Mass Spectrometry - Science method

Recent advancements in mass spectrometry for ionizing non-volatile compounds include techniques like extractive-liquid sampling electron ionization (E-LEI-MS) and nanopore ion sources, which enhance sensitivity and reduce sample loss. These innovations enable real-time analysis and broaden the range of analytes that can be effectively studied.

STAT60 is primarily designed for RNA isolation and does not specifically cater to protein extraction suitable for mass spectrometry. While it can be used to co-isolate proteins during RNA extraction, the resulting protein samples may not be optimal for high-precision applications like mass spectrometry. The presence of contaminants such as guanidine salts and phenol, commonly used in RNA isolation processes, can interfere with mass spectrometry analysis and may necessitate additional purification steps.

For more reliable protein isolation specifically for mass spectrometry, methods like filter-aided sample preparation (FASP), S-Trap, or SP3 protocols are recommended. These methods ensure efficient protein digestion and purification, reducing contaminants that can affect mass spectrometric analysis. Additionally, optimized commercial kits, such as Thermo Fisher’s mass spec sample prep kits, have shown to yield reproducible and high-quality protein preparations from tissues for LC-MS/MS applications.

If you're focusing on protein extraction for mass spectrometry, selecting a method or kit designed explicitly for this purpose is advisable.

Differences in the numbers to the right of the dot in calculated and found m/z values in mass spectrometry can arise from factors such as mass resolution, natural isotopic variations, fragmentation patterns, and instrument calibration. These elements can lead to discrepancies between theoretical predictions and actual measurements.

Calculating the collision cross-section (CCS) for sodiated adduct ions and multiply charged ions involves experimental and theoretical approaches. Here's how to proceed:

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1. Experimental Approach (Using Ion Mobility Spectrometry - IMS)

Sodiated adduct ions ([M+Na]⁺) and multiply charged ions (e.g., [M+2H]²⁺) are injected into the IMS instrument.

Measure the drift time () under a known electric field, gas type (e.g., N₂ or He), pressure, and temperature.

Use the Mason-Schamp equation to calculate CCS ():

\Omega = \frac{{3ze}}{{16N}} \left( \frac{{2\pi}}{{k_BT\mu}} \right)^{1/2} \frac{{t_dE}}{{L}}

Where:

: Charge of the ion.

: Elementary charge.

: Gas number density.

: Boltzmann constant.

: Temperature.

: Reduced mass of the ion-gas pair.

: Drift time.

: Electric field strength.

: Drift tube length.

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2. Theoretical Approach (Trajectory Method)

For sodiated or multiply charged ions:

Generate 3D structures of the ions using computational methods like density functional theory (DFT).

Simulate ion-neutral interactions using programs like MOBCAL or IMoS to estimate CCS based on ion dynamics.

Account for the effects of sodium addition or multiple charges on the ion's structure and interaction with drift gas.

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Key Considerations:

Multiply Charged Ions: CCS typically decreases with increasing charge due to compaction from Coulombic interactions.

Sodiated Adduct Ions: The CCS may increase slightly due to the larger ionic radius of sodium.

Use accurate experimental conditions and computational models to ensure precision in CCS calculations.

Let me know if you need specific software recommendations or detailed examples!

GlycoBlue does not significantly affect mass spectrometry with RNA samples as it is primarily an inert glycogen-based co-precipitant used to enhance RNA recovery. However, its dye component might introduce minor background signals in highly sensitive analyses. To avoid potential interference, ensure thorough washing of RNA pellets. For critical applications, alternative co-precipitants like pure glycogen can be used.

Perfect, thank you so much again for the help and the insights!

The [M-H+114]⁻ adduct you observed in negative mode LC/MS could be due to an uncommon contamination or reagent interaction. Although TFA is not present, the 114 Da addition might be related to other substances or reagents present in your system (e.g., a plasticizer or solvent impurity). The fragment ions at m/z 113 and 69 suggest a specific structure breaking down in a predictable way. It's essential to check for possible contaminants in your solvents, equipment, or even the plant extract itself. You could also run a blank to see if the adduct is present without the sample.

Export the data as xml or csv format into a spreadsheet, then write a simple formula to copy the specific columns of data.

