microRNA - Science topic

Making real time PCR primers for microRNA isn't too difficult nowadays. First make sure your RNA is isolated with columns that will retain microRNA fraction of RNA. So don't assume that your traditional RNA column which you used for mRNA expression study will have miRNA fraction. There are excellent spin columns from many vendors, QIAGEN & ThermoFisher coming to mind as top choices (though others will work as well :)). For the primers/probe target you should probably aim for the active strand of the miRNA which is either the 3' or 5' strand depending on the microRNA. A good database to screen for the active strand is miRBase, an example for hsa-miR-124-1 is here: https://mirbase.org/hairpin/MI0000443. In this case, miR-124-3p seems to be the activate strand as most NGS reads are placed on the 3' end of the miRNA (click "Show Histogram" in Sequence tab).

You will also need a miRNA reverse transcription kit, you will have to be mindful of how the miRNA is reverse transcribed as that will determine what kind of primers or probes will be possible with that assay. If you don't want to optimize much, Thermo has TaqMan assays that take away a lot of the hassle of optimization with their reverse transcription + TaqMan probe assays (https://www.thermofisher.com/order/catalog/product/4366596). If you have a decent budget and you don't feel comfortable with primer + probe design, I highly recommend looking into Thermo's Taqman and Exiqon LNA assays by QIAGEN. Exiqon makes it very convenient to run plate assays (96/384) with all the necessary normalization/QC checks in an assay setup for a decent price. After that, microRNA interpretation is very similar to mRNA expression analysis so follow all the best practices that you are familiar with for real time PCR.

1. Silver

2. yes, 25 nt DNA ladder

3. Yes, I do have access to a

In miRNA TaqMan assays, exogenous control standardization is crucial for accurate and reliable quantification of microRNA (miRNA) expression levels. Exogenous controls, also known as spike-in controls, are synthetic RNA molecules that are added to each sample at a known concentration before the RNA isolation step. These controls serve as reference points to normalize variations in sample preparation, RNA isolation efficiency, and reverse transcription efficiency across different samples.

As long as you gather your Cq values at a valid threshold on a per target basis, If you have same cDNA and same master mix, your comparisons will be valid. However, to avoid interplate variability, it is best practice to pipette all samples for a given target, and reference genes on a single plate.

MicroRNA (miRNA) encoding genes produce non-coding RNA molecules that regulate gene expression post-transcriptionally by binding to target mRNAs while protein-coding genes produce mRNA transcripts that are translated into proteins. MiRNA genes often produce long primary transcripts (pri-miRNAs) processed into precursor miRNAs (pre-miRNAs) and mature miRNAs, whereas protein-coding genes contain exons and introns and undergo transcription, mRNA processing, and translation. The transcription of both types of genes is regulated, but miRNA genes may have distinct regulatory elements. MiRNA biogenesis involves processing steps mediated by specific complexes, leading to mature miRNA incorporation into RNA-induced silencing complexes (RISC). MiRNAs function post-transcriptionally to destabilize or repress the translation of target mRNAs by binding to complementary sequences in their 3' UTRs, while proteins translated from protein-coding genes perform diverse cellular functions. These differences highlight the distinct roles, structures, regulations, biogenesis pathways, and functions of miRNA-encoding genes compared to protein-coding genes.

You will use the blood RNA extraction kit, such as 1154G84 Quick-RNA Whole Blood Kit (50 Preps), however RNA degrades quite quickly even in -80 so if your samples several weeks old it is quite likely that you will extract highly degraded RNA.

A Genbank entry is not considered a publication. But in your Researchgate profile there is a place to add unpublished work (Research from the profile menu, then Research Items, then Research.)

