The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson’s disease
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Abstract
Astrocytes are essential cells of the central nervous system, characterized by dynamic relationships with neurons that range from functional metabolic interactions and regulation of neuronal firing activities, to the release of neurotrophic and neuroprotective factors. In Parkinson’s disease (PD), dopaminergic neurons are progressively lost during the course of the disease, but the effects of PD on astrocytes and astrocyte-to-neuron communication remain largely unknown. This study focuses on the effects of the PD-related mutation LRRK2 G2019S in astrocytes generated from patient-derived induced pluripotent stem cells. We report the alteration of extracellular vesicle (EV) biogenesis in astrocytes and identify the abnormal accumulation of key PD-related proteins within multivesicular bodies (MVBs). We found that dopaminergic neurons internalize astrocyte-secreted EVs and that LRRK2 G2019S EVs are abnormally enriched in neurites and fail to provide full neurotrophic support to dopaminergic neurons. Thus, dysfunctional astrocyte-to-neuron communication via altered EV biological properties may participate in the progression of PD.
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Author Response:
Reviewer #1:
In this manuscript titled "The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson's disease", Jacquet and colleagues investigated the role of Parkinsonism gene mutation LRRK2 G2019S in hiPSC-differentiated astrocytes. By isolating extracellular vesicles from ACM and examining astrocytes with various electron microscopy techniques, the authors found that LRRK2 G2019S affects the morphology and distribution of MVBs and the morphology of secreted EVs in hiPSC-differentiated astrocytes. Furthermore, the authors observed that astrocyte-derived EVs can be internalized by dopaminergic neurons and such EVs support neuronal survival. However, LRRK2 G2019S EVs lost the ability of promoting neuronal …
Author Response:
Reviewer #1:
In this manuscript titled "The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson's disease", Jacquet and colleagues investigated the role of Parkinsonism gene mutation LRRK2 G2019S in hiPSC-differentiated astrocytes. By isolating extracellular vesicles from ACM and examining astrocytes with various electron microscopy techniques, the authors found that LRRK2 G2019S affects the morphology and distribution of MVBs and the morphology of secreted EVs in hiPSC-differentiated astrocytes. Furthermore, the authors observed that astrocyte-derived EVs can be internalized by dopaminergic neurons and such EVs support neuronal survival. However, LRRK2 G2019S EVs lost the ability of promoting neuronal survival. This is an interesting study showing a non-cell autonomous contribution to dopaminergic neuron loss in PD.
The proposed idea of how LRRK2 G2019S dysregulates EV-mediated astrocyte-to-neuron communication is novel and exciting. However, the authors present some conflicting data that is not addressed during the discussion: they first conclude upregulated exosome biogenesis by RNAseq in G2019S vs WT astrocytes, but later show a decrease in the number of <120nm particles in G2019S mutants suggesting a decrease in the classical exosome-sized vesicle secreted compared to WT. Lastly, their MVB images show less CD63 gold particles in G2019S compared to WT control (though this was not quantified). Do the authors suggest and increase or decrease in exosome biogenesis in G2019S mutants? How do they reconcile these seemingly contradicting data? Several experiments, controls and additional analyses are needed to fully demonstrate the validity of the proposed mechanism.
The RNA-sequencing data of LRRK2 G2019S astrocytes showed an enrichment in genes associated with the “extracellular exosome” gene ontology term but not with the MVB/EV trafficking or secretion pathways. While we found CD82 and Rab27b to be upregulated, the classical biogenesis markers of MVB/EV trafficking and secretion (e.g. VTA1, VPS4, ALIX) were not dysregulated. Instead, the gene list shows an overwhelming dysregulation of genes coding for EV-enclosed proteins which do not have known roles in MVB/EV biogenesis or function (we now discuss this point in the main text, see highlights in italics below). As a result, we do not believe that exosome biogenesis is upregulated but instead propose the working hypothesis that the EV pathway may contribute to LRRK2 G2019S astrocyte dysfunction. To complement the sequencing data, our study provides a characterization of this pathway by (i) describing the cellular distribution of CD63+ structures in astrocytes, (ii) measuring the size of secreted EVs, and (iii) analyzing the neurotrophic potential of control and LRRK2 G2019S astrocyte-secreted EVs. We have not characterized the cellular biology of exosome/EV biogenesis in depth, and we do not propose a mechanism by which the LRRK2 G2019S mutation dysregulates these pathways. These questions are beyond the scope of our study, which is focused on the role of astrocytes in neurodegeneration.
The reviewer also referred to the CD63 immunogold staining used in Figures 4C and 6A to localize MVBs. After careful quantification of the number of CD63 gold particles in WT and LRRK2 G2019S MVBs, we conclude that there are no differences between the two genotypes and we apologize for selecting non-representative images. We have now replaced these with representative images. Regarding the shift in the size of WT vs. LRRK2 G2019S vesicles, we complemented our cryo-EM analysis with new data generated using Nanoparticle Tracking Analysis (NTA) (Figure 3C,D). The NTA analysis enabled the quantification of a greater number of particles, and we found that both WT and LRRK2 G2019S astrocytes secrete a significant number of particles in the 0-120 nm range. The cryo-EM data suggested that mutant astrocytes secreted fewer particles in this size range, but this is not observed in the NTA analysis. This discrepancy could be explained by the following: (i) in contrast to cryo-EM, NTA does not distinguish EVs from cell debris, which could bias the quantification and increase the number of small particles quantified (Noble et al., 2020), and (ii) studies showed that the size distributions between NTA and cryo-EM differ, the latter enabling the identification of larger particles (Noble et al., 2020). These two techniques are therefore complementary in the study of secreted EV and our manuscript now presents data generated using these two approaches (Figure 3C-G) (see italicised text below).
Results
Expression of exosome components in iPSC-derived astrocytes is altered by the LRRK2 G2019S mutation Gene ontology (GO) analysis revealed that components of the extracellular compartment are up-regulated in LRRK2 G2019S astrocytes – these include GO terms corresponding to the extracellular region, extracellular matrix and extracellular exosomes (Figure 1D,F). The exosome component is one of the most significantly up-regulated GO terms in both isogenic and non-isogenic astrocytes, and is comprised of a total of 67 (isogenic pair) or 95 (non isogenic pair) genes (Supplementary Tables 1 and 2). The large majority (~ 98 %) of these gene products are described to be enclosed in exosomes (e.g. CBR1) but do not perform specific functions related to EV formation or secretion. Only a few genes are associated with exosome biogenesis (e.g. CD82) and trafficking (e.g. Rab27b) (Andreu & Yanez-Mo, 2014; Chiasserini et al., 2014; Ostrowski et al., 2010) and we did not detect differences in the expression of canonical factors that regulate MVB formation (e.g. VTA1, VPS4 or ALIX).
