High-Resolution CE-SSCP Offers Fast, Accurate Spinal Muscular Atrophy Test

Patricia Inacio, PhD avatar

by Patricia Inacio, PhD |

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shutterstock_222886591A study entitled “A simple and precise diagnostic method for spinal muscular atrophy using a quantitative SNP analysis system” published in Electrophoresis describes a system for diagnosis of spinal muscular atrophy that is both fast and accurate.

Spinal Muscular Atrophy (SMA) is caused by mutations in the Smn1 gene that encodes SMN1 protein, a key protein for motor neuron survival. The lack of SMN protein leads to motor neuron dysfunction and death triggering subsequent generalized muscle atrophy. SMA is the leading cause of death in infants and currently there is no cure. In humans, an additional gene Smn2 is present, however, it differs in two nucleotides, in exons 7 and 8, and that by a process known as alternative splicing only produces a small amount of its coded protein is functional.

Thus, the SMA phenotype is caused by differences in sequence that are important to identify and quantify for a correct diagnosis of SMA and its severity. For that, SMN gene copy number and single-nucleotide difference in exon 7 should be analyzed.

SMA diagnosis is performed with examinations such as electromyography (EMG), creatine kinase test, and nerve conduction study. However, genetic analysis of Smn gene is still the most reliable method. The current available methods — Multiplex ligation-dependent probe amplification (MLPA) and sequence-sensitive DNA separation using hydroxyethyl cellulose (HEC) and hydroxypropyl cellulose (HPC) blended polymer matrix – required long hybridization periods and have low resolution.

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Here the authors describe a new method — high-resolution CE-SSCP — capable of simultaneously discriminating SMN eons 7 and 8. Specifically, the authors combined simple one-step multiplex PCR with high-resolution CE-SSCP improved resolution, when compared to previous methods, leading to a precise and accurate quantification of Smn genes.

Thus, the authors suggest that this innovative method has the potential to be used in clinics to diagnose SMA.