Questions related to Quantitative RT-PCR
I am optimizing qPCR assay using a pooled cDNA sample. I have several target genes (100-200bp).
current template dilution: 1:30 (30x)
final primer concentrationn: 0.27 uM
annealing temp: 60C
extension temp: 72C
Ct values I get using this template dilution range from 32 to 35, which I think are too high (aren't they?). Increasing the template to 1:10 (10x) and decreasing the annealing temp close to primer Tm (55C) didn't do much.
All melt curves show single peaks at expected Tm. No problem with primer dimers and specificity.
Do you think I should increase the primer concentration to 0.5 uM to lower the Cts?
Hellow fellow academics
I am currently in a dilemma and I would really appreciate some suggestions/guidance on the matter.
I have overexpressed my gene of interest (GOI) from wheat in Arabidopsis using the floral dip method and with strict screening on MS-Hygromycin, obtained my T3 transgenics. Now the problem is that while the selection, on the media has been successful, I am not able to get a band of my GOI on agarose gel after doing semi- RT-PCR. Initially, I thought that maybe my overexpression was unsuccessful so I took the T3 seeds to screen again on the media, but, the result was the same; the overexpression was successful and met the segregation ratio requirement of 100% germination. As this is my first time working with transgenics, please enlighten me on where I could be going wrong.
Please advise. Thank you for your time in advance.
Is there anyone who has done TaqMan assays using average regular use PCR mastermix (not the TaqMan assay specific mastermixe) using cDNA as template for the qPCR test? I wanted to know the ins and outs of the procedure and the optimization you did to get accurate results.
Thanks in advance.
I have an unusual question: I am working on a Erasmus internship project with Drosophila mutants at 2 different timepoints and with WT, KO and KI condition. A company analyzed the data using DESeq2 and I have only got loads of PDFs and the results_apeglm.xlsx file.
This contains: Transcripts per million for each gene, replicate and timepoint with the comparison for looking at DEGs - so I have a padj and log2FC value. A snippet is attached as an example.
I now want to construct a graph and clustering where genes that are going in changing directions between WT and KO over time become visible out of the hundreds of candidate DEGs. With this I want to narrow down the long list to make it verifiable with qPCR and serve as a marker for transformation from presymptomatic to symptomatic.
I am setting up my analysis in R and want to use the degPatterns() function from DEGReport, as it gives a nice visual output and clusters the genes for me.
How can I now transform my Excel sheets, to a matrix format that I can use with degPatterns()? The example with the Summarized Experiment given in the vignette is not really helpful to me, sadly.
Thank you all for reading, pondering and helping with my question! I would be very happy if there´s a way to solve my data wrangling issue.
All the best,
Trying to design my first set of methylated primers. Ran a temperature gradient and think I’ve narrowed down a functional range of annealing temperatures across primer concentrations.
Now, I wanted to finally test efficiency across a few annealing more select temperatures. However, I’m realizing that in my labs qPCR protocols, most of them don’t carry an extension step, only the annealing and denature step.
My understanding is that since the templates and products are small (100bp) the copy is usually completed during the ramp up to the denature step. I’ll add that my Tms are approximately 64C, and my estimated annealing is consequently in the 55-61C range. We are using IQ sybr green super mix (iTAQ polymerase).
Just wanted to inquire if this was indeed the case, and if I should rerun my temperature gradient with an added extension step or just proceed with piloting.
On an agarose gel, I didn’t see any double banding across temperatures (suggesting high primer specificity?) albeit brighter bands were detected at specific temperatures, within 2C-6C of the Tm, so I wanted to run sample dilutions across a few degrees to maximize efficiency.
thanks for any help!
Hi ResearchGate community,
I have been trying to learn more about the optical differences between block-based real-time PCR machines like ABI StepOne versus rotor-based machines such as MIC or RotorGene systems.
I understand that some systems rely on ROX as a passive reference dye while others state that it is optional to incorporate it and others do not need such a factor at all.
My question is if you add this fluorescent dye to your master mix, would it interfere with the detection when it is being amplified using one of the systems that do not need such normalization?
