{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T21:18:32Z","timestamp":1790630312210,"version":"4.1.0"},"reference-count":30,"publisher":"American Chemical Society (ACS)","issue":"22","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":365,"URL":"https:\/\/pubs.acs.org\/page\/policy\/authorchoice_ccbyncnd_termsofuse.html"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM12757"],"award-info":[{"award-number":["R01GM12757"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01AI136979"],"award-info":[{"award-number":["R01AI136979"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000071","name":"National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R01HD092659"],"award-info":[{"award-number":["R01HD092659"]}],"id":[{"id":"10.13039\/100000071","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007812","name":"University of Washington","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/100007812","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/na","name":"Department of Medicinal Chemistry, University of Washington","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/na","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (\u201cstats\u201d module), generating informative plots (\u201cplotting\u201d module), identifying lipid species at different confidence levels (\u201cidentification\u201d module), and carrying out all functions using a user-friendly text-based interface (\u201cinteractive\u201d module). To support lipid identification, we assembled a comprehensive experimental database of m\/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.<\/jats:p>","DOI":"10.1021\/acs.analchem.0c02560","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T13:59:38Z","timestamp":1603979978000},"page":"14967-14975","source":"Crossref","is-referenced-by-count":60,"title":["LiPydomics: A Python Package for Comprehensive Prediction\nof Lipid Collision Cross Sections and Retention Times and Analysis\nof Ion Mobility-Mass Spectrometry-Based Lipidomics Data"],"prefix":"10.1021","volume":"92","author":[{"given":"Dylan H.","family":"Ross","sequence":"first","affiliation":[{"name":"University of Washington , , 1959 NE Pacific Street, HSB H-172 , , ,","place":["Seattle, Washington, United States, 98195"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang Ho","family":"Cho","sequence":"additional","affiliation":[{"name":"University of Washington , , 1959 NE Pacific Street, HSB H-172 , , ,","place":["Seattle, Washington, United States, 98195"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rutan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Washington , , 1959 NE Pacific Street, HSB H-172 , , ,","place":["Seattle, Washington, United States, 98195"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9125-5268","authenticated-orcid":true,"given":"Kelly M.","family":"Hines","sequence":"additional","affiliation":[{"name":"University of Washington , , 1959 NE Pacific Street, HSB H-172 , , ,","place":["Seattle, Washington, United States, 98195"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1021-5200","authenticated-orcid":true,"given":"Libin","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Washington , , 1959 NE Pacific Street, HSB H-172 , , ,","place":["Seattle, Washington, United States, 98195"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"2026090211333905400_cit1","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1038\/nrm.2017.138","article-title":"Understanding the diversity\nof membrane lipid composition","volume":"19","author":"Harayama","year":"2018","journal-title":"Nat. Rev. Mol.\nCell Biol."},{"key":"2026090211333905400_cit2","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1038\/nrm2335","article-title":"Lipid signalling\nin disease","volume":"9","author":"Wymann","year":"2008","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"2026090211333905400_cit3","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.copbio.2016.11.008","article-title":"Recent advances in expanding the coverage of the lipidome","volume":"43","author":"Tumanov","year":"2017","journal-title":"Curr. Opin. Biotechnol."},{"key":"2026090211333905400_cit4","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1021\/acs.analchem.7b04836","article-title":"Analytical Challenges\nand Recent Advances in Mass Spectrometry\nBased Lipidomics","volume":"90","author":"Rustam","year":"2018","journal-title":"Anal. Chem."