There is accumulating interest in proteomic analysis of CSF and CSF-derived EVs to identify biomarkers for neurodegenerative disorders. Here, we report the first proteomic analysis of CSF-derived EV samples isolated from former NFL players. Our proteomic analysis reports a slightly higher enrichment rate of EV-specific molecules in our CSF-derived EV preparation than previous reports, despite the lower number of identified molecules (Street et al., 2012; Chiasserini et al., 2014; Lee et al., 2016; Manek et al., 2018). The MagCapture exosome isolation kit employed here has been reported to have a higher purity of EV than other exosome isolation kits, resulting in the lower protein yields (Nakai et al., 2016). For these reasons, although we used 3 mL of CSF per sample in the present study, the number of identified proteins was lower than other have reported. In this individual proteomics study, we analyzed 4 former NFL players and 5 CTRL groups but found no significant differences between the groups. This could be due to the small sample size and some of the NFL players may not have CTE although they are high risk group. A further study is necessary with increased sample size and follow up with the former NFL players. When our proteomics dataset was compared with the CSF proteome dataset, only 114 proteins were commonly detected between total CSF and CSF-derived EVs, and they were mostly non-EV molecules (Dayon et al., 2019). On the other hand, our proteomic profiling data indicated that CSF-derived EVs are enriched in molecules from microglia, astrocytes, oligodendrocytes, and neurons to a lesser extent. Moreover, in CSF derived EVs, proteins specific to such brain cell as microglia (S100A9, CTSD, CST3, LTF, HSPA1A, CD14, S1008, ITGB2, SPP1), astrocytes (MFGE8, SLC39A12, ATP1A2, AGT, A2M), oligodendrocytes (GSN), and neurons (CRABP1) were identified commonly in the present study as well as by previously published proteomics studies on pooled human CSF samples (Chiasserini et al., 2014). Canonical pathway analysis revealed that enrichment of acute phase response and LXR/RXR activation, suggesting the elevation of inflammatory conditions and lipid biosynthesis pathway in the CSF-derived EVs from former NFL players. Researchers have reported the LXR/RXR pathway has important neuroprotective roles in neurodegenerative disease, including stroke, ALS, AD, and PD (Nam et al., 2016). In AD, LXR/RXR pathway has a role in microglial activity or in clearance of amyloid beta from the brain (Moutinho and Landreth, 2017). In former NFL players with risk of CTE, the LXR/RXR pathway might be able to affect neuronal survival and regulate microglia activity. Future exploration is needed to elucidate the activation of this pathway in CTE.

In summary, we have quantified tau levels and proteomic profiles in CSF-derived EVs from former NFL players in this study, identified unique enrichment of Alzheimer's disease pathway and Age/Telomere Length ontology, canonical liver/retinoid X receptor activation pathway, and predicted MAPT (tau) as an upstream regulator in former NFL players. Fluid and neuroimaging biomarkers have been mainly focused on tau: Tau PET imaging has been developed as an important tool for CTE diagnosis (Stern et al., 2019) and total tau and exosomal tau were identified as blood-based biomarker in CTE (Stern et al., 2016). The CSF is an attractive biofluid for biomarker discovery research with abundant proteins associated with neurodegenerative diseases in early stages in comparison to blood samples (Alosco et al., 2018). Our results suggested that p-tau181 in CSF-derived EVs and a combination of several cell type-specific molecules are potential monitoring biomarkers in former NFL players at risk for CTE. Further investigations are necessary to confirm these biomarkers, including additional validation sets, and to clarify the implications of the correlation of this marker with disease, and to elucidate the mechanisms of EV secretion from the brain cell types in former NFL players.

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study can be found in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015358 (http://www. proteomexchange.org).

#### ETHICS STATEMENT

fnins-13-01059 October 4, 2019 Time: 18:35 # 11

The studies involving human participants were reviewed and approved by the Boston University School of Medicine. The patients/participants provided their written informed consent to participate in this study.

#### AUTHOR CONTRIBUTIONS

SM, RS, and TI designed the research. SM, MJ, HT, and AD performed the research. SM, MJ, SG, TT, and TI analyzed the data. RS provided the CSF samples. SM and TI wrote the manuscript. SM, MJ, AD, SI, RS, and TI edited the manuscript.

#### REFERENCES

#### FUNDING

This work was in part funded by the Aethlon Medical, Inc. (TI), Cure Alzheimer's Fund (TI), NIH RF1AG054199 (TI), NIH R56AG057469 (TI), R01NS078337 (RS), U01NS093334 (RS), P30AG13846 (RS), and AMED 17dk0207042s0101 (TT).

### ACKNOWLEDGMENTS

We thank the Ikezu lab members (K. Yukawa-Takamatsu and M. B. Botros) for experimental supports.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins. 2019.01059/full#supplementary-material

FIGURE S1 | NTA plot of average size and concentration of EVs from former NFL player and CTRL CSF: The black line shows the fitting curve. Red line represents the error bar. The y axis is the concentration of particles. The x axis is the size of particle. (A) Former NFL player, (B) CTRL.

TABLE S1 | The sample size of each groups in this study.

TABLE S2 | Proteins identified and quantified in TMT-labeled experiment.

TABLE S3 | Peptides identified and quantified in TMT-labeled experiment.

relation to blood-brain barrier permeability in older adults. J. Proteome Res. 18, 1162–1174. doi: 10.1021/acs.jproteome.8b00809

**Conflict of Interest:** TI has sponsored research agreement from Aethlon Medical, Inc., for this study.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Muraoka, Jedrychowski, Tatebe, DeLeo, Ikezu, Tokuda, Gygi, Stern and Ikezu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Considerations and Implications in the Purification of Extracellular Vesicles – A Cautionary Tale

Yi Xin Fiona Lee1,2, Henrik Johansson3 , Matthew J. A. Wood1 and Samir El Andaloussi1,4 \*

1 Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom, 2 Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore, 3 Cancer Proteomics Mass Spectrometry, Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden, 4 Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden

Extracellular vesicles (EVs) are nano-sized particles constitutively released from cells

into all biological fluids. Interestingly, these vesicles contain genetic cargoes including proteins, RNA and bioactive lipids that can be functionally delivered and affect recipient cells. As a result, there is growing interest in studying EVs in pathological conditions, including central nervous system (CNS)-related diseases, as EVs may be used for diagnostic purposes or as therapeutic agents. However, one major bottleneck is the need for better EV purification strategies when considering complex biological sources such as serum/protein-rich media or plasma. In this study, we have performed a systematic comparison study between the current gold-standard method: ultracentrifugation, to an alternative: size-exclusion chromatography (LC), using induced pluripotent stem cell (iPSC) derived complex media as a model system. We demonstrate that LC allows for derivation of purer EVs from iPSCs, which was previously impossible with the original UC method. Importantly, our study further highlights the various drawbacks when using the conventional UC approach that lead to misinterpretation of EV data. Lastly, we describe novel data on our iPSC-EVs; how they could relate to stem cell biology and discuss their potential use as EV therapeutics for CNS diseases.

Keywords: extracellular vesicles, stem cells, proteomics, exosomes, isolation, purification

## INTRODUCTION

The very first report on secretory material from cells was by Wolf in 1967; here he observed the presence of minute phospholipid-rich, pro-coagulant substance from fresh platelet-free plasma which he then termed as "platelet-dusts" (Wolf, 1967). Subsequently, more studies revealed that these "dusts" were actually membranous nanosized particles that contained functional molecules, had downstream effects on recipient cells and could be collected from various bodily fluids (Raz et al., 1978; Trams et al., 1981). Based on the state of cells and mechanisms of release, these particles can be broadly classified into three main populations: "exosomes"- a pool of vesicles originating from the multivesicular endosome; "microvesicles"- a heterogeneous set of particles which directly shed off the plasma membrane, and "apoptotic bodies"- a much larger group of micelles which bleb off dying cells undergoing apoptosis (Harding et al., 1984; Pan et al., 1985; Raposo et al., 1996). Despite such known differences, there still appears to be significant overlaps in both molecular composition and size across all three populations. As it is challenging to purify each individual

#### Edited by:

Grant Thomas Corbett, Brigham and Women's Hospital and Harvard Medical School, United States

#### Reviewed by:

In-Hyun Park, Yale University, United States Valentina Bonetto, Istituto Di Ricerche Farmacologiche Mario Negri, Italy

> \*Correspondence: Samir El Andaloussi<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

Received: 08 July 2019 Accepted: 24 September 2019 Published: 18 October 2019

#### Citation:

Lee YXF, Johansson H, Wood MJA and El Andaloussi S (2019) Considerations and Implications in the Purification of Extracellular Vesicles – A Cautionary Tale. Front. Neurosci. 13:1067. doi: 10.3389/fnins.2019.01067

**80**

group, there has been an active shift to use "extracellular vesicles (EVs)" as a collective term to instead describe these secretory vesicles (Bobrie et al., 2012; Gould and Raposo, 2013).

