Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Summary

This protocol describes how to perform absolute quantification assays of target proteins within complex biological samples using selected reaction monitoring. It was used to accurately quantify proteins of the mouse macrophage chemotaxis signaling pathway. Target peptide selection, assay development, and qualitative and quantitative assays are described in detail.

Abstract

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of G_i2 (a heterotrimeric G-protein α-subunit) is described in detail.

Introduction

Proteomic experiments that use mass spectrometry (MS) can be designed to use either non-targeted (shotgun) or targeted methods. Discovery proteomics generally relies on bottom-up shotgun MS, either by using a traditional data-dependent acquisition mode, or by using one of the recently developed data-independent techniques (e.g., MS^E, SWATH)^1,2. Shotgun proteomics is a powerful tool for high-throughput peptide identification and relative quantification, but it is generally unsuitable for absolute quantification or for targeting small, defined sets (~tens) of proteins. The MS method most often used for targeted proteomics is selected reaction monitoring (SRM) because of its high sensitivity, speed, and dynamic range^3-5. Alternatives to SRM include parallel reaction monitoring, which takes advantage of high-resolution, full MS scanning⁶.

SRM is usually performed using a nano-flow reversed-phase high-performance liquid chromatography (nano-RP-LC) instrument coupled to a nano-electrospray ionization (nano-ESI) ion source attached to a triple quadrupole mass spectrometer (QqQ-MS). In a typical experiment, sample proteins are proteolytically digested, and the resulting peptides are chromatographically separated, desorbed, and ionized. The resulting precursor ions are m/z filtered by the first quadrupole (Q1) and fragmented in the second quadrupole (q2) by colliding them with a collision gas. The resulting fragment ions are m/z-filtered in the third quadrupole (Q3) and quantified by a dynode. Each precursor and fragment ion pair is referred to as a transition, and each transition is monitored for a specified time period (the dwell time; typically 2-50 msec). During LC-SRM, the QqQ-MS cycles through a predefined list of transitions (the duty cycle is typically ≤3 sec), and a chromatogram of each transition is produced.

Alternative strategies for protein quantification typically use immunoassays such as dot blots, Western blots, ELISAs, antibody microarrays, reverse phase protein microarrays, microfluidic immunoassays, digital ELISAs, and microsphere-based immunoassays⁷. The best immunoassays can be significantly more sensitive than LC-SRM, and sample throughput of immunoassays can be significantly higher than that of LC-SRM⁵. However, developing immunoassays can be expensive and/or time consuming, and the resulting assays can be vulnerable to cross-reactivity and/or interference, incompatible with cell/tissue lysis/homogenization methods, and/or not amenable to multiplexing^5,8. Some of these issues can be addressed by coupling antibody- and MS-based techniques. For example, target proteins can be enriched using immunoprecipitation prior to proteolysis and LC-SRM^9-12. Alternatively, the SISCAPA technique employs immunoprecipitation subsequent to proteolysis at the peptide level^13,14. In addition to immunoenrichment strategies, immunodepletion of high abundance proteins can be employed to increase LC-SRM sensitivity by reducing interference by coeluting analytes^15,16.

MS-based protein quantification can be divided into relative and absolute quantification, and also into label-free and stable isotope labeling (e.g., metabolic labeling, chemical labeling, and heavy-labeled protein and peptide internal standards). Label-free techniques can be useful for relative protein quantification, but are unsuitable for accurate absolute quantification. By comparison, labeling techniques have reduced error associated with sample preparation and MS variance, and are often used for relative protein quantification¹⁷. For example, stable isotope labeled proteome (SILAP) standards prepared using a cultured human cell line enabled relative quantification of potential biomarkers via LC-SRM of human serum¹⁸. Accurate absolute protein quantification by MS requires that purified, quantified, isotopically-labeled protein or peptide internal standards be spiked-into biological samples prior to MS. The incorporation of heavy isotope labeled internal standards into an LC-SRM workflow enables absolute quantification that has been shown to be highly reproducible and transferable between laboratories^16,19.

