Universal microbial diagnostics using random DNA probes

Early identification of pathogens is essential for limiting development of
therapy-resistant pathogens and mitigating infectious disease outbreaks.
Most bacterial detection schemes use target-specific probes to
differentiate pathogen species, creating time and cost inefficiencies in
identifying newly discovered organisms. In this talk I will present a novel
universal microbial diagnostics (UMD) platform to screen for microbial
organisms in an infectious sample, using a small number of random DNA
probes that are agnostic to the target DNA sequences. Our platform
leverages the theory of sparse signal recovery (compressive sensing) to
identify the composition of a microbial sample that potentially contains
novel or mutant species. We validated the UMD platform in vitro using five
random probes to recover 11 pathogenic bacteria. We further demonstrated in
silico that UMD can be generalized to screen for common human pathogens in
different taxonomy levels. UMD’s unorthodox sensing approach opens the door
to more efficient and universal molecular diagnostics.