CdSe/CdS-quantum rods: fluorescent probes for in vivo two-photon laser scanning microscopy.

CdSe/CdS-Quantum-dots-quantum-rods (QDQRs) with an aspect ratio of ∼ 6 are prepared via the seeded growth method, encapsulated within a shell of crosslinked poly(isoprene)-block-poly(ethylene glycol) (PI-b-PEG) diblock copolymer, and transferred from the organic phase into aqueous media. Their photoluminescence quantum yield (PLQY) of 78% is not compromised by the phase transfer. Within a period of two months the PLQY of QDQRs in aqueous solution at neutral pH decreases only slightly (to ∼ 65%). The two-photon (TP) action cross sections of QDQRs (∼ 10(5) GM) are two orders of magnitude higher than those of CdSe/CdS/ZnS-core/shell/shell quantum dots (QDs, ∼ 10(3) GM) with comparable diameter (∼ 5 nm). After applying PI-b-PEG encapsulated QDQRs onto the small intestinal mucosa of mice in vivo, their strong red fluorescence can easily be observed by two-photon laser scanning microscopy (TPLSM) and clearly distinguished from autofluorescent background. Our results demonstrate that PI-b-PEG encapsulated CdSe/CdS-QDQRs are excellent probes for studying the uptake and fate of nanoparticles by two-photon imaging techniques in vivo.

Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy.

Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.