Stable isotope labeled internal standards for absolute protein quantification by MS include peptide standards prepared using solid phase synthesis²⁰, proteins composed of concatenated protease-cleavable peptide standards²¹, and full-length protein standards²². Target protein covalent modification and incomplete sample preparation (i.e., incomplete sample lysis and homogenization, and incomplete protein solubilization, denaturation, alkylation, and proteolysis) can undermine accurate quantification. Internal protein standards are the least likely to be affected by most of these potential problems, but they are usually the most difficult to prepare. An alternative is to analyze each target protein using multiple internal peptide standards which are designed to include amino- and carboxy-terminal native flanking residues. Regardless of which type of internal standard is employed, it should be spiked-into the biological samples at as early a point during sample preparation as possible. Also, multiple sample preparation techniques (e.g., different denaturation conditions) should be tested. The usage of multiple orthogonal experimental techniques (experimental cross-validation) is a viable strategy for overcoming most potential quantification challenges^23-25.

LC-SRM quantification of proteins is a highly flexible technique that has been used in a wide variety of applications. Notably, it has been used to study peptide and protein biomarkers within clinical samples such as serum, core biopsies, and fine needle aspirates⁵. LC-SRM has also been used to measure the stoichiometry of protein complexes^5,26, to detect botulinum neurotoxins²⁷, to quantify protein phosphorylation dynamics within signaling pathways⁵, and to quantify changes in protein conformation²⁸.

Our laboratory is using LC-SRM to quantify the signaling proteins that mediate macrophage chemotaxis to support the development of chemotaxis pathway simulations. The overall scheme of the protocol (Figure 1) begins with ranking the tentative target peptides. Subsequently, crude external peptide standards are synthesized and used to develop LC-SRM assays for qualitative analyses of biological samples. If the biological sample-derived target peptide is detected, purified heavy-labeled internal peptide standards are prepared for quantitative LC-SRM. This protocol can be used to accurately quantify proteins from a wide variety of biological samples, and to support investigations of a wide variety of protein targets.

Protocol

NOTE: This method has been previously described⁵⁶.

1. Peptide Target Selection

1. Compile a list of the target proteins, and include a small number of housekeeping proteins for normalization across biological samples, and also include an internal protein standard (e.g., firefly luciferase). Digest the target proteins into tryptic peptides in silico using a software tool such as Protein Digestion Simulator^29,30.
2. Require that the peptides are fully tryptic and contain no missing trypsin cleavage sites. Avoid peptides with neighboring trypsin cleavage sites to avoid potentially incomplete trypsin digestion³¹.
3. Require that the length of each peptide is 5-20 residues. Consider longer peptides, but note that they are generally more expensive to synthesize.
4. Avoid a peptide if it corresponds to a natural genetic variant (e.g., a single-nucleotide polymorphism). Also confirm that the trypsin sites are unaffected (Step 1.2).
5. Avoid a peptide if it corresponds to a site of posttranslational modification (PTM). Similarly, avoid a peptide if it would be prone to chemical modification during LC-MS sample preparation. Specifically, avoid peptides prone to cysteine and methionine oxidation, asparagine and glutamine deamidation, and amino-terminal glutamine formation of pyroglutamate. Confirm that the trypsin sites are unaffected (Step 1.2).
   1. Furthermore, avoid peptides that originate from protein amino- and carboxy-termini because protein termini are prone to posttranslational modification (amino-terminal methionine loss and acetylation, and carboxy-terminal amidation).
6. Require that each target peptide is unique to each target protein. If this is not possible, require that the peptide is unique to a set of isoforms/homologues. Treat peptides differing only by leucine/isoleucine substitution as if they were identical when determining peptide uniqueness because these perform nearly identically during LC-SRM.
   1. If a comprehensive transcriptomic analysis of all of the biological samples has been performed (e.g., RNA-seq), determine peptide uniqueness using in silico translations of the transcript sequences³² instead of using the entire proteome of the species.
7. Require that the target peptides are proteotypic. Identify proteotypic peptides using shotgun mass spectrometry^1,2 of the biological samples being studied. Alternatively, retrieve peptide identifications of proteotypic peptides from online proteomics databases such as the NIST mass spectrometry libraries³³ (http://peptide.nist.gov/), The Global Proteome Machine³⁴ (X! HUNTER spectral libraries from http://www.thegpm.org/HUNTER/index.html, and GPMDB peptide identifications from http://gpmdb.thegpm.org/), PeptideAtlas³⁵ (http://www.peptideatlas.org/), and the PRIDE database³⁶ (http://www.ebi.ac.uk/pride/archive/).
8. If quantitative transcript-level analyses of the biological samples have been performed (e.g., RNA-seq, qPCR), then avoid targeting peptides that correspond to the relatively low abundance transcripts.
9. If needed, select target peptides that could be used for LC-SRM assays of target protein orthologs (e.g., to assay both the human and the mouse forms of a target protein).
10. Select at least two target peptides per target protein (if possible).

