Questions related to Proteomics
Hi, I am proteomics resercher.
I ask for your understanding that writing in English may be lacking.
Because this is my first time to post question in the researchgate and i am not a native English speaker.
I am doing proteomics research comparing protein concentration(Label Free Quantification) in two groups using LC-MS.
We have 6 samples and two groups to analyze (3 samples/group).
we are planning to run only two samples in one day and other four samples several weeks later.
Is it possible to statistically analyze (ex, t-test) 6 samples together in this case?
I think it is impossible because Retention Time drift and machine performance change.
Please let me know your answer. (If you know manuscript about this issue, please let me know.)
I've conjugated a PEG4-maleimide (MW 613.66) onto an antibody fragment (52 kDa), but I'm trying to figure out how this affects A260/280 measurements on a Nanodrop afterwards. After conjugation and removal of unconjugated material on a PD-10 column, I measure the concentration of the eluted aliquots but the measurements seem really off, and the A260/A280 ratio is nowhere close to 0.5. I blanked the Nanodrop with the PD-10 elution buffer. Seems strange that such a small molecule could affect the readings that much. Does anyone have experience with PEG and if/how it could affect concentration measurements after conjugation?
I know this might be a bit too general question but:
In proteomic analysis (working with Perseus) when you deal with raw LFQ analysis. Do you always use Z- score? And do you always log2 transform your data?
It doesn't seem to me that is always needed. Besides, no matter if you do or don't the results on the graphs should come out the same, only differently scaled, right?
Maybe it's a dumb question but thank you regardless!
I extracted protein from various tissues in mouse with RIPA buffer (added protease-/phosphatase- inhibitor cocktails and PMSF).
Quantified the protein concentration via Bradford and loaded equal total protein amounts.
My housekeeping gen is quite stable across my various organs but not 100% same.
For publication I would like to have a blot where all bands are equal - this is why I adjust the protein loaded according to the previous blot. With protein from cell culture this works fine but with a variety of mouse tissue organs I do not receive an equalized normalizer band.
Why is that? Is there anybody with experience doing multi-organ blots and has a good protocol or advise where to look for one ?
Thanks for any advise or help :)
Can you help with the issue of a better kit for the depletion of more abundant proteins (albumin, and all immunoglobulins) in the plasma of mice for use in proteomics analyses? Thank you very much.
I have done SMD of protein at applying constant velocity using NAMD software and CHARMM ff. Since, this is my first time in performing Steered MD, I am not sure as to should I do umbrella sampling alongwith SMD? Are the results of SMD without any umbrella sampling significant ? It would be helpful if I could get some references as well.
I have tissue samples digested by SDS lysis buffer which i would like to use for lipidomics analysis. what do you suggest? do you believe is gonna be possible?
I am trying to upload my proteomics data in Proteomexchange in "Complete Submission" type.
But the File validation (the first step after selecting data) is stuck in 50%.
I gave it time even for an hour, but nothing happened.
How can I solve the problem?
Thank you in advance
Is anyone aware of an SPE approach to isolating plasma metabolites, or at least substantially diminishing the protein/metabolite concentration ratio? The aim is to reach a balance of concentrations such that vibrational spectroscopic methods can pick up meaningful spectroscopic contributions from metabolites - contributions that are otherwise inaccessible as a result of overwhelming protein absorptions. Note that there need not be selectivity among the proteins - we are not interested in removing only the high-abundance ones (as is the case for proteomic MS work for example). Ideally, we want ALL of the proteins gone.
P.S. I am fully aware of the various lab methods in common use - crashing proteins out with organics (e.g. acetonitrile, methanol), and ultrafiltration methods. Interested in knowing more about less obvious options.
Could anyone recommend some good online Proteomics courses and/or books for both beginners and advanced students?
I am using desthiobiotin instead of biotin for pulling down a particular protein via streptavidin beads. The desthiobiotin is clicked to the chemical probe I am using for the protein. Post enrichment, I am digesting the proteins 'on beads' and then eluting the desthiobiotinylated peptides. I am submitting the digested sample for proteomic analysis to determine site of modification. However, I am not sure if the desthiobiotin is intact during the proteomics mass spectrometry analysis and hence can't predict the exact mass difference expected.
How can I reduce the viscosity of saliva samples for the proteomics project and homogenize the samples viscosely?
It should be noted that I do not want proteins to be removed in this way
Long story short, I need to degrade 30ug of RNA and i need to do it at 4C. I want to use only as much RNAse A as necessary.
So if performing the reaction at 4C, how long of incubation time and how much RNAse A would i need to degrade 30ug of RNA?
For example, would 1ug of RNAse A(20ug/ml conc.) for 15min at 4C be enough?
I have three MS spectra of an unknown protein. The protein has been separated and directly analyzed with a MS without trypsinization (top down approach). How can I know the identity of the protein? I suppose I can search for a matched spectrum in a library. However, I have no experience with top down proteomics and I don't know which software to use for the protein identification. Any help please?
This might be a trivial question and an operation do to, but I'm not experienced and haven't been able to find a direct solution online. I'm pretty sure I've overlooked something as this should be a simple task, so I'm asking here.
I have a list of some 1500 protein IDs identified in a proteomic experiment coming from a bacterial origin.
I would like to get GO annotation for those proteins so I could categorize them according to the "biological process"and "cellular function". Is there a web service or a simple program that could get those GO annotations ?
I'm confident that the GO enrichment analysis offered on the main page is not appropriate for the data I have and the information I want (I may be wrong), and my organism is not available in the list.
Does anyone have any suggestions ?
I got a set of around 4000 protein ids from a proteomic experiment and I would like to globally analyse if the particular groups of proteins in my experiment are significantly more hydrophobic and/or aggregation-prone compared with other groups. I am looking for an R programming library or a web tool that will enable me to obtain some quantitative value for hydrophobicity per protein for my sets. One thing I may do is to just simply calculate the sequence length adjusted number of hydrophobic amino acids C, L, V, I, M, F, W but this seems to be a little naive and I am not sure about the biological relevance of such a simple calculation not taking into account the whole structural aspects of the sequences...I would be glad for ideas on any smarter approaches...please help
I'm planning on running my peptide samples on a high-resolution LC-MS instrument. I'm going to use a ZipTip C18 tip for the extraction of peptides and desalting of the sample. However, I'll be sending these samples overseas and it might take 2-3 days for them to reach their destination. I can potentially keep them in dry ice throughout their delivery, but it is very costly and we had few issues before where the dry ice evaporated until it reached the destination.
If I free-dry my peptide samples, do you think they are going to be stable for couple of days? Considering there won't be any humidity where the enzymes can work on the peptides, but I just wanted to get the opinion of people who has lots of proteomics experience.
Multiplexed samples labelled with TMT tags. I am trying to quantify the ion intensity for each channel, however they are all being reported as 0.
Current search engines for MS/MS protein identifications such as: Mascot, MS Amanda, Sequest, etc., currently rely on the creation of a search library composed of computationally generated potential peptides through the cleavage by proteases (e.g., trypsin) of proteins from a given database. Different PTMs can be added to these computationally generated peptides, so that the search could be extended to address specific scientific questions, but this leads to significantly higher computational costs.
I have recently come across a case, where a highly enriched short protein could not be identified by a standard search, given that it was only generating a single peptide that had 2 fixed modifications. The modifications were not the most common there were and finding the right combination to use was time and computationally expensive.
I would like to open a discussion on the fact that pre-made peptidome libraries are a much better alternative to de-novo generated libraries of proteomes. Let’s get into the details!
As an example, I will use the ACE2 receptor, now infamously known to be the entry gate of Covid-19 into human cells.
The human ACE2 receptor undergoes a series of post translational event, such as: proteolytic cleavage by ADAM17 resulting in a soluble proteoform, glycosylation and phosphorylation of tyrosine-781 and serine 783.
