Author

C. Blumenscheit

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 3 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Assay:proteomics· 3 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Assay:metagenomics· 1

Frequent Co-Authors

A. Schneider· 3 shared P. Lasch· 3 shared J. Doellinger· 2 shared A. Viehweger· 1 shared B. König· 1 shared C. Brandt· 1 shared C. John· 1 shared C. Lübbert· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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3 shared papersMicrobiology

A. Schneider

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4 papers

Recent·Most cited

Increasing proteome depth while maintaining quantitative precision in short gradient data-independent acquisition proteomics

J. Doellinger, C. Blumenscheit, A. Schneider, P. Lasch

This paper shows that reducing data points per peak in short gradient DIA-MS increases proteome depth while maintaining quantitative precision.

bioRxiv · Sep 12, 2022 · PreprintBiochemistry

Context-aware genomic surveillance reveals hidden transmission of a carbapenemase-producing Klebsiella pneumoniae

A. Viehweger, C. Blumenscheit, N. Lippmann, K. L. Wyres, C. Brandt, J. B. Hans, M. Hölzer, L. Irber, S. Gatermann, C. Lübbert, M. Pletz, K. E. Holt, B. König

The paper identifies hidden transmission of a carbapenemase-producing Klebsiella pneumoniae strain using genomic and metagenomic data from multiple studies.

bioRxiv · Jun 7, 2021Microbiology

Unbiased antimicrobial resistance detection from clinical bacterial isolates using proteomics

C. Blumenscheit, Y. Pfeifer, G. Werner, C. John, A. Schneider, P. Lasch, D. Joerg

This study presents an unbiased proteomics workflow for detecting bacterial species and antimicrobial resistance proteins from clinical isolates in under 4 hours with high sensitivity and specificity.

bioRxiv · Nov 17, 2020 · PreprintMicrobiology

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data

P. Lasch, A. Schneider, C. Blumenscheit, J. Doellinger

This study presents a rapid, accurate, and automatable method for microbial identification using LC-MS1 data and an in silico peptide mass library.

bioRxiv · Dec 10, 2019Microbiology