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Processing node 49: Detect Compounds
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1. General Settings:
- Mass Tolerance [ppm]: 3 ppm
- Min. Peak Intensity: 100000
- Min. Scans per Peak: 6
- Use Most Intense Isotope Only: True
2. Trace Detection:
- Max. Number of Gaps to Correct: 2
- Min. Number of Adjacent Non-Zeros: 2
3. Peak Detection:
- Chromatographic S/N Threshold: 3
- Remove Baseline: False
- Gap Ratio Threshold: 0.35
- Max. Peak Width [min]: 1
- Min. Relative Valley Depth: 0.1
4. Isotope Pattern Detection:
- Group Isotopes for: Br; Cl
- Use Peak Quality for Isotope Grouping: True
- Filter out Features with Bad Peaks Only: True
- Zig-Zag Index Threshold: 0.5
- Jaggedness Threshold: 0.5
- Modality Threshold: 0.9
- Remove Potentially False Positive Isotopes: True
5. Compound Detection:
- Ions: [M+H]+1; [M-H]-1
- Base Ions: [M+H]+1; [M-H]-1
- Remove Singlets: True
6. AcquireX Settings:
- Detect Persistent Background Ions: True
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Processing node 55: Assign Compound Annotations
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1. General Settings:
- Mass Tolerance: 5 ppm
2. Data Sources:
- Data Source 1: MassList Search
- Data Source 2: mzCloud Search (Compound Class, KEGG ID, mzCloud ID, mzCloud Library)
- Data Source 3: ChemSpider Search (CSID, SMILES, InChI Key)
- Data Source 4: Predicted Compositions
- Data Source 5: (not specified)
- Data Source 6: (not specified)
- Data Source 7: (not specified)
3. Scoring Rules:
- Use mzLogic: False
- Use Spectral Distance: True
- SFit Threshold: 20
- SFit Range: 20
4. Reprocessing:
- Clear Names: False
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Processing node 51: Search mzVault
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1. Search Settings:
- mzVault Library: mzVault_HILIC_neg.db|mzVault_HILIC_pos.db
- Max. Results: 10
- Match Factor Threshold: 50
- Search Algorithm: HighChem HighRes
- Match Analyzer Type: True
- IT Fragment Mass Tolerance: 0.4 Da
- FT Fragment Mass Tolerance: 10 ppm
- Use Retention Time: False
- Precursor Mass Tolerance: 3 ppm
- Apply Intensity Threshold: True
- Match Ionization Method: True
- Ion Activation Energy Tolerance: 20
- Match Ion Activation Energy: Any
- Match Ion Activation Type: False
- Compound Classes: All
- Remove Precursor Ion: False
- RT Tolerance [min]: 2
------------------------------------------------------------------
Processing node 58: Search mzCloud
------------------------------------------------------------------
1. General Settings:
- Compound Classes: All
- Precursor Mass Tolerance: 3 ppm
- FT Fragment Mass Tolerance: 10 ppm
- IT Fragment Mass Tolerance: 0.4 Da
- Library: Reference
- Post Processing: Recalibrated
- Max. Results: 10
- Annotate Matching Fragments: False
- Search MSn Tree: False
2. DDA Search:
- Identity Search: HighChem HighRes
- Match Activation Type: True
- Match Activation Energy: Match with Tolerance
- Activation Energy Tolerance: 20
- Apply Intensity Threshold: True
- Similarity Search: Similarity Forward
- Match Factor Threshold: 75
3. DIA Search:
- Use DIA Scans for Search: False
- Max. Isolation Width [Da]: 500
- Match Activation Type: False
- Match Activation Energy: Any
- Activation Energy Tolerance: 100
- Apply Intensity Threshold: False
- Match Factor Threshold: 20
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Processing node 50: Search Mass Lists
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1. Search Settings:
- Mass Lists: iHILIC_neg_2021.massList|iHILIC_pos_2021.massList
- Mass Tolerance: 3 ppm
- Use Retention Time: True
- RT Tolerance [min]: 0.5
------------------------------------------------------------------
Processing node 57: Search ChemSpider
------------------------------------------------------------------
1. Search Settings:
- Database(s): BioCyc; Human Metabolome Database; KEGG
- Search Mode: By Formula or Mass
- Mass Tolerance: 3 ppm
- Max. of results per compound: 100
- Max. of Predicted Compositions to be searched per Compound: 3
