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

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...

You might also read

Related Articles

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

Sort by

Same author

Early onset primary angle closure and foveal retinoschisis associated with a pseudo-homozygous CRB1 pathogenic variant.

Documenta ophthalmologica. Advances in ophthalmology·2026

Same author

Predicting and Co-Optimizing the Taste and Aroma of Green Tea During Spreading Using the TabPFN Model.

Foods (Basel, Switzerland)·2026

Same author

Association between capsulectomy shunt revision and Ahmed glaucoma valve implant exposure--Shanghai Ahmed glaucoma valve implant exposure (AGVIE) study.

BMC ophthalmology·2026

Same author

Efficient Image Debiased Contrastive Clustering.

IEEE transactions on neural networks and learning systems·2026

Same author

Flexible Sensorized Tube for Pipeline Defect Detection Based on Bending and Pressure Sensing.

Sensors (Basel, Switzerland)·2026

Same author

The genetic repertoire of deep-sea microbiome: From sequence to structure and function.

Cell host & microbe·2026

Same journal

Targeted Delivery of Indole-3-Pyruvic Acid Suppresses Macrophage Ferroptosis to Enhance CD8<sup>+</sup> T Cell-Mediated Immunotherapy Response in Bladder Cancer.

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

Same journal

Pathological Copper Overload Reprograms SOD1 Activation via COMMD1 to Promote Senescence and Fibrosis.

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

Same journal

Bending-Resistant Intimate 3D Graphene-Metal Heterojunctions for Highly Sensitive and Robust Flexible Sensors.

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

Same journal

A Pathology-Instructed Theranostic Platform with Mechanoadaptive and ROS-Powered Nanobreathing Functions for Precision Myocardial Repair.

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

Same journal

Targeting p21-High Senescent Kupffer Cells Nanotherapeutically Potentiates Antitumor Immunity in Advanced Hepatocellular Carcinoma with Portal Vein Tumor Thrombus.

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

Same journal

A Ceramic Network for Hybrid Solid Electrolyte Lithium Metal Batteries.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 10, 2025

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

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

Published on: March 12, 2021

Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD.

Zetian Zheng¹, Junyi Chen², Xingjian Chen¹

¹Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|February 10, 2023

Summary

This summary is machine-generated.

A new transfer learning model infers single-cell drug sensitivities by leveraging bulk RNA sequencing data. This approach reveals cell subpopulations with varying drug responses and informs novel anti-cancer combination therapies.

Keywords:

drug response annotation single-cell sequencing transfer learning

More Related Videos

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Aug 10, 2025

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

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

Published on: March 12, 2021

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational biology
Genomics
Pharmacology

Background:

Single-cell RNA sequencing (scRNA-seq) offers cellular resolution of gene expression, contrasting with bulk RNA sequencing (bulk RNA-seq).
Understanding cellular drug sensitivity is crucial for deciphering anti-cancer response heterogeneity and drug resistance.
Limited pharmacogenomic data for scRNA-seq hinders direct drug sensitivity inference at the single-cell level.

Purpose of the Study:

To develop a transfer learning model for inferring drug sensitivities at the single-cell level.
To bridge the gap between limited scRNA-seq pharmacogenomic data and the need for cellular-resolution drug response insights.
To explore potential anti-cancer drug combinations by analyzing drug sensitivities across cell subpopulations.

Main Methods:

A transfer learning framework was designed to learn from bulk transcriptome profiles and pharmacogenomics data of cell lines.
Knowledge was transferred from a large public dataset to infer drug efficacy for individual cells.
The model was applied to identify pre-existing cell subpopulations with distinct drug sensitivities before drug exposure.

Main Results:

The model successfully inferred drug sensitivities at the single-cell level, highlighting its suitability for pre-clinical cell line data.
Drug-resistant subpopulations were found to be sensitive to alternative drugs, suggesting potential combination therapies (e.g., Gefitinib + Vorinostat).
Identified drug sensitivity biomarkers provided insights into tumor heterogeneity and cellular-level treatment strategies.

Conclusions:

Transfer learning is effective for inferring single-cell drug sensitivities from bulk RNA sequencing data.
The model aids in understanding and predicting anti-cancer drug response heterogeneity and resistance.
Findings support novel drug combination strategies and personalized treatment approaches based on cellular drug sensitivity profiles.