Biognosys in Publications 2019 - Biognosys

Biognosys in Publications 2019

Publications by Biognosys and Collaborators

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris WHM, Hager J, Valsesia A, Dayon L, and Reiter L. Molecular & Cellular Proteomics

We have established a robust capillary-flow DIA platform capable of measuring a large number of clinical samples in a fast and reproducible manner. The platform allowed the acquisition of 31 plasma proteomes per day, and a total of 1508 samples from the dietary intervention study DiOGenes measured on a single column. Our workflow revealed distinct biological reactions to weight loss and maintenance, and the comparison to independent studies showed the robustness for potential biomarker discovery.


Surpassing 10 000 Identified and Quantified Proteins in a Single Run by Optimizing Current LC-MS Instrumentation and Data Analysis Strategy.

Muntel J, Gandhi T, Verbeke L, Bernhardt OM, Treiber T, Bruderer R, and Reiter L. Molecular Omics

In this study we showed how improvements in chromatography and data analysis can push the proteome coverage in human tissue beyond 10,000 proteins, spanning six orders of magnitude in dynamic range. Such remarkable coverage was achieved using Hybrid Libraries, a novel approach that combines the depth of resource libraries with the high precision of directDIA. Thousands of these proteins could be linked to the hallmarks of testis cancer.


Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time.

Muntel J, Kirkpatrick J, Bruderer R, Huang T, Vitek O, Ori A, and Reiter L. Journal of Proteome Research

Here we compare the performance of the two most popular quantitative workflows employed in discovery proteomics, label-free DIA, and isobaric-labeled TMT 10-plex. Workflows were tested on the same spike-in samples, with matched instrument time and reciprocal assessment in two facilities. Both workflows delivered high proteome coverage with low missing values at the protein level. While TMT performed slightly better in coverage and quantitative precision, DIA showed higher scalability and quantitative accuracy.


Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition.

Huang T, Bruderer R, Muntel J, Xuan Y, Vitek O, and Reiter L. Molecular & Cellular Proteomics

In this publication, we report a novel statistical procedure that takes advantage of the two independent quantitative spaces available in DIA data, i.e., MS1 and MS2, for the detection of differentially abundant proteins. The use of these two levels improves precision and reduces the effect of the interferences. This new approach was applied to six sets of controlled mixtures, showing a consistent improvement in the ability to find differential changes at the protein level.


Publications Mentioning Biognosys or their Technology and Tools


Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites.

Lundby A, Franciosa G, Emdal KB, Refsgaard JC, Gnosa SP, Bekker-Jensen DB, Secher A, Maurya SR, Paul I, Mendez BL, Kelstrup CD, Francavilla C, Kveiborg M, Montoya G, Jensen LJ, and Olsen J V. Cell

Researchers from the Olsen group developed an MS-based interaction proteomics approach to unravel the EGF-dependent phosphotyrosine signaling network in vivo. This approach is based on cutting-edge MS instrumentation and DIA, which together enabled the rapid and high-throughput screening of over a thousand phosphotyrosine sites. The authors showed how several cancer mutations near phosphotyrosine docking sites altered the assembly of protein-complexes and rewired signaling networks.


Sensitive Quantitative Proteomics of Human Hematopoietic Stem and Progenitor Cells by Data-independent Acquisition Mass Spectrometry.

Amon S, Meier-Abt F, Gillet LCJ, Dimitrieva S, Theocharides APA, Manz MG, and Aebersold R. Molecular & Cellular Proteomics

Researchers from the Aebersold group presented a robust proteomic workflow for the quantitative profiling of minute amounts of sorted cells. Following isolation by fluorescence-activated cell sorting, the authors applied DIA to reproducibly quantify the proteome of hematopoietic stem and progenitor cells. The subpopulations of hematopoietic cells were analyzed in sets of 25,000, and on average over 5,800 protein groups were covered, representing a relevant technical and scientific advance.


Prosit: Proteome-wide Prediction of Peptide Tandem Mass Spectra by Deep Learning.

