Tau polarizes an aging transcriptional signature to excitatory neurons and glia
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eLife assessment
This is an important study that defines cell type changes upon the expression of a mutant tau protein and put it into context of human postmortem tissue. Technically, the single cell mRNA sequencing data are convincing but the loss of neurons already at the earliest stages assessed in this model may rather suggest there are also elements of developmental toxicity.
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Abstract
Aging is a major risk factor for Alzheimer’s disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing in Drosophila with pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell-type-specific manner. Integration of cellular abundance and gene expression pinpoints nuclear factor kappa B signaling in neurons as a marker for cellular vulnerability. We also highlight the conservation of cell-type-specific transcriptional patterns between Drosophila and human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy.
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Author Response
Reviewer #1 (Public Review):
Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They …
Author Response
Reviewer #1 (Public Review):
Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They then examined innate immune pathways downstream of NFkB, which they had already uncovered in their previous bulk studies to be associated with tau expression. Also at the scRNAseq level, they find these pathways to be deregulated after expression of tau. In addition, in control cell types that are lost when tau is expressed, they find an inverse correlation of the expression of these pathways and cellular loss, suggesting they might be predictors of neurodegeneration severity. Finally, they use this finding uncovered in Drosophila and reexamined human Alzheimer's disease snRNAseq datasets, were they also find the NFkB pathway to be deregulated.
This study has several strengths. It demonstrates the power of studying taueffects in a tractable model and then using the obtained knowledge to pin-point relevant pathways in cross-sectional studies of human tauopathy, which are otherwise not easy to interpret given the overlayed effects of other disease triggers. By examining the single-cell level they uncover cell type specific effects, which would otherwise be hidden. This study also represents a valuable resource. Given that the authors have included multiple time points the dataset provides an opportunity to understand the evolution of cell-type specific tau effects over time. The authors have also included a replication dataset, which confirms the results of the primary analysis of neuronal loss. I also appreciate the efforts to understand the apparent increase in glia cell number after expression of tau. By combining computational and experimental methods the authors reach the well supported conclusion that in fact glial cell numbers remain constant but only appear increased due to the proportional nature of the scRNAseq data and profound loss of some neurons. Overall, it is interesting that the authors nominate the innate immunity and NFkB pathways in tauopathy, based on deregulated genes and also based on vulnerable neurons. Nevertheless, this is a correlative finding and as such does not proof that it is causal.
As noted [R3], above, we agree that our findings of NFkB dysregulation are correlative. We have performed new experiments to directly test the hypothesis that neuronal immune pathways are causally linked to tau-mediated neurodegeneration; however, the results were negative. These data are included in the revision and we also carefully discuss published work from other fly models of aging and neurodegeneration as well as mouse tauopathy that strongly suggest NFkB can directly modulate neurodegeneration.
The authors correctly point out the importance of aging as a risk factor for Alzheimer's disease. However, it is unclear whether their models actually capture age-dependent neurodegeneration. Alternatively, they might represent neurodevelopmental tau toxicity. In Figure 1B it can be seen that all vulnerable cell types are already lost at day 1, most notably a'/b'-KC, a/b-KC and G-KC with a >4-fold decrease. This raises the question whether the lost cells might developmentally have not correctly formed, as suggested by a study that the authors cite (Kosmidis et al., 2010). This distinction is important in order to strengthen the translational value of the study to human tauopathies.
The elav>tauR406W model manifests both developmental toxicity and age-dependent neurodegenerative changes. Our revision includes new data highlighting specific examples and includes a more balanced discussion of these issues.
The analysis of tau expression levels relative to its impact across cell types in Figure S8 is interesting, however has caveats. The profound neuronal loss makes the interpretation of the correlation analysis of tau levels vs. neuronal vulnerability difficult - since it might be that the individual surviving a'/b'KC, a/b-KC and G-KC cells are the ones that expressed little amounts of tau, while those that are missing used to express high tau. In addition, it is unclear from the methods whether the 3' UTR from the transformation vector to generate the models was included in the counting. The majority of reads would be expected to be there.
