Label-free Selected Reaction Monitoring Enables Multiplexed

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Label-free Selected Reaction Monitoring enables multiplexed quantitation of S100 protein isoforms in cancer cells Juan Martínez-Aguilar, and Mark P Molloy J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400251t • Publication Date (Web): 20 Jun 2013 Downloaded from http://pubs.acs.org on June 24, 2013

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Journal of Proteome Research

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Label-free Selected Reaction Monitoring enables multiplexed quantitation of S100 protein isoforms in cancer cells Juan Martínez-Aguilar1 and Mark P. Molloy1,2* 1

Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney,

Australia. 2

Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, Australia.

AUTHOR INFORMATION *Corresponding author Assoc. Prof. Mark P. Molloy APAF Macquarie University Sydney, Australia, 2109. Tel: +612 9850 6218 Fax: +612 9850 6200 [email protected]

ACS Paragon Plus Environment


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ABSTRACT In humans, the S100 protein family is composed of 21 highly related low molecular weight (~10kDa) proteins. These proteins are known to have diagnostic, prognostic and predictive value in a variety of cancers, but their small size and high sequence homology present a challenging scenario for quantitative bioanalytical procedures. Here, we developed a multiplexed, label-free Selected Reaction Monitoring (SRM) assay to specifically measure the S100 protein isoform family in cancer cells. Several normalisation parameters associated with label-free SRM quantitation were investigated to derive a method with optimal precision. We detected eleven S100 isoforms across a panel of nine colon and breast cancer cell lines. The quantitative potential of the S100 assays for biomarker discovery was demonstrated by studying the isogenic cell lines SW480 and SW620, a cellular model of colon cancer progression. Our findings were shown to be in agreement with previously published polysomal mRNA level quantitation for S100 genes in these cell lines. Comparison of the quantitation results using label-free SRM with those obtained using stable-isotope labelled peptide standards demonstrated reliability of the method. These data support the use of SRM to quantitate S100 protein isoforms as these are important players in a broad range of human diseases.

Keywords: S100 proteins, Selected Reaction Monitoring, Label-free quantitation, Cancer biomarkers


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INTRODUCTION The human S100 protein family comprises 21 protein isoforms of low molecular weight (∼9-13 kDa). Seventeen of the family members are located on Ch 1 having arisen from gene duplication and are characterised by two calcium-binding EF-hand motifs.1-3 There is growing evidence for the clinical significance of several members of the S100 protein family. Dysregulation of S100 proteins in many human diseases including malignant neoplasms has been consistently observed and some of them have even been proposed as therapeutic targets or predictive markers of therapeutic response.4-6 Their observed intracellular functions include regulation of protein phosphorylation, enzyme activity, cell growth and differentiation; extracellularly, some of the S100 isoforms exhibit chemotactic, neurotrophic or angiogenic activities.3, 4, 7 A number of S100 proteins interact with the p53 tumour suppressor8, 9 and it is also commonly suggested that the clustered organization of most S100 genes on human chromosome 1q21, a frequently rearranged region in cancer, is linked to their dysregulated expression.1, 3, 10 Indeed, many S100 proteins have been identified as potential markers of a variety of cancers including melanoma, pancreatic, gastric, bladder, thyroid, breast and colorectal cancer (CRC).4 For example, S100A4, S100A6, S100A8, S100A9 and S100P have all been described as candidate markers in gastric and colorectal cancer.11-19 Likewise, S100A4 and S100A11 have been reported in bladder and prostate cancer.20-23 In malignant melanoma, serum S100B has prognostic and predictive significance.24, 25 In different inflammatory diseases (e.g. rheumatoid and psoriatic arthritis, inflammatory bowel disease) S100A8, S100A9 and S100A12 have also been found.26-29 Previous proteomic studies focused on the analysis of multiple S100 proteins are scarce.30, 31 Here, we developed a targeted mass spectrometry analysis of the S100 protein family by Selected Reaction Monitoring (SRM). We utilised a bioinformatics approach to identify unique tryptic



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Page 4 of 34 peptide sequences for S100 isoform discrimination, then optimised multiplexed SRM assays using a panel of nine cancer cell lines. We investigated the analytical precision attained with different normalization/comparisons associated with label-free quantitation and then applied the approach to quantitate S100 proteins in colon and breast cancer cell lines. Our data show that the labelfree SRM method supports the simultaneous and straightforward evaluation of S100 family protein expression in cancer cells.