In current search engines, the tryptic peptides generated would be generated from the first Methionine to the next positively charged residue and so on until the very last residue of the protein. If one would like to detect this protein in a sample and asses the presence of the mentioned PTMS, you would need to look for at least 2 phosphorylation sites per peptide and also check for S and Y phosphorylation. The search engine will then generate all possible combinations of SY single and double phosphorylate tryptic peptides to search for, which leads to exponentially increasing computational costs.
Since the protein is also cleaved by another protease in vivo, the 2 peptides before and after this site will not be accounted for as they do not end/begin after a positive residue. Since this is not a small protein, other peptides will probably still be detected, and the protein will eventually be identified.
I imagine a tool which would be used to generate the tryptic peptides as before, only accounting for the known PTM sites. In case of the ACE2 2 almost adjacent phosphorylation sites, this would lead to only 3 additional peptides (pY, pS, and pYpS). If the research question being asked is to identify novel phosphorylation sites, then only 1 phospho-site per peptide while looking for STY phosphorylation might already suffice, since the known ones will have already been accounted for. This can be applied to any combination of PTMs, massively reducing computational requirements. It is of course counterproductive to looking for PTMs in sterically inaccessible regions for example (e.g., hydrophobic core of the fold)
Databases of know annotated PTM sites of entire proteomes of many organisms are readily available. The tool could have a modular design in allowing the user to create a customized peptidome having any or all the following characteristics: trypsin/other enzyme used and/or accounting for known endogenous cleavage sites and/or accounting for known PTMs sites and/or accounting for natural variants.
I see a long list of advantages using this method and I would like to list the most important ones:
1. Identification of additional hits that could have been missed due to several reasons (e.g., tryptic peptides contain fixed modifications while not searching for these specific modifications due to computational resource limitation, or worse, small protein that would normally only yield in a single peptide that has 2 fixed modifications, one of which might be exotic)
2. Reduced computational time when trying to identify novel PTM sites
3. Lower false discovery rate since the peptidome used will be a much more closely related dataset to the actual sample composition than just a simple tryptic proteome and as a result newly identified spectra of interest can be more confidently assigned as the risk of artefacts is lower.
4. Single nucleotide polymorphisms can be analyzed analogously to PTM sites and would not result in exponentially larger search database.
5. More unique peptides could be assigned: If 2 proteins share a tryptic peptide, but one is known to be phosphorylated in this peptide but not the other, one could distinguish the phosphorylated peptide as having come only from one of the 2. In case of glycosylation this makes even more sense since some types of glycosylation only appear in a limited number of proteins, depending on their cellular localization
As the human proteoform project is taking on, maybe this would be the way of MS based proteomics to quickly catch up and help this project while advancing itself.
What are you thought on this? Are there any ongoing projects that would aim to do just that?
I am looking to perform Protein extraction from Human Aortas to send for Mass spectrometry analysis. Anyone has previous experience with these tissues, and would be willing to share their protocol with me?
Thank you in advance for any help you may provide :)
I have a very small knowledge in bioinformatics, and part of my research project is based on analysis of proteomics and metabolomics data. However, I am struggling to find some resources (webinars, courses, websites, ...) to help me get started with understanding and analyzing my data. I would appreciate it if anyone can give me some suggestions.
As the protein buffer exchange is important for efficient protein immobilization. However, most times we lose some of the protein during the exchange process.
could we escape this step if the dilution factor is high, Ex; 50X or 100X? is there a reference for that?
Thank you in advance.
I am looking for a tool (online, R, Python, or otherwise) which I can use to highlight peptide sequences on the full protein sequence in a visually nice way for publications and presentations.
Extended description: In several of my bottom-up proteomics research projects, I have identified proteins of interest for a given condition/disease. Often, these proteins are activated/deactivated by cleavage (e.g. the complement system, coagulation system, angiotensinogen, etc.). Therefore, I commonly perform a peptide-centric analysis after the protein centric analysis, to identify changing peptides and then I manually map these to the protein sequence. I am looking for a tool to help me with this; where I can submit the list of peptide sequences and have these visually mapped to the full protein sequence of origin. Ideally, the tool should include known cleavage products (e.g. from UniProt KB).