## EVs as Communicators of Physiology and Disease

Historically, EVs were postulated to be the "garbage trucks" of cells; helping to remove unwanted molecular material out of cells. However, there are an increasing number of studies describing dedicated protein and RNA machineries that regulate the sorting of specific molecular contents (proteins and RNA) into and onto these vesicles (Szostak et al., 2014; Villarroya-Beltri et al., 2014; Janas et al., 2015; Hung and Leonard, 2016; Ragusa et al., 2017; Moreno-Gonzalo et al., 2018; Sork et al., 2018). Subsequently, these EVs serve as delivery men by following the unique "addresses" on their surface. Thus, EVs ferry their wellprotected molecular cargoes within the bi-lipid boundaries and elicit phenotypic effects on recipient targets, some of which may locate at distal sites from their origin.

The presence and involvement of EVs in biology is best understood in the areas of immunology and cancer. Notably, EVs from dendritic cells can mimic their parental source to aid in antigen presentation and elicit a T cell response in normal physiological processes (Raposo et al., 1996; Zitvogel et al., 1998; Théry et al., 2001; Utsugi-Kobukai et al., 2003; Segura et al., 2005a,b). However, not all EV-mediated communications are beneficial, as in disease settings EVs can aid in the spread of pathogenesis. For example, EVs from primary cancer cells assist in the preparation of the pre-metastasis niche for colonization of migratory cancer cells (Janowska-Wieczorek et al., 2005; Peinado et al., 2012). Interestingly, one study found that subpopulations of EVs are secreted by a single pool of breast cancer cells and that each EV subtype can influence the formation of micrometastases in the different organs, depending on the types of surface integrins (Hoshino et al., 2015).

## EVs Contribute to Intercellular Brain Communication

The brain is a complex organ consisting of myriad neural networks between the multitudes of cell types. Hence, it is no surprise that there has been a steadily increasing body of evidence showing how EVs contribute to normal brain function and in central nervous system (CNS)-related diseases (Thompson et al., 2016). As in other body systems, all cell types of the brain, including neurons, oligodendrocytes, astrocytes and microglial cells, release EVs (Fauré et al., 2006; Krämer-Albers et al., 2007; Antonucci et al., 2012; Wang et al., 2012). Depending on the cell source and the physiological state, the characteristics of secreted EVs can vary accordingly and induce different functions in the brain. For example, EVs from neurons present the AMPA receptor subunit GluR2 and this contributes to synapse maturation and plasticity (Lachenal et al., 2011). Alternatively, EVs from neurons are enriched in syndecans and tetraspanins (e.g., CD81), which are important signaling cues (Bahi and Dreyer, 2005; Hienola et al., 2006). Hence, EVs can assist in neurite outgrowth and axon path finding. In other instances, EVs can act as important tools for the cross talk between different cell types in the brain, such as when oligodendrocytes release EVs in response to neuronal activity. These EVs carry metabolites such as catalase, which can be delivered back to the neurons for trophic support (Frühbeis et al., 2013). Similarly, Schwann cells have been shown to release EVs upon axonal damage (Krämer-Albers et al., 2007), where they could be active at the injury site and aid in axonal regeneration.

## Technical Issues in EV Research-Purification Strategies

Despite growing knowledge of the importance of EVs to multicellular functions, there is still a lack of consensus on the methodologies for purifying EVs from cells. To date, the most common strategy remains differential ultracentrifugation (UC) due to its accessibility in most laboratories (Théry et al., 2006). However, with improved technologies for EV phenotyping, it is becoming increasingly evident that this "gold-standard" UC protocol suffers from several drawbacks; most of which are related to the yield, purity and physical integrity of EVs (Cvjetkovic et al., 2014; Nordin et al., 2015).

To address this issue, alternative methods for EV purification have been exploited. These include precipitation-based strategies; some of which can be coupled with antibody-capture methods on microfluidic devices or the use of magnetic nanowires (Ghosh et al., 2014; Kanwar et al., 2014; Lim et al., 2019; Sunkara et al., 2019). However, these methods may not be scalable, often compromise on EV purity (Yamada et al., 2012) and require more studies on if the purified EVs can be efficiently eluted and remain complementary to use for any downstream functional characterization. One other method which has gained popularity in recent years is size-exclusion liquid chromatography (LC). Unlike UC, the LC process does not require high speed spins and is scalable for large volume samples when combined with a pre-concentration step. All in all, many supporting reports have verified the feasibility of using LC for EV purification in several serum-free cell culture-based systems and in biological fluids (Chen et al., 2011; Arslan et al., 2013; Böing et al., 2014; Nordin et al., 2015; Welton et al., 2015; Mol et al., 2017; Oeyen et al., 2018; Lane et al., 2019; Takov et al., 2019).

Generally in EV research, it is required to use serum-free or exosome-cleared FBS for collection of conditioned media, as regular serum contains numerous microvesicles that can contaminate and confound the downstream analysis of the intended cellular EVs (Noerholm et al., 2012; Shelke et al., 2014; Wei et al., 2016; Kornilov et al., 2018; Lehrich et al., 2018). However, this becomes an issue when considering cell types which will change in serum-depleted media, such as stem cells. In all the previously studies on protocol comparison (Rood et al., 2010; Alvarez et al., 2012; Tauro et al., 2012; Rekker et al., 2014; Zhang et al., 2014; Livshits et al., 2015; Stranska et al., 2018; Takov et al., 2019), none have compared purification of EVs from protein-rich, complex media sources such as stem cell media. Here, we describe a systematic comparison study between UC and LC purified EVs from iPSCs. Interestingly, we find that their molecular contents vary based on protocol, which will have implications on the interpretation of EV content and functionality. Lastly, we cross compared EVs to their parental sources and discuss the feasibility of using these iPSCs for CNS related research.

## MATERIALS AND METHODS

fnins-13-01067 October 18, 2019 Time: 15:31 # 3

## Cell Culture of Pluripotent Stem Cell Lines and Embryonic Fibroblasts

Mouse embryonic fibroblasts (MEFs, derived from Dr. Paul Fairchild's lab) were grown in complete MEF growth media comprising of DMEM (Life Technologies) supplemented with 15% FBS and 50 µg/ml Penicillin/Streptomycin (P/S). To prepare feeders for stem cell culture, MEFs were treated with mitomycin-C (Sigma, United Kingdom) at 1 mg/ml of MEF media for 2 h. The treated MEFs were then washed three times with PBS and re-plated on fresh culture flasks. Mouse stem cell lines [embryonic stem cells (ESCs): ESF121, ESF116 and induced pluripotent stem cells (iPSCs): iMEF14, iMEF19]; all derived from Dr. Paul Fairchild's lab (Fairchild et al., 2000) were first cultured on mitomycin-C treated MEFs feeders. When the cells reached 80% confluence, one-tenth of the cells were plated on fresh feeders while the remainder were plated on 0.1% gelatin-coated plates. For both feeder and feeder-free conditions, cells were cultured in complete mouse stem cell media comprised of DMEM (Lonza, United Kingdom) supplemented with 15% knockout serum-replacement supplement (KOSR) (Life Technologies, United Kingdom), 2 mM L-glutamine (Life Technologies), 1 mM sodium pyruvate (Life Technologies), 0.1 mM non-essential amino acids (Life Technologies), 50 (µg/ml of P/S, 0.2 mM 2-mercaptoethanol (Sigma) and 106 units of mouse leukaemia inhibitory factor (mLIF, Miltenyi Biotec, United Kingdom). All cells were cultured at 37◦C with 5% CO2.

## Collection of Conditioned Media (CM) for EV Purification

For the initial data shown in **Supplementary Figure S1**, prespun stem cell (PS) media described here was complete stem cell media that had been pre-spun at 120,000 g for 16 h prior to use. The OptiMEM (OM) media was OptiMEM (Life Technologies) supplemented with 50 µg/ml of P/S. For both ESC and iPSC cell lines, the stem cells were cultured twice on 0.1% gelatincoated plates to get rid of any contaminating feeder cells prior to use for EV collection. For each cell line, 1M cells were initially seeded on a single 10 cm plate. When cells reached 70% confluence (48 or 72 h after plating), the growth media was removed; cells were washed with PBS and replaced with fresh stem cell media, PS or OM depending on the experimental setup. Conditioned media (CM), ranging from 150–200 ml, was then collected 48 h after media change; cells were harvested by trypsin and counted with a hemocytometer. For each collection, the total CM was first centrifuged at 300 g, 5 min to get rid of cellular debris. The supernatant was decanted and further centrifuged at 2,000 g, 10 min to get rid of larger particles before subjecting to filtration through a 0.22 µm syringe filter. Subsequently, the CM was split equally into two volumes for UC and LC purification concurrently.

## Purification of EVs

#### Purification of EVs by Differential Centrifugation Protocol (UC)

The UC protocol used for purification of EVs is based on an established protocol described by Théry et al. (2006). Briefly, the filtered CM was spun at 120,000 g, 70 min to pellet EVs. To eliminate any protein contaminations, the pellet was resuspended in 25 ml of PBS and spun again at 120,000 g for 70 min. All centrifugation steps were performed at 4◦C. The resultant pellet was then re-suspended in 100 µl of PBS and used fresh for analysis or kept at −80◦C for further proteomics analysis.

