An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced <em>Medicago truncatula</em> Mutants

Mutants are invaluable genetic resources for gene function studies. To generate mutant collections, three types of mutagens can be utilized, including ...

University of Oklahoma Health Science Center

The present paper summarizes our first 12 months' experiences of rapid prenatal diagnosis using commercially available diagnostic fluorescence in situ hybridization (FISH) probes for chromosomes X, Y, 13, 18 and 21. The data clearly demonstrate that the advantage of using FISH as an adjunct technique is the fast and reliable determination of the common fetal chromosomal aneuploidies; the results are available in less than 24 hours instead of the 7-14 days with standard techniques.

Rapid prenatal diagnosis of trisomies 13, 18, 21 and sex chromosome anomalies by fluorescence in situ hybridization: a year's experience.

A 32-year-old man was newly diagnosed with acute myelocytic leukemia, classified as acute myeloblastic leukemia with maturation (AML-M2) according to the French-American-British classification system. Conventional chromosome analysis before chemotherapy treatment revealed an abnormal karyotype: a possible segmental duplication of 11q23, plus a translocation between chromosomes 15 and 17 [t(15;17) (q22;q21.1)] in the majority of cells analyzed. Fluorescence in situ hybridization analysis using commercially available probes confirmed the cytogenetic findings. To our knowledge, this is the first report of a combination of t(15;17) and a segmental duplication of 11q23 in a patient with AML-M2.

Identification of t(15;17) and a segmental duplication of chromosome 11q23 in a patient with acute myeloblastic leukemia M2.

Visual search and identification of analyzable metaphase chromosomes using optical microscopes is a very tedious and time-consuming task that is routinely performed in genetic laboratories to detect and diagnose cancers and genetic diseases. The purpose of this study is to develop and test a computerized scheme that can automatically identify chromosomes in metaphase stage and classify them into analyzable and un-analyzable groups. Two independent datasets involving 170 images are used to train and test the scheme. The scheme uses image filtering, threshold, and labeling algorithms to detect chromosomes, followed by computing a set of features for each individual chromosome as well as for each identified metaphase cell. Two machine learning classifiers including a decision tree (DT) based on the features of individual chromosomes and an artificial neural network (ANN) using the features of the metaphase cells are optimized and tested to classify between analyzable and un-analyzable cells. Using the DT based classifier the Kappa coefficients for agreement between the cytogeneticist and the scheme are 0.83 and 0.89 for the training and testing datasets, respectively. We apply an independent testing and a 2-fold cross-validation method to assess the performance of the ANN-based classifier. The area under and receiver operating characteristic (ROC) curve is 0.93 for the complete dataset. This preliminary study demonstrates the feasibility of developing a computerized scheme to automatically identify and classify metaphase chromosomes.

Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images.

We developed and tested a new automated chromosome karyotyping scheme using a two-layer classification platform. Our hypothesis is that by selecting most effective feature sets and adaptively optimizing classifiers for the different groups of chromosomes with similar image characteristics, we can reduce the complexity of automated karyotyping scheme and improve its performance and robustness. For this purpose, we assembled an image database involving 6900 chromosomes and implemented a genetic algorithm to optimize the topology of multi-feature based artificial neural networks (ANN). In the first layer of the scheme, a single ANN was employed to classify 24 chromosomes into seven classes. In the second layer, seven ANNs were adaptively optimized for seven classes to identify individual chromosomes. The scheme was optimized and evaluated using a "training-testing-validation" method. In the first layer, the classification accuracy for the validation dataset was 92.9%. In the second layer, classification accuracy of seven ANNs ranged from 67.5% to 97.5%, in which six ANNs achieved accuracy above 93.7% and only one had lessened performance. The maximum difference of classification accuracy between the testing and validation datasets is <1.7%. The study demonstrates that this new scheme achieves higher and robust performance in classifying chromosomes.

Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme.

The authors developed an integrated computer-aided detection (CAD) scheme for detecting and classifying metaphase chromosomes as well as assessing its performance and robustness. This scheme includes an automatic metaphase-finding module and a karyotyping module and it was applied to a testing database with 200 digital microscopic images. The automatic metaphase-finding module detects analyzable metaphase cells using a feature-based artificial neural network (ANN). The ANN-generated outputs are analyzed by a receiver operating characteristics (ROC) method and an area under the ROC curve is 0.966. Then, the automatic karyotyping module classifies individual chromosomes of this cell into 24 types. In this module, a two-layer decision tree-based classifier with eight ANNs established in its connection nodes was optimized by a genetic algorithm. Chromosomes are first classified into seven groups by the ANN in the first layer. The chromosomes in these groups are then separately classified by seven ANNs into 24 types in the second layer. The classification accuracy is 94.5% in the first layer. Six ANNs achieved the accuracy above 95% and only one had lessened performance (80.6%) in the second layer. The overall classification accuracy is 91.5% as compared to 86.7% in the previous study using two independent datasets randomly acquired from our genetic laboratory. The results demonstrate that our automated scheme achieves high and robust performance in identification and classification of metaphase chromosomes.

Development and Assessment of an Integrated Computer-Aided Detection Scheme for Digital Microscopic Images of Metaphase Chromosomes.

Trisomy 8: a common finding in mouse embryonic stem (ES) cell lines.

The variability of a small supernumerary marker chromosome (sSMC)-related phenotype is determined by the molecular component, the size, and shape of the marker chromosome. As fluorescence in situ hybridization has limitations regarding the resolution, efficiency, and accuracy. Recently, array comparative genomic hybridization (aCGH) was used for sSMC characterization. In this study, twenty cases with sSMCs were characterized by aCGH and FISH. Chromosomal origin of the marker chromosomes were successfully identified in seventeen of them. For the three cases with negative aCGH results, two of them were more likely due to that the sSMCs only contained centromere heterochromatin, whereas the reason for the remaining case with negative aCGH finding was uncertain. In order to establish a stronger genotype-phenotype correlation for clinical service in the future and avoid miss characterization, more sSMC cases were needed to be detailed characterized. This will help to clarify the variable clinical characteristics of sSMCs and provide additional information to aid clinical service and future research.

Molecular characterization of 20 small supernumerary marker chromosome cases using array comparative genomic hybridization and fluorescence in situ hybridization.

Shibo Li

Genetics Laboratory

University of Oklahoma Health Science Center

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

Shibo Li

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Genetics Laboratory

University of Oklahoma Health Science Center

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

Genetics Laboratory