There is an ever increasing necessity for the rapid identification of microbiological pathogens in human clinical samples and agricultural samples. Generating information such as strain level identification, in a rapid, robust and automated way, is the way forward for microbiology. Traditional techniques rely on sometimes subjective morphological and biochemical information which requires a high level of technical expertise and expensive reagents. We aim to replace traditional microbiology identification workflows with a single simple mass spectrometry-based protocol that allows walk up users to quickly and accurately identify a disease causing organism from multiple collected samples.
We have developed a new pathovarience technique which is an analysis technique that uses Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI-MS) to identify microorganisms from a range of clinical samples. In short, a small amount of cells are grown (or sampled) and prepared according to a proprietary protocol to extract molecules that can be analysed by the mass spectrometer. Currently this technology is limited, with several major pathogen groups, such as Staphylococcus spp., Streptococcus spp., and Escherischia coli/Shigella subgenus indistinguishable beyond the species level.
We have significantly improved on this current technology by developing a proprietary sample preparation protocol and computational package that allows the differentiation and identification of a range of organisms at strain and sub strain level with a high level of accuracy. We have termed this new technology the Karora Pathovarience System (patent pending).