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

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

RNA-seq

RNA-seq

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 microarray-based...

You might also read

Related Articles

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

Sort by

Same author

CDK10 loss primes tumor cells for transcriptional vulnerability and sensitizes to CDK12/13 inhibition.

Cell death & disease·2026

Same author

Pyrrhotite-driven early-stage terrestrial alteration in Ryugu grains.

Nature communications·2026

Same author

Clinical Characteristics of Dynapenia and Sarcopenia in Hospitalized Patients with Cardiovascular Disease.

International heart journal·2026

Same author

Clinical Characteristics of Cardiovascular Disease Patients With Dynapenia and the Effects of Outpatient Physical Therapy.

Circulation reports·2026

Same author

Right Bundle Branch Conduction in Left Bundle Branch Area Pacing.

Journal of arrhythmia·2026

Same author

A novel CDK12 inhibitor induces homologous recombination deficiency to enhance PARP inhibitor efficacy in uterine serous carcinoma.

Molecular cancer therapeutics·2026

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.

Masato Ishikawa¹, Seiichi Sugino², Yoshie Masuda²

¹Institute for Life and Medical Sciences, Kyoto University, Kyoto, 606-8507, Japan. ishikawa.masato.7v@kyoto-u.ac.jp.

Communications Biology

|December 28, 2023

Summary

RENGE, a new method, accurately infers gene regulatory networks from time-series single-cell CRISPR data by modeling knockout effects over time. This approach improves upon static snapshots for understanding complex biological systems.

More Related Videos

Investigation of the Transcriptional Role of a RUNX1 Intronic Silencer by CRISPR/Cas9 Ribonucleoprotein in Acute Myeloid Leukemia Cells

Investigation of the Transcriptional Role of a RUNX1 Intronic Silencer by CRISPR/Cas9 Ribonucleoprotein in Acute Myeloid Leukemia Cells

Published on: September 1, 2019

Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

Published on: May 23, 2025

Related Experiment Videos

Last Updated: Jun 8, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Investigation of the Transcriptional Role of a RUNX1 Intronic Silencer by CRISPR/Cas9 Ribonucleoprotein in Acute Myeloid Leukemia Cells

Investigation of the Transcriptional Role of a RUNX1 Intronic Silencer by CRISPR/Cas9 Ribonucleoprotein in Acute Myeloid Leukemia Cells

Published on: September 1, 2019

Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes

Published on: May 23, 2025

Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) and CRISPR perturbations are powerful tools for inferring gene regulatory networks (GRNs).
Static snapshots of scRNA-seq data following CRISPR gene knockouts may not capture the dynamic, multi-layered effects of genetic perturbations over time, limiting accurate GRN inference.
Understanding causal relationships in gene regulation is crucial for deciphering biological systems.

Purpose of the Study:

To develop a computational method, RENGE, for inferring gene regulatory networks using time-series single-cell CRISPR datasets.
To accurately model the temporal propagation of gene knockout effects within a regulatory network.
To distinguish between direct and indirect regulatory interactions and infer regulations involving non-knockout genes.

Main Methods:

Developed RENGE, a computational method that utilizes time-series single-cell CRISPR data.
Modeled the propagation dynamics of gene knockout effects through the regulatory network.
Incorporated algorithms to differentiate direct and indirect gene regulations.

Main Results:

RENGE accurately infers gene regulatory networks by accounting for temporal dynamics after gene knockout.
The method successfully distinguishes direct from indirect regulatory relationships.
Application of RENGE to human-induced pluripotent stem cell data generated a GRN consistent with existing biological databases and literature.
RENGE demonstrates superior accuracy in GRN inference compared to current methods.

Conclusions:

RENGE provides a robust framework for accurate gene regulatory network inference from time-series single-cell CRISPR data.
The ability to model temporal effects and distinguish regulation types enhances the reliability of inferred networks.
Accurate GRN inference using RENGE can facilitate the identification of critical regulatory factors in diverse biological contexts.