Spatial transcriptomics is rapidly advancing in the wake of scRNA-seq. Recognized as Method of the Year by Nature in 2020, spatially resolved transcriptomics has since experienced a CAGR of 23.20% from 2020 to 2025. 

This workshop is designed to provide an in-depth look at the latest techniques and tools for analyzing spatial biology data. Participants will learn about innovative methods for data analysis, modeling, and visualization, and how these techniques are applied to real-world biological research. The session will cover practical approaches to spatial data interpretation and highlight their relevance in fields such as drug discovery, clinical diagnostics, and cancer research. 

Target Audience: 

This workshop is aimed at pathologists, clinical scientists, researchers in biology and related fields and pharma/industry professionals.  

Key Learnings:  

• Gain insights into the novel techniques for spatial data analysis and visualization 

• Develop a deeper understanding of the challenges and solutions in spatial data interpretation and integration  

• Understand how spatial biology can be applied in drug discovery and personalized medicine 

Pre-Requisites: 

• Basic understanding of fundamental biological concepts, including cellular and molecular biology 

• Familiarity with data analysis concepts and software (R preferably) 

• Understanding of spatial biology fundamentals  

• Download R Studio in advance and load the datasets provided on personal laptops 

Format: 

11:40 Pitfalls and opportunities: a practical tutorial for spatial transcriptomics analysis of human tissue data  

• Spatial vs single-cell: biases and sources of noise  

• Cell typing in spatial data: coarse and fine types  

• Integration with single cell  

• Tissue domain detection  

• Multi-sample and case-control analysis  

• Cell-cell communication networks 

Ilya Korsunsky, Principal Investigator, Brigham and Women’s Hospital   

12:10 Applications in Translational Research  

Title: Multi-omic spatial analysis to understand the mechanisms of tumor progression and immune interactions in renal cell carcinoma 

• Quick summary on impact of spatial biology in translational research and importance of multi-omic approach to understand tumor dynamics 

• Brief background of renal cell carcinoma and topic of this work 

• Transformation of clear cell renal cell carcinoma to sarcomatoid – highly aggressive tumor that occurs through epithelial to mesenchymal transition; highly immune infiltrated and increased response to immunotherapy 

• Better understanding of tumor cell mechanisms and immune crosstalk will lead to improved biomarkers and treatments 

• Summary of workflow 

• Patient cohort and specimen collection  

• Nanostring CosMx (single cell spatial transcriptomics) 

• Nanostring GeoMx (Segemented bulk spatial transcriptomics, full transcriptome) 

• Vectra Polaris & CellScape (multiplex immunoflourescence) 

• In vitro work to explore mechanisms suggested by spatial work 

• Findings 

• Novel transition state found along continuum of clear cell to sarcomatoid transformation 

• Demonstrate impact of spatial resolution as well as single cell resolution in finding this 

• How we’ve applied this to other datasets for validation 

• Ongoing work to develop this into biomarker to inform adjuvant immunotherapy in renal cell carcinoma 

• Tumor cell/macrophage crosstalk identified 

• Demonstrate how to use spatial single cell to study cell to cell interactions 

• Potential therapeutic targets and ongoing work 

• Summary/re-cap 

Allison May, Assistant Professor of Urology, University of Virginia 

12:40 Spatial Data Analysis 

Comparative Modeling and Analysis of Spatio-Temporal Transcriptomics Data 

• comparative modeling of spatial data between two or more conditions and across multiple time points 

• application of generative AI and optimal transport algorithms 

• garnering insights into cellular ecosystems (microenvironments) 

Abul Hassan Samee, Assistant Professor, Integrative Physiology, Baylor College of Medicine