Abstract
In the present work, the potential of Trapped Ion Mobility Spectrometry coupled to TOF Mass Spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150 – 250) prior to mass analysis. Five peptide biomarkers were separated, identified and quantified using offline nanoESI-TIMS-CID-TOF MS and results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as marker from the same proteins and, eventually, other proteins.
Graphical abstract
Introduction
The level of chemical complexity during proteomic analysis and the large dynamic range of commonly studied and potential biomarkers represents an analytical challenge that requires the further development of high throughput, orthogonal, reproducible and robust analytical platforms. Nowadays, mass spectrometry based analysis offers an unparalleled, non-targeted, analysis tool for dissecting complex protein samples at the molecular level; however, prior to mass spectrometry analysis, pre-separation techniques, such as high performance liquid chromatography (HPLC) and nano-liquid chromatography (nanoLC), are often required to enhance the peak capacity of the analysis. In addition, these pre-separation methods can provide advantages by reducing problems associated with ion suppression during competitive ionization of complex samples, a phenomenon that is more typically observed during biomarker detection across large dynamic range [1, 2]. However, traditional LC-based protocols require long separation times in order to analytically separate most of the compounds of interest, and high solvent volumes per sample; which for large scale profiling, represents a major obstacle in analysis due to added time and cost [3–5]. In addition, these techniques still suffer from poor separation of isobaric species, which significantly challenges protein sequencing and identification using bottom-up approaches. These challenges become major hindrances for the analysis of a complex biological system, such as cancer proteomic samples, which typically contain a myriad of molecular species. In addition, large-scale profiling in bottom-up proteomics is often limited by the sensitivity of the current mass spectrometry instruments to isolate and detect parent and fragment ions during tandem MS analysis of complex mixtures [6]. For example, current bottom-up proteomic strategies require the chemical treatment of samples (i.e., trituration, protein extraction, enzymatic digest) prior to analysis which result in highly complex mixtures which then require further separation and preparation prior to MS analysis [7].
An alternative or complementary approach is the use of gas-phase, post-ionization separations such as ion mobility spectrometry coupled to mass spectrometry (IMS-MS), which promises further gains in the speed, sensitivity and selectivity for the analysis of complex biological mixtures [8, 9]. Specifically, the added mobility dimension of separation yields an increase in peak coverage [6, 10–12], a factor that has often inhibited the analysis of complex mixtures with MS-only detection. The IMS-MS coupling readily enhances peptide/protein coverage and identification by allowing more ions, specifically isomers, to be resolved while simultaneously reducing chemical noise [13, 14]. Previous studies have illustrated the advantages of IMS-MS in terms of profiling mixtures [8, 15–19], making it one of the most powerful platforms for identification and characterization of proteins and peptides in biological samples. Our group has been working on the development of alternative, time-independent IMS approaches based on Trapped Ion Mobility Spectrometry coupled to MS (TIMS-TOF MS and TIMS-FT-ICR MS) for the study and manipulation of gas-phase molecular ions [10, 20–33]. Briefly, the concept behind TIMS is the use of an electric field to hold ions stationary against a moving gas, so that the drift force is compensated by the electric field and ion packets are separated based on their respective ion mobilities [20, 21, 27]. This concept follows the idea of a parallel flow ion mobility analyzer [34], with the main difference that ions are also confined radially using a quadrupolar field to guarantee higher ion transmission and sensitivity [20, 21]. Since the introduction of TIMS-MS in 2011 [20, 21], our group [10, 22–33, 35] and others [8, 36–44] have shown the potential of TIMS-MS for fast, gas-phase separation and for molecular structural elucidation. In particular, we have demonstrated the advantages of TIMS over traditional IMS analyzers for fast screening [22] and targeted [10, 35] analysis of molecular ions from complex chemical mixtures; the study of isomerization kinetics of small molecules [23, 24], peptides [25], DNA [33], proteins [28, 29], DNA-protein complexes and protein-protein complexes in their native and denatured states [32]. In a more recent report, we showed the isomer separation of polybrominated diphenyl ether metabolites using nanoESI-TIMS-TOF MS with mobility resolutions of up to 400 (the highest reported mobility resolution for singly charged species) [30].
