Proteomics - Science topic

Brief storage of partially dried peptide samples at 4 °C over a weekend is unlikely to significantly affect peptide stability or LC-MS quantification. Proceeding with analysis as planned should be fine, but complete drying and colder storage are best for long-term stability.

Hi did you figure out the problem? I also have been having the same problem

Certainly detergent based lysis is necessary. Choose SDS concentration based on MS tolerance level. Talk to company's tech department what is the maximum amount of SDS you can use. Alternatively you can rupture your EV by sonication.

Good luck.

I would recommend R&D, and BioLegend

Abdelhak Maghchiche I tried EASY-IC for real-time internal calibration. It worked great for positive ion mode, but mass error was still significant for negative ion mode. Have you had any issues with negative ion mode internal calibration? Thanks!

Corning® DeckWorks™ low binding tips

Thank you for your answer Nikhil Dev Narendradev ,

I had a suspicion that this would be the case. I have looked at the correlation of TMT and LFQ fold changes, and it's not great unfortunately (plot attached). However, proteins with increased abundance in one technique do tend to show similar changes in the other technique.

I have decided to focus my analysis on the 25 proteins showing significance in both methods - this is a manageable list! I've not though to focus on proteins by those following a linear relationship however, that's an interesting idea.

Hi Tehzeeb,

Using XTandem, are you able to match any IDs in the test sample?

The only quantifiable entity in your results is Intensity of a particular species at a particular m/z

You can utilize the XCalibur or the analytical software for your mass spectrometer to integrate area under curve of the particular species at particular m/z in your mixture.

Good luck,

Hediye

Hello Bankole Abdul-Quadri

Genomics can improve diagnostic accuracy, predict which drugs are likely to be effective in patients, and contribute to the monitoring, treatment and control of infectious disease in individuals and in populations. One may explore the genomic structure of infectious agents, the implication of acquisition or loss of nucleotides, genes and plasmids on pathogenicity, evaluate how sequencing of the genome of infective organisms can be used for diagnosis, sub-classification and strain identity, and the sensitivity of a pathogen to drug treatment.

Few examples of genomics in clinical diagnosis of infectious diseases.

A. The application of nucleic acid sequencing and sequence-dependent detection methods may be used for the diagnosis and management of viral infections such as the blood-borne viruses human immunodeficiency virus (HIV) and hepatitis C virus (HCV).

B. Integration of genomic technologies into routine antimicrobial resistance (AMR) surveillance in health-care facilities has the potential to generate rapid, actionable information for patient management and inform infection prevention and control measures. Detection of drug resistance in plasma-borne viruses using DNA sequencing has been a mainstay for the clinical management of HIV-infected patients. This method is reliable and fast compared to the alternative, gold standard, cell-based phenotyping in culture and is also used to manage other viral infections that are treated with antivirals including HCV, hepatitis B virus, influenza and herpesviruses.

Proteomics expression provides biomarker identification by comparing the profile of protein expression between normal samples and those affected by the disease. When protein expression changes in biological pathways during disease conditions, monitoring of these altered proteins in tissue, blood, urine, or other biological samples can provide indicators of the disease.

Disease-specific biomarkers can be categorized into diagnostic for early detection; prognostic to predict disease recurrence; and treatment-predictive biomarkers. Predictive biomarkers classify patients into categories of responders and non-responders. This classification also is important in drug design applications. So, these biomarkers, in general, could reflect how patients feel and survive.

Few examples of proteomics in clinical diagnosis of infectious diseases.

A. Proteomic methods can be applied for the diagnosis of tuberculosis. Proteomic methods enable for the establishment of proteins secreted by the clinical isolates in vitro. Among these, rRv0566c, rRv3874, and rRV3369 have shown potential as sero-diagnostic antigens with a sensitivity of 43%, 74%, and 60%, and a specificity of 84%, 97%, and 96%, respectively. Therefore, kit-based serum screening test can be performed by using such groups of proteins.

B. Proteomics has played a significant role in the diagnosis of sera proteins during periods of SARS-CoV-2 outbreak. Severe acute respiratory syndrome is a viral infectious disease which was the cause of a number of deaths. For the treatment and control of this disease, a precise diagnostic approach would be important. Therefore, MS-based proteomic techniques can be used to detect SARS-CoV-2 viral proteins. Sera proteomic study of SARS patients can reveal possible protein markers of truncated forms of α 1-antitrypsin (TF-α 1-AT) that are consistently detected with higher concentrations in SARS patients than healthy individuals. These markers are proven useful as therapeutic targets, vaccine targets, and diagnostic tool for SARS patients.

The article attached below will be helpful!

https://onlinelibrary.wiley.com/doi/10.1155/2023/5510791#:~:text=Proteomics%20expression%20provides%20biomarker%20identification,those%20affected%20by%20the%20disease.

Best.

Hi Kay, i'm having the same problem. i used the Click-iT Enrichment Kit (e.g., Thermo C10416), but the enrichment results show significant non-specific protein binding.Did you find a solution? Thank you

Dear Enrique,

this file extension is specific for all Agilent platforms (HPLC, GC-MS, LC-MS, etc.). You will probably need OpenLab ChemStation or Agilent MassHunter to read and analyse the data contained in the files.

Best

Michael

Simultaneous RNA-Seq and proteomics are uncommon due to several technical challenges. First, RNA and proteins require different extraction protocols, and attempting to isolate both from the same sample can compromise yield and quality. RNA and proteins also differ in stability, turnover rates, and abundance, complicating simultaneous extraction. Proteomics is more complex, as proteins undergo post-translational modifications (PTMs) that require specialized mass spectrometry techniques, unlike RNA-Seq. Additionally, quantifying transcripts and proteins involves different methods, making it difficult to correlate their levels. Limited sample availability can further reduce material for both analyses, and the lack of standardized protocols for integrating RNA-Seq and proteomics data adds to the difficulty of adopting this approach.

Hello Giulia,

I too had thought the same. Maybe you are right. The shelf life of RIPA lysis buffer is 1 month when stored at 4 deg C.

Best.

Isoforms most likely. You also see it when you go from protein to gene or vice versa. You also have the complication of different species.

Some barley related articles.

Regards,

Joachim

Hi Aura,

Expression level and activity are two different things. It might be possible that your treatment is increasing the activity of cathepsin B (i.e. by PTMs) and therefore the expression gets compensatory downregulated to regulate to a normal level.

Best,

Murat

I would also recommend the albumin and IgG depletion, it will help with the identification of lower abundance proteins.

Manys thanks, Hussain Nizam !

I cannot really answer your questions, but wondered, if this will be of some help. I have seen a review article about how lactobacilli degrade milk protein, and the resulting short peptides have medicinal properties. For instance: "Many studies focused on ACE inhibitor peptides, probably

due to the ease of use of in vitro anti-ACE assays. The well-known

Val-Pro-Pro and Ile-Pro-Pro peptides are produced during milk

fermentation by some Lb. helveticus strains. ... An additional

ACE inhibitory peptide sequence (Ala-Ile-Pro-Pro-Lys-Lys-Asn-

Gln-Asp) was also identified in milk fermented by Lb. helveticus."

Raveschot, C., Cudennec, B., Coutte, F., Flahaut, C., Fremont, M., Drider, D., & Dhulster, P. (2018). Production of bioactive peptides by Lactobacillus species: from gene to application. Frontiers in Microbiology, 9, 409606.

As mentioned above by Dr. Albert Lee, you can use protein A or protein G to remove heavy chain. You can also use cation exchange chromatography to separated light chain from heavy chain, especially when the amount of the light chains you need is small since you can use an high resolution analytical cation exchange chromatography column.

Hi, you can check with Phenoswitch Bioscience, now Allumiq.

Here is their website: https://allumiqs.com/

James M Fulcher Thanks for your answer! I agree it is probably not trivial, because you cannot assume that *all* ionisations are with sodium rather than a proton, so it sort of explodes combinatorically at the MS2 level.

Unfortunately, I cannot rerun the samples, because I am trying to apply a new analysis pipeline to existing data on PRIDE, and I have found this phenomenon in other researchers' published data.

I have tried setting up sodiation as a PTM on MaxQuant, and I think it could work, but it increases the analysis time from minutes per sample to days per sample, so might be useful for a proof-of-concept, but if anyone has suggestions for a better solution – or knows of a way to this in MaxQuant already – I would love to hear them!

Hi Linoy,

answered this question already 2 years ago:

Best,

Murat

İsmail Emir Akyildiz Meryem wrote they add 15% TFA, so that's probably not a final concentration.

If you have A normalized to C, and B normalized to C, then you can compare A and B and set a cut-off differentiating the two. This will work if it is quantitative setting.

It is always better to include an internal standard (deuterated isotopes or any analog compound) to uncover any system or operator-related bias...

If you don't have any IS in your experiment, an alternative way to do this is tracking the intensity (and RT) of an inherently available compound (consider this approach as visualization of housekeeping proteins as normalization targets in western blot analysis).

