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Updated: Dec 21, 2025

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

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

Published on: February 25, 2017

MAPS-seq: magnetic bead-assisted parallel single-cell gene expression profiling.

Munsu Park¹, Dongin Lee², Duhee Bang³

¹Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, Seoul, Korea.

Experimental & Molecular Medicine

|May 15, 2020

Summary

This summary is machine-generated.

We developed magnetic bead-assisted parallel single-cell gene expression sequencing (MAPS-seq), a cost-effective and accessible method for single-cell RNA sequencing. This technique reduces reagent waste and simplifies sample preparation for transcriptomic analysis.

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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

Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic profiling.
Existing scRNA-seq methods often require specialized equipment and significant reagent consumption.
There is a need for more accessible and resource-efficient scRNA-seq techniques.

Purpose of the Study:

To introduce magnetic bead-assisted parallel single-cell gene expression sequencing (MAPS-seq).
To provide a simplified, less wasteful, and more accessible scRNA-seq method.
To validate MAPS-seq for transcriptomic analysis and drug response studies.

Main Methods:

MAPS-seq is a microwell-based method pooling samples before reverse transcription.
Utilizes universal reagents and standard molecular biology instrumentation.
Applied to analyze gene expression in human myelogenous leukemia cells.

Main Results:

MAPS-seq successfully generated single-cell gene expression data.
Transcriptional changes in leukemia cells in response to drug treatment were identified.
Marker genes indicating drug response were discovered.
MAPS-seq data quality is comparable to existing scRNA-seq methods.

Conclusions:

MAPS-seq offers a more accessible and less wasteful alternative for single-cell transcriptomics.
The method simplifies sample preparation and reduces reagent costs.
MAPS-seq is suitable for investigating cellular responses, including drug effects.