Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples. (22nd April 2022) - de Haan, Noortje; Pučić-Baković, Maja; Novokmet, Mislav; Falck, David; Lageveen-Kammeijer, Guinevere; Razdorov, Genadij; Vučković, Frano; Trbojević-Akmačić, Irena; Gornik, Olga; Hanić, Maja; Wuhrer, Manfred; Lauc, Gordan; et al.
Abstract: Glycans expand the structural complexity of proteins by several orders of magnitude, resulting in a tremendous analytical challenge when including them in biomedical research. Recent glycobiological research is painting a picture in which glycans represent a crucial structural and functional component of the majority of proteins, with alternative glycosylation of proteins and lipids being an important regulatory mechanism in many biological and pathological processes. Since interindividual differences in glycosylation are extensive, large studies are needed to map the structures and to understand the role of glycosylation in human (patho)physiology. Driven by these challenges, methods have emerged, which can tackle the complexity of glycosylation in thousands of samples, also known as high-throughput (HT) glycomics. For facile dissemination and implementation of HT glycomics technology, the sample preparation, analysis, as well as data mining, need to be stable over a long period of time (months/years), amenable to automation, and available to non-specialized laboratories. Current HT glycomics methods mainly focus on protein N-glycosylation and allow to extensively characterize this subset of the human glycome in large numbers of various biological samples. The ultimate goal in HT glycomics is to gain better knowledge and understanding of the complete human glycome using methods that are easy to adapt and implement in (basic) biomedical research. Aiming to promote wider use and development of HT glycomics, here, we present currently available, emerging, and prospective methods and some of their applications, revealing a largely unexplored molecular layer of the complexity of life.