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Related Experiment Video

Updated: Jun 27, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

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

Juanru Guo1,2, Simona Sarafinovska1,3, Ryan A Hagenson2,4

  • 1Department of Genetics, Washington University in St. Louis School of Medicine, Saint Louis, MO, USA.

Nature Communications
|June 25, 2026
PubMed

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Summary

We developed SMURF (Segmentation and Manifold UnRolling Framework), a deep learning tool for accurately assigning mRNA to cells in spatial transcriptomics. SMURF reveals tissue architecture and gene expression patterns, offering new biological insights.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate transcript assignment to single cells is crucial for high-resolution spatial transcriptomics.
  • Existing computational methods face challenges in complex tissue architectures.

Purpose of the Study:

  • To introduce SMURF (Segmentation and Manifold UnRolling Framework), a novel deep learning algorithm for soft-segmentation in spatial transcriptomics.
  • To enhance the accuracy of mapping mRNAs to individual cells and analyzing tissue organization.

Main Methods:

  • SMURF employs deep learning to map mRNAs from capture spots to nearby nuclei.
  • The algorithm unrolls complex tissue architectures by projecting cells onto Cartesian coordinates.
  • Applied to Visium HD data for segmenting over 400,000 cells in the mouse ileum.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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Last Updated: Jun 27, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Main Results:

  • SMURF demonstrates superior accuracy in assigning mRNAs to single cells compared to existing methods.
  • Successfully unrolled complex tissues, revealing zonated transcriptional programs and cell-type organization.
  • Identified zonated gene expression programs and regulatory transcription factors in the maturing intestinal villus.
  • Revealed gene expression gradients along the proximal-distal axis accumulating in the upper villus.
  • Showcased reprogramming of upper villus gene expression by environmental lumenal signals.

Conclusions:

  • SMURF is a powerful, cross-platform framework for analyzing gene expression within native tissue contexts.
  • The study establishes environmental inputs as key determinants of regional transcriptional identity in the intestine.
  • SMURF enables detailed analysis of cell-type organization and gene expression gradients in complex tissues.