Gene Expression - Science topic

Hi, My HL-60 cells also aggregates sometime...

Did you resolve that problem??

Was it really Mycoplasma contamination?

In bacterial studies, certain genes are often used as references due to their stable presence and relatively conserved sequences across different bacterial species. These reference genes serve as internal controls for gene expression studies, aiding in normalizing data for accurate comparisons. Some commonly used reference genes in bacterial studies include: such as 16S rRNA, gyrB (Gyrase subunit B),

rpoB (RNA polymerase subunit B), recA (Recombinase A), and tuf (elongation factor Tu), These genes are not only used as references for gene expression studies but also for phylogenetic analyses, species identification, and other molecular biology applications in bacterial research.

Hi

in a last paper I saw that 44 cell death genes were identified (based on array results) and I guess it could be more since it depends on lot of things, that's why I proposed this way.

all the best

fred

Go to this site (https://xenabrowser.net/datapages/?cohort=TCGA%20Colon%20Cancer%20(COAD)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443) and click on gene expression RNAseq -> IlluminaHiSeq* and then you can find download link.

1. It is not uncommon to get skewness even after normalization of the data. Some degree of skewness is inherent to RNA-seq data due to the natural variation in gene expression.

2. It is normal to get some common differentially expressed genes between two different conditions. This might occur due to perturbation in common biological processes or pathways.

3. You do not require any sophisticated formula to calculate fold change. The simplest way is:

a. For each gene, you calculate the average expression value across all replicates in each condition (conditions 1 and 2).

b. Divide the mean expression in condition 2 by the mean expression in condition 1.

Hi Oenone Rodgers,

I have previously completed over 10 projects on miRNA analysis. If you require any assistance with analysis, I can provide support until your publication. I am available to work as a freelance support (my email is [email protected]). Alternatively, you can reach out to us at [email protected].

Good luck with your project. We can connect with you for better clarity and assistance.

Regards,

Generating OVA (Ovalbumin)-expressing B16F10 melanoma cells involves introducing the gene encoding ovalbumin into B16F10 cells so that these cells express ovalbumin as a model antigen. This process is typically done to create a cancer model that presents a known antigen (OVA in this case), which can then be used in immunological experiments to study antitumor immune responses, vaccine efficacy, and T cell responses. Here's a simplified overview of how to generate such a cell line:

This process requires a good understanding of molecular biology techniques, cell culture, and genetic manipulation. If you're planning to undertake this project, ensure you have access to the necessary facilities and regulatory approvals, especially if you're working with viral vectors or genetically modified organisms (GMOs).

l Perhaps this protocol list can give us more information to help solve the problem.

Dear Colleague,

Identifying target sequences along with Protospacer Adjacent Motif (PAM) sites for CRISPR/Cas9-mediated genome editing involves a series of strategic steps. The CRISPR/Cas9 system requires the design of a guide RNA (gRNA) that is complementary to the target DNA sequence, adjacent to a PAM site, which is essential for Cas9 nuclease binding and DNA cleavage. Here is a methodical approach to identify suitable target sequences for your gene of interest:

By following these steps, you can identify appropriate target sequences with PAM sites for CRISPR/Cas9-mediated gene editing. This technology offers unparalleled precision in genome engineering, but its success hinges on careful target selection, design, and validation.

Best regards.

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

Look up some pre-designed qRT-PCR assay available in the market (e.g. ThermoFisher, IDT etc) already. You can find if that has been used in publications.

I think that if you have a good yield almost every RNA isolation technique may work (total RNA, micro RNA if you are interested). Do you use a kit or a classical trizol + cloroform/ethanol extraction?

Also it depends if you want to focus on one type of RNA: if you want to enrich in mRNA transcripts you can use oligo-dT Beads (as Lucia Hronska commented), if you want to focus on microRNAs or tRNAs you may use a micro-RNA isolation kit, if you want ribosomal RNA you can perform a gradient centrifuge (with sucrose for instance) and then isolate the enriched eluted fractions and later peform the RNA isolation (Ref: Liang S, Bellato HM, Lorent J, Lupinacci FCS, Oertlin C, van Hoef V, Andrade VP, Roffé M, Masvidal L, Hajj GNM, Larsson O. Polysome-profiling in small tissue samples. Nucleic Acids Res. 2018 Jan 9;46(1):e3. doi: 10.1093/nar/gkx940)

What sequencing technique will you use?? And in what kind of RNA are you interested?