If the system is Orbitrap, EASY-IC calibrant performance may also indicate some hints. A baffled system for ESI fluidics infusing the leucine enkephalin in the Waters system may also be beneficial...

shifted position for a sample in PCA is more common for biological replicates but not for technical replicates. If the instrument performance is stable during analysis, it is probably caused by either sample prep or autosample operation failure.

My best way to answer this:

following an articel by Gianetto (2016 - Uses and misuses of the fudge factor... DOI 10.1002/pmic.201600132) I downloaded the siggenes package for R and perfomed analysis on my dataset. I guess this is the only rigorous way of doing it.

Hi Rohit,

I can not give any suitable match time and alignment time window since it is dependent on your LC-MS performance and the level of possible drift of retention times from the first to the last sample. maybe you can try to inclease the retention time window in steps of +30 seconds and look if you get more identifications.

In relation to your FASTA, I would suggest to take a combined FASTA from all available fungal species in close taxonomy to your species of interest to increase the database by entries of very homologue sequences from other very similar fungal species.

Best,

Murat.

Post cross-linking, the sum of the proteins linked by cross-linker is what you should expect to see. In practice however, this is more complicated since cross linking is not a clean experiment and since there are intra- (within the protein) as well as many mono-linkages formed as reaction products.

So, if you are observing a cross linked product smaller than expected, is that because you already know what it is linking with?

Hediye.

Hi Tarabryn,

to be honest, I have never used LFQ quantification on single peptide level, However there are a multitute of settings to define the calculation for LFQ.

Maybe, most important is to set the min peptide ratio for LFQ from 2 (default) to 1.

It also makes sense to disable the fast LFQ function.

Then set the type of peptides which should be considered for LFQ (i.e. Unique, Razor etc. and modified or unmodified only (depending on your experimental question.

I hope you can find the way to recover the LFQ values with the newer MQ versions.

Good luck!

Murat

Hi Jan Gebauer

I've also had some funny looking volcano plots from PD data. Discussion here: https://www.researchgate.net/post/Artefact_in_volcano_plot

I never got to the root cause, but the funny lines of points lining up were an artefact of normalisation - mind you, the underlying table of PSMs from PD was the original source.

Unfortunately I never got around to identifying why PD gave odd output.

Sorry I can't be more helpful!

Sam

Check the electrodes. If you see them covered with whitish stuff, remove it with wet tissue or brush until the metal surface is exposed.

"Kindly" take the time to perform a keyword search on the web (e.g. Google, Bing) to find related articles and documents on this topic. *Learning how to research the answer to a question is one of the most important skills you can learn as person and student.

Murat Eravci

Thank you for your kind reply.

I will try searching HLA peptide atlas.

Best regards

Jaekwan.

Albert Lee : the issue with doing this is it makes the fold changes entirely arbitrary. Imagine I have a protein I detect in my test samples at "arbitrary value 10" but do not detect in my control samples at all.

If I call the ctrl value 0.5, then 0.5 vs 10.5 = 20 fold increase.

If I call the ctrl value 0.1, then 0.1 vs 10.1 = 100 fold increase.

If I call the ctrl value 0.0001, then 0.0001 vs 10.0001 = 100,000 fold increase.

In reality, the increase is effectively "infinite fold", but what this is really highlighting is that fold changes are not an appropriate metric here.

A lot (most) of statistical analysis is predicated on the measurement of change in values, not "present/absent" scenarios.

For disease biomarkers, for example, something that is present/absent is of use as a diagnostic biomarker, but not as a monitoring biomarker: you can say "if you see this marker at all, you have the disease", but you cannot really use it to track therapeutic efficacy, because all values of this marker other than "N/A" are indicative of disease.

For monitoring biomarkers you really want "healthy" and "diseased" values such that you can track the shift from one to the other.

David Genisys: I agree with Jochen Wilhelm , and would not plot my data in this manner.

A lot will depend on the kind of reviewers you get, and the type of paper you're trying to produce, but it would be more appropriate to note that these markers are entirely absent in one group, and then to comment on the robustness of their detection in the other. You wouldn't run stats necessarily, because as noted, stats are horrible for yes/no markers, but you could use the combination of presence/absence and actual level of the former to make inferences as to biological effect. If a marker goes from "not detected" to "detected but barely", then it might be indicative of dysregulated, aberrant expression behaviour, or perhaps stochastic low-level damage. Interesting, but perhaps not of biological import or diagnostic utility. If instead if goes from "not detected" to "readily detected, at high levels", then it's probably very useful as a diagnostic biomarker, and also indicative of some active biological process, be it widespread damage/release, or active expression of novel targets.

In either case you can make biological inferences without resorting to making up numbers so you can stick them on a volcano plot (and to be honest, if you get the kind of reviewers that demand volcano plots, you can always use the trick Albert suggests).

Volcano plots are primarily a way to take BIG DATA and present it in a manner that allows you to highlight the most interesting targets that have changed between groups: if you have whole swathes of genes that are instead present/absent, then those could be presented as a table, perhaps sorted by GO terms or something (if it looks like there are shared ontological categories you could use to infer underlying biology).

To gain insights from your proteomic data in the context of pathways:

1. Protein-protein interaction networks: Construct protein-protein interaction (PPI) networks using available databases or tools. These networks represent the physical interactions between proteins and can provide insights into functional relationships and pathway associations. Analyze the network topology, identify highly connected proteins (hubs), and explore protein clusters or modules that may represent enriched pathways.

2. Functional enrichment analysis: Perform functional enrichment analysis using tools such as DAVID, Enrichr, or g:Profiler. These tools allow you to input a list of proteins and assess enrichment of Gene Ontology (GO) terms, biological pathways, or other functional annotations. This analysis can help identify overrepresented functions or pathways in your protein dataset.

3. Cross-referencing with gene-level data: If available, consider integrating your proteomic data with gene-level or transcriptomic data from the same samples or a related study. By mapping proteins to corresponding genes, you can leverage gene-level pathway analysis methods and identify pathways enriched with differentially expressed genes associated with the proteins of interest.

4. Literature-based analysis: Conduct a literature search to explore existing knowledge and studies related to the proteins identified in your proteomic dataset. Look for studies that have investigated the functions, interactions, or pathways associated with these proteins. This qualitative analysis can provide valuable insights into the potential involvement of specific pathways in your disease sample.

5. Pathway databases: Explore curated pathway databases such as Reactome, KEGG, or WikiPathways. These databases provide well-annotated pathways and can serve as a reference to investigate potential connections between your identified proteins and known pathways. Look for proteins within your dataset that are annotated to specific pathways of interest.

Remember that pathway analysis based solely on proteomic data has limitations.

Hope it helps:credit AI

For this you can see some available protein databases and tandem -LCMS data from various mass spectrometry sites.

You may use antioxidants to inhibit the oxidation. For this, catalase, and methionine are typically used but you may add water-soluble antioxidants such as phenolic acids can also do the job. FASP is inherently an extended protocol and smaller sample volumes, and larger cut-off values of the membrane can reduce the required uptime. Thus, the whole process can be concluded relatively faster to limit met oxidation.

BTW, you may also look for S-TRAP as an alternative way of SDS-removal

and on-membrane cleavage technique. I did not compare but papers declare it has the same efficiency as FASP but completes the process faster...

Hi Jaekwan Kim,

my first question is on howyou performed the nanodrop analysis of the peptide concentration? Have you set the baseline (Blank) correctly by using the respective buffers for the different solutions (sample loading buffer, washing buffer, elution buffer). If you have done the blank measurement with dest Water for all solutions your results might be not correct.

However, there are some point in the stage tip procedure that can be the reason of bad recovery.

The first is if you put the C18 disks to loose into the tip there might be a leak where the buffer can flow through without retention of the peptides at the C18 matrix. The sam will happen if the disk stacks of the produced stage tip has wrinkanges at the side (by usage of a two large diameter of the blunt ended hamilton syringe needle for punching out the disks and push into the tip / 16gauge size in the original protocol Rappsilber et al. 2007). I would allways check freshly prepared stage tips for a leakage with a short centrifugation step before possibly wasting important samples.

Another point which is rarely addressed in protocols is the activation state of the C18 matrix in the stage tip which is active for proper peptide bilding/retention as long as the material remains wet. I.e, if you activate your C18 matrix with 100% methanol but leave the tips for a longer time before proceeding with the next step, so that the matrix dries and the C18 become inactive and your peptides will flow through. The drying of the C18 can also happen if your centrifugation steps between activation and sample loading are too long or the centrifugation speed is to fast so that every liquid is drained away from the C18. The setup of the centrifugation time and speed is depending on how tight the C18 disk stacks are packed (which could be very variant). The tighter the stacks in the stage tip are packed the longer the centrifugation or the higher the speed will be required. For this reason I would recommend (and also because of the drying issue mentioned above) to evaluate time and speed for the individual type of tightness of the stage tips.

Good luck,

Murat

The Thermo Scientific Q Exactive mass spectrometer is a popular instrument for proteomics research, but it is primarily associated with bottom-up proteomics, where proteins are digested into peptides before analysis. While it is possible to use the Q Exactive for top-down proteomics, it may not be as effective as other specialized instruments for this approach.

Top-down proteomics involves analyzing intact proteins without prior digestion into peptides. This approach can provide valuable information about protein isoforms, post-translational modifications, and protein complexes. However, it comes with its own set of challenges, as intact proteins are larger and more complex than peptides.

Here are some factors to consider regarding the effectiveness of the Q Exactive for top-down proteomics:

In summary, the Thermo Scientific Q Exactive can be used for top-down proteomics, and it offers high-resolution mass spectrometry capabilities that are beneficial for intact protein analysis. However, the field of top-down proteomics has advanced with the development of specialized instruments and methods, such as FT-ICR (Fourier-transform ion cyclotron resonance) and Orbitrap instruments. Researchers often choose these specialized platforms when focusing on top-down proteomics due to their improved performance and tailored features. Therefore, the choice of instrument for top-down proteomics depends on the specific research goals and the resources available to the researcher.

I think a lot of cell biologists could look at their cells under a microscope and immediately tell if their cell culture was contaminated, although they might not be able to identify which type of contamination. Trying to distinguish which type of contamination could be difficult. Personally, I don't think that microbiologists would be interested in the application. There might be more interest from biologists who study non bacterial eukaryotic cell types like HEK293, Hela, cancer cells, and other types of mammalian cells.

Size of prokaryotic cells is about 1 - 5 microns and size of eukaryotic cells is at least 10 microns and larger. On the basis of size at least by eye, it is easy to separate eukaryotic cells from prokaryotic bacterial cells.

Bacterial cells can be groups by size and morphology. They can be spheres, rods, curved rods, etc. and grow in isolate, chains, or clusters. That to some degree tells you if you are looking at Escherichia coli, vibrio cholera, staphylococcus aureus, etc, although false positives are possible because some bacterial cell types have the same morphology and growth pattern.

A lot of mammalian cell culture biologists complain about Mycoplasma contamination because the size of the mycoplasma bacteria is smaller than 1 micron so biologists might be interested in a application that could identify mycoplasma contamination. But cell biologists would probably look under the microscope immediately see the mycoplasma contamination because it is such a problem.

ImageJ is pretty standard for analyzing biological images. It is very powerful and you can write scripts for it, but it is kind of easy, but I don't think it is already automated for tasks like this.

When microbiologists complain about contamination, it is viral contamination of bacteriophages that they are complaining about. Bacteriophages are like nanometers in size so they can't be seen with a microscope but evidence of contamination is detected by lack of microbe cell growth due to lysis and the lysis of bacteria cells I believe you could detect from the images. Maybe microbiologists would be interested in an application that could detect lysed bacteria cells.

Hi Eef Dirksen,

Thanks for your comment.

I have some serum and tissue samples from patients with a certain disease, and I want to identify signature proteins or peptides that can differentiate them from healthy controls. I am wondering what is the best method to extract and analyze (top down or bottom up) these biomarkers.We have a Q-Exactive mass spectrometer for this experiment. how to handle the data analysis and interpretation? Is there any specific software or tools to process and visualize the results? 

How about protein A or G resins...The melon gel works as flow-through mode of purification, protein A is bind & elute mode (orthogonal strategies). Without knowing about a cost comparison, this application works well and recovery would be satisfying. If you investigate the IgG CDR domains (fab) nsmol kit is ready to use and a promising tool as a front-end approach.

Heritable diseases tend to be the more obvious ones and are caused by genomic mutations, often at a single locus. This makes finding the causative locus easier. The underlying mechanisms are often then subsequently probed. Diseases caused by environmental exposure and lifestyle are harder to probe as they often involve the interaction of multiple gene products.

Here is the instruction manual for this resin.

To prepare the resin for the first use, you have only to replace the ethanol with the starting buffer until equilibrium is reached.

Regeneration is to be done by washing with a strong NaCl solution (1 M or 2M), which should elute just about anything that is bound by an ionic interaction.

If the column needs to be cleaned of hydrophobic substances, wash it with 0.01 M NaOH, then re-equilibrate it with the binding buffer until the pH is back to where it should be.

Hi Waldo, I'd like to know if you solved this problem. Because when I want to make standard curve for Bradford assay, BSA cannot dissolve in the urea/thiourea buffer even after a day.

As an update to this discussion, I have decided to reduce my sample size and incorporate a pooled reference channel. Mostly to open up the possibility of integrating additional samples and conditions in the future.

Sam

The best way for this is to use the MALDI-TOF MS configuration. For ESI top-down or native-MS-like approach is needed for the analysis of either heavy or light chains. You may combine SEC and native analysis for ESI-LCMS or gas phase fragmentation of antibody light chain to select SRM-like produced peptide for quantification. ESI produces multiple charge states for large molecules therefore occurring charge envelopes reduce the precursor intensity if the top-down quantification is aimed. MALDI gives reduced charge states thus either the identification or quantification approach would be more easy/more practical if the sample is not a protein complex but a purified antibody.

Rather than the selection of MS or acquisition technique, it is more important to choose the sample prep strategy herein. Garbage in garbage out for any MS system. What is your consideration about the sample prep and what is your sample matrix? how would you purify/clean, reduce, and fractionate your sample? It is more critical to assess prior to MS detection. Otherwise many interfering compounds, and protein peptides make your quantification worse and that is why it is recommended to use MRM and signature proteolytic peptide identification is more appropriate to perform absolute quantification...

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

When doing research in many SAIF labs it is commonly used (MilliQ-models) and proven results with better health of instrument. If you are planning to buy it for your laboratory, I refer to any advance /latest models of milliQ.

Addition information I will provide in some time with real user's reviews.

I'd suggest you look at PeptideShaker, it's easy to use, well integrated and supported;

Vaudel, Marc, Julia M Burkhart, René P Zahedi, Eystein Oveland, Frode S Berven, Albert Sickmann, Lennart Martens, and Harald Barsnes. “PeptideShaker Enables Reanalysis of MS-Derived Proteomics Data Sets.” Nature Biotechnology 33, no. 1 (January 2015): 22–24. https://doi.org/10.1038/nbt.3109.

http://compomics.github.io/projects/peptide-shaker

Here are the other points, you may lean on;

If the purpose is quantification or purity check, LC-UV would be nice to use since the octapeptide you have several aromatic rings and would be highly responsive,

secondly, 500 ppb may be a low conc. to conduct a full scan..especially when the ionization efficiency is low.

Third, I would prefer combined flow scanning in place of infusion...In this mode, you are not taking benefits of the mobile phases which present donors to improve ionization...You should combine the lc flow and infusion (acid and/or DMSO additives in phases for pos ESI in this case) and retest the response...

By solving the peptide in an alkaline condition you are directing the peptide to deprotonation and this makes the peptide more amenable to neg ESI...If it is soluble in ACN directly..prepare your stock in ACN and dilute it with the same solvent, I prefer not to use aggressive pH which is not convenient for most of the peptides to the unintended H exchanges...

Last but not least, If the peptide is hydrophobic and dissolves only in organic solvents this is susceptible to be efficiently ionized in APCI, APPI rather than ESI...You may look for these alternative ionization techniques if MS analysis is the bottleneck and the abovementioned suggestions are useless...

Good Luck...

Sadie rose Grant The decision to wash the cecum tissue with phosphate-buffered saline (PBS) versus taking the cecum contents straight from the GI tract can depend on the specific goals of your experiment and the nature of the microbial community you are trying to study.

Here are some considerations:

1. Sample composition: Washing with PBS can help remove transient bacteria and mucus, thereby allowing you to focus more on the microbes tightly adhering to or embedded within the gut lining. This could be beneficial if you're interested in the interaction between the host and microbiota. However, if you want a more comprehensive view of the total microbial community, including transient and luminal bacteria, you might prefer to avoid washing and take the contents directly.

2. Downstream analysis: Consider how washing might impact your downstream analysis. For instance, washing with PBS could potentially dilute the proteins you are trying to analyze, making it more challenging to detect low-abundance species. However, it might also reduce potential contaminants, making your proteomics data cleaner.

3. Sample handling consistency: It's crucial to handle all samples in a consistent manner to avoid introducing unnecessary variability into your dataset. If you're comparing cecum contents with other types of samples that are washed with PBS (like fecal samples), it might make sense to wash the cecum as well.

4. Study Reproducibility: If you're building upon previous work, consider the methods used in those studies. Replicating their methods can help make your results more comparable.

As for the ideal way to handle mouse cecum samples in bottom-up proteomics, it will ultimately depend on the specific scientific question you want to answer, and it may be worth testing both methods (i.e., with and without washing) on a small number of samples to see how much of an effect it has on your results. Also, make sure to control for any potential effects by including appropriate reference samples and replicate measurements.

