In recent years, dielectrophoretic force has been used to manipulate colloids, inert particles, and biological microparticles, such as red blood cells, white blood cells, platelets, cancer cells, bacteria, yeast, microorganisms, proteins, DNA, etc. This specific electrokinetic technique has been used for trapping, sorting, focusing, filtration, patterning, assembly, and separating biological entities/particles suspended in a buffer medium. Dielectrophoretic forces acting on particles depend on various parameters, for example, charge of the particle, geometry of the device, dielectric constant of the medium and particle, and physiology of the particle. Therefore, to design an effective micro-/nanofluidic separation platform, it is necessary to understand the role of the aforementioned parameters on particle motion. In this paper, we review studies particularly related to dielectrophoretic separation in microfluidic devices. Both experimental and theoretical works by several researchers are highlighted in this article covering AC and DC DEP. In addition, AC/DC DEP, which uses a combination of low frequency AC and DC voltage to manipulate bioparticles, has been discussed briefly. Contactless DEP, a variation of DC DEP in which electrodes do not come in contact with particles, has also been reviewed. Moreover, dielectrophoretic force-based field flow fractionations are featured to demonstrate the bioparticle separation in microfluidic device. In numerical front, a comprehensive review is provided starting from the most simplified effective moment Stokes-drag (EMSD) method to the most advanced interface resolved method. Unlike EMSD method, recently developed advanced numerical methods consider the size and shape of the particle in the electric and flow field calculations, and these methods provide much more accurate results than the EMSD method for microparticles.

This paper describes the preconcentration of the biomarker cardiac troponin I (cTnI) and a fluorescent protein (R-phycoerythrin) using cationic isotachophoresis (ITP) in a 3.9 cm long poly(methyl methacrylate) (PMMA) microfluidic chip. The microfluidic chip includes a channel with a 5× reduction in depth and a 10× reduction in width. Thus, the overall cross-sectional area decreases by 50× from inlet (anode) to outlet (cathode). The concentration is inversely proportional to the cross-sectional area so that as proteins migrate through the reductions, the concentrations increase proportionally. In addition, the proteins gain additional concentration by ITP. We observe that by performing ITP in a cross-sectional area reducing microfluidic chip we can attain concentration factors greater than 10 000. The starting concentration of cTnI was 2.3 μg mL−1 and the final concentration after ITP concentration in the microfluidic chip was 25.52 ± 1.25 mg mL−1. To the author’s knowledge this is the first attempt at concentrating the cardiac biomarker cTnI by ITP. This experimental approach could be coupled to an immunoassay based technique and has the potential to lower limits of detection, increase sensitivity, and quantify different isolated cTnI phosphorylation states.

Background: Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline-from obtaining large quantities of root samples through image based trait processing and analysis. Results: This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional autoencoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions: This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

The separation of biomolecules and other nanoparticles is a vital step in several analytical and diagnostic techniques. Towards this end we present a solid state nanopore-based set-up as an efficient separation platform. The translocation of charged particles through a nanopore was first modeled mathematically using the multi-ion model and the surface charge density of the nanopore membrane was identified as a critical parameter that determines the selectivity of the membrane and the throughput of the separation process. Drawing from these simulations a single 150 nm pore was fabricated in a 50 nm thick free-standing silicon nitride membrane by focused-ion-beam milling and was chemically modified with (3-aminopropyl)triethoxysilane to change its surface charge density. This chemically modified membrane was then used to separate 22 and 58 nm polystyrene nanoparticles in solution. Once optimized, this approach can readily be scaled up to nanopore arrays which would function as a key component of next-generation nanosieving systems.

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications=14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from .3,300 maize roots and .1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.

This paper describes the preconcentration of the biomarker cardiac troponin I (cTnI) and a fluorescent protein (R-phycoerythrin) using cationic isotachophoresis (ITP) in a 3.9 cm long poly(methyl methacrylate) (PMMA) microfluidic chip. The microfluidic chip includes a channel with a 5× reduction in depth and a 10× reduction in width. Thus, the overall cross-sectional area decreases by 50× from inlet (anode) to outlet (cathode). The concentration is inversely proportional to the cross-sectional area so that as proteins migrate through the reductions, the concentrations increase proportionally. In addition, the proteins gain additional concentration by ITP. We observe that by performing ITP in a cross-sectional area reducing microfluidic chip we can attain concentration factors greater than 10 000. The starting concentration of cTnI was 2.3 μg mL–1 and the final concentration after ITP concentration in the microfluidic chip was 25.52 ± 1.25 mg mL–1. To the author's knowledge this is the first attempt at concentrating the cardiac biomarker cTnI by ITP. This experimental approach could be coupled to an immunoassay based technique and has the potential to lower limits of detection, increase sensitivity, and quantify different isolated cTnI phosphorylation states.

No abstract