Towards many colors in FISH on 3D-preserved interphase nuclei

The article reviews the existing methods of multicolor FISH on nuclear targets, first of all, interphase chromosomes. FISH proper and image acquisition are considered as two related components of a single process. We discuss (1) M-FISH (combinatorial labeling + deconvolution + widefield microscopy); (2) multicolor labeling + SIM (structured illumination microscopy); (3) the standard approach to multicolor FISH + CLSM (confocal laser scanning microscopy; one fluorochrome - one color channel); (4) combinatorial labeling + CLSM; (5) non-combinatorial labeling + CLSM + linear unmixing. Two related issues, deconvolution of images acquired with CLSM and correction of data for chromatic Z-shift, are also discussed. All methods are illustrated with practical examples. Finally, several rules of thumb helping to choose an optimal labeling + microscopy combination for the planned experiment are suggested. Copyright (c) 2006 S. Karger AG, Basel

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

A Platform for Fast Detection of Let-7 Micro RNA Using Polyaniline Fluorescence and Image Analysis Techniques

The project describes a new strategy for transducing hybridization events through modulating intrinsic properties of the electroconductive polymer polyaniline (PANI). When DNA based probes electrostatically interact with PANI, its fluorescence properties are increased, a phenomenon that can be enhanced by UV irradiation. Hybridization of target nucleic acids results in dissociation of probes causing PANI fluorescence to return to basal levels. By monitoring restoration of base PANI fluorescence as little as 10-11 M (10 pM) of target oligonucleotides could be detected within 15 minutes of hybridization. Detection of complementary oligos was specific, with introduction of a single mismatch failing to form a target-probe duplex that would dissociate from PANI. Furthermore, this approach is robust and is capable of detecting specific RNAs in extracts from animals. This sensor system improves on previously reported strategies by transducing highly specific probe dissociation events through intrinsic properties of a conducting polymer without the need for additional labels. The change in fluorescence property of PANI by oligo immobilization and hybridization with mimic let-7 is measured by fluorescence microscope and the image analyzed by MATLAB. A heuristic algorithm determines color threshold of the fluorescent active image. This image segmentation helps to determine the average pixel intensity representing the active image foreground of PANI fluorescence triggered by DNA immobilization and hybridization process. This would help us to quantify response of PANI based biosensor for detecting micro RNA let-7

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Image registration and visualization of in situ gene expression images.

In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be registered or organized into a common coordinate system for alignment to a reference or atlas images. We use three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in-situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work. Our results suggest that the user-defined landmark method is best when considering images from different modalities; automated landmark detection is best when the images are expected to have a high degree of consistency; and the mutual information methodology is useful when the images are from the same modality

Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs [preprint]

Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 μm thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues

Single-molecule FISH in Drosophila muscle reveals location dependent mRNA composition of megaRNPs [preprint]

Single-molecule fluorescence in-situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, methods for its accurate application to tissues are lacking. The utility of smFISH in thick tissue sections must overcome several challenges, including probe penetration of fixed tissue, accessibility of target mRNAs for probe hybridization, high fluorescent background, spherical aberration along the optical axis, and image segmentation of organelles. Here we describe how we overcame these obstacles to study mRNA localization in Drosophila larval muscle samples that approach 50 μm thickness. We use sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3-dimensional multiple-organelle segmentation. The latter allows using independent thresholds for different regions of interest within an image stack. Our approach therefore facilitates accurate measurement of mRNA location in thick tissues

Automated Image Analysis of Cancer Tissue Adapted for Virtual Microscopy

Emerging large-scale digitization of microscopic tissue samples (i.e. virtual microscopy) in biomarker research and clinical pathology enables rapid, objective and repeatable computational analysis of the images. Automated image analysis is likely to be especially useful in personalized medicine, where high-throughput analysis is required for risk prediction, advanced diagnostics and targeted treatment of patients. Malignant tumors are profiled in detail to identify clinically relevant mutations and aberrant protein expression levels. Human observers are still predominantly visually interpreting the increasing number of biomarker assays with fluorescence in situ hybridization (FISH) and immunohistochemical (IHC) stainings. To aid in these quantification tasks, novel applications for automated image analysis of cancer tissues are needed. Virtual microscopy samples require large digital storage space, and image size reduction techniques should be addressed prior to archiving of the images. In this thesis, tools for high-throughput biomarker research in a digital microscopy environment were developed, assessed and adapted to a virtual microscopy setting. The first algorithm developed is intended for automated quantitative assessment of FISH signals to determine the HER2 gene amplification status in breast cancer tissue images, and proved to be comparable to visual scoring. The extent of Ki-67 staining determined in breast cancer tissue images by the second automated algorithm was a significant predictor of patient outcome in both uni- and multivariate analyses. The third algorithm for automated segmentation of tissue images divided the colorectal cancer images into epithelial and stromal compartments with high accuracy. In addition, image compression and scaling led to significant reductions in image sizes without compromising the results of the second and third algorithms introduced previously. The algorithms developed in this thesis are freely accessible to be used by the research community, facilitating external validation of the algorithms. After further validation studies, the algorithms can potentially be applied in clinical pathology especially within diagnostics, risk prediction and targeted treatment of cancer patients in a personalized medicine setting.Patologin työ sisältää runsaasti mikroskooppinäytteiden tulkintaa. Perinteisesti näytteitä on katsottu tavallisella mikroskoopilla, ja näytteistä tarvittavat merkkiaineiden laskennat on tehty silmämääräisesti. Varsinkin laajojen tutkimusaineistojen tulkintaan tarvitaan vaihtoehtoinen menetelmä, sillä työ vaatii paljon aikaa, ja patologien määrä on rajallinen. Tietokoneiden ja digitaalisen kuvantamisen kehittyminen on mahdollistanut virtuaalimikroskopian eli mikroskooppinäytteiden kuvaamisen kokonaisuudessaan suurella tarkkuudella. Kuvattuja näytteitä voidaan katsella internetin välityksellä tavallisella verkkoselaimella. Digitoidut mikroskooppinäytteet mahdollistavat myös kuvien automaattisen tulkinnan tietokoneavusteisella konenäöllä. Yksi virtuaalimikroskopian suurimmista haasteista on kuvien vaatima suuri tallennustila. Tässä väitöskirjassa kehitettiin kolme erilaista menetelmää digitoitujen mikroskooppinäytteiden automaattiseen tulkintaan. Tavoitteena oli päästä patologin tulkintaa vastaaviin tuloksiin. Näytteet olivat peräisin laajoista rinta- ja paksusuolisyöpäsarjoista, ja potilaista oli saatavilla taustatiedot pitkältä seuranta-ajalta. Patologin tekemiä laskentoja verrattiin tilastollisesti konenäön tuottamiin tuloksiin. Väitöskirjatyössä tutkittiin myös kuvapakkauksen ja -skaalauksen vaikutusta konenäön antamiin tuloksiin. Kehitetyt konenäkömenetelmät kykenivät toistamaan luotettavasti patologin antamien laskentojen tulokset. Yksi menetelmistä kykeni jopa ennustamaan potilaan selviytymistä paremmin kuin patologin laskennan perusteella voitiin ennustaa. Konenäkömenetelmät sietivät kohtalaisen suurta kuvapakkausta ja -skaalausta ilman menetelmien tuottamien tulosten heikkenemistä. Väitöskirja osoittaa, että esitetyt automaattiset konenäkömenetelmät ovat luotettavia ja niillä voidaan korvata työläitä silmämääräisiä laskentoja tutkimuskäytössä. Menetelmät pitää vielä luotettavasti validoida jatkotutkimuksissa, jotta niitä voitaisiin hyödyntää myös potilastyössä. Menetelmien avulla voidaan kohdentaa rajallisia patologiresursseja työläistä laskennoista muualle. Konenäkö työskentelee väsymättömästi, joten esimerkiksi yöajat voidaan hyödyntää mikroskooppikuvien automaattiseen tulkintaan. Virtuaalimikroskopian vaatimaa tallennustilaa tietokoneilla voidaan vähentää käyttämällä kuvapakkausta ja -skaalausta, joiden ei todettu heikentävän tässä väitöskirjassa esiteltyjen konenäkömenetelmien toimintaa