Dataset Information

Rapid, automated, parallel quantitative immunoassays using highly integrated microfluidics and AlphaLISA.

ABSTRACT: Immunoassays represent one of the most popular analytical methods for detection and quantification of biomolecules. However, conventional immunoassays such as ELISA and flow cytometry, even though providing high sensitivity and specificity and multiplexing capability, can be labor-intensive and prone to human error, making them unsuitable for standardized clinical diagnoses. Using a commercialized no-wash, homogeneous immunoassay technology ('AlphaLISA') in conjunction with integrated microfluidics, herein we developed a microfluidic immunoassay chip capable of rapid, automated, parallel immunoassays of microliter quantities of samples. Operation of the microfluidic immunoassay chip entailed rapid mixing and conjugation of AlphaLISA components with target analytes before quantitative imaging for analyte detections in up to eight samples simultaneously. Aspects such as fluid handling and operation, surface passivation, imaging uniformity, and detection sensitivity of the microfluidic immunoassay chip using AlphaLISA were investigated. The microfluidic immunoassay chip could detect one target analyte simultaneously for up to eight samples in 45 min with a limit of detection down to 10 pg mL(-1). The microfluidic immunoassay chip was further utilized for functional immunophenotyping to examine cytokine secretion from human immune cells stimulated ex vivo. Together, the microfluidic immunoassay chip provides a promising high-throughput, high-content platform for rapid, automated, parallel quantitative immunosensing applications.

SUBMITTER: Yu ZT

PROVIDER: S-EPMC4466892 | biostudies-other | 2015 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Identifying antibodies (Abs) that neutralize infectious agents is the first step for developing therapeutics, vaccines, and diagnostic tools for these infectious agents. However, current approaches for identifying neutralizing Abs (nAbs) typically rely on dilution-based assays that are costly, inefficient, and only survey a small subset of the entire repertoire. There are also intrinsic biases in many steps of conventional nAb identification processes. More importantly, conventional assays rely on simple Ab-antigen binding assays, which may not result in identifying the most potent nAbs, as the strongest binder may not be the most potent nAb. Droplet microfluidic systems have the capability to overcome such limitations by conducting complex multi-step assays with high reliability, resolution, and throughput in a pico-liter volume water-in-oil emulsion droplet format. Here, we describe the development of PRESCIENT (Platform for the Rapid Evaluation of antibody SucCess using Integrated microfluidics ENabled Technology), a droplet microfluidic system that can enable high-throughput single-cell resolution identification of nAb repertoires elicited in response to viral infection. We demonstrate PRESCIENT's ability to identify Abs that neutralize a model viral agent, Murine coronavirus (murine hepatitis virus), which causes high mortality rates in experimentally infected mice. In-droplet infection of host cells by the virus was first demonstrated, followed by demonstration of in-droplet neutralization by nAbs produced from a single Ab-producing hybridoma cell. Finally, fluorescence intensity analyses of two populations of hybridoma cell lines (nAb-producing and non-nAb-producing hybridoma cell lines) successfully discriminated between the two populations. The presented strategy and platform have the potential to identify and investigate neutralizing activities against a broad range of potential infectious agents for which nAbs have yet to be discovered, significantly advancing the nAb identification process as well as reinvigorating the field of Ab discovery, characterization, and development.

Project description:BACKGROUND:Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. PURPOSE:To develop a rapid, robust, and automated liver QSM for clinical practice. STUDY TYPE:Prospective. POPULATION:13 healthy subjects and 22 patients. FIELD STRENGTH/SEQUENCES:1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence. ASSESSMENT:Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2 *-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (simultaneous phase unwrapping and removal of chemical shift, SPURS) in healthy subjects (n = 5). Reproducibility was assessed over four scanners at two field strengths from two manufacturers using healthy subjects (n = 8). Clinical feasibility was evaluated in patients (n = 22). STATISTICAL TESTS:IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R2 *, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). RESULTS:Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T2 *-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between -0.06 to 0.07 ppm, ICC 0.97). DATA CONCLUSION:Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications. LEVEL OF EVIDENCE:1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.

Dataset Information

Rapid, automated, parallel quantitative immunoassays using highly integrated microfluidics and AlphaLISA.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets