In recent years, the field of drug discovery and development has undergone a remarkable transformation thanks to the advent of single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) – the profiling of gene expression at the level of individual cells – and its accompanying computational tools. Combine this with the wealth of publicly available data resources, and the way we can search for new drugs has been revolutionized.
Improved disease understanding is at the core of scRNA-seq’s impact on drug discovery and development. “ScRNA-seq enables more efficient, direct and safer treatments,” explained Ester Gil, a consultant in Clarivate’s bioinformatics team.
Now that this new field of cellular understanding has been upon us for some years, we take the chance to examine which parts of the R&D landscape scRNA-seq will impact the most as the industry searches for pathological and biological breakthroughs.
Preclinical modelling
ScRNA-seq can aid in the identification and validation of potential drug targets by characterizing the gene expression profiles of individual cells within diseased tissues. By comparing the transcriptomic profiles of healthy and diseased cells, researchers can identify specific cell types or subpopulations that contribute to disease progression. This information can guide the selection of therapeutic targets and help prioritize molecules for further development.
Equally, this single cell sequencing could be invaluable in the selection of relevant preclinical disease models, facilitating the creation of models that more faithfully recapitulate the cellular heterogeneity observed in human diseases.
By closely examining gene expression patterns at the single-cell level, researchers can identify and refine the most suitable models for testing novel drug candidates. Additionally, scRNA-seq provides insights into the mechanisms of action of drugs, shedding light on their interactions with specific cell types and uncovering potential off-target effects.
Traditional scRNA-seq techniques capture gene expression data from individual cells but lack spatial information about their location within the tissue. However, recent advancements have allowed the integration of scRNA-seq with spatial information, enabling the use of scRNA-seq in spatial transcriptomics. This process provides crucial spatial context to molecular data, enhancing our understanding of diseases and informing clinical decision-making.
Precision medicine
The biopharma industry is increasingly moving towards a precision medicine approach that enables patients to be treated at an earlier stage of disease for better outcomes. Key to precision medicine is patient stratification, and scRNA-seq can aid in the discovery of novel biomarkers and therapeutic targets for various diseases, including cancer, neurological disorders, and autoimmune conditions.
By improving biomarker identification, scRNA-seq enables more accurate patient stratification, matching individuals with targeted therapies that are most likely to benefit them. Furthermore, this technology allows for the precise monitoring of drug response and disease progression at the cellular level.
ScRNA-seq also enables the identification of cell subtypes and states within diseased tissues. By characterizing the transcriptomic profiles of individual cells, researchers can classify disease subtypes with higher resolution, potentially leading to improved diagnostic and prognostic markers.
By closely tracking changes in gene expression profiles, clinicians can make informed decisions regarding treatment adjustments, leading to more personalized and effective therapeutic interventions.
This knowledge allows new opportunities for identifying potential targets and developing targeted therapies. Furthermore, the integration of highly multiplexed functional genomics screens with scRNA-seq allows for enhanced target credentialing and prioritization, streamlining the selection of promising candidates for further investigation.
Some of these topics were discussed during a recent Clarivate webinar on unleashing the power of scRNA-seq, featuring insights from the bioinformatics team on novel techniques emerging in the field of scRNA-seq analysis, understanding cell-cell interactions, and assessing cell heterogeneity in preclinical and clinical studies.
One of the crucial roles scRNA-seq can play is in the repurposing of existing therapies by uncovering their effects on different cell types. By comparing the gene expression profiles of cells treated with different drugs, researchers can identify similarities in transcriptional responses, suggesting potential therapeutic overlaps or repurposing opportunities. This approach can expedite the drug discovery process by leveraging existing drugs with known safety profiles and facilitating the development of new treatment options for various diseases.
Improved processes
While the technology and analysis are well established, there are, of course, processes in sequencing single cells that could be improved. “Some of the methods that the industry uses are not as statistically sound as they could be,” said Gil. “With the onset of machine learning, some of the less robust methods may be replaced by new cutting-edge tools,” she explained. Gil is part of Clarivate’s bioinformatics consulting team that enable discovery, pre-clinical and translational activities through a combination of trusted data, powerful tools, and high quality analysis.
Standardization of experimental protocols and data analysis methods is crucial to ensure reproducibility and comparability across different studies. Additionally, the complexity of scRNA-seq data and the need for sophisticated computational tools require continuous advancements in bioinformatics and data management. Collaborative efforts between academia, industry, and regulatory bodies will be key in overcoming these challenges and unlocking the full potential of scRNA-seq for the benefit of patients worldwide.
To learn more about the applications of single-cell RNA sequencing in drug discovery, please view the webinar, Unleashing the power of single-cell RNA sequencing.
