Metabolomic and Proteomic Analysis of ApoE4-Carrying H4 Neuroglioma Cells in Alzheimer’s Disease Using OrbiSIMS and LC-MS/MS
Open accessAbstract
Growing clinical evidence reveals that systematic molecular alterations in the brain occur 20 years before the onset of AD pathological features. Apolipoprotein E4 (ApoE4) is one of the most significant genetic risk factors for Alzheimer's disease (AD), which is not only associated with the AD pathological features such as amyloid-β deposition, phosphorylation of tau proteins, and neuroinflammation but is also involved in metabolism, neuron growth, and synaptic plasticity. Multiomics, such as metabolomics and proteomics, are applied widely in identifying key disease-related molecular alterations and disease-progression-related changes. Despite recent advances in the development of analytical technologies, screening the entire profile of metabolites remains challenging due to the numerous classes of compounds with diverse chemical properties that require different extraction processes for mass spectrometry. In this study, we utilized Orbitrap Secondary Ion Mass Spectrometry (OrbiSIMS) as a chemical filtering screening tool to examine molecular alterations in ApoE4-carried neuroglioma cells compared to wild-type H4 cells. The findings were compared using liquid chromatography (LC)-MS/MS targeted metabolomics analysis for the confirmation of specific metabolite classes. Detected alterations in peptide fragments by OrbiSIMS provided preliminary indications of protein changes. These were extensively analyzed through proteomics to explore ApoE4's impact on proteins. Our metabolomics approach, combining OrbiSIMS and LC-MS/MS, revealed disruptions in lipid metabolism, including glycerophospholipids and sphingolipids, as well as amino acid metabolism, encompassing alanine, aspartate, and glutamate metabolism; aminoacyl-tRNA biosynthesis; glutamine metabolism; and taurine and hypotaurine metabolism. Further LC-MS/MS proteomics studies confirmed the dysfunction in amino acid and tRNA aminoacylation metabolic processes, and highlighted RNA splicing alterations influenced by ApoE4.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A study integrating metabolomics and proteomics in APOE ε4 carriers revealed disruptions in sphingolipid and glycerophospholipid pathways.
“A study integrating metabolomics and proteomics in APOE ε4 carriers revealed disruptions in sphingolipid and glycerophospholipid pathways.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [293]
The Results and Discussion report disrupted glycerophospholipids and sphingolipids in ApoE4-expressing H4 cells, and the paper also includes proteomic analysis. But the claim’s reference to “APOE ε4 carriers” implies people, whereas the Conclusions specify that the studied H4 neuroglioma cells were not derived from patients. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe kernel-based model used in that study was non-transparent and performed poorly in non-European cohorts, highlighting the challenge of cross-population generalizability.
“However, the kernel-based model used was non-transparent and performed poorly in non-European cohorts, highlighting the challenge of cross-population generalizability159,293.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [293]
The Introduction describes metabolomic and proteomic analysis of ApoE4-related cellular mechanisms, not a kernel-based prediction model. Searches for a non-transparent kernel model and performance in non-European cohorts found no passage addressing either point; the relevant part of the paper has no page number in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
New insights into the genetic etiology of Alzheimer’s disease and related dementias
Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01024-z · Cited by 2,498 · see citing works
