What is Drug-seq? High-Throughput Transcriptomics Guide

DRUG-seq changes how researchers approach drug discovery. The method eliminates RNA extraction and works with as few as 1,000 cells. It enables high-throughput RNA sequencing for large-scale screening at a fraction of traditional costs. Users can select from flexible service packages, including Full-Length Transcripts Sequencing for deeper analysis. DRUG-seq detects up to 12,000 genes with only 2–13 million reads per well, as shown in the table below.

DRUG-seq supports biomarker discovery, mechanism analysis, and precision medicine. The platform provides cost-effective, scalable solutions for modern pharmaceutical research.

Key Takeaways

• Drug-seq eliminates RNA extraction, allowing researchers to analyze gene expression directly from cell lysates.

• The platform can work with as few as 1,000 cells, making it ideal for rare samples and small tissue slices.

• Drug-seq detects up to 12,000 genes with only 2–13 million reads per well, providing a comprehensive view of gene expression.

• This method is cost-effective, with sequencing costs around $3 per sample, significantly lower than traditional RNA-seq.

• Drug-seq supports high-throughput screening, enabling researchers to test hundreds to thousands of compounds in parallel.

• The technology enhances biomarker discovery and mechanism analysis, helping teams make informed decisions in drug development.

• Fast turnaround times, as quick as 10 business days, accelerate research and decision-making processes.

• CD Genomics offers flexible service packages, allowing researchers to choose options that fit their specific project needs.

Drug Discovery Bottlenecks

Limits of Traditional HTS

Drug discovery relies on high-throughput screening (HTS) to identify promising compounds. Traditional HTS methods use automation and miniaturization to test thousands or millions of compounds quickly. This approach speeds up the identification of initial hits and increases the efficiency of drug development pipelines. However, these methods still face several challenges.

Traditional HTS often limits the number of compounds that researchers can test. This restriction can cause missed opportunities, as some potential drug candidates may never reach the screening stage. Many HTS platforms focus on single endpoints, such as cell viability or enzyme activity. These readouts do not capture the full complexity of cellular responses. As a result, researchers may overlook important gene expression changes that signal toxicity or off-target effects.

Another challenge involves the cost and time required for large-scale screens. Even with automation, traditional HTS can become expensive and labor-intensive when scaling up. The need for specialized equipment and skilled personnel adds to the burden. These factors slow the pace of drug discovery and limit the success rate of projects.

Need for Scalable Transcriptomics

Modern pharmaceutical research demands more comprehensive data. High-complexity transcriptomics studies provide a deeper understanding of how compounds affect gene expression across the entire transcriptome. Drug-seq addresses this need by enabling scalable, high-throughput transcriptomics for drug discovery.

• Multi-modal fusion in transcriptomics integrates diverse data types, offering a complete view of experimental results.

• This integration supports advanced analyses, such as drug toxicity assessment and cancer cell line comparison.

• Spatial transcriptomics combines histological techniques with high-throughput RNA sequencing. This method preserves spatial information, which is crucial for understanding tissue and cellular heterogeneity.

• Researchers can explore spatial gene expression patterns, identify signature genes for specific cell types, and study intercellular interactions.

Drug-seq makes these advanced approaches accessible. It allows scientists to profile gene expression from as few as 1,000 cells, making it suitable for rare samples and organoids. The platform supports hundreds to thousands of drug treatments or genetic perturbations in parallel. Researchers gain insights into mechanism of action, biomarker discovery, and personalized medicine.

Drug-seq streamlines workflows by eliminating RNA extraction and reducing turnaround times. This efficiency helps pharmaceutical teams accelerate discovery and improve data quality.

With scalable transcriptomics, drug-seq empowers researchers to overcome traditional bottlenecks and drive innovation in drug development.

What is Drug-seq?

Drug-seq stands as a transformative platform for high-throughput transcriptomics in drug discovery. Researchers use drug-seq to perform whole transcriptome analysis directly from cell lysates, organoids, or tissue slices. This method eliminates the need for RNA extraction and supports profiling from as few as 1,000 cells. CD Genomics offers flexible service packages, including standard, ultra-low input, and full-length transcript options. These packages address diverse research needs, from large-scale compound screening to deep molecular characterization.

