journal article·2012-09

Detecting molecules and cells labeled with magnetic particles using an atomic magnetometer

Dindi Yu, Songtham Ruangchaithaweesuk, Li Yao, Shoujun Xu

Journal of Nanoparticle Research· Volume 14· Issue 9
Publisher page

Abstract

The detection of magnetically labeled molecules and cells involves three essential parameters: sensitivity, spatial resolution, and molecular specificity. We report on the use of atomic magnetometry and its derivative techniques to achieve high performance in terms of all these parameters. With a sensitivity of 80 fT/√Hz for dc magnetic fields, we show that 7,000 streptavidin-conjugated magnetic microparticles magnetized by a permanent magnet produce a magnetic field of 650 pT; this result predicts that a single such particle can be detected during one second of signal averaging. Spatial information is obtained using a scanning magnetic imaging scheme. The spatial resolution is 20 μm with a detection distance of more than 1 cm; this distance is much longer than that in previous reports. The molecular specificity is achieved using force-induced remnant magnetization spectroscopy, which currently uses an atomic magnetometer for detection. As an example, we perform measurement of magnetically labeled human CD4+ T cells, whose count in the blood is the diagnostic criterion for human immunodeficiency virus infection. Magnetic particles that are specifically bound to the cells are resolved from nonspecifically bound particles and quantitatively correlate with the number of cells. The magnetic particles have an overall size of 2.8 μm, with a magnetic core in nanometer regime. The combination of our techniques is predicted to be useful in molecular and cellular imaging.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Atomic and Subatomic Physics Research · Characterization and Applications of Magnetic Nanoparticles · Advanced MRI Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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