Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

Targeted proteomic approaches are fast becoming the method of choice for validation of proteins from short-term proteomics-based experiments or from molecular biology-based experiments. Multiple reaction monitoring is one such targeted approach which is increasingly being used to detect and quantify proteins from biological samples. In this study we have walked through the various steps involved in detecting and quantifying proteins from human brain tissue with an intention of introducing the readers to the basic requirements of an MRM experiment.

Weigh around 50 mg of brain tissue, and wash the tissue with 300 microliters of 1x phosphate-buffered saline. This step is performed to remove any blood on the external surface of the tissue, and hence it is advisable to remove as much blood as possible. Following washes with PBS, add 300 microliters of lysis buffer to the tube containing the tissue.

Lyse the tissue using a probe sonicator while keeping the tube on an ice bath. Continue with tissue homogenization using a bead beater to completely lyse the tissue. Centrifuge the contents of the tube at 6000 g for 15 minutes at four degrees centigrade.

Transfer the supernatant into a fresh tube and mix homogeneously. Take a small aliquot of the supernatant, and quantify the protein content using a standard protein assay. Here we show an example of protein estimation using Bradford Assay.

Take 50 micrograms of protein in a microcentrifuge tube and reduce the contents by adding TCEP such that the final concentration is 20 millimolar. Incubate the contents at 37 degrees for one hour. Following incubation, alkylate the reduced proteins by adding iodoacetamide to the tube, such that the final concentration is 40 millimolar.

Incubate the tube in dark for 30 minutes. Now add Buffer B to the tube contained the reduced and alkylated proteins such that the final concentration of urea is less than one molar. Add trypsin in 1 as to 30 enzyme-to-protein ratio and incubate the tubes overnight at 37 degrees with constant shaking.

Following digestion, concentrate the digested peptides in a vacuum concentrator. At this step, the peptides can either be reconstituted and desalted or stored at minus 80 for future use. Desalting, or peptide clean-up, is essential before loading the samples on any LC-MS MS.Salts and other contaminants in the sample can clog the column and affect the sensitivity of the instrument in the long run.

This process can be performed using commercially available C18 STAGE tips or columns. Here we have depicted the workflow for peptide desalting using a C18 STAGE tip. Following desalting, dry the peptides in a vacuum concentrator.

Reconstitute these clean peptides and proceed for quantification using the Scopes method. For this, load two microliters of the sample in a microdrop plate and calculate the concentration as shown in the slide. Transition list preparation is a crucial step in an SRM or MRM experiment.

A transition list contains all the information required by the mass spectrometer to monitor the transition. This include the m by z values of the precursor and product ions, collision energy of the transition, polarity of the instrument, and the time range for data acquisition. Prepare the transition list using Skyline.

Prepare a background proteome using Human Proteome Fasta file from UniProt and ensure it is selected under the Background tab of Peptide Settings. In the Filter tab, set a minimum length of 8 and maximum length of 25. Continue to use the default settings if there are no other modifications in the peptides.

Once the peptide settings have been made, click on Transition Settings. Under the Filter tab, set Precursor charges as 2 and 3. Set Ion charges as 1 and set Ion types to y.

Product ions can be selected depending on the user's choice. Leave all the parameters as default. Now insert the accession IDs of the selected target proteins.

This can be done by clicking on Edit tab then selecting Insert and finally pasting the accession IDs. The IDs will be mapped to the proteins in the background proteome and a transition list created based on the peptide and transition settings set earlier. Export this transition list.

Ensure that the right instrument is selected by clicking on the Instrument type dropdown option. In this case, the instrument is Thermo Altis. Since the undefined transition list has too many transitions, keep a limit of only 350 transitions per list and export as multiple methods.

A binary solvent system is used. Solvent A is 1%FA and Solvent B is 80%ACN. Keep the flow rate at 450 microliter per minute and the gradient as shown.

Set column compartment temperature at 45 degrees Celsius. For further details about column and LC system, refer text. Ensure the MS settings in your TSQ Altis are as shown here.

Import a single transition list to create a new method. We suggest to keep the resolution for quadrupole 1 and quadrupole 3 at 7. These can be further optimized if required.

To ensure consistency in runs and consequently the data quality, prepare your run sequence as shown. Start with a couple of blanks, refer to the text for composition of blank mixture, followed by a QC standard light BSA of known concentration to monitor any variation in the day-wise response of the instrument. The QC done should be just before the samples.

Next line up all the samples with blanks in between. Inject equal volumes of each sample, accounting for 25 to 1 nanogram of peptides per injection. To make a refined method, first run the undefined methods against whole samples.

Import results into Skyline and manually check each peptide. For each peptide, select one precursor showing consistently good peaks in all the samples. Delete precursors and peptides that are not showing good peaks.

Make sure all the peaks are annotated properly. To select the right peak and further refine the transitions under each precursor, a library can be used. A library is a set of MS-MS spectral data matched with peptides and transitions of the current experimental data.

Pick peaks that have DART P values closer to 1 and remove transitions that do not match the library. A DART P value denotes how good the match is between the experimental peak and library peak. After one or two rounds of optimization, a refined list will be ready.

This refined list can be run for individual samples. Import the refined runs into Skyline and annotate peaks properly. Use the Annotation tab under Document Settings to annotate each sample in the document grid carefully.

Use the Group Comparison feature to create comparisons between Condition 1 and Condition 2 of your experiment. This will give table with adjusted P values and fold change values between the two groups. To our knowledge, this is the first study employing the use of MRM to detect peptides for the proteins Apolipoprotein A-1, Vimentin, and Nicotinamide phosphoribosyltransferase in human brain tissue samples.

Optimizing various parameters such as LC gradient, dwell time, cycle time, and collision energy can greatly influence the success of an MRM experiment. We believe that this technique can be used to develop patterns of biomarkers with ability to distinguish between different clinical conditions and diseases.