2019 Alzheimer’s disease facts and figures.
Alzheimer's Association (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.01.010 · Cited by 2,461 · see citing works
Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data
Doncheva, N. T., Morris, J. H., Gorodkin, J., & Jensen, L. J. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00702 · Cited by 2,316 · see citing works
Preclinical Alzheimer’s Disease: Definition, Natural History, and Diagnostic Criteria
Dubois, B., Hampel, H., Feldman, H. H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.02.002 · Cited by 1,925 · see citing works
NMDA receptor trafficking in synaptic plasticity and neuropsychiatric disorders
Lau, C. G., & Zukin, R. S. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2153 · Cited by 1,159 · see citing works
Metabolomics-based methods for early disease diagnostics
Gowda, G. A. N., Zhang, S., Gu, H., Asiago, V. M., Shanaiah, N., & Raftery, D. (2008) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.8.5.617 · Cited by 690 · see citing works
Linking Aβ and Tau in Late-Onset Alzheimer's Disease: A Dual Pathway Hypothesis
Small, S. A., & Duff, K. (2008) · Neuron · doi:10.1016/j.neuron.2008.11.007 · Cited by 546 · see citing works
Alzheimer’s disease drug development pipeline: 2023
Cummings, J., Zhou, Y., Lee, G., Zhong, K., Fonseca, J., & Cheng, F. (2023) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12385 · Cited by 477 · see citing works
Passarelli, M. K., Pirkl, A., Moellers, R., Grinfeld, D., Kollmer, F., Havelund, R., Newman, C. F., Marshall, P. S., Arlinghaus, H., Alexander, M. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4504 · Cited by 469 · see citing works
Self-citationYin, F. (2023) · The FEBS Journal · doi:10.1111/febs.16344 · Cited by 441 · see citing works
Raj, T., Li, Y. I., Wong, G., Humphrey, J., Wang, M., Ramdhani, S., Wang, Y.-C., Ng, B., Gupta, I., Haroutunian, V., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0238-1 · Cited by 430 · see citing works
ApoE4 Impairs Neuron-Astrocyte Coupling of Fatty Acid Metabolism
Qi, G., Mi, Y., Shi, X., Gu, H., Brinton, R. D., & Yin, F. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108572 · Cited by 379 · see citing works
Zhao, J., Fu, Y., Yamazaki, Y., Ren, Y., Davis, M. D., Liu, C.-C., Lu, W., Wang, X., Chen, K., Cherukuri, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19264-0 · Cited by 374 · see citing works
Chen, Y., Durakoglugil, M. S., Xian, X., & Herz, J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914984107 · Cited by 368 · see citing works
Glutamate Metabolism in the Brain Focusing on Astrocytes
Schousboe, A., Scafidi, S., Bak, L. K., Waagepetersen, H. S., & McKenna, M. C. (2014) · Advances in neurobiology · doi:10.1007/978-3-319-08894-5_2 · Cited by 366 · see citing works
ApoE Cascade Hypothesis in the pathogenesis of Alzheimer’s disease and related dementias
Martens, Y. A., Zhao, N., Liu, C.-C., Kanekiyo, T., Yang, A. J., Goate, A. M., Holtzman, D. M., & Bu, G. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.004 · Cited by 316 · see citing works
Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., Marcora, E., Huang, K.-L., Porter, T., Laws, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18534-1 · Cited by 272 · see citing works
Towards a future where Alzheimer’s disease pathology is stopped before the onset of dementia
van der Flier, W. M., de Vugt, M. E., Smets, E. M. A., Blom, M., & Teunissen, C. E. (2023) · Nature Aging · doi:10.1038/s43587-023-00404-2 · Cited by 220 · see citing works
Lee, S., Devanney, N. A., Golden, L. R., Smith, C. T., Schwartz, J. L., Walsh, A. E., Clarke, H. A., Goulding, D. S., Allenger, E. J., Morillo-Segovia, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112196 · Cited by 203 · see citing works
Xu, J., Begley, P., Church, S. J., Patassini, S., Hollywood, K. A., Jüllig, M., Curtis, M. A., Waldvogel, H. J., Faull, R. L. M., Unwin, R. D., et al. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2016.03.001 · Cited by 163 · see citing works
Plasma lipidome is dysregulated in Alzheimer’s disease and is associated with disease risk genes