Any advice is most welcome and thank you for your time.
Tue Bjerg Bennike
I have done siRNA mediated knockdown of a low expressed protein in SKOV3 cell lines followed by proteomic analysis in biological triplicates. Proteomics was repeated three time. After retrieving the date I found that my desired protein(knockdown protein) in not present in transfected and even control (Non-transfected) group. However, I am getting bands of protein in western blot analysis. How can I justify my proteomics data.
I am looking to design a proteomics experiment looking at three treatment concentrations (Control, low-dose, high-dose) and two timepoints (24h, 48h) in an attempt to discover an unknown mechanism for lipid accumulation in THP-1 macrophages. I have never stepped into the omics world before so I thought I would start by asking:
What do you know now that you wish you had known when you started?
In fact, we know that both active transport and facilitated diffusion can using the carrier protein. And in the processes, there are involving conformation change, that change energy depends on a)facilitated diffusion→ligands disruption of the carrier intermoleuclar forces, b)atp as energy
however it is interesting that if free collision of ligands to receptor can lead conformation, then why or what molecular mechanism driven that in the case of against concentration gradient have to depends on the atp(atlases case) but not the ligands receptorinteraction changing the interaction?
to ask in the other way, can I engeering a new protein that they are able to transport again concentration gradient but no need atp /cotransporter, only depends on the channel interaction and free flowing of the ligands (collision frequency)? thank you
I have generated spheroids (UN-KC6141, pancreatic cancer cell line) and want to do proteomics.
Anyone who has a protocol for this procedure?
I have been working on optimizing lysis conditions to do whole proteome lysis from liver tissue and have a head-scratcher. Using the BioRad detergent compatible BCA analysis kit, I get a woefully low estimation of protein extracted when compared to doing a mass balance (weighing empty tube, liver, then remaining pellet after extraction)... I've washed the liver tissue as much as possible to remove blood (non-perfused at harvest) and the samples aren't bloody looking. Does anyone have any suggestions of expected protein extracted per wet liver weight? If I know that, I can at least have a better idea of which number I should use (BCA or mass balance).
we are interested in label-free quantification of shotgun proteomics experiments using the Waters SYNAPT G2-S HDMS instrument.
Does anybody have any experience of such data being used with MaxQuant?
What other freeware could be used for such application?
We are not sure if MSe mode or Survey mode would work best.
Thanks for your help!
I want to learn how to work with these databases? I do not know how to learn them step by step, and there is no instructional video.
Thanks for all your help.
I am using zeba desalting column to purify my protein + plasma sample in the beginning step of protein enrichment procedure to get rid of all unwanted stuff. After performing desalting process, I am going through the enrichment and digestion procedure but on LC-MS my peptide peak shape getting poor as the more number of injections injected. Initially peak shape is perfect but after injecting about 30 to 40 samples, chromatography getting poor. Earlier, samples prepared without zeba desalting have not shown any poor chromatography. Anyone have any idea about troubleshooting?
I have some interest in the interaction between protein A and B but I barely know about proteomics so I leave the question here.
To specify the exact interaction sites, I made four site-directed mutated plasmids having a GST tag using the quick-change method. The mutated sites are on the cold shock domain of protein B. Because I read this phrase "systematic alanine scanning mutagenesis has revealed that the substitution of an amino acid residue by alanine in these hot spot regions lowers the binding affinity by at least 2 kcal/mol (Bogan and Thorn 1998).", I changed every mutagenesis site into alanine.
And then I did a GST pull-down assay after co-expression of MYC-A and GST-B (treated with RNase, DNase, and MNase). This is the question. I could not understand the results I got from this experiment. The affinity between protein A and mutated B(all four mutations!!!) is so much higher(>100 folds) than that between protein A and wild-type protein B. They interact much stronger via these mutated sites. Do you have any idea what it is? And can it be a clue for finding exact interaction sites?