- Result Order (for Max. of results per compound): Order By Reference Count (DESC)
2. Predicted Composition Annotation:
- Check All Predicted Compositions: False
------------------------------------------------------------------
Processing node 40: Predict Compositions
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1. Prediction Settings:
- Mass Tolerance: 3 ppm
- Min. Element Counts: C H
- Max. Element Counts: C90 H190 Br3 Cl4 N10 O18 P3 S5
- Min. RDBE: 0
- Max. RDBE: 40
- Min. H/C: 0.1
- Max. H/C: 3.5
- Max. Candidates: 10
- Max. Internal Candidates: 200
2. Pattern Matching:
- Intensity Tolerance [%]: 30
- Intensity Threshold [%]: 0.1
- S/N Threshold: 3
- Min. Spectral Fit [%]: 30
- Min. Pattern Cov. [%]: 90
- Use Dynamic Recalibration: True
3. Fragments Matching:
- Use Fragments Matching: True
- Mass Tolerance: 5 ppm
- S/N Threshold: 3
Samples were randomly assigned into the autosampler, and metabolites were separated on an iHILIC®-(P) Classic HPLC column (HILICON AB, 100 x 2.1 mm; 5 µm; 200 Å, Sweden) with a flow rate of 100 µl/min delivered through an Ultimate 3000 HPLC system (Thermo Fisher Scientific, Germany). The stepwise gradient started at 90% A (ACN) and took 21 min to 60% B (25 mM ammonium bicarbonate) followed by 5 min hold at 80% B and subsequent equilibration phase at 90% A with a total run time of 35 min.
"],"publication":["Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma. 10.1038/s44321-026-00498-2. PMID:42581140"],"submitter_affiliation":["Medical University of Graz","Vienne BioCenter Core Facilities GmbH"],"submitter_name":["Gerlinde Grabmann","Gustav Jonsson"],"organism_part":["urine","Solvent"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Metabolites were extracted from each sample by mixing 20 μl of urine supernatants with 200 μl methanol. Samples were subsequently dried down in a vacuum centrifuge and resuspended in 0.1% formic acid. Creatinine levels were determined in a targeted LC-MS/MS experiment. Normalized to the amount of creatinine determined, another aliquot of each extracted sample was evaporated and resuspended in 130 μl ACN:H2O (80:20). Samples were then centrifuged at 4°C for 10 min at 16000 g and transferred to a glass HPLC vial. 2 μl of all samples were pooled and used as a quality control (QC) sample.
"],"organism":["solvent blank","Homo sapiens","Quality Control"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS12025"],"author":["Gustav Jonsson. Institute of Molecular Biotechnology. gustav.jonsson@medunigraz.at.","Gerlinde Grabmann. Vienna BioCenter Core Facilities. Dr. Bohr-Gasse 3, 1030 Vienna, Austria. gerlinde.grabmann@vbcf.ac.at."],"data_transformation_protocol":["The obtained data set was processed by “Compound Discoverer 3.3 SP2” (Thermo Fisher Scientific). Compounds were annotated through searching against our internal mass list database which was generated with authentic standard solutions. Additional compound annotation was conducted by searching the mzCloud database.
"],"study_factor":["Disease"],"submitter_email":["gerlinde.grabmann@vbcf.ac.at","gustav.jonsson@medunigraz.at"],"sample_collection_protocol":["The study was approved by the ethical commission at the Medical University of Vienna (EthikKommission Medizinische Universität Wien), study number 2224/2021, project title: Urinproteomik zur Validierung von Biomarkern für das klarzellige Nierenkarzinom – Pilotstudie (UrineProt).
Urine was collected from 40 patients who presented with a suspected primary renal mass at the Department of Urology of the Medical University of Vienna. Out of the 40 patients, 9 were excluded due to the renal mass being identified as something other than a renal cell carcinoma, such as oncocytomas, cysts, angiomyolipoma, papillary adenoma or a kidney-lodged metastasis. From the remaining 31 patients, 22 patients were characterized as ccRCC, 8 as pRCC and 1 as chromophobe RCC through histological assessment by a trained pathologist.
For metabolomics, the 22 ccRCC samples and 12 controls were used for downstream analysis.