Core Methodology

Drug-seq leverages genome-wide transcriptional profiling to screen compounds efficiently. The platform detects over 10,000 genes directly, providing a comprehensive view of gene expression changes. Researchers benefit from accurate clustering and the identification of unique differential genes. Drug-seq supports mechanism of action analysis and pathway enrichment, including mitochondrial functions. The table below highlights key methodological differences between drug-seq and other transcriptomics platforms:

Drug-seq employs hierarchical clustering and mechanism of action analysis. It also incorporates connectivity mapping to infer mRNA profiles, supporting robust data interpretation.

Direct Lysis & Multiplexing

Drug-seq simplifies workflows by using direct cell lysis. This approach preserves sample integrity and reduces hands-on time. Multiplexing enables the analysis of hundreds to thousands of samples in parallel. Researchers can profile rare clinical samples, organoids, or tissue slices with minimal input. The ultra-low input service from CD Genomics works with as few as 1,000 cells, making drug-seq suitable for precious or limited samples.

Tip: Direct lysis and multiplexing not only save time but also lower costs, making drug-seq accessible for large-scale studies.

Evolution of Drug-seq

The evolution of drug-seq reflects advances in sequencing and single-cell technologies. In 2009, single-cell RNA sequencing began to impact pharmaceutical research. Subsequent improvements enabled deeper insights into genomics and drug development. Today, drug-seq integrates these advances, supporting target identification, candidate selection, and clinical research. The platform continues to expand its applications in neuroscience, oncology, and personalized medicine.

Researchers now rely on drug-seq for scalable, accurate, and cost-effective transcriptome profiling. The technology empowers teams to accelerate discovery and gain actionable insights from whole transcriptome analysis.

Drug-seq Workflow

Cell Lysis & RNA Capture

Drug-seq begins with a streamlined cell lysis and RNA capture process. Researchers prepare cells, organoids, or tissue lysates directly, skipping the traditional RNA extraction step. This approach preserves RNA integrity and reduces hands-on time. Drug-seq enables unbiased whole transcriptome profiling from as few as 1,000 cells. The method supports hundreds of samples in a single experiment, making it ideal for high-throughput drug screening.

Drug-seq’s direct lysis method lowers costs and allows analysis of rare or precious samples. Multiplexing further increases efficiency, letting scientists process many samples at once.

RT & Barcoding

Reverse transcription (RT) and barcoding form the next critical step in the drug-seq workflow. During RT, the system converts captured RNA into complementary DNA (cDNA). Each sample receives a unique barcode, which encodes treatment conditions and sample identity. This precise encoding ensures accurate tracking of each sample throughout the process.

• The barcoding strategy in drug-seq allows researchers to cluster samples based on drug treatments.

• Functional barcodes represent drug combinations, supporting robust data analysis.

• Studies show that this method does not introduce bias, as clustering by drug combination yields higher silhouette scores than random grouping.

Dual-Index Strategy

Drug-seq uses a dual-index strategy to further enhance sample identification. Each sample receives two unique indices, one for the plate and one for the well. This dual system allows for the simultaneous processing of hundreds or thousands of samples. It also reduces the risk of sample misidentification and cross-contamination.

Figure 2: The Power of Multiplexing: Dual-Index Barcoding Strategy. Each well is assigned a unique combination of Well Barcodes and Plate Barcodes during Reverse Transcription (RT). After pooling, Unique Molecular Identifiers (UMIs) are used to eliminate PCR duplication bias, ensuring accurate gene quantification for every sample.

Molecular Barcodes Explained

Molecular Barcodes play a key role in drug-seq. Molecular barcodes are short sequences added to each RNA molecule before amplification. They help distinguish between original molecules and PCR duplicates. This feature improves data accuracy and ensures reliable quantification of gene expression.

Library Pooling & Sequencing

After barcoding, drug-seq pools all libraries for sequencing. Pooling optimizes costs and increases statistical power. By combining samples, researchers reduce within-group variability and detect biological effects with fewer samples. Pooling also helps manage noise from low-abundance genes, making differential gene expression analysis more robust.

Drug-seq’s workflow supports scalable, high-throughput sequencing. Careful selection of pool size and sequencing depth ensures high-quality data. CD Genomics delivers results quickly, with standard turnaround times as fast as 10 business days. Their dedicated bioinformatics team provides comprehensive data analysis and reporting, supporting every stage of the drug-seq process.

