Spectronaut software

Manufactured by Biognosys

Sourced in Switzerland

Spectronaut is a software platform developed by Biognosys for the analysis of mass spectrometry data. It provides tools for the identification and quantification of proteins and peptides in complex biological samples.

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Lab products found in correlation

Irt kit, by Biognosys (2 mentions) Ultimate 3000 uplc system, by Thermo Fisher Scientific (1 mentions) Q exactive plus instrument, by Thermo Fisher Scientific (1 mentions) Tripletof 5600 instrument, by AB Sciex (1 mentions) Nanoacquity chromatographic system, by Waters Corporation (1 mentions) Orbitrap eclipse tribrid mass spectrometer, by Thermo Fisher Scientific (1 mentions) Markerview, by AB Sciex (1 mentions) Peakview, by AB Sciex (1 mentions) Total protein extraction kit, by Nanjing Jiancheng (1 mentions) Spectronaut pulsar x, by Biognosys (1 mentions) Proteome discoverer v2, by Thermo Fisher Scientific (1 mentions)

23 protocols using spectronaut software

Proteomic Analysis of CD8+ T Cells

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Cell samples for proteome analysis were prepared from CD8+ T cells of three donors, including the conditions non-treated (nt), mock electroporated, and BCL11B KO 22 days after activation with TransAct. Whole protein was extracted, protein concentration was determined, and peptide solutions were prepared by an SP3 bead-based protocol^{26 (link)} and digestion with trypsin. LC-MS/MS analysis was performed on the peptides in data-independent acquisition (DIA) mode using an Ultimate 3000 UPLC system coupled to a QExactive Plus instrument (Thermo Scientific, USA). Further details on protein preparation and LC-MS/MS analysis can be found in Supplementary Table 1, and data acquisition details are compiled in Supplementary Table 3.
Analysis of mass spectrometric raw data was carried out using Spectronaut software (v14.9., Biognosys, Germany). Statistical data analysis was conducted using an in-house developed R tool. Further details on quantitation algorithms are provided in Supplementary Table 3 and on raw data analysis and statistical analysis in Supplementary Table 1.
Finally, differential abundant proteins (absolute fold change ≥ 1.5 and p ≤ 0.05) were identified by the statistical analysis using the ROPECA algorithm^{27 (link)} applied on peptide level.

Forkel H., Grabarczyk P., Depke M., Troschke-Meurer S., Simm S., Hammer E., Michalik S., Hentschker C., Corleis B., Loyal L., Zumpe M., Siebert N., Dorhoi A., Thiel A., Lode H., Völker U, & Schmidt C.A. (2022). BCL11B depletion induces the development of highly cytotoxic innate T cells out of IL-15 stimulated peripheral blood αβ CD8+ T cells. Oncoimmunology, 11(1), 2148850.

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Spectronaut-based DIA-MS Data Analysis

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The DIA-MS data were analyzed using the Spectronaut software (version 12; Biognosys, Schlieren, Schwitzerland) as described previously (22) . Due to the explorative nature of the study, no protein FDR on the identification level was applied. Quantification is based on the summed up abundances of proteotypic peptides with data filtering set to Qvalue required.
Abundance values were normalized based on retention time dependent local regression model (23) .

Petrera A., von Toerne C., Behler J., Huth C., Thorand B., Hilgendorff A, & Hauck S.M. (2021). Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling. Journal of proteome research, 20(1).

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Nano-LC-MS/MS Data Analysis

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For analysis of samples acquired by nano‐LC‐MS/MS in DIA mode, data were submitted to Spectronaut software (Biognosys) and analysed using a library‐based search. A spectral library created in‐house was used as library. Search and extraction settings were kept as default (BGS Factory settings). Human proteome data from UniProt (www.uniprot.org) with 20,374 entries were selected as the proteome background.
For reliable label‐free quantification, only proteins with ≥2 unique peptides were considered for further analysis. Subsequently, average normalized abundances (determined using Spectronaut) were calculated for each protein and used to determine the ratio between patient samples and corresponding controls. Finally, Student's t‐test p‐values were calculated for each protein using MS Excel.

Athamneh M., Daya N., Hentschel A., Gangfuss A., Ruck T., Marina A.D., Schara‐Schmidt U., Sickmann A., Güttsches A., Deschauer M., Preusse C., Vorgerd M, & Roos A. (2024). Proteomic studies in VWA1‐related neuromyopathy allowed new pathophysiological insights and the definition of blood biomarkers. Journal of Cellular and Molecular Medicine, 28(8), e18122.