I was wondering if anyone knows-
which statistical test I should use in order to find whether a sample is an outlier in my proteomic data? It's obvious when looking at the PCA, but how should one calculate this?
Many thanks for your help!
Hi everybody. I have been in metabolomics 10 years and proteomics 20 years. However I feel confuse today that I am not going anywhere. It is endless questions and endless work to do? Is it good or bad for my future? Have I going wrong direction on the beginning? Today, every manuscript you read it has something to do with programming. Is it future? Should change my career as soon as possible?
Are there any strong books or tutorials on the concepts underpinning manual inspection and editing of alignments garnered by our alignment algorithms? Applying to both pairwise and MSAs, and I know the difference is perhaps stark between the two, though not sure.
I am trying to sort cells using FACS and the sorted cells will be processed for downstream study, sequencing, proteomics, or metabolics, for example. I am gonna use two reporter lines to tell the given cells from each other, tdTomato and GFP. But now the problem is that our BD Aria II doesnot have a 561 laser line, so tdTomato-positive cells cannot be sorted out, I am trying to use anti-tdTomato Ab that is conjugated to 633 to stain the cell suspension, but I donot know if it can make it. Usually, the Ab is specially produced for flow cytometry, and also it only recognize the antigen on the cellular membrane. tdTomato is expressed intracellularly, I have to use a blocking buffer containing detergent to puncture the membrane, Trition-100 for example, and let the Ab enter into the cells and bind, if in that case, it means there must be intracellular content efflux, which might cripple the downstream experiment, proteomics and metabolics for example.
Anyone can let me know how to deal with this in my case.
Thanks a lot !
in Western blotting, we use loading controls such as alpha tubulin and beta actin are used as loading controls to normalize samples. Can this method also be applied to normalize results obtained for shotgun proteomics?
We are using HRP conjugated secondary antibody for blotting and we employ DAB as developer. Some times the bands appear very much intense (dark brown) whereas sometimes the bands don't appear intensely with same protein concentration. We have changed all the stock buffers as well as tried a new vial of antibody but the problem persists. Why does this happen? Is it due to variation in the HRP activity?
We use R&D Proteome Profiler Rat Cytokine Array for detection of multiple cytokins in tissue samples. The standard protocol utilizes streptavidin-HRP with a chemiluminescent detection reagent that is not specified in the product documents. Does anyone know, what is the two reagents provided to the kit (chemi reagent 1, chemi reagent 2)?
Have anyone tried another detection system for visualizing (either chemiluminescent or other) the dots?
I studied the whole-cell protein profile in cyanobacteria undergoing abiotic stress. I have two questions regarding the data.
1. Is it mandatory/preferable to submit whole-cell proteomics data to a repository/database before publishing?
2. If it is, what is the best repository/database especially for cyanobacteria?
A primary look at imputation methods feels like we are just inventing values to make the data fit.
For data where we impute to improve the replicate clustering: aren't we forcing the replicates to agree?
For data where the protein is missing but we impute some kind of probabilistic value, what if the protein is actually absent?
For data where the absent values are non-random and non-ignorable, do we know the technical cause of missing values to impute?
How do we know whether our imputation has made the data better or caused us to introduce artefacts?
Hello dear fellow scientists,
I would like to ask some basic naive questions:
1) when scientists perform a transcriptomic study, lets say to compare a mutant to a Wild type plant, they tend to look at the genes that are at least 2 times more or two times less expressed between the two samples, why not all genes that are differentially expressed between the two genotypes? is it because it is more reliable ?
2) Usually when you perform a transcriptomic and a proteomic study (on the same sample and same conditions) you only find a low number of genes that show the same expression pattern (up-regulation or downregulation) between the two experiments, why ??
I did a transcriptomic and a proteomic study on a mutant and I found a small overlap between the differentially expressed genes and the differentially expressed proteins,
I mean its not surprising overall but I can't think of an explanation,
is it related to the degradation of transcripts ? post-translational regulations ?
I hope my questions are clear..
