The sensitive and specific detection of glycans via mass spectrometry (MS) remains a significant challenge due to their low abundance in complex biological mixtures, inherent lack of hydrophobicity, and suppression by other, more abundant biological molecules (proteins/peptides) or contaminants. A new strategy for the sensitive and selective MS analysis of glycans based on fluorous chemistry is reported. Glycan reducing ends were derivatized with a hydrophobic fluorinated carbon tag, increasing glycan ionization efficiency during MS by more than an order of magnitude. More importantly, the fluorinated carbon tag enabled efficient fluorous solid-phase extraction (FSPE) to specifically enrich the glycans from contaminated solutions and protein mixtures. Finally, we successfully analyzed the N-glycome in human serum using this new method.

Glycosylation is estimated to be found in over 50% of human proteins. Aberrant protein glycosylation and alteration of glycans are closely related to many diseases. More than half of the cancer biomarkers are glycosylated-proteins, and specific glycoforms of glycosylated-proteins may serve as biomarkers for either the early detection of disease or the evaluation of therapeutic efficacy for treatment of diseases. Glycoproteomics, therefore, becomes an emerging field that can make unique contributions to the discovery of biomarkers of cancers. The recent advances in mass spectrometry (MS)-based glycoproteomics, which can analyze thousands of glycosylated-proteins in a single experiment, have shown great promise for this purpose. Herein, we described the MS-based strategies that are available for glycoproteomics, and discussed the sensitivity and high throughput in both qualitative and quantitative manners. The discovery of glycosylated-proteins as biomarkers in some representative diseases by employing glycoproteomics was also summarized.

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.

A general and simple labeling method, termed glycan reductive isotope-coded amino acid labeling (GRIAL), was developed for mass spectrometry-based quantitative N-glycomics.

A novel implementation of in situ protein digestion supported by a graphene oxide-immobilized enzyme reactor (GO-IMER) in the MALDI imaging mass spectrometry (IMS) workflow is reported, which enables the simultaneous diagnostic identity and distribution attributes of the proteome on tissue.