Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

While several techniques are available in proteomics, LC-MS based analysis of complex protein/peptide mixtures has turned out to be a mainstream analytical technique for quantitative proteomics. Significant technical advances at both sample preparation/separation and mass spectrometry levels have revolutionized comprehensive proteome analysis. Moreover, automation and robotics for sample handling process permit multiple sampling with high throughput.For LC-MS based quantitative proteomics, sample preparation turns out to be critical step, as it can significantly influence sensitivity of downstream analysis. Several sample preparation strategies exist, including depletion of high abundant proteins or enrichment steps that facilitate protein quantification but with a compromise of focusing on a smaller subset of a proteome. While several experimental strategies have emerged, certain limitations such as physiochemical properties of a peptide/protein, protein turnover in a sample, analytical platform used for sample analysis and data processing, still imply challenges to quantitative proteomics. Other aspects that make analysis of a proteome a challenging task include dynamic nature of a proteome, need for efficient and fast analysis of protein due to its constant modifications inside a cell, concentration range of proteins that exceed dynamic range of a single analytical method, and absence of appropriate bioinformatics tools for analysis of large volume and high dimensional data. This paper gives an overview of various LC-MS methods currently used in quantitative proteomics and their potential for detecting differential protein expression. Fundamental steps such as sample preparation, LC separation, mass spectrometry, quantitative assessment and protein identification are discussed.For quantitative assessment of protein expression, both label and label free approaches are evaluated for their set of merits and demerits. While most of these methods edge on providing "relative abundance" information, absolute quantification is achieved with limitation as it caters to fewer proteins. Isotope labeling is extensively used for quantifying differentially expressed proteins, but is severely limited by successful incorporation of its heavy label. Lengthy labeling protocols restrict the number of samples that can be labeled and processed. Alternatively, label free approach appears promising as it can process many samples with any number of comparisons possible but entails reproducible experimental data for its application.

Recent advances in mass spectrometry-based metabolomics have created the potential to measure the levels of hundreds of metabolites that are the end products of cellular regulatory processes. In this study, we investigate the metabolic changes in genetically engineered cell lines in response to radiation exposure. "Shrinkage t" statistic and partial least-squares-discriminant analysis methods are utilized to identify peaks whose signal intensities were significantly altered by radiation. This is accomplished through pairwise comparison of radiation treated cell lines at various time points following radiation against untreated cell lines. A pathway analysis is performed following identification of the metabolites represented by the selected peaks. The results indicate an ATM regulated induction of major pathways in response to radiation treatment.