Dataset Information

Deep and fast label-free Dynamic Organellar Mapping - DDA data

ABSTRACT: The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. This repository contains all DDA-LFQ datasets used in our work: A reference dataset acquired in single shot on 100 min gradients and three fractionated datasets providing measured libraries for DIA searches on 100 min, 44 min and 21 min gradients.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

SUBMITTER: Julia Schessner

LAB HEAD: Georg H.H.

PROVIDER: PXD034962 | Pride | 2023-08-25

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	100minbenchmark.zip	Other
	100minbenchmark_reprocessed.zip	Other
	100minlibrary.zip	Other
	20210225_EXPL1_ViAl_SA_HeLa_DDA100min_MAP1_12K.raw	Raw
	20210225_EXPL1_ViAl_SA_HeLa_DDA100min_MAP1_1K.raw	Raw

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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.

Dataset Information

Deep and fast label-free Dynamic Organellar Mapping - DDA data

Dataset's files

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