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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K

You might also read

Related Articles

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

Sort by
Same author

Myofibroblast- specific autophagy drives cyst growth in autosomal dominant polycystic kidney disease.

bioRxiv : the preprint server for biology·2026
Same author

A comprehensive survey of computer vision methods for spatial transcriptomics.

Briefings in bioinformatics·2026
Same author

Multi-Organ Physiologic Deficits During Exercise Identify Clinical and Molecular Predisposition to Heart Failure with Preserved Ejection Fraction.

Circulation·2026
Same author

Proteogenomic Analysis of Coronary Artery Calcification in Human Populations.

Arteriosclerosis, thrombosis, and vascular biology·2026
Same author

Signal Strength Aware Latent Spaces Reveal Molecularly Distinct Substructures within Human Kidney Tissue.

bioRxiv : the preprint server for biology·2026
Same author

Unbiased Characterization of Atrial Fibrillation Phenotypic Architecture Provides Insight Into Genetic Liability and Clinically Relevant Outcomes.

Circulation. Genomic and precision medicine·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
Same journal

CLABP: a contrastive learning framework integrating protein language models and structural information for antibacterial peptide prediction.

Briefings in bioinformatics·2026
Same journal

Toward the regularization of E value from BLAST similarity search into a dissimilarity measure as distance function, and the metrication of protein sequence space.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 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

5.0K

stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and

Yu Wang1,2, Haichun Yang3, Ruining Deng4

  • 1Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN 37232, United States.

Briefings in Bioinformatics
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics (ST) analysis is enhanced by stImage, an R package integrating gene expression, histology, and spatial data. It offers 54 strategies to improve biological insights into tissue architecture.

Keywords:
deep learninghistology imagesintegrationoptimizingspatial transcriptomics

More Related Videos

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

448
En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis
08:57

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis

Published on: July 8, 2025

522

Related Experiment Videos

Last Updated: Sep 9, 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

5.0K
Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

448
En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis
08:57

En face Cryosectioning of Mouse Retina for High-dimensional Spatial Molecular Analysis

Published on: July 8, 2025

522

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) combines gene expression with tissue spatial organization.
  • Current ST methods often analyze spatial coordinates or histology separately.
  • Existing approaches show inconsistent performance across different datasets.

Purpose of the Study:

  • To introduce stImage, an open-source R package for comprehensive ST analysis.
  • To develop a unified framework integrating gene expression, histology, and spatial coordinates.
  • To provide flexible strategies for optimizing ST data integration.

Main Methods:

  • Generating deep learning-derived histology features.
  • Implementing 54 integrative strategies within the stImage package.
  • Utilizing a diagnostic graph to guide strategy selection.

Main Results:

  • stImage effectively integrates transcriptional profiles, histology images, and spatial information.
  • The package demonstrates consistent performance across multiple datasets.
  • The diagnostic graph aids users in selecting optimal integration strategies.

Conclusions:

  • stImage offers a comprehensive and flexible solution for spatial transcriptomics analysis.
  • The package optimizes ST by synergizing diverse data types for enhanced biological insights.
  • stImage advances the understanding of tissue architecture through integrated analysis.