Complex morphology and functional dynamics of vital murine intestinal mucosa revealed by autofluorescence 2-photon microscopy

The mucosa of the gastrointestinal tract is a dynamic tissue composed of numerous cell types with complex cellular functions. Study of the vital intestinal mucosa has been hampered by lack of suitable model systems. We here present a novel animal model that enables highly resolved three-dimensional imaging of the vital murine intestine in anaesthetized mice. Using intravital autofluorescence 2-photon (A2P) microscopy we studied the choreographed interactions of enterocytes, goblet cells, enteroendocrine cells and brush cells with other cellular constituents of the small intestinal mucosa over several hours at a subcellular resolution and in three dimensions. Vigorously moving lymphoid cells and their interaction with constituent parts of the lamina propria were examined and quantitatively analyzed. Nuclear and lectin staining permitted simultaneous characterization of autofluorescence and admitted dyes and yielded additional spectral information that is crucial to the interpretation of the complex intestinal mucosa. This novel intravital approach provides detailed insights into the physiology of the small intestine and especially opens a new window for investigating cellular dynamics under nearly physiological conditions

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

Media 4: Probing the immune and healing response of murine intestinal mucosa by time-lapse 2-photon microscopy of laser-induced lesions with real-time dosimetry

Originally published in Biomedical Optics Express on 01 October 2014 (boe-5-10-3521

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