2. Preparation of Peptide Standards 

NOTE: This section of the protocol describes the preparation of a set of twenty lyophilized peptide standards (each being 1 nmol in quantity) for downstream analyses. For a different number of peptides, or for different peptide quantities, it will need to be adjusted accordingly.

1. Prepare lyophilized peptide standards (≥1 nmol) using a peptide synthesis technology such as SPOT synthesis^20,37.
   1. Ensure that cysteine residues of external peptide standards are carbamidomethylated (the chemical structure formed by iodoacetamide alkylation). In contrast, ensure that cysteine residues of internal peptide standards are unmodified (these will be alkylated during sample preparation, described below).
   2. Design internal peptide standards so that they contain amino- and carboxy-terminal native flanking residues (to control for trypsin digestion efficiency; each is typically three to six residues long).
   3. Design internal peptide standards so that they are stable isotope labeled. Consider the natural isotopic profile of the target peptide and the mass measurement accuracy of the MS when selecting a peptide labeling strategy. Do not use deuterated peptide standards because peptide deuteration causes reversed-phase LC retention times to shift³⁸.
      Note: Typically, tryptic internal peptide standards are synthesized using ~98% pure [¹³C₆, ¹⁵N₄]Arg and [¹³C₆,¹⁵N₂]Lys incorporated at the carboxy-termini.
   4. Purify internal peptide standards using HPLC³⁹, and accurately quantify them⁴⁰.
2. Add 100 µl of 0.1% v/v formic acid, 20% v/v acetonitrile (ACN) to each 1 nmol lyophilized peptide standard to produce a peptide concentration of 10 µM (use care as the lyophilized peptide can have a very low-density and can be lost easily). Vortex the samples for 2 min and bath sonicate them for 5 min to ensure that peptide dissolution is complete.
3. Pool the dissolved peptides, and concentrate the resulting mixture in a vacuum concentrator to a final volume of 80 µl. Add 20 µl of ACN to dissolve any precipitated peptide.
   Note: The sample now contains 10 µM of each peptide and 20% v/v ACN.
4. For internal peptide standards, prepare a dilution series for quantitative LC-SRM:
   1. Prepare 100 µl of a 1 µM dilution: combine 90 µl of 20% v/v ACN and 10 µl of the 10 µM peptide mixture.
   2. Prepare 100 µl of a 100 nM dilution: combine 90 µl of 20% v/v ACN and 10 µl of the 1 µM peptide mixture.
5. For external peptide standards, prepare 100 µl of a 1 µM dilution for LC-MS by combining 90 µl of 0.1% v/v formic acid and 10 µl of the 10 µM peptide mixture.
   Note: The sample now contains 1 µM of each peptide, 0.1% v/v formic acid, and 2% v/v ACN, and is ready to be used for LC-SRM assay development.

