The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Single App knock-in mouse models of Alzheimer’s disease
Abstract
Experimental studies of Alzheimer's disease have largely depended on transgenic mice overexpressing amyloid precursor protein (APP). These mice, however, suffer from artificial phenotypes because, in addition to amyloid β peptide (Aβ), they overproduce other APP fragments. We generated knock-in mice that harbor Swedish and Beyreuther/Iberian mutations with and without the Arctic mutation in the APP gene. The mice showed typical Aβ pathology, neuroinflammation and memory impairment in an age-dependent manner.
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In the citing paper
“We elected to use the AppNL-G-F knock-in mouse model for this study as it expresses App under its endogenous promoter in a physiological manner with regards to both cell type and amount, and avoids the issue of APP overexpression and abnormal expression patterns.”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [33]
In the citing paper
“AppNL-G-F mice [C57BL/6-App<tm3(NL-G-F)Tcs> (No.RBRC06344)] were provided by the RIKEN BRC through the National BioResource Project of the MEXT, Japan [33].”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [33]
In the citing paper
“we employed APPNLGF knock-in mice that express a humanized and triple-mutated APP in the endogenous App locus to circumvent transgenic mouse artifacts.”
From Oligodendrocytes produce amyloid-β and contribute to plaque formation alongside neurons in Alzheimer’s disease model mice (2024) · cites this paper as [34]
In the citing paper
“While novel APP knock-in models, such as APPNL, APPNL-F, or APPNL-G-F mice overproduce Aβ1–42 in the absence of potentially confounding APP overexpression [139], these mice still contain one or more independent FAD mutations, which might result in an abnormal Aβ conformation [140].”
From Neuron Loss in Alzheimer’s Disease: Translation in Transgenic Mouse Models (2020) · cites this paper as [139]
In the citing paper
“By 6 months of age, App NL‐G‐F mice exhibit a substantial amyloid plaque burden in the brain”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“These were bred to the App NL‐G‐F AD‐like model to determine responses in the context of amyloid‐driven pathology (Saito et al. 2014).”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“The App NL‐G‐F mice are characterized by progressive amyloid deposition from 2 months to cognitive decline by 6 months (Saito et al. 2014).”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“The App NL‐G‐F knock‐in model, however, has physiological App expression levels, avoiding the overexpression artifacts found in other transgenic models”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“but is strongly biased toward very high Aβ42:40 ratios (~10–100:1) because of the familial mutations”
From Alzheimer's Protection by PLCγ2 Compacts Plaques, Redistributes Microglia, and Protects Synapses in App NL ‐G‐F Mice (2026) · cites this paper as Saito et al. 2014
In the citing paper
“In AppNL-G-F mice, which harbor all three mutations, Aβ amyloidosis is aggressive, and neuroinflammation is observed in subcortical structures as well as cortical regions [10–12].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“By contrast, AppNL mice that carry only the Swedish mutation produce significantly higher levels of Aβ40 and Aβ42 without overt AD-related brain pathology such as extracellular Aβ plaques or neuroinflammation [10, 11, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“alternative mouse models have been generated via knock-in (KI) of a humanized Aβ sequences harboring familial AD mutations (Swedish (NL), Beyreuther/Iberian (F), and Arctic (G)) in order to model Aβ amyloidosis without non-physiological overexpression of APP [10].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“By contrast, AppNL mice that carry only the Swedish mutation produce significantly higher levels of Aβ40 and Aβ42 without overt AD-related brain pathology such as extracellular Aβ plaques or neuroinflammation [10, 11, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“To overcome these issues, KI mouse models with familial AD mutations have been generated [10, 24, 31, 32].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“despite overproducing human Aβ40 and Aβ42 in their brains [10, 13].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“These results are consistent with recent studies reporting negligible cognitive deficits and the lack of Aβ plaque formation in AppNL/NL mice (Fig. 6) [10, 11, 13, 14, 24].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [10]
In the citing paper
“Both models express human APP at physiological levels.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“The AppNL-G-F and AppNL-F show neuropathology and hippocampus-dependent cognitive impairments at 6 or 18 months, respectively.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“Our result in female mice is consistent with the cognitive phenotype in the Y maze reported in this genotype25,42.”
From Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice (2021) · cites this paper as [25]
In the citing paper
“Human APP knockin (KI) models develop robust Aβ pathology, yet do not exhibit artifacts associated with APP over-expression, such as elevated levels of APP cleavage products (Saito et al., 2014).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These studies all suffer because they exhibit artifacts arising from the over-expression APP (WT or mutant) and in some cases mutant presenilins (Balducci and Forloni, 2011; Saito et al., 2014; Sasaguri et al., 2017).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“Human MAPT knock-in models have also been developed, and these models produce tau pathology, but only very late in the murine life span (~15 months) (Saito et al., 2014; Sakakibara et al., 2018; Sasaguri et al., 2022).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“However, over-expressing APP and presenilin 1 cause effect resulting from the over-expression that are unrelated to the disease process (Saito et al., 2014; Sakakibara et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These mice develop robust plaque pathology beginning as early as 3 months of age, however they develop little tau pathology and little neurodegeneration (Saito et al., 2014; Sakakibara et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“These patterns of labeling or Aβ and MAPT are consistent with prior studies of APPNL-G-F and P301S MAPT mice (Yoshiyama et al., 2007; Saito et al., 2014; Sasaguri et al., 2017; Apicco et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Saito et al., 2014
In the citing paper
“As expected,15 22-month-old AppNL-G-F mice had robust AD-related pathology, including amyloid deposition and neuroinflammation (Figures 1C and 1D).”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“As suggested,15,20 App KI mice may model pre-clinical stages of AD, characterized by progressive amyloidosis and gliosis without prominent cognitive deterioration, tau pathology, or neurodegeneration.”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“Humanized App knockin (KI) and APP transgenic mouse models mimic certain aspects of AD pathogenesis, including progressive amyloidosis, gliosis, and neuroinflammation, as well as network hyperexcitability and cognitive impairment, albeit to different degrees of severity across models and paradigms.15-27”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [15]
In the citing paper
“Whereas plaque deposition only increases by less than twofold between 9 and 12 months of age, it increases tenfold between 12 and 18 months (Saito et al., 2014).”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“By contrast, NHE6 ablation led to glial activation and decreased plaque load in ApoeAPOE4 (Sullivan et al., 1997) and AppNL-F (Saito et al., 2014) double knockin mice.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“The AppNL-F mouse model used in our study develops plaques at 12 months (Saito et al., 2014) in the presence of murine ApoE.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“In AppNL-F mice, plaques can be identified as early as 9 months of age.”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“These mice, however, do not show cognitive impairments in spatial learning tests like Morris water maze (Saito et al., 2014) (own unpublished observations).”
From NHE6 depletion corrects ApoE4-mediated synaptic impairments and reduces amyloid plaque load (2021) · cites this paper as Saito et al., 2014
In the citing paper
“We show in a well-studied App knock-in mouse model (Saito et al. , 2014) the gradual establishment of a multicellular co-expressed gene response encompassing 57 Plaque-Induced Genes or PIGs in the microenvironments of amyloid plaques (Figure 5).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Saito et al., 2014
In the citing paper
“App NL-G-F knock-in (Saito et al., 2014) mice express Swedish (KM670/671NL), Beyreuther/Iberian (I716F), and Arctic (E693G) mutations in the App gene under the endogenous promoter on the C57Bl/6J background.”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Saito et al., 2014
In the citing paper
“AD mice, in which APP with a humanised Aβ region containing 3 AD-related mutations (APPNL-G-F) is knocked in (49), to avoid artefacts associated with over-expressing APP, were crossed with NG2-DsRed mice in which pericytes express DsRed (19).”
From Amyloid β oligomers constrict human capillaries in Alzheimer's disease via signaling to pericytes (2019) · cites this paper as [49]
In the citing paper
“In contrast, knock-in of Swedish and Iberian mutations results in plaque development beginning at 6 months, and gliosis, synaptic alterations and memory impairment from 18 months [129].”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
In the citing paper
“Additional knock-in of the Arctic mutation into these mice results in more rapid pathology development including plaque development beginning at 2 months that is more widespread throughout the brain and memory impairment from 6 months [129].”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
In the citing paper
“In contrast, initial studies show that more physiological knock-in mouse models develop cognitive impairment many months after plaque development [129], which is more similar to humans.”
