How to Process Image Data to Achieve Single cell Resolution Gene Expression on Stereo-seq
This webinar provides a comprehensive introduction to the critical role of image data in Stereo-seq spatial transcriptomics analysis, tailored for researchers and beginners entering the field. Stereo-seq technology revolutionizes our understanding of tissue architecture by preserving spatial context while capturing gene expression profiles. However, unlocking its full potential also relies heavily on effective image data processing.
Key Topics Covered:
1.From Microscope to Cell Bin:
-Microscopy image acquisition.
Image processing essentials: image registration, segmentation, and cell bin spatial matrices generation.
2.Integrating Image and Spatial Transcriptomic Data:
Linking Cell Bin coordinates with gene expression matrices to map RNA signals to specific cells, enabling precise cell-level analysis.
Understand quality control strategies to ensure data accuracy and reliability.
3.Practical Tools & Tips:
Characteristics of official pipelines and third-party tools (e.g., Cellpose, QuPath, etc). Pros, cons, and use-case scenarios.
Common image anomalies and parameter-tuning guidance.
4.Limitations & Future Directions:
Current challenges and emerging solutions.