A DNA microarray, often called a gene chip, allows scientists to analyze the activity or structure of thousands of genes simultaneously in a single experiment. This technology utilizes a small solid surface, usually glass or silicon, onto which microscopic spots of specific DNA sequences are precisely arranged in a grid pattern. Each spot contains probes that act as molecular anchors for genetic material extracted from a biological sample, such as cells or tissue. The primary conclusion drawn from a microarray is a massive dataset that represents a snapshot of the genetic state of the sample at a given moment.
Determining Differential Gene Expression
The most common conclusion derived from DNA microarrays is the identification of differential gene expression, which compares the activity levels of genes between two distinct samples. This comparison often involves contrasting a diseased cell sample with a healthy control sample, or a treated cell with an untreated one. The core of this analysis involves measuring the amount of messenger RNA (mRNA) present for each gene, as mRNA levels are a direct indicator of how active a gene is.
To achieve this, the mRNA from each sample is converted into complementary DNA (cDNA) and labeled with a unique fluorescent dye, typically red for the test sample and green for the control sample. When these labeled cDNAs are mixed and applied to the microarray, they bind to their complementary DNA probes on the chip. The resulting color intensity at each spot reveals the relative activity of that specific gene between the two samples.
A spot that fluoresces primarily red indicates that the gene is “up-regulated,” meaning it is significantly more active in the test sample than in the control. Conversely, a spot that is predominantly green signals “down-regulation,” showing the gene is less active in the test sample. A yellow spot, resulting from the equal mixing of red and green signals, indicates a gene that is expressed at a similar level in both samples.
The final conclusion is a comprehensive list of genes that show a significant change in activity, often quantified as a “fold change.” This molecular fingerprint helps researchers understand the biological processes that are turned up or down in a particular condition, such as the heightened activity of growth genes in a tumor or the suppression of immune genes during infection.
Identifying Genomic Abnormalities
A different, yet equally important, conclusion is drawn from a specialized version of the technology called array-based Comparative Genomic Hybridization (aCGH). Unlike gene expression analysis, which measures what the cell is doing by looking at RNA, aCGH measures what the cell has by analyzing the structure of the DNA itself. This technique is primarily used to identify structural changes in the genome, which are often associated with genetic disorders and cancer.
The conclusion derived from aCGH is the detection of Copy Number Variations (CNVs), which are segments of the DNA that have been duplicated (gained) or deleted (lost) in the test sample compared to a reference genome. In this method, the genomic DNA from a patient and a healthy control are labeled with different fluorescent dyes and hybridized to the array. The intensity ratio of the two colors at each spot reveals the copy number status of that genomic region.
If the patient’s DNA has a deletion in a region, the reference DNA signal will be stronger, indicating a loss of genetic material in the patient. Conversely, if the patient’s DNA has a duplication, the patient’s signal will be stronger, indicating a gain. These conclusions are crucial for diagnosing conditions like intellectual disability, autism spectrum disorder, and for classifying tumors based on their unique pattern of chromosomal gains and losses.
Interpreting Functional Significance
Moving beyond the raw data of individual gene lists or CNV calls, the ultimate conclusions drawn from microarray experiments relate to the functional significance of these genetic changes. The final step in the analysis is to convert a list of differentially expressed genes or structural changes into a map of cellular function. This higher-level interpretation is achieved through advanced bioinformatic analysis that groups the affected genes.
One key conclusion is the identification of affected biological pathways, often called Pathway Analysis. By determining whether the genes that are up- or down-regulated belong to the same functional group, researchers can conclude that a specific cellular process, such as inflammation, metabolism, or cell death, is being actively modulated. This shifts the focus from a single gene to an entire mechanism, providing a more complete picture of the disease state.
Microarray data also leads to conclusions about disease classification and prognosis. By analyzing the overall gene expression signature, scientists can classify seemingly similar diseases into distinct molecular subtypes, such as differentiating between high-risk and low-risk tumors. This molecular profiling helps predict patient outcomes and guide therapeutic decisions. Furthermore, the analysis can identify which specific genes or pathways are driving the disease, thereby pinpointing potential therapeutic targets for new drugs.