Best regards!

Francesc

Yordan Manasiev , thank you!!

You can extract RNA from the cell lines (treated group and control) and then do qPCR for the expression of your three genes of interest. For your natural products use a chlorofom:methanol:water extraction and use a rotary evaporator to dry before resuspending in a small volume of DMSO. You can then add a volume of that to your cell culture media. As for how good your cell line will be for modelling cervical softening, I cannot tell. I advise using a positive control.

The Ct value depends not only on the gene expression but also on the amount of biological material used, its quality (integrity), assay parameters (e.g. background correction, selection of a threshold value) and the assay performance, all of which can differ between samples and/or runs. Only after somehow controlling all these possible influences you may interpret differences in observed Ct values as differences in gene expression. This is typically achieved (at least in part) by measuring the CT values of some internal control with presumably constant expression under all conditions and in all samples and using plate calibrators where measurements should be compared across plates or runs. And further, Ct values can be quite variable between samples, what mean that it needs some statistics check the statistical significance of observed differences.

Given all this was done but just not communicate by you, then the observed result may indicate a counter-regulation. If so, it should also dampen at even later time-points.

Of course it can. Why should this be a problem?

Dear Kiriakos Athanasiadis

If you have a knockout-animal, you could try it.

However, in knockout animals you usually don't remove the entire gene, but have a transcript that cannot be translated into a full protein.

I am also wondering:

Why would you want to confirm a PCR with a knockout-animal? You could just use a different set of primers for a different region of the cDNA and/or perform Western Blot with an appropriate antibody...

Thanks for your response. I have sent you a message. Regards

Hello Tiantian Fu,

DESeq2 can generate such files, usually the best one is VST normalized table, where you plot each gene alone with very little variations among samples.

Regarding the top 10, they are the 10 genes with the lowest FDR values from the DESeq2 results table, then you can go back to the VST normalized table and calculate the Z scores of the top 10 genes. After that you can use GraphPad prism to plot a heatmap.

Hope this helps

Use a promoter from one of the circadian rhythm genes? Or you could try a quorum sensing based repressilator.

Certainly! Here's a more human-friendly version:

When we talk about gene expression, we often use a measure called the Ct value in a technique called quantitative PCR. This value tells us at which cycle a sample's reaction shows the detection of a specific piece of genetic material. A lower Ct value means there's more of the target genetic material, and a higher Ct value suggests less or some issues.

For something like SARS-CoV-2, the virus responsible for COVID-19, the Ct value can be linked to the amount of viral genetic material present. So, if both a control group and a treated group show an increase in gene expression based on their Ct values, it seems like there's more viral genetic material in both.

However, the fold change, which compares the gene expression of the treated group to the control group, might show a decrease. This could mean that, relatively speaking, the gene expression in the treated group is lower when compared to the control group or a reference gene. There are several reasons why this might happen, including various biological factors or issues in the PCR process.

In simpler terms, even if the Ct values go up in both groups (indicating more viral genetic material), the fold change could go down, suggesting a relative decrease in gene expression. This could be due to a range of factors that affect the PCR process.

Citations:

[1] https://bitesizebio.com/24581/what-is-a-ct-value/

[2] https://publichealthproviders.sccgov.org/sites/g/files/exjcpb951/files/Documents/FAQs-CT-values-from-covid-19-PCR-tests.pdf

[3] https://www.thermofisher.com/blog/behindthebench/understanding-ct-values/

[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386165/

[5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9562486/

Hello Alifia Rahma

I agree with Mohamed Khashan . The calibrator is used to correct for technical variances between different runs. You will have to normalize each sample with its reference gene (GAPDH) of the treated group.

The untreated group may be used as a baseline relative to the expression/detection level of target gene in the treated group. The relative quantity of a target gene in a treated group could be expressed as the fold change relative to an untreated group, using GAPDH, a reference gene, as a normalizer.

You may understand the calculations much better if you refer to the link below. The link below presents data from an experiment where the expression levels of a target gene(c- myc) and an endogenous control (GAPDH) are evaluated. The levels of these amplicons in a series of drug-treated samples are compared to an untreated sample.

