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Fine-Tuning Electrokinetic Injections Considering Nonlinear Electrokinetic Effects in Insulator-Based Devices.

ABSTRACT: The manner of sample injection is critical in microscale electrokinetic (EK) separations, as the resolution of a separation greatly depends on sample quality and how the sample is introduced into the system. There is a significant wealth of knowledge on the development of EK injection methodologies that range from simple and straightforward approaches to sophisticated schemes. The present study focused on the development of optimized EK sample injection schemes for direct current insulator-based EK (DC-iEK) systems. These are microchannels that contain arrays of insulating structures; the presence of these structures creates a nonuniform electric field distribution when a potential is applied, resulting in enhanced nonlinear EK effects. Recently, it was reported that the nonlinear EK effect of electrophoresis of the second kind plays a major role in particle migration in DC-iEK systems. This study presents a methodology for designing EK sample injection schemes that consider the nonlinear EK effects exerted on the particles being injected. Mathematical modeling with COMSOL Multiphysics was employed to identify proper voltages to be used during the EK injection process. Then, a T-microchannel with insulating posts was employed to experimentally perform EK injection and separate a sample containing two types of similar polystyrene particles. The quality of the EK injections was assessed by comparing the resolution (Rs) and number of plates (N) of the experimental particle separations. The findings of this study establish the importance of considering nonlinear EK effects when planning for successful EK injection schemes.

SUBMITTER: Miller A

PROVIDER: S-EPMC8227112 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Insulator-based dielectrophoresis is a relatively new analytical technique with a large potential for a number of applications, such as sorting, separation, purification, fractionation, and preconcentration. The application of insulator-based dielectrophoresis (iDEP) for biological samples, however, requires the precise control of the microenvironment with temporal and spatial resolution. Temperature variations during an iDEP experiment are a critical aspect in iDEP since Joule heating could lead to various detrimental effects hampering reproducibility. Additionally, Joule heating can potentially induce thermal flow and more importantly can degrade biomolecules and other biological species. Here, we investigate temperature variations in iDEP devices experimentally employing the thermosensitive dye Rhodamin B (RhB) and compare the measured results with numerical simulations. We performed the temperature measurement experiments at a relevant buffer conductivity range commonly used for iDEP applications under applied electric potentials. To this aim, we employed an in-channel measurement method and an alternative method employing a thin film located slightly below the iDEP channel. We found that the temperature does not deviate significantly from room temperature at 100 ?S/cm up to 3000 V applied such as in protein iDEP experiments. At a conductivity of 300 ?S/cm, such as previously used for mitochondria iDEP experiments at 3000 V, the temperature never exceeds 34 °C. This observation suggests that temperature effects for iDEP of proteins and mitochondria under these conditions are marginal. However, at larger conductivities (1 mS/cm) and only at 3000 V applied, temperature increases were significant, reaching a regime in which degradation is likely to occur. Moreover, the thin layer method resulted in lower temperature enhancement which was also confirmed with numerical simulations. We thus conclude that the thin film method is preferable providing closer agreement with numerical simulations and further since it does not depend on the iDEP channel material. Overall, our study provides a thorough comparison of two experimental techniques for direct temperature measurement, which can be adapted to a variety of iDEP applications in the future. The good agreement between simulation and experiment will also allow one to assess temperature variations for iDEP devices prior to experiments.

Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

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Fine-Tuning Electrokinetic Injections Considering Nonlinear Electrokinetic Effects in Insulator-Based Devices.

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