Spheroscope: A custom-made miniaturized microscope for tracking tumour spheroids in microfluidic devices.

3D cell culture models consisting of self-assembled tumour cells in suspension, commonly known as tumour spheroids, are becoming mainstream for high-throughput anticancer drug screening. A usual measurable outcome of screening studies is the growth rate of the spheroids in response to treatment. This is commonly quantified on images obtained using complex, expensive, optical microscopy systems, equipped with high-quality optics and customized electronics. Here we present a novel, portable, miniaturized microscope made of low-cost, mass-producible parts, which produces both fluorescence and phase-gradient contrast images. Since phase-gradient contrast imaging is based on oblique illumination, epi-illumination is used for both modalities, thus simplifying the design of the system. We describe the system, characterize its performance on synthetic samples and show proof-of-principle applications of the system consisting in imaging and monitoring the formation and growth of lung and pancreas cancer tumour spheroids within custom made microfluidic devices.

Validation tool for traction force microscopy.

Traction force microscopy (TFM) is commonly used to estimate cells' traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.

Numerical estimation of 3D mechanical forces exerted by cells on non-linear materials.

The exchange of physical forces in both cell-cell and cell-matrix interactions play a significant role in a variety of physiological and pathological processes, such as cell migration, cancer metastasis, inflammation and wound healing. Therefore, great interest exists in accurately quantifying the forces that cells exert on their substrate during migration. Traction Force Microscopy (TFM) is the most widely used method for measuring cell traction forces. Several mathematical techniques have been developed to estimate forces from TFM experiments. However, certain simplifications are commonly assumed, such as linear elasticity of the materials and/or free geometries, which in some cases may lead to inaccurate results. Here, cellular forces are numerically estimated by solving a minimization problem that combines multiple non-linear FEM solutions. Our simulations, free from constraints on the geometrical and the mechanical conditions, show that forces are predicted with higher accuracy than when using the standard approaches.

Non-rigid consistent registration of 2D image sequences.

We present a novel algorithm for the registration of 2D image sequences that combines the principles of multiresolution B-spline-based elastic registration and those of bidirectional consistent registration. In our method, consecutive triples of images are iteratively registered to gradually extend the information through the set of images of the entire sequence. The intermediate results are reused for the registration of the following triple. We choose to interpolate the images and model the deformation fields using B-spline multiresolution pyramids. Novel boundary conditions are introduced to better characterize the deformations at the boundaries. In the experimental section, we quantitatively show that our method recovers from barrel/pincushion and fish-eye deformations with subpixel error. Moreover, it is more robust against outliers--occasional strong noise and large rotations--than the state-of-the-art methods. Finally, we show that our method can be used to realign series of histological serial sections, which are often heavily distorted due to folding and tearing of the tissues.

Influence of dietary macronutrient composition on adiposity and cellularity of different fat depots in Wistar rats

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The aim of this study was to investigate the role of dietary macronutrient content on adiposity parameters and adipocyte hypertrophy/hyperplasia in subcutaneous and visceral fat depots from Wistar rats using combined histological and computational approaches. For this purpose, male Wistar rats were distributed into 4 groups and were assigned to different nutritional interventions: Control group (chow diet); high-fat group, HF (60% E from fat); high-fat-sucrose group, HFS (45% E from fat and 17% from sucrose); and high-sucrose group, HS (42% E from sucrose). At day 35, rats were sacrificed, blood was collected, tissues were weighed and fragments of different fat depots were kept for histological analyses with the new softwareAdiposoft. Rats fed with HF, HFS and HS diets increased significantly body weight and total body fat against Control rats, being metabolic impairments more pronounced on HS rats than in the other groups. Cellularity analyses usingAdiposoft revealed that retroperitoneal adipose tissue is histologically different than mesenteric and subcutaneous ones, in relation to bigger adipocytes. The subcutaneous fat pad was the most sensitive to the diet, presenting adipocyte hypertrophy induced by HF diet and adipocyte hyperplasia induced by HS diet. The mesenteric fat pad had a similar but attenuated response in comparison to the subcutaneous adipose tissue, while retroperitoneal fat pad only presented adipocyte hyperplasia induced by the HS diet intake after 35 days of intervention. These findings provide new insights into the role of macronutrients in the development of hyperplastic obesity, which is characterized by the severity of the clinical features. Finally, a new tool for analyzing histological adipose samples is presented (AU)

Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT.

Animal models of lung disease are gaining importance in understanding the underlying mechanisms of diseases such as emphysema and lung cancer. Micro-CT allows in vivo imaging of these models, thus permitting the study of the progression of the disease or the effect of therapeutic drugs in longitudinal studies. Automated analysis of micro-CT images can be helpful to understand the physiology of diseased lungs, especially when combined with measurements of respiratory system input impedance. In this work, we present a fast and robust murine airway segmentation and reconstruction algorithm. The algorithm is based on a propagating fast marching wavefront that, as it grows, divides the tree into segments. We devised a number of specific rules to guarantee that the front propagates only inside the airways and to avoid leaking into the parenchyma. The algorithm was tested on normal mice, a mouse model of chronic inflammation and a mouse model of emphysema. A comparison with manual segmentations of two independent observers shows that the specificity and sensitivity values of our method are comparable to the inter-observer variability, and radius measurements of the mainstem bronchi reveal significant differences between healthy and diseased mice. Combining measurements of the automatically segmented airways with the parameters of the constant phase model provides extra information on how disease affects lung function.

Halton sampling for autofocus.

Reliable autofocusing is a critical part of any automated microscopy system: by precisely positioning the sample in the focal plane, the acquired images are sharp and can be accurately segmented and quantified. The three main components of an autofocus algorithm are a contrast function, an optimization algorithm and a sampling strategy. The latter has not been given much attention in the literature. It is however a very important part of the autofocusing algorithm, especially in high content and high throughput image-based screening. It deals with the problem of sampling the focus surface as sparsely as possible to reduce bleaching and computation time while with sufficient detail as to permit a faithful interpolation. We propose a new strategy that has higher performance compared to the classical square grid or the hexagonal lattice, which is based on the concept of low discrepancy point sets and in particular on the Halton point set. We tested the new algorithm on nine different focus surfaces, each under 24 different combinations of Signal-to-Noise ratio (SNR) and sampling rate, obtaining that in 88% of the tested conditions, Halton sampling outperforms its counterparts.

Influence of dietary macronutrient composition on adiposity and cellularity of different fat depots in Wistar rats.

The aim of this study was to investigate the role of dietary macronutrient content on adiposity parameters and adipocyte hypertrophy/hyperplasia in subcutaneous and visceral fat depots from Wistar rats using combined histological and computational approaches. For this purpose, male Wistar rats were distributed into 4 groups and were assigned to different nutritional interventions: Control group (chow diet); high-fat group, HF (60% E from fat); high-fat-sucrose group, HFS (45% E from fat and 17% from sucrose); and high-sucrose group, HS (42% E from sucrose). At day 35, rats were sacrificed, blood was collected, tissues were weighed and fragments of different fat depots were kept for histological analyses with the new softwareAdiposoft. Rats fed with HF, HFS and HS diets increased significantly body weight and total body fat against Control rats, being metabolic impairments more pronounced on HS rats than in the other groups. Cellularity analyses usingAdiposoft revealed that retroperitoneal adipose tissue is histologically different than mesenteric and subcutaneous ones, in relation to bigger adipocytes. The subcutaneous fat pad was the most sensitive to the diet, presenting adipocyte hypertrophy induced by HF diet and adipocyte hyperplasia induced by HS diet. The mesenteric fat pad had a similar but attenuated response in comparison to the subcutaneous adipose tissue, while retroperitoneal fat pad only presented adipocyte hyperplasia induced by the HS diet intake after 35 days of intervention. These findings provide new insights into the role of macronutrients in the development of hyperplastic obesity, which is characterized by the severity of the clinical features. Finally, a new tool for analyzing histological adipose samples is presented.

Blind spectral unmixing of M-FISH images by non-negative matrix factorization.

Multi-color Fluorescent in-Situ Hybridization (M-FISH) selectively stains multiple DNA sequences using fluorescently labeled DNA probes. Proper interpretation of M-FISH images is often hampered by spectral overlap between the detected emissions of the fluorochromes. When using more than two or three fluorochromes, the appropriate combination of wide-band excitation and emission filters reduces cross-talk, but cannot completely eliminate it. A number of approaches -both hardware and software-have been proposed in the last decade to facilitate the interpretation of M-FISH images. The most used and efficient approaches use linear unmixing methods that algorithmically compute and correct for the fluorochrome contributions to each detection channel. In contrast to standard methods that require prior knowledge of the fluorochrome spectra, we present a new method, Non-Negative Matrix Factorization (NMF), that blindly estimates the spectral contributions and corrects for the overlap. Our experimental results show that its performance in terms of residual cross-talk and spot counting reliability outperforms the non-blind state-of-the-art method, the Non-Negative Least Squares (NNLS) algorithm.

Automatic segmentation of histological structures in mammary gland tissue sections.

Real-time three-dimensional (3-D) reconstruction of epithelial structures in human mammary gland tissue blocks mapped with selected markers would be an extremely helpful tool for diagnosing breast cancer and planning treatment. Besides its clear clinical application, this tool could also shed a great deal of light on the molecular basis of the initiation and progression of breast cancer. We present a framework for real-time segmentation of epithelial structures in two-dimensional (2-D) images of sections of normal and neoplastic mammary gland tissue blocks. Complete 3-D rendering of the tissue can then be done by surface rendering of the structures detected in consecutive sections of the blocks. Paraffin-embedded or frozen tissue blocks are first sliced and sections are stained with hematoxylin and eosin. The sections are then imaged using conventional bright-field microscopy and their background corrected using a phantom image. We then use the fast-marching algorithm to roughly extract the contours of the different morphological structures in the images. The result is then refined with the level-set method, which converges to an accurate (subpixel) solution for the segmentation problem. Finally, our system stacks together the 2-D results obtained in order to reconstruct a 3-D representation of the entire tissue block under study. Our method is illustrated with results from the segmentation of human and mouse mammary gland tissue samples.

