SimLipid®database is a scalable relational database containing 9 lipid categories namely, Carnitines, Fatty acyls, Glycerolipids, Glycerophospholipids, Polyketides, Prenols, Saccharolipids, Sphingolipids and Sterols. The database stores 40,298 lipids, and 1,509,305 structure-specific in-silico MS/MS characteristic ions. Besides this, the database also stores 5227 transitions of precursor ion scan and neutral loss scan triple quadrupole mass spectrometry methods corresponding to 1,179, 393, and 221 unique fatty acyls from glycerophospholipids, glycerolipids, and sphingolipids respectively.
User can create custom databases* to store curated lipids along with retention times, and drift time. Once a custom database is created, MS, and MS/MS database search for structural identification of lipids can be performed using retention time, and drift time tolerances as initial search predicates. This functionality facilitates users to create LC-MS or LC-Ion Mobility-MS templates; thereby enabling them to exploit the state of the art LC-MS and IMS technologies for their research.
Other database links include Kyoto Encyclopedia of Genes and Genomes (KEGG), Human Metabolome Database (HMDB), Chemical Entities of Biological Interest (CEBI), PubChem Substance database, LIPIDBANK, and LIPIDAT. The database is continuously being updated.
Figure 1: Schematic representation of SimLipid database.
Please Note: * The functionality is available in the Enterprise Edition of the program.
SimLipid® provides a comprehensive project management, associating lipid analysis results with input profiles and search parameters. You can create any number of projects. You can also import hundreds of raw data files in batch mode. The complete raw data from an experimental MS run: total ion chromatogram, identified lipids, MS or MS/MS spectrum, and their corresponding database information are displayed in a single workbench.
Figure 2: Typical graphical user interface of SimLipid software: A single workbench that enables you to visualize complete raw data of an LC-MS, ESI-MS, MALDI-MS experimental scan, results after performing database search, and corresponding information of an identified lipid.
SimLipid® provide following additional features:
SimLipid® enables high throughput lipid profiling from multiple MS peak list data by searching lipid precursor ion against the known lipid structures available in the SimLipid® database for Carnitines, Fatty acyls, Glycerolipids, Glycerophospholipids, Polyketides, Prenols, Saccharolipids, Sphingolipids and Sterols. Users can perform MS Lipid Search for high resolution data with an error tolerance less than or equal to 50 ppm or 2000 milliDalton (mDa). SimLipid® supports [M+H], [M+NH4], [M+Na], [M+C5H12N], [M+Li] ions in the positive ion mode and [M-H], [M+AcO], [M+Cl], [M-CH3], [M+OAcO] and [M+HCOO] in the negative ion mode. Users can also trigger product ion data analysis from an MS lipid profile. The profiled lipids corresponding to a precursor m/z can be verified using product ion data for candidate lipid species.
Users can import the ion species for precursor m/z values that have been included in a high throughput search, from a predefined list of m/z values and corresponding ion species. This facilitates MS/MS search in variable ion modes and for different adducts. Multiple options are available for selecting MS and MS/MS profiles to be included in a batch run based on retention time, precursor m/z and intensity.
SimLipid® supports structural elucidation of lipids using precursor and product ion m/z data from MS/MS and MSE spectra of Carnitines, Fatty acyls, Glycerolipids, Glycerophospholipids, Prenols, Saccharolipids, Sphingolipids and Sterols. Identified lipids are scored based on an innovative ranking algorithm that indicates the relative degree of proximity of theoretical lipids with the experimental data. High resolution accurate mass data with error tolerance between 0.001-50 parts per million (ppm) and 0.1 to 2,000 milliDaltons (mDa) can be analyzed.
Data from multiple PIS/NLS can be imported into SimLipid software using either native file formats or standard formats. Users can model experimental designs in SimLipid by classifying data files according to biological/technical replicates. Identification of lipids based on target fatty acyls/head group fragment ions or their neutral losses is processed in batch mode.
