Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data

Area of Science:

• Microbiology
• Analytical Chemistry
• Bioinformatics

Background:

• Mass spectrometry is vital for microbial identification, but subspecies-level analysis is hindered by similar peptide profiles.
• Previous methods like PCA, kNN, and Pearson correlation have limitations for strain-level differentiation.
• 1D cross-correlation has proven effective for small molecules and peptide identification but not for microbial strain analysis using LC-MS.

Purpose of the Study:

• To develop and demonstrate a novel method for microbial strain identification using 2D cross-correlation of LC-MS data.
• To address the challenge of differentiating closely related microbial strains based on their mass spectral data.
• To adapt cross-correlation principles for high-resolution LC-MS data in microbial strain typing.

Main Methods:

• Collected high-resolution LC-MS-Orbitrap data for 30 yeast isolates across 5 distinct strains.
• Generated reference mass spectra for each yeast strain by combining data from its respective isolates.
• Applied 2D cross-correlation between sample spectra and reference spectra, incorporating correction factors for asymmetry.

Main Results:

• Successfully predicted sample strains by computing 2D cross-correlation against strain-specific reference spectra.
• Demonstrated the efficacy of 2D cross-correlation in differentiating between yeast strains with high peptide similarity.
• The method showed potential for accurate microbial strain identification using LC-MS data.

Conclusions:

• 2D cross-correlation of LC-MS data offers a promising approach for microbial strain identification.
• This method overcomes limitations of previous techniques in distinguishing closely related microbial strains.
• The developed technique provides a new tool for high-resolution microbial strain typing and analysis.