A targeted amplicon next-generation sequencing assay for tryptase genotyping to support personalized therapy in mast cell-related disorders.

Tryptase, the most abundant mast cell granule protein, is elevated in severe asthma patients independent of type 2 inflammation status. Higher active ß tryptase allele counts are associated with higher levels of peripheral tryptase and lower clinical benefit from anti-IgE therapies. Tryptase is a therapeutic target of interest in severe asthma and chronic spontaneous urticaria. Active and inactive allele counts may enable stratification to assess response to therapies in asthmatic patient subpopulations. Tryptase gene loci TPSAB1 and TPSB2 have high levels of sequence identity, which makes genotyping a challenging task. Here, we report a targeted next-generation sequencing (NGS) assay and downstream bioinformatics analysis for determining polymorphisms at tryptase TPSAB1 and TPSB2 loci. Machine learning modeling using multiple polymorphisms in the tryptase loci was used to improve the accuracy of genotyping calls. The assay was tested and qualified on DNA extracted from whole blood of healthy donors and asthma patients, achieving accuracy of 96%, 96% and 94% for estimation of inactive α and ßΙΙΙFS tryptase alleles and α duplication on TPSAB1, respectively. The reported NGS assay is a cost-effective method that is more efficient than Sanger sequencing and provides coverage to evaluate known as well as unreported tryptase polymorphisms.

Quantitative Comparison of Conventional and t-SNE-guided Gating Analyses.

Dimensionality reduction using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm has emerged as a popular tool for visualizing high-parameter single-cell data. While this approach has obvious potential for data visualization it remains unclear how t-SNE analysis compares to conventional manual hand-gating in stratifying and quantitating the frequency of diverse immune cell populations. We applied a comprehensive 38-parameter mass cytometry panel to human blood and compared the frequencies of 28 immune cell subsets using both conventional bivariate and t-SNE-guided manual gating. t-SNE analysis was capable of stratifying every general cellular lineage and most sub-lineages with high correlation between conventional and t-SNE-guided cell frequency calculations. However, specific immune cell subsets delineated by the manual gating of continuous variables were not fully separated in t-SNE space thus causing discrepancies in subset identification and quantification between these analytical approaches. Overall, these studies highlight the consistency between t-SNE and conventional hand-gating in stratifying general immune cell lineages while demonstrating that particular cell subsets defined by conventional manual gating may be intermingled in t-SNE space.

Quality assessment and interference detection in targeted mass spectrometry data using machine learning.

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions' ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.

Comparison of Enrichment Methods for Intact N- and O-Linked Glycopeptides Using Strong Anion Exchange and Hydrophilic Interaction Liquid Chromatography.

Heterogeneity of protein glycosylation poses great challenges for analysis that is key to understand structure and function of glycoproteins. Resolving this conundrum requires efficient and specific enrichment of intact glycopeptides for identification and quantitation. To this end, hydrophilic interaction chromatography (HILIC) has been commonly used to enrich intact N- and O-linked glycopeptides. However, its effectiveness to enrich isobarically labeled glycopeptides remains unclear. Here, we studied three different enrichment methods for enrichment of N- and O-linked glycopeptides. It was found that removal of N-glycans prior to enrichment of O-linked glycopeptides by HILIC improved identification of O-linked glycopeptides by mass spectrometry. We also compared the enrichment of intact N- and O-linked glycopeptides using other chromatography methods and found that using cartridges containing materials for strong anion exchange (SAX) chromatography increased yield and identification of N- and O-linked glycopeptides. The enrichment of O-linked glycopeptides was further improved when a Retain AX cartridge (RAX) was used. In particular, isobaric tag labeled glycopeptides after C18 desalting could be readily enriched by SAX and RAX cartridges but not by HILIC to enable quantitative glycoproteomics. It is anticipated that the use of SAX and RAX cartridges will facilitate broad applications of identifications and quantitation of glycoproteins.

Classification of Tandem Mass Spectra for Identification of N- and O-linked Glycopeptides.

