Adaptive Segmentation of DAPI-stained, C-banded, Aggregated and Overlapping Chromosomes.

Existing algorithms for automated segmentation of chromosomes and centromeres do not work well for condensed, C-banded and DAPI-stained chromosomes and centromeres. Overlapping and aggregation, which frequently occur in metaphase spreads, introduce additional challenges to the counting of chromosomes and centromeres in the Dicentrics Chromosome Assay (DCA). In this paper, we introduce adaptive algorithms, for segmentation of difficult metaphase spreads that include overlapping and aggregated chromosomes. In order to enhance and segment chromosomes, two optimizations are done: (1) the best algorithm among several options is automatically chosen based on predefined figures of merit, (2) the algorithm is automatically optimized with a binary search to modify its parameters to achieve predefined thresholds. These algorithms are designed to separate mildly or moderately aggregated chromosomal clusters. The clusters are segmented by skeleton junctions, reduction of the overall object thickness, and the watershed algorithm. The chromosomes are characterized by rules we establish, using minimal assumptions. Centromeres are detected by detecting bright spots on the surface of the chromosomes, and then using cluster analysis and shape and intensity profiles to identify them as centromeres. High sensitivity and specificity for chromosome and centromere detection were achieved.

A DAPI-Based Modified C-banding Technique for a Rapid Achieving High Photographic Contrast of Centromeres on Chromosomes.

Many chromosome assays rely on the quantification of chromosome abnormalities in cells, and one important abnormality is the existence of more than one centromere for each chromosome. The quantification of such abnormalities has been studied before. However, this process is labor-intensive and time consuming. Thus, this assay is challenging for ex-laboratory applications, where speed is required. We present a visualization method that uses a cheap stain-DAPI, long (e.g., high-resolution) chromosomes and our modified C-banding method for labeling chromosomes. The labeled chromosomes can then be easily seen with a conventional and readily available fluorescence microscopy system. This method achieves an acceleration of the detection of the presence of constitutive heterochromatin in chromosomal centromeres by more than 10 times, to ~2 h, in Human lymphocyte cells and in cells of the human Jurkat line. This new procedure will ultimately provide an easier and cheaper alternative to FISH/PNA probes, or the classic Giemsa staining method. Simplification and reduction in time of the overall procedure will enable the utilization of centromere-counting assays in laboratory and ex-laboratory applications, including in emergency response.

Metaphase Cells Enrichment for Efficient Use in the Dicentric Chromosome Assay.

The dicentric chromosome assay (DCA), is considered the 'gold standard' for radiation biodosimetry. Yet, DCA, as currently implemented, may be impractical for emergency response applications, especially when time is of the essence, owing to its labor-intensive and time-consuming nature. The growth of a primary lymphocyte culture for 48 h in vitro is required for DCA, and manual scoring of dicentric chromosomes (DCs) requires an additional 24-48 h, resulting in an overall processing time of 72-96 h for dose estimation. In order to improve this timing. we introduce a protocol that will detect the metaphase cells in a population of cells, and then will harvest only those metaphase cells. Our metaphase enrichment approach is based on fixed human lymphocytes incubated with monoclonal, anti-phosphorylated H3 histone (ser 10). Antibodies against this histone have been shown to be specific for mitotic cells. Colcemid is used to arrest the mitotic cells in metaphase. Following that, a flow-cytometric sorting apparatus isolates the mitotic fraction from a large population of cells, in a few minutes. These mitotic cells are then spread onto a slide and treated with our C-Banding procedure [Gonen et al. 2022], to visualize the centromeres with DAPI. This reduces the chemical processing time to ~2 h. This reduces the time required for the DCA and makes it practical for a much wider set of applications, such as emergency response following exposure of a large population to ionizing radiation.

The role of AGG interruptions in the FMR1 gene stability: A survey in ethnic groups with low and high rate of consanguinity.

BACKGROUND: The prevalence and the role of AGG interruptions within the FMR1 gene in the normal population is unknown. In this study, we investigated the frequent of AGG loss, in one or two alleles within the normal population. The role of AGG in the FMR1 stability has been assessed by correlating AGG loss to the prevalence of premutation/full mutation in two ethnic groups differing in their consanguinity rate: high versus low consanguinity rate (HCR vs. LCR). METHODS: The CGG repeat allele size and AGG presence were measured in 6,865 and 6,204 females belonging to the LCR (5%) and HCR (>45%) groups, respectively, by Tripled-Primed-PCR technique. RESULTS: A lower prevalence of the premutation was observed in the HCR (1:158) as compared to the LCR group (1:128). No full mutation was found in the HCR females while in the LCR group the prevalence found was 1:1,149. Homozygosity rate was higher in the HCR population compared to the LCR group.The overall AGG loss was higher in the HCR population than in the LCR and increased with increased CGG repeat number in both ethnic groups. CONCLUSIONS: Although we observed a significantly higher rate of homozygosity and AGG loss in the HCR group, this did not affect the prevalence of the premutation and full mutation in this population. Their prevalence was significantly lower than in the LCR population. Finally, we discuss whether the loss of AGG could be also a polymorphic event but not only a stabilizing factor.

Early non-invasive detection of breast cancer using exhaled breath and urine analysis.

The main focus of this pilot study is to develop a statistical approach that is suitable to model data obtained by different detection methods. The methods used in this study examine the possibility to detect early breast cancer (BC) by exhaled breath and urine samples analysis. Exhaled breath samples were collected from 48 breast cancer patients and 45 healthy women that served as a control group. Urine samples were collected from 37 patients who were diagnosed with breast cancer based on physical or mammography tests prior to any surgery, and from 36 healthy women. Two commercial electronic noses (ENs) were used for the exhaled breath analysis. Urine samples were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS). Statistical analysis of results is based on an artificial neural network (ANN) obtained following feature extraction and feature selection processes. The model obtained allows classification of breast cancer patients with an accuracy of 95.2% ±â€¯7.7% using data of one EN, and an accuracy of 85% for the other EN and for urine samples. The developed statistical analysis method enables accurate classification of patients as healthy or with BC based on simple non-invasive exhaled breath and a urine sample analysis. This study demonstrates that available commercial ENs can be used, provided that the data analysis is carried out using an appropriate scheme.