As suggested, we have repeated the alignment and analysis of MAPT expression including the short SV40 3’UTR (135bp) from the transformation vector. The result appears very similar to that from the previous analysis, and we have updated Figure 3–figure supplement 4 with these data. Based on the feedback from Reviewer 2, we also include a new plot highlighting the non-relation between MAPT expression and cell abundance changes (Figure 3–figure supplement 4B). We acknowledge the caveat that “missing” / dead cells may have previously expressed high levels of tau, leaving behind survivors with low tau levels. We have added mention of this possible caveat in the Results (lines 197-198). However, while this scenario might impact our interpretation of cell abundance changes, it is a less likely to confound our analyses of differential expression, in which the number of differentially expressed genes show very poor correlation, if any, with MAPT transgene expression (Figure 3–figure supplement 4C).
It would be relevant to know whether the animals were in the same genetic background. I.e. is UAS-TauR406W in the same background of the fly that was crossed to elav-Gal4 to serve as the control. This is not mentioned in the paper and also not in Mangleburg et al., 2020 which the authors refer to. There is a lot of tau-induced DEGs (~1/3 of the detected genes) and it would be relevant to know whether some of them might be due to genetic background.
Our experimental design mitigates the possibility of a substantial impact from genetic background; however, we have added text in the revision noting that this is an important consideration and possible confounder.
The finding of the authors that NFkB pathways are higher in cell types that degenerate more is interesting. However, in Figure 4D it is also apparent that multiple cell types that do not degenerate have comparably high expression. Therefore, it is not a sufficient factor to explain why some neurons are vulnerable vs. others are not, but rather predicts amongst the vulnerable neurons how much they will be lost. It would be helpful to make this distinction clear in the text.
We agree with the reviewer’s interpretation, and we have tried to make this more clear in both the results and discussion (lines 286-288; 387-390). Indeed, the NFkB expression level seems to be a marker for the severity of tau-triggered cell loss among the vulnerable cells.
Reviewer 2 (Public Review):
Wu et al. conducted longitudinal single-nucleus RNA sequencing in a Drosophila transgenic line expressing pathogenic tau (Arg406 ->Trp) and control to study presenile degenerative dementia with bitemporal atrophy. Their data is consistent with previous findings on Tau neurotoxicity, which significantly affects excitatory neurons in human brain samples and transgenic mice. Authors identify aging-like signatures, and an innate immune glial response, including the NFKB pathway, in the transgenic animals.
Strength: This is a great resource for the dissection of dynamic, age-dependent gene expression changes at cellular resolution for the fly community. The article's conclusions are largely supported by the data.
We thank the reviewer for recognizing the value of this work as a resource for the field.
Weakness: No additional orthogonal validation is done on the identified pathways using immunohistochemistry. Also, the authors hypothesized that innate immune signatures might serve as predictors of neuronal subtype vulnerability in tauopathies. Although their data support stronger immune responses in the mutant lines, these findings are not validated. Moreover, the Authors need to use appropriate control animals to compare the mutant Tau animals.
Our original manuscript included experimental validation demonstrating that (1) the apparent increases in glial cell abundance is likely due to changes in cell proportions, and we also (2) confirmed the expression of Relish in both neurons and glia of the adult fly brain. For our revision, we were guided by the requested Essential Revision #3 [R3]. We have therefore performed additional experiments directly testing whether manipulation of Relish/NFkB in neurons alters tau-induced neurodegeneration. While the results of these experiments were negative, we have incorporated them into the results and discussion, along with discussion of related published studies that support a causal role for NFkB immune pathways in tauopathy. Lastly, in our response to Essential Revision #4 [R4], we address concerns about the conservation of neurotoxic mechanisms between mutant and wildtype froms of MAPT and the use of mutant MAPT transgenic models for investigations of AD, along with the caveats. New analyses have been performed and textual revisions have been made in response this feedback
Reviewer3 (Public Review):
Understanding the changes in the brain during the progression of neurodegenerative diseases may provide a critical entry point towards medical treatments. Many genes have been directly or indirectly implemented in an array of neurodegenerative diseases, including the microtubule associated protein tau (MAPT). Various studies have shown that misexpression of tau can cause behavioral, genetic as well as molecular phenotypes that display properties of human neurodegenerative diseases connected to tauopathies. Here the authors use the fruit fly as model to assess phenotypic defects at single-cell resolution. Pan-neuronal misexpression of a mutant form of tau (R406W) and single-cell RNAseq at different time points provides the basis for the investigation.
The authors assess which cell-types are affected (by comparing it with previously described brain cell atlas identities) and find that certain cell types are missing (or less abundant) while other appear unaffected. They do this comparison in relative abundance; both neurons and glia cells are affected.
