09-12, 14:00–15:00 (Europe/Berlin), Workshop room II - MW 0234
This goal of this workshop is to demonstrate how spatial transcriptomics data can be used to extract subcellular insights about cell biology, primarily emphasizing analysis with Bento – a package in the Scverse ecosystem – that utilizes the SpatialData framework enabling interoperability with Scanpy and Squidpy. The workshop will briefly introduce motivations and applications of spatial transcriptomics technologies for subcellular measurements. Then we will walk through how Bento models single-molecule resolution data, computes spatial features, and finally how they are utilized for functional analysis i.e. annotating subcellular spatial patterns and domains, modeling gene colocalization, and measuring cell morphology. We will conclude with a short Q&A session for more open-ended discussion.
Learning Objectives
The participants will gain an understanding of current spatial transcriptomic technologies, including advantages and limitations of each platform. They will also gain technical insight into data modeling and feature representation in Bento and its utilization of SpatialData. Here are specific learning objectives we hope attendees will take away from this workshop:
- Comprehensive understanding of various spatial transcriptomics technologies, their capabilities, advantages, and limitations.
- Exposure to methods for subcellular measurements with spatial transcriptomics data to study RNA localization and dynamics.
- Understanding of challenges unique to single-molecule resolution analysis of spatial transcriptomic data in contrast to single-cell or tissue resolution analysis.
Why do we care about subcellular transcriptomic measurements? (10-15 minutes)
- Relevant Biological Concepts: RNA localization, polarization, compartmentalization, aggregation
- Spatial technologies: Explain the technology platforms that have the resolution necessary to make subcellular insights as well as the current limitations of such methods.
Computing Subcellular Feature Spaces with Bento (20-30 minutes)
- Case study: Explore methods and technical considerations such as data models, spatial indexing, and feature spaces
- Localization patterns: How to compute spatial features to annotate localization patterns for groups of molecules
- Subcellular spatial domains: Intro to RNA Flux (method in Bento), its principles, and its application in identifying spatial domains and measuring gene set enrichment in spatial transcriptomics data
No previous knowledge expected
I’m a bioinformatics scientist exploring the intersection of spatial genomics, ML/AI, and cell biology. I am currently a Postdoctoral Researcher in the Yeo Lab at UC San Diego, recently graduated from the UC San Diego Bioinformatics & Systems Biology PhD Program, co-advised by Dr. Gene Yeo and Dr. Hannah Carter. During my PhD, I created bento-tools, an open-source Python toolkit that unifies novel machine learning algorithms and statistics for studying RNA and cell biology. Outside of the lab, I enjoy spending time with my dog in sunny San Diego, CA at my favorite coffee shops and breweries.