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The human metabolome provides a direct physiological read-out of an individual's actual health state and includes biomarkers that may predict disease or response to a treatment. The discovery and validation of these metabolomic biomarkers requires large-scale cohort studies, typically involving thousands of samples. This analytical challenge drives novel technological developments to enable faster, cheaper, and more comprehensive metabolomic analysis: more for less. This review summarises recent (2012–2018) developments towards this goal in all aspects of the analytical workflow, in relation to NMR but primarily to mass spectrometry (MS). Recent trends include miniaturisation and automation of extraction techniques, online coupling to fast analysis methods including direct infusion ion mobility MS, integrated microfluidic devices, and sharing and standardizing metabolomics software and data. The technological advances in metabolomics support its widespread application, integration with other -omics fields, and ultimately disease prediction and precision medicine. • Overviews trends towards faster, cheaper and more comprehensive metabolomics. • Features automation and miniaturisation to increase throughput and reduce costs. • Reports progress in fast separations and sample introduction for mass spectrometry. • Describes emergence of open-source and independent, automated, data analysis tools. • Substantiates the increasing power of metabolomics as a tool in precision medicine. [ABSTRACT FROM AUTHOR]

Sample preparation is a challenge for high-throughput analysis, especially for volume-limited samples with low-abundant analytes. Ideally, sample preparation enriches the analytes of interest while removing the interferents to reduce the matrix effect and improve both sensitivity and quantification. In this study, a three-phase electroextraction (EE) method hyphenated to fast online liquid chromatography–mass spectrometry (LC-MS) was developed. Four model acidic drugs of relevance for drug monitoring in plasma, i.e. naproxen, fenoprofen, flurbiprofen, and ibuprofen, were utilized for the optimization and evaluation of the method. A Design of Experiment approach (Box-Behnken design) was used to optimize the critical parameters of the method, i.e., the type of organic solvent, pH of the sample and acceptor phase, and the extraction voltage and time. Good fitness (P < 0.02, R2 > 0.95) was observed for the developed quadratic model. Extraction could be achieved in less than 2 min (115 s) with enrichment factors (EF) up to 190 and extraction recoveries (ER) up to 38% for academic samples. Additionally, the optimized three-phase EE method was successfully applied to spiked plasma samples with low-abundant (50 ng mL−1) analytes and a low sample volume of 15 μL plasma in 10-fold diluted samples. Finally, two crucial contributors to the matrix effect of three-phase EE application on plasma samples were determined. Specifically, the ion-suppression effect in the MS source was reduced by the fast LC separation, and the matrix effect during extraction was negligible for the diluted protein-precipitated plasma samples. The developed three-phase EE method is easy to operate and provides fast and online extraction of trace-level acidic analytes from volume-limited biological samples. Therefore, this method can provide a potential solution for sample-preparation bottlenecks in high-throughput bioanalysis workflows. [Display omitted] • Fast extraction of acidic analytes, less than 2 min. • High enrichment factors, up to 190-fold. • Low sample consumption, down to 15 μL. • Hyphenation to fast LC-MS. • Limit of detection as low as 1.05 ng mL−1. [ABSTRACT FROM AUTHOR]

While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing. • Satellite resolution trade-offs hamper accurate surface melt detection in Antarctica. • We present UMelt: An Antarctic-wide, high-resolution surface melt record. • UMelt was developed by merging multi-source remote sensing data using deep learning. • UMelt detects intricate surface melt events that were difficult to capture before. [ABSTRACT FROM AUTHOR]

Sample preparation is often reported as the main bottleneck of analytical processes. To meet the requirements of both high-throughput and high sensitivity, improved sample-preparation methods capable of fast analyte preconcentration are urgently needed. To this end, a new three-phase electroextraction (EE) method is presented that allows for ultrafast electroextraction hyphenated to flow-injection analysis mass spectrometry (FIA-MS). Four model compounds, i.e., propranolol, amitriptyline, bupivacaine, and oxeladin, were used to optimize and evaluate the method. Within only 30 s extraction time, enrichment factors (EF) of 105–569 and extraction recoveries (ER) of 10.2%–55.7% were achieved for these analytes, with limits of detection (LODs) ranging from 0.36 to 3.21 ng mL−1, good linear response function (R2 > 0.99), low relative standard deviation (0.6%–17.8%) and acceptable accuracy (73–112%). Finally, the optimized three-phase EE method was successfully applied to human urine and plasma samples. Our three-phase electroextraction method is simple to construct and offers ultrafast, online extraction of trace amounts of analytes from biological samples, and therefore has great potential for high-throughput analysis. Image 1 • Ultrafast analyte extraction, down to 30 s. • High enrichment factors of up to 569-fold. • Direct hyphenation to MS. • Simple extraction setup. • Limit of detection as low as 360 pg/mL. [ABSTRACT FROM AUTHOR]

• Absence of stationary-phase material in 1D channel deteriorates analyte transfer. • Porous materials in both 1D and 2D lead to reduced band broadening. • Thin or no spacers in the 2D are preferred in terms of band broadening. • Fabrication imperfections of 3D-printed devices increase A-term dispersion. • Pressure resistance is affected by the thickness of the encasing of devices. Conventional one-dimensional column-based liquid chromatographic (LC) systems do not offer sufficient separation power for the analysis of complex mixtures. Column-based comprehensive two-dimensional liquid chromatography offers a higher separation power, yet suffers from instrumental complexity and long analysis times. Spatial two-dimensional liquid chromatography can be considered as an alternative to column-based approaches. The peak capacity of the system is ideally the product of the peak capacities of the two dimensions, yet the analysis time remains relatively short due to parallel second-dimension separations. Aspects affecting the separation efficiency of this type of systems include flow distribution to homogeneously distribute the mobile phase for the second-dimension (2D) separation, flow confinement during the first-dimension (1D) separation, and band-broadening effects during analyte transfer from the 1D separation channel to the 2D separation area. In this study, the synergy between computational fluid dynamics (CFD) simulations and rapid prototyping was exploited to address band broadening during the 2D development and analyte transfer from 1D to 2D. Microfluidic devices for spatial two-dimensional liquid chromatography were designed, simulated, 3D-printed and tested. The effects of presence and thickness of spacers in the 2D separation area were addressed and leaving these out proved to be the most efficient solution regarding band broadening reduction. The presence of a stationary-phase material in the 1D channel had a great effect on the analyte transfer from the 1D to the 2D and the resulting band broadening. Finally, pressure limit of the fabricated devices and printability are discussed. [ABSTRACT FROM AUTHOR]

Abstract: An experimental study was performed to investigate the effects of column parameters and gradient conditions on the separation of intact proteins using styrene-based monolithic columns. The effect of flow rate on peak width was investigated at constant gradient steepness by normalizing the gradient time for the column hold-up time. When operating the column at a temperature of 60°C a small C-term effect was observed in a flow rate range of 1–4μL/min. However, the C-term effect on peak width is not as strong as the decrease in peak width due to increasing flow rate. The peak capacity increased according to the square root of the column length. Decreasing the macropore size of the polymer monolith while maintaining the column length constant, resulted in an increase in peak capacity. A trade-off between peak capacity and total analysis time was made for 50, 100, and 250mm long monolithic columns and a microparticulate column packed with 5μm porous silica particles while operating at a flow rate of 2μL/min. The peak capacity per unit time of the 50mm long monolithic column with small pore size was superior when the total analysis time is below 120min, yielding a maximum peak capacity of 380. For more demanding separations the 250mm long monolith provided the highest peak capacity in the shortest possible time frame. [Copyright &y& Elsevier]