Best.

There is no cut-off for judging a fold-change (btw: fold-change already is a relative measure; it is superfluous to stress that you your fold-change is relative) being biologically relevant, as this depends on too many factors (what gene, involved in what signally/metabolic processes, in what cells, what is the proportion of relevant cells in the sample, at what time, are the cells synchronized, is the mean change considered or are expression bursts relevant, what compensatory effects can exist, can it be an unspecific side-effect without practical relevance in the cell, it the gene expression change counter-balanced by post-transcriptional or post-translational regulation, etc etc etc). It's your job as expert biologists to make such a judgement.

As Can wrote, the significance test only judges the information from your sample data to compare the sample estimate (b) to a hypothetical population value (h): d = b-h. If the information is considered sufficient, then b can be believed to be "on the correct side" of h, so the test tells us if we may have confidence in the sign of d, or that we can statistically distinguish b from h. This hypothetical value if often zero (h=0, so d = b), it is about interpreting sign of b (here: the sign of the log fold-change calculated from your sample).

Note that the point estimate b is associated with uncertainty. Consider the typical case that h=0. A test tells then that you can have confidence that the sign of b is correct, but not that b itself is correct. It might be that b is rather large and the p-value is small, but a irrelevantly small hypothetical value close to zero would not be statistically distinguishable from this h. So the data may be compatible with irrelevant values of h. If relevance (not statistical significance) is of interest, it's more useful to interpret the confidence interval (CI), which is the range of all possible values of h that would be statistically distinguishable from b (giving low p-values in a test). Only if the limit closest to zero is large enough to be considered relevant, then the information from the sample is sufficient to exclude irrelevantly small values of h and claim a "relevant effect".

But these are all technicalities. How large a relevant effect has to be is an expert judgement and cannot be answered statistically. Very often, biologists don't have much of a clue, so the best one can do to claim that one identified the direction of regulation (and avoid to say that the amount of regulation may not be of any biological relevance).

Dear Malcolm Nobre,

Thank you very much for your time and valuable answer.

Are there other suitable cell lines for this study? I found a cell line named NK-92. Do you think it is common to study this type of cell line, especially for my goal?

The best,

Introduction

RNA interference (RNAi) is a biological process in which RNA molecules are involved in sequence-specific suppression of gene expression by double-stranded RNA (dsRNA), through translational or transcriptional repression. RNAi is a powerful tool for gene silencing and can be used to study gene function and develop new therapies.

Different methods of RNA interference

There are a number of different methods of RNAi, but they can be broadly divided into two categories:

RNA-mediated RNAi

RNA-mediated RNAi involves the introduction of dsRNA into a cell. The dsRNA is then processed by enzymes into small interfering RNAs (siRNAs). siRNAs are short, double-stranded RNA molecules that are about 21-25 nucleotides long. siRNAs bind to messenger RNA (mRNA) molecules that are complementary to their sequence and cause them to be degraded. This prevents the mRNA molecules from being translated into proteins.

There are a number of different ways to introduce dsRNA into cells. Some of the most common methods include:

DNA-mediated RNAi

DNA-mediated RNAi involves the introduction of a DNA sequence into a cell that encodes a short hairpin RNA (shRNA). shRNAs are double-stranded RNA molecules that are about 21-25 nucleotides long. shRNAs are processed by enzymes into siRNAs, which can then silence gene expression.

DNA-mediated RNAi is a more stable and long-lasting method of RNAi than RNA-mediated RNAi. This is because the DNA sequence can be stably integrated into the genome of the cell.

There are a number of different ways to introduce DNA sequences into cells. Some of the most common methods include:

Applications of RNAi

RNAi has a wide range of applications in research and medicine. Some of the most common applications of RNAi include:

Conclusion

RNAi is a powerful tool for gene silencing and has a wide range of applications in research and medicine. RNAi is still a relatively new technology, but it is rapidly evolving and has the potential to revolutionize the way we study and treat diseases.

Proximity labeling (BioID-TurboID biotin labeling) in an expression system or co-IP proteomic would be helpful...