Could someone point me in the right direction as to which tool will allow me to analyse a proteomics dataset and filter the list of proteins in it based on their function (e.g. proteins related to cytoskeleton, metastasis etc) yielding a list of such proteins/ network?
Data extracted from Nano-LC-MS,
I want to submit the mass spectrometry proteomics data to the ProteomeXchange Consortium via the PRIDE
We have used Agilent nanoLC QTOF MS/MS system for proteomics studies. The format of raw data file is .d and searched files from Spectrum Mill software are in .ssv format. How we can convert these searched file format into mzIdentML or mzTab ??
Suggestions or answers will be highly appreciated.
I would appreciate any experience-based input on free software tools to analyze proteomics data. I have read the theoretical basis but no hands-on experience...
Thanks in advance
I need to deposit our mass spectrometry proteomics data to one of the good proteomics repositories acceptable for publication.
I found many published papers deposited their data to the ProteomeXchange Consortium via the PRIDE partner repository.
I am completely new on doing this and quite confused about what is the most simple and straightforward way for depositing the data to ProteomeXchange Consortium via the PRIDE.
I would highly appreciate if you could advise me in this regard. What is their requirements for mass spectrometry proteomics datasets? Which tools is the best to do it? How long does usually it take to get an identifier?
Also, can we only deposit our proteomics datasets in PRIDE? and not to ProteomeXchange?
Many thanks for your great help and advice.
I am working on cell cultures and labeling nascent translated proteins with methionine analog - AHA. Then I perform "click reaction" on cell lysates to tag freshly translated proteins with biotin. Can anyone advise me on what type of magnetic dynabeads is best for pulling down full AHA-biotin labeled proteins? I have successfully used M-270 dynabeads to pull down tryptic AHA-biotin labeled peptides but for full proteins I have good enrichment only for small <35 kDa proteins. Are some other dynabeads like C1 streptavidin better for full proteins? What can I do and what types of beads are best for pull-down of larger proteins. All suggestions and advice are welcome.
Greetings! I am looking forward for Liquid nitrogen free method of Protein isolation (from leaves) for Proteomics. I intend to follow TCA method for the purpose. It would be a great favour if someone guide me in this regard. Also, guide me how to clean mortar and pestle to avoid protein contamination while crushing different samples. Thanks in advance!
How long can fusion protein be stored at -20°C? If you need your protein samples for longer periods of time, is there any trick for storing them at -80°C?
Thanks in advance.
In past I was using MASCOT to search, identify and explore the proteomics data. I am looking for any windows based free software to analyze my big proteomics data files mostly in mgf format.
I have been working on pancreatic islet isolation and have no prior experience with pancreatic islets. Our aim is to use the islets for omics studies. I would like to know the best strategy for homogenizing the freshly isolated islets for proteomics studies (buffers, time, temperature, etc.) if someone already has prior experience with them.
We're supposed to delete rows that have blank/Zero as LFQ intensities and those that have <0.75 individual localization probabilities for all replicates in one or both groups. (If they have average loc. prob. <0.75 they will be deleted prior to this). My question is how many replicates in a group must have below threshold values in order to be deleted. 50%? If in a group with 6 replicates, there's only one replicate that has loc. prob more than 0.75, should we retain it?
to gain access to proteins inside of Lactobacillus casei and L. plantarum vesicles and to perform LC-MS/MS, I want to establish a lysis protocol. The protein concentrations before and after the lysis are determined with a BCA assay.
I already tried Triton X 100 buffer (4h, on ice) and RIPA buffer, but the results were not reproducible or it didn't work at all (same protein concentration before and after lysis).
I also tried a sonication bath after incubation with buffer and 30s with 50% intensity digital sonifier. Lyophilization and solving in Methanol didn't work either.
Did anyone have similar problems? Do you know a working lysis protocol for these (seemingly very stable) EV's?
