Sample Submission Guidelines
What is DRUG-seq?
DRUG-seq, or Digital RNA perturbation of genes sequencing, is a cutting-edge RNA sequencing technology that enables high-throughput drug screening and in-depth mechanism-of-action studies. Unlike traditional RNA-seq methods that require large amounts of starting material and complex RNA extraction steps, DRUG-seq delivers comprehensive transcriptome profiling directly from cell lysates, organoids, or tissue slices—even when sample amounts are extremely limited.
With DRUG-seq, researchers can analyze hundreds to thousands of drug treatments or genetic perturbations in parallel, uncovering gene expression signatures, discovering novel biomarkers, and revealing intricate pathways impacted by therapeutic compounds. Whether for drug discovery, toxicity testing, or personalized medicine development, DRUG-seq offers the scalability, speed, and precision required in modern research.
Our DRUG-seq Service Packages
At CD Genomics, we understand that every research project is unique. That's why our Drug-seq services are offered as flexible, modular packages designed to match your scientific goals, budget, and sample constraints.
Standard DRUG-seq
- 3' RNA-seq focused on gene expression quantification
- Suitable for screening large libraries of compounds
- Supports 96- or 384-well plate formats
- Cost-effective solution for high-throughput needs
- Applications:
- Mechanism-of-action (MOA) studies
- Phenotypic screening
- Toxicity assessments
DRUG-seq2
- Ultra-low input starting from as few as 1,000 cells
- Perfect for organoids, tissue biopsies, or rare clinical samples
- Direct-from-lysate workflow—no RNA extraction required
- Delivers higher sensitivity and faster turnaround times
- Broad applications:
- Tumor tissue-of-origin tracing
- Biomarker discovery
- Personalized medicine insights
Full-Length DRUG-seq
- Comprehensive full-length transcript coverage
- Detects splicing variants, fusion transcripts, and isoform-specific drug responses
- Recommended for projects requiring deeper molecular insights
- Higher sequencing depth (5–20 million reads per sample) ensures robust results
- Ideal for:
- Alternative splicing analysis
- Detailed MOA exploration
- Complex biological systems and organoid studies
Why Choose Our DRUG-seq CRO Services?
- No RNA Extraction Required
Streamline your workflow and preserve sample integrity with direct-from-lysate library preparation. - Ultra-Low Sample Input
Analyze scarce or precious samples—including single organoids or small tissue sections—with as few as 1,000 cells. - Scalable High-Throughput Solutions
Screen hundreds or thousands of compounds simultaneously, reducing time and costs for large-scale studies. - Fast Turnaround Times
Receive high-quality data in as little as 10 business days for standard DRUG-seq, and three weeks for full-length transcriptomics. - Regulatory-Grade Quality Control
We maintain rigorous QC standards to ensure reliability and reproducibility of your results. - Dedicated Bioinformatics Expertise
Our bioinformaticians provide both standard analyses and custom solutions tailored to your experimental needs. - US- and International Operations
With facilities in the US and partnerships worldwide, we deliver global support and local service.
Key Applications of DRUG-seq Services
Drug Mechanism of Action (MOA) Studies
- Identify how drugs impact gene expression pathways
- Reveal off-target effects and secondary mechanisms
- Compare transcriptional profiles across compound libraries
CRISPR Perturbation Profiling
- Study combined effects of genetic edits and drug treatments
- Pinpoint synergistic or antagonistic interactions at the transcriptomic level
- Accelerate functional genomics research for drug target validation
Tumor Tissue-of-Origin Tracing
- Use transcriptomic signatures to classify tumors by origin
- Inform diagnostic strategies and personalized treatment decisions
- Analyze scarce biopsy samples with ultra-low input protocols
Biomarker Discovery
- Detect molecular markers predictive of drug response
- Support personalized medicine and targeted therapy development
- Integrate transcriptomic data into multi-omics workflows
Organoid and Cell Culture Optimization
- Optimize growth conditions for complex cell models
- Identify compounds that promote organoid viability or differentiation
- Enhance reproducibility and translatability of in vitro studies
From high-throughput screening to precision medicine breakthroughs, our DRUG-seq CRO services empower your drug seq analysis projects with unmatched scalability and depth.
