| Title |
Quantifying Nanoparticle Internalization Using a High Throughput Internalization Assay
|
|---|---|
| Published in |
Pharmaceutical Research, July 2016
|
| DOI | 10.1007/s11095-016-1984-3 |
| Pubmed ID | |
| Authors |
Sarah K. Mann, Ewa Czuba, Laura I. Selby, Georgina K. Such, Angus P. R. Johnston |
| Abstract |
The internalization of nanoparticles into cells is critical for effective nanoparticle mediated drug delivery. To investigate the kinetics and mechanism of internalization of nanoparticles into cells we have developed a DNA molecular sensor, termed the Specific Hybridization Internalization Probe - SHIP. Self-assembling polymeric 'pHlexi' nanoparticles were functionalized with a Fluorescent Internalization Probe (FIP) and the interactions with two different cell lines (3T3 and CEM cells) were studied. The kinetics of internalization were quantified and chemical inhibitors that inhibited energy dependent endocytosis (sodium azide), dynamin dependent endocytosis (Dyngo-4a) and macropinocytosis (5-(N-ethyl-N-isopropyl) amiloride (EIPA)) were used to study the mechanism of internalization. Nanoparticle internalization kinetics were significantly faster in 3T3 cells than CEM cells. We have shown that ~90% of the nanoparticles associated with 3T3 cells were internalized, compared to only 20% of the nanoparticles associated with CEM cells. Nanoparticle uptake was via a dynamin-dependent pathway, and the nanoparticles were trafficked to lysosomal compartments once internalized. SHIP is able to distinguish between nanoparticles that are associated on the outer cell membrane from nanoparticles that are internalized. This study demonstrates the assay can be used to probe the kinetics of nanoparticle internalization and the mechanisms by which the nanoparticles are taken up by cells. This information is fundamental for engineering more effective nanoparticle delivery systems. The SHIP assay is a simple and a high-throughput technique that could have wide application in therapeutic delivery research. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 30% |
| Student > Master | 6 | 16% |
| Student > Doctoral Student | 3 | 8% |
| Student > Bachelor | 3 | 8% |
| Researcher | 3 | 8% |
| Other | 5 | 14% |
| Unknown | 6 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 9 | 24% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 19% |
| Agricultural and Biological Sciences | 4 | 11% |
| Chemical Engineering | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 7 | 19% |
| Unknown | 6 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 September 2016.
All research outputs
#15,384,989
of 22,889,074 outputs
Outputs from Pharmaceutical Research
#2,235
of 2,860 outputs
Outputs of similar age
#226,096
of 355,077 outputs
Outputs of similar age from Pharmaceutical Research
#29
of 46 outputs
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