Gessulat S, Schmidt T, Zolg DP, Samaras P, Schnatbaum K, Zerweck J, Knaute T, Rechenberger J, Delanghe B, Huhmer A, Reimer U, Ehrlich H-C, Aiche S, Kuster B, and Wilhelm M. Nature Methods

Researchers at the Technical University of Munich trained a deep neural network, termed Prosit, to predict spectra for proteases, generate high-quality spectral libraries for DIA, and improve the analysis of metaproteomes. Prosit could learn and predict the retention time and fragment ion intensity of any peptide and has proven to exceed the quality of the experimental data. This tool can generate custom spectral libraries for any organism just from peptide sequence information.


Lysine Harvesting is an Antioxidant Strategy and Triggers Underground Polyamine Metabolism.

Olin-Sandoval V, Yu JSL, Miller-Fleming L, Alam MT, Kamrad S, Correia-Melo C, Haas R, Segal J, Peña Navarro DA, Herrera-Dominguez L, Méndez-Lucio O, Vowinckel J, Mülleder M, and Ralser M. Nature


Lipid Signaling Drives Proteolytic Rewiring of Mitochondria by YME1L.

MacVicar T, Ohba Y, Nolte H, Mayer FC, Tatsuta T, Sprenger H-G, Lindner B, Zhao Y, Li J, Bruns C, Krüger M, Habich M, Riemer J, Schwarzer R, Pasparakis M, Henschke S, Brüning JC, Zamboni N, and Langer T. Nature


Abundance of Bacterial Type VI Secretion System Components Measured by Targeted Proteomics.

Lin L, Lezan E, Schmidt A, and Basler M. Nature Communications


Combining Rapid Data Independent Acquisition and CRISPR Gene Deletion for Studying Potential Protein Functions: A Case of HMGN1.

Mehnert M, Li W, Wu C, Salovska B, and Liu Y. Proteomics


Glyco-DIA: A Method for Quantitative O-glycoproteomics with in Silico-boosted Glycopeptide Libraries.

Ye Z, Mao Y, Clausen H, and Vakhrushev SY. Nature Methods


Dietary Sugars Alter Hepatic Fatty Acid Oxidation via Transcriptional and Post-translational Modifications of Mitochondrial Proteins.

Softic S, Meyer JG, Wang G-X, Gupta MK, Batista TM, Lauritzen HPMM, Fujisaka S, Serra D, Herrero L, Willoughby J, Fitzgerald K, Ilkayeva O, Newgard CB, Gibson BW, Schilling B, Cohen DE, and Kahn CR. Cell Metabolism


Comparison of Drosophila Melanogaster Embryo and Adult Proteome by SWATH-MS Reveals Differential Regulation of Protein Synthesis, Degradation Machinery, and Metabolism Modules.

Fabre B, Korona D, Lees JG, Lazar I, Livneh I, Brunet M, Orengo CA, Russell S, and Lilley KS. Journal of Proteome Research


Metastatic-Niche Labeling Reveals Parenchymal Cells with Stem Features.

Ombrato L, Nolan E, Kurelac I, Mavousian A, Bridgeman VL, Heinze I, Chakravarty P, Horswell S, Gonzalez-Gualda E, Matacchione G, Weston A, Kirkpatrick J, Husain E, Speirs V, Collinson L, Ori A, Lee J-H, and Malanchi I. Nature


Systematic Comparison of Strategies for the Enrichment of Lysosomes by Data Independent Acquisition.

Singh J, Kaade E, Muntel J, Bruderer R, Reiter L, Thelen M, and Winter D. Journal of Proteome Research


Plasma Proteome Profiling Discovers Novel Proteins Associated with Non-alcoholic Fatty Liver Disease.

Niu L, Geyer PE, Wewer Albrechtsen NJ, Gluud LL, Santos A, Doll S, Treit P V, Holst JJ, Knop FK, Vilsbøll T, Junker A, Sachs S, Stemmer K, Müller TD, Tschöp MH, Hofmann SM, and Mann M. Molecular Systems Biology


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