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Quantitative Yeast Proteome Profiling

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For protein profiling, single colonies of respective yeast strains were grown to mid-log phase (OD₆₀₀ ~0.7) in selective medium containing 2 % glucose, harvested by centrifugation and snap-frozen in aliquots equalling 10 OD₆₀₀ units. Sample preparation was carried out as previously described ^{60 (link)}. SWATH LC-MS/MS analysis was performed essentially as previously described ^{60 (link)} on a TripleTOF5600 instrument (SCIEX) online coupled to a nanoACQUITY chromatographic system (Waters) operating at 3 μL/min flow rate. Data was analyzed with Spectronaut software (version 14, Biognosys AG) and post-processed in statistical language R. Principal component analysis was carried out using the unfiltered data set and a prcomp function. Protein fold change was calculated with reference to the wild type strain, and differential abundance was defined as a fold change > 1.5 and FDR-corrected p-value < 0.01. Pathway enrichment was performed in String-db v11 ^{111 (link)}, and Reactome pathways ^{112 (link)} were reported as significantly enriched if FDR-corrected enrichment p-value < 0.05.

Vowinckel J., Hartl J., Marx H., Kerick M., Runggatscher K., Keller M.A., Mülleder M., Day J., Weber M., Rinnerthaler M., Yu J.S., Aulakh S., Lehmann A., Mattanovich D., Timmermann B., Zhang N., Dunn C.D., MacRae J.I., Breitenbach M, & Ralser M. (2021). The metabolic growth limitations of petite cells lacking the mitochondrial genome. Nature metabolism, 3(11), 1521-1535.

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Label-free Quantitative Proteomics Analysis

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For the analysis of the samples acquired with nano-LC-MS/MS in DIA mode, the data were introduced to the Spectronaut software (Biognosys, Schlieren, Switzerland) and analyzed with a library-based search. For the library, an in house created spectral library was used. Search and extraction settings were kept as standard (BGS Factory settings). For the proteome background, the human proteome data were selected from UniProt (www.uniprot.org, accessed on 23 July 2018) containing 20,374 entries.
For reliable label-free quantification, only proteins identified with ≥2 unique peptides were considered for further analysis. Following this, the average normalized abundances (obtained by Spectronaut) were calculated for each protein and were used to determine the ratios between patient muscle samples with their respective controls. Lastly, for each protein, log2 transformation of the generated ratios and Student’s t-test p-values were calculated using MS Excel.

Braun F., Hentschel A., Sickmann A., Marteau T., Hertel S., Förster F., Prokisch H., Wagner M., Wortmann S., Della Marina A., Kölbel H., Roos A, & Schara-Schmidt U. (2021). Muscular and Molecular Pathology Associated with SPATA5 Deficiency in a Child with EHLMRS. International Journal of Molecular Sciences, 22(15), 7835.

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Protein Quantification by Spectronaut Software

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MS data (raw file) were processed
with Spectronaut software (Ver. 15.4.21 Biognosys, Schlieren, Switzerland).^{31 (link)} Database searching included all entries from
the Homo sapiens UniProt database (downloaded
in April 2020, taxonomy ID: 9606) and contaminant database.^{32 (link)} This database was concatenated with one composed
of all protein sequences in the reversed order. The search parameters
were as follows: up to two missed cleavage sites, 7–52 peptide
length, carbamidomethylation of cysteine residues (+57.021 Da) as
static modifications, acetylation of N-terminal residues and oxidation
of methionine residues as variable modifications, and protein names
from FASTA for implicit protein grouping and for quantification strategy.
Precursor ions were adjusted to a 1% false discovery rate. Max LFQ
was selected for the protein label-free quantitative method. Protein
quantitation values were exported for further analysis in Microsoft
Excel or Prism 9.

Muraoka S., Hirano M., Isoyama J., Ishida M., Tomonaga T, & Adachi J. (2022). Automated Proteomics Sample Preparation of Phosphatidylserine-Positive Extracellular Vesicles from Human Body Fluids. ACS Omega, 7(45), 41472-41479.

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SWATH-MS Data Analysis Pipeline

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The SWATH-MS data were obtained and analyzed using Skyline (Department of Genome Sciences, University of Washington) and Spectronaut software (Biognosys). In the SWATH acquisition mode, 20 isolation windows were used to cover all the precursors in the range of 400 to 800 m/z by SWATH mode. All raw files were screened using peptide length of 6 to 25 and imported for quantitation using reference spectral library. All detected peaks were reintegrated using mprophet peak scoring model, and the q-value annotation was added to each peak. The protein quantitation result was exported with MSstats for further bioinformatics analysis.