"],"omics_type":["Metabolomics"],"study_design":["hydrophilic interaction chromatography","pooled quality control sample","Metabolomics","high-resolution mass spectrometry","urine","untargeted analysis","Thermo Scientific Dionex UltiMate 3000 RSLC System","solvent blank","Homo sapiens","Clear cell renal cell carcinoma","Compound Discoverer","sample preparation blank","experimental sample","untargeted metabolite profiling","Control","LC-MS","Thermo Scientific Q Exactive Focus","Solvent","clear cell renal carcinoma","Quality Control"],"curator_keywords":["hydrophilic interaction chromatography","Metabolomics","pooled quality control sample","high-resolution mass spectrometry","untargeted analysis","urine","Thermo Scientific Dionex UltiMate 3000 RSLC System","solvent blank","Homo sapiens","Clear cell renal cell carcinoma","Compound Discoverer","sample preparation blank","experimental sample","untargeted metabolite profiling","Control","LC-MS","Thermo Scientific Q Exactive Focus","Solvent","clear cell renal carcinoma","Quality Control"],"mass_spectrometry_protocol":["Sample spectra were acquired by a high-resolution tandem mass spectrometer (Q-Exactive Focus, Thermo Fisher Scientific, Germany) in full MS mode. Metabolites were ionized via electrospray ionization in polarity switching mode after Hydrophilic Interaction Liquid Chromatography (HILIC) separation. Ionization potential was set to +3.5/-3.0 kV, the sheet gas flow was set to 20, and an auxiliary gas flow of 5 was used. Samples were subjected to randomized analysis, flanked by a blank and a QC sample for background correction and data normalization, respectively, occurring after every set of 8 samples. QC samples were additionally measured in data-dependent and confirmation mode to obtain MS/MS spectra for identification.
"],"pubmed_abstract":["Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy. Yet, no rapid, non-invasive biomarkers are available for diagnosis or screening. Urine represents an ideal analyte matrix due to its accessibility, low invasiveness, longitudinal sampling, and the kidney's central role in filtration. Here, we integrated proteomic, lipidomic, and metabolomic analyses of urine from ccRCC patients and controls to identify diagnostic biomarkers. Multi-omics profiling revealed urogenital metabolic dysregulation in ccRCC, including increased lipid metabolism, altered mitochondrial respiration signatures, and elevated urinary lipid content. We identified three urinary protein biomarkers: serum amyloid A1 (SAA1), haptoglobin (HP), and lipocalin 15 (LCN15). Using a parallel reaction monitoring mass spectrometry workflow, we developed a rapid and sensitive assay and combined these markers into a diagnostic UrineScore. The UrineScore achieved 0.96 in an area under the receiver operating characteristic curve analysis in the discovery cohort, and 0.95 in an independent validation cohort. Together, these results support the feasibility of multi-omics-guided urinary biomarker discovery and represent a step toward accessible diagnostic platforms for ccRCC."],"pubmed_title":["Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma."],"pubmed_authors":["Jonsson Gustav G, Oliveira Tiago T, Hofmann Maura M, Lemberger Ursula U, Stejskal Karel K, Krššáková Gabriela G, Sakic Irma I, Novatchkova Maria M, Mereiter Stefan S, Grabmann Gerlinde G, Köcher Thomas T, Koglgruber Rubina R, Kikic Zeljko Z, Camano Páez Sonia S, Luna Sanchez Bárbara B, Díez Nicolás Víctor V, Rechberger Gerald N GN, Züllig Thomas T, Hagelkruys Astrid A, Englinger Bernhard B, Schmidinger Manuela M, Penninger Josef M JM"],"additional_accession":[]},"is_claimable":false,"name":"Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma","description":"Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy. Yet, no rapid, non-invasive biomarkers are available for diagnosis or screening. Urine represents an ideal analyte matrix due to its accessibility, low invasiveness, longitudinal sampling and the kidney’s central role in filtration. Here, we integrated proteomic, lipidomic, and metabolomic analyses of urine from ccRCC patients and controls to identify diagnostic biomarkers. Multi-omics profiling revealed urogenital metabolic dysregulation in ccRCC, including increased lipid metabolism, altered mitochondrial respiration signatures, and elevated urinary lipid content. We identified three urinary protein biomarkers: serum amyloid A1 (SAA1), haptoglobin (HP), and lipocalin 15 (LCN15). Using a parallel reaction monitoring mass spectrometry workflow, we developed a rapid and sensitive assay and combined these markers into a diagnostic UrineScore. The UrineScore achieved 96% accuracy in receiver operating characteristic analysis in the discovery cohort and 95% accuracy in an independent validation cohort. Together, these results support the feasibility of multi-omics-guided urinary biomarker discovery and represent a step toward accessible diagnostic platforms for ccRCC.
","dates":{"publication":"2026-08-16","submission":"2024-12-27"},"accession":"MTBLS12025","cross_references":{"pubmed":["42581140"]}}