#### Purification of EVs by Size-Exclusion Liquid Chromatography Protocol (LC)

The LC protocol used for purification of EVs was based on the method described by Taylor et al. (2011), with some slight modifications. Briefly, the filtered CM was concentrated using the Amicon 100k-Da molecular weight cut-off (MWCO) filters (Millipore, United Kingdom) at 3,500 g for 15 min. The concentrate retentate was then loaded onto a Sephacryl S-400 16/60 LC column (GE Healthcare, Sweden) and run with PBS at 0.5 ml/min. Fixed-volume 2 ml fractions of the eluted solutions were then collected with a fraction collector. Based on the 280 nm LC chromatograph, fractions covering the first 280 nm LC peak, designated as "F1," were pooled and concentrated with Amicon 10-kDa MWCO filters (Millipore) at 3,500 g for 15 min down to 100 µl of PBS and used fresh for analysis or kept at −80◦C for further proteomics analysis. All centrifugation and LC processes were done at 4◦C.

## Sucrose Gradient Density Centrifugation of EVs

The sucrose gradient density centrifugation protocol used here was based from the protocol described by Théry et al. (2006). Briefly, hydroxyethylpiperazine-N 0 -2-ethanesulfonic acid (HEPES)/sucrose stock solution was prepared by mixing 428 g of protease-free sucrose (Sigma) in 500 ml of 20 mM of HEPES buffer. The pH was adjusted to 7.4 with 1M sodium hydroxide (NaOH) and stored at 4◦C. Prior to construction of the sucrose gradient, the HEPES/sucrose stock solution was diluted with 20 mM HEPES buffer to generate 10 concentrations of sucrose solutions (0.25–2.5M with 0.25 increments). The EV sample collected after UC and LC was pre-mixed with either 0.25 or 2.5 M of HEPES/sucrose stock to 1 ml total volume and loaded at either at the top or bottom of the linear sucrose gradient respectively. The sucrose gradient was then centrifuged at 200,000 g for 16 h or 72 h at 4◦C in a SW 40 swing rotor (Beckman Coulter). One ml fractions were carefully collected from the top and each fraction was weighed to obtain an estimated density of each fraction. Each 1ml sucrose fraction was subsequently diluted in 25 ml of PBS and centrifuged at 120,000 g for 70 min at 4◦C to wash and pellet the particles. The resultant UC pellet of each fraction was re-suspended in 50 µl of PBS and subjected to molecular analysis.

## Quantification and Characterization of EVs

#### Nanoparticle Tracking Analysis (NTA)

fnins-13-01067 October 18, 2019 Time: 15:31 # 4

NTA allows for the quantification of total particle amounts and size distribution of particles based on Brownian motion of particles. All NTA was done with the NTA2.3 software on the NS500 Nanosight machine (Nanosight, Malvern, United Kingdom). Before each run, the NS500 measurements were calibrated with known concentrations of 100 nm silica microspheres to obtain optimum acquisition detector settings and post-acquisition settings. For all our recordings, we used a camera level of 14 (shutter speed 600, camera gain 250) and automatic function for all post-acquisition settings: detection threshold level 5, blur and minimum expected particle size. EV samples were diluted in PBS prior measurement, starting at an initial dilution of 1:100, and then further adjusted for each sample individually to achieve a particle count of between 2 × 108 per ml to 1 × 109 per ml. Once the dilution of the sample was determined, the sample was loaded in the sample chamber and the camera focus was adjusted to make the particles appear as sharp dots of light. Using the script control facility on the NTA2.3 software, we recorded five 30 s videos for each sample; incorporating a sample advance and 5 s delay between each recording. The measurements were then analyzed using the batch process facility and results were exported as Microsoft Excel spreadsheets for further analysis. If the profiles were not in agreement, measurements were then repeated.

### Protein Quantification of EVs and Cell Lysates

Extracellular vesicles and cell lysates were quantified using the microBCA or the BCA assay kit (Thermo Scientific Fisher) respectively as indicated by the manufacturer's instructions.

## Western Blotting (WB)

Depending on the experimental set-up, either a fixed volume or a set number of particles (as calculated by NTA) from the re-suspension of EV pellet was used. For cells, after trypsin treatment, cells were collected with PBS and spun at 1,500 g, 5 min to pellet cells. Cells were washed in PBS and pellet at 1,500 g, 5 min again. The supernatant was decanted and the cell pellet was lysed in radioimmunoprecipitation assay (RIPA) buffer for 1 h at 4◦C. The mixture was then spun at 16,000 g, 20 min. The supernatants were then measured for protein concentration and fixed proteins amounts were used for WB. The EV/cell sample was mixed with 2x Laemilli sample buffer (Bio-Rad, United Kingdom) containing 5% β-mercaptanol and heated at 100◦C for 10 min. Samples were then spun-down briefly before being loaded in 1.5 mm, 12% home-made Tris/Glycine SDS-polyacrylamide gels and ran at 170 V for 70 min in running buffer, until the dye front reaches the bottom of the tank. Proteins on the gel were transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore) at 100 V for 70 min in transfer buffer containing 20% methanol. Membranes were then incubated in blocking buffer [5% fat free milk in Tris Buffer Saline with 0.1% Tween-20 (TBS-T, Sigma)] for 60 min at room temperature (RT) on a rocker with gentle shaking. After blocking, the membrane was incubated with freshly prepared primary antibody solution [from Abcam, United Kingdom: anti-Alix, ab117600; anti-Tsg101, ab30871; anti-CD9, ab92726; anti-Calnexin, ab22595; anti-Oct4, ab19857, anti-β-actin, ab8268, all used at 1:1000; from Santa Cruz: anti-CD81(H-121), sc-9158 used at 1:100] overnight at 4◦C or 2 h at RT. Membranes were then washed three times 10 min each using washing buffer (TBS-T) with vigorous shaking before adding the secondary antibody solution [all from LiCOR, United Kingdom: Goat anti-mouse IgG IRDye R 800CW (925-32210) and 680RD (925-68070); Goat antirabbit IgG IRDye R 800CW (925-32211) abd 680RD (925-68071), all used at 1:10000] and incubated for 2 h at RT. After secondary incubation, membranes were washed three times 10 min each with TBS-T and visualized by scanning both 700- and 800 nm channels on the LI-COR Odyssey CLx infrared imaging system. For re-probing on the same membrane, the membrane was first washed three times 10 min each before re-incubation with the

## Transmission Electron Microscopy (TEM)

next primary antibody.

A 200 mesh nickel carbon/formvar grid (AgarScientific) was placed onto a 10 µl droplet of the EV suspension for 15 min. The grid was then blotted dry with filter paper, immediately transferred to a 15 µl droplet of 2% uranyl acetate for 1 min and protected from light. The grid was again blotted dry with filter paper before being transferred to a 15 µl droplet of filtered distilled and deionized water (ddH20) for 1 min. The grid was then blotted dry and left to air dry on the bench top for 15 min. EVs negatively stained on this grid was then visualized with a JEOL 1010 transmission electron microscope (JEOL, Tokyo, Japan).

## Trizol Extraction of RNA From EVs and Cells

Total RNA from EVs and cells were extracted based on the manufacturer's protocol. Briefly, 75 µl of the EV suspension or 2 × 105 cells were mixed in Trizol LS (Life Technologies) and incubated for 5 min at RT. Sixty µl of chloroform was then added and tubes were shaken for 15 s. The mixed samples were then incubated for 15 min at RT before being centrifuged at 12,000 g for 15 min at 4◦C to derive the three distinct phases. The upper colorless phase was transferred to a new tube and 150 µl of isopropanol with 1 µl of glycogen was added. The sample was vortex briefly and incubated at RT for 10 min. The sample was then centrifuged at 12,000 g for 10 min at 4 ◦C and the supernatant were discarded. The remaining white RNA pellet was washed with 300 µl of 75% ethanol and then spun down at 7,500 g for 5 min at 4◦C. The ethanol was then discarded, and the pellet was air-dried for 5–10 min, till it turned transparent, and then re-dissolved in 20 µl of RNase-free water. The mixture was then incubated at 55–60◦C for 15 min on a heat block. RNA concentrations of the samples were then measured using the Quant-iTTM RiboGreen R RNA Assay Kit (Life Technologies).

## RNA Profiling With Agilent Bioanalyzer Software

Quality and size of the EV and cellular RNA were detected using capillary electrophoresis with the Agilent RNA 6000 Pico kit and Agilent RNA small RNA kit on an Agilent 2100 Bioanalyzer R (Agilent Technologies, Santa Clara, CA, United States) according to the manufacturer's protocol.

### LC/MS/MS Proteomic Analysis

As both mouse iPSCs and ESCs were derived off the same strain background (CBA/Ca) and derived using the same methodology, we proceeded with proteomics analysis on one of the miPSC line- iMEF14 and one of the mESC line- ESF121. For each cell and EV sample, three biological replicates were prepared and analyzed (**Supplementary Table S1**). All cells and EVs were concentrated by speedvac and lysed with 1% SDS, 25 mM HEPES, 1 mM DTT. Lysates were heated to 95◦C for 5 min followed by sonication for 1 min and centrifugation at 14,000 g for 15 min. The supernatant was mixed with 1 mM DTT, 8 M urea, 25 mM HEPES, pH 7.6 and transferred to a 10-kDa cutoff centrifugation filtering unit (Pall, Nanosep R ), and centrifuged at 14,000 g for 15 min, followed by an addition of the 8 M urea buffer and centrifugation again. Proteins were alkylated by 50 mM iodoacetamide (IAA) in 8 M urea, 25 mM HEPES for 10 min, The proteins were then centrifuged at 14,000 g for 15 min followed by 2 more additions and centrifugations with 8 M urea, 25 mM HEPES. Trypsin (Promega) in 250 mM urea, 50 mM HEPES was added to the cell lysate at a ratio of 1:50 trypsin:protein and incubated overnight at 37◦C. The filter units were centrifuged at 14,000 g for 15 min followed by another centrifugation with milli-Q water (MQ) and the flow-through was collected. Peptides were cleaned by a strata-X-C-cartridge (Phenomenex).

Before analysis on the Q Exactive (Thermo Fischer Scientific, San Jose, CA, United States), peptides were separated using an Agilent 1200 nano-LC system. Samples were trapped on a Zorbax 300SB-C18, and separated on a NTCC-360/100-5-153 (Nikkyo Technos., Ltd.) column using a gradient of A [3% acetonitrile (ACN), 0.1% formic acid (FA)] and B (95% ACN, 0.1% FA), ranging from 7 to 40% B in 240 min with a flow of 0.4 µl/min. The Q Exactive was operated in a data dependent manner, selecting top 5 precursors for fragmentation by HCD. The survey scan was performed at 70,000 resolution from 300–1700 m/z, using lock mass at m/z 445.120025, with a max injection time of 100 ms and target of 1 × 106 ions. For generation of HCD fragmentation spectra, a max ion injection time of 500 ms and AGC of 1 × 105 were used before fragmentation at 30% normalized collision energy, 17,500 resolution. Precursors were isolated with a width of 2 m/z and put on the exclusion list for 70 s. Single and unassigned charge states were rejected from precursor selection.

Proteome discoverer 1.3 with sequest-percolator was used for protein identification. Precursor mass tolerance was set to 10 ppm and for fragments to 0.02 Da. Oxidized methionine and was set as dynamic modification, and carbamidomethylation as static modification. Spectra were matched to a combined Mus musculus and Bos taurus ensembl 72 database, and results were filtered to 1% FDR. Identifications in Bos taurus was considered to originate from FBS and removed. For this set of LC-MS/MS, the reference list used was generated based on all proteins identified within this stem cell set. In this proteomics run, only a short gradient of the cellular and vesicular proteome was subjected to analysis. Hence, the overall proteome of the cell described here may only recapitulate a portion of the total proteome as expected. For classification of commonly identified proteins when comparing two sample types, we calculated the ratio of area of proteins of one sample over the other. Proteins that had a twofold change in ratio were designated to be "up-regulated." These groups were then analyzed in the online platform: Panther to evaluate enrichment of Gene ontology (GO) (Mi et al., 2013, 2019).

## Statistics

The Student's t-test was used when comparing EVs derived by the UC versus LC purification.

## RESULTS

## Alternative Forms of Serum-Depleted Media Not Feasible for Stem Cells

In our study, mouse induced pluripotent stem cells (iPSCs) were used as a model for sensitive cells that require complex media types for their cultivation. Unlike differentiated cells, serum is crucial for the maintenance of stem cells in their undifferentiated state. However, in EV research, the use of serum is problematic as serum contains microvesicles, which could be co-purified and interfere with the downstream analysis of EVs. Hence, we used knockout serum replacement (KOSR), which is devoid of serum microvesicles. However, KOSR could still contain other undefined components, which could confound the EV purity.

To evaluate the background particulate counts in KOSRsupplemented (SR) media, we applied the UC purification protocol to unconditioned media and analyzed the pellet. In parallel, two other media types commonly used in EV research were compared; pre-spun SR-media (PS) and serum-free OptiMEM (OM). Interestingly, NTA measurements indicated the presence of nanoparticles in all pellets, most significantly in the SR-media pellet (**Supplementary Figures S1A,B**). Notably, the mode sizes of these nanoparticles appeared to be similar to that of EVs. Next, we assessed the growth and morphology of stem cells in these alternative media types. After a 48 h incubation period, we found that both iPSCs (**Supplementary Figures S1C,D**) and ESCs (**Supplementary Figures S1F,G**) grew differently in these three media types. In PS media, cells appeared less viable based on cell morphology and had much lower total cell counts as compared to regular SR media. In OM media, stem cells proliferated at a higher rate and some colonies displayed altered morphology. As for EVs, the greatest number of particles was recovered from SR CM, followed by OM and lastly PS media (**Supplementary Figures S1E,H**). As we were concerned with the cellular changes caused by the switch in media type, we decided to continue with SR media for subsequent comparisons between UC and LC protocol.

## LC Enabled Purification of Stem Cell EVs in a Reproducible Fashion

Since SR media does not contain serum, the detection of vesicles by NTA in the UC pellet of SR media led us to question the nature of these particles. We hypothesized that these "particles" were macromolecular structures of protein aggregates that formed during the high-speed centrifugation.

In LC, there is a lack of high-speed spins. Hence, we fractionated CM from stem cells on the size-exclusion column. Generally, the total protein and particle number profiles overlapped well, such that the fractions with the highest amounts of protein corresponded to the fractions with the highest number of particles (**Figure 1A**). Overall, we grouped and pooled the fractions into four general fractions: F1–F4. NTA profiles of these four fractions showed that the greatest number of particles to be in F1 followed by F2, while F3 and F4 contained very few particles (**Figure 1B**). When we compared their particle size distribution graphs, the peak of particle concentration in F1 was significantly higher in cell-derived samples than that of media alone (**Figure 1C**). Moreover, the presence of EVs exclusively in F1 was confirmed through the detection of common EV markers (Alix and CD9) (**Figure 1D**). Importantly, this showed that stem cell media contains a fair amount of macromolecular proteins that may appear as particles on NTA. Unlike in LC, these contaminating proteins are inseparable from EVs in the UC process. Furthermore, the LC process was highly reproducible across replicate runs, as seen by the protein chromatographs, western blotting and small RNA profile analysis of replicate collections (**Figures 1E–H**).

#### UC Pelleting Led to Co-precipitation of Non-vesicular Proteins and RNAs With EVs

When comparing UC and LC side-by-side, we detected higher particle yield and total protein and RNA amounts after UC as compared to LC (**Figures 2A–D**). However, this contradicted our previous study on serum-free culture samples (Nordin et al., 2015). To understand the reasons underlying the differences, we analyzed the expression levels of common EV markers following both types of collection. Interestingly, when normalized by particle numbers, we detected stronger expression of both Alix and CD9 in LC purified than UC purified samples, whereas OCT-4, a marker of pluripotency, showed the reverse trend (**Figure 2E**). Next, we compared the expression of the same EV markers in parental cells and EVs based on a fixed protein amount. Unexpectedly, there was no enrichment of EV markers in the UC pellet as compared to cells. To complement our molecular profiling, we next assessed how the biophysical properties of EVs differed by purification protocol. Overall, TEM imaging showed a general cloudy background in the UC samples, which made it difficult to discriminate EVs. In contrast, LC samples had a cleaner background where particles were more easily detected (**Figure 2G**). Lastly, we calculated the particle per protein ratio (P/µg) and compared them to benchmark values suggested for EV purity (**Table 1**). Here, the P/µg ratio for UC sample was classified as impure, while the LC samples had slightly higher P/µg ratios and were considered of low vesicular purity (**Table 2**).

One of the methods proposed for deriving pure, clinicalgrade EVs is by floating EVs on a discontinuous sucrose gradient overnight (Lamparski et al., 2002), where pure EVs should float at a specific density range of 1.15–1.19 g/ml (Théry et al., 2006). Here, we compared EV purity after loading the sucrose gradient either above (bottom-loading) or below the UC pellet (toploading). Interestingly, we found a discrepancy in the fractions enriched for EV markers and peak particle counts between the two loading types. After bottom-loading, the purified sample unexpectedly peaked in fractions 7–8, which correspond to a density of 1.21–1.22 g/ml (**Figures 3A,B**). These fractions also contained the greatest amounts of protein (**Figure 3C**). Hence, we speculated that non-vesicular proteins attached to EVs during the UC process and that this prevented the migration of EVs to their real densities. When further increasing the fractionation time in the sucrose gradient to 72 h, we began to detect more expression of the EV marker CD81 in the expected fractions of pure EVs (**Figures 3D–I**). Overall, we here show that initial quantification of particle counts and molecular content (proteins and RNAs) in UC pellets were inaccurate. Despite additional prolonged periods of clean-up by either sucrose gradient centrifugation or LC (**Supplementary Figure S2**), we achieved only a marginal improvement in the purity of EVs isolated by UC.

