Current Prices (3+ replicates per condition are required, 5-8 optimal)
|Service||University of California||Non-Profit||For-Profit|
|Typical Cost per Replicate||$179||$252||$310|
|Raw Data Only (no analysis or prep)||$73||$114||$140|
* These are bundled prices, for our official prices please see http://proteomics.ucdavis.edu/prices-jan-2015
A note about our Prices: Profiling can get expensive as you typically need at least 3-5 biological replicates per condition. Optimally you need many more, but calculating power can only really be done correctly after the experiment is finished (mainly due to sample preparation and biological variability which we cannot predict accurately before we do the experiment). To save money I suggest doing as much of the sample preparation and data analysis yourself.
- The above prices do not include the costs of extracting the proteins from tissue or cells. Unfortunately we have to charge extra for that.
- Unfortunately we have to charge a little extra if you sample requires precipitation. Usually we need to precipitate samples that are in a solution with detergents or salts that cannot eaisly be removed
Area Under the Curve (AUC) and 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
ITRAQ and TMT labeled profiling
Currently we do not routinely do this type of analysis. Although we can if requested.
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 Here are some other nice references to get you started with label free proteomics (I will try and update these soon)
Data Independent Analysis
We can now offer Data independent analysis (DIA) using our Q-exactives and Skyline. I’ll add more information soon, but DIA is a lot less expensive than MRM/SRM as there is virtually no method development (hence the I in DIA). Here is a good paper to get you started
- Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis
Here are some other nice references to get you started with label free proteomics (I will try and update these soon)
- 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
Labeling Methods (SILAC, iTRAQ, TMT etc)
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)