It's also important to remember that the state of the cecum (e.g., whether it's relaxed or contracted) can also affect the amount of luminal content and, thus the microbial composition, so careful and consistent sample collection is crucial.

Quite honestly if your protein isn't too large, i.e., to many amino acids for it I would just use AlphaFold or ESMFold and compare the best model with the resolved one by aligning on this region. I think the models (or variants of it that participated listed in the previous post all do have lower performance in the last CASP competitions then AF had. Although I haven't checked this ^^

RosettaFold would also be a good option.

Of course homology modeling can still work pretty well, but usually only if you have good templates and ideally many of them. But if you have regions that basically are missing in your templates and those are significant it usually doesn't really work that well.

Hi, I don't know the extent of your shiny application, but you can also use EnrichR (in R), which is also good, though, it upload the data to server during calculation.

Plus, I am curious how you will handle the missing IDs during ID mapping? ClusterProfiler can map IDs, but there are always some % of ID that are missing.

I would extend that and say we really need to be careful using upregulated. To me, that means the expression of the protein is increased, so there is a fundamental change in the amount of protein quantified as a result of that. I'd suggest we use the term increased (or decreased) abundance when quantifying the protein, unless we have clear evidence to say otherwise. There are a lot of reasons protein quant differs between samples, and it's not always due to expression level changes. I agree you will find such terms used interchangeably, that does not mean it's correct, or a good idea.

Hi Peter,

There is also

1. MassIVE which is curated by University of California San Diego, USA. It is under the Center for Computational Mass Spectrometry of UC San Diego.

Once a deposition is under "complete", with peak files matching raw files and mzID results files, the MassIVE will also register/ produce ProteomeExchange number which may be required by some journals.

2. Another resource is "GPM" or Generalized Proteomics data Meta-analysis

Best,

Hediye.

Hi all,

I've narrowed down the cause of this phenomenon, (in case anyone else has this same problem), namely there's an issue with the dataset I'm using. Running the same analysis on a different dataset using the same script does not produce this pattern.

Notably, the pattern varies as a function of the normalisation method used in MSstats.

Sam

MetaboAnalyst is primarily designed for metabolomics data analysis, but it also supports proteomics data analysis. Therefore, it is possible to use MetaboAnalyst for multivariate analysis of proteomics data, but there may be some limitations.

One potential issue is that the data preprocessing methods for proteomics data might differ from those used for metabolomics data. For example, the normalization and scaling methods for proteomics data might not be the same as those used for metabolomics data. Additionally, the statistical tests available in MetaboAnalyst might not be optimized for proteomics data.

However, despite these limitations, MetaboAnalyst is still a powerful tool that can be used for proteomics data analysis. It provides a user-friendly interface for data uploading, preprocessing, and multivariate analysis. It also supports a wide range of multivariate analysis techniques, including PCA, PLS-DA, OPLS-DA, and clustering analysis, which can be used to identify patterns and relationships in the data.

If you are looking for a specialized tool for proteomics data analysis, there are other software packages available, such as Progenesis QI for Proteomics, MaxQuant, and Perseus. These tools are designed specifically for proteomics data analysis and offer a wide range of advanced features and analysis methods.

The product of the reaction of iodoacetamide with the side chain of cysteine is not a primary amino group. It is an acetamide. The reactivity of an amide is not the same as the reactivity of an amine.

If I understand correctly, this is the sample you want to use for your proteomic analysis. Therfore, you don't want to bring in media or serum, as this would totally mess up your proteomics!

Wash the cells 3x with PBS and transfer them into the Eppendorf tube for freezing of the cell pellet.

Later, you will use your lydis buffer and prepare the sample for the analysis.

Hi Filipe,

Here is a good publication which I followed and obtained results (protein identifications) from FFPE slides:

Thank you. I was thinking of doing enzymatic degradation next. thanks for sharing the detailed protocol. Did you get it from some literature. can you please share the paper?

Antonis Tsintarakis thank you for your reply! I will look into the BEST tool you have suggested.

Thanks Marwan for your advice. I also spoke to some fellow researchers and they also suggested the following: REVIGO for GO enrichment, DAVID for functional annotation, and iDEP for proteomic analysis (it is very similar to metaboanalyst). Links below.

REVIGO: http://revigo.irb.hr/

DAVID: https://david.ncifcrf.gov/

iDEP: http://bioinformatics.sdstate.edu/idep/

How to digest mucin in saliva?

