1: 2.5uL of 30-50ng/uL cDNA should give you good results. You can check 1 each of control and treatment for each target gene before setting up reaction

2: instead of going for 5 ref genes, try finding two most stable genes. For e.g. you can use GeNorm software for determining gene stability

3: unclear. Run samples in triplicate (or duplicate) with ref and target samples/genes in same plate

Dear Vishnutej Ellur,

to validate your primer i would propose to perform in silico analyses for theoretical bindin and hairpin formation. Moreover, to validate your primer for binding and production of the expected amplicon, you can use one (maybee pooled) cDNA sample (of a source known to express your gene of interest). Using this sample you can perform a gradient RT-(q)PCR to find an optimal annealing temperature. Melting point analyses will show you whether only one product is generated or maybe more than one (specificity of your oligos). After that you should have information on: optimal RT-(q)PCR condition and specificty of your primer pair.

Regarding reference gene expression you can use 5 or more genes of non-modified expression under our experimental conditions). However, it is also ok to use one, two or three references for expression normalization (see various publication available via NCBI Pubmed).

Dear Vishnutej ,

As explained by Andreas, I would run first an in silico specificity check (UCSC for example but I am not sure that the chickpea plant genome reference is included into their database).

For your first question, I would use one of each group sample (Control and treated) to run an qPCR efficacy assay. You need at least 5 serial dilutions of the cDNA (1/2, 1/5 or 1/10) and run your qPCR in the selected conditions (time and temperatures of different cycle phases, number of amplification cycles) in a SYBR Green assay. I would recommend to run triplicates for each dilution and samples. This will give you 2 important results: 1-qPCR efficiency & 2-a single product after melting curve.

Regarding your reference genes, I will run at least 3 reference genes identified using Genorm (as discussed by Harsh) or other software for identifying best reference genes. Using 3 genes will allow you to choose the 2 better ones.

Always run your qPCR assays in triplicates first and then, if you find very low sd between replicates after many runs, move to duplicates in order to decrease the "assay cost".

IfI well understand you have one gene of interest (GOI) and 5 reference genes (HKG). Is it mandatory to run all the 5 HKG? (I will follow my recommandation of using only 3) 96 wells plate will allow you to run 8 samples per plate:

• 4 genes (1 GOI and 3 HKG) in triplicate as shown in the attached pdf

if you have more questions/issues, feel free to interact directly by message.

Best

Typically the best way to begin to set up your experiment is to load the same total RNA per cDNA reaction. You used 1ug total RNA, which is pretty typical for making cDNA. I don't quantify the cDNA using a Nanodrop because there is contaminating primers and protein from the enzymes that limit the accuracy of measuring your cDNA directly from the reaction mix. It's overkill, in my opinion to precipitate the cDNA for downstream RT-PCR. I think a good general place to start is by diluting your cDNA mix 1:20 or 1:40. I usually do 1:40 because it doubles my sample volume and also only changes the Ct value by a factor of 2. This dilution will work for most gene expression analysis. To validate your primer pairs, I recommend designing them around exon-exon boundaries to limit any genomic DNA contamination. I use IDT Primer Quest tool and import the desired mRNA sequence from PubMed. I then go to the custom section and put in the nucleotide numbers where the mRNA sequence notes an exon boundary. I usually test 2-4 primer pairs by running them on control cDNA from cells where I know those genes will be expressed. For example, I would test Il1b gene expression on macrophage cells stimulated with LPS to make them expression Il1b. Be sure to confirm that your reference gene expression doesn't change. You'll be able to do this, since you used 1ug total RNA in your cDNA reactions. If you compare raw Ct values, your control genes should not be significantly different from each other (since you used the same amount of total RNA in your cDNA reactions). If you see a significant difference between experimental and control samples with your reference genes, you cannot use that one as a reference gene. I find TATA-binding protein (Tbp) is very stable in my samples, but beta-actin expression is actually not and will massively alter my results. For setting up your plate, do whatever is convenient for you. I use Bio-Rad, and know that the data export makes columns of Ct values going across rows (i.e. A1, A2, A3, A4.... A12, B1, B2, B3.... etc.). So I usually run my samples of -RT (no reverse transcriptase control, which controls for any genomic DNA in my RNA sample) in duplicate, then my cDNA samples in duplicate next to them. I repeat so on and so forth, then run a couple wells of no template control (NTC) at the end of all my samples. As a note, I usually digest my RNA with the NEB DNase enzyme which is very efficient and really cleans up my samples. This is especially necessary if you don't have as much RNA in your cDNA reaction.