Citation: | Wentao Li, Chongyu Shao, Chang Li, Huifen Zhou, Li Yu, Jiehong Yang, Haitong Wan, Yu He. Metabolomics: A useful tool for ischemic stroke research[J]. Journal of Pharmaceutical Analysis, 2023, 13(9): 968-983. doi: 10.1016/j.jpha.2023.05.015 |
J.B. German, B.D. Hammock, S.M. Watkins, Metabolomics: Building on a century of biochemistry to guide human health, Metabolomics 1 (2005) 3-9.
|
D.S. Wishart, Applications of metabolomics in drug discovery and development, Drugs R D 9 (2008) 307-322.
|
R. Kaddurah-Daouk, B.S. Kristal, R.M. Weinshilboum, Metabolomics: A global biochemical approach to drug response and disease, Annu. Rev. Pharmacol. Toxicol. 48 (2008) 653-683.
|
S. Paul, E. Candelario-Jalil, Emerging neuroprotective strategies for the treatment of ischemic stroke: An overview of clinical and preclinical studies, Exp. Neurol. 335 (2021), 113518.
|
M. Katan, A. Luft, Global burden of stroke, Semin. Neurol. 38 (2018) 208-211.
|
L.G. De Lima, B.G.O. Soares, H. Saconato, et al., Beta-blockers for preventing stroke recurrence, Cochrane Database Syst. Rev. (2013), CD007890.
|
Manolescu, Oprea, Mititelu, et al., Dietary anthocyanins and stroke: A review of pharmacokinetic and pharmacodynamic studies, Nutrients 11 (2019), 1479.
|
S. Xu, J. Lu, A. Shao, et al., Glial cells: Role of the immune response in ischemic stroke, Front. Immunol. 11 (2020), 294.
|
T. Russo, G. Felzani, C. Marini, Stroke in the very old: A systematic review of studies on incidence, outcome, and resource use, J. Aging Res. 2011 (2011), 108785.
|
L. Wang, Z. Wang, J. Shi, et al., Inhibition of proprotein convertase subtilisin/kexin type 9 attenuates neuronal apoptosis following focal cerebral ischemia via apolipoprotein E receptor 2 downregulation in hyperlipidemic mice, Int. J. Mol. Med. (2018) 2098-2106.
|
I. Deguchi, S. Mizuno, S. Kohyama, et al., Drip-and-ship thrombolytic therapy for acute ischemic stroke, J. Stroke Cerebrovasc. Dis. 27 (2018) 61-67.
|
P. Li, R.A. Stetler, R.K. Leak, et al., Oxidative stress and DNA damage after cerebral ischemia: Potential therapeutic targets to repair the genome and improve stroke recovery, Neuropharmacology 134 (2018) 208-217.
|
A.M. Gori, B. Giusti, B. Piccardi, et al., Inflammatory and metalloproteinases profiles predict three-month poor outcomes in ischemic stroke treated with thrombolysis, J. Cereb. Blood Flow Metab. 37 (2017) 3253-3261.
|
D. Zou, M. Luo, Z. Han, et al., Activation of alpha-7 nicotinic acetylcholine receptor reduces brain edema in mice with ischemic stroke and bone fracture, Mol. Neurobiol. 54 (2017) 8278-8286.
|
H. Chen, S. Qi, J. Shen, One-compound-multi-target: Combination prospect of natural compounds with thrombolytic therapy in acute ischemic stroke, Curr. Neuropharmacol. 15 (2016) 134-156.
|
X. Wang, H. Sun, A. Zhang, et al., Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: As Pillars of the bridge between Chinese and Western medicine, J. Pharm. Biomed. Anal. 55 (2011) 859-868.
|
A. Zhang, H. Sun, Z. Wang, et al., Metabolomics: Towards understanding traditional Chinese medicine, Planta Med. 76 (2010) 2026-2035.
|
H. Luan, X. Wang, Z. Cai, Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders, Mass Spectrom. Rev. 38 (2019) 22-33.
|
D.S. Wishart, Advances in metabolite identification, Bioanalysis 3 (2011) 1769-1782.
|
G.A. Nagana Gowda, D. Raftery, NMR-based metabolomics. Cancer Metabolomics. Cham: Springer, 2021: 19-37.
|
D.S. Wishart, NMR metabolomics: A look ahead, J. Magn. Reson. 306 (2019) 155-161.
|
A. Zhang, H. Sun, P. Wang, et al., Modern analytical techniques in metabolomics analysis, Analyst 137 (2012) 293-300.
|
W.B. Dunn, N.J.C. Bailey, H.E. Johnson, Measuring the metabolome: Current analytical technologies, Analyst 130 (2005) 606-625.
|
E.M. Weaver, A.B. Hummon, Imaging mass spectrometry: From tissue sections to cell cultures, Adv. Drug Deliv. Rev. 65 (2013) 1039-1055.
|
J. Balog, L. Sasi-Szabo, J. Kinross, et al., Intraoperative tissue identification using rapid evaporative ionization mass spectrometry, Sci. Transl. Med. 5 (2013), e3005623.
|
C.H. Cullen, G.J. Ray, C.M. Szabo, A comparison of quantitative nuclear magnetic resonance methods: Internal, external, and electronic referencing, Magn. Reson. Chem. 51 (2013) 705-713.
