1. Zhang, H. et al. Degenerate mapping of environmental location presages deficits in object-location encoding and memory in the 5xFAD mouse model for Alzheimer’s disease. Neurobiol Dis 176, 105939 (2023).
  2. Tran, K. M. et al. A Trem2(R47H) mouse model without cryptic splicing drives age- and disease-dependent tissue damage and synaptic loss in response to plaques. Mol Neurodegener 18, 12 (2023).
  3. Sasner, M., Territo, P. R. & Sukoff Rizzo, S. J. Meeting report of the annual workshop on Principles and Techniques for Improving Preclinical to Clinical Translation in Alzheimer’s Disease research. Alzheimers Dement (2023).
  4. Rezaie, N., Reese, F. & Mortazavi, A. PyWGCNA: a Python package for weighted gene co-expression network analysis. Bioinformatics 39 (2023).
  5. Quinney, S. K. et al. STOP-AD portal: Selecting the optimal pharmaceutical for preclinical drug testing in Alzheimer’s disease. Alzheimers Dement (2023).
  6. Pandey, R. S., Kotredes, K. P., Sasner, M., Howell, G. R. & Carter, G. W. Differential splicing of neuronal genes in a Trem2*R47H mouse model mimics alterations associated with Alzheimer’s disease. BMC Genomics 24, 172 (2023).
  7. Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 3, 100498 (2023).
  8. Milinkeviciute, G. & Green, K. N. Clusterin/apolipoprotein J, its isoforms and Alzheimer’s disease. Front Aging Neurosci 15, 1167886 (2023).
  9. Jullienne, A. et al. Cortical cerebrovascular and metabolic perturbations in the 5xFAD mouse model of Alzheimer’s disease. Front Aging Neurosci 15, 1220036 (2023).
  10. Bohlson, S. S. & Tenner, A. J. Complement in the Brain: Contributions to Neuroprotection, Neuronal Plasticity, and Neuroinflammation. Annu Rev Immunol 41, 431-452 (2023).
  11. Tsai, A. P. et al. PLCG2 is associated with the inflammatory response and is induced by amyloid plaques in Alzheimer’s disease. Genome Med 14, 17 (2022).
  12. Reagan, A. M., Onos, K. D., Heuer, S. E., Sasner, M. & Howell, G. R. Improving mouse models for the study of Alzheimer’s disease. Curr Top Dev Biol 148, 79-113 (2022).
  13. Reagan, A. M. et al. The 677C > T variant in methylenetetrahydrofolate reductase causes morphological and functional cerebrovascular deficits in mice. J Cereb Blood Flow Metab 42, 2333-2350 (2022).×221122644
  14. Onos, K. D. et al. Pharmacokinetic, pharmacodynamic, and transcriptomic analysis of chronic levetiracetam treatment in 5XFAD mice: A MODEL-AD preclinical testing core study. Alzheimers Dement (N Y) 8, e12329 (2022).
  15. Oblak, A. L. et al. Plcg2(M28L) Interacts With High Fat/High Sugar Diet to Accelerate Alzheimer’s Disease-Relevant Phenotypes in Mice. Front Aging Neurosci 14, 886575 (2022).
  16. Oblak, A. L. et al. Prophylactic evaluation of verubecestat on disease- and symptom-modifying effects in 5XFAD mice. Alzheimers Dement (N Y) 8, e12317 (2022).
  17. Lin, X. et al. Spatial coding defects of hippocampal neural ensemble calcium activities in the triple-transgenic Alzheimer’s disease mouse model. Neurobiol Dis 162, 105562 (2022).
  18. Kotredes, K. P. et al. Corrigendum: Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2(*)R47H. Front Aging Neurosci 14, 857628 (2022).
  19. Jullienne, A., Trinh, M. V. & Obenaus, A. Neuroimaging of Mouse Models of Alzheimer’s Disease. Biomedicines 10 (2022).
  20. Jullienne, A. et al. Progressive Vascular Abnormalities in the Aging 3xTg-AD Mouse Model of Alzheimer’s Disease. Biomedicines 10 (2022).
  21. Henningfield, C. M., Arreola, M. A., Soni, N., Spangenberg, E. E. & Green, K. N. Microglia-specific ApoE knock-out does not alter Alzheimer’s disease plaque pathogenesis or gene expression. Glia 70, 287-302 (2022).
  22. Gordon, M. N. et al. Impact of COVID-19 on the Onset and Progression of Alzheimer’s Disease and Related Dementias: A Roadmap for Future Research. Alzheimers Dement 18, 1038-1046 (2022).
  23. Foley, K. E. et al. The APOE (ε3/ε4) Genotype Drives Distinct Gene Signatures in the Cortex of Young Mice. Front Aging Neurosci 14, 838436 (2022).
  24. Foley, K. E. et al. APOE ε4 and exercise interact in a sex-specific manner to modulate dementia risk factors. Alzheimers Dement (N Y) 8, e12308 (2022).
  25. Dunham, S. J. B. et al. Longitudinal Analysis of the Microbiome and Metabolome in the 5xfAD Mouse Model of Alzheimer’s Disease. mBio 13, e0179422 (2022).
  26. Tsai, A. P. et al. INPP5D expression is associated with risk for Alzheimer’s disease and induced by plaque-associated microglia. Neurobiol Dis 153, 105303 (2021).
  27. Szu, J. I. & Obenaus, A. Cerebrovascular phenotypes in mouse models of Alzheimer’s disease. J Cereb Blood Flow Metab 41, 1821-1841 (2021).×21992462
  28. Oblak, A. L. et al. Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study. Front Aging Neurosci 13, 713726 (2021).
  29. Maguire, E. et al. PIP2 depletion and altered endocytosis caused by expression of Alzheimer’s disease-protective variant PLCγ2 R522. Embo j 40, e105603 (2021).
  30. Li, Y. et al. Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer’s disease progression. Alzheimers Dement (Amst) 13, e12140 (2021).
  31. Kotredes, K. P. et al. Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H. Front Aging Neurosci 13, 735524 (2021).
  32. Javonillo, D. I. et al. Systematic Phenotyping and Characterization of the 3xTg-AD Mouse Model of Alzheimer’s Disease. Front Neurosci 15, 785276 (2021).
  33. Forner, S. et al. Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer’s disease. Sci Data 8, 270 (2021).
  34. Crapser, J. D., Arreola, M. A., Tsourmas, K. I. & Green, K. N. Microglia as hackers of the matrix: sculpting synapses and the extracellular space. Cell Mol Immunol 18, 2472-2488 (2021).
  35. Balderrama-Gutierrez, G. et al. Single-cell and nucleus RNA-seq in a mouse model of AD reveal activation of distinct glial subpopulations in the presence of plaques and tangles. bioRxiv, 2021.2009.2029.462436 (2021).
  36. Baglietto-Vargas, D. et al. Generation of a humanized Aβ expressing mouse demonstrating aspects of Alzheimer’s disease-like pathology. Nat Commun 12, 2421 (2021).
  37. Arreola, M. A. et al. Microglial dyshomeostasis drives perineuronal net and synaptic loss in a CSF1R(+/-) mouse model of ALSP, which can be rescued via CSF1R inhibitors. Sci Adv 7 (2021).
  38. Wyman, D. et al. A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification. bioRxiv, 672931 (2020).
  39. Wan, Y. W. et al. Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models. Cell Rep 32, 107908 (2020).
  40. Vitek, M. P. et al. Translational animal models for Alzheimer’s disease: An Alzheimer’s Association Business Consortium Think Tank. Alzheimers Dement (N Y) 6, e12114 (2020).
  41. Sukoff Rizzo, S. J. et al. Improving preclinical to clinical translation in Alzheimer’s disease research. Alzheimers Dement (N Y) 6, e12038 (2020).
  42. Silverman, J. L., Nithianantharajah, J., Der-Avakian, A., Young, J. W. & Sukoff Rizzo, S. J. Lost in translation: At the crossroads of face validity and translational utility of behavioral assays in animal models for the development of therapeutics. Neurosci Biobehav Rev 116, 452-453 (2020).
  43. Preuss, C. et al. A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease. Mol Neurodegener 15, 67 (2020).
  44. Oblak, A. L. et al. Model organism development and evaluation for late-onset Alzheimer’s disease: MODEL-AD. Alzheimers Dement (N Y) 6, e12110 (2020).
  45. Mukherjee, S. et al. Author Correction: Molecular estimation of neurodegeneration pseudotime in older brains. Nat Commun 11, 6307 (2020).
  46. Milind, N. et al. Transcriptomic stratification of late-onset Alzheimer’s cases reveals novel genetic modifiers of disease pathology. PLoS Genet 16, e1008775 (2020).
  47. Jadhav, V. S. et al. Trem2 Y38C mutation and loss of Trem2 impairs neuronal synapses in adult mice. Mol Neurodegener 15, 62 (2020).
  48. Greenwood, A. K. et al. The AD Knowledge Portal: A Repository for Multi-Omic Data on Alzheimer’s Disease and Aging. Curr Protoc Hum Genet 108, e105 (2020).
  49. Fernández-Mendívil, C., Arreola, M. A., Hohsfield, L. A., Green, K. N. & Lopez, M. G. Aging and Progression of Beta-Amyloid Pathology in Alzheimer’s Disease Correlates with Microglial Heme-Oxygenase-1 Overexpression. Antioxidants (Basel) 9 (2020).
  50. Crapser, J. D. et al. Microglia facilitate loss of perineuronal nets in the Alzheimer’s disease brain. EBioMedicine 58, 102919 (2020).
  51. Chintapaludi, S. R. et al. Staging Alzheimer’s Disease in the Brain and Retina of B6.APP/PS1 Mice by Transcriptional Profiling. J Alzheimers Dis 73, 1421-1434 (2020).
  52. Pandey, R. S. et al. Genetic perturbations of disease risk genes in mice capture transcriptomic signatures of late-onset Alzheimer’s disease. Mol Neurodegener 14, 50 (2019).
  53. Onos, K. D. et al. Enhancing face validity of mouse models of Alzheimer’s disease with natural genetic variation. PLoS Genet 15, e1008155 (2019).
  54. Wang, X. et al. A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer’s Disease. Genetics 209, 51-64 (2018).
  55. Cheng-Hathaway, P. J. et al. The Trem2 R47H variant confers loss-of-function-like phenotypes in Alzheimer’s disease. Mol Neurodegener 13, 29 (2018).
Zhang, H. et al. Degenerate mapping of environmental location presages deficits in object-location encoding and memory in the 5xFAD mouse model for Alzheimer’s disease. Neurobiol Dis 176, 105939 (2023).