Analytical chemistry studies and uses instruments and methods to separate, identify, and quantify matter. In practice, separation, identification or quantification may constitute the entire analysis or be combined with another method. Separation isolates analytes. Qualitative analysis identifies analytes, while quantitative analysis determines the numerical amount or concentration.
Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.
Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.
The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.
In a chemical analysis, the internal standard method involves adding the same amount of a chemical substance to each sample and calibration solution. The internal standard responds proportionally to changes in the analyte and provides a similar, but not identical, measurement signal. It must also be absent from the sample matrix to ensure there is no other source of the internal standard present. Taking the ratio of analyte signal to internal standard signal and plotting it against the analyte concentrations in the calibration solutions will result in a calibration curve. The calibration curve can then be used to calculate the analyte concentration in an unknown sample.
Pyrolysis–gas chromatography–mass spectrometry is a method of chemical analysis in which the sample is heated to decomposition to produce smaller molecules that are separated by gas chromatography and detected using mass spectrometry.
Protein mass spectrometry refers to the application of mass spectrometry to the study of proteins. Mass spectrometry is an important method for the accurate mass determination and characterization of proteins, and a variety of methods and instrumentations have been developed for its many uses. Its applications include the identification of proteins and their post-translational modifications, the elucidation of protein complexes, their subunits and functional interactions, as well as the global measurement of proteins in proteomics. It can also be used to localize proteins to the various organelles, and determine the interactions between different proteins as well as with membrane lipids.
Mass spectrometry imaging (MSI) is a technique used in mass spectrometry to visualize the spatial distribution of molecules, as biomarkers, metabolites, peptides or proteins by their molecular masses. After collecting a mass spectrum at one spot, the sample is moved to reach another region, and so on, until the entire sample is scanned. By choosing a peak in the resulting spectra that corresponds to the compound of interest, the MS data is used to map its distribution across the sample. This results in pictures of the spatially resolved distribution of a compound pixel by pixel. Each data set contains a veritable gallery of pictures because any peak in each spectrum can be spatially mapped. Despite the fact that MSI has been generally considered a qualitative method, the signal generated by this technique is proportional to the relative abundance of the analyte. Therefore, quantification is possible, when its challenges are overcome. Although widely used traditional methodologies like radiochemistry and immunohistochemistry achieve the same goal as MSI, they are limited in their abilities to analyze multiple samples at once, and can prove to be lacking if researchers do not have prior knowledge of the samples being studied. Most common ionization technologies in the field of MSI are DESI imaging, MALDI imaging, secondary ion mass spectrometry imaging and Nanoscale SIMS (NanoSIMS).
Matrix-assisted laser desorption electrospray ionization (MALDESI) was first introduced in 2006 as a novel ambient ionization technique which combines the benefits of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). An infrared (IR) or ultraviolet (UV) laser can be utilized in MALDESI to resonantly excite an endogenous or exogenous matrix. The term ‘matrix’ refers to any molecule that is present in large excess and absorbs the energy of the laser, thus facilitating desorption of analyte molecules. The original MALDESI design was implemented using common organic matrices, similar to those used in MALDI, along with a UV laser. The current MALDESI source employs endogenous water or a thin layer of exogenously deposited ice as the energy-absorbing matrix where O-H symmetric and asymmetric stretching bonds are resonantly excited by a mid-IR laser.
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Laser ablation electrospray ionization (LAESI) is an ambient ionization method for mass spectrometry that combines laser ablation from a mid-infrared (mid-IR) laser with a secondary electrospray ionization (ESI) process. The mid-IR laser is used to generate gas phase particles which are then ionized through interactions with charged droplets from the ESI source. LAESI was developed in Professor Akos Vertes lab by Dr. Peter Nemes in 2007 and it was marketed commercially by Protea Biosciences, Inc until 2017. Fiber-LAESI for single-cell analysis approach was developed by Dr. Bindesh Shrestha in Professor Vertes lab in 2009. LAESI is a novel ionization source for mass spectrometry (MS) that has been used to perform MS imaging of plants, tissues, cell pellets, and even single cells. In addition, LAESI has been used to analyze historic documents and untreated biofluids such as urine and blood. The technique of LAESI is performed at atmospheric pressure and therefore overcomes many of the obstacles of traditional MS techniques, including extensive and invasive sample preparation steps and the use of high vacuum. Because molecules and aerosols are ionized by interacting with an electrospray plume, LAESI's ionization mechanism is similar to SESI and EESI techniques.
