I am familiar with DIA-NN. However I have been given a Linux HPC and not familiar with CL and unable to run DIA-NN since None of the Linux version comes with a GUI so its CL only. However I got to know FragPipe https://fragpipe.nesvilab.org/ in the apps available via Open OnDemand (OOD) which as DIA-NN as part of its pipeline and it comes with a GUI.

​

Is it possible to only use DIA-NN features from FragPipe? Anyone has a manual for this?

My lab is out of c18 ziptips and I need about 20 tomorrow. However, we do have some c18-resin for preparing self-made stagetips.

What would the difference be between commercial ziptips and if I made my own ziptips by placing one or two discs of c18-resin in the bottom of a 200 uL pipette tip?

There is a concern about the impact of antibiotics on male fertility, so this study looked into how various fertility parameters changed in the sperm of a young man taking H. pylori treatment.

The methodology was as follows:

A young man (23 years old) with Helicobacter pylori infection was treated with antibiotic therapy (amoxicillin 2 g/day for 15 days, clarithromycin 1 g/day for 15 days). During the medication (29 March–11 April 2022), semen samples were taken approximately every 3 days for a total of 5 times. After drug withdrawal (12–28 April 2022), semen samples were also taken approximately every 3 days for a total of 5 times.

The findings on the sperm parameters are given in the supplementary material.

Now, what I am confused by is that when I look at table S4 (which gives numbers for morphology and sperm count, not proteins), at least as I read the numbers, it seems to me that whatever worriesome effects are introduced by the treatment seem to peter out in the withdrawal period and yet the authors conclude the following:

Our results suggested that the abnormal regulation events occurring in human sperm proteins may play a significant role in sperm quality and reproductive potential after amoxicillin and clarithromycin therapy. Based on our results, more comparative sperm proteomics studies could be designed and performed for exploring the mechanism of sperm development, the mechanism of fertilization, pathogenesis of male infertility, the action mechanism of some drugs, and so on.

So they seem to be implying that there may be some permanent effect on reproductive potential in the wake of antibiotic therapy, but they also seem to be attributing it to what happens to the proteins, rather than to sperm count and morphology, and I am not knowledgeable enough to interpret that, so could you guys tell me whether you see anything in the protein data that indicates to you there may be a permanent deleterious change?

I'm not sure how the authors are reaching this conclusion since they took no samples before treatment, so how do they know that the difference in proteins during vs post-treatment doesn't simply represent a regression to baseline, rather than some pathological change? They even say in the study, Additionally, the biological functions of differently expressed proteins in this study didn’t been studied.

Just a fun question, do you have a favourite enzyme or chemcial for proteolytic digestion.

If so why ? Do you have a cool story for it, or equally a bad experience that steered you away?

My personal favourite is using hydroxylamine just because it seems cool. Honourable mention to lysarginase.

I have just received the results of my data analysis in form of a summary excel sheet. However, I struggle to perform post analysis with python or R, mainly because I feel like a lot of data is missing. For example, in the spectronaut file I have information about the detected peptides and protein groups and can thereby calculate the number of identifications. But I have no intensity values (unless PG.quantity is another term for this?). Furthermore, I am not sure how much information I need to perform PCA since I am used to the software doing it automatically.

I guess the question is, do I need to go back to the department and load my data in the respective softwares again or can I do all these stats with the summary export files as well?

I am just learning proteomics. I had one fundamental question. In one of the experiment where I digest a purified protein, I would like to use two proteases for protein digestion. I wonder why not these two protease don't chop each other decreasing the efficiency of target protein digestion. Even in single protease condition, won't it self cleavage? If you have a good reference article (protocol etc.) to understand rationale in each step would be helpful.
Thanks!

Anyone have copy of Proteome discoverer 2.5 user guide? I could only get 2.2 but not 2.5.

Thanks.

I was a prolific user of the Plasma Proteome Database back in grad school, but now the site appears nonfunctional. Not sure if this is temporary or permanent. I've emailed the authors of the original paper without any luck.

Curious if anyone knows the back story on this, or has suggested alternatives? (The Human Protein Atlas is great, but it doesn't provide the comprehensive view of plasma concentrations that PPD did)

ProCan has published a paper outlining the sample prep method they have used for over 10 thousand cancer samples, the paper can be found here https://pubs.acs.org/doi/10.1021/acs.analchem.3c04708
While working there, I made a video outlining the lab process and have just uploaded it to YouTube, which can be found here: https://youtu.be/i2LGXr3Rfzw
If anyone is interested in any of the 3D printed tools used in this video, the STL files can be found here: https://www.printables.com/@SteveW91/collections/139924

​

I’ve used both XC and RPS tips but they seem similar in terms of withstanding high and low ph fractionations. Can someone who knows more about these product tell me the difference between them? I seems to be able to get away with similar conditions for both.

Hello Guys,

Can you give me the standard sample preparation technique for proteomics which I can apply in the lab easily? I just found out the paper from nature discussing SP3, but i am wondering if there is another simpler method?

Thank you

I'm a researcher who is interested in developing educational games. Especially visualizations. (https://prajwalsouza.github.io/denovo-peptide-sequencing). I want to try a community driven approach to design. So, do you have an idea for games that might be helpful, interesting, especially for teaching proteomics.

So, far in my research, I've come across an idea of using denovo sequencing as a game. But this is in development. But if you have any more recommendations, I'd like to hear them. :)

There have been a few suspicious posts looking to obtain market research or asking for advice on particular companies for investment purposes. I think it is getting a bit out of hand. The subreddit is focused on discussing proteomics research, asking proteomics related questions regarding experiments, publications, software etc... I understand it is a tech driven field and it is fair to discuss different technologies, company strategies, advantages and disadvantages. But when you have investment bankers asking questions about a specific company ... I think it is taking it a bit too far.

To those trying to easily obtain said information: Stop being cheap/lazy and consult a proteomics scientist to get their expertise.

I have performed an experiment using BioID and I have gotten back MGF files from the sequencing results. Does anyone know how I could analyse these files in FragPipe and then use the SAINTexpress plug in?

I have tried using the LFQ-MBR workflow after converting the files to mzML. However I keep getting the same error message at the IonQuant stage: Process 'IonQuant' finished, exit code: 1.

I tried using mzXML files instead and the same issue arose.

Any help would be most appreciated!

I have an HPLC system with an analytical column with ID 4.6mm, length 250mm and flow rate of 1ml/min. I’ve been asked to miniaturize the system to detect 5% of the analyte that I am currently using. What will be the ID of my new column?

PS. this is all the info I have. Calculated it but unsure of my answer. Kindly help.

Hi! I am just starting out in this field but are working on improving sensitivity and sample recovery on experiments with low sample inputs. I performed a pilot experiment on 10000 HeLa cells with an estimation of roughly 250 pg of protein per cell and noticed about 80% sample loss, estimated by nanodrop. Even if the amount isn't 100% accurate, this is quite a bit of sample loss.

I am wondering what are the critical aspects in the workflow that cause sample loss, especially with such low cell numbers? I am using the Eppendorf LoBind tubes so I thought surface adsorption is almost no problem. I am also drying my samples at 40 degrees but again always suspected that the temperatures weren't high enough for peptides to evaporate.

Thoughts?

Hello,

Sorry, stupid question, truly a beginner. Upon receiving my PD result, I saw that it contained a large amount of data with low confidence. My question is, is it possible to publish a paper by mentioning the low confidence? So I set up the workflow becoming a low confidence level. My goal is just to know how many proteins are detected with the method used.

Thanks

Hello everyone, I'm a PhD student delving into the world of proteomics. I wanted to ask if anyone has any suggestions on how to proceed once you have the data analysis file in hand (generated by Spectronaut, MaxQuant, etc.). I'm struggling a bit to navigate in order to extract useful information from this wealth of data: should I proceed with an enrichment analysis for different ontologies, look at proteins individually for the most significant ones? Of course, this all gets more complicated when it comes to integrating proteomics data with metabolism data. I realize that each experiment is unique and the approach varies depending on the question, but I feel a bit lost when faced with these omics data.

👋 I'm struggling to find a reference database to use for a proteomic analysis. However, there is a sequenced genome, do you know how to obtain a protein database from the genomic data?