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EXPERIMENTAL SECTION Cell culture and cellular stimulation Colorectal cancer cells (HT-29, SW480, SW620, SW837, SW1116, HCT-116) and breast cancer cells (MCF-7, SKBR3) were cultured in Dulbecco's Modified Eagle Medium (DMEM) with Glutamax media (Gibco), supplemented with 10 % Fetal bovine serum (FBS, Gibco) and 1 % PenicillinStreptomycin (Gibco). MDA-MB-231 was cultured in RPMI 1640 medium (Gibco), supplemented as above. Cells were maintained at 37 °C in a humidified atmosphere with 5 % CO2. Upon reaching 70-90% confluence, they were washed with phosphate buffered saline (PBS), harvested and stored at -80 °C until use. 1D-Gel fractionation/In-gel reduction, alkylation and trypsin digestion Cells were suspended in lysis buffer (50 mM Tris, 1% Triton X-100, 150 mM NaCl, 0.1 % SDS, 0.5 % sodium deoxycholate, 1 mM EDTA, pH 7.4) with complete, EDTA-free protease inhibitor cocktail (Roche) and lysed on ice with sonication (three cycles of 20 s pulses). Samples were centrifuged (12 000g, 5 min, 4 °C) and the supernatants were transferred to new tubes. Protein quantitation was performed with a BCA protein assay kit (Thermo Scientific). Aliquots from each cell line were mixed with NuPAGE LDS sample buffer (Invitrogen) with 50 mM DTT, incubated at 70 °C for 10 min and loaded onto 4-12 % Bis-Tris NuPAGE gels (Invitrogen). 40 ug of protein per lane along with unstained molecular weight markers (Bio-Rad) were electrophoretically fractionated with MES running buffer at 200 V for 25 min. Proteins were fixed with 50% ethanol/10% acetic acid for 1 h and stained overnight with colloidal Coomassie G-250 (Sigma). After short destaining with 50% methanol/10% acetic acid, gel bands from around 8-14 kDa were excised, cut into approximately 1×1 mm cubes and further destained with 50% ACN in 50 mM Ammonium bicarbonate (AmBic, pH 8.0). Gel pieces were dehydrated in 100% ACN, speed-vac dried and incubated in 10 mM DTT/100



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Page 6 of 34 mM Ambic at 56 ° for 1h followed by treatment with 55 mM Iodoacetamide/100 mM AmBic at RT in the dark for 45 min. Samples were dehydrated, speed-vac dried and rehydrated again with trypsin solution (0.4 ug sequencing grade modified trypsin (Promega) in 50 mM AmBic) on ice for 1 h. Excess trypsin solution was discarded, replaced by 50 mM AmBic and samples were incubated overnight at 37 °C. Peptides were extracted twice with 50% ACN/1% TFA and once with 100% ACN in ultrasonic waterbath for 10 min. Pooled extracts were dried in a speed-vac. Western Blot 10 ug of protein from cell lysates of HT-29, SW480, SW620 and MDA-MB-231 were separated on 4-12 % Bis-Tris NuPAGE gels as above and electroblotted onto nitrocellulose (Bio-Rad). The membrane was blocked with 5% skim milk in TBS for 1 h at RT prior to overnight incubation at 4 °C with rabbit polyclonal anti-S100A4 (1:100, AbCam) and rabbit polyclonal anti-GAPDH (1:2000, Cell signaling). The primary antibody against S100A4 had been tested for cross reactivity with S100A1, S100A2, S100A6 and S100B by the manufacturer. The secondary antibody was fluorescencelabelled IRDye 680RD goat anti-rabbit IgG (H+L) (1:10 000 in 5% skim milk in TBST, LI-COR), incubated at RT for 1h and visualized with the Oddysey infrared imaging system. Development of the Selected Reaction Monitoring assay ∙ Selection of precursor-fragment ions for SRM Protein sequences for all 21 annotated human S100 family proteins were compared by BLAST to identify unique tryptic peptides containing more than six amino acids and no methionine. Preliminary selection of SRM transitions was derived from MS/MS spectra acquired on a Thermo Finnigan LTQ and information obtained from SRM Atlas that helped to set up SRM-triggered IDA MS/MS experiments on an AB Sciex QTRAP 5500. Tandem mass spectra were searched against the SwissProt database with Mascot v. 2.3.0. Fragment ions were selected giving priority to those


