Novel methods of tandem mass spectrometry in life sciences

Abstract: Mass spectrometry (MS) is a powerful analytical technique widely employed for isotopic analysis and species identification in numerous research fields. Isotopic analysis involves precisely measuring stable isotopes within a sample, offering valuable insights into its origin, environment, and biological processes. Collagen, a structural protein abundant in connective tissues, serves as a vital clue for identifying various animal species and geographical locations. Mass spectrometry's precision in determining isotopic ratios and protein sequencing greatly aids in pinpointing collagen sources. This capability holds immense significance in archaeology, forensic science, and biological research. For instance, collagen samples obtained from archaeological or forensic remains can be subject to peptide mass fingerprinting (PMF) analysis. This technique relies on mass spectrometry to discern the species of origin. While mass spectrometry plays a pivotal role in isotopic analysis and species identification, there are some challenges in both mentioned analyses within the conventional methods used in the field. For instance, the required sample quantity, typically 2 milligrams for each element, the challenges in obtaining compound-resolved isotopic ratios in isotopic analysis, and the need for more robustness in species identification using the PMF technique. In this thesis, we have developed a novel high-resolution orbitrap-based mass spectrometry technique to measure the isotopic ratio of carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) elements in biogenic samples in individual amino acids using microgram quantities. We have also introduced a pipeline for species identification using tandem mass spectrometry (MS/MS) as a first step toward building a comprehensive species search engine. In paper I, we described our developed method, Fourier Transform Isotopic Ratio Mass Spectrometry (FT IsoR MS), a comprehensive and validated approach utilizing high-resolution orbitrap-based mass spectrometry for analyzing heavy stable isotopes of C, H, N, and O in proteins. Our approach demonstrates remarkable per mil-range precision in isotopic ratio measurements within aliphatic residues from proteins. Additionally, we introduced a novel software tool called Protein Amino Acid- Resolved Isotopic Ratio Mass Spectrometry (PAIR-MS), designed for the efficient extraction of isotopic ratio data from raw data files acquired using an Orbitrap mass spectrometer. In paper II, we presented unexpected findings regarding the δ2H values in individual amino acids, particularly proline and hydroxyproline residues extracted from the bone collagen of grey seals. Bone samples from grey seals were among the first samples we analyzed during our FT IsoR MS development. Our observations revealed an unprecedented deuterium enrichment, with δ2H values more than three times higher than in any previously reported biogenic sample, challenging the conventional belief that dietary factors solely determine isotopic composition. Paper III used the FT IsoR MS method to confirm the isotopic adaptation of the bacterium E. coli to cultivation in a medium with different isotopic compositions. In this project, we studied the simultaneous depletion of heavy isotopes of C, H, N, and O on E. coli and the enzymes it produces, as changes in the level of heavy isotopes can significantly differ in the physicochemical properties of inorganic materials. We found that 13C and 15N were depleted 20 and 10 times in the most abundant amino acids, respectively. This observation showed the capability of FT IsoR MS for detecting isotopic compositions when the isotope ratios are extremely far away from the natural isotopic abundances. In paper IV, as a part of a collaborative study, we provided a variation of our FT IsoR MS method, using a similar type of analysis, to measure the overall occupancy level oxidation modification on tyrosine (single) or phenylalanine (double) residues in collagen. Using our adjusted FT IsoR MS method, we have shown that this modified residue, dihydroxyphenylalanine (DOPA), was commonly present in collagen derived from various connective tissues. In paper V, we present our species prediction model based on peptide sequences and abundances (Species Identification and Prediction by Mass Spectrometry or SIP-MS model) as the initial step toward a Species Search Engine (SSE). Collagen and DNA sequences are pivotal molecular markers for species identification, offering unique advantages. While DNA sequencing has traditionally been the gold standard due to its specificity and sensitivity, collagen as an abundant and stable protein is valuable in identifying species from ancient and degraded samples. However, current collagen analysis methods need a more rigorous statistical framework, posing challenges in reliability and robustness. To address these limitations, we present an improved statistical analysis and peptide aggregation approach for species identification using collagen analysis. Leveraging PMF and MS/MS, we introduced a species-specific collagenous peptide dataset encompassing eight species, enabling precise identification and phylogenetic analysis. Our model exhibits high accuracy and sensitivity in predicting the taxonomy of unknown samples. This advancement paves the way for SSE development and signifies a significant step forward in species identification through protein analysis.