An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

Abstract

Introduction It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. Objectives Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines. Methods We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the ‘exometabolome’ or metabolic footprint). Results Our method measures some 4000–5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other. Conclusion Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

“…Experimental mass spectra were generated from pooled human serum or from chemical standards using an Orbitrap mass spectrometer and the methods described in 16,79 with standard settings, and were visualised using Compound Discoverer (Thermo).…”

“…We also produced a second completely independent test set of over 1000 separate molecules from ZINC 56M, fragmented as below, that were tested single-blind. Finally, we utilised experimental mass spectra generated as part of our current metabolomics studies 16,79 In silico fragmentation method…”

“…Throughout we assume that we have high-resolution mass spectral data with a precision of better than 5ppm, as is nowadays common (and is easily achieved in our own Orbitrap instruments 16 ). The present paper utilises only ES+ data.…”

“…Experimentally, commonly just 10% of molecules can be identified from what are reproducible spectral features in complex matrices such as serum (e.g. [14][15][16][17][18], despite the existence of many heuristics [19][20][21][22] . Solving the mass spectral molecular identification problem is thus seen widely as the key unsolved problem of metabolomics 4,11,17,[23][24][25][26][27][28][29][30][31][32][33][34][35][36] .…”

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

“…With untargeted metabolomics, huge numbers of peaks can be produced. Although some of the uncharacterized peaks can be correlated with cellular conditions or physiology conditions, including diseases [ 5 , 6 , 7 ], most studies have focused on analyzing characterized peaks, and uncharacterized peaks have been ignored [ 8 ]. This is because the biological interpretation of characterized peaks is much easier, and usually they have higher priority when the number of metabolites that can be investigated is limited.…”