3. LC-SRM Assay Development

1. Analyze the mixtures of the external peptide standards (roughly 1-10 pmol of each peptide per injection) by shotgun MS using a nano-flow HPLC system coupled to a triple quadrupole mass spectrometer (LC-QqQ-MS(/MS)).
   1. Use an LC-MS system equipped with a capillary column packed with C-18 media (≤5 µm diameter, ~200 Å pores, length ≥10 cm, i.d. = 50-100 µm) and a nano-ESI tip (typically produced using a laser-puller). Ensure that each LC-MS run includes a ~60 min linear gradient (typically, 0-40% Solvent B), a column regeneration step (~80% Solvent B), and a column re-equilibration step (~0% Solvent B) (Solvent A = 0.1% v/v formic acid in H₂O, Solvent B = 0.1% v/v formic acid in ACN, flow rate = 200-800 nl/min, ESI voltage = 1,800 V, Q3 isolation width = 0.7 m/z, q2 argon pressure = 1.5 mTorr).
   2. For each precursor ion scan, dynamically select the top ~10 most intense precursor ions for tandem mass spectrometry (MS/MS). Run each sample in duplicate so that two different collision energy ramps are used: one designed to optimally fragment +2 precursor ions, and the other for +3 precursor ions⁴¹.
   3. Perform the LC-QqQ-MS(/MS) analyses using Q3 precursor ion scanning (Q1 precursor ion scanning can cause tailing of the precursor ion peaks resulting from low-energy argon collisions in q2).
   4. Make sure that the LC-MS system is performing adequately by analyzing QC samples and technical replicates. Prevent sample carryover by using freshly made LC columns and by running multiple blanks between samples.
2. Analyze the resulting shotgun LC-QqQ-MS(/MS) data using database searching against the sequences of the external peptide standards⁴². If appropriate, discard ambiguous peptide identifications using the peptide identification confidence scores (e.g., expectation values) or by using statistical modeling⁴². Regardless, manually review all of the peptide identifications⁴³ to ensure that they are all unambiguous.
3. Use the shotgun MS peptide identifications to construct a spectrum library using a software program such as Skyline.
   1. Prepare an LC-SRM transition list using the 3-10 most intense transitions per precursor ion (+2 and +3 precursor ions; +1 and +2 fragment ions; y-, b-, and a-ions that are ≥2 residues long).
   2. Discard a transition if the precursor and fragment ion m/z values overlap within the mass measurement accuracy of the QqQ-MS (precursor ion fragmentation is sometimes incomplete; remember to consider the monoisotopic and the heavy natural isotope forms, as well as the unlabeled and heavy-labeled forms).
   3. Similarly, discard a transition if the fragment ion m/z overlaps that of a different fragment ion from the same precursor ion.
4. Use the resulting transition lists to perform LC-SRM analyses of the mixtures of the external peptide standards (perform mass spectrometry as described in Step 3.1; Q1 and Q3 isolation width = 0.7 m/z; dwell time = 2-50 msec). 
5. Manually review the resulting LC-SRM data and delete any poorly performing assays (the analysis of LC-SRM data is described in Section 5).

4. LC-SRM Assays of Biological Samples  

1. Prepare the dishes of cultured cells⁴⁴. If multiple experimental conditions are being compared, block and randomize the samples to reduce any possible systematic biases⁴⁵.
2. For each dish of cells, aspirate the cell culture medium to waste and suspend the cells in a serum free buffer⁴⁴. If necessary, wash the cells in a serum free buffer to remove any remaining extracellular protein.
3. For each sample, count the number of viable and total cells by using a viability stain (e.g., trypan blue) and a hemocytometer or an automated cell counter⁴⁴.
4. Pellet the cells by centrifugation, and aspirate the supernatants to waste⁴⁴ (a typical cell pellet volume is ~30 µl).
5. Add 400 µl of Urea Lysis Buffer or Surfactant Lysis Buffer to each cell pellet (100 mM HEPES∙NaOH pH 8, 10 µM bestatin hydrochloride, 10 µM pepstatin A, and either 8 M urea or 0.1% w/v surfactant (respectively); freshly prepared; use an acid-labile MS-compatible surfactant such as RapiGest SF or PPS Silent Surfactant). If using other protease inhibitors, ensure that they do not inhibit trypsin.
6. Mix each sample using gentle pipetting, and transfer each to a 2 ml tube (with a screw cap with an O-ring) containing ~100 µl of 0.1 mm zirconia/silica beads (note that the beads can cause snap-cap tubes to leak). Lyse the cells by vortexing the samples for 5 min at full speed (this lyses the cells by bead beating).
   1. Alternatively, lyse the cells using a different mechanical method (e.g., using a French press or homogenization tips).
      Note: Avoid centrifuging cell lysates because this might pellet precipitated protein that could otherwise be tryptically digested and detected by LC-MS.
7. For the surfactant denatured samples, incubate them at 90 °C for 10 min to assist sample homogenization and protein denaturation.
8. Bath sonicate the samples for 10 min at room temperature to assist homogenization and protein denaturation.
   Note: The lysates and lysis buffer can be stored at -80 °C (the lysis buffer will be needed for Steps 4.9 and 4.13).
9. Perform a protein concentration assay⁴⁶ (e.g., a bicinchoninic acid assay) of the lysates (and of the lysis buffer as a control experiment).
10. For qualitative analyses (i.e., no internal peptide standards will be spiked-into the samples), pipette 200 µg (protein mass) of cell lysate into a fresh 1.5 ml microcentrifuge tube. For quantitative analyses, prepare four such samples for a stable isotope dilution series.
11. For each sample, add an internal protein standard (e.g., add ~5 pmol of ~98% pure firefly luciferase).
12. For quantitative analyses, add the stable isotope dilution series of the equimolar mixture of the internal peptide standards (e.g., 0, 0.2, 2, 20 pmol of each peptide) to the samples.
    1. In parallel, prepare control samples using the internal peptide standards alone (i.e., no cell lysate) (e.g., 0, 0.2, 2, 20 pmol of each peptide).
    2. Also prepare control samples using the external and internal peptide standards (for example: 2 pmol of each external peptide standard, and 0, 0.2, 2, and 20 pmol of each internal peptide standard).
       Note: Be sure that the peptide standards are free of formic acid as it may interfere with the trypsin digestion.
13. Add lysis buffer so that all of the samples are identical volumes. Note: A typical sample volume is 70 µl, so this volume will be used for this protocol.
14. Reduce protein cysteine residues by adding 0.7 µl of 1 M DTT (freshly prepared) to each sample (the final DTT concentration is 10 mM) and incubating the samples at 60 °C for 30 min.
15. Alkylate protein cystines by adding 7 µl of buffered iodoacetamide (500 mM iodoacetamide, 1 M HEPES∙NaOH pH 8; freshly prepared) to each sample (the final iodoacetamide concentration is 50 mM) and incubating the samples at room temperature for 20 min in darkness (iodoacetamide is light sensitive). If necessary, quench the remaining iodoacetamide by adding DTT to a final concentration of 50 mM.
16. For each of the samples denatured using urea, add 482 µl of 100 mM HEPES∙NaOH pH 8 so that the final urea concentration is 1 M (trypsin is significantly inhibited at >1 M urea⁴⁷).
17. Tryptically digest the proteins into peptides.
    1. For each sample that contains cell lysate, add 8 µl of 0.5 µg/µl sequencing grade modified trypsin so that the final trypsin concentration is 1:50 (protein w:w) trypsin:sample, and incubate the sample at 37 °C for 18 hr.
    2. For each sample that does not contain cell lysate, add 0.5 µL of 0.5 µg/µl sequencing grade modified trypsin, and incubate the sample at 37 °C for 2 hr (each of these samples are for control experiments, and typically contain only 10 ng-10 µg of peptide + protein mass).
18. For each of the urea denatured samples, add 440 µl of 2% v/v formic acid (the final formic acid concentration is 1% v/v). Confirm that these samples are pH ~3.
    1. For each of the surfactant denatured samples, add 914 µl of 0.5% v/v TFA (the final TFA concentration is 0.5% v/v). Confirm that these samples are pH ~1.5. Incubate these samples at 37 °C for 60 min to hydrolyze the surfactant.
19. Microcentrifuge all of the samples at 21,000 × g for 20 min at room temperature to pellet the surfactant tail group and any other precipitates that would clog a C-18 SPE cartridge.
20. Solid phase extract each of the supernatants using a disposable C-18 SPE cartridge(Buffer A = 0.1% v/v formic acid; Buffer B = 0.1% v/v formic acid, 80% v/v ACN; ~100 mg C-18 resin per cartridge). Force the mobile phases through the C-18 SPE cartridge using a flat-surfaced rubber bulb, using an extraction manifold, or by centrifuging the cartridge in a 15 ml centrifuge tube at 10 × g for 5 min.
    1. Wet the column by applying 1 ml of Buffer B, equilibrate it by applying 1 ml of Buffer A twice, apply the sample, and wash the cartridge by applying 1 ml of Buffer A twice. Elute the peptides by applying 1 ml of Buffer B slowly (~2 min).
21. Concentrate each eluate in a vacuum concentrator to a final volume of 100 µl to evaporate away ACN. Add 200 µl of H₂O to each sample, and concentrate each in a vacuum concentrator to a final volume of 98 µl to evaporate away any possible remaining ACN.
22. Add 2 µL of 5% v/v formic acid in ACN to each sample (the final sample concentrations are 0.1% v/v formic acid, 2% v/v ACN).
    Note: The samples can be stored at -80 °C.
23. Analyze the samples using LC-SRM (as in Step 3.4). For qualitative LC-SRM analyses, run the biological samples and the external peptide standards, and analyze all of the resulting data together. For quantitative LC-SRM analyses, run the isotope dilution series of each biological sample, and also run the samples consisting of the peptide standards alone, and analyze all of the resulting data together.