Thanks for sharing, indeed, for a low mass resolution spectrum is simple, usually these masses are rounded and collected by centroid mode so that easy to align it among multiple spectra. However, as in most of the cases people tend to use high resolution mass spectrometer and store them in profile mode, how to align the profile data currently is challenging, I saw Xcalibur the software did a very good job, that why I raised this question. But still, thanks a lot, probably an easy way to solve my problem is just to centroid my data and then align it.

Hi Asra,

I have used Maxquant to anlyse TurboID data (see pdf attached).

Best,

Murat

Abdullah Aldasem see this paper. you should be good to go

Hi there! I´ve used DETARCHI software which is indeed ver easy to use in the calculation of CCA and CCB.

You may contact the department of Chemistry from the University of Burgos, Spain to get the software.

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.

Hi Amir,

I think I know the issue with your data because am currently at the same stage of analysis. I used to get the same error when I run my samples which were converted to MZXML formate using MSConvert tool in maxquant. the thing I discovered that Maxquant doesn't recognize any MZXML files except the ones created by OpenMS (TOPPAS) tool which have a specific option to make it recognizable by maxquant ''Force_MaxQuant_compatibility''.

To make it easier please follow step by step;

1) open Galaxy Europe (a free online tool that provides graphical access to multiple genomic and proteomic tools as OpenMS)using the link ------https://usegalaxy.eu/

2) create an account

3) search for FileConverter tool from the toolbars on the left of the page, and click on it

4) Then load the files that you wish to convert under input files to convert, (note you can load all the files at once)

5) then, specify the output type formate to mzxml, and under advance options set yes option to [mzXML output only] Make sure that MaxQuant can read the mzXML and set the msManufacturer to 'Thermo Scientific'

6) and then click run tool , it will take few mins and your desired converted files will appear on the right bar on the page, then you can download the converted files one by one.

one more hint; when setting the parameters on maxqaunt keep the instrument as orbitrap and make sure to run the analysis using a PC with a high processor and space or cloud space as the quanting can take upto days of continuous run

and that's all. all the best :)

Dirk Siekmeier Have you tried using deionized water? I always thought HPLC grade water is cleaner than distilled water.. Maybe I should try using distilled water!

You can try search and match those compounds in Adam's Database, based on the RI, KI value. I think, this will help you to clarify.

Comparison and Recommendation: For Analyzing H₂ in Exhaust Gas:

Electron Impact Ionization (EI): When used with mass spectrometry, EI offers detailed and sensitive analysis of H₂ due to its consistent ionization properties. This makes it particularly effective for detecting and quantifying hydrogen specifically.

Mass Spectrometry (MS): As a standalone technique, MS is highly effective for analyzing various gases in exhaust emissions, including hydrogen. When paired with appropriate ionization methods like EI, it becomes a powerful tool for comprehensive exhaust gas analysis.

Recommendation:

Mass Spectrometry with Electron Impact Ionization (EI) is the superior choice for analyzing H₂ in exhaust gas. This combination utilizes the high sensitivity and specific ionization characteristics of EI along with the versatility and precision of MS, making it ideal for detecting and quantifying hydrogen in complex gas mixtures like exhaust emissions.

I think you already answered the main possibility. Coordinates of the autosampler must be confirmed as well as the septa and liner used should also be checked carefully. Everything in the sample introduction configuration must be fully compatible and properly used. Secondly, storing the SPME arrow and holder with residual KOH without rinsing well may also present some clogging or corrosive contaminant in SPME which may lead to the plunger malfunctioning.

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.

For SEC (size exclusion chromatography), can you use Sephadex LH-20? This can be run in an open column (gravity column).

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!

If you are using purified proteins, the simplest method (if you can find a lab that has the necessary equipment) to detect phosphorylation would be intact protein mass spectrometry, either by electrospray or MALDI. Those methods would easily distinguish the mass change due to addition of phosphate. No antibodies needed.

Hi Keller, Thanks for your reply!!

ahah hello All !

UPLC spectrum?... Did you mean UPLC chromatogram or LC-UV spectrum/spectral view?

If you mean chromatogram and resulting different retention times with the same masses, these may be the isomeric variants of the same molecules. This can be clarified by the use of a high-resolution power of the mass spec. High-resolution m/z values (say 40.000 and above FWHMs) can confirm if these are probably the isomeric species.