Renato Zenobi is a Swiss chemist. He is Professor of Chemistry at ETH Zurich. Throughout his career, Zenobi has contributed to the field of analytical chemistry.
In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.
Exometabolomics, also known as 'metabolic footprinting', is the study of extracellular metabolites and is a sub-field of metabolomics.
Cynthia Larive is an American scientist and academic administrator serving as the chancellor of University of California, Santa Cruz. Larive's research focuses on nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry. She was previously a professor of chemistry and provost and executive vice chancellor at the University of California, Riverside. She is a fellow of AAAS, IUPAC and ACS, associate editor for the ACS journal Analytical Chemistry and editor of the Analytical Sciences Digital Library.
Jonathan V Sweedler is an American chemist specializing in bioanalytical chemistry, neurochemistry and cell to cell biology and behavior. He is the James R. Eiszner Family Endowed Chair in Chemistry at the University of Illinois at Urbana-Champaign. Additionally, he holds a faculty appointment in the Beckman Institute. He is also an Elected Fellow to the American Chemical Society, for which he is also the society's Editor in Chief for the journal Analytical Chemistry.
Peter Nemes is a Hungarian-American chemist, who is active in the fields of bioanalytical chemistry, mass spectrometry, cell/developmental biology, neuroscience, and biochemistry.
Gary Siuzdak is an American chemist best known for his work in the field of metabolomics, activity metabolomics, and mass spectrometry. His lab discovered indole-3-propionic acid as a gut bacteria derived metabolite in 2009. He is currently the Professor and Director of The Center for Metabolomics and Mass Spectrometry at Scripps Research in La Jolla, California. Siuzdak has also made contributions to virus analysis, viral structural dynamics, as well as developing mass spectrometry imaging technology using nanostructured surfaces. The Siuzdak lab is also responsible for creating the research tools eXtensible Computational Mass Spectrometry (XCMS), METLIN, METLIN Neutral Loss and Q-MRM. As of January 2021, the XCMS/METLIN platform has over 50,000 registered users.
XCMS Online is a cloud version of the original eXtensible Computational Mass Spectrometry (XCMS) technology, created by the Siuzdak Lab at Scripps Research. XCMS introduced the concept of nonlinear retention time alignment that allowed for the statistical assessment of the detected peaks across LCMS and GCMS datasets. XCMS Online was designed to facilitate XCMS analyses through a cloud portal and as a more straightforward way to analyze, visualize and share untargeted metabolomic data. Further to this, the combination of XCMS and METLIN allows for the identification of known molecules using METLIN's tandem mass spectrometry data, and enables the identification of unknown via similarity searching of tandem mass spectrometry data. XCMS Online has also become a systems biology tool for integrating different omic data sets. As of January 2021, the XCMSOnline /METLIN platform has over 44,000 registered users.
David S. Wishart is a Canadian researcher and a Distinguished University Professor in the Department of Biological Sciences and the Department of Computing Science at the University of Alberta. Wishart also holds cross appointments in the Faculty of Pharmacy and Pharmaceutical Sciences and the Department of Laboratory Medicine and Pathology in the Faculty of Medicine and Dentistry. Additionally, Wishart holds a joint appointment in metabolomics at the Pacific Northwest National Laboratory in Richland, Washington. Wishart is well known for his pioneering contributions to the fields of protein NMR spectroscopy, bioinformatics, cheminformatics and metabolomics. In 2011, Wishart founded the Metabolomics Innovation Centre (TMIC), which is Canada's national metabolomics laboratory.