Heather Macpherson oh yay that is much better. I think edgeR or limma would be highly appropriate to process your data. The edgeR and limma user guide is an excellent resource and has many tutorials on its proper use. as Jochen Wilhelm explained very well you will not want to subset. In edgeR and limma you can filter by experiment which would require a design matrix. i would also generate a contrast matrix for the group comparisons. After your groupwise comparisons you can subset as you like. highlight those genes in a volcano plot or smear plot. If you have not done this before, I would highly suggest starting from homer step 1 and then directly to the edgeR user guide vignette. Good luck!! http://homer.ucsd.edu/homer/basicTutorial/index.html

Most standard cloning vectors have an ampicillin resistant gene (which is beta-lactamase). So those are nearly universal.

Unless you are performing protein purification for biochemical analysis, you don't require an optimized cassette. Most the naturally found genes for resistance will express adequately to confer resistance. So you just PCR out the genes from any plasmid carrying that resistance marker.

Source: Artificial Intelligence Tools

There are a few reasons why column names might be converted to "na.xx" when reading headers in R.

One reason is that the data source does not actually contain headers in the first row. If this is the case, you can use the read.csv() function with the header argument set to FALSE.

Another reason is that the headers are not formatted correctly. R expects the headers to be on a single line, separated by commas or whitespace. If the headers are not formatted correctly, R may assign default names like "na.xx" to the columns.

Finally, it is possible that there are special characters or problematic characters in the header names. R may struggle to read these characters, which can cause the header names to be converted to "na.xx".

Here are some tips for resolving the issue of column names being converted to "na.xx" when reading headers in R:

If you are still having trouble reading the headers in your data, you can try using the readLines() function to read the first row of the data file and then use the names() function to assign the headers to the columns.

Here is an example of how to do this

# Read the first row of the data file headers <- readLines("my_data.csv", n = 1) # Assign the headers to the columns names(data) <- headers # Read the rest of the data file data <- read.csv("my_data.csv", header = FALSE)

This should ensure that the column names are read correctly, even if they contain special characters or problematic characters.

I would suggest triplicates but I can't make suggestions about the variation in CT for just one set of primers.

Hi Daryl

you can test GEO profile (https://www.ncbi.nlm.nih.gov/geoprofiles/) to search for the kind of datasets you want to compare.

fred

This review should at least et you started:

Finally figured out what's causing low Cts. I'll share it here.

It was the Extension temp. I was using a 3-step protocol (separate extension at 72C from annealing) according to the manufacturer's suggestion for primer Tm < 60C. The 18S ref gene did not have any problem with the 3-step, only the GOIs. After tweaking cDNA input, primer conc., and annealing temp without success, I decided to revert to the standard 2-step qPCR protocol (annealing/extension at 60C), and then the Cts all went down to 20's. This was a surprise though since I've read that the 3-step protocol is supposed to increase amplification efficiency, with Taq operating at its optimal temp. How come a single annealing/extension temp was better?

Thanks to all those who suggested answers.

In single-cell RNA sequencing (scRNA-seq) data analysis, the preprocessing steps, including denoising and normalization, are essential to ensure that the data is suitable for downstream differential expression analysis. While it may seem intuitive to directly use denoised raw data without normalization for differential analysis, this approach is generally not recommended for several reasons:

In your example with C. elegans muscle cells at different ages, it's important to normalize the data to account for differences in library size between cells. This ensures that any observed changes in gene expression are more likely to reflect true biological differences rather than technical artifacts.

That said, you should still perform differential expression analysis on the normalized data. If a gene like Cox-4 is significantly downregulated with age, the normalization should help highlight this effect by reducing the influence of technical factors.

In summary, while it may seem reasonable to directly use denoised raw data for differential analysis, it's generally recommended to perform proper normalization to account for technical variations and ensure the robustness and biological relevance of your results in scRNA-seq data analysis

I assume the two tanks in system 1 cannot be independently temperature controlled and the same for system 2.

If the two systems have identical tanks and control/ciiculation mechanisms, then you could assume no tank effect and hence no need for tank replicates. If there are differences you could do a repeat of the whole experiment swapping the control and treatment tank system.

Dear Morvarid,

There are a number of methods to extract RNA. If you are intending to use an RNA extraction kit, any reliable company offers high quality kits. However, more conventional methods also result in high quality RNA. Depending on your aim (whether you test blood cells in an experiment or not) RNA can be extracted from blood and from isolated PBMC. In order to have high quality RNA, you must have highly viable cell pellet. Either extract RNA right after spinning your cells, OR snap-freeze using liquid nitrogen and store in a reliable -80 freezer until RNA extraction.