Expressing short hairpin RNA (shRNA) versus microRNA (miRNA) primary transcripts in a lentiviral vector for gene silencing purposes involves leveraging different aspects of the cell's natural RNA interference (RNAi) pathway. Each approach has its advantages, depending on the context of the research or therapeutic goal. Here's an overview of the advantages of using shRNA and miRNA primary transcripts in lentiviral vectors:

Advantages of shRNA Expression

Advantages of Expressing miRNA Primary Transcripts

Conclusion

The choice between shRNA and miRNA primary transcripts depends on the specific requirements of the experiment or therapeutic application. shRNAs offer a direct and efficient way to silence specific targets, making them suitable for applications where potent knockdown of a single gene is desired. In contrast, expressing miRNA primary transcripts offers a way to harness the cell's natural gene regulation mechanisms for more nuanced control over gene expression, potentially reducing off-target effects and providing the ability to target multiple genes within a pathway. The decision should consider the desired outcome, potential for off-target effects, and the biological context of the target cells or tissues.

l Take a look at this protocol list; it could assist in understanding and solving the problem.

Ideally the authors would state it somewhere in the paper, but there is a database where you can get an idea of which one is the active strand using miRBase. In the link below, look at the "Sequence" segment and click on "Show Histogram" which is based on NGS reads. This will give you an idea of which strand is likely to be active for your microRNA of interest. If the authors gave you the microRNA primer sequence, you can also try to map it back to 5' or 3' ends.

Really, any PCR-based reaction will tend to produce weird stuff if you leave it long enough: when the reaction is exponential, anything, anything that the primers can bind (including the taqman probe, and themselves) to will tend to end up being amplified. Even if the binding affinity is terrible and the chances are almost zero, it only needs to happen once to get something that can be amplified by subsequent rounds.

But that's usually ok: Cq of 33 is very, very close to the barest minimum quantification anyway (rule of thumb is that Cq 35 roughly corresponds to a single target molecule per well), and if your miR of interest is routinely present at Cq values comparable to your 'background' Cq of 33, then it's safe to conclude that your miR of interest isn't meaningfully present.

In my experience, miRs are usually fairly robustly expressed (Cq 20-26) or not at all (Cq 33+).

It honestly sounds to me like you're wasting a lot of time, energy and effort trying to remove non-existent contamination: this isn't contamination, it's just the way your assay works, and that's...ok: assays are rarely perfect.

Hsu Lei Wai Well, I typically use 25-30mL for larger blots. But this is dependent on the size of the bottle you are using and the width of your blot. Even though the protocol suggests lower volumes, I try to make sure the membrane is sufficiently covered with buffer while it is rotating.

It depends on the design of your study, if those are important, you can show them, otherwise, no. You can create a PCA plot and see if they clustered together or search for their targets and see if their targets cluster together.

Hello Sreedevi M J

How have you designed the primers for the MicroRNA? You can try setting up a real time experiment and run the products on an agraose gel if the primers are working fine. Also, try to check the process for cDNA preparation as the cDNA preparation for MicroRNA requires a different approach.

Hope this helps. Good luck.

Lenin Kumar Bompalli Thank you so much sir.

Thank you so much for your suggestion.

Dapi stains most of the immune cells. However, the positions of cells on the plot determine the live and dead if the dots on your plot for a concentrated line or any group on the right side(away from the y-axis) are the dead cells.

I'm not sure about micro RNA, I used it for the innate lymphoid cells.

Victoria Cairoli

Hello vectoria, firstly thank you for your response.

I dont make any replicate and many samples in my plates have undetermined ct values and values above 35, so from this I dont know how I can deal with this to complete my calculation ?

the other thing also I have 2 control , so here also take geomean or arithmatic mean ?

You could use a T7 self splicing intron to make your RNA circular which would protect against exonucleases.

Dear Dr. Pal

It gets frustrating when one faces contamination during PCR. The unexpected signal could be caused by contamination of reagents with template, DNA contamination or amplicon contamination.

I have listed down a few measures which you could adopt to prevent contamination.

1. Use separate rooms, one to prepare the PCR master mix and the other to add the template. This will help prevent introduction of template into the reagents. As far as possible, use a benchtop PCR hood to prepare the master mix as this will help minimize the risk of contamination, and a benchtop hood is simple to decontaminate.

2. Try to keep the components of the PCR mix like the enzyme, primers and probes, nuclease free water as well as the consumables like the pipettes, microfuge tubes and tips in a room that is not used to isolate or store the template.

3. Pipettes are a common source of contamination by means of aerosols. So, make use of filter tips that will act as a barrier between the pipette and the liquid being measured, thus preventing the transfer of aerosols into reagents and samples. Also, make use of positive displacement pipettes that will help limit the risk of aerosol contamination.