As next step they compare this with the cell-cluster changes during aging and compare both types of analysis; the investigation here includes the analysis of differentially expressed genes in defined cell clusters. One particularly affected pathway in response to tau is the NFκB signaling pathway. The authors investigate the gene expression changes of the NFκB signaling pathway in the current dataset in more detail. In the last section the authors compare singlecell transcriptomic analyses between fly and human postmortem tissue, showing that the NFκB signaling pathway might be a conserved aspect of neurodegeneration.
The manuscript is overall an elegant example of how single-cell RNAseq can be employed as tool to study the impact of genetic modulators of neurodegeneration (in this case tau) and that it allows direct comparison with human tissues. The results are clean, logically presented and accordingly discussed. It shows that such approaches are indeed powerful for genetic dissection of mechanisms at a descriptive level and opening doors for functional studies.
We thank the reviewer for this positive summary of our manuscript.
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eLife assessment
This is an important study that defines cell type changes upon the expression of a mutant tau protein and put it into context of human postmortem tissue. Technically, the single cell mRNA sequencing data are convincing but the loss of neurons already at the earliest stages assessed in this model may rather suggest there are also elements of developmental toxicity.
-
Reviewer #1 (Public Review):
Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They then examined …
Reviewer #1 (Public Review):
Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They then examined innate immune pathways downstream of NFkB, which they had already uncovered in their previous bulk studies to be associated with tau expression. Also at the scRNAseq level, they find these pathways to be deregulated after expression of tau. In addition, in control cell types that are lost when tau is expressed, they find an inverse correlation of the expression of these pathways and cellular loss, suggesting they might be predictors of neurodegeneration severity. Finally, they use this finding uncovered in Drosophila and reexamined human Alzheimer's disease snRNAseq datasets, were they also find the NFkB pathway to be deregulated.
This study has several strengths. It demonstrates the power of studying tau-effects in a tractable model and then using the obtained knowledge to pin-point relevant pathways in cross-sectional studies of human tauopathy, which are otherwise not easy to interpret given the overlayed effects of other disease triggers. By examining the single-cell level they uncover cell type specific effects, which would otherwise be hidden. This study also represents a valuable resource. Given that the authors have included multiple time points the dataset provides an opportunity to understand the evolution of cell-type specific tau effects over time. The authors have also included a replication dataset, which confirms the results of the primary analysis of neuronal loss. I also appreciate the efforts to understand the apparent increase in glia cell number after expression of tau. By combining computational and experimental methods the authors reach the well supported conclusion that in fact glial cell numbers remain constant but only appear increased due to the proportional nature of the scRNAseq data and profound loss of some neurons. Overall, it is interesting that the authors nominate the innate immunity and NFkB pathways in tauopathy, based on deregulated genes and also based on vulnerable neurons. Nevertheless, this is a correlative finding and as such does not proof that it is causal.
The authors correctly point out the importance of aging as a risk factor for Alzheimer's disease. However, it is unclear whether their models actually capture age-dependent neurodegeneration. Alternatively, they might represent neurodevelopmental tau toxicity. In Figure 1B it can be seen that all vulnerable cell types are already lost at day 1, most notably a'/b'-KC, a/b-KC and G-KC with a >4-fold decrease. This raises the question whether the lost cells might developmentally have not correctly formed, as suggested by a study that the authors cite (Kosmidis et al., 2010). This distinction is important in order to strengthen the translational value of the study to human tauopathies.
The analysis of tau expression levels relative to its impact across cell types in Figure S8 is interesting, however has caveats. The profound neuronal loss makes the interpretation of the correlation analysis of tau levels vs. neuronal vulnerability difficult - since it might be that the individual surviving a'/b'-KC, a/b-KC and G-KC cells are the ones that expressed little amounts of tau, while those that are missing used to express high tau. In addition, it is unclear from the methods whether the 3' UTR from the transformation vector to generate the models was included in the counting. The majority of reads would be expected to be there.
It would be relevant to know whether the animals were in the same genetic background. I.e. is UAS-TauR406W in the same background of the fly that was crossed to elav-Gal4 to serve as the control. This is not mentioned in the paper and also not in Mangleburg et al., 2020 which the authors refer to. There is a lot of tau-induced DEGs (~1/3 of the detected genes) and it would be relevant to know whether some of them might be due to genetic background.