Highly appreciate any insight in this regard.
Hello, I am working on microRNA expression studies on Regeneration. I isolated RNA from my sample and converted it into cDNA using Poly A polymerase and Reverse transcriptase enzyme. Previously I designed a miRNA-specific Forward primer at the melting temperature of 60°C. For Reverse primer, I used Universal Reverse primer from a commercial kit. But now I need to design a miRNA reverse primer for myself. Kindly suggest me method to design a reverse primer for Poly A-tailed miRNA. Thanks in advance.
Is it possible that the amplification failure products can be visualized in electrophoresis? Due to the failed amplification results it shows bands in my electrophoresis with bands that are quite clear. My amplification curve clearly shows amplification failure, but when I look back at it with electrophoresis there are some obvious bands, how is that possible?
Hi everyone. I have searched all around the internet and literature for the answer to this question but haven’t been able to find any info regarding my specific situation.
I have multiple experiments consisting of qPCR data but can’t figure out how to best analyse it. I have WT and KO cells which I apply 3 treatments to and I have a control (no treatment) for both genotypes, and I check 15 genes. What I really want to show is if the genes are up/downregulated when I add a treatment in ko vs wt, so I want to make my comparison between the genotypes. But I can’t compare them directly, because at baseline they have quite different expression levels already, so I want to take the control for each into consideration. Before I was plotting -Dct (normalised to housekeeping gene only) and would compare each treatment in each genotype to its own control. But my group didn’t like this, which I understand, because the graphs are cluttered and I don’t show the comparison I’m really trying to make. I worked with a bioinformatician with my idea to normalise the Dct for each genotype/treatment to its own control and in that way make DDct, and then I compare the -DDct between genotypes for each treatment using an unpaired t-test. I don’t do fold change. These graphs are much nicer to look at, but my supervisor says it doesn’t make statistical sense this way, and wants to keep the graphs the original way.
can anyone help me out? What is the best way to analyse and graph my data?
Is it necessary to measure the quantity of single-strand cDNA after RT-PCR and before qPCR? TaqMan Universal PCR Master Mix manual (TaqMan Gene Expression Assay) (at picture) describes the desired quantity of template (1-100 ng) per reaction. So, I should measure the quantity of cDNA after RT-PCR (for instance, on Nanodrop spectrophotometer) and bring this amount to the desired range if needed?
Hello, I have a probable inhibition problem with q-PCR.
Always starting from the same RNA extraction, I synthesized the cDNA in two different ways. In both cases, I used the ImProm-II Reverse Transcriptase kit (promega).
For the first synthesis I treated the RNA with the TURBO DNA-free™ kit (Ambion).
The second time using the promega RQ1 RNase-freeDNase kit, because in the laboratory where I work now they mainly use this kit.
I noticed that in the qPCR done using RQ1 treated cDNA the gene expression values are significantly lower. While using the cDNA treated with the turbo DNA free kit everything was fine. These results are confirmed because I have access to the DEGs information of the two tested samples.
the main difference between the two kits is that the ambion kit allows the reagents elimination, while the promega kit does not.
Do you think it is possible that there is inhibition caused by the DNase kit? or could the problem be something else?
I did several tests with different dilutions and amount of cDNA. Diluting it improves the results but I don't achieve the expected results.
Thanks to anyone who can give me some advice
I have a group of five patients who were tested for expression of 11 genes using qPCR. Each of these patients have a different deletion size, and so my aim is to check the differences in expression levels for the presence versus absence of each of these genes. I calculated their 2^-ddCt values (relative fold change). What I want to do is get an average fold change when a particular gene is present or absent, collectively in this group of five patients. I want this change in logarithmic terms, in order for the up- and down-regulation to be represented equally.
My question is: Say, I want to get an average fold change in expression, for example upregulation, for a gene X. Should I take log base 10 of the average for all 2^-ddCt in the patients showing upregulation? Or should I take log base 10 of individual 2^-ddCt values showing upregulation and then take average?
Hi all , I am confirming gene expression using RT-PCR. If a gene is down-regulated does that mean it is not expressed in the strain?
I have two types of template (RNA) derived from two different types of tissue (normal and tumor). Which one I should add to well with endogenous control primers/probe (GAPDH) at qPCR plate? I know that there is no difference because it is a control gene (it is suggested that its expression will be equal in both types of tissue, but, nevertheless, I would like to know which source of template is usually used in this case (template from normal or tumor sample)? And, additionally, should I make all templates' concentrations equally (in every well at plate) before start of qPCR (TaqMan Gene Expression Assay)?
I am trying to design a primer for a specific gene and the Tm is coming near to 60 degrees when I select a minimum of 30-35 bp, for forward as well as for reverse primer. As the template is too long I think it might lead to non-specific binding, what are the possible solutions for this problem? I am using Snapgene to design primers for cloning
I am doing absolute quantification RT-qPCR for the beta actin gene. I start with RNA, turn this into cDNA and use that for qPCR. I am going to get a custom oligo of the PCR amplification product to use for making the standard required for absolute quantification.
This is how I got the sequence for the custom oligo. First, I input my primers into this website: http://www.bioinformatics.org/sms2/pcr_products.html along with the mRNA sequence for beta actin. The website gave me the amplified product, which is 61 bp as expected.
My question is should I use this result sequence as my oligo, or should I reverse complement it? I am wondering this because I input the mRNA sequence into the website to get the amplicon, but I am using cDNA which would be the complementary sequence. Please let me know. Thank you
Based on the Rt-PCR analysis results of the oligos I have designed, it appears that there is a consistent signal from the NTC and negative patient samples with Ct values ranging from 30 to 32. However, I have noticed that in the positive patient samples, the signal sometimes resembles that of the NTC and sometimes it does not. I am curious about the possible reasons for these false positives in the NTC and false negatives in the positive samples. Could this be attributed to a faulty probe or potentially caused by dimerization?
I've done a qPCR reaction using Bio-Rad CFX Duet, 10 ul reaction, using EvaGreen dye.
as you can see, image 01 is what I think as a good amplification plot.
Today I do the qPCR again using a home-made qPCR master mix (also using Taq Polymerase produced in our lab).
Here is the recipe for it:
20 mM Tris.Cl pH 8.0
10 mM (NH4)2SO4
10 mM KCl
2 mM MgSO4
0.1% Triton X-100
200 uM dNTPs
300 ng of Taq Polymerase (in 10% Glycerol, 25 mM Tris.Cl pH 8.0, and 500 mM Imidazole)
a week ago I using the same recipe for the assay, but it show great amplification plot
but today, it show amplification plot like image 04 (sorry i haven't moved the data yet so i just using Paint to show what the amplification plot looks like)
what can cause the weird shape of it?
I have quantified plasma protein (ELISA) and mRNA (whole blood; RT-PCR) for a candidate molecule. Independently, a comparison of plasma levels between 2 groups shows a significant difference; whereas, comparison of dCt is not statistically significant. Also, dCt vs conc ng/ul shows a negative correlation.
1. Should I compare dCt between the two groups or rely on fold-change to assess whether my molecule is expressed in the case group vs the control group?
2. How do I justify the negative correlation obtained while comparing plasma protein levels vs whole blood mRNA levels in the same set of patients?
I desperately need your help and advice. Based on the TCGA data analysis that i have done, i would like to validate the expression of two miRNAs in my own ffpe samples. I am comparing these miRNA expressions in samples with high vs low macrophage density.
Let's say the miRs are miR-A and miR-B. I would like to compare the expression difference in breast cancer tissue samples with high macrophage density vs low. The hypothesis is that samples with high macrophage density would have high expression of miR-A, but low expression of miR-B, and vice versa.
The question is, how do i calculate the miR expression? I am using U6 as my HKG. I do not have a reference sample nor do I have treated vs untreated samples. Most calculations i see involve these two. I am merely comparing the expression pattern between samples with high macs vs low. Also, what statistical method shall i use for this analysis?
Hello there, I would like to correlate the mRNA expression of a gene obtained from patient blood samples with surrogate parameters like HbA1c and data like body weight, age, etc. However, I am unsure and confused about the correct way to do it. I did not find a solution on the web so far.
Which qPCR-data should you I use (dCT, ddCT, 2^-ddCT)?
If it favorable to use log-transformed data, or does it distort the results?
Is it statistically correct to simply use an xy-graph and do a correlation analysis?
We are trying to analyse differential expression of cell free mRNA (which is totally free on the blood) on blood samples of breast cancer patients. I need articles to use as references. Thanks!
I am performing qPCR on drosophila mtDNA. Because of the nature of my work, I use two separate primer sets. With one of them, the qPCR works as expected - good amplification with mtDNA and nothing/ very high Cq primer-dimers in NTCs.
With my second set of primers, I continually get amplification in the NTCs that is at about the same amplitude as the template samples, and have identical melt curves. I have tried using new primers, new readymix, and new water. I have also bought new primer stock, but still have this issue. I have used these primers in PCR and they have worked fine when verified with gels (PCRs are without the readymix, that is the only difference - I make up my own solution).
Because the first primers work fine, it seems like it doesn't seem to be a technical/environmental issue, or contamination of the water or mastermix. The only difference is the primers which I initially thought may have been contaminated, but given new stock solution yields the same results and PCR without the readymix is also fine, that does not seem to be the case.
Is it a thing for primers to be incompatible with certain readymixes? If so, why is that? I haven't found any explanations in my searches.
I am currently trying to determine the number of archaea present in an ecosystem with the 16S rRNA gene.
I am normalizing the mcrA gene with the total archaea 16S rRNA gene. The mcrA gene measures the methanogenic acivity.
The problem that I am currently facing is that some archaea have multiple 16S rRNA genes, which makes it difficult to normalize to the mcrA gene.
Does anyone know how to solve this problem?
Thanks in advance!
does anyone know why there is an air gap at the end of the pipette tip every now and then after aspirating? it seems to occur more often when pipetting larger volumes on a P1000 but can also occur when say using a multichannel pipette on lower volumetric pipettes. pardon the poor paint image to depict what im trying to get across
is this a operator error or is it natural to occur? if an operator error, are there any tips to avoid this?
I have IgG-secreting hybridoma cell lines, and I am trying to knock out a gene in these cell lines. I tried K/D but, each time I get a very low RNA yield (to assess using qPCR) and K/D is unsuccessful.
I am trying to determine relative expression values for specific genes in different life-stages of my organisms (adult, larval, and microfilaria). For each of the three life-stages, I have three biological replicates and performed all PCRs in triplicate. I have two reference genes to compare with my genes of interest, but I am unsure how to calculate fold expression changes if there are no treatment groups/control groups with the ΔΔCt method since I am only assessing life-stage differences in expression. Also, with two reference genes, I am unsure at which point in the analysis I need to account for this.
Any advice would be greatly appreciated!
I am performing PCR quantification of 5 inflammatory markers on 180 samples. As you can imagine, I therefore work on several 384-well plates. To compare them, I introduced a duplicate amplification control per primer for each plate to check if my amplification is indeed the same from one run to another.
Now, I have experimented with all my plates and I would like to run them through a statistical test to verify that the experiment is comparable from one plate to another. What statistical test should I use?
I'll let the stats pros answer! :)
I have the results of both qPCR CT values and RNA-seq TPM values. Now that I have 2 sets of data, is it proper to compare expression fold change (2^ of delta delta CT) with log2 of TPM values?
Thanks in advance,
#qPCR, #RNA-sequence analysis, #TPM.
I am running a qPCR assay. I chose gradient temperature option for each of my primer to get the best conditions the amplification happens (without heterodimers- NA in negative controls). However, I have seen that my housekeeping gene and one of my target gene have different annealing temperature. Can I run another qPCR set-up just for this gene by choosing gradient temperature option ? For instance; my gene in question in a row with 54C and housekeeping gene in a row with 60C. I think as far as the machine reads the signals at the same time, it won't pose a problem but I just want to make sure.