4. Clean pipettes and the working surface area with 5% bleach. You may also use UV sterilization to decontaminate the equipment including the pipettes and the tube racks.

5. Use a no ‘RT’ control in your experiment by omitting reverse transcriptase in the reverse transcription step. This will help you to identify genomic DNA contamination in RNA preparation.

6. Try to store your stock solutions in aliquots for one time use to minimize the risk of contamination of the stock solutions. Also, if you observe contamination, you may always repeat the experiment with fresh aliquots.

Since you have observed contamination, my advice to you is to repeat the experiment with fresh stocks. You may have to discard the existing reagents.

Regards,

Malcolm Nobre

One study identified 55 miRNAs that were altered in COVID-19 patients during early-stage disease, compared to healthy controls. The most strongly upregulated miRNA was miR-31-5p, which is associated with inflammatory disorders. The study also found a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) that could independently classify COVID-19 cases with high accuracy .

Another study suggested that SARS-CoV-2, the virus that causes COVID-19, may act as a “miRNA sponge” and disrupt the normal function of host miRNAs. The study found that some miRNAs, such as hsa-miR-302c-5p and hsa-miR-16-5p, may be targeted by the virus and affect the expression of genes related to the ACE2 receptor, the entry point of the virus into human cells .

These studies indicate that miRNAs may be involved in the molecular mechanism and clinical outcome of COVID-19, and may have potential applications as biomarkers or therapeutic targets. However, more research is needed to validate and elucidate the role of miRNAs in COVID-19.

If you want to learn more about miRNA related with COVID-19, you can check out these web search results:

The PowerUp SYBR Green Master Mix from Thermo Fisher Scientific is a commonly used reagent for real-time PCR, and it is compatible with a variety of real-time PCR instruments, including the QuantStudio 5 from Thermo Fisher.

However, when using different kits and reagents from different manufacturers, it's important to ensure compatibility and optimize the conditions for your specific experiment. Here are some considerations:

Before proceeding with the large-scale analysis of patient samples, it's advisable to perform a pilot experiment with a subset of samples to optimize the assay conditions and validate the performance of the primer sets, cDNA synthesis, and real-time PCR using the PowerUp SYBR Green Master Mix.

Always follow the recommended protocols provided by the respective manufacturers for each kit and reagent. If you encounter any issues or have specific questions about compatibility, you may also consider reaching out to technical support from both Qiagen and Thermo Fisher Scientific for guidance.

I would try again but follow the kit guidelines for using 75 ug. Even if you don't need that much, it is likely that the kit works better with the suggested amount. For example, you might need to saturate binding to the beads. If you really want to use less, scale down proportionally.

The number of participants and their demographics could be a factor. Race and differences in cardiometabolic issues might be influencing the differences miRNA expression. Try look at the DEGs associated with the miRNAs that didn't match up, this will give you more insight into what's happening. It could be technical differences like choice of housekeeping genes, normalization process.

Ethtidium bromide makes double stranded DNA run about 10% slower than EB free dna so it is possible that the secondary structure of the RNA is intercalating EB which is slowing its migration down

miRNA impacts several processes of the cells. Their activity depends on their bindings with mRNA and a single miRNA can bind with multiple mRNA. You can go through these reviews which have described many miRNAs and their role in Covid-19.

Regards

Saurabh

Could it be that cells with different ages express different levels of laminin-1? On this paper DOI: 10.1002/aja.1002030404, I have found that "Different temporal patterns of laminin alpha 1, beta 1, and gamma 1 subunit chain expression were observed" ...

Hello Sameena Alam

The main difference between TaqMan and SYBR green assay is specificity.

In SYBR green assay, the dye binds to any double-stranded DNA sequence. This means that you could also detect fluorescence emitted from non-specific qPCR products, such as primer dimers. This can result in false positive results.

The TaqMan assay only measures amplification progression of the target sequence, as the probes are target specific. Also, you can monitor the quantity of various qPCR products in a single reaction by adding different primers and TaqMan probes with different reporter dyes to the master mix. This multiplex approach allows you to detect several fluorescent signals at the end of every thermal cycle.

