How does your Ultra-Low Input method differ from standard Ribo-seq kits?

Standard ribosome profiling kits typically require 10^7 cells and use sucrose gradients to separate ribosomes, which leads to high sample loss. Our service uses a proprietary, gradient-free size-exclusion chemistry that allows us to capture ribosomes from as few as 200 cells, making it possible to study rare cell populations.

Do I need to perform RNA-seq alongside Ribo-seq?

Yes, we highly recommend it. Ribo-seq measures the density of ribosomes. To calculate Translational Efficiency (TE)—which tells you if a gene is being translated more or less efficiently relative to its abundance—you need to normalize the Ribo-seq data against total mRNA levels derived from standard RNA Sequencing.

What is the typical rRNA contamination rate?

Ribosomal RNA (rRNA) is the enemy of Ribo-seq. Without depletion, it can account for 90% of reads. Our optimized ribo-seq protocol consistently achieves rRNA rates of <15%, ensuring the vast majority of your data maps to coding sequences (CDS).

Can you detect non-canonical ORFs or neoantigens?

Yes. Because our protocol avoids enzymatic bias at the 5' end and utilizes high sequencing depth, we can precisely map Translation Initiation Sites (TIS) in 5' UTRs, 3' UTRs, and lncRNAs. This is the gold standard for neoantigen discovery in cancer research.

What reference databases do you use for analysis?

We utilize major repositories like Ensembl and RefSeq, but we can also integrate specialized data from a ribosome profiling database (like RPFdb or GWIPS-viz) or use your custom de novo assembly.

Is this service suitable for clinical diagnostics?

No. Our Ribo-seq service is for Research Use Only (RUO). While it is a powerful tool for biomarker discovery, the data is not validated for clinical decision-making.

Translatomics
Ribo-Seq (Ribosome Footprinting)

Ultra-Low Input Ribo-seq (Ribosome Profiling) Service

Q: Can you detect non-canonical ORFs or neoantigens?

Yes. Because our protocol avoids enzymatic bias at the 5' end and utilizes high sequencing depth, we can precisely map Translation Initiation Sites (TIS) in 5' UTRs, 3' UTRs, and lncRNAs. This is the gold standard for neoantigen discovery in cancer research.

Q: What reference databases do you use for analysis?

We utilize major repositories like Ensembl and RefSeq, but we can also integrate specialized data from a ribosome profiling database (like RPFdb or GWIPS-viz) or use your custom de novo assembly.

Q: Is this service suitable for clinical diagnostics?

No. Our Ribo-seq service is for Research Use Only (RUO). While it is a powerful tool for biomarker discovery, the data is not validated for clinical decision-making.

The Transcriptome is Potential. The Translatome is Reality.

Bridge the critical gap between gene expression and protein synthesis. We offer the industry's first gradient-free Ribo-seq platform designed specifically for rare and precious samples.

Why CD Genomics?

Break the Input Barrier: Profile as few as 200 cells (Oocytes, CTCs, FACS-sorted neurons).
Single-Nucleotide Resolution: Map P-sites with >70% periodicity to detect Ribosome Stalling and uORFs.
Clean Data: Proprietary depletion reduces rRNA contamination to <15%.
Discovery Ready: Uncover tumor neoantigens and micropeptides invisible to RNA-seq.

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Ultra-low input Ribo-seq platform for translatome and TE analysis

Overview Advantages Comparison Workflow Bioinformatics Samples Results Case Study FAQ

Overview: Bridging the "Genotype-to-Phenotype" Gap

What is Ribo-seq (Ribosome Profiling)?

Ribo-seq (Ribosome Profiling) is a next-generation sequencing technique that captures ribosome-protected fragments (RPFs) to map the exact positions of ribosomes on mRNA. It allows researchers to quantify translational efficiency (TE) and discover non-canonical open reading frames (ORFs) with single-nucleotide resolution, bridging the critical gap between transcriptomics and proteomics.

Why the Transcriptome is Not Enough

While RNA-seq has revolutionized biology, it suffers from a fundamental blind spot: it assumes that mRNA levels linearly predict protein abundance. Decades of research have proven this assumption false. The correlation between the transcriptome and the proteome is often as low as 0.4, driven by a complex layer of translational regulation.