Wang C., Zhao R., Zhao Z., Liu N., Cheng J, & Guo M. (2023). Proteomic characterization and comparison of milk fat globule membrane proteins of Saanen goat milk from 3 habitats in China using SWATH-MS technique. Journal of dairy science, 106(4).

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Quantitative Proteomics Analysis Pipeline

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All raw mass spectrometric data were imported into Spectronaut software (Biognosys AG, Schlieren, Switzerland) to generate the in-house-developed spectral library. The proteomics data were searched against the UniProt human database appended with the iRT sequence. The parent ion tolerance and fragment ion tolerance were set to 10 ppm and 0.05 Da, respectively. Not more than two missed cleavage sites for trypsin digestion were allowed. Carbamidomethylation of cysteines was considered the fixed modification, and oxidation of methionine was set as the variable modification. Then, the DIA raw files were searched against the spectral library. The cross-run normalization was based on the local regression. The Q value was set to 0.01 for proteomics data filtering. The summed peak areas in MS2 were used for peptide quantification. The gene expression matrix files with an official gene symbol were obtained from the DIA platform after normalization for DEP analysis. The thresholds for DEP identification were an absolute log2(fold-change) (log FC) value of > 1 and an adjusted p value (q) of < 0.05. The UniProt database was used for gene symbol standardization [16 (link)].

Chen Z., Wu J., Wang W., Tang X., Zhou L., Lv Y, & Zheng Y. (2023). Investigation of the Pathogenic Mechanism of Ciprofloxacin in Aortic Aneurysm and Dissection by an Integrated Proteomics and Network Pharmacology Strategy. Journal of Clinical Medicine, 12(4), 1270.

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Relative Protein Quantification by Spectronaut

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The acquired data were imported into Spectronaut software (Biognosys). The human proteome data from UniProt (www.uniprot.org), containing 20,374 entries, were selected as the proteome background. Processing settings were as follows: enzyme was trypsin, minimum and maximum peptide length were set to 7 and 52, respectively, and missed cleavages were set to 2. Carbamidomethyl for cysteine was set as a fixed modification. Acetyl (protein N-term) and oxidation of methionine were set as variable modifications. All settings related to library generation including tolerances, identification, filters, iRT calibration, and workflow were left at factory defaults. For relative quantification, the Top N max 3 option was chosen, meaning that for each protein the average of the 3 peptides identified with the highest intensity was taken to calculate the quantitative value for that protein. The analyzed data were matched against an in-house spectral library [11 (link)] and protein identification was performed using the pulsar search engine included in Spectronaut.

Hentschel A., Meyer N., Kohlschmidt N., Groß C., Sickmann A., Schara-Schmidt U., Förster F., Töpf A., Christiansen J., Horvath R., Vorgerd M., Thompson R., Polavarapu K., Lochmüller H., Preusse C., Hannappel L., Schänzer A., Grüneboom A., Gangfuß A, & Roos A. (2023). A Homozygous PPP1R21 Splice Variant Associated with Severe Developmental Delay, Absence of Speech, and Muscle Weakness Leads to Activated Proteasome Function. Molecular Neurobiology, 60(5), 2602-2618.

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Quantitative Proteomics Workflow for Plasma

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DDA data were identified using MaxQuant (version 1.5.3.30) [64 (link)]. The reference database sequences were obtained from the UniProt Homo sapiens proteome database (172,419 sequences). The spectral library was built using peptide/protein entries that satisfied a false discovery rate (FDR) ≤ 1%. Carbamidomethyl (C) was set as a fixed modification, and oxidation (M) and acetyl (protein N-term) were set as variable modifications. DIA data were processed using Spectronaut software (Biognosys, https://biognosys.com/shop/spectronaut) [65 ] against the self-built plasma spectral library to achieve deeper proteome identification and quantification. The FDR was estimated using the mProphet scoring algorithm with 1% FDR control at the peptide-spectrum match, peptide, and protein levels. Next, the R package Msstats was used for log2 transformation, normalization, and p value calculation of the data [66 (link)]. Differentially expressed proteins (DEPs; p < 0.05 and fold-change ≥1.5 or p < 0.05 and fold-change <0.67) were identified for further analysis. The p values were then adjusted using the Benjamini-Hochberg correction (p adjust <0.05). The DEPs are listed in Supplementary Table S1.

Li H., Li X., Wu Q., Wang X., Qin Z., Wang Y., He Y., Wu Q., Li L, & Chen H. (2022). Plasma proteomic and metabolomic characterization of COVID-19 survivors 6 months after discharge. Cell Death & Disease, 13(3), 235.

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