It's a very fundamental question. First, a appropriate cell line is essential for constructing a stable transgenic cell line. According to the cell line, an efficient method to transfer gene to the cell line is need, such as expression plasmid, lentivirus. Finally, the cell lines that stably expressed swine interleukin 2 can be obtained after a series of screening.

An article may be helpful as follow.

Thank You Anubhav Singh Nahar Sir for your response.

Were the annotations to the gene IDs result in hypothetical protein? Did you use KEGG mapper? If not you can find relevant EC numbers or use uniport id retrieval (https://www.uniprot.org/id-mapping). If nothing works, you can search for homologus genes and their IDs try to map your pathway.

If you don't want to use sra-tools, you can search for the SRA id at https://www.ebi.ac.uk/ena/browser/ and download the fastq files directly.

Great, thanks for your answer. But the viral genes have different lengths. I am wondering if I use the EDgR package. It will not take into consideration these differences. Thanks in advance

Use the same _amount_ of RNA in each reaction, wherever possible. Don't dilute your RNA prior to the reaction, there's no point. Dilute it as part of the reaction (i.e. add X ul RNA + Y ul H2O).

A lot depends on the max vol your kit can take, but if I know that I can use say...8ul of RNA total, and most of my RNAs are 200ng.ul-1 or above, I'll use '1600ng' as my target. If any of my RNA is less than 200ng.ul-1 (so I can't physically add 1600ng without exceeding the 8ul cap), I'll just add 8ul.

Most kits can take anything up to about 2ug, so that's your upper limit. 1ug would be fine, for example.

Once you've made your cDNA, dilute all of it to the same extent (1/10 dilution for everything). Definitely dilute it, though.

You're not just diluting out the target molecule population, you're also diluting out the cDNA synthesis buffer components so they don't interfere with your PCR reaction.

Yes, that’s correct. When a gene is down-regulated, it means that the expression of the gene is decreased. However, it does not mean that the gene is not being expressed at all. The product it encodes for is also decreased as a result of the decreased expression of the gene

Dear all,

thanks for the insightful commentaries of my colleagues. However, I beg to differ in some detail as this problem goes a bit deeper. (Just my 0.02$)

1. Throwing three samples into one is, of course, a bad idea because you waste ressources. Why not make three differently barcoded libraries and then send them for sequencing on one lane. You do not loose information and you can always ask for more reads if your sequencing depth is not sufficient. Thus you save in sequencing costs but keep all the options.

2. Do not make technical replicates. If you master the technique they will be +/- identical. If you have technical problems no replicate will help you anyway.

3. If you run biological replicates I wouldn't use the classical R-programs. Most assume that there is a "true" value that you can't measure because of random variation in your method/sample. However that is not exactly what happens in nature. Imagine you derive three transgenic cell lines with an inducible transcription factor to find target genes. Now you compare 3 times TFon/TFoff. You get following values:

TFon TFoff

geneA

sampleA: 1000 100

sampleB: 100 10

sampleC: 10 1

It is clear that geneA is very interesting. However, if you define sampleA,B,C as triplicate most analysis programs will throw this gene out because base-line expression has a higher variation as the overall difference between on/off.

Alas, geneA may be a perfect and important target gene as you do not control the overall concentration of the transcripiton factor in the transgenic cells. So it may be perfectly ok that you see this large variation in base expression. Fold-change is what counts here. In biology a value very frequently depends on more than one factor and not all can be controlled. Classical statistics fails in this cases.

Therefore I'd recommend to run barcoded libraries - evaluate each one individually and look for the intersection of genes that come up as interesting in all three instances. Then follow up on these.

In the end no statistics can replace the good old biological confirmatory experiment anyway (although "wet" biology seems to be out of fashion nowadays).

Good luck with your experiment.

Best

Robert

search for bicistronic plasmids in addgene

thank you

Chat-GPT really is a jabber-box. Context must be critically reworked by a person knowing the topic. Answers like the one posted by Rana above are not only not useful but also potentially misleading, not to say wrong. But most relevantly, it has nothing to do with a scientific interaction of researchers.