The "Black Box" of Translation Several mechanisms decouple transcription from translation, creating a "black box" that standard RNA-seq cannot penetrate:

Translational Repression: Many mRNAs are transcribed but held in a dormant state (e.g., in oocytes or neurons) until a specific signal triggers translation. RNA-seq sees these as "expressed," but phenotypically, they are silent.
Ribosome Stalling: Ribosomes may bind to an mRNA but stall due to codon bias, amino acid starvation, or drug interaction. A stalled ribosome produces no protein, yet the mRNA remains stable and abundant.
Non-Canonical Translation: Standard annotation ignores vast regions of the genome—5' UTRs, 3' UTRs, and lncRNAs—that actually encode functional micropeptides or regulatory upstream Open Reading Frames (uORFs).

How Ribo-seq Solves It: The Footprint Principle Ribo-seq functions like a molecular "freeze-frame." By treating the cell lysate with a non-specific nuclease (RNase I), we digest all RNA except the specific segment protected by the ribosome.

The Footprint: The ribosome protects a segment of exactly 28–30 nucleotides (the approximate width of the ribosome).
The Position: By sequencing these footprints, we know exactly where the ribosome was (down to the specific codon in the P-site).
The Density: The number of footprints correlates directly with the number of proteins being synthesized, providing a true measure of Translational Efficiency (TE).

Core Advantages: The "Low-Input" Revolution

Traditional ribosome profiling protocols are notoriously difficult. They rely on sucrose gradient ultracentrifugation, require massive sample inputs (>10^7 cells), and take days to execute. This has historically excluded researchers working with rare samples like oocytes, FACS-sorted neurons, or clinical biopsies.

CD Genomics has solved this bottleneck.

Our Proprietary "Gradient-Free" Chemistry

We use a specialized size-exclusion and affinity-capture workflow that eliminates the need for ultracentrifugation.

Picogram Sensitivity: We can generate high-quality libraries from 200–1,000 cells or 5–50 mg of tissue.
High Purity: Our optimized rRNA depletion module reduces ribosomal RNA contamination to <15%, maximizing your useful sequencing reads.
Unbiased 5' Mapping: Unlike some ribo-seq kits that introduce enzymatic bias, our protocol preserves the native 5' end of the fragment, which is critical for identifying translation initiation sites (TIS).

Technology Comparison: Which Tool Do You Need?

Translatomics is not "one size fits all." We offer a tiered portfolio of translational profiling services. Use this matrix to select the right technology for your research question.

Feature	Ribo-seq (Our Flagship)	Polysome Profiling	RNC-seq
Primary Output	Ribosome Footprints (28–32 nt)	Optical Density Profiles (Traces)	Full-length mRNA Transcripts
Resolution	Single Nucleotide (Codon)	Global / Gene Level	Transcript Level
Key Application	Mechanism: Stalling, P-sites, uORFs, Neoantigens.	Global Status: Monosome vs. Polysome ratio.	Screening: Which mRNAs are being translated?
Sample Input	Low: ~200 cells	High: >10^7 cells	High: >10^7 cells
Method	RNase Digestion + Size Selection	Sucrose Gradient Fractionation	Sucrose Cushion / Affinity
Best For...	Deep Mechanistic Insight	Phenotype Validation	High-Throughput Screening

Director's Note: For a comprehensive study, many clients combine approaches. You might use RNC-seq for an initial high-throughput screen of actively translated genes, and then follow up with Ribo-seq for high-resolution mapping of specific regulatory mechanisms.

Step-by-Step Workflow

Our workflow is engineered to solve the historical failure points of ribosome profiling: sample loss during ultracentrifugation and rRNA contamination. Below is the technical breakdown of our 5-stage process.

Phase 1: Cell Lysis & Translation Arrest

Success begins at the bench. Ribosomes are highly dynamic; without immediate stabilization, they run off the mRNA, distorting the data.

Translation Arrest: We utilize Cycloheximide (CHX) or Tigecycline during lysis to "lock" the ribosome in the elongation state. This preserves the snapshot of active translation.
Non-Denaturing Lysis: We use a proprietary lysis buffer optimized for low-input samples (200–1,000 cells) that solubilizes the cytoplasm while keeping the 80S monosomes intact.

