PXD039543

PXD039543 is an original dataset announced via ProteomeXchange.

Title	Unified workflow for the rapid and in-depth characterization of bacterial proteomes
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
HostingRepository	MassIVE
AnnounceDate	2023-07-03
AnnouncementXML	Submission_2023-07-03_05:00:42.726.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Christina Ludwig
SpeciesList	scientific name: Bacillus cereus; NCBI TaxID: 1396; scientific name: Pseudomonas aeruginosa; NCBI TaxID: 287; scientific name: Anoxybacillus flavithermus; NCBI TaxID: 33934; scientific name: Bacillus subtilis; NCBI TaxID: 1423; scientific name: Escherichia coli; common name: E. coli; NCBI TaxID: 562; scientific name: Staphylococcus aureus; NCBI TaxID: 1280; scientific name: Acetobacter aceti; NCBI TaxID: 435; scientific name: Brevundimonas diminuta; NCBI TaxID: 293; scientific name: Burkholderia cepacia; NCBI TaxID: 292; scientific name: Clostridium sporogenes; NCBI TaxID: 1509; scientific name: Corynebacterium glutamicum; NCBI TaxID: 1718; scientific name: Cutibacterium acnes; NCBI TaxID: 1747; scientific name: Deinococcus radiodurans; NCBI TaxID: 1299; scientific name: Enterococcus faecalis; NCBI TaxID: 1351; scientific name: Lactobacillus delbrueckii; NCBI TaxID: 1584; scientific name: Listeria monocytogenes; NCBI TaxID: 1639; scientific name: Micrococcus luteus; NCBI TaxID: 1270; scientific name: Mycolicibacterium smegmatis; NCBI TaxID: 1772; scientific name: Prevotella melaninogenica; NCBI TaxID: 28132; scientific name: Sphingobacterium spiritivorum; NCBI TaxID: 258; scientific name: Vibrio proteolyticus; NCBI TaxID: 671; scientific name: Bifidobacterium bifidum; NCBI TaxID: 1681; scientific name: Streptococcus mitis; NCBI TaxID: 28037;
ModificationList	Acetyl; Oxidation
Instrument	Orbitrap Exploris 480; Orbitrap Fusion Lumos; Q Exactive HF-X

Revision	Datetime	Status	ChangeLog Entry
0	2023-01-18 09:18:10	ID requested
⏵ 1	2023-07-03 05:00:43	announced

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, 22(8):100612(2023) [pubmed]

submitter keyword: proteomics, bacteria, global proteome profiling, DDA, DIA, microflow, nanoflow

Christina Ludwig
contact affiliation	BayBioMS, Technical University of Munich
contact email	tina.ludwig@tum.de
lab head
Christina Ludwig
contact affiliation	BayBioMS, Technical University of Munich (TUM)
contact email	tina.ludwig@tum.de
dataset submitter

MassIVE dataset URI

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