Related Experiment Video
Updated: Jan 7, 2026
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Spatial transcriptomics: integrating platforms and computational approaches for clinical insights
Wei Song1, Duo Wang2, Jinming Li1
1National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/ National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
Spatial transcriptomics (ST) advances life science by mapping gene expression to locations. This review compares ST methods, applications, and challenges, aiming to guide future research and clinical use.
Area of Science:
- Life Science Research
- Molecular Biology
- Pathological Diagnosis
Background:
- Spatial transcriptomics (ST) enables transcriptome analysis at spatial location levels, moving beyond bulk and single-cell methods.
- This technology offers unprecedented insights into biological processes, disease mechanisms, and clinical diagnosis.
- Despite advances, ST faces challenges in technical complexity, data analysis, and standardization.
Purpose of the Study:
- To provide a comprehensive comparison of ST technical principles and data analysis.
- To summarize the latest applications and current standardization status of ST.
- To offer researchers a framework for understanding ST progress, challenges, and future directions.
Main Methods:
- Review and comparison of existing spatial transcriptomics technologies.
- Analysis of data processing pipelines for spatial transcriptomics data.
- Summary of current applications and standardization efforts in the field.
Main Results:
- Detailed comparison of technical principles and data analysis workflows for ST.
- Overview of recent advancements and diverse applications of ST in biological research.
- Identification of key challenges including technical hurdles, analytical difficulties, and standardization gaps.
Conclusions:
- ST is a transformative technology with vast potential in biology and medicine.
- Addressing current challenges is crucial for the further development and clinical translation of ST.
- This review aims to facilitate understanding and promote the advancement of ST technologies.