Profiling WT and LRRK2 G2019S EVs secreted by iPSC-derived astrocytes
The astrocyte-derived EV pellet is enriched in exosomes, as demonstrated by the expression of 8 exosomal markers and the absence of cellular contamination (Supplementary Figure 3D). NTA quantification showed that the number of secreted EVs does not differ between LRRK2 G2019S and isogenic control (Figure 3C), and it appears that LRRK2 G2019S particles have a slightly different size distribution compared to WT particles (Figure 3D). It should be noted that TEM and NTA are methods traditionally used to estimate the size distribution of EVs, but their accuracy is often challenged by sample processing artifacts and technical biases (Pegtel & Gould, 2019). To overcome these limitations, we complemented the NTA results with cryo-EM analysis of the size of EVs secreted by WT and LRRK2 G2019S isogenic astrocytes. EVs mostly displayed a circular morphology (as opposed to the cup-shaped morphology observed by TEM) (Figure 3E), but a variety of other shapes were also observed (Supplementary Figure 3E). Cryo-EM analysis confirmed that WT astrocyte-secreted EVs display a large range of sizes, from 80 nm to greater than 600 nm in diameter, with differences between WT and mutant populations (Figure 3F). The cryo-EM data suggested that mutant astrocytes secreted fewer particles in the 0-120 nm size range, and the discrepancy with the NTA results could be explained by the following: (i) in contrast to cryo-EM, NTA does not discriminate EVs from cell debris, which could bias the quantification and increase the number of small particles quantified (Noble et al., 2020), and (ii) studies showed that the size distributions between NTA and cryo EM differ, the latter enabling the identification of larger particles (Noble et al., 2020). However, cryo-EM is a low throughput methodology that limits data collection to a small sample size and has therefore a lower statistical power than NTA. Quantification of the number of simple vs. multiple EV structures did not reveal differences between the two lines, and represent up to 16% of the EV population (Figure 3G). We then sought to complement our EV profiling experiments with an analysis of secreted CD63+ particles, which form one of the known exosomal sub-populations. We previously showed that WT and LRRK2 G2019S MVBs contain similar levels of the CD63 tetraspanin (Figure 2E, Supplementary Figure 3A,B), and an ELISAbased quantification confirmed that the number of CD63+EVs remained unchanged between the two genotypes (Supplementary Figure 3F). We conclude from these results that the total number and morphology of EVs produced by WT and LRRK2 G2019S astrocytes are similar, but mutant EVs may have a different size distribution compared to WT vesicles.
Major concerns:
- In figure 1 A authors demonstrate iPSC-derived astrocytes characterization. Since there is no one unified and validated method for astrocytes differentiation, there is a need for more accurate characterization of iPSC-derived astrocytes. Authors should demonstrate the percentage of cells positive to astrocytic markers and to prove that obtained astrocytes are functional (able to promote synaptogenesis and uptake glutamate). I would also recommend analyzing the iPSC-derived astrocyte cultures for expression of more specific astrocytic markers as GLT1, SOX9 in addition to those which have been analyzed. Moreover, it is highly important to know what is the proportion of astrocytes derived from LRRK2 G2019S line and its isogenic control in order to be able to compare their effect on neurons.
We thank the reviewer for these suggestions. It is true that there exist many different astrocyte differentiation protocols, and this study uses a protocol developed by TCW et al. that has been further optimized by our lab to derive astrocytes from a midbrain-patterned population of neural progenitor cells (NPCs) (de Rus Jacquet, 2019; Tcw et al., 2017). The protocol is published, and shows that these astrocytes are functional – they respond to inflammatory factors and alter secretion of the IL-6 cytokine. Furthermore, Supplementary Figure 2D shows a whole transcriptome analysis (by RNA-seq) of the cell populations produced for this study and demonstrates that iPSC-derived astrocytes cluster with human primary midbrain astrocytes and away from iPSCs or NPCs in an unsupervised cluster analysis. However, we agree that in-depth characterization of iPSC-derived astrocytes is essential, and the updated manuscript now shows that (i) the astrocyte differentiation protocol yields 100 % GFAP+ cells with both WT and mutant lines (Supplementary Figure 2B), (ii) expression of six astrocyte markers (GLT1, SOX9, APOE, BHLHE41, CD44, GLUD1) (Supplementary Figure 2Aii, B), as well as (iii) transient intracellular calcium signaling (Supplementary Figure 2E), and (iv) synaptosome uptake (Supplementary Figure 2F) in both WT and LRRK2 G2019S astrocytes. We also updated the text as follows (italicised):
Results section
Midbrain-patterned NPCs carrying the LRRK2 G2019S mutation or its isogenic control were differentiated into astrocytes as described previously (de Rus Jacquet, 2019; Tcw et al., 2017). As expected, astrocytes expressed the markers GFAP, vimentin, and CD44 as demonstrated by immunofluorescence (Figure 1A) and flow cytometry analyses (Supplementary Figure 2A). Differentiation was equally effective in WT and LRRK2 G2019S cells, with 100 % of the differentiated astrocytes expressing GFAP (Supplementary Figure 2Bi). To further demonstrate the successful differentiation of iPSCs into astrocytes, we analyzed gene expression using RNA-sequencing analysis (RNA-seq), including primary human midbrain astrocyte samples in the RNA-seq study to serve as a positive control for human astrocyte identity. iPSC-derived and human midbrain astrocytes expressed similar levels of genes markers of astrocyte identity, including SOX9 and GLUT1 (Supplementary Figure 2B). In addition, principal component and unsupervised cluster analyses separated undifferentiated iPSCs, iPSC-derived NPCs and iPSC-derived astrocytes into independent clusters, demonstrating that our differentiation strategy produces distinct cell types (Supplementary Figure 2C-D). Importantly, the transcriptome of iPSC-derived astrocytes showed more similarities to fetal human midbrain astrocytes than to NPCs or iPSCs, further validating their astrocyte identity (Supplementary Figure 2D). Lastly, control and LRRK2 G2019S astrocytes showed classic astrocytic functional phenotypes such as spontaneeous and transient calcium signaling and synaptosome uptake (Supplementary Figure 2E-F).
