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

Lineage Commitment01:21

Lineage Commitment

3.0K
Commitment is the  process whereby stem cells:
3.0K
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

CycleVI: isolating cell cycle variation with an interpretable deep generative model.

Bioinformatics (Oxford, England)·2026
Same author

5PSeq Explorer: interactive analysis of co-translational mRNA decay and ribosome dynamics.

RNA biology·2026
Same author

Mechanisms of gene regulation by SRCAP and H2A.Z.

Nature communications·2026
Same author

A portable, low-cost, point-of-care DNA amplification kit with impedance-based detection for decentralized antimicrobial resistance diagnostics.

Lab on a chip·2026
Same author

Electrostatic properties of disordered regions control transcription factor search and pioneer activity.

Nature communications·2026
Same author

A U3 snoRNA is required for the regulation of chromatin dynamics and antiviral response in Drosophila melanogaster.

Nucleic acids research·2025

Related Experiment Video

Updated: Jun 29, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K

Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets.

A S Eisele1, M Tarbier2, A A Dormann3

  • 1Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland. almut.eisele@epfl.ch.

Nature Communications
|March 30, 2024
PubMed
Summary

Gene Expression Memory-based Lineage Inference (GEMLI) reconstructs cell lineages from single-cell RNA sequencing data without needing experimental lineage tracing. This computational tool reveals new insights into cell differentiation and cancer progression.

More Related Videos

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
05:45

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

2.3K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K

Related Experiment Videos

Last Updated: Jun 29, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.5K
Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
05:45

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

2.3K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.3K

Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Lineage tracing is crucial for understanding cell differentiation but is technically challenging and often lacks temporal resolution.
  • Most single-cell RNA sequencing (scRNA-seq) datasets do not contain inherent lineage information.
  • Existing methods struggle to identify small to medium-sized cell lineages.

Purpose of the Study:

  • To introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational tool for inferring cell lineages solely from scRNA-seq data.
  • To enable the study of heritable gene expression, cell fate decisions, and multicellular structure reconstruction.
  • To identify novel gene expression changes associated with cancer invasiveness.

Main Methods:

  • GEMLI utilizes scRNA-seq data to computationally reconstruct cellular lineage trees.
  • The method analyzes gene expression patterns to infer lineage relationships.
  • Application to human breast cancer biopsies to identify early invasive changes.

Main Results:

  • GEMLI successfully identifies small to medium-sized cell lineages from scRNA-seq data.
  • The tool can discriminate between symmetric and asymmetric cell fate decisions.
  • Novel gene expression alterations at the onset of cancer invasiveness were discovered in human breast cancer samples.

Conclusions:

  • GEMLI provides a robust computational approach to infer cell lineages without experimental lineage tracing.
  • The tool facilitates the study of cell lineage dynamics in various physiological and pathological contexts.
  • GEMLI offers a universal applicability for investigating the role of cell lineages in vivo.