Herein, we present for the first time a nanoESI-TIMS-CID-TOF MS workflow, developed for fast, gas-phase ion separation and accumulation, with efforts focused on targeted quantitative analysis and discovery measurements of breast cancer markers. The ability of TIMS-CID-TOF MS to separate and sequence isobaric peptides in a complex mixture is illustrated. We address typical challenges and targeted and discovery monitoring strategies using isotopically-labeled internal standards for effective peptide identification and sequencing. While LC-TIMS-MS separations has been recently shown in the case of peptide markers [8], the presented workflow targets offline separations in order to shorten the MS analysis time while tailoring the TIMS analysis for high mobility separation and sensitivity.
Experimental
Tumor protein extraction and tryptic digestion
A patient-derived mouse xenograft model of luminal B human breast cancer - Washington University Human-in-Mouse (WHIM16) was used for all the studies [45]. The WHIM16 xenograft tumor pieces were transferred into pre-cooled Covaris Tissue-Tube 1 Extra (TT01xt) bags (Covaris no. 520007) and processed in a Covaris CP02 Cryoprep device using an impact setting of 3 (all tumor tissue wet weights were less than 100 mg). The tissue powder was then transferred into precooled cryovials (Corning no. 430487). All procedures were carried out on dry ice and liquid nitrogen to maintain tissue in a powdered, frozen state. Approximately 50 mg of WHIM16 tumor tissue was homogenized in 600 µL of lysis buffer (8 M urea, 100 mM NH4HCO3, pH 7.8, 0.1% NP-40, 0.5% sodium deoxycholate, 10 mM NaF, phosphatase inhibitor cocktails 2 and 3, 20 µM PUGNAc). Protein concentrations of tissue lysates were determined by BCA assay (Pierce). Proteins were reduced with 5 mM dithiothreitol for 1 h at 37 °C, and subsequently alkylated with 10 mM iodoacetamide for 1 hour at room temperature in the dark. Samples were diluted 1:2 with Nanopure water, 1 mM CaCl2 and digested with sequencing grade modified trypsin (Promega, V5113) at 1:50 enzyme-to-substrate ratio. After 4 h of digestion at 37 °C, samples were diluted 1:4 with the same buffers and another aliquot of the same amount of trypsin was added to the samples and further incubated at room temperature overnight (~16 h). The digested samples were then acidified with 10% trifluoroacetic acid to ~pH 3. Tryptic peptides were desalted on strong cation exchange (SCX) SPE (SUPELCO, Discovery-SCX, 52685-U) and reversed-phase C18 SPE columns (SUPELCO Discovery, 52601-U) and dried using Speed-Vac.
Tryptic peptide fractionation
The tryptic peptide sample was separated on a Waters reversed phase XBridge C18 column (250 × 4.6 mm, 5-µm and protected by a 4.6 mm × 20 mm guard column) using an Agilent 1200 HPLC System. After sample loading, the column was washed for 35 min with 10 mM triethylammonium bicarbonate, pH 7.5(solvent A), before applying a 102-min LC gradient in combination with 10 mM triethylammonium bicarbonate, pH 7.5, 90% acetonitrile (solvent B). The LC gradient started with a linear increase to 10% B in 6 min, then to 30% B in 86 min, 42.5% B in 10 min, 55% B in 5 min and 100% solvent B in another 5 min. The flow rate was 0.5 mL/min. A total of 96 fractions were collected into a 96 well plate throughout the LC gradient. These fractions were concatenated into 48 fractions by combining 2 fractions that are 48 fractions apart (i.e., combining fractions #1 and #49; #2 and #50; and so on)[46]. The concatenated fractions were dried in a Speed-Vac and stored at −80°C. Fractions of various volumes were prepared at PNNL based upon BCA analyses to have total peptide concentrations of 0.5 µg/µL and shipped for analysis at FIU. The fractions of interest were selected apriori based on LC-MS/MS analyses conducted at PNNL (to be reported separately) with known presence of the target peptides of interest in this study (see Table 1). Heavy standards of the target peptides were purchased from ThermoFisher and used as received. The last residue of the sequence (Arg or Lys) was modified with 13C6 and 15N4 or 13C6 and 15N2, respectively. Light (non-isotopically labelled) standards of the target peptides were also purchased from GenScript and used without further purification. All samples were diluted with Optima grade 0.1% Formic Acid.