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Page 7 of 34 with higher signal response and m/z above the precursor ion, except in few cases where a particularly more intense signal was found below that m/z value. Three transitions per peptide were monitored. To improve precision during the quantitative analysis, an empirical cut-off for the peak areas was set at 5x104 counts, whereby fewer split peaks or divergence of peak shape similitude was observed. ∙ LC-SRM-MS Samples were analysed using a Waters nanoACQUITY UPLC system coupled to the QTRAP 5500. Weak (A) and strong (B) elution solvents were 0.1 % FA in water and 0.1 % FA in ACN, respectively. 4 uL of sample were loaded using partial loop injection onto a Waters Symmetry C18 trapping column (180 um x 20 mm, 5 um particle size) and peptides were separated on a Waters BEH C18 nano UPLC column (100 um x 100 mm, 1.7 um particle size) at 35 °C under constant flow rate of 0.4 uL/min. In the preliminary tests, two elution gradients were used: 28-min gradient began with 3% solvent B for 1 min followed by 3-60% B in 24 min, 60-85% B in 3 min holding for 3 min and returning to 3% B in 1 min. 33-min gradient began with 3% solvent B for 1 min followed by 3-50% B in 30 min, 50-85% B in 2 min holding for 3 min and returning to 3% B in 1 min. The latter was employed in subsequent analyses. Samples were reconstituted with 9 uL of 2 % ACN/0.1 % TFA and 1 uL of 178 fmol/uL 13C stable isotope-labelled peptide ESDTSYVSLK from human C-reactive protein (Auspep, Australia), used as spiked external standard. Three injections from different biological samples were performed as replicates, with blank injections between them. The complete panel of S100 peptides was targeted across three different SRM transition lists (three methods) with a cycle time of 1.5-1.6 s and dwell times of 15 ms. The nano-ESI source was operated in positive mode at 2.5 kV with interface heater temperature of 150 °C. Curtain gas was set to 20 psi, declustering potential at 70 V, collision cell



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Page 8 of 34 exit potential at 13 V and both Q1/Q3 were set at unit resolution. Collision energies were calculated based on the equation CE(2+)= 0.055*m/z+5, where m/z is the mass to charge ratio of the precursor ion. SRM data were processed with Multiquant 2.1 (AB Sciex) with the MQ4 integration algorithm. ∙ Normalization methods The summed raw fragment ion peak areas were normalized with the corresponding signal responses from either a spiked peptide standard (ESDTSYVSLK, CRP protein) or a cellular reference peptide (GGEIQPVSVK; 10 kDa Heat shock protein, Hsp10).The normalised peak areas were then compared to the SW480 cell line used as a reference for fold-difference determination. ∙ Validation with stable-isotope labelled standards. Six 13C, 15N stable-isotope labelled peptide standards (SpikeTides TQL, JPT Peptide Techonologies, Germany) were employed to compare the results obtained by the label-free SRM approach: ELPSFVGEK (S100A2), ELPSFLGK (S100A4), LQDAEIAR (S100A6), DPGVLDR (S100A11), LIGELAK (S100A13) and AVIVLVENFYK (S100A16). These peptide standards were added in different amounts (5-3000 fmol on-column) before in-gel digestion to test the linearity of the SRM response. The transition areas of each peptide were summed and the ratio relative to the endogenous light peptides was calculated and plotted against the standard peptide amount. Then, equal amounts of these labelled peptides (100-1200 fmol on-column, to obtain an approximate ratio 1:1 between the peak areas of light/heavy peptides in SW480 samples) were added to samples before trypsin digestion. A time-scheduled SRM assay (detection window 180s, target scan time 1 s) was applied for the multiplexed analysis of both light and heavy versions of S100 peptides. The respective fragment ion peak areas of endogenous and added standards were summed and their relative ratio was calculated.