Drug-seq’s efficient workflow, from cell lysis to data delivery, accelerates discovery and supports large-scale transcriptomics projects.

Bioinformatics Pipeline

Drug-seq generates large volumes of transcriptomics data. CD Genomics provides a robust bioinformatics pipeline to help researchers interpret results quickly and accurately. The pipeline begins after sequencing and continues through several analytical stages. Researchers receive support at every step, ensuring high-quality gene expression data for drug discovery.

The process starts with quality control. Analysts examine sequencing data for read length and base call accuracy. This step ensures that only reliable data move forward. Alignment quality control follows. The team maps reads to the reference genome and checks for completeness. Accurate mapping is essential for trustworthy gene expression profiles.

Quantitative gene expression analysis comes next. Algorithms calculate transcript abundance using normalized values such as TPM (Transcripts Per Million). This approach allows researchers to compare gene expression across samples and conditions. Differential gene expression analysis identifies genes that change significantly between treated and control groups. These results reveal how compounds affect cellular pathways.

Pathway analysis provides deeper insights. Analysts use enrichment methods to find biological pathways altered by drug treatment. This information helps researchers understand mechanisms of action and identify potential biomarkers.

The table below summarizes the main stages of the Drug-seq bioinformatics workflow:

Researchers benefit from a streamlined workflow that eliminates RNA extraction and reduces hands-on time. The bioinformatics pipeline transforms raw sequencing reads into actionable gene expression matrices. Drug-seq enables unbiased discovery and supports high-throughput screening for pharmaceutical research.

Figure 1: Streamlined Drug-seq Workflow vs. Traditional RNA-seq. Unlike traditional methods that require labor-intensive RNA purification, Drug-seq utilizes a direct-to-lysis approach. Samples are lysed, barcoded, and pooled immediately, enabling high-throughput processing of thousands of wells in parallel.

Drug-seq Advantages

Unbiased Discovery

Drug-seq enables researchers to explore gene expression changes without bias. The platform profiles thousands of genes directly from cell lysates. Scientists can identify new mechanisms of action and potential drug targets. Published studies confirm the power of this approach.

Researchers use drug-seq to uncover gene signatures and pathway changes. The method works well for mechanism analysis and biomarker discovery. It supports studies in neuroscience, oncology, and personalized medicine.

Drug-seq provides a comprehensive view of cellular responses, helping teams make informed decisions in early drug development.

Cost & Sample Efficiency

Drug-seq reduces both costs and sample requirements. The platform eliminates RNA extraction and uses direct lysis. This approach saves time and preserves sample integrity. Researchers can profile gene expression from as few as 1,000 cells.

• The cost of preparing NGS libraries with traditional kits is about $45–$47 per sample.

• BOLT-seq, a related method, prepares up to 96 libraries in 4 hours, with only 2 hours of hands-on time.

• BOLT-seq uses unpurified cell lysates and skips purification steps, cutting time and cost.

Drug-seq follows a similar streamlined workflow. Scientists can process rare or precious samples, such as organoids or clinical biopsies. The method supports large-scale studies without high expenses.

Fast turnaround and low input needs make drug-seq ideal for high-throughput screening and projects with limited material.

High Throughput Screening

Drug-seq supports high-throughput screening for drug discovery. The platform processes hundreds to thousands of samples in parallel. Researchers can test many compounds or genetic perturbations at once.

• TORNADO-seq, another high-throughput platform, uses targeted RNA sequencing to study organoids.

• TORNADO-seq can analyze cell mixtures and differentiation states, improving drug efficacy studies.

Drug-seq matches these strengths. It enables scalable, cost-effective transcriptomics for large compound libraries. Scientists gain insights into drug effects, toxicity, and off-target responses.

Drug-seq accelerates discovery by combining speed, scalability, and robust data quality.

Drug-seq vs. RNA-seq

Library Prep Differences

Drug-seq and RNA sequencing both play important roles in drug discovery. However, their workflows differ in key ways. Drug-seq uses a direct lysis approach. This method skips the RNA extraction step. Researchers can add cell lysates straight into the workflow. RNA sequencing, on the other hand, requires careful RNA extraction from each sample. This step adds time and complexity.

