Array Comparative Genomic Hybridization (Array CGH) is a cutting-edge technique used to detect chromosomal imbalances, such as deletions, duplications, and amplifications in the DNA. It provides a high-resolution genome-wide analysis, offering insights into genetic changes that may not be visible through traditional methods like karyotyping. Here’s a step-by-step breakdown of how Array CGH works:
1. DNA Extraction
The first step involves extracting DNA from both the test sample (such as a patient’s DNA) and a reference sample (typically from a healthy individual or control).
2. Labeling the DNA
After extracting the DNA, it is labeled with fluorescent come funziona array cgh dyes. The test DNA is labeled with one color (commonly red), and the reference DNA is labeled with a different color (typically green). These labels allow researchers to distinguish between the two DNA samples when analyzed.
3. Hybridization onto a Microarray
The labeled test and reference DNA are mixed together and then hybridized onto a microarray. A microarray is a glass slide or chip that contains thousands of DNA probes. These probes represent specific regions of the human genome and are designed to hybridize (bind) with complementary sequences from the test and reference DNA.
The DNA probes on the microarray are arranged in a grid, with each probe corresponding to a unique region in the genome. When the labeled DNA samples are added, they will bind to the specific probes that match their sequences.
4. Scanning the Microarray
After hybridization, the microarray is scanned using a fluorescence scanner. This scanner detects the fluorescence emitted by the labeled DNA samples. The scanner records the intensity of the red (test) and green (reference) signals for each probe.
- If there is no genetic imbalance in a particular region, both the test and reference DNA will hybridize equally, and the fluorescence intensity will be balanced, showing a mixture of red and green.
- If the test DNA has more copies of a specific region (a duplication), the red signal will be stronger.
- If the test DNA has fewer copies of a region (a deletion), the green signal will dominate.
5. Data Analysis
The fluorescence signals are analyzed to identify copy number variations (CNVs), which are regions where there is a gain (duplication) or loss (deletion) of genetic material. The intensity of the red and green signals is compared for each region of the genome:
- Red signal dominance indicates a duplication (extra copies of genetic material in the test sample).
- Green signal dominance indicates a deletion (missing genetic material in the test sample).
- Equal signals suggest no imbalance in that genomic region.
This allows researchers to pinpoint areas of the genome with genomic imbalances that could be associated with disease.
6. Identifying and Interpreting Results
The results are visualized as a genomic map, showing the regions where imbalances (such as deletions or duplications) are present. This map helps in identifying specific genetic alterations linked to various diseases, such as cancers, developmental disorders, and other genetic conditions.
- The detected imbalances can be cross-referenced with databases to identify whether they are associated with known genetic disorders.
- The identified copy number variations (CNVs) can be used to make a diagnosis or guide further clinical decisions.
Advantages of Array CGH:
- High resolution: Array CGH can detect very small chromosomal imbalances that are missed by traditional methods like karyotyping.
- Genome-wide analysis: The technique can scan the entire genome, identifying imbalances across all chromosomes.
- Sensitive detection: It is capable of detecting subtle genetic variations, such as microdeletions and microduplications.
Conclusion:
Array CGH works by using fluorescently labeled DNA samples from a patient and a reference, which are then hybridized to a microarray. By analyzing the fluorescent signals, the technique identifies genomic imbalances, such as deletions or duplications, offering a powerful tool for genetic analysis and disease diagnosis. It is particularly useful in identifying small genetic changes that might not be detected using older methods.