If you are meaning UV spectral shift differences (distinct UV max absorption wavelengths) but the same masses, probably they are different molecules. You can use the abovementioned second option if you did not apply before.

Alternatively MS/MS and MS to the n experiments would be more beneficial to discriminate the molecules. EAD, CID, HCD, UVPD, and ETD are the orthogonal approaches that can also be used for molecular structure elucidation.

Good luck,

İEA

Hey there Junlong Huang,

It's great to hear about your progress with the TDS-GC-MS method for VOCs analysis! Now, regarding your question about the final oven temperature and its influence on the repeatability of low boiling point analytes, let's dive into it.

The fluctuations in intensity you're observing, particularly with dichloromethane, benzene, and heptane, can indeed be puzzling. The change in final oven temperature seems to have a significant impact on the stability of these compounds, with lower temperatures showing better repeatability for benzene and heptane but not so much for dichloromethane.

One potential explanation for this could be the volatility and thermal stability of the compounds. Lower boiling point analytes like dichloromethane are more sensitive to temperature changes, and even a slight variation in final oven temperature can lead to fluctuations in their intensity. On the other hand, compounds with higher boiling points like naphthalene and pentadecane are less affected by these temperature changes, hence the more stable RSD.

Additionally, considering the column pressure under high-temperature conditions is crucial. High pressures can exacerbate the volatility of low boiling point analytes, leading to increased intensity variation.

In summary, the interplay between compound volatility, thermal stability, and column pressure under high-temperature conditions likely contributes to the observed phenomenon. Fine-tuning the method parameters, such as oven temperature and column pressure, can potentially mitigate these fluctuations and improve repeatability.

Hope this sheds some light on the issue! Let me know if you Junlong Huang need further clarification or assistance.

You can start with Wilson and Walker proteomics chapter. It is very basic and easy to understand

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.

Hi,

This is my go-to site for that purpose: https://prospector.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msproduct

Why not look at monoisotopic mass?

The difference in masses obtained can be due to the way the elemental masses are set in the algorithm of the calculator: a difference in digits/rounding can result in large differences in peptide masses, let alone that of complete proteins..!

There is no 'correct' answer, I guess, it's more about the accuracy taken into consideration.

Hope this helps.

INFICON FabGuard Explorer Operating Manual (manualmachine.com)

This link may be helpful foor you, I have never used this.

No one can answer this question as it depends entirely on the LC-MS METHOD used (conditions, column, settings etc). Using an LC-MS/MS system is not the same as using a basic lab instrument such as a spectrophotometer. Please contact an experienced LC-MS chromatographer at your institution (or outside lab) who can assist you in the preparation of your sample(s) and also the development of a selective method of analysis. Once the method has been established and a loading study run, then you will know what concentration and volume to use for your analysis. This process will allow you to obtain better quality data for analysis.

Contact me

Murat Eravci

Thank you for your kind reply.

I will try searching HLA peptide atlas.

Best regards

Jaekwan.

Hello Mariano,

It is pretty common to have carry-over analyzing plasma/serum samples. Someone recommended me the use of Trifluoroethanol to wash out all retained peptides. Basically you just use it as a sample and inject 1 or 2ul direct onto the column with a relative short gradient. It does not damage the column in my experience, so maybe you can give it a try in the future.

Good luck!

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).

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

Hi Constanza,

Throughout the electrophoresis steps, please use powder free gloves to prevent contamination from skin keratins. It is also important to dedicate apparatus for electrophoresis and not to use any surfaces used for Western blots. Otherwise, you will encounter eg., casein and BSA contamination arising from blocking of membranes during Western blots.

The best way to clean any glass apparatus to remove background proteins is the following procedure: this is the same procedure our proteomics laboratory at MSKCC used, for high sensitivity sequencing of peptides:

1: RInse glassware with 80% acetic acid (v/v)

2: Follow 1 above with MQ filtered water

3. Follow 2 with 100% methanol

4: Follow 3 with MQ filtered water.

This protocol of stringent washes can completely remove any protein/peptide contamination from your elelctrophoresis apparatus. Please use a fume hood for this cleaning experiment. Also, make sure your electrophoresis containers are glass eg;, pyrex glass containers that can be used for stainng and washing of the gels.

