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• W4361286446 abstract "•Optimal gas-phase fractionated (GPF) library for urinary EV phosphoproteomics.•Integrating EVtrap, PolyMAC, and GPF DIA for thousands of unique EV phosphosite identification.•Linear discriminant analysis (LDA) correctly clustered the grades of RCC patients.•Chronic kidney disease (CKD) served as a better control for RCC biomarker screening. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Renal cell carcinoma (RCC) is currently the eighth leading cause of cancer death in the United States, affects nearly 300,000 individuals worldwide each year, and is responsible for more than 100,000 deaths annually (1Attalla K. Weng S. Voss M.H. Hakimi A.A. Epidemiology, risk assessment, and biomarkers for patients with advanced renal cell carcinoma.Urol. Clin. North Am. 2020; 47: 293-303Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar, 2Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2019.CA. Cancer J. Clin. 2019; 69: 7-34Crossref PubMed Scopus (15310) Google Scholar). RCC originates from the renal cortex or the renal epithelial cells and accounts for more than 90% of all kidney cancers (3Padala S.A. Kallam A. Clear Cell Renal Carcinoma. StatPearls Publ, Treasure Island, FL2022Google Scholar, 4Hsieh J.J. Purdue M.P. Signoretti S. Swanton C. Albiges L. Schmidinger M. et al.Renal cell carcinoma.Nat. Rev. Dis. Prim. 2017; 3: 17009Crossref PubMed Scopus (1413) Google Scholar). In the past decades, the incidence of RCC has been increasing steadily, and a diverse set of RCC subtypes has been recognized. The primary histologic subtypes are clear cell (70–80%), papillary (15%), chromophobe (5%), and unclassified RCC (5Jonasch E. Gao J. Rathmell W.K. Renal cell carcinoma.BMJ. 2014; 349: g4797Crossref PubMed Scopus (437) Google Scholar, 6Gray R.E. Harris G.T. Renal cell carcinoma: diagnosis and management richard.Am. Fam. Physician. 2019; 99: 179-184PubMed Google Scholar). Distinct cytogenetic and immunohistochemical profiles characterize each subtype and prognoses as reflected by staging severity, with the lower stage being associated with longer survival rates (7Ng C.S. Wood C.G. Silverman P.M. Tannir N.M. Tamboli P. Sandler C.M. Renal cell carcinoma: diagnosis, staging, and surveillance.Am. J. Roentgenol. 2008; 191: 1220-1232Crossref PubMed Scopus (159) Google Scholar). Clear cell RCC is the most common among these subtypes and accounts for the majority of RCC-related deaths. Due to the lack of symptoms until locally advanced or metastatic, renal cell cancer is typically detected incidentally when localized without warning. Currently, the detection and classification of renal masses rely on radiologic examinations, including ultrasound, computed tomography, magnetic resonance imaging, and so on. (8Ljungberg B. Bensalah K. Canfield S. Dabestani S. Hofmann F. Hora M. et al.EAU guidelines on renal cell carcinoma: 2014 update.Eur. Urol. 2015; 67: 913-924Abstract Full Text Full Text PDF PubMed Scopus (1901) Google Scholar). In recent years, the frequent use of imaging for unrelated clinical symptoms of other diseases has led to a higher number of incidental diagnoses of RCC (9Escudier B. Porta C. Schmidinger M. Rioux-Leclercq N. Bex A. Khoo V. et al.Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.Ann. Oncol. 2019; 30: 706-720Abstract Full Text Full Text PDF PubMed Scopus (607) Google Scholar). Once identified the majority of renal masses are operated on without knowledge of subtype or grade. There are a number of explanations for this approach including a high number of historical nondiagnostic results, the risk of tumor seeding, and the risk of complications including primarily bleeding and pain as well as limited access to quality interventional radiology (10Patel H.D. Johnson M.H. Pierorazio P.M. Sozio S.M. Sharma R. Iyoha E. et al.Diagnostic accuracy and risks of biopsy in the diagnosis of a renal mass suspicious for localized renal cell carcinoma: systematic review of the literature.J. Urol. 2016; 195: 1340-1347Crossref PubMed Scopus (1) Google Scholar, 11Corapi K.M. Chen J.L.T. Balk E.M. Gordon C.E. Bleeding complications of native kidney biopsy: a systematic review and meta-analysis.Am. J. Kidney Dis. 2012; 60: 62-73Abstract Full Text Full Text PDF PubMed Scopus (296) Google Scholar, 12Tøndel C. Vikse B.E. Bostad L. Svarstad E. Safety and complications of percutaneous kidney biopsies in 715 children and 8573 adults in Norway 1988-2010.Clin. J. Am. Soc. Nephrol. 