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Updated: May 2, 2026

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

Validation of high-throughput single cell analysis methodology.

Alison S Devonshire¹, Marc-Olivier Baradez¹, Gary Morley¹

¹LGC, Teddington, Middlesex TW11 0LY, UK.

Analytical Biochemistry

|March 18, 2014

Summary

This summary is machine-generated.

High-throughput quantitative polymerase chain reaction (qPCR) workflows optimize single-cell gene expression profiling. Direct cell capture in reverse transcription-preamplification reagent improves precision for neural stem cell analysis.

Keywords:

Digital PCR Gene expression Normalization RT–qPCR Single cell mRNA

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Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

High-throughput quantitative polymerase chain reaction (qPCR) enables multi-gene profiling in single cells, crucial for understanding complex biological processes.
Applications include monitoring cancer cells and advancing stem cell-based therapies, but require validated workflows to minimize variation for clinical use.

Purpose of the Study:

To investigate and validate the performance of single-cell high-throughput qPCR workflows.
To assess efficiency, precision, and limit of detection for lysis, reverse transcription (RT), preamplification (PA), and nanofluidic qPCR steps.
To compare different cell capture and reagent protocols for optimizing gene expression profiling.

Main Methods:

Studied neural stem cell lines to evaluate single-cell qPCR workflow components.
Compared protocols: separate lysis buffer versus direct cell capture in RT-PA reagent.
Utilized digital PCR to correlate preamplified template copy numbers with Cq values and analyzed calibration/normalization strategies.

Main Results:

Both lysis methods showed similar efficiencies, but direct RT-PA capture demonstrated improved precision.
Digital PCR identified low-quality signals impacting analysis.
Calibration and data normalization strategies were found to enhance data comparability and minimize inter-experimental variation.

Conclusions:

The study validates high-throughput qPCR workflows for single-cell gene expression profiling.
Provides guidance on protocol selection and data analysis for improved accuracy and reproducibility.
Highlights the importance of precision and data normalization in single-cell analysis for diagnostics and research.