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SIRV: spatial inference of RNA velocity at the single-cell resolution
Tamim Abdelaal1,2,3, Laurens M Grossouw4, R Jeroen Pasterkamp4
1Department of Radiology, Leiden University Medical Center, 2333ZC Leiden, The Netherlands.
NAR Genomics and Bioinformatics
|August 7, 2024
Summary
Spatial RNA velocity analysis reveals cellular differentiation trajectories within tissues. The new SIRV method integrates spatial transcriptomics with single-cell RNA sequencing data to map these dynamic processes at single-cell resolution.
Area of Science:
- Single-cell genomics
- Developmental biology
- Spatial transcriptomics
Background:
- RNA velocity infers cellular differentiation from scRNA-seq data.
- Studying tissue development requires spatial context.
- Current spatial transcriptomics methods struggle to capture mRNA dynamics.
Purpose of the Study:
- To develop a method for inferring spatial RNA velocities at single-cell resolution.
- To enable the study of cellular differentiation dynamics within the spatial context of tissues.
Main Methods:
- Introducing SIRV (Spatially Inferring RNA Velocity).
- Enriching spatial transcriptomics data with spliced and unspliced mRNA from reference scRNA-seq data.
- Applying SIRV to mouse brain development, organogenesis, chicken heart, and human osteosarcoma data.
Main Results:
- SIRV successfully inferred spatial differentiation trajectories in developing mouse brain, including midbrain-hindbrain boundary cells and forebrain origins.
- The method revealed spatial differentiation patterns not detectable by scRNA-seq alone.
- Robust spatial differentiation trajectories were obtained for mouse organogenesis and validated on chicken heart and human osteosarcoma datasets.
Conclusions:
- SIRV enables single-cell resolution spatial RNA velocity inference.
- This facilitates the study of tissue development and cellular differentiation dynamics in spatial contexts.
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