Table 1.
Peptide sequence, m/z of light and heavy peptides, ion-neutral collision cross section (CCS) and total in-fraction peptide concentration of the five targeted peptides in the fractions.
Peptidem | m/z | CCSN2 (Å2) | Concentration (nM) |
---|---|---|---|
| |||
DFTPAELR | 948.483 [M+H]+ | 298 | 182 ± 7.0 |
DFTPAELR | 958.486 [M+H]+ | 298 | |
| |||
TTILQSTGK | 948.521 [M+H]+ | 295 | 21 ± 5.0 |
TTILQSTGK | 956.555 [M+H]+ | 295 | (19 ± 3.0*) |
| |||
DVVICPDASLEDAKK | 801.904 [M+2H]+2 | 430 | 18 ± 5.0 |
| |||
DVVICPDASLEDAKK | 834.422 [M+2H]+2 | 436 | (19.0 ± 3.0*) |
| |||
LSASTASELSPK | 595.815 [M+2H]+2 | 380 | 17 ± 4.8 |
LSASTASELSPK | 599.824 [M+2H]+2 | 380 | |
| |||
VFDKDGNGYISAAELR | 877.925 [M+2H]+2 | 452 | 16 ± 4.5 |
VFDKDGNGYISAAELR | 882.944 [M+2H]+2 | 452 | (15± 3.0*) |
LC-ESI-MS/MS concentrations using the 535.2 → 187.0 DA (DVVICPDASLEDAKK), 585.7 → 201.1 Da (VFDKDGNGYISAAELR) and 474.9 → 130.1 Da (TTILQSTGK) channels are denoted with an asterisk (*).
Trapped Ion Mobility Spectrometry – Mass Spectrometry Analysis
Individual fractions, each spiked with the corresponding internal heavy peptide standard, were analyzed by directly infusing the sample via nanoESI into the TIMS-MS spectrometer. A detailed overview of the TIMS analyzer and its operation can be found elsewhere [20, 21, 27]. The nitrogen bath gas flow is defined by the pressure difference between entrance funnel P1 = 1.8–2.6 mbar and the exit funnel P2 = 0.6–1.0 mbar at ca. 300 K. The TIMS analyzer is comprised of three regions: an entrance funnel, analyzer tunnel (46 mm axial length), and exit funnel. A 880 kHz and 200 Vpp RF potential was applied to each section creating a dipolar field in the funnel regions and a quadrupolar field inside the tunnel. In TIMS operation, multiple ion species are trapped simultaneously at different E values resulting from a voltage gradient applied across the TIMS tunnel. After thermalization, species are eluted from the TIMS cell by decreasing the electric field in stepwise decrements (referred to as the “ramp”) and can be described by a characteristic voltage (i.e., Velution − Vout). Eluted ions are then mass analyzed and detected by a maXis impact Q-ToF MS (Bruker Daltonics Inc, Billerica, MA).