|
Q. Liang, Q. Wang, Y. Wang, et al., Quantitative 1H-NMR spectroscopy for profiling primary metabolites in mulberry leaves, Molecules 23 (2018), 554.
|
S. Singh, R. Roy, The application of absolute quantitative 1H NMR spectroscopy in drug discovery and development, Expert Opin. Drug Discov. 11 (2016) 695-706.
|
C. Simmler, J.G. Napolitano, J.B. McAlpine, et al., Universal quantitative NMR analysis of complex natural samples, Curr. Opin. Biotechnol. 25 (2014) 51-59.
|
Y. Shao, W. Le, Recent advances and perspectives of metabolomics-based investigations in Parkinson’s disease, Mol. Neurodegener. 14 (2019), 3.
|
J.H. Grimes, T.M. O’Connell, The application of micro-coil NMR probe technology to metabolomics of urine and serum, J. Biomol. NMR 49 (2011) 297-305.
|
Y. Xi, J.S. de Ropp, M.R. Viant, et al., Improved identification of metabolites in complex mixtures using HSQC NMR spectroscopy, Anal. Chim. Acta 614 (2008) 127-133.
|
G. Theodoridis, H.G. Gika, I.D. Wilson, Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies, Mass Spectrom. Rev. 30 (2011) 884-906.
|
Z. Yu, H. Huang, A. Reim, et al., Optimizing 2D gas chromatography mass spectrometry for robust tissue, serum and urine metabolite profiling, Talanta 165 (2017) 685-691.
|
R.A. Neese, E.W. Gertz, J.A. Wisneski, et al., A stable isotope technique for investigating lactate metabolism in humans, Biomed. Mass Spectrom. 10 (1983) 458-462.
|
O. Fiehn, J. Kopka, P. Dormann, et al., Metabolite profiling for plant functional genomics, Nat. Biotechnol. 18 (2000) 1157-1161.
|
G.A. Theodoridis, H.G. Gika, E.J. Want, et al., Liquid chromatography-mass spectrometry based global metabolite profiling: A review, Anal. Chim. Acta 711 (2012) 7-16.
|
M. Venti, L. Parnetti, V. Gallai, Genetics of ischemic stroke, Clin. Exp. Hypertens. 24 (2002) 531-534.
|
S. Djebali, C.A. Davis, A. Merkel, et al., Landscape of transcription in human cells, Nature 489 (2012) 101-108.
|
A.F. Palazzo, E.S. Lee, Non-coding RNA: What is functional and what is junk? Front. Genet. 6 (2015), 2.
|
J. Zhao, J. Xu, W. Wang, et al., Long non-coding RNA LINC-01572: 28 inhibits granulosa cell growth via a decrease in p27 (Kip1) degradation in patients with polycystic ovary syndrome, SSRN Electron. J. (2018): 526-538.
|
A. Akella, S. Bhattarai, A. Dharap, Long noncoding RNAs in the pathophysiology of ischemic stroke, NeuroMolecular Med. 21 (2019) 474-483.
|
X. Chen, H. Chen, M. Xu, et al., Targeting reactive nitrogen species: A promising therapeutic strategy for cerebral ischemia-reperfusion injury, Acta Pharmacol. Sin. 34 (2013) 67-77.
|
C.L. Allen, U. Bayraktutan, Oxidative stress and its role in the pathogenesis of ischaemic stroke, Int. J. Stroke 4 (2009) 461-470.
|
M. Sun, H. Jin, X. Sun, et al., Free radical damage in ischemia-reperfusion injury: An obstacle in acute ischemic stroke after revascularization therapy, Oxid. Med. Cell. Longev. 2018 (2018), 3804979.
|
A. Cherubini, C. Ruggiero, M.C. Polidori, et al., Potential markers of oxidative stress in stroke, Free. Radic. Biol. Med. 39 (2005) 841-852.
|
J.T. Coyle, P. Puttfarcken, Oxidative stress, glutamate, and neurodegenerative disorders, Science 262 (1993) 689-695.
|
T. Sugawara, P.H. Chan, Reactive oxygen radicals and pathogenesis of neuronal death after cerebral ischemia, Antioxid. Redox Signal. 5 (2003) 597-607.
|
F.M. Faraci, Reactive oxygen species: Influence on cerebral vascular tone, J. Appl. Physiol. 100 (2006) 739-743.
|
R.W. Neumar, Molecular mechanisms of ischemic neuronal injury, Ann. Emerg. Med. 36 (2000) 483-506.
|
S.E. Gariballa, T.P. Hutchin, A.J. Sinclair, Antioxidant capacity after acute ischaemic stroke, QJM 95 (2002) 685-690.
|
I. Margaill, M. Plotkine, D. Lerouet, Antioxidant strategies in the treatment of stroke, Free. Radic. Biol. Med. 39 (2005) 429-443.
|
K. Duris, Z. Splichal, M. Jurajda, The role of inflammatory response in stroke associated programmed cell death, Curr. Neuropharmacol. 16 (2018) 1365-1374.
|
A. Tuttolomondo, D. Di Raimondo, R. di Sciacca, et al., Inflammatory cytokines in acute ischemic stroke, Curr. Pharm. Des. 14 (2008) 3574-3589.
|
N.T. Lee, L.K. Ong, P. Gyawali, et al., Role of purinergic signalling in endothelial dysfunction and thrombo-inflammation in ischaemic stroke and cerebral small vessel disease, Biomolecules 11 (2021), 994.
|
J.Y. Kim, M. Kawabori, M.A. Yenari, Innate inflammatory responses in stroke: Mechanisms and potential therapeutic targets, Curr. Med. Chem. 21 (2014) 2076-2097.
|
T. Okada, H. Suzuki, Z.D. Travis, et al., The stroke-induced blood-brain barrier disruption: Current progress of inspection technique, mechanism, and therapeutic target, Curr. Neuropharmacol. 18 (2020) 1187-1212.
|
I. Perez-de-Puig, F. Miro-Mur, M. Ferrer-Ferrer, et al., Neutrophil recruitment to the brain in mouse and human ischemic stroke, Acta Neuropathol. 129 (2015) 239-257.
|
M.I. Cuartero, I. Ballesteros, A. Moraga, et al., N2 neutrophils, novel players in brain inflammation after stroke: Modulation by the PPARγ agonist rosiglitazone, Stroke 44 (2013) 3498-3508.
|
G. Morris, M. Berk, A.F. Carvalho, et al., Why should neuroscientists worry about iron? The emerging role of ferroptosis in the pathophysiology of neuroprogressive diseases, Behav. Brain Res. 341 (2018) 154-175.
|
Y. Liu, J. Liu, H. Liu, et al., Investigation of cerebral iron deposition in aged patients with ischemic cerebrovascular disease using susceptibility-weighted imaging, Ther. Clin. Risk Manag. 12 (2016) 1239-1247.
|
Y.L. Chang, S.H. Hung, W. Ling, et al., Association between ischemic stroke and iron-deficiency Anemia: A population-based study, PLoS One 8 (2013), e82952.
|
J. Petrova, V. Manolov, V. Vasilev, et al., Ischemic stroke, inflammation, iron overload-connection to a hepcidin, Int. J. Stroke 11 (2016) NP16-NP17.
|
Q.Z. Tuo, P. Lei, K.A. Jackman, et al., Tau-mediated iron export prevents ferroptotic damage after ischemic stroke, Mol. Psychiatry 22 (2017) 1520-1530.
|
N. Li, X. Wang, C. Sun, et al., Change of intestinal microbiota in cerebral ischemic stroke patients, BMC Microbiol. 19 (2019) 1-8.
|
Q. Zhao, C.O. Elson, Adaptive immune education by gut microbiota antigens, Immunology 154 (2018) 28-37.
|
A. Bartley, T. Yang, R. Arocha, et al., Increased abundance of lactobacillales in the colon of beta-adrenergic receptor knock out mouse is associated with increased gut bacterial production of short chain fatty acids and reduced IL17 expression in circulating CD4+ immune cells, Front. Physiol. 9 (2018), 1593.
|
M. Carabotti, A. Scirocco, M.A. Maselli, et al., The gut-brain axis: Interactions between enteric microbiota, central and enteric nervous systems, Ann. Gastroenterol. 28 (2015) 203-209.
|
H. Wang, Y. Wang, Gut microbiota-brain axis, Chin. Med. J. 129 (2016) 2373-2380.
|
V. Singh, S. Roth, G. Llovera, et al., Microbiota dysbiosis controls the neuroinflammatory response after stroke, J. Neurosci. 36 (2016) 7428-7440.
|
J. Yin, S. Liao, Y. He, et al., Dysbiosis of gut microbiota with reduced trimethylamine-N-oxide level in patients with large-artery atherosclerotic stroke or transient ischemic attack, J. Am. Heart Assoc. 4 (2015), e002699.
|
Y. Chen, J. Liang, F. Ouyang, et al., Persistence of gut microbiota dysbiosis and chronic systemic inflammation after cerebral infarction in Cynomolgus monkeys, Front. Neurol. 10 (2019), 661.
|
K. Machiels, M. Joossens, J. Sabino, et al., A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitziidefines dysbiosis in patients with ulcerative colitis, Gut 63 (2014) 1275-1283.
|
U. Gophna, T. Konikoff, H.B. Nielsen, Oscillospira and related bacteria - From metagenomic species to metabolic features, Environ. Microbiol. 19 (2017) 835-841.
|
M.W. Bourassa, I. Alim, S.J. Bultman, et al., Butyrate, neuroepigenetics and the gut microbiome: Can a high fiber diet improve brain health? Neurosci. Lett. 625 (2016) 56-63.
|
F. Pan, L. Zhang, M. Li, et al., Predominant gut Lactobacillus murinus strain mediates anti-inflammaging effects in calorie-restricted mice, Microbiome 6 (2018), 54.
|
V. Chesnokova, R.N. Pechnick, K. Wawrowsky, Chronic peripheral inflammation, hippocampal neurogenesis, and behavior, Brain Behav. Immun. 58 (2016) 1-8.
|
A. Kiryk, R. Pluta, I. Figiel, et al., Transient brain ischemia due to cardiac arrest causes irreversible long-lasting cognitive injury, Behav. Brain Res. 219 (2011) 1-7.
|
G.C. Medeiros, D. Roy, N. Kontos, et al., Post-stroke depression: A 2020 updated review, Gen. Hosp. Psychiatry 66 (2020) 70-80.
|
L. Radenovic, M. Nenadic, M. Ulamek-Koziol, et al., Heterogeneity in brain distribution of activated microglia and astrocytes in a rat ischemic model of Alzheimer’s disease after 2 years of survival, Aging 12 (2020) 12251-12267.
