Click here to download this whitepaper as a PDF
Introduction
In a single human tissue sample, billions of cells interact in a complex, dynamic dance – each with its own unique genetic script. Until recently, scientists could only glimpse fragments of this intricate performance. Spatial transcriptomics is changing that, offering an unprecedented window into the molecular choreography of life itself.
Spatially resolved gene expression is rapidly transforming the field of bioinformatics.
This groundbreaking technology provides a quantitative readout of either the whole transcriptome or targeted gene expression, precisely mapped to specific locations within a tissue section. By capturing this spatial context, researchers gain a powerful new lens for understanding cellular composition and activity in their native environment.
Two primary approaches drive spatial transcriptomics. Next-generation sequencing-based platforms map mRNA within tissue sections and then sequence these transcripts outside the original tissue (ex vivo). By contrast, imaging-based approaches – often called microscopy-based spatial methods – directly image hundreds to thousands of mRNAs within the tissue itself (in situ).
These complementary techniques can be further enhanced by integrating histological staining or immunofluorescence protein detection, providing researchers with a multi-layered view of tissue complexity. Key metrics for evaluating spatial transcriptomics technologies include the resolution of spatial biology, gene plexity (the number of genes detected), and gene sensitivity (the linear dynamic range of accurate transcript detection).
For pharmaceutical and biotech companies, spatial transcriptomics is opening new avenues for drug discovery, disease research, and therapeutic development. By providing a detailed view of cellular interactions and functions within their spatial context, this technology is poised to revolutionize our understanding of biology and accelerate the development of novel therapeutics.
The Power of Spatial Context
Traditional transcriptomics, like bulk RNA seq data, has been instrumental in understanding gene expression; however, it lacks the crucial element of spatial context. This is why spatial transcriptomics is proving so powerful: researchers can capture much more information to help make bigger and better discoveries. By preserving spatial information, spatial transcriptomics allows researchers to:
Identify cellular interactions: Understand how cells communicate and influence each other within their microenvironment.
Delineate functional tissue regions: Define areas with distinct gene expression patterns, revealing the organization of complex tissues.
Investigate disease mechanisms: Analyze how gene expression changes in diseased tissues, pinpointing the origin and progression of diseases.
Discover potential drug targets: Identify spatially localized genes and pathways crucial for disease development, enabling targeted therapies.
Applications in Drug Discovery and Development
Spatial transcriptomics is poised to transform drug discovery and development in several ways:
Target identification and validation: Pinpoint specific cell types or tissue regions affected by disease, leading to more precise drug targets.
Drug efficacy assessment: Evaluate the impact of drugs on gene expression within specific tissue regions, providing insights into drug activity and potential side effects.
Biomarker discovery: Identify spatially localized biomarkers that can be used for early disease detection, treatment response prediction, and patient stratification.
Personalized medicine: Develop tailored therapies based on the unique spatial gene expression profiles of individual patients.
Overcoming the Data Integration Challenge
In our experience working with spatial transcriptions, we’ve seen firsthand that integrating datasets generated from different experimental conditions or technologies can be difficult. Batch effects, or non-biological variations between datasets, can obscure meaningful biological patterns. Advanced computational methods are crucial for addressing this challenge. While traditional methods like Seurat and Harmony have limitations in considering spatial organization, newer algorithms are being developed to effectively integrate spatial transcriptomics data. These algorithms leverage spatial information to identify and correct for batch effects, ensuring accurate and reliable analysis.
Conclusion
Spatial transcriptomics is empowering life science companies with a deeper understanding of tissue biology and disease complexity. By providing spatially resolved gene expression data, this technology is accelerating drug discovery, improving disease research, and paving the way for more effective and personalized therapies. As spatial transcriptomics continues to advance and mature, its impact on bioinformatics and the development of novel therapeutics is expected to grow exponentially.
Outsourcing spatial transcriptomics to a specialized bioinformatics service provider (BSP) offers numerous advantages for life science companies. BSPs bring extensive expertise and experience in spatial transcriptomics technologies, data analysis, and result interpretation. They provide access to cutting-edge platforms and software, ensuring companies leverage the most advanced tools without significant upfront investment.
Outsourcing also offers cost-effectiveness, scalability, and faster turnaround times, enabling companies to accelerate their research and development efforts. By delegating specialized analysis to experts like Bridge Informatics, companies can focus on their core competencies.
Contact us to learn how to leverage the power of spatial transcriptomics for your drug discovery and development pipeline.