},{"key":"2026090211333905400_cit5","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.trac.2019.03.017","article-title":"The emerging role\nof ion mobility-mass spectrometry in lipidomics to facilitate lipid\nseparation and identification","volume":"116","author":"Tu","year":"2019","journal-title":"TrAC Trends Anal.\nChem."},{"key":"2026090211333905400_cit6","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1194\/jlr.E400004-JLR200","article-title":"A comprehensive classification\nsystem for lipids","volume":"46","author":"Fahy","year":"2005","journal-title":"J. Lipid Res."},{"key":"2026090211333905400_cit7","doi-asserted-by":"publisher","first-page":"S9","DOI":"10.1194\/jlr.R800095-JLR200","article-title":"Update of the LIPID MAPS comprehensive classification\nsystem for\nlipids","volume":"50 Suppl","author":"Fahy","year":"2009","journal-title":"J. Lipid Res."},{"key":"2026090211333905400_cit8","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1194\/jlr.M033506","article-title":"Shorthand\nnotation for lipid structures derived from mass spectrometry","volume":"54","author":"Liebisch","year":"2013","journal-title":"J. Lipid Res."},{"key":"2026090211333905400_cit9","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1021\/acs.accounts.6b00030","article-title":"Chemical Derivatization\nand Ultrahigh Resolution and\nAccurate Mass Spectrometry Strategies for \u2033Shotgun\u2033\nLipidome Analysis","volume":"49","author":"Ryan","year":"2016","journal-title":"Acc. Chem. Res."},{"key":"2026090211333905400_cit10","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1039\/C5AN02062J","article-title":"Uncovering biologically\nsignificant lipid isomers with liquid chromatography,\nion mobility spectrometry and mass spectrometry","volume":"141","author":"Kyle","year":"2016","journal-title":"Analyst"},{"key":"2026090211333905400_cit11","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1194\/jlr.D074724","article-title":"Assessment\nof Altered Lipid Homeostasis\nby HILIC-Ion Mobility-Mass Spectrometry-Based Lipidomics","volume":"58","author":"Hines","year":"2017","journal-title":"J. Lipid Res."},{"key":"2026090211333905400_cit12","doi-asserted-by":"publisher","first-page":"e00492","DOI":"10.1128\/mSphere.00492-17","article-title":"Characterization\nof the Mechanisms\nof Daptomycin Resistance among Gram-Positive Bacterial Pathogens by\nMultidimensional Lipidomics","volume":"2","author":"Hines","year":"2017","journal-title":"mSphere"},{"key":"2026090211333905400_cit13","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.chemphyslip.2019.01.007","article-title":"Lipidomic consequences of phospholipid\nsynthesis defects in Escherichia\ncoli revealed by HILIC-ion mobility-mass spectrometry","volume":"219","author":"Hines","year":"2019","journal-title":"Chem. Phys. Lipids"},{"key":"2026090211333905400_cit14","doi-asserted-by":"publisher","first-page":"9559","DOI":"10.1021\/acs.analchem.7b02625","article-title":"LipidCCS: Prediction\nof Collision Cross-Section Values for Lipids with High Precision To\nSupport Ion Mobility-Mass Spectrometry-Based Lipidomics","volume":"89","author":"Zhou","year":"2017","journal-title":"Anal. Chem."},{"key":"2026090211333905400_cit15","doi-asserted-by":"publisher","first-page":"10758","DOI":"10.1021\/acs.analchem.8b01527","article-title":"Increasing\nCompound Identification Rates in Untargeted Lipidomics Research with\nLiquid Chromatography Drift Time-Ion Mobility Mass Spectrometry","volume":"90","author":"Blaz\u030cenovic\u0301","year":"2018","journal-title":"Anal. Chem."},{"key":"2026090211333905400_cit16","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1038\/s41587-020-0531-2","article-title":"A lipidome atlas in\nMS-DIAL 4","volume":"38","author":"Tsugawa","year":"2020","journal-title":"Nat. Biotechnol."},{"key":"2026090211333905400_cit17","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1038\/s41467-019-08897-5","article-title":"Ion mobility\nconformational\nlipid atlas for high confidence lipidomics","volume":"10","author":"Leaptrot","year":"2019","journal-title":"Nat. Commun."