From Alzheimer's disease: experimental models and reality (2017) · cites this paper as [129]
Citations
Cites
Correlative Memory Deficits, Aβ Elevation, and Amyloid Plaques in Transgenic Mice
Hsiao, K., Chapman, P., Nilsen, S., Eckman, C., Harigaya, Y., Younkin, S., Yang, F., & Cole, G. (1996) · Science · doi:10.1126/science.274.5284.99 · Cited by 4,210
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.263 · Cited by 3,459
Amyloid deposition as the central event in the aetiology of Alzheimer’s disease
Hardy, J., & Allsop, D. (1991) · Trends in Pharmacological Sciences · doi:10.1016/0165-6147(91)90609-v · Cited by 2,538
Reducing Endogenous Tau Ameliorates Amyloid ß-Induced Deficits in an Alzheimer's Disease Mouse Model
Roberson, E. D., Scearce-Levie, K., Palop, J. J., Yan, F., Cheng, I. H., Wu, T., Gerstein, H., Yu, G.-Q., & Mucke, L. (2007) · Science · doi:10.1126/science.1141736 · Cited by 1,892
Citron, M., Oltersdorf, T., Haass, C., McConlogue, L., Hung, A. Y., Seubert, P., Vigo-Pelfrey, C., Lieberburg, I., & Selkoe, D. J. (1992) · Nature · doi:10.1038/360672a0 · Cited by 1,757
A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline
Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., Stefansson, H., Sulem, P., Gudbjartsson, D., Maloney, J., et al. (2012) · Nature · doi:10.1038/nature11283 · Cited by 1,738
Two amyloid precursor protein transgenic mouse models with Alzheimer disease-like pathology
Sturchler-Pierrat, C., Abramowski, D., Duke, M., Wiederhold, K.-H., Mistl, C., Rothacher, S., Ledermann, B., Bürki, K., Frey, P., Paganetti, P. A., et al. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.24.13287 · Cited by 1,507
Saido, T. C., Iwatsubo, T., Mann, D. M. A., Shimada, H., Ihara, Y., & Kawashima, S. (1995) · Neuron · doi:10.1016/0896-6273(95)90301-1 · Cited by 587
Self-citationAβ Secretion and Plaque Formation Depend on Autophagy
Nilsson, P., Loganathan, K., Sekiguchi, M., Matsuba, Y., Hui, K., Tsubuki, S., Tanaka, M., Iwata, N., Saito, T., & Saido, T. C. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.08.042 · Cited by 459
Self-citationSarnyai, Z., Sibille, E. L., Pavlides, C., Fenster, R. J., McEwen, B. S., & Tóth, M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.26.14731 · Cited by 396
Flagged first, then by citations
Cited by
Sorted by citations
The amyloid hypothesis of Alzheimer's disease at 25 years
Selkoe, D. J., & Hardy, J. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201606210 · Cited by 6,164
Defeating Alzheimer's disease and other dementias: a priority for European science and society
Winblad, B., Amouyel, P., Andrieu, S., Ballard, C., Brayne, C., Brodaty, H., Cedazo-Minguez, A., Dubois, B., Edvardsson, D., Feldman, H., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00062-4 · Cited by 1,676
Network abnormalities and interneuron dysfunction in Alzheimer disease
Palop, J. J., & Mucke, L. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.141 · Cited by 1,022
Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future
Zhang, Y., Chen, H., Li, R., Sterling, K., & Song, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01484-7 · Cited by 988
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949
Self-citationSala Frigerio, C., Wolfs, L., Fattorelli, N., Thrupp, N., Voytyuk, I., Schmidt, I., Mancuso, R., Chen, W.-T., Woodbury, M. E., Srivastava, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.099 · Cited by 926
Self-citationAlzheimer's disease: experimental models and reality
Drummond, E., & Wisniewski, T. (2017) · Acta Neuropathologica · doi:10.1007/s00401-016-1662-x · Cited by 810
APP mouse models for Alzheimer’s disease preclinical studies.
Sasaguri, H., Nilsson, P., Hashimoto, S., Nagata, K., Saito, T., De Strooper, B., Hardy, J., Vassar, R., Winblad, B., & Saido, T. C. (2017) · The EMBO Journal · doi:10.15252/embj.201797397 · Cited by 793
Self-citationAmyloid β oligomers constrict human capillaries in Alzheimer's disease via signaling to pericytes
Nortley, R., Korte, N., Izquierdo, P., Hirunpattarasilp, C., Mishra, A., Jaunmuktane, Z., Kyrargyri, V., Pfeiffer, T., Khennouf, L., Madry, C., et al. (2019) · Science · doi:10.1126/science.aav9518 · Cited by 682
“AD mice, in which APP with a humanized Ab region containing three AD-related mutations (App) is knocked in (49) to avoid artifacts associated with overexpressing APP, were crossed with NG2-DsRed mice in which pericytes express DsRed (19).”
