Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unsupervised transfer learning enables multi-animal tracking without training annotation.

Nature methods·2026
Same author

Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces.

Nature communications·2026
Same author

High-fidelity intravital imaging of biological dynamics with latent-space-enhanced digital adaptive optics.

Nature biotechnology·2026
Same author

Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising.

Science (New York, N.Y.)·2026
Same author

Sub-second volumetric 3D printing by synthesis of holographic light fields.

Nature·2026
Same author

A lung CT vision foundation model facilitating disease diagnosis and medical imaging.

Nature communications·2025

Related Experiment Video

Updated: Jun 18, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K

Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.

Bohan Li1, Feng Bao2,3,4, Yimin Hou1

  • 1School of Artificial Intelligence, Beihang University, Beijing, China.

Nature Communications
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

soScope enhances spatial omics data quality and resolution for various molecular profiles. This unified framework improves tissue architecture identification and corrects biases across diverse spatial technologies.

More Related Videos

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

695
Tissue Characterization after a New Disaggregation Method for Skin Micro-Grafts Generation
09:30

Tissue Characterization after a New Disaggregation Method for Skin Micro-Grafts Generation

Published on: March 4, 2016

21.5K

Related Experiment Videos

Last Updated: Jun 18, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K
Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

695
Tissue Characterization after a New Disaggregation Method for Skin Micro-Grafts Generation
09:30

Tissue Characterization after a New Disaggregation Method for Skin Micro-Grafts Generation

Published on: March 4, 2016

21.5K

Area of Science:

  • Spatial omics
  • Computational biology
  • Bioinformatics

Background:

  • Spatial omics technologies profile molecular categories beyond transcriptomics.
  • Limited spatial resolution hinders detailed tissue architecture characterization.
  • Existing computational methods lack adaptability for diverse spatial omics data.

Purpose of the Study:

  • Introduce soScope, a unified generative framework.
  • Enhance data quality and spatial resolution for diverse spatial omics data.
  • Address limitations of current computational methods.

Main Methods:

  • soScope aggregates multimodal tissue information (omics, spatial relations, images).
  • Jointly infers omics profiles at enhanced resolutions using omics-specific modeling and distribution priors.
  • Evaluated on Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq platforms.

Main Results:

  • Improved identification of biologically meaningful intestine and kidney architectures.
  • Revealed fine embryonic heart structures not resolvable at original resolution.
  • Corrected sample and technical biases from sequencing and processing.
  • Extended to spatial multiomics (e.g., CITE-seq, ATAC-seq) for simultaneous enhancement.

Conclusions:

  • soScope is a versatile tool for improving spatial omics data utilization.
  • Enhances resolution and quality across diverse spatial omics technologies.
  • Facilitates deeper understanding of tissue architecture and biological processes.