Spatial Transcriptomics: Mapping Gene Expression in 3D Tissue Space
Map Gene Expression in Space—Unlock Tissue-Level Insights with Spatial Transcriptomics
About This Course
Spatial transcriptomics is transforming biological research by enabling scientists to study gene expression within its native tissue context, preserving spatial relationships between cells. Unlike traditional bulk or single-cell sequencing, spatial methods allow researchers to map where genes are expressed across tissues, providing deeper insights into tumor microenvironments, developmental biology, and disease progression.
This workshop introduces the end-to-end spatial transcriptomics workflow, including tissue handling, library preparation principles, sequencing strategies, and downstream computational analysis. Participants will focus on dry-lab components such as data preprocessing, spatial mapping, clustering, and visualization using tools like Seurat, Scanpy, and specialized spatial analysis packages. Real-world datasets will be used to interpret biological patterns and identify spatial biomarkers.
Aim
This workshop aims to provide a comprehensive understanding of spatial transcriptomics, covering the complete workflow from sample preparation to data analysis. It focuses on preserving spatial context while analyzing gene expression patterns within tissues. Participants will learn how to integrate experimental design with computational pipelines to uncover tissue architecture and cellular interactions. The program bridges wet-lab concepts with dry-lab data analytics for translational research.
Workshop Objectives
- Understand spatial transcriptomics technologies and experimental design.
- Learn preprocessing and quality control of spatial datasets.
- Apply clustering and spatial domain detection techniques.
- Visualize gene expression across tissue architecture.
- Identify spatial biomarkers and tissue-specific gene patterns.
Workshop Structure
Day 1:
- Introduction to spatial transcriptomics as the next step after single-cell sequencing
- Understanding location-based gene expression in intact tissues
- Sample collection, preservation, and tissue quality essentials
- Best practices for tissue sectioning and spatial sample preparation
- Overview of major spatial transcriptomics assays and platforms
- Choosing the right assay based on resolution, tissue type, and research goal
- Integrating single-cell and spatial data for deeper biological insight
- Applications of spatial transcriptomics in cancer research and tumor microenvironment analysis
Day 2:
- Software and pipelines for spatial transcriptomics data processing
- Handling raw data from sequencing and imaging platforms
- Quality control, preprocessing, and troubleshooting strategies
- Interpreting spatial gene expression patterns and tissue architecture
- Visualization of cellular neighborhoods, spatial domains, and biomarkers
- Collaboration with bioinformatics and core facilities for better outcomes
- Best practices for planning future spatial experiments
- Hands-on session: Practical workflow using SpatialTouchstone / MOSAIK / SAGE-FM / PersiST
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
04/05/2026
IST 7:00 PM
Workshop Dates
04/05/2026 – 04/07/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Understand spatial transcriptomics workflows from experiment to analysis.
- Analyze spatial gene expression data using modern computational tools.
- Identify spatial domains and cellular interactions within tissues.
- Interpret results for disease mechanisms and biomarker discovery.
- Build reproducible pipelines for spatial omics analysis.
Fee Structure
Student Fee
₹2799 | $60
Ph.D. Scholar / Researcher Fee
₹3799 | $70
Academician / Faculty Fee
₹4799 | $85
Industry Professional Fee
₹5799 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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