Hi, I used 8M urea buffer to lysate my tissue sample. And, lowered the Urea conc to 2M after alkylation and reduction. Then, added trypsin 1:50 (w/w) protein to enzyme ratio and kept in shaker at RT overnight. When I was about to acidify the samples the next day, I think the samples smelled sweet or funny. Is this normal?

I still have to freeze dry them and run in the mass spec but I am worried if my samples went bad during the overnight digestion.

I would really appreciate your feedback. It is my first time trying to do the peptide prep for proteomic study.

Thank you in advance!

I used thermo's 10-plex TMT labels for an experiment and set the TMT label as a fixed modification in PD for the peptide N-terminus and on lysine residues. The overall number of proteins ID'd in the TMT-labeled runs is about half of what I was identifying before the TMT labeling (before TMT labeling = just 1 sample, after TMT labeling = pool of 10 samples). I am assuming that this is because the labeling efficiency wasn't great, and having TMT as a static modification means that any unlabeled peptides weren't identified. Is there a quick way to calculate TMT labeling efficiency? I have seen this value reported in different TMT methods papers, but don't understand how they calculated it. Any advice is much appreciated!

Hi everyone,

I have a question related to the Maxquant setting. Is that possible that I add 2 fasta sequence (the same species but different cultivars) in Maxquant to identify peptides? The reason I want to do it is the cultivar that I used is not that common in my research field and the other cultivar has more protein information from previous research. Also I will add another fungi fasta sequnce together since my experimental material was treated by fungi.

Do anyone have suggestions?

Thank you in advance.

Cell pellets were easy, but now we dealing with tissue samples...

Was looking forward to the bead mill homogenizer we just got but wow, so bad. Whips up the proteins too much they crash out.

Probe sonicator was hit or miss and felt awful to use...

Mortar/pestle + N2 worked best but we don't wanna do that for however many hundred samples coming up.

Anybody got experience with those Dremel like things? Can I test that with a $50 rotary tool and drill bits from hardware store? Cuz we got some explaining to do, why we need more equipment money to homogenize tissue when we just spent so much on a bead mill...

Boss not gonna be impressed if we get 9K+ proteins out of cell lines but not tissues...cuz he said to collaborator we gonna see at least 10K out of tissues LOL.

I'd appreciate any recommendations on how y'all handle frozen tissues. Also any tips for breaking up FFPE samples or can you just boil them?

​

​

Has anyone has experience analysing phohorylation data from DIA-NN output?

I am looking to analyze downstream statistical data analysis and don't know how to.

I'd be grateful if anyone could assist me with a existing pipeline.

Hi Folks,

When analyzing multi samples (even though we are sure about injecting known amount of digested peptides) how should I perform data normalization for DIA data?

DEP package suggest variance stabilizing transformation method. How does this looks like?

Do I have to normalize to Total peak area for each sample (like RPKM/FPKM normalization methods in RNAseq), or

is there any alternative?

I'm a postgrad student who started working in a proteomics lab 2 months ago (where I'll also be writing my thesis in). I had a course on proteomics last semester and had a few opportunities with hands on experience using the MS. I would say that I am fine with the basics.

Yet I am incredibly amazed and jealous at all the knowledge coming from the rest of the lab, even students who just began their PhD last year. I would also like to participate in discussions, evaluate sample processing approaches and most of all, be able to come up with fresh ideas. Everybody seems to have some sort of side project and our lab heavily invites experimentation.

We are a technical lab and I feel like this is such a unique opportunity to learn about MS and different analysis strategies. My project will focus on phosphoproteomics in particular.

Tldr; Are there any intermediate resources out there (books, videos, blogs, papers...) to make that jump from novice territory? Anything that perhaps helped you become more well versed in MS based proteomics?

​

This figure shows the sequence logo analysis of A) milk B) kefir 1 C) kefir 2. The line represents the cleavage sites of the enzyme. For plot A, K stands for the amino acid that is present at that position, which is next to the cleavage site. What about E?

Hello, stupid question: how do we know if our experiment results in lower coverage? Is there a benchmark or reference for that? Many thanks.