The plastic containers used for electrophoresis can be rinsed with dilute methanol and water. You can omit the acetic acid for these plastic containers.

Good luck!

Hediye.

Hi ,

I want to ask, after adding 1% SDS buffer, is a sonication step needed, since in this case DNA is not needed?

There are multiple answers to this depending on the application. The first relates to protein ID. If you have sequenced (via tandem MS) more than one peptide from the same protein, that is multiple assignments. The second relates to charge state at the MS-level. If you have multiple charge states the peaks corresponding to more than one charge state constitue multiple assignments (i.e., a series of charge state peaks). The third relates to the isotopic series. You can use the heights of the isotopic pattern to create a vector that can be compared to the predicted isotopic vector of an elemental composition using isotopic vector angle analysis.

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.

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? 

Assuming that the labeled glucose powder is not sterile, you have 3 options.

1. Prepare a concentrated stock solution of the labeled glucose and pass it through a sterile syringe filter into a sterile container.

2. Prepare the medium from powder, including the labeled glucose powder, and then sterilize it in the usual way.

3. Add the non-sterile labeled glucose as powder or stock solution directly to sterile medium, then re-sterilize the medium by membrane filtration.

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.

Hi Himani,

If you know which organisms are included in your samples you can work with a joined database fused from downloaded fasta format databases (i.e. from Uniprot) of the respective organisms.

With Maxquant you can than look at the number of unique/razor peptides to check from which organisms the proteins are more likely.

Best,

Murat

Hi Dina,

What is the purpose of the analysis your performing? If you'd like to get an idea of the yield of a protein purification, I don't think you would need a protein digestion protocol; you could consider running LC-UV instead.

How did you determine the yield of the individual steps of your protocol?

And how are you desalting after digesting?

What is your 'running solution'?

Thanks in advance for providing some additional context and information!

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

When performing mass spectrometry analysis of lipid classes using a lipidomics standard like the EQUISPLASH product from Avanti Polar Lipids, adding the standard to your test sample serves as an internal standard for quantification. This approach helps account for variations that can occur during sample preparation, extraction, and analysis. Here's how it works:

1. Internal Standard Principle:An internal standard is a compound that is added to both your standard samples and your test samples in a known amount. It's chemically similar to the analytes of interest but should be easily distinguishable in the mass spectrometry analysis. By adding a known amount of the internal standard, you can correct for variations that might occur during sample preparation and analysis.

2. Adding the Internal Standard:When you add the EQUISPLASH lipidomics standard to your test sample during the extraction process, it becomes a reference compound with a known concentration that you control. This internal standard will undergo the same extraction and analysis steps as your sample, which helps compensate for any losses, variations, or biases introduced during these steps.

3. Quantitative Data Analysis:To obtain quantitative data from your mass spectrometry analysis, you'll follow these steps:

The rationale behind this is that the internal standard's known concentration serves as a reference point. If the extraction and analysis are consistent, the ratio of the analyte's peak area to the internal standard's peak area should be proportional to the ratio of their concentrations.

4. Calibration Curve:To convert the peak area ratio to quantitative concentration, you'll need to create a calibration curve. This involves analyzing a series of standard solutions with known concentrations of the lipid classes using the same extraction and analysis procedures. The calibration curve plots the concentration of the internal standard against the measured peak area ratio.

5. Quantification:Using the calibration curve, you can then determine the concentration of your lipid classes in your test samples based on the peak area ratio. The formula to calculate the concentration is derived from the linear relationship shown in the calibration curve.

By adding the internal standard and utilizing a calibration curve, you can obtain quantitative data for your lipid classes in your test samples, even considering any variations introduced during sample preparation and analysis. This internal standard approach enhances the accuracy and reliability of your quantitative results. Subhadip Kundu

To get qualified and specific answers you need to provide a bit more information about your experimental approach. Ismail suggested many different options that may be suitable depending on what your overall experimental setup is. As i read your question you are capturing interactors on a Ni-NTA resin with the bait protein bound and then eluting them with imidazole.

There are a wealth of proteomics sample prep methods nowadays that would get you past the imidazole issues, such as protein aggregation capture (PAC) or SP3, which serve to precipitate your protein onto a bead matrix that can be washed thoroughly before trypsin digestion (if bottom-up proteomics is your method). Alternatively, you may be able to digest your protein directly on the Ni-NTA matrix. A C18 desalting step is usually standard procedure before LC-MS analysis and that should give you nice clean samples overall. Gel-band analysis is another approach that should also work fine (if a bit more laborious).