- In Figure 1, the authors found a significant upregulation of exosome components in astrocytes, demonstrating an important role of LRRK2 G2019S in EV signaling pathway. In the discussion, the authors briefly mentioned 'sub-populations of CD63- EVs may be differentially secreted in mutant astrocytes'. Since the authors have obtained the RNA-seq data, it would be nice to dig deep into the data and comment on potential EV sub-populations which can be differentially secreted. This information can be very beneficial for follow-up studies in the PD and LRRK2 field. Furthermore, the authors should assess the expression of Rab27a and CD82 in WT and LRRK2 G2019S astrocytes by western blots to verify RT-qPCR data. Furthermore, the authors should present specifically exosome biogenesis or secretion genes are altered to provide further insight into the stage of exosome biogenesis that is affected (ESCRT0-3, VPS4, ALIX, etc).
In the first comment, the reviewer refers to the observation that the number of total and CD63-positive EVs secreted by astrocytes is unchanged between the WT and LRRK2 G2019S genotypes. The classification of different EV sub-populations based on marker proteins is an evolving field of research, and an important study by Kowal et al. defined generic and sub population-specific EV markers (Kowal et al., 2016). Our RNA-seq dataset revealed five upregulated genes identified in the Kowal study, namely actin, GAPDH, actinin, complement and fibronectin, but unfortunately there is no clear pattern correlated with specific EV sub populations. For example, actin and GAPDH are two upregulated proteins that can be found in multiple types of EVs, actinin is enriched in large and medium-sized EVs, and complement and fibronectin are enriched in high density but small EVs (Kowal et al., 2016). The majority of dysregulated genes identified in our sequencing experiment are not proteins classically used to categorize EVs, so unfortunately our data does not allow us to address the reviewer’s question. To make sure that the data is readily accessible to the scientific community, we have prepared a supplementary table with a list of extracellular exosome-related genes identified in the RNA sequencing study. To respond to the reviewer’s comment on a specific stage of EV biogenesis/secretion altered in LRRK2 G2019S, the sequencing data presented in this manuscript does not allow to conclude that there is such a dysregulation. Our gene list corresponding to the “extracellular exosome” gene ontology term contains a large majority of genes coding for proteins enclosed within EVs that do not play a role in biogenesis/secretion. For example, the gene list does not contain ESCRT0-3, VPS4, ALIX or other classical markers involved in EV biogenesis and we cannot conclude anything about the alteration of MVB/EV biogenesis or defects in specific stages of MVB trafficking or EV secretion. In addition, we thank the reviewer for suggesting the validation of RT-qPCR data by western blot. The purpose of the RT-qPCR experiment was to validate the gene expression data collected by RNA-seq. Given that our objective was to confirm gene expression levels, and that we do not further study CD82 and Rab27b, we think that collecting protein expression levels is not necessary in the context of this study.
We agree with the reviewer’s suggestion, and we added images showing the subcellular localization of CD63 in both WT and LRRK2 G2019S MVBs by immunogold staining (Figure 2E). Validation experiments available in Figure 1A and Supplementary Figure 2A and 2B confirm that our astrocytes express CD44 as well as markers of mature astrocytes (BHLHE41, SOX9, GLUT1, APOE, GLUD1). The reason for showing CD44 instead of a more mature marker such as GFAP in Figure 2 of the manuscript is because CD44 is a membrane marker, and it therefore enables a clear visualization of the astrocyte surface area. We also note that, as shown in Supplementary Figure 2Aii, iPSC-derived and human fetal astrocytes express CD44, but iPSCs and NPCs do not significantly express this marker gene. In addition, as suggested by the reviewer, we added information related to MVB maturation in the introduction and the new text reads as follows (changes in italics):
Introduction section The sorting and loading of exosome cargo is an active and regulated process (Temoche-Diaz et al., 2019), and the regulatory factors involved in EV/exosome biogenesis are just beginning to be identified. Among the well-known factors, Rab proteins are essential mediators of MVB trafficking and they regulate endosomal MVB formation/maturation as well as microvesicle budding directly from the plasma membrane (Pegtel & Gould, 2019; T. Wang et al., 2014). In addition, membrane remodeling is an essential aspect of MVB/EV formation that appears to be regulated, at least in part, by the endosomal sorting complex required for transport (ESCRT) machinery (Pegtel & Gould, 2019; Schoneberg, Lee, Iwasa, & Hurley, 2017).
- In Figure 2A and B, data shows that both WT and LRRK2 G2019S astrocytes produce MVBs and MVBs in LRRK2 G2019S astrocytes is smaller than in WT astrocytes. In Figure 2E, the authors showed the abundance of CD63 localized within MVBs in WT astrocytes but did not show the CD63 localization in MVBs in G2019S astrocytes. However, it is important to show CD63 localization in MVBs in G2019S astrocytes to fully support the conclusion that CE63+ MVBs are present in LRRK2 G2019S astrocytes. In addition, CD44 is a marker for astrocyte-restricted precursor cells. Although CD44+ positive cells are committed to give rise to astrocytes, it is crucial to include another astrocyte marker to ensure these cells are indeed mature astrocytes. -Related, authors should consider citing some of the MVB maturation literature to guide the readers.
We agree with the reviewer’s suggestion, and we added images showing the subcellular localization of CD63 in both WT and LRRK2 G2019S MVBs by immunogold staining (Figure 2E). Validation experiments available in Figure 1A and Supplementary Figure 2A and 2B confirm that our astrocytes express CD44 as well as markers of mature astrocytes (BHLHE41, SOX9, GLUT1, APOE, GLUD1). The reason for showing CD44 instead of a more mature marker such as GFAP in Figure 2 of the manuscript is because CD44 is a membrane marker, and it therefore enables a clear visualization of the astrocyte surface area. We also note that, as shown in Supplementary Figure 2Aii, iPSC-derived and human fetal astrocytes express CD44, but iPSCs and NPCs do not significantly express this marker gene. In addition, as suggested by the reviewer, we added information related to MVB maturation in the introduction and the new text reads as follows (changes in italics):
Introduction section
The sorting and loading of exosome cargo is an active and regulated process (Temoche-Diaz et al., 2019), and the regulatory factors involved in EV/exosome biogenesis are just beginning to be identified. Among the well-known factors, Rab proteins are essential mediators of MVB trafficking and they regulate endosomal MVB formation/maturation as well as microvesicle budding directly from the plasma membrane (Pegtel & Gould, 2019; T. Wang et al., 2014). In addition, membrane remodeling is an essential aspect of MVB/EV formation that appears to be regulated, at least in part, by the endosomal sorting complex required for transport (ESCRT) machinery (Pegtel & Gould, 2019; Schoneberg, Lee, Iwasa, & Hurley, 2017).
