Supplementary Fig. 1. S-1. Supplementary Fig. 2. S-2. Supplementary Fig. 3. S-3. Supplementary Fig. 4. S-4. Supplementary Fig. 5.

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1 Supplementary Information - An IonStar experimental strategy for MS1 ion current-based quantification using ultra-high-field Orbitrap: reproducible, in-depth and accurate protein measurement in larger cohorts Xiaomeng Shen 1, 3 ; Shichen Shen 2, 3 ; Jun Li 1, 3 ; Qiang Hu 4 ; Lei Nie 3,5 ; Chengjian Tu 1, 3 ; Xue Wang 4 ; Benjamin Orsburn 6 ; Jianmin Wang 4 ; Jun Qu 1, 3 1 Dept.of Pharmaceutical Sci. at SUNY at Buffalo, Buffalo, NY; 2 Dept. Biochemistry of at SUNY at Buffalo, Buffalo, NY; 3 Center of Excellence in Bioinformatics & Life Sci., Buffalo, NY; 4 Rosewell Park Cancer Institute, Buffalo, NY; 5 Shandong University, China; 6 ThermoFisher Scientific, PA Table of Contents: Supplementary Fig. 1. S-1 Supplementary Fig. 2. S-2 Supplementary Fig. 3. S-3 Supplementary Fig. 4. S-4 Supplementary Fig. 5. S-5 Supplementary Fig. 6. S-6 Supplementary Methods S-8 Supplementary Tables Excel

2 Supplementary Fig. 1 Supplementary Figure 1. LC setup with a zero-dead-volume(zdv) sensor and a large ID trap. A large-id trap (300-µm ID) and 100cm-long, 75-µm ID C18 nano column, heated at 50 C, were connected by a 6-port valve. A ZDV conductivity sensor was placed between the column and the trap. Workflow of analysis cycle is shown in the figure. S-1

3 Supplementary Fig. 2 Supplementary Figure 2. Investigation of the effects of column lengths and gradient times on protein identification (N=3/condition). A human cell digest was used for this investigation and an ultra-high-field Oribtrap Fusion Lumos was employed as the detector. By balancing throughput and depth of identification, 2.5h gradient on 1-meter-long column was determined optimal. S-2

4 Supplementary Fig P ro te in # in c re a s e b y in c re a s e d lo a d in g w e ig h t P ro te in N u m b e r g 4 g P e p tid e # in c re a s e b y in c re a s e d lo a d in g w e ig h t P e p tid e N u m b e r g 4 g Supplementary Figure 3. Increased sample loading weight enables increased peptide/protein quantification. 4-µg loading gives 25% more protein quantification and 26.7% more peptide quantification than 0.5 -µg loading S-3

5 Supplementary Fig K 60 K 30 K Supplementary Figure 4. High resolution MS1 scan greatly increases mass accuracy. Delta mass distribution of identification results from MS1 scan using different resolutions, i.e. 30K, 60K and 120K. Figures were from plots provided by Scaffold. S-4

6 Supplementary Fig. 5 A B C D E E.Coli. Spike-in percentile 0% 1% 2% 3% 4% 5% 6% 7% Supplementary Figure 5. Design of the spiked-in sample set used to benchmark the analytical method. E. Coli protein lysate (true positives) was spiked at low and variable levels into high and constant levels of human cell lysate (true negatives). Each of the five spiked-in levels has 4 samples (N=20 in total). After removing species-shared peptides, totally ~900 E. Coli proteins and ~5400 human proteins, both spanning a wide concentration dynamic range, were quantified with >2 peptides/protein and low protein and peptide identification FDRs. S-5

7 Supplementary Fig. 6 A. B. C. S-6

8 Supplementary Figure 6. Application of IonStar proteomics experimental strategy to characterize proteome dynamics in combinational drug treatment. Gemcitabine is the standard-of-care chemotherapeutics for treating pancreatic cancer (PaCA), however is subjected to high occurrence of drug resistance largely because of induced epithelial-mesenchymal transition (EMT) and eventually tumor metastasis. Therefore, combining drugs capable of inhibiting EMT appears to be a viable solution to Gem resistance, and one potential candidate is fibroblast growth factor receptor (FGFR) inhibitors. In previous studies, we have shown that Gemcitabine combining BGJ398, an FGFR inhibitor, significantly suppressed cell growth/mobility and triggered sustained cell cycle arrest in both Gem-sensitive and Gem-resistant human PaCA cell lines. To further investigate the underlying mechanisms of this combination, we applied IonStar proteomics experimental strategy to characterize related proteome dynamics. A) 39 biological samples were quantified in this proteomics study, which are collected across 4 different time points (i.e. 24 hours, 48 hours, 72 hours and 96 hours) and subjected to three different treatments (i.e. Gemcitabine, BGJ398 and combination of the two), while each treatment/time group contains three biological replicates. B) Heaptmap of ~6000 unique protein groups quantified without any missing values in the 39 samples. C) By applying a ratio threshold of 1.4-fold change and Anova p-value threshold of 0.05, 1302 proteins were determined as significantly changed. DAVID GO annotation reveals that these proteins are enriched in oncology associated function categories such as cell cycle, cell apoptosis, cell-cell adhesion and migration. S-7

