High-throughput screening and high-content screening are foundational to the early stages of drug discovery and development pipelines. The pharmaceutical industry harnesses both approaches to sift through vast libraries of compounds or genetic perturbations in parallel to identify promising therapeutic candidates, study diverse biological systems, and identify potential molecular targets, pathways, and cytotoxic effects crucial for downstream decision making.
High-throughput screening and high-content screening differ in their applications, strengths, and weaknesses, but employing both in tandem is an increasingly powerful way to accelerate exploration while de-risking drug discovery, thanks to the speedy generation of vast amounts of information for more compounds or experimental conditions than ever before.
Let’s take a look at the trade-offs between high-content screening and high-throughput screening, where both fields are heading in the big data era, and how increasingly scalable, data-rich high-throughput screening technologies are entering the high-content sphere to allow researchers to ‘have it all’.
What is high-throughput screening?
High-throughput screening lets researchers simultaneously assess the biological effects of thousands (sometimes millions) of potentially therapeutic compounds on specific targets in various in vitro and cell-based assays (Wildey et al., 2017). The ultimate aim is often to identify active compounds or “hits” showing potential therapeutic effects. Once identified, researchers further validate these hits with secondary and tertiary screening, using more complex and physiologically relevant approaches such as high-content screens that further confirm the specificity or activity of the hits.
The approach uses automated robotics, combined with 96, 384, or 1536-well microtiter plates, where each well contains a drug, dosage, or other experimental condition of interest. Because of the vast number of compounds or conditions tested and the speed required, common screening assays are generally relatively simple and need automation-compatible assay designs and data processing.
The two flavors of high-throughput screening include biochemical assays and cell-based assays.
- In biochemical screens, researchers often use a purified target protein of interest to measure the binding of ligands or the inhibition of enzymatic activity in vitro. It includes techniques like fluorescence resonance energy transfer (FRET) to determine if two proteins interact.
- Cell-based screens are the biological counterpart to biochemical screens because they often test the effect of compounds on live cells instead of on purified targets. This allows the capture of more complex, physiologically relevant responses. Assays include cell viability, reporter gene, second messenger, and high-throughput microscopy assays.
What are the limitations of high-throughput screening?
While high-throughput screening approaches are powerful, informative, and increasingly essential to drug discovery and development pipelines, they often have distinct limitations that are steadily being overcome.
For instance, the need for simple, easy-to-automate assays was a major limitation of high-throughput screens because, although many samples could be rapidly assessed, the biological information was restricted to relatively basic fluorescent read-outs, required prior target selection, or was only suited to adherent, easy-to-culture cell types (Attene-Ramos et al., 2014). These requirements limited the use of highly informative but traditionally slow and expensive transcriptomics in high-throughput screening because, despite its utility, it was challenging to achieve sufficient sample throughput at an acceptable cost.
High-throughput screening goes transcriptomic
Innovations in transcriptomic library preparation technologies now provide researchers with highly scalable, easy-to-automate, and cost-effective alternatives to traditional RNA-seq approaches. It allows pipelines to rapidly interrogate samples with a transcriptome-wide perspective to gain deeper, target- and hypothesis-agnostic insights into the overall effects of compounds or perturbations across the entire biological space (Alpern et al., 2019).
Technologies like MERCURIUS™ DRUG-seq allow researchers to perform high-throughput transcriptomic screening using the 384-well plate compound treatment format familiar to traditional workflows, but with data-rich outputs not possible with other biochemical or cell-based assays. It also aligns with sustainability goals due to low consumable usage made possible by its RNA-extraction-free and massively multiplexed workflow optimized for automation.
Screening assays are also now embracing more physiologically relevant 3D organoid systems alongside less informative adherent monoculture cell lines, depending on the research question.
What is high-content screening?
While high-throughput screening focuses on speed and throughput for initial target-based assays, high-content screening traditionally refers to slower image-based approaches using automated microscopy and quantitative data analysis at slightly lower throughput (Seal et al., 2024). Its strength lies in providing a broader understanding of the overall impact of compounds on cellular phenotypes, as it can report on diverse features like morphology, organelle structure, and protein localization, even at the single-cell level, to help identify the most promising drug candidates with favorable safety profiles.
It is suited to confirmatory secondary, tertiary, or lead optimization screens as it analyzes multiple cellular parameters simultaneously to inform on alterations in cellular functions in response to compound treatment or genetic perturbation, rather than a single target or endpoint.
Although “high‑content” traditionally refers to imaging-based assays, the same principle of rich, multiparametric data now applies to high-throughput transcriptomic approaches like MERCURIUS™ DRUG-seq, which are increasingly termed high-content transcriptomics.
What are the limitations of high-content screening?
While high-throughput screening excels with scalability, high-content screening approaches sacrifice some high-throughput capabilities in return for greater biological and phenotypic complexity in assay endpoints. Scalability isn’t at the level of high-throughput screening just yet, but thanks to approaches like Cell Painting, combines panels of dyes with automated imaging to inform on thousands of cellular morphological features simultaneously, and advances in instrumentation, automation, and data analysis, progress is accelerating (Seal et al., 2024).
High-content screening also generates an overwhelming amount of incredibly rich imaging data that often acts as a significant bottleneck for researchers. However, with the onset of artificial intelligence and machine learning algorithms, researchers are now overcoming this hurdle with accurate, simplified read-outs to aid quicker go/no-go decision making, crucial for high-stakes drug development.
Are high-throughput and high-content screens stronger together?
High-throughput and high-content screening aren’t mutually exclusive. They’re complementary approaches that provide unprecedented amounts of data-rich information crucial to understanding the biological effects of compounds in relevant systems, simultaneously or at different stages of the development pipeline.
For instance, high-throughput screening is ideal for early-stage triage of vast compound or genetic perturbation libraries, whereas high-content screening adds depth, helping prioritize or de-risk leads by offering functional and phenotypic context. With high-throughput transcriptomics like MERCURIUS™ DRUG-seq now offering a ‘high-content’ deep dive into the broad molecular effects of chemical or genetic perturbations, screening pipelines are poised to further de-risk drug discovery with information that enables swift, confident decision-making necessary for successful pipelines.
Hybrid screening workflows will eventually become the norm, with cost-effective, streamlined, high-throughput transcriptomic screening and high-content methods playing an increasingly important role in allowing researchers to ‘have it all,’ accelerating new drugs through development to the patients who need them.
References
- Alpern, D., Gardeux, V., Russeil, J., Mangeat, B., Meireles-Filho, A.C., Breysse, R., Hacker, D. and Deplancke, B., 2019. BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome biology, 20, pp.1-15.
- Attene-Ramos, M.S., Austin, C.P. and Xia, M. (2014) "High throughput screening," Encyclopedia of Toxicology, pp. 916–917
- Seal, S., Trapotsi, M.A., Spjuth, O., Singh, S., Carreras-Puigvert, J., Greene, N., Bender, A. and Carpenter, A.E., 2024. Cell Painting: a decade of discovery and innovation in cellular imaging. Nature methods, pp.1-15.
- Wildey, M.J., Haunso, A., Tudor, M., Webb, M. and Connick, J.H., 2017. High-throughput screening. Annual Reports in Medicinal Chemistry, 50, pp.149-195.