The finding of the authors that NFkB pathways are higher in cell types that degenerate more is interesting. However, in Figure 4D it is also apparent that multiple cell types that do not degenerate have comparably high expression. Therefore, it is not a sufficient factor to explain why some neurons are vulnerable vs. others are not, but rather predicts amongst the vulnerable neurons how much they will be lost. It would be helpful to make this distinction clear in the text.
-
Reviewer #2 (Public Review):
Wu et al. conducted longitudinal single-nucleus RNA sequencing in a Drosophila transgenic line expressing pathogenic tau (Arg406 ->Trp) and control to study presenile degenerative dementia with bitemporal atrophy. Their data is consistent with previous findings on Tau neurotoxicity, which significantly affects excitatory neurons in human brain samples and transgenic mice. Authors identify aging-like signatures, and an innate immune glial response, including the NFKB pathway, in the transgenic animals.
Strength: This is a great resource for the dissection of dynamic, age-dependent gene expression changes at cellular resolution for the fly community. The article's conclusions are largely supported by the data.
Weakness: No additional orthogonal validation is done on the identified pathways using …
Reviewer #2 (Public Review):
Wu et al. conducted longitudinal single-nucleus RNA sequencing in a Drosophila transgenic line expressing pathogenic tau (Arg406 ->Trp) and control to study presenile degenerative dementia with bitemporal atrophy. Their data is consistent with previous findings on Tau neurotoxicity, which significantly affects excitatory neurons in human brain samples and transgenic mice. Authors identify aging-like signatures, and an innate immune glial response, including the NFKB pathway, in the transgenic animals.
Strength: This is a great resource for the dissection of dynamic, age-dependent gene expression changes at cellular resolution for the fly community. The article's conclusions are largely supported by the data.
Weakness: No additional orthogonal validation is done on the identified pathways using immunohistochemistry. Also, the authors hypothesized that innate immune signatures might serve as predictors of neuronal subtype vulnerability in tauopathies. Although their data support stronger immune responses in the mutant lines, these findings are not validated. Moreover, the Authors need to use appropriate control animals to compare the mutant Tau animals.
-
Reviewer #3 (Public Review):
Understanding the changes in the brain during the progression of neurodegenerative diseases may provide a critical entry point towards medical treatments. Many genes have been directly or indirectly implemented in an array of neurodegenerative diseases, including the microtubule associated protein tau (MAPT). Various studies have shown that misexpression of tau can cause behavioral, genetic as well as molecular phenotypes that display properties of human neurodegenerative diseases connected to tauopathies. Here the authors use the fruit fly as model to assess phenotypic defects at single-cell resolution. Pan-neuronal misexpression of a mutant form of tau (R406W) and single-cell RNAseq at different time points provides the basis for the investigation.
The authors assess which cell-types are affected (by …
Reviewer #3 (Public Review):
Understanding the changes in the brain during the progression of neurodegenerative diseases may provide a critical entry point towards medical treatments. Many genes have been directly or indirectly implemented in an array of neurodegenerative diseases, including the microtubule associated protein tau (MAPT). Various studies have shown that misexpression of tau can cause behavioral, genetic as well as molecular phenotypes that display properties of human neurodegenerative diseases connected to tauopathies. Here the authors use the fruit fly as model to assess phenotypic defects at single-cell resolution. Pan-neuronal misexpression of a mutant form of tau (R406W) and single-cell RNAseq at different time points provides the basis for the investigation.
The authors assess which cell-types are affected (by comparing it with previously described brain cell atlas identities) and find that certain cell types are missing (or less abundant) while other appear unaffected. They do this comparison in relative abundance; both neurons and glia cells are affected.
As next step they compare this with the cell-cluster changes during aging and compare both types of analysis; the investigation here includes the analysis of differentially expressed genes in defined cell clusters. One particularly affected pathway in response to tau is the NFκB signaling pathway. The authors investigate the gene expression changes of the NFκB signaling pathway in the current dataset in more detail. In the last section the authors compare single-cell transcriptomic analyses between fly and human postmortem tissue, showing that the NFκB signaling pathway might be a conserved aspect of neurodegeneration.
The manuscript is overall an elegant example of how single-cell RNAseq can be employed as tool to study the impact of genetic modulators of neurodegeneration (in this case tau) and that it allows direct comparison with human tissues. The results are clean, logically presented and accordingly discussed. It shows that such approaches are indeed powerful for genetic dissection of mechanisms at a descriptive level and opening doors for functional studies.
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