2012; 7: 1591-1597Crossref PubMed Scopus (197) Google Scholar, 13Andersen M.F.B. Norus T.P. Tumor seeding with renal cell carcinoma after renal biopsy.Urol. Case Rep. 2016; 9: 43-44Crossref PubMed Scopus (30) Google Scholar, 14Vogel C. Ziegelmüller B. Ljungberg B. Bensalah K. Bex A. Canfield S. et al.Imaging in suspected renal-cell carcinoma: systematic review.Clin. Genitourin. Cancer. 2019; 17: e345-e355Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar). Although recently renal mass biopsy has improved and utilization has increased, having an alternative office-based test that could predict tumor type, aggressiveness, and the need for surgical intervention while obviating the need for biopsy has been the elusive “holy grail” for the practicing urologist (8Ljungberg B. Bensalah K. Canfield S. Dabestani S. Hofmann F. Hora M. et al.EAU guidelines on renal cell carcinoma: 2014 update.Eur. Urol. 2015; 67: 913-924Abstract Full Text Full Text PDF PubMed Scopus (1901) Google Scholar, 15Sircar K. Rao P. Jonasch E. Monzon F.A. Tamboli P. Contemporary approach to diagnosis and classification of renal cell carcinoma with mixed histologic features.Chin. J. Cancer. 2013; 32: 303-311Crossref PubMed Scopus (16) Google Scholar). Considering that most of the identified tumors are low-grade, the alternative approach could allow the urologists to make decisions on a case-by-case basis depending on the grades of cancer, which might require active surveillance and different therapeutic strategies instead of surgical procedures. Considering the limitations of current approaches, it is necessary to develop a novel diagnostic technique for early intervention of RCC. Therefore, early diagnosis and identification of RCC subtypes and tumor grades are essential to provide proper and effective treatment to increase the survival rate of patients. Recent studies suggest that extracellular vesicles (EVs) found in biofluids, such as urine, plasma, and saliva, can be a promising source for disease diagnosis (16Nilsson J. Skog J. Nordstrand A. Baranov V. Mincheva-Nilsson L. Breakefield X.O. et al.Prostate cancer-derived urine exosomes: a novel approach to biomarkers for prostate cancer.Br. J. Cancer. 2009; 100: 1603-1607Crossref PubMed Scopus (619) Google Scholar, 17Melo S.A. Luecke L.B. Kahlert C. Fernandez A.F. Gammon S.T. Kaye J. et al.Glypican-1 identifies cancer exosomes and detects early pancreatic cancer.Nature. 2015; 523: 177-182Crossref PubMed Scopus (1998) Google Scholar, 18Sun Y. Huo C. Qiao Z. Shang Z. Uzzaman A. Liu S. et al.Comparative proteomic analysis of exosomes and microvesicles in human saliva for lung cancer.J. Proteome Res. 2018; 17: 1101-1107Crossref PubMed Scopus (110) Google Scholar). As shown in Figure 1A, EVs (e.g., exosomes and microvesicles) are membrane-covered particles containing bioactive molecules such as RNA, DNA, proteins, and lipids secreted by all types of cells that are crucial for cell-to-cell communications (19Abels E.R. Breakefield X.O. Introduction to extracellular vesicles: biogenesis, RNA cargo selection, content, release, and uptake.Cell Mol. Neurobiol. 2016; 36: 301-312Crossref PubMed Scopus (942) Google Scholar, 20van Niel G. D’Angelo G. Raposo G. Shedding light on the cell biology of extracellular vesicles.Nat. Rev. Mol. Cell Biol. 2018; 19: 213-228Crossref PubMed Scopus (4006) Google Scholar). Exosomes are nanoscale vesicles ranging from 30 to 120 nm with spherical or cup-like morphology, whereas microvesicles are irregular in shape and tend to be larger with a wide range of sizes up to approximately 1500 nm (21Xu R. Greening D.W. Zhu H.-J. Takahashi N. Simpson R.J. Extracellular vesicle isolation and characterization: toward clinical application.J. Clin. Invest. 2016; 126: 1152-1162Crossref PubMed Scopus (576) Google Scholar, 22Kalra H. Drummen G.P.C. Mathivanan S. Focus on extracellular vesicles: introducing the next small big thing.Int. J. Mol. Sci. 2016; 17: 170Crossref PubMed Scopus (512) Google Scholar). EVs secreted by cancer cells can promote cell growth and survival, shape the tumor microenvironment, and increase metastatic activities (23Chang W.H. Cerione R.A. Antonyak M.A. Extracellular Vesicles and Their Roles in Cancer Progression.in: Methods Mol. Biol.2174. Humana Press Inc, Totowa, NJ2021: 143-170Crossref Scopus (58) Google Scholar). Furthermore, EVs produced by cancer cells function as key mediators of cancer cell signaling and communication, causing adjacent or distant healthy cells to respond with phenotypic changes, which promote multiple aspects of tumor progression (23Chang W.H. Cerione R.A. Antonyak M.A. Extracellular Vesicles and Their Roles in Cancer Progression.in: Methods Mol. Biol.2174. Humana Press Inc, Totowa, NJ2021: 143-170Crossref Scopus (58) Google Scholar, 24Maacha S. Bhat A.A. Jimenez L. Raza A. Haris M. Uddin S. et al.Extracellular vesicles-mediated intercellular communication: roles in the tumor microenvironment and anti-cancer drug resistance.Mol. Cancer. 