I would recommend the use of TaqMan assay for your project as it is highly sensitive and will help in specific quantification compared to SYBR green assay. If you have enough funds, you should go for the TaqMan assay because TaqMan probes are expensive.

Best.

Jochen Wilhelm, Thank you so much for the elaborated and scientific answer :-)

It depends. Did you extract only the miRNA or the total RNA? If it's a micro RNA specific kit, a low RIN is to be expected, since it will filter out most of the ribosomal RNA that is required for the analysis. If you extracted total RNA, it could mean that your samples are degraded and this could then influence your downstream analysis.

Removing DNA contamination during miRNA (microRNA) isolation is crucial to obtain pure miRNA samples for downstream applications such as quantitative PCR (qPCR) or next-generation sequencing (NGS). Here are some steps you can take to minimize or eliminate DNA contamination during miRNA isolation:

Use DNase Treatment:

Add DNase I (RNase-free) to your RNA sample after the initial miRNA extraction step. DNase I will digest any contaminating DNA.

Incubate the sample with DNase I according to the manufacturer's instructions, typically at 37°C for 15-30 minutes.

Ensure Proper DNase Inactivation:

After DNase treatment, inactivate the DNase by adding an appropriate inactivation reagent (e.g., EDTA or heat inactivation) and incubate at the recommended temperature and time.

Use RNase-free Materials:

Use RNase-free tubes, pipette tips, and other labware to minimize the risk of introducing RNases, which can degrade RNA.

Perform a Mock Reaction:

Include a negative control sample in your miRNA isolation process. This sample should be processed in parallel but without the addition of reverse transcriptase or any other enzymes used in downstream applications. This control will help identify any residual DNA contamination.

Monitor DNA Contamination:

Assess the presence of DNA contamination by running an aliquot of your miRNA samples on an agarose gel or using a DNA-specific quantitative PCR assay. This will help you confirm the effectiveness of the DNase treatment.

Optimize RNA Extraction Method:

Consider using a miRNA-specific RNA extraction kit or method, as these are designed to selectively isolate miRNA and minimize DNA contamination.

Quantify RNA Purity:

Measure the RNA concentration and purity using a spectrophotometer or a specialized microRNA quantification kit. Verify that the A260/A280 and A260/A230 ratios are within the acceptable range for pure RNA.

RNA Integrity Assessment:

Assess the integrity of your RNA samples using gel electrophoresis or a bioanalyzer. Intact RNA should show distinct bands corresponding to miRNA species.

Repeat DNase Treatment If Necessary:

If you still detect DNA contamination after the initial DNase treatment, you may need to repeat the treatment or use a more rigorous DNase digestion protocol.

Use RNA Isolation Columns:

RNA isolation kits that use columns with RNA-binding silica membranes can effectively remove DNA contamination by washing away DNA during the purification process.

By following these steps and maintaining strict laboratory practices, you can minimize or eliminate DNA contamination during miRNA isolation, ensuring the purity of your miRNA samples for further experiments

Removing tags aligning to exons and introns is a common preprocessing step in miRNA (microRNA) analysis for several reasons:

Focus on miRNAs: The primary goal of miRNA analysis is to identify and quantify miRNAs, which are short non-coding RNAs involved in post-transcriptional gene regulation. Exons and introns are parts of protein-coding genes, and tags aligning to these regions do not represent miRNAs. Removing them helps to focus the analysis specifically on miRNAs of interest.

Reducing noise: Sequencing data can contain various types of noise and artifacts. Tags aligning to exons and introns may arise due to background noise, experimental artifacts, or RNA degradation products. Removing these non-miRNA tags reduces noise and increases the accuracy of miRNA quantification.

Avoiding misinterpretation: Including tags from exons and introns in miRNA analysis can lead to misinterpretation of the results. For example, if you count tags from these regions as miRNAs, it can lead to overestimation of miRNA expression levels and false positives.

Streamlining downstream analysis: Many downstream miRNA analysis tools and pipelines are designed to work with data that only contains miRNA-specific reads. Removing tags aligning to exons and introns makes the data more compatible with these tools and simplifies the analysis process.