|
S.M. Houten, Metabolomics: Unraveling the chemical individuality of common human diseases, Ann. Med. 41 (2009) 402-407.
|
D.S. Wishart, Emerging applications of metabolomics in drug discovery and precision medicine, Nat. Rev. Drug Discov. 15 (2016) 473-484.
|
D.S. Wishart, Y.D. Feunang, A. Marcu, et al., HMDB 4.0: The human metabolome database for 2018, Nucleic Acids Res 46 (2018) D608-D617.
|
S.H. Shah, W.E. Kraus, C.B. Newgard, Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: Form and function, Circulation 126 (2012) 1110-1120.
|
R. Zhang, J. Meng, X. Wang, et al., Metabolomics of ischemic stroke: Insights into risk prediction and mechanisms, Metab. Brain Dis. 37 (2022) 2163-2180.
|
W. Guo, Y. Wang, M. Fan, et al., Integrating metabolomics and network pharmacology to explore the protective effect of gross saponins of Tribulus terrestris L. fruit against ischemic stroke in rat, J. Ethnopharmacol. 263 (2020), 113202.
|
L. Wu, C. Chen, Y. Li, et al., UPLC-Q-TOF/MS-based serum metabolomics reveals the anti-ischemic stroke mechanism of nuciferine in MCAO rats, ACS Omega 5 (2020) 33433-33444.
|
Y. Wang, H. Zhao, Y. Liu, et al., GC-MS-based metabolomics to reveal the protective effect of gross saponins of Tribulus terrestris fruit against ischemic stroke in rat, Molecules 24 (2019), 793.
|
L. Luo, J. Kang, Q. He, et al., A NMR-based metabonomics approach to determine protective effect of a combination of multiple components derived from Naodesheng on ischemic stroke rats, Molecules 24 (2019), 1831.
|
J.Y. Jung, H.S. Lee, D.G. Kang, et al., 1H-NMR-based metabolomics study of cerebral infarction, Stroke 42 (2011) 1282-1288.
|
Z. Jiang, J. Sun, Q. Liang, et al., A metabonomic approach applied to predict patients with cerebral infarction, Talanta 84 (2011) 298-304.
|
W.T. Kimberly, Y. Wang, L. Pham, et al., Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke, Stroke 44 (2013) 1389-1395.
|
M. Liu, K. Zhou, H. Li, et al., Potential of serum metabolites for diagnosing post-stroke cognitive impairment, Mol. BioSyst. 11 (2015) 3287-3296.
|
X. Ding, R. Liu, W. Li, et al., A metabonomic investigation on the biochemical perturbation in post-stroke patients with depressive disorder (PSD), Metab. Brain Dis. 31 (2016) 279-287.
|
W. Zhang, X. Zhang, A novel urinary metabolite signature for non-invasive post-stroke depression diagnosis, Cell Biochem. Biophys. 72 (2015) 661-667.
|
Z. Hu, Z. Zhu, Y. Cao, et al., Rapid and sensitive differentiating ischemic and hemorrhagic strokes by dried blood spot based direct injection mass spectrometry metabolomics analysis, J. Clin. Lab. Anal. 30 (2016) 823-830.
|
M. Ruiz-Canela, E. Toledo, C.B. Clish, et al., Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial, Clin. Chem. 62 (2016) 582-592.
|
J. Xiao, J. Zhang, D. Sun, et al., Discriminating poststroke depression from stroke by nuclear magnetic resonance spectroscopy-based metabonomic analysis, Neuropsychiatr. Dis. Treat. 12 (2016) 1919-1925.
|
M. Guasch-Ferre, Y. Zheng, M. Ruiz-Canela, et al., Plasma acylcarnitines and risk of cardiovascular disease: Effect of Mediterranean diet interventions1, Am. J. Clin. Nutr. 103 (2016) 1408-1416.
|
Y. Zheng, F.B. Hu, M. Ruiz-Canela, et al., Metabolites of glutamate metabolism are associated with incident cardiovascular events in the PREDIMED PREvencion con DIeta MEDiterranea (PREDIMED) trial, J. Am. Heart Assoc. 5 (2016) e003755.
|
D.D. Wang, E. Toledo, A. Hruby, et al., Correction to: Plasma ceramides, Mediterranean diet, and incident cardiovascular disease in the PREDIMED trial (prevencion con dieta mediterranea), Circulation 140 (2019): 2028-2040.
|
L. Yang, P. Lv, W. Ai, et al., Lipidomic analysis of plasma in patients with lacunar infarction using normal-phase/reversed-phase two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry, Anal. Bioanal. Chem. 409 (2017) 3211-3222.
|
Y. Lee, A. Khan, S. Hong, et al., A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: A retrospective cohort study, Mol. BioSyst. 13 (2017) 1109-1120.
|
P. Liu, R. Li, A.A. Antonov, et al., Discovery of metabolite biomarkers for acute ischemic stroke progression, J. Proteome Res. 16 (2017) 773-779.
|
D.B. Kell, Systems biology, metabolic modelling and metabolomics in drug discovery and development, Drug Discov. Today 11 (2006) 1085-1092.
|
H. Cao, A. Zhang, H. Zhang, et al., The application of metabolomics in traditional Chinese medicine opens up a dialogue between Chinese and Western medicine, Phytother. Res. 29 (2015) 159-166.
|
R. Verpoorte, D. Crommelin, M. Danhof, et al., Commentary: “a systems view on the future of medicine: Inspiration from Chinese medicine?”, J. Ethnopharmacol. 121 (2009) 479-481.
|
S. Ahmad, N.M. Elsherbiny, R. Haque, et al., Sesamin attenuates neurotoxicity in mouse model of ischemic brain stroke, NeuroToxicology 45 (2014) 100-110.
|
J. Wang, S.C. Bondy, L. Zhou, et al., Protective effect of Tanshinone IIA against infarct size and increased HMGB1, NFκB, GFAP and apoptosis consequent to transient middle cerebral artery occlusion, Neurochem. Res. 39 (2014) 295-304.
|
M. Chien, C.H. Chuang, C.M. Chern, et al., Salvianolic acid A alleviates ischemic brain injury through the inhibition of inflammation and apoptosis and the promotion of neurogenesis in mice, Free. Radic. Biol. Med. 99 (2016) 508-519.
|
S. Xu, A. Zhong, H. Ma, et al., Neuroprotective effect of salvianolic acid B against cerebral ischemic injury in rats via the CD40/NF-κB pathway associated with suppression of platelets activation and neuroinflammation, Brain Res. 1661 (2017) 37-48.
|
B.P. Gaire, O.W. Kwon, S.H. Park, et al., Neuroprotective effect of 6-paradol in focal cerebral ischemia involves the attenuation of neuroinflammatory responses in activated microglia, PLoS One 10 (2015), e0120203.
|
X. Xue, X. Qu, Y. Yang, et al., Baicalin attenuates focal cerebral ischemic reperfusion injury through inhibition of nuclear factor κB p65 activation, Biochem. Biophys. Res. Commun. 403 (2010) 398-404.
|
Y. Zhang, P. Zhang, Y. Liu, Neuroprotective effect of WYY026B on cerebral ischemia/reperfusion injury in rodents through activation of Nrf2/HO-1 pathway, SSRN Electron. J. (2022): 639-898.
|
Y. Gao, X. Xu, S. Chang, et al., Totarol prevents neuronal injury in vitro and ameliorates brain ischemic stroke: Potential roles of Akt activation and HO-1 induction, Toxicol. Appl. Pharmacol. 289 (2015) 142-154.
|
B. Xu, X. He, Y. Sui, et al., Ginkgetin aglycone attenuates neuroinflammation and neuronal injury in the rats with ischemic stroke by modulating STAT3/JAK2/SIRT1, Folia Neuropathol. 57 (2019) 16-23.
|
J. Feng, X. Chen, S. Lu, et al., Naringin attenuates cerebral ischemia-reperfusion injury through inhibiting peroxynitrite-mediated mitophagy activation, Mol. Neurobiol. 55 (2018) 9029-9042.
|
Z. Wen, W. Hou, W. Wu, et al., 6’-O-galloylpaeoniflorin attenuates cerebral ischemia reperfusion-induced neuroinflammation and oxidative stress via PI3K/akt/Nrf2 activation, Oxid. Med. Cell. Longev. 2018 (2018) 1-14.
|
C. Ke, C. Pan, Y. Zhang, et al., Metabolomics facilitates the discovery of metabolic biomarkers and pathways for ischemic stroke: A systematic review, Metabolomics 15 (2019), 152.
|
X. Fu, J. Wang, S. Liao, et al., 1H NMR-based metabolomics reveals refined-Huang-Lian-Jie-du-decoction (BBG) as a potential ischemic stroke treatment drug with efficacy and a favorable therapeutic window, Front. Pharmacol. 10 (2019), 337.
|
Y. Qi, Q. Zhang, H. Zhu, Huang-Lian Jie-Du Decoction: A review on phytochemical, pharmacological and pharmacokinetic investigations, Chin. Med. 14 (2019) 1-22.
|
Y.S. Hwang, C.Y. Shin, Y. Huh, et al., Hwangryun-Hae-Dok-Tang (Huanglian-Jie-Du-Tang) extract and its constituents reduce ischemia-reperfusion brain injury and neutrophil infiltration in rats, Life Sci. 71 (2002) 2105-2117.
|
P. Wang, J. Wang, C. Zhang, et al., Huang-Lian-Jie-Du-Decotion induced protective autophagy against the injury of cerebral ischemia/reperfusion via MAPK-mTOR signaling pathway, J. Ethnopharmacol. 149 (2013) 270-280.
|
B. Zhu, H. Cao, L. Sun, et al., Metabolomics-based mechanisms exploration of Huang-Lian Jie-Du Decoction on cerebral ischemia via UPLC-Q-TOF/MS analysis on rat serum, J. Ethnopharmacol. 216 (2018) 147-156.
|
Q. Zhang, J. Wang, S. Liao, et al., Optimization of Huang-Lian-Jie-du-decoction for ischemic stroke treatment and mechanistic study by metabolomic profiling and network analysis, Front. Pharmacol. 8 (2017), 165.
|
Q. Zhang, J. Wang, C. Zhang, et al., The components of Huang-Lian-Jie-Du-Decoction act synergistically to exert protective effects in a rat ischemic stroke model, Oncotarget 7 (2016) 80872-80887.