Analysis of intact glycopeptides by mass spectrometry is essential to determining the microheterogeneity of protein glycosylation. Higher-energy collisional dissociation (HCD) fragmentation of glycopeptides generates mono- or disaccharide ions called oxonium ions that carry information about the structure of the fragmented glycans. Here, we investigated the link between glycan structures and the intensity of oxonium ions in the spectra of glycopeptides and utilized this information to improve the identification of glycopeptides in biological samples. Tandem spectra of glycopeptides from fetuin, glycophorin A, ovalbumin and gp120 tryptic digests were used to build a spectral database of N- and O-linked glycopeptides. Logistic regression was applied to this database to develop model to distinguish between the spectra of N- and O-linked glycopeptides. Remarkably, the developed model was found to reliably distinguish between the N- and O-linked glycopeptides using the spectral features of the oxonium ions using verification spectral set. Finally, the performance of the developed predictive model was evaluated in HILIC enriched glycopeptides extracted from human serum. The results showed that pre-classification of tandem spectra based on their glycosylation type improved the identification of N-linked glycopeptides. The developed model facilitates interpretation of tandem mass spectrometry data for assignment of glycopeptides.

Integrated Proteomic and Glycoproteomic Analyses of Prostate Cancer Cells Reveal Glycoprotein Alteration in Protein Abundance and Glycosylation.

Prostate cancer is the most common cancer among men in the U.S. and worldwide, and androgen-deprivation therapy remains the principal treatment for patients. Although a majority of patients initially respond to androgen-deprivation therapy, most will eventually develop castration resistance. An increased understanding of the mechanisms that underline the pathogenesis of castration resistance is therefore needed to develop novel therapeutics. LNCaP and PC3 prostate cancer cell lines are models for androgen-dependence and androgen-independence, respectively. Herein, we report the comparative analysis of these two prostate cancer cell lines using integrated global proteomics and glycoproteomics. Global proteome profiling of the cell lines using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and two- dimensional (2D) liquid chromatography-tandem MS (LC-MS/MS) led to the quantification of 8063 proteins. To analyze the glycoproteins, glycosite-containing peptides were isolated from the same iTRAQ-labeled peptides from the cell lines using solid phase extraction followed by LC-MS/MS analysis. Among the 1810 unique N-linked glycosite-containing peptides from 653 identified N-glycoproteins, 176 glycoproteins were observed to be different between the two cell lines. A majority of the altered glycoproteins were also observed with changes in their global protein expression levels. However, alterations in 21 differentially expressed glycoproteins showed no change at the protein abundance level, indicating that the glycosylation site occupancy was different between the two cell lines. To determine the glycosylation heterogeneity at specific glycosylation sites, we further identified and quantified 1145 N-linked glycopeptides with attached glycans in the same iTRAQ-labeled samples. These intact glycopeptides contained 67 glycan compositions and showed increased fucosylation in PC3 cells in several of the examined glycosylation sites. The increase in fucosylation could be caused by the detected changes in enzymes belonging to the glycan biosynthesis pathways of protein fucosylation observed in our proteomic analysis. The altered protein fucosylation forms have great potential in aiding our understanding of castration resistance and may lead to the development of novel therapeutic approaches and specific detection strategies for prostate cancer.

GPQuest: A Spectral Library Matching Algorithm for Site-Specific Assignment of Tandem Mass Spectra to Intact N-glycopeptides.

Glycoprotein changes occur in not only protein abundance but also the occupancy of each glycosylation site by different glycoforms during biological or pathological processes. Recent advances in mass spectrometry instrumentation and techniques have facilitated analysis of intact glycopeptides in complex biological samples by allowing the users to generate spectra of intact glycopeptides with glycans attached to each specific glycosylation site. However, assigning these spectra, leading to identification of the glycopeptides, is challenging. Here, we report an algorithm, named GPQuest, for site-specific identification of intact glycopeptides using higher-energy collisional dissociation (HCD) fragmentation of complex samples. In this algorithm, a spectral library of glycosite-containing peptides in the sample was built by analyzing the isolated glycosite-containing peptides using HCD LC-MS/MS. Spectra of intact glycopeptides were selected by using glycan oxonium ions as signature ions for glycopeptide spectra. These oxonium-ion-containing spectra were then compared with the spectral library generated from glycosite-containing peptides, resulting in assignment of each intact glycopeptide MS/MS spectrum to a specific glycosite-containing peptide. The glycan occupying each glycosite was determined by matching the mass difference between the precursor ion of intact glycopeptide and the glycosite-containing peptide to a glycan database. Using GPQuest, we analyzed LC-MS/MS spectra of protein extracts from prostate tumor LNCaP cells. Without enrichment of glycopeptides from global tryptic peptides and at a false discovery rate of 1%, 1008 glycan-containing MS/MS spectra were assigned to 769 unique intact N-linked glycopeptides, representing 344 N-linked glycosites with 57 different N-glycans. Spectral library matching using GPQuest assigns the HCD LC-MS/MS generated spectra of intact glycopeptides in an automated and high-throughput manner. Additionally, spectral library matching gives the user the possibility of identifying novel or modified glycans on specific glycosites that might be missing from the predetermined glycan databases.

