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Model-Based Spectral Library for Bacterial Identification

Description:

 

Background

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.

 

Description

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.

 

Advantages

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

 

Related Documents

Accepted manuscript, Analytical Chemistry

Provisional patent serial 62/809,285

 

 

 

UNR19-012

Patent Information:
For Information, Contact:
Shannon Sheehan
Manager, Technology Commercialization
University of Nevada, Reno
ssheehan@unr.edu
Inventors:
Keywords: