Of the key molecular diagnostics technologies, only microarrays and NGS are designed to look at multiple areas of the genome simultaneously. Let’s see where they stand when it comes to clinical applications, and what the future might hold.
What are microarrays?
DNA microarrays are chips containing tens of thousands of oligonucleotide sequences (oligos), each representing a single gene, part of a gene, or RNA transcript, as spots on a solid surface. They enable you to detect the corresponding genes or transcripts through fluorescently labeling the samples, hybridizing them to the oligos on the chip, and imaging under fluorescence excitation.
Both DNA and RNA (after converting to cDNA) can be analyzed by microarray and, using different labels, two samples (e.g. test and reference) can be compared directly in a multiplexed assay.
Pros and cons of microarrays
Microarrays have been a common sight in clinical labs for decades. The assays are well validated, and clinicians tend to be familiar with the tests and their outcomes. This familiarity is a strength of the technology that has helped maintain its place as a key diagnostic tool, notably above next generation sequencing (NGS).
Unlike other molecular diagnostic techniques, like qPCR and fluorescence in situ hybridization (FISH), which I review in part two of this series, microarrays enable you to probe a huge number of genes simultaneously, thanks to the enormous number of oligos that can be placed on a single chip, and in high resolution, as you can use multiple probes per gene.
The technology is also well suited for detecting large chromosomal abnormalities, such as copy number variation (CNV). This is achieved by array comparative genomic hybridization (aCGH), where test and reference samples are multiplexed with different fluorescent labels. It’s also suited to detecting smaller mutations, such as single nucleotide polymorphisms (SNPs), though this requires arrays with allele-specific probes.
This brings us to the main drawback of microarray technology: its dependence on (oligo) probes for detection. Though they enable high resolution assays, the results are completely dependent on the probe selection. Unlike NGS, you need to have detailed knowledge of the mutations you’re studying, making it suitable only for well-characterized aberrations.
Clinical applications of microarrays
Diagnostic laboratories often use microarrays for gene expression profiling, studying mRNA levels by first converting to cDNA and comparing against a reference. Abnormal expression levels are a trait of diseases like cancers, and high-throughput high-capacity techniques like microarrays are well suited to tracking the complexities of these diseases.
It’s also common for clinical scientists to use microarrays to study conditions that fall under Mendelian diseases and reproductive health, and in cancer diagnostics. They use array comparative genomic hybridization (aCGH) to look at large aberrations, like CNV, comparing two closely related samples at a higher resolution than is possible by FISH.
Microarrays going forward
In the short-to-medium term, using microarrays for genetic testing in the clinic is likely to continue and grow. For studying multiple areas of the genome simultaneously, this approach is more efficient than any of the other established techniques in molecular diagnostics. It’s a trusted and time-tested technology for clinical scientists doing comprehensive genomic analysis.
In the longer term, the success of microarray technology will largely depend on how it stacks up against diagnostic assays based on next-generation sequencing (NGS). Right now, NGS is more expensive and still relatively new to the molecular diagnostics space. But that balance will likely shift in the next few years as NGS becomes cheaper and more widely recognized.
What is DNA sequencing?
DNA sequencing refers to a range of techniques that analyze sections of the genome to single-nucleotide resolution. The traditional Sanger method is still popular in clinical applications, but high-throughput next generation sequencing (NGS) techniques are on the rise. NGS enables whole-genome, whole-exome, transcriptome, and targeted sequencing with relative ease.
Pros and cons of next-generation sequencing (NGS)
The key reason for choosing NGS for clinical diagnostics is its combination of high throughput, speed, and resolution. Like microarrays, NGS assays can efficiently analyze the entire genome or exome, or focus on a specified number of targeted locations in the genome.
NGS’s single-nucleotide resolution also enables it to detect even the smallest possible mutations (SNPs) without necessarily requiring knowledge of the mutation in advance. As the technology improves, the combined detection of SNP and larger abnormalities is becoming easier, providing an all-in-one solution for detecting multiple types of mutations.
One of the challenges that NGS faces in the clinic is that it doesn’t yet have the long and proven track record or familiarity of other technologies.However, this is changing rapidly, with continuing innovation and decreases in the cost-per-base.
NGS technology also requires a fair bit of expertise to run assays and interpret data, but this too is changing. Recent progress on guidelines from the US Food and Drug Administration and “game-changing” FDA cancer panel approvals are removing these barriers and leading to increased investment and clinical adoption.
Key clinical applications of NGS
The biggest market for NGS is currently in reproductive health, more specifically non-invasive prenatal testing (NIPT), where it’s gradually replacing array-based techniques such as array comparative genomic hybridization (aCGH). NIPT provides a safer alternative to invasive tests as it analyzes fetal cell-free DNA (cfDNA) from the mother’s circulation, making detection of genetic disorders such as Down syndrome easier.
Use of NGS is also growing in oncology, Mendelian diseases, complex diseases, and infectious diseases . Clinical scientists can use NGS assays either for diagnosis or for decisions on treatment by studying both small mutations (e.g. SNPs and indels) and larger abnormalities (e.g. CNV) at the same time.
Refinement of cancer diagnoses is a particular growth area for NGS. As treatments become more personalized, there’s a need for classifying cancers in terms of their underlying mutations to help direct treatment options in the clinic. NGS plays a key role in this trend towards precision medicine, helping to minimize the human and financial costs of ineffective cancer treatments.
The future of NGS
Out of the four key molecular diagnostics techniques discussed in this series (qPCR, FISH, microarrays, and NGS), NGS held the smallest share of the molecular diagnostics market in 2015 (3.3%), but also the highest growth rate. This is likely because of its potential and ability to provide comprehensive data on a whole range of abnormalities.
The speed at which NGS will grow in different clinical markets will probably depend on pushing sequencing costs down further, as well as developments in the regulatory landscape. As clinical scientists get more familiar with the possibilities with NGS, they can also contribute to higher growth through demand.
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