1932

Abstract

Amyloid-related diseases, such as Alzheimer's and Parkinson's disease, are devastating conditions caused by the accumulation of abnormal protein aggregates known as amyloid fibrils. While assays involving animal models are essential for understanding the pathogenesis and developing therapies, a wide array of standard analytical techniques exists to enhance our understanding of these disorders. These techniques provide valuable information on the formation and propagation of amyloid fibrils, as well as the pharmacokinetics and pharmacodynamics of candidate drugs. Despite ethical concerns surrounding animal use, animal models remain vital tools in the search for treatments. Regardless of the specific animal model chosen, the analytical methods used are usually standardized. Therefore, the main objective of this review is to categorize and outline the primary analytical methods used in in vivo assays for amyloid-related diseases, highlighting their critical role in furthering our understanding of these disorders and developing effective therapies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061622-023326
2024-07-17
2025-04-29
Loading full text...

Full text loading...

/deliver/fulltext/anchem/17/1/annurev-anchem-061622-023326.html?itemId=/content/journals/10.1146/annurev-anchem-061622-023326&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Wadsworth JDF, Asante EA, Collinge J. 2010.. Contribution of transgenic models to understanding human prion disease. . Neuropathol. Appl. Neurobiol. 36:(7):57697
    [Crossref] [Google Scholar]
  2. 2.
    Van Dam D, De Deyn PP. 2011.. Animal models in the drug discovery pipeline for Alzheimer's disease. . Br. J. Pharmacol. 164:(4):1285300
    [Crossref] [Google Scholar]
  3. 3.
    Boelsterli UA. 2003.. Animal models of human disease in drug safety assessment. . J. Toxicol. Sci. 28:(3):10921
    [Crossref] [Google Scholar]
  4. 4.
    World Health Organ. 2019.. Global dementia observatory: data and statistics. https://www.who.int/data/gho/data/themes/global-dementia-observatory-gdo
    [Google Scholar]
  5. 5.
    Chiti F, Dobson CM. 2017.. Protein misfolding, amyloid formation, and human disease: a summary of progress over the last decade. . Annu. Rev. Biochem. 86::2768
    [Crossref] [Google Scholar]
  6. 6.
    United Nations. 2023.. Global issues: population. https://www.un.org/en/global-issues/population
    [Google Scholar]
  7. 7.
    Alexander AG, Marfil V, Li C. 2014.. Use of Caenorhabditis elegans as a model to study Alzheimer's disease and other neurodegenerative diseases. . Front. Genet. 5::279
    [Crossref] [Google Scholar]
  8. 8.
    Jeon Y, Lee JH, Choi B, Won S-Y, Cho KS. 2020.. Genetic dissection of Alzheimer's disease using Drosophila models. . Int. J. Mol. Sci. 21:(3):884
    [Crossref] [Google Scholar]
  9. 9.
    Chia K, Klingseisen A, Sieger D, Priller J. 2022.. Zebrafish as a model organism for neurodegenerative disease. . Front. Mol. Neurosci. 15::940484
    [Crossref] [Google Scholar]
  10. 10.
    Exner CRT, Willsey HR. 2021.. Xenopus leads the way: frogs as a pioneering model to understand the human brain. . Genesis 59:(1–2):e23405
    [Crossref] [Google Scholar]
  11. 11.
    Mckean NE, Handley RR, Snell RG. 2021.. A review of the current mammalian models of Alzheimer's disease and challenges that need to be overcome. . Int. J. Mol. Sci. 22:(23):13168
    [Crossref] [Google Scholar]
  12. 12.
    Myers A, McGonigle P. 2019.. Overview of transgenic mouse models for Alzheimer's disease. . Curr. Protoc. Neurosci. 89:(1):e81
    [Crossref] [Google Scholar]
  13. 13.
    Lee Y, Dawson VL, Dawson TM. 2012.. Animal models of Parkinson's disease: vertebrate genetics. . Cold Spring Harb. Perspect. Med. 2:(10):a009324
    [Crossref] [Google Scholar]
  14. 14.
    Edrey YH, Medina DX, Gaczynska M, Osmulski PA, Oddo S, et al. 2013.. Amyloid beta and the longest-lived rodent: the naked mole-rat as a model for natural protection from Alzheimer's disease. . Neurobiol. Aging 34:(10):235260
    [Crossref] [Google Scholar]
  15. 15.
    von Horsten S, Schmitt I, Nguyen HP, Holzmann C, Schmidt T, et al. 2003.. Transgenic rat model of Huntington's disease. . Hum. Mol. Genet. 12:(6):61724
    [Crossref] [Google Scholar]
  16. 16.
    Welchko RM, Lévêque XT, Dunbar GL. 2012.. Genetic rat models of Parkinson's disease. . Parkinsons Dis. 2012::16
    [Crossref] [Google Scholar]
  17. 17.
    Shively CA, Lacreuse A, Frye BM, Rothwell ES, Moro M. 2021.. Nonhuman primates at the intersection of aging biology, chronic disease, and health: an introduction to the American Journal of Primatology Special Issue on aging, cognitive decline, and neuropathology in nonhuman primates. . Am. J. Primatol. 83:(11):e23309
    [Crossref] [Google Scholar]
  18. 18.
    Emborg ME. 2017.. Nonhuman primate models of neurodegenerative disorders. . ILAR J. 58:(2):190201
    [Crossref] [Google Scholar]
  19. 19.
    Tanila H. 2018.. Testing cognitive functions in rodent disease models: present pitfalls and future perspectives. . Behav. Brain Res. 352::2327
    [Crossref] [Google Scholar]
  20. 20.
    Castillo-Rangel C, Marín G, Diaz-Chiguer DL, Zarate-Calderon CJ, Viveros-Martinez I, et al. 2023.. Animal models in Alzheimer's disease: biological plausibility and mood disorders. . Neurol. Perspect. 3:(1):100110
    [Crossref] [Google Scholar]
  21. 21.
    Wolf A, Bauer B, Abner EL, Ashkenazy-Frolinger T, Hartz AMS. 2016.. A comprehensive behavioral test battery to assess learning and memory in 129S6/Tg2576 mice. . PLOS ONE 11:(1):e0147733
    [Crossref] [Google Scholar]
  22. 22.
    McIlwain KL, Merriweather MY, Yuva-Paylor LA, Paylor R. 2001.. The use of behavioral test batteries: effects of training history. . Physiol. Behav. 73:(5):70517
    [Crossref] [Google Scholar]
  23. 23.
    Puzzo D, Lee L, Palmeri A, Calabrese G, Arancio O. 2014.. Behavioral assays with mouse models of Alzheimer's disease: practical considerations and guidelines. . Biochem. Pharmacol. 88:(4):45067
    [Crossref] [Google Scholar]
  24. 24.
    Webster SJ, Bachstetter AD, Nelson PT, Schmitt FA, Van Eldik LJ. 2014.. Using mice to model Alzheimer's dementia: an overview of the clinical disease and the preclinical behavioral changes in 10 mouse models. . Front. Genet. 5::88
    [Crossref] [Google Scholar]
  25. 25.
    Botto R, Callai N, Cermelli A, Causarano L, Rainero I. 2022.. Anxiety and depression in Alzheimer's disease: a systematic review of pathogenetic mechanisms and relation to cognitive decline. . Neurol. Sci. 43:(7):410724
    [Crossref] [Google Scholar]
  26. 26.
    Pentkowski NS, Rogge-Obando KK, Donaldson TN, Bouquin SJ, Clark BJ. 2021.. Anxiety and Alzheimer's disease: behavioral analysis and neural basis in rodent models of Alzheimer's-related neuropathology. . Neurosci. Biobehav. Rev. 127::64758
    [Crossref] [Google Scholar]
  27. 27.
    Walf AA, Frye CA. 2007.. The use of the elevated plus maze as an assay of anxiety-related behavior in rodents. . Nat. Protoc. 2:(2):32228
    [Crossref] [Google Scholar]
  28. 28.
    Seibenhener ML, Wooten MC. 2015.. Use of the open field maze to measure locomotor and anxiety-like behavior in mice. . J. Visualized Exp. (96):e52434
    [Google Scholar]
  29. 29.
    Bourin M, Hascoët M. 2003.. The mouse light/dark box test. . Eur. J. Pharmacol. 463:(1–3):5565
    [Crossref] [Google Scholar]
  30. 30.
    Slattery DA, Cryan JF. 2012.. Using the rat forced swim test to assess antidepressant-like activity in rodents. . Nat. Protoc. 7:(6):100914
    [Crossref] [Google Scholar]
  31. 31.
    Steru L, Chermat R, Thierry B, Simon P. 1985.. The tail suspension test: a new method for screening antidepressants in mice. . Psychopharmacology 85:(3):36770
    [Crossref] [Google Scholar]
  32. 32.
    Anisman H, Merali Z. 2001.. Rodent models of depression: learned helplessness induced in mice. . Curr. Protoc. Neurosci. 14::8.10C.115
    [Crossref] [Google Scholar]
  33. 33.
    Liu M-Y, Yin C-Y, Zhu L-J, Zhu X-H, Xu C, et al. 2018.. Sucrose preference test for measurement of stress-induced anhedonia in mice. . Nat. Protoc. 13:(7):168698
    [Crossref] [Google Scholar]
  34. 34.
    Wu C, Yang L, Li Y, Dong Y, Yang B, et al. 2020.. Effects of exercise training on anxious-depressive-like behavior in Alzheimer rat. . Med. Sci. Sports Exerc. 52:(7):145669
    [Crossref] [Google Scholar]
  35. 35.
    Kim S, Kwon S-H, Kam T-I, Panicker N, Karuppagounder SS, et al. 2019.. Transneuronal propagation of pathologic α-synuclein from the gut to the brain models Parkinson's disease. . Neuron 103:(4):62741.e7
    [Crossref] [Google Scholar]
  36. 36.
    Sun J, Li H, Jin Y, Yu J, Mao S, et al. 2021.. Probiotic Clostridium butyricum ameliorated motor deficits in a mouse model of Parkinson's disease via gut microbiota–GLP-1 pathway. . Brain Behav. Immun. 91::70315
    [Crossref] [Google Scholar]
  37. 37.
    Manfré G, Novati A, Faccini I, Rossetti AC, Bosch K, et al. 2018.. BACHD rats expressing full-length mutant huntingtin exhibit differences in social behavior compared to wild-type littermates. . PLOS ONE 13:(2):e0192289
    [Crossref] [Google Scholar]
  38. 38.
