Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnosis of COVID-19 depends on quantitative reverse transcription PCR (qRT-PCR), which is time-consuming and requires expensive instrumentation. Here, we report an ultrasensitive electrochemical biosensor based on isothermal rolling circle amplification (RCA) for rapid detection of SARS-CoV-2. The assay involves the hybridization of the RCA amplicons with probes that were functionalized with redox active labels that are detectable by an electrochemical biosensor. The one-step sandwich hybridization assay could detect as low as 1 copy/μL of N and S genes, in less than 2 h. Sensor evaluation with 106 clinical samples, including 41 SARS-CoV-2 positive and 9 samples positive for other respiratory viruses, gave a 100% concordance result with qRT-PCR, with complete correlation between the biosensor current signals and quantitation cycle (Cq) values. In summary, this biosensor could be used as an on-site, real-time diagnostic test for COVID-19.

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on “pheno–molecular AST”, a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment–machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.

Management of curable sexually-transmitted infections (STI) such as Chlamydia can be revolutionized by highly sensitive nucleic acid testing that is deployable at the point-of-care (POC). Here we report the development of a mobile nucleic acid amplification testing (mobiNAAT) platform utilizing a mobile phone and droplet magnetofluidics to deliver NAAT in a portable and accessible format. By using magnetic particles as a mobile substrate for nucleic acid capture and transport, fluid handling is reduced to particle translocation on a simple magnetofluidic cartridge assembled with reagents for nucleic acid purification and amplification. A mobile phone user interface operating in tandem with a portable Bluetooth-enabled cartridge-processing unit facilitates process integration. We tested 30 potentially Chlamydia trachomatis (CT)-infected patients in a hospital emergency department and confirmed that mobiNAAT showed 100% concordance with laboratory-based NAAT. Concurrent evaluation by a nontechnical study coordinator who received brief training via an embedded mobile app module demonstrated ease of use and reproducibility of the platform. This work demonstrates the potential of mobile nucleic acid testing in bridging the diagnostic gap between centralized laboratories and hospital emergency departments.

Many cancers comprise heterogeneous populations of cells at primary and metastatic sites throughout the body. The presence or emergence of distinct subclones with drug-resistant genetic and epigenetic phenotypes within these populations can greatly complicate therapeutic intervention. Liquid biopsies of peripheral blood from cancer patients have been suggested as an ideal means of sampling intratumor genetic and epigenetic heterogeneity for diagnostics, monitoring and therapeutic guidance. However, current molecular diagnostic and sequencing methods are not well suited to the routine assessment of epigenetic heterogeneity in difficult samples such as liquid biopsies that contain intrinsically low fractional concentrations of circulating tumor DNA (ctDNA) and rare epigenetic subclonal populations. Here we report an alternative approach, deemed DREAMing (Discrimination of Rare EpiAlleles by Melt), which uses semi-limiting dilution and precise melt curve analysis to distinguish and enumerate individual copies of epiallelic species at single-CpG-site resolution in fractions as low as 0.005%, providing facile and inexpensive ultrasensitive assessment of locus-specific epigenetic heterogeneity directly from liquid biopsies. The technique is demonstrated here for the evaluation of epigenetic heterogeneity at p14ARF and BRCA1 gene-promoter loci in liquid biopsies obtained from patients in association with non-small cell lung cancer (NSCLC) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN), respectively.

Accurate and timely diagnostics are critical for managing bacterial infections. The current gold standard, culture-based diagnostics, can provide clinicians with comprehensive diagnostic information including bacterial identity and antimicrobial susceptibility, but they often require several days of turnaround time, which leads to compromised clinical outcome and promotes the spread of antibiotic resistance. Nucleic acid amplification tests such as PCR have significantly accelerated the detection of specific bacteria but generally lack the capacities for broad-based bacterial identification or antimicrobial susceptibility testing. Here, we report an integrated assay based on PCR and high-resolution melt (HRM) for rapid diagnosis for bacterial infections. In our assay, we measure bacterial growth in the presence or absence of certain antibiotics with real-time quantitative PCR or digital PCR to determine antimicrobial susceptibility. In addition, we use HRM and a machine learning algorithm to identify bacterial species based on melt-curve profiles of the 16S rRNA gene in an automated fashion. As a demonstration, we correctly identified the bacterial species and their antimicrobial susceptibility profiles for multiple unknown samples in blinded tests within ∼6.5 h.