Programme Parameters
Foundation
2 Days (3 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Choosing a Spatial Technology
• Sequencing-based Visium and imaging-based Xenium, MERFISH and CosMx
• The resolution against gene-panel-breadth trade-off that defines the choice
• Spot-level capture is not single cell — the point most analyses get wrong
Sample Preparation and Quality
• FFPE and fresh-frozen workflows and the RNA integrity each demands
• Sectioning, orientation and permeabilisation optimisation
• Quality metrics that determine whether a section is worth sequencing at all
From Raw Data to a Spatial Object
• Space Ranger output and building objects in Seurat, Squidpy or Scanpy
• Normalisation for spatial data and the confound of tissue density
• Registering expression to histology and reading the two together
Spatial Statistics
• Deconvolution with cell2location or RCTD against a single-cell reference
• Spatially variable gene detection and spatial domain identification
• Neighbourhood and ligand-receptor analysis, and the co-location fallacy
Interpretation and 3D Reconstruction
• Tumour microenvironment structure and immune exclusion patterns
• Serial section alignment for pseudo-3D reconstruction and its distortions
• Validation with immunofluorescence or in situ hybridisation before claiming a finding
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | BWA |
| Covered Tool / Platform | SAMtools |
| Covered Tool / Platform | GATK |
| Covered Tool / Platform | FastQC |
| Covered Tool / Platform | Trimmomatic |
| Covered Tool / Platform | R/Bioconductor |
| Covered Tool / Platform | IGV |
| Covered Tool / Platform | PLINK |
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