    Kodera K, Matsui H. 2022.. Zebrafish, medaka and turquoise killifish for understanding human neurodegenerative/neurodevelopmental disorders. . Int. J. Mol. Sci. 23:(3):1399
    [Crossref] [Google Scholar]
  39. 39.
    Sahoo PK, Aparna S, Naik PK, Singh SB, Das SK. 2021.. Bisphenol A exposure induces neurobehavioral deficits and neurodegeneration through induction of oxidative stress and activated caspase-3 expression in zebrafish brain. . J. Biochem. Mol. Toxicol. 35:(10):e22873
    [Crossref] [Google Scholar]
  40. 40.
    Pecio Ł, Kozachok S, Brinza I, Boiangiu RS, Hritcu L, et al. 2022.. Neuroprotective effect of Yucca schidigera Roezl ex Ortgies bark phenolic fractions, yuccaol B and gloriosaol A on scopolamine-induced memory deficits in zebrafish. . Molecules 27:(12):3692
    [Crossref] [Google Scholar]
  41. 41.
    Aparna S, Patri M. 2021.. Benzo[a]pyrene exposure and overcrowding stress impacts anxiety-like behavior and impairs learning and memory in adult zebrafish, Danio rerio. . Environ. Toxicol. 36:(3):35261
    [Crossref] [Google Scholar]
  42. 42.
    Giacomini AC, Bueno BW, Marcon L, Scolari N, Genario R, et al. 2020.. An acetylcholinesterase inhibitor, donepezil, increases anxiety and cortisol levels in adult zebrafish. . J. Psychopharmacol. 34:(12):144956
    [Crossref] [Google Scholar]
  43. 43.
    Tamagno WA, Santini W, Alves C, Vanin AP, Pompermaier A, et al. 2022.. Neuroprotective and antioxidant effects of pitaya fruit on Cu-induced stress in adult zebrafish. . J. Food Biochem. 46:(7):e14147
    [Crossref] [Google Scholar]
  44. 44.
    Shenoy A, Banerjee M, Upadhya A, Bagwe-Parab S, Kaur G. 2022.. The brilliance of the zebrafish model: perception on behavior and Alzheimer's disease. . Front. Behav. Neurosci. 16::861155
    [Crossref] [Google Scholar]
  45. 45.
    Tierney KB. 2011.. Behavioural assessments of neurotoxic effects and neurodegeneration in zebrafish. . Biochim. Biophys. Acta Mol. Basis Dis. 1812:(3):38189
    [Crossref] [Google Scholar]
  46. 46.
    Ilie O-D, Duta R, Jijie R, Nita I-B, Nicoara M, et al. 2022.. Assessing anti-social and aggressive behavior in a zebrafish (Danio rerio) model of Parkinson's disease chronically exposed to rotenone. . Brain Sci. 12:(7):898
    [Crossref] [Google Scholar]
  47. 47.
    Lassen LB, Gregersen E, Isager AK, Betzer C, Kofoed RH, Jensen PH. 2018.. ELISA method to detect α-synuclein oligomers in cell and animal models. . PLOS ONE 13:(4):e0196056
    [Crossref] [Google Scholar]
  48. 48.
    Pan T, Chang B, Wong P, Li C, Li R, et al. 2005.. An aggregation-specific enzyme-linked immunosorbent assay: detection of conformational differences between recombinant PrP protein dimers and PrPSc aggregates. . J. Virol. 79:(19):1235564
    [Crossref] [Google Scholar]
  49. 49.
    Tenreiro S, Eckermann K, Outeiro TF. 2014.. Protein phosphorylation in neurodegeneration: friend or foe?. Front. Mol. Neurosci. 7::42
    [Crossref] [Google Scholar]
  50. 50.
    Thomsen M, Hansen H, Timmerman M, Mikkelsen J. 2010.. Cognitive improvement by activation of α7 nicotinic acetylcholine receptors: from animal models to human pathophysiology. . Curr. Pharm. Des. 16:(3):32343
    [Crossref] [Google Scholar]
  51. 51.
    Chen G, Chen P, Tan H, Ma D, Dou F, et al. 2008.. Regulation of the NMDA receptor–mediated synaptic response by acetylcholinesterase inhibitors and its impairment in an animal model of Alzheimer's disease. . Neurobiol. Aging 29:(12):1795804
    [Crossref] [Google Scholar]
  52. 52.
    Hur J-Y. 2022.. γ-Secretase in Alzheimer's disease. . Exp. Mol. Med. 54:(4):43346
    [Crossref] [Google Scholar]
  53. 53.
    Biasini E, Tapella L, Mantovani S, Stravalaci M, Gobbi M, et al. 2009.. Immunopurification of pathological prion protein aggregates. . PLOS ONE 4:(11):e7816
    [Crossref] [Google Scholar]
  54. 54.
    Betzer C, Kofoed RH, Jensen PH. 2019.. The use of co-immunoprecipitation to study conformation-specific protein interactions of oligomeric α-synuclein aggregates. . Springer Protoc. 144::2336
    [Google Scholar]
  55. 55.
    Wagner WJ, Gross ML. 2024.. Using mass spectrometry–based methods to understand amyloid formation and inhibition of alpha-synuclein and amyloid beta. . Mass Spectrom. Rev. https://doi.org/10.1002/mas.21814
    [Google Scholar]
  56. 56.
