The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automated Lipid Identification and Quantification by Multidimensional Mass Spectrometry-Based Shotgun Lipidomics
Open accessAbstract
This article presents the strategies underlying the automated identification and quantification of individual lipid molecular species through array analysis of multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) data, which are acquired directly from lipid extracts after direct infusion and intrasource separation. The automated analyses of individual lipid molecular species in the program employ a strategy in which MDMS-SL data from building block analyses using precursor ion scans, neutral loss scans, or both are used to identify individual molecular species, followed by quantitation. Through this strategy, the program screens and identifies species in a high-throughput fashion from a built-in database of over 36,000 potential lipid molecular species constructed employing known building blocks. The program then uses a two-step procedure for quantitation of the identified species possessing a linear dynamic range over 3 orders of magnitude and reverifies the results when necessary through redundant quantification of multidimensional mass spectra. This program is designed to be easily adaptable for other shotgun lipidomics approaches that are currently used for mass spectrometric analysis of lipids. Accordingly, the development of this program should greatly accelerate high-throughput analysis of lipids using MDMS-based shotgun lipidomics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Han, X., Yang, K., & Gross, R. W. (2008) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.3595 · Cited by 190
Self-citation
Cited by
Cellular mechanism of insulin resistance in nonalcoholic fatty liver disease
Kumashiro, N., Erion, D. M., Zhang, D., Kahn, M., Beddow, S. A., Chu, X., Still, C. D., Gerhard, G. S., Han, X., Dziura, J., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113359108 · Cited by 585
Self-citationHan, X., Rozen, S., Boyle, S. H., Hellegers, C., Cheng, H., Burke, J. R., Welsh-Bohmer, K. A., Doraiswamy, P. M., & Kaddurah-Daouk, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021643 · Cited by 429
Self-citationCircadian Clocks and Feeding Time Regulate the Oscillations and Levels of Hepatic Triglycerides
Adamovich, Y., Rousso-Noori, L., Zwighaft, Z., Neufeld-Cohen, A., Golik, M., Kraut-Cohen, J., Wang, M., Han, X., & Asher, G. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2013.12.016 · Cited by 410
Self-citationLiu, Q., Trotter, J., Zhang, J., Peters, M. M., Cheng, H., Bao, J., Han, X., Weeber, E. J., & Bu, G. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4067-10.2010 · Cited by 268
Self-citationHepatocyte-Macrophage Acetoacetate Shuttle Protects Against Tissue Fibrosis
Puchalska, P., Martin, S. E., Huang, X., Lengfeld, J. E., Daniel, B., Graham, M. J., Han, X., Nagy, L., Patti, G. J., & Crawford, P. A. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.10.015 · Cited by 138
Self-citationFitz, N. F., Nam, K. N., Wolfe, C. M., Letronne, F., Playso, B. E., Iordanova, B. E., Kozai, T. D. Y., Biedrzycki, R. J., Kagan, V. E., Tyurina, Y. Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23762-0 · Cited by 129
Self-citationComprehensive Lipidome Profiling of Isogenic Primary and Metastatic Colon Adenocarcinoma Cell Lines
Fhaner, C. J., Liu, S., Ji, H., Simpson, R. J., & Reid, G. E. (2012) · Analytical Chemistry · doi:10.1021/ac302154g · Cited by 121
APOE2 eases cognitive decline during Aging: Clinical and preclinical evaluations
Shinohara, M., Kanekiyo, T., Yang, L., Linthicum, D., Shinohara, M., Fu, Y., Price, L., Frisch‐Daiello, J. L., Han, X., Fryer, J. D., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24628 · Cited by 111
Self-citationAPOE3 -Jacksonville (V236E) variant reduces self-aggregation and risk of dementia
Liu, C.-C., Murray, M. E., Li, X., Zhao, N., Wang, N., Heckman, M. G., Shue, F., Martens, Y., Li, Y., Raulin, A.-C., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abc9375 · Cited by 108
Self-citationJohnson, S. M., Bao, H., McMahon, C. E., Chen, Y., Burr, S. D., Anderson, A. M., Madeyski-Bengtson, K., Lindén, D., Han, X., & Liu, J. (2024) · Nature Communications · doi:10.1038/s41467-024-49224-x · Cited by 100
