Accurate and efficient microbial diagnosis is crucial for effective molecular diagnostics, especially in the field of human healthcare. The gold standard equipment widely employed for detecting specific microorganisms in molecular diagnosis is quantitative real-time polymerase chain reaction (qRT-PCR). However, its limitations in low metagenomic DNA yield samples necessitate exploring alternative approaches. Digital PCR, by quantifying the number of copies of the target sequence, provides absolute quantification results for the bacterial strain. In this study, we compared the diagnostic efficiency of qRT-PCR and digital PCR in detecting a particular bacterial strain (Staphylococcus aureus), focusing on skin-derived DNA samples. Experimentally, specific primer for S. aureus were designed at transcription elongation factor (greA) gene and the target amplicon were cloned and sequenced to validate efficiency of specificity to the greA gene of S. aureus. To quantify the absolute amount of microorganisms present on the skin, the variable region 5 (V5) of the 16S rRNA gene was used, and primers for S. aureus identification were used to relative their amount in the subject's skin. The findings demonstrate the absolute convenience and efficiency of digital PCR in microbial diagnostics. We suggest that the high sensitivity and precise quantification provided by digital PCR could be a promising tool for detecting specific microorganisms, especially in skin-derived DNA samples with low metagenomic DNA yields, and that further research and implementation is needed to improve medical practice and diagnosis.
Keyphrases
- healthcare
- single molecule
- circulating tumor
- cell free
- real time pcr
- staphylococcus aureus
- microbial community
- genome wide
- primary care
- soft tissue
- wound healing
- copy number
- nucleic acid
- high resolution
- quality improvement
- antibiotic resistance genes
- circulating tumor cells
- gene expression
- genome wide identification
- cystic fibrosis
- biofilm formation
- social media
- amino acid
- molecularly imprinted
- silver nanoparticles
- bioinformatics analysis