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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

  1. Aurelie de Rus Jacquet  Is a corresponding author
  2. Jenna L Tancredi
  3. Andrew L Lemire
  4. Michael C DeSantis
  5. Wei-Ping Li
  6. Erin K O'Shea
  1. Janelia Research Campus, Howard Hughes Medical Institute, United States
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Cite this article as: eLife 2021;10:e73062 doi: 10.7554/eLife.73062

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.

Introduction

The loss of dopaminergic neurons in the substantia nigra pars compacta is associated with the severe and debilitating motor dysfunction observed in Parkinson’s disease (PD) patients (Massano and Bhatia, 2012). The cause of dopaminergic neuron degeneration has been under intense investigation and has revealed the roles played by cellular stressors such as oxidative stress, mitochondrial dysfunction, and disruption of protein degradation pathways (Dawson and Dawson, 2003; Lynch-Day et al., 2012). The dysregulation of these pathways within the neurons themselves reflects cell-autonomous mechanisms of neurodegeneration, but neurons exist in a highly dynamic multicellular environment in which non-neuronal cells may also contribute to neuron loss (Allen and Lyons, 2018; Liddelow and Barres, 2017). Among these non-neuronal populations, astrocytes are specialized in detecting and responding to neuronal signals to support neuronal health and function (Allen and Eroglu, 2017; Banker, 1980), suggesting that impaired astrocytic functions could initiate or accelerate neuron loss. Neurons rely on astrocytes for regulation of their firing activities (Deemyad et al., 2018), for trophic and metabolic support (Allen and Lyons, 2018), and detoxification of harmful factors (Ioannou et al., 2019). However, astrocytes can change from a resting to a reactive state in response to injury (Zamanian et al., 2012) or normal aging (Clarke et al., 2018). In fact, depending on the nature of the extracellular signals released after injury, astrocytes can protect neurons or induce neurotoxicity (Liddelow et al., 2017; Zamanian et al., 2012). The role of astrocytes in promoting neuron loss has been the focus of a number of studies across multiple neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) (Di Giorgio et al., 2008; Haidet-Phillips et al., 2011), Huntington disease (HD) (Valenza et al., 2015), and PD (di Domenico et al., 2019; Gu et al., 2010). For example, studies showed that astrocytes with the ALS-causing mutation SOD1 G93A produced factors toxic to motor neurons (Di Giorgio et al., 2008), as well as failed to secrete factors that support the viability of neurons (Basso et al., 2013). Similarly, astrocytes with the LRRK2 G2019S mutation were found to alter neuronal morphology in a non-cell-autonomous fashion and shuttle detrimental astrocyte-derived alpha-synuclein (αSyn) to co-cultured neurons (di Domenico et al., 2019). These observations suggest that disease astrocytes undergo important functional changes that ultimately affect the viability of neurons. Further studies of the effects of PD-related mutations on the functionality of astrocytes and their ability to support neuronal survival are greatly needed as much remains to be discovered to fully appreciate the unique contributions of astrocytes to PD pathogenesis.

Cell-to-cell communication is mediated by several highly regulated mechanisms including the secretion of signaling factors directly into the extracellular space or enclosed within extracellular vesicles (EVs). While astrocyte paracrine signaling via the secretion of soluble factors into the extracellular space has been the focus of many studies (Allen and Eroglu, 2017; Spampinato et al., 2019), astrocyte communication via secreted EVs has not been well characterized. EVs represent a heterogeneous population of vesicles that can be further classified into different subtypes according to their size, biogenesis pathways, and cargo. Microvesicles (~100 nm to 1 µm diameter) are formed by budding of the plasma membrane directly into the extracellular space, and their cargo resembles the cytoplasmic signature of the cell of origin (Pegtel and Gould, 2019). Exosomes (~40–120 nm) are formed by a regulated inward budding of early endosomes to form exosome-containing multivesicular bodies (MVBs), as well as by budding of the plasma membrane (Pegtel and Gould, 2019). 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 and Gould, 2019; 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 and Gould, 2019; Schöneberg et al., 2017). Accumulating evidence suggests that the PD-related protein LRRK2 plays a role in vesicle trafficking, possibly via phosphorylation of Rab GTPase substrates (Alessi and Sammler, 2018; Steger et al., 2016). The PD-causing mutation G2019S occurs within the kinase domain of LRRK2, resulting in a hyper-active kinase form (West et al., 2005), which could affect the MVB/EV pathway. Furthermore, the nature of the EV cargo varies in health and disease (Chaudhuri et al., 2018; Lamontagne-Proulx et al., 2019). The composition of EVs isolated from the biofluids of PD patients is different from that of healthy controls (Fraser et al., 2016b; Lamontagne-Proulx et al., 2019; Shi et al., 2014) and contains proteins associated with PD, such as LRRK2 and αSyn. The clinical observation that the EV profile is altered in PD patients, and the proposed role of LRRK2 in vesicle trafficking, opens exciting opportunities to further investigate the relationships between EVs and PD pathogenesis in disease-relevant cell types.

Here we made use of human-based genetic models of PD using induced pluripotent stem cells (iPSCs) derived from PD patients with the LRRK2 G2019S mutation and its gene-corrected isogenic control (Reinhardt et al., 2013), as well as a second independent pair of iPSCs prepared from a LRRK2 G2019S PD patient and a sex- and age-matched control. The LRRK2 G2019S mutation is one of the most common genetic determinants associated with an increased risk to develop sporadic or familial PD (Bonifati, 2006; Kumari and Tan, 2009). We show that the LRRK2 G2019S mutation alters the phenotype of MVBs and secreted EVs by affecting the morphology and distribution of MVBs, the morphology of secreted EVs, and by overaccumulating the PD-related proteins LRRK2 and phospho-S129 αSyn in MVBs. Importantly, we show that astrocyte-derived EVs are internalized by dopaminergic neurons, and WT but not LRRK2 G2019S EVs supports neuronal survival. Overall, this study implicates a non-cell-autonomous contribution in dopaminergic neuron loss in PD and suggests pathological dysregulation of EV-mediated astrocyte-to-neuron communication by the PD-related mutation LRRK2 G2019S.

Results

Differentiation of LRRK2 G2019S iPSCs and gene-corrected isogenic controls into NPCs and astrocytes

We obtained iPSCs reprogrammed from the dermal fibroblasts of two patients with the LRRK2 G2019S mutation, and one line was gene-corrected to produce an isogenic control (Reinhardt et al., 2013) while the second line was paired with iPSCs derived from a sex- and age-matched healthy individual (Figure 1—figure supplement 1A-C). This enables the direct comparison of two independent pairs of iPSCs to take into consideration the inherent variability that exists between different patient-derived cell lines. Of particular importance, one of our disease and control iPSC pair differs only in a single-point mutation in LRRK2, which is essential to ensure that the experimental observations are a consequence of the mutation of interest, and do not result from differences in genetic background, or from differences that could arise during fibroblast reprogramming of each non-isogenic pair (Gore et al., 2011; Soldner et al., 2009). Since PD pathology significantly affects the region of the midbrain, we prepared astrocytes from neural progenitor cells (NPCs) patterned toward a midbrain fate. We adapted the protocol developed by Kriks et al. to generate a population of renewable midbrain-patterned NPCs that can differentiate into astrocytes or dopaminergic neurons, depending on exogenous cues (de Rus Jacquet, 2019; Kriks et al., 2011; Tcw et al., 2017). The midbrain and floor plate identity of the NPCs was systematically validated for each iPSC-to-NPC differentiation by RT-qPCR (Figure 1—figure supplement 1B), and the differentiated NPCs expressed characteristic NPC markers such as vimentin, SOX1, nestin, and notch1 (Figure 1—figure supplement 1C). 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 (Figure 1—figure supplement 2A). Differentiation was equally effective in WT and LRRK2 G2019S cells, with 100% of the differentiated astrocytes expressing GFAP (Figure 1—figure supplement 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 SLC2A1 (Figure 1—figure supplement 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 (Figure 1—figure supplement 2C and 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 (Figure 1—figure supplement 2D). Lastly, control and LRRK2 G2019S astrocytes showed classic astrocytic functional phenotypes such as spontaneous and transient calcium signaling and synaptosome uptake (Figure 1—figure supplement 2E and F).

Figure 1 with 2 supplements see all
Expression of exosome components is dysregulated in LRRK2 G2019S astrocytes.

(A) Astrocytes were prepared from induced pluripotent stem cells (iPSCs) carrying the Parkinson’s disease (PD) mutation LRRK2 G2019S or its isogenic control. Confocal images of immunostained iPSC-derived astrocytes show expression of astrocyte markers GFAP (green), vimentin (red), merged GFAP (green) and vimentin (red) with the nuclear marker DAPI (blue), and merged astrocyte marker CD44 (red) with DAPI (blue). (B) Heatmap representing the hierarchical clustering of significantly upregulated and downregulated genes in LRRK2 G2019S vs. WT astrocytes using a 1.4-fold threshold for upregulated genes and a 0.7-fold threshold for downregulated genes, and a false discovery rate of 0.05. Sequencing counts were normalized using the median of ratios method and calculated by the EBseq package in R, as described in Materials and methods, then transformed using log10(1 + normalized sequencing counts). The genes are separated into three categories based on their log10(1 + normalized sequencing counts) value using k-means clustering with k = 3 to reveal groups of low-, moderate-, or high-expression genes. Genes encoding exosome-related components are indicated on the right (purple lines, with each indicating one gene). (C) Heatmap representing the differential expression of 20 genes encoding exosome components in WT and LRRK2 G2019S astrocytes, across three independent biological replicates (labeled 1–3). The values represent the log10(1 + normalized sequencing counts) transformation as described in Materials and methods, log2FC represents LRRK2 G2019S vs. WT fold change in gene expression. Genes are sorted by their log2FC value in order of descending fold change. (D–H) Gene ontology analysis of non-isogenic and isogenic lines showing upregulated components identified by RNA sequencing, and Benjamini–Hochberg adjusted p-values were obtained from the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (D, F). Gene expression validation by qPCR of two exosome components, Rab27b (E, G) and CD82 (H) in non-isogenic and isogenic lines. Data are from three (B, C, D, F) four (G, H), or five (E) independent biological replicates; error bars represent mean + standard error of the mean (SEM). Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal standard deviation (s.d.) (*p≤0.05, **p<0.01).

Expression of exosome components in iPSC-derived astrocytes is altered by the LRRK2 G2019S mutation

We analyzed the effects of the LRRK2 G2019S mutation on global gene expression patterns in iPSC-derived astrocytes using RNA-seq analysis. Differential gene expression analysis revealed that the isogenic pair differed by a total of 348 genes (Figure 1B), and the non-isogenic pair by 563 genes. Classification of these 348 genes using k-means clustering showed that they are distributed between transcripts expressed at high, moderate, and low levels based on their log10(1 + normalized sequencing counts) value, suggesting that LRRK2 G2019S affects a broad range of targets (Figure 1B). Gene ontology (GO) analysis revealed that components of the extracellular compartment are highly upregulated and include the extracellular region, extracellular matrix, and extracellular exosomes (Figure 1D and F). The exosome component is one of the most significantly upregulated GO terms in both isogenic and non-isogenic astrocytes, and comprises a total of 67 (isogenic pair) or 95 (non-isogenic pair) genes (Supplementary files 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 and trafficking, and we did not detect differences in the expression of canonical factors that regulate MVB formation (e.g., VTA1, VPS4A, or PDCD6IP). Quantification of gene expression by RT-qPCR showed a 5-fold increase in RAB27B (associated with exosome trafficking) and a 64-fold increase in CD82 (associated with exosome biogenesis) (Andreu and Yáñez-Mó, 2014; Chiasserini et al., 2014; Ostrowski et al., 2010), in LRRK2 G2019S astrocytes compared to WT astrocytes (Figure 1E, G and H).

LRRK2 G2019S affects the size of MVBs in iPSC-derived astrocytes

The exosome pathway is characterized by the formation of MVBs, endosome-derived structures that form exosomes via inward budding of the endosomal membrane (Pegtel and Gould, 2019). Using transmission electron microscopy (TEM), we first confirmed that iPSC-derived astrocytes produce MVB-like structures. We found that while both WT and LRRK2 G2019S astrocytes produce MVBs (Figure 2A), their average size is genotype-dependent. The area of MVBs in LRRK2 G2019S astrocytes is smaller than in WT astrocytes, with an average of 0.26 µm2 (SEM = 0.02079) in LRRK2 G2019S and 0.44 µm2 (SEM = 0.03472) in WT cells (Figure 2B). MVBs with an area smaller than 0.2 µm2 are over-represented in LRRK2 G2019S compared to WT astrocytes, which range in area from 0.1 to 1.2 µm2 (Figure 2C). 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 (Jørgensen 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 level of CD63 localized within MVBs in WT and LRRK2 G2019S astrocytes (Figure 2E, Figure 3—figure supplement 1A and B), confirming that CD63 can be used as a marker of MVBs and exosomes in iPSC-derived astrocytes. These results demonstrate morphological defects in the MVBs produced by astrocytes derived from PD patients. Antibodies that target the CD63 protein accurately label astrocytic MVB and are therefore valuable markers to further characterize LRRK2 G2019S-dependent alterations in MVB-related pathways.

Multivesicular bodies (MVBs) in LRRK2 G2019S astrocytes are smaller than in WT astrocytes.

(A) Transmission electron microscopy (TEM) images of MVBs in WT and LRRK2 G2019S astrocytes. For illustration purposes, the red boxes in the top panel indicate MVBs in the cytoplasm of astrocytes and the lower panel shows a zoomed-in view of the MVBs. (B, C) Quantification of mean area (B) and size distribution (C) of MVBs identified in TEM images of WT and LRRK2 G2019S astrocytes. Data are sampled from at least 20 cells (≥40 MVBs) in each experimental condition; error bars represent mean + SEM for two independent biological samples (B). Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (****p<0.0001). (D) Representative confocal images of astrocytes labeled by immunofluorescence with the exosome marker CD63 (green) and the astrocyte marker CD44 (red). (E) Electron microscopy image showing immunogold labeling of CD63 (large gold) in astrocytes. Dashed lines delineate MVB membranes.