- In Figure 3, it is impressive that the authors are able to image EVs using cyro-EM approach and analyze their sizes. The authors also observed different shapes of EVs. Is there any shape difference between WT EVs and G2019S EVs? Is there a way that the authors could categorize these shapes and do a detailed analysis in EV shapes? Also, In Figure 3D, both WT EV and G2019S EV images should present side by side for comparison. -Related, the size frequencies of EVs presented suggest a difference in the types of EV's released. Interestingly, exosomes are classically known to range from ~50-120nm and this population is significantly decreased in G2019S compared to WT. What does this suggest?
As suggested by the reviewer, we classified the two main EV shapes as “simple” and “multiple” EVs, and found no quantitative differences between WT and LRRK2 G2019S. This new data and side-by-side images of WT and LRRK2 G2019S EV images are available in Figure 3E-G, and the text has been updated accordingly (see text in italics below). One of the observations of Figure 3 is that there exist genotype-specific differences in the size distribution of EVs, which suggests that different classes of vesicles may be preferably produced by WT vs. LRRK2 G2019S astrocytes. This could be the result of differences in dynamics related to cargo loading, or a shift from MVB-released exosomes to membrane budding and microvesicle production. These observations are of great interest and we added a short discussion (in italics below) but they are beyond the scope of this study focused on EV neurotrophic properties, and we do not currently have evidence to support these hypotheses.
Results - LRRK2 G2019S affects the size of EVs secreted by iPSC-derived astrocytes
EVs mostly displayed a circular morphology (as opposed to the cup-shaped morphology observed by TEM) (Figure 3E), but a variety of other shapes were also observed (Supplementary Figure 3C). (…) Quantification of the number of simple vs. multiple EV structures did not differ between the two lines, and represent up to 16 % of the EV population (Figure 3G).
Discussion – Dysregulation of iPSC-derived astrocyte-mediated EV biogenesis in Parkinson’s disease
The observation that LRRK2 G2019S MVBs are less frequently located in the perinuclear area suggests that they may spend less time loading cargo at the Trans-Golgi network, which could in turn produce smaller MVBs and EVs with a different size range compared to WT (Edgar, Eden, & Futter, 2014; Pegtel & Gould, 2019). We did not observe a difference in the number of secreted EVs (total and CD63+ subpopulation) between WT and LRRK2 G2019S astrocytes (Figure 3C,H), suggesting that the secretion of at least one population of EVs is independent of the astrocyte genotype.
- In figure 3c, SBI ELISA claims to quantify CD63+ vesicles, the authors should present more standardized particle quantification data (either by CD63 FACs for isolated EVs in WT vs G2019S or ZetaView/QNano particle tracking). The authors should also directly quantify the total number of EVs secreted in WT vs G2019S conditions (not only CD63+).
The updated manuscript now contains the NTA analysis of WT and LRRK2 G2019S EVs (Figure 3C,D) which provides the total number of EVs secreted by WT and LRRK2 G2019S astrocytes.
- In Figure 4, the authors quantify LRRK2+/CD63+ particles by imaging. Importantly, it appears that there are less CD63 "large gold" particles in MVB of G2019S compared to control. This CD63 baseline quantification in MVB of WT vs. G2019S should be presented in this figure. These data are not convincing and should be quantified by FACS in secreted EV. Supplementary figure 3 should be brought into this figure.
As suggested by the reviewer, we quantified the number of CD63 large gold particles per MVB in WT and LRRK2 G2019S lines (Supplementary Figure 3A,B), and we re-introduced Supplementary Figure 3 into the main text (Figure 4E). We also updated the text (see in italics below). Additionally, we present extensive quantification of LRRK2 levels in MVBs and secreted EVs via imaging and biochemical analysis (ELISA), two different but complementary analytical methods.
Results - LRRK2 G2019S affects the size of MVBs in iPSC-derived astrocytes
Tetraspanins are transmembrane proteins, and the tetraspanin CD63 is enriched in exosomes and widely used as an exosomal marker (Escola et al., 1998; Men et al., 2019). However, cell type specificities in the expression of exosomal markers such as CD63 have been documented (Jorgensen et al., 2013; Yoshioka et al., 2013). We therefore confirmed the presence of CD63- positive MVBs in iPSC-derived isogenic astrocytes by immunofluorescence (Figure 2D) and immunogold electron microscopy (IEM) (Figure 2E). Analysis of IEM images showed an abundance and similar levels of CD63 localized within MVBs in WT and LRRK2 G2019S astrocytes (Figure 2E, Supplementary Figure 3A,B), confirming that CD63 can be used as a marker of MVBs and exosomes in iPSC-derived astrocytes.
- In Figure 5, using CD63 as a MVB marker is not the most accurate approach. ESCRT markers should be co-stained with these experiments to truly show MVB localization (CD63 can localize to MVBs but is known to have a wider distribution throughout the cell compared to TSG1010 or other ESCRT complex proteins). Additionally, the authors must show their Supplemental Figure 3 ELISA quantification of p-aSyn in this main figure, and comment on why they conclude higher p-aSyn content in MVBs based on their IEM but then find no differences in aSyn in secreted EVs in WT vs. G2019S by ELISA.