|
Q. Zhang, X. Fu, J. Wang, et al., Treatment effects of ischemic stroke by berberine, baicalin, and jasminoidin from Huang-Lian-Jie-du-decoction (HLJDD) explored by an integrated metabolomics approach, Oxid. Med. Cell. Longev. 2017 (2017) 1-20.
|
Y. Guo, S. Yan, L. Xu, et al., Use of Angong Niuhuang in treating central nervous system diseases and related research, Evid. Based Complementary. Altern. Med. 2014 (2014), 346918.
|
Y. Luo, Angongniuhuang pill treatment of acute stroke clinical observation of 64 cases, Chin. J. Ethnomed. Ethnopharmacy 10 (2009) 45-46.
|
Z. Wei, Therapeutic effects of Angongniuhuang pill on 34 patients with cerebral stroke, Hebei J. Tradit. Chin. Med. 27 (2005) 13-14.
|
B. Tsoi, X. Chen, C. Gao, et al., Neuroprotective effects and hepatorenal toxicity of Angong Niuhuang Wan against ischemia-reperfusion brain injury in rats, Front. Pharmacol. 10 (2019), 593.
|
F. Xia, A. Li, Y. Chai, et al., UPLC/Q-TOFMS-based metabolomics approach to reveal the protective role of other herbs in an-Gong-Niu-Huang Wan against the hepatorenal toxicity of cinnabar and realgar, Front. Pharmacol. 9 (2018), 618.
|
Y. Zhang, X. Liu, J. Long, et al., Exploring active compounds and mechanisms of Angong Niuhuang Wan on ischemic stroke based on network pharmacology and molecular docking, Evid. Based Complementary Altern. Med. 2022 (2022) 1-13.
|
L. Zhang, X. Cheng, R. Chen, et al., Protective effect of effective composite of Chinese medicine prescription Naodesheng (脑得生) against focal cerebral ischemia in rats, Chin. J. Integr. Med. 15 (2009) 377-383.
|
L. Luo, L. Zhen, Y. Xu, et al., 1H NMR-based metabonomics revealed protective effect of Naodesheng bioactive extract on ischemic stroke rats, J. Ethnopharmacol. 186 (2016) 257-269.
|
L. Luo, S. Wu, X. Zhu, et al., The profiling and identification of the absorbed constituents and metabolites of Naoshuantong capsule in mice biofluids and brain by ultra- fast liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry, J. Chromatogr. B 1129 (2019), 121791.
|
Y. Gu, P. Huang, T. Cheng, et al., A multiomics and network pharmacological study reveals the neuroprotective efficacy of Fu-Fang-Dan-Zhi Tablets against glutamate-induced oxidative cell death, Comput. Biol. Med. 148 (2022), 105873.
|
J. Liu, L. Yang, Y. Niu, et al., Potential therapeutic effects of mi-Jian-Chang-Pu decoction on neurochemical and metabolic changes of cerebral ischemia-reperfusion injury in rats, Oxid. Med. Cell. Longev. 2022 (2022), 7319563.
|
P. Giraudeau, NMR-based metabolomics and fluxomics: Developments and future prospects, Analyst 145 (2020) 2457-2472.
|
D.J. Williamson, G.L. Burn, S. Simoncelli, et al., Machine learning for cluster analysis of localization microscopy data, Nat. Commun. 11 (2020), 1493.
|
R.S.G. Sealfon, L.H. Mariani, M. Kretzler, et al., Machine learning, the kidney, and genotype-phenotype analysis, Kidney Int. 97 (2020) 1141-1149.
|
B. Curry, D.E. Rumelhart, MSnet: A neural network which classifies mass spectra, Tetrahedron Comput. Methodol. 3 (1990) 213-237.
|
D.A. Cirovic, Feed-forward artificial neural networks: Applications to spectroscopy, Trac Trends Anal. Chem. 16 (1997) 148-155.
|
K.M. Mendez, D.I. Broadhurst, S.N. Reinke, The application of artificial neural networks in metabolomics: A historical perspective, Metabolomics 15 (2019), 142.
|
Y. Truong, X. Lin, C. Beecher, Learning a complex metabolomic dataset using random forests and support vector machines, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. August 22 - 25, 2004, Seattle, WA, USA. New York: ACM, (2004) 835-840.
|
M.P.S. Brown, W.N. Grundy, D. Lin, et al., Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. U. S. A. 97 (2000) 262-267.
|
E. Saccenti, H.C.J. Hoefsloot, A.K. Smilde, et al., Reflections on univariate and multivariate analysis of metabolomics data, Metabolomics 10 (2014) 361-374.
|
W.G. Touw, J.R. Bayjanov, L. Overmars, et al., Data mining in the Life Sciences with Random Forest: A walk in the park or lost in the jungle? Brief. Bioinform. 14 (2013) 315-326.
|
R.G. Brereton, G.R. Lloyd, Support Vector Machines for classification and regression, Analyst 135 (2010) 230-267.
|
S. Min, B. Lee, S. Yoon, Deep learning in bioinformatics, Brief. Bioinform. 18 (2017) 851-869.
|
I.T. Jolliffe, J. Cadima, Principal component analysis: A review and recent developments, Phil. Trans. R. Soc. A. 374 (2016), 20150202.
|
G.J. McLachlan, Cluster analysis and related techniques in medical research, Stat. Meth. Med. Res. 1 (1992) 27-48.
|
A. Au, Metabolomics and lipidomics of ischemic stroke, Adv. Clin. Chem. 85 (2018) 31-69.
|
T.H. Shin, H.A. Kim, J.Y. Jung, et al., Analysis of the free fatty acid metabolome in the plasma of patients with systemic lupus erythematosus and fever, Metabolomics 14 (2018), 14.
|
K. Ben Salem, A. Ben Abdelaziz, Principal component analysis (PCA), Tunis. Med. 99 (2021) 383-389.
|
G. Ivosev, L. Burton, R. Bonner, Dimensionality reduction and visualization in principal component analysis, Anal. Chem. 80 (2008) 4933-4944.
|
J. Bartel, J. Krumsiek, F.J. Theis, Statistical methods for the analysis of high-throughput metabolomics data, Comput. Struct. Biotechnol. J. 4 (2013), e201301009.
|
J.M. Fonville, S.E. Richards, R.H. Barton, et al., The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping, J. Chemom. 24 (2010) 636-649.
|
J. Trygg, S. Wold, Orthogonal projections to latent structures (O-PLS), J. Chemom. 16 (2002) 119-128.
|
M. Bylesjo, M. Rantalainen, O. Cloarec, et al., OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification, J. Chemom. 20 (2006) 341-351.
|
T. Wang, K. Shao, Q. Chu, et al., Automics: An integrated platform for NMR-based metabonomics spectral processing and data analysis, BMC Bioinform. 10 (2009), 83.
|
M. Jacob, A.L. Lopata, M. Dasouki, et al., Metabolomics toward personalized medicine, Mass Spectrom. Rev. 38 (2019) 221-238.
|
J. Xia, D.I. Broadhurst, M. Wilson, et al., Translational biomarker discovery in clinical metabolomics: An introductory tutorial, Metabolomics 9 (2013) 280-299.
|
G. la Marca, Mass spectrometry in clinical chemistry: The case of newborn screening, J. Pharm. Biomed. Anal. 101 (2014) 174-182.
|
R. Kaddurah-Daouk, R. Weinshilboum, P.R. Network, Metabolomic signatures for drug response phenotypes: Pharmacometabolomics enables precision medicine, Clin. Pharmacol. Ther. 98 (2015) 71-75.
|
B.C. Sallustio, LC-MS/MS for immunosuppressant therapeutic drug monitoring, Bioanalysis 2 (2010) 1141-1153.
|
D.J.A.R. Moes, R.R. Press, J.W. de Fijter, et al., Liquid chromatography-tandem mass spectrometry outperforms fluorescence polarization immunoassay in monitoring everolimus therapy in renal transplantation, Ther. Drug Monit. 32 (2010) 413-419.
|
N.K. Zgheib, R.F. Frye, T.S. Tracy, et al., Validation of incorporating flurbiprofen into the Pittsburgh cocktail, Clin. Pharmacol. Ther. 80 (2006) 257-263.
|
K. Suhre, S.Y. Shin, A.K. Petersen, et al., Human metabolic individuality in biomedical and pharmaceutical research, Nature 477 (2011) 54-60.
|
S.Y. Shin, E.B. Fauman, A.K. Petersen, et al., An atlas of genetic influences on human blood metabolites, Nat. Genet. 46 (2014) 543-550.
|
R. Zenobi, Single-cell metabolomics: Analytical and biological perspectives, Science 342 (2013), e1243259.
|
B.B. Misra, S.M. Assmann, S. Chen, Plant single-cell and single-cell-type metabolomics, Trends Plant Sci. 19 (2014) 637-646.
|
S.S. Rubakhin, E.J. Lanni, J.V. Sweedler, Progress toward single cell metabolomics, Curr. Opin. Biotechnol. 24 (2013) 95-104.
|
A. Oikawa, K. Saito, Metabolite analyses of single cells, Plant J. 70 (2012) 30-38.
|
F.R. Pinu, D.J. Beale, A.M. Paten, et al., Systems biology and multi-omics integration: Viewpoints from the metabolomics research community, Metabolites 9 (2019), 76.
|
X. Luo, L. Li, Metabolomics of small numbers of cells: Metabolomic profiling of 100, 1000, and 10000 human breast cancer cells, Anal. Chem. 89 (2017) 11664-11671.
|
T. Huang, M. Armbruster, R. Lee, et al., Metabolomic analysis of mammalian cells and human tissue through one-pot two stage derivatizations using sheathless capillary electrophoresis-electrospray ionization-mass spectrometry, J. Chromatogr. A 1567 (2018) 219-225.
|
Z. Pan, D. Raftery, Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics, Anal. Bioanal. Chem. 387 (2007) 525-527.
|
A.Svatos, Single-cell metabolomics comes of age: New developments in mass spectrometry profiling and imaging, Anal. Chem. 83 (2011) 5037-5044.
|
D. Wang, S. Bodovitz, Single cell analysis: The new frontier in ‘omics’, Trends Biotechnol. 28 (2010) 281-290.
|
Y. Li, J.H. Jang, C. Wang, et al., Microfluidics cell loading-dock system: Ordered cellular array for dynamic lymphocyte-communication study, Adv. Biosys. 1 (2017), 1700085.
|
Y. Li, J.D. Motschman, S.T. Kelly, et al., Injection molded microfluidics for establishing high-density single cell arrays in an open hydrogel format, Anal. Chem. 92 (2020) 2794-2801.