Profiling WT and LRRK2 G2019S EVs secreted by iPSC-derived astrocytes

The alteration of MVB size and the dysregulated expression of key components involved in MVB formation (such as RAB27B) suggests that astrocytic EV signaling may be affected by the G2019S mutation. We therefore investigated if the LRRK2 G2019S mutation could affect the release and morphology of astrocyte-secreted EVs. We first analyzed iPSC-derived astrocytes by TEM and confirmed the presence of EVs that appear to be secreted by exocytosis (Figure 3A), and by a mechanism that resembles membrane budding or shedding (Figure 3—figure supplement 1C). We then collected and processed astrocyte conditioned media (ACM) by ultracentrifugation. Successive rounds of ultracentrifugation resulted in the isolation of an EV-enriched pellet, immediately prepared for cryogenic electron microscopy (cryo-EM) and nanoparticle tracking analysis (NTA), or stored for later use (Figure 3B). EVs form a heterogeneous population differing in their biogenesis pathway (endosomal or plasma membrane budding) and cargo (Pegtel and Gould, 2019; Temoche-Diaz et al., 2019). The unique characteristics of different EV populations are not fully understood, but the documentation of their size range and molecular signatures is commonly used to describe EV populations. The astrocyte-derived EV pellet is enriched in exosomes, as demonstrated by the expression of eight exosomal markers and the absence of cellular contamination (Figure 3—figure supplement 1D). 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 and 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 (Figure 3—figure supplement 1E). 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. The discrepancy with the NTA results could be explained by the following: (1) 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 (2) 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 mutant and WT, 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 subpopulations. We previously showed that WT and LRRK2 G2019S MVBs contain similar levels of the CD63 tetraspanin (Figure 3—figure supplement 1A and B), and an ELISA-based quantification confirmed that the number of CD63+ EVs remained unchanged between the two genotypes (Figure 3—figure supplement 1F). 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.

Figure 3 with 1 supplement see all
The size distribution of extracellular vesicles (EVs) secreted by astrocytes is altered in the LRRK2 G2019S mutant.

(A) Transmission electron microscopy (TEM) images demonstrate that induced pluripotent stem cell (iPSC)-derived astrocytes actively produce and secrete EVs by exocytosis. The dashed line delineates the plasma membrane. The white arrows indicate EVs. (B) Overview of the procedure to isolate EVs by ultracentrifugation. ACM: astrocyte conditioned medium; Cryo-EM: cryogenic electron microscopy. (C) Nanoparticle tracking analysis (NTA) quantification of the number of particles (i.e., EVs) isolated in WT or LRRK2 G2019S ACM by ultracentrifugation as described in (B). (D) Graph showing the distribution of isolated EVs by particle size. Data in (C, D) are from two independent biological replicates; error bars represent mean + SEM. Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (ns: not significant). (E–G) Secreted EVs were imaged by cryo-EM (E) and their diameter (F) and morphology (G) was analyzed. Data are from ≥177 EVs isolated from ACM taken from 30.4 × 106 plated astrocytes for each experimental condition; error bars are mean + SEM for three independent biological replicates. Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (*p≤0.05).

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 colocalization 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). However, this immunofluorescence analysis does not discriminate between LRRK2 signal localized at the periphery or within CD63+ structures. To reveal the localization of LRRK2 in finer detail, we analyzed IEM images, which demonstrated that LRRK2 (small gold) is localized both in the vicinity of and inside CD63+ MVBs (large gold) (Figure 4C). Importantly, small gold LRRK2 was more frequently localized inside MVBs in LRRK2 G2019S astrocytes compared to WT astrocytes – 65% of MVBs in LRRK2 G2019S astrocytes are LRRK2+/CD63+ vs. only 44% in WT astrocytes. LRRK2 G2019S astrocytes contained an average of 1.8 LRRK2 small gold particles per MVB compared to only 0.9 small gold particles in WT astrocytes, and MVB populations containing more than five LRRK2 small gold particles were only observed in LRRK2 G2019S astrocytes (Figure 4D). We then performed biochemical assays to determine if astrocytes secrete LRRK2 in EVs, and ELISA analysis confirmed the presence of LRRK2 in EV-enriched fractions isolated from ACM (Figure 4E). We conclude that LRRK2 is associated with MVBs in astrocytes and is secreted in astrocyte-derived EVs, and the PD-related LRRK2 G2019S mutation results in the abnormal accumulation of LRRK2 inside MVBs and EVs.

LRRK2 is associated with multivesicular bodies (MVBs) in astrocytes and accumulates in the MVBs of LRRK2 G2019S mutant.

(A) Representative images of WT and LRRK2 G2019S isogenic astrocytes labeled by immunofluorescence with CD63 (green), LRRK2 (purple), and DAPI nuclear stain (blue) (Ai, Aiv). The images were analyzed using Imaris software to identify CD63+ MVBs (green surfaces) colocalized with LRRK2 (purple dots) and show the localization of the nucleus (DAPI, blue) (Aii, Av). Zoomed-in images shows two populations of CD63+ surfaces: CD63+/LRRK2+ (red arrowhead), and CD63+/LRRK2- (white arrowhead) (Aiii, Avi). (B) Percentage of CD63-labeled surfaces that are also LRKK2 positive in WT and LRRK2 G2019S astrocytes, quantified with Imaris software using object-based colocalization. Data are from three independent biological replicates, and ≥40 astrocytes (>3000 MVBs) were analyzed per experimental condition; error bars represent mean + SEM. Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (ns: not significant). (C) Immunogold electron microscopy shows the presence of LRRK2 (small gold, red arrowheads) inside and in the vicinity of CD63+ MVBs (large gold) in WT and LRRK2 G2019S astrocytes. The dashed lines indicate the contour of MVBs. (D) Distribution of CD63+ MVBs according to their number of internal LRRK2 gold particles. Data are sampled from at least 20 astrocytes (≥59 MVBs) in each experimental condition. The distribution is significantly different in LRRK2 G2019S astrocytes compared to WT astrocytes (p-value = 0.0084, chi-square test). (E) Quantification of the amount of LRRK2 in WT or LRRK2 G2019S EV-enriched fractions by ELISA. Data are from at least three independent biological replicates; error bars represent mean + SEM; statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (ns: not significant).

The LRRK2 mutation G2019S alters MVB distribution in iPSC-derived astrocytes

The proposed role of LRRK2 in vesicle trafficking (Alessi and Sammler, 2018; Steger et al., 2016), in addition to the dysregulation of factors associated with exosome/MVBs in mutant astrocytes (Figure 1C–H), and accumulation of LRRK2 in mutant MVBs (Figure 4D) suggested that the spatial distribution of vesicles could be disrupted in LRRK2 G2019S astrocytes. To gain further insight into the functional role of LRRK2 in the MVB pathway, we used immunofluorescence to probe the spatial distribution of CD63+ MVBs in WT and LRRK2 G2019S astrocytes (Figure 5A). We measured the distance of CD63+ structures to the nucleus and found that CD63+ MVBs accumulated primarily in the perinuclear region of WT astrocytes in both isogenic and non-isogenic pairs (Figure 5B and D). In contrast, CD63+ MVBs were less clustered near the nucleus in LRRK2 G2019S cells. The median distance of CD63+ MVBs to the nuclear membrane was 9.2 µm in LRRK2 G2019S astrocytes compared to 4.8 µm in isogenic controls, and 11.5 µm in LRRK2 G2019S astrocytes compared to 7.5 µm in the non-isogenic control. Furthermore, 75% of the isogenic control CD63+ MVBs were localized at a distance to the nucleus smaller than 11.23 µm compared to 19.34 µm in LRRK2 G2019S astrocytes (Figure 5B and C). In non-isogenic lines, 75% of the control CD63+ MVBs were localized at a distance to the nucleus smaller than 15.82 µm compared to 23.79 µm in LRRK2 G2019S astrocytes (Figure 5D and E). The observation that LRRK2 G2019S disrupts the perinuclear distribution of CD63+ MVBs suggests that their localization is influenced by the LRRK2 protein, and this cellular organization is altered in the astrocytes of PD patients.

The spatial distribution of multivesicular bodies (MVBs) in LRRK2 G2019S astrocytes is altered.

(A) Representative immunofluorescence images of WT and LRRK2 G2019S astrocytes labeled with the exosome marker CD63 (green), the astrocyte marker CD44 (red), and the nuclear marker DAPI (dark blue). The bottom images show the corresponding Imaris software rendering of CD63+ MVBs, color-coded by distance to the nucleus, from blue (closest) to white (farthest). The plain white lines indicate the cell boundary (outer line) and nucleus (inner circle). (B–E) Quantification of the distance of CD63+ MVBs from the nuclear membrane in WT and LRRK2 G2019S isogenic (B, C) or non-isogenic (D, E) astrocytes using Imaris software ‘vesicles distance to closest nucleus’ calculation. The violin plot shows the median (blue dashed line) and interquartile range (red solid line) (B, D). Data are from three independent biological replicates, 40–70 astrocytes (>3300 MVBs) were analyzed for each experimental condition. Statistical analysis was performed using a Mann–Whitney test (****p<0.0001).

The LRRK2 G2019S mutation increases the amount of phosphorylated alpha synuclein (Ser129) in MVBs

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). 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.

Alpha-synuclein and phospho-S129 alpha-synuclein are associated with multivesicular bodies (MVBs) in astrocytes and accumulate in the MVBs of LRRK2 G2019S mutant.

(A) Immunogold labeling of phospho-S129 alpha-synuclein (p-αSyn, small gold, red arrowheads) and CD63 (large gold) shows localization of p-αSyn inside and in the vicinity of MVBs in astrocytes. The low abundance of small gold particles in the control sample is consistent with the observation that healthy brain tissues contain low levels of p-αSyn (Fujiwara et al., 2002). The dashed lines indicate the boundary of the MVBs. (B) Distribution of CD63+ MVBs according to their number of internal p-αSyn gold particles, in WT and LRRK2 G2019S astrocytes. Data are sampled from at least 20 astrocytes (30–79 MVBs) for each experimental condition. The distribution is significantly different in LRRK2 G2019S astrocytes compared to WT astrocytes (p-value=0.0014, chi-square test). (C) Quantification of the amount of αSyn in WT and LRRK2 G2019S extracellular vesicle (EV)-enriched fractions by ELISA. Data are from seven independent biological replicates; error bars represent mean + SEM; statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (ns: not significant).

Exosomes secreted by iPSC-derived astrocytes are internalized by iPSC-derived dopaminergic neurons

EVs have been suggested to mediate astrocyte paracrine signaling and affect neuronal health (Basso et al., 2013; Chaudhuri et al., 2018; Pascua-Maestro et al., 2018); it is therefore important to understand the pattern of astrocyte-derived EV uptake by dopaminergic neurons in health and disease. We investigated the internalization of WT and LRRK2 G2019S astrocyte-derived exosomes by dopaminergic neurons to determine if there exist genotype-dependent alterations in astrocyte-to-neuron exosome shuttling. To monitor production of EVs by astrocytes and assess uptake by neurons, we fluorescently labeled exosomes in astrocytes with one color (CD63-GFP) and labeled neurons with a second color (tdTomato). Subsequently, astrocytes and neurons were co-cultured, and the uptake of green-labeled exosomes by dopaminergic neurons was monitored by live-cell confocal microscopy (Figure 7A and B). We produced a 3D reconstitution of neurons and exosomes, and segmented intracellular and extracellular exosomal populations (Figure 7B). We observed the presence of astrocyte-derived exosomes in the somas and neurites of dopaminergic neurons (Figure 7Bvi), and endocytosis of extracellular exosomes (Figure 7—figure supplement 1). We then sought to determine if the efficiency of exosome uptake by dopaminergic neurons was influenced by the astrocyte genotype. We co-cultured dopaminergic neurons with either WT or G2019S astrocytes, but did not observe a difference in the proportion of exosome-containing neurons. On average, 40% of the dopaminergic neurons had internalized exosomes regardless of the astrocyte genotype (Figure 7C). Although there was no difference in the amount of exosomes internalized by neurons exposed to either WT or LRRK2 G2019S astrocytes (Figure 7D), exosomes were significantly over-represented in the neurites of neurons exposed to LRRK2 G2019S astrocytes (Figure 7E and F). These results show that astrocyte-secreted CD63+ exosomes are internalized by neurons, but in the presence of PD astrocytes the pattern of uptake of exosomes is changed, suggesting that the astrocyte-to-neuron shuttling of EV-enclosed proteins may be affected.

Figure 7 with 1 supplement see all
Dopaminergic neurons internalize exosomes secreted by WT and LRRK2 G2019S astrocytes.

(A) WT dopaminergic neurons were transduced with rh10-CAG-tdTomato (red), and WT or LRRK2 G2019S astrocytes were transduced with lenti-CD63-GFP (green) to produce green-labeled exosomes. Neurons and astrocytes were co-cultured, and uptake of CD63-GFP exosomes by neurons was monitored by live-cell confocal microscopy, followed by deconvolution and Imaris modeling. (B) Confocal images of tdTomato neurons (Bi), CD63-GFP astrocytes (Bii), and the merged image (Biii). The corresponding Imaris software rendering represents tdTomato neurons in yellow (Biv), and the CD63-GFP exosomes in blue (outside the neurons) or purple (inside the neurons) (Bv). A transverse view of a neuron shows purple-labeled exosomes inside the somas and neurites (Bvi). (C) Quantification of the percentage of neurons with internalized WT or LRRK2 G2019S CD63-GFP exosomes at the time of live-cell imaging. (D) Quantification of the number of CD63-GFP exosomes per unit of neuronal volume. (E) Confocal images of tdTomato neurons co-cultured with WT or G2019S CD63-GFP isogenic astrocytes, and the corresponding Imaris software rendering representing neurons in white and CD63-GFP exosomes in red. White arrowheads show exosomes inside neurites. Scale bar: 5 µm. (F) Quantification of the number of CD63-GFP exosomes per unit of soma or neurite volume. For all datasets: data are from three independent biological replicates, ≥80 neurons were analyzed for each experimental condition. The scatter plot shows the median value. Statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (C, D), or one-way ANOVA with Newman–Keuls multiple comparisons (E) (ns: not significant, *p≤0.05).

