In recent years, magnetic particle spectroscopy (MPS) has become a highly sensitive and versatile sensing technique for quantitative bioassays. It relies on the dynamic magnetic responses of magnetic nanoparticles (MNPs) for the detection of target analytes in the liquid phase. There are many research studies reporting the application of MPS for detecting a variety of analytes including viruses, toxins, nucleic acids, and so forth. Herein, we report a modified version of the MPS platform with the addition of a one-stage lock-in design to remove the feedthrough signals induced by external driving magnetic fields, thus capturing only MNP responses for improved system sensitivity. This one-stage lock-in MPS system is able to detect as low as 781 ng multi-core Nanomag50 iron oxide MNPs (micromod Partikeltechnologie GmbH) and 78 ng single-core SHB30 iron oxide MNPs (Ocean NanoTech). We first demonstrated the performance of this MPS system for bioassay-related applications. Using the SARS-CoV-2 spike protein as a model, we have achieved a detection limit of 125 nM (equal to 5 pmole) for detecting spike protein molecules in the liquid phase. In addition, using a streptavidin-biotin binding system as a proof-of-concept, we show that these single-core SHB30 MNPs can be used for Brownian relaxation-based bioassays while the multi-core Nanomag50 cannot be used. The effects of MNP amount on the concentration-dependent response profiles for detecting streptavidin were also investigated. Results show that by using a lower concentration/ amount of MNPs, concentration-response curves shift to a lower concentration/amount of target analytes. This lower concentration-response indicates the possibility of improved bioassay sensitivities by using lower amounts of MNPs.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531866PMC
http://dx.doi.org/10.1021/acs.jpcc.1c05126DOI Listing

Publication Analysis

Top Keywords

one-stage lock-in
12
magnetic particle
8
particle spectroscopy
8
target analytes
8
liquid phase
8
mps system
8
multi-core nanomag50
8
iron oxide
8
oxide mnps
8
single-core shb30
8

Similar Publications

Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud.

Sensors (Basel)

October 2022

Ingenium College of Liberal Arts, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea.

Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative method in GNNs, in the context of computer vision, utilize conventional Convolutional Neural Networks (CNNs) to process data supported by graphs. This paper proposes a one-stage GCN approach for 3D object detection and poses estimation by structuring non-linearly distributed points of a graph.

View Article and Find Full Text PDF

Magnetic Particle Spectroscopy with One-Stage Lock-In Implementation for Magnetic Bioassays with Improved Sensitivities.

J Phys Chem C Nanomater Interfaces

August 2021

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States.

In recent years, magnetic particle spectroscopy (MPS) has become a highly sensitive and versatile sensing technique for quantitative bioassays. It relies on the dynamic magnetic responses of magnetic nanoparticles (MNPs) for the detection of target analytes in the liquid phase. There are many research studies reporting the application of MPS for detecting a variety of analytes including viruses, toxins, nucleic acids, and so forth.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!