Microscopia multimodal prática: registro automático de imagens de microscopia ótica e de microscopia eletrônica de varredura

A discriminação de fases que são praticamente indistinguíveis ao microscópio ótico de luz refletida ou ao microscópio eletrônico de varredura (MEV) é um dos problemas clássicos da microscopia de minérios. Com o objetivo de resolver este problema vem sendo recentemente empregada a técnica de microscopia colocalizada, que consiste na junção de duas modalidades de microscopia, microscopia ótica e microscopia eletrônica de varredura. O objetivo da técnica é fornecer uma imagem de microscopia multimodal, tornando possível a identificação, em amostras de minerais, de fases que não seriam distinguíveis com o uso de uma única modalidade, superando assim as limitações individuais dos dois sistemas. O método de registro até então disponível na literatura para a fusão das imagens de microscopia ótica e de microscopia eletrônica de varredura é um procedimento trabalhoso e extremamente dependente da interação do operador, uma vez que envolve a calibração do sistema com uma malha padrão a cada rotina de aquisição de imagens. Por esse motivo a técnica existente não é prática. Este trabalho propõe uma metodologia para automatizar o processo de registro de imagens de microscopia ótica e de microscopia eletrônica de varredura de maneira a aperfeiçoar e simplificar o uso da técnica de microscopia colocalizada. O método proposto pode ser subdividido em dois procedimentos: obtenção da transformação e registro das imagens com uso desta transformação. A obtenção da transformação envolve, primeiramente, o pré-processamento dos pares de forma a executar um registro grosseiro entre as imagens de cada par. Em seguida, são obtidos pontos homólogos, nas imagens óticas e de MEV. Para tal, foram utilizados dois métodos, o primeiro desenvolvido com base no algoritmo SIFT e o segundo definido a partir da varredura pelo máximo valor do coeficiente de correlação. Na etapa seguinte é calculada a transformação. Foram empregadas duas abordagens distintas: a média ponderada local (LWM) e os mínimos quadrados ponderados com polinômios ortogonais (MQPPO). O LWM recebe como entradas os chamados pseudo-homólogos, pontos que são forçadamente distribuídos de forma regular na imagem de referência, e que revelam, na imagem a ser registrada, os deslocamentos locais relativos entre as imagens. Tais pseudo-homólogos podem ser obtidos tanto pelo SIFT como pelo método do coeficiente de correlação. Por outro lado, o MQPPO recebe um conjunto de pontos com a distribuição natural. A análise dos registro de imagens obtidos empregou como métrica o valor da correlação entre as imagens obtidas. Observou-se que com o uso das variantes propostas SIFT-LWM e SIFT-Correlação foram obtidos resultados ligeiramente superiores aos do método com a malha padrão e LWM. Assim, a proposta, além de reduzir drasticamente a intervenção do operador, ainda possibilitou resultados mais precisos. Por outro lado, o método baseado na transformação fornecida pelos mínimos quadrados ponderados com polinômios ortogonais mostrou resultados inferiores aos produzidos pelo método que faz uso da malha padrão.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

Clustering algorithms and shape factor methods to discriminate among small GTPase phenotypes using DIC image analysis.

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès. En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants. Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive.

A novel method for measuring lag times in division of individual bacterial cells using image analysis

A method is presented for determining the time to first division of individual bacterial cells growing on agar media. Bacteria were inoculated onto agar-coated slides and viewed by phase-contrast microscopy. Digital images of the growing bacteria were captured at intervals and the time to first division estimated by calculating the "box area ratio". This is the area of the smallest rectangle that can be drawn around an object, divided by the area of the object itself. The box area ratios of cells were found to increase suddenly during growth at a time that correlated with cell division as estimated by visual inspection of the digital images. This was caused by a change in the orientation of the two daughter cells that occurred when sufficient flexibility arose at their point of attachment. This method was used successfully to generate lag time distributions for populations of Escherichia coli, Listeria monocytogenes and Pseudomonas aeruginosa, but did not work with the coccoid organism Staphylococcus aureus. This method provides an objective measure of the time to first cell division, whilst automation of the data processing allows a large number of cells to be examined per experiment. (c) 2005 Elsevier B.V. All rights reserved.

Confocal laser scanning microscopy elucidation of the micromorphology of the leaf cuticle and analysis of its chemical composition

Electron microscopy techniques such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) have been invaluable tools for the study of the micromorphology of plant cuticles. However, for electron microscopy, the preparation techniques required may invariably introduce artefacts in cuticle preservation. Further, there are a limited number of methods available for quantifying the image data obtained through electron microscopy. Therefore, in this study, optical microscopy techniques were coupled with staining procedures and, along with SEM were used to qualitatively and quantitatively assess the ultrastructure of plant leaf cuticles. Leaf cryosections of Triticum aestivum (wheat), Zea mays (maize), and Lupinus angustifolius (lupin) were stained with either fat-soluble azo stain Sudan IV or fluorescent, diarylmethane Auramine O and were observed under confocal laser scanning microscope (CLSM). For all the plant species tested, the cuticle on the leaf surfaces could be clearly resolved in many cases into cuticular proper (CP), external cuticular layer (ECL), and internal cuticular layer (ICL). Novel image data analysis procedures for quantifying the epicuticular wax micromorphology were developed, and epicuticular waxes of L. angustifolius were described here for the first time. Together, application of a multifaceted approach involving the use of a range of techniques to study the plant cuticle has led to a better understanding of cuticular structure and provides new insights into leaf surface architecture.

Image analysis of modified cellulose fibers from sugarcane bagasse by zirconium oxychloride

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A method for the digital image analysis of ceramic grains based on shape factor segmentation

A very simple and robust method for ceramics grains quantitative image analysis is presented. Based on the use of optimal imaging conditions for reflective light microscopy of bulk samples, a digital image processing routine was developed for shading correction, noise suppressing and contours enhancement. Image analysis was done for grains selected according to their concavities, evaluated by perimeter ratio shape factor, to avoid consider the effects of breakouts and ghost boundaries due to ceramographic preparation limitations. As an example, the method was applied for two ceramics, to compare grain size and morphology distributions. In this case, most of artefacts introduced by ceramographic preparation could be discarded due to the use of perimeter ratio exclusion range.

Morphometric study by image analysis of Ag-stained nucleoli in thyroids bearing proliferating lesions

The Ag-NOR staining technique and image analysis were used to evaluate morphological parameters (area, perimeter and axis ratio) in nucleoli from normal thyroids and from thyroids bearing proliferating lesions (carcinomas, adenomas and hyperplasias). Regions with normal appearance located close to adenomatous and carcinomatous regions, in the thyroid of every patient, were also analyzed for comparison with the respective pathological regions and with normal thyroids. Statistical analysis of data for the nucleolar area and perimeter allowed the separation of adenomas and carcinomas from hyperplasias and normal tissue but not the two components in each of these two groups. However, if we look at the numbers, a sequence of increasing nucleolar mean areas in the order: normal, hyperplasia, adenoma and carcinoma may be observed, indicating the sequence of increasing rRNA requirements in these different kinds of cells. The axis ratio that denotes the nucleolar shape (round or oblong) did not show significant differences among tissues, suggesting that shape is not important in the characterization of these pathologies. Differences in nucleolar areas and perimeter between normal and affected regions from each patient were statistically significant for adenomas and carcinomas. When these normal regions were compared with the normal thyroids, significant differences were not obtained in the three evaluated parameters. The observations and their importance for histopathological diagnosis are discussed.

A New Technique for Firn Grain-Size Measurement Using SEM Image Analysis

Firn microstructure is accurately characterized using images obtained from scanning electron microscopy (SEM). Visibly etched grain boundaries within images are used to create a skeleton outline of the microstructure. A pixel-counting utility is applied to the outline to determine grain area. Firn grain sizes calculated using the technique described here are compared to those calculated using the techniques of Cow (1969) and Gay and Weiss (1999) on samples of the same material, and are found to be substantially smaller. The differences in grain size between the techniques are attributed to sampling deficiencies (e.g. the inclusion of pore filler in the grain area) in earlier methods. The new technique offers the advantages of greater accuracy and the ability to determine individual components of the microstructure (grain and pore), which have important applications in ice-core analyses. The new method is validated by calculating activation energies of grain boundary diffusion using predicted values based on the ratio of grain-size measurements between the new and existing techniques. The resulting activation energy falls within the range of values previously reported for firn/ice.

Comparison of co-located independent ground-based middle atmospheric wind and temperature measurements with numerical weather prediction models

High-resolution, ground-based and independent observations including co-located wind radiometer, lidar stations, and infrasound instruments are used to evaluate the accuracy of general circulation models and data-constrained assimilation systems in the middle atmosphere at northern hemisphere midlatitudes. Systematic comparisons between observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses including the recent Integrated Forecast System cycles 38r1 and 38r2, the NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses, and the free-running climate Max Planck Institute–Earth System Model–Low Resolution (MPI-ESM-LR) are carried out in both temporal and spectral dom ains. We find that ECMWF and MERRA are broadly consistent with lidar and wind radiometer measurements up to ~40 km. For both temperature and horizontal wind components, deviations increase with altitude as the assimilated observations become sparser. Between 40 and 60 km altitude, the standard deviation of the mean difference exceeds 5 K for the temperature and 20 m/s for the zonal wind. The largest deviations are observed in winter when the variability from large-scale planetary waves dominates. Between lidar data and MPI-ESM-LR, there is an overall agreement in spectral amplitude down to 15–20 days. At shorter time scales, the variability is lacking in the model by ~10 dB. Infrasound observations indicate a general good agreement with ECWMF wind and temperature products. As such, this study demonstrates the potential of the infrastructure of the Atmospheric Dynamics Research Infrastructure in Europe project that integrates various measurements and provides a quantitative understanding of stratosphere-troposphere dynamical coupling for numerical weather prediction applications.