Dataset Information

Evaluating the influence of MS-acquisition parameters on DDA label-free proteomics analyses

ABSTRACT: Label-free proteomics enables the unbiased quantification of thousands of proteins across large sample cohorts. Commonly used mass spectrometry-based proteomic workflows rely on data dependent acquisition (DDA). However, its stochastic selection of peptide features for fragmentation-based identification inevitably results in high rates of missing values, which prohibits the integration of larger cohorts as the number of recurrently detected peptides is a limiting factor. Peptide identity propagation (PIP) can mitigate this challenge, allowing to transfer sequencing information between samples. However, despite the promise of these approaches, current methods remain limited either in sensitivity or reliability and there is a lack of robust and widely applicable software. Here we prepared a tool spike-in data set which can be used to evaluate the influence of changing Top-N, gradient length and sample injection amounts on DDA label-free proteomics results. It also includes analysis by data-independent acquisition (DIA) which allows direct comparison of DDA and DIA for label-free proteomics analyses.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

TISSUE(S): Permanent Cell Line Cell

SUBMITTER: Mathias Kalxdorf

LAB HEAD: Jeroen Krijgsveld

PROVIDER: PXD019777 | Pride | 2021-07-10

REPOSITORIES: Pride

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Publications

IceR improves proteome coverage and data completeness in global and single-cell proteomics.

Kalxdorf Mathias M Müller Torsten T Stegle Oliver O Krijgsveld Jeroen J

Nature communications 20210809 1

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates simila ...[more]

PMID: 34373457

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Project description:The branched-chain amino acid (BCAA) pathway and high levels of BCAA transaminase 1 (BCAT1) have recently been associated with aggressiveness in several cancer entities. However, the mechanistic role of BCAT1 in this process remains largely uncertain. Here, by performing high-resolution proteomic analysis of human acute myeloid leukaemia (AML) stem-cell and non-stem-cell populations, we find the BCAA pathway enriched and BCAT1 protein and transcripts overexpressed in leukaemia stem cells. We show that BCAT1, which transfers α-amino groups from BCAAs to α-ketoglutarate (αKG), is a critical regulator of intracellular αKG homeostasis. Further to its role in the tricarboxylic acid cycle, αKG is an essential cofactor for αKG-dependent dioxygenases such as Egl-9 family hypoxia inducible factor 1 (EGLN1) and the ten-eleven translocation (TET) family of DNA demethylases. Knockdown of BCAT1 in leukaemia cells caused accumulation of αKG, leading to EGLN1-mediated HIF1α protein degradation. This resulted in a growth and survival defect and abrogated leukaemia-initiating potential. By contrast, overexpression of BCAT1 in leukaemia cells decreased intracellular αKG levels and caused DNA hypermethylation through altered TET activity. AML with high levels of BCAT1 (BCAT1high) displayed a DNA hypermethylation phenotype similar to cases carrying a mutant isocitrate dehydrogenase (IDHmut), in which TET2 is inhibited by the oncometabolite 2-hydroxyglutarate11,12. High levels of BCAT1 strongly correlate with shorter overall survival in IDHWTTET2WT, but not IDHmut or TET2mutAML. Gene sets characteristic for IDHmut AML13 were enriched in samples from patients with an IDHWTTET2WTBCAT1high status. BCAT1highAML showed robust enrichment for leukaemia stem-cell signatures14,15, and paired sample analysis showed a significant increase in BCAT1 levels upon disease relapse. In summary, by limiting intracellular αKG, BCAT1 links BCAA catabolism to HIF1α stability and regulation of the epigenomic landscape, mimicking the effects of IDH mutations. Our results suggest the BCAA–BCAT1–αKG pathway as a therapeutic target to compromise leukaemia stem-cell function in patients with IDHWTTET2WT AML.

Dataset Information

Evaluating the influence of MS-acquisition parameters on DDA label-free proteomics analyses

Publications

IceR improves proteome coverage and data completeness in global and single-cell proteomics.

Similar Datasets

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