Issue 15, 2023

A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

Abstract

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS “barcodes” (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Graphical abstract: A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

Supplementary files

Article information

Article type
Paper
Submitted
19 Oct 2022
Accepted
10 May 2023
First published
04 Jul 2023

Lab Chip, 2023,23, 3361-3369

Author version available

A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

N. S. Ha, J. R. Onley, K. Deng, P. Andeer, B. P. Bowen, K. Gupta, P. W. Kim, N. Kuch, M. Kutschke, A. Parker, F. Song, B. Fox, P. D. Adams, M. de Raad and T. R. Northen, Lab Chip, 2023, 23, 3361 DOI: 10.1039/D2LC00980C

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