You're right to be thinking critically about your qPCR analysis strategy, and your intuition is on the right track. When your hypothesis involves comparing gene expression levels between mutant and wild-type strains under different conditions (with and without phage infection), the most appropriate approach is to use the 2^-ΔΔCt method, not just 2^-ΔCt. The ΔCt value (calculated as Ct of the gene of interest minus Ct of the housekeeping gene) is used to normalize for sample-to-sample variation in RNA quantity or cDNA input. However, analyzing 2^-ΔCt values directly with a t-test doesn’t capture the biological comparison you are aiming to test, namely, the difference in gene expression between mutant and wild-type strains under infection.

The 2^-ΔΔCt method, on the other hand, is designed exactly for this purpose. It compares the normalized expression of a gene in a treatment group relative to a reference condition. In your case, a logical reference would be the wild-type strain without phage infection. You first calculate ΔCt for each sample, then subtract the average ΔCt of your reference group to get ΔΔCt, and finally use 2^-ΔΔCt to express the fold change in gene expression relative to the baseline. This approach allows you to quantify how much gene expression changes in the mutant compared to the wild type under infection, which aligns directly with your stated hypothesis.

It’s also important to note that for statistical testing, you should not use the 2^-ΔΔCt or 2^-ΔCt values directly, because these are derived from exponential transformations and are not normally distributed. Instead, perform statistical comparisons (e.g., t-tests or ANOVA) on the raw ΔCt values, which are log-transformed and better suited for statistical analysis. The 2^-ΔΔCt values should be used for data visualization and interpretation, such as fold-change plots, but not for the statistical tests themselves.

In summary, the best approach is to calculate ΔCt for all samples, compute ΔΔCt relative to your control condition, convert to 2^-ΔΔCt for fold change interpretation, and apply statistical tests to the ΔCt values. This will give you both statistically sound results and a clear, interpretable picture of how gene expression differs between your experimental groups.

Halley K. C. Soares Thank you for clearing up the confusion!

To analyze gene expression in a study comparing mutant and wild-type (WT) strains under conditions with and without phage infection, you should approach the experiment using a 2x2 factorial design. This involves four groups: WT without phage, WT with phage, mutant without phage, and mutant with phage. Such a design allows you to investigate not only the individual effects of genotype (mutant vs. WT) and treatment (phage infection vs. no infection) but also the interaction between the two—specifically, whether the phage response differs between the WT and mutant strains. Begin by conducting RNA-seq or another transcriptomic assay. Use appropriate tools like STAR or Hisat2 for aligning your reads to the genome, and then use featureCounts or HTSeq to quantify gene expression. The output should be a matrix of raw gene counts for each sample. Next, prepare a metadata table that includes each sample's identifier along with its genotype (WT or mutant) and treatment condition (phage or no phage). For differential expression analysis, software like DESeq2 (in R), edgeR, or limma-voom is typically used.

Zahid Ahmad Thoker Thank you! I will check that strategy out.