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Page 9 of 34 RESULTS AND DISCUSSION Proteotypic S100 peptides Supplementary Table 1 lists the amino acid sequences of all unique S100 isoform tryptic peptides that were used as the basis for SRM assays. Conventional proteomic analysis of these proteins is challenging due to their high sequence identity and relatively small size that affords only a few tryptic peptides for each isoform. Ideally, peptides with readily modified amino acids (e.g. oxidation of methionine and tryptophane, deamidation of aparagine, etc.) are avoided in SRM analysis.32 However, given the limited number of possible S100 peptide candidates per protein, only those with methionine were excluded in this work. We used a ‘training sample set’ of six colorectal cancer cell lines and three breast cancer cell lines to evaluate these assays. Colorectal and breast cancer cell lines were used since many S100 proteins have been linked to the appearance or progression of these cancers.4, 11, 12 Table 1 summarizes the S100 proteotypic peptides detected in this study using the cancer cell lines training sample set. In total, eleven S100 protein isofroms were detected: S100A1, S100A2, S100A3, S100A4, S100A6, S100A10, S100A11, S100A13, S100A14, S100A16 and S100P. At least two peptides were detected for all proteins, except in the cases of S100A1, S100A2 and S100A3 with only one peptide detected. The detection of each S100 peptide was confirmed by MS/MS and spectra are shown in the supplementary section (Supp. Figs. 1-25). To the best of our knowledge, this is the most comprehensive list of S100 proteins that has been definitively detected in a single mass spectrometry experiment and incorporates most of the S100 proteins that have been suggested as possible biomarkers in cancer.



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Page 10 of 34 Label-free SRM of S100 proteins We investigated the analytical performance of the SRM assay panel with label-free quantitation in preparation for quantitative comparisons in relevant experimental models. We followed a sample preparation protocol that involved total protein quantitation, 1D-gel fractionation, addition of external standard and RP-LC-SRM-MS. This protocol exploits the characteristic small size of all S100 proteins and takes advantage of the size-based separation readily achieved by SDS-PAGE. Further advantages of sample fractionation are the enhanced sensitivity, reduced potential of detrimental ion suppression and the reduced likelihood of isobaric interferences in SRM.33, 34 Manual inspection of the SRM traces of all the S100 peptides ensured accurate detection and peak integrations. Retention times were typically within 10s between samples under the same chromatographic conditions. Peak areas of transitions of each peptide were summed and were first compared under different elution gradients carried out several weeks apart. This strategy was aimed to: identify potential SRM interferences, assess ion suppression/enhancement effects and choose an appropriate normalization method to correct for the variation in signal responses. We employed two elution gradients, one “short gradient” of 28 min and one “long gradient” of 33 min, where a minimum of nine points were recorded across each peak. Cell lysates of SW480, HT29 and MDA-MB-231 were used for testing. In general, we found coefficients of variation (CV) 30% and hence excluded from subsequent quantitation. The correspondence in transition ratios between samples was considered to represent interference-free assay conditions for the succeeding analyses.