Phase 2: Nuclease Digestion (The Critical Step)

This is the most sensitive step in the protocol. We treat the lysate with RNase I.

Optimization: The enzyme concentration is titrated to ensure complete digestion of exposed RNA while leaving the ribosome-protected fragment (RPF) intact. Over-digestion destroys the footprint; under-digestion leaves large RNA fragments that clutter the library.
Output: The result is a mixture of 80S ribosomes (containing the RPF) and free nucleotides.

Phase 3: Ribosome Enrichment (The Innovation)

Traditional Method: The industry standard uses sucrose density gradient centrifugation. This requires layering the lysate onto a sucrose solution and spinning at >100,000 x g for 4 hours. It is labor-intensive, requires visible pellets (millions of cells), and manual fractionation.

The CD Genomics Solution: We utilize a Size-Exclusion Chromatography (SEC) and affinity-based approach.

Mechanism: The digestion mixture is passed through a specialized resin. The bulky 80S ribosomes (~3 MDa) flow through the void volume or are captured via affinity tags, while smaller proteins, free RNase, and digested RNA fragments are retarded or washed away.
Advantage: This happens in a micro-column format suitable for picogram inputs, taking minutes instead of hours, with >90% recovery of ribosomes.

Phase 4: Library Construction & rRNA Depletion

Once the RPFs are extracted (phenol-chloroform purification), we must convert them into a sequencing library.

End Repair (T4 PNK): RNase I leaves a 3'-phosphate and a 5'-hydroxyl group, which are incompatible with adapter ligation. We use T4 Polynucleotide Kinase (T4 PNK) to phosphorylate the 5' end and remove the 3' phosphate, preparing the fragment for ligation.
rRNA Depletion: Ribosomal RNA makes up ~80% of the RPF mass. We use a probe-based subtraction method specifically designed for the fragmented nature of RPFs. This brings rRNA reads down to <15%, vastly increasing the "effective sequencing depth" for your target genes.
cDNA Synthesis: Using Reverse Transcriptase and circularization or molecular barcodes to minimize PCR duplication bias.

Phase 5: Sequencing

We sequence on the Illumina NovaSeq X Plus platform using PE150 mode, generating >30 million raw reads per sample to ensure coverage of low-abundance transcripts.

Vertical ultra-low input Ribo-seq workflow (gradient-free ribosome profiling)

Bioinformatics: The "Ribo-seq Analysis Pipeline."

Generating the data is only half the battle. Our bioinformatics team uses a proprietary ribo-seq analysis pipeline integrating tools like RiboCode, ORFquant, and custom R scripts (similar to Data Analysis with R) to deliver actionable insights.

Standard Analysis Package:

Quality Control: rRNA contamination rate, Read length distribution, Periodicity check.
Mapping: Alignment to reference genome (HG38, mm10, etc.).
Quantification: RPF counts per gene (RPKM/TPM).
Translational Efficiency (TE): (Requires paired RNA-seq) Ratio of RPF/mRNA.
Differential Translation Analysis: Identification of genes with significant changes in TE.

Advanced Analysis (Custom):

uORF & sORF Discovery: Identification of non-canonical micropeptides.
Ribosome Stalling Analysis: Detection of pause sites at specific codons.
Isoform-Specific Translation: Deconvolution of transcript isoforms.
Neoantigen Prediction: For oncology/immunotherapy research.

Sample Requirements

Sample Type	Recommended Input (Low Input)	Standard Input	Storage Condition
Adherent Cells	1×10³−1×10⁵ cells	1×10⁷ cells	Flash Frozen / Lysis Buffer
Suspension Cells	1×10³−1×10⁵ cells	1×10⁷ cells	Flash Frozen Cell Pellet
Animal Tissue	5 - 50 mg	>100 mg	Flash Frozen (Liquid N2)

Applications: Translating Data into Discovery

Ribo-seq is not just for counting ribosomes. It is a discovery engine for fields where standard transcriptomics has hit a ceiling.

A. Oncology: Neoantigen Discovery

Immunotherapy relies on T-cells recognizing tumor-specific antigens presented on MHC-I molecules.

The Problem: Many tumor-specific peptides do not come from canonical exons. They arise from "non-coding" regions, lncRNAs, or out-of-frame translation events that RNA-seq ignores.
The Ribo-seq Solution: By detecting active translation in these "silent" regions, Ribo-seq identifies Cryptic MHC-I Peptides. These are high-value targets for mRNA cancer vaccines and TCR-T therapies.
Data Insight: Our pipeline maps reads to lncRNAs and calls sORFs (small Open Reading Frames) that are translationally active in tumor cells but not in normal tissue.

B. Neuroscience: Local Translation in Dendrites

Neurons are polarized cells. An mRNA may be transported to a distal dendrite and translated only when a synaptic signal is received.

The Problem: Standard bulk RNA-seq averages the cell body and dendrites, missing this spatial nuance. Furthermore, sorting specific neuronal subtypes yields very few cells.
The Ribo-seq Solution: Our Low-Input (200 cell) capability allows researchers to profile FACS-sorted neuronal subpopulations or even synaptosome fractions. This reveals the "Local Translatome" underlying synaptic plasticity and memory formation.

C. Developmental Biology: The Oocyte-to-Embryo Transition

The early embryo is transcriptionally silent. It relies entirely on the translation of stored maternal mRNAs.

The Problem: Transcript levels in an oocyte are static. They tell you nothing about which genes are driving the first cleavage divisions.
The Ribo-seq Solution: Ribo-seq reveals the precise timing of maternal mRNA activation. By profiling individual oocytes (impossible with gradient methods), we can map the "wake-up call" of the genome during embryogenesis.

D. Drug Discovery: Ribosome Stalling Profiling

Many small molecule drugs (and antibiotics) work by inhibiting the ribosome.

The Application: Ribo-seq can determine the exact Mechanism of Action (MoA).
The Signal: If a drug blocks the ribosome tunnel, you will see a massive accumulation of Ribo-seq reads at the specific codon where the ribosome is stuck. This creates a "Pause Peak" in the data, pinpointing the molecular interaction site.

Demo Results: Visualizing Ribo-seq Quality

High-quality ribo-seq data analysis is defined by specific quality control metrics. Below are the standard visualizations you will receive in your report.

Ribo-seq read length distribution peak at 28–32 nt

Ribo-seq 3-nt periodicity P-site mapping (frame 0 dominant)

Translational efficiency plot comparing RNA-seq and Ribo-seq

1. Read Length Distribution

What it shows: A sharp peak at 28–32 nt.

Why it matters: This confirms that RNase I digestion was successful and that we have captured true "Ribosome Protected Fragments" (RPFs) rather than random RNA degradation products.

2. 3-Nucleotide Periodicity (P-Site Mapping)

What it shows: A characteristic "sawtooth" pattern where the read density peaks at the first base of each codon (Frame 0).
Why it matters: This is the "fingerprint" of active translation. It proves the reads are generated by the stepping motor of the ribosome (which moves 3 nucleotides at a time) and allows for precise P-site offset calculation.

3. Metagene Analysis

What it shows: Aggregate read density around Start and Stop codons across the genome.
Why it matters: It visualizes global translational regulation, such as ribosome pausing at termination sites or accumulation at initiation sites (TIS).

4. Translational Efficiency (TE): RNA-seq vs Ribo-seq

What it shows: TE is calculated per gene as RPF (Ribo-seq) / mRNA (RNA-seq), often shown as an RNA–RPF scatter plot plus a TE ranking.

Why it matters: It finds genes where mRNA abundance doesn't match protein synthesis, revealing translational repression (high mRNA, low RPF) and enhanced translation (low mRNA, high RPF), and supports differential TE between conditions.

Case Study: Uncovering the "Hidden" Translatome

To demonstrate the power of Ribo-seq, we highlight a landmark study that utilizes high-resolution ribosome profiling to map the translatome of human tissue. This case mirrors the depth of data provided by CD Genomics.

Title: The translational landscape of the human heart

Source: Nature Communications (2019).