We thank the reviewer for the suggestion to use ESCRT proteins as MVB markers. We decided to use CD63 because it is recognized in the literature as an MVB and EV marker (Beatty, 2008; Edgar et al., 2014), and we now refer to these two studies in the manuscript to support this choice (see text in italics below). Using ESCRT complex proteins as MVB markers is an interesting alternative, but we note that proteins associated with this complex are also found to regulate other biological processes such as autophagy (Takahashi et al., 2018) and plasma membrane repair (Jimenez et al., 2014), and so they can co-localize to non-MVB structures (e.g. autophagosomes or plasma membrane). Similarly, TSG101 can also localize to non-MVB structures such as the nucleus and Golgi complex (Xie, Li, & Cohen, 1998), and also lipid droplet (LD) membranes where it promotes LD-mitochondria contact (J. Wang et al., 2021). As suggested by the reviewer, Supplemental Figure 3 has been re-introduced into the main text (Figure 6C). Regarding αSyn, the immunogold staining specifically detects the phosphorylated form of αSyn (p-αSyn), while the ELISA detects all forms of αSyn (total αSyn). We observed increased p-αSyn in LRRK2 G2019S MVBs, but similar levels of total αSyn in WT vs LRRK2 G2019S EVs. This observation suggests that the phosphorylated form of αSyn, but not the total amount of αSyn, is affected by the experimental conditions. The text has been updated and reads as follows (changes in italics).
Results - LRRK2 is associated with MVBs and EVs in iPSC-derived astrocytes
In light of our observations that mutations in LRRK2 result in altered astrocytic MVB and EV phenotypes, we asked if LRRK2 is directly associated with MVBs in astrocytes and if this association is altered by the LRRK2 G2019S mutation. We analyzed and quantified the co localization of LRRK2 with CD63 (Figure 4A), a marker for MVBs (Beatty, 2008; Edgar et al., 2014), and found that the proportion of LRRK2+ /CD63+ structures remains unchanged between WT and LRRK2 G2019S isogenic astrocytes (Figure 4B).
Results - The LRRK2 G2019S mutation increases the amount of phosphorylated alpha synuclein (Ser129) in MVBs
Since the MVB/EV secretion pathway is altered in our LRRK2 G2019S model of PD, we reasoned that mutant astrocytes might produce αSyn-enriched EVs by accumulating the protein in its native or phosphorylated form in MVBs or EVs. IEM analysis revealed an abundance of p-αSyn (small gold) inside and in the vicinity of MVBs of LRRK2 G2019S iPSC-derived astrocytes, but not isogenic control astrocytes (Figure 6A). We observed that 55 % of the CD63+ (large gold) MVBs in LRRK2 G2019S astrocytes are also p-αSyn+ (small gold), compared to only 16 % in WT MVBs. LRRK2 G2019S astrocytes contained on average 1.3 p-αSyn small gold particles per MVB compared to only 0.16 small gold particles in isogenic control astrocytes, and MVB populations containing more than 3 p-αSyn small gold particles were only observed in LRRK2 G2019S astrocytes (Figure 6B). When we analyzed the content of EVs by ELISA, we found that total αSyn levels (phosphorylated and non-phosphorylated) in EV-enriched fractions are similar between isogenic controls and LRRK2 G2019S (Figure 6C). These results suggest that astrocytes secrete αSyn-containing EVs, and the LRRK2 G2019S mutation appears to alter the ratio of p-αSyn/total αSyn in MVB-related astrocyte secretory pathways.
- In figure 6, it is even more clear that there is a stark difference between the CD63 presence in/near MVBs between WT and G2019S conditions. Since the authors normalize several pieces of data to CD63 (MVB localization, LRRK2 co-localization, etc), it is critical to quantify the number of baseline CD63 gold particles in MVBs in WT vs G2019S.
After careful quantification of the number of CD63 gold particles in WT and LRRK2 G2019S MVBs (available in Supplementary Figure 3A,B), we conclude that there are no significant differences between the two genotypes, and the MVB images initially selected in Figure 6 are not representative. We therefore replaced Figure 6A with new images.
- In Figure 7, the authors used the co-culture of astrocytes and neurons to assess astrocyte-derived EV uptake by dopaminergic neurons. Although 3D reconstitution of neurons and exosomes can be precise, the data may not be 100% clean. It would be better if the authors collect ACM containing EV fraction from WT astrocyte and G2019S astrocytes and then incubate dopaminergic neurons with ACM containing EV fraction. In this way, only dopaminergic neurons are in the culture and there will be no CD63-GFP expressed astrocytes to contaminate the CD63-GFP signal in neurons.
We understand the concerns raised by the reviewer, and we can ensure that state-of-the-art imaging technologies and image post-processing techniques have been used to prevent astrocytic CD63 signal from contaminating the neuronal signal. We performed confocal microscopy with a 63X oil objective lens (numerical aperture = 1.4), and the images were processed with a Gaussian Filter (0.18 μm filter width) to reduce background noise in the MAP2 channel, and deconvolved (10 iteration) to enhance confocal image resolution in the CD63 channel. Furthermore, CD63-positive structures were detected with background subtraction enabled.
- In Figure 9, the authors must show their ACM control. They show untreated, EV-free, and EV-rich ACM, but do not show unmanipulated ACM control.
The results of dendrite length analysis for unmanipulated ACM was initially available in Figures 8E and 8F. For clarity, we prepared a new Figure 9 that shows treatment with unmanipulated ACM, EV-free ACM, and EV-enriched fractions.
Reviewer #2:
In this manuscript by de Rus Jacquet et al., authors present an interesting study to detect changes in extracellular vesicles in human PD patient derived iPSC-derived astrocytes carrying the LRRK2 G2019S mutation. Isogenic gene corrected iPSCs were used as controls in all experiments. Authors first performed RNA-Seq for global gene expression changes between G2019S and "WT" gene corrected astrocytes. GO analysis showed an upregulation of extracellular compartments (including exosome compartments) in LRRK2 astrocytes. Subsequent experiments focusing on extracellular vesicles (EVs) and multivesicular bodies (MVBs), showed specific differences of MVB area and the size of secreted EVs. Secreted EVs from G2019S astrocytes also contained more LRRK2 particles and G2019S EVs contained more phosphorylated aSyn particles. Co-culture of LRRK2 astrocytes with human dopamine neurons showed accumulation of CD63+ exosomes in neurites, compared to co-culture with WT astrocytes. Co-culture with LRRK2 astrocytes decreased viability of TH+ neurons and LRRK2 dendrites/neurites were also shorter. These co-culture findings were replicated using EV-enriched conditioned media. Finally, authors showed that the trophic effect of astrocytes on neurons was due both to soluble factors released into the media, and production and release of EVs. Overall, this is a well-written and systematically performed study. This reviewer has several comments as detailed below.