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Page 11 of 34 Label-free SRM assay reproducibility An important parameter that was addressed by making deliberate changes in the chromatographic conditions dealt with finding an optimum normalization procedure that could offer reliable results for quantitative analyses. By performing the analyses at different times the additional factor of MS system drift could also be taken into account to develop a reproducible quantitative strategy. Fig. 1 shows the ratio of S100 proteotypic peptides responses of HT29 and MDA-MB-231 cell lysates between the long and short gradients employing the raw peptide peak areas and two normalization methods. An increase in the raw peak areas of many of the S100 peptides was seen with the long gradient. Fig. 2 illustrates that one of the reasons for this result is related to a change in ionization efficiencies, enhancing the response probably due to a reduced number of coeluting species during the long gradient run (median increment in peak areas = 1.5). Fig. 2 also shows that the changes in peptide ion abundances from a sample analysed in the same day are relatively uniform, unlike the more extreme differences displayed in Fig. 1. The presence of high fold differences in these last two cases might be explained by variation in other factors such as tryptic digestion efficiency, peptide extraction or a change in system response. Analyses on the reproducibility of S100 peptide abundances among samples prepared at different times but run as a single batch using the long gradient showed comparable results (see Supp. Figs. 26 and 27). This finding suggested that the LC-MS system is one of the main sources of the variation, rather than sample preparation steps. We tested two normalization methods to account for the differences in signal responses. The first normalization step used an endogenous internal standard (GGEIQPVSVK, from Hsp10) and a spiked external standard (ESDTSYVSLK, from CRP protein). The choice of endogenous reference peptides for peak area normalization in label-free SRM has been previously considered35 to correct for



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Page 12 of 34 sample losses and variable injection volumes. However, as can be seen in Supp. Figs. 28 and 29, neither the endogenous reference peptide nor the spiked external standard alone could modulate the variable quantitative response of the S100 peptides, which was shown to be peptide-specific. Finding a protein or set of proteins with stable expression among samples from different biological conditions is not an easy task and can certainly complicate the analysis (e.g. in studies of cell response to drug stimulation). As shown in Supp. Figs. 30 and 31, it is highly advisable, however, to include a spiked external standard to compensate for distinct sample loadings within LC runs. Supp. Fig. 32 shows that the signal responses between gradients, first normalized with the spiked peptide ESDTSYVSLK, are similar between the three cell lines HT29, MDA-MB-231 and SW480. This leads to the deduction that a comparison of peptide-to-peptide normalized areas could be made by using a reference sample (e.g. SW480). Although this approach has been previously reported, our work with the simultaneous analysis of several S100 peptides shows the compensatory effect with clarity, since many of the ratios of peak areas approach unity (see Fig.1). This result might be explained given that comparison of peptide samples prepared in the same way and analysed under very similar conditions helps to balance changes including differences in LC-MS responses. This in fact forms the basis of the high precision achievable by the use of stable-isotope labelled peptides, which nevertheless holds the key advantage of having the reference peptides in exactly the same proteomic matrix.36 The median CVs of the comparisons of peptide amounts between the above mentioned tests with two elution gradients were below 20% (see Supp. Figs. 33 and 34). The application of this label-free SRM-based protocol to the analysis of S100 proteins in HT29 cells 14 months later (see Supp. Fig. 35) showed a median CV of 22 %, in good agreement with related studies.37 Given the robust performance achieved with this reference sample normalization approach we adopted it for subsequent analyses.


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Page 13 of 34 Part of the variability could be ascribed to the intrinsic susceptibility of some S100 proteotypic peptides to chemical modifications. Others peptides like ISSPTETER from S100A11 hold possible phosphorylation sites. A strong detrimental effect by the presence of such amino acids was not observed in our study, but one peptide from S100A10, DPLAVDK , continuously exhibited the highest CVs and thus was not used for quantitation. The aspartic acid-proline bond is known to be weak, acid-labile38 and that could, for instance, explain the quantitative behaviour of the peptide, although in contrast, DPGVLDR form S100A11 did not demonstrate the same variation in the samples under study. In summary, while there are different potential sources of error in label-free LC-SRM-MS, normalization with an external standard peptide and comparison of every peptide abundance against a reference sample prepared in the same manner and analysed under the same system conditions constitutes an optimal quantitative method. This approach allowed for assessment of S100 protein isoform differential expression and can be readily used as a less expensive quantitative screening method from which synthetic labelled standards can be selected whenever higher precision or absolute concentration is required. Indeed, label-free SRM has been suggested as an alternative to antibody-based methods.36 Our results regarding method precision are in keeping with other studies. For example, in experiments where proteins were spiked into cell lysate or human plasma, Zhang et al.36 observed median CV values of 17.7% by label-free SRM quantitation with a spiked external standard, which improved to 9.9% with the use of isotope-labelled peptides. In clinical specimens prepared by in-gel digestion, both approaches detected the same significant differences in protein expression. Application of the label-free SRM assay panel for biomarker discovery To demonstrate the applicability of the multiplexed label-free SRM quantitation method we compared SW480 colon cells with SW620 cells. These isogenic cell lines are widely employed as



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Page 14 of 34 cellular models in colon cancer progression39, 40. The former was originated from a Duke’s stage B colon carcinoma and the latter corresponds to a Duke’s stage C, from a lymph-node metastatic site from the same patient. We detected nine S100 proteins: S100A2, S100A3, S100A4, S100A6, S100A10, S100A11, S100A13, S100A16 and S100P (see Fig. 3) and achieved high overall reproducibility within replicates (median CVs: SW480 10.8% and SW620 11.3%). During the analysis of these cell lines it became apparent that the peptide TDEAAFQK from S100A4 displayed large variance and was thus omitted from further analysis. TDEAAFQK is a hydrophilic peptide eluting very early in the gradient and more susceptible to the initial spray instability during the LCMS run, which could be one of the reasons for this poor precision. Fig. 3 summarises the differential expression of S100 proteins in these two colon cancer cell lines. Statistical analysis was performed using an unpaired Student t-test of the log-transformed values of peak areas (normalized to the spiked ESDTSYVSLK peptide) averaged across all replicates. Four S100 proteins were found down-regulated in the metastatic SW620 cellular model: S100A2, S100A3, S100A10 and S100A11. S100A6 was found up-regulated in SW620 cells, where only S100P was detected. Actual fold differences and their estimated errors are shown in Table 2. When two or more peptides per protein were analysed, these yielded similar trends within the experimental error. Comparison with polysomal mRNA expression Previous work by Provenzani et al. 41 compared the global alterations of gene expression in SW480 and SW620 cell lines by measuring polysomal mRNA with microarrays (NCBI GEO database, accession GSE2509). In Fig. 4 we compared these results for S100 isoforms with those quantitated using the label-free SRM approach. Fig. 4 displays the striking concordance between the profile of translationally active S100 mRNAs (polysomal loading) and the S100 proteins profiles. Five out of the six S100 proteins regarded as differentially expressed by SRM were also observed as differentially abundant in polysomal mRNA with the microarray. Only S100A6, which showed up-


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Page 15 of 34 regulation at the protein level, was not deemed to have significantly changed when measured by microarray of polysomal mRNA. The magnitude of change in S100 protein abundances between the SW480 and SW620 cells was consistent between quantitation techniques (see Table 3). Moreover, it was found that of the S100 proteins not detected by SRM, each had very low microarray probe intensity counts, less than 100 units, or was even reported as absent (see Supp. Fig. 36). These results support the validity and the robust analytical performance of the label-free SRM assay for the relative quantitation of S100 protein isoforms. Validation with stable-isotope labelled peptide standards To ensure the validity of the label-free quantitation method we utilized six 13C, 15N stable-isotope labelled S100 peptide standards. SRM parameters (collision energy, CE; declustering potential, DP; and collision cell exit potential, CEXP) were optimised by incremental steps following the methodology described by MacLean et al42. In some cases, the optimisation of CE offered a sensitivity gain as high as 2-fold; the effect of CEXP was smaller and no improvement was observed while changing DP for these peptides. The optimum CE values were close to the ones predicted by the equation included in the Skyline software (CE(2+)=0.036*m/z+8.857), which was then used to estimate the CE for the peptides not detected but listed in Supp. Table 1. Optimised parameters and corresponding retention times (see Supp. Table 2) were used to create a new, time-scheduled SRM assay that included both the light and the isotopically labelled version of the peptides. We tested the linearity of SRM response by spiking different amounts of the standards (5 fmol3000 fmol on-column) into samples from SW480 cells immediately prior to proteolytic digestion. Supp. Figure 37 shows the correlation between the peak areas and the amount of peptide injected on-column. A linear relationship (r2 > 0.98) was found in all cases within the tested concentration



