Spatial Transcriptome

Spatial transcriptome analysis is an advanced molecular technique that enables the measurement of gene expression while preserving the spatial context of tissue architecture. Unlike conventional RNA sequencing methods—such as bulk RNA-seq or single-cell RNA-seq—that require dissociation of tissue and, consequently, result in the loss of positional information, spatial transcriptomics captures where specific transcripts are located within the intact tissue section. This spatially resolved information is critical for understanding the organization and function of complex tissues.By combining histological imaging with high-throughput gene expression profiling, spatial transcriptomics allows researchers to visualize and quantify RNA molecules in situ. This approach provides a multidimensional view of the tissue, revealing not only which genes are expressed, but also where they are expressed in relation to cell types, tissue structures, and neighboring microenvironments. For example, it can distinguish gene expression differences across tumor margins, identify localized immune responses in inflamed tissues, or map developmental gradients in embryonic tissues.

Several cutting-edge platforms have been developed to perform spatial transcriptomics, including 10x Genomics Visium, MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), Slide-seq, seqFISH, and Nanostring GeoMx. These methods vary in their resolution, throughput, and transcriptome coverage—some providing whole-transcriptome profiling at lower spatial resolution, and others enabling targeted high-resolution single-molecule analysis.

IMG
By the end of the spatial transcriptomics course, participants will understand the core principles and technologies used to study gene expression in tissue context, including platforms like 10x Visium and MERFISH. They will learn to process raw spatial data, perform quality control, analyze spatial gene expression patterns, identify tissue regions through clustering, and integrate single-cell RNA-seq for cell type mapping. Students will also gain experience using tools such as Seurat to perform spatial analysis, visualize results, and interpret biological significance. Ultimately, learners will be equipped to independently analyze spatial transcriptomics datasets and apply these skills to research projects.

👉 Enroll now and take the first step toward a future in data-driven biology and beyond.

## 10% Discount if you register before 15th October, 2025. Hurry up!!

  • RNASeq [R]
  • RNASeq [D]
  • scRNA-Seq
  • lncRNA-Seq
  • miRNA-Seq
  • PSI-Seq
  • ChipSeq
  • ATAC-Seq
  • scATA-Seq
  • Spatial Transcriptome
  • DNASeq
  • MethylSeq
  • Whole Genome de novo Assembly
  • Targeted metagenomics
  • Whole Genome Metagenomics
  • Metatranscriptomics
  • RAD-Seq
  • SNAP-ChIP

Course Information
Course Spatial Transcriptomics
Duration online 7 Days Training [ 2 Hours Daily [ Monday To Friday ] ]

Slots

Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM
For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.

Mode

👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive)
👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference.
👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning.
👉 For doubt clear there will be extra support will be provided based on the requirement.
👉 Certificate will be provided
Sequencing Platform Illumina /Ion Torrent/PacBio/Nanopore
Raw data Candidate can include maximum 4 datasets of their own during training. Publication standards figures and tables will be generated.
Training Fees
Module-NGS Spatial Transcriptomics
    📘 Introduction to Spatial Transcriptomics
    - What is spatial transcriptomics?
    - Comparison with bulk and single-cell RNA-seq
    - Key applications (cancer, neuroscience, immunology, etc.)
    - Overview of current platforms
    - Tissue sectioning and imaging
    - RNA capture and library preparation
    - Understanding barcoded arrays or in situ hybridization
    - Best practices to avoid batch effects and RNA degradation
    - Understanding NGS and different file formats

    📘 Linux Basics & Environment Setup
    - Linux Command Line Basics
    - Installing Spatial Transcriptome Analysis Tools
    - Using Conda and Shell Scripting

    📘 Introduction to R/Bioconductor
    - Installing packages with CRAN and Bioconductor
    - Data types and standardized data container
    - Data manipulation

    📘 Data Preprocessing & Quality Control
    - Image alignment and registration
    - Running pipeline
    - Filtering low-quality spots
    - Visualization of QC metrics (UMIs, gene counts, mitochondrial genes)

    📘 Downstream Analysis – Spatial Expression Mapping
    - Loading data into Seurat or Scanpy
    - Normalization and scaling
    - PCA, UMAP/t-SNE for dimensionality reduction
    - Identifying spatial patterns of gene expression

    📘 Clustering & Spatial Domain Detection
    - Unsupervised clustering of spots
    - Spatially informed clustering
    - Identifying marker genes per region or domain
    - Integrating histological images with expression data

    📘 Spatial Differential Expression
    - Detecting spatially varying genes
    - Statistical models (SpatialDE, SPARK, etc.)
    - Visualizing expression gradients

    📘 Cell–Cell Interaction & Ligand-Receptor Analysis
    - Inferring spatial interactions between cells
    - Interpreting interaction networks

    📘 Additional Post Analysis
    - Different plots (Heatmap/volcano plot)
    - Functional annotation and pathway enrichment (clusterProfiler, Enrichr, KEGG)
    - Network analysis using STRING-db and Cytoscape for spatial DEGs

Preparation

Spatial_transcriptomics : https://en.wikipedia.org/wiki/Spatial_transcriptomics
- NCBI : https://pmc.ncbi.nlm.nih.gov/articles/PMC10433685/

Instructor

Industry Experienced

Target Audiance

This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.

