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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data.

Axel Andersson¹, Andrea Behanova¹, Christophe Avenel¹

¹Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|July 3, 2024

Summary

This summary is machine-generated.

Points2Regions is a new computational tool that rapidly identifies biologically relevant regions in spatial transcriptomics data. It efficiently discovers tissue structures and cell types across multiple scales without needing extra data or lengthy optimization.

Keywords:

clustering in situ sequencing interactive spatial omics spatial transcriptomics visualization

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Area of Science:

Computational Biology
Bioinformatics
Spatial Transcriptomics

Background:

Spatial transcriptomics generates point-based mRNA data, requiring identification of biologically significant regions.
Current methods for region identification are often scale-specific, require complementary data, or involve lengthy optimization.
This limits their utility in exploratory analysis across different biological scales.

Purpose of the Study:

To introduce Points2Regions, a novel computational tool for identifying regions with similar mRNA compositions in spatial transcriptomics data.
To enable rapid, multi-scale region discovery without reliance on pre-segmented cells or extensive optimization.
To provide a user-friendly tool for exploratory analysis of spatial transcriptomics data.

Main Methods:

Feature extraction via rasterizing mRNA points onto a pyramidal grid.
Efficient clustering using a hybrid approach of hierarchical and k-means clustering.
Integration into TissUUmaps and as a Napari plugin for interactive visualization.

Main Results:

Points2Regions achieves performance comparable to state-of-the-art methods on simulated datasets.
The tool is significantly faster than existing methods and does not require segmented cells.
Analysis of real-world datasets confirms the identification of biologically relevant regions consistent with prior studies.

Conclusions:

Points2Regions offers a fast, efficient, and versatile solution for multi-scale region discovery in spatial transcriptomics.
The tool enhances exploratory data analysis by enabling rapid identification of tissue structures and cell-type compositions.
Its integration into existing platforms (TissUUmaps, Napari) improves user experience and accessibility.