It is essential to develop more efficient diagnostic methods to mitigate the spread infectious diseases. Current methods of bacterial identification use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) which is protein based mass spectra that cannot differentiate closely related bacterial species. Therefore, we propose use of a special library for building representative datasets to accurately characterize bacteria.
Researchers at the University of Nevada, Reno have developed a model-based spectral library to analyze MALDI-TOF-MS data of bacterial membrane glycolipids like Lipid A from Gram-negative bacteria and related species from Gram-positive bacteria (LASL). This method uses our novel algorithm to identify and characterize bacteria without using bacterial cultures. This approach does not require theoretical mass spectra, as our algorithm is based on acquired data, the stochastic nature of bacterial glycolipid ions is reflected. The machine learning model can select key ions in glycolipid mass spectra during its training runs. Thus, it can work better in identifying glycolipid mass spectra than algorithms designed for protein mass spectra.
More efficient than (Biotyper – Bruker Daltonics, Spectral Archive and Microbial Identification System SARAMICS – bioMeriux)
Our spectral library approach can be applied in many other areas such as proteomics, lipidomics, and metabolomics and used by public health practitioners, researchers, and hospitals.
Our invention makes it possible to do bacteria/phenotype identifications with or without biological cultures
Users can rapidly identify bacteria, treat patients, and control the spread of infection at low cost
Accepted manuscript, Analytical Chemistry
Provisional patent serial 62/809,285