High-resolution spatial transcriptomics and imaging-based technologies are now generating large-scale, multi-dimensional datasets that are beginning to reshape how we understand tissue organisation, disease mechanisms and cellular interactions.
At the same time, the field is facing an array of challenges. Scaling spatial data across tissues, integrating multi-omic layers, and translating complex spatial outputs into biologically meaningful insights remain key barriers to adoption.
This three-part webinar series brings together leading researchers to explore how spatial biology is evolving – from data generation to integrated, multi-omic and AI-enabled tissue insights.
What You Will Gain
By attending this webinar series, you will be able to:
- Benchmark the market: Compare high-resolution vs. high-plex systems to determine the true “cost-per-cell” for your research and experimental design decisions.
- Master multi-omics integration: Learn approaches to integrate DNA, RNA and protein layers without sacrificing spatial context or signal-to-noise performance.
- Overcome the data bottleneck: Discover how researchers are leveraging foundation models and AI-driven segmentation to transform spatial datasets into actionable biomarkers.
Please note, by registering for one webinar in the series, you will automatically gain access to the subsequent webinars.
Webinar 1: Spatial Technologies – Benchmarking in the Era of High Throughput
Wednesday 15th July 2026 | 3pm BST | 4pm CEST | 10am EST
As spatial platforms evolve to generate increasingly high-throughput maps of gene expression, the field is facing a critical benchmarking challenge.
This session brings together experts to provide a reality check on the current technology landscape. We move beyond surface-level comparisons to examine resolution vs. scale trade-offs, whole-transcriptome scaling constraints, and the real cost-per-cell across tissue types.
Talk 1: Choosing the Right Spatial Biology Platform: Trade-offs, Limitations and Multi-Omic Integration
Shani Perera, Technical Specialist Cambridge Stem Cell Institute, University of Cambridge
- How spatial biology platforms differ in resolution, sensitivity and tissue compatibility
- The importance of selecting technologies based on biological questions
- Complementary strengths of spatial transcriptomics and proteomics approaches
- Key bottlenecks including panel size limits, sample preservation, single-cell resolution and clinical translation barriers
Talk 2: Applying Spatial Biology to Real-World Clinical Samples: Challenges and Opportunities in Metastatic Cancer Research
Kerstin Haase, Senior Research Fellow, University College London
- Application of spatial transcriptomics and proteomics in metastatic cancer research
- Practical limitations in clinical samples, including preservation, segmentation and postmortem tissue challenges
- Why spatial profiling reveals tumour–microenvironment interactions missed by bulk approaches
Webinar 2: Beyond Transcriptomics – Multi-Omics in Tissue Context
Wednesday 22nd July 2026 | 3pm BST | 4pm CEST | 10am EST
Spatial biology is moving beyond the transcriptome. RNA, protein and DNA are increasingly being integrated within intact tissue contexts, enabling a more complete understanding of cellular state, lineage and function in situ.
However, integrating multiple molecular layers introduces both experimental and analytical challenges.
Talk 1: Bridging Targeted and Exploratory Spatial Proteomics: Toward Integrated Tissue Biology
Fabian Coscia, Group Leader, Max Delbrück Center
- Targeted vs exploratory spatial proteomics approaches and their complementary roles
- How multi-scale spatial proteomics and computational tools are expanding tissue analysis in cancer and neuroscience
- Efforts to improve accessibility, standardisation and data processing in spatial proteomics
Talk 2: Spatial Organisation and Tumour Plasticity: Understanding the Architecture of Cancer Progression
Victoria Sanz-Moreno, Professor and Group Leader: Cytoskeleton and Cancer Metastasis, The Institute of Cancer Research
- How spatial transcriptomics and metabolomics reveal tumour regions, signalling environments and cellular states
- Why tumour borders may contain early metastatic signatures missed by bulk approaches
- The importance of spatial and temporal context in understanding tumour plasticity and microenvironment interactions
Webinar 3: Spatial to Systems Biology – Computational Approaches and Data Analysis
Wednesday 29th July 2026 | 3pm BST | 4pm CEST | 10am EST
Artificial intelligence and machine learning are increasingly being applied to spatial and histological data to identify patterns, infer cellular interactions and build predictive models of tissue behaviour.
This webinar explores how spatial biology is evolving from descriptive mapping towards systems-level, model-driven biology — including early concepts of digital tissues and predictive modelling.
Talk 1: Analysing Spatial Biology Data: Statistical Challenges, Cellular Organisation and Computational Methods
Mark Robinson, Professor of Statistical Genomics, University of Zurich
- Statistical methods for identifying spatial relationships between cell types
- How to uncover multicellular structures within tissues
- Key computational challenges including segmentation, spatial statistics and data interpretation
Talk 2: Computational Workflows for High-Resolution Spatial Transcriptomics in Brain Research
Maria Richter, Bioinformatics Researcher, Helmholtz Munich
- Computational workflows for interpreting high-resolution spatial transcriptomics
- Challenges in assigning transcripts to specific cell types and tissue regions
- Applications in brain research, including neuronal integration in disease models
Talk 3: AI and Foundation Models for Spatial Proteomics: Advancing Patient Stratification in Cancer
Shimrit Mayer, Senior Postdoctoral Researcher (Raza Ali Lab), Cancer Research UK Cambridge Institute
- Machine learning and deep learning approaches for spatial proteomic analysis
- Use of foundation models and pathology imaging for predictive risk stratification
- Integrating spatial, imaging and clinical datasets to support precision oncology