BackgroundSelf-citationHe, Z., Guo, J. L., McBride, J. D., Narasimhan, S., Kim, H., Changolkar, L., Zhang, B., Gathagan, R. J., Yue, C., Dengler, C., et al. (2018) · Nature Medicine · doi:10.1038/nm.4443 · Cited by 645
Kim, M.-S., Kim, Y., Choi, H., Kim, W., Park, S., Lee, D., Kim, D. K., Kim, H. J., Choi, H., Hyun, D.-W., et al. (2020) · Gut · doi:10.1136/gutjnl-2018-317431 · Cited by 614
ScaleS: an optical clearing palette for biological imaging
Hama, H., Hioki, H., Namiki, K., Hoshida, T., Kurokawa, H., Ishidate, F., Kaneko, T., Akagi, T., Saito, T., Saido, T. C., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4107 · Cited by 600
Self-citationMicroglia in Neuroinflammation and Neurodegeneration: From Understanding to Therapy
Muzio, L., Viotti, A., & Martino, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.742065 · Cited by 581
Animal models of neurodegenerative diseases
Dawson, T. M., Golde, T. E., & Lagier-Tourenne, C. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0236-8 · Cited by 576
Type I interferon response drives neuroinflammation and synapse loss in Alzheimer disease
Roy, E. R., Wang, B., Wan, Y.-W., Chiu, G., Cole, A., Yin, Z., Propson, N. E., Xu, Y., Jankowsky, J. L., Liu, Z., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci133737 · Cited by 563
Cryo-EM structures of amyloid-β 42 filaments from human brains
Yang, Y., Arseni, D., Zhang, W., Huang, M., Lövestam, S., Schweighauser, M., Kotecha, A., Murzin, A. G., Peak-Chew, S. Y., Macdonald, J., et al. (2022) · Science · doi:10.1126/science.abm7285 · Cited by 546
Inconsistencies and Controversies Surrounding the Amyloid Hypothesis of Alzheimer's Disease
Morris, G. P., Clark, I. A., & Vissel, B. (2014) · Acta Neuropathologica Communications · doi:10.1186/s40478-014-0135-5 · Cited by 522
Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 493
“(h) Immunofluorescence closeups of myelin (MBP labelled) in plaque proximity (labelled with 6E10) in WT, 5xFAD and AppNLGF cortex.”
InfluentialSelf-citationPractical considerations for choosing a mouse model of Alzheimer’s disease
Jankowsky, J. L., & Zheng, H. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0231-7 · Cited by 449
Duncombe, J., Kitamura, A., Hase, Y., Ihara, M., Kalaria, R. N., & Horsburgh, K. J. (2017) · Clinical Science · doi:10.1042/cs20160727 · Cited by 416
Kobayashi, Y., Sugahara, H., Shimada, K., Mitsuyama, E., Kuhara, T., Yasuoka, A., Kondo, T., Abe, K., & Xiao, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-13368-2 · Cited by 411
Rodent models for Alzheimer disease
Götz, J., Bodea, L., & Goedert, M. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0054-8 · Cited by 422
RIPK1 mediates a disease-associated microglial response in Alzheimer’s disease
Ofengeim, D., Mazzitelli, S., Ito, Y., DeWitt, J. P., Mifflin, L., Zou, C., Das, S., Adiconis, X., Chen, H., Zhu, H., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714175114 · Cited by 395
Emerging diagnostics and therapeutics for Alzheimer disease
Self, W. K., & Holtzman, D. M. (2023) · Nature Medicine · doi:10.1038/s41591-023-02505-2 · Cited by 381
The epigenetics of aging and neurodegeneration
Lardenoije, R., Iatrou, A., Kenis, G., Kompotis, K., Steinbusch, H. W. M., Mastroeni, D., Coleman, P., Lemere, C. A., Hof, P. R., van den Hove, D. L. A., et al. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.05.002 · Cited by 381
Immune Signaling in Neurodegeneration
Hammond, T. R., Marsh, S. E., & Stevens, B. (2019) · Immunity · doi:10.1016/j.immuni.2019.03.016 · Cited by 336
The clinical promise of biomarkers of synapse damage or loss in Alzheimer’s disease
the Synaptic Health Endpoints Working Group, Colom-Cadena, M., Spires-Jones, T., Zetterberg, H., Blennow, K., Caggiano, A., DeKosky, S. T., Fillit, H., Harrison, J. E., Schneider, L. S., et al. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00588-4 · Cited by 328
Elucidating the Role of TREM2 in Alzheimer’s Disease
Ulrich, J. D., Ulland, T. K., Colonna, M., & Holtzman, D. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.042 · Cited by 325
Enhancing protective microglial activities with a dual function TREM2 antibody to the stalk region
Schlepckow, K., Monroe, K. M., Kleinberger, G., Cantuti‐Castelvetri, L., Parhizkar, S., Xia, D., Willem, M., Werner, G., Pettkus, N., Brunner, B., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201911227 · Cited by 324
Adult Hippocampal Neurogenesis in Aging and Alzheimer's Disease
Babcock, K. R., Page, J. S., Fallon, J. R., & Webb, A. E. (2021) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.01.019 · Cited by 330