Imaging of N-linked glycans from formalin-fixed paraffin-embedded tissue sections using MALDI mass spectrometry.

Aberrant glycosylation is associated with most of the diseases. Direct imaging and profiling of N-glycans on tissue sections can reveal tissue-specific and/or disease-associated N-glycans, which not only could serve as molecular signatures for diagnosis but also shed light on the functional roles of these biomolecules. Mass spectrometry imaging (MSI) is a powerful tool that has been used to correlate peptides, proteins, lipids, and metabolites with their underlying histopathology in tissue sections. Here, we report an MSI technique for direct analysis of N-glycans from formalin-fixed paraffin-embedded (FFPE) tissues. This technique consists of sectioning FFPE tissues, deparaffinization, and rehydration of the sections, denaturing tissue proteins, releasing N-linked glycans from proteins by printing peptide-N-glycosidase F over the sections, spray-coating the tissue with matrix, and analyzing N-glycans by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Brain sections from a C57BL/6 mouse were imaged using this technique at a resolution of 100 µm. Forty-two N-glycans were analyzed from the mouse brain section. The mass spectrometry images were used to study the relative abundance of oligomannose, nonfucosylated, and fucosylated complex N-glycans in different brain areas including isocortex, hippocampal formation, and brainstem and specific glycans associated with different areas of the brain were identified. Furthermore, glioblastoma tumor xenografts in a NOD/SCID mouse were imaged. Several glycans with differential expression in tumor versus normal brain tissues were identified. The MSI technique allows for imaging of N-glycans directly from FFPE sections. This method can potentially identify tissue-specific and/or disease-associated glycans coexpressed with other molecular signatures or within certain histological structures.

Glycoform analysis of recombinant and human immunodeficiency virus envelope protein gp120 via higher energy collisional dissociation and spectral-aligning strategy.

Envelope protein gp120 of human immunodeficiency virus (HIV) is armored with a dense glycan shield, which plays critical roles in envelope folding, immune-evasion, infectivity, and immunogenicity. Site-specific glycosylation profiling of recombinant gp120 is very challenging. Therefore, glycoproteomic analysis of native viral gp120 is still formidable to date. This challenge promoted us to employ a Q-Exactive mass spectrometer to identify low abundant glycopeptides from virion-associated gp120. To search the HCD-MS data for glycopeptides, a novel spectral-aligning strategy was developed. This strategy depends on the observation that glycopeptides and the corresponding deglycosylated peptides share very similar MS/MS pattern in terms of b- and y-ions that do not contain the site of glycosylation. Moreover, glycopeptides with an identical peptide backbone show nearly resembling spectra regardless of the attached glycan structures. For the recombinant gp120, this "copy-paste" spectral pattern of glycopeptides facilitated identification of 2224 spectra using only 18 spectral templates, and after precursor mass correction, 1268 (57%) spectra were assigned to 460 unique glycopeptides accommodating 19 N-linked and one O-linked glycosylation sites (glycosites). Strikingly, we were able to observe five N- and one O-linked glycosites in native gp120. We further revealed that except for Asn276 in the C2 region, glycans were processed to contain both high mannose and hybrid/complex glycans; an additional four N-linked glycosites were decorated with high mannose type. Core 1 O-linked glycan Gal1GalNAc1 was seen for the O-linked glycosite at Thr499. This direct observation of site-specific glycosylation of virion-derived gp120 has implications in HIV glycobiology and vaccine design.

