Spatial transcriptomics [Neurosurgery Wiki]

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====== Spatial transcriptomics ======


Spatial [[transcriptomics]] is an emerging field in [[genomics]] that combines traditional [[RNA sequencing]] ([[RNA-Seq]]) with spatial information. It allows [[researcher]]s to analyze [[gene expression]] within the context of the spatial organization of cells within a [[tissue]] or sample. This [[technology]] provides a more comprehensive understanding of how [[gene]]s are expressed in specific regions of tissues, which is crucial for understanding complex biological processes, such as [[development]], [[disease]], and tissue organization. 

===== Key points =====


Principle: Spatial transcriptomics aims to capture the spatial distribution of RNA molecules within tissue samples. It involves the simultaneous measurement of gene expression and the physical location of each RNA molecule, preserving the spatial context of the tissue.

Methods:

In Situ Hybridization: Traditional methods like in situ hybridization have been used for decades to visualize gene expression at the cellular level, but they are limited in throughput and the number of genes that can be studied.

Spatially Resolved Transcriptomics Technologies: Recent advancements have led to the development of spatial transcriptomics technologies that enable high-throughput, genome-wide gene expression profiling at a spatial resolution. Some popular methods include Slide-seq, Visium by 10x Genomics, and spatial transcriptomics by 10x Genomics.

Workflow:

Tissue Sectioning: The process begins with the preparation of thin tissue sections.

Capture and Sequencing: The tissue sections are then placed on a specialized slide or platform, and each spot or region on the slide corresponds to a specific location in the tissue. These regions are equipped with barcoded beads, and mRNA molecules from the tissue are captured on these beads.

Library Preparation and Sequencing: The captured mRNA molecules are used to generate sequencing libraries, followed by high-throughput RNA sequencing.

Data Analysis: The resulting data is analyzed with specialized software and bioinformatics tools to determine the spatial distribution of gene expression across the tissue. This can involve clustering cells by their gene expression profiles, visualizing gene expression patterns on tissue images, and identifying different cell types or regions based on gene expression.

Applications:

Developmental Biology: Spatial transcriptomics helps elucidate the molecular events that underlie tissue development and differentiation.

Cancer Research: It can reveal the molecular heterogeneity within tumors and the tumor microenvironment, aiding in the development of targeted therapies.

Neuroscience: Researchers can study the distribution of specific genes or neuronal markers in different brain regions.

Pathology: It can assist in identifying disease-associated gene expression patterns and markers.

Drug Discovery: Understanding spatial gene expression can be valuable for drug target identification and validation.

Challenges: Spatial transcriptomics is a complex and rapidly evolving field, and there are challenges in data analysis, imaging, and the need for specialized equipment and expertise.

Spatial transcriptomics is a powerful tool for bridging the gap between molecular biology and histology, providing a holistic view of gene expression in the context of tissue organization. It is advancing our understanding of various biological processes and diseases.