Single Cell Biology

Spatial Transcriptomics in Single Cells: An Overview

Hui Lang^* Department of Infectious Diseases, Xiangya Hospital, Central South University, Hunan, China
^*Correspondence: Hui Lang, Department of Infectious Diseases, Xiangya Hospital, Central South University, Hunan, China, Email:

Received: 01-Dec-2023, Manuscript No. SCPM-23-23434; Editor assigned: 04-Dec-2023, Pre QC No. SCPM-23-23434 (PQ); Reviewed: 18-Dec-2023, QC No. SCPM-23-23434; Revised: 25-Dec-2023, Manuscript No. SCPM-23-23434 (R); Published: 01-Jan-2024, DOI: 10.35248/2168-9431.24.12.077

Description

The spatial organization of cells within tissues is a fundamental aspect of biology. From the arrangement of cells in organs to the intricate networks formed in neural circuits, spatial context plays a important role in determining how cells function and communicate with one another. Traditional transcriptomics techniques, such as RNA sequencing, have greatly advanced our understanding of gene expression. However, they often provide a global view of gene expression without accounting for the spatial distribution of transcripts within tissues.

Spatial transcriptomics is an innovative approach that seeks to close this discrepancy by preserving the spatial information of individual individual cells' gene expression patterns within tissues. This emerging field offers the potential of a holistic view of cellular biology by allowing us to explore gene expression in the context of where it occurs. In this article, we explore the field of spatial transcriptomics, highlighting its importance, technologies, applications, and potential to revolutionize our understanding of biology.

Spatial transcriptomics technologies

Spatially barcoded RNA sequencing: Spatially barcoded RNA sequencing methods use specialized capture or imaging techniques to link the spatial location of individual cells within a tissue to their transcriptomic profiles. Examples include Slide- seq, 10 X Genomics Visium, and GeoMx DSP (Digital Spatial Profiling). These technologies rely on barcoded molecules that capture RNA from spatially defined regions, enabling the reconstruction of gene expression maps.

In situ hybridization techniques: In situ hybridization methods, such as RNA Fluorescence In Situ Hybridization (FISH) and single-molecule RNA FISH, allow the visualization of RNA molecules directly within tissues. While they do not provide genome-wide transcriptomic data, they offer high spatial resolution and are useful for studying specific genes or cell types within complex tissues.

Imaging mass cytometry: Imaging mass cytometry combines cytometry and imaging to simultaneously analyze multiple proteins and transcripts within individual cells in a spatial context. It enables the mapping of cellular phenotypes and their associated gene expression patterns within tissues.

Applications of spatial transcriptomics

Understanding developmental processes: Spatial transcriptomics has provided critical insights into embryonic development and organogenesis by revealing the spatially regulated gene expression patterns that guide tissue formation and cellular differentiation.

Investigating disease pathology: Researchers have used spatial transcriptomics to study various diseases, including cancer, neurodegenerative disorders, and autoimmune diseases. By mapping gene expression changes in affected tissues, scientists gain a deeper understanding of disease mechanisms and potential therapeutic targets.

Analysing the neural circuits: In neuroscience, spatial transcriptomics is transforming our understanding of the brain. It allows researchers to map gene expression patterns in specific brain regions and cell types, foucsing on the molecular basis of neural circuitry and function.

Advancing immunology: Spatial transcriptomics is helping revealing the complexities of the immune system by focusing on the spatial distribution of immune cell subsets and their gene expression profiles within tissues. This knowledge is valuable for understanding immune responses and immunotherapies.

Spatial omics integration: Integrating spatial transcriptomics with other omics data, such as single-cell genomics, proteomics, and epigenomics, offers a more comprehensive view of cellular biology. Combining multiple layers of spatially resolved information enhances our ability to dissect the molecular mechanisms underlying complex biological processes.

Challenges and future directions

Despite its promise, spatial transcriptomics faces several challenges, including data analysis complexities, the need for improved spatial resolution, and the development of standardized protocols. Future directions in the field include the refinement of existing technologies, the development of novel computational methods for spatial data analysis, and the creation of comprehensive spatial atlases for various tissues and species.

Conclusion

Spatial transcriptomics is reshaping our understanding of biology by providing a spatial context for gene expression within tissues. It has the potential to revolutionize our knowledge of development, disease, neuroscience, immunology, and many other fields. As this technology continues to evolve and become more accessible.