SMURF: soft-segmentation for single-cell reconstruction and topological analysis of spatial transcriptomic data.

Area of Science:

• Computational biology
• Genomics
• Tissue engineering

Background:

• High-resolution spatial transcriptomics (ST) methods like Visium HD require advanced computational tools.
• Accurate assignment of transcripts to individual cells within complex tissue substructures is crucial for ST data analysis.

Purpose of the Study:

• Introduce the Segmentation and Manifold UnRolling Framework (SMURF), a novel computational tool for analyzing high-resolution ST data.
• Enable accurate transcript assignment to cells and analysis of cells within complex tissue architectures.
• Investigate gene expression gradients and their regulation in the mouse small intestine.

Main Methods:

• Developed SMURF, featuring a "soft-segmentation" approach for aggregating and assigning mRNAs from capture spots to nearby cells.
• Implemented a manifold unrolling technique to standardize cell coordinates within complex tissue structures.
• Applied SMURF to high-resolution ST data from the mouse small intestine.

Main Results:

• SMURF accurately assigns transcripts to cells, facilitating detailed analysis of tissue organization.
• Identified the zonation of metabolic programs along the maturing intestinal villus and associated regulatory transcription factors.
• Discovered that proximal-to-distal gene expression gradients in the intestine accumulate at the villus tip, influenced by luminal environmental signals.

Conclusions:

• SMURF enhances the analysis of high-resolution ST data by improving mRNA-to-cell assignment and enabling manifold analysis.
• Environmental signals in the lumen are significant determinants of regional transcriptional identity in the intestine.
• SMURF provides a powerful toolbox for studying regional transcriptional programs along tissue manifolds.