Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer

Muntel, Jan; Xuan, Yue; Berger, Sebastian Tobias; Reiter, Lukas; Bachur, Richard G.; Kentsis, Alex; Steen, Hanno

doi:10.1021/acs.jproteome.5b00826

Cited by 109 publications

(113 citation statements)

References 40 publications

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“…DDA is a sequential operation, analyzing one peptide at a time. However, under-sampling is a problem due to the molecular complexity and dynamic expression range of urine proteins [90]. An alternative method, similar to SRM, is data-independent acquisition (DIA) in which many peptides at a time are subjected to simultaneous fragmentation [18].…”

Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

“…have developed a data-independent acquisition (DIA) workflow in which a urinary spectral library is first created from a data-dependent acquisition (DDA) analysis of numerous urine samples using multiple MS/MS instruments [90]. In Muntel's study, urine from children diagnosed with ovarian cyst (n=12), urinary tract infection (n=11), and an abdominal pain control group (n=64) were analyzed by LC-MS/MS to create a spectral library [90]. A subset of these specimens (n=23) were analyzed in the same manner to create a second spectral library [90].…”

Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

“…In Muntel's study, urine from children diagnosed with ovarian cyst (n=12), urinary tract infection (n=11), and an abdominal pain control group (n=64) were analyzed by LC-MS/MS to create a spectral library [90]. A subset of these specimens (n=23) were analyzed in the same manner to create a second spectral library [90]. When combined with a publicly available human spectral library, a comprehensive spectral library of over 10,000 proteins was generated.…”

Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

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Current state of the art for enhancing urine biomarker discovery

Harpole

Davis

Espina

2016

Expert Review of Proteomics

109

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Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state. High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

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Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

Section: Advancements In Mass Spectrometry Based Urinary Proteomicsmentioning

confidence: 99%

See 1 more Smart Citation

Current state of the art for enhancing urine biomarker discovery

Harpole

Davis

Espina

2016

Expert Review of Proteomics

109

View full text Add to dashboard Cite

show abstract

“…Urine represents a cheap and easily available noninvasive specimen, a rich source of information about differentially expressed proteins, peptides and pathways depending on patients' mutated genotype as well as biomarkers of disease [15]. In 2001, Spahr et al [26] started mapping the urinary proteome identifying 124 proteins; the number of identified proteins grew to over 1,500 in 2006 when Adachi et al [27] performed an extensive proteomic analysis, up to more than 2,500 proteins when using prefractionation and combining the proteomic data from several urine specimens [15,28]. Gel-based or gel-free methods are suitable for urinary proteome analysis.…”

Section: Proteomic Strategies and Applications In Iemsmentioning

confidence: 99%

“…Excluding the strictly kidney-related pathologies, the systemic nature of urine has allowed to study the molecular basis and research urinary biomarkers for a large variety of diseases of different origin (in example appendicitis, prostate cancer, tuberculosis, and major depressive disorder) [28]. …”

Section: Proteomic Strategies and Applications In Iemsmentioning

confidence: 99%

Integration of Proteomics and Metabolomics in Exploring Genetic and Rare Metabolic Diseases

et al. 2017

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Background: Inherited metabolic disorders or inborn errors of metabolism are caused by deficiency of enzymatic activities in the catabolism of amino acids, carbohydrates, or lipids. These disorders include aminoacidopathies, urea cycle defects, organic acidemias, defects of oxidation of fatty acids, and lysosomal storage diseases. Inborn errors of metabolism constitute a significant proportion of genetic diseases, particularly in children; however, they are individually rare. Clinical phenotypes are very variable, some of them remain asymptomatic, others manifest metabolic decompensation in neonatal age, and others encompass mental retardation at later age. The clinical manifestation of these disorders can involve different organs and/or systems. Some disorders are easily managed if promptly diagnosed and treated, whereas in other cases neither diet, vitamin therapy, nor transplantation appears to prevent multi-organ impairment. Summary: Here, we discuss the principal challenges of metabolomics and proteomics in inherited metabolic disorders. We review the recent developments in mass spectrometry-based proteomic and metabolomic strategies. Mass spectrometry has become the most widely used platform in proteomics and metabolomics because of its ability to analyze a wide range of molecules, its optimal dynamic range, and great sensitivity. The fast measurement of a broad spectrum of metabolites in various body fluids, also collected in small samples like dried blood spots, have been facilitated by the use of mass spectrometry-based techniques. These approaches have enabled the timely diagnosis of inherited metabolic disorders, thereby facilitating early therapeutic intervention. Due to its analytical features, proteomics is suited for the basic investigation of inborn errors of metabolism. Modern approaches enable detailed functional characterization of the pathogenic biochemical processes, as achieved by quantification of proteins and identification of their regulatory chemical modifications. Key Message: Mass spectrometry-based “omics” approaches most frequently used to study the molecular mechanisms underlying inherited metabolic disorders pathophysiology are described.

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Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library

et al. 2023

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High‐grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5‐year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced‐stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high‐quality ovary‐specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan‐human spectral library (DPHL), this spectral library provides 10% more ovary‐specific and 3% more ovary‐enriched proteins. This library was then applied to analyze data‐independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six‐protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log‐rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary‐specific spectral library for targeted proteome analysis, and propose a six‐protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT‐IDS treatment.

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Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer

Cited by 109 publications

References 40 publications

Current state of the art for enhancing urine biomarker discovery

Current state of the art for enhancing urine biomarker discovery

Integration of Proteomics and Metabolomics in Exploring Genetic and Rare Metabolic Diseases

Resistance prediction in high‐grade serous ovarian carcinoma with neoadjuvant chemotherapy using data‐independent acquisition proteomics and an ovary‐specific spectral library

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