Analysis of 8000 proteins and reduced carry over significantly increase the throughput of single-shot proteomics

In the field of LC-MS based proteomics, increases in sampling depth and proteome coverage have mainly been accomplished by rapid advances in mass spectrometer technology. The comprehensiveness and quality of data that can be generated do however also depend on the performance provided by nano liquid chromatography (nanoLC) separations. Proper selection of reversed-phase separation columns can be of paramount importance to provide the MS instrument with peptides at the highest possible concentration and separated at the highest possible resolution. As an alternative to traditional packed bed LC column technology that uses beads packed into capillary tubing, we present a novel LC column format based on photolithographic definition and Deep Reactive Ion Etching (DRIE) into silicon wafers. With a next generation pillar array column designed for universal use in bottom-up proteomics, the critical dimensions of the stationary phase support structures have been reduced by a factor of 2 to provide further increases in separation power. To demonstrate the potential for single-shot proteomics workflows, we report on a series of optimization and benchmarking experiments where we combine LC separation on a new generation of pillar array columns using Vanquish Neo UHPLC with fast Orbitrap Tribrid MS data-dependent acquisition (DDA) and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS). In addition to providing superior proteome coverage, robust operation over more than 1 month with a single nanoESI emitter and reduction of the column related sample carry over are additional figures of merit that can help improve proteome research sensitivity, productivity and standardization.

Progressive Cellular Architecture in Microscale Gas Chromatography for Broad Chemical Analyses

Gas chromatography is widely used to identify and quantify volatile organic compounds for applications ranging from environmental monitoring to homeland security. We investigate a new architecture for microfabricated gas chromatography systems that can significantly improve the range, speed, and efficiency of such systems. By using a cellular approach, it performs a partial separation of analytes even as the sampling is being performed. The subsequent separation step is then rapidly performed within each cell. The cells, each of which contains a preconcentrator and separation column, are arranged in progression of retentiveness. While accommodating a wide range of analytes, this progressive cellular architecture (PCA) also provides a pathway to improving energy efficiency and lifetime by reducing the need for heating the separation columns. As a proof of concept, a three-cell subsystem (PCA3mv) has been built; it incorporates a number of microfabricated components, including preconcentrators, separation columns, valves, connectors, and a carrier gas filter. The preconcentrator and separation column of each cell are monolithically implemented as a single chip that has a footprint of 1.8 × 5.2 cm2. This subsystem also incorporates two manifold arrays of microfabricated valves, each of which has a footprint of 1.3 × 1.4 cm2. Operated together with a commercial flame ionization detector, the subsystem has been tested against polar and nonpolar analytes (including alkanes, alcohols, aromatics, and phosphonate esters) over a molecular weight range of 32–212 g/mol and a vapor pressure range of 0.005–231 mmHg. The separations require an average column temperature of 63–68 °C within a duration of 12 min, and provide separation resolutions >2 for any two homologues that differ by one methyl group.

Thermally-modulated cell separation columns using a thermoresponsive block copolymer brush as a packing material for the purification of mesenchymal stem cells

Cell therapy using mesenchymal stem cells (MSCs) is used as effective regenerative therapy. Cell therapy requires effective cell separation without cell modification and cell activity sustainment. In this study, we...

Machine and Deep Learning Approaches for Flooding Prevention in Distillation and Extraction Columns

Machine Learning (ML) algorithms can be combined with the modular automation protocol (MTP) and recognize the flooding behavior of laboratory fluids separation columns. Hence, artificial intelligence (AI) tools with deep learning (DL) offer a high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice. This article provides an overview on how artificial intelligence-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns and the system is adapted to the existing modular automation standard of the Module Type Package (MTP).