Universal microbial diagnostics using random DNA probes

Amirali Aghazadeh, Adam Y. Lin, Mona A. Sheikh, Allen L. Chen, Lisa M. Atkins, Coreen L. Johnson, Joseph F. Petrosino, Rebekah A. Drezek, Richard G. Baraniuk*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article numbere1600025
JournalScience Advances
Volume2
Issue number9
DOIs
StatePublished - Sep 2016
Externally publishedYes

ASJC Scopus subject areas

  • General

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