ABSTRACT
Little is the need to demonstrate the capability of a one-step solution to the detection and identication of microbes by MSP using ABOid™ software. The following examples are demonstration enough. Some of these have been published in the open peer-reviewed literature in shorter versions to conrm the acceptance of this approach by the scientic community. The honeybee paper has had more than 70,000 views and when published was included on the front page of more than 100 newspapers worldwide. The obvious conclusion after reading these examples is that this methodology/approach is indicated where accurate microbe detection is desired.
One reason to develop a one-step method to detect microbes is the possibility of epidemic. This creates a need for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology that meets this capability is a high-throughput MS-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identication. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using LC-MS/MS. Bioinformatic tools that provide bacterial classication were used to evaluate the proteomic approach. Results show that bacteria in all of the double-blind samples were accurately identied with no false-positive assignment. The MSP approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identied the sample to the peptide experimental record stored in the database. The MSP approach proved capable of identifying and classifying organisms within a microbial mixture.