I am new to extracting RNA from fruit flesh. Currently, I'm extracting RNA from sweet cherry flesh using Fruitmate and Nucleospin Takara Bio. I have tried using RNeasy kit Qiagen but I failed (but with another fruit sample I can extract it).
For shredding the tissue I used multibeads shocker and store the sample at -80 degree celsius freezer.
I have tried to follow their instructions and try to create a clean workspace as possible. My problem is that the extraction always resulted in low concentration (both 1x and 2x elution) and absorption is also very small (0.xxx of A260 and A280), also A260/A280 ratio of 1.5 - 1.89. I want to know where did I do wrong but I don't know how to evaluate it. Please help.
I am currently undergoing my end of year research project which is testing if DNA can be found in a secondary transfer when multiple transfers have occurred, one of my replicates have a Ct value of 0, do I include this or find an average of the remaining replicates as when I have calculated out the fold change of my replicates it has a value of 20.29
I am a beginner at qPCR and would highly appreciate help and thoughts on this.
The melt curve analysis states that there are multiple Tm peaks in all of the NTCs, however these NTC peaks are quite small compared to the single peak from the samples I'm analyzing (i.e. reactions with template) and therefore I am wondering how much of a problem it is. Most of the NTCs don't reach detectable amplification (image below). Is there a certain amplitude in the melt curve under which one can disregard signal as "background"?
I'm doing PCR and qPCR frequently. My assay is extremely sensitive so I need to pipette a very exact volume from my SOP. From my experience and also my school lab course, I learned to press the pipette to the 1st stop to get the desired volume, and then press all the way to the second stop to expel all the liquid. However, I was harshly judged by my supervisor for using such skill. My supervisor said it must be done only at 1st stop, picking the liquid at 1st stop and expelling the liquid at 1st stop. I tried the above way but always left some residue on the wall of the tip, which raised my concern a lot.
My supervisor made a judgment that if I can't expel liquid at the 1st stop, it is my tech skill problem. I also asked some co-workers ( sophisticated), and some of them indeed stop at the 1st stop to dispense an accurate amount, and ignore the residue on the tip. One coworker advised because of the air pressure, angle, and speed when you pick the liquid, it might over-pick the liquid so dispensing at 1st stop is accurate enough.
My liquid usually thawed cold serum, enzyme, probes, and buffer, so I'm less concerned about evaporation. I'm grateful for any suggestions here!
If protein expression in blood monocytes was low (detected by western blot) and protein level in serum (detected by ELISA) was high? what dose it mean?
Note: the protein should not leave the nucleus because it’s a DNA-binding protein.
I have never isolated RNA from blood, let alone serum and I really don't know which is the best strategi. Also I don't know which kit to use. For now, I found 2 Qiagen kits: miRNeasy Serum/Plasma Advanced Kit and QIAamp RNA Blood Mini Kit and I don't know which one is more suitable. After isolation, I'll perform RT-PCR. What is your experience?
I am trying to set up a qPCR multiplex reaction using HEX and Cy3 probes; however, when using the HEX probe alone, I can definitely see a significant signal in the Cy3 channel. Has anyone ever observed something like that? I thought I could put them together but it seems like it's not going to be the case.
I would be grateful to get any advice on how to make this reaction better.
Just to give more details, I am using QS5 from ThermoF so the channel I am setting up are VIC (for HEX) and TAMRA (for Cy3).
Thanks in advance for your help!
So i'm testing samples for RNA viruses. We are using the phenol chloroform RNA extraction method, nanodropping to find concentration of RNA and quality, diluting the samples (in NF water) to 200 ng/microliter, converting the RNA to cDNA, then running q-PCR on each sample (in triplicate) with a no template negative control. All of the wells show DNA growth at various cycles. I had my mentor run everything from scratch as well, and he is also getting product in the NTC wells. Has anyone seen something like this happen before?
What protocol is best for determining the protein expression level in the brain using quantitative RT-PCR?
Brain tissue preservation suitable for the protocol and how to analyse
I'm designing a diagnostic qPCR triplex assay for three parasitic species and have this weird weird thing that keeps happening where I test my assays in singleplex and they're specific but then in triplex one of them isn't. I have done every possible BLAST and bioinformatic analysis to verify that the assay isn't cross amplifying, so I'm certain it's not a specificity issue. There's no genomic explanation and I've also contacted both ThermoFisher (the maker of the machine) and IDT (where we get our primers) and they said there was nothing that should be causing this problem.
The three probes I'm using are FAM, VIC, and TAMRA - recommended by a ThermoFisher rep and our machines are calibrated for all of these (I've seen this weird thing happen on multiple machines). The VIC assay is picking up the FAM target at almost the exact same Cq values as the FAM assay is picking up the FAM target. To me, that means the two species would have to be present in equal parts in the sample and it's not just a contamination problem since I'm putting in just one species' DNA in each well. The VIC assay still picks up the VIC target, but LATER than it picks up the FAM target. So apparently the VIC assay is picking up a completely unrelated species far better than it's own target species?? I think not.
I've done so much to verify that it's not a specificity issue, including running the VIC assay in singleplex with the FAM target DNA and only two wells of a positive control to make sure the qPCR worked - it showed absolutely no amplification in any of the FAM target wells. My NTC wells are always negative and my positive controls always work. I've reached out to anyone I can think of for help because I don't know what on earth is going on and no one has been able to give me even any possibilities. I have this weird feeling it's something with the settings on the StepOne Plus, but I'm not sure what.
Has anyone else had anything like this happen? Or does anyone have suggestions of other things to try?
Dear RG comunity,
I am analyzing my qPCR data of male and female, treated and non treated groups. Considering there are reasons to assume treatment may diffrently affect expression of genes of interest according to sex, two-way ANOVA seems appropriate (assumptions checked).
What I ask for help is the normalization process in the 2-ΔΔCt calculation, for which average ΔCt values of a reference/control group are important to calculate expression in the treated group. If I normalize values separated to sex (e.g. use ΔCt values of untreated males to calculate 2-ΔΔCt for treated males and do in the same way with females), would I still be able to compare sex or treatment x sex effects of a 2-way ANOVA?
I've determined the LOD/LOQ value for a qPCR assay (1 primer pair, 1 taqman probe) and it's higher than expected. I've been trying to figure out why - i've tried to find information about this but all I can find online is explanations of what the LOD is or reporting LOD values from different assays.
Could anyone point me in the right direction?
I have done my qPCR experiments and gave me some results, I used the DDCt method and I calculated the 2^(-DDCt), I transformed my data in base 10 logarithm and separated my samples between control and patients. I want to ask if I see that there is for example a fold change 4 times higher in patients for my gene of interest then I use one-tail or two-tail t-test, and what if the distribution is not normal, will I do non-parametric test, or I can skip the outliers and do the t-test. I am very confused in that statistical conundrum.
Hi to all.
My question is how can I optimize my RTqPCR if the cDNA dilutions ended up in similar Cq?
I synthesized my cDNA from 350 ng total RNA, assuming 1:1 production I should have 350 ng cDNA in 20 ul right? Then I did a dilution of 1/2, 1/5, 1/10 and 1/20 (I know the first three are consider quite a lot to be used in the run) and used them in a 20 ul run. The gene is a ref. gene: GAPDH. Interestingly the Cq values aren't that different between the dilutions (~29, ~30, ~31 and ~30). Obviously these aren't good values but I don't know what can I do to optimize the run.
I've run RNAseq and qPCR on a set of genes, and while the log2 expression values are consistent between the tests for most of the genes in the set, there are a handful that appear to be unregulated according to the RNAseq results and down regulated according to the qPCR results (and vice versa). Is there any possible reason that could explain this, other than just human error?
I've read that I need cDNA standard curves to test primer efficencies.