#### EVs From iPSCs and ESCs Have Similar Size Distribution Profiles and Expression of EV Markers

Using the LC method, we next proceeded to purify EVs from two mouse ESCs lines and two iPSCs lines. From the LC chromatograph, it was observed that all four EV fractionation processes generally showed a similar 280 nm absorbance pattern across the eluted volume (**Figures 4A,C**). As we were only interested in the EVs, we pooled LC fractions across the region where EVs eluted (F1) and analyzed the particles with NTA. As shown in **Figures 4B,D,E**, the particle size distribution profiles overlapped within each cell type. Moreover, TEM on EVs from both mouse ESCs and iPSCs corroborated the NTA measurements and showed that the particles from both cell types appeared similar. Furthermore, similar levels of EV markers (Alix and CD9) were detected in both mouse iPSC and ESC-derived EVs (**Figures 4F,G**). Lastly, when analyzing the P/µg ratio of EVs from all four cell lines, we found that all samples were of similar purity (**Figure 4H**).

## EVs From ESCs and iPSCs Contain Similar Proteins That Differ Greatly From Their Parental Cells

To characterize our EVs more deeply, unbiased LC-MS/MS proteomics analysis were performed on both cell lysates and EVs from our iPSC and ESC lines. As we suspected remnants of contaminating proteins within our EV samples, LC-MS/MS was first performed on unconditioned stem cell media. Overall, 35 cow and 29 mouse proteins were detected (**Table 3**),

western blots for EV markers (Alix and CD9) in mESC CM. NTA size distribution profiles of the EVs collected from LC-F1 in three replicates (R1–R3) of CM from miPSC (E) and mESC (F). (G) Representative western blotting pictures for EV markers (Alix and CD9) in duplicate EVs samples (R1–R2) from miPSCs and mESCs. (H) Small RNA Bioanalyzer profiles of duplicate EV samples (R1–R2) from miPSCs and mESCs.

with the majority of proteins belonging to common protein groups such as albumin, fibronectin, heat shock protein and keratins (**Tables 4**–**6**). Importantly, many of these proteins were also detected in high abundance within our EV samples (**Supplementary Figure S3**). To avoid ambiguity, we removed the entire set of proteins present in media and our EV samples from downstream analysis.

After filtering, a total of 1387 proteins were detected in EVs from either iPSCs or ESCs, where 33.1% of them were present in both groups (**Figure 5A**). Although there were many more proteins found exclusively in iPSC-EVs than in ESC-EVs (593 vs. 35), GO annotations showed that many of these proteins had similar molecular functions, such as protein binding and cytosolic localization. Next, we considered the 459 proteins which

**87**

from (10 µg) of cell lysate (CL) and EV samples. (F) Representative TEM images of EVs purified by UC or LC. The scale bar corresponds to 100 nm.

TABLE 1 | Table showing the suggested purity index by Webber and Clayton 2013.

TABLE 2 | Table showing the purity ratios of UC and LC samples from both stem cell types.

were common to both types of EVs and divided them into three groups: proteins with similar expression, proteins overrepresented in iPSC-EVs and proteins over-represented in ESC-EVs. In general, most of these common proteins were found at similar levels in both groups (**Figure 5B**). As expected, both EVs were enriched in proteins with GO terms such as EV, extracellular exosome, membrane-bound exosome and vesicle (**Figure 5C**). Moreover, known EV markers like CD9 and CD81 were abundant in both samples and were cross validated by western blotting (**Figure 5D**). On the other hand, a few GO terms were significantly different between the two groups. For example, GO terms for translation initiation factor activity, translation factor activity, RNA binding and protein hetero oligomerization were over-represented in EVs from iPSCs, while proteins associated with the plasma membrane were over-represented in EVs from ESCs (**Figure 5E**).

To analyze if the similarities in the EVs was related to their parental cells, we next compared the cellular proteomes of matched iPSCs and ESCs to their respective EVs. As expected, more proteins were detected in cells than in EVs (3565 versus 1387). Of the proteins identified, 71.6% overlapped in both cell types (**Figure 6A**). As for proteins exclusive to each cell type, most of them mapped to the same GO terms (e.g., nucleotide and RNA binding, organelle and membrane). Similar to before, common proteins were separated into three groups; those expressed similarly in both, those over-represented in iPSCs and those over-represented in ESCs. Although there were variations in GO term enrichments between the two cell types, none of these differences were found to be significant. Interestingly, the scatter plot of individual proteins showed the exception of two outliers: mitochondrial ribosomal protein S25 (MRPS25) and activating signal co-integrator 1 complex subunit 3 (ASCC3), both of which were more abundant in ESCs than in iPSCs (**Figure 6B**).

Next, we compared each EV sample back to their parent cells. Interestingly, the overlap of cellular proteins in their corresponding EVs was quite low; 28.5% for iPSCs and 15.5% for ESCs (**Figures 6C,E**). This was further supported by the scatter plots showing correlation expression of all identified proteins in EVs versus cells (**Figures 6D,F**). To understand the nature of these differences, we applied GO analysis to the different groups of proteins. In the iPSC set, GO terms for translation initiation factor activity, translation factor activity, RNA binding, extracellular matrix and extracellular space were all found to be over-represented in EVs. In contrast, proteins with GO terms for structural constituent of ribosome, mRNA binding, cytosolic part and intracellular nonmembrane-bounded organelle were over-represented in cells (**Figure 6G**). As proteins detected in ESC-EVs, we observed an enrichment in GO terms for glycolipid binding, G-protein beta/gamma-subunit complex binding, external side of plasma membrane and extracellular matrix component in the EVs. In contrast, ESC proteins were over-represented in proteins with GO terms for the structural constituent of cytoskeleton, histone binding, protein-DNA complex assembly and chromatin assembly or disassembly, as compared to EVs (**Figure 6H**). Interestingly, there were some GO terms which were similarly enriched in EVs and cells across both stem cell types. For example, the GO term basal lamina was enriched in EVs from both iPSCs and ESCs when compared to their parent cells. On the other hand, GO terms for protein-DNA complex subunit organization, DNA replication-dependent nucleosome organization, nuclear chromosome and nuclear nucleosome were enriched in both cell samples as compared to their EVs (**Figure 6I**).

#### DISCUSSION

In this study, we cross compared the isolation of EVs from complex, protein rich media using two methods: the goldstandard of UC versus an LC method. Due to the presence of microvesicles in serum, many researchers have turned to alternative media types like pre-spun media or entirely serumfree solutions reconstituted with recombinant protein substitutes for serum component (Li et al., 2015). Unfortunately, there have been doubts about the clearance efficiency of microvesicles from pre-spun media (Kornilov et al., 2018), and it is not feasible to reconstitute serum components for certain sensitive cell types. Here, we have demonstrated the phenotypic changes that occur in stem cells after short term incubation in these different media types. Although serum-free alternatives for cell types such as neurons and astrocytes have been developed (Brewer, 1995; Pozzi et al., 2017), these remain poorly defined, protein rich alternatives that may induce unwanted molecular changes and lead to growth of selective populations (Balvers et al., 2013). Despite the growing number of studies comparing EV isolation methods, there is still only limited knowledge about the molecular profiles of EVs collected from protein rich media types and their implications on molecular contents of EVs.

In our findings, we highlight various problems associated with the UC protocol. First, we identify an issue with high particle counts in UC pellets from unconditioned media, despite the lack of serum in KOSR. However, when we fractionated the unconditioned media with LC, most particles were found in later fractions corresponding with high protein amounts. Hence, this led us to speculate that the high-speed spins in UC promote the precipitation of protein aggregates, which then appear as nano-sized particles on NTA.

gradient fractions from UC samples centrifuged on the sucrose gradient for either 16 h (H) or 72 h (I).

In our side-by-side comparison of UC and LC, the UC pellet contained more particles, protein and RNA than the LC sample. This result was rather unexpected, as the opposite trend was previously reported in serum-free CM (Nordin et al., 2015). As we are aware that protein aggregates in the UC pellet can appear as particles on the NTA, we remain unsure of the reliability of our NTA and protein quantifications. Thus, we used multitude of other technologies for our subsequent detailed comparison and characterization studies.

First, we checked the expression levels of well-established EV and stem cell markers when loading equal numbers of particles. Surprisingly, the expression level of OCT-4 was inversely correlated to that of the EV markers. Although OCT-4 is reported to be present in ESC-EVs (Ratajczak et al., 2006),

TABLE 3 | Table showing the list of cow proteins exclusively identified in the media sample.

TABLE 4 | Table showing the list of mouse proteins exclusively identified in the media sample.

