IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Systems Glycobiology: Past, Present, and Future

Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome

Biomedical analysis of formalin-fixed, paraffin-embedded tissue samples: The Holy Grail for molecular diagnostics

More than a century ago in 1893, a revolutionary idea about fixing biological tissue specimens was introduced by Ferdinand Blum, a German physician. Since then, a plethora of fixation methods have been investigated and used. Formalin fixation with paraffin embedment became the most widely used types of fixation and preservation method, due to its proper architectural conservation of tissue structures and cellular shape. The huge collection of formalin-fixed, paraffin-embedded (FFPE) sample archives worldwide holds a large amount of unearthed information about diseases that could be the Holy Grail in contemporary biomarker research utilizing analytical omics based molecular diagnostics. The aim of this review is to critically evaluate the omics options for FFPE tissue sample analysis in the molecular diagnostics field

Developing an Integrative Glycobiology Workflow for the Identification of Disease Markers for Pancreatic Cancer

A deeper understanding of dysregulated glycosylation in pancreatic cancer can provide insights into disease mechanisms and the identification of novel disease markers. Recent improvements in mass spectrometry techniques have been instrumental in profiling biologically relevant tissue sections in order to identify disease marker candidates, but have either not yet been adopted for studying glycosylation or applied directly to pancreatic cancer. In the dissertation herein, new methods have been developed and adapted to the study of aberrant glycosylation in pancreatic cancer, with the ultimate goal of identifying novel disease marker candidates. For the first time, we describe a mass spectrometry imaging approach to study the localization of N-glycans. This technique demonstrated a histology-derived localization of N-glycans across tissue sections, with identifications displaying remarkable consistency with documented studies. Furthermore, the technique provides superior structural information compared to preexisting methodologies. In the analysis of diseased specimen, changes in glycosylation can be linked to aberrations in glycosyltransferase expression. When applied to pancreatic cancer in a high-throughput and high-dimensional analysis, panels of glycans displayed an improved ability to differentiate tumor from non-tumor tissues compared to current disease markers. Furthermore, the data suggest that glycosylation can identify premalignant lesions, as well as differentiate between malignant and benign conditions. These observations overcome significant limitations that hinder the efficacy of current disease markers. In an effort to link aberrant glycosylation to the modified protein, a subset of glycosylated proteins were enriched and analyzed by mass spectrometry to identify proteins that are integral to disease progression and can be probed for the early detection of pancreatic cancer. Known disease markers were among the glycoproteins identified, validating the utility of the enrichment and detection strategy outlined. This approach also differentiated the role of N- and O-glycosylation in antigen expression. Finally, we outline an integrated workflow that takes advantage of the unique capabilities of high resolution mass spectrometers. This workflow can capitalize on prior glycomic and proteomic experiments to provide a comprehensive analysis of dysregulated protein glycosylation in pancreatic cancer

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future

Unleashing the Power of Proteomics to Develop Blood-Based Cancer Markers

BACKGROUND: There is an urgent need for blood-based molecular tests to assist in the detection and diagnosis of cancers at an early stage, when curative interventions are still possible, and to predict and monitor response to treatment and disease recurrence. The rich content of proteins in blood that are impacted by tumor devel-opment and host factors provides an ideal opportunity to develop noninvasive diagnostics for cancer. CONTENT: Mass spectrometry instrumentation has ad-vanced sufficiently to allow the discovery of protein alterations directly in plasma across no less than 7 or-ders of magnitude of protein abundance. Moreover, the use of proteomics to harness the immune response in the form of seropositivity to tumor antigens has the potential to complement circulating protein bio

Determination of N-Linked Glycosylation Changes in Hepatocellular Carcinoma and the Associated Glycoproteins for Enhanced Biomarker Discovery and Therapeutic Targets

With hepatocellular carcinoma (HCC) remaining as the fifth most common cancer in the world, causing more than 700,000 deaths annually, the need for reliable, early stage diagnoses and preventive treatments is crucial. While serum glycoproteins are hepatic in origin, making them excellent targets for HCC biomarkers, they can originate from both cancerous and non-cancerous regions and direct analysis of cancerous tissue itself is lacking. To counteract this, I hypothesized that direct tissue analysis combined with proteomic analysis could be utilized to identify more potential targets specific to HCC for early detection. This was done with a primary focus on glycosylation—as most clinically approved biomarkers are glycoproteins—and examined direct tissue glycomics in conjunction with glycoproteomic techniques through two specific aims: 1) Determining patterns of N-linked glycan changes in HCC tissue using MALDI imaging mass spectrometry to compare to previously published serum changes and 2) identifying glycopeptides containing changes in observed patterns of N- linked glycans in HCC samples using a targeted glycoproteomic approach. In Aim 1, HCC tissue was examined using MALDI imaging mass spectrometry to v verify changes in glycosylation via direct tissue analysis. Here, it was found that increased branching and fucosylation were directly associated with the cancerous tissue when compared to normal or cirrhotic. To further identify changes in glycosylation, two methods (one novel and one adapted for imaging) were implemented on tissue to further classify N-linked glycan isoforms through linkage analysis, specifically for sialic acids and core fucose. Again, it was shown that core fucose is most directly related to HCC tissue, thus confirming serum findings in the literature. For Aim 2, the novel method of determining core fucosylation was used in conjunction with glycoproteomic techniques to further elucidate the core fucosylated glycoproteins of interest. With the tag left behind following the enzymatic cleavage, targeted glycoproteomics was used to determine glycoproteins of interest while eliminating some biases inherent in the method, such as low ionization efficiencies for more complex N-glycans. This work outlines the first in-depth analysis of HCC tissue specifically regarding N- glycan changes, a novel application to determine N-glycan isoforms, and the application of these methods for glycoproteomic enhancement. With these findings, new trends in glycosylation related to the disease state could be further uncovered, as well as provide new biomarker candidates or therapeutic targets for future studies

Key biological processes driving metastatic spread of pancreatic cancer as identified by multi-omics studies.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, characterized by a high metastatic burden, already at the time of diagnosis. The metastatic potential of PDAC is one of the main reasons for the poor outcome next to lack of significant improvement in effective treatments in the last decade. Key mutated driver genes, such as activating KRAS mutations, are concordantly expressed in primary and metastatic tumors. However, the biology behind the metastatic potential of PDAC is not fully understood. Recently, large-scale omic approaches have revealed new mechanisms by which PDAC cells gain their metastatic potency. In particular, genomic studies have shown that multiple heterogeneous subclones reside in the primary tumor with different metastatic potential. The development of metastases may be correlated to a more mesenchymal transcriptomic subtype. However, for cancer cells to survive in a distant organ, metastatic sites need to be modulated into pre-metastatic niches. Proteomic studies identified the influence of exosomes on the Kuppfer cells in the liver, which could function to prepare this tissue for metastatic colonization. Phosphoproteomics adds an extra layer to the established omic techniques by unravelling key functional signaling. Future studies integrating results from these large-scale omic approaches will hopefully improve PDAC prognosis through identification of new therapeutic targets and patient selection tools. In this article, we will review the current knowledge on the biology of PDAC metastasis unravelled by large scale multi-omic approaches