    Grasso G. 2011.. The use of mass spectrometry to study amyloid-β peptides. . Mass Spectrom. Rev. 30:(3):34765
    [Crossref] [Google Scholar]
  57. 57.
    Woods LA, Radford SE, Ashcroft AE. 2013.. Advances in ion mobility spectrometry–mass spectrometry reveal key insights into amyloid assembly. . Biochim. Biophys. Acta Proteins Proteom. 1834:(6):125768
    [Crossref] [Google Scholar]
  58. 58.
    Xue C, Lin TY, Chang D, Guo Z. 2017.. Thioflavin T as an amyloid dye: fibril quantification, optimal concentration and effect on aggregation. . R. Soc. Open Sci. 4:(1):160696
    [Crossref] [Google Scholar]
  59. 59.
    Espargaró A, Medina A, Di Pietro O, Muñoz-Torrero D, Sabate R. 2016.. Ultra rapid in vivo screening for anti-Alzheimer anti-amyloid drugs. . Sci. Rep. 6:(1):23349
    [Crossref] [Google Scholar]
  60. 60.
    Elghetany MT, Saleem A, Barr K. 1989.. The Congo red stain revisited. . Ann. Clin. Lab. Sci. 19:(3):19095
    [Google Scholar]
  61. 61.
    Klunk WE, Pettegrew JW, Abraham DJ. 1989.. Quantitative evaluation of Congo red binding to amyloid-like proteins with a beta-pleated sheet conformation. . J. Histochem. Cytochem. 37:(8):127381
    [Crossref] [Google Scholar]
  62. 62.
    Klunk W. 1995.. Chrysamine-G binding to Alzheimer and control brain: autopsy study of a new amyloid probe. . Neurobiol. Aging 16:(4):54148
    [Crossref] [Google Scholar]
  63. 63.
    Styren SD, Hamilton RL, Styren GC, Klunk WE. 2000.. X-34, a fluorescent derivative of Congo red: a novel histochemical stain for Alzheimer's disease pathology. . J. Histochem. Cytochem. 48:(9):122332
    [Crossref] [Google Scholar]
  64. 64.
    Velasco A, Fraser G, Delobel P, Ghetti B, Lavenir I, Goedert M. 2008.. Detection of filamentous tau inclusions by the fluorescent Congo red derivative FSB [(trans, trans)-1-fluoro-2,5-bis(3-hydroxycarbonyl-4-hydroxy)styrylbenzene]. . FEBS Lett. 582:(6):9016
    [Crossref] [Google Scholar]
  65. 65.
    Kim D, Moon H, Baik SH, Singha S, Jun YW, et al. 2015.. Two-photon absorbing dyes with minimal autofluorescence in tissue imaging: application to in vivo imaging of amyloid-β plaques with a negligible background signal. . J. Am. Chem. Soc. 137:(21):678189
    [Crossref] [Google Scholar]
  66. 66.
    Navarro S, Ventura S. 2014.. Fluorescent dye ProteoStat to detect and discriminate intracellular amyloid-like aggregates in Escherichia coli. . Biotechnol. J. 9:(10):125966
    [Crossref] [Google Scholar]
  67. 67.
    Xu M, Ren W, Tang X, Hu Y, Zhang H. 2016.. Advances in development of fluorescent probes for detecting amyloid-β aggregates. . Acta Pharmacol. Sin. 37:(6):71930
    [Crossref] [Google Scholar]
  68. 68.
    Capponi PC, Mari M, Ferrari E, Asti M. 2023.. Radiolabeled chalcone derivatives as potential radiotracers for β-amyloid plaques imaging. . Molecules 28:(7):3233
    [Crossref] [Google Scholar]
  69. 69.
    Fu H, Cui M, Zhao L, Tu P, Zhou K, et al. 2015.. Highly sensitive near-infrared fluorophores for in vivo detection of amyloid-β plaques in Alzheimer's disease. . J. Med. Chem. 58:(17):697283
    [Crossref] [Google Scholar]
  70. 70.
    Sarroukh R, Goormaghtigh E, Ruysschaert J-M, Raussens V. 2013.. ATR-FTIR: a “rejuvenated” tool to investigate amyloid proteins. . Biochim. Biophys. Acta Biomembr. 1828:(10):232838
    [Crossref] [Google Scholar]
  71. 71.
    Han S, Hill AF. 2008.. Analysis of PrP conformation using circular dichroism. . Methods Mol. Biol. 459::14559
    [Crossref] [Google Scholar]
  72. 72.
    Eckert A, Hauptmann S, Scherping I, Meinhardt J, Rhein V, et al. 2008.. Oligomeric and fibrillar species of β-amyloid (Aβ42) both impair mitochondrial function in P301L tau transgenic mice. . J. Mol. Med. 86:(11):125567
    [Crossref] [Google Scholar]
  73. 73.
    Dahal E, Ghammraoui B, Badano A. 2020.. Feasibility of a label-free X-ray method to estimate brain amyloid load in small animals. . J. Neurosci. Methods 343::108822
    [Crossref] [Google Scholar]
  74. 74.
    Morris KL, Serpell LC. 2012.. X-ray fibre diffraction studies of amyloid fibrils. . Methods Mol. Biol. 849::12135
    [Crossref] [Google Scholar]
  75. 75.
    Wille H, Bian W, McDonald M, Kendall A, Colby DW, et al. 2009.. Natural and synthetic prion structure from X-ray fiber diffraction. . PNAS 106:(40):1699095
    [Crossref] [Google Scholar]
  76. 76.
    Kreutzer AG, Hamza IL, Spencer RK, Nowick JS. 2016.. X-ray crystallographic structures of a trimer, dodecamer, and annular pore formed by an Aβ17–36 β-hairpin. . J. Am. Chem. Soc. 138:(13):463442
    [Crossref] [Google Scholar]
  77. 77.
    Sabate R, Espargaró A, Busquets MA, Estelrich J. 2015.. Amyloids in solid-state nuclear magnetic resonance: potential causes of the usually low resolution. . Int. J. Nanomed. 10::697583
    [Crossref] [Google Scholar]
  78. 78.
    Tycko R. 2015.. Amyloid polymorphism: structural basis and neurobiological relevance. . Neuron 86:(3):63245
    [Crossref] [Google Scholar]
  79. 79.
    Lv G, Kumar A, Giller K, Orcellet ML, Riedel D, et al. 2012.. Structural comparison of mouse and human α-synuclein amyloid fibrils by solid-state NMR. . J. Mol. Biol. 420:(1–2):99111
    [Crossref] [Google Scholar]
  80. 80.
    Pedrero-Prieto CM, Flores-Cuadrado A, Saiz-Sánchez D, Úbeda-Bañón I, Frontiñán-Rubio J, et al. 2019.. Human amyloid-β enriched extracts: evaluation of in vitro and in vivo internalization and molecular characterization. . Alzheimers Res. Ther. 11:(1):56
    [Crossref] [Google Scholar]
  81. 81.
    Höfling C, Morawski M, Zeitschel U, Zanier ER, Moschke K, et al. 2016.. Differential transgene expression patterns in Alzheimer mouse models revealed by novel human amyloid precursor protein–specific antibodies. . Aging Cell 15:(5):95363
    [Crossref] [Google Scholar]
  82. 82.
    Szegő ÉM, Boß F, Komnig D, Gärtner C, Höfs L, et al. 2021.. A β-wrapin targeting the N-terminus of α-synuclein monomers reduces fibril-induced aggregation in neurons. . Front. Neurosci. 15::696440
    [Crossref] [Google Scholar]
  83. 83.
    Sahara N, Lewis J, DeTure M, McGowan E, Dickson DW, et al. 2002.. Assembly of tau in transgenic animals expressing P301L tau: alteration of phosphorylation and solubility. . J. Neurochem. 83:(6):1498508
    [Crossref] [Google Scholar]
  84. 84.
    van Dyck CH. 2018.. Anti-amyloid-β monoclonal antibodies for Alzheimer's disease: pitfalls and promise. . Biol. Psychiatry 83:(4):31119
    [Crossref] [Google Scholar]
  85. 85.
    Siwek ME, Müller R, Henseler C, Trog A, Lundt A, et al. 2015.. Altered theta oscillations and aberrant cortical excitatory activity in the 5XFAD model of Alzheimer's disease. . Neural Plast. 2015::781731
    [Crossref] [Google Scholar]
  86. 86.
    Kim B, Shin J, Kim Y, Choi JH. 2020.. Destruction of ERP responses to deviance in an auditory oddball paradigm in amyloid infusion mice with memory deficits. . PLOS ONE 15:(3):e0230277
    [Crossref] [Google Scholar]
  87. 87.
    Schneider F, Baldauf K, Wetzel W, Reymann KG. 2014.. Behavioral and EEG changes in male 5xFAD mice. . Physiol. Behav. 135::2533
    [Crossref] [Google Scholar]
  88. 88.
    Dossena S, Imeri L, Mangieri M, Garofoli A, Ferrari L, et al. 2008.. Mutant prion protein expression causes motor and memory deficits and abnormal sleep patterns in a transgenic mouse model. . Neuron 60:(4):598609
    [Crossref] [Google Scholar]
  89. 89.
    Sur S, Sinha V. 2009.. Event-related potential: an overview. . Ind. Psychiatry J. 18:(1):7073
    [Crossref] [Google Scholar]
  90. 90.
    Teruya PY, Farfán FD, Pizá ÁG, Soletta JH, Lucianna FA, Albarracín AL. 2021.. Quantifying muscle alterations in a Parkinson's disease animal model using electromyographic biomarkers. . Med. Biol. Eng. Comput. 59:(9):173549
    [Crossref] [Google Scholar]
  91. 91.
    Ebrahimi MJ, Aliaghaei A, Boroujeni ME, Khodagholi F, Meftahi G, et al. 2018.. Human umbilical cord matrix stem cells reverse oxidative stress–induced cell death and ameliorate motor function and striatal atrophy in rat model of Huntington disease. . Neurotox. Res. 34:(2):27384
    [Crossref] [Google Scholar]
  92. 92.
    Zhang R, Chen Y, Wang X, Tian H, Liu H, et al. 2021.. Spreading of pathological TDP-43 along corticospinal tract axons induces ALS-like phenotypes in Atg5+/− mice. . Int. J. Biol. Sci. 17:(2):390401
    [Crossref] [Google Scholar]
  93. 93.
    Molleman A. 2002.. Patch Clamping: An Introductory Guide to Patch Clamp Electrophysiology. New York:: Wiley
    [Google Scholar]
  94. 94.