To achieve this, researchers typically use alignment tools like Bowtie, STAR, or HISAT2 to map sequencing reads to a reference genome, and then they filter out reads that align to exonic and intronic regions before proceeding with miRNA-specific analysis. This preprocessing step helps ensure that the subsequent analysis is focused on the biologically relevant miRNAs and provides more accurate insights into their expression and function.

Salah A. Alshehade can I start my work depending on what I had on nanodrop to start making cDNA synthesis and after that panel of miRNA because the all idea from the research is doing the miRNA panel and see if there are differences between healthy control samples and patients samples?

Use -20 degree C at lest.

Here is the general protocol. You’ll find lots of related pipelines for each step(s) in github.

1. Data Preprocessing: Clean and preprocess your datasets, ensuring data quality and consistency.

2. Identify Differentially Expressed Genes: Use appropriate tools to identify differentially expressed genes (DEGs) for each dataset (lncRNA, mRNA, miRNA, and proteome) between different conditions (e.g., infected vs. uninfected).

3. miRNA-lncRNA-mRNA Interactions: Predict miRNA-lncRNA and miRNA-mRNA interactions using bioinformatics tools or databases. This helps establish the ceRNA interactions.

4. Construct ceRNA Network: Combine the interactions from step 3 to construct the ceRNA network, where lncRNAs, mRNAs, and miRNAs form nodes, and their interactions form edges.

5. Network Analysis: Perform network analysis to identify hub genes or central nodes. You can use centrality measures like degree, betweenness, closeness, or eigenvector centrality to identify these hubs.

6. Integration with Proteome Data: Integrate proteome data with the ceRNA network by mapping DEGs to proteins. This can help you identify hub genes that are regulated at both the RNA and protein levels.

7. Functional Analysis: Perform functional enrichment analysis on hub genes to understand their potential roles and pathways in the host-pathogen interaction.

As it is a poly-A that means the end of your sequence is several A codons in a row. Therefore your reverse primer needs to be poly-T.

glass beads should be avoided when processing samples for DNA/RNA extraction because nucleic acid tends to stick to glass. If you’re working with RNA, it’s wise to use beads pre-treated to be RNase-free: https://lab.plygenind.com/mastering-bead-selection-for-effective-homogenization

Hello,

Try two-tailed rt-qpcr

Hi Ahmad

before going to the software, be sure the samples were extracted with the right protocol and kits to allow miRNA to be selected or selected in the whole samples.

fred

Ahmad Kodous Are you asking whether it makes sense to write a review article about the mentioned topic or do you expect the experts in this forum to write a review article for you? In the latter case, this forum is neither for doing your homework nor a ChatGPT chatbot.

Start reading, e.g.

Hello, @Rucha Wadapurkar

have found a good tool, I have the same issue. If you found please let me know

June Leslie i would recommend you to prepare cDNA as soon as you isolate RNA and then freeze the remaining RNA in -80 because you freezing and thawing will decrease the efficiency of of RNA as cDNA are more stable than RNA.

hi Seyyed Navid Mousavinejad

As Steven mentioned, now we can extract microRNA from almost all kinds of body fluids such as blood, serum, plasma, CSF, urine, tissue fluid, etc. Some kits are readily available to isolate miRNA of such small size from even tiny sample sizes. Best way to detect miRNA is qPCR. How we isolate miRNA from samples, based on that we have miRNA specific cDNA synthesis kit. Both SYBr and Taqman probe platform available to detect using qPCR setup. Like Steven, I would also suggest look for Qiagen kits or miRNA works. We have also used Qiagen.

Regards

Saurabh

We (Laura Leighton and myself) suggested that the RNAs of the transfected samples are "contaminated" withe vector/plasmid DNA.

see troubleshooting guide

Only, in the first lane (far left) in the RNA extracted from non-transfected cells there might be some residual genomic DNA, indicating that there is room for improvement in the RNA isolation procedure, while the RNA on the far right of the gel looks very good and free of genomic DNA.

Hi Hanie,

I've used the specially designed Qiagen miRNA kits before with good results to perform qPCR measurement of miRNAs further down the line, machery nagel also produce some cheaper kits however with lower yield obtained in my experience.

