Chou C-C, Vest R, Prado MA, Wilson-Grady J, Paulo JA, Shibuya Y, Moran-Losada P, Lee T-T, Luo J, Gygi SP, Kelly JW, Finley D, Wernig M, Wyss-Coray T, Frydman J. Proteostasis and lysosomal quality control deficits in Alzheimer's disease neurons. 2023 Mar 27 10.1101/2023.03.27.534444 (version 1) bioRxiv.

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  1. I think that transdifferentiation is a valuable, complementary approach to iPSC models. Both capture the genetic background of the individuals from whom the cells were derived. The aging components that are captured with transdifferentiation also are an advantage of the method. The advantage of iPSCs (and the reason my lab continues to focus on using this experimental system) is that it is much easier to generate large numbers of neurons, astrocytes, microglia, etc., from iPSCs, and it is possible to share this experimental system broadly. Because there is a virtually unlimited supply of iPSCs once a line is established, one can use tools to genetically manipulate the cells in a variety of ways, and then use the same base cell line to generate any type of cell in the body. With fibroblasts, there is a limited number of times these can be propagated, with additional technical challenges to overcome. Nonetheless, both are quite valuable and complementary.

    View all comments by Tracy Young-Pearse

  2. Chou et al. report effects of aging and AD on the human proteome using AD patient’s fibroblast and transdifferentiated tNeurons, confirming, in both cell models, previous studies in AD and AD models demonstrating prominent dysfunction of the autophagy-lysosomal (A-L) pathway. An important take-home message of the tNeuron model is that, in late-age-onset diseases, it is critical to incorporate the interaction of cell aging mechanisms in the model. This is especially true in AD, where failing A-L functions, which are tied fundamentally to the mechanism of cell aging, are progressive, profound, and central to all stages of AD pathogenesis. Early stages of this failure have been reproduced nicely in IPSC neurons derived from diseased fibroblasts but even long duration cell culture cannot reproduce the progression of intraneuronal pathology and consequent extracellular pathology that can be seen in human AD and the mouse models.

    Chou et al. can be congratulated for introducing an aging element by use of the tNeuron system, demonstrating certain features of the more advanced A-L cytopathology described in our A-L studies over several decades in AD brain, cells, and the mouse models. Comparing the findings made in tNeurons to those in our recent characterizations of the neuronal A-L failure, from its origin to late consequences in varied AD mouse models (Lee et al., 2022), the Chou et al. tNeuron study confirms our observations, in brain and cell systems, of lysosomal acidification and vATPase abnormality (Lee et al., 2010; Lee et al., 2015; Wolfe et al., 2013) and their reversal by re-acidification (Lee et al., 2020), as well as abnormal enlargement of lysosomes/autolysosomes and their accumulation of Aβ immunoreactivity. Moreover, lysosome damage, reported by us as progressive Lysosomal Membrane Permeability (LMP) (Lee et al., 2022), was also detected by the authors as galectin-3 and CHMP2B positivity—two markers linked to lysosomal damage. As might be expected, these frail lysosomes, and, interestingly, even the ones in aged fibroblasts, are more sensitive to toxic damage. Whether the appearance of CHMP2B positivity is a contributor to damage or a victim of the digestion failure affecting many substrates, is an interesting issue to explore. Additionally, the evidence that the damaging Aβ accumulated in the models is in lysosomes rather than, or in addition to, autolysosomes—the main organelle accumulating in the mouse neurons—could be further considered. In this regard, the authors observed Aβ immunoreactivity residing in LC3B/LAMP-positive puncta, which would suggest autolysosomes—a closer parallel than lysosomes to the abnormality we see in mice and humans.

    Immunoreactive Aβ accumulation in tNeurons notwithstanding, there seems to be scarce evidence presented that tNeuron models generate β-amyloid species of Aβ. Aβ immunoreactivity by 6E10 is quite different from the Thioflavin S -positive fibrillar Aβ we were the first to identify, intralumenally, within ER structures in intact terminal stage neurons (PANTHOS). The perinuclear coalescence of Aβ into an intracellular plaque in the PANTHOS neuron yields the extracellular senile plaque when the cell dies (Lee et al., 2022). The suggestion in the Chou report that the demonstrated tNeuron pathology models these last cellular stages, and the inside-out sequence of plaque formation, lacks substantiation without the direct evidence provided by reports from our and the Glabe lab (Pensalfini et al., 2014), neither of which were cited in this context. Indeed, the lack of reference in the results and elsewhere to reports from various labs that informed the authors’ interpretations of findings could lead some readers to conclude that these abnormalities were first shown in tNeurons, which likely was not the intention.

    The data on cytokines and inflammasome marker upregulation is an interesting new feature of the damage to lysosome-related compartments nicely modeled in the tNeuron system and may well contribute to the PANTHOS-related premature death of select neurons in AD.

    In conclusion, there are important features of AD brain pathobiology and pathology dependent on brain aging which are able to be modeled in the integrated cellular milieu of the intact mouse brain. At least as of yet, these aspects of AD pathobiology are beyond the scope of pathologies that can be reproduced in cell models. Nevertheless, the tNeuron model is a valuable advance in technology toward creating the most optimal in vitro model for investigations of AD and other late-age onset neurodegenerative diseases.  

    References:

    Lee JH, Yang DS, Goulbourne CN, Im E, Stavrides P, Pensalfini A, Chan H, Bouchet-Marquis C, Bleiwas C, Berg MJ, Huo C, Peddy J, Pawlik M, Levy E, Rao M, Staufenbiel M, Nixon RA. Faulty autolysosome acidification in Alzheimer's disease mouse models induces autophagic build-up of Aβ in neurons, yielding senile plaques. Nat Neurosci. 2022 Jun;25(6):688-701. Epub 2022 Jun 2 PubMed.

    Lee JH, Yang DS, Goulbourne CN, Im E, Stavrides P, Pensalfini A, Chan H, Bouchet-Marquis C, Bleiwas C, Berg MJ, Huo C, Peddy J, Pawlik M, Levy E, Rao M, Staufenbiel M, Nixon RA. Faulty autolysosome acidification in Alzheimer's disease mouse models induces autophagic build-up of Aβ in neurons, yielding senile plaques. Nat Neurosci. 2022 Jun;25(6):688-701. Epub 2022 Jun 2 PubMed.

    Lee JH, McBrayer MK, Wolfe DM, Haslett LJ, Kumar A, Sato Y, Lie PP, Mohan P, Coffey EE, Kompella U, Mitchell CH, Lloyd-Evans E, Nixon RA. Presenilin 1 Maintains Lysosomal Ca(2+) Homeostasis via TRPML1 by Regulating vATPase-Mediated Lysosome Acidification. Cell Rep. 2015 Sep 1;12(9):1430-44. Epub 2015 Aug 20 PubMed.

    Lee JH, Wolfe DM, Darji S, McBrayer MK, Colacurcio DJ, Kumar A, Stavrides P, Mohan PS, Nixon RA. β2-adrenergic Agonists Rescue Lysosome Acidification and Function in PSEN1 Deficiency by Reversing Defective ER-to-lysosome Delivery of ClC-7. J Mol Biol. 2020 Apr 3;432(8):2633-2650. Epub 2020 Feb 24 PubMed.

    Pensalfini A, Albay R 3rd, Rasool S, Wu JW, Hatami A, Arai H, Margol L, Milton S, Poon WW, Corrada MM, Kawas CH, Glabe CG. Intracellular amyloid and the neuronal origin of Alzheimer neuritic plaques. Neurobiol Dis. 2014 Nov;71:53-61. Epub 2014 Aug 1 PubMed.

    Wolfe DM, Lee JH, Kumar A, Lee S, Orenstein SJ, Nixon RA. Autophagy failure in Alzheimer's disease and the role of defective lysosomal acidification. Eur J Neurosci. 2013 Jun;37(12):1949-61. PubMed.

    View all comments by Ralph Nixon

  3. This new pre-print from Judith Frydman’s team is both provocative and interesting, suggesting that fibroblast-derived neurons (tNeurons) from aged and AD subjects displayed profound abnormalities in proteostasis and lysosomal biology. Important caveats to this otherwise impressive work are that the sample sizes were fairly small—3-4 fibroblast lines from young, aged, and AD subjects—and that the differentiation efficiency was quite variable across these lines. Variability in neuron differentiation efficiency across patient cell lines can have major impacts on cellular phenotypes, sometimes overshadowing actual disease phenotypes, both in studies that differentiate neurons from fibroblasts and in studies that use iPSCs (e.g. Workman et al., 2023).

    A challenge that many of us face is that it can be difficult, in practice, to perform complex assays – such as those used in this study - across sufficient numbers of patient cell lines to distinguish disease phenotypes from those driven by cell line variability. iPSC models enable isogenic correction of patient mutations for familial forms of neurodegeneration, thereby potentially providing ideal controls. However, iPSC models may not recapitulate key aspects of aging that could interact with disease-related pathways, such as those described in this paper. How to best address issues of variability across different cell lines versus practical considerations for experimental design, and how to model aging in iPSC-derived cells are major questions yet to be addressed in our field.

    View all comments by Priyanka Narayan

  4. The use of human neurons derived from stem cells has revolutionized the in vitro modelling of neurodegenerative diseases, such as Alzheimer’s disease. However, given that most protocols used to derive these neurons mimic embryonic development, one caveat has been that they are largely fetal and require long, in vitro culture periods to undergo pre- to postnatal developmental transitions (Gordon et al., 2021)—the developmental regulation of tau splicing is a good example of this (Sposito et al., 2015). This means it can be difficult to look at the impact of cellular age in these models, and indeed it is possible that stem-cell-derived neurons can be highly protected against certain disease insults—for example, high activity of proteostasis networks is necessary because the presence of aggregates at such early developmental time points can impact the long-term viability of the cell/organism.

    Transdifferentiated neurons are rapidly emerging as a complementary tool to incorporate aging in our in vitro models. Through the direct conversion of somatic cells directly to neurons, cells retain the epigenetic signatures of aging, display mature tau splicing, and show age related defects in nuclear-cytoplasmic transport and mitochondrial function (Capano et al., 2022; Mertens et al., 2015; Kim et al., 2018). Here, Chou et al. show, beautifully, the defects in the endosomal and autophagy-lysosomal pathways in transdifferentiated neurons from PSEN1 carriers and from late-onset AD cases. This builds on previous work showing disrupted lysosome and autophagosomes in iPSC-neurons from individuals with monogenic AD (Hung and Livesey, 2018).  It will be interesting to compare transdifferentiated neurons with iPSC-derived neurons from the same individuals, then decipher the role of epigenetic aging marks on proteostasis network function. This will help to determine the role that aging plays in neuronal susceptibility to protein turnover changes, potentially contributing to neurodegeneration.

    Thus, transdifferentiated neurons represent an important tool for the study of aging and AD phenotypes. However, as the authors point out, they come with their own set of challenges, including the need for efficient and scalable neuronal conversion, their variability due to donor background, and the need to develop multicellular models to study neuronal-glial interactions. The relative ease with which CRISPR can be used to generate isogenic iPSC lines also makes it easier to overcome variability from donor background. Nevertheless, this study adds to the growing body of work showing the power of cultured human neurons to unravel disease mechanisms in a physiologically relevant system and the importance of using multiple complementary strategies for disease modelling.

    References:

    Gordon A, Yoon SJ, Tran SS, Makinson CD, Park JY, Andersen J, Valencia AM, Horvath S, Xiao X, Huguenard JR, Pașca SP, Geschwind DH. Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat Neurosci. 2021 Mar;24(3):331-342. Epub 2021 Feb 22 PubMed.

    Sposito T, Preza E, Mahoney CJ, Setó-Salvia N, Ryan NS, Morris HR, Arber C, Devine MJ, Houlden H, Warner TT, Bushell TJ, Zagnoni M, Kunath T, Livesey FJ, Fox NC, Rossor MN, Hardy J, Wray S. Developmental regulation of tau splicing is disrupted in stem cell-derived neurons from frontotemporal dementia patients with the 10 + 16 splice-site mutation in MAPT. Hum Mol Genet. 2015 Sep 15;24(18):5260-9. Epub 2015 Jul 1 PubMed.

    Capano LS, Sato C, Ficulle E, Yu A, Horie K, Kwon JS, Burbach KF, Barthélemy NR, Fox SG, Karch CM, Bateman RJ, Houlden H, Morimoto RI, Holtzman DM, Duff KE, Yoo AS. Recapitulation of endogenous 4R tau expression and formation of insoluble tau in directly reprogrammed human neurons. Cell Stem Cell. 2022 Jun 2;29(6):918-932.e8. PubMed.

    Mertens J, Paquola AC, Ku M, Hatch E, Böhnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy JR, Gonçalves JT, Toda T, Kim Y, Winkler J, Yao J, Hetzer MW, Gage FH. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell. 2015 Oct 6; PubMed.

    Kim Y, Zheng X, Ansari Z, Bunnell MC, Herdy JR, Traxler L, Lee H, Paquola AC, Blithikioti C, Ku M, Schlachetzki JC, Winkler J, Edenhofer F, Glass CK, Paucar AA, Jaeger BN, Pham S, Boyer L, Campbell BC, Hunter T, Mertens J, Gage FH. Mitochondrial Aging Defects Emerge in Directly Reprogrammed Human Neurons due to Their Metabolic Profile. Cell Rep. 2018 May 29;23(9):2550-2558. PubMed.

    Hung CO, Livesey FJ. Altered γ-Secretase Processing of APP Disrupts Lysosome and Autophagosome Function in Monogenic Alzheimer's Disease. Cell Rep. 2018 Dec 26;25(13):3647-3660.e2. PubMed.

    View all comments by Rebecca Gabriele

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This paper appears in the following:

News

  1. Better Cell Model? Transdifferentiated Neurons Capture AD-Like Changes

Mutations

  1. PSEN1 A246E
  2. PSEN1 A431E
  3. PSEN1 F386L