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J Control Release. 2015 Dec 28;220(Pt A):37-43. doi: 10.1016/j.jconrel.2015.10.021. Epub 2015 Oct 18.

Minimizing biases associated with tracking analysis of submicron particles in heterogeneous biological fluids.

Author information

1
Department of Biophysics, Johns Hopkins University, 3400 North Charles St, Baltimore, MD 21218, USA.
2
UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, USA.
3
Division of Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, USA.
4
Mathematics Department, University of Florida, 1400 Stadium Road, Gainesville, FL 32611, USA.
5
UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, USA; Division of Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina School of Medicine, 125 Mason Farm Road, Chapel Hill, NC 27599, USA. Electronic address: lai@unc.edu.

Abstract

Tracking the dynamic motion of individual nanoparticles or viruses offers quantitative insights into their real-time behavior and fate in different biological environments. Indeed, particle tracking is a powerful tool that has facilitated the development of drug carriers with enhanced penetration of mucus, brain tissues and other extracellular matrices. Nevertheless, heterogeneity is a hallmark of nanoparticle diffusion in such complex environments: identical particles can exhibit strongly hindered or unobstructed diffusion within microns of each other. The common practice in 2D particle tracking, namely analyzing all trackable particle traces with equal weighting, naturally biases towards rapidly diffusing sub-populations at shorter time scales. This in turn results in misrepresentation of particle behavior and a systematic underestimate of the time necessary for a population of nanoparticles to diffuse specific distances. We show here via both computational simulation and experimental data that this bias can be rigorously corrected by weighing the contribution by each particle trace on a 'frame-by-frame' basis. We believe this methodology presents an important step towards objective and accurate assessment of the heterogeneous transport behavior of submicron drug carriers and pathogens in biological environments.

KEYWORDS:

Diffusion; Mucus; Multiple particle tracking; Single particle tracking; Transport

PMID:
26478013
PMCID:
PMC4688199
DOI:
10.1016/j.jconrel.2015.10.021
[Indexed for MEDLINE]
Free PMC Article

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