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Page 16 of 34 ranges. In order to compare the results from the stable-isotope dilution (SID) and label-free approaches, the standard peptides were added in equal amounts into the in-gel protein extracts of SW480, SW620 and HT29 cells before trypsin digestion. Table 4 and corresponding Fig. 5 show the excellent agreement between the results obtained by SID and label-free quantitation methods. Median CVs were as follows: HT29 Label-free 13.7%, SID 13.3%; SW620 Label-free 2%, SID 4.5%; SW480 Label-free 25%, SID 2.5%. Only in the case of SW480 was there considerable discrepancy between the precision for the two techniques. Upon investigation we identified this to be artificially high due to inaccurate addition of the CRP peptide to one of the samples, which was used to normalise the SRM responses for the label-free quantitation. Nonetheless, the correspondence between fold differences among the two quantitative approaches indicates that matrix effects did not compromise the label-free SRM approach. This can be explained by our sample preparation method that used SDS-PAGE to enrich S100 proteins and minimise interferences that could be caused by the matrix background. The results listed in Table 4 obtained using a scheduled acquisition method also agreed with the original label-free SRM results shown in Table 2 and Supp. Fig. 33. In addition, use of the labelled peptides supported the confident analysis of LIGELAK (S100A13), which was previously detected with the label-free SRM method but excluded from quantitation due to >30% CV of transition ratios. After optimisation of the SRM parameters, S100A14 was detected in SW620 cells (see Table 1). The reliable detection of these peptides demonstrates the utility of stable-isotope labelled peptide standards where SRM assay optimisation conditions can be explored (fragmentation conditions, retention time and SRM transition ratios calculation) to improve biomarker discovery.


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Page 17 of 34 Biological insights of S100 protein expression in colon cancer Some of the key biological findings from our experiments are discussed below. The peptide ELPSFVGEK from S100A2, showed the largest decrease in abundance in the lymph-node origin SW620 cells compared with SW480 cells. To our knowledge, S100A2 has not been reported as a candidate biomarker for CRC, but its decreased expression has been associated with lymph-node metastasis in other cancers including gastric and head and neck cancer.43, 44 S100A3 was also found at reduced levels in SW620 cells. Smith et al. 45 identified the down-regulation of the S100A3 gene as part of a gene classifier associated with high risk of metastasis and death from colon cancer. In the case of S100A11, down-regulated in SW620 cells, Meding et al 46 recently reported its underexpression in colon tumours and S100A11 was proposed as a marker of lymph-node positive colon cancer, although other reports have associated S100A11 up-regulation with tumour progression.47 In accordance with our SRM-based results, up-regulation of the S100A6 protein has also recently been reported in SW620 cells.48 Komatsu et al. 12 found association of S100A6 expression in colorectal adenocarcinomas with nodal status and lymphatic permeation. The S100P protein was detected by SRM and polysomal mRNA41 in SW620 cells. In SW480 cells we could not detect the protein, but this was unsurprising given it was only reported with very low abundance on the microarray. Fuentes et al. 49 also observed absence of S100P in SW480 cells by RT-PCR and that addition of exogenous S100P stimulates proliferation and migration. Over-expression of S100P has been detected in colorectal and other cancer tumours and has been considered as a therapeutic target.6, 14 We did not find significant overexpression of S100A4 in SW620 cells but its up-regulation and association with CRC metastasis and poor prognosis has been reported elsewhere.11, 50 We confirmed the S100A4 protein expression in SW480, SW620, HT-29 and MDAMB-231 cells by Western Blot, finding good agreement with the SRM-based quantitative data (Fig. 6).



