MassIVE MSV000091088

Partial Public PXD039543

Unified workflow for the rapid and in-depth characterization of bacterial proteomes

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Description

Among the kingdoms of life, bacteria are by far the most abundant, but also the most diverse organisms. Due to this excessive diversity, finding a unified, comprehensive and safe workflow for quantitative bacterial proteomics is highly challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition and data analysis strategies in the context of bacterial proteomics. In order to mimic bacterial diversity, we investigated workflow performances on six representative species with highly different physiologic properties. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in solution digest. Subsequently, proteomes were acquired in a 30-minute microflow data-independent acquisition mass spectrometric measurement and analyzed with DIA-NN using a predicted spectral library ('library-free'). Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs and biological safety. With this rapid workflow, on average over 40% of all encoded genes could be detected per bacterial species. Finally, we demonstrate the general applicability of our workflow on a set of 23 taxonomically and physiologically bacterial species. In this dataset we could confidently identify over 30,000 bacterial proteins which have not been described before. Our work thereby provides a valuable resource for the microbial scientific community. The fast and biological safe proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom [doi:10.25345/C5VD6PF37] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: proteomics, bacteria, global proteome profiling, DDA, DIA, microflow, nanoflow

Contact

Principal Investigators:
(in alphabetical order) 		Christina Ludwig, BayBioMS, Technical University of Munich, Germany
Submitting User: ludwig_bbm

Publications

Abele M, Doll E, Bayer FP, Meng C, Lomp N, Neuhaus K, Scherer S, Kuster B, Ludwig C.
Unified workflow for the rapid and in-depth characterization of bacterial proteomes.
Mol Cell Proteomics. Epub 2023 Jun 28.

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Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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