Plasma doesn't have much in it: it's a cell free fluid.

What RNA it does contain is...mostly miRs, and those are small (so don't contribute much in terms of RNA mass).

I extract from serum and typically get yields of "I am not even going to bother speccing this" (i.e. looow), so I just base my reverse transcription on volume rather than quantity. I can confirm that even [RNA] too low to measure still has LOADS of miRs.

People attach entirely unnecessary value to specific methods, often to the point where they forget what the data actually is.

You don't need a calibrator. It adds nothing here.

Your questions are "Does miR-A go up with macrophage content" and "does miR-B go down with macrophage content"

You can measure miR-A and miR-B in (hopefully) multiple samples that you have designated "high macrophage" and "low macrophage". You can also measure U6 in those samples, and you assume U6 will not change with macrophage content.

Now you normalise miR expression to U6 expression (miR-A - U6, miR-B - U6) for EACH sample: these are your dCt values for miR-A and miR-B, in each sample, and represent the normalised expression of those miRs within those samples.

These are your numbers, though they are on an inverted log scale, so smaller numbers = higher expression.

Multiply all numbers by -1 to make it more intuitive.

Now these are your numbers on a log scale (which is where you want them, because qPCR data is normally distributed when left as log scale).

Are all the miR-A numbers in high macrophage samples bigger than those in the low macrophage samples? Is the reverse true for miR-B?

Those are your questions, and you now have the numbers to answer them.

What would a calibrator do here? The question you are asking is "is this set of numbers different from this other set of numbers", and that isn't a question that needs anything to be "calibrated to 1". The numbers themselves are entirely arbitrary, and if you used a different reference gene, might all be completely different, but the relationship between those numbers should stay the same.

-dCt values of 2, 3, 2 vs -1, -1, -2 : that's a significant difference.

If you add 5 to all the values such that you now have 7, 8, 7 vs 4, 4, 3, it's still a significant difference (it's exactly the same significance value, too).

It's the relationship between the numbers you are testing, not the numbers themselves.

Also note, the mean difference between the two sets IS your fold change, on a log2 scale. For the numbers above, it's 3.666 (in both cases), corresponding to a ~10-fold difference. I'm using -dCt values here, so the first set is higher expression: the first set has ~10x as much expression as the second set.

I don't think you need to explicitly describe this process at all: you have Ct values, you used your reference to obtain dCt values (normalised data), you then tested that. That's statistically valid and entirely appropriate here.

What MIGHT be useful is to include (in your manuscript) a ballpark figure for the raw Cq values, because a major issue with normalising qPCR data is that all abundance information is discarded.

If you had the data above, and presented that, showing a ~10-fold difference in expression, but also noted that "Cq values for the second set of samples were ~28-30", this tells the reader (and you) that what you're looking at here is low expression becoming effectively "no expression" (Cqs in the 30s are near the limit of detection, and are unlikely to be of biological relevance).

If instead you said "Cq values for the second set of samples were ~20-22", this represents MUCH more abundant expression, so here biological mechanism is "loads of expression" becoming "slightly less expression", with concomitant effects on how you interpret these changes.

In essence, the difference between the two sets of numbers is what you're finding out. The approximate Cq values of your GOI gives you rough idea of abundance. The combination of these two allows you to make biological inferences.

If you are observing only one clear band (~800bp) on your gel after extracting total RNA using a miRNA isolation kit, it suggests that the majority of the RNA in your sample is likely degraded.

Total RNA extracted from cells or tissues typically contains a mixture of different RNA species, including ribosomal RNA (rRNA) and messenger RNA (mRNA). The rRNA molecules, such as the 18S and 28S rRNA, are usually the most abundant RNA species in a total RNA sample. They are larger in size and should appear as distinct bands on a gel.

The fact that you are not observing the expected bands corresponding to rRNA suggests that the RNA in your sample has undergone degradation, which can occur during various stages of sample preparation or storage. Degradation can be caused by endogenous ribonucleases, improper sample handling, or long-term storage at suboptimal conditions.

