Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Abstract

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Figure 1

Application of spatial transcriptomics in drug research and development.

Figure 2

Current mainstream spatial transcriptomics methods. ISC involves the specific binding of mRNA from corresponding cellular locations with spots or beads on slides carrying capture probes, followed by further NGS of the captured RNA molecules. ISC offers high sensitivity and specificity, enabling the detection of rare transcripts and precise spatial localization, while achieving single-cell resolution with this technology is currently challenging. Image-based methods, including ISH and ISS. ISH involves the specific hybridization of RNA in tissues with multiple fluorescently labelled short oligonucleotide probes, reflecting the abundance and spatial localization of specific gene transcripts. ISH detects low-abundance transcripts with high sensitivity and relatively high resolution, while the limited signal-to-noise ratio during imaging necessitates high-magnification imaging systems, limiting the area of detection. ISS involves the hybridization of padlock probes with cDNA, followed by RCA to form RCP. ISS allows for the sequencing of RNA molecules directly on tissue sections, providing higher-resolution data, while it has lower sensitivity, and padlock probes may introduce significant probe-specific biases, increasing the complexity of image processing.

Figure 3

Application of spatial transcriptomics in target identification. By identifying cell types through spatial ST, it is possible to reveal the distribution of specific cell types within tissues and understand their function and state. The spatial expression profile depicts the expression of mRNAs in spatial dimensions, aiding in the identification of characteristic DEGs involved in disease processes. Clustering analysis helps in discovering specific cell populations associated with diseases that may be closely linked to the occurrence, progression, or prognosis of the disease. By analysing interactions between cells, key interactions involved in cell communication and signal transduction can be identified. Integrating this information can provide insights into the process of disease occurrence and reveal potential therapeutic targets.

Figure 4

Application of spatial transcriptomics in drug discovery and drug development. ST aids in identifying new targets, facilitating drug screening. ST holds immense potential in drug repurposing and natural product drug discovery. ST utilizes spatial expression maps after drug treatment to identify key genes and predict treatment pathways. ST constructs a drug action atlas, enabling the localization of drug effects and the study of drug resistance and interactions. By analysing the spatial cell composition of pharmacological models, assessing model similarity to cases, and studying intercellular interactions, ST assists in building pharmacological research models. ST is being applied across various stages of drug discovery and development, and these applications are enhancing the probability of drug discovery success and improving therapeutic outcomes.

Figure 5

Spatial transcriptomics in personalized medicine. ST identifies biomarkers of drug response by analysing the spatial transcriptome information of different drug-responsive populations. The spatial transcriptome was used to analyse the spatial expression information of patient biomarkers to achieve personalized medicine.

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Rapidly advancing spatial transcriptomics technology and its application prospects. The integration of spatial transcriptome technology with other emerging technologies promotes understanding of disease and the construction of human spatial maps.