Abstract:
: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. We 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.
journal_name
Sci Advjournal_title
Science advancesauthors
Aghazadeh A,Lin AY,Sheikh MA,Chen AL,Atkins LM,Johnson CL,Petrosino JF,Drezek RA,Baraniuk RGdoi
10.1126/sciadv.1600025subject
Has Abstractpub_date
2016-09-28 00:00:00pages
e1600025issue
9issn
2375-2548pii
1600025journal_volume
2pub_type
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