Microglia in Alzheimer Disease: Well-Known Targets and New Opportunities
Hemonnot, A.-L., Hua, J., Ulmann, L., & Hirbec, H. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00233 · Cited by 298
ApoE facilitates the microglial response to amyloid plaque pathology
Ulrich, J. D., Ulland, T. K., Mahan, T. E., Nyström, S., Nilsson, K. P., Song, W. M., Zhou, Y., Reinartz, M., Choi, S., Jiang, H., et al. (2018) · Journal of Experimental Medicine · doi:10.1084/jem.20171265 · Cited by 297
De Schepper, S., Ge, J. Z., Sierksma, A., Crowley, G., Ferreira, L. S. S., Garceau, D., Toomey, C. E., Sokolova, D., Rueda-Carrasco, J., Shin, S.-H., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01257-z · Cited by 295
Molecular pathology of neurodegenerative diseases by cryo-EM of amyloids
Scheres, S. H. W., Falcon, B., & Goedert, M. (2023) · Nature · doi:10.1038/s41586-023-06437-2 · Cited by 308
Susaki, E. A., Shimizu, C., Kuno, A., Tainaka, K., Li, X., Nishi, K., Morishima, K., Ono, H., Ode, K. L., Saeki, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15906-5 · Cited by 294
Self-citationCalpain research for drug discovery: challenges and potential
Ono, Y., Saido, T. C., & Sorimachi, H. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.212 · Cited by 292
Self-citationThe gut microbiome in Alzheimer’s disease: what we know and what remains to be explored
Chandra, S., Sisodia, S. S., & Vassar, R. J. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00595-7 · Cited by 278
Park, H., Oh, J., Shim, G., Cho, B., Chang, Y., Kim, S., Baek, S., Kim, H., Shin, J., Choi, H. G., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0352-0 · Cited by 277
Xiang, X., Wind, K., Wiedemann, T., Blume, T., Shi, Y., Briel, N., Beyer, L., Biechele, G., Eckenweber, F., Zatcepin, A., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abe5640 · Cited by 268
Site-specific phosphorylation of tau inhibits amyloid-β toxicity in Alzheimer’s mice
Ittner, A., Chua, S. W., Bertz, J., Volkerling, A., van der Hoven, J., Gladbach, A., Przybyla, M., Bi, M., van Hummel, A., Stevens, C. H., et al. (2016) · Science · doi:10.1126/science.aah6205 · Cited by 263
Entorhinal cortex dysfunction in Alzheimer's disease
Igarashi, K. M. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.11.006 · Cited by 276
Sobue, A., Komine, O., Hara, Y., Endo, F., Mizoguchi, H., Watanabe, S., Murayama, S., Saito, T., Saido, T. C., Sahara, N., et al. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-01099-x · Cited by 253
Self-citationAlzheimer’s-Causing Mutations Shift Aβ Length by Destabilizing γ-Secretase-Aβn Interactions
Szaruga, M., Munteanu, B., Lismont, S., Veugelen, S., Horré, K., Mercken, M., Saido, T. C., Ryan, N. S., De Vos, T., Savvides, S. N., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.004 · Cited by 248
Self-citationSaito, T., Mihira, N., Matsuba, Y., Sasaguri, H., Hashimoto, S., Narasimhan, S., Zhang, B., Murayama, S., Higuchi, M., Lee, V. M. Y., et al. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.009487 · Cited by 248
Self-citationCognitive deficits in single App knock-in mouse models
Masuda, A., Kobayashi, Y., Kogo, N., Saito, T., Saido, T. C., & Itohara, S. (2016) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2016.07.001 · Cited by 248
Self-citationClayton, K., Delpech, J. C., Herron, S., Iwahara, N., Ericsson, M., Saito, T., Saido, T. C., Ikezu, S., & Ikezu, T. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00440-9 · Cited by 245
Self-citationBassil, F., Brown, H. J., Pattabhiraman, S., Iwasyk, J. E., Maghames, C. M., Meymand, E. S., Cox, T. O., Riddle, D. M., Zhang, B., Trojanowski, J. Q., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.10.010 · Cited by 246
Analyzing dendritic spine pathology in Alzheimer’s disease: Problems and opportunities
Dorostkar, M. M., Zou, C., Blazquez-Llorca, L., & Herms, J. (2015) · Acta Neuropathologica · doi:10.1007/s00401-015-1449-5 · Cited by 235
CXCR6 orchestrates brain CD8+ T cell residency and limits mouse Alzheimer’s disease pathology
Su, W., Saravia, J., Risch, I., Rankin, S. L., Guy, C. S., Chapman, N. M., Shi, H., Sun, Y., Anil, K. C., Li, W., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01604-z · Cited by 222
Peripheral immune system in aging and Alzheimer’s disease
Cao, W., & Zheng, H. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0284-2 · Cited by 214
Mouse Models of Alzheimer’s Disease
Yokoyama, M., Kobayashi, H., Tatsumi, L., & Tomita, T. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.912995 · Cited by 213
Dysregulation of neuronal calcium homeostasis in Alzheimer's disease – A therapeutic opportunity?
