The development of new drugs is a complex and time-consuming process that involves identifying potential drugs and their targets, optimizing and synthesizing drug candidates, and testing their efficacy and safety in preclinical and clinical trials¹. Traditional methods for drug screening, i.e., the systematic assessment of libraries of candidate compounds for therapeutic purposes, involve the use of animal models or cell-based assays to test the effects on specific targets or pathways. While these methods have been successful in identifying drug candidates, they often did not provide sufficient insights into the complex molecular mechanisms underlying drug efficacy and also toxicity and mechanisms of potential side effects.

Assessing genome-wide transcriptional states presents a powerful approach to overcome current limitations in drug screening, as it enables comprehensive assessments of gene expression in response to drug treatments². By measuring RNA transcripts in a genome-wide fashion expressed at a given time, transcriptomics aims to provide a holistic view of the transcriptional changes that occur in response to drugs, including changes in gene expression patterns, alternative splicing, and non-coding RNA expression³. This information can be used to determine drug targets, predict drug efficacy and toxicity, and optimize drug dosing and treatment regimens.

One of the key benefits of combining transcriptomics with unbiased drug screening is the potential to identify new drug targets that have not been previously considered. Conventional drug screening approaches often focus on established target molecules or pathways, hindering the identification of new targets and potentially resulting in drugs with unforeseen side effects and restricted effectiveness. Transcriptomics can overcome these limitations by providing insights into the molecular changes that occur in response to drug treatment, uncovering potential targets or pathways that may not have been previously considered².

In addition to the identification of new drug targets, transcriptomics can also be used to predict drug efficacy and toxicity. By analyzing the gene expression patterns associated with drug responses, biomarkers can be developed that can be used to predict a patient's response to a particular drug or treatment regimen. This can also help to optimize drug dosing and reduce the risk of adverse side effects⁴.

Despite its potential benefits, the cost of transcriptomics remains a significant barrier to its widespread application in drug screening. Transcriptomic analysis requires specialized equipment, technical expertise, and data analysis, which can make it challenging for smaller research teams or organizations with limited funding to utilize transcriptomics in drug screening. However, the cost of transcriptomics has been steadily decreasing, making it more accessible to the research communities. Additionally, advancements in technology and data analysis methods have made transcriptomics more efficient and cost-effective, further increasing its accessibility².

In this protocol, we describe a high-dimensional and explorative system for transcriptome-based drug screening, combining miniaturized perturbation cultures with mini-bulk transcriptomics analysis⁵^,⁶. With this protocol, it is possible to reduce the cost per sample to 1/6^th of the current cost of commercial solutions for full-length mRNA sequencing. The protocol requires only standard laboratory equipment, the only exception being the use of short-read sequencing technologies, which can be outsourced if sequencing instruments are not available in-house. The optimized mini-bulk protocol provides information-rich biological signals at cost-effective sequencing depth, enabling extensive screening of known drugs and new molecules.

The aim of the experiment is to screen for drug activity on PBMCs in different biological contexts. This protocol can be applied to any biological question where several drugs should be tested with a transcriptomic readout, giving a transcriptome-wide view of the cellular effect of the treatment.

MRNA Sequencing to Understand the Cellular Effect of Drug Treatment