Assay Rapidly Detects Bacteria in a Single Blood Droplet

Weian Zhao PhD Assistant Professor at the Sue and Bill Gross Stem Cell Research Center, Chao Family Comprehensive Cancer Center, Department of Biomedical Engineering, Edwards Lifesciences Center for Advanced Cardiovascular Technology and Department of Pharmaceutical Sciences at University of California, Irvine. Founder of Velox Biosystems MedicalResearch.com Interview with:
Weian Zhao PhD
Assistant Professor at the Sue and Bill Gross Stem Cell Research Center, Chao Family Comprehensive Cancer Center, Department of Biomedical Engineering, Edwards Lifesciences Center for Advanced Cardiovascular Technology and Department of Pharmaceutical Sciences at University of California, Irvine.

Medical Research: What is the background for this study?

Dr. Zhao: Bloodstream infections are a major cause of illness and death. In particular, infections associated with antimicrobial-resistant pathogens are a growing health problem in the U.S. and worldwide. According to the Centers for Disease Control & Prevention, more than 2 million people a year globally get antibiotic-resistant blood infections, with about 23,000 deaths. The extremely high mortality rate for blood infections is due, in part, to the inability to rapidly diagnose and treat patients in the early stages. The present gold standard to detect a blood infections, is a blood culture and it takes 2-5 days for the detection and the identification of the bacteria. Recent molecular diagnosis methods, including polymerase chain reaction, can reduce the assay time to hours but are often not sensitive enough to detect bacteria that occur at low concentrations in blood, as is common in patients with blood infections. Therefore, less expensive and less technically demanding methods are urgently needed for the rapid and sensitive identification of blood infections.

Medical Research: What are the main findings?

Dr. Zhao: Here we developed a new platform technology termed ‘Integrated Comprehensive Droplet Digital Detection’ (IC 3D) that can selectively detect bacteria directly from milliliters of blood at single-cell sensitivity in a one-step, culture- and amplification-free process within 1-4 hours. The IC 3D technology differs from other diagnostic techniques in that it converts blood samples directly into billions of very small droplets and fluorescent DNA sensor solution can be encapsulated into the droplets to detect those with bacterial markers, lighting them up with an intense fluorescent signal in blood droplets containing bacteria. This confinement of the blood sample into so many small drops (picoliter) minimizes the interference of other components in blood, making it possible to directly detect target bacteria without the purification typically required in conventional assays. To identify bacteria-containing droplets among billions of others in a timely fashion, our team incorporated a three-dimensional particle counter developed by UCI biomedical engineer Enrico Gratton and his colleagues that tags fluorescent particles within several minutes.

Medical Research: What should clinicians and patients take away from your report?

Dr. Zhao: Our study shows the potential of IC 3D to improve diagnosis for the blood infection in early stage where the treatment is most effective. As a platform technology, it can be applied to detect and screen other diseases including cancer, viruses (e.g., HIV, Ebola) and neurological disorders. It is the diagnostics that ensure ever patient gets the right treatment early which will improve patient outcome and reduce health care burden.

Medical Research: What recommendations do you have for future research as a result of this study?

Dr. Zhao: Our ongoing work focuses on clinical validation of these new tests with head-to-head comparison to existing methods. Therefore, we actively seek for new collaborations and partnerships with clinicians and industry to further develop these products.

Citation:

Rapid detection of single bacteria in unprocessed blood using Integrated Comprehensive Droplet Digital Detection

Dong-Ku Kang, M. Monsur Ali, Kaixiang Zhang, Susan S. Huang, Ellena Peterson, Michelle A. Digman, Enrico Gratton & Weian Zhao

Nature Communications5, Article number: 5427 doi:10.1038/ncomms6427
Published 13 November 2014