5. LC-SRM Data Analysis  

NOTE: Peptide identification and quantification can be highly simplified and partially automated using software such as Skyline, but it is still strongly recommended that all data annotation be manually reviewed. Also, it is best to exclude protein level information during manual annotation of LC-SRM data to prevent bias.

1. For each peptide identification, confirm that the shape of each transition elution profile is roughly Gaussian. For each transition, confirm that the elution profile is the product of multiple signals measured by the MS detector (i.e., is not the product of one or two random spikes of MS noise).
2. For each peptide identification, ensure that the entire peptide elution profile is selected. Avoid manually adjusting the elution profile boundaries of subcomponents of the peptide identification (the heavy-labeled peptide form, the unlabeled form, individual precursor ions, and individual transitions).
3. Confirm that each transition elution profile has a signal-to-noise ratio (s/n) ≥3. Note: “Noise” refers to random MS noise, not to reproducible signal from coeluting analytes. Chromatogram smoothing functions can greatly assist the analysis of noisy data.
4. For each peptide identification, confirm that all of the transitions have nearly identical elution profiles (not just equal peak apex times; the elution profiles may have different amplitudes).
5. For LC-SRM analyses of a large quantity of the peptide standards (typically ≥100 fmol of each peptide), confirm that the corresponding peptide elution profiles are very intense (typically s/n ≥100), and that the peptide identifications are unambiguous. Note: These highly confident identifications will be critical for the identification of the corresponding biological sample-derived peptides.
6. For each peptide identification, confirm that the relative transition intensities match those of the confidently identified peptide standard. If necessary, discard noisy, relatively low-intensity transition elution profiles, but confirm that none of the relatively intense transitions are missing. Disregard relatively intense transition elution profiles that are significantly and unambiguously affected by a coeluting contaminant.
7. For each peptide identification, estimate the probability that it was produced randomly by background signal (random MS noise plus reproducible signal from coeluting analytes).
   1. Estimate this probability manually by examining the entire LC-SRM chromatogram and estimating the uniqueness of each peptide identification. Produce a set of confident peptide identifications that has an estimated false discovery rate (FDR) of 5%. Use stricter criteria (e.g., FDR ≤1%) if a higher confidence set of identifications is required (use of looser criteria is not recommended).
   2. Alternatively, use the mProphet algorithm⁴⁸ or the AuDIT algorithm⁴⁹, but manually review the results.
8. For each peptide identification, confirm that the observed elution time is consistent with the hydrophobicity of the peptide (Skyline is designed to perform this calculation).
9. For each peptide, confirm that its elution time is consistent across the LC runs.
   NOTE: LC-SRM analyses sometimes produce elution profiles that are systematically skewed between runs. Systematic shifts are to be expected after instrument alterations such as replacing a column. Also, early eluting peptides can be prone to shifting (especially if the column is loaded close to capacity), and late eluting peptides can also be prone to shifting. Peptide identification is now complete.
10. To improve peptide quantification, reinspect each peptide identification and discard a transition elution profile if it is especially noisy compared to the other transition elution profiles of the peptide identification. Discard a transition elution profile if its relative transition intensity is wrong (compared to the peptide standard).
11. Inspect the boundaries of each precursor ion and transition elution profile. If necessary, carefully adjust the elution profile boundaries to crop away background signal (or to improve background signal estimation).
12. For each internal peptide standard, check if it is contaminated with a detectable amount of light peptide (determined by the LC-SRM analyses of the internal peptide standards alone, described above). Subsequently, discard any light peptide elution profiles that were significantly compromised by light-peptide contamination within the internal peptide standards.
