In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine... Show moreIn recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research. Show less
Falck, D.; Lechmann, M.; Momcilovic, A.; Thomann, M.; Koeleman, C.A.M.; Jany, C.; ... ; Reusch, D. 2022
A relatively low clearance is one of the prominent favorable features of immunoglobulin G1-based therapeutic monoclonal antibodies (mAbs). Various studies have observed differential clearance of... Show moreA relatively low clearance is one of the prominent favorable features of immunoglobulin G1-based therapeutic monoclonal antibodies (mAbs). Various studies have observed differential clearance of mAb glycoforms, including oligomannose glycoforms, which are considered a critical quality attribute because they show higher clearance than complex type glycoforms. Glycoform clearance, however, has not previously been studied after subcutaneous injection or in a porcine model system. Here, we performed glycoform-resolved pharmacokinetic (PK) analysis of two mAbs in Gottingen minipigs. We found glycoform effects on clearance to be largely the same for subcutaneous and intravenous injection and in line with observations in other species. Oligomannose glycoforms were cleared up to 25% faster and monoantennary glycoforms up to 8% faster than agalactosylated complex glycoforms. Sialylated glycoforms were cleared at approximately the same rate as fully galactosylated glycoforms. Importantly, we report here an impact of galactosylation on the PK of a mAb for the first time. Whether increased galactosylation led to slower or faster clearance seemed to depend on the overall glycosylation profile. When clearance of galactosylated glycoforms was slower, the mAb showed higher galactosylation in serum at maximum concentration after subcutaneous injection compared to both intravenous injection and the injected material. Whether this higher galactosylation after subcutaneous injection has consequences for therapeutic efficacy remains to be investigated. In conclusion, preferential clearance of antibody glycoforms can be simulated in the minipig model with intravenous as well as subcutaneous injections. Furthermore, we observed a glycoform bias in the absorption from skin into circulation after subcutaneous injection based on galactosylation. Show less
Kantae, V.; Ogino, S.; Noga, M.; Harms, A.C.; Dongen, R.M. van; Onderwater, G.L.J.; ... ; Hankemeier, T. 2017
Bottom-up glycoproteomics by liquid chromatography-mass spectrometry (LC-MS) is an established approach for assessing glycosylation in a protein- and site-specific manner. Consequently, tools are... Show moreBottom-up glycoproteomics by liquid chromatography-mass spectrometry (LC-MS) is an established approach for assessing glycosylation in a protein- and site-specific manner. Consequently, tools are needed to automatically align, calibrate, and integrate LC-MS glycoproteomics data. We developed a modular software package designed to tackle the individual aspects of an LC-MS experiment, called LaCyTools. Targeted alignment is performed using user defined m/z and retention time (t(r)) combinations. Subsequently, sum spectra are created for each user defined analyte group. Quantitation is performed on the sum spectra, where each user defined analyte can have its own tr, minimum, and maximum charge states. Consequently, LaCyTobls deals with multiple charge states, which gives an output per charge state if desired, and offers various analyte and spectra quality criteria. We compared throughput and performance of LaCyTools to combinations of available tools that deal with individual processing steps. LaCyTools yielded relative quantitation of equal precision (relative standard deviation <0.5%) and higher trueness due to the use of MS peak area instead of MS peak intensity. In conclusion, LaCyTools is an accurate automated data processing tool for high-throughput analysis of LC-MS glycoproteomics data. Released under the Apache 2.0 license, it is freely available on GitHub (https://github.com/Tarskin/LaCyTools). Show less
