We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.
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To overcome these challenges, we have developed LipidMatch. LipidMatch currently contains the most comprehensive lipid fragmentation libraries of freely available software, when ranked by the number of lipid types. LipidMatch includes in silico libraries with over 250,000 lipid species across 56 lipid types, including oxidized lipids. LipidMatch incorporates user-modifiable, rule-based lipid identification, which allows for accurate lipid annotation in regards to structural resolution. In addition, if multiple identifications exist for one feature, LipidMatch outputs include all possible identifications ranked by summed fragment intensities.
For each lipid class, structurally indicative fragments were compiled using other MS/MS databases (LIPID MAPS [10], LipidBlast [11], and MS-DIAL [12]), literature, and/or experimentally derived fragmentation. Using multiple sources to obtain fragmentation allowed for cross-validation of fragments and generation of lipid class-specific fragmentation rules (see video tutorial 6 of the Additional file 1 for details). Fragment masses calculated were validated with MS/MS of internal standards obtained using HCD fragmentation [13] on a high-resolution orbitrap mass spectrometer, or literature searches. The following internal standards were used for verification (acronyms are defined in Additional file 2: Table S1): CE(17:0), CE(19:0), CE(2:0), Cer(d18:1/17:0), Cer(d18:1/25:0), MAG(17:0), DAG(14:0/14:0), DAG(19:2/19:2), DAG(20:0/20:0), GlcCer(d18:1/12:0), LPA(17:0), LPC(17:0), LPC(19:0), LPE(14:0), MG(17:0), OxPC(16:0/9:0(CHO)), PA(14:0/14:0), PC(14:1/14:1), PC(17:0/17:0), PC(19:0/19:0), PE(15:0/15:0), PE(17:0/17:0), PG(14:0/14:0), PG(15:0/15:0), PG(17:0/17:0), PI(8:0/8:0), PS(14:0/14:0), PS(17:0/17:0), SM(d18:1/17:0), SM(d18:1/6:0), TAG(13:0/13:0/13:0), TAG(15:0/15:0/15:0), TAG(17:0/17:0/17:0), TAG(17:1/17:1/17:1) and TAG(19:0/19:0/19:0). All internal standards were obtained from Avanti Polar Lipids (Alabaster, Alabama), except TAG species, which were purchased from Sigma-Aldrich (St. Louis, MO), and cholesterol esters, which were obtained from Nu-Chek Prep (Elysian, MN).
In Step 4, fragments assigned a 1 are considered observed based on the threshold criteria discussed above. Lipids are identified if they contain the necessary observed fragments. For example, for PCs measured as formate adducts, both negative ions of the fatty acyl constituents must be observed (Step 5 of Fig. 3), while for protonated PCs the PC head group ion 184.0733 must be observed, along with at least one fatty acyl indicative fragment if the lipid is to be characterized at the level of fatty acyl constituents. Default fragments which must be observed for each lipid class were determined using high collisional induced dissociation (HCD) on a Q-Exactive orbitrap mass spectrometer of internal standards, or endogenous lipids verified in literature. Users can modify which fragment ions for each lipid class must be observed for identification using a simple Excel sheet as outlined in the 6th video tutorial. In certain cases it may be important to optimize fragment criteria for applications not employing HCD fragmentation with an orbitrap analyzer. Experimental protocols including mobile phase (adducts observed), low and high mass cutoff, resolution, and type of fragmentation (e.g. HCD, CID, or UV) will determine what fragment ions are necessary for each lipid type to be identified. Therefore, for applications other than those using HCD fragmentation and orbitrap detection, we strongly recommend checking the existing fragmentation rules against MS/MS obtained in-house. Fragments chosen for confirmation should be of relative high intensity and distinguish the lipid structure from other lipids with similar fragmentation. It is important to note that while fragmentation measured on other high resolution instruments, such as qTOF platforms, can result in significant changes in the relative fragment intensities, in most cases the fragment masses observed are the same. Therefore, since LipidMatch does not include intensity in in silico fragmentation libraries and does not include relative intensities in identification, criteria for identification will often be similar between instruments.
All four programs use different identification strategies. MS-DIAL and LipidSearch include intensity to rank lipid identification by a similarity score. Greazy includes a similarity score as well as a false discovery probability based on the total number of fragments observed, thus solely relying on m/z. Both LipidMatch and LipidSearch include rule-based identification, which allows correct annotation of lipid structure based on fragments observed (correct structural resolution). While all other open-source software sort identifications by similarity score, LipidMatch sorts lipid identifications by summed fragment intensity. For each lipid species identified, all expected fragment ions are summed (using the scan with the highest intensity for each fragment). Fragment ions to sum are determined from the in silico fragment m/z values for that species and include fragments not necessary for lipid identification (for example the loss of the PC head group for PCs when the m/z 184.0733 PC fragment is observed). For each feature, the lipid ions are ranked from maximum to minimum summed intensity.
LipidMatch is a freely available tool with the potential to be incorporated into a diverse range of lipidomics workflows, including imaging, direct-infusion, and LC-MS/MS experiments with both low and high mass resolution. For LC-MS/MS workflows, LipidMatch can be used with any feature processing software, such as MZmine, XCMS, or MS-DIAL. LipidMatch contains the greatest diversity in lipid types of any current open-source software platform and a unique rule-based strategy for identification and summed fragment intensity based strategy for ranking top hits. Compared to other software, LipidMatch is highly customizable. For example, users can select which fragments are necessary for confirmation and develop their own fragmentation libraries in Excel. Additional tools allow the user to pool results from multiple identification software platforms into one feature table. Compared to MS-DIAL and Greazy, LipidMatch was found to provide the most lipid identifications for Red Cross plasma. For features with identifications using all 3 software platforms, identifications were comparable at the level of fatty acid constituents. 92% and 98% of LipidMatch identifications were corroborated by at least one of the other software platforms in positive and negative mode, respectively.
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For the Low-Income Housing Tax Credit program, users should refer to the FY 2021 Multifamily Tax Subsidy Project income limits available at The formula used to compute theseincome limits is as follows: take 120 percent of the Very Low-Income Limit. Do notcalculate income limit percentages based on a direct arithmetic relationship with themedian family income; there are too many exceptions made to the arithmetic rule incomputing income limits. 2ff7e9595c
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