I'd like to know if I could use genomic DNA instead, since I've got too many different conditions from which different cDNAs have been synthesized.
In the end, depending on the efficencies, I've read that I may use either the Double Delta or the Pfaffl method to quantify the relative expression of genes.
Hello ResearchGate community,
I have run standard curves on cDNA synthesised from whole mouse liver RNA and for two of my genes of interest I have gotten melt curves that have a smaller secondary peak (see attached images 1 and 2). I am using a SYBR Green Master Mix and running the samples on a Quantstudio 12k flex machine - the rest of my genes of interest appear to be working well and producing a single clean peak (see attached image 3). I am unsure of whether this indicates primer dimerization, non-specific amplification, or is of no concern as I cannot find anything online about what a peak after the primary amplicon peak means... Please help!
Thank you for your time.
I am performing QPCR with the E-cadherin gene and getting ct values around 11-12. I had done the same experiment two years back but back then the ct values ranged around 25-26. Can anybody explain what's happening?
Are ct values around 11-12 for a gene like ECAD acceptable?
Thanks in advance,
I have values of two treatment groups with similar gene target and all 3 groups have the same beta actin value. Treated 1 = 21.00 cq and Treated 2 = 20.50 cq, while both treatments having the same Control = 19.00 cq. From this rough data, is it reliable to say Treated 2 have higher expression than Treated 1 against Control due to lower cq value by 0.5? Thank you in advance
My problem is that unfortunately I am getting a good Ct value in NTC (NFW). I have tried three different primer sets (Target of three different genes) with NFW from different sources and surprisingly each primer set returned with a good Ct values.
Believing that SYBR-green (fast) could have been contaminated so, I used a totally new SYBR-green reagent yet there was no significant change observed upon RT-PCR.
I ordered new set of primers for same genes thinking that might be stock primers were contaminated and repeated RT-PCR with new SYBR green and fresh NFW yet I see there is very mild yet not a big difference in Ct values.
I use gloves and filter tips all the time.
I will be very grateful if anyone can give me some useful suggestion or insight to fix this never ending problem.
Reaction volume per well= 20ul
Primer conc.- 1uM each (FP/RP)
amplicon size ≈ 70-80 bp
Gene 1 Ct= 19
Gene 2 Ct ≈ 26-28
Hello, I would appreciate getting help to do appropriate statistical analyses on my qPCR data in GraphPad Prism for the sake of the statistical tools and graphing power.
I had already done the delta delta Ct analysis in Excel. I am dissatisfied because Excel doesn't offer much statistical power or good data visualization. Additionally, looking at the 2^-deltadeltaCt does not follow a Gaussian distribution, so nonparametric tests are required.
My experimental design is as follows:
3 technical replicates were done per sample
2 biological replicates were done per condition
I have 1 control (untreated sample) and 5 additional conditions
I have 2 genes of interest and am normalizing to GAPDH
So, for example, for each gene of interest tested (as well as GAPDH), I had two wells of iPSC untreated, isolated the RNA from each, then used the RNA from each well to do 3 PCRs for 1 GOI.
If I just try giving graphpad the 2^-deltadeltaCt to compare and analyze, then it will only see a sample size of 2 for each condition and there will be no statistical significance found between conditions.
How would you recommend I analyze this data in Graphpad?
Since I am still relatively new to GraphPad and not a statistician, it would be appreciated if more of a step-by-step solution is offered.
I've got a Roche LightCycler that I use to run my qPCRs. It used to take 1h 37 min to run a qPCR but now it take 1h 45 min using the same exact settings. Could the problem be that the machine is taking longer to reach certain temperatures? Does that mean it needs servicing?
The first few qPCR runs the primers worked and I was getting signals in my samples/positive controls. However, now when I run another qPCR with the same primers I get no signal. I've tried using a fresh primer aliquot but that hasn't worked. The primer stock has been defrosted several times and is less than a year old so I'm not if its degraded or not. If it's degraded, would it make sense to slightly increase the primer concentration in my PCR reactions to get a signal?
