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Analysis of single‐cell microbial mass spectra profiles from single‐particle aerosol mass spectrometry
Author(s) -
Liu Chaowu,
Li Boning,
Liu Cong,
Li Mei,
Zhou Zhen
Publication year - 2021
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.9069
Subject(s) - chemistry , mass spectrometry , analytical chemistry (journal) , mass spectrum , aerosol , ion , particle (ecology) , bioaerosol , spectral line , chromatography , oceanography , physics , organic chemistry , astronomy , geology
Rationale Single‐particle aerosol mass spectrometry is a practical method for studying microbial aerosols. However, the related mass spectral characteristics of single‐cell microorganisms have not yet been studied systematically; hence, further investigations are necessary. Methods 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. Results 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 spectral 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 the peaks of the average spectrum, it covers most of the characteristic 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(CH 3 ) 3 + and 74 N(CH 3 ) 4 + , and 12 peak variables) led to an identification accuracy of approximately 92.4% with the random forest algorithm. Conclusions The results indicate that single‐cell mass spectral profiles can be used to distinguish microbial aerosols and further illustrate their origin in a laboratory setting.