LRRK2 G2019S iPSC-derived astrocytes induce a non-cell-autonomous loss of iPSC-derived dopaminergic neurons

To further determine the functional consequences of the LRRK2 G2019S mutation in astrocyte-to-neuron communication, we investigated the impact of WT or LRRK2 G2019S astrocytes on neuronal health. We co-cultured astrocytes and dopaminergic neurons for 14 days and observed a loss of and morphological changes to dopaminergic neurons in presence of mutant astrocytes (Figure 8A–D). The neuronal dendrites shortened from an average of 60 µm in WT to 45 µm in LRRK2 G2019S isogenic cultures (Figure 8C). Similar morphological changes were observed when dopaminergic neurons were co-cultured with the second line of LRRK2 G2019S astrocytes compared to non-isogenic controls (Figure 8E and F), and these results are in agreement with a previous report that LRRK2 G2019S astrocytes induce loss of dopaminergic neurons compared to astrocytes derived from a healthy (non-isogenic) donor (di Domenico et al., 2019). To further confirm our results, we measured neuron survival using mouse primary midbrain cultures that consist of purified post-mitotic dopaminergic and GABAergic neurons co-cultured with purified astrocytes. A 14 days, co-culture of WT neurons with LRRK2 G2019S astrocytes resulted in loss of 37% more of the dopaminergic neurons than co-cultures containing WT astrocytes (Figure 8G and H). Moreover, this neuronal death was selective for dopaminergic neurons (Figure 8I), recapitulating a key feature of PD. LRRK2 G2019S astrocytes also reduced the viability of LRRK2 G2019S dopaminergic neurons (Figure 8J), but did not affect non-dopaminergic neuronal populations (Figure 8K).

LRRK2 G2019S astrocytes affect the viability and morphology of dopaminergic neurons.

(A) Confocal images showing neurons labeled by immunofluorescence with the pan-neuronal and dendrite marker MAP2 (red), the marker for dopaminergic neurons TH (green), and the merged images (MAP2, red, and TH, green). MAP2: microtubule-associated protein 2; TH: tyrosine hydroxylase. (B–F) Quantification of neuron viability (number of dopaminergic neurons remaining in culture) (B), average dendrite length (C, E), and dendrite length distribution (D, F) after 14 days in culture with WT or LRRK2 G2019S isogenic (B–D) or non-isogenic (E, F) astrocytes. Viability data are from five (B, D) independent biological replicates, and at least 500 neurons were counted per experimental condition and biological replicate. WT and 530 LRRK2 G2019S neurons were counted (B). Dendrite length data are from three independent biological replicates, and more than 300 (E, F) or 500 (C, D) neurites were measured for each experimental condition. (G) Confocal images showing WT mouse primary neurons co-cultured with WT or LRRK2 G2019S mouse primary astrocytes. Neurons are labeled by immunofluorescence and images show MAP2 (red), TH (green), and the merged images (MAP2, red, and TH, green). White arrowheads mark TH+/MAP2+ dopaminergic neurons. (H, I) The cells were scored for TH+/MAP2+ dopaminergic (H) or TH-/MAP2+ non-dopaminergic neuron survival (I) after 14 days co-culture with WT or LRRK2 G2019S astrocytes. (J, K) LRRK2 G2019S mouse primary neurons were co-cultured with mouse WT or LRRK2 G2019S astrocytes as described in (H, I) and scored for relative viability of TH+/MAP2+ dopaminergic (J) or TH-/MAP2+ non-dopaminergic neurons (K). Viability data for all mouse primary culture experiments are from three independent biological replicates, and at least 200 neurons were counted for each experimental condition and biological replicate. For all datasets, error bars represent mean + SEM; statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (*p≤0.05, **p<0.01, ****p<0.0001). Results shown in panel (B) support similar observations recently documented in a non-isogenic induced pluripotent stem cell (iPSC)-based model system (di Domenico et al., 2019).

To determine if these morphological changes are mediated by direct contact between astrocytes and neurons or if they rely solely on astrocyte secreted factors, WT dopaminergic neurons were cultured in ACM prepared from LRRK2 G2019S or isogenic control astrocytes. After 14 days in culture, the average dendrite length of dopaminergic neurons exposed to WT ACM was 64.4 µm compared to 50.3 µm if the dopaminergic neurons were exposed to basal NB/B27 medium (Figure 9A). This observation suggests that WT ACM, but not basal medium, contains neurotrophic factors necessary for dendrite outgrowth. LRRK2 G2019S ACM mimicked the phenotype observed by direct co-culture, with a 26% increase in the number of short neurites (0–40 µm), and the absence of long neurites (160–200 µm) compared to neurons exposed to WT ACM (Figure 9B). The average dendrite length of dopaminergic neurons cultured in basal medium or LRRK2 G2019S ACM is equivalent (50.3 µm vs. 49.1 µm, respectively) (Figure 9A). When mouse primary midbrain neurons are exposed to ACM prepared from LRRK2 G2019S mouse midbrain astrocytes, shortening of dendrite length is also observed in vulnerable dopaminergic neuron populations (Figure 9C and D). In addition, we found that LRRK2 G2019S mouse primary dopaminergic neuron survival is affected by LRRK2 G2019S but not WT ACM (Figure 9—figure supplement 1). We conclude that the morphological atrophy in dopaminergic neurons may result from a lack of neurotrophic support by the LRRK2 G2019S astrocyte secretome.

Figure 9 with 1 supplement see all
WT but not LRRK2 G2019S astrocytes release neurotrophic factors via two distinct secretory pathways to support the growth of dopaminergic neurons.

(A–D) WT dopaminergic neurons were cultured for 14 days with basal medium (-), WT or LRRK2 G2019S astrocytes conditioned media (ACM), and the resulting dendrite length average (A, C) and distribution (B, D) were quantified. Data in (A, B) were collected using the isogenic induced pluripotent stem cell (iPSC)-based model, data in (C, D) used the primary mouse culture system. The data are from three independent biological replicates, and ≥450 (A, B) or 170 (C, D) neurites were measured per experimental condition. (E, F) Quantification of the average (E) and distribution (F) of dendrite lengths of WT dopaminergic neurons cultured for 14 days with basal medium (-), or WT or LRRK2 G2019S extracellular vesicle (EV)-free ACM. Data are from four independent biological replicates, and ≥650 neurites were measured for each experimental condition. (G, H) Quantification of the average (G) and distribution (H) of dendrite lengths of WT dopaminergic neurons cultured for 14 days with basal medium (-), or WT or LRRK2 G2019S EV-enriched fractions. Data are from three independent biological replicates, and ≥450 dendrite were measured for each experimental condition. For all datasets, error bars represent mean + SEM, and statistical analysis was performed using two-tailed unpaired Student’s t-test with equal s.d. (C) or one-way ANOVA with Tukey’s multiple comparisons correction (A, E, G) (ns: not significant, *p≤0.05, ****p<0.0001).

Two distinct secretory pathways contribute to the trophic support of iPSC-derived dopaminergic neurons by WT but not LRRK2 G2019S iPSC-derived astrocytes

The astrocyte secretome comprises factors directly secreted into the medium as well as factors enclosed in EVs. To determine which of these two populations are responsible for the effects we observe on neurons, we assessed the neurotrophic potential of EVs vs. EV-free medium prepared from LRRK2 G2019S or isogenic control astrocytes. We collected ACM and prepared EV-free and EV-enriched fractions using sequential ultracentrifugation (Figure 3B). Dopaminergic neurons were cultured for 14 days in basal medium, or EV-free medium prepared from LRRK2 G2019S or isogenic control astrocytes. WT EV-free medium promoted neuronal health, as evidenced by an average dendrite outgrowth of 60.3 µm compared to 51 µm in basal medium (Figure 9E). In contrast, LRRK2 G2019S EV-free medium did not support dendrite outgrowth, as evidenced by an average length of 48.7 µm. The analysis of the distribution of dendrite lengths shows a greater proportion of short neurites (0–40 µm) in dopaminergic neurons exposed to basal medium or LRRK2 G2019S EV-free medium (Figure 9F). We conclude that soluble factors found in WT but not LRRK2 G2019S EV-free ACM mediate neuron growth.

Next, we tested the hypothesis that failure of mutant astrocytes to directly secrete trophic factors into the media was also accompanied by a failure to secrete EV-enclosed beneficial molecules., We therefore cultured dopaminergic neurons with basal medium, or with WT or LRRK2 G2019S EV-enriched fractions. WT EVs supported a 30% increase in dendrite length compared to basal medium (Figure 9G), which is higher than the 18% increase supported by WT soluble factors (Figure 9E). Interestingly, LRRK2 G2019S EVs were not as efficient as their WT counterparts, but they supported a 11% increase in dendrite length (Figure 9G) and reduced the proportion of short neurites (0–40 µm) (Figure 9H). These results suggest that WT astrocytes secrete neurotrophic factors not only via direct release into the extracellular compartment, but also via production and release of EVs. Supplementation of the neuronal basal medium with WT or LRRK2 G2019S EVs resulted in neuron growth, but LRRK2 G2019S EVs provided only a marginal trophic support compared to WT EVs. Together with the previous observation that LRRK2 G2019S affects the exosome/EV component in astrocytes and the distribution of internalized EVs in neurons, these data support a model of astrocyte-to-neuron trophic support via both direct secretion and EV-mediated shuttling of factors, and these systems appear to be dysregulated in the LRRK2 G2019S model of PD.

The nature of the secreted factors, either those secreted directly into the media or those enclosed in EVs, could be proteins, small signaling molecules, or essential lipids. A GO analysis of downregulated genes in LRRK2 G2019S astrocytes revealed a defect in cholesterol as well as isoprenoid biosynthetic processes (Figure 10A and B) in both isogenic and non-isogenic lines. Furthermore, genes associated with cell growth and survival were significantly downregulated in mutant astrocytes, including BIRC5, FLOT1, HTRA1, PAK1, KCNMA1, and LZTS1 (Figure 10C). These genes regulate neuronal growth (BIRC5, FLOT1, PAK1, LZTS1) and/or function (FLOT1, PAK1, LZTS1) (Contet et al., 2016; Du et al., 2020; Kawaue et al., 2019; Ko et al., 2011; Launay et al., 2008; Pan et al., 2015; Swanwick et al., 2010), and can be found in EVs (FLOT1, HTRA1, KCNMA1) (Skog et al., 2008), suggesting that they could be shuttled between neurons and astrocytes via this communication pathway.

Cholesterol biogenesis is impaired in LRRK2 G2019S astrocytes.

Gene ontology analysis of isogenic (Ai) and non-isogenic (Aii) LRRK2 G2019S vs. WT induced pluripotent stem cell (iPSC)-derived astrocytes showing downregulated components identified by RNA sequencing. Benjamini–Hochberg adjusted p-values were obtained from the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. (B, C) Heatmaps showing top significantly downregulated genes associated with lipid biogenesis (B) and cell survival (C) in LRRK2 G2019S vs. WT astrocytes using a 0.7-fold threshold and a false discovery rate of 0.05. Data shows log2(CPM) values calculated for WT and LRRK2 G2019S astrocytes prepared using the isogenic or non-isogenic lines, as well as human midbrain fetal astrocytes to use as a reference. Abbreviation: CPM, counts per million.

Discussion

Dysregulation of iPSC-derived astrocyte-mediated EV biogenesis in PD

Here we document the dysregulation of the MVB-mediated secretory pathway in human iPSC-derived astrocytes containing the PD-related mutation LRRK2 G2019S. Transcriptomic profiling revealed the misregulated expression of genes encoding proteins found in exosomes in LRRK2 G2109S vs. WT astrocytes (Figure 1). Both LRRK2 G2019S MVBs and secreted EVs were smaller than their WT counterparts (Figures 2 and 3D and G), and the mutant CD63+ MVBs lost their perinuclear distribution (Figure 5). 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 et al., 2014; Pegtel and 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 and H), suggesting that the secretion of at least one population of EVs is independent of the astrocyte genotype. However, we cannot rule out that sub-populations of CD63- EVs may be differentially secreted in mutant astrocytes.

Accumulating evidence supports a role for LRRK2 in vesicular membrane-related pathways, which could be mediated by the kinase activity of LRRK2 and the resulting phosphorylation of a subset of Rab GTPases (Alessi and Sammler, 2018; Steger et al., 2016). Rab GTPases are master regulators of vesicle biogenesis and coordinate intracellular trafficking of vesicular organelles including autophagosomes, lysosomes, and MVBs (Alessi and Sammler, 2018). The LRRK2 mutation G2019S results in a gain-of-function phenotype with increased kinase activity and increased phosphorylation of Rab substrates (Steger et al., 2016). Thus, the observation that the distribution and phenotype of MVBs and EVs is altered by LRRK2 G2019S (Figures 2 and 3) may correlate with dysregulation of Rab GTPase signaling cascades. In fact, genetic manipulation of Rab27b in HeLa cells altered the size and distribution of CD63+ MVBs, but did not change the size or protein content of the secreted EVs (Ostrowski et al., 2010). This suggests that while Rab27b may mediate LRRK2 G2019S-related alterations in MVB phenotype in astrocytes, it is likely that additional pathways are affected and alter EV morphology and cargo loading. For example, future studies could elucidate whether the increased kinase activity resulting from the LRRK2 G2019S mutation prevents EV maturation, as well as the rate of astrocyte-derived EV production and release.