Александровна, П. Е., Pchitskaya, E. I., & Борисович, Б. И. (2016) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2016.09.053 · Cited by 218
Brain/MINDS: A Japanese National Brain Project for Marmoset Neuroscience
Okano, H., Sasaki, E., Yamamori, T., Iriki, A., Shimogori, T., Yamaguchi, Y., Kasai, K., & Miyawaki, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.018 · Cited by 213
Calcium/calmodulin-dependent kinase II and Alzheimer’s disease
Ghosh, A., & Giese, K. P. (2015) · Molecular Brain · doi:10.1186/s13041-015-0166-2 · Cited by 205
Meftah, S., & Gan, J. (2023) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2023.1129036 · Cited by 205
Walgrave, H., Balusu, S., Snoeck, S., Vanden Eynden, E., Craessaerts, K., Thrupp, N., Wolfs, L., Horré, K., Fourne, Y., Ronisz, A., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.05.001 · Cited by 175
Nutma, E., Fancy, N. N., Weinert, M., Tsartsalis, S., Marzin, M. C., Muirhead, R. C. J., Falk, I. N., Breur, M., de Bruin, J., Hollaus, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40937-z · Cited by 204
Self-citationTransmission of amyloid-β protein pathology from cadaveric pituitary growth hormone
Purro, S. A., Farrow, M. A., Linehan, J., Nazari, T., Thomas, D. X., Chen, Z., Mengel, D., Saito, T., Saido, T., Rudge, P., et al. (2018) · Nature · doi:10.1038/s41586-018-0790-y · Cited by 191
Self-citationUpdates on mouse models of Alzheimer’s disease
Zhong, M. Z., Peng, T., Duarte, M. L., Wang, M., & Cai, D. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00712-0 · Cited by 189
An aberrant sugar modification of BACE1 blocks its lysosomal targeting in Alzheimer's disease
Kizuka, Y., Kitazume, S., Fujinawa, R., Saito, T., Iwata, N., Saido, T. C., Nakano, M., Yamaguchi, Y., Hashimoto, Y., Staufenbiel, M., et al. (2015) · EMBO Molecular Medicine · doi:10.15252/emmm.201404438 · Cited by 185
Self-citationMicroglia regulation of central nervous system myelin health and regeneration
Kent, S. A., & Miron, V. E. (2024) · Nature Reviews Immunology · doi:10.1038/s41577-023-00907-4 · Cited by 183
Zhang, H., Wu, L., Pchitskaya, E. I., Zakharova, O. A., Saito, T., Saido, T. C., & Борисович, Б. И. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1034-15.2015 · Cited by 185
Self-citationLiu, Y., Cheng, A., Li, Y.-J., Yang, Y., Kishimoto, Y., Zhang, S., Wang, Y., Wan, R., Raefsky, S. M., Lu, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09897-1 · Cited by 176
Self-citationLiu, M., Wang, Y., Zhang, Y.-B., Hu, D., Tang, L., Zhou, B., & Yang, L. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02112-8 · Cited by 189
Factors other than hTau overexpression that contribute to tauopathy-like phenotype in rTg4510 mice
Gamache, J., Benzow, K. A., Forster, C. L., Kemper, L. J., Hlynialuk, C. J. W., Furrow, E., Ashe, K. H., & Koob, M. D. (2019) · Nature Communications · doi:10.1038/s41467-019-10428-1 · Cited by 174
A multi-pronged human microglia response to Alzheimer’s disease Aβ pathology
Mancuso, R., Fattorelli, N., Martinez-Muriana, A., Davis, E., Wolfs, L., Van Den Daele, J., Geric, I., Preman, P., Serneels, L., Poovathingal, S., et al. (2022) · doi:10.1101/2022.07.07.499139 · Cited by 169
Nrf2 Suppresses Oxidative Stress and Inflammation in App Knock-In Alzheimer’s Disease Model Mice
Uruno, A., Matsumaru, D., Ryoke, R., Saito, R., Kadoguchi, S., Saigusa, D., Saito, T., Saido, T. C., Kawashima, R., & Yamamoto, M. P. (2020) · Molecular and Cellular Biology · doi:10.1128/mcb.00467-19 · Cited by 168
Self-citationDisrupted Place Cell Remapping and Impaired Grid Cells in a Knockin Model of Alzheimer's Disease
Jun, H., Bramian, A., Soma, S., Saito, T., Saido, T. C., & Igarashi, K. M. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.023 · Cited by 170
Self-citationFujita, K., Motoki, K., Tagawa, K., Chen, X., Hama, H., Nakajima, K., Homma, H., Tamura, T., Watanabe, H., Katsuno, M., et al. (2016) · Scientific Reports · doi:10.1038/srep31895 · Cited by 160
Self-citationFerroptosis and NRF2: an emerging battlefield in the neurodegeneration of Alzheimer's disease