In a TIMS device, the total analysis time can be described as:
(1) |
where, ttrap is the thermalization/trapping time, ToF is the time after the mobility separation, and Vramp and tramp are the voltage range and time required to vary the electric field, respectively. The elution voltage was experimentally determined by varying the ramp time (tramp = 100, 200, 300, 400 and 500 ms) for a constant ramp voltage. This procedure also determines the time ions spend outside the separation region to (e.g., ion trapping and time-of-flight). The TIMS cell was operated using a fill/trap/ramp/wait sequence of 10/10/50–500/50 ms. The ToF analyzer was operated at 10 kHz (m/z 100–3500). The data was summed over 100 analysis cycles yielding an analysis time of ~50 s for the largest trapping times (tramp=500 ms). Mobility calibration was performed using the Tuning Mix calibration standard (G24221A, Agilent Technologies, Santa Clara, CA) in positive ion mode (e.g., m/z 322, K0 = 1.376 cm2 V−1 s−1 and m/z 622, K0 = 1.013 cm2 V−1 s−1) [27]. The TIMS operation was controlled using in-house software, written in National Instruments Lab VIEW, and synchronized with the maXis Impact Q-ToF acquisition program [20]. A custom-built source using pulled capillary nanoESI emitters was utilized for all the experiments. Quartz glass capillaries (O.D.: 1.0 mm and I.D.: 0.70 mm) were pulled utilizing a P-2000 micropipette laser puller (Sutter Instruments, Novato, CA) and loaded with 10 µL aliquot of the 20× diluted sample solution. A typical nanoESI source voltage of +600–1200 V was applied between the pulled capillary tips and the TIMS-MS instrument inlet. Ions were introduced via a stainless steel inlet capillary (1/16 × 0.020”, IDEX Health Science, Oak Harbor, WA) held at room temperature into the TIMS cell.
Reduced mobility values (K0) were correlated with Collisional cross section (Ω) using the equation:
(2) |
where z is the charge of the ion, kB is the Boltzmann constant, N* is the number density and mi and mb refer to the masses of the ion and bath gas, respectively [47]. All IMS resolving power (RIMS = Ω/ΔΩ) and mass resolution (RMS= m/Δm) values were determined from Gaussian peak fits using OriginPro (version 8.0).
LC-ESI-MS/MS Analysis
Confirmation studies using tandem mass spectrometry were performed by a QTRAP 5500 Triple-Quadrupole mass spectrometer (AB Sciex, Concord, Ontario, Canada) equipped with a Turbo V ion source (ESI) operated in the positive mode. Solutions of peptides and heavy analogs (5.0 µM) in 50% acetonitrile, 0.1% formic acid in water were directly infused (10 µL/min) into the TurboV ion source. Once suitable species (usually [M+2H]+2) were detected in manual tuning mode, automatic optimization was performed of the collision energy (CE), declustering potential (DP) and collision cell exit potential (CXP) to obtain best parameters for MS/MS via collision-induced dissociation (CID). A multiple reaction monitoring (MRM) detection method was thus developed for each peptide and heavy analog, using the two most intense transitions observed for quantitative and confirmation purposes. HPLC separations (40 µL injections) used a reverse phase column (Dionex Acclaim 120 C18 Column, 250×2.1 mm, 5 µm) and a Shimadzu Prominence LC-20AD Ultra-Fast Liquid Chromatograph. Mobile phase gradient was performed between 0.1% formic acid dissolved in water (mobile phase A) and 0.1% formic acid dissolved in acetonitrile (mobile phase B), all purchased commercially and of Optima LC-MS grade. The auto sampler was kept at 4° C. Analysis was performed at 35° C with a flow rate of 0.80 mL/min, according to the following 11.0 min program: hold 10% B for 0.25 min; ramp to 65% B in 4.5 min; ramp to 98% in 0.1 min; hold for 1.65 min; return to 10% B in 0.5 min; hold for 4 min until end.
Results and Discussion
Commonly used peptide biomarkers during detection of protein DJ-1 [48], calmodulin [49], parafibromin [50, 51], MAP7 domain-containing protein 1 [52–54] and membrane-associated progesterone receptor component 1 [55, 56] (sequences: DVVICPDASLEDAKK, VFDKDGNGYISAAELR, TTILQSTGK, LSASTASELSPK, DFTPAELR, respectively) were used in this study (see Table 1). The selection of the targeted peptides was guided towards covering a diverse protein abundance range based on previous analyses of the patient-derived breast cancer mouse xenograft tissue sample (WHIM16) using LC-QQQ by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [57]. Single peptide standards and their respective heavy versions were analyzed using TIMS-MS in order to determine the charge state distribution (CSD) and collision cross section (CCS) when sprayed from the same starting solvent conditions as those of the WHIM16 tryptic digested fractions. Peptides DFTPAELR and TTILQSTGK showed similar CSDs with the [M+H]+ producing the most abundant signal, while LSASTASELSPK, VFDKDGNGYISAAELR, and DVVICPDASLEDAKK showed larger abundance for the [M+2H]+2 charge state (Figure 1). In addition to targeting m/z peaks, based on their abundance as a function of the charge state, a second criteria utilized was the simplicity of the CCS profiles for the [M+H]+ and [M+2H]+2 charge states in order to avoid potential interferences. A typical mobility resolving power of R > 200 was obtained for the [M+H]+ and [M+2H]+2 charge states and CCS values correlate well with previously identified peptide mobility trend lines observed during IMS-MS analysis [58]. Comparison of the IMS profiles of the targeted peptides and the heavy analogs present the same distribution and CCS values (Table 1); moreover, an exception to this rule was observed for CAM modified heavy analogs customized to prevent disulfide association. For the latter, the confirmation and quantification was made based on the targeted and heavy analog m/z and CCS values.
Figure 1.
Typical mass spectra and IMS projection plots of the five targeted peptide of interest. Notice the high mobility resolution obtained using nanoESI-TIMS-TOF MS for single and double charged molecular ions.
The power of the TIMS-MS for peptide characterization was further examined by sequencing two of the targeted peptides possessing the same nominal mass (e.g., m/z 948.479 and m/z 948.536 for DFTPAELR and TTILQSTGK, respectively). While traditional proteomics analysis is based on peptide identification using MS/MS strategies, for TIMS-CID-TOF MS the m/z and CCS characterization of the parent ion can be complemented with CID without the need for m/z preselection if separation in the CCS domain is achieved (Figure 2). Inspection of the experimental 2D IMS-MS contour plots of the isobaric peptide mixture shows the fragment ions of DFTPAELR and TTILQSTGK peptides falling directly in line with the mobilities of their respective parent ion (Figure 2a). The incorporation of IMS prior to CID holds multiple advantages for molecular identification since direct correlation of fragment ions with precursors ion can be performed in the 2D-IMS-MS domain [59–65]. Analysis using TIMS provided baseline separation of the targeted peptides, where a minimum resolving power of RIMS~ 100–150 (i.e., CCS of 295 and 298 Å2) is required for near baseline separation (Figure 2b). Closer inspection of the product ions (mostly b and y type fragments and some internal fragments) permitted the verification of the peptide sequences (Figure 2c). The advantage of this approach when compared to traditional LC-MS/MS proteomics is that the CCS values (or profiles) of each parent and corresponding fragments are common parameters to the IMS separation and can be used as additional identification confirmation. That is, the precursor and product ions will share the same CCS, while characteristic LC elution times may depend on several experimental conditions and may not be as reproducible, nor specific, to a given peptide. However, because the CCS is a property of the peptide parent ion, the possibility to uniquely trap the mobility range of interest in a TIMS analyzer significantly enhances the multiple reaction monitoring capabilities of the TIMS-MS analyzer by ultimately reducing chemical noise and increasing the TIMS selectivity of the parent and fragment ions.
Figure 2.
a) TIMS-CID-TOF MS of isolated isobaric precursor ions for the DFTPAELR and TTILQSTGK peptides and the corresponding ladder fragmentation pattern. B) Mobility selected MS showing the separation of the precursor ions. C) CID spectra of the fragments corresponding to DFTPAELR and TTILQSTGK.
