Rapid and highly sensitive detection of DNA is critical in diagnosing genetic diseases. Conventional approaches often rely on cumbersome, semi-quantitative amplification of target DNA to improve detection sensitivity. In addition, most DNA detection systems (microarrays, for example), regardless of their need for target amplification, require separation of unhybridized DNA strands from hybridized stands immobilized on a solid substrate, and are thereby complicated by solution-surface binding kinetics. Here, we report an ultrasensitive nanosensor based on fluorescence resonance energy transfer (FRET) capable of detecting low concentrations of DNA in a separation-free format. This system uses quantum dots (QDs) linked to DNA probes to capture DNA targets. The target strand binds to a dye-labelled reporter strand thus forming a FRET donor-acceptor ensemble. The QD also functions as a concentrator that amplifies the target signal by confining several targets in a nanoscale domain. Unbound nanosensors produce near-zero background fluorescence, but on binding to even a small amount of target DNA (approximately 50 copies or less) they generate a very distinct FRET signal. A nanosensor-based oligonucleotide ligation assay has been demonstrated to successfully detect a point mutation typical of some ovarian tumours in clinical samples.

CT screening can reduce death from lung cancer. We sought to improve the diagnostic accuracy of lung cancer screening using ultrasensitive methods and a lung cancer-specific gene panel to detect DNA methylation in sputum and plasma. This is a case-control study of subjects with suspicious nodules on CT imaging. Plasma and sputum were obtained preoperatively. Cases ( = 150) had pathologic confirmation of node-negative (stages I and IIA) non-small cell lung cancer. Controls ( = 60) had non-cancer diagnoses. We detected promoter methylation using quantitative methylation-specific real-time PCR and methylation-on-beads for cancer-specific genes (, and ). DNA methylation was detected in plasma and sputum more frequently in people with cancer compared with controls ( < 0.001) for five of six genes. The sensitivity and specificity for lung cancer diagnosis using the best individual genes was 63% to 86% and 75% to 92% in sputum, respectively, and 65% to 76% and 74% to 84% in plasma, respectively. A three-gene combination of the best individual genes has sensitivity and specificity of 98% and 71% using sputum and 93% and 62% using plasma. Area under the receiver operating curve for this panel was 0.89 [95% confidence interval (CI), 0.80-0.98] in sputum and 0.77 (95% CI, 0.68-0.86) in plasma. Independent blinded random forest prediction models combining gene methylation with clinical information correctly predicted lung cancer in 91% of subjects using sputum detection and 85% of subjects using plasma detection. High diagnostic accuracy for early-stage lung cancer can be obtained using methylated promoter detection in sputum or plasma. .

This paper reports a droplet microfluidic, sample-to-answer platform for the detection of disease biomarkers and infectious pathogens using crude biosamples. The platform exploited the dual functionality of silica superparamagnetic particles (SSP) for solid phase extraction of DNA and magnetic actuation. This enabled the integration of sample preparation and genetic analysis within discrete droplets, including the steps of cell lysis, DNA binding, washing, elution, amplification and detection. The microfluidic device was self contained, with all reagents stored in droplets, thereby eliminating the need for fluidic coupling to external reagent reservoirs. The device incorporated unique surface topographic features to assist droplet manipulation. Pairs of micro-elevations were created to form slits that facilitated efficient splitting of SSP from droplets. In addition, a compact sample handling stage, which integrated the magnet manipulator, the droplet microfluidic device and a Peltier thermal cycler, was built for convenient droplet manipulation and real-time detection. The feasibility of the platform was demonstrated by analysing ovarian cancer biomarker Rsf-1 and detecting Escherichia coli with real time polymerase chain reaction and real time helicase dependent amplification.