We found that two important PD-related proteins, LRRK2 and αSyn, were localized inside MVBs and secreted in astrocyte-derived EVs (Figures 4 and 6). Furthermore, the LRRK2 G2019S mutation in astrocytes induced the accumulation of LRRK2 (Figure 4C and D) and p-αSyn (Figure 6A and B) within MVBs. We observed a trend towards greater levels of LRRK2 in mutant EVs compared to WT, but no genotype-dependent changes in αSyn levels (Figure 6C). However, the accumulation of p-αSyn in LRRK2 G2019S MVBs suggests that this aggregation-prone form of αSyn may also accumulate in mutant EVs and is consistent with studies showing that cerebral levels of p-αSyn are elevated in LRRK2 G2019S rodents (Longo et al., 2017). Furthermore, the LRRK2 mutation G2019S increases LRRK2 autophosphorylation at serine 1,292 (pS1292), and pS1292-LRRK2 was found in higher levels in EVs isolated from PD patients compared to healthy controls (Fraser et al., 2016a; Wang et al., 2017). Further studies are needed to determine if pS1292-LRRK2 and p-αSyn differentially accumulate in EVs secreted from LRRK2 G2019S vs. WT astrocytes, as differences in EV profile between EV derived from healthy or PD astrocytes could open new avenues in the field of biomarker research. The investigation of biofluid-isolated EVs as noninvasive disease diagnostic tools in PD is an emerging field (Thompson et al., 2016), and the search for sensitive biomarkers is facing challenges. Many cell types, including cells of the central nervous system (Fiandaca et al., 2015, Shi et al., 2014) but also non-disease-relevant cell types, contribute to the pool of EVs isolated from biofluids. As a result, EV subpopulations that would best serve as markers of the disease are diluted in the total pool of EVs, which limits the sensitivity of a biomarker assay. Given the dysregulation of the EV secretory pathway in LRRK2 G2019S astrocytes, future studies could evaluate if astrocyte-derived EVs may provide a sensitive measure of disease progression. Astrocyte-derived exosomes could be isolated based on their expression of the Glial Fibrillary Acidic Protein (GFAP) or Glutamine Aspartate Transporter (GLAST) astrocyte markers (Venturini et al., 2019; Willis et al., 2017; Winston et al., 2019), and their cargoes compared between healthy donors and PD patients.

Significance of EV trophic support in PD

We also show that dopaminergic neurons internalize EVs secreted by WT or LRRK2 G2019S astrocytes (Figure 7). For this investigation, we focused our observations on a well-defined subpopulation of EVs that can be genetically labeled with GFP, thus enabling the documentation of their internalization in live neurons. The EV genotype did not affect the amount of CD63-GFP exosomes taken up by dopaminergic neurons (Figure 7C), but LRRK2 G2019S exosomes accumulated to greater levels in the neurites compared to the somas (Figure 7F). Our observations suggest that the localization of internalized EVs can vary based on the genotype of the donor astrocyte. However, the functional consequences of EV localization in the recipient neurons are unclear. Studies found that the composition of EVs and the health status of donor and recipient cells can affect EV secretion and uptake (French et al., 2017), and could therefore alter astrocyte-to-neuron EV-mediated communication. Our study identified the PD-related proteins LRRK2 and αSyn in EV-enriched fractions (Figures 4F and 6), but their effect in the recipient neurons is unknown. Further studies including proteomics-based characterization of WT and LRRK2 G2019S astrocyte-derived EVs are needed to more thoroughly determine their composition, and which relevant cargoes (or lack of) affect the physiology of the recipient neurons.

In support of our observations suggesting the dysregulation of EV-related pathways in LRRK2 G2019S astrocytes, we found that conditioned medium and EV-enriched fractions prepared from mutant astrocytes failed to provide neurotrophic support compared to WT astrocytes (Figure 9). We showed that in the presence of LRRK2 G2019S ACM or EVs dopaminergic neurons were not able to maintain their neurite length, as observed by a shortening of the dendrites equivalent to that of neurons cultured in the absence of trophic factors. However, we did not observe excessive neurite shortening (≤50% WT length) or fragmentation indicative of the presence of toxic factors (di Domenico et al., 2019; Liddelow et al., 2017). Neurons rely heavily on astrocytes via mechanisms involving metabolic coupling whereby astrocytes produce and supply a unique combination of cholesterol, cholesterol precursors, and fatty acids in the form of lipoproteins (Pfrieger and Ungerer, 2011). Our study revealed cholesterol and isoprenoid biosynthesis as two of the most significantly downregulated pathways in LRRK2 G2019S astrocytes (Figure 10). This finding suggests that mutant astrocytes may have limited capability to produce and supply essential lipids necessary for neuronal growth, function (Zhang and Liu, 2015) and lipid-mediated signaling (Lane-Donovan et al., 2014). A previous study showed that increased levels of cholesterol derivatives in the developing midbrain promote the growth of dopaminergic neurons (Theofilopoulos et al., 2019), and the lack of these astrocyte-secreted factors in our co-culture model could mediate neuron atrophy. Furthermore, our findings of an altered cholesterol profile in patient-derived astrocytes corroborate previous studies showing reduced cholesterol levels in the serum of PD patients and a correlation between serum cholesterol and disease progression (Huang et al., 2007; Jin et al., 2019). Altered membrane lipid composition and cholesterol metabolism appear to be a common pathological feature among several neurodegenerative disorders including AD, HD, and Niemann–Pick type C disease (Vance, 2012), and warrant further investigation as a mechanism of non-cell-autonomous neurodegeneration. In particular, cholesterol appears to be an essential factor in MVB formation and function (Kobuna et al., 2010), and impairment of the MVB/EV pathway in LRRK2 G2019S astrocytes could be mediated by changes in cholesterol biogenesis in these cells. Another outcome of our RNA-seq study is the identification of downregulated genes involved in cell growth and survival (Figure 10C). For example, decreased levels of the lipid raft protein flotillin-1 (FLOT1 gene) have been associated with changes in membrane fluidity and reduced glutamate uptake by astrocytes with a PD-related mutation in PARK7 (Kim et al., 2016), which could lead to neurodegeneration. In addition, flotillin-1 contributes to the function of dopaminergic neurons by forming microdomains responsible for the internalization and localization of the dopamine transporter at the plasma membrane (Cremona et al., 2011). Flotillin-1 has been detected in EVs (Skog et al., 2008), suggesting that it could be shuttled between cell types. Collectively, these observations suggest that LRRK2 G2019S astrocytes fail to deliver important trophic factors to dopaminergic neurons. However, without identifying the molecules responsible, we cannot rule out the possibility that release of neurotoxic molecules by astrocytes also contributes to the neuronal phenotype. Indeed, a previous study proposed a neurotoxic effect of LRRK2 G2019S astrocytes on dopaminergic neurons, possibly mediated by the transfer and accumulation of astrocytic αSyn in neurons (di Domenico et al., 2019). Our work, together with that of di Domenico, suggests that astrocytes may impact dopaminergic neuron function by secreting neurotoxic molecules as well as by failing to secrete EV-enclosed trophic factors and essential lipids.

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 that appears to be mediated, at least in part, by impaired 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.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
AntibodyAlexa Fluor-conjugated secondary antibodiesThermo Fisher ScientificMultipleIF (1:1000)
AntibodyAnti-alpha-synuclein (rabbit monoclonal)Abcamab138501IF (1:200)
AntibodyAnti-APC magnetic beadsMiltenyi130-090-855MACS (1:5)
AntibodyAnti-CD133, APC-conjugated (mouse monoclonal)BD Biosciences566596MACS (1:20)
AntibodyAnti-CD44 (rat monoclonal)BD Biosciences550538IF (1:500)
AntibodyAnti-CD63 (mouse monoclonal)Thermo Fisher ScientificMA1-19281IF (1:500).IEM: (1:40)
AntibodyAnti-GFAP (mouse monoclonal)BD Biosciences556328IF (1:500)
AntibodyAnti-LRRK2 (rabbit monoclonal)Abcamab133474IF (1:200).IEM (1:20)
AntibodyAnti-MAP2 (chicken polyclonal)Thermo Fisher ScientificPA1-10005IF (1:3000)
AntibodyAnti-mouse IgG conjugated to 12 nm gold particles (goat polyclonal)Jackson ImmunoResearch115-205-146IEM (1:20)
AntibodyAnti-mouse IgG conjugated to 6 nm gold particles (goat polyclonal)Jackson ImmunoResearch115-195-146IEM (1:40)
AntibodyAnti-phospho alpha-synuclein (S129) (rabbit monoclonal)Abcamab51253IEM (1:20)
AntibodyAnti-rabbit IgG conjugated to 6 nm gold particles (goat polyclonal)Jackson ImmunoResearch111-195-144IEM (1:40)
AntibodyAnti-TH (rabbit polyclonal)PhosphoSolutions2025-THRABIF (1:500)
AntibodyAnti-vimentin (rat monoclonal)R&DMAB2105IF (1:100)
Cell line (Homo sapiens)Human midbrain astrocytesScienCell1850
Cell line (Homo sapiens)Isogenic control iPSCsReinhardt et al., 2013IM2-GCProf. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute)
Cell line (Homo sapiens)LRRK2 G2019S iPSCsReinhardt et al., 2013IM2Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute)
Cell line (Homo sapiens)Healthy control iPSCsRUCDR Infinite BiologicsRUID: NN0004085, line ID ND36091Dr. Randall T. Moon (Howard Hughes Medical Institute/University of Washington)
Cell line (Homo sapiens)LRRK2 G2019S iPSCsRUCDR Infinite BiologicsRUID: NN0004061, line ID ND33879Dr. Randall T. Moon (Howard Hughes Medical Institute/University of Washington)
Strain, strain background(Mus musculus)WT mouseTaconicC57BL/6NTac
Strain, strain background(Mus musculus)LRRK2 G2019S constitutive knock-in mouseTaconicC57BL/6-Lrrk2tm4.1Arte
Chemical compound, drugAscorbic acidSigmaA92902
Chemical compound, drugCHIR99021STEMCELL Technologies72054
Chemical compound, drugCytosine β-D-arabinofuranoside (AraC)SigmaC1768
Chemical compound, drugDAPTTocris Bioscience2634
Chemical compound, drugDibutyryl cAMPMillipore Sigma28745
Chemical compound, drugLDN193189 hydrochlorideSigmaSML0559
Chemical compound, drugPurmorphamineMillipore Sigma540223
Chemical compound, drugROCK inhibitor (Y-27632)STEMCELL Technologies72304
Chemical compound, drugSB431542STEMCELL Technologies72234
Commercial assay or kitControl library phiXIlluminaFC-110-3001
Commercial assay or kitExoELISA-ULTRA Complete Kit (CD63 Detection)System BiosciencesEXEL-ULTRA-CD63-1
Commercial assay or kitExoGlow -Protein EV Labeling Kit (Red)System BiosciencesEXOGP100A-1
Commercial assay or kitKAPA library quantification kitRoche7960298001
Commercial assay or kitKAPA SYBR FAST qPCR kitRoche7959397001
Commercial assay or kitNextera XT DNA library preparation kitIlluminaFC-131-1024
Commercial assay or kitRevertAid First Strand cDNA Synthesis KitThermo Fisher ScientificK1622
Commercial assay or kitRNA purification kitQiagen74104
Commercial assay or kitTaqMan LRRK2 SNP genotyping assayThermo Fisher Scientific4351379
Commercial assay or kitTaqMan hPSC Scorecard PanelThermo Fisher ScientificA15871
Other96-well plates with optically clear bottomIbidi89626
OtherLD columnsMiltenyi Biotech130-042-901
OtherPolycarbonate aluminum bottles with cap assemblyBeckman Coulter355618
OtherProlong Diamond with DAPIThermo Fisher ScientificP36962
Peptide, recombinant proteinBDNF (recombinant human)PeproTech450-02
Peptide, recombinant proteinEGF (recombinant human)Thermo Fisher ScientificPHG0311
Peptide, recombinant proteinFGF basic (146 aa) (recombinant human)R&D Systems233-FB-025
Peptide, recombinant proteinGDNF (recombinant human)PeproTech450-10
Peptide, recombinant proteinSHH(C25II) (recombinant mouse)R&D Systems464-SH-200
Peptide, recombinant proteinTGF-β3 (recombinant human)R&D Systems8420-B3-025
Recombinant DNA reagentpCT-CD63-GFPSystem BiosciencesCYTO120-PA-1
Sequence-based reagentHuman CD82_FThis paperqPCR primer5’GGTTTCGTGGAAGGAAGC3’
Sequence-based reagentHuman CD82_RThis paperqPCR primer5’AAGATCAAGTTGAAGAGGAAGAG 3’
Sequence-based reagentHuman Rab27b_FThis paperqPCR primer5’AACTGGATGAGCCAACTG3’
Sequence-based reagentHuman Rab27b_RThis paperqPCR primer5’CTTGCCGTTCATTGACTTC3’
Software, algorithmBeacon Designer Lite 8.16Premier Biosoft (San Francisco, CA)N/A
Software, algorithmDatabase for Annotation, Visualization and Integrated Discovery (DAVID) v6.8N/Ahttps://david.ncifcrf.gov
Software, algorithmEBseq v3.8Leng et al., 2013http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html
Software, algorithmComplexHeatmap v3.11Gu et al., 2016http://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
Software, algorithmImaris 9.3Bitplane (Belfast, UK)N/A
Software, algorithmSTAR v.2.7.3aDobin et al., 2013https://github.com/alexdobin/STAR/releases, Alexander, 2021
Software, algorithmStarcode v1.1Zorita et al., 2015https://github.com/gui11aume/starcode, Guillaume, 2021

Culture of iPSCs

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Patient-derived iPSCs with the LRRK2 mutation G2019S and the corresponding isogenic control were provided by Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute). The second line of patient-derived iPSCs with the LRRK2 mutation G2019S and the sex- and age-matched healthy control were provided by Dr. Randall T. Moon (Howard Hughes Medical Institute/University of Washington). These iPSCs were produced in collaboration with the Tom and Sue Ellison Stem Cell Core (Institute for Stem Cell & Regenerative Medicine, University of Washington) via episomal reprogramming of human primary fibroblasts as described previously (Okita et al., 2011; Yu et al., 2009). On day 0, 1 × 106 cells were electroporated with 1.5 µg each of pCXLE-hsk (Addgene plasmid #27078), pCXLE-hul (Addgene plasmid #27080), and pCXLS-hOCT3/4 (Addgene plasmid #27076) episomal vectors (Okita et al., 2011), resuspended in fibroblast media (DMEM high glucose, 10% FBS, 100 U/ml penicillin/streptomycin) and plated into a gelatin-coated 10 cm dish. On day 1, the cultures were replenished with fresh fibroblast media and on day 3 or 4 the transfected cells were passaged onto a gelatin-coated 10 cm dish and maintained in fibroblast media. On day 7, the cells were passaged at three different densities (1 × 105, 5 × 104, 2 × 104, and 1 × 104) onto 10 cm dishes pre-plated with mouse embryonic fibroblasts (MEFs), and subsequently cultured in DMEM/F12 media supplemented with 0.1 mM sodium butyrate, 50 nM SAHA and 15% KnockOut serum replacement. Media was changed every 2–3 days and observed daily to identify iPSC colony formation. Colonies were picked and plated onto 24-well plates pre-plated with MEFs and cultured in DMEM/F12 media supplemented with 20% KnockOut serum replacement, 1% sodium pyruvate, 1% non-essential amino acids, 100 µM 2-mercaptoethanol, 100 U/ml penicillin/streptomycin, and 20 ng/ml FGF2. IPSC clones were subsequently expanded on MEF feeders and subcultured using Dispase (Thermo Fisher Scientific) until the lines demonstrated robust growth (usually 3–5 passages). The culture medium was then replaced with mTesR1 or mTesR Plus medium (STEMCELL Technologies, Vancouver, Canada) and MEFs were replaced by a Geltrex basement membrane matrix (Thermo Fisher Scientific). Successful fibroblast reprogramming into iPSCs was validated by confirming expression of pluripotency markers and demonstrating trilineage differentiation potential, as described in the Immunofluorescence and RNA isolation and RT-qPCR sections of Materials and methods.

The isogenic and non-isogenic iPSCs were genotyped to confirm the presence of the LRRK2 mutation G2019S using a TaqMan SNP genotyping assay (Thermo Fisher Scientific, Waltham, MA), and karyotyped every 2 months to confirm genomic stability (Cell Line Genetics, Madison, WI). The isogenic and non-isogenic iPSCs were cultured in mTesR1 or mTesR Plus medium (STEMCELL Technologies), regularly passaged as aggregates using ReLeSR (STEMCELL Technologies), and cultured on Geltrex-coated plates (Thermo Fisher Scientific).

Preparation and culture of iPSC-derived NPCs, dopaminergic neurons, and astrocytes

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The step-by-step protocols, media recipes, and quality control experiments to prepare iPSC-derived NPCs, dopaminergic neurons, and astrocytes have been described in extensive detail (de Rus Jacquet, 2019). Briefly, iPSCs were plated at a density of 1.6 × 106 cells/well of a Geltrex-coated 6-well plate and neuralized by dual SMAD inhibition (Kriks et al., 2011): day 0 (100 nM LDN193189, 10 μM SB431542), days 1 and 2 (100 nM LDN193189, 10 μM SB431542, SHH 100 ng/ml, purmorphamine 2 μM), days 3 and 5 (100 nM LDN193189, 10 μM SB431542, 100 ng/ml SHH, 2 μM purmorphamine, 3 μM CHIR99021), and days 7 and 9 (100 nM LDN193189, 3 μM CHIR99021). At day 11, the cells were harvested and replated at 7 × 106 cells/well on a Geltrex-coated 6-well plate. At days 11 and 12, the medium was supplemented with 3 μM CHIR99021, 20 ng/ml FGF2, and 20 ng/ml EGF. At day 13, the differentiation of the iPSCs into midbrain floor-plate NPCs was complete, and the cells were maintained in NPC medium (Neurobasal/B27 without vitamin A, 20 ng/ml FGF2, and 20 ng/mL EGF) supplemented with ROCK inhibitor (Y-27632, STEMCELL Technologies) at each single-cell passage.

To prepare dopaminergic neurons, NPCs were harvested in Accumax cell dissociation solution (Innovative Cell Technologies, San Diego, CA) and plated at 7 × 106 cells/well on a Geltrex-coated 6-well plate. The next day, NPCs were washed with DPBS and incubated in neuron differentiation medium (NB/B27, 0.5 mM dibutyryl cAMP, 10 μM DAPT, 0.2 mM ascorbic acid, 20 ng/ml BDNF, 20 ng/ml GDNF, 1 ng/ml TGF-β3) (Kriks et al., 2011). Neuron differentiation medium was replaced every 2 days for a total of 7–10 days. The resulting culture consisted of neurons and undifferentiated NPCs. 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.

To prepare astrocytes, NPCs were plated on Geltrex-coated plates at 15,000 cells/cm2 in ScienCell astrocyte medium (ScienCell Research Laboratories, Carlsbad, CA), passaged every 7 days at 15,000 cells/cm2, and cryopreserved after 28 days of differentiation or used for experiments (de Rus Jacquet, 2019; Tcw et al., 2017).

Culture of primary human astrocytes

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Fetal human midbrain astrocytes were obtained from ScienCell Research Laboratories. To match the gender of the iPSCs used in this study, the primary astrocytes were selected from a female donor. The cells were cultured in Astrocyte Medium (ScienCell Research Laboratories), plated at a density of 40,000 cells/cm2, cultured for 48 hr, and harvested in TRIzol for RNA-seq analysis.

Preparation of mouse primary cultures

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Primary cultures were prepared via dissection of P1 to P3 mouse pups obtained from C57BL/6NTac WT mice or constitutive LRRK2 G2019S knock-in mice generated and maintained on the C57BL/6NTac background (Matikainen-Ankney et al., 2016) (Taconic Biosciences, NY) using methods approved by the Janelia Institutional Animal Care and Use Committee. The mesencephalic region containing the substantia nigra and ventral tegmental area was isolated stereoscopically, and the tissue was digested with papain (Worthington Biochemical, NJ) for 30 min at 37°C, gently triturated to dissociate the cells, filtered with a cell strainer, and centrifuged to remove papain and cell debris.

Astrocyte isolation procedure

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Astrocytes were prepared as described before with some modifications (McCarthy and de Vellis, 1980). The cells were resuspended in Astrocyte Media (ScienCell Research Laboratories) and plated at a density of 70,000 cells/cm2 in rat collagen-coated flasks. The next day, the old media was removed and replaced with fresh astrocyte media every 3 days until the culture became confluent (5–7 days), at which point the cells were frozen, used for experiments, or expanded by passaging once and no more than twice. For neuron-astrocyte co-cultures, the astrocytes were plated at a density of 9600 cells/well of a 96-well plate.

Neuron isolation procedure and culture

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The cells were resuspended in buffer (phenol-red free HBSS with calcium and magnesium, 1% sodium pyruvate, 1% GlutaMAX, 1% HEPES, 0.5% w/v BSA, 5% FBS, 100 U/ml penicillin and streptomycin) and neurons were isolated by MACS using a mouse neuron isolation kit as recommended by the manufacturer (Miltenyi Biotech). Monocultures of neurons were used for ACM experiments and plated at a density of 45,000 cells/well in Geltrex-coated black 96-well plates with clear bottom. The cultures were maintained in Neurobasal A media supplemented with 2% B27 without vitamin A, BDNF (20 ng/ml), GDNF (20 ng/ml), TGFb (4 ng/ml), cAMP (500 μM), 0.5% FBS, 1% GlutaMAX, and 100 U/ml penicillin and streptomycin. At day 0 of the treatment, the maintenance medium was replaced with 100% ACM and replenished every 3–5 days until analysis. For co-culture experiments, neurons were plated directly onto astrocytes at a density of 15,000–20,000 neurons/well in a 96-well plate, and cultured in Neurobasal A medium supplemented with 0.5% FBS, 1% GlutaMAX, and 100 U/ml penicillin and streptomycin. A 50% medium change was performed every 3 days to replenish nutrients while ensuring the presence of secreted factors in the culture. After 24 hr in culture, isolated or co-cultured neurons were treated with 15 µM 5-fluoro-2'-deoxyuridine for 10 hr to prevent growth of non-neuronal cells.

Preparation of ACM

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Astrocytes were first grown to 80–90% confluence, and astrocyte media was replaced by phenol red-free Neurobasal media supplemented with 2% B27 without vitamin A, 1% GlutaMAX, and 100 U/ml penicillin and streptomycin. The volumes used were 10 ml per T75 flasks and 3.3 ml per T25 flasks. When preparing ACM from iPSC-derived astrocytes, 1% FBS was added to the media to improve survival. ACM was collected after 3 days, stored at 4°C, and the flasks were replenished with fresh media for a second ACM collection. The two batches were then pooled and centrifuged to remove cell debris, sterile filtered, and frozen as single-use aliquots at –20°C.

Isolation of EVs and preparation of EV-free ACM

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EVs were isolated from FBS-free ACM by ultracentrifugation following an established protocol (Purushothaman, 2019). Astrocytes were plated in ScienCell astrocyte medium at a density of 40,000 cells/cm2 in 15 cm dishes (5–8 dishes depending on the number of astrocytes available) and grown to confluence for 24 hr. The cells were subsequently triple washed with PBS to remove cell debris and traces of FBS, and 25 ml of NB/B27 medium was added to each 15 cm dish. The ACM was collected twice, at 48 hr intervals, and processed for EV isolation at 4°C. First, large debris was removed by serial centrifugation steps in a Sorvall ST8R centrifuge (Thermo Fisher Scientific) at 300 × g for 10 min and at 2000 × g for 20 min, followed by 12,000 rpm for 30 min using a fixed angle type 70 Ti rotor (Beckman Coulter, Brea, CA) and polycarbonate aluminum bottles with cap assembly (Beckman Coulter). The cleared supernatant was subsequently centrifuged at 40,000 rpm for 70 min to pellet the EVs using the type 70 Ti rotor in an Optima XPN-80 ultracentrifuge (Beckman Coulter). The supernatant resulting from this high-speed centrifugation is the EV-free ACM, and it was filtered through a 0.22 µm membrane and stored at –80°C. The EV pellet was resuspended in PBS and centrifuged at 40,000 rpm for 70 min using the type 70 Ti rotor in an Optima XPN-80 ultracentrifuge (Beckman Coulter). The pellet obtained after this last centrifugation step was the purified EV fraction, which was resuspended in PBS and processed immediately for cryo-EM or aliquoted and stored at –80°C. The number of CD63+ EVs collected in the EV-enriched fractions was estimated by ELISA (System Biosciences). The ELISA standards provided in the kit are calibrated by NTA to measure the number of exosomes and establish a standard curve based on exosome abundance, and therefore enable an estimation of the number of exosomes in the experimental sample (Figure 3H). Furthermore, the total number of EVs and their size distribution was quantified by NTA (System Biosciences).

Cryo-electron microscopy

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Cryo grids were prepared using two types of TEM grids: 400 mesh Quantifoil R1.2/1.3 grids with empty holes and 400 mesh Quantifoil R1.2/1.3 grids coated with a 2 nm thin carbon layer (Quantifoil Micro Tools GmbH, Germany). The grids were rendered hydrophilic by glow discharge using a Pelco easiGlow glow discharger (Ted Pella Inc, Redding, CA) for 60 s, with a current of 15 mA and a vacuum of 0.39 mbar. The cryo grids were then prepared using a Mark IV Vitrobot (FEI Company, OR) with the operation chamber cooled to 4°C at a set humidity level of 100%. A total of 3 µl of fresh EVs was applied to a glow-discharged grid. The grid was subsequently blotted for 3 s from both sides with blot force 3 and plunged into a liquid ethane bath for vitrification.

The cryo grids were loaded into a Tecnai F20 transmission electron microscope (FEI Company, OR) for imaging through a side entry Gatan 626 cryo holder (Gatan Inc, CA). The microscope was operated at 200 kV and was equipped with a standard Field Emission Gun (s-FEG) and a Gatan K2 Summit direct electron detector (Gatan Inc). Data collection was carried out using the automation software package SerialEM (University of Colorado). To identify the localization of the EVs on the grid, survey maps were first taken for various grid squares at a nominal magnification of 1700×, with a calibrated pixel size of 2.15 nm (corresponding to a calibrated magnification of 2325.6×). Once the EVs were identified on the survey maps, high-magnification images were taken at a nominal magnification of 29,000× with a pixel size of 0.15 nm (corresponding to a calibrated magnification of 33,333×). The illumination was set to produce a dose rate of 10.5 electrons per pixel per second, and the K2 camera was operated in counted mode with dose fractionation of 8 s total exposure time for 40 frames (0.2 s per frame). Frames were aligned for motion correction using the default algorithm in SerialEM.

Transmission electron microscopy

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WT and LRRK2 G2019S astrocytes were cultured in 60 mm dishes to 80% confluence and immersion fixed in freshly prepared 2% (w/v) paraformaldehyde (Sigma-Aldrich) and 2% glutaraldehyde (Electron Microscopy Sciences, Hatfield, PA) in Hanks’ balanced salt solution (20 mM HEPES, pH 7.4) for 2 hr at room temperature. To facilitate handling, cells were pelleted and resuspended in 2% (w/v) agar as described before (Paez-Segala et al., 2015). Cell pellets were then post-fixed in 1% osmium tetroxide (Electron Microscopy Sciences, Hatfield) for 1 hr at room temperature, dehydrated in graded ethanol solutions (30, 50, 70, 80, 95, and 100%), and embedded in Eponate-12 (Ted Pella Inc). Ultrathin sections (60 nm) were cut using a Leica UC6 ultramicrotomes, placed on Formvar/carbon-coated copper grids, and stained with 3% aqueous uranyl acetate (15 min) and Sato’s lead solution (5 min). Sections were imaged using a FEI Spirit BioTWIN transmission electron microscope operating at 80 kV, equipped with a 4k × 4k Gatan OneView Camera.

Immunogold electron microscopy

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Cultured astrocytes were fixed in 4% paraformaldehyde and 0.2% glutaraldehyde in Hanks’ balanced salt solution (20 mM HEPES, pH 7.4) for 2 hr at room temperature. After pelleting, samples were transferred to carriers filled with 20% BSA and frozen in a HPF Compact 01 high-pressure-freezing machine (M. Wohlwend GmbH, Sennwald, Switzerland) for cryo-immobilization. Samples were freeze substituted in acetone containing 0.1% uranyl acetate plus 3% distilled water in a freeze substitution unit (Leica EM AFS2, Leica Microsystems, Buffalo Grove, IL) at −90°C for 20 hr. Afterwards, the temperature was brought up slowly to −45°C; samples were rinsed with pure acetone, and infiltrated with 30, 50, and 70% Lowicryl HM20 resin (Polysciences Inc, Warrington, PA) in acetone for 2 hr each followed by 100% HM20 resin overnight. Samples were embedded in Lowicryl HM20 and UV polymerized for 2 days at −45°C and 2 days at 20°C. Postembedding immunogold labeling was performed on ultrathin HM20 sections (70 nm) of astrocytes to detect CD63. The ultrathin sections were conditioned in blocking buffer containing 1% BSA (w/v, Sigma-Aldrich), 0.01% (v/v) Triton X-100, and 0.01% (v/v) Tween 20 in PBS for 20 min at room temperature, and incubated overnight at 4°C with an anti-CD63 primary antibody (1:20 dilution, Thermo Fisher Scientific, cat# MA1-19281) in blocking buffer. After extensive rinses in PBS, sections were incubated for 2 hr at room temperature with a secondary antibody conjugated to 6 nm gold particles (Jackson ImmunoResearch, cat# 115-195-146) diluted to 1:40 in blocking buffer. Samples were rinsed in PBS, and then fixed with 1% (v/v) glutaraldehyde in PBS for 10 min. Gold particles were amplified with silver enhancement (HQ silver kit from Nanoprobes, Yaphank, NY) for 4 min at room temperature and contrast stained with 3% UA and Sato’s triple lead.

For double-labeling experiments, cultured cells were fixed in 4% paraformaldehyde and 0.2% glutaraldehyde in Hanks’ balanced salt solution (20 mM HEPES, pH 7.4) for 2 hr at room temperature, pelleted in 2% (w/v) agar, cut into small blocks, and infused in 2 M sucrose containing 15% (w/v) polyvinylpyrrolidone (10 kDa) for freezing. Samples were frozen and ultrathin sections were prepared using a Leica UC6 ultramicrotome equipped with a Leica EM FC6 cryochamber. The cryo-sections were lifted onto nickel grids and stored overnight on gelatin at 4°C. Before immunolabeling, the gelatin was liquefied at 37°C, and the sections were washed by floating the sections on droplets of PBS. For immunogold localization, the grids with the attached thin sections were conditioned on droplets containing 1% (w/v) BSA, 0.01% (v/v) Triton X-100, and 0.01% (v/v) Tween 20 in PBS (blocking buffer) for 10 min at room temperature. Grids were incubated for 2 hr in the presence of anti-LRRK2 (1:20 dilution, Abcam, cat# ab133474) and anti-CD63 antibodies (1:40 dilution), or anti-p-αSyn antibody (1:20 dilution, Abcam, cat# ab51253) with mouse monoclonal anti-CD63 (1:40 dilution) in blocking buffer. After extensive rinses in PBS, the sections were incubated for 1 hr at 35°C with a secondary antibody conjugated to 6 nm gold particles (1:40 dilution, Jackson ImmunoResearch, cat# 111-195-144) and a secondary antibody conjugated to 12 nm gold particles (1:20 dilution, Jackson ImmunoResearch, cat# 115-205-146) in blocking buffer. Lastly, grids with the attached thin sections were rinsed in PBS, fixed with 2% (v/v) glutaraldehyde in PBS for 10 min, and negative or positive contrast stained. Electron micrographs were taken with a FEI Spirit BioTWIN transmission electron microscope operating at 80 kV, equipped with a 4k × 4k Gatan OneView camera.

Immunofluorescence

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Cells were grown on German glass coverslips (Electron Microscopy Sciences) or 96-well plates with an optically clear bottom (Ibidi, Gräfelfing, Germany) coated with Geltrex. The cells were washed once with PBS and fixed by adding 4% paraformaldehyde (Thermo Fisher Scientific) for 20 min at room temperature. The cells were subsequently washed once with PBS and blocked/permeabilized in blocking buffer (0.3% TX-100, 1% BSA, 10% FBS in PBS) for 1 hr at room temperature. The cells were then washed once with PBS and incubated in primary antibody overnight at 4°C with the following antibodies: anti-MAP2 (1:3000, Thermo Fisher Scientific, cat# PA1-10005), TH (1:500, PhosphoSolutions, Aurora, CO, cat# 2025-THRAB), anti-CD63 (1:500, Thermo Fisher Scientific, cat# MA1-19281), anti-LRRK2 (1:200, Abcam, cat# ab133474), anti-GFAP (1:500, BD Biosciences, cat# 556328), anti-vimentin (1:100, R&D, Minneapolis, MN, cat# MAB2105), and anti-CD44 (1:500, BD Biosciences, cat# 550538) in BSA buffer (1% BSA in PBS). Alternatively, iPSCs were incubated with primary antibodies provided in the StemLight Pluripotency antibody kit (Cell Signaling Technologies, cat#9656) as per the manufacturer’s instructions. The next day, the cells were washed twice with DPBS, then incubated in Alexa-conjugated secondary antibodies (Thermo Fisher Scientific) diluted 1:1000 in BSA buffer for 1 hr at room temperature. The cells were washed three times with DPBS, then coverslips were mounted on slides with ProLong Diamond with DAPI (Thermo Fisher Scientific) and cured for 24 hr in the dark at room temperature.

RNA isolation and RT-qPCR

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Confluent cell monolayers grown into individual wells of a Geltrex-coated 6-well plate were first washed with DPBS to remove the cell culture medium and cell debris, then homogenized in TRIzol (Thermo Fisher Scientific), and processed immediately or stored at –80°C. Chloroform (20% v/v) was added to the TRIzol samples, which were shaken vigorously for 20 s and allowed to sit at room temperature for 3 min. The samples were subsequently centrifuged at 12,000 × g for 15 min, at 4°C, to separate the aqueous solution from the organic phase. The top, aqueous phase was collected, and mixed 1:1 with ethanol. The RNA-containing samples were processed using an RNeasy kit (Qiagen, Hilden, Germany, cat#74104) following the manufacturer’s instructions, and the isolated RNA was processed immediately or stored at –80°C. cDNA synthesis was carried out with a RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific) as described in the manufacturer’s instructions. Forward and reverse primers for RT-qPCR were designed at exon-exon junctions, using Beacon Designer Lite 8.16 (Premier Biosoft, San Francisco, CA). The primer sequences were as follows:

  • Human Rab27b: 5’AACTGGATGAGCCAACTG3’ (forward) and 5’CTTGCCGTTCATTGACTTC3’ (reverse).

  • Human CD82: 5’GGTTTCGTGGAAGGAAGC3’ (forward) and 5’AAGATCAAGTTGAAGAGGAAGAG 3’ (reverse).

Each RT-qPCR measurement was performed in technical duplicates, with a total of four independent biological replicates. Relative mRNA levels were calculated for each gene using the formula

2ΔΔCt=2{(Ct,TGCt,βactin)LRRK2G20195(Ct,TGCt,βactin)WT

where ‘Ct, TG’ represents the cycle threshold (Ct) for the target gene (TG), and ‘Ct, βactin’ represents the cycle threshold for the loading reference Actb (β-actin). The KAPA SYBR FAST qPCR (Roche, Basel, Switzerland) master mix was used to prepare the reactions, and RT-qPCR was performed using a Roche LightCycler 480.

Tri-lineage differentiation potential of the newly reprogrammed, non-isogenic iPSCs was assessed using a validated protocol based on embryoid body (EB) formation and spontaneous differentiation (Thermo Fisher Scientific, Pub. No. MAN0013784). Briefly, iPSCs were dissociated as large aggregates using ReLeSR (STEMCELL Technologies), resuspended in EB culture medium (DMEM/F12, 20% KnockOut Serum Replacement, 1% MEM non-essential amino acids, 55 µm 2-mercaptoethanol, 100 U/ml penicillin/streptomycin) supplemented with 4 ng/ml FGF2, and plated on non-adherent tissue culture dishes. After 24 hr, the growth medium was changed to fresh EB medium without FGF2 and replaced every other day. On day 10, EBs were dissociated using Accutase, homogenized in TRIzol, and mRNA and cDNA were obtained as described above. Expression of endoderm, ectoderm, mesoderm, and self-renewal marker genes was assessed using a TaqMan hPSC Scorecard Panel (Thermo Fisher Scientific) as per the manufacturer’s instructions.

RNA-sequencing and analysis

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Samples were homogenized in TRIzol, then total RNA was isolated according to the manufacturer’s instructions, quantified by Nanodrop (Thermo Fisher Scientific), and diluted to 1 ng/µl in nuclease-free water. A total of 1 ng of RNA was added to 2.5 µl of cell lysis buffer (nuclease-free water with 0.2% v/v Triton X-100 and 0.1 U/µl RNase inhibitor), and subjected to cDNA synthesis and amplification as described before (Cembrowski et al., 2018). Libraries were prepared using a modified Nextera XT DNA protocol (Illumina, San Diego, CA) where 5 µM P5NEXTPT5 was substituted for the i5 primers in the kit. Libraries were quantified by qPCR (Roche), normalized to 2 nM, then pooled and sequenced on a NextSeq550 flowcell with 25 bases in read 1, 8 bases in the i7 index read, and 50 bases in read 2. The control library phiX (Illumina) was spiked in at a final concentration of 15% to improve color balance in read 1.

STARsolo analysis pipeline

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Sequencing adapters were trimmed from the reads with Cutadapt v2.10 (Martin, 2011) prior to alignment with STAR v2.7.5c (Dobin et al., 2013) to the Homo sapiens GRCh38.95 genome assembly (Ensembl). Gene counts were generated with the STARsolo algorithm using the following additional parameters: “--soloType CB_UMI_Simple --soloCBwhitelist smartscrb_whitelist.txt --soloCBstart 2 --soloCBlen 8 --soloUMIstart 10 --soloUMIlen 10 --soloBarcodeReadLength 0 --soloCBmatchWLtype 1 MM_multi_pseudocounts --soloStrand Forward --soloFeatures Gene --soloUMIdedup 1 MM_All --soloUMIfiltering MultiGeneUMI --soloCellFilter None”. The full set of 384 barcodes designed for this assay was used as the whitelist. Gene counts for the subset of barcodes used in this experiment were extracted using custom R scripts.

Differential gene expression analysis was performed using EBseq v3.8 in R (Leng et al., 2013), with condition 1 being LRRK2 G2019S and condition 2 being WT. EBseq calculated a median normalization of the sequencing counts using the median of ratios methods (Anders and Huber, 2010). First, the geometric mean of the sequencing counts was calculated for each gene and across all samples to create a pseudo-reference sample, and the ratio of the sequencing counts to the pseudo-reference counts was calculated for every gene. Then, for each sample, the median value of all the ratios was taken as the normalization factor. Finally, the median normalized sequencing counts were calculated by dividing the sample’s sequencing counts by the sample’s normalization factor. A false discovery rate of 0.05 and a fold change threshold of 1.4 or 0.7 were used to identify dysregulated genes in LRRK2 G2019S vs. WT astrocytes. A k-means clustering algorithm (k = 3) was used to group genes into low, moderate, and highly expressed transcripts based on their log10(1 + median normalized counts) value, using ComplexHeatmap v3.11 in R (Gu et al., 2016). GO enrichment analysis was done using the Database for Annotation, Visualization, and Integrated Discovery (DAVID v6.8, https://david.ncifcrf.gov/) (Huang et al., 2009a; Huang et al., 2009b). A list of all genes detected in all the samples was exported for use as the background gene set in DAVID.

Accession numbers

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Raw RNA-seq datasets have been deposited in the GEO data repository (NCBI), accession number GSE152768.

Internalization of CD63+ exosomes by dopaminergic neurons

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The study of the internalization of astrocyte-derived EVs by dopaminergic neurons followed a specific multistep protocol that is detailed below: (1) prepare separate cultures of dopaminergic neurons and astrocytes, (2) transduce each cell type to express a protein of interest (tdTomato or CD63-GFP), (3) harvest the astrocytes and plate them directly on the neurons, and (4) monitor the internalization of EVs by live cell microscopy. As a first step to this protocol, astrocytes were plated at 40,000 cells/cm2 in ScienCell astrocyte medium and transduced the next day with a lentivirus expressing CD63-GFP (produced from pCT-CD63-GFP, System Biosciences, Palo Alto, CA). The astrocytes expressed GFP between 48 and 72 hr after transduction. In parallel, iPSC-derived dopaminergic neurons were plated at 70,000 cells/well in a Geltrex-coated 96-well optically clear plate (Ibidi) in neuron differentiation medium. After 2–3 days in culture, the dopaminergic neurons were transduced with an adeno-associated virus rh10 vector expressing CAG-tdTomato. After 72 hr, the rh10-CAG-tdTomato-containing medium was removed, and 20,000 CD63-GFP astrocytes were plated in each neuron-containing well of the 96-well plate, on top of the tdTomato dopaminergic neurons. To ensure a healthy co-culture, the cells were maintained in NB/B27 with 0.5% FBS for 3 days before imaging. At the time of imaging, the cell culture medium was changed to prewarmed phenol red-free medium to reduce nonspecific background fluorescence. The uptake of astrocyte-secreted CD63-GFP EVs by dopaminergic neurons was monitored for 2 hr by live-cell confocal microscopy using a temperature- and CO2-controlled incubation chamber mounted on a Zeiss LSM 880 inverted laser scanning confocal microscope.

Confocal and widefield microscopy

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The following microscopes were used: (1) Zeiss LSM 880 inverted laser scanning confocal microscope equipped with a plan-apochromatic 63× oil objective lens (Zeiss, NA = 1.4), 40× oil objective lens (Zeiss, NA = 1.3), 405 nm, 488 nm, and 561 nm laser lines, and ZEN software (Zeiss, Oberkochen, Germany). (2) Nikon Eclipse TiE inverted microscope equipped with a CFI Plan Apo Lambda 10× (Nikon Instruments, NA = 0.45) and a CFI Plan Apo VC 60× oil objective lens (Nikon Instruments, NA = 1.4), sCMOS camera (Zyla 4.2, Andor), SPECTRA X light engine illumination source (Lumencor, Beaverton, OR), and NIS-Elements software (Nikon Instruments, Tokyo, Japan). The experimental details for each imaging experiment are provided in the appropriate subsection in Materials and methods.

