Electrospray and Nanoelectrospray Ionization-Mass Spectrometry Systems for the High-Throughput Analysis of Microfluidic Droplets
Year: 2019
Type: dissertation
Abstract: The field of microfluidics has seen notable growth in the past few decades, driven by potential advantages in sample processing such as reduced volumes, improved spatial and thermal control, and higher throughput than with conventional methods. Droplet microfluidics is a variation of microfluidics wherein liquid streams are segmented into discrete “droplet” samples by an immiscible carrier phase. This approach augments the benefits of conventional microfluidics by also promoting rapid internal mixing and boundaries to restrict molecular movement. Combining droplet microfluidics and mass spectrometry (MS) has created enabling platforms for studies in drug discovery, biocatalysis, and biochemistry. Described in this dissertation are new approaches for paring droplet microfluidics and MS. A system for robust nanoelectrospray ionization (nESI)-MS analysis of microfluidic droplets was developed, being the first system to demonstrate continuous analysis of 1000’s of droplets. Also achieved was the analysis of lower volumes (65 pL) and higher throughputs (10 droplets/s) than previously shown. Linear concentration-based responses with < 3% droplet-to-droplet carry-over were achieved, showing features crucial for quantitative analysis. Finally, amine transaminase (ATA)-117 enzymatic activity in droplets was observed, showing capability for applications like drug discovery and enzyme evolution. Droplet nESI-MS was then applied to neurochemical monitoring in vivo. Acetylcholine, glutamine, glutamate, and gamma aminobutyric acid were all detected from high-saline artificial cerebral spinal fluid (aCSF) sampling matrix in vitro with detection limits applicable for in vivo studies. The assay was then paired with a novel in vivo push-pull probe and droplet segmentation of sample streams, with < 30 s changes in neurochemical levels observed upon microinjection of high potassium aCSF. This overall system for monitoring neurochemical dynamics pushed the capabilities for in vivo sampling by combining high spatiotemporal sampling with MS analysis. The droplet nESI-MS method was also applied in the analysis of synthetic reactions as a screening approach for robust, high-throughput MS analysis that incorporated highly gentle analyte ionization. The reactivity of 17 different drug/drug-like substrates across 3 alkylation reactions was examined, showing the ability of nESI-MS to detect a variety of reaction products. Throughput as high as three droplets/s was achievable with < 10% droplet-to-droplet carry-over. Methods for overcoming variable analyte ionization between samples were successfully applied to droplet samples. A screen of 72 different conditions for the trifluoromethylation of caffeine was performed, with 19F-NMR validation of the top 5 conditions all showing > 20% product yield. Finally, ESI-MS and droplet microfluidics were applied to create a platform for the screening of flow reactions. Introduction of 4-7 nL sample droplets to a sheath sprayer allowed simultaneous dilution and ESI-MS analysis post-reaction with 10x higher throughput than conventional screening systems. Droplet samples were found to be stable for 1 hr under oscillatory flow patterns, enabling flow reactions to be performed over extended incubation times without observing sample diffusion into carrier streams. Addition of reagents to individual droplets enabled performing reactions with each sample only consuming ~3 nL of reagents, which is a marked improvement over conventional screening systems that consume µL volumes. As a whole, the work outlined in this thesis further develops the combination of droplet microfluidics and MS. The applications explored herein, namely enzymology, neurochemistry, photoredox catalysis, and synthetic flow chemistry not only show the capability of droplet-based MS, but also highlight the broad applicability of such systems.
Author: Daniel J. Steyer
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3129548862