This would be Chi-square or Fisher's exact test on the contingency table like below:

High Low

gene_A 500 500

gene_B 450 550

gene_C 300 700

I believe Rob is correct.

Since you are using the intersect function, the numbers in your figure (e.g. 365 and 154) are the number of genes without any intersection.

The total genes of each set (e.g. OE21) will be the sum of all the numbers in each intersection + genes with no intersection. I couldn't do full sum for you as the core intersection number is missing.

Mohamed Khashan thanks!

Kishor Gawade It is not uncommon to get such results in case of siRNA.

Pls refer to the following comparative study which presents a protocol to avoid false positive results.

Hahn P, Schmidt C, Weber M, Kang J, Bielke W. RNA interference: PCR strategies for the quantification of stable degradation-fragments derived from siRNA-targeted mRNAs. Biomol Eng. 2004 Nov;21(3-5):113-7. doi: 10.1016/j.bioeng.2004.09.001. PMID: 15567105.

Hello! I like to use the "qpcR" R software to fit curves, but it isn't necessary. you can use the "delta delta Ct" method. Here is the gold standard instructions for qPCR experiments. Good Luck! https://www.gene-quantification.de/national-measurement-system-qpcr-guide.pdf

If you only care about normalized data to make a heatmap, you can use recount3. NCBI itself is also starting to offer raw and normalized RNA-Seq counts (beta version). You can also take a look at GEOexplorer

https://academic.oup.com/nar/article/50/W1/W367/6591531

OK, given the context of the research, you are going to want to start by using regular PCR to see if the region of the gDNA can be amplified at all.

In-silico predictions often have errors. You'll save yourself a LOT of stress if you can show that the genomic region exists in the DNA.

Besides, you'll need to clone that region of the genome to make a standard curve for your qPCR. It's a "win win" to check for the gDNA first.

Environmental factors such as soil nutrients, temperature, water availability and light intensity influence the genetic and chemical diversity of plant populations. These environmental conditions can exert strong selection pressures; they could even determine the evolutionary course of plant populations. Gene expression can be altered by environmental factors such as food, drugs or exposure to toxins. These changes can range from small to so significant that certain genes in our system can be turned off or on when they are supposed to be the opposite way. Environmental factors such as soil nutrients, temperature, water availability and light intensity influence the genetic and chemical diversity of plant populations. Genetic factors as well as local conditions affect the growth of an adult plant. The growth of an animal is controlled by genetic factors, food intake, and interactions with other organisms and each species have a typical adult size range. Primary abiotic factors are light, temperature, water, atmospheric gases, and ionizing radiation, influencing the form and function of the individual. For each environmental factor, an organism has a tolerance range in which it is able to survive.

You need whole protein to analyze gene expression. How is just one unit help in studying the expression, when you never know how many regulatory sites might be present in the other half.

The results of the transcriptome analysis of F1 hybrids suggest that the relationship between expression levels and allelic levels can be complex. However, the researchers were able to identify some of the factors that contribute to this complexity. This information could be used to develop new methods for predicting the expression of genes in F1 hybrids.

There are a number of factors that can contribute to the complexity of the relationship between expression levels and allelic levels. One factor is that the expression of a gene can be affected by a number of different factors, including the environment, the developmental stage of the organism, and the presence of other genes. Another factor is that the expression of a gene can be influenced by the interaction of different alleles. One allele may be dominant over another, meaning that it is expressed more strongly.

Bertrand Cornu Can Kiessling Audrys G. Pauža Mohamed Khashan Dino Santos Matias Thank u all. After many trials I have decided to use single temperature. I have to admit that still qPCR experiment is not so objective to me (changes according to conditions very easily). However, since everyone use the same method, and what matters is to compare the mrna level for the same protein, I guess it is fine.

Heatmap is not an analysis, it is just a visualization method of the results from analysis. There are many tools which can be used to make heatmap in R, python etc. If you have dataframe ready, any visualization tool can be used.

In R pheatmap or heatmap3 are examples.

Any transient transfection can be titrated to achieve a desired expression level. The CMV promoter is very strong and will work in most cell types. The mRNA and protein levels for your gene will vary depending on RNA stability, size, translational efficiency, etc. So, by transfecting varying amounts of your expression vector mixed with something inert, like Salmon sperm DNA, you can empirically determine how much you wish to express. For 100mm plate we use 6ug DNA with lipofectamine or fugene. But that can be 100 ng of vector with 5.9 ug inert DNA. Vector can be anywhere from 100ng to 6 ug.

When there is differences of more than 2 fold in a gene for a condition is known to be differentially expressed.

There are several machine learning algorithms that can be used for clustering and classification of gene expression data, including deep learning algorithms such as Convolutional Neural Networks (CNNs), as I mentioned in my previous answer.

Another popular deep learning algorithm for clustering and classification tasks is the Deep Belief Network (DBN). DBNs have been used for gene expression analysis and have shown good performance in identifying gene clusters associated with diseases and other phenotypes.

Other commonly used machine learning algorithms for clustering and classification of gene expression data include:

Ultimately, the choice of machine learning algorithm will depend on the specific characteristics of your dataset and the problem you are trying to solve. It's recommended to experiment with different algorithms and compare their performance to identify the best approach for your specific problem.

Honestly, DESeq2 takes almost no time to run: it's by far the easiest part of the entire pipeline.

So...do it all. All comparisons.

Male treated vs male untreated -> effects of treatment on males specifically

Female treated vs female untreated -> effects of treatment on females specifically

Male and female treated vs male and female untreated -> sex-agnostic effects of treatment

Male untreated vs female untreated -> sex-specific differences under normal conditions

Male treated vs female treated -> sex-specific differences under treated conditions

male (all) vs female (all) -> treatment-agnostic effect of sex

What you'd hope is that the same genes would crop up under the same sort of comparisons (i.e. the best hits for male treated vs male untreated would be the same as those for female treated vs female untreated), and any genes that bucked the trend by being highly altered in males but not females (or vice versa) would also pop out of the other datasets, allowing you to determine whether the differences reflect sex alone, or are a consequence of sex + treatment.

RNAseq data always gives you far, far more candidates than you can realistically handle, so you can be quite aggressive in your culling of "interesting but not that interesting" candidates, but making multiple comparisons in this manner allows you to interpret your results with more nuance. And it's really quick and easy to do.

Mitra Riasi

Many R packages are available for DEGs analysis. You may go through the following sources.

1. ccb.jhu.edu/software/stringtie/index.shtml?t=manual#deseq

2. rnabio.org/course/#module-03-expression

3. www.nature.com/articles/nprot.2012.016

4. www.nature.com/articles/nprot.2016.095

The TCGA gene expression units in the PANCAN batch effects normalized gene expression data available on the Xena browser are in the form of log2-transformed normalized expression values, where the normalization method used is not explicitly stated.

The normalization method used for the EB++AdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena dataset is not specified on the Xena browser website. However, based on the description of the normalization units, it is possible that the normalization was performed using the log2(TPM+1) or log2(FPKM+1) method.

The normalization units, log2(norm_value+1), suggest that the data may have been normalized using a method that produces values similar to TPM or FPKM. The "+1" is added to the value to avoid taking the log of zero, which can cause errors in downstream analysis.

To get more information on the normalization method used, you can try contacting the TCGA or Xena browser support team for more details. Alternatively, you can check the original publications or data sources for the dataset to see if the normalization method is explicitly stated there.

Just to add a bit more complexity, unless you have very strong molecular evidence to back up the gene model, any PREDICTED introns/exons/promotors/etc are just predictions. Bioinformatics tools use "generally true, most of the time" rules. But if you care about a particular gene, you'll have to spend time either in the literature or at the bench to see the evidence that supports/refutes that model for that specific gene.

The "intron" might not even actually be an intron.

Hello Viktoriia Iakovleva

I know about two companies that might be of help to you. See the details given below.

1) Synbio technologies

2) GenScript

Best.

The indicator (aka "dummy") variable is a variable that takes the values 0 or 1 to indicate the membership to a group. It takes the value 1 if the corresponding response value (the dCt) is from the "treatment group", and it is 0 otherwise.

In the linear predictor, this variable is multiplied by the coefficient coding the treatment effect as the expected difference in the response (the dCt values) between the indicated group (treatment) and the reference group (the group not being represented by an indicator variable; here: the control group). Nothe that the difference in dCt values is called ddCt.