Liu, Y., Thalamuthu, A., Mather, K. A., Crawford, J. D., Ulanova, M., Wong, M. W. K., Pickford, R., Sachdev, P. S., & Braidy, N. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01362-2 · Cited by 134 · see citing works
Fitz, 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 · see citing works
Brain Glutamate Levels Are Decreased in Alzheimer’s Disease: A Magnetic Resonance Spectroscopy Study
Fayed, N., Modrego, P. J., Rojas-Salinas, G., & Aguilar, K. (2011) · American Journal of Alzheimer's Disease & Other Dementias® · doi:10.1177/1533317511421780 · Cited by 128 · see citing works
Li, T., Lu, L., Pember, E., Li, X., Zhang, B., & Zhu, Z. (2022) · Cells · doi:10.3390/cells11121925 · Cited by 109 · see citing works
Self-citationMembrane-Derived Phospholipids Control Synaptic Neurotransmission and Plasticity
García‐Morales, V., Montero, F., González‐Forero, D., Bey, G. R., Gomez-Perez, L. J., Medialdea-Wandossell, M. J., Domínguez-Vías, G., García‐Verdugo, J. M., & Moreno‐López, B. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002153 · Cited by 83 · see citing works
The role of APOE in transgenic mouse models of AD
Balu, D., Karstens, A. J., Loukenas, E., Maldonado Weng, J., York, J. M., Valencia-Olvera, A. C., & LaDu, M. J. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2019.134285 · Cited by 81 · see citing works
Presence of d-alanine in proteins of normal and Alzheimer human brain
D’Aniello, A., Vetere, A., Fisher, G. H., Cusano, G. P., Chávez, M., & Petrucelli, L. (1992) · Brain Research · doi:10.1016/0006-8993(92)91656-y · Cited by 80 · see citing works
A “multi-omics” analysis of blood–brain barrier and synaptic dysfunction in <i>APOE4</i> mice
Barisano, G., Kisler, K., Wilkinson, B., Nikolakopoulou, A. M., Sagare, A. P., Wang, Y., Gilliam, W., Huuskonen, M. T., Hung, S.-T., Ichida, J. K., et al. (2022) · Journal of Experimental Medicine · doi:10.1084/jem.20221137 · Cited by 78 · see citing works
Protein identification by 3D OrbiSIMS to facilitate in situ imaging and depth profiling
Kotowska, A. M., Trindade, G. F., Mendes, P. M., Williams, P. M., Aylott, J. W., Shard, A. G., Alexander, M. R., & Scurr, D. J. (2020) · Nature Communications · doi:10.1038/s41467-020-19445-x · Cited by 76 · see citing works
Self-citationAlanine metabolism, transport, and cycling in the brain
Bröer, S., Bröer, A., Hansen, J. T., Bubb, W. A., Balcar, V. J., Nasrallah, F. A., Garner, B., & Rae, C. D. (2007) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2007.04654.x · Cited by 60 · see citing works
Chen, P., Han, X., Shaw, T. I., Fu, Y., Sun, H., Niu, M., Wang, Z., Jiao, Y., Teubner, B. J. W., Eddins, D. M., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00290-0 · Cited by 60 · see citing works
Xie, K., Qin, Q., Long, Z., Yang, Y., Peng, C., Xi, C., Li, L., Wu, Z., Daria, V., Zhao, Y., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.602887 · Cited by 40 · see citing works
Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype
Suvannapruk, W., Edney, M. K., Kim, D., Scurr, D. J., Ghaemmaghami, A. M., & Alexander, M. R. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c01375 · Cited by 37 · see citing works
Self-citationChiang, G. C.-Y., Insel, P. S., Tosun, D., Schuff, N. W., Truran‐Sacrey, D., Raptentsetsang, S. T., Thompson, P. M., Reiman, E. M., Jack, C. R., Fox, N. C., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.12.010 · Cited by 31 · see citing works
Meurs, J., Scurr, D. J., Lourdusamy, A., Storer, L. C. D., Grundy, R. G., Alexander, M. R., Rahman, R., & Kim, D. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c05087 · Cited by 24 · see citing works
Self-citationLinke, F., Johnson, J. E. C., Kern, S., Bennett, C., Lourdusamy, A., Lea, D., Clifford, S. C., Merry, C. L. R., Stolnik, S., Alexander, M. R., et al. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01496-4 · Cited by 23 · see citing works
Self-citationMolecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets
Edney, M. K., Kotowska, A. M., Spanu, M., Trindade, G. F., Wilmot, E., Reid, J., Barker, J. F., Aylott, J. W., Shard, A. G., Alexander, M. R., et al. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04898 · Cited by 16 · see citing works
Self-citationIDSL.GOA: gene ontology analysis for interpreting metabolomic datasets
Mahajan, P., Fiehn, O., & Barupal, D. K. (2024) · Scientific Reports · doi:10.1038/s41598-024-51992-x · Cited by 15 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
From Stress to Synapse: The Neuronal Atrophy Pathway to Mood Dysregulation
Krasner, H., Ong, C. V., Hewitt, P., & Vida, T. A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26073219 · Cited by 36 · see citing works
Șerban, M., Toader, C., & Covache-Busuioc, R.-A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26157364 · Cited by 28 · see citing works
Ivanich, K. L., Yackzan, A. T., Flemister, A., Chang, Y., Xing, X., Chen, A., Yanckello, L. M., Sun, M., Aware, C. B., Govindarajan, M., et al. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70216 · Cited by 8 · see citing works
Saleh, A. M., Hassan, R. Y. A., Badawey, A. M., & Marzouk, H. M. (2026) · RSC Advances · doi:10.1039/d6ra00286b · Cited by 4 · see citing works
Astrocytic spermidine insufficiency contributes to enhanced pain sensitivity associated with ApoE4
Yu, K., Zhou, X., Li, B., Sun, J., Yang, T., Li, W., Wang, N., Gu, X., Cui, S., & Cao, R. (2025) · The Journal of Headache and Pain · doi:10.1186/s10194-025-02054-8 · Cited by 4 · see citing works
Wang, Y., Chai, J., Guo, S., Wang, Z., Wang, S., Yu, X., He, X., Zhang, A., & Qiu, S. (2025) · Food Bioscience · doi:10.1016/j.fbio.2025.106138 · Cited by 4 · see citing works
Shon, H. K., Yeun, J., Lee, G. S., Moon, J. H., Im, S. G., Lee, T. G., & Son, J. G. (2025) · Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · doi:10.1116/6.0005061 · Cited by 3 · see citing works
Kshirsagar, S., Islam, M. A., Reddy, A. P., & Reddy, P. H. (2025) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823241310716 · Cited by 3 · see citing works
Li, B., Yadav, S., Zhou, S. W., Du, Y., Qian, L., Karas, Z., Zhang, Y., Shen, L., Lee, J. J., Vardarajan, B. N., et al. (2023) · medRxiv · doi:10.1101/2023.04.06.23288268 · Cited by 3 · see citing works
Guo, H., Zheng, S., Sun, S., Shi, X., Wang, X., Yang, Y., Ma, R., & Li, G. (2025) · Biomedicines · doi:10.3390/biomedicines13020362 · Cited by 2 · see citing works
Ren, D., Xiang, C., Chai, X., Zhang, Z., Zhu, X., Zhao, Y., Wei, J., Yi, C., Chen, S., Li, E., et al. (2025) · Brain Behavior and Immunity Integrative · doi:10.1016/j.bbii.2025.100148
Hakim, M. A., & Li, L. (2026) · Metabolites · doi:10.3390/metabo16030206
Gut Microbial Metabolism as a Dynamic Interface in Neurodegenerative Diseases
Kang, Z., Meng, R., Nie, M., & Wang, T. (2026) · Metabolites · doi:10.3390/metabo16090642
Chen, Y., Zhang, L., Chen, N., Han, Y., & Lang, G.-P. (2026) · Journal of Clinical Insight · doi:10.1016/j.jci.2026.100008
- Authors
- Lu, L., Kotowska, A. M., Kern, S., Fang, M., Rudd, T. R., Alexander, M. R., Scurr, D. J., & Zhu, Z.
- Affiliations
- University of Nottingham (GB)
- Medicines and Healthcare products Regulatory Agency (GB)
- Corresponding author
- Scurr, David J.
- Zhu, Zheying
- ORCID
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-07-11
- Volume
- 96
- Issue
- 29
- Pages
- 11760-11770
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- PMC
- PMC11270533
- OpenAlex
- W4400560921
- Cited by
- 15
- References
- 39
- Citations by year
- 2026: 4 · 2025: 10 · 2023: 1
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Analytical Chemistry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/P029868/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- APOE ε4, Alzheimer's disease, metabolomics, proteomics, lipid metabolism, glycerophospholipids, sphingolipids, amino acid metabolism, aminoacyl-tRNA biosynthesis, glutamine metabolism, taurine and hypotaurine metabolism, RNA splicing
- MeSH terms
- Liquid Chromatography-Mass Spectrometry, Alzheimer Disease, Chromatography, Liquid, Glioma, Humans, Proteomics, Cell Line, Tumor, Apolipoprotein E4, Tandem Mass Spectrometry, Metabolomics
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