- Based on their data, authors conclude that astrocyte-to-neuron signaling and trophic support mediated by EVs is disrupted in LRRK2 G2019S astrocytes. Have authors measured the differences in trophic factors released by LRRK2 astrocytes in EVs and in conditioned media?
This is an important question, and we have not measured the levels of various neurotrophic factors in the medium. We concluded that LRRK2 G2019S astrocytes failed to secrete neurotrophic factors based on the neuron viability data. Healthy neurons cultured with disease astrocytes displayed dendrite shortening equivalent to that of neurons cultured in basal medium lacking neurotrophic factors. Furthermore, the morphological alterations occurred over a long period of time (2 weeks) and did not recapitulate the rapid and high level of neuron death and neurite fragmentation typically observed as a result of exposure to neurotoxins (Liddelow et al., 2017). However, we performed a new analysis of our RNA-seq data and identified dysregulated trophic processes of interest in LRRK2 G2019S astrocytes.
- Authors differentiate cells (astrocytes and neurons) from midbrain lineage NPCs. The data show convincing effects of the LRRK2 derived astrocytes on neurons, but one question is whether this is specific to dopaminergic cells. Would this genotype specific effect also be expected in other lineages, e.g. cortical neurons? Authors should discuss this point.
The reviewer is making an excellent point. We prepared mouse primary midbrain cultures, and co-cultured WT midbrain neurons with WT or LRRK2 G2019S astrocytes. We found that the survival of WT midbrain dopaminergic neurons was significantly affected by LRRK2 G2019S astrocytes, but the viability of non-dopaminergic midbrain neurons was not changed when co cultured with WT or disease astrocytes. A previous study by di Domenico et al. also showed that dopaminergic neurons are more sensitive to the effect of LRRK2 G2019S astrocytes compared to non-dopaminergic cell types (di Domenico et al., 2019).
- Prior work has demonstrated reductions in neurite length in neurons derived from LRRK2 G2019S iPSCs (not specific to dopaminergic neurons in LRRK2 cells) (for example Reinhard et al 2013). It is curious that the LRRK2 G2019S mutation itself can cause such a phenotype in neurons mono-cultures, and as shown in the current study, that LRRK2 G2019S astrocytes also induce a similar effect on WT neurons in co-culture. Can authors expand on this point in the Discussion?
We thank the reviewer for this question, and we added a new point of discussion in our manuscript, which reads as follows (changes in italics):
Evidence from this study and previous reports indicates that the LRRK2 G2019S mutation affects neurons through a variety of mechanisms. Here, we show a non-cell autonomous effect on neuronal viability via impairment of essential astrocyte-to-neuron trophic signaling, but the LRRK2 G2019S mutation can also mediate cell-autonomous dopaminergic neurodegeneration (Reinhardt et al., 2013). These observations support the idea that the LRRK2 kinase may be involved in a large number of pathways essential to maintain cellular function, cell-cell communication and brain homeostasis, and disruption of LRRK2 in one cell type has cascading effects on other neighboring cell types. In conclusion, our study suggests a novel effect of the PD-related mutation LRRK2 G2019S in astrocytes, and in their ability to support dopaminergic neurons. This study supports a model of astrocyte-to-neuron signaling and trophic support mediated by EVs, and dysregulation of this pathway contributes to LRRK2 G2019S astrocyte mediated dopaminergic neuron atrophy.
- Authors should provide data on % dopaminergic neurons generated in the cultures.
We agree that this is important information, and we updated the latest version of the manuscript with this information (see below in italics). We estimate that the neuron cultures consist of 50 to 70 % dopaminergic neurons, and they are depleted of non-neuronal cells as explained in Material and Methods.
Material and Methods - Preparation and culture of iPSC-derived NPCs, dopaminergic neurons and astrocytes
To isolate a pure neuronal population, the cells were harvested in Accumax medium, diluted to a density of 1 × 106 cells in 100 µl MACS buffer (HBSS, 1 % v/v sodium pyruvate, 1 % GlutaMAX, 100 U/ml penicillin/streptomycin, 1 % HEPES, 0.5 % bovine serum albumin) supplemented with CD133 antibody (5 % v/v, BD Biosciences, San Jose, CA, cat. # 566596), and the CD133+ NPCs were depleted by magnetic-activated cell sorting (MACS) using an LD depletion column (Miltenyi Biotech, San Diego, CA), as described previously (de Rus Jacquet, 2019). The final cultures are depleted of non-neuronal cells and contain approximately 70 % dopaminergic neurons, the remaining neurons consisting of uncharacterized non-dopaminergic populations.
- p7. Authors refer to phosphorylated a-synuclein as accelerating PD pathogenesis, but the references cited do not show this. In fact, Gorbatyuk et al 2008, showed that overexpression of S129 with constitutive phosphorylation eliminated a-synuclein induced nigrostriatal degeneration. The Fujiwara et al 2002 reference showed the presence of phospho a-syunclein in Lewy bodies and neurites. Authors should revise their statement that phospho a-synuclein is associated with accelerated pathology.
The reviewer is correct. We meant to highlight that there is a correlation between phosphorylated αSyn levels and PD pathogenesis, not that phosphorylated αSyn causes an acceleration of PD pathogenesis. We rephrased the sentence as follows, and replaced the study by Gorbatyuk et al. with a study by Anderson et al. that shows presence of phosphorylated αSyn in Lewy bodies (new text in italics):
EVs isolated from the biofluids of PD patients exhibit accumulation of αSyn (Lamontagne Proulx et al., 2019; Shi et al., 2014; Zhao et al., 2018), a hallmark protein whose phosphorylation at the serine residue 129 (p-αSyn) is correlated with PD pathogenesis (Anderson et al., 2006; Fujiwara et al., 2002).
- Please provide details on the number of iPSC lines used for these experiments.
Experiments in the first version of this manuscript were performed using a single LRRK2 G2019S iPSC line and its gene-corrected control. The manuscript now presents the results collected using a second, independent non-isogenic iPSC line, as well as mouse primary cultures.
- Clarify whether the WT neurons used for co-culture were derived from the isogenic human neurons?
We confirm that the WT neurons used for co-culture experiments were derived from isogenic controls. We added subtitles to our figures to clarify when data show results from isogenic or non-isogenic iPSC-derived cells.