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Page 18 of 34 The relative profiles of S100 proteins expression from the cell lines SW480, SW620, HT29 and MDA-MB-231 is illustrated in Fig. 7. Relative peptide abundances were averaged for the corresponding protein. Among these cells, MDA-MB-231 presented higher values of S100A11 and S100A13; the same was found for S100A6 in HT29 and S100A2 in SW480. The fewest number of proteins were detected in the breast cancer cell lines (Table 1), as exemplified by MDA-MB-231 cells in Fig. 7. The proteins detected in all cell lines were S100A6, S100A10, S100A11, S100A13 and S100A16. S100A8 and S100A9 are commonly found in tumours4, 13 and their lack of detection in all the cancer cells analysed correlates with several literature reports that point out their higher expression in tumour-associated immune cells rather than epithelial cancer cells.13, 15 Taken together, these results have shown the applicability of the targeted mass-spectrometric approach for the analysis of highly related S100 family members, whose concurrent expression has been demonstrated in several cancer cell lines.

CONCLUSIONS The aim of this study was to develop a multiplexed quantitative SRM assay for the broad analysis of S100 protein isoforms in cancer cells. With the use of several cancer cell lines, we detected most of the S100 proteins previously identified in cancer studies as molecular flags of disease. The precision of the label-free SRM analysis benefited from the use of an external standard to account for possible loading anomalies and, to a limited extent, system fluctuations. The latter were seen to be better addressed by relative comparisons using a particular cell line as a reference for normalisation. We demonstrated reliability of the label-free SRM quantitation by comparison with isotopically labelled peptide standards. The latter showed some additional benefits due to optimisation of SRM instrumental parameters and confirmation of relative abundances of fragment ions. We propose that label free SRM has utility for reliable quantitation in the discovery


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Page 19 of 34 phase of biomarker studies with added time and cost advantages over the use of peptide standards. These standards can then be obtained to optimise SRM assay performance and ensure signals are free of interferences once peptide targets have been established. SRM of S100 peptides in SW480 and SW620 colon cancer cells revealed significant dysregulation of seven S100 proteins, confirming the differential expression of several members of the S100 family in the context of cancer progression. Taken together, the general concordance between the results offered by the label-free LC-SRM method for the isogenic cell lines and the available data reported from previous studies with the same cell lines and clinical samples emphasizes the great potential of the SRM assay to evaluate the expression of the complete panel of S100 proteins. The optimized list of proteotypic peptides and transitions (Table 1) can serve as the basis for the quantitative screening of these proteins not only in cancer cells but also in clinical specimens and other samples where their participation has been observed. For the remaining S100 protein isoforms that were not observed in this study Supp. Table 1 and 2 provide their SRM parameters as a starting point for assay optimisation.

ASSOCIATED CONTENT Supplementary Figures and Supplementary Table 1 and 2 with the complete list of SRM transitions are available free of charge via the Internet at http://pubs.acs.org ACKNOWLEDGMENTS JMA is the recipient of an MQRes PhD scholarship from Macquarie University. MPM acknowledges support from the Cancer Institute NSW (Research Equipment grant). Aspects of this research were conducted at the Australian Proteome Analysis Facility supported by the Australian Government’s National Collaborative Research Infrastructure Scheme and Education Investment Fund.



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Page 25 of 34 Figure legends

Figure 1. Investigation of LC gradients and normalization approaches for label-free SRM using (A) HT29 cells (B) MDA-MB-231 cells . Error bars are standard error of the mean (SEM) Figure 2. Peptide peak areas between long/short gradient. Error bars are SEM Figure 3. SRM quantitation of S100 peptide abundances between SW480 and SW620 cells. *p

Label-free Selected Reaction Monitoring Enables Multiplexed

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