2019; 18: 55Crossref PubMed Scopus (242) Google Scholar). In addition, EVs are stably present in different body fluids, such as plasma and urine, which offer a useful and promising resource for non-invasive cancer biomarker discovery. In other words, EVs reflect the current disease state of cells by carrying bioactive components that can potentially be used as early biomarkers of RCC. Phosphoproteins in EVs offer valuable surrogates for monitoring disease states, as phosphorylation is a key regulator of proteins involved in different cellular functions such as cell growth, differentiation, and apoptosis (25Singh V. Ram M. Kumar R. Prasad R. Roy B.K. Singh K.K. Phosphorylation: implications in cancer.Protein J. 2017; 36: 1-6Crossref PubMed Scopus (160) Google Scholar). Alterations in phosphorylation pathways are often associated with devastating diseases such as cancer (25Singh V. Ram M. Kumar R. Prasad R. Roy B.K. Singh K.K. Phosphorylation: implications in cancer.Protein J. 2017; 36: 1-6Crossref PubMed Scopus (160) Google Scholar). Some well-known signaling pathways, such as MAPK, EGFR/HER, CDK, and Cadherin–catenin complex, are major cell cycle players and deregulation of their phosphorylation–dephosphorylation activity has been shown to lead to the formation of various types of cancers. Previous studies from our group have identified numerous EV phosphoproteins as potential disease markers in urine and plasma for patients with breast cancer, chronic kidney disease, kidney cancer, and Parkinson’s disease (26Chen I.H. Xue L. Hsu C.C. Paez J.S.P. Panb L. Andaluz H. et al.Phosphoproteins in extracellular vesicles as candidate markers for breast cancer.Proc. Natl. Acad. Sci. U. S. A. 2017; 114: 3175-3180Crossref PubMed Scopus (278) Google Scholar, 27Iliuk A. Wu X. Li L. Sun J. Hadisurya M. Boris R.S. et al.Plasma-derived extracellular vesicle phosphoproteomics through chemical affinity purification.J. Proteome Res. 2020; 19: 2563-2574Crossref PubMed Scopus (38) Google Scholar, 28Hadisurya M. Li L. Kuwaranancharoen K. Wu X. Lee Z.-C. Alcalay R.N. et al.Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define diagnostic and prognostic biosignatures for Parkinson’s Disease.medRxiv. 2022; ([preprint])https://doi.org/10.1101/2022.01.18.22269096Crossref Scopus (0) Google Scholar). A number of data-independent acquisition (DIA) methods have been introduced with increased coverage, sensitivity, and reproducibility compared to the more commonly used data-dependent acquisition (DDA) method and are powerful tools for disease biomarker discovery (29Pino L.K. Just S.C. MacCoss M.J. Searle B.C. Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries.Mol. Cell Proteomics. 2020; 19: 1088-1103Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar). Inspired by recent works by Searle et al. and Pino et al. (29Pino L.K. Just S.C. MacCoss M.J. Searle B.C. Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries.Mol. Cell Proteomics. 2020; 19: 1088-1103Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar, 30Searle B.C. Pino L.K. Egertson J.D. Ting Y.S. Lawrence R.T. MacLean B.X. et al.Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry.Nat. Commun. 2018; 9: 5128Crossref PubMed Scopus (217) Google Scholar), we made our efforts to adapt the DIA method to analyze urinary EV phosphoproteomics. This study aimed to present an EV phosphoproteomics approach based on optimized DIA by comparing different DIA strategies for data acquisition methods, such as several windowing schemes and forbidden zones where no precursor could possibly exist (31Frahm J.L. Howard B.E. Heber S. Muddiman D.C. Accessible proteomics space and its implications for peak capacity for zero-, one- and two-dimensional separations coupled with FT-ICR and TOF mass spectrometry.J. Mass Spectrom. 