To confirm whether your RNA sample is indeed degraded, you can perform additional tests:

If your RNA sample is indeed degraded, it may be necessary to optimize your RNA extraction protocol or modify your sample handling/storage procedures to minimize degradation. Additionally, ensure that you are using appropriate reagents and following the manufacturer's instructions for the miRNA isolation kit.

Keep in mind that the specific cause of degradation in your case may require further investigation, and it's best to consult with experts or reference materials specific to your experimental setup for troubleshooting advice.

These video playlists might be helpful to you:

I also love the QIAGEN brand kits. Make sure to read the instructions carefully since there are several different ways to complete the protocol depending on the starting materials.

Hi Jay

maybe you could try the StringDb solution with the multiple proteins option (https://string-db.org).

it's free and easy to use, otherwise there is also DAVID, or you can install the cytoscape solution but it's more difficult to take the hand on.

all the best

fred

Hi Serena, you can do this with TargetScan (the stand-alone version; https://www.targetscan.org/cgi-bin/targetscan/data_download.vert80.cgi)

The installation is not super straight-forward but once this is done, you will be all set; the tool is super flexible. Good luck!

Dear Farhad Kamandi

I sincerely appreciate your precise explanation.

Saurabh Mandal Thank you

Hi Tiziana,

We, at RealSeq Biosciences, do have a SYBR based qPCR technology to detect any small RNA, I just sent you an email. Let me know if you are interested.

Md. Niaz Morshed Ok, thank you very much

The "mRNA trap" by circRNA does not mean it binds mRNA. Instead, this is an expression used to describe a process in which a non-translatable circular RNA is formed as an alternative splice to the translatable mRNA. This is a possible way of regulating the expression of the mRNA and the protein it codes. it could be also called alternative circularization.

I described this process in my review article:

You can also there refer to the original articles that described the process

The key step is cDNA synthesis. If you have total RNA then Qiagen has something miRScript RT II kit which converts all miRNA present in total RNA into miRNA-specific cDNA. That cDNA you can use with SYBr and amplify those cDNA with miRNA primer (of your interest). This kit is compatible with total RNA and SYBr. Otherwise, you will need to look at some other reagents and find compatibility with your stuff i.e your onetube PCR system should able to amplify miRNA. regular cDNA synthesis will work properly for miRNA detection.

Many miRNAs with the same name have different sequences across species. Even if the mature miRNA sequence is identical, they could have differences in the pri-miRNA sequence which will affect which strand is loaded onto Ago. I would not do the type of analysis that you are suggesting without rigorously checking the sequences of each miRNA in both species.

If you need very accurate estimation of the concentration of the miRNA, like for example if you are intending to do RNAseq, the advice is to use Qubit Fluorometers which measure the actual RNA amount in your sample after staining with RNA specific dye.

https://www.thermofisher.com/fi/en/home/industrial/spectroscopy-elemental-isotope-analysis/molecular-spectroscopy/fluorometers/qubit.html?ef_id=EAIaIQobChMI-rHuzuW3_QIVz5BoCR1pCwE1EAAYASAAEgISUfD_BwE:G:s&s_kwcid=AL!3652!3!555768009241!e!!g!!qubit%20fluorometer!7851124051!80648209174&cid=bid_pca_aqb_r01_co_cp1359_pjt0000_bid00000_0se_gaw_bt_pur_con&gclid=EAIaIQobChMI-rHuzuW3_QIVz5BoCR1pCwE1EAAYASAAEgISUfD_BwE

If the Qubit is not available so, you have to rely more on nanodrop (in my opinion) because the Bioanalyzer is intended mainly to evaluate the integrity of the sample and not to quantitate despite some people rely on it for quantification. The accuracy of the nanodrop in my understanding will rely on the purity of your sample.

See these answers for a similar question:

And these are some references:

primer efficiency depends above all on their design.

you can add DMSO at 5% if you don't want to change dramatically any of the contents.

fred

hi Tosca van Gelderen

A very interesting topic for discussion. I came across a few papers which may help you to get your answer.

Key points: Binding strongly or weakly with mRNA plays a crucial role but the expected degree of translational suppression and thermodynamic equilibrium for accessibility in RNA antisense interactions is very important.

This paper is about target prediction tool however showed the importance of seed regions for target suppression.

