Browsing by Subject "Microarrays"

Now showing 1 - 3 of 3

Open Access
Femtosecond laser fabrication of fiber based optofluidic platform for flow cytometry applications
(SPIE, 2017) Serhatlioglu, Murat; Elbuken, Çağlar; Ortac, Bülend; Solmaz, Mehmet E.
Miniaturized optofluidic platforms play an important role in bio-analysis, detection and diagnostic applications. The advantages of such miniaturized devices are extremely low sample requirement, low cost development and rapid analysis capabilities. Fused silica is advantageous for optofluidic systems due to properties such as being chemically inert, mechanically stable, and optically transparent to a wide spectrum of light. As a three dimensional manufacturing method, femtosecond laser scanning followed by chemical etching shows great potential to fabricate glass based optofluidic chips. In this study, we demonstrate fabrication of all-fiber based, optofluidic flow cytometer in fused silica glass by femtosecond laser machining. 3D particle focusing was achieved through a straightforward planar chip design with two separately fabricated fused silica glass slides thermally bonded together. Bioparticles in a fluid stream encounter with optical interrogation region specifically designed to allocate 405nm single mode fiber laser source and two multi-mode collection fibers for forward scattering (FSC) and side scattering (SSC) signals detection. Detected signal data collected with oscilloscope and post processed with MATLAB script file. We were able to count number of events over 4000events/sec, and achieve size distribution for 5.95μm monodisperse polystyrene beads using FSC and SSC signals. Our platform shows promise for optical and fluidic miniaturization of flow cytometry systems. © 2017 SPIE.
Open Access
Functional genomics in translational cancer research: focus on breast cancer
(Oxford University Press, 2008) Yulug, I. G.; Gur-Dedeoglu, B.
Conventional molecular and genetic methods for studying cancer are limited to the analysis of one locus at a time. A cluster of genes that are regulated together can be identified by DNA microarray, and the functional relationships can uncover new aspects of cancer biology. Breast cancer can be used to provide a model to demonstrate the current approaches to the molecular analysis of cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes to increase power in clinical and biological studies across different sets of data. Recently, meta-analysis approaches have been applied to large collections of microarray datasets to investigate molecular commonalities of multiple cancer types not only to find the common molecular pathways in tumour development but also to compare the individual datasets to other cancer datasets to identify new sets of genes. Several investigators agree that microarray results should be validated. One commonly used method is quantitative reverse transcription PCR (qRT-PCR) to validate the expression profiles of the target genes obtained through microarray experiments. qRT-PCR is attractive for clinical use, since it can be automated and performed on fresh or archived formalin-fixed, paraffin-embedded tissue samples. The outcome of these analyses might accelerate the application of basic research findings into daily clinical practice through translational research and may have an impact on foreseeing the clinical outcome, predicting tumour response to specific therapy, identification of new prognostic biomarkers, discovering targets for the development of novel therapies and providing further insights into tumour biology. © The Author 2008. Published by Oxford University Press.
Open Access
Investigation and comparison of the preprocessing algorithms for microarray analysis for robust gene expression calculation and performance analysis of technical replicates
(IEEE, 2006) İlk, H. G.; İlk, Ö.; Konu, Özlen; Özdağ, H.
Preprocessing of microarray data involves the necessary steps of background correction, normalization and summarization of the raw intensity data obtained from cDNA or oligo-arrays before statistical analysis. Several algorithms, namely RMA, dChip, and MAS5 exist for the preprocessing of Affymetrix microarray data. Previous studies have identified RMA as one of most accurate algorithms while MAS5 was characterized with lower accuracy and sensitivity levels. In this study, performance of different preprocessing algorithms have been compared in terms of ROC characteristics of pairwise intensity differences of microarray replicates. Our findings indicated that all three algorithms predicted in similar order the quality of the technical replicates obtained from a selected set of latin square experiments [1]. On the other hand, RMA exhibited higher performance in terms of accuracy by maximizing the area under the receiver operating curve. The proposed method also is useful for detection of global and/or local artifacts associated within the technical replicas of a microarray experiment. Therefore this study is unique in the sense that it provides an extensive investigation and comparison of preprocessing algorithms and proposes a novel method for the detection and identification of fine technical replicate pair.