The table below highlights the main differences in library preparation:

Drug-seq allows researchers to process hundreds of samples at once. This high throughput makes it ideal for large-scale screens. Rna sequencing often limits throughput because each sample needs extraction and cleanup. Drug-seq also reduces the risk of sample loss or degradation. The streamlined workflow saves time and resources.

Tip: Direct lysis in drug-seq preserves RNA quality and speeds up the process. This feature benefits studies with rare or precious samples.

Read Depth & Coverage

Read depth and coverage are important factors in transcriptomics. Rna sequencing typically uses deep sequencing to capture the full transcriptome. Researchers may sequence each sample with 30 to 50 million reads. This approach detects rare transcripts and provides broad coverage.

Drug-seq uses a more targeted strategy. Most experiments require only 2 to 13 million reads per well. This read depth detects up to 12,000 genes in a single sample. While rna sequencing may detect more genes, drug-seq covers the majority of biologically relevant transcripts. For high-throughput screens, this balance between depth and efficiency is crucial.

Drug-seq supports parallel analysis of hundreds or thousands of treatments. RNA sequencing, with its higher read requirements, often becomes costly and time-consuming at scale. Drug-seq enables rapid profiling without sacrificing data quality.

• Drug-seq: 2–13 million reads per well, 11,000–12,000 genes detected

• RNA sequencing: 30–50 million reads per sample, up to 17,000 genes detected

Researchers should choose the method that matches their project goals. For broad discovery, RNA sequencing offers maximum coverage. For screening and mechanism studies, drug-seq provides efficient and reliable results.

Cost Comparison

Cost plays a major role in method selection. RNA sequencing involves several steps, including RNA extraction, library preparation, and deep sequencing. Each step adds to the total cost. Drug-seq eliminates RNA extraction and uses direct lysis. This change lowers reagent and labor expenses.

On average, drug-seq costs about $3 per sample for sequencing. RNA sequencing can cost much more, especially for large projects. The need for high read depth and extra preparation steps increases expenses. Drug-seq’s streamlined workflow makes high-throughput transcriptomics affordable for most labs.

Researchers can screen more compounds or conditions with the same budget. This advantage accelerates drug discovery and supports innovation. Drug-seq’s cost efficiency, combined with high throughput, sets it apart from traditional RNA sequencing.

Note: Lower costs do not mean lower quality. Drug-seq delivers robust gene expression data suitable for mechanism analysis and biomarker discovery.

Choosing the Right Method

Selecting the best approach for high-throughput transcriptomics in drug discovery depends on project goals, sample type, and resource availability. Both Drug-seq and RNA-seq offer powerful gene expression profiling, but each method excels in different scenarios.

Key factors to consider:

Tip: Drug-seq streamlines high-throughput transcriptomics for drug discovery, especially when researchers need to screen many compounds or work with limited material.

When to choose Drug-seq:

• Screening hundreds or thousands of compounds in parallel.

• Working with rare, precious, or low-input samples such as organoids or clinical biopsies.

• Needing rapid turnaround and cost-effective gene expression profiling.

• Focusing on mechanism of action, biomarker discovery, or toxicity screening.

When to choose RNA-seq:

• Requiring maximum transcriptome coverage, including rare or novel transcripts.

• Investigating complex splicing events or isoform-specific responses.

• Conducting in-depth studies on a smaller number of samples.

Researchers in pharmaceutical R&D often select Drug-seq for large-scale, unbiased discovery. RNA-seq remains valuable for deep molecular characterization and validation. CD Genomics offers flexible service packages, allowing teams to match the method to their scientific objectives.

Choosing the right transcriptomics platform ensures efficient use of resources and maximizes the impact of drug discovery projects.

Applications in Neuroscience Drug Discovery

Neuroscience drug discovery faces unique challenges. Researchers need tools that can handle rare samples, organoids, and clinical biopsies. Drug-seq offers scalable solutions for drug response profiling in these settings. The platform also supports studies in cancer and tumor biology, making it valuable for both neuroscience and oncology research.

Figure 3: High-Throughput Screening of Neural Organoids. Drug-seq enables the profiling of complex models like brain organoids in a 384-well format. By analyzing gene expression signatures (Heatmap) rather than simple phenotypic changes, researchers can identify compounds that modulate disease pathways with high specificity.

Compound Screening

Drug-seq enables high-throughput compound screening in neuroscience drug discovery. Scientists can test hundreds of molecules on neural cells, organoids, or tissue slices. The method works with as few as 1,000 cells, which is ideal for rare samples. Researchers use drug-seq to identify gene expression changes after treatment. This approach helps find promising compounds for further study.

• Drug-seq supports parallel screening of cancer and tumor models.

• The platform detects subtle transcriptomic shifts, revealing early drug effects.

• Scientists can compare responses across neural, cancer, and tumor cell lines.

Mechanism of Action

Understanding how drugs work is essential in neuroscience drug discovery. Drug-seq provides detailed gene expression profiles, helping researchers map drug mechanisms. The technology reveals pathway changes in neural, cancer, and tumor cells. Scientists can link specific gene signatures to drug action.

• Drug-seq uncovers molecular pathways affected by compounds.

• The platform supports mechanism studies in cancer and tumor organoids.

• Researchers use pathway analysis to predict drug efficacy and safety.

Q: What insights does drug-seq offer for mechanism of action?
A: Drug-seq identifies gene networks and pathways altered by drugs in neural and tumor models.

Off-Target & Toxicity

Drug-seq excels at detecting off-target effects and toxicity in neuroscience drug discovery. The platform profiles gene expression changes linked to adverse reactions. Researchers use curated datasets to predict toxicity in neural, cancer, and tumor cells.

Researchers visualize safety profiles using UMAP plots. Machine learning models, such as LightGBM, improve toxicity prediction accuracy.

Hit-to-Lead Case Study

A recent study used drug-seq to screen neural organoids for hit-to-lead optimization. Scientists identified compounds that modulated neural pathways without affecting cancer or tumor markers. The team validated hits using transcriptomic data and pathway analysis (Ye et al., 2018). Drug-seq enabled rapid selection of safe and effective leads for further development.

Drug-seq supports neuroscience drug discovery, cancer research, and personalized medicine. The platform works with rare samples, organoids, and clinical research projects.

Q: Can drug-seq help select safer drug candidates?
A: Yes. Drug-seq detects off-target and toxicity signals in neural, cancer, and tumor cells, guiding hit-to-lead selection.

Data Analysis & Bioinformatics

From Reads to Matrices

Drug-seq produces a high-dimensional readout that captures gene expression changes across many samples. The data analysis journey starts with raw sequencing reads. Analysts first clean the data by removing low-quality sequences and adapter fragments. They then align the cleaned reads to a reference transcriptome using tools such as STAR or HISAT2. This step ensures that each read matches the correct gene or transcript. After alignment, quality control checks remove poorly mapped reads. The final step counts how many reads map to each gene, creating a raw count matrix. This matrix forms the foundation for all downstream analysis.

The count matrix provides a snapshot of gene activity for each sample, making it possible to compare drug effects across experiments.

DGE Analysis

Differential gene expression (DGE) analysis helps scientists identify which genes respond to drug treatments. Drug-seq datasets often use statistical methods adapted from single-cell RNA sequencing. These methods handle the unique distribution of molecular barcodes counts. Analysts choose between parametric and non-parametric approaches. Parametric models include Zero Inflated Negative Binomial and Hurdle models. These models account for excess zeros and variability in the data. Popular R packages for these methods include MAST and ZINB-wave. Non-parametric tools, such as D3E and EMDomics, offer flexibility for comparing two groups. Each method helps researchers detect significant gene expression changes with confidence.

• Parametric methods: Handle complex data distributions, suitable for large datasets.

• Non-parametric methods: Useful for simple group comparisons, robust to outliers.

Pathway & Network Insights

Once DGE analysis identifies key genes, pathway and network analysis reveal the biological meaning. Analysts use enrichment tools to map differentially expressed genes to known pathways. This process uncovers which cellular processes drugs affect most. Network analysis links genes into functional modules, showing how drug treatments reshape cellular networks. These insights help researchers understand mechanisms of action and predict off-target effects. Drug-seq enables rapid discovery of pathway changes, supporting biomarker identification and target validation.

Pathway analysis transforms raw data into actionable knowledge, guiding drug development decisions.