Lane, D. J. R., Metselaar, B., Greenough, M., Bush, A. I., & Ayton, S. (2021) · Essays in Biochemistry · doi:10.1042/ebc20210017 · Cited by 160
Castillo, E., Leon, J., Mazzei, G., Abolhassani, N., Haruyama, N., Saito, T., Saido, T., Hokama, M., Iwaki, T., Ohara, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17999-3 · Cited by 158
Self-citationRecent advances in the modeling of Alzheimer’s disease
Sasaguri, H., Hashimoto, S., Watamura, N., Sato, K., Takamura, R., Nagata, K., Tsubuki, S., Ohshima, T., Yoshiki, A., Sato, K., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.807473 · Cited by 157
Self-citationToward more predictive genetic mouse models of Alzheimer’s disease
Onos, K. D., Sukoff Rizzo, S. J., Howell, G. R., & Sasner, M. (2016) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2015.12.003 · Cited by 156
Animal models of Alzheimer’s disease: Applications, evaluation, and perspectives
Chen, Z.-Y., 北京大学生命科学学院膜生物学国家重点实验室, 北. 1., State Key Laboratory of Membrane Biology, C. O. L. S., & Zhang, Y. (2022) · Zoological Research · doi:10.24272/j.issn.2095-8137.2022.289 · Cited by 155
Xie, J., Cools, L., Imschoot, G. V., Wonterghem, E. V., Pauwels, M. J., Vlaeminck, I., Witte, C. D., Andaloussi, S. E., Wierda, K. D. B., Groef, L. D., et al. (2023) · Journal of Extracellular Vesicles · doi:10.1002/jev2.12306 · Cited by 161
Astrocyte calcium dysfunction causes early network hyperactivity in Alzheimer’s disease
Shah, D., Gsell, W., Wahis, J., Luckett, E. S., Jamoulle, T., Vermaercke, B., Preman, P., Moechars, D., Hendrickx, V., Jaspers, T., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111280 · Cited by 164
Kwak, S. S., Washicosky, K. J., Brand, E., von Maydell, D., Aronson, J., Kim, S., Capen, D. E., Cetinbas, M., Sadreyev, R., Ning, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15120-3 · Cited by 151
Fibrillar Aβ triggers microglial proteome alterations and dysfunction in Alzheimer mouse models
Sebastian Monasor, L., Müller, S. A., Colombo, A. V., Tanrioever, G., König, J., Roth, S., Liesz, A., Berghofer, A., Piechotta, A., Prestel, M., et al. (2020) · eLife · doi:10.7554/elife.54083 · Cited by 147
Self-citationMehla, J., Lacoursiere, S. G., Lapointe, V., McNaughton, B. L., Sutherland, R. J., McDonald, R. J., & Mohajerani, M. H. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.026 · Cited by 147
Pathological phenotypes of astrocytes in Alzheimer’s disease
Kim, J., Yoo, I. D., Lim, J., & Moon, J. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-01148-0 · Cited by 154
Haass, C., & Selkoe, D. (2022) · PLOS Biology · doi:10.1371/journal.pbio.3001694 · Cited by 145
Pang, K., Jiang, R., Zhang, W., Yang, Z., Li, L., Shimozawa, M., Tambaro, S., Mayer, J., Zhang, B., Li, M., et al. (2021) · Cell Research · doi:10.1038/s41422-021-00582-x · Cited by 147
Self-citationOverview of Transgenic Mouse Models for Alzheimer's Disease
Myers, A., & McGonigle, P. (2019) · Current Protocols in Neuroscience · doi:10.1002/cpns.81 · Cited by 148
Zhang, H., Sun, S., Wu, L., Pchitskaya, E. I., Zakharova, O. A., Tacer, K. F., & Борисович, Б. И. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1188-16.2016 · Cited by 140
Current and emerging avenues for Alzheimer's disease drug targets
Loera‐Valencia, R., Cedazo-Mínguez, Á., Kenigsberg, P.-A., Page, G., Duarte, A. I., Giusti, P., Zusso, M., Robert, P. H., Frisoni, G. B., Cattaneo, A., et al. (2019) · Journal of Internal Medicine · doi:10.1111/joim.12959 · Cited by 142
An epoxide hydrolase inhibitor reduces neuroinflammation in a mouse model of Alzheimer’s disease
Ghosh, A., Comerota, M. M., Wan, D., Chen, F., Propson, N. E., Hwang, S. H., Hammock, B. D., & Zheng, H. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.abb1206 · Cited by 139
Mallach, A., Zielonka, M., van Lieshout, V., An, Y., Khoo, J. H., Vanheusden, M., Chen, W.-T., Moechars, D., Arancibia-Carcamo, I. L., Fiers, M., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114216 · Cited by 135
