Dataset Information

High-throughput mixed-species quantitative benchmark with varying sample amounts

ABSTRACT: Samples were generated by mixing commercially available tryptic digests (human: K562, HeLa; yeast (S.cerevisiae); E.coli) in defined proportions, to create a dataset with known ground truth, for benchmarking of the quantitative performance and scalability of DIA raw data processing software. Acquisition was performed in high throughput using the Evosep One system operated in microflow mode coupled to Bruker timsTOF Ultra in dia-PASEF mode. The experiment design is specified in the “Experiment_design.xlsx” file attached and achieves the following: - The human background is perturbed in a controlled manner, by mixing K562 and HeLa digests in defined proportions, to model the biological variation observed in applications. The E.coli digest within the samples serves for normalisation, whereas the yeast digest is added in different proportions relative to E.coli in different samples and therefore allows the evaluate the quantitative accuracy of the method. - The scale of the experiment allows to evaluate the effect of the number of samples on the quantitative performance of the software. - The experiment involves acquisitions with medium-high to very low load, modelling the rapidly emerging applications of high-sensitivity DIA proteomics.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human) Escherichia Coli Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Vadim Demichev

LAB HEAD: Vadim Demichev

PROVIDER: PXD055981 | Pride | 2025-12-09

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 15

Similar Datasets

Project description:Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance, but the source of such heterogeneity is often insufficiently understood. Exchange interactions between metabolite producing and consuming cells are believed to be one possible cause, but detecting metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), which monitors amino acid incorporation into peptides with multiple occurrences of the same amino acid. DILAC is used to differentiate producer and consumer cells of a particular amino acid within an isogenic cell population. We applied DILAC to young, morphologically undifferentiated yeast colonies and reveal that they contain sub-populations of lysine producers and consumers which emerge due to nutrient gradients. DILAC can deconvolute the proteome of subpopulations from bulk measurements which indicated an in situ cross feeding situation where fast growing cells ferment and provide the slower growing, respiring cells with ethanol as substrate. Finally, by combining DILAC with FACS, we show that the metabolic states that differ between isogenic cells, confer resistance to the antifungal drug amphotericin B. Overall, this novel and broadly applicable methodological approach captures previously unnoticed metabolic heterogeneity, providing experimental evidence for the role of metabolic specialisation and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations. This submission contains 8 raw files as well as the following additional files relating to Dataset 3: DIA-NN_pipeline_PRIDE.pipeline - The DIA-NN pipeline file containing all the settings to run the analysis. In the first step, a modified spectral library with the required fixed and variable isotope labels was predicted. In the second step, the data files are analysed; 20210225_yeastspeclib.tsv.speclib - The original spectral library; sSwath_yeastspeclib_C13K.predicted.speclib - The isotope-modified library; sSwath_yeastspeclib_C13K.predicted.ExcludeFromAssay.speclib.tsv - The isotope-modified library with unsuitable fragments excluded using the ExcludeFromAssay column; modify_library.ipynb - Jupyter notebook used to add ExcludeFromAssay column to library to filter suitable fragments for quantification; 20220428_Ex8_rerun_ExcludeFromAssay.tsv - The main DIA-NN report; hit_calling_PRIDE.ipynb - Jupyter notebook containing code for hit calling. This submission contains an additional 7 raw files as well as the following additional files relating to the spike-in experiment shown in Extended View 4 E-G.

			Action	DRS
	200SPD_DIA_075ng_Final.zip	Other
	200SPD_DIA_15ng.zip	Other
	200SPD_DIA_1_5ng.zip	Other
	200SPD_DIA_3ng.zip	Other
	500SPD_DIA_15ng.zip	Other

Dataset Information

High-throughput mixed-species quantitative benchmark with varying sample amounts

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets