Rapid pathogen identification and phenotypic antimicrobial susceptibility directly from urine specimens.

Implementing effective antimicrobial therapy close to the onset of infection lowers morbidity and mortality and attenuates the spread of antimicrobial resistance. Current antimicrobial susceptibility testing (AST) methods, however, require several days to determine optimal therapies. We present technology and an automated platform that identify (ID) Urinary Tract Infection pathogens in 45 min and provide phenotypic AST results in less than 5 h from urine specimens without colony isolation. The ID and AST tests count cells fluorescently labeled with specific rRNA probes using non-magnified digital imaging. The ID test detected five pathogens at ≤ 7,000 CFU/mL and had a linear range of ~ 4 orders of magnitude. For contrived specimens, AST tests gave 93.1% categorical agreement with 1.3% Very Major Errors (VME), 0.3% Major Errors (ME), and 6.3% minor Errors (mE) compared to the broth microdilution (BMD) reference method. For clinical specimens, the ID test had 98.6% agreement and the AST test had 92.3% categorical agreement with 4.2% mE, 3.4% ME and 4.0% VME compared to BMD. Data presented demonstrates that direct-from-specimen AST tests can accurately determine antimicrobial susceptibility/resistance for each pathogen in a specimen containing two pathogens. The method is robust to urine matrix effects and off-target commensal and contaminating bacteria.

A Rapid, Accurate, Single Molecule Counting Method Detects Clostridium difficile Toxin B in Stool Samples.

We describe a new rapid and accurate immunoassay-based technology capable of counting single target molecules using digital imaging without magnification. Using the technology, we developed a rapid test for Clostridium difficile toxin B, which is responsible for the pathology underlying potentially fatal C. difficile infections (CDI). There are currently no tests for CDI that are rapid, sensitive, and specific. The MultiPath C. difficile toxin B test images and counts complexes of target-specific magnetic and fluorescent particles that have been tethered together by toxin B molecules in minimally processed stool samples. The performance characteristics of the 30 minute test include a limit of detection of 45 pg/mL, dynamic range covering 4-5 orders of magnitude, and coefficient of variation of less than 10%. The MultiPath test detected all toxinotypes and ribotypes tested, including the one most commonly occurring in the US and EU; shows no cross reactivity with relevant bacterial species; and is robust to potential interferants commonly present in stool samples. On a training set of 320 clinical stool samples, the MultiPath C. difficile toxin B test showed 97.0% sensitivity (95% CI, 91.4-99.4%); 98.3% specificity (95% CI, 96.8-99.2%); and 98.2% accuracy (95% CI, 96.7-99.0%) compared to the cellular cytotoxicity neutralization assay (CCNA) reference method. Based on these compelling performance characteristics, we believe the MultiPath technology can address the lack of rapid, sensitive, specific, and easy-to-use diagnostic tests for C. difficile.

An automated system for rapid non-destructive enumeration of growing microbes.

BACKGROUND: The power and simplicity of visual colony counting have made it the mainstay of microbiological analysis for more than 130 years. A disadvantage of the method is the long time required to generate visible colonies from cells in a sample. New rapid testing technologies generally have failed to maintain one or more of the major advantages of culture-based methods. PRINCIPAL FINDINGS: We present a new technology and platform that uses digital imaging of cellular autofluorescence to detect and enumerate growing microcolonies many generations before they become visible to the eye. The data presented demonstrate that the method preserves the viability of the microcolonies it detects, thus enabling generation of pure cultures for microbial identification. While visual colony counting detects Escherichia coli colonies containing about 5x10(6) cells, the new imaging method detects E. coli microcolonies when they contain about 120 cells and microcolonies of the yeast Candida albicans when they contain only about 12 cells. We demonstrate that digital imaging of microcolony autofluorescence detects a broad spectrum of prokaryotic and eukaryotic microbes and present a model for predicting the time to detection for individual strains. Results from the analysis of environmental samples from pharmaceutical manufacturing plants containing a mixture of unidentified microbes demonstrate the method's improved test turnaround times. CONCLUSION: This work demonstrates a new technology and automated platform that substantially shortens test times while maintaining key advantages of the current methods.