Analysis of single‐cell microbial mass spectra profiles from single‐particle aerosol mass spectrometry

Abstract

Single-particle aerosol mass spectrometry is a practical method for studying microbial aerosols. However, the related mass spectra characteristics of single-cell microorganisms have not yet been studied systematically; hence, further investigations are necessary. Different microbial cells were grown and directly aerosolized in the laboratory. These aerosols were then drawn into a single-particle mass spectrometer platform, and single-cell mass spectra profiles were obtained in real-time. The biological characteristics, ion variation trends, and microbial types were analyzed with either laser pulse energy or laser fluence. The single-particle mass spectra contained prominent peaks that could be attributed to the presence of biological matter, such as organic phosphate and nitrogen, amino acids, and spore-associated calcium complexes. Limited types of average mass spectrum patterns were present, and a significant correlation was found between the ion intensity trend (presence and absence of peaks) and laser ionization energy (expressed by the total positive ion intensity). Although a single spectral data point does not contain all peaks of the average spectrum, it covers most of the characteristics peaks and could be identified using a machine learning algorithm. After the analysis of single-particle mass spectra, we found that using multi-group features (e.g., peak intensity ratio of m/z +47 and +41, peak intensity ratio of 59 N(CH3 )3 + and 74 N(CH3 )4 + , and 12 peak variables) led to an identification accuracy of approximately 92.4% with the random forest algorithm. The results indicate that single-cell mass spectra profiles can be used to distinguish microbial aerosols and further illustrate their origin in a laboratory setting.