Here said, 3′ pairing quality matters a lot.

These articles described miRNA influence capturing.

Regards

Saurabh

Smearing of amplified dna is often caused by over amplification when the amplimer is annealing with other amplimer molecules and amplifying longer pcr products. This is very often caused by pcr product contamination. Are your no template controls showing zero product as they should do?

Not letting the gel solidify and cool completely can have this effect as can running the gel at too high a voltage when the gel gets too hot and the ladder and samples run badly. If the buffer is hot to touch at the end of the gel run then this may be the case

hi Debora Olioso

I have checked the Diana Mirpath, MirTargetLink 2.0, Tarbase. They are functional. Links are attached below. I added a few more, may find helpful.

Regards

Saurabh

Mohammad Ahmad using nanodrop to quantify miRNA is not very reliable. The best way to quantify is to proceed further and quantify by qPCR.

To design primers for a specific miRNA using the NCBI database, follow these steps:

While designing primers for miRNA, it is important to consider the following factors:

Gayatri Gandhi Saurabh Mandal

thanks a million for helping me

Hi,

Just found this. https://diana.e-ce.uth.gr/lncbasev3/interactions

I don't know, how helpful it would be for you.

Regards

Saurabh

NO, even additional options for identifying mature, physiologically active miRNA is there but i think not for reverse transcription of RNA.

Hello Pablo Llevenes

Lipofectamine transfection reagent is a cationic-lipid transfection reagent that relies on cationic lipids to facilitate nuclei acid delivery into cells. The cationic lipids form a unilamellar liposomal structure with a positive surface charge and fuse with nucleic acids to form a transfection complex. By charged interactions between the cationic lipid headgroups with the negatively charged cell membrane, the transfection complex localizes to the cell surface for endocytosis and transfection.

Serum contains albumin which negatively interacts with the lipofectamine limiting their ability to interact with cellular membrane receptors. Albumin in FBS will block the aggregation necessary for your reagent with the nucleic acid to sediment and the positive charge on these particles necessary to enter the cells, thereby hindering their efficiency.

Use of Opti‐MEM which is a serum‐reduced media contains insulin, transferrin, hypoxanthine, thymidine, and trace elements that will help compensate for the adverse impact of FBS supplementation reduction with no change in the growth rate or morphology, and at the same time will help to increase the transfection efficiency. Also, absence of serum will help to avoid possible contamination with RNases.

Best.

Studies clearly suggest there is a difference in miRNA expression in plasma & serum. Begin from sample collection, RNA isolation, cDNA synthesis, qPCR, spikes, and endogenous control influences the process. Furthermore, the sample's condition like normal and disease all matters. So until you do it will be very unpredictable.

Please find some cardio papers that discussed the difference between plasma and serum samples for miRNA expression.

Regards

Saurabh

Could you please elaborate what is the reason for the higher miRNA yield from your isolation? If I am pointing correctly, then sample id 1--5 and 16-20, which may be 'without proteinase K', and if we compare those samples with others, there is no big difference.

Average estimation describes miRNA comprises ~0.01% of total RNA by weight. Even using standard kit isolation sometime miRNA isolation may not meet expectations. if your samples preparation using a different kit or you have done miRNA-specific treatment and then any difference emerges then that needs further analysis. However, You can analyze the miRNA of interest in these isolated samples using qRTPCR. Then see what happens.

Run your reactions on an ordinary gel. You should see bands. Check that this is the case. If yes, something is wrong with the fluorescence acquisition. If no, something is wrong with your melting curves. But as Katie A S Burnette said, it‘s likely the former.

If you want to lower your chance of having a batch effect bias and/or correct for it, option 2. is much better. In general, the more randomization you have, the better. If you, for any reason, you must use option 1. then mix your replicates between the flow cells. For example - WT condition Replicate 1 would go to flow cell 1, and WT condition Replicate 2 would go to flow cell 2. If use option 1. and you put both replicates on the same flow cell you would not be able to correct for the batch effect.

There is a nice paper about this Missing data and technical variability in single-cell RNA-sequencing experiments (https://academic.oup.com/biostatistics/article/19/4/562/4599254). Don't forget to check their Supplemental Figure 1.