Implementation: In-House vs. Outsourcing

Pharmaceutical teams often face a choice between running drug-seq workflows in-house or outsourcing to a specialized service provider. This decision impacts project speed, data quality, and resource allocation. Drug-seq offers scalable transcriptomics for drug discovery, but technical barriers can affect implementation.

Technical Barriers

Equipment Needs

Setting up drug-seq in-house requires advanced laboratory equipment. Teams need automated liquid handlers, high-throughput sequencers, and robust data storage systems. Many labs also need specialized software for sample tracking and data analysis. These investments can be significant, especially for groups with limited budgets or space. Maintenance and calibration add ongoing costs. Not every research group can justify these expenses for occasional projects.

Batch Effects

Batch effects can introduce unwanted variability in transcriptomics data. Inconsistent sample handling, reagent lots, or instrument performance may cause these effects. Drug-seq workflows demand strict quality control to minimize batch-to-batch differences. In-house teams must standardize protocols and monitor every step. Even small deviations can impact downstream analysis and interpretation.

Note: Outsourcing to an experienced provider helps control batch effects through validated workflows and centralized processing.

Benefits of Service Providers

Contract research organizations (CROs) like CD Genomics offer several advantages. They provide access to state-of-the-art equipment and expert personnel. Their teams follow validated protocols, reducing technical variability. Service providers deliver fast turnaround times, often within 10 business days for standard drug-seq projects. Dedicated bioinformatics support ensures accurate data analysis and reporting.

CD Genomics stands out with flexible service packages. Researchers can choose standard, ultra-low input, or full-length transcript options. Custom solutions address unique project needs, such as rare samples or organoids. Outsourcing allows teams to focus on experimental design and interpretation, rather than technical setup.

CRO Selection Metrics

Selecting the right CRO involves careful evaluation. Key metrics include:

• Experience: Look for providers with a proven track record in drug-seq and high-throughput transcriptomics.

• Turnaround Time: Fast data delivery accelerates decision-making.

• Data Quality: Assess quality control measures and bioinformatics support.

• Service Flexibility: Flexible packages and custom solutions accommodate diverse research needs.

• Communication: Responsive support ensures smooth project management.

Tip: Partnering with a CRO like CD Genomics streamlines drug-seq implementation and maximizes research impact.

Future of Transcriptomic Screening

Transcriptomics continues to transform drug discovery. Drug-seq technology from CD Genomics leads this change. Researchers now use transcriptomics to profile gene expression quickly and cost-effectively. Drug-seq enables high-throughput screening, mechanism analysis, and biomarker discovery. These advances support personalized medicine and accelerate pharmaceutical research.

Key trends shaping the future of transcriptomic screening include:

• Automation and Miniaturization:
  Laboratories invest in automated platforms. These systems handle thousands of samples with minimal human intervention. Miniaturized workflows reduce reagent use and lower costs. Drug-seq already eliminates RNA extraction, setting a standard for streamlined processes.

• Artificial Intelligence (AI) Integration:
  AI tools analyze large transcriptomics datasets. Machine learning models predict drug responses and identify biomarkers. Researchers use AI to uncover hidden patterns in gene expression. Drug-seq data supports these advanced analytics.

• Single-Cell and Spatial Transcriptomics:
  Scientists explore gene expression at single-cell resolution. Spatial transcriptomics maps gene activity within tissues. These methods reveal cellular heterogeneity and tissue architecture. Drug-seq adapts to low-input samples, making it compatible with organoids and rare specimens.

• Personalized Medicine:
  Transcriptomic screening tailors drug selection to individual patients. Researchers match gene expression profiles with targeted therapies. Drug-seq enables rapid profiling of clinical samples, supporting precision medicine.

Note: Drug-seq’s flexibility and scalability position it as a core technology for future drug discovery.

Case study:
A recent study used Drug-seq to screen neural organoids for drug responses (Ye et al., 2018). The team identified compounds that modulated neural pathways without affecting cancer markers. This approach accelerated hit-to-lead selection and improved safety assessment.

Researchers expect transcriptomic screening to become even more accessible. Lower costs and faster turnaround times will drive adoption. Drug-seq from CD Genomics will continue to support innovation in pharmaceutical R&D. Scientists will use transcriptomics to answer complex questions and develop safer, more effective drugs.

