Real-Time Signal Analysis with Wider Dynamic Range and Enhanced Sensitivity in Multiplex Colorimetric Immunoassays Using Encoded Hydrogel Microparticles.

The simultaneous quantification of multiple proteins is crucial for accurate medical diagnostics. A promising technology, the multiplex colorimetric immunoassay using encoded hydrogel microparticles, has garnered attention, due to its simplicity and multiplex capabilities. However, it encounters challenges related to its dynamic range, as it relies solely on the colorimetric signal analysis of encoded hydrogel microparticles at the specific time point (i.e., end-point analysis). This necessitates the precise determination of the optimal time point for the termination of the colorimetric reaction. In this study, we introduce real-time signal analysis to quantify proteins by observing the continuous colorimetric signal change within the encoded hydrogel microparticles. Real-time signal analysis measures the "slope", the rate of the colorimetric signal generation, by focusing on the kinetics of the accumulation of colorimetric products instead of the colorimetric signal that appears at the end point. By developing a deep learning-based automatic analysis program that automatically reads the code of the graphically encoded hydrogel microparticles and obtains the slope by continuously tracking the colorimetric signal, we achieved high accuracy and high throughput analysis. This technology has secured a dynamic range more than twice as wide as that of the conventional end-point signal analysis, simultaneously achieving a sensitivity that is 4-10 times higher. Finally, as a demonstration of application, we performed multiplex colorimetric immunoassays using real-time signal analysis covering a wide concentration range of protein targets associated with pre-eclampsia.

Multiplex genotyping of SNPs in genomic DNA via hydrogel-based assay mediated with MutS and polyethylene glycol.

The simultaneous genotyping of multiple single nucleotide polymorphisms (SNPs) in genomic DNA derived from organisms holds significant potential for applications such as precision medicine and food product authentication. However, conventional assay technologies including qPCR-based techniques, microarrays, and hydrogel-based assays face limitations in efficient multiplexing of SNPs, particularly for large-size DNA beyond kilobase scales, due to constraints in multiplex capability, specificity, or sensitivity. In this study, a hydrogel-based multiplex SNP genotyping platform specifically designed for genomic DNA is presented. This platform integrates the ligation detection reaction (LDR) and rolling circle amplification (RCA) techniques within a hydrogel-based multiplex sensing system, enabling adaptable and sensitive SNP genotyping for genomic DNA. To enhance the specificity of the assay, MutS protein and polyethylene glycol are introduced into the protocol, reducing the non-specific ligation and RCA reactions synergistically. With significant specificity improvement of over 10-fold, three types of SNPs within an artificially constructed ∼1000 bp double-stranded DNA (dsDNA) are successfully genotyped with double-digit picomolar sensitivity. Furthermore, the practical applicability of the developed process for the origin identification of raw materials is demonstrated by genotyping three types of SNPs within genomic DNA obtained from two closely related plant species, Korean ginseng (Panax ginseng) and American ginseng (Panax quinquefolius), containing ca. 3.5 gigabase genome size. Of notable significance, this study marks the premiere achievement in PCR-free multiplex genotyping of SNPs in genomic DNA using a single fluorophore.

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles.

Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of HMPs with diverse graphical codes. However, previous related studies have found the use of plain particles as data to be disadvantageous for accurate analyses of HMPs loaded with functional nanomaterials. Furthermore, the manual data annotation method used in existing approaches is highly labor-intensive and time-consuming. In this study, we present an efficient deep-learning-based analysis of encoded HMPs with diverse graphical codes and functional nanomaterials, utilizing the auto-annotation and synthetic data mixing methods for model training. The auto-annotation enhanced the throughput of dataset preparation up to 0.11 s/image. Using synthetic data mixing, a mean average precision of 0.88 was achieved in the analysis of encoded HMPs with magnetic nanoparticles, representing an approximately twofold improvement over the standard method. To evaluate the practical applicability of the proposed automatic analysis strategy, a single-image analysis was performed after the triplex immunoassay for the preeclampsia-related protein biomarkers. Finally, we accomplished a processing throughput of 0.353 s per sample for analyzing the result image.

Encoded hydrogel microparticles with universal mismatch-incorporated DNA probes for highly specific multiplex detection of SNPs.

Hydrogel microparticle-based nucleic acid assays are an attractive detection platform based on their multiplexing capabilities with high sensitivity and specificity. A particular area of interest is single-nucleotide polymorphism (SNP) sensing, where multiple SNPs should be identified in a highly reliable yet economical manner. However, hydrogel microparticles leveraging probe-target hybridization as a key mechanism are hampered by small duplex stability differences arising from single base-pair mismatch. We have developed encoded hydrogel microparticles with DNA probes tailored for multiplex SNP detection. Within the DNA probes, we adopt a widely used base analog (5-nitroindole) so that it substitutes one of the base sequences among DNA probes. The effects of the modification of the probes' structure on SNP sensing has been tested from multiple perspectives, such as specificity, sensitivity, and available assay temperatures at a given ionic strength. We have validated that our hydrogel microparticles exhibit much higher specificity for a single base-pair mismatch with minimal reduction in sensitivity. Our particles can also detect multiple SNPs located in different target strands, which is a significant challenge for conventional particles.

Degassed micromolding lithography for rapid fabrication of anisotropic hydrogel microparticles with high-resolution and high uniformity.

Replica molding techniques, which are used to synthesize microparticles inside anisotropic micromolds, have been developed to enable the mass production of hydrogel particles. However, these techniques are limited in their ability to synthesize only a narrow range of particle compositions and shapes because of the difficulty in loading precursors into the micromolds as well as the low particle homogeneity due to the uneven evaporation of the precursors. Herein, we describe a simple yet powerful technique, called degassed micromolding lithography, which can load precursors within 1 min regardless of the wettability. This technique is based on the gas-solubility of a degassed micromold that acts as a suction pump to completely fill the mold by drawing precursor liquids in. The semi-closed system within the micromold prevents the uneven evaporation of the precursor, which is essential for the production of homogeneous particles. Furthermore, controlled uniformity of the hydrogel microparticles (C.V. < 2%) can be achieved by engineering the design of the micromold array.

Quantitative Measurements of Size-Dependent Magnetoelectric Coupling in Fe3O4 Nanoparticles.

Bulk magnetite (Fe3O4), the loadstone used in magnetic compasses, has been known to exhibit magnetoelectric (ME) properties below ∼10 K; however, corresponding ME effects in Fe3O4 nanoparticles have been enigmatic. We investigate quantitatively the ME coupling of spherical Fe3O4 nanoparticles with uniform diameters (d) from 3 to 15 nm embedded in an insulating host, using a sensitive ME susceptometer. The intrinsic ME susceptibility (MES) of the Fe3O4 nanoparticles is measured, exhibiting a maximum value of ∼0.6 ps/m at 5 K for d = 15 nm. We found that the MES is reduced with reduced d but remains finite until d = ∼5 nm, which is close to the critical thickness for observing the Verwey transition. Moreover, with reduced diameter the critical temperature below which the MES becomes conspicuous increased systematically from 9.8 K in the bulk to 19.7 K in the nanoparticles with d = 7 nm, reflecting the core-shell effect on the ME properties. These results point to a new pathway for investigating ME effect in various nanomaterials.