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Spatial Transcriptomics: Bridging Gaps in Cell Analysis and Revolutionizing Our Understanding of Tissue Dynamics

NextEdge Admin

09 Feb 2024

2 min 55 sec

Spatial transcriptomics,neuroscience,disease-driving

Over the past decade, single-cell transcriptomics (scRNA-seq) has evolved into a pivotal tool in medical research, particularly in developmental biology, cancer, immunology, and neuroscience. However, several commercially available scRNA-seq methods necessitate intact and viable cells, limiting the study of specific cell types and sacrificing spatial context.

Spatial transcriptomics, a cutting-edge field within genomics, has emerged as a revolutionary approach to analyse gene expression within intact tissues while preserving spatial organization. This becomes particularly crucial when studying deeply embedded cells, such as neurons in the brain. It overcomes the limitations of traditional scRNA-seq methods that often require specialized dissociation protocols, making it challenging to study certain cell types.

The significance of spatial transcriptomics lies in its ability to provide insights into cell biology, including cell-cell interactions, signals from neighbouring cells, and subcellular mRNA molecule localization.

Notably recognized as the ‘Method of the Year 2020’ by Nature Methods, its rapid growth is fuelled by advancements in next-generation sequencing initiatives like the Human Cell Atlas and the BRAIN Initiative Cell Census Consortium, increased computing capacity, and improvements in microscopy and imaging technologies.

Spatial transcriptomics finds diverse applications in medical research, with commercial platforms like Spatial Transcriptomics, 10X Genomics’ Visium and Nanostring’s GeoMx & CosMx making the technology more accessible.

In cancer research, it has unveiled gene expression patterns in tumour tissues, aiding in identifying specific cell types associated with poor survival. In reproductive biology, it has elucidated mechanisms regulating cellular differentiation in the endometrium, while in neuroscience, it has provided insights into gene expression programs near amyloid plaques in Alzheimer’s disease models.

With varied technologies and applications, spatial transcriptomics holds immense potential for personalized medicine. Understanding the spatial distribution of genes in tissues can identify disease-driving cell types and niches, offering new insights into tissue architecture.