Please ask for an estimate if you want to do profiling
TMT can be expensive especially if we do the labeling and sample prep. If you do the labeling you will save a lot of time and money!
DIA costs about 20% more than TMT even with the TMT label costs. This is due to you not being able to multiplex with DIA and the need for DIA library creation (not strictly required, but it should make the data better). Essentially you need more LC-MS runs with DIA which costs more than the TMT labels .The exception to this is where you want us to do the TMT labeling. In that case DIA is a lot cheaper (especially if you are off campus) due to the massive decrease in labor needed for the experiment.
for our official prices please see http://proteomics.ucdavis.edu/prices-jan-2015
TMT labeled profiling (recommended for protein amt > 20 ug or single cell proteomics)
TMT is a reporter ion isotope labeled balanced set of tag’s (currently 11) .
Essentially (simplifying a bit)
- you digest your proteins into peptides,
- label with TMT, which labels free amines (K and N-term)
- Mix your samples together (yep you multiplex them)
- Fractionate your samples using high pH if your sample is very complex (lysate for example)
- Analyze your peptide fractions using SPS MS3 on our Fusion lumos
I know, protein amounts over 20ug or single cell proteomics? How can this be ? Well first we really can’t do proteomics on single cells, but it is possible with TMT to use one of the tags as a carrier sample. This does two really cool things, it helps your proteins get into the Mass spec and not stick to your tube, and it amplifies your signal so the MAss spec will trigger on the peptides and fragment it
Here is a presentation of a dataset we did recently. The author of the above publication helps us with the analysis. Make sure you read the notes as most of the detailed info is located there.
Also with our new Fusion Lumos we routinely do MS3 TMT 10 or 11 plex’s. We currently recommend 10 plexes with MS3 over 6 plexes with MS2
Currently we like to do multiples of 8 samples (8 Samples + 2 pooled references per 10 plex) so 16 samples minimum if the total number of samples is > 10. If less than 10 we do not have to use pooled reference. So e.g. if you have greater than 10 samples you should do a minimum of 16 with multiples of 8 after that (16,24,32 etc…_)
Data Independent Acquisition (recommended for < 20 ug or when you don’t want to TMT label)
Been around for a few years , but only recently has the software become available that allows non mass spec people or technical experts to analyze the data and get any sort of clarity and insight in a reasonable period of time
ScaffoldDIA (based on EncyclopeDIA)
It’s a little hard to wrap your head around, but essentially you fragment all peptides in a certain mass window, (4Da, 25Da typically), and then you extract the Y and B ions you expect for all theoretical peptides in that window and then see if they line up by retention time. If they do, you score them and say they are identified and then you quantify the peptides by integrating the area of those y and b ions over time.
This differs from traditional DDA (Spectral Counting or LFQ) where you isolate individual the peptide ions (or at least try to) and then fragment them separately. DDA is stochastic, you will get a slightly different set of peptides every time you do the experiment) DIA is the same every time. Hence you are able to sample the peptides more consistently and your CV’s are much better for the quantitation. DIA is generally considered a little less sensitive than DDA I think. Although from my experience, i’m not certain . The quantitative ability blows spectral counting out of the water.
DIA also works a lot better on our current instruments (due to he cycle speed) and we only have 1 of these so your turn qround time will be slower
Some DIA data generated in the core (2018)
Spectral Counting (okay for pulldowns, currently not recommended otherwise )
Label Free Quantitation (LFQ) aka Area Under the Curve (AUC) or Differential Mass Spectrometry(dMS)
- Do a traditional bottomup shotgun proteomics experiment.
- Do a spectral counting analysis (usually using Scaffold)to determine which proteins are differentially expressed
- Take the interesting proteins that are significantly differentially expressed (or interesting and not significantly different) and make a list out of them
- Take all the MS/MS spectra you acquired from the shotgun experiment and generate a MS/MS library using skyline
- Take the sequences of the differential or interesting proteins and put them into skyline to generate AUC numbers of all your proteins from step 3 in all your replicates (technical or biological)
- Export these numbers from skyline into R
- Use R to calculate the statistics
You can also use a MRM targeted proteomics approach after say step 3. MRM on a triple quadrupole (low resolution, low accuracy, but direct beam and specific) has its advantages and disadvantages compared to AUC analysis on traditional high accuracy high resolution instruments.
Here is a an example paper where they did a similar approach (There are others too I will add shortly)
Targeted Proteomics Assays (TPA’s I guess)
These are mixtures of 100’s or even up to 1000 heavy isotope labeled peptides that map to 100’s of proteins. These peptides are used as internal standards either for AUC on MS data or MRM QQQ analysis. Usually these peptides are chosen carefully and the assay is validated to some extent to make sure the peptides behave linearly over a wide rage of concentrations. Choosing the peptides and validating the assay can take 100’s of hours and cost thousands of dollars. There are only two places that I know that do this
If this is something that interests you , let me know. I can see about getting one up and running here, or purchasing a TPA assay like Spiketides set for tumor associated antigens . All I need is the interest from people on (or off) campus
We have done this in the past and can generate the data for it. Just let us know if it interests you. The data in this paper was generated in our facility . I personally do not like SILAC…It makes the MS1 space way to complex and it’s difficult to pick out SILAC pairs that have low s/n
- Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue
- Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments.
- Evaluation and Improvement of Quantification Accuracy in Isobaric Mass Tag-Based Protein Quantification Experiments
- MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics.
- Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics
- Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics
- A Model for Random Sampling and Estimation of Relative Protein Abundance in Shotgun Proteomics
- Comparison of label-free methods for quantifying human proteins by shotgun proteomics.
- Statistical Analysis of Membrane Proteome Expression Changes in Saccharomyces cerevisiae
- Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation
- Spectral Index for Assessment of Differential Protein Expression in Shotgun Proteomics
- Quantitative mass spectrometry in proteomics: a critical review
- Comprehensive Profiling of Cartilage Extracellular Matrix Formation and Maturation Using Sequential Extraction and Label-free Quantitative Proteomics
- Quantitative Analysis of Complex Peptide Mixtures Using FTMS and Differential Mass Spectrometry
- Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures.
- Quantitative Profiling of Proteins in Complex Mixtures Using Liquid Chromatography and Mass Spectrometry
- The Association of Biomolecular Resource Facilities Proteomics Research Group 2006 Study
- Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes.
- Comparative LC-MS: A landscape of peaks and valleys (Great Review!)
- Evaluation of SIEVE as a label-free mass spectrometry protein quantification method
Abundant Protein Depletion
This is a great paper to read if your planning to look for biomarkers in Plasma!
Other important papers
One of the most recent and best studies on the correlation of mRNA and protein expression
Does a really nice job of comparing iBAQ, APEX and emPAI label free methods
- Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli.
Another really nice paper that uses Spectral couting, XIC and RNA-seq
- Comparative Analysis of Different Label-Free Mass Spectrometry Based Protein Abundance Estimates and Their Correlation with RNA-Seq Gene Expression Data
A nice recent paper on differential expression on transcripts using RNASeq compared to proteomics using SILAC
Recent Paper comparing Various Label Free methods of Quantitation (we can do all of these if you’re interested)