Image analysis

CD63+ marker distribution in astrocytes

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Images in Zeiss CZI file format were properly converted to Imaris native IMS file format and analyzed in Imaris 9.3 (Bitplane, Belfast, UK). Each image was composed of three channels representing the CD44 membrane marker, CD63 exosome/MVB marker, and DAPI nuclear marker. The components in each fluorescence channel were detected using the cell creation wizard without the need for additional image preprocessing. Intensity thresholds were calculated for each channel such that the values were applied to all datasets since they were acquired using identical acquisition settings to minimize user bias; however, for cases in which the cytoplasmic staining was heterogeneous, the intensity thresholds were manually adjusted. For nucleus detection, a size filter was used to remove small and irregular shapes. For cell detection, a surface was generated by expanding it on a single nucleus accompanied by a size filter. For vesicle detection, CD63+ structures were modeled as spots with an estimated diameter of 0.8 µm with region growing disabled. Statistics were exported for each cell object, and ‘Vesicles Distance to Closest Nucleus’ statistics were used to analyze CD63+ MVB distribution in astrocytes.

Colocalization of LRRK2 and CD63

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Images in Zeiss CZI file format were properly converted to Imaris native IMS file format and analyzed in Imaris 9.3. All fluorescence channels were first deconvolved using the ‘robust’ algorithm (15 iterations) in Imaris ClearView, which estimated the PSF based on a widefield fluorescence template and the acquisition parameters used. A surface object was created for the CD63 (exosome/MVB) channel with background subtraction enabled (Gaussian filter width of 1 µm). LRRK2 was detected as spots with an estimated diameter of 0.4 µm and with background subtraction enabled; ‘region growing on spots’ was used to more accurately determine their intersections with surfaces. Once LRRK2 spots were created, they were segregated into two populations depending on if the distances from the spot centers to the exterior of CD63 surface objects were greater than 0.2 µm (half the estimated diameter); this was accomplished using the ‘Find Spots Close To Surface’ MATLAB (MathWorks, Natick, MA) algorithm from Imaris XTensions. A cell object was then created by importing CD63 surface objects as cells and LRRK2-associated spots as vesicles. The relevant statistics for each LRRK2 spot, including the cell IDs corresponding to the associated CD63 surface objects, were exported. The data were aggregated in MATLAB to generate histograms.

Internalization of CD63-GFP EVs by dopaminergic neurons

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Confocal z-stack images were properly converted from Zeiss CZI file format to Imaris native IMS file format and analyzed in Imaris 9.3. The green channel with the CD63-GFP signal was first deconvolved (10 iterations) in Imaris ClearView, which estimated the PSF based on a confocal fluorescence template and the acquisition parameters used. The red channel with the tdTomato signal was processed with a Gaussian filter (0.18 µm filter width) to reduce image noise. A total of three layers were created to represent the total neuronal surfaces, soma-only surfaces, and CD63-GFP EVs. Neuronal surfaces encompassing the soma and neurites were modeled using the ‘surface’ tool with background subtraction and a sphericity filter to remove background signal. The soma layer was modeled using the ‘surface’ tool with background subtraction enabled, ‘split touching objects’ enabled, and with seed point diameters of 10 µm to eliminate non-soma surfaces. The resulting surfaces created by Imaris were manually curated if necessary to trim and delete non-soma surfaces and ensure an accurate representation of the somas. CD63-GFP exosomes were detected as spots with an estimated diameter of 0.4 µm and background subtraction enabled. Once CD63 spots were created, they were segregated into two populations (intracellular and extracellular) depending on their distances from the spot centers to the exterior of neuron or soma surface objects; this was accomplished using the ‘Find Spots Close To Surface’ MATLAB (MathWorks) algorithm from Imaris XTensions.

Astrocyte effects on dopaminergic neuron viability and morphological changes

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Dopaminergic neurons were identified as MAP2+/TH+ populations, and the number of MAP2+/TH+ neurons after experimental treatments was counted as a measure of neuronal viability. The length of the dendrites was measured by tracing the MAP2 signal of dopaminergic neurons using the NIS Elements (Nikon Instruments) tracing tool. Sample identity was masked using a numerical code to blind the experimenter during image analysis.

Statistical analysis

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Data was analyzed using GraphPad Prism version 8.0 (La Jolla, CA). p-Values were calculated using two-tailed unpaired Student’s t-test assuming equal standard deviation (Figure 1E,G,H, Figure 2B, Figure 3C,H, Figure 4B,E, Figure 6C , Figure 7C,D, Figure 8B,C,E,H-K, Figure 9C), Mann–Whitney test (Figure 5B and D), chi-square test (Figures 4D and 6B), or using one-way ANOVA with Newman–Keuls multiple comparisons (Figure 7F) or Tukey’s multiple comparisons correction (Figure 9A, E and G).

Data availability

Sequencing data have been deposited in GEO under accession codes GSE152768.

The following data sets were generated
    1. Jacquet AdR
    2. Tancredi JL
    3. Lemire AL
    4. DeSantis MC
    5. W-P Li
    6. O'Shea EK
    (2021) NCBI Gene Expression Omnibus
    ID GSE152768. 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.

References

    1. Liddelow SA
    2. Barres BA
    (2017) Reactive astrocytes: Production
    Function, and Therapeutic Potential. Immunity 46:957–967.
    https://doi.org/10.1016/j.immuni.2017.06.006

Decision letter

  1. Hugo J Bellen
    Reviewing Editor; Baylor College of Medicine, United States
  2. Huda Y Zoghbi
    Senior Editor; Texas Children's Hospital, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The contribution of glia to the long-term health of neurons is of high interest. Using genetic models of Parkinson's disease (PD), this study shows that PD-related LRRK2 astrocytes derived from patient induced pluripotent stem cells leads to alterations in astrocyte-released extracellular vesicle size, distribution, and content. Moreover, it reveals that these vesicles are taken up by dopaminergic neurons but that they contain diminished neurotrophic support when derived from PD-related LRRK2 astrocytes. Together, this suggests a non-cell autonomous effect on the health of dopaminergic neurons, leading to the selective vulnerable cells that are typically lost in PD.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "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" for consideration by eLife. Your article has been reviewed by two peer reviewers and a reviewing editor, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

The discussion between reviewers and editors centered on a few key points. First, all reviewer's felt that it is of utmost important 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 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.

For these reasons, the study, while of interest, is felt too preliminary to consider further revisions.

Reviewer #1:

Jacquet et al., 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.

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 proposed mechanism.

1. 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.

2. 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).

3. 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.

4. 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?

5. 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+).

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

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.

1) 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?

2) 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.

3) 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?

4) Authors should provide data on % dopaminergic neurons generated in the cultures.

5) 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.

6) Please provide details on the number of iPSC lines used for these experiments.

7) Clarify whether the WT neurons used for co-culture were derived from the isogenic human neurons?

https://doi.org/10.7554/eLife.73062.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

First, all reviewer's felt that it is of utmost important 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.

We appreciate the reviewers’ concerns, and have now collected additional data using (i) a second pair of iPSCs with the LRRK2 G2019S mutation and corresponding age-and sex- matched non-isogenic control, and (ii) primary midbrain cultures isolated from a LRRK2 G2019S mouse model of PD. We demonstrate that experiments performed with the non-isogenic pair and the primary mouse cultures reproduce findings obtained using the isogenic iPSC line. In addition, we note that some of the data on neuron atrophy have been independently described in a recent publication utilizing distinct iPSC lines (di Domenico et al., 2019), supporting our observations. The updated manuscript now contains (i) a second RNA-sequencing (RNA-seq) analysis of astrocytes derived from the non-isogenic pair (Figure 1D,E), (ii) quantification of the distance of CD63+ MVBs from the nuclear membrane in WT and LRRK2 G2019S isogenic and non-isogenic astrocytes (Figure 5D,E), (iii) analysis of dendrite length and viability of dopaminergic and nondopaminergic neurons exposed to WT or mutant astrocytes using the second non-isogenic iPSC pair and mouse primary cultures (Figure 8E-K, Supplementary Figure 5), (iv) quantification of dendrite length in mouse primary dopaminergic neurons exposed to conditioned medium prepared from mouse primary WT or mutant astrocytes (Figure 9C,D), and (v) an analysis of down-regulated genes by RNA-seq in both isogenic and non-isogenic astrocyte pairs. We have updated the results and material and methods section to reflect the new data:

Results – Differentiation of LRRK2 G2019S iPSCs and gene-corrected isogenic controls into neural progenitor cells and astrocytes

“We obtained iPSCs reprogrammed from the dermal fibroblasts of two patients with the LRRK2 G2019S mutation, and one line was gene-corrected to produce an isogenic control (Reinhardt et al., 2013) while the second line was paired with iPSCs derived from a sex- and age-matched healthy individual (Supplementary Figure 1A-C). This enables the direct comparison of two independent pairs of iPSCs to take into consideration the inherent variability that exists between different patient-derived cell lines. Of particular importance, one of our disease and control iPSC pair differs only in a single point mutation in LRRK2, which is essential to ensure that the experimental observations are a consequence of the mutation of interest, and do not result from differences in genetic background, or from differences that could arise during fibroblast reprogramming of each non-isogenic pair (Gore et al., 2011; Soldner et al., 2009).”

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 highly up-regulated, and include 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 Files 1 and 2).”

Results – The LRRK2 mutation G2019S alters MVB distribution in iPSC-derived astrocytes The median distance of CD63+ MVBs to the nuclear membrane was 9.2 µm in “LRRK2 G2019S astrocytes compared to 4.8 µm in isogenic controls, and 11.5 µm in LRRK2 G2019S astrocytes compared to 7.5 µm in the non-isogenic control. Furthermore, 75 % of the isogenic control CD63+ MVBs were localized at a distance to the nucleus smaller than 11.23 µm, compared to 19.34 µm in LRRK2 G2019S astrocytes (Figure 5B-C). In non-isogenic lines, 75 % of the control CD63+ MVBs were localized at a distance to the nucleus smaller than 15.82 µm, compared to 23.79 µm in LRRK2 G2019S astrocytes (Figure D-E).”

Results – LRRK2 G2019S iPSC-derived astrocytes induce a non-cell autonomous loss of iPSC-derived dopaminergic neurons

“The neuronal dendrites shortened from an average of 60 µm in WT to 45 µm in LRRK2 G2019S isogenic cultures (Figure 8C). Similar morphological changes were observed when dopaminergic neurons were co-cultured with the second line of LRRK2 G2019S astrocytes compared to nonisogenic controls (Figure 8E-F), and these results are in agreement with a previous report that LRRK2 G2019S astrocytes induce loss of dopaminergic neurons compared to astrocytes derived from a healthy (non-isogenic) donor (di Domenico et al., 2019). To further confirm our results, we measured neuron survival using mouse primary midbrain cultures that consist of purified postmitotic dopaminergic and GABAergic neurons co-cultured with purified astrocytes. A 14-day coculture of WT neurons with LRRK2 G2019S astrocytes resulted in loss of 37 % more of the dopaminergic neurons than co-cultures containing WT astrocytes (Figure 8G, H). Moreover, this neuronal death was selective for dopaminergic neurons (Figure 8I), recapitulating a key feature of PD. LRRK2 G2019S astrocytes also reduced the viability of LRRK2 G2019S dopaminergic neurons (Figure 8J), but did not affect non-dopaminergic neuronal populations (Figure 8K).”

“To determine if these morphological changes are mediated by direct contact between astrocytes and neurons or if they rely solely on astrocyte secreted factors, WT dopaminergic neurons were cultured in ACM prepared from LRRK2 G2019S or isogenic control astrocytes. After 14 days in culture, the average dendrite length of dopaminergic neurons exposed to WT ACM was 64.4 µm, compared to 50.3 µm if the dopaminergic neurons were exposed to basal NB/B27 medium (Figure 9A). This observation suggests that WT ACM, but not basal medium, contains neurotrophic factors necessary for dendrite outgrowth. LRRK2 G2019S ACM mimicked the phenotype observed by direct co-culture, with a 26 % increase in the number of short neurites (0-40 µm), and the absence of long neurites (160-200 µm) compared to neurons exposed to WT ACM (Figure 9B). The average dendrite length of dopaminergic neurons cultured in basal medium or LRRK2 G2019S ACM is equivalent (50.3 µm vs 49.1 µm respectively) (Figure 9A). When mouse primary midbrain neurons are exposed to ACM prepared from LRRK2 G2019S mouse midbrain astrocytes, shortening of dendrite length is also observed in vulnerable dopaminergic neuron populations (Figure 9C,D). In addition, we found that LRRK2 G2019S mouse primary dopaminergic neuron survival is affected by LRRK2 G2019S but not WT ACM (Supplementary Figure 5).”