Technical Specifications
| Specification | Details |
|---|---|
| Supported Formats | 96-well plates 384-well plates 1536-well plates (for large-scale studies) |
| RNA Extraction | Not required – direct-from-lysate workflows |
| Sequencing Platforms | Illumina and Nanopore |
| Sequencing Depth (Standard / DRUG-seq2) | ~1 million reads per sample (typical) |
| Sequencing Depth (Full-Length DRUG-seq) | 5–20 million reads per sample for comprehensive coverage |
| Data Deliverables | FASTQ files Alignment reports Gene count matrices Optional advanced analysis |
DRUG-seq Service Workflow
Step 1 – Sample Preparation
- Prepare cells, organoids, or tissue lysates according to our detailed submission guidelines.
- No RNA extraction required—proceed directly from cell lysates.
Step 2 – Sample Shipment
- Ship frozen plates or lysates to our designated service center.
- Our team is available to advise on proper packing and shipping conditions.
Step 3 – Library Preparation
- Assign unique barcodes to each sample.
- Build sequencing libraries using our optimized DRUG-seq or Full-Length DRUG-seq protocols.
Step 4 – Quality Control Checkpoint
- Evaluate library quality with Qubit assays, Fragment Analyzer, and shallow sequencing.
- Report QC results for client approval before deep sequencing.
Step 5 – Deep Sequencing
- Perform high-throughput sequencing on Illumina NovaSeq or other compatible platforms.
- Sequencing depth tailored to your experimental goals.
Step 6 – Data Analysis & Reporting
- Align reads to your genome of choice.
- Generate gene count matrices, QC reports, and optional differential gene expression analyses.
Step 7 – Data Delivery
Deliver all data securely, including:
- FASTQ files
- Alignment summaries
- Gene count matrices
- Optional custom analyses
From initial sample preparation to comprehensive bioinformatics analysis, CD Genomics offers a reliable, transparent workflow that puts your research timelines first.
Bioinfomatics Analysis
Sample Requirements
| Service Package | Recommended Cell Input (per well) | Notes |
|---|---|---|
| Standard DRUG-seq | 2,000 – 20,000 cells | Suitable for large-scale compound screens |
| DRUG-seq2 | As few as 1,000 cells | Ideal for organoids, tissue biopsies, or rare clinical samples |
| Full-Length DRUG-seq (96-well) | 5,000 – 25,000 cells | Minimum ~80,000 cells total per pool recommended for optimal quality |
| Full-Length DRUG-seq (384-well) | 2,000 – 10,000 cells | Same as above |
| Sample Type | Cells, organoids, tissue lysates | Contact us for handling recommendations |
Gene Expression Heatmap
PCA Plot
Volcano Plot
Pathway Enrichment Bar Chart
Splicing / Isoform Plot (Full-Length DRUG-seq Only)
FAQs
1. What is the minimum cell number required for DRUG-seq?
For standard DRUG-seq, we recommend starting with at least 2,000–20,000 cells per well. However, our DRUG-seq2 service can work with as few as 1,000 cells per well, making it ideal for limited or precious samples such as organoids or tissue sections.
2. Is RNA extraction necessary before sequencing?
No. One of the major advantages of DRUG-seq is that it does not require RNA extraction. Libraries are prepared directly from cell lysates, simplifying workflows and preserving RNA integrity.
3. Can DRUG-seq analyze organoid samples?
Absolutely. Our DRUG-seq and DRUG-seq2 services are highly compatible with organoid models and other complex 3D cell systems, even with ultra-low input amounts.
