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Related Experiment Video

Updated: Jul 1, 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

Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport.

John P Bryan1, Samouil L Farhi2, Brian Cleary3,4,5,6,7

  • 1Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.

Nature Communications
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

New computational methods track cell development over time using spatial transcriptomics (ST) data. Our new method, SOCS, preserves tissue structure for more accurate developmental trajectory inference.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • Single-cell resolution methods analyze molecular profiles and developmental trajectories.
  • Tissues contain spatially contiguous units (e.g., ovarian follicles, kidney tubules) that develop as a whole.
  • Existing time-series spatial transcriptomics (ST) methods yield biologically incoherent trajectories that disrupt structural units.

Purpose of the Study:

  • To develop a novel trajectory inference method for time-series ST data.
  • To ensure trajectory inferences preserve the structural integrity of contiguous biological units.
  • To improve the accuracy and biological plausibility of developmental trajectory analysis.

Main Methods:

  • We present Spatiotemporal Optimal transport with Contiguous Structures (SOCS), an Optimal Transport-based method.
  • SOCS is designed for time-series ST data.
  • The method integrates gene expression similarity and global geometric structure while preserving contiguous structures.

Main Results:

  • SOCS produces trajectory inferences that maintain the spatial coherence of biological structures over time.
  • It overcomes the limitations of existing methods that generate biologically incoherent trajectories.
  • SOCS enables more accurate trajectory inference and biological insight compared to other approaches.

Conclusions:

  • SOCS offers a significant advancement in analyzing developmental trajectories from time-series ST data.
  • The method successfully preserves the structural integrity of biological units during trajectory inference.
  • This approach leads to more biologically plausible and accurate insights into developmental processes.