It may be helpful to read some books about (general) linear models.

Oooh this is an interesting one. There's quite a bit here.

Firstly - I don't think you can assume that if mouse or human primers DO work, that means the cells are human/mouse and not canine. You could check the primer sequences against the dog genome with BLAST and see how similar they are.

Second - Sanity check your reaction setup. How much RNA did you put into each cDNA synthesis, and also what kind of primers and how much? How much cDNA template did you put into your qPCR?

Third - yes you can check if your cDNA synthesis worked. Check the concentration using Nanodrop or Qubit. If it's less than about 20ng/uL, your reaction failed. If it's more than that, run it on a 1.5% agarose gel and see what the size distribution looks like. You should see a broad smear from ~50nt up to ~1000nt - if it's very skewed towards low molecular weight fragments, the problem is that your RNA was degraded.

I would avoid wasting further RT or qPCR reagents until you know if your cDNA synthesis worked and what the size distribution is like.

Also - unorthodox suggestion but it just might work - would any of your canine-specific primer sets detect canine gDNA? If so - does anyone you work with have a pet dog? You would be able to extract a modest amount of canine gDNA from a cotton swab vigorously rubbed on the gums of any dog. This would at least let you check whether your canine-specific primers do recognise the dog genome. (If they are mRNA specific primers, you're out of luck here.)

Thank you Juan Pablo Tosar

Not necessarily. The level of gene expression does not always directly correlate with the amount of protein produced. There are many factors that can affect protein expression, including post-transcriptional modifications, translation efficiency, protein stability, and degradation.

Hi,

You can do qPCR to check the expression of the target genes.

As Katie said, degraded "smear" RNA looks the same as intact RNA in Nanodrop. If you want to prevent RNA degradation from frozen livers, I recommend using RNAlater. If you are using an extraction method such as TRIzol, it is better to use a column method such as RNeasy. And in some cases, using 50% ethanol (instead of 70% ethanol) may increase RNA yields from liver samples.

for alignment search? https://blast.ncbi.nlm.nih.gov/Blast.cgi

@Don Ashley,

Please provide the procedure of purification by Ficoll-Hypaque method.

ddPCR (Digital Droplet PCR) is a PCR-based method that partitions the reaction into thousands of tiny droplets, each containing a single copy of the template DNA. The droplets are then analyzed using flow cytometry to determine the number of positive and negative droplets, which in turn reflects the number of copies of the target DNA.

The reason why ddPCR does not require a housekeeping gene is that it is inherently a very sensitive and specific method. The droplet partitioning allows for a high degree of multiplexing, meaning that you can test for multiple genes at once, and the digital nature of the method enables very precise quantification of the target DNA. The high sensitivity of ddPCR means that even low levels of target gene expression can be detected and quantified, which reduces the need for normalization to a housekeeping gene.

That being said, it is always good practice to check for the suitability of the gene of interest as a reference gene and the specificity of the primers, in order to avoid unspecific amplification or cross-reactivity. Additionally, if you are interested in comparing the expression of your target genes across different samples or conditions, it may be useful to include a housekeeping gene as a reference to control for variations in sample quality or RNA integrity.

Hello Bruna Cunha de Alencar

It is known that CMV promoter has a tendency to lose its transcriptional activity and this is theorized to be due to gradual methylation at its CpG sites which may lead to low-level expression. In that case EF-1a would be better.

I have attached few papers for your reference.

Best.

You generally dilute samples to 1) preserve some DNA/cDNA for later use and 2) Lessen the concentration of inhibitors. Regarding "if we dilute DNA concentration by 5:1, after getting the raw number from qPCR machine, should we multiply the received number by 5 to reach exact gene expression copy number?", yes you would have to multiply by the dilution factor to get the quantity in the starting tube (this is assuming absolute quantification method via real time PCR). Alternatively, you can also use digital PCR if you have access to get absolute quantification without the use of standards. Some materials for you to look at:

https://www.gene-quantification.de/strategy.html

Generally the entire plasmid is imported into the nucleus, you can often find multiple copies of the plasmid in the cell. Transient expression is often achieved without any sort of integration into the nuclear genome, but it does go away with time. For stable expression you need to select for integrants.