9 Supplementary Methods Cell Culture, treatment and collection MIA PaCa-2 cells were purchased from American Type Culture Collection (ATCC), and cultured in Dulbecco s modified Eagle s medium (Cellgro, Manassa, VA) supplemented with 10 % (v/v) fetal bovine serum. Cells were incubated in a 95 % humidified atmosphere containing 5 % CO 2 at 37. MIA PaCa-2 cells were grown as monolayers in 10 cm culture plates (Corning Inc, Corning, NY). The cells were treated with IC 50 of gemcitabine (23 nm), IC 50 of trabectedin (0.8 nm) and their combinations for 0, 12, 24, 36, 48 and 72 h. Sterile double distilled water and DMSO was added as vehicle control for gemcitabine and trabectedin. At each time point, the culture medium containing floating cells was collected in 15 ml tube. The attached cells were washed with PBS followed by trypsinization, the detached cells were transferred to the 15 ml tubes, the plates were washed with medium for 3 times. All cell solutions including PBS and medium were also collected in tube. The pellets were obtained after centrifugation at 2250 rpm for 5 min. The pellets then were washed with PBS for three times, and ready for protein extraction (composition described in Experimental Procedures: Protein Extraction). MS parameters The instrument was operated in the data dependent mode: MS1 spectral were collected at a resolution of , with an automated gain control (AGC) target of , and a max injection time of 50 ms. Maximal duty cycle time was set at 3s. The m/z range for MS1 full scan is Previously interrogated precursors were excluded using a dynamic window (60s ± 10 ppm). Precursors were filtered by quadrupole using isolation window of 1 Th and fragmented by highenergy collision dissociation (HCD) at a normalized collision energy of 35%. MS2 spectra were S-8

10 collected at a resolution of in Orbitrap, with AGC target of , and a max injection time of 50 ms. Protein Identification The individual raw files (.raw) generated by LC-MS analysis were matched to the human databased and concatenated human-e.coli database and human database, respectively, with and entries, using the MS-GF+ searching engines(released on May 17, 2013){Kim, 2014 #30}. The search parameters set were listed as follows: 1) Precursor ion mass tolerance: 20 ppm; 2) instrument: Q-Exactive; 3) 1 match per spectrum is allowed; 4) Fixed modification: carbamidomethylation of cysteine; 5) Dynamic modification: oxidation of methionine and acetylation of N-terminal; 6) 2 miss cleavage is permitted. Protein/peptide filtering and control of the false discovery rate (FDR) was accomplished by IDPicker{Ma, 2009 #32} using a target-decoy search strategy with a concatenated database containing both forward and reverse sequences{elias, 2005 #33}. Both protein and peptide FDR were controlled at < 1%, while minimum unique peptide number of 2 was required. Protein Quantification Quantitative data analysis was achieved by using an ion current-based strategy, using SIEVE and IonStarStat R package. Chromatographic alignment and ion intensity-based MS1 feature detection/extraction was performed using functions in SIEVE v2.2 (collaboratively with ThermoFisher Scientific){Lopez, 2011 #34}. The main processes in SIEVE include: 1) Chromatographic alignment among LC-MS runs using the ChromAlign algorithm modified based on a previous work{sadygov, 2006 #35}, whereas quality control of the alignment of LC-MS runs was achieved by monitoring and benchmarking the alignment scores (>0.8) and base-peak intensity; 2) Data-independent MS1 feature generation: generate features for all precursors S-9

11 associated with existing MS/MS scans and extract ion current in the aligned collective dataset by using defined m/z and retention time window width centered the existed precursor ion (i.e. 10ppm and 1 min are respectively used for analysis of our datasets). 3) The resulted feature intensities were then exported and associated with identified PSM by scan number using an in-house R script. Then the quantitative features were subjected to duplicated frame removal, normalization, multivariate outlier detection/rejection and aggregation to protein level using an in-house R package IonStarStat (available at Details are in the help file of IonStarStat. Experimental details of the sample set described in Fig. 5 In order to evaluate quantitative accuracy and precision using different MS1 resolutions, we used a spike-in sample set, by spiking small, variable levels (3µg and 9µg) of DH5α E. Coli digest (representing altered proteins) into a large, constant level (90µg) of human cell Panc-1 cell digest. So the theoretical spike in ratio is 3 fold, and each spike-in level contains 3 technical replicates. LC/MS analysis were performed using optimized procedures described in Experimental Procedures, except the resolution of MS1 were set at different resolutions (i.e. 30K, 60K, 120K) for three batches of analysis. For protein quantitation, ion current extraction windows were set as 30ppm, 20ppm and 10ppm respectively for 30K, 60K and 120K data. S-10