Species Class of Name of Accession

more recent work has shown that OCT-4 can also be secreted from cells directly into the extracellular environment (Wang and Jauch, 2014). Hence, we speculate that some portion of this secreted OCT-4 protein in the CM might pellet together with stem cell EVs during the UC process. This hypothesis would explain the high particle counts and levels of OCT-4 detected by western blotting in UC-purified EVs. Apart from proteins, we also observed significant differences in other EV properties after isolation with UC and LC, such as their small RNA profiles and TEM images of vesicles. In the latter, we hypothesized that the presence of free protein in the UC pellet would contribute to the cloudy background that interfered with our visualization of the EVs. Furthermore, the P/µg ratio was lower after UC than LC purification; which corroborated our data suggesting that the UC pellet displayed lower vesicle purity. However, here we realize that interpretation of the vesicle purity ratio (P/µg) may need to

be re-considered for protein rich substances. Since some protein aggregates may be mistakenly counted as EVs when using NTA, purified samples with more protein aggregates would appear to have a higher purity ratio than they do.

Next, we subjected the UC pellet to additional clean-up strategies to test for the presence of contaminating proteins. The first clean up strategy we attempted was sucrose gradient centrifugation. Previously, it has been reported that EVs float at 1.15–1.19 g/ml, which is slightly different to vesicles originating from cellular organelles such as the endoplasmic reticulum

TABLE 5 | Table showing the list of mouse proteins exclusively identified in the gelatin sample.

List excludes the detection of 8 uncharacterized sus scrofa proteins.

TABLE 6 | Table showing the list of proteins commonly identified in both the media and gelatin sample.

List excludes 2 uncharacterized sus scrofa proteins.

(1.18–1.25 g/ml) or the Golgi (1.05–1.12 g/ml) (Théry et al., 2006). These floatation intervals have been verified on EVs from different cellular sources and found to be reproducible regardless of the method of loading the EV sample on the top or bottom of the sucrose gradient. However, others have debated that overnight centrifugation was insufficient for vesicles to efficiently penetrate in the denser fractions and reach density equilibrium within 16 h centrifugation period (Yuana et al., 2014; Iwai et al., 2016). Hence, we here tested both loading strategies (top versus bottom) and at two different lengths of centrifugation timings (16 h versus 72 h). Unexpectedly, there was great inconsistency in the fractions showing the highest total particle counts and expression of EV markers between the two loading strategies. In the bottom-loaded sample, the gradient fractions positive for EV markers overlapped with those containing non-vesicular protein bands. Hence, we hypothesize that the EVs in the UC pellet may be associated with protein aggregates and thus float at these higher densities instead of their actual density.

Interestingly, with the longer overnight centrifugation, the expression of CD81 became more diverse. We hypothesized that after this longer incubation period, a portion of EVs were able to escape from the protein aggregates and float at their expected densities. In hindsight though, CD81 expression still appeared relatively higher in the higher density fractions. Despite this data, it is very difficult to conclude whether the particles appearing at higher densities and stained positively for CD81 are EVs bound to non-vesicular proteins or they could be true EVs that float at a higher density. However, there is the possibility that some of these EV-protein associations are transient, whilst others may be more permanent due the high gravitational forces inherent in the UC procedure. Another strategy we tried was to fractionate the UC pellet on an LC column. Interestingly, this revealed an additional protein peak immediately after the initial EV peak, though the particles in this second peak did not express any of the EV markers we tested. This result further supports our previous deduction that proteins can appear as particles on the NTA. From these results, we conclude that UC purification of EVs may require additional clean-up to increase the purity of EVs, despite that these steps would compromise the final EV yield as seen by another recent study (Onódi et al., 2018).

When applying LC to stem cell CM, the fractionation pattern was reproducible across replicates and consistent across the two stem cell types. However, we noticed that there was a lack of distinct cut-off points between the particle and protein peaks in the LC profile of stem cell CM. This led us to speculate that, due to the high protein content in the media, the resolution of fractionation may require more improvements; this can be in the form of sequential LC steps or with an alternative taller column. We briefly tested the sequential LC approach here and noticed that in the second LC run there was slightly lower expression of the transmembrane protein CD9, while the opposite was true for the intraluminal protein (Tsg101) (**Supplementary Figure S4**). Interestingly, this suggests that more caution is required when checking the expression of established EV markers identification. In this scenario, the lower expression of CD9 in the second LC may indicate that some CD9 initially detected might be contributed by non-EV proteins.

Despite our efforts to clean-up the EV product, there remains the presence of huge protein bands as seen by Ponceau S staining. From literature, KOSR contains high amounts of albumin and other proteins to replace the serum component. We postulated that the pre-concentration step with spin filters might have caused the capture of these protein aggregates in the filters. To bypass this, one could consider the use of tangential flow filtration (TFF) devices, which function through diafiltrationbased processes. On the other hand, some contaminating proteins on EVs may be due to the presence of cell adhesion molecules like connexins, integrins and cadherins (Albelda and Buck, 1990; Sakisaka, 2005; Shimaoka et al., 2019) which would make EVs particularly adhesive to free proteins in the circulation.

Unlike previous studies on stem cell derived-EVs, which were collected from serum-free conditions (Ratajczak et al., 2006; Yuan et al., 2009; Katsman et al., 2012), we used serum-replacement media to avoid the differentiation of our stem cells in the absence of serum. To identify the background of proteins in the unconditioned media, we applied the use of a mass spectrometry method. We found a relatively high amount of cow albumin and keratin proteins. As the serum-replacement media contained AlbuMaxTM (Life Technologies, United Kingdom), we believed that this was the main source of cow albumin in our samples and pre-filtered these proteins out of downstream analysis.

Generally, the EVs from iPSCs and ESCs have relatively similar proteomes. Although there was a disparity in the total number of proteins detected in the iPSC-EVs and ESC-EVs, most of the specifically expressed proteins did not show a significant

difference in GO terms enrichments and may not impact on cell functionality. Furthermore, our proteomics data highlighted the identities of other EV markers, which could serve as useful indicators for future studies on stem cell EVs.

Next, to trace if the similarities between EVs was related to their parental cells, we analyzed the proteomes of the cells from which the EVs were derived from. Although the proteomes of both stem cell types were highly similar, there was much less overlap between EVs and their corresponding parental source. This was largely due to the lower overall number of detected proteins in EVs than in cells. This is not at all unexpected, as it can be attributed to the size differences between a vesicle (100 nm) and a cell (>5 µm). However, the enrichment of specific GO terms in EVs over cells illustrates the differences between their biological contents and may be linked to their downstream functionality. In hindsight though, we cannot be certain how the background from the basal media may have also affected the LC-MS run and the downstream protein readout.

There is an overwhelming amount of research commenting on the similarities and differences between ESCs and iPSCs (Boulting et al., 2011; Narsinh et al., 2011; Phanstiel et al., 2011), and the variation between ESC and iPSC clones due to the type of donor cells (Ward et al., 2004; Osafune et al., 2008; Narsinh et al., 2011), their methylation status and transcriptomes (Kim et al., 2011; Lister et al., 2011; Ohi et al., 2011).

On the other hand, There have only been a handful of published proteomics studies on mouse (Graumann et al., 2007) and human iPSCs and ESCs (Phanstiel et al., 2011; Munoz et al., 2014), all of which have been performed on different ESC and iPSC lines. There have also been some recent studies on EVs from iPSCs and iPSC-derivatives (Zhou et al., 2016; Liu et al., 2017; Zhu et al., 2017; Ding et al., 2018; Dougherty et al., 2018; La Greca et al., 2018; Oh et al., 2018), though there is yet to be any proteomic data on EVs from pluripotent cell sources. Hence, due to the variation between lines, the biological data here might not be applicable to all other pluripotent stem cells.

In neurological diseases, standard methods of monitoring such as CT or MRI scans are costly, less sensitive and requires expertise interpretation. The use of traditional tissue biopsy or blood liquid biopsy in CNS-related disease has been elusive due to the presence of the blood brain barrier functions (Daneman and Prat, 2015).  However, the lumbar tap procedure for CSF extraction is painful and often only very small volumes of CSF can be extracted. Hence, we envision here that our study will assist other EV researchers on the methodology for EV purification from these precious sources.

Besides biology, EVs can also be exploited by loading therapeutic cargoes; both naturally and after engineering. In the former, there are many excellent reviews on how EVs from mesenchymal stem cells (MSCs) can alleviate disease through EV therapy in CNS-related diseases (Kawikova and Askenase, 2015; Koniusz et al., 2016; Luarte et al., 2016). On the other hand, others have cleverly designed targeting moieties on the surface of EVs and encapsulated genetic cargoes or drugs for therapeutic purposes (Alvarez-Erviti et al., 2011; El Andaloussi et al., 2013; Kalani et al., 2016; Usman et al., 2018). Here, we describe EVs from iPSCs that may serve as an alternative resource. Furthermore, we performed unbiased protein characterization of these EVs with the hope that this can assist in the future design and molecular engineering of EVs for specific personalized therapy.

