Instant magnetic labeling of tumor cells by ultrasound in vitro
Source abstract
Abstract
Abstract Magnetic labeling of living cells creates opportunities for numerous biomedical applications. Here we describe an instantly cell magnetic labeling method based on ultrasound. We present a detailed study on the ultrasound performance of a simple and efficient labeling protocol for H-22 cells in vitro. High frequency focus ultrasound was investigated as an alternative method to achieve instant cell labeling with the magnetic particles without the need for adjunct agents or initiating cell cultures. Mean diameter of 168 nm dextran-T40 coated superparamagnetic iron oxide (SPIO) nanoparticles were prepared by means of classical coprecipitation in solution in our laboratory. H-22 tumor cells suspended in phosphate-buffered saline (PBS, pH=7.2) were exposed to ultrasound at 1.37 MHz for up to 120 s in the presence of SPIOs. The cellular uptake of iron oxide nanoparticles was detected by prussion blue staining. The viability of cells was determined by a trypan blue exclusion test. At 2 W power and 60 s ultrasound exposure in presence of 410 μg/ml SPIOs, H-22 cell labeling efficiency reached 69.4±6.3% and the labeled cells exhibited an iron content of 10.38±2.43 pg per cell. Furthermore, 95.2±3.2% cells remained viable. The results indicated that the ultrasound protocol could be potentially applied to label cells with large-sized magnetic particles. We also calculated the shear stress at the 2 W power and 1.37 MHz used in experiments. The results showed that the shear stress threshold for ultrasonically induced H-22 cell reparable sonoporation was 697 Pa. These findings provide a quantitative guidance in designing ultrasound protocols for cell labeling.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Ultrasound and Hyperthermia Applications · Microfluidic and Bio-sensing Technologies · Ultrasound and Cavitation Phenomena
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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