Scekic‐Zahirovic, J., Oussini, H. E., Mersmann, S., Drenner, K., Wagner, M., Sun, Y., Allmeroth, K., Dieterle, S., Sinniger, J., Dirrig‐Grosch, S., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1687-9 · Cited by 135
Birnbaum, J. H., Wanner, D., Gietl, A. F., Saake, A., Kündig, T. M., Hock, C., Nitsch, R. M., & Tackenberg, C. (2018) · Stem Cell Research · doi:10.1016/j.scr.2018.01.019 · Cited by 135
β-amyloid redirects norepinephrine signaling to activate the pathogenic GSK3β/tau cascade
Zhang, F., Gannon, M., Chen, Y., Yan, S., Zhang, S., Feng, W., Tao, J., Sha, B., Liu, Z., Saito, T., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6931 · Cited by 133
Self-citationEwers, M., Biechele, G., Suárez‐Calvet, M., Sacher, C., Blume, T., Morenas‐Rodriguez, E., Deming, Y., Piccio, L., Cruchaga, C., Kleinberger, G., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012308 · Cited by 133
Fujii, Y., Nguyen, T. T. T., Fujimura, Y., Kameya, N., Nakamura, S., Arakawa, K., & Morita, H. (2019) · Bioscience Biotechnology and Biochemistry · doi:10.1080/09168451.2019.1644149 · Cited by 135
3D culture models of Alzheimer’s disease: a road map to a “cure-in-a-dish”
Choi, S. H., Kim, Y. H., Quinti, L., Tanzi, R. E., & Kim, D. Y. (2016) · Molecular Neurodegeneration · doi:10.1186/s13024-016-0139-7 · Cited by 132
Seizures in Alzheimer’s disease
Born, H. A. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.11.051 · Cited by 139
Transgenic Mouse Models of Alzheimer’s Disease: An Integrative Analysis
Sanchez-Varo, R. M., Mejias‐Ortega, M., Fernandez-Valenzuela, J. J., Nuñez‐Diaz, C., Cáceres-Palomo, L., Vegas‐Gomez, L., Sánchez‐Mejías, E., Trujillo‐Estrada, L., García‐León, J. A., Moreno‐González, I., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23105404 · Cited by 136
Critical review: involvement of endoplasmic reticulum stress in the aetiology of Alzheimer's disease
Hashimoto, S., & Saido, T. C. (2018) · Open Biology · doi:10.1098/rsob.180024 · Cited by 129
Self-citationDionísio, P. A., Amaral, J. D., Ribeiro, M. F., Lo, A. C., D'Hooge, R., & Rodrigues, C. M. P. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.08.034 · Cited by 127
Fairley, L. H., Lai, K. O., Wong, J. H., Chong, W. J., Vincent, A. S., D’Agostino, G. A., Wu, X., Naik, R. R., Jayaraman, A., Langley, S. R., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209177120 · Cited by 132
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Sobue, A., Komine, O., & Yamanaka, K. (2023) · Inflammation and Regeneration · doi:10.1186/s41232-023-00277-3 · Cited by 134
Petrache, A. L., Rajulawalla, A., Shi, A., Wetzel, A., Saito, T., Saido, T. C., Harvey, K., & Ali, A. B. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz016 · Cited by 124
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Metadata
- Authors
- Saito, T., Matsuba, Y., Mihira, N., Takano, J., Nilsson, P., Itohara, S., Iwata, N., & Saido, T. C.
- Affiliations
- RIKEN Center for Brain Science (JP)
- Corresponding author
- Saido, Takaomi C
- Venue
- Nature Neuroscience
- Published
- 2014-04-13
- Volume
- 17
- Issue
- 5
- Pages
- 661-663
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2090620788
- Cited by
- 1,366
- References
- 26
- Citations by year
- 2026: 159 · 2025: 164 · 2024: 209 · 2023: 167 · 2022: 157 · 2021: 155 · 2020: 92 · 2019: 73 · 2018: 64 · 2017: 49 · 2016: 37 · 2015: 24 · 2014: 13
- FWCI
- 15.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (23680039, 26290019)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, knock-in mice, amyloid precursor protein, APP mutations, Swedish mutation, Arctic mutation, amyloid-beta pathology, neuroinflammation, memory impairment, age-dependent phenotype
- MeSH terms
- Age Factors, Alzheimer Disease, Analysis of Variance, Animals, Disease Models, Animal, Enzyme-Linked Immunosorbent Assay, Humans, Mice, Inbred C57BL, Mice, Transgenic, Mutation, Amyloid beta-Peptides, Amyloid beta-Protein Precursor, Maze Learning, Mice, Gene Knock-In Techniques
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