#### CONCLUSION

Here, we describe a study investigating the isolation of EVs from protein-rich complex media types, using stem cells as a model. Initially, our step-by-step comparison between UC and LC contradicted our expectations. Using additional technologies, we illustrate the possible causes and sources of protein contamination in the UC pellet and how they can be reduced with additional clean-up steps. However, these processes are somewhat inefficient and compromise on the final output. On the other hand, although LC was a better alternative to UC, the resultant EV population still appears to be of low purity. The purity of EVs is of great importance in all forms of EV research, from deducing biological functions to use as therapeutic agents, as contaminations may confound interpretations. Furthermore, we highlight the need to be cautious when using current tools for the interpretation of EVs purified from complex media or biological fluids. Lastly, we analyzed the protein content of these EVs and briefly discuss their potential in future CNSrelated research.

## DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the manuscript/**Supplementary Files**.

## AUTHOR CONTRIBUTIONS

YL performed all the experiments and wrote the manuscript. HJ performed the proteomics. MW and SE conceptualized the study and provided critical discussions on the overall study and manuscript.

## FUNDING

YL is funded by the Agency for Science Technology and Research, Singapore. SE is funded by the Swedish Medical Research Council, the Swedish Foundation for Strategic Research (SSF-IRC), and Evox Therapeutics.

## ACKNOWLEDGMENTS

We would like to thank Mr. Tim Davis and Dr. Paul Fairchild for kindly sharing their mouse ESCs and iPSCs lines. We would also like to thank Dr. Daniel Hagey for critical and useful discussions on the manuscript.

#### SUPPLEMENTARY MATERIAL

fnins-13-01067 October 18, 2019 Time: 15:31 # 19

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins. 2019.01067/full#supplementary-material

FIGURE S1 | Alternative types of serum-depleted media for EV collection from stem cells. (A) NTA size distribution profiles of UC pellets after centrifugation of the three types of media: KOSR-supplemented stem cell media (SR), Pre-spun stem cell media (PS) and serum-free media/OptiMEM (OM). (B) Table showing the mode size (nm) and concentration of particles (x109 /ml) detected in the UC pellet of each media type. Representative bright-field images of mouse iPSCs (C) and mouse ESCs (F) after 48 h culture in SR, PS, or OM. Graphs comparing the total number of viable cells in the miPSCs (D) and mESCs (E) cultures at CM collection. Graphs comparing the total concentration of particles (x1010/ml) detected by NTA in the UC pellet of miPSCs (G) and mESCs (H) in the three different media types.

FIGURE S2 | LC fractionation of UC pellets reveals co-precipitation of non-vesicular proteins with EVs in the UC pellet. (A) Schematic outline of the LC fractionation protocol of UC pellets. (B) LC chromatograph showing the 280 nm absorbance of the elution course from the LC column for three replicate samples (R1–R3). The first fraction (pink box) corresponds to the region where EVs elute. The second fraction (orange box) shows the appearance of another peak after the EVs. (C) NTA size distribution profiles of particles in the original UC sample (blue), and in the first (box 1; UC-LC1) and second (box 2; UC-LC2) LC fractions. (D) Graph on the left shows the mode size (nm) of particles in the original UC

#### REFERENCES

pellet, UC-LC1 and UC-LC2 (n = 3, bar represents mean ± SD). Graph on the right shows the percentage of particles detected in the UC pellet versus UC-LC1 (n = 3, bar represents mean ± SD). (E) Representative western blots for EV (Alix and CD9) and pluripotency (OCT4) markers when loading the same amount of particles from the UC pellet and UC-LC1 fraction. (F) Total protein staining of the UC pellet, UC-LC1 and UC-LC2. (G) Representative TEM images of the UC pellet and UC-LC1. Here the UC-LC1 sample appears to have a decreased background as compared to UC. The scale bar corresponds to 100 nm.

FIGURE S3 | Detection of non-mouse proteins in mouse derived EVs. Scatter plot showing the areas of proteins identified in the preliminary analysis of iPSC- and ESC-EV samples.

FIGURE S4 | Sequential LC fraction improves EV purity marginally. (A) Schematic outline of the sequential LC fractionation set-up. (B) LC chromatograph showing the 280 nm absorbance of the elution course from the LC column for three replicate samples (R1–R3). The first fraction (green box) corresponds to the region where EVs elute. (C) NTA size distribution profiles of particles in the first LC sample versus the second LC run (LC01). (D) On the left, the mode size of particles in the original LC sample and the LC01 sample appears to be similar (n = 3). On the right, graph showing overall percentage of particles recovered as compared to the input material. (E) Representative western blotting pictures when loading the same amount of particles for LC and LC'1. (F) Total protein staining of the membrane with LC and LC'1 samples.

TABLE S1 | Table showing the protein expression of replicate runs of cells and EVs purified from mouse ESC and mouse iPSC.

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**Conflict of Interest:** MW and SE are founders and shareholders of Evox Therapeutics.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Lee, Johansson, Wood and El Andaloussi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# miR-212 and miR-132 Are Downregulated in Neurally Derived Plasma Exosomes of Alzheimer's Patients

#### Edited by:

Efrat Levy, New York University, United States

#### Reviewed by:

Scott Edward Counts, Michigan State University, United States Daniela Galimberti, University of Milan, Italy P. Hemachandra Reddy, Texas Tech University Health Sciences Center, United States

#### \*Correspondence:

Dominic M. Walsh<EMAIL_ADDRESS>
†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

Received: 21 August 2019 Accepted: 25 October 2019 Published: 26 November 2019

#### Citation:

Cha DJ, Mengel D, Mustapic M, Liu W, Selkoe DJ, Kapogiannis D, Galasko D, Rissman RA, Bennett DA and Walsh DM (2019) miR-212 and miR-132 Are Downregulated in Neurally Derived Plasma Exosomes of Alzheimer's Patients. Front. Neurosci. 13:1208. doi: 10.3389/fnins.2019.01208 Diana J. Cha1† , David Mengel1,2,3† , Maja Mustapic4 , Wen Liu1 , Dennis J. Selkoe1 , Dimitrios Kapogiannis4 , Douglas Galasko5 , Robert A. Rissman5,6, David A. Bennett7 and Dominic M. Walsh1,8 \*

1 Laboratory for Neurodegenerative Disease Research, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 2 Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany, 3 German Center for Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany, 4 Laboratory of Neurosciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States, 5 Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States, 6 VA San Diego Healthcare System, La Jolla, CA, United States, 7 Rush Alzheimer's Disease Center, Rush Medical College, Chicago, IL, United States, 8 Alzheimer's Disease and Dementia Research Unit, Biogen Inc., Cambridge, MA, United States

It was recently discovered that brain cells release extracellular vesicles (EV) which can pass from brain into blood. These findings raise the possibility that brain-derived EV's present in blood can be used to monitor disease processes occurring in the cerebrum. Since the levels of certain micro-RNAs (miRNAs) have been reported to be altered in Alzheimer's disease (AD) brain, we sought to assess miRNA dysregulation in AD brain tissue and to determine if these changes were reflected in neural EVs isolated from blood of subjects with AD. To this end, we employed high-content miRNA arrays to search for differences in miRNAs in RNA pools from brain tissue of AD (n = 5), high pathological control (HPC) (n = 5), or cognitively intact pathology-free controls (n = 5). Twelve miRNAs were altered by >1.5-fold in AD compared to controls, and six of these were also changed compared to HPCs. Analysis of hits in brain extracts from 11 AD, 7 HPCs and 9 controls revealed a similar fold difference in these six miRNAs, with three showing statistically significant group differences and one with a strong trend toward group differences. Thereafter, we focused on the four miRNAs that showed group differences and measured their content in neurally derived blood EVs isolated from 63 subjects: 16 patients with early stage dementia and a CSF Aβ42+ tau profile consistent with AD, 16 individuals with mild cognitive impairment (MCI) and an AD CSF profile, and 31 cognitively intact controls with normal CSF Aβ42+ tau levels. ROC analysis indicated that measurement of miR-132-3p in neurally-derived plasma EVs showed good sensitivity and specificity to diagnose AD, but did not effectively separate

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individuals with AD-MCI from controls. Moreover, when we measured the levels of a related miRNA, miR-212, we found that this miRNA was also decreased in neural EVs from AD patients compared to controls. Our results suggest that measurement of miR-132 and miR-212 in neural EVs should be further investigated as a diagnostic aid for AD and as a potential theragnostic.

Keywords: blood biomarker, extracellular vesicles, L1CAM, micro-RNA, mild cognitive impairment, qRT-PCR

#### INTRODUCTION

fnins-13-01208 November 23, 2019 Time: 16:4 # 2

Alzheimer's disease is a devastating disorder for which there is no cure or effective treatment.  Advances in brain imaging and the development of robust immunoassays to measure tau and amyloid β-protein (Aβ) in cerebrospinal fluid (CSF) have greatly aided diagnosis (Blennow et al., 2012). Specifically, measurement of tau and Aβ in CSF, or quantitation of amyloid or tangle pathology by PET imaging, can be used to identify mild cognitive impairment (MCI), a frequent precursor of AD (Jack et al., 2013; Bao et al., 2017), and use of these markers is now common in clinical research (Blennow et al., 2012; Jack et al., 2013). But PET imaging is expensive and is restricted to use in certain geographies. Assessment of markers in CSF is more amendable for general use, but CSF sampling remains unpopular with patients. Thus, there is a pressing need for less costly and intrusive, and more widely available biomarkers that can replace or supplement current CSF and PET markers (Bateman et al., 2019). Measurement of a blood-based analyte would be ideal since blood collection is widely accepted by patients and can be done almost anywhere.

