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A series of [ZnIILnIII] complexes (Ln = Sm, Eu, Tb and Dy) are synthesized by Salamo-type ligand H2L. Only [ZnIISmIII] shows the characteristic transitions of Sm3+ ion, indicating the effective energy transfer between the ligand and Sm3+ ion.

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The densely glycosylated spike (S) proteins that are highly exposed on the surface of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) facilitate viral attachment, entry, and membrane fusion. We have previously reported all the 22 N-glycosites and site-specific N-glycans in the S protein protomer. Herein, we report the O-glycosylation landscapes of SARS-CoV-2 S proteins, which were characterized through high-resolution mass spectrometry. Following digestion with trypsin and trypsin/Glu-C, and de-N-glycosylation using PNGase F, we determined the GalNAc-type O-glycosylation pattern of S proteins, including O-glycosites and the six most common O-glycans occupying them, via Byonic identification and manual validation. Finally, 255 intact O-glycopeptides composed of 50 peptides sequences and 43 O-glycosites were discovered by higher energy collision-induced dissociation (HCD), and three O-glycosites were confidently identified by electron transfer/higher energy collision-induced dissociation (EThcD) in the insect cell-expressed S protein. Most glycosites were modified by non-sialylated O-glycans such as HexNAc(1) and HexNAc(1)Hex (1). In contrast, in the human cell-expressed S protein S1 subunit, 407 intact O-glycopeptides composed of 34 peptides sequences and 30 O-glycosites were discovered by HCD, and 11 O-glycosites were unambiguously assigned by EThcD. However, the measurement of O-glycosylation occupancy hasn’t been made. Most glycosites were modified by sialylated O-glycans such as HexNAc(1)Hex (1)NeuAc (1) and HexNAc(1)Hex (1)NeuAc (2). Our results reveal that the SARS-CoV-2 S protein is an O-glycoprotein; the O-glycosites and O-glycan compositions vary with the host cell type. These comprehensive O-glycosylation landscapes of the S protein are expected to provide novel insights into the viral binding mechanism and present a strategy for the development of vaccines and targeted drugs.

Protein O-glycosylation has long been recognized to be closely associated with many diseases, particularly with tumor proliferation, invasion, and metastasis. The ability to efficiently profile the variation of O-glycosylation in large-scale clinical samples provides an important approach for the development of biomarkers for cancer diagnosis and for therapeutic response evaluation. Therefore, mass spectrometry (MS)-based techniques for high throughput, in-depth and reliable elucidation of protein O-glycosylation in large clinical cohorts are in high demand. However, the wide existence of serine and threonine residues in the proteome and the tens of mammalian O-glycan types lead to extremely large searching space composed of millions of theoretical combinations of peptides and O-glycans for intact O-glycopeptide database searching. As a result, an exceptionally long time is required for database searching, which is a major obstacle in O-glycoproteome studies of large clinical cohorts. More importantly, because of the low abundance and poor ionization of intact O-glycopeptides and the stochastic nature of data-dependent MS2 acquisition, substantially elevated missing data levels are inevitable as the sample number increases, which undermines the quantitative comparison across samples. Therefore, we report a new MS data processing strategy that integrates glycoform-specific database searching, reference library-based MS1 feature matching and MS2 identification propagation for fast identification, in-depth, and reproducible label-free quantification of O-glycosylation of human urinary proteins. This strategy increases the database searching speeds by up to 20-fold and leads to a 30%–40% enhanced intact O-glycopeptide quantification in individual samples with an obviously improved reproducibility. In total, we identified 1300 intact O-glycopeptides in 36 healthy human urine samples with a 30%–40% reduction in the amount of missing data. This is currently the largest dataset of urinary O-glycoproteome and demonstrates the application potential of this new strategy in large-scale clinical investigations.