Hi Sir,

You can check gene expression at protein level using Human Protein Atlas (https://www.proteinatlas.org). Also, NCI Proteomic Data Commons can be explored (https://pdc.cancer.gov/pdc/)

Roberto,

Here is the article partly covering this issue.

Roberto, both approaches should work, and, as they are orthogonal, the results should nicely confirm each other.

Hi Antonio,

the amount of extracellular vesicles (EV) required for a proteomic approach is dependent on the protein content of the EVs of interest which could differ in different sources. I would suggest that you perform a protein analysis from you EV preparation and adjust the amount that you need for your proteomics approach from this protein analysis. In one of our recent publications, Cilibrasi et al. 2022 "Definition of an Inflammatory Biomarker Signature in Plasma-Derived Extracellular Vesicles of Glioblastoma Patients" we have used 100ng of proteins from EVs to perform an SDS PAGE and do InGel digestion of 5 slices from each lane to get tryptic peptides for downstream LC-MS analysis.

This amount worked good for us but the required amount can be very different in relation to the method and LC-MS equipment you are planning to use.

I hope this helps.

All the best,

Murat

The issue you described is not a problem for proteomic analysis and your statistical tests will be valid provided you appropriately normalize the data. Most current proteomic search tools (e.g., MaxQuant, Fragpipe, ProteomeDiscoverer) will correct for mass and retention time drift. Of course, it is always best practice to ensure your instrument is calibrated before running your samples. Once you have protein-level data output from your search software, there are several statistical tools for controlling intensity (abundance) normalization. One of the more common methods is to perform Tukey's median polish. After you have performed some type of statistical test between your groups, remember to correct for multiple hypothesis testing using Benjamini-Hochberg or Bonferroni procedure. This will ensure you have adequately controlled your false-discovery rate (FDR).

Cynthia,

Please contact me by email at

Sherman@mvpharm.com. I will try to "talk you through" the method of using the refractive index and UV absorption to calculate the protein and PEG concentrations without attempting a BCA assay or other chemical assay of the protein concentration. Our method is based on the Kunitani reference that I cited in this string of discussions in February 2015.

Best wishes,

Merry

Working with log-transformed intensity (or concentration) data is very convenient. Protein expression is approximately log-normal, so it's approximately normal at the log scale, where you can use the standard toolbox for analysis (i.e. linear models like t-tests, ANOVA, regression, ANCOVA etc).

I think visualization is also better done with log concentrations or log fold-changes. The resulting picture (and possibly the interpretation) should not depend on the perspective, that is, which group is chose as the "reference" and wich as the "experimental" condition. To give an example: you study the expression of protein X in males and in females. The fold-change male/female is 5 (wow what a drastic increase!), so the fold change female/male is 0.2 (eh, well, yes, some kind of reduction). The numbers look very differently impressive althought the express the very same difference (effect, ratio if you like). The (base-2-)logarithms are +2.3 and -2.3: same number, opposite sign. This is (imho) easier to understand and faithfully reflects the symmetry of the problem.

Using z-scores serves a different purpose. Here, z-scores are calculed from the log intensities, for the reasons explained above. The z-scores more closely resemble what a t-test or ANOVA "sees" from the data, as here the differences (from the mean) are related to the variability (the standard deviation). What gets lost is the information about the base intensity (expression level), what is often less interesting than the difference in expession levels between samples and groups.

This could happen due to pipetting errors, or the proteins leaching out of the wells while applying on gels. The whole idea of using a house keeping gene is to nullify this effect. If you express the intensity of protein of interest with respect to that of the house keeping gene minor differences will not be a problem.

Please go through this article. Might help! https://www.sciencedirect.com/science/article/abs/pii/S0003269710001351#:~:text=In%20conclusion%2C%20we%20have%20shown,no%20longer%20than%2010%20min.

Do let me know if that help!

I beileve this is highly dependent on what you are seeking. DIA has better reproducibility and higher data completeness as Dr. Zepeda said, however, in terms of quantification, DIA has lower sensitivity than DDA as the complete spectrum must be scanned, reducing the acquisition time per data point.

I think DDA is suitable if you would like to perform A vs. B as it focuses on highly-abundant proteins.

If you afford to use it, try the Multiple Affinity Removal System (MARS) from Agilent. Mouse Multiple Affinity Removal Columns and Cartridges are ready-to-use columns for simultaneous removal of major 3 interfering proteins Albumins, transferring, and IgG. Otherwise, you need to follow a relatively cheap, tandem and, long procedure as Dr.Wolfgang Schechinger indicated.

Sharmi Mazumder could you figure out how to pull and fix multiple atoms in smd ? i'm interested to know about the same.