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Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry
Published on: March 1, 2013
Quantification of nanoparticle dose and vesicular inheritance in proliferating cells
Huw D Summers1, Martyn R Brown, Mark D Holton
1Centre for Nanohealth, College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, United Kingdom. h.d.summers@swansea.ac.uk
ACS Nano
|June 19, 2013
Summary
Quantifying nanoparticle dose per cell is challenging. This study calibrates flow cytometry with electron microscopy to accurately measure nanoparticle uptake, revealing asymmetric distribution during cell division.
Area of Science:
- Nanotechnology
- Cell Biology
- Biophysics
Background:
- Accurate nanoparticle dose assessment in cellular interactions is complex.
- Particle uptake is governed by multiple, poorly understood mechanisms and metrics.
- Standard methods lack the resolution to quantify internalized nanoparticle numbers per cell.
Purpose of the Study:
- To develop a method for quantifying nanoparticle dose per cell.
- To calibrate high-throughput cytometry with high-resolution microscopy.
- To analyze nanoparticle distribution during cell division.
Main Methods:
- Correlating transmission electron microscopy (TEM) with flow cytometry (FCM).
- Quantifying quantum dot uptake in U-2 OS cells using both low- and high-resolution imaging.
- Developing probability distribution functions for vesicles and nanoparticles per vesicle.
Main Results:
- Established a calibration factor to convert FCM fluorescence intensity to particle dose per cell.
- Determined a mean nanoparticle dose of 2.4 million particles per cell.
- Observed near-symmetric vesicle inheritance but highly asymmetric particle dose in daughter cells due to variable vesicle loading.
Conclusions:
- Accurate nanoparticle dose quantification is achievable through correlative microscopy and cytometry.
- Cell division leads to unequal nanoparticle distribution between daughter cells.
- Understanding particle partitioning is crucial for nanoparticle-based therapies and diagnostics.

