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Updated: Sep 2, 2025

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

Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in

L Mathur^1,2, B Szalai^3,4,5, N H Du⁶

¹Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.

Nature Communications

|August 1, 2022

Summary

This summary is machine-generated.

This study introduces Combi-Seq, a microfluidic method for screening numerous anti-cancer drug combinations using single-cell transcriptomics. It efficiently identifies effective drug pairings and pathway activities from minimal patient samples.

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Last Updated: Sep 2, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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Published on: January 7, 2020

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Area of Science:

Oncology
Genomics
Bioengineering

Background:

Anti-cancer therapies often have limited efficacy due to acquired drug resistance, leading to tumor relapse.
Drug combinations offer a promising strategy to overcome resistance, but identifying optimal combinations is complex.
Current high-throughput screening methods require substantial biological material, often unavailable from patient biopsies.

Purpose of the Study:

To develop a scalable microfluidic workflow, Combi-Seq, for high-throughput screening of drug combinations.
To utilize single-cell transcriptome changes as a readout for drug effects.
To enable drug combination screening using limited patient-derived samples.

Main Methods:

A microfluidic system was designed to screen hundreds of drug combinations in picoliter droplets.
A deterministic combinatorial DNA barcoding method was developed to encode treatment conditions.
Single-cell RNA sequencing was employed to analyze transcriptome-wide gene expression changes.

Main Results:

Combi-Seq successfully screened 420 drug combinations using approximately 250 single-cell droplets per condition.
The workflow accurately predicted synergistic and antagonistic drug pairs.
Pathway activities influenced by drug combinations were identified.

Conclusions:

Combi-Seq provides a scalable and material-efficient approach for drug combination screening.
This method enables gene expression-based analysis of drug effects in a highly multiplexed manner.
Combi-Seq has the potential to accelerate the discovery of effective combination therapies for cancer treatment.