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###Reviewer #2:
In this manuscript by de Rus Jacquet et al., authors present an interesting study to detect changes in extracellular vesicles in human PD patient derived iPSC-derived astrocytes carrying the LRRK2 G2019S mutation. Isogenic gene corrected iPSCs were used as controls in all experiments. Authors first performed RNA-Seq for global gene expression changes between G2019S and "WT" gene corrected astrocytes. GO analysis showed an upregulation of extracellular compartments (including exosome compartments) in LRRK2 astrocytes. Subsequent experiments focusing on extracellular vesicles (EVs) and multivesicular bodies (MVBs), showed specific differences of MVB area and the size of secreted EVs. Secreted EVs from G2019S astrocytes also contained more LRRK2 particles and G2019S EVs contained more phosphorylated aSyn particles. …
###Reviewer #2:
In this manuscript by de Rus Jacquet et al., authors present an interesting study to detect changes in extracellular vesicles in human PD patient derived iPSC-derived astrocytes carrying the LRRK2 G2019S mutation. Isogenic gene corrected iPSCs were used as controls in all experiments. Authors first performed RNA-Seq for global gene expression changes between G2019S and "WT" gene corrected astrocytes. GO analysis showed an upregulation of extracellular compartments (including exosome compartments) in LRRK2 astrocytes. Subsequent experiments focusing on extracellular vesicles (EVs) and multivesicular bodies (MVBs), showed specific differences of MVB area and the size of secreted EVs. Secreted EVs from G2019S astrocytes also contained more LRRK2 particles and G2019S EVs contained more phosphorylated aSyn particles. Co-culture of LRRK2 astrocytes with human dopamine neurons showed accumulation of CD63+ exosomes in neurites, compared to co-culture with WT astrocytes. Co-culture with LRRK2 astrocytes decreased viability of TH+ neurons and LRRK2 dendrites/neurites were also shorter. These co-culture findings were replicated using EV-enriched conditioned media. Finally, authors showed that the trophic effect of astrocytes on neurons was due both to soluble factors released into the media, and production and release of EVs. Overall, this is a well-written and systematically performed study. This reviewer has several comments as detailed below.
Based on their data, authors conclude that astrocyte-to-neuron signaling and trophic support mediated by EVs is disrupted in LRRK2 G2019S astrocytes. Have authors measured the differences in trophic factors released by LRRK2 astrocytes in EVs and in conditioned media?
Authors differentiate cells (astrocytes and neurons) from midbrain lineage NPCs. The data show convincing effects of the LRRK2 derived astrocytes on neurons, but one question is whether this is specific to dopaminergic cells. Would this genotype specific effect also be expected in other lineages, e.g. cortical neurons? Authors should discuss this point.
Prior work has demonstrated reductions in neurite length in neurons derived from LRRK2 G2019S iPSCs (not specific to dopaminergic neurons in LRRK2 cells) (for example Reinhard et al 2013). It is curious that the LRRK2 G2019S mutation itself can cause such a phenotype in neurons mono-cultures, and as shown in the current study, that LRRK2 G2019S astrocytes also induce a similar effect on WT neurons in co-culture. Can authors expand on this point in the Discussion?
Authors should provide data on % dopaminergic neurons generated in the cultures.
p7. Authors refer to phosphorylated a-synuclein as accelerating PD pathogenesis, but the references cited do not show this. In fact, Gorbatyuk et al 2008, showed that overexpression of S129 with constitutive phosphorylation eliminated a-synuclein induced nigrostriatal degeneration. The Fujiwara et al 2002 reference showed the presence of phospho a-syunclein in Lewy bodies and neurites. Authors should revise their statement that phospho a-synuclein is associated with accelerated pathology.
Please provide details on the number of iPSC lines used for these experiments.
Clarify whether the WT neurons used for co-culture were derived from the isogenic human neurons?
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###Reviewer #1:
In this manuscript titled "The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson's disease", Jacquet and colleagues investigated the role of Parkinsonism gene mutation LRRK2 G2019S in hiPSC-differentiated astrocytes. By isolating extracellular vesicles from ACM and examining astrocytes with various electron microscopy techniques, the authors found that LRRK2 G2019S affects the morphology and distribution of MVBs and the morphology of secreted EVs in hiPSC-differentiated astrocytes. Furthermore, the authors observed that astrocyte-derived EVs can be internalized by dopaminergic neurons and such EVs support neuronal survival. However, LRRK2 G2019S EVs lost the ability of promoting neuronal survival. This is an …
###Reviewer #1:
In this manuscript titled "The LRRK2 G2019S mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human iPSC-derived model of Parkinson's disease", Jacquet and colleagues investigated the role of Parkinsonism gene mutation LRRK2 G2019S in hiPSC-differentiated astrocytes. By isolating extracellular vesicles from ACM and examining astrocytes with various electron microscopy techniques, the authors found that LRRK2 G2019S affects the morphology and distribution of MVBs and the morphology of secreted EVs in hiPSC-differentiated astrocytes. Furthermore, the authors observed that astrocyte-derived EVs can be internalized by dopaminergic neurons and such EVs support neuronal survival. However, LRRK2 G2019S EVs lost the ability of promoting neuronal survival. This is an interesting study showing a non-cell autonomous contribution to dopaminergic neuron loss in PD.
The proposed idea of how LRRK2 G2019S dysregulates EV-mediated astrocyte-to-neuron communication is novel and exciting. However, the authors present some conflicting data that is not addressed during the discussion: they first conclude upregulated exosome biogenesis by RNAseq in G2019S vs WT astrocytes, but later show a decrease in the number of <120nm particles in G2019S mutants suggesting a decrease in the classical exosome-sized vesicle secreted compared to WT. Lastly, their MVB images show less CD63 gold particles in G2019S compared to WT control (though this was not quantified). Do the authors suggest and increase or decrease in exosome biogenesis in G2019S mutants? How do they reconcile these seemingly contradicting data? Several experiments, controls and additional analyses are needed to fully demonstrate the validity of the proposed mechanism.
Major concerns:
In figure 1 A authors demonstrate iPSC-derived astrocytes characterization. Since there is no one unified and validated method for astrocytes differentiation, there is a need for more accurate characterization of iPSC-derived astrocytes. Authors should demonstrate the percentage of cells positive to astrocytic markers and to prove that obtained astrocytes are functional (able to promote synaptogenesis and uptake glutamate). I would also recommend analyzing the iPSC-derived astrocyte cultures for expression of more specific astrocytic markers as GLT1, SOX9 in addition to those which have been analyzed. Moreover, it is highly important to know what is the proportion of astrocytes derived from LRRK2 G2019S line and its isogenic control in order to be able to compare their effect on neurons.
