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

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

You might also read

Related Articles

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

Sort by

Same author

Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.

Nature communications·2026

Same author

PHD-MS: Multiscale Domain Identification for Spatial Transcriptomics via Persistent Homology.

ArXiv·2025

Same author

OTMol: Robust Molecular Structure Comparison via Optimal Transport.

Journal of chemical information and modeling·2025

Same author

OTMol: Robust Molecular Structure Comparison via Optimal Transport.

ArXiv·2025

Same author

Supervised Gromov-Wasserstein Optimal Transport with Metric-Preserving Constraints.

SIAM journal on mathematics of data science·2025

Same author

SYNCHRONIZED OPTIMAL TRANSPORT FOR JOINT MODELING OF DYNAMICS ACROSS MULTIPLE SPACES.

SIAM journal on applied mathematics·2025

Same journal

A computational method to design broad-spectrum T cell-inducing vaccines applied to Betacoronaviruses.

Cell reports methods·2026

Same journal

MalDeepSeq panel: A targeted ultra-deep sequencing approach to trace drug resistance markers in Plasmodium falciparum.

Cell reports methods·2026

Same journal

Induced pluripotent stem cell-derived macrophages enable broad modeling of human inflammasome signaling.

Cell reports methods·2026

Same journal

Rapid discovery of cell-surface glycosylation regulators using a lectin-based magnetic CRISPR screen.

Cell reports methods·2026

Same journal

A real-time FRET ubiquitin transfer assay for quantitative characterization of ternary complexes in targeted protein degradation.

Cell reports methods·2026

Same journal

A high-throughput, end-to-end pipeline for extracellular miRNA biomarker discovery from human biofluids.

Cell reports methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 2, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Multiscale domain identification for spatial transcriptomics via persistent homology.

Perry Beamer¹, Zixuan Cang²

¹Department of Mathematics, North Carolina State University, Raleigh, NC, USA.

Cell Reports Methods

|March 31, 2026

Summary

This summary is machine-generated.

This study introduces Persistent Homology for Domains at Multiple Scales (PHD-MS), a novel method using topological data analysis to identify spatial domains across various scales in tissues. PHD-MS accurately captures multiscale tissue structures, outperforming traditional clustering techniques.

Keywords:

CP: computational biology CP: systems biology multiscale domains spatial transcriptomics topological data analysis

More Related Videos

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

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

Related Experiment Videos

Last Updated: Apr 2, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

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

Area of Science:

Computational Biology
Genomics
Data Science

Background:

Spatial transcriptomics (ST) reveals gene expression within tissue microenvironments.
Identifying spatial domains is crucial for understanding tissue organization.
Current methods often fail to capture multiscale spatial domain characteristics.

Purpose of the Study:

To develop a novel method for identifying spatial domains at multiple morphological scales.
To address limitations of single-scale clustering in spatial transcriptomics.
To leverage topological data analysis for enhanced biological data interpretation.

Main Methods:

Development of Persistent Homology for Domains at Multiple Scales (PHD-MS).
Application of topological data analysis (TDA) to spatial transcriptomics data.
Comparison of PHD-MS identified domains with expert-annotated ground truth.

Main Results:

PHD-MS effectively identifies spatial domains that persist across multiple morphological scales.
The method highlights multiscale spatial domains across diverse tissue types and ST technologies.
PHD-MS demonstrates superior performance compared to traditional clustering approaches.

Conclusions:

PHD-MS offers a robust framework for analyzing multiscale spatial organization in tissues.
This approach enhances the interpretation of spatial transcriptomics data by considering hierarchical structures.
PHD-MS represents a significant advancement in computational methods for tissue analysis.