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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: May 7, 2025

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

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications.

Qianwen Wang¹, Hongyuan Zhu¹, Lin Deng¹

¹Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.

|January 6, 2025

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) offers new ways to study gene expression in tissues. This review covers ST technologies, computational tools, and their use in understanding brain complexity and diseases.

Keywords:

bioinformatics computational tools neuroscience spatial transcriptomics

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Last Updated: May 7, 2025

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Area of Science:

Molecular Biology
Neuroscience
Genomics

Background:

Spatial transcriptomics (ST) revolutionizes gene expression analysis within tissue architecture.
Understanding spatial gene expression is crucial for complex biological systems like the brain.

Purpose of the Study:

To review advancements in ST technologies and computational tools.
To highlight the applications of ST in neuroscience research.
To discuss the impact of ST on brain atlas projects, development, and diseases.

Main Methods:

Historical overview of ST techniques from image-based to sequence-based methods.
Discussion of computational approaches for ST data analysis (preprocessing, annotation, clustering, etc.).
Integration of 3D multi-slices data.

Main Results:

ST provides insights into brain complexity, development, and neuroimmune disorders.
Applications include Alzheimer's, schizophrenia, and brain tumor research.
ST enhances understanding of neuronal vulnerability.

Conclusions:

ST has profoundly impacted neuroscience, offering transformative potential for brain research.
Challenges include improving sequencing technologies and computational tools.
ST paves the way for new therapeutic insights.