Despite the LC pre-fractionation step, the samples of interest provided highly complex spectra with multiple peaks present at the nominal mass level in the 2D-IMS-MS domain (Figure 3a). The 2D IMS-MS contour plots showed that each fraction contained two main trend lines, corresponding to singly and doubly charged species [8]. Closer inspection confirmed that the TTILQSTGK [M+H]+ molecular ion (m/z 948.536) was accompanied by two other compounds within 5 mDa, which are not distinctly separated in the MS domain alone, despite the high mass resolution of the TOF analyzer (RMS ~30–40k). When combined with TIMS analysis, however, the three signals can be easily separated, distinguished, and identified (Figure 3b). Further comparison of the targeted peptide and the corresponding heavy analog IMS projections of TTILQSTGK [M+H]+ (m/z 956.555) confirmed the assignment in the 2D-IMS-MS contour plots (Figure 3c).
Figure 3.
a) Typical 2D-IMS-MS contour plot using nanoESI-TIMS-TOF MS for a fraction containing the target peptide TTILQSTGK. The 2D-IMS-MS profile highlights the complexity of each fraction and shows the charge state specific trend lines. b) The 2D IMS-MS at the level of nominal mass depicts the isomeric interferences in the region of the targeted peptide and heavy analog. c) IMS projection plots for the targeted and corresponding heavy peptide using 5mDa window show identical IMS profiles.
After verifying the TIMS-MS workflow for high reproducibility and accuracy in measuring and identifying the targeted peptides from the WHIM16 tryptic digested fractions, the potential for quantitative analysis via TIMS-MS was evaluated. To mimic matrix effects, one of the fractions, with confirmed absence of the target peptide, was spiked with known concentrations of the light and heavy peptide standards. The use of internal heavy standards accounted for variations in the nanoESI spray between experiments and from sample to sample, as well as changes in the spraying conditions as a function of time. Figure 4 shows a linear dependence between the TIMS peak area and the sample concentration for the case of the TTILQSTGK [M+H]+ peptide Figure 4a), regardless of the TIMS trapping time (e.g., tramp = 100–500 ms) and analytical ramp slope (Figure 4b). The robustness of this TIMS-MS quantitation procedure was also confirmed for all the peptides of interest at the low concentration (e.g., 1, 5, 10 and 20 nM) and the use of internal heavy peptide analogs accounted for all the potential nanoESI spray variability (Figure 4c).
Figure 4.
a) Typical IMS profiles of a pure heavy standard and in-fraction light peptide standard (TTILQSTGK [M+H]+) as a function of the concentration. b) Trapping efficiency of TIMS-TOF MS analysis illustrating that trapping time does not impact the calculated concentration or linear response and c) Measured light peptide concentrations for each targeted peptide, via TIMS-TOF MS, in relation to the heavy analog displaying a linear response.
The analysis and quantitation of targeted compounds in complex mixtures using direct infusion ESI (and nanoESI) can be subject to ion suppression effects [66, 67]. To further evaluate this consequence, dilution (up to 20 times) of a WHIM16 tryptic digested fraction with known spiked concentrations of targeted and heavy standards showed less than 10% variability in the TIMS-MS quantification results (Figure 5a, top). Complementary LC-ESI-MS/MS based traditional proteomic analyses of the same sample showed similar ion suppression effects (Figure 5a, bottom).
Figure 5.
a) Response curves for the LSASTASELSPK [M+2H]+2 peptide standards analyzed in fraction (matrix effect) and in the blank via nanoESI-TIMS-TOF MS and LC-QqQ-MS and b) comparisons of targeted peptide concentrations measured in fractions by nanoESI-TIMS-TOF MS and LC-ESI-MS/MS.