    Hille B. 2001.. Ion Channels of Excitable Membranes. Oxford, UK:: Oxford Univ. Press. , 3rd ed..
    [Google Scholar]
  95. 95.
    Sepulveda FJ, Parodi J, Peoples RW, Opazo C, Aguayo LG. 2010.. Synaptotoxicity of Alzheimer beta amyloid can be explained by its membrane perforating property. . PLOS ONE 5:(7):e11820
    [Crossref] [Google Scholar]
  96. 96.
    Heggland I, Kvello P, Witter MP. 2019.. Electrophysiological characterization of networks and single cells in the hippocampal region of a transgenic rat model of Alzheimer's disease. . eNeuro 6:(1):ENEURO.0448-17.2019
    [Crossref] [Google Scholar]
  97. 97.
    Humphrey DR, Schmidt EM. 1990.. Extracellular single-unit recording methods. . In Neurophysiological Techniques: Neuromethods, ed. AA Boulton, GB Baker, CH Vanderwolf , pp. 164. Totowa, NJ:: Humana
    [Google Scholar]
  98. 98.
    Vegas-Suárez S, Morera-Herreras T, Requejo C, Lafuente JV, Moratalla R, et al. 2022.. Motor cortico-nigral and cortico-entopeduncular information transmission and its modulation by buspirone in control and after dopaminergic denervation. . Front. Pharmacol. 13::953652
    [Crossref] [Google Scholar]
  99. 99.
    Spira ME, Hai A. 2013.. Multi-electrode array technologies for neuroscience and cardiology. . Nat. Nanotechnol. 8:(2):8394
    [Crossref] [Google Scholar]
  100. 100.
    Lee S, Kim TK, Choi JE, Choi Y, You M, et al. 2022.. Dysfunction of striatal MeCP2 is associated with cognitive decline in a mouse model of Alzheimer's disease. . Theranostics 12:(3):140418
    [Crossref] [Google Scholar]
  101. 101.
    Rajamohamedsait HB, Sigurdsson EM. 2012.. Histological staining of amyloid and pre-amyloid peptides and proteins in mouse tissue. . Methods Mol. Biol. 849::41124
    [Crossref] [Google Scholar]
  102. 102.
    Sarkar S, Raymick J, Cuevas E, Rosas-Hernandez H, Hanig J. 2020.. Modification of methods to use Congo-red stain to simultaneously visualize amyloid plaques and tangles in human and rodent brain tissue sections. . Metab. Brain Dis. 35:(8):137183
    [Crossref] [Google Scholar]
  103. 103.
    Souder DC, Dreischmeier IA, Smith AB, Wright S, Martin SA, et al. 2021.. Rhesus monkeys as a translational model for late-onset Alzheimer's disease. . Aging Cell 20:(6):e13374
    [Crossref] [Google Scholar]
  104. 104.
    Mathis CA, Bacskai BJ, Kajdasz ST, McLellan ME, Frosch MP, et al. 2002.. A lipophilic thioflavin-T derivative for positron emission tomography (PET) imaging of amyloid in brain. . Bioorg. Med. Chem. Lett. 12:(3):29598
    [Crossref] [Google Scholar]
  105. 105.
    Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, et al. 2004.. Imaging brain amyloid in Alzheimer's disease with Pittsburgh compound-B. . Ann. Neurol. 55:(3):30619
    [Crossref] [Google Scholar]
  106. 106.
    Bacskai BJ, Hickey GA, Skoch J, Kajdasz ST, Wang Y, et al. 2003.. Four-dimensional multiphoton imaging of brain entry, amyloid binding, and clearance of an amyloid-β ligand in transgenic mice. . PNAS 100:(21):1246267
    [Crossref] [Google Scholar]
  107. 107.
    Shankar GM, Leissring MA, Adame A, Sun X, Spooner E, et al. 2009.. Biochemical and immunohistochemical analysis of an Alzheimer's disease mouse model reveals the presence of multiple cerebral Aβ assembly forms throughout life. . Neurobiol. Dis. 36:(2):293302
    [Crossref] [Google Scholar]
  108. 108.
    Gong B, Kielar C, Morton AJ. 2012.. Temporal separation of aggregation and ubiquitination during early inclusion formation in transgenic mice carrying the Huntington's disease mutation. . PLOS ONE 7:(7):e41450
    [Crossref] [Google Scholar]
  109. 109.
    Taguchi T, Ikuno M, Hondo M, Parajuli LK, Taguchi K, et al. 2020.. α-Synuclein BAC transgenic mice exhibit RBD-like behaviour and hyposmia: a prodromal Parkinson's disease model. . Brain 143:(1):24965
    [Crossref] [Google Scholar]
  110. 110.
    Schmidt M, Wiese S, Adak V, Engler J, Agarwal S, et al. 2019.. Cryo-EM structure of a transthyretin-derived amyloid fibril from a patient with hereditary ATTR amyloidosis. . Nat. Commun. 10:(1):5008
    [Crossref] [Google Scholar]
  111. 111.
    Stoeckli M, Knochenmuss R, McCombie G, Mueller D, Rohner T, et al. 2006.. MALDI MS imaging of amyloid. . Methods Enzymol. 412::94106
    [Crossref] [Google Scholar]
  112. 112.
    Kaya I, Jennische E, Lange S, Tarik Baykal A, Malmberg P, Fletcher JS. 2020.. Brain region–specific amyloid plaque–associated myelin lipid loss, APOE deposition and disruption of the myelin sheath in familial Alzheimer's disease mice. . J. Neurochem. 154:(1):8498
    [Crossref] [Google Scholar]
  113. 113.
    Kavkova M, Zikmund T, Kala A, Salplachta J, Proskauer Pena SL, et al. 2021.. Contrast enhanced X-ray computed tomography imaging of amyloid plaques in Alzheimer disease rat model on lab based micro CT system. . Sci. Rep. 11:(1):5999
    [Crossref] [Google Scholar]
  114. 114.
    Ni R. 2021.. Magnetic resonance imaging in animal models of Alzheimer's disease amyloidosis. . Int. J. Mol. Sci. 22:(23):12768
    [Crossref] [Google Scholar]
  115. 115.
    Cao L, Kong Y, Ji B, Ren Y, Guan Y, Ni R. 2022.. Positron emission tomography in animal models of tauopathies. . Front. Aging Neurosci. 13::761913
    [Crossref] [Google Scholar]
  116. 116.
    Kaur A, New EJ, Sunde M. 2020.. Strategies for the molecular imaging of amyloid and the value of a multimodal approach. . ACS Sens. 5:(8):226882
    [Crossref] [Google Scholar]
  117. 117.
    Kurniawan ND. 2018.. MRI in the study of animal models of neurodegenerative diseases. . Methods Mol. Biol. 1718::34775
    [Crossref] [Google Scholar]
  118. 118.
    Ni R, Kindler DR, Waag R, Rouault M, Ravikumar P, et al. 2019.. fMRI reveals mitigation of cerebrovascular dysfunction by bradykinin receptors 1 and 2 inhibitor noscapine in a mouse model of cerebral amyloidosis. . Front. Aging Neurosci. 11::27
    [Crossref] [Google Scholar]
  119. 119.
    Ni R. 2022.. Magnetic resonance imaging in tauopathy animal models. . Front. Aging Neurosci. 13::791679
    [Crossref] [Google Scholar]
  120. 120.
    Ni R. 2021.. Positron emission tomography in animal models of Alzheimer's disease amyloidosis: translational implications. . Pharmaceuticals 14:(11):1179
    [Crossref] [Google Scholar]
  121. 121.
    Kuebler L, Buss S, Leonov A, Ryazanov S, Schmidt F, et al. 2021.. [11C]MODAG-001—towards a PET tracer targeting α-synuclein aggregates. . Eur. J. Nucl. Med. Mol. Imaging 48:(6):175972
    [Crossref] [Google Scholar]
  122. 122.
    Fuchigami T, Kawasaki M, Watanabe H, Nakagaki T, Nishi K, et al. 2020.. Feasibility studies of radioiodinated pyridyl benzofuran derivatives as potential SPECT imaging agents for prion deposits in the brain. . Nucl. Med. Biol. 90–91::4148
    [Crossref] [Google Scholar]
  123. 123.
    Jokar S, Behnammanesh H, Erfani M, Sharifzadeh M, Gholami M, et al. 2020.. Synthesis, biological evaluation and preclinical study of a novel 99mTc-peptide: a targeting probe of amyloid-β plaques as a possible diagnostic agent for Alzheimer's disease. . Bioorg. Chem. 99::103857
    [Crossref] [Google Scholar]
  124. 124.
    Morales-Zavala F, Jara-Guajardo P, Chamorro D, Riveros AL, Chandia-Cristi A, et al. 2021.. In vivo micro computed tomography detection and decrease in amyloid load by using multifunctionalized gold nanorods: a neurotheranostic platform for Alzheimer's disease. . Biomater. Sci. 9:(11):417890
    [Crossref] [Google Scholar]
  125. 125.
    Ong SS, Proia AD, Whitson HE, Farsiu S, Doraiswamy PM, Lad EM. 2019.. Ocular amyloid imaging at the crossroad of Alzheimer's disease and age-related macular degeneration: implications for diagnosis and therapy. . J. Neurol. 266:(7):156677
    [Crossref] [Google Scholar]
  126. 126.
    Matei N, Leahy S, Blair NP, Burford J, Rahimi M, Shahidi M. 2022.. Retinal vascular physiology biomarkers in a 5XFAD mouse model of Alzheimer's disease. . Cells 11:(15):2413
    [Crossref] [Google Scholar]
  127. 127.
    Oakley H, Cole SL, Logan S, Maus E, Shao P, et al. 2006.. Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer's disease mutations: potential factors in amyloid plaque formation. . J. Neurosci. 26:(40):1012940
    [Crossref] [Google Scholar]
  128. 128.
    Zhao Y, Zeng C, Li X, Yang T, Kuang X, Du J. 2020.. Klotho overexpression improves amyloid-β clearance and cognition in the APP/PS1 mouse model of Alzheimer's disease. . Aging Cell 19:(10):e13239
    [Crossref] [Google Scholar]
  129. 129.
    Luheshi LM, Tartaglia GG, Brorsson A-C, Pawar AP, Watson IE, et al. 2007.. Systematic in vivo analysis of the intrinsic determinants of amyloid β pathogenicity. . PLOS Biol. 5:(11):e290
    [Crossref] [Google Scholar]
  130. 130.
