π-Conjugated Chromophore Incorporated Polystyrene Nanobeads as Single Optical Agent for Three-Channel Fluorescent Probe in Bioimaging Application.
Sarabjot Kaur MakkadAsha S KPublished in: ACS biomaterials science & engineering (2017)
Fluorescent polystyrene (PS) nanobeads in the size range ∼70-120 nm incorporating perylene bisimide (PBI-PS) and/or oligo(p-phenylenevinylene) (OPV-PS) was developed by miniemulsion polymerization technique. A dye loading content (DLC) of <3% was sufficient to impart high fluorescence emission capability to the PS beads. OPV-PS exhibited emission in the range 400-550 nm with peak emission at 450 nm (λex = 350 nm; ϕFL = 26%); PBI-PS showed emission from 520-650 nm with peak emission at 545 nm (λex = 490 nm; ϕFL = 9.7%) in 1× PBS buffer, whereas OPV(PBI)-PS nanobeads incorporating both the fluorophores exhibited multicolor emission capabilities (λex from 350 to 490 nm). The nanoparticles were characterized by field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and dynamic light scattering (DLS) for size and zeta potential for surface charge. For bioimaging applications, the PS nanoparticles were incubated with HeLa cells. Cell viability analysis involving HeLa cells showed more than 90% cell viability confirming the biocompatibility of the PS beads. The cellular uptake of the nanoparticles was confirmed by flow cytometry analysis and confocal laser scanning microscopy (CLSM) images. The subcellular localization of the nanoparticles in the cytoplasm could be precisely established by their simultaneous multicolor emission. The PS-based single optical agent presented here that can function as three-channel fluorescent probe to meet the requirements for multicolor bioimaging is advantageous.
Keyphrases
- fluorescent probe
- living cells
- electron microscopy
- photodynamic therapy
- flow cytometry
- high resolution
- cell cycle arrest
- induced apoptosis
- quantum dots
- single molecule
- solid state
- high speed
- cell death
- light emitting
- oxidative stress
- deep learning
- risk assessment
- convolutional neural network
- signaling pathway
- label free
- raman spectroscopy
- pi k akt