4. What is the typical turnaround time for DRUG-seq services?
For standard DRUG-seq projects, data can be delivered in as little as 10 business days. Full-Length DRUG-seq projects, which include more comprehensive transcript analysis, typically require around three weeks.
5. What types of data do I receive from DRUG-seq?
Standard deliverables include:
- Raw FASTQ sequencing data
- Alignment reports
- Gene count matrices
- Optional differential gene expression and pathway analysis reports
6. What sequencing depth is recommended for DRUG-seq projects?
- Standard DRUG-seq / DRUG-seq2: ~1 Gb per well is recommended for reliable gene expression profiling.
- Full-Length DRUG-seq: 5–20 million reads per sample are recommended for comprehensive transcript coverage and isoform analysis.
7. Does cell lysis time affect cDNA quality?
Yes. Overly long lysis times can lead to RNA degradation and result in a higher proportion of small fragments in the cDNA library. Always follow the recommended incubation times to ensure high-quality data.
8. How does DRUG-seq identify individual samples in pooled sequencing runs?
Each well is uniquely labeled using a well barcode and a molecular barcode during cDNA synthesis. After pooling and sequencing, bioinformatics pipelines use these barcodes to accurately demultiplex the data, restoring gene expression profiles for each sample.
9. Can the same well barcode and molecular barcode be reused for multiple experiments?
No. Each molecular barcode combination is intended for single-use only to prevent cross-contamination between samples. Always handle barcodes according to protocol instructions, including thawing on ice and proper mixing.
10. What applications is DRUG-seq suitable for?
- Drug mechanism-of-action studies
- CRISPR perturbation profiling
- Tumor tissue-of-origin tracing
- Biomarker discovery
- Toxicity screening
- Organoid model optimization
11. Can you provide custom bioinformatics analysis?
Yes. Our bioinformatics team offers both standardized and fully customized data analysis solutions to match your scientific objectives.
12. Is DRUG-seq cost-effective for large screens?
Yes. DRUG-seq's high multiplexing capacity significantly reduces per-sample costs, making it an excellent choice for high-throughput compound screening and large-scale transcriptomics projects.
Case Study: High-Throughput MoA Profiling of 433 Compounds using DRUG-seq
Ye, C. et al. "DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery." Nature Communications 9, 4307 (2018).
Method & Technical Approach
- High-well multiplexing: Utilized DRUG-seq platform in 384- and 1536-well formats, enabling simultaneous profiling of 433 compounds across 8 dose levels.
- Barcode + UMI labeling: Early barcoding during reverse transcription, pooling samples post-RT, eliminating need for individual RNA extraction .
- Sequencing depth: Used 1–2 million reads per well—sufficient to quantify ~11,000 genes per sample, compared to ~17,000 with bulk RNA-seq, all at ~1% of the cost.
📈Key Results
- Mechanism-based clustering:
t-SNE analysis of DRUG-seq data grouped compounds according to shared MoA, even distinguishing between inhibitors targeting the same protein. - Dose-response sensitivity:
Gene expression changes were clearly dose-dependent, matching expected compound behavior across multiple target families. - CRISPR vs. compound comparison:
Demonstrated that DRUG-seq can differentiate transcriptional outcomes from CRISPR-mediated gene knockout versus pharmacological inhibition of the same target.
🧠Conclusion & Impact
- Scalability & cost-efficiency:
High-throughput profiling across hundreds of compounds with minimal per-sample cost (~$2–4). - Rich mechanistic insight:
Robust MoA clustering validates transcriptional signatures, enabling off-target detection and target validation. - Flexible assay compatibility:
Demonstrated applicability in both chemical and genetic perturbation contexts, making DRUG-seq a versatile tool for drug discovery pipelines.
Figure: DRUG-seq Mechanistic Clustering
t-SNE plot showing clustering of compound-treated samples. Each point represents a compound-dose combination, color-coded by mechanism-of-action (e.g., CDK inhibitors, epigenetic modulators). Clusters reflect functional grouping (Fig. 3a in Ye et al. 2018).