2006; 41: 281-288Crossref PubMed Scopus (49) Google Scholar), to profile phosphoprotein landscape in urinary EVs from patients with RCC and controls. A windowing scheme with windows edged by forbidden zones, designed to lessen the quadrupole transmission edge effects, maximizes the precursor ion transmission in the window range. As a result, there was a slight improvement in phosphopeptide identification. The windowing schemes were designed to be staggered, allowing peptide signal collection from multiple regions of the precursor space in the same MS2 and using the MS2 nearby in different precursor space regions to demultiplex signals specific to each region computationally. The demultiplexing during data processing separates the staggered precursor isolation windows into their effective parts, improves precursor selectivity by nearly a factor of 2, and eliminates potential noises (32Amodei D. Egertson J. MacLean B.X. Johnson R. Merrihew G.E. Keller A. et al.Improving precursor selectivity in data-independent acquisition using overlapping windows.J. Am. Soc. Mass Spectrom. 2019; 30: 669-684Crossref PubMed Scopus (64) Google Scholar). In addition, we also compared two different strategies for DIA library building, including gas-phase fractionated (GPF) DIA library and direct DIA (an implementation of a library-free DIA method; Biognosys AG) (33Bekker-Jensen D.B. Bernhardt O.M. Hogrebe A. Martinez-Val A. Verbeke L. Gandhi T. et al.Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries.Nat. Commun. 2020; 11: 787Crossref PubMed Scopus (160) Google Scholar). Searle et al. demonstrated that the GPF DIA library has the advantage of ensuring that the library is experiment-specific, always up-to-date, and accounts for variation across different instrument platforms without the need of doing offline fractionation for library generation (34Searle B.C. Swearingen K.E. Barnes C.A. Schmidt T. Gessulat S. Küster B. et al.Generating high quality libraries for DIA MS with empirically corrected peptide predictions.Nat. Commun. 2020; 11: 1548Crossref PubMed Scopus (109) Google Scholar). Moreover, the narrow window used by GPF-DIA provides parallel reaction monitoring quality data for every peptide inside the scanned range. On the other hand, direct DIA offers the benefit of performing DIA analysis without the need to build a library first. Taken together, we found that the 400 to 1100 m/z precursor range with GPF library was the most ideal for urinary EV phosphoprotein and phosphopeptide detection. After we optimized the DIA phosphoproteomics method for urinary EV samples, we carried out DIA phosphoproteomics in urinary EVs derived from 57 individuals with low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease (CKD), and healthy control (HC) to differentiate clear cell low-grade from CKD, controls, and clear cell high-grade RCC. We discovered that the GPF library provides the most comprehensive information for our clinical sample quantification. Some prominent cancer pathways, such as ErbB signaling, proteoglycans in cancer, renal cell carcinoma, and regulation of actin cytoskeleton, were found to be upregulated only in the high-grade clear cell RCC urinary EV samples. Furthermore, some phosphosites were uniquely upregulated in either clear cell low-grade or high-grade RCC. Several phosphosites known to be involved in RCC, such as PAK1 (T185) and BRAF (S365), were upregulated in clear cell high-grade. Moreover, the renal cell carcinoma pathway was only enhanced in clear cell high grade RCC. These findings offer opportunities for further exploration to develop novel EV phosphoprotein-based biomarkers and open the door for an effective early-stage clinical diagnosis for clear cell RCC. Samples were collected at the Indiana University Simon Cancer Center and Methodist Research Institute in Indianapolis. The urine samples were collected on the same day right before the renal mass removal surgery. The collected urine samples were aliquoted into two or more cryotubes (5 ml) and stored in a −80 °C freezer. After the surgery, the obtained tumor tissue samples were used for immunohistochemistry analysis to diagnose the cancer subtypes and the grade based on the WHO/ISUP grading system for RCC (35Moch H. Cubilla A.L. Humphrey P.A. Reuter V.E. Ulbright T.M. The 2016 WHO classification of tumours of the urinary system and male genital organs—Part A: renal, penile, and testicular tumours.Eur. Urol. 2016; 70: 93-105Abstract Full Text