Home » The ToxLearn4EU Project » WP4: New approaches, methodologies and models » WP4: Online Course 1

NEW APPROACHES, METHODOLOGIES AND MODELS

Gene Expression Analysis using Real-Time PCR

INDEX

1. Description

Gene expression analysis is a fundamental tool in molecular biology, with widespread applications in biomedical research, toxicology, pharmacology, and environmental sciences. This online course provides a comprehensive introduction to quantitative PCR (qPCR), one of the most powerful and widely used techniques for measuring gene expression.

Understanding gene expression patterns is crucial for identifying biomarkers, studying disease mechanisms, assessing toxicological effects, and developing new therapeutic strategies. In the context of toxicology, qPCR plays a vital role in evaluating how biological systems respond to toxic substances at the molecular level. The course is designed to equip students, researchers, and professionals with the essential knowledge and skills to successfully implement qPCR in their research.

Through a series of expert-led lectures available on the project’s YouTube channel, participants will explore key aspects of qPCR, including its principles, methodological considerations, and data interpretation. By the end of this course, learners will have a strong foundation in applying qPCR to gene expression analysis and will understand its significance in modern scientific research and public health.

2. Learning outcomes

Upon completing this course, participants will be able to:

  • Understand the fundamental principles and mechanisms of qPCR and its application in gene expression analysis.
  • Explain the methodological workflow, including RNA extraction, reverse transcription, primer design, and reaction setup.
  • Differentiate between fluorescence-based detection chemistries, such as SYBR Green and TaqMan probes.
  • Apply best practices for qPCR data normalization, including reference gene selection and internal controls.
  • Analyze and interpret qPCR results using key metrics such as Ct values, reaction efficiency, and relative quantification methods.
  • Evaluate the use of qPCR in toxicology research, understanding its role in assessing environmental contaminants and cellular responses.
  • Critically assess the strengths, limitations, and potential pitfalls of qPCR in comparison with other molecular techniques.

This course provides an accessible and engaging learning experience, making it ideal for students and professionals looking to enhance their expertise in gene expression analysis and its applications in toxicology and life sciences.

3. Structure of the course

Introduction to Transcriptomics and Gene Expression Analysis

Understanding gene expression is fundamental in modern biology, toxicology, and medicine. In this lecture, we introduce transcriptomics, the study of RNA transcripts, and its critical role in comparing gene expression across different cell types, healthy and diseased tissues, and responses to environmental stressors like toxic compounds. We explore key gene expression analysis methods, including qPCR (quantitative PCR), Northern blot, microarrays, and RNA sequencing, highlighting their advantages, limitations, and applications in research. Learn how scientists analyze gene activity, measure mRNA levels, and use transcriptomics to uncover insights into disease mechanisms, drug responses, and toxicology assessments. Whether you’re a student, researcher, or professional in molecular biology, this lecture will provide you with a clear foundation in gene expression techniques and their impact on risk assessment, biomedical research, and environmental science. Join us as we break down the principles of RNA isolation, sequencing technologies, and qPCR applications for accurate and reliable gene expression analysis.

The Do’s and Don’ts of Primer Design in Real-Time PCR

Primer design is one of the most critical steps in real-time PCR (qPCR) and can directly impact the accuracy and reliability of your gene expression results. In this lecture, we dive deep into the key principles of primer design, covering essential factors such as amplicon length, primer melting temperature, self-complementarity, and exon-exon junction binding. Learn why improper primer selection can lead to nonspecific amplification, primer dimer formation, and unreliable CQ values. We’ll also discuss best practices for ensuring specificity, reducing experimental errors, and using online tools like Primer3Plus for optimal primer selection. Whether you’re a researcher, student, or professional in molecular biology, toxicology, or risk assessment, this detailed walkthrough will equip you with the skills to design primers that improve the accuracy of your qPCR experiments. Stay tuned for a hands-on demonstration where we guide you step by step through the process of designing primers for your own studies.

Step-by-Step Guide to qPCR Primer Design

In this step-by-step tutorial, we dive into qPCR primer design, an essential part of ensuring accurate and reliable gene expression analysis. Using NCBI Gene Database and Primer3Plus, we demonstrate how to efficiently design primers for real-time PCR (qPCR), focusing on key factors like amplicon length, primer melting temperature (Tm), GC content, and exon-exon boundaries to prevent genomic DNA amplification. We also explore how to check primer specificity using NCBI BLAST, ensuring they target only the gene of interest while avoiding off-target effects. Whether you’re a researcher, student, or lab scientist, this guide will equip you with the skills needed to design, validate, and optimize primers for high-precision qPCR experiments. Learn how to avoid common pitfalls, improve your primer efficiency, and take your quantitative PCR (qPCR) analysis to the next level. Watch now and start designing primers for gene expression studies with confidence!

How to Accurately Measure Primer Efficiency in qPCR

Understanding primer efficiency is crucial for accurate qPCR (quantitative PCR) analysis and reliable gene expression studies. In this lecture, we dive into the importance of measuring primer efficiency, explaining how to set up a serial dilution, interpret amplification curves, and calculate efficiency using standard curves. You’ll learn why an efficiency range of 90-110% is ideal, how deviations impact qPCR data reliability, and what factors—such as annealing temperature, primer concentration, magnesium levels, and PCR inhibitors—affect efficiency. We also break down common mistakes in primer design and how to optimize your reactions for high-quality gene expression analysis. Whether you’re a researcher, student, or professional working with real-time PCR, this tutorial will help you ensure your primers are performing optimally. Plus, we provide a step-by-step demonstration of efficiency calculations to improve your qPCR workflows. Stay tuned for practical examples and exercises to reinforce your learning!

Step-by-Step Guide to Primer Efficiency Calculation

Accurate primer efficiency is essential for reliable qPCR results, and in this step-by-step demonstration, you’ll learn exactly how to calculate it using serial dilutions and standard curves. We’ll walk through the entire process—from preparing a pooled cDNA sample and setting up a dilution series to running qPCR reactions and analyzing the data using CQ values, trend lines, and R² correlation coefficients. You’ll also discover common primer efficiency pitfalls, including low efficiency, excessive deviation from 100% efficiency, and the impact of PCR inhibitors on qPCR performance. Whether you’re a researcher, scientist, or student, this guide will help you optimize your qPCR experiments and ensure precise gene expression analysis. Stay ahead in molecular biology, transcriptomics, and PCR analysis by mastering primer efficiency testing—because even the best-designed primers can fail in the lab if their efficiency isn’t properly evaluated. Watch now to improve the accuracy and reproducibility of your results!

Why Normalizing qPCR Data is Crucial for Accurate Results

Understanding qPCR data normalization is crucial for obtaining accurate and reliable gene expression results. In this lecture, we dive deep into the importance of normalizing qPCR data, discussing how technical variation—such as differences in RNA concentration, sample size, and PCR efficiency—can impact your results. You’ll learn why housekeeping genes are often used for normalization, the pitfalls of assuming their stability, and how validated reference genes help minimize errors. We also explore different normalization strategies, the impact of biological vs. technical variation, and how to correctly apply data correction methods to ensure meaningful qPCR analysis. Additionally, we introduce the MIQE guidelines—an essential reporting framework that promotes transparency and reproducibility in qPCR experiments. Whether you’re new to qPCR data analysis or looking to refine your methodology, this lesson provides practical insights into making your gene expression studies more reliable. Stay tuned for a detailed demonstration of normalization techniques and a discussion on common mistakes to avoid in qPCR research.

How to Analyze qPCR Data Step by Step

Understanding qPCR data analysis is crucial for obtaining accurate gene expression results, but many researchers unknowingly make mistakes in data normalization. In this detailed tutorial, we walk you through a complete step-by-step analysis of qPCR data, including selecting the best reference genes, applying the ΔCq method, and using the GreyNorm algorithm to ensure reliable normalization. You’ll learn how to minimize technical variation, interpret relative quantification (RQ) values, and visualize data with bar charts and error bars in Excel. This session also highlights why proper data normalization is essential for meaningful biological conclusions and how to avoid common pitfalls in real-time PCR (qPCR) experiments. Whether you’re working with toxicology studies, gene expression research, or advanced molecular biology, this guide provides clear, practical insights to improve the quality and reliability of your qPCR results. Don’t let incorrect normalization compromise your research—follow these best practices to get accurate, reproducible data every time!