The asialoglycoprotein receptor (ASGPR) and the mannose receptor C-type 1 (MRC1) are well known for their selective recognition and clearance of circulating glycoproteins. Terminal galactose and N-Acetylgalactosamine are recognized by ASGPR, while terminal mannose, fucose, and N-Acetylglucosamine are recognized by MRC1. The effects of ASGPR and MRC1 deficiency on the N-glycosylation of individual circulating proteins have been studied. However, the impact on the homeostasis of the major plasma glycoproteins is debated and their glycosylation has not been mapped with high molecular resolution in this context. Therefore, we evaluated the total plasma N-glycome and plasma proteome of ASGR1 and MRC1 deficient mice. ASGPR deficiency resulted in an increase in O-acetylation of sialic acids accompanied by higher levels of apolipoprotein D, haptoglobin, and vitronectin. MRC1 deficiency decreased fucosylation without affecting the abundance of the major circulating glycoproteins. Our findings confirm that concentrations and N-glycosylation of the major plasma proteins are tightly controlled and further suggest that glycan-binding receptors have redundancy, allowing compensation for the loss of one major clearance receptor.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.

Secondary mutation, T790M, conferring tyrosine kinase inhibitors (TKIs) resistance beyond oncogenic epidermal growth factor receptor (EGFR) mutations presents a challenging unmet need. Although TKI-resistant mechanisms are intensively investigated, the underlying responses of cancer cells adapting drug perturbation are largely unknown. To illuminate the molecular basis linking acquired mutation to TKI resistance, affinity purification coupled mass spectrometry was adopted to dissect EGFR interactome in TKI-sensitive and TKI-resistant non-small cell lung cancer cells. The analysis revealed TKI-resistant EGFR-mutant interactome allocated in diverse subcellular distribution and enriched in endocytic trafficking, in which gefitinib intervention activated autophagy-mediated EGFR degradation and thus autophagy inhibition elevated gefitinib susceptibility. Alternatively, gefitinib prompted TKI-sensitive EGFR translocating toward cell periphery through Rab7 ubiquitination which may favor efficacy to TKIs suppression. This study revealed that T790M mutation rewired EGFR interactome that guided EGFR to autophagy-mediated degradation to escape treatment, suggesting that combination therapy with TKI and autophagy inhibitor may overcome acquired resistance in non-small cell lung cancer.

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Characterization of highly glycosylated biopharma-ceuticals by mass spectrometry is challenging because of the huge chemical space of coexistent glycoforms present. Here, we report the use of an array of HPLC-mass spectrometry-based approaches at different structural levels of released glycan, glycopeptide, and hitherto unexplored intact glycoforms to scrutinize the biopharmaceutical Myozyme, containing the highly complex lysosomal enzyme recombinant acid α-glucosidase. The intrinsic heterogeneity of recombinant acid α-glucosidase glycoforms was unraveled using a novel strong anion exchange HPLC-mass spectrometry approach involving a pH-gradient of volatile buffers to facilitate chromatographic separation of glycoforms based on their degree of sialylation, followed by the acquisition of native mass spectra in an Orbitrap mass spectrometer. Upon considering the structures of 60 different glycans attached to seven glycosylation sites in the intact protein, the large set of interdependent data acquired at different structural levels was integrated using a set of bioinformatic tools and allowed the annotation of intact glycoforms unraveling more than 1,000,000 putative intact glycoforms. Detectable isoforms also included several mannose-6-phosphate variants, which are essential for directing the drug toward its target, the lysosomes. Finally, for the first time, we sought to validate the intact glycoform annotations by integrating experimental data on the enzymatically dissected proteoforms, which reduced the number of glycoforms supported by experimental evidence to 42,104. The latter verification clearly revealed the strengths but also intrinsic limitations of this approach for fully characterizing such highly complex glycoproteins by mass spectrometry.

Targeted mass spectrometry (MS)-based proteomic assays, such as multiplexed multiple reaction monitoring (MRM)-MS assays, enable sensitive and specific quantification of proteotypic peptides as stoichiometric surrogates for proteins. Efforts are underway to expand the use of MRM-MS assays in clinical environments, which requires a reliable strategy to monitor proteolytic digestion efficiency within individual samples. Towards this goal, extended stable isotope-labeled standard (SIS) peptides (hE), which incorporate native proteolytic cleavage sites, can be spiked into protein lysates prior to proteolytic (trypsin) digestion, and release of the tryptic SIS peptide (hT) can be monitored. However, hT measurements alone cannot monitor the extent of digestion and may be confounded by matrix effects specific to individual patient samples; therefore, they are not sufficient to monitor sample-to-sample digestion variability. We hypothesized that measuring undigested hE, along with its paired hT, would improve detection of digestion issues compared to only measuring hT. We tested the ratio of the SIS pair measurements, or hE/hT, as a quality control (QC) metric of trypsin digestion for two MRM assays: a direct-MRM (398 targets) and an immuno-MRM (126 targets requiring immunoaffinity peptide enrichment) assay, with extended SIS peptides observable for 54% (216) and 62% (78) of the targets, respectively. We evaluated the quantitative bias for each target in a series of experiments that adversely affected proteolytic digestion (e.g., variable digestion times, pH, and temperature). We identified a subset of SIS pairs (36 for the direct-MRM, 7 for the immuno-MRM assay) for which the hE/hT ratio reliably detected inefficient digestion that resulted in decreased assay sensitivity and unreliable endogenous quantification. The hE/hT ratio was more responsive to a decrease in digestion efficiency than a metric based on hT measurements alone. For clinical-grade MRM-MS assays, this study describes a ready-to-use QC panel and also provides a road map for designing custom QC panels.