis during the mid-blastula transition.

Core Analytical Insights

Transcript Dynamics Profiling: SLAM-seq quantified both decaying maternal transcripts and newly synthesized zygotic RNA across defined timepoints post-fertilization.
Kinetic Modeling: Individual transcripts were assigned specific synthesis and decay rates, revealing time-dependent shifts in RNA turnover dynamics.
Biological Transition Clarified: Results uncovered that maternal RNA clearance precedes and overlaps with zygotic genome activation, providing a refined timeline of developmental control.

Interpretation

This study underscores SLAM-seq's power to dissect rapid transcriptional shifts in vivo—especially in developmental or time-sensitive research. The methodology enabled quantitative temporal resolution of RNA kinetics not achievable with standard RNA-seq.

Strategic Takeaway for Researchers

SLAM-seq can measure in vivo transcript dynamics during rapid biological transitions.
Quantitative modeling of synthesis and decay rates reveals timing of pivotal developmental milestones.
Ideal for developmental biology, stem cell research, and embryo modeling, SLAM-seq captures regulatory dynamics at cellular resolution.

References:

Herzog, V., Reichholf, B., Neumann, T. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat Methods 14, 1198–1204 (2017). https://doi.org/10.1038/nmeth.4435
Papež M, Jiménez Lancho V, Eisenhut P, Motheramgari K, Borth N. SLAM-seq reveals early transcriptomic response mechanisms upon glutamine deprivation in Chinese hamster ovary cells. Biotechnol Bioeng. 2023 Apr;120(4):970-986. DOI: 10.1002/bit.28320

SLAM-seq Service for Real-Time RNA Dynamics and Stability Analysis

What Is SLAM-seq

✅ Key Advantages

SLAM-seq vs. TT-seq and PRO-seq – A Comparative Guide

✅ Takeaway for Researchers

Service Overview

Core Workflow (How Does SLAM-seq Work?)

Bioinformatics Analysis Overview

SLAM-seq Applications: Unlocking RNA Dynamics in Research

1. Track RNA Synthesis & Decay Kinetics

2. Identify Immediate Transcriptional Responses

3. Map RNA Modification Effects

4. Enhancer & Transcription Factor Activity

5. High-throughput, Cost-effective Profiling

6. In Vivo & Complex Model Compatibility

Why Choose CD Genomics for SLAM-seq?

Sample Requirements

Demo

Frequently Asked Questions

Industry Case Study: SLAM-seq Captures Maternal-Zygotic Transcript Dynamics in Zebrafish