What is DRUG-seq

DRUG-se represents a cutting-edge high-throughput drug screening platform based on RNA sequencing. This technology is designed for transcriptomic profiling from minimal cell quantities in 96- or 384-well plates. It utilizes well barcoding and Unique Molecular Identifier (UMI) quantification to enable full-length transcript amplification. DRUG-seq constitutes a crucial step in the early stages of drug candidate selection during Chemistry, Manufacturing, and Controls (CMC) submission. This methodology allows for the simultaneous detection of transcriptomes across all samples in large-scale high-throughput screening, involving hundreds of candidate compounds and experimental conditions. The resulting data provides comprehensive insights into drug mechanisms of action.

Figure 1. DRUG-seq overview.

The advent of high-throughput sequencing technologies has markedly transformed the field of pharmacological research, especially in the identification and validation of novel therapeutic compounds. Within this spectrum, DRUG-seq has emerged as a cornerstone technique, effectively merging the comprehensive capabilities of full-length RNA sequencing with the scalability essential for industrial-level drug screening. This methodology facilitates the precise quantification of gene expression, thereby allowing researchers to identify specific metabolic pathways influenced by drug treatments.

In the realm of drug discovery, it is crucial to evaluate the transcriptional changes induced by compounds at the cellular level. DRUG-seq fulfills this requirement by enabling the simultaneous transcriptomic profiling of numerous candidate compounds within a single experiment. Furthermore, the technology is adaptable to a variety of experimental conditions, encompassing different cell types, drug dosages, and time points. By offering a detailed perspective on how compounds modulate gene expression, DRUG-seq significantly contributes to the identification of promising drug candidates and enhances the probability of success in subsequent drug development stages.

Applications of DRUG-seq

1. Mechanism of Action Determination: DRUG-seq facilitates the validation of known target compound mechanisms or the prediction of potential mechanisms for compounds with unidentified targets.
2. Dose-Response Kinetics: This technique enables the investigation of dose-response kinetics across different compounds targeting indirect pathways.
3. Functional Divergence Analysis: It provides insights into biological functional differences elicited by distinct compounds.
4. Off-Target Effects Assessment: DRUG-seq allows for the evaluation of biological functions affected by compounds acting on non-target genes.

Advantages of DRUG-seq Technology

1. High Throughput: DRUG-seq is capable of assessing gene expression at the transcriptome level for all cells in either 96- or 384-well plates in a single experiment.
2. Short Experimental Cycle: Simultaneous processing of 96- or 384-well samples significantly reduces the overall experimental duration.
3. Cost Efficiency: Pooling and sequencing of 96- or 384-well samples results in substantially lower costs compared to traditional RNA-seq methods.
4. Low Starting Material Requirement: DRUG-seq is effective with as few as 2,000 to 20,000 cells per sample.
5. Increased Resolution: Compared to conventional high-throughput screening methods that focus on cellular phenotypes, DRUG-seq offers gene-level resolution through RNA sequencing, enabling more precise drug screening.
6. Comprehensive Bioinformatics Analysis: DRUG-seq supports both standardized and customized bioinformatic analyses. Differential gene expression analyses can reveal specific metabolic pathways affected by drug treatments while also uncovering other pharmacological effects, providing a multidimensional assessment of drug mechanisms.

Sequencing Protocol

Sequencing Platform: Illumina NovaSeq

Data Output: 1–2 GB per well

Bioinformatics Analysis

Core Analysis

1. Quality Control of Raw Data: Comprehensive evaluation of sequencing data quality, including read length, base call accuracy, and adapter contamination.
2. Alignment Quality Control: Assessment of the alignment results, ensuring the accuracy and completeness of reads mapped to the reference genome.
3. Quantitative Gene Expression Analysis: Calculation of transcript abundance using appropriate algorithms, providing normalized expression levels (e.g., TPM, FPKM).
4. Principal Component Analysis (PCA): Dimensionality reduction technique applied to assess variation in the gene expression profiles across samples.
5. Sample and Gene Clustering Analysis: Hierarchical clustering or other methods applied to both genes and samples, revealing potential patterns in gene expression data.
6. Differential Gene Expression Analysis: Identification of genes exhibiting statistically significant changes in expression levels between experimental conditions (e.g., treated vs. control groups).
7. Gene Ontology (GO) Functional Analysis: Categorization of differentially expressed genes (DEGs) into biological processes, molecular functions, and cellular components based on the GO database.
8. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis: Enrichment analysis of DEGs to identify pathways potentially altered by the drug treatment.
9. Reactome Pathway Analysis: Utilization of the Reactome database to map DEGs onto known biological pathways, offering insights into drug-induced biological responses.
10. Gene Set Enrichment Analysis (GSEA): A non-biased method to determine whether a predefined set of genes shows statistically significant differences between experimental groups.

Customized Analysis

1. Gene Interaction Network Analysis: Post-drug-treatment analysis of gene-gene interactions, focusing on disease-related genes and their networks of interactions. This can elucidate how compounds impact disease mechanisms at the molecular level.
2. Co-expression Network Analysis: Investigation of co-expressed gene groups post-treatment, identifying gene clusters associated with disease mechanisms and drug responses.
3. Trend Analysis: Examination of gene expression trends over varying drug concentrations or across different time points post-treatment. Time-series analyses are employed to identify dynamic changes in gene expression profiles.

Further Custom Analyses

Additional tailored bioinformatic analyses are available upon request, providing a more specific investigation into particular drug effects, pathways, or gene interactions. This can include advanced statistical modeling, in-depth pathway interrogation, or integration with other omics data types for a multi-layered approach to understanding drug efficacy and mechanism.

Recommendations for DRUG-seq Sample Submission

Notes

• The provided sample handling instructions are critical for maintaining the integrity of the cellular material and ensuring optimal results during subsequent DRUG-seq analyses.
• Adherence to recommended shipping methods is essential to prevent degradation of lysate samples, which may adversely affect downstream applications and data quality.
• It is imperative to process and ship samples on the same day to preserve cellular integrity and ensure reliable results.

DRUG-seq vs. RNA-seq

DRUG-seq and RNA-seq are both powerful techniques used to study gene expression at the transcriptome level, but they serve distinct purposes and offer different advantages in specific contexts. Below is a detailed comparison of the two methods:

Case Study

Validation of Compound Mechanisms of Action Using DRUG-seq Technology

In a study involving the validation of 433 compounds across eight different dosages, transcriptomic profiles obtained through the DRUG-seq technology successfully facilitated the functional classification of these compounds based on their anticipated mechanisms of action (MoAs). Significant variations in transcriptional alterations were detected among compounds targeting the same biological entities, providing evidence for the utility of DRUG-seq in elucidating both on-target and off-target activities of compounds.

The findings underscore the capacity of DRUG-seq to differentiate between the perturbations in transcriptomic responses associated with distinct compounds, thereby contributing to a deeper understanding of the complex biological interactions inherent to pharmaceutical agents. This capability enhances the assessment of therapeutic potential and supports the optimization of drug discovery processes.