DOI: 10.1038/s41467-019-10168-z

Cardiomyopathies are traditionally studied using standard RNA Sequencing. However, the researchers hypothesized that the "active translatome" (protein synthesis) might diverge significantly from mRNA abundance, potentially hiding novel therapeutic targets in the non-coding regions of the genome.

The research team performed high-depth Ribosome Profiling on human heart tissue. They integrated this with transcriptome data to identify Small Open Reading Frames (sORFs)—regions previously thought to be "junk DNA" that actually code for micropeptides.

ribo-seq-data-identifying-novel-sorfs-in-human-heart-tissue Figure a: Identification of a novel micropeptide. Ribo-seq read density (middle tracks) reveals active translation in a region previously annotated as non-coding. (Source: van Heesch et al., Nat Commun, 2019).

The study proved that RNA-seq alone missed these targets. Ribo-seq was the only tool capable of identifying these functional micropeptides.

Relevance to Your Research: CD Genomics utilizes the same 3-nucleotide periodicity analysis and ORF-calling algorithms used in this study, enabling you to discover neoantigens or micropeptides in your own oncology or developmental biology samples.

Frequently Asked Questions

- Q: How does your Ultra-Low Input method differ from standard Ribo-seq kits?
  - A: Standard ribosome profiling kits typically require 107 cells and use sucrose gradients to separate ribosomes, which leads to high sample loss. Our service uses a proprietary, gradient-free size-exclusion chemistry that allows us to capture ribosomes from as few as 200 cells, making it possible to study rare cell populations.
- Q: Do I need to perform RNA-seq alongside Ribo-seq?
  - A: Yes, we highly recommend it. Ribo-seq measures the density of ribosomes. To calculate Translational Efficiency (TE)—which tells you if a gene is being translated more or less efficiently relative to its abundance—you need to normalize the Ribo-seq data against total mRNA levels derived from standard RNA Sequencing.
- Q: What is the typical rRNA contamination rate?
  - A: Ribosomal RNA (rRNA) is the enemy of Ribo-seq. Without depletion, it can account for 90% of reads. Our optimized ribo-seq protocol consistently achieves rRNA rates of <15%, ensuring the vast majority of your data maps to coding sequences (CDS).
- Q: Can you detect non-canonical ORFs or neoantigens?
  - A: Yes. Because our protocol avoids enzymatic bias at the 5' end and utilizes high sequencing depth, we can precisely map Translation Initiation Sites (TIS) in 5' UTRs, 3' UTRs, and lncRNAs. This is the gold standard for neoantigen discovery in cancer research.
- Q: What reference databases do you use for analysis?
  - A: We utilize major repositories like Ensembl and RefSeq, but we can also integrate specialized data from a ribosome profiling database (like RPFdb or GWIPS-viz) or use your custom de novo assembly.
- Q: Is this service suitable for clinical diagnostics?
  - A: No. Our Ribo-seq service is for Research Use Only (RUO). While it is a powerful tool for biomarker discovery, the data is not validated for clinical decision-making.

References:

Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS. Science, 2009. (The foundational paper establishing Ribo-seq technology)
The translational landscape of the human heart van Heesch S, Witte F, Schneider-Lunitz V, et al. Nature Communications, 2019. (Our featured Case Study: Discovery of micropeptides and sORFs in cardiac tissue)
Pervasive downstream RNA hairpins dynamically dictate start-codon selection Xiang Y, Huang W, Tan L, et al. Nature, 2023. (Key evidence for translational control mechanisms via uORFs and RNA structure)
DNA damage induces p53-independent apoptosis through ribosome stalling Boon NJ, Soto-Feliciano YM, et al. Science, 2024; 384(6697): 785-792.
Ribosome profiling reveals the what, when, where and how of protein synthesis Brar GA, Weissman JS. Nature Reviews Molecular Cell Biology, 2015. (Authoritative review on the comprehensive capabilities of the technology)
The m6A reader IGF2BP2 regulates glutamine metabolism and represents a therapeutic target in acute myeloid leukemia Weng H, Huang F, Yu Z, et al. Cancer Cell, 2022. (Example of Ribo-seq revealing translational efficiency changes in leukemia)