In Figure 1, the authors found a significant upregulation of exosome components in astrocytes, demonstrating an important role of LRRK2 G2019S in EV signaling pathway. In the discussion, the authors briefly mentioned 'sub-populations of CD63- EVs may be differentially secreted in mutant astrocytes'. Since the authors have obtained the RNA-seq data, it would be nice to dig deep into the data and comment on potential EV sub-populations which can be differentially secreted. This information can be very beneficial for follow-up studies in the PD and LRRK2 field. Furthermore, the authors should assess the expression of Rab27a and CD82 in WT and LRRK2 G2019S astrocytes by western blots to verify RT-qPCR data. Furthermore, the authors should present specifically exosome biogenesis or secretion genes are altered to provide further insight into the stage of exosome biogenesis that is affected (ESCRT0-3, VPS4, ALIX, etc).
In Figure 2A and B, data shows that both WT and LRRK2 G2019S astrocytes produce MVBs and MVBs in LRRK2 G2019S astrocytes is smaller than in WT astrocytes. In Figure 2E, the authors showed the abundance of CD63 localized within MVBs in WT astrocytes but did not show the CD63 localization in MVBs in G2019S astrocytes. However, it is important to show CD63 localization in MVBs in G2019S astrocytes to fully support the conclusion that CE63+ MVBs are present in LRRK2 G2019S astrocytes. In addition, CD44 is a marker for astrocyte-restricted precursor cells. Although CD44+ positive cells are committed to give rise to astrocytes, it is crucial to include another astrocyte marker to ensure these cells are indeed mature astrocytes. -Related, authors should consider citing some of the MVB maturation literature to guide the readers.
In Figure 3, it is impressive that the authors are able to image EVs using cyro-EM approach and analyze their sizes. The authors also observed different shapes of EVs. Is there any shape difference between WT EVs and G2019S EVs? Is there a way that the authors could categorize these shapes and do a detailed analysis in EV shapes? Also, In Figure 3D, both WT EV and G2019S EV images should present side by side for comparison. -Related, the size frequencies of EVs presented suggest a difference in the types of EV's released. Interestingly, exosomes are classically known to range from ~50-120nm and this population is significantly decreased in G2019S compared to WT. What does this suggest?
In figure 3c, SBI ELISA claims to quantify CD63+ vesicles, the authors should present more standardized particle quantification data (either by CD63 FACs for isolated EVs in WT vs G2019S or ZetaView/QNano particle tracking). The authors should also directly quantify the total number of EVs secreted in WT vs G2019S conditions (not only CD63+).
In Figure 4, the authors quantify LRRK2+/CD63+ particles by imaging. Importantly, it appears that there are less CD63 "large gold" particles in MVB of G2019S compared to control. This CD63 baseline quantification in MVB of WT vs. G2019S should be presented in this figure. These data are not convincing and should be quantified by FACS in secreted EV. Supplementary figure 3 should be brought into this figure.
In Figure 5, using CD63 as a MVB marker is not the most accurate approach. ESCRT markers should be co-stained with these experiments to truly show MVB localization (CD63 can localize to MVBs but is known to have a wider distribution throughout the cell compared to TSG1010 or other ESCRT complex proteins). Additionally, the authors must show their Supplemental Figure 3 ELISA quantification of p-aSyn in this main figure, and comment on why they conclude higher p-aSyn content in MVBs based on their IEM but then find no differences in aSyn in secreted EVs in WT vs. G2019S by ELISA.
In figure 6, it is even more clear that there is a stark difference between the CD63 presence in/near MVBs between WT and G2019S conditions. Since the authors normalize several pieces of data to CD63 (MVB localization, LRRK2 co-localization, etc), it is critical to quantify the number of baseline CD63 gold particles in MVBs in WT vs G2019S.
In Figure 7, the authors used the co-culture of astrocytes and neurons to assess astrocyte-derived EV uptake by dopaminergic neurons. Although 3D reconstitution of neurons and exosomes can be precise, the data may not be 100% clean. It would be better if the authors collect ACM containing EV fraction from WT astrocyte and G2019S astrocytes and then incubate dopaminergic neurons with ACM containing EV fraction. In this way, only dopaminergic neurons are in the culture and there will be no CD63-GFP expressed astrocytes to contaminate the CD63-GFP signal in neurons.
In Figure 9, the authors must show their ACM control. They show untreated, EV-free, and EV-rich ACM, but do not show unmanipulated ACM control.
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##Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
###Summary:
The discussion between reviewers and editors centered on a few key points. First, all reviewers felt that it is of utmost importance that a justified and appropriate number of hiPSCs and their appropriate controls are utilized throughout. In particular, there is concern that G2019S-related phenotypes may be more variable than other presumed monogenetic causes of disease, for example a low penetrance of disease causation associated with G2019S in people (e.g., 20% lifetime penetrance for PD) that may …
##Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
###Summary:
The discussion between reviewers and editors centered on a few key points. First, all reviewers felt that it is of utmost importance that a justified and appropriate number of hiPSCs and their appropriate controls are utilized throughout. In particular, there is concern that G2019S-related phenotypes may be more variable than other presumed monogenetic causes of disease, for example a low penetrance of disease causation associated with G2019S in people (e.g., 20% lifetime penetrance for PD) that may necessitate more lines analyzed than usual, and possible lines from carriers of the mutation that appear resilient to disease. Studies in the past decade that use only one or a few lines of G2019S hIPSCs have generally failed to replicate in more than one laboratory, possibly due to low power. The reviewer's were not sure how rigorous the study was in this regard. Second, reviewer's felt there was over-interpretation and speculation regarding the possible roles of differential trophic factors released by the astrocytes in EVs and conditioned media without many measures of specific trophic factors, or rescue experiments, to help define the mechanism. Third, the EV data are not broadly supported by NTA (like Zeta or nanosight) or quantitative measures fairly standard in the EV field. For example, the authors did not clearly quantify the total number of EVs secreted in WT vs. G2019S conditions, which would be a basic experiment needed to create interest in the study in the EV community.
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