I'm currently delving into proteomics head first, which is entirely new to me. My collaborators will be carrying out tandem mass tag spectrometry with fractionation on my samples (cases versus controls, tissue is postmortem brain tissue) and will be sending processed results my way, which include # Peptides, # Unique Peptides, values scaled to QC, % CV, abundances, normalized abundances. I'm interested in case versus control differences so what would be the best analyses to do? I'm only familiar with RNA-seq analyses, so any workshops, youtube tutorials, tips and advice would be GREATLY appreciated!
Hi. Recently I am working on proteomic data analysis, and I need to know how many times A protein is expressed more than B protein (mass or copy numbers). I know there are multiple ways of proteomic methods to compare between-sample levels, in other words, the relative number of one protein in two or more samples. I'm wondering whether can I quantify the relative number of protein A over protein B in the same sample. If this can be done, how to deal with the proteomic results (e.g. the intensity matrix of peptides or proteins) to get the relative number I want? Thanks!
Some studies say that the Random forest method could be the best. But I'd like to get more opinions since many people seem to be using many different methods. It would be nice if someone could provide any resources for carrying out the methods too (Tutorials, R code, etc)
Hi. Since missing value imputation is determined by nearby data, should we separate control and treatment groups and perform MVI separately for each? Context: This is for mass spectrometry data.
I am working with some materials that require desalting of the peptide samples prior testing. My samples contain a salt of potassium. With a standard sample, I would use R3 or C18 resins with low pH solvents for desalting (Formic acid + MeCN, for example). However, I am working with a modification that is unstable at low pH but completely stable at high pH.
Can you de-salt peptide digest using C18 or similar with basic pH solvents as if you were doing high pH RPLC fractionation? In our lab we always desalt prior high pH fractionation but we do it with acidic conditions. But with high pH, would the peptides be retained in the C18 stationary phase? And if it's possible, what kind of solvents would be good for it? (Keep in mind that the peptides are analyzed by LC-MS/MS).
Thank you for your advice.
I am experiencing some difficulties in preparing brain tissue for proteomic analysis - most likely due to high lipid content that interferes with proteomic data acquisition.
We need to isolate proteins in their native state including larger molecular weight complexes and protein aggregates. This means that we are limited to bead beating in native lysis buffer (1 mM MgCl2, 150 mM KCl, 100 mM HEPES, pH 7.4) and a very mild centrifugation step (800xg, 5 min) before using the supernatant for tryptic digest, C18-cleanup and HPLC-MS.
However, I commonly observe column clogging or ion suppression using these samples, which I suspect is due to the presence of contaminating lipids.
Does anyone have a good protocol to remove lipids without denaturing proteins or for removing lipids after the tryptic digest?
Any advice is highly appreciated! Many thanks in advance!
Its hard to detect amyloids even in brain tissue of Alzheimer patients using untarget proteomic methods. I am curious of what kind of protein is hard to detect by mass spectrometry, whether there are commonalities in their structures?
I'm conducting proteomic research on a complex in plasma. I found when I set the criteria as at least 2 unique peptides, lots of low molecule proteins (~10 kD) were excluded despite they have relatively high PSM. In my experience, these proteins have already been validated composing this complex.
I am wondering if I could comfirm all unqiue peptides of a protein in theoretically? Is there any database I can seek? Or for some low molecular proteins, only 1 unique peptide enough to identify their existence in proteomic study? P.S. These proteins were usually members of a superfamily.
I am looking for an active and productive collaboration with experienced researchers in the area of analysing (wet lab validation and bioinformatics) human genome, transcriptions, proteome, metabolome, single cell omics etc. for various human traits and disease related research.
Please contact at email@example.com
I have to use mass spectrometry proteome data (analyzed data from Maxquant) to carry out post-translational modification analysis using Proteome Discoverer software. I'm searching for a proper workflow for this purpose. Any suggestions/links to useful sources, etc. would be very welcome.
I am interested in how plants shape microbial metabolism in the soil and I was wondering if we could extract "soil proteins" as we do for genomic DNA extraction from soil and perform proteomics to understand how plants shape soil metabolism? If it is possible, would you suggest some references to read and follow?