The ultimate test for the TIMS-MS workflow consisted of blind analysis of WHIM16 tryptic digested fractions known to have the targeted peptides. Briefly, the highest concentration of the targeted peptide observed was for DFTPAELR at 182 ± 7.0 nM (364 fmol/µg), follow by TTILQSTGK peptide at 21.0 ± 5.0 nM (42 fmol/µg), VFDKDGNGYISAAELR peptide at 16.0 ± 4.5 nM (32 fmol/µg), DVVICPDASLEDAKK peptide at 18 ± 5.0 nM (35 fmol/µg) and LSASTASELSPK peptide at 17 ± 4.8 nM (33 fmol/µg). The results of the TIMS-MS quantification procedure and their comparison to LC-ESI-MS/MS based traditional proteomic analyses of the same sample are summarized in Figure 5b and Table 1. Overall, comparable results for targeted peptides per fraction were observed using nanoESI-TIMS-MS and LC-ESI-MS/MS; moreover, it is worth stating that while nanoESI-TIMS-MS was routinely done in five minutes, each LC-ESI-MS/MS analysis typically required 20–25 minutes. In addition, while TIMS-MS measurements were geared toward the separation, identification and quantitation of five targeted peptides, simultaneously, discovery TIMS-MS measurements were collected without compromising the targeted analysis. That is, the TIMS-MS analysis allowed for the identification of multiple tryptic peptides (both targeted and untargeted) from the above five specified proteins. For example, TIMS-MS data analysis revealed 15 to 60% protein sequence coverage, using peptide IDs that were not initially targeted, over the various fractions analyzed (see Figure S1 and Table S1 in the Supporting Information). While new biomarker detection is not in the scope the present study, it should be noted that a posteriori screening (or discovery) of potential biomarkers of interest is an inherent potential of the current TIMS-MS workflow. That is, the use of TIMS-MS workflow as a broad measurement technique, concurrently with the targeted quantitative approach, effectively reduces the problems associated with individual analyses and holds the advantage of increasing proteome coverage and new biomarker detection while maintaining speed, accuracy and sensitivity.
Conclusions
The demand for fast, accurate, and sensitive analytical tools for the detection and quantification of biomolecules is increasing as a way to offset the challenge of drug discovery and biomarker identification. While several strategies have been developed, some current efforts are focused on reducing sample preparation and analysis time, while increasing detection limits and peak capacity by using complementary, orthogonal separation techniques. In the present work, the concepts of characterizing proteomes using offline nanoESI-TIMS-MS were evaluated by performing targeted and discovery analysis of cancer biomarkers from a human-in-mouse xenograft tumor tissue. Results showed that targeted peptide separation, identification and sequencing can be performed based on accurate CCS, mass and fragmentation pattern measurements, and that peptide quantitation can be routinely achieved utilizing heavy peptide analogs as internal standards. The capacity of the TIMS analyzers for selective mobility trapping with high resolving power increases the selectivity and sensitivity of the analysis and provides unique advantages for offline targeted studies compared with traditional LC-ESI-MS-MS proteomic strategies. A good agreement was obtained between the quantitation using offline nanoESI-TIMS-MS and LC-ESI-MS/MS. This work serves as a stepping-stone and proof of concept for quantitative proteomics of targeted peptides without the need for online LC separation, an aspect that can significantly lower the analysis cost and lead to increased sample throughput during targeted biomarker detection.
Supplementary Material
Acknowledgments
This work was supported by the National Cancer Institute (NCI) through Leidos Biomedical Inc. (No. 14X2333 to FFL) and National Institute of General Medicine (Grant No. R00GM106414 to FFL). The PNNL efforts were performed in the Environmental Molecular Sciences Laboratory, a U.S. Department of Energy (DOE) national scientific user facility located at PNNL in Richland, Washington. The author would like to acknowledge the support from Kristin E. Burnum-Johnson and Marina A. Gritsenko for the liquid chromatography fractionation and peptide selection per fraction. PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the DOE under Contract DE-AC05-76RL01830.
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