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

Related Concept Videos

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

Comparison of Fludarabine Versus Bendamustine as a Lymphodepleting Chemotherapy Prior to CAR-T for Large Cell Lymphoma.

Transplantation and cellular therapy·2026

Same author

Prolonged Cytopenia After Idecabtagene Vicleucel for Multiple Myeloma is Associated with Poor Overall Survival.

Transplantation and cellular therapy·2026

Same author

Outcomes of Hematopoietic Cell Transplantation in Patients with Plasmablastic Lymphoma.

Transplantation and cellular therapy·2026

Same author

Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.

Briefings in bioinformatics·2026

Same author

Retinoic acid drives cell fate specification, maturation and retinal regionality in human retinal organoids.

Nature communications·2026

Same author

Erratum: Novel R Shiny Tool for Survival Analysis With Time-Varying Covariate in Oncology Studies: Overcoming Biases and Enhancing Collaboration.

JCO clinical cancer informatics·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

Same journal

scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Briefings in bioinformatics·2026

Same journal

scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

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

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

Published on: July 6, 2022

Multi-view gene panel characterization for spatially resolved omics.

Daniel Kim^1,2,3,4, Wenze Ding^1,5, Akira Nguyen Shaw^1,4

¹Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia.

Briefings in Bioinformatics

|October 4, 2025

Summary

This summary is machine-generated.

We developed panelScope and panelScope-OA to improve gene panel design for spatial transcriptomics. These tools offer quantitative insights for creating tailored panels, balancing cell type capture with transcriptional variation.

Keywords:

gene panel large language models multi-objective optimization spatial transcriptomics

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Related Experiment Videos

Last Updated: Jan 16, 2026

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

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

Published on: July 6, 2022

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Spatially resolved transcriptomics offers cellular resolution but relies on pre-selected gene panels.
Current gene panel design often prioritizes cell type identification over other crucial factors.
Effective panel design requires consideration of transcriptional variation, pathway coverage, and gene redundancy.

Purpose of the Study:

To develop a framework for comprehensive gene panel characterization and optimization for spatial transcriptomics.
To introduce panelScope for holistic panel comparison and panelScope-OA for automated panel optimization.
To provide quantitative, multi-dimensional insights for designing tailored gene panels.

Main Methods:

Developed panelScope, a platform for characterizing gene panels from multiple perspectives.
Created panelScope-OA, a genetic algorithm integrating characterization metrics for automated panel optimization.
Applied panelScope and panelScope-OA to analyze nine gene panels across four datasets.

Main Results:

Computationally designed gene panels demonstrated competitive performance in capturing major cell types.
Manual curation showed advantages in identifying and capturing minor cell types.
panelScope and panelScope-OA provided quantitative insights for panel design.

Conclusions:

The developed framework offers a multi-dimensional approach to gene panel design for spatial transcriptomics.
Automated and characterization tools can support the creation of customized gene panels.
Balancing computational design with expert curation may yield optimal gene panels for diverse research needs.