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

Proteomics01:33

Proteomics

7.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.2K
Genomics02:02

Genomics

35.8K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
35.8K

You might also read

Related Articles

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

Sort by
Same author

AI-Assisted Digital Single-Molecule Activity Tracker for Decoupling Intrinsic Heterogeneity from Photo-Oxidative Damage in High-Photon-Flux Enzymology.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A Hybrid Deep Learning Framework with Supervised Contrastive Learning for Robust Seizure Detection in Long-Term EEG.

Journal of medical systems·2026
Same author

Electrostatic-Driven Nucleobase Discrimination by Covalent Organic Framework Nanosheets for Deoxyribonucleic Acid Methylation Profiling.

Journal of the American Chemical Society·2026
Same author

Brain-Derived Extracellular Vesicle Subpopulations: from Bulk Measurements to Single-Entity Assays.

JACS Au·2026
Same author

CNN-Autoformer: Automated EEG-Based Seizure Detection and Localization Using Hybrid Deep Learning.

Biomedical signal processing and control·2025
Same author

Deep Learning-Enabled Real-Time Single-Shot Refocusing of Microwell Array for Digital Melting Curve Analysis.

Analytical chemistry·2025

Related Experiment Video

Updated: Jun 2, 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

4.8K

Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.

Changxiang Huan1,2, Jinze Li1, Yingxue Li1

  • 1CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.

BME Frontiers
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

Spatial multiomics integrates tissue structure with biomolecule data for biological insights. Advancements in spatial omics techniques are highlighted, alongside challenges in data analysis and proposed integration strategies.

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

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

Published on: March 12, 2021

3.5K

Related Experiment Videos

Last Updated: Jun 2, 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

4.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.1K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

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

Published on: March 12, 2021

3.5K

Area of Science:

  • Life Sciences
  • Biotechnology
  • Genomics

Background:

  • Spatial monoomics and multiomics are crucial for understanding biological functions and cellular identities.
  • Recent advancements have improved spatial resolution, throughput, efficiency, and sample compatibility in omics technologies.
  • Existing data analysis frameworks for spatial omics lag behind technological progress, posing significant challenges.

Purpose of the Study:

  • To systematically review recent developments in spatial monoomics and multiomics techniques.
  • To identify and address current challenges in spatial omics data analysis pipelines.
  • To propose a novel data integration strategy for spatial multiomics data.

Main Methods:

  • Systematic literature review of spatial monoomics and multiomics techniques.
  • Analysis of current data analysis pipelines and their limitations.
  • Development of a proposed data integration strategy: cross-platform, cross-slice, and cross-modality.

Main Results:

  • Significant technological advancements in spatial omics, including epigenomics, genomics, transcriptomics, proteomics, and metabolomics.
  • Identification of critical challenges in spatial omics data analysis, including incomplete pipelines and lack of established strategies.
  • Proposal of a comprehensive data integration strategy to address current analytical limitations.

Conclusions:

  • Spatial multiomics technology offers powerful capabilities for biological discovery and understanding cellular heterogeneity.
  • Addressing data analysis challenges is crucial for fully realizing the potential of spatial multiomics.
  • Spatial multiomics is poised to significantly impact biology and precision medicine by integrating structural and molecular data.