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

Research progress in precision medicine for type 2 diabetes based on the GLP-1.

Frontiers in endocrinology·2026
Same author

Case report: Minimally invasive management of synchronous early-stage ascending colon adenocarcinoma and type 1 papillary renal cell carcinoma presenting with severe anemia: a rare Chinese case.

Frontiers in oncology·2026
Same author

High-Performance HfS<sub>2</sub>-HfO<sub>X</sub>-WSe<sub>2</sub> P-i-N Photodetector Based on Self-Oxidized HfS<sub>2</sub>.

Small methods·2026
Same author

Robust 2D/0D/2D MXene@TiO<sub>2</sub>/ZnIn<sub>2</sub>S<sub>4</sub> ternary integrated heterojunction with efficient multi-interface charge transport and enhanced surface adsorption for gas sensing.

Journal of hazardous materials·2026
Same author

Natural product databases for drug discovery: Features and applications.

Pharmaceutical science advances·2026
Same author

Corrigendum to "Natural product databases for drug discovery: Features and applications" [Pharmaceut. Sci. Adv. 2 (2024) 100050].

Pharmaceutical science advances·2026

Related Experiment Video

Updated: Jun 28, 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

stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Kongming Li1,2, Jiahao Li1,2, Yuhao Tao1,2

  • 1Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China.

Briefings in Bioinformatics
|April 17, 2024
PubMed
Summary
This summary is machine-generated.

stDiff enhances spatial transcriptomics (ST) by using gene expression relationships from single-cell RNA sequencing (scRNA-seq) data. This novel method accurately reconstructs spatial patterns and improves cell population identification.

Keywords:
diffusion modelimputationscRNA-seq dataspatial transcriptomics data

More Related Videos

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

18.5K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.9K

Related Experiment Videos

Last Updated: Jun 28, 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
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

18.5K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.9K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) reveals gene expression in tissues but faces limitations in gene detection and imaging.
  • Current ST enhancement methods often rely on scRNA-seq cell similarity.
  • Imaging-based methods offer high resolution but are restricted in gene number or detection sensitivity.

Purpose of the Study:

  • To introduce stDiff, a novel method for enhancing spatial transcriptomics data.
  • To leverage gene expression abundance relationships in scRNA-seq data for ST enhancement.
  • To improve cell population identification and spatial pattern reconstruction in ST data.

Main Methods:

  • stDiff utilizes a conditional diffusion model based on scRNA-seq data.
  • The model employs two Markov processes: one for noise introduction and one for denoising.
  • Original ST data is integrated into the denoising process to predict missing information.

Main Results:

  • stDiff demonstrated exceptional preservation of cell topological structures across 16 datasets.
  • The model accurately reconstructed diverse spatial expression patterns and delineated spatial boundaries.
  • Enhancement outcomes closely mirrored actual ST data, unifying observed and predicted segments.

Conclusions:

  • stDiff offers a robust solution for cell population identification in spatial transcriptomics.
  • The method effectively enhances ST data by leveraging scRNA-seq gene expression relationships.
  • stDiff is poised to advance spatial transcriptomics imputation methodologies.