},{"key":"2026090211333905400_cit18","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1038\/s41467-019-14044-x","article-title":"Trapped ion\nmobility spectrometry and PASEF enable in-depth lipidomics from minimal\nsample amounts","volume":"11","author":"Vasilopoulou","year":"2020","journal-title":"Nat. Commun."},{"key":"2026090211333905400_cit19","doi-asserted-by":"publisher","first-page":"4548","DOI":"10.1021\/acs.analchem.9b05772","article-title":"Breaking Down Structural Diversity\nfor Comprehensive Prediction of Ion-Neutral Collision Cross Sections","volume":"92","author":"Ross","year":"2020","journal-title":"Anal. 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Chem."},{"key":"2026090211333905400_cit22","doi-asserted-by":"publisher","first-page":"7624","DOI":"10.1039\/C7CC04257D","article-title":"Collision\ncross section predictions\nusing 2-dimensional molecular descriptors","volume":"53","author":"Soper-Hopper","year":"2017","journal-title":"Chem.\nCommun."},{"key":"2026090211333905400_cit23","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.chroma.2018.02.025","article-title":"Prediction\nof collision cross section and retention time for broad scope screening\nin gradient reversed-phase liquid chromatography-ion mobility-high\nresolution accurate mass spectrometry","volume":"1542","author":"Mollerup","year":"2018","journal-title":"J. Chromatogr.,\nA"},{"key":"2026090211333905400_cit24","doi-asserted-by":"publisher","first-page":"5191","DOI":"10.1021\/acs.analchem.8b05821","article-title":"Predicting\nIon Mobility Collision Cross-Sections Using a Deep Neural Network:\nDeepCCS","volume":"91","author":"Plante","year":"2019","journal-title":"Anal. Chem."},{"key":"2026090211333905400_cit25","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1093\/bioinformatics\/bty661.","article-title":"LipidIMMS Analyzer: Integrating multi-dimensional\ninformation\nto support lipid identification in ion mobility - mass spectrometry\nbased lipidomics","volume":"35","author":"Zhou","year":"2019","journal-title":"Bioinformatics"},{"key":"2026090211333905400_cit26","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/s13361-016-1579-6","article-title":"LipidPioneer\n: A Comprehensive User-Generated Exact Mass Template for Lipidomics","volume":"28","author":"Ulmer","year":"2017","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"2026090211333905400_cit27","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific\ncomputing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"2026090211333905400_cit28","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J.\nMach Learn. Res."},{"key":"2026090211333905400_cit29","doi-asserted-by":"publisher","first-page":"7329","DOI":"10.1021\/acs.analchem.6b01728","article-title":"Evaluation of Collision\nCross Section Calibrants for Structural Analysis of Lipids by Traveling\nWave Ion Mobility-Mass Spectrometry","volume":"88","author":"Hines","year":"2016","journal-title":"Anal. Chem."},{"key":"2026090211333905400_cit30","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1093\/jac\/dkz562","article-title":"Occurrence of cross-resistance\nand beta-lactam seesaw effect in glycopeptide-,\nlipopeptide- and lipoglycopeptide-resistant MRSA correlates with membrane\nphosphatidylglycerol levels","volume":"75","author":"Hines","year":"2020","journal-title":"J. Antimicrob.\nChemother."}],"container-title":["Analytical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.analchem.0c02560","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/92\/22\/14967\/66920457\/acs.analchem.0c02560.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/92\/22\/14967\/66920457\/acs.analchem.0c02560.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:33:46Z","timestamp":1788363226000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/ancham\/article\/92\/22\/14967\/826098\/LiPydomics-A-Python-Package-for-Comprehensive"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,29]]},"references-count":30,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,10,29]]},"published-print":{"date-parts":[[2020,11,17]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.analchem.0c02560","relation":{},"ISSN":["0003-2700","1520-6882"],"issn-type":[{"value":"0003-2700","type":"print"},{"value":"1520-6882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,29]]}}}