Full Text PDF PubMed Scopus (1771) Google Scholar). We have collected diverse RCC subtypes; however, we only utilized the low-grade and high-grade clear cell RCC for this study. Some samples were collected and used for the method optimization. We also collected urine samples from non-cancer healthy individuals (HC) and patients with chronic kidney disease (CKD). Here, we were also interested in investigating whether CKD samples could serve as a better control group than HC group when they were utilized as a control group for clear cell low-grade and clear cell high-grade RCC differentiation. In total, we processed 60 samples (15 HC, 15 CKD, 15 clear cell low-grade, 15 clear cell high-grade) (See supplemental Data S1 for complete clinical characteristics) chosen randomly from a larger sample cohort. All samples were collected under IRB approved protocol no. 1011004282, Development of a Biorepository for IU Health Enterprise Clinical Research Operations from Indiana Biobank. The study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki. All 60 samples were processed separately by implementing the statistical principles in experimental designs, including replication, randomization, and blocking when applicable (36Oberg A.L. Vitek O. Statistical design of quantitative mass spectrometry-based proteomic experiments.J. Proteome Res. 2009; 8: 2144-2156Crossref PubMed Scopus (200) Google Scholar). The EVtrap beads were provided by Tymora Analytical Operations and were used as described previously (37Wu X. Li L. Iliuk A. Tao W.A. Highly efficient phosphoproteome capture and analysis from urinary extracellular vesicles.J. Proteome Res. 2018; 17: 3308-3316Crossref PubMed Scopus (44) Google Scholar). EVtrap, magnetic beads with functionalized lipophilic and hydrophilic groups, binds to the lipid bilayer membranes of EVs that enables fast and reproducible EV capture from urine samples. The 60 urine samples (approximately 10–15 ml each) and the sample pool (60 samples combined for a total volume of 180 ml) were centrifuged at 2500g for 10 min and frozen at −80 °C. After thawing, the sample pool was split into 6 × 50 ml tubes (30 ml in each tube) to facilitate the EVtrap incubation. Magnetic EVtrap beads were added to each urine sample at the ratio of 20 μl beads per 1 ml of urine. The samples, including the sample pool, were then incubated for 1 h by end-over-end rotation, allowing for ample movement of the beads. Afterward, the solution was removed with the aid of a magnetic separator. After three washes with PBS, the beads were incubated two times for 10 min by shaking with fresh 100 mM triethylamine to elute the EVs. After that, the eluted sample containing the EVs was collected, combined, and dried in a vacuum centrifuge. The extracellular vesicles were solubilized with the help of a Phase Transfer Surfactant (PTS) buffer (38Masuda T. Saito N. Tomita M. Ishihama Y. Unbiased quantitation of Escherichia coli membrane proteome using phase transfer surfactants.Mol. Cell Proteomics. 2009; 8: 2770-2777Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar, 39Masuda T. Sugiyama N. Tomita M. Ishihama Y. Microscale phosphoproteome analysis of 10 000 cells from human cancer cell lines.Anal. Chem. 2011; 83: 7698-7703Crossref PubMed Scopus (63) Google Scholar). The PTS buffer included 0.5% (w/v)/12 mM sodium deoxycholate (SDC), 0.35% (w/v)/12 mM sodium lauroyl sarcosinate, and 50 mM Tris-Cl, 10 mM tris-(2-carboxyethyl)phosphine to assist reduction, 40 mM chloroacetamide to help alkylation, and phosphatase inhibitor to protect the phosphoproteins and phosphopeptides from phosphatases. 100 μl of PTS was added to each sample. The samples were boiled for 10 min at 95 °C in the dark and were diluted fivefold using 50 mM TEAB for digestion. Then, a BCA assay was performed to identify the amounts of proteins per sample. Lys-C was added at the fixed 1:50 w/w ratio, and samples were digested at 37 °C for 3 h in a shaker at 1100 rpm. Afterward, trypsin was added at the fixed ratio of 1 μg per 50 μg protein per sample, and samples were digested overnight at 37 °C. Each sample was acidified with 50 μl of 10% trifluoroacetic acid (TFA) solution to adjust its final concentration to 1% TFA. 650 μl of ethyl acetate was added to the sample and vortexed for 2 min to precipitate the detergents completely. The samples were then centrifuged at 20,000g for 3 min to differentiate the aqueous and organic phases. The upper layer (organic phase) was removed, and the samples were dried for about 1.5 h until less than 150 μl remained. Another 1 ml of ethyl acetate was added to the samples, and the previous steps of vortexing and centrifugation were repeated. This time, the samples were dried completely. TopTip C-18 (10–200 μl) (Glygen, Part number: TT2C18.96) tips were used to desalt the samples according to the manufacturer’s instructions. Protein concentration was checked using a Pierce Colorimetry peptide concentration assay kit, normalized for the same amount of peptides for each sample, and dried completely. The sample pool utilized 50 mg Sep-Pak columns (Waters) for desalting according to the manufacturer’s instructions. After the concentration examinations using the Pierce Colorimetry peptide concentration assay, the appropriate peptide concentration for the GPF library (seven injections) was placed into several tubes (for PolyMAC preparation) and dried completely. Each sample, including the pooled samples, was subjected to phosphopeptide enrichment using the PolyMAC Phosphopeptide Enrichment kit (Tymora Analytical) according to the manufacturer's instructions (40Iliuk A.B. Martin V.A. Alicie B.M. Geahlen R.L. Tao W.A. In-depth analyses of kinase-dependent tyrosine phosphoproteomes based on metal ion-functionalized soluble nanopolymers.Mol. Cell Proteomics. 2010; 9: 2162-2172Abstract Full Text Full Text PDF PubMed Scopus (142) Google Scholar). The eluted phosphopeptides were dried completely in a vacuum centrifuge. The phosphoproteomic samples were spiked with an 11-peptide indexed Retention Time internal-standard mixture (Biognosys) to normalize the LC-MS signal between the samples. All samples were captured on a 2-cm Acclaim PepMap trap column and separated on a heated 50-cm Acclaim PepMap column (Thermo Fisher Scientific) containing C18 resin. The mobile phase buffer consisted of 0.1% formic acid in HPLC grade water (buffer A) with an eluting buffer containing 0.1% formic acid in 80% (vol/vol) acetonitrile (buffer B) run with a linear 85-min gradient of 5 to 35% buffer B at a flow rate of 300 nl/min. The UHPLC was coupled online with a Q-Exactive HF-X mass spectrometer (Thermo Fisher Scientific, see supplemental Data S2 for instrument settings). For DDA experiments, the mass spectrometer was run in the data-dependent mode, in which a full-scan MS (from m/z 375–1500 with the resolution of 60,000) was followed by MS/MS of the 15 most intense ions (30,000 resolution; normalized collision energy - 28%; automatic gain control target (AGC) - 2E4, maximum injection time - 200 ms; 60s exclusion). For DIA experiments, the mass spectrometer was run in the data-independent mode, in which a full-scan MS (polarity - positive; scan range 389.8–1109.8 m/z with the resolution of 60,000; automatic gain control target (AGC) - 1E6, maximum injection time - 60 ms; spectrum data type - centroid) was followed by MS/MS with 8.0 m/z staggered-isolation windows schemes as described in Searle et al. and Pino et al. (polarity - positive; 15,000 resolution; normalized collision energy - 27%; AGC - 1E6, maximum injection time - 20 ms; loop count - 88; spectrum data type - centroid) (29Pino L.K. Just S.C. MacCoss M.J. Searle B.C. Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries.Mol. Cell Proteomics. 2020; 19: 1088-1103Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar, 30Searle B.C. Pino L.K. Egertson J.D. Ting Y.S. Lawrence R.T. MacLean B.X. et al.Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry.Nat. Commun. 2018; 9: 5128Crossref PubMed Scopus (217) Google Scholar). The GPF spectra library was generated using Spectronaut Pulsar search (Biognosys, v15, Switzerland) according to Searle et al. and Pino et al. (29Pino L.K. Just S.C. MacCoss M.J. Searle B.C. Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries.Mol. Cell Proteomics. 2020; 19: 1088-1103Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar, 30Searle B.C. Pino L.K. Egertson J.D. Ting Y.S. Lawrence R.T. MacLean B.X. et al.Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry.Nat. Commun. 2018; 9: 5" @default.
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• W4361286446 title "Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation" @default.
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• W4361286446 doi "https://doi.org/10.1016/j.mcpro.2023.100536" @default.
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