Automatic registration of serial mammary gland sections.

We present two methods for automatic registration of microscope images of consecutive tissue sections. They represent two possibilities for the first step in the 3-D reconstruction of histological structures from serially sectioned tissue blocks. The goal is to accurately align the sections in order to place every relevant shape contained in each image in front of its corresponding shape in the following section before detecting the structures of interest and rendering them in 3D. This is accomplished by finding the best rigid body transformation (translation and rotation) of the image being registered by maximizing a matching function based on the image content correlation. The first method makes use of the entire image information, whereas the second one uses only the information located at specific sites, as determined by the segmentation of the most relevant tissue structures. To reduce computing time, we use a multiresolution pyramidal approach that reaches the best registration transformation in increasing resolution steps. In each step, a subsampled version of the images is used. Both methods rely on a binary image which is a thresholded version of the Sobel gradients of the image (first method) or a set of boundaries manually or automatically obtained that define important histological structures of the sections. Then distance-transform of the binary image is computed. A proximity function is then calculated between the distance image of the image being registered and that of the reference image. The transformation providing a maximum of the proximity function is then used as the starting point of the following step. This is iterated until the registration error lies below a minimum value.

Applications of quantitative digital image analysis to breast cancer research.

Our studies of radiogenic carcinogenesis in mouse and human models of breast cancer are based on the view that cell phenotype, microenvironment composition, communication between cells and within the microenvironment are important factors in the development of breast cancer. This is complicated in the mammary gland by its postnatal development, cyclic evolution via pregnancy and involution, and dynamic remodeling of epithelial-stromal interactions, all of which contribute to breast cancer susceptibility. Microscopy is the tool of choice to examine cells in context. Specific features can be defined using probes, antibodies, immunofluorescence, and image analysis to measure protein distribution, cell composition, and genomic instability in human and mouse models of breast cancer. We discuss the integration of image acquisition, analysis, and annotation to efficiently analyze large amounts of image data. In the future, cell and tissue image-based studies will be facilitated by a bioinformatics strategy that generates multidimensional databases of quantitative information derived from molecular, immunological, and morphological probes at multiple resolutions. This approach will facilitate the construction of an in vivo phenotype database necessary for understanding when, where, and how normal cells become cancer.

A geometric model for 3-D confocal image analysis.

In this paper, we use partial-differential-equation-based filtering as a preprocessing and post processing strategy for computer-aided cytology. We wish to accurately extract and classify the shapes of nuclei from confocal microscopy images, which is a prerequisite to an accurate quantitative intranuclear (genotypic and phenotypic) and internuclear (tissue structure) analysis of tissue and cultured specimens. First, we study the use of a geometry-driven edge-preserving image smoothing mechanism before nuclear segmentation. We show how this filter outperforms other widely-used filters in that it provides higher edge fidelity. Then we apply the same filter, with a different initial condition, to smooth nuclear surfaces and obtain sub-pixel accuracy. Finally we use another instance of the geometrical filter to correct for misinterpretations of the nuclear surface by the segmentation algorithm. Our prefiltering and post filtering nicely complements our initial segmentation strategy, in that it provides substantial and measurable improvement in the definition of the nuclear surfaces.

Segmentation of confocal microscope images of cell nuclei in thick tissue sections.

Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.

Automated FISH spot counting in interphase nuclei: statistical validation and data correction.

The evaluation of an automated system for Fluorescence In Situ Hybridization (FISH) spot counting in interphase nuclei is presented in this paper. Different types of experiments have been performed with samples from known populations. In all of them the goal is to detect mosaicism of chromosome X in leukocytes from mixtures in known proportions of healthy male and female blood. First the initial results from the automatic FISH analysis system were obtained and evaluated. Then the analysis was modified to reduce systematic errors, so that the results are closer to what an experienced human operator would have obtained (system calibration step). Finally, an additional control probe of chromosome Y was used to detect and discard cells where incorrect hybridization or other abnormal situations had occurred. In each step the system sensitivity was determined by the use of two statistical validation tests, so that the improvement brought about by the correction methods could be assessed. The results obtained in the study showed that, using both corrections, the system is able to detect 10% monosomies with a significance level alpha = 0.1%.

Evaluation of autofocus functions in molecular cytogenetic analysis.

This work describes a systematic evaluation of several autofocus functions used for analytical fluorescent image cytometry studies of counterstained nuclei. Focusing is the first step in the automatic fluorescence in situ hybridization analysis of cells. Thirteen functions have been evaluated using qualitative and quantitative procedures. For the last of these procedures a figure-of-merit (FOM) is defined and proposed. This new FOM takes into account five important features of the focusing function. Our results show that functions based on correlation measures have the best performance for this type of image.