A comprehensive cytogenetics tutorial program, encompassing changeable G-band resolutions

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Abstract

Chromosome analysis is a basic science with medical implication. Karyotyping is a procedure to study an individual's chromosome make-up. It is time consuming to train students and clinical technologists to recognize patterns of G-banded chromosomes because of the dynamic nature of G-band resolutions in different metaphase spreads. High resolution G-bands are desirable because they provide detailed information for structural analysis. However, it is challenging to identify chromosomes at higher resolution levels even for many cytogenetics technologists. In response to the need for training students to identify human chromosomes at variable G-band resolutions, we present in this paper an advanced version of virtual reality (VR)-based interactive karyotyping program capable of manipulating G-band resolutions for human cytogenetics education. The program can generate different metaphase spreads ranging from short and well separate chromosomes at low G-band resolutions to long, curved, and overlapped chromosomes at high G-band resolutions. Other features include a scoring system, helping strategies, and the progress reports. The traditional “cut and paste” karyotyping method for chromosome separation is incorporated in the software. This method is compared with the “simple clicking” method which is based on an edge detection technique for outlining each chromosome. The comprehensive program is suitable for in-depth training of advanced students.

Introduction

Virtual reality (VR) is a young and growing field that has a wide range of applications, such as education, biomedical training, health care, manufacturing, military, and entertainment. VR is especially useful for biomedical training because of the complexity of living systems which depend heavily on visualization and experimentation for understanding and learning. VR is therefore an effective way to simplify and clarify the comprehension of complex biological phenomena [1].

Cytogenetics, the study of chromosomes, is an important part of genetics education. In most universities and medical schools, the laboratory portion of human cytogenetics are either absent or limited to observing the images of metaphase spreads with pre-labeled chromosome numbers which may be used by students for cutting and pasting on a karyotype sheet [2], [3]. Attempts to improve the lab exercise have been made by developing more attractive online karyotyping programs [4], [5] and karyotyping software [2]. However, these approaches are entirely based on static chromosome images and thus have limited functions for training purposes. To further advance human cytogenetics laboratory education, we recently introduced a virtual reality based human cytogenetics learning program [6]. The program was developed with the model building for each of the 24 (22 autosomes + X and Y) chromosomes based on original digitized G-banded chromosome images captured from the microscope. By means of Matlab and OpenGL, the chromosome models were visibly identical to original images but more adaptable because of their dynamic nature. Many features, such as changing of chromosome shapes, separation of overlapping chromosomes, and changing of metaphase spreads with random chromosome distributions, made this program useful and attractive for learning.

Whereas our recent work [6] was intended for general human cytogenetics education, the present paper represents an advanced version of human cytogenetics program with new algorithms for changing G-band resolutions. Thus, the present work is specifically designed for advanced cytogenetics students as well as for training clinical technologists.

The term chromosome banding refers both to the process of producing banding patterns on chromosomes and to the patterns themselves [7]. Chromosome banding is crucial for cytogenetics studies. Many banding techniques have been developed for specific purposes [8], [9], [10]. These techniques can be divided into two main groups: (1) techniques that produce horizontal bands along the length of the entire chromosome to provide landmarks for individual chromosome identification (e.g., G-, Q-, and R-banding) and (2) techniques that stain only a specific band or region of the chromosome (e.g., C-, T-, and NOR-banding) [11].

Despite the diversity of banding techniques, the chromosome learning program is typically based on G-banded chromosomes, since G-banding is the most widely used technique in clinical genetics laboratories for identifying normal and abnormal chromosomes.

G-band patterns at high resolutions are crucial for detecting chromosome abnormalities such as deletions, insertion, inversion, and translocations. Thus, high resolution G-banding is important for detecting structure changes. The identification of high resolution G-banded chromosomes, however, has inherent difficulties because the less condensed chromosomes tend to curve, twist, and overlap with one another. Furthermore, the G-band patterns at higher resolutions are considerably different from those at lower resolutions [9], and the banding changes between low and high resolutions are not strictly linear. Those who are accustomed to identify chromosomes at 350–450 band levels may have difficulties in identifying chromosomes at higher resolutions. The present program, which provides students with the opportunity to recognize chromosomes with changeable G-band resolutions, is therefore designed for advanced cytogenetics courses and clinical training. Highly trained cytogenetics technologists are crucial for accurate diagnosis of chromosomal-based diseases.

Section snippets

Basic features of dynamic models

G-banded human chromosome images originally captured from metaphase spreads on microscope slides were selected for model building. Static chromosome models were initially developed with programs in MatLab and OpenGL from the fixed G-banded images. These models were then transformed into dynamic virtual chromosomes so that their shapes can be altered by curving at any points with any angles through our recently developed algorithms [6]. Abnormal chromosomes were also modeled for training

Variable virtual metaphase spreads for karyotyping

With the availability of virtual chromosomes at variable band resolution levels, along with other features such as changeable shapes, the chromosome features in metaphase spreads for karyotyping are greatly enriched. Users may learn karyotyping progressively from low G-band resolution levels with relatively straight and individually separate chromosomes to longer, higher G-band levels with bent and overlapping chromosomes. Additional features such as chromosome aberrations may be added to

Database and management

The database was designed in MySQL for keeping records for both instructors and students. It contains three tables: instructor table, student table, and student record table. For administrating and monitoring student learning progress, instructors need to log in their accounts in order to search the records of student performances. Adding new or deleting existing student accounts is also possible.

The database is for instructors to manage any student's account and for students to view their

Conclusions

The software of a comprehensive cytogenetics tutorial program was developed. The dynamic human chromosome models can be randomly mixed to generate different metaphase spreads with different resolutions and bending angles. Since the G-banding pattern of each chromosome is not static, it is essential that the simulated chromosome models are capable of changing shapes, G-band resolutions, and structure for showing chromosome-based diseases. The program provides a virtual learning environment which

Mode of availability

Our program will be made available to readers who are interested in using this program by contacting the corresponding author through the following email address: [email protected].

Conflicts of interest

None.

Acknowledgements

This work was co-funded by NSF (National Science Foundation) and NWICG (North West Indiana Computing Grid).

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Area of interest: 5.19 Theory and Applications for Computer Science and Software Engineering. Second area of interest: 4.21 Medical Signal Processing.

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