Metabolic profiling of material-limited cell samples by dimethylaminophenacyl bromide derivatization with UPLC-MS/MS analysis

The ability to dissect the intracellular metabolome is vital in the study of diverse biological systems and models. However, limited cell availability is a challenge in metabolic profiling due to the low concentrations affecting the sensitivity. This is further exacerbated by modern technologies such as 3D microfluidic cell culture devices that provide a physiologically realistic environment, compared to traditional techniques such as cell culture in 2D well-plates. Attempts to address sensitivity issues have been made via advances in microscale separation such as CE and micro/nano-LC coupled to mass spectrometers with low-diameter ionization emitter sources. An alternative approach is sample derivatization, which improves the chromatographic separation, enhances the MS ionization, and promotes favourable fragmentation in terms of sensitivity and specificity. Although chemical derivatization is widely used for various applications, few derivatization methods allow sensitive analysis below 1 × 10 4 cells. Here, we conduct RPLC-MS/MS analysis of HepG2 cells ranging from 250 cells to 1 × 10 5 cells, after fast and accessible derivatization by dimethylaminophenacyl bromide (DmPABr), which labels the primary amine, secondary amine, thiol and carboxyl submetabolome, and also utilizes the isotope-coded derivatization (ICD). The analysis of 1 × 10 4 HepG2 cells accomplished quantification of 37 metabolites within 7-minute elution, and included amino acids, N-acetylated amino acids, acylcarntines, fatty acids and TCA cycle metabolites. The metabolic coverage includes commonly studied metabolites involved in the central carbon and energy-related metabolism, showing applicability in various applications and fields. The limit of detection of the method was below 20 nM for most amino acids, and sub 5 nM for the majority of N-acetylated amino acids and acylcarnitines. Good linearity was recorded for derivatized standards in a wide biological range representing expected metabolite levels in 2–10,000 cells. Intraday variability in 5 × 10 3 HepG2 cells was below 20% RSD for concentrations measured of all but two metabolites. The method sensitivity at the highest dilution of cell extract, 250 HepG2 cells, enabled the quantification of twelve metabolites and the detection of three additional metabolites below LLOQ. Where possible, performance parameters were compared to published methodologies that measure cell extract samples. The presented work shows a proof of concept for harnessing a derivatization method for sensitive analysis of material-limited biological samples. It offers an attractive tool with further potential for enhanced performance when coupled to low-material suitable technologies such as CE-MS and micro/nano LC-MS.