Epithelium percentage estimation facilitates epithelial quantitative protein measurement in tissue specimens.

BACKGROUND: The rapid advancement of high-throughput tools for quantitative measurement of proteins has demonstrated the potential for the identification of proteins associated with cancer. However, the quantitative results on cancer tissue specimens are usually confounded by tissue heterogeneity, e.g. regions with cancer usually have significantly higher epithelium content yet lower stromal content. OBJECTIVE: It is therefore necessary to develop a tool to facilitate the interpretation of the results of protein measurements in tissue specimens. METHODS: Epithelial cell adhesion molecule (EpCAM) and cathepsin L (CTSL) are two epithelial proteins whose expressions in normal and tumorous prostate tissues were confirmed by measuring staining intensity with immunohistochemical staining (IHC). The expressions of these proteins were measured by ELISA in protein extracts from OCT embedded frozen prostate tissues. To eliminate the influence of tissue heterogeneity on epithelial protein quantification measured by ELISA, a color-based segmentation method was developed in-house for estimation of epithelium content using H&E histology slides from the same prostate tissues and the estimated epithelium percentage was used to normalize the ELISA results. The epithelium contents of the same slides were also estimated by a pathologist and used to normalize the ELISA results. The computer based results were compared with the pathologist's reading. RESULTS: We found that both EpCAM and CTSL levels, measured by ELISA assays itself, were greatly affected by epithelium content in the tissue specimens. Without adjusting for epithelium percentage, both EpCAM and CTSL levels appeared significantly higher in tumor tissues than normal tissues with a p value less than 0.001. However, after normalization by the epithelium percentage, ELISA measurements of both EpCAM and CTSL were in agreement with IHC staining results, showing a significant increase only in EpCAM with no difference in CTSL expression in cancer tissues. These results were obtained with normalization by both the computer estimated and pathologist estimated epithelium percentage. CONCLUSIONS: Our results show that estimation of tissue epithelium percentage using our color-based segmentation method correlates well with pathologists' estimation of tissue epithelium percentages. The epithelium contents estimated by color-based segmentation may be useful in immuno-based analysis or clinical proteomic analysis of tumor proteins. The codes used for epithelium estimation as well as the micrographs with estimated epithelium content are available online.

Glycomic analysis by glycoprotein immobilization for glycan extraction and liquid chromatography on microfluidic chip.

Glycosylation is one of the most common protein modifications and profoundly regulates many biological processes. Aberrant glycosylation is reported to associate with diseases such as cancers, human immunodeficiency virus, and immune disorders. It is considerably important to study protein glycosylation and the associated glycans for diagnostics and disease prognostics. Unlike other protein modifications, glycans attached to proteins are enormously complex. Therefore, the comprehensive analysis of glycans from biological or clinical samples is an unmet technical challenge. Development of the high-throughput method will facilitate the glycomics analysis. In this study, we developed a novel method for the high-throughput analysis of N-glycans from glycoproteins using glycoprotein immobilization for glycan extraction (GIG) coupled with liquid chromatography (LC) in an integrated microfluidic platform (chipLC). The separated glycans were then analyzed by mass spectrometry. Briefly, proteins were first immobilized on a solid support. Glycans on immobilized glycoproteins were modified on solid phase to increase the detection and structure analysis. N-Glycans were then enzymatically released and subsequentially separated by porous graphitized carbon particles packed in the same device. By applying the GIG-chipLC for glycomic analysis of human sera, we identified N-glycans with 148 distinct N-glycan masses. The platform was used to analyze N-glycans from mouse heart tissue and serum. The extracted N-glycans from tissues indicated that unique unsialylated N-glycans were detected in tissues that were missing from the proximal or distal serum, whereas common N-glycans from tissues and serum have mature and sialylated structures. The GIG-chipLC provides a simple and robust platform for glycomic analysis of complex biological and clinical samples.

Targeted analyte detection by standard addition improves detection limits in matrix-assisted laser desorption/ionization mass spectrometry.

Matrix-assisted laser desorption/ionization (MALDI) has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications.