Improvements on segment based contours method for DNA microarray image segmentation

DNA microarray is an efficient biotechnology tool for scientists to measure the expression levels of large numbers of genes, simultaneously. To obtain the gene expression, microarray image analysis needs to be conducted. Microarray image segmentation is a fundamental step in the microarray analysis process. Segmentation gives the intensities of each probe spot in the array image, and those intensities are used to calculate the gene expression in subsequent analysis procedures. Therefore, more accurate and efficient microarray image segmentation methods are being pursued all the time. In this dissertation, we are making efforts to obtain more accurate image segmentation results. We improve the Segment Based Contours (SBC) method by implementing a higher order of finite difference schemes in the partial differential equation used in our mathematical model. Therefore, we achieved two improved methods: the 4th order method and the 8th order method. The 4th order method could be applied to segment both the cDNA microarray images and the Affymetrix GeneChips, while the 8 th order method could be applied to segment only the cDNA microarray images, due to the limitation of the current image resolution. The mathematical derivation shows that both our 4th order method and 8th order method are better approximating the C-V model [Chan & Vese, 2001] than the SBC method, which means they will offer more accurate segmentation results than the SBC method. Besides mathematical proof, we do the practical experiments to double check the conclusion drawn from the mathematical derivation. Both the 4th order method and the 8th order method are used to segment microarray images, and the output segmentation results—the intensities of each probe cell in the microarray image—are being compared to the results from the SBC method and two other mainstream microarray image segmentation methods, the Globaly Optimal Geodesic Active Contours (GOGAC) method and the GeneChip Operating System (GCOS) software, for more valid evaluation. To give the ground true values of intensities as the standard for different segmentation methods comparison, a microarray image simulator is introduced to generate the simulated images used in our experiments. The simulated microarray images have all the characteristics that real microarray images have, and the true intensity values of each probe spot in the image are provided by this simulator. Intensity values segmented by those segmentation methods are compared to the true intensity values. Therefore, we could evaluate that one segmentation method is more accurate than the other methods if its intensity values are closer to the true values. We conduct several analysis procedures in the segmentation results comparison part to convince our analysis results. Intensity analysis, paired t-test and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) hierarchy cluster experiments are applied to analyze intensity values of those methods. The segmentation output analysis results show that our 4th order method and the 8th order method could offer more accurate segmentation than the SBC method, the GCOS method and the GOGAC method on some kinds of the microarray images. There are accuracy improvements achieved with the 8 th order method over the 4th order method on the cDNA microarray image. On the Bovine type Affymetrix GeneChip image, there is no significant difference between the 4th order method and the 8th order method

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

Identification of genes involved in leukaemia and differentiation induced by activated mutants of the GM-CSF receptor β subunit.

Interleukin (IL)-3, IL-5 and granulocyte-macrophage-colony-stimulating factor (GM-CSF) are cytokines that affect the growth, survival and differentiation of many cells within the haematopoietic system. The functions of these factors are mediated by membrane bound receptor complexes that are composed of specific ligand binding subunits (α)and a common signal transducing subunit(hβc). Constitutively activated mutants of hβc have been previously identified that are able to confer factor-independent signalling in a number of haematopoietic cell lines (including FDC-P1 and FDB-1). These activated mutants fall into two classes defined by the location of the mutation and their biochemical and leukaemogenic properties. In particular, the transmembrane mutant, V449E, causes an acute myeloid leukaemia in vivo, whereas the extracellular mutants (FI∆ or I374N) cause chronic myeloproliferative disorders. The work described in this thesis used the activated hβc mutants to uncover novel transcriptional events induced by the GM-CSF/IL-3/IL-5 receptor complex and to define pathways associated with proliferation and differentiation. Large-scale gene expression profiling techniques were used to investigate the genes involved in these biological processes in the murine myelomonocytic cell line FDC-P1, and the bi-potent FDB-1 myeloid cell line, which are responsive to IL-3 and GM-CSF. Membrane arrays were used to identify differences in gene expression between I374N and V449E expressing FDC-P1 cells. This technique revealed that the gene Ptpmt1 was differentially expressed between V449E and I374N, which was subsequently confirmed by Northern blotting. This finding suggested that the phosphatase encoded by Ptpmt1 may be involved in the different outcomes induced by these two hβc mutants. Northern analysis also revealed Ptpmt1, Nab1 and Ddx26b to be regulated in response to human GM-CSF in FDC-P1 cells expressing human GM CSFα and hβc. A large-scale cDNA microarray experiment was also performed to identify genes that are selectively expressed during differentiation of FI∆ expressing FDB-1 cells, compared to proliferating V449E expressing FDB-1 cells over 24 hours. A comprehensive analysis approach was adopted to examine the microarray data and identify differentially expressed genes. Among the genes displaying differential expression were Btg1, S100a9, Cd24, and Ltf found to be differentiation-associated and Bnip3, Cd34, Myc, Nucleophosmin, and Nucleostemin found to be proliferation-associated. Hipk1, Klf6, Sp100, and Sfrs3 were also identified as potential transcriptional regulators during growth and differentiation. Northern analysis was used to confirm differences in expression for these 13 genes between FI∆ and V449E expressing FDB-1 cells. Eleven of the 13 genes examined were confirmed to be differentially expressed between FI∆ and V449E expressing FDB-1 cells over 24 hours. Furthermore, six genes (Btg1, Hipk1, Cd24, Cd34, Klf6 and Nucleostemin) examined over 72 hours revealed differences in gene expression at early (6-12 hours) and late (48-72 hours) time points. Cell surface expression of CD24 protein was also shown to be induced upon FI∆ expression or GM-CSF induced differentiation of FDB-1 cells, consistent with elevated levels of Cd24 mRNA in FI∆ cells over time. Based on their confirmed gene expression differences seen on the microarrays and Northern analysis, four genes (Btg1, Cd24, Klf6 and Nucleostemin) were selected for over-expression analysis in FDC-P1 or FDB-1 cells, in order to gain insights into the function of these genes. Optimisation of the retroviral infection process was performed so that the role of these genes in proliferation and differentiation could be investigated in the FDB-1 model. Such preliminary functional experiments in FDB-1 cells will enable prioritisation of the genes for further analysis of their function in primary cells. Thus, the work in this thesis describes the first use of microarrays to identify gene expression differences between hβc mutants with differential activities affecting myeloid growth and differentiation.Thesis (PhD)-- School of Medicine, 200

maigesPack: A Computational Environment for Microarray Data Analysis

Microarray technology is still an important way to assess gene expression in molecular biology, mainly because it measures expression profiles for thousands of genes simultaneously, what makes this technology a good option for some studies focused on systems biology. One of its main problem is complexity of experimental procedure, presenting several sources of variability, hindering statistical modeling. So far, there is no standard protocol for generation and evaluation of microarray data. To mitigate the analysis process this paper presents an R package, named maigesPack, that helps with data organization. Besides that, it makes data analysis process more robust, reliable and reproducible. Also, maigesPack aggregates several data analysis procedures reported in literature, for instance: cluster analysis, differential expression, supervised classifiers, relevance networks and functional classification of gene groups or gene networks

Can graph-cutting improve microarray gene expression reconstructions?

Microarrays produce high-resolution image data that are, unfortunately, permeated with a great deal of “noise” that must be removed for precision purposes. This paper presents a technique for such a removal process. On completion of this non-trivial task, a new surface (devoid of gene spots) is subtracted from the original to render more precise gene expressions. The graph-cutting technique as implemented has the benefits that only the most appropriate pixels are replaced and these replacements are replicates rather than estimates. This means the influence of outliers and other artifacts are handled more appropriately (than in previous methods) as well as the variability of the final gene expressions being considerably reduced. Experiments are carried out to test the technique against commercial and previously researched reconstruction methods. Final results show that the graph-cutting inspired identification mechanism has a positive significant impact on reconstruction accuracy

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Digital detection of exosomes by interferometric imaging

Exosomes, which are membranous nanovesicles, are actively released by cells and have been attributed to roles in cell-cell communication, cancer metastasis, and early disease diagnostics. The small size (30–100 nm) along with low refractive index contrast of exosomes makes direct characterization and phenotypical classification very difficult. In this work we present a method based on Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS) that allows multiplexed phenotyping and digital counting of various populations of individual exosomes (>50 nm) captured on a microarray-based solid phase chip. We demonstrate these characterization concepts using purified exosomes from a HEK 293 cell culture. As a demonstration of clinical utility, we characterize exosomes directly from human cerebrospinal fluid (hCSF). Our interferometric imaging method could capture, from a very small hCSF volume (20 uL), nanoparticles that have a size compatible with exosomes, using antibodies directed against tetraspanins. With this unprecedented capability, we foresee revolutionary implications in the clinical field with improvements in diagnosis and stratification of patients affected by different disorders.This work was supported by Regione Lombardia and Fondazione Cariplo through POR-FESR, project MINER (ID 46875467); Italian Ministry of Health, Ricerca Corrente. This work was partially supported by The Scientific and Technological Research Council of Turkey (grant #113E643). (Regione Lombardia; 46875467 - Fondazione Cariplo through POR-FESR, project MINER; Italian Ministry of Health, Ricerca Corrente; 113E643 - Scientific and Technological Research Council of Turkey)Published versio

Biophotonic Tools in Cell and Tissue Diagnostics.

In order to maintain the rapid advance of biophotonics in the U.S. and enhance our competitiveness worldwide, key measurement tools must be in place. As part of a wide-reaching effort to improve the U.S. technology base, the National Institute of Standards and Technology sponsored a workshop titled "Biophotonic tools for cell and tissue diagnostics." The workshop focused on diagnostic techniques involving the interaction between biological systems and photons. Through invited presentations by industry representatives and panel discussion, near- and far-term measurement needs were evaluated. As a result of this workshop, this document has been prepared on the measurement tools needed for biophotonic cell and tissue diagnostics. This will become a part of the larger measurement road-mapping effort to be presented to the Nation as an assessment of the U.S. Measurement System. The information will be used to highlight measurement needs to the community and to facilitate solutions