|
X. Feng, B. Liu, J. Li, et al., Advances in coupling microfluidic chips to mass spectrometry, Mass Spectrom. Rev. 34 (2015) 535-557.
|
S. Gavasso, S.E. Gullaksen, J. Skavland, et al., Single-cell proteomics: Potential implications for cancer diagnostics, Expert Rev. Mol. Diagn. 16 (2016) 579-589.
|
A. Gross, J. Schoendube, S. Zimmermann, et al., Technologies for single-cell isolation, Int. J. Mol. Sci. 16 (2015) 16897-16919.
|
M. Fessenden, Metabolomics: Small molecules, single cells, Nature 540 (2016) 153-155.
|
L. Zhang, A. Vertes, Single-cell mass spectrometry approaches to explore cellular heterogeneity, Angew. Chem. Int. Ed. 57 (2018) 4466-4477.
|
K.D. Duncan, J. Fyrestam, I. Lanekoff, Advances in mass spectrometry based single-cell metabolomics, Analyst 144 (2019) 782-793.
|
C. Muschet, G. Moller, C. Prehn, et al., Removing the bottlenecks of cell culture metabolomics: Fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method, Metabolomics 12 (2016), 151.
|
L. Zhang, A. Vertes, Energy charge, redox state, and metabolite turnover in single human hepatocytes revealed by capillary microsampling mass spectrometry, Anal. Chem. 87 (2015) 10397-10405.
|
M.K. Passarelli, C.F. Newman, P.S. Marshall, et al., Single-cell analysis: Visualizing pharmaceutical and metabolite uptake in cells with label-free 3D mass spectrometry imaging, Anal. Chem. 87 (2015) 6696-6702.
|
A. Amantonico, P.L. Urban, S.R. Fagerer, et al., Single-cell MALDI-MS as an analytical tool for studying intrapopulation metabolic heterogeneity of unicellular organisms, Anal. Chem. 82 (2010) 7394-7400.
|
N. Pan, W. Rao, N.R. Kothapalli, et al., The single-probe: A miniaturized multifunctional device for single cell mass spectrometry analysis, Anal. Chem. 86 (2014) 9376-9380.
|
A.M. Kleinfeld, J.P. Kampf, C. Lechene, Transport of 13C-oleate in adipocytes measured using multi imaging mass spectrometry, J. Am. Soc. Mass Spectrom. 15 (2004) 1572-1580.
|
N.J. Schoffelen, W. Mohr, T.G. Ferdelman, et al., Single-cell imaging of phosphorus uptake shows that key harmful algae rely on different phosphorus sources for growth, Sci. Rep. 8 (2018), 17182.
|
H. Li, X. Hua, Y. Long, Graphene quantum dots enhanced ToF-SIMS for single-cell imaging, Anal. Bioanal. Chem. 411 (2019) 4025-4030.
|
M.E. Duenas, J.J. Essner, Y.J. Lee, 3D MALDI mass spectrometry imaging of a single cell: Spatial mapping of lipids in the embryonic development of zebrafish, Sci. Rep. 7 (2017), 14946.
|
B. Yang, N.H. Patterson, T. Tsui, et al., Single-cell mass spectrometry reveals changes in lipid and metabolite expression in RAW 264.7 cells upon lipopolysaccharide stimulation, J. Am. Soc. Mass Spectrom. 29 (2018) 1012-1020.
|
T.A. Zimmerman, E.B. Monroe, K.R. Tucker, et al., Chapter 13 imaging of cells and tissues with mass spectrometry. Methods in Cell Biology. Amsterdam: Elsevier, (2008) 361-390.
|
B. Shrestha, A. Vertes, in situ metabolic profiling of single cells by laser ablation electrospray ionization mass spectrometry, Anal. Chem. 81 (2009) 8265-8271.
|
R. Liu, N. Pan, Y. Zhu, et al., T-probe: An integrated microscale device for online in situ single cell analysis and metabolic profiling using mass spectrometry, Anal. Chem. 90 (2018) 11078-11085.
|
Q. Huang, S. Mao, M. Khan, et al., Dean flow assisted cell ordering system for lipid profiling in single-cells using mass spectrometry, Chem. Commun. 54 (2018) 2595-2598.
|
E.K. Neumann, J.F. Ellis, A.E. Triplett, et al., Lipid analysis of 30 000 individual rodent cerebellar cells using high-resolution mass spectrometry, Anal. Chem. 91 (2019) 7871-7878.
|
M. Qi, M.C. Philip, N. Yang, et al., Single cell neurometabolomics, ACS Chem. Neurosci. 9 (2018) 40-50.
|
I. Lanekoff, V.V. Sharma, C. Marques, Single-cell metabolomics: Where are we and where are we going? Curr. Opin. Biotechnol. 75 (2022), 102693.
|
Y. Fangma, H. Zhou, C. Shao, et al., Hydroxysafflor yellow A and anhydrosafflor yellow B protect against cerebral ischemia/reperfusion injury by attenuating oxidative stress and apoptosis via the silent information regulator 1 signaling pathway, Front. Pharmacol. 12 (2021), 739864.
|
M.J. Taylor, J.K. Lukowski, C.R. Anderton, Spatially resolved mass spectrometry at the single cell: Recent innovations in proteomics and metabolomics, J. Am. Soc. Mass Spectrom. 32 (2021) 872-894.
|