Metabolomics: The Final Frontier?

The connection between genomics, proteomics and metabolomics is evident in even the most simplistic of scientific models. Genes give rise to mRNA. Proteins are translated from mRNA and then proceed to carry out a myriad of functions within the cell, including the metabolism of small molecules such as glucose and adenosine triphosphate. Not that many years ago, scientists used to study the ‘big 4 ’ biomolecules under the guise of genes, transcripts, protein and metabolites. The last decade of biomedical research, however, has been witness to the growth of the ‘omics ’ industries. Genomics, transcriptomics and proteomics have become core technologies within almost every major academic or industrial research program around the world. What was missing was the final piece of the omics puzzle: metabolomics. Mass spectrometry or nuclear magnetic resonance? From a technology perspective, metabolomics has come along at precisely the right time. The two major technologies used to gather metabolomics data, mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, have both reached fantastic heights of data gathering capability [1,2]. Comparatively speaking, however, MS and NMR spectroscopy have their own specific advantages and disadvantages when conducting metabolomic studies. The main advantage of MS is sensitivity, as state-of-the-art mass spectrometers can detect analytes routinely in the femtomolar to attomolar range. Coupling MS with liquid chromatography (LC) or gas chromatography (GC) enables the measurement of hundreds of individual species within a single sample. The combination of mass accuracy and real-time tandem MS available with many mass spectrometers, along with increasingly comprehensive databases, is making the identification of these metabolites more routine. One of the major weaknesses of MS in metabolomics is quantification. The MS signal intensity of any compound is affected by the type of sample preparation used and its molecula

Using a Global Proteomic Approach to Identify Proteins Affected by Estrogen Therapy

With the increase in technological capabilities for measuring biological molecules, there is a greater trend to conduct non-biased, discovery-driven studies that collect information on hundreds of molecules in a single study. The hope is that novel findings can be detected within these large datasets. For protein analysis, these non-biased studies are particularly challenging as no technology is presently capable of providing a view of the entire proteome. The ability of non-biased studies to accurately detect specific differences within the proteomes of samples obtained from differentially treated individuals must be conclusively demonstrated before investigators will routinely adopt these methods as part of their experimental protocols. This need is especially true for clinical and epidemiological studies in which limited amounts of samples are available

Concentration of Endogenous Estrogens and Estrogen Metabolites in the NCI-60 Human Tumor Cell Lines

BACKGROUND: Endogenous estrogens and estrogen metabolites play an important role in the pathogenesis and development of human breast, endometrial, and ovarian cancers. Increasing evidence also supports their involvement in the development of certain lung, colon and prostate cancers. METHODS: In this study we systemically surveyed endogenous estrogen and estrogen metabolite levels in each of the NCI-60 human tumor cell lines, which include human breast, central nerve system, colon, ovarian, prostate, kidney and non-small cell lung cancers, as well as melanomas and leukemia. The absolute abundances of these metabolites were measured using a liquid chromatography-tandem mass spectrometry method that has been previously utilized for biological fluids such as serum and urine. RESULTS: Endogenous estrogens and estrogen metabolites were found in all NCI-60 human tumor cell lines and some were substantially elevated and exceeded the levels found in well known estrogen-dependent and estrogen receptor-positive tumor cells such as MCF-7 and T-47D. While estrogens were expected to be present at high levels in cell lines representing the female reproductive system (that is, breast and ovarian), other cell lines, such as leukemia and colon, also contained very high levels of these steroid hormones. The leukemia cell line RMPI-8226 contained the highest levels of estrone (182.06 pg/106 cells) and 17β-estradiol (753.45 pg/106 cells). In comparison, the ovarian cancer cell line with the highest levels of these estrogens contained only 19.79 and 139.32 pg/106 cells of estrone and 17β-estradiol, respectively. The highest levels of estrone and 17β-estradiol in breast cancer cell lines were only 8.45 and 87.37 pg/106 cells in BT-549 and T-47D cells, respectively. CONCLUSIONS: The data provided evidence for the presence of significant amounts of endogenous estrogens and estrogen metabolites in cell lines not commonly associated with these steroid hormones. This broad discovery of endogenous estrogens and estrogen metabolites in these cell lines suggest that several human tumors may be beneficially treated using endocrine therapy aimed at estrogen biosynthesis and estrogen-related signaling pathways

Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE): Advances and Perspectives

The recent trend in science is to assay as many biological molecules as possible within a single experiment. This trend is evident in proteomics where the aim is to characterize thousands of proteins within cells, tissues, and organisms. While advances in mass spectrometry have been critical, developments made in two-dimensional PAGE (2D-PAGE) have also played a major role in enabling proteomics. In this review, we discuss and highlight the advances made in 2D-PAGE over the past 25 years that have made it a foundational tool in proteomic research

Mass Spectrometry: M/Z 1983-2008

While definitely not a new technology, mass spectrometry (MS) has seen incredible growth over the past 25 years. Mass spectrometry has rapidly evolved to the forefront of analytical techniques; its ability to analyze proteins is the major driving force in the field of proteomics. MS instrumentation has increased approximately 5-fold in sensitivity every three years. The level of performance that is achievable with MS today allows scientists to study proteins in ways that were inconceivable a quarter century ago. This review of the history of MS over the past 25 years is timely in that it encompasses two of the biggest developments, electrospray and matrix-assisted laser desorption/ionization (MALDI), which have enabled many of the uses of this technology today

How close is the bench to the bedside? Metabolic profiling in cancer research

Metabolic profiling using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) is integral to the rapidly expanding field of metabolomics, which is making progress in toxicology, plant science and various diseases, including cancer. In the area of oncology and metabolic phenotyping, researchers have probed the known changes in malignant cellular pathways using new experimental techniques to gain more insights, and others are exploiting these same cellular pathways for therapeutic drug targets and for novel cancer biomarkers, with the ultimate goal of translation to the clinic. Here, we discuss the challenges and opportunities in metabolic phenotyping for discovering novel cancer biomarkers, and we assess the clinical applicability of MS and NMR

Sampling and Analytical Strategies for Biomarker Discovery Using Mass Spectrometry

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology

Pressure-Assisted Protein Extraction: A Novel Method for Recovering Proteins from Archival Tissue for Proteomic Analysis

Formaldehyde-fixed, paraffin-embedded (FFPE) tissue repositories represent a valuable resource for the retrospective study of disease progression and response to therapy. However, the proteomic analysis of FFPE tissues has been hampered by formaldehyde-induced protein modifications, which reduce protein extraction efficiency and may lead to protein misidentification. Here, we demonstrate the use of heat augmented with high hydrostatic pressure (40,000 psi) as a novel method for the recovery of intact proteins from FFPE mouse liver. When FFPE mouse liver was extracted using heat and elevated pressure, there was a 4-fold increase in protein extraction efficiency, a 3-fold increase in the extraction of intact proteins, and up to a 30-fold increase in the number of nonredundant proteins identified by mass spectrometry, compared to matched tissue extracted with heat alone. More importantly, the number of nonredundant proteins identified in the FFPE tissue was nearly identical to that of matched fresh-frozen tissue

Diagnostic Proteomics: Serum Proteomic Patterns for the Detection of Early Stage Cancers

The ability to interrogate thousands of proteins found in complex biological samples using proteomic technologies has brought the hope of discovering novel disease-specific biomarkers. While most proteomic technologies used to discover diagnostic biomarkers are quite sophisticated, proteomic pattern analysis has emerged as a simple, yet potentially revolutionary, method for the early diagnosis of diseases. Utilizing this technology, hundreds of clinical samples can be analyzed per day and several preliminary studies suggest proteomic pattern analysis has the potential to be a novel, highly sensitive diagnostic tool for the early detection of cancer