I would suggest your consult with a proteomics specialist at your institution to figure out the most viable approach for your experiment.

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...

Alternatively go back over your sample preparation protocol and determine the source of the PEG contamination.

If your laboratory results are showing increased zinc levels despite collecting samples in EDTA tubes, there could be several potential reasons for this discrepancy:

To resolve the discrepancy and ensure accurate results, it is essential to review the entire testing process, including sample collection, handling, and analytical methodology. If there are concerns about the results, it is recommended to consult with the laboratory or a qualified healthcare professional for further investigation and appropriate interpretation of the findings.

Thanks Murat

I suggest using phree-phenomenex (bimodal/phospholipids and protein removal) protein precipitation plates...This will remove the abundant large molecules of serum such as proteins (albumins, globulins...and phospholipid derivates)...with this, you may combine protein precipitation and spe clean-up into a single prep step...In addition, MS/MS fragmentation and using alternative ions for your SRM acquisition may improve specificity and give more non-interfered spectra...If sensitivity is low I also recommend nitrogen evoporation followed by buffer exchange and internal standard normalization approach for absolute quantification...for hydrophobic compounds such as curcumin I also use APCI instead of ESI to get a more intense signal if your configuration is applicable to implement...

Good luck

No. You can never assume that different compounds will ionize in the same manner. MS analysis is not a "universal" detection technique.

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.

Yes, this is flying as a protonated adduct.

Noel W Davies thank you very much

Dear Hongjie Hu,

Please share the data it will easy to discuss.

I would do an on-bead digestion with trypsin, which will give you the peptides that can then be followed by cleanup (if needed) and MS. This is quite a common approach for elution and any mass spec service should be able to advise you on it.

To my understanding, acetonitrile, methanol, isopropoanol etc. act as electron donors in ESI conditions, enhancing ionization in ESI+. This is one of the reason why ionization in ESI+ is enhanced by solvents (the other is their faster vaporization than water in ESI conditions).

some solvents are better electron donors than others, and you may notice signal enhancement switching from one to another...

hope this helps...

Dear Simon Thain,

Thank you for the answer! Yes, our case was caused by losing BIOS settings, we restored them and now Synapt is running. But absence of the manual for EPC is looks strange for me. battery in EPC is not eternal, so why did they don't add any information in user manual or even their web-site? It looks like Waters don't even think about situation, when their instrument will be online after more than 5 years :-)

Warm regards,

Azamat

Noel W Davies

Thank you so much Prof. Noel

Let me work on your advice and will come back to you for feedback.

Thanks

İsmail Emir Akyildiz that is really a good suggestion. I would try this out

Dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) are pretty much universal techniques. AFM might be demanding for sample preparation, but it is also possible.

Shift in the surface plasmon resonance (SFR) UV-VIS absorption peak works in some specific cases.

Dear Itay,

do you see no signals at all, or is the identification/quantification of sugars connected to peptides the problem (e.g. in a software) ? Do you have a chromatography in front or do you only want to analyse single proteins and thus have a direct injection setup?

There are several causes for the presence of outlyers. Impurities, errors during the extraction, the needle that for some reason did not inject the same amount of sample, and samples that are naturally different from the other ones.

You can solve the problem using this checklist.

- check the look of the chromatograms and verify that all of them look more or less the same.

- check IS intensities and verify that their peak area is within an acceptable range for all the samples.

- normalize the data across samples.

- scale the data using UV scaling.

- evaluate the presence of outlyers using RobustPCA.

good luck

Late answer but TiO2 and FeNTA are not suitable for phosphotyrosines( i.e I quantified 43 phosphotyrosines by a sample from which I quantified nearly 9000 S/T phosphosites so much less than 1% with TiO2) If you don't have sample quantity problems(not working on clinical data) pY100 antibodies are the best for phosphotyrosines. Not really an expert but if you can quantify more than 300 phosphotyrosines its a success.

You need to freeze them immediately using liquid nitrogen in order to stop the metabolism of the cell. After this you can keep the cells until the analysis day.