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SimLipid®

Upgrades

SimLipid version 5.60 Released

The new version includes the following enhancements:

  • An updated database with 40,298 lipid structures and 1,509,305 structure-specific in-silico MS/MS characteristic ions.
  • 32,208 structure specific characteristic ions of wax mono esters have been added enabling accurate identification of lipid species from wax mono ester class using MS/MS database search.

  • New criteria for filtering lipids based on lipid ontology has been introduced to limit the MS, and MS/MS database search to specific lipids. Users can instruct the program to report lipids based on even/odd numbers of carbon (C) atoms in their fatty acid chains' compositions. Ontology filter is applicable only to Glycerolipids (GL), Glycerophospholipids (GP) and Sphingolipids (SP) categories.

  • MS/MS Score filter has been introduced to screen the candidate lipids during MS, and MS/MS database search, based on the attributes of the observed characteristic ions, namely, (i) types of the characteristic ions – head group, fatty acyls; (ii) threshold value of the relative intensity of characteristic ions – 50% of the base peak for at least 3 structure-specific characteristic ions.

  • HTP search filter to automatically select only those MS/MS scans that did not return any match in previous searches and re-perform MS/MS database search using a different set of search parameters. This enables users to identify lipid species quickly using the MS/MS data where precursor ion species are unknown.

  • For PIS/NLS based shotgun lipidomics workflows, a heatmap showing color coded percentage composition of all the lipid species identified for a target mass in a sample can now be generated with a click of a button.

  • Sub-class filter is now added to restrict the MS and MS/MS database search to only a group of specified lipid sub-classes.

SimLipid version 5.51 Released

The upgrade accommodates fixes to reported issues.

SimLipid version 5.50 Released

The new version includes the following enhancements:

  • The database now contains 40,234 lipid structures

  • The database now contains 1,487,728 structure-specific in-silico MS/MS characteristic ions. The following new characteristic ions in negative ion mode MS/MS spectra of lipids are observed from:
  1. Plasmalogen glycerophosphocholine
    - Loss of the Sn1 chain with 1-(1Z-alkenyl) bond
    - Loss of the Sn2 chain with 2-(1Z-alkenyl) bond

  2. Glycerophosphoglycerols
    - Loss of Sn2 chain along with Glycerol (C3O2H6)
    - Loss of Sn2 chain along with Glycerol (C3O2H6) and H2O

  3. Glycerophosphates class
    - Loss of Sn2 chain- CO2

  4. Glycerophosphoinositol
    - C3H6O5P (characteristic ion of PI class)
  • Support for Waters' native data file (.raw). You can now directly load raw data generated by Waters's triple quadrupole mass spectrometers, namely Xevo TQ-XS, Xevo TQ-S, Xevo TQD, etc.; and Quadrupole Time-of-Flight Mass Spectrometers, namely Xevo G2-XS QTof, and SYNAPT G2-Si, without having to use a third party data conversion tool

  • Support for Shimadzu's native data file (.lcd). You can now import raw data directly from Shimadzu's triple quadrupole mass spectrometers namely LCMS-8050 instrument and other LCMS – triple quadrupole mass spectrometers

  • Compatibility with Bruker Daltonic's latest CompassXtract version (v. 3.2.3)

  • Automated data analysis protocol for Multiplexed Precursor Ion Scan (MPIS) and Neutral Loss Scan (NLS) based Shotgun Lipidomics QqQ workflow. Profiled lipids can be aligned across multiple scans and biological samples within an experiment based on short name (i.e., #C:#DB), similar fatty acyls (disregarding the position of Sn1, Sn2, Sn3 chains and position of double bonds) or common name/abbreviation

  • Multiple samples can now be compared at a click of a button and the comparative reports generated can be exported for further downstream analysis

  • Custom target masses for precursor ion scan (PIS) and neutral loss scan (NLS) based QqQ mass spectrometry lipidomics experiments can now be added or edited to target a specific lipid class or subclass

  • Lipid structures can be exported from the SimLipid database and novel lipid structures can be imported to the database for further analysis

  • Data from multiple scans within specified m/z and retention time tolerances can now be auto-averaged while loading the data itself. The process not only considerably reduces the time taken for data processing before a search but also improves the quality of the data

  • A quality peak picking process is now introduced in which average data from MS scans having relative TIC and BPC greater than a user specified threshold is picked up
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A high throughput informatics tool for identification and relative quantitation of lipids using data from LC-, MALDI-, Shotgun-, Mass Spectrometry Workflows

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.

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  • Robust Lipid Structure Database
  • Project Management and Data Visualization
  • High Throughput MS Lipid Search
  • High Throughput Lipid Structural Elucidation using MS/MS and MSE Data
  • Multiplexed Precursor Ion Scan (PIS) and Neutral Loss Scan (NLS) Triple Quadrupole Mass Spectrometry Methods Based Lipid Profiling and Quantitation
  • LC MS and LC- MS/ MS High Throughput Data Processing
  • Mass Spectra Annotation with Identified Lipids
  • Generate Report
  • Database Search

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