bin2cell

bin2cell reconstructs cells from Visium HD (10X Genomics) 2 μm bins by combining morphology image segmentation and gene expression to enhance spatial transcriptomic mapping of FFPE samples.

Key Features:

• High-Resolution Data Reconstruction: Reconstructs cells from 2 μm bins using morphology image segmentation and gene expression, providing higher granularity than default 8 μm bins.
• Visium HD (10X Genomics) support: Operates on Visium HD data derived from archived FFPE blocks and aligns transcriptomic data with reference morphology images.
• Compatibility with Python-based tools: Integrates with established Python-based single-cell and spatial transcriptomics software for downstream analysis.
• No GPU requirement: Functions without specialized hardware such as GPUs.
• Efficiency: Processes data in minutes to enable rapid reconstruction of cells from high-resolution bins.
• Improved downstream analysis: Produces reconstructed cells that enhance downstream analyses relative to 8 μm bin approaches, as demonstrated on benchmark datasets.

Scientific Applications:

• Neuroscience Research: Enables finer spatial transcriptomic mapping in complex tissues such as mouse brain to study cellular heterogeneity and organization.
• Cancer Research: Facilitates identification of sub-cellular transcriptional differences in human colorectal cancer to interrogate tumor microenvironments.

Methodology:

Leverages morphology image segmentation alongside gene expression information to reconstruct cells from 2 μm Visium HD bins.