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Related Experiment Video

Updated: May 8, 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

A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and

Visanu Wanchai1,2, Nancy C Bustamante-Gomez1, Alongkorn Kurilung1,2

  • 1Center for Musculoskeletal Disease Research, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

Biorxiv : the Preprint Server for Biology
|May 7, 2026
PubMed
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This summary is machine-generated.

TENGU, a new bioinformatic software, improves spatial transcriptomics by prioritizing transcript data for cell segmentation, overcoming limitations in dense or complex tissues. This transcript-first approach enhances accuracy in analyzing cellular microenvironments.

Area of Science:

  • Spatial transcriptomics
  • Bioinformatics
  • Computational biology

Background:

  • Accurate cell segmentation is crucial for spatial transcriptomics, but traditional image-based methods fail in dense, inflamed, or mineralized tissues.
  • Existing segmentation approaches lead to transcriptomic bleed-through and inaccurate clustering in challenging microenvironments.
  • Visium HD platform offers near-single-cell resolution, necessitating improved computational tools for precise cell boundary definition.

Purpose of the Study:

  • To develop a novel bioinformatic software package, TENGU (Transcript-signal Enrichment and Grouping Unit), for robust cell segmentation in spatial transcriptomics.
  • To overcome the limitations of image-dependent segmentation in complex tissue architectures.
  • To enable accurate cell-type annotation and spatially aware cell-cell communication analysis.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Main Methods:

  • TENGU utilizes a transcript-first segmentation strategy, prioritizing transcript-signal density over histological images.
  • A transcriptomic-driven cell simulation algorithm refines initial boundaries through iterative optimization.
  • The pipeline integrates tissue segmentation, cell-type annotation, and basic cell-cell communication analysis.

Main Results:

  • TENGU demonstrated superior transcriptomic distinctness in murine brain tissue compared to existing pipelines.
  • The software successfully segmented matrix-embedded osteocytes in challenging bone microenvironments.
  • TENGU identified critical osteoimmune signaling networks in osteomyelitis and pro-tumorigenic signaling in colorectal cancer xenografts.

Conclusions:

  • TENGU provides a robust and adaptable computational framework for accurate cell segmentation in spatial transcriptomics.
  • By mitigating image-dependency, TENGU enables precise decoding of functional micro-anatomy in diverse healthy and pathological tissues.
  • The transcript-first approach enhances the analysis of complex cellular interactions and molecular signatures.