Drug-seq from CD Genomics removes barriers in drug discovery. The platform speeds up research, increases scalability, and lowers costs. Scientists use drug-seq for biomarker discovery in large and small projects. The technology improves data quality and supports biomarker discovery in rare samples. Teams gain deeper insights into mechanism analysis and biomarker discovery for personalized medicine. Future advances in drug-seq will drive innovation in pharmaceutical research.

FAQ

Q: What sample types does Drug-seq support?

Drug-seq from CD Genomics works with cell lysates, organoids, and tissue slices. Researchers can use as few as 1,000 cells. The platform supports high-throughput transcriptomics for rare or precious samples.

Q: Can Drug-seq detect subtle gene expression changes?

Yes. Drug-seq profiles thousands of genes per sample. The method detects subtle transcriptomic shifts after drug treatment. Researchers use these data for mechanism analysis and biomarker discovery.

Q: How does Drug-seq compare to traditional RNA-seq?

Drug-seq eliminates RNA extraction and uses direct lysis. This approach reduces cost and hands-on time. Drug-seq supports high-throughput screening, while traditional RNA-seq offers deeper coverage for focused studies.

Q: Is Drug-seq suitable for low-input or clinical samples?

Absolutely. Drug-seq can analyze samples with as few as 1,000 cells. This feature makes it ideal for organoids, rare clinical specimens, and small tissue biopsies.

Q: What bioinformatics support does CD Genomics provide?

CD Genomics offers comprehensive bioinformatics analysis. The team delivers gene count matrices, differential expression results, and pathway insights. Researchers receive clear, actionable reports.

Q: Can Drug-seq be used for personalized medicine research?

Yes. Drug-seq enables rapid gene expression profiling from clinical samples. Researchers use these data to identify biomarkers and support personalized medicine strategies.

Q: Can Drug-seq analyze low-input samples like organoids or rare clinical specimens?
Drug-seq from CD Genomics supports high-throughput transcriptomics with very low input. Researchers can profile gene expression from as few as 1,000 cells. This feature makes Drug-seq ideal for rare samples, organoids, or small tissue slices. The platform preserves RNA integrity by skipping extraction. Scientists can use Drug-seq for projects where sample material is limited.

Q: What types of samples work best with Drug-seq?
Drug-seq works with cell lysates, organoids, and tissue slices. The method does not require purified RNA. This flexibility allows researchers to study a wide range of biological models. Drug-seq adapts to both standard and ultra-low input projects.

Tip: Drug-seq’s ultra-low input service helps teams maximize data from precious or rare samples.

Q: How does Drug-seq differ from single-cell RNA sequencing (scRNA-seq)?
Drug-seq profiles bulk populations of cells, not individual cells. This approach enables high-throughput screening of hundreds or thousands of samples in parallel. scRNA-seq analyzes gene expression at the single-cell level. It provides detailed cellular heterogeneity but requires more complex workflows and higher costs.

Q: When should researchers choose Drug-seq over scRNA-seq?
Drug-seq fits large-scale compound screening, mechanism studies, and projects with limited material. scRNA-seq suits studies focused on cell heterogeneity or rare cell types. Drug-seq offers faster turnaround and lower costs for high-throughput transcriptomics.

Q: How does drug-seq improve compound screening in neuroscience?
A: Drug-seq allows rapid, unbiased analysis of gene expression in neural and tumor cells, even with low input.

Reference

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2. Li J, Ho DJ, et al. DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery. ACS Chem Biol. 2022 Jun 17;17(6):1401-1414. doi: 10.1021/acschembio.1c00920. Epub 2022 May 4. PMID: 35508359; PMCID: PMC9207813.

3. Subramanian, A., Narayan, R., Corsello, S. M., et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6), 1437-1452.e17.

4. Ziegenhain, C., Vieth, B., Parekh, S., et al. (2017).Comparative Analysis of Single-Cell RNA Sequencing Methods. Molecular Cell, 65(4), 631-643.e4.

5. Norkin M, Huelsken J. TORNADO-seq: A Protocol for High-Throughput Targeted RNA-seq-Based Drug Screening in Organoids. Methods Mol Biol. 2023;2650:65-75. doi: 10.1007/978-1-0716-3076-1_6. PMID: 37310624.

6. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.

For more information on Drug-seq, high-throughput transcriptomics, and CD Genomics services, visit the official CD Genomics Drug-seq page.