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Related Concept Videos

Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: May 18, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
10:23

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

RT-qPCR work-flow for single-cell data analysis.

Anders Ståhlberg1, Vendula Rusnakova, Amin Forootan

  • 1TATAA Biocenter, Gothenburg, Sweden; Sahlgrenska Cancer Center, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden. anders.stahlberg@neuro.gu.se

Methods (San Diego, Calif.)
|October 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a practical workflow for preprocessing single-cell data from reverse transcription quantitative real-time PCR. It addresses challenges with existing methods, offering guidance for handling missing and scaled data in single-cell gene expression analysis.

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Last Updated: May 18, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

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

Published on: February 25, 2017

Area of Science:

  • Molecular Biology
  • Genomics
  • Biotechnology

Background:

  • Individual cells possess unique properties crucial for understanding biological processes.
  • Traditional gene expression analysis methods are unsuitable for single-cell data.
  • New techniques for single-cell analysis necessitate adapted statistical workflows.

Purpose of the Study:

  • To present a simple and practical preprocessing workflow for single-cell data.
  • To provide guidance for researchers using reverse transcription quantitative real-time PCR for single-cell profiling.
  • To address specific challenges in single-cell data analysis, such as missing data and data scaling.

Main Methods:

  • Development of a preprocessing workflow tailored for single-cell reverse transcription quantitative real-time PCR data.
  • Demonstration of the workflow on a dataset profiling 41 genes in 303 single cells.
  • Evaluation and recommendation of strategies for handling missing data and scaling data for multivariate analysis.

Main Results:

  • The proposed workflow effectively preprocesses single-cell gene expression data.
  • Specific strategies for managing missing data and scaling are presented and discussed.
  • The workflow is validated on a relevant dataset, showing its practical applicability.

Conclusions:

  • The developed workflow offers a valuable guide for the growing community of single-cell researchers.
  • Standard statistical methods require adaptation for single-cell reverse transcription quantitative real-time PCR data.
  • This work facilitates more robust downstream analysis of single-cell gene expression profiles.