13. Quantify each transition elution profile by calculating its LC peak area. If appropriate, deduct the estimated background signal. Quantify each peptide elution profile by summing the corresponding transition quantification values (hereafter, this is referred to as the “Sum_Peak_Area” value). For each biological sample peptide Sum_Peak_Area value, calculate the biological sample peptide molar abundance using data from LC-SRM of internal or external peptide standards (use only the transitions that were successfully quantified by both the biological sample peptide LC-SRM and that of the peptide standard).
    1. Quantification using external peptide standards (not recommended):
       1. Plot the external peptide standard Sum_Peak_Area values versus the external peptide standard molar abundance values. Produce a standard curve by fitting a linear regression to the data (typically, only the linear component of the dynamic range should be used; any nonlinear component should be used with extreme caution). For each biological sample peptide Sum_Peak_Area value, use the standard curve to calculate the peptide molar abundance.
          Note: This quantification method is not recommended because it requires that the sample preparation and LC-SRM be demonstrability robust because the biological samples and external peptide standards are prepared and analyzed by LC-SRM separately. Also, it does not account for matrix effects (effects due to components of a sample other than the analyte).
    2. Quantification using internal peptide standards (recommended):
       1. For each biological sample peptide Sum_Peak_Area value and corresponding heavy-labeled internal peptide standard Sum_Peak_Area value, calculate the light/heavy ratio. Use this ratio as a measure of the corresponding light/heavy peptide molar abundance ratio to calculate the molar abundance of the biological sample peptide. Specifically, the biological sample peptide molar abundance equals the internal peptide standard molar abundance multiplied by the light/heavy Sum_Peak_Area ratio.
       2. For each biological replicate and target peptide, identify the linear range of the LC-SRM quantification by creating a standard curve using a linear regression. Specifically, plot the internal peptide standard molar abundance values versus the heavy/light Sum_Peak_Area ratio values (note that the biological sample mass is constant across the dataset). Require that each biological sample peptide Sum_Peak_Area value is within the linear range (typically, only the linear component of the dynamic range should be used; any nonlinear component should be used with extreme caution). Likewise, perform this step for each target peptide transition.
          Note: Analyte quantification by LC-QqQ-SRM should be linear over a wide dynamic range (~10,000). At the low end, background signal (random MS noise plus reproducible signal from coeluting analytes) will reduce quantification accuracy and precision. At the high end, detector saturation will cause nonlinearity (ultimately, this can cause elution profiles to be flat starting at a specific signal intensity).
14. If appropriate, perform a global normalization across the samples, such as a central tendency normalization⁵⁰, to correct for slight differences in sample quantity prior to the spiking-in of the internal peptide standards. If necessary, use only the housekeeping proteins from Step 1.1.
15. If necessary, impute missing quantification values^51,52.
    Note: It is improper to simply replace missing values with half the lower limit of quantification (LLOQ) or half the limit of detection (LOD) because doing so would artificially reduce quantification variance, and could result in a statistical test (e.g., an ANOVA) producing a false positive result (a type I error). However, ignoring missing values can also be problematic. For example, if half the true abundance values are below the LOD, and the other half are above the LLOQ, then the mean observed abundance would overestimate the mean true abundance.
16. Ensure that the assays satisfy the broadly accepted guidelines for LC-SRM^23-25. Notably, require that the coefficient of variation of the quantification values is typically ≤25% for clinical assays, and ≤35% for non-clinical assays²⁵. For LC-SRM assays related to healthcare or veterinary products or services that are to be considered for regulatory approval, require that the assays satisfy the stricter precision requirements for such assays: “The precision determined at each concentration level should not exceed 15% of the coefficient of variation (CV) except for the LLOQ, where it should not exceed 20% of the CV”^23,24.