I am doing qPCR to quantify silencing some of the proteins I work with. but I see that my silenced values are greater than that of the control ones.
I got higher values in silenced samples after the double delta ct analysis. Essentially, my silenced samples are showing higher transcript levels compared to the un-silenced ones.
I am currently using the Quanti-Nova SYBR Green PCR Kit from Qiagen and I would like to know how much cDNA (ng) will be sufficient for amplification of my genes of interest (BMP2, 6 and SMAD6). Does anyone have worked with this kit previously?
And another question if at a qPCR experiment we use the same amount and some of the samples are not amplified, this means that they are undetected or I must modify the concentration so that qPCR can detect the very low concentration for the sake of statistical analysis?
I need to validate a series of reference genes (i.e. housekeeping genes) for my qPCR assay and I need to construct a standard curve for each primer pair to check for efficiency. I am using range of snail embryo tissue samples, as well as a snail adult tissue sample.
Due to the limited quantity of RNA from the embryos, I would prefer to use the adult tissue (of high RNA yield) to conduct the standard curves. However, I am planning to use both embryo and adult tissue samples for validating my primers, but it's likely the adult tissue samples will be excluded from the comparison of expression data.
Thus, my question is: would it be acceptable to use cDNA from the adult tissue to examine primer efficiency (e.g. constructing standard curves) as long as the sample is tested (and included in primer validation experiments), but not used in the final analysis?
I am working on genes differentially expressing between normal and diseased conditions. I finished qPCR and obtained 2^-DDCT values for 9 different genes. I am using Graphpad Prism for visualization of the data. I need help regarding statistical analysis of this data, whether to use unpaired T-test or 2 way ANOVA for this data.
I performed a qPCR to assess the collagen type 1 gene expression in the mesenchymal cell line.
prior to the test, I got a little amount of total RNA than what I regularly get (10 ng/ul) as the cell growth was low.
so during performing the qPCR I found out that my housekeeping gene has a Ct of about 30 but the target gene (collagen 1) has no Ct in 40 cycles. so I decided to extend the cycle number to 60 cycles. I observed the Ct for collagen type 1 on cycle 50.
if there were no primer dimer and nonspecific products in the result, is this result reliable for gene expression assessment?
thank you for your kind guidance in advance.
I have used GAPDH, SDHA, B2M and 18S. All of these HKGs are showing varying Ct values.
1. I am starting with diff. cell numbers but am using the same RNA amount for the cDNA synthesis.
2. I am isolating RNA from a monolayer and the supernatant. The GAPDH and 18S Ct values for the monolayer are almost identical. There is less deviation for the supernatant 18S CT values. I have used GAPDH for WB with the same treatment and same cell line and it showed to be a good HKG.
the cells I am collecting in the supernatant is a mixture of live and dead cells. Could it be that my HKGs are being degraded causing high Ct values?
Is there a good HKG for dying cells?
Is there a way of separating live and dead cells from the supernatant?
Does anyone have recommendations (design and/or protocols) for carrying out SNP genotyping with HRM on a CFX96 without the precision melt analysis package? Is it possible?
My understanding is the sensitivity is gated by the machine (and possibly the calibration kit), so I'm not sure why the software is even needed when open source analysis packages are available.
Thanks in advance!
So, I’ve gotten to a new lab, I used to use PEX4 as a qPCR reference for Arabidopsis expression. I’ve gotten to a new lab that works on mice and humans, they have used here HSP60 as qPCR reference, I see it’s not as reliable as I’d like, the molecular biology “department” here was one person before me that left 4 days after I was hired, so it’s just me now.
I’m looking at papers and I see there a few dozen recommendations for different housekeeping genes, with varying levels of reliability, can anyone point me to specific gene and/or primers that work for them personally for mice and/or humans?
* Edit as I have not worked with mice before, I was unaware of the need for this information, thank you John Hildyard, we are working predominantly with C57 mouse line and its MDX mutant (male and female) and with quadricep, gastrocnemius, diaphragm, liver and hart tissues.