Processing

Significance: Fluorescence polarization (FP) and fluorescence anisotropy (FA) microscopy are powerful imaging techniques that allow to translate the common IT assay capabilities into the in vitro and in vivo cellular domain. As a result, they have found potential for mapping drug- protein or protein-protein interactions. Unfortunately, these imaging modalities are ratiometric in nature and as such they suffer from excessive noise even under regular imaging conditions, preventing accurate image-feature analysis of fluorescent molecules behaviors. Aim: We present a high dynamic range (HDR)-based FA imaging modality for improving image quality in FA microscopy. Approach: The method exploits ad hoc acquisition schemes to extend the dynamic range of individual FP channels, allowing to obtain FA images with increased signal-to-noise ratio. Results: A direct comparison between FA images obtained with our method and the standard, clearly indicates how an HDR-based FA imaging approach allows to obtain high-quality images, with the ability to correctly resolve image features at different values of FA and over a substantially higher range of fluorescence intensities. Conclusion: The method presented is shown to outperform standard FA imaging microscopy narrowing the spread of the propagated error and yielding higher quality images. The method can be effectively and routinely used on any commercial imaging system and could be also translated to other microscopy ratiometric imaging modalities. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

Fluorescence acquisition and image display over a high dynamic range is highly desirable. However, the limited dynamic range of current photodetectors and imaging charge-coupled devices impose a limit on the fluorescence intensities that can be simultaneously captured during a single image acquisition. This is particularly troublesome when imaging biological samples, where protein expression fluctuates considerably. As a result, biological images will often contain regions with signal that is either saturated or hidden within background noise, causing information loss. In this paper, we summarize recent work from our group and others, to extended conventional to high dynamic range fluorescence imaging. These strategies have many biological applications, such as mapping of neural connections, vascular imaging, biodistribution studies or pharmacologic imaging at the single cell and organ level.

Mitochondria, which are essential organelles in resting and replicating cells, can vary in number, mass and shape. Past research has primarily focused on short-term molecular mechanisms underlying fission/fusion. Less is known about longer-term mitochondrial behavior such as the overall makeup of cell populations’ morphological patterns and whether these patterns can be used as biomarkers of drug response in human cells. We developed an image-based analytical technique to phenotype mitochondrial morphology in different cancers, including cancer cell lines and patient-derived cancer cells. We demonstrate that (i) cancer cells of different origins, including patient-derived xenografts, express highly diverse mitochondrial phenotypes; (ii) a given phenotype is characteristic of a cell population and fairly constant over time; (iii) mitochondrial patterns correlate with cell metabolic measurements and (iv) therapeutic interventions can alter mitochondrial phenotypes in drug-sensitive cancers as measured in pre-versus post-treatment fine needle aspirates in mice. These observations shed light on the role of mitochondrial dynamics in the biology and drug response of cancer cells. On the basis of these findings, we propose that image-based mitochondrial phenotyping can provide biomarkers for assessing cancer phenotype and drug response.

In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

Intravital fluorescence microscopy, through extended penetration depth and imaging resolution, provides the ability to image at cellular and subcellular resolution in live animals, presenting an opportunity for new insights into in vivo biology. Unfortunately, physiological induced motion components due to respiration and cardiac activity are major sources of image artifacts and impose severe limitations on the effective imaging resolution that can be ultimately achieved in vivo. Here we present a novel imaging methodology capable of automatically removing motion artifacts during intravital microscopy imaging of organs and orthotopic tumors. The method is universally applicable to different laser scanning modalities including confocal and multiphoton microscopy, and offers artifact free reconstructions independent of the physiological motion source and imaged organ. The methodology, which is based on raw data acquisition followed by image processing, is here demonstrated for both cardiac and respiratory motion compensation in mice heart, kidney, liver, pancreas and dorsal window chamber.

Current intraoperative imaging systems are typically not able to provide sharp’ images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix. ((c) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Absorption and emission optical projection tomography (OPT), alternatively referred to as optical computed tomography (optical-CT) and optical-emission computed tomography (optical-ECT), are recently developed three-dimensional imaging techniques with value for developmental biology and ex vivo gene expression studies. The techniques’ principles are similar to the ones used for x-ray computed tomography and are based on the approximation of negligible light scattering in optically cleared samples. The optical clearing is achieved by a chemical procedure which aims at substituting the cellular fluids within the sample with a cell membranes’ index matching solution. Once cleared the sample presents very low scattering and is then illuminated with a light collimated beam whose intensity is captured in transillumination mode by a CCD camera. Different projection images of the sample are subsequently obtained over a 360 degrees full rotation, and a standard backprojection algorithm can be used in a similar fashion as for x-ray tomography in order to obtain absorption maps. Because not all biological samples present significant absorption contrast, it is not always possible to obtain projections with a good signal-to-noise ratio, a condition necessary to achieve high-quality tomographic reconstructions. Such is the case for example, for early stage’s embryos. In this work we demonstrate how, through the use of a random noise removal algorithm, the image quality of the reconstructions can be considerably improved even when the noise is strongly present in the acquired projections. Specifically, we implemented a block matching 3D (BM3D) filter applying it separately on each acquired transillumination projection before performing a complete three-dimensional tomographical reconstruction. To test the efficiency of the adopted filtering scheme, a phantom and a real biological sample were processed. In both cases, the BM3D filter led to a signal-to-noise ratio increment of over 30 dB on severe noise-affected reconstructions revealing original-noise-hidden-image details. These results show the utility of the BM3D approach for OPT under typical conditions of very low light absorption, suggesting its implementation as an efficient alternative to other filtering schemes such as for example the median filter.