Proteome Coverage after Simultaneous Proteo-Metabolome Liquid–Liquid Extraction

Abstract

Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid−liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol−chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.

GEMCAT – A new algorithm for gene expression-based prediction of metabolic alterations

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Silybin A fromSilybum marianumreprograms lipid metabolism to induce a cell fate-dependent class switch from triglycerides to phospholipids

“…Phospholipids, TGs and fatty acids in the lower organic phase were evaporated to using an Acquity TM Ultraperformance LC system (Waters) as described before [195][196][197] . Alternatively, phospholipids and TGs were separated by an ExionLC™ AD UHPLC (Sciex, Framingham, MA, USA) [198][199][200] . In brief, phospholipids were analyzed at a flow rate of 0.75 ml/min at 45°C using acetonitrile/water (95/5) and 2 mM ammonium acetate as mobile phase A and water/acetonitrile (90/10) and 2 mM ammonium acetate as mobile phase B.…”

“…35 eV, and the collision cell exit potential to 26 V200 .Free fatty acids were analyzed by single ion monitoring in the negative ion mode193 and SM by multiple reaction monitoring in the positive ion mode based on the detection of the choline headgroup (m/z = 184) 193 . Absolute lipid quantities were normalized to internal standards and either cell number or tissue weight.…”

The MTORC1-AHR pathway sustains translation and autophagy in tumours under tryptophan stress

“…After 24 h, the cells were washed three times with PBS. For proteome extraction, a simultaneous proteo-metabolome liquid-liquid extraction was used (van Pijkeren et al, 2022). The cell metabolism was quenched by addition of 500 µL ice-cold methanol (Fisher Chemical, 10653963) and 500 µL MS-grade water (Millipore, Direct Water Purification System).…”