SimLipid® enables quantifying lipids corresponding to the peaks observed on the MS spectra. Quantification is done by normalizing the intensity values based on the observed intensity of a chosen internal standard. The program facilitates specification of endogenous/exogenous lipids as internal standards. To enable quantification of lipids from low resolution spectra, SimLipid® corrects the intensities for isotopic overlaps. This facilitates accurate quantification of lipids from biological mixtures. The quantified data along with other information such as peak m/z, molecular formula, corrected, uncorrected and normalized intensities in different samples can be exported to a spreadsheet compatible .csv and .xls formats.
Figure 3: Schematic representation of the lipid profiling and quantitation workflow of SimLipid software using data from MPIS/NLS QqQ MS method.
SimLipid® processes the LC-MS and MS/MS data for peak detection, smoothing, chromatogram deconvolution, peak deisotoping, and molecular feature finding. The program aligns detected peak m/z values to corresponding MS/MS spectra enabling seamless identification of lipid structures using precursors and product ions data from MS and MS/MS data.
SimLipid® also facilitates comparative and quantitative analysis of lipids identified from different biological samples. The peaks detected from different peaklists are aligned based on the agreement of retention time, m/z value, observed intensity and charge state using the RANSAC techniques. Up to 200 peaklists can be aligned and the results can be exported to HTML, CSV and MS Excel formats.
SimLipid software provides a single workbench view of the LC-MS peaklist; list of identified lipids in the experimental LC-MS run; Structure of the identified lipid at a particular retention time; associated information of the identified lipid in the database; and total ion chromatogram with a vertical bar at the retention time of the identified lipid.
Figure 4: Typical graphical user interface of SimLipid software: A single workbench view of LC-MS peaklist, list of identified lipids at retention time points, structure of a lipid at a selected retention time point, and chromatogram of the sample with a vertical line indicating the retention time point of the displayed lipid structure.
SimLipid® annotates mass spectra with the lipids, and fragment ions identified for MS, MS/MS as well as MSEdata. This helps in interpreting mass spectra by highlighting the experimental m/z values that match those of theoretical lipid structures from the product database.
The annotated mass spectra can be adjudged/re-sized either to fit on a page or based on the area of interest. A user can zoom in/out into a specific plot location using the mouse cursor or by specifying the m/z range. Further, users can export the annotated spectra as images (in PNG and JPEG formats) that can be included in MS PowerPoint, facilitating information sharing amongst research groups.
Figure 5: Typical graphical user interface of SimLipid software: Annotated MS/MS spectrum of the identified lipid TG(56:3). The two most intense observed peaks in the spectrum correspond to the characteristic ions of the unique fatty acid chains 18:1, and 20:1 respectively of the structure.
After analyzing mass spectrometric data, users can generate goal-oriented reports in HTML, csv and MS Excel formats from 50,000 MS and MS/MS profiles. Users can set SimLipid® to launch the report automatically after performing the analysis. The reports that can be generated are for:
You can also search for a lipid structure of interest by lipid abbreviation/common name, mass, chemical composition or lipid ID.
The main biological functions of lipids include energy storage, acting as structural components of cell membranes, and participating as important signaling molecules.
Comparative studies of complex lipid mixtures found in cells and tissues could potentially reveal lipid biomarkers. The presence of lipids in membranes is reflective of the physiological state of an organism at a given time. Targeted and non-targeted approaches based on chromatography and mass spectrometry are employed for such studies. Both the approaches involve identification and measurement of lipids in the samples.
The challenges with mass spectrometry based lipidomics are the chemical complexity and large range of concentration of thousands of lipid species that are present in biological samples. Moreover, identification of lipids requires sophisticated software since automated interpretation of lipid MS/MS spectra is more challenging as compared to other biopolymers such as DNA, carbohydrates or peptides since lipids show much less standardized fragment mass spectra. Each lipid class has its own fragmentation patterns as well as ionization efficiency.