The future of healthcare requires innovations in evidence-based medicine that promote disease prevention and healthy living on a population level. Nutrition, including diet, lifestyle and environment, play critical roles in chronic disease risk. However, they are not modeled adequately by genetic markers, and are poorly assessed by self-referral questionnaires when evaluating thousands of possible causative exposures, including over 25,000 different compounds in various foods (1). Since metabolites are molecular endpoints of gene expression closely associated with clinical outcomes and environmental exposures, untargeted metabolite profiling (metabolomics) offers a novel approach to decipher the complex and poorly understood determinants of human health (2).

A major challenge in mass spectrometry (MS)-based metabolomics is the low sample throughput, high costs, and complicated data processing, particularly when coupled to conventional separation systems. In this case, major infrastructure investment is required for large-scale epidemiological studies, which are often performed massively in parallel across multiple instrumental platforms. Alternatively, direct infusion and flow injection techniques coupled to high-resolution MS can greatly enhance sample throughput with appropriate sample pretreatment (3).

However, high efficiency separations are still needed for resolution of isobars/isomers, and minimizing ion suppression/enhancement effects while supporting the identification of unknown metabolites of clinical importance (4). These features are also critical for reducing false discoveries, as well as improving method accuracy and robustness – especially when analyzing large numbers of samples from biorepositories that vary widely between subjects, such as urine. As a result, various strategies have been introduced to shorten analysis times and duty cycle, including new column technologies operating at high flow rates, ambient ionization methods for direct sampling and ultra-fast separations based on microfluidic devices and ion mobility spectrometry. Yet, is there a cost-effective approach to boost sample throughput in discovery metabolomics without sacrificing analytical performance, peak capacity and data fidelity?

Is there a cost-effective approach to boost sample throughput in discovery metabolomics without sacrificing analytical performance, peak capacity and data fidelity?

Our group has recently introduced a simple approach for multiplexing separations in metabolomics (5) based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). In this case, seven or more discrete segments of sample can be injected in series within a single capillary that enhances sample throughput up to one order of magnitude without compromising separation performance and data quality. Moreover, MSI-CE-MS encodes spectral information temporally via signal pattern recognition that allows for unambiguous peak picking, feature identification and noise filtering within an accelerated metabolomics data workflow (6). Ongoing work is now focused on evaluating the long-term performance of MSI-CE-MS in validating lifestyle interventions that promote health on an individual level, as well as markers for improved screening and diagnosis of human diseases (7). Thus, new advances in separation science play critical roles in expanding the performance MS-based chemical analyses that are no longer limited by long elution times while analyzing “one sample at a time”.