Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.

Noa Konforti^1,2,3, Tal Goldberg^1,2,3, Michal Danino-Levi^1,2,3

¹The Alexander Kofkin Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel.

Briefings in Bioinformatics

|June 22, 2026

Summary

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

InSituPREP enables 3D single-cell mapping of interaction-associated gene programs in the breast cancer tumor microenvironment.

Nucleic acids research·2026

Same author

Super-Resolved Spatial Transcriptomics Reveals Early Changes in RNA Localization in the 5xFAD Hippocampus.

bioRxiv : the preprint server for biology·2025

Same author

Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events.

RNA (New York, N.Y.)·2024

Same author

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets.

International journal of molecular sciences·2022

Same author

Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems.

Science (New York, N.Y.)·2021

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

This summary is machine-generated.

ModelistsGCN enhances spatial single-cell cell typing by integrating gene expression, spatial data, and cell morphology. This novel framework improves cell-type identification accuracy, even with limited gene data, advancing spatial transcriptomics research.

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics offers cellular resolution but faces a trade-off between spatial and molecular detail.
High-resolution methods like MERFISH and Expansion Sequencing (ExSeq) profile limited gene panels, complicating cell-type identification.
Sparse transcriptomic coverage per cell hinders accurate cell typing in spatial single-cell data.

Purpose of the Study:

To introduce ModelistsGCN, a semi-supervised multimodal graph convolutional framework for spatial single-cell cell typing.
To integrate gene expression, spatial proximity, and cellular morphology for improved cell-type inference.
To address the challenge of accurate cell identification in spatial transcriptomic datasets with limited gene coverage.

Main Methods:

Keywords:

cell type identification graph convolutional network multimodal data integration semi-supervised learning single-cell spatial transcriptomics

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Jun 23, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Developed ModelistsGCN, a graph convolutional framework utilizing gene expression, spatial location, and cell morphology.
Employed a semi-supervised approach using high-confidence cells to guide clustering.
Integrated spatial neighborhood information and morphological features to compensate for sparse gene expression data.

Main Results:

ModelistsGCN demonstrated higher agreement with reference annotations on mouse visual cortex and breast cancer datasets.
The method showed improved cluster separation and stronger marker-gene coherence compared to existing approaches.
Successfully enhanced cell-type inference in spatial single-cell transcriptomic data with limited gene coverage.

Conclusions:

ModelistsGCN effectively integrates multimodal data for robust spatial single-cell cell typing.
The framework overcomes limitations of sparse gene expression data in high-resolution spatial transcriptomics.
Offers a significant advancement for cell-type identification in complex biological tissues.