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

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

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

Sort by

Same author

Tumor microbial burden drives immune responses through regulation of Interferon signaling.

Cancer discovery·2026

Same author

Multiscale EEG feature fusion for recognizing 3D object shapes through active touch.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Host metabolism can produce many indoles and phenols independently of the microbiome.

Nature metabolism·2026

Same author

Prevalence and psychosocial interventions of mental disorders following traumatic spinal cord injury: a systematic review and meta-analysis based on knowledge graphs.

BMC psychiatry·2026

Same author

The WHO Classification of Genetic Tumour Syndromes: Considerations for Genetics.

Clinical genetics·2026

Same author

LPAR4 mediates resistance to interferon-induced stress in soft tissue sarcoma.

Science advances·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

Same journal

3D pathology-guided microdissection.

Nature methods·2026

Same journal

Challenges and recommendations in establishing national human diversity genomic projects.

Nature methods·2026

Same journal

Reclone: a global research community building equitable access to reagents.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Resolving tissue complexity by multimodal spatial omics modeling with MISO.

Kyle Coleman^1,2, Amelia Schroeder³, Melanie Loth³

¹Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. kyle.coleman@cshs.org.

|January 15, 2025

Summary

This summary is machine-generated.

MISO is a new algorithm that integrates multiple types of spatial omics data, enabling better understanding of tissue complexity and function. It efficiently analyzes large datasets with high spatial resolution.

More Related Videos

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

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

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

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jun 8, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

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

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

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

Published on: September 20, 2024

Area of Science:

Biomedical research
Computational biology
Genomics

Background:

Spatial molecular profiling reveals links between cell location and tissue function.
Modeling multimodal spatial omics data is key to understanding tissue complexity.
High-resolution spatial technologies generate large datasets requiring efficient computational methods.

Purpose of the Study:

To introduce MISO (MultI-modal Spatial Omics), a versatile algorithm for feature extraction and clustering.
To demonstrate MISO's capability in integrating diverse spatial omics modalities.
To address the need for computationally efficient methods for large-scale, high-resolution spatial omics data.

Main Methods:

Developed MISO, an algorithm for feature extraction and clustering.
Applied MISO to integrate multiple modalities including gene expression, protein expression, epigenetics, metabolomics, and histology.
Evaluated MISO's performance on various spatial omics datasets.

Main Results:

MISO effectively integrates multimodal spatial omics data with high spatial resolution.
The algorithm demonstrates superior performance in identifying biologically relevant spatial domains compared to existing methods.
MISO shows computational efficiency and scalability for large-scale datasets.

Conclusions:

MISO represents a significant advancement in multimodal spatial omics analysis.
The algorithm enhances the understanding of tissue complexity and function through integrated data analysis.
MISO's efficiency makes it suitable for current and future high-resolution spatial omics technologies.