Robust centrifugal microfluidic miniaturization and automation of target enrichment for protease substrate identification
Final Report Abstract
For large-scale analysis of complex protein mixtures, liquid chromatography – tandem mass spectrometry (LC-MS/MS) has been proven to be one of the most versatile tools due to its high sensitivity and ability to both identify and quantify thousands of proteins in a single measurement. Sample preparation typically comprises site-specific cleavage of proteins into peptides, followed by desalting and concomitant peptide enrichment, which is commonly performed by solid phase extraction. Desalting workflows may include multiple liquid handling steps and are thus error prone and labour intensive. To improve the reproducibility of sample preparation for low amounts of protein, we present a centrifugal microfluidic disk that automates all liquid handling steps required for peptide desalting by solid phase extraction (DesaltingDisk). Evaluation of the microfluidic disk was performed by LC-MS/MS analysis of tryptic HEK-293 eukaryotic cell peptide mixtures desalted either using the microfluidic disk or a manual workflow. A comparable number of peptides were identified in the disk and manual set with 19775 and 20212 identifications, respectively. For a core set of 10444 peptides that could be quantified in all injections, intensity coefficients of variation were calculated based on label-free quantitation intensities. The disk set featured smaller variability with a median CV of 9.3% compared to the median CV of 12.6% for the manual approach. The presented centrifugal microfluidic DesaltingDisk thus demonstrates the potential to improve reproducibility in the sample preparation workflow for proteomic mass spectrometry. Microfluidics allows the miniaturization of biochemical analyses. The small dimensions reduce sample consumption and enhance reaction rates. A downside is that the high surface to volume ratios increase the unspecific binding of proteins to the substrate material. The resulting sample loss and reagent depletion decrease the sensitivity and specificity of protein-based assays, especially if low concentrations are analyzed. Here, we introduce a hydrophobin coating of microfluidic chips made of cyclic olefin copolymers (COC). The recombinant hydrophobin H*Protein B self-assembles into stable monolayers on hydrophobic surfaces, making them hydrophilic and thus reducing hydrophobic interactions between the chip surfaces and proteins. The substrate and sealing layers of the microfluidic chip were dip coated and subsequently assembled by thermo-diffusion bonding. The efficiency of the protein-repellent coating was evaluated by depletion experiments in microfluidic chips. Recoveries of 90 % were observed with total protein amounts of 10 ng, even for microfluidic channels up to 835 mm in length. For comparison, only 30 % of protein was recovered in uncoated microfluidic channels.
Publications
-
Automation of Peptide Desalting by Centrifugal Microfluidics; 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences (μTAS), 10-14 October 2021, Palm Springs, USA
J.-N. Klatt, T.J. Dinh, O. Schilling, R. Zengerle, F. Schmidt, N. Paust & T. Hutzenlaub
-
Automation of peptide desalting for proteomic liquid chromatography – tandem mass spectrometry by centrifugal microfluidics. Lab on a Chip, 21(11), 2255-2264.
Klatt, J.-N.; Dinh, T. J.; Schilling, O.; Zengerle, R.; Schmidt, F.; Hutzenlaub, T. & Paust, N.
-
Automation of Solid Phase Extraction for Peptide Desalting by Centrifugal Microfluidics; 2021, 37th International Symposium on Microscale Separations and Bioanalysis, July 12-15, Boston, USA
J.-N. Klatt, T.J. Dinh, O. Schilling, R. Zengerle, F. Schmidt, T. Hutzenlaub & N. Paust
-
Blocking Protein Adsorption in Microfluidic Chips by a Hydrophobin Coating. ACS Applied Polymer Materials, 3(7), 3278-3286.
Klatt, Jan-Niklas; Hutzenlaub, Tobias; Subkowski, Thomas; Müller, Tanja; Hennig, Stefan; Zengerle, Roland & Paust, Nils
-
Microfluidic automation of sample preparation for mass spectrometry based proteomics, PhD thesis, 2022
J.-N. Klatt
