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

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

13.6K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
13.6K
DNA Microarrays02:34

DNA Microarrays

17.4K
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...
17.4K
What is Gene Expression?01:42

What is Gene Expression?

167.5K
Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
167.5K
RNA-seq03:21

RNA-seq

9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Genomic analysis of oesophageal carcinoma (EC) identifies recurrent mutations in histone methyltransferases as a distinctive subset.

Oncogene·2026
Same author

Activation of the endocytosis pathway stratifies subtypes and therapeutic sensitivity in colorectal cancer.

Research square·2026
Same author

BIO26-035: Validation of an Electronic Health Record Phenotype for Advanced Solid Cancer Across Five University of California Health Systems.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

BIO26-035: MTAP Deficiency in Thoracic Oncology: Bridging Genomic Loss, Survival Deficits, and Immunosuppressive Microenvironments.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Prognostic Implications of Codon-Specific <i>KRAS</i> Mutations in Localized and Advanced Stages of Pancreatic Cancer.

JCO precision oncology·2026
Same author

Tucatinib plus trastuzumab for chemotherapy-refractory, HER2 + , RAS wild-type metastatic colorectal cancer (MOUNTAINEER): final analysis.

Nature communications·2026

Related Experiment Video

Updated: Jun 29, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.7K

q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.

Myrl G Marmarelis1, Russell Littman2, Francesca Battaglin3

  • 1Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA. myrlm@isi.edu.

Communications Biology
|April 2, 2024
PubMed
Summary

q-diffusion enhances single-cell RNA sequencing analysis by capturing gene coexpression structures. This advanced method improves precision medicine for metastatic colorectal cancer and refines cell classification and clustering in human tissues.

More Related Videos

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.5K
Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
10:23

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

11.0K

Related Experiment Videos

Last Updated: Jun 29, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.7K
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.5K
Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
10:23

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

11.0K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNAseq) offers high-resolution insights into cellular heterogeneity.
  • Analyzing the complex coexpression patterns within scRNAseq data remains a significant challenge.
  • Current analytical tools often fall short in capturing the full dimensionality of gene interactions.

Purpose of the Study:

  • To introduce q-diffusion, a novel framework for analyzing gene coexpression structures in scRNAseq data.
  • To demonstrate the efficacy of q-diffusion in improving statistical significance and classification accuracy.
  • To showcase q-diffusion's utility in both bulk and spatial scRNAseq applications.

Main Methods:

  • Development of the q-diffusion framework for capturing gene coexpression.
  • Application of q-diffusion to analyze the CALGB/SWOG 80405 clinical trial data.
  • Benchmarking q-diffusion against existing scRNAseq classification and clustering methods using PBMC and Tabula Sapiens datasets.
  • Implementation of a local distributional segmentation approach for spatial scRNAseq.

Main Results:

  • q-diffusion identified statistically significant differential effects on patient outcomes in metastatic colorectal cancer.
  • The method demonstrated superior accuracy in discriminating IFN-γ stimulation in PBMCs compared to existing tools.
  • q-diffusion improved unsupervised cell clustering performance on the Tabula Sapiens human atlas.
  • A spatial scRNAseq analysis using q-diffusion yielded interpretable structures in human cortical tissue.

Conclusions:

  • q-diffusion provides a powerful framework for enhancing the analysis of scRNAseq data.
  • The method offers potential for precision guidance in cancer treatment and improved understanding of cellular atlases.
  • q-diffusion advances the analysis of both bulk and spatial transcriptomic data, unlocking richer biological insights.