Material and Methods – Culture of iPSCs

“Patient-derived iPSCs with the LRRK2 mutation G2019S and the corresponding isogenic control were provided by Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute). The second line of patient-derived iPSCs with the LRRK2 mutation G2019S and the sex- and age-matched healthy control were provided by Dr. Randall T. Moon (Howard Hughes Medical Institute/University of Washington). These iPSCs were produced in collaboration with the Tom and Sue Ellison Stem Cell Core (Institute for Stem Cell and Regenerative Medicine, University of Washington) via episomal reprogramming of human primary fibroblasts as described previously (Okita et al., 2011; Yu et al., 2009). On Day 0, 1x106 cells were electroporated with 1.5 µg each of pCXLE-hsk (Addgene plasmid #27078), pCXLE-hul (Addgene plasmid #27080) and pCXLS-hOCT3/4 (Addgene plasmid #27076) episomal vectors (Okita et al., 2011), resuspended in fibroblast media (DMEM high glucose, 10 % FBS, 100 U/ml penicillin/streptomycin) and plated into a gelatin-coated 10 cm dish. On Day 1, the cultures were replenished with fresh fibroblast media and on Day 3 or 4 the transfected cells were passaged onto a gelatin-coated 10 cm dish and maintained in fibroblast media. On Day 7, the cells were passaged at three different densities (1x105, 5x104, 2x104 and 1x104) onto 10 cm dishes preplated with mouse embryonic fibroblasts (MEFs), and subsequently cultured in DMEM/F12 media supplemented with 0.1 mM sodium butyrate, 50 nM SAHA and 15 % KnockOut serum replacement. Media was changed every 2 to 3 days and observed daily to identify iPSC colony formation. Colonies were picked and plated onto 24-well plates pre-plated with MEFs and cultured in DMEM/F12 media supplemented with 20 % KnockOut serum replacement, 1 % sodium pyruvate, 1 % non-essential amino acids, 100 µM 2-Mercaptoethanol, 100 U/ml penicillin/streptomycin and 20 ng/mL FGF2. IPSC clones were subsequently expanded on MEF feeders and subcultured using Dispase (Thermo Fisher Scientific) until the lines demonstrated robust growth (usually three to five passages). The culture medium was then replaced with mTesR1 or mTesR Plus medium (StemCell Technologies, Vancouver, Canada) and MEFs were replaced by a Geltrex basement membrane matrix (Thermo Fisher Scientific). Successful fibroblast reprogramming into iPSCs was validated by confirming expression of pluripotency markers and demonstrating tri-lineage differentiation potential, as described in the Immunofluorescence and RNA isolation and RT-qPCR sections of the Material and Methods.”

Material and Methods – Preparation of mouse primary cultures

“Primary cultures were prepared via dissection of P1 to P3 mouse pups obtained from C57BL/6NTac WT mice or constitutive LRRK2 G2019S knock-in mice generated and maintained on the C57BL/6NTac background (Matikainen-Ankney et al., 2016) (Taconic Biosciences, NY) using methods approved by the Janelia Institutional Animal Care and Use Committee. The mesencephalic region containing the substantia nigra and ventral tegmental area was isolated stereoscopically, and the tissue was digested with papain (Worthington Biochemical, NJ) for 30 min at 37 °C, gently triturated to dissociate the cells, filtered with a cell strainer and centrifuged to remove papain and cell debris.

Astrocyte isolation procedure. Astrocytes were prepared as described before with some modifications (McCarthy and de Vellis, 1980). The cells were resuspended in Astrocyte Media (ScienCell Research Laboratories) and plated at a density of 70,000 cells/cm2 in rat collagencoated flasks. The next day, the old media was removed and replaced with fresh astrocyte media every 3 days until the culture became confluent (5 to 7 days), at which point the cells were frozen, used for experiments, or expanded by passaging once and no more than twice. For neuronastrocyte co-cultures the astrocytes were plated at a density of 9,600 cells/well of a 96-well plate.”

“Neuron isolation procedure and culture. The cells were resuspended in buffer (phenol-red free HBSS with calcium and magnesium, 1 % sodium pyruvate, 1 % Glutamax, 1 % HEPES, 0.5 % w/v BSA, 5 % FBS, 100 U/ml penicillin and streptomycin) and neurons were isolated by magneticactivated cell sorting (MACS) using a mouse neuron isolation kit as recommended by the manufacturer (Miltenyi Biotech). Monocultures of neurons were used for ACM experiments, and plated at a density of 45,000 cells/well in Geltrex-coated black 96-well plates with clear bottom. The cultures were maintained in Neurobasal A media supplemented with 2 % B27 without vitamin A, BDNF (20 ng/mL), GDNF (20 ng/mL), TGFb (4 ng/mL), cAMP (500 uM), 0.5 % FBS, 1 % Glutamax, 100 U/ml penicillin and streptomycin. At day 0 of the treatment, the maintenance medium was replaced with 100 % ACM and replenished every 3 to 5 days until analysis. For coculture experiments, neurons were plated directly onto astrocytes at a density of 15,000 to 20,000 neurons/well in a 96-well plate, and cultured in Neurobasal A medium supplemented with 0.5 % FBS, 1 % Glutamax, 100 U/ml penicillin and streptomycin. A 50 % medium change was performed every 3 days to replenish nutrients while ensuring the presence of secreted factors in the culture. After 24 h in culture, isolated or co-cultured neurons were treated with 15 µM 5-Fluoro-2'deoxyuridine for 10 h to prevent growth of non-neuronal cells.”

Material and Methods – Immunofluorescence

“Alternatively, iPSCs were incubated with primary antibodies provided in the StemLight Pluripotency antibody kit (Cell Signaling Technologies, cat. #9656) as per the manufacturer’s instructions.”

Material and Methods – RNA isolation and RT-qPCR

“Tri-lineage differentiation potential of the newly reprogrammed, non-isogenic iPSCs was assessed using a validated protocol based on embryoid body (EB) formation and spontaneous differentiation (ThermoFisher Scientific, Pub. No. MAN0013784). Briefly, iPSCs were dissociated as large aggregates using ReLeSR (StemCell Technologies), resuspended in EB culture medium (DMEM/F12, 20 % KnockOut Serum Replacement, 1 % MEM non-essential amino acids, 55 µm 2-Mercaptoethanol, 100 U/ml penicillin/streptomycin) supplemented with 4 ng/mL FGF2, and plated on non-adherent tissue culture dishes. After 24 h, the growth medium was changed to fresh EB medium without FGF2, and replaced every other day. On Day 10, EBs were dissociated using Accutase, homogenized in Trizol, and mRNA and cDNA were obtained as described above. Expression of endoderm, ectoderm, mesoderm and self-renewal marker genes was assessed using a TaqMan hPSC Scorecard Panel (Thermo Fisher Scientific) as per the manufacturer’s instructions.”

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 hep define mechanism.

The isolation of specific neurotrophic factors contained in the astrocyte conditioned medium (ACM)/extracellular vesicles (EVs) and rescue experiments are essential to understand the molecular mechanisms involved in the loss of dopaminergic neurons. While this is beyond the scope of our study, the new version of the manuscript now provides a summary of down-regulated genes of interest identified by RNA-seq analysis of LRRK2 G2019S and control astrocytes (new Figure 10). Gene ontology analysis reveals impaired cholesterol and isoprenoid biosynthesis in both our isogenic and non-isogenic lines of iPSC-derived mutant astrocytes, and we include two heatmaps showing the most significantly down-regulated genes associated with lipid biogenesis (Figure 10B) and cell survival (Figure 10C) in LRRK2 G2019S vs WT astrocytes. Identification of an impaired cholesterol pathway in LRRK2 G2019S astrocytes is particularly interesting because astrocytes are the main producers and suppliers of cholesterol in the brain, and cholesterol is not only a signaling factor but also essential for cellular growth and metabolism (Czuba, Steliga, Lietzau, and Kowianski, 2017; Pfrieger and Ungerer, 2011). Therefore, it is reasonable to hypothesize that dopaminergic neurons in our models lack astrocyte-derived cholesterol supplied via EVs or lipoproteins, which in turn limits their growth and survival. The manuscript has been updated to reflect these findings, and the new text reads as follows:

Results section:

“The nature of the secreted factors, either those secreted directly into the media or those enclosed in EVs, could be proteins, small signaling molecules, or essential lipids. A gene ontology analysis of down-regulated genes in LRRK2 G2019S astrocytes revealed a defect in cholesterol as well as isoprenoid biosynthetic processes (Figure 10 A,B) in both isogenic and non-isogenic lines. Furthermore, genes associated with cell growth and survival were significantly downregulated in mutant astrocytes, including BIRC5, FLOT1, HTRA1, PAK1, KCNMA1 and LZTS1 (Figure 10C). These genes regulate neuronal growth (BIRC5, FLOT1, PAK1, LZTS1) and/or function (FLOT1, PAK1, LZTS1) (Contet, Goulding, Kuljis, and Barth, 2016; Du et al., 2020; Kawaue et al., 2019; Ko et al., 2011; Launay et al., 2008; Pan et al., 2015; Swanwick, Shapiro, Vicini, and Wenthold, 2010), and can be found in EVs (FLOT1, HTRA1, KCNMA1) (Skog et al., 2008), suggesting that they could be shuttled between neurons and astrocytes via this communication pathway.”

Discussion section:

“Neurons rely heavily on astrocytes via mechanisms involving metabolic coupling whereby astrocytes produce and supply a unique combination of cholesterol, cholesterol precursors and fatty acids in the form of lipoproteins (Pfrieger and Ungerer, 2011). […] Collectively, these observations suggest that LRRK2 G2019S astrocytes may fail to deliver important factors to dopaminergic neurons. Further experimentation is needed to test this hypothesis.”

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.

As suggested by the reviewer, we have now included a nanotracking particle analysis (NTA) to complement our cryo-EM approach. We quantified the total number of EVs secreted in WT vs. G2019S astrocytes by NTA and find that there are no significant differences in the number of EVs secreted by WT and LRRK2 G2019S (Figure 3C). The updated text reads as follows:

Results section:

“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). [...] 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.”

Material and Methods:

“The ELISA standards provided in the kit are calibrated by NTA to measure the number of exosomes and establish a standard curve based on exosome abundance, and therefore enable an estimation of the number of exosomes in the experimental sample (Figure 3H). Furthermore, the total number of EVs and their size-distribution was quantified by NTA (System Biosciences).”

For these reasons, the study, while of interest, is felt too preliminary to consider further revisions.

Reviewer #1:

Jacquet et al., 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.

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 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). 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.

In his/her concerns, 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).

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 upregulated 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 nonisogenic astrocytes, and is comprised of a total of 67 (isogenic pair) or 95 (non-isogenic pair) genes (Supplementary Files 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 and 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). […] 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.”

1. 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:

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 RNAsequencing 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 iPSCderived astrocytes into independent clusters, demonstrating that our differentiation strategy produces distinct cell types (Supplementary Figure 2C-D). Importantly, the transcriptome of iPSCderived 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 spontaneous and transient calcium signaling and synaptosome uptake (Supplementary Figure 2E-F).”

2. 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 CD63positive 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 subpopulation-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 RTqPCR 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.

3. 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 CD63+ 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:

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 and 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 and Gould, 2019; Schoneberg, Lee, Iwasa, and Hurley, 2017).”

4. 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 3EG, and the text has been updated accordingly. 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 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, and Futter, 2014; Pegtel and 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.”

5. 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. We refer the reviewer to General comments, Comment no 3 for a more detailed response.

6. 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. 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, celltype 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 CD63positive 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.”

7. 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. 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, and 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.

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 colocalization 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 α 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.”

8. 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.

9. 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.

10. 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.

1) 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. We would like to refer the reviewer to General comments, comment no 2 for more detailed explanation of the new data and accompanying text.

2) 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 cocultured 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). We refer the reviewer to our response to reviewer 1, comment no 13 more detailed explanations of the new data and accompanying text.

3) 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:

“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 PDrelated 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 astrocytemediated dopaminergic neuron atrophy.”

4) 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. 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.”

5) 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:

“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).”

6) 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. We would like to refer the reviewer to General comments, comment no 1. for detail explanations of the new models and data collected.

7) 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 nonisogenic iPSC-derived cells.

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https://doi.org/10.7554/eLife.73062.sa2

Article and author information

Author details

  1. Aurelie de Rus Jacquet

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    aureliederus@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5548-8045
  2. Jenna L Tancredi

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Andrew L Lemire

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0624-3789
  4. Michael C DeSantis

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Data curation, Formal analysis, Methodology, Software, Validation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7214-2740
  5. Wei-Ping Li

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  6. Erin K O'Shea

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Validation, Writing – review and editing
    Competing interests
    is Chief Scientific Officer and a Vice President at the Howard Hughes Medical Institute, one of the three founding funders of eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2649-1018

Funding

No external funding was received for this work.

Acknowledgements

We thank the members of the O’Shea lab and our colleagues Dr. Deepika Walpita, Dr. Jennifer Lippincott-Schwartz, Dr. Ulrike Heberlein, and Dr. Erik Snapp for helpful discussions and feedback, and Michelle Quiambao for administrative support. We also thank Dr. Damien Alcor and the Light Microscopy Center at Janelia for assistance setting up the imaging platform, Dr. Zhiheng Yu and the staff at Janelia’s Cryo-Electron Microscopy and Electron Microscopy Center, and the staff at Janelia’s Virus Services. We thank Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R Schöler (Max-Planck Institute) for providing the iPSCs with the LRRK2 G2019S mutation and isogenic control, and Dr. Randall T Moon (Howard Hughes Medical Institute/University of Washington) for providing the non-isogenic iPSC lines. This work was supported by the Howard Hughes Medical Institute.

Ethics

Patient-derived induced pluripotent stem cells were obtained from RUCDR Infinite Biologics, a University-based biorepository. A second line was provided by Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute), for which informed consent was obtained from all patients prior to cell donation and the Ethics Committee of the Medical Faculty and the University Hospital Tübingen previously approved this consent form (see publication by Reinhardt et al., 2013. Cell Stem Cell, doi 10.1016/j.stem.2013.01.008).

Primary cultures were prepared via dissection of P1 to P3 mouse pups obtained from C57BL/6NTac WT mice or constitutive LRRK2 G2019S knock-in mice generated and maintained on the C57BL/6NTac background (Matikainen-Ankney et al., 2016) (Taconic Biosciences, NY) using methods approved by the Janelia Institutional Animal Care and Use Committee (IACUC protocol #18-168).

Senior Editor

  1. Huda Y Zoghbi, Texas Children's Hospital, United States

Reviewing Editor

  1. Hugo J Bellen, Baylor College of Medicine, United States

Publication history

  1. Preprint posted: July 2, 2020 (view preprint)
  2. Received: August 15, 2021
  3. Accepted: September 28, 2021
  4. Accepted Manuscript published: September 30, 2021 (version 1)
  5. Version of Record published: October 13, 2021 (version 2)

Copyright

© 2021, de Rus Jacquet et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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