Results

The development of predictive computational models of signal transduction pathways is one of the fundamental goals of systems biology⁵³. Unfortunately, even for signaling pathways that have been studied extensively and have a high clinical significance, it is still not generally possible to quantitatively predict pathway behavior in response to perturbations (e.g., this is true for the MAPK/ERK pathway⁵⁴). Recently, an investigation employed targeted proteomics, transcriptomics, and computa...

Discussion

Absolute protein quantification is essential for a very diverse range of biomedical applications such as biomarker validation and signal transduction pathway modeling. Recently, targeted proteomics using LC-SRM has benefited from improvements to numerous technologies including peptide standard preparation, HPLC, QqQ-MS, and LC-SRM data analysis. Consequently, it has become a powerful alternative to immunoassays. Immunoassays can be extremely sensitive and high-throughput, but developing a robust immunoassay can be extrem...

Materials

Name	Company	Catalog Number	Comments
Acetonitrile (ACN), LC-MS grade	Fisher	A955-1
BCA (bicinchoninic acid) protein assay kit	Fisher	23235
Beads for bead beating, zirconia-silica, 0.1 mm	BioSpec Products	11079101z
Bestatin hydrochloride	Sigma	B8385-10MG
Cell culture DMEM (with glucose, without L-glutamine)	Lonza	12-614F
Cell culture EDTA, 500 mM, pH8	Gibco	15575
Cell culture fetal bovine serum (FBS)	Atlanta Biologicals	S11550
Cell culture L-glutamine	Sigma	G8540-25G
Cell culture phosphate buffered saline (PBS) pH 7.4	Gibco	10010-049
Cell culture Trypan Blue viability stain, 0.4% w/v	Lonza	17-942E
Cellometer Auto T4 cell counter	Nexcelom Bioscience	Cellometer Auto T4
Cellometer Auto T4 disposable counting chambers	Nexcelom Bioscience	CHT4-SD100-014
Dithiothreitol (DTT)	Sigma	D5545-5G
Formic acid, LC-MS grade, ampules	Fisher	A117-10X1AMP
Hemocytometer, Neubauer-improved, 0.1 mm deep	Marienfeld-Superior	0640030
HEPES, 1 M, pH 7.2	Mediatech	25-060-CI
Hydrochloric acid, 37% w/w	VWR	BDH3028-2.5LG
Iodoacetamide	Sigma	I1149-5G
Laser Based Micropipette Puller	Sutter Instrument Co.	P-2000
LC coated silica capillary, 50 µm id	Polymicro Technologies	1068150017
LC vial, autosampler, 12 mm x 32 mm polypropylene	SUN SRI	200-268
LC vial screw cap, autosampler, pre-slit PTFE/silicone	SUN SRI	500-061
Luciferase, from Photinus pyralis	Sigma	L9506-1MG
Pepstatin A	EMD Millipore	516481-25MG
pH strips colorpHast (pH 0.0-6.0)	EMD Chemicals	9586-1
PhosStop phosphatase inhibitor cocktail	Roche	04906837001
RapiGest SF	Waters	186001861
Sep-Pak SPE, C18 1 ml 100 mg cartridge	Waters	WAT023590
Sep-Pak SPE, extraction manifold, 20 position	Waters	WAT200609
Sep-Pak SPE, flat-surfaced rubber bulb	Fisher	03-448-25
Sodium hydroxide (NaOH)	Fisher	S318-500
SpeedVac vacuum concentrator	Fisher	SPD111V
Trifluoroacetic acid (TFA), LC-MS grade	Fisher	A116-50
Trypsin, sequencing grade, modified	Promega	V5113
Tube decapper for Micronic tubes	USA Scientific	1765-4000
Tubes, 2 ml microcentrifuge, o-ring screw-cap, sterile	Sarstedt	72.694.006
Urea	Sigma	U0631-500g
Water, LC-MS grade	Fisher	W6-1