Contact

Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.

Course Information
Course Spatial Transcriptomics
Duration online 15 Days Training [ 2 Hours Daily [ Monday To Friday ] ]

Slots

Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM
For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.

Mode

👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive)
👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference.
👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning.
👉 For doubt clear there will be extra support will be provided based on the requirement.
👉 Certificate will be provided
Sequencing Platform Illumina /Ion Torrent/PacBio/Nanopore
Raw data Candidate can include maximum 4 datasets of their own during training. Publication standards figures and tables will be generated.
Training Fees
Module-I Advanced Bioinformatics & basic programming
Topics
    📘 Introduction to Bioinformatics
    - Overview of bioinformatics and its applications
    - Key concepts in computational biology
    - Role of bioinformatics in genomics, transcriptomics, and proteomics

    📘 Understanding NGS and Genomics Bioinformatics
    - Basics of Next-Generation Sequencing (NGS)
    - Types of NGS data (RNA-seq, WGS, WES)
    - Overview of NGS data formats: FASTQ, BAM, VCF
    - Introduction to pipelines and tools for NGS data analysis

    📘 Databases & Data Retrieval (NCBI and UCSC)
    - Learning how to retrieve biologically correct data
    - Performing complete batch retrieval (e.g., whole exome)
    - NCBI: understanding gene-level data retrieval
    - UCSC: handling large-scale data retrieval
    - UCSC Genome Browser and Table Browser usage
    - Batch Coordinate Retrieval and Genomic Data downloads
    - GFF/GTF gene annotation formats and how to retrieve them
    - Using BLAT for sequence-based search and alignment

    📘 Gene Prediction and Functional Annotation
    - Gene prediction approaches and tools
    - Functional annotation using Gene Ontology (GO)
    - Pathway analysis using KEGG, Reactome
    - Interpreting gene sets and biological relevance

    📘 Standalone/Offline BLAST for Large-Scale Genomic Data
    - Installing and setting up standalone BLAST
    - Running local BLAST for batch sequence alignment
    - Applications in genome-wide homology searches
    - Custom BLAST databases and performance optimization

    📘 PCR Primer Designing and Specificity Check
    - Designing accurate primers for PCR amplification
    - Tools: Primer3, NCBI Primer-BLAST
    - Checking primer specificity using genome-wide BLAST
    - Avoiding non-target amplification through design best practices

    📘 Understanding Python Programming
    - Introduction to Python for bioinformatics
    - Scripting basics: variables, loops, functions
    - Libraries like Biopython, pandas for biological data handling
    - Automating genomic workflows with Python scripts
AND
Module-II Next Generation Sequencing (NGS) - Spatial Transcriptomics
Topics
    📘 Introduction to Spatial Transcriptomics
    - What is spatial transcriptomics?
    - Comparison with bulk and single-cell RNA-seq
    - Key applications (cancer, neuroscience, immunology, etc.)
    - Overview of current platforms
    - Tissue sectioning and imaging
    - RNA capture and library preparation
    - Understanding barcoded arrays or in situ hybridization
    - Best practices to avoid batch effects and RNA degradation
    - Understanding NGS and different file formats

    📘 Linux Basics & Environment Setup
    - Linux Command Line Basics
    - Installing Spatial Transcriptome Analysis Tools
    - Using Conda and Shell Scripting

    📘 Introduction to R/Bioconductor
    - Installing packages with CRAN and Bioconductor
    - Data types and standardized data container
    - Data manipulation

    📘 Data Preprocessing & Quality Control
    - Image alignment and registration
    - Running pipeline
    - Filtering low-quality spots
    - Visualization of QC metrics (UMIs, gene counts, mitochondrial genes)

    📘 Downstream Analysis – Spatial Expression Mapping
    - Loading data into Seurat or Scanpy
    - Normalization and scaling
    - PCA, UMAP/t-SNE for dimensionality reduction
    - Identifying spatial patterns of gene expression

    📘 Clustering & Spatial Domain Detection
    - Unsupervised clustering of spots
    - Spatially informed clustering
    - Identifying marker genes per region or domain
    - Integrating histological images with expression data

    📘 Spatial Differential Expression
    - Detecting spatially varying genes
    - Statistical models (SpatialDE, SPARK, etc.)
    - Visualizing expression gradients

    📘 Cell–Cell Interaction & Ligand-Receptor Analysis
    - Inferring spatial interactions between cells
    - Interpreting interaction networks

    📘 Additional Post Analysis
    - Different plots (Heatmap/volcano plot)
    - Functional annotation and pathway enrichment (clusterProfiler, Enrichr, KEGG)
    - Network analysis using STRING-db and Cytoscape for spatial DEGs

Preparation

Spatial_transcriptomics : https://en.wikipedia.org/wiki/Spatial_transcriptomics
- NCBI : https://pmc.ncbi.nlm.nih.gov/articles/PMC10433685/

Instructor

Industry Experienced

Target Audiance

This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.

Contact

Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.