Bento

Bento facilitates subcellular analysis of spatial transcriptomics data by leveraging single-molecule coordinates and segmentation boundaries to define subcellular domains, annotate localization patterns, and quantify gene–gene colocalization within the Scverse ecosystem.

Key Features:

• Subcellular Scale Analysis: Leverages single-molecule information to perform spatial analyses at resolutions that capture subcellular domains.
• Data Ingestion and Integration: Ingests molecular coordinates and segmentation boundaries and integrates with single-cell analysis tools in the Scverse ecosystem.
• Defining Subcellular Domains: Identifies distinct regions within cells based on molecular localization patterns.
• Annotating Localization Patterns: Annotates patterns of molecular distribution within identified subcellular domains.
• Quantifying Gene–Gene Colocalization: Assesses proximity between different genes or proteins to quantify colocalization events at the subcellular level.

Scientific Applications:

• Subcellular molecular organization studies: Enables analysis of molecular organization and functional compartmentalization within individual cells.
• MERFISH datasets: Supports analysis of MERFISH (Multiplexed Error-Robust FISH) single-molecule spatial transcriptomics data.
• seqFISH+ datasets: Supports analysis of seqFISH+ (sequential FISH) single-molecule spatial transcriptomics data.
• Molecular Cartography datasets: Supports analysis of Molecular Cartography single-molecule spatial transcriptomics data.
• Xenium datasets: Supports analysis of Xenium single-molecule spatial transcriptomics data.

Methodology:

Ingests molecular coordinates and segmentation boundaries; leverages single-molecule localization to perform subcellular spatial analyses; identifies subcellular domains from molecular localization, annotates localization patterns, and computes gene–gene colocalization metrics; integrates results with Scverse-compatible single-cell analysis tools.