Self-citationSaadat, L., Dupree, J. L., Kilkus, J., Han, X., Traka, M., Proia, R. L., Dawson, G., & Popko, B. (2010) · Glia · doi:10.1002/glia.20930 · Cited by 61
Self-citationZhao, J., Lu, W., Ren, Y., Fu, Y., Martens, Y. A., Shue, F., Davis, M. D., Wang, X., Chen, K., Li, F., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02361-9 · Cited by 58
Self-citationYenilmez, B., Wetoska, N., Kelly, M., Echeverria, D., Min, K., Lifshitz, L., Alterman, J. F., Hassler, M. R., Hildebrand, S., DiMarzio, C., et al. (2022) · Molecular Therapy · doi:10.1016/j.ymthe.2021.11.007 · Cited by 46
Self-citationThe Insulin-Sensitizer Pioglitazone Remodels Adipose Tissue Phospholipids in Humans
Palavicini, J. P., Chavez-Velazquez, A., Fourcaudot, M., Tripathy, D., Pan, M., Norton, L., DeFronzo, R. A., & Shannon, C. E. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.784391 · Cited by 36
Garbarino, V. R., Palavicini, J. P., Melendez, J., Barthelemy, N. R., He, Y., Kautz, T. F., Lopez-Cruzan, M., Mathews, J. J., Xu, P., Zhang, B., et al. (2025) · Neurotherapeutics · doi:10.1016/j.neurot.2025.e00591 · Cited by 26
“Data processing including ion peak selection, baseline correction, data transfer, peak intensity comparison, 13 C deisotoping, and quantitation were conducted using a custom programmed Microsoft Excel macro after considering the principles of lipidomics [31].”
Lipidomics Profiling of Myelin
Wang, C., Pablo Palavicini, J., & Han, X. (2018) · Methods in Molecular Biology · doi:10.1007/978-1-4939-7862-5_4 · Cited by 15
Self-citationGRAMD1B is a regulator of lipid homeostasis, autophagic flux and phosphorylated tau
Acosta Ingram, D., Turkes, E., Kim, T. Y., Vo, S., Sweeney, N., Bonte, M.-A., Rutherford, R., Julian, D. L., Pan, M., Marsh, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58585-w · Cited by 12
Self-citationWnt/Wingless signaling promotes lipid mobilization through signal-induced transcriptional repression
Liu, M., Hemba-Waduge, R.-U.-S., Li, X., Huang, X., Liu, T.-H., Han, X., Wang, Y., & Ji, J.-Y. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322066121 · Cited by 12
Self-citationA Membrane Thickness Sensor in TREK-1 Channels Transduces Mechanical Force
Nayebosadri, A., Petersen, E. N., Cabanos, C., & Hansen, S. B. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3155650 · Cited by 12
Yesiltepe, M., Yin, T., Tambini, M., Bao, H., Pan, M., d’Abramo, C., Giliberto, L., Han, X., & D’Adamio, L. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01832-2 · Cited by 10
“These steps were conducted using a custom-programmed Microsoft Excel macro, as previously described [57].”
MethodSelf-citationStrickland, M. R., Wang, Z., Golden, L. R., Wang, H., Lu, P., Ren, Y., Tabor, G. T., Zhao, N., Ulrich, J. D., Han, X., et al. (2026) · Journal of Lipid Research · doi:10.1016/j.jlr.2026.101000 · Cited by 8
Self-citationMulti-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain
Hopp, S. C., Rogers, J. G., Smith, S., Campos, G., Miller, H., Barannikov, S., Kuri, E. G., Wang, H., Han, X., Bieniek, K. F., et al. (2023) · doi:10.1101/2023.12.06.570499 · Cited by 4
“Data processing including ion peak selection, baseline correction, data transfer, peak intensity comparison, 13 C deisotoping, and quantitation were conducted using a custom programmed Microsoft Excel macro after considering the principles of lipidomics(47).”
MethodSelf-citationGutierrez-Kuri, E., Marie Garcia-Rogers, J. L., Perez, J., Smith, S., Kenwood, M. R., Archuleta, K. S., Xiao, Y., Campos, G., Barannikov, S., Wang, H., et al. (2026) · doi:10.64898/2026.07.13.738356
Self-citationChen, K., Chen, Y., Ren, Y., Wang, Z., Huang, Y., Pan, M., Li, Z., Casey, M. C., Qiao, W., Shi, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-78407-x
Self-citation
Metadata
- Authors
- Yang, K., Cheng, H., Gross, R. W., & Han, X.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-05-01
- Volume
- 81
- Issue
- 11
- Pages
- 4356-4368
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2079325827
- Cited by
- 415
- References
- 1
- FWCI
- 9.72
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.