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We present a statistical framework for classifying cells according to the set of peptide masses obtained by mass spectrometric analysis of digestions of whole cell protein extracts. The digest is separated by high performance liquid chromatography (HPLC) coupled directly to a mass spectrometer either by an electrospray interface or by collection to a matrix-assisted laser desorption/ionization target plate. Here, the mass to charge ratio, intensity, and HPLC retention time of the peptides are measured. We have used defined bacterial strains to test this approach. For each bacterium, this process is repeated for extracts obtained at different points in the growth curve in order to try and define an invariant set of signals that uniquely identify the bacterium. This paper presents algorithms for the creation of this cell fingerprint database and develops a Bayesian classification scheme for deciding whether or not an unknown bacterium has a match in the database. Our initial testing based on a limited data set of three bacteria indicates that our approach is feasible. Via a jack-knife test, our Bayesian classification scheme correctly identified the bacterium in 67.8% of the cases.
Zhou et al. (Sun,) studied this question.