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Tracking and quantifying polymer therapeutic distribution on a cellular level using 3D dSTORM.

ABSTRACT: We used a single-molecule localization technique called direct stochastic optical reconstruction microscopy (dSTORM) to quantify both colocalization and spatial distribution on a cellular level for two conceptually different N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer conjugates. Microscopy images were acquired of entire cells with resolutions as high as 25nm revealing the nanoscale distribution of the fluorescently labeled therapeutic components. Drug-free macromolecular therapeutics consisting of two self-assembling nanoconjugates showed slight increase in nanoclusters on the cell surface with time. Additionally, dSTORM provided high resolution images of the nanoscale organization of the self-assembling conjugates at the interface between two cells. A conjugate designed for treating ovarian cancer showed that the model drug (Cy3) and polymer bound to Cy5 were colocalized at an early time point before the model drug was enzymatically cleaved from the polymer. Using spatial descriptive statistics it was found that the drug was randomly distributed after 24h while the polymer bound dye remained in clusters. Four different fluorescent dyes were used and two different therapeutic systems were tested to demonstrate the versatility and possible general applicability of dSTORM for use in studying drug delivery systems.

SUBMITTER: Hartley JM

PROVIDER: S-EPMC5065329 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Tracking and quantifying polymer therapeutic distribution on a cellular level using 3D dSTORM.

Hartley Jonathan M JM Zhang Rui R Gudheti Manasa M Yang Jiyuan J Kopeček Jindřich J

Journal of controlled release : official journal of the Controlled Release Society 20160216

We used a single-molecule localization technique called direct stochastic optical reconstruction microscopy (dSTORM) to quantify both colocalization and spatial distribution on a cellular level for two conceptually different N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer conjugates. Microscopy images were acquired of entire cells with resolutions as high as 25nm revealing the nanoscale distribution of the fluorescently labeled therapeutic components. Drug-free macromolecular therapeutics con ...[more]

PMID: 26855050

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Project description:BackgroundThe technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.ResultsQuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.ConclusionsThe presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.

Dataset Information

Tracking and quantifying polymer therapeutic distribution on a cellular level using 3D dSTORM.

Publications

Tracking and quantifying polymer therapeutic distribution on a cellular level using 3D dSTORM.

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