    Karthick C, Nithiyanandan S, Essa MM, Guillemin GJ, Jayachandran SK, Anusuyadevi M. 2019.. Time-dependent effect of oligomeric amyloid-β (1-42)-induced hippocampal neurodegeneration in rat model of Alzheimer's disease. . Neurol. Res. 41:(2):13950
    [Crossref] [Google Scholar]
  131. 131.
    De Plano LM, Calabrese G, Conoci S, Guglielmino SPP, Oddo S, Caccamo A. 2022.. Applications of CRISPR-Cas9 in Alzheimer's disease and related disorders. . Int. J. Mol. Sci. 23:(15):8714
    [Crossref] [Google Scholar]
  132. 132.
    Yang S, Chang R, Yang H, Zhao T, Hong Y, et al. 2017.. CRISPR/Cas9-mediated gene editing ameliorates neurotoxicity in mouse model of Huntington's disease. . J. Clin. Investig. 127:(7):271924
    [Crossref] [Google Scholar]
  133. 133.
    US Food Drug Admin. (FDA)-Natl. Inst. Health (NIH) Biomark. Work. Group. 2016.. BEST (Biomarkers, EndpointS, and Other Tools) Resource. Silver Spring/Bethesda, MD:: FDA/NIH. https://www.ncbi.nlm.nih.gov/books/NBK326791/
    [Google Scholar]
  134. 134.
    Alzforum. 2021.. Alzbiomarker. https://www.alzforum.org/alzbiomarker
    [Google Scholar]
  135. 135.
    Leuzy A, Mattsson-Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. 2022.. Blood-based biomarkers for Alzheimer's disease. . EMBO Mol. Med. 14:(1):e14408
    [Crossref] [Google Scholar]
  136. 136.
    Wang R, Sweeney D, Gandy SE, Sisodia SS. 1996.. The profile of soluble amyloid β protein in cultured cell media. . J. Biol. Chem. 271:(50):31894902
    [Crossref] [Google Scholar]
  137. 137.
    de Souza ID, Queiroz MEC. 2023.. Advances in sample preparation and HPLC-MS/MS methods for determining amyloid-β peptide in biological samples: a review. . Anal. Bioanal. Chem. 415:(18):400321
    [Crossref] [Google Scholar]
  138. 138.
    Clarke NJ, Tomlinson AJ, Ohyagi Y, Younkin S, Naylor S. 1998.. Detection and quantitation of cellularly derived amyloid β peptides by immunoprecipitation-HPLC-MS. . FEBS Lett. 430:(3):41923
    [Crossref] [Google Scholar]
  139. 139.
    Celik Topkara K, Kilinc E, Cetinkaya A, Saylan A, Demir S. 2022.. Therapeutic effects of carvacrol on amyloid β–induced impairments in in vitro and in vivo models of Alzheimer's disease. . Eur. J. Neurosci. 56:(9):571426
    [Crossref] [Google Scholar]
  140. 140.
    Jiang J, Wang Z, Yu R, Yang J, Tian H, et al. 2022.. Effects of electroacupuncture on the correlation between serum and central immunity in AD model animals. Evidence Based Complement. . Altern. Med. 2022::3478847
    [Google Scholar]
  141. 141.
    Zhou AL, Sharda N, Sarma VV, Ahlschwede KM, Curran GL, et al. 2022.. Age-dependent changes in the plasma and brain pharmacokinetics of amyloid-β peptides and insulin. . J. Alzheimers Dis. 85:(3):103144
    [Crossref] [Google Scholar]
  142. 142.
    Julku U, Xiong M, Wik E, Roshanbin S, Sehlin D, Syvänen S. 2022.. Brain pharmacokinetics of mono- and bispecific amyloid-β antibodies in wild-type and Alzheimer's disease mice measured by high cut-off microdialysis. . Fluids Barriers CNS 19:(1):99
    [Crossref] [Google Scholar]
  143. 143.
    Ramakrishnan V, Friedrich C, Witt C, Sheehan R, Pryor M, et al. 2023.. Quantitative systems pharmacology model of the amyloid pathway in Alzheimer's disease: insights into the therapeutic mechanisms of clinical candidates. . CPT Pharmacom. Syst. Pharmacol. 12:(1):6273
    [Crossref] [Google Scholar]
  144. 144.
    Chen Z-Y, Zhang Y. 2022.. Animal models of Alzheimer's disease: applications, evaluation, and perspectives. . Zool. Res. 43:(6):102640
    [Crossref] [Google Scholar]
  145. 145.
    Esquerda-Canals G, Montoliu-Gaya L, Güell-Bosch J, Villegas S. 2017.. Mouse models of Alzheimer's disease. . J. Alzheimers Dis. 57:(4):117183
    [Crossref] [Google Scholar]
  146. 146.
    Singh JV, Thakur S, Kumar N, Singh H, Mithu VS, et al. 2022.. Donepezil-inspired multitargeting indanone derivatives as effective anti-Alzheimer's agents. . ACS Chem. Neurosci. 13:(6):73350
    [Crossref] [Google Scholar]
  147. 147.
    Matsubara E, Bryant-Thomas T, Pacheco Quinto J, Henry TL, Poeggeler B, et al. 2003.. Melatonin increases survival and inhibits oxidative and amyloid pathology in a transgenic model of Alzheimer's disease. . J. Neurochem. 85:(5):11018
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-anchem-061622-023326
Loading
/content/journals/10.1146/annurev-anchem-061622-023326
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error