Unlike CSF, the contents of blood are influenced by many organs and therefore changes in blood analytes are often not sensitive to minor changes that occur in brain. For instance, while the levels of Aβ42 in CSF change early in AD, measurement of Aβ42 in plasma has not proved as useful (Janelidze et al., 2016). The discovery that brain cells release extracellular vesicles (EVs) and a portion of these enter the bloodstream offers the potential of monitoring changes occurring in the brain by isolating EVs from venous blood. EVs are small packages of cytosol encapsulated by a lipid bilayer and are released by all cells, including neurons and glia (Fruhbeis et al., 2012; Raposo and Stoorvogel, 2013; Paolicelli et al., 2019). Exosome is the term most frequently applied to EVs, but there are many classes of EVs, primarily defined by their cellular origin. Exosomes arise through the endolysosomal pathway and are specifically formed by inward budding of multivesicular bodies to produce intraluminal vesicles. After maturation, multivesicular bodies can fuse with the plasma membrane and exosomes are released (Pan et al., 1985; Johnstone et al., 1987).

A series of recent papers on neural exosomes isolated from blood plasma (Fiandaca et al., 2015; Goetzl et al., 2015a,b; Kapogiannis et al., 2015; Abner et al., 2016) has caused huge excitement among the neuroscience biomarker community (Galasko, 2015; Heinzelman et al., 2016; Vella et al., 2016). The method used employs a proprietary reagent, ExoQuick, to precipitate total "exosomes" from plasma, followed by enrichment of material of "neural" origin using an antibody to the neural cell adhesion molecules, L1CAM (Fiandaca et al., 2015). In two independent studies, analysis of samples prepared using this protocol revealed significant increases in pT181-tau, pS396 tau and Aβ1-42 in patients with AD vs. those from age-matched normal cognitively intact controls (Fiandaca et al., 2015; Winston et al., 2016). Other protein cargo in neural exosomes have also been found to change in AD (Goetzl et al., 2019). However, the ability to discriminate AD from AD-MCI and controls required a statistical approach that constrains comparison across cohorts analyzed on different occasions. Furthermore, a more recent publication employing a similar method failed to detect elevation of pT181-tau or Aβ1-42 in neural exosomes from AD subjects (Guix et al., 2018).

Micro-RNAs (miRNA) are small non-coding RNA molecules which base-pair with complementary sequences within mRNA to decrease post-transcriptional gene expression (O'Brien et al., 2018) and accumulating evidence indicates that the expression of certain miRNAs are altered in AD (Lau et al., 2013; Patrick et al., 2017; Pichler et al., 2017; El Fatimy et al., 2018; Swarbrick et al., 2019). However, it is unknown whether miRNA dysregulation in the brain is reflected in neural exosomes. To this end, we performed an unbiased analysis of miRNAs in human brain tissue to inform miRNA candidates for targeted analysis in neurally derived plasma exosomes. First, we measured miRNAs in pooled RNA isolated from five individuals with AD, five cognitively intact high pathology controls (HPCs), and five pathology-free controls. HPC refers to subjects with sufficient amyloid plaques and neurofibrillary tangles to be diagnosed as AD, but at the time of death these individuals had no evidence of cognitive impairment. Recently, it had been suggested to consider such individuals as AD stage 1 and 2 (Jack et al., 2018). Results were highly similar across the pooled and individual brain samples. In a total of 27 individual brain samples: 9 controls, 7 HPC and 11 AD, miR-182-5p, miR-591, miR-32-3p, and miR-132-3p were dysregulated in AD vs. both controls and HPC.

Armed with this information, we asked whether the miRNAs changed in AD brain were also altered in neural exosomes isolated from plasma. Since, diagnosis of AD based on clinical criteria alone is uncertain (Monsell et al., 2015) we were careful to use specimens from individuals whose clinical diagnosis was confirmed by the best current biomarkers – CSF Aβ42 and tau. Using plasma from patients that conformed to these criteria we prepared neural exosomes from 31 cognitively intact biomarkernegative controls, 16 CSF biomarker-positive MCI, and 16

CSF biomarker-positive AD. RNA was extracted from neural exosomes, reversed transcribed and analyzed by qRT-PCR for the four miRNAs most altered in AD brains. miR-132 was decreased by ∼9-fold in AD exosomes vs. controls, and ∼5.4 fold in AD vs. AD-MCI. Since miRNAs within the same cluster are often co-regulated, we also measured miR-212, a miRNA in tandem to miR-132. Strikingly, the levels of miR-212 were reduced fourfold in AD samples compared to controls. These results suggest that concerted dysregulation of miR-132 and miR-212 occurs in AD and that measuring the levels of these miRNAs in neurally derived exosomes may offer a window on changes occurring in AD brain. Our results in neural exosomes are consistent with prior studies that detected down-regulation of miR-132 and miR-212 in AD brain (Pichler et al., 2017) and down-regulation of miR-132 in blood (Denk et al., 2018).

### MATERIALS AND METHODS

#### Brain Donors

Human specimens were obtained from Rush Alzheimer's Disease Center, Rush University Medical Center and used in accordance with the Partners Institutional Review Board (Protocol: Walsh BWH 2011). Samples of frozen frontal cortex were from the Religious Orders Study (ROS), a longitudinal clinicalpathological cohort study in aging and Alzheimer's Disease (Bennett et al., 2018). Participants from religious communities were enrolled at a time when they were free of known dementia. The study was approved by the Institutional Review Board of Rush University Center. All participants signed an informed consent, Anatomic Gift Act, and a repository consent to allow their data and biospecimens to be shared for research. They were followed over time until brain donation. Overall follow-up and autopsy rates were about 95%. Each participant underwent uniform structured annual cognitive and clinical evaluations. A diagnosis AD dementia required evidence of meaningful cognitive decline in two domains of cognition, one of which was episodic memory. Clinical summary diagnostic opinion was made post-mortem based on all available clinical information by a neurologist blinded to pathologic data. A neuropathologic diagnosis of high (1), intermediate (2), low (3), or no AD (4) was based on the modified NIA-Reagan Diagnosis of AD. This assessment relies on the severity and distribution of both neurofibrillary tangles and neuritic plaques (Bennett et al., 2006). Individuals were categorized as controls, high pathology controls (HPC), or AD based on combined clinical and pathological criteria (**Table 1**). Individuals with an NIA-Reagan score of 1 or 2 and a clinical diagnosis of AD dementia were defined AD (n = 11), whereas patients with an NIA-Reagan Score of 1 or 2 and no evidence of dementia were designated HPC (n = 8). Patients in the control group had an NIA-Reagan score of 3 and no evidence of dementia.

## Study Participants, CSF and Blood Collection

Specimens were from research participants in the UCSD Shiley-Marcos Alzheimer's Disease Research Center, collected and used in accordance with IRB approval (**Table 2**). Each participant donated both ETDA plasma and CSF, which were obtained using standardized protocols. CSF was collected into polypropylene tubes, centrifuged at 1,500 × g for 10 min, and aliquoted into polypropylene storage tubes. Blood was drawn on the same day as lumbar puncture, and plasma was isolated and aliquoted. On the day of analysis, samples were thawed at room temperature, and used for subsequent exosome isolation.

Subjects were designated as AD, MCI, and controls based on cognitive testing (MMSE) and CSF biomarker criteria. Control subjects (n = 16) had an MMSE ≥ 28, a CSF Innotest tau/Aβ ratio < 0.5 and Aβ > 500 pg/mL (mean Innotest tau level: 248.6 ± 111.5 pg/mL; mean Innotest Aβ1-42 level: 911.2 ± 245.1 pg/mL).  AD subjects (n = 31) had a CSF AD biomarker profile (mean Innotest tau level: 699.6 ± 219.7 pg/mL; mean Innotest Aβ1-42 level: 500.0 ± 81.9 pg/mL) and an MMSE score of 15–24 points, whereas AD-MCI (n = 16) subjects had a CSF AD biomarker profile (mean Innotest tau level: 723.1 ± 218.8 pg/mL; mean Innotest Aβ1–42 level: 470.3 ± 90.5 pg/mL) and an MMSE score of 25–29 points. CSF levels of Aβ and tau yielded clear separation of diagnostic groups (**Supplementary Figure S1**).

### Generation of Induced Neurons (iNs) From Human Induced Pluripotent Stem Cells (iPSCs) and Isolation of Exosomes From Conditioned Media

Neurogenin 2 (Ngn2)-induced human neurons were prepared as before (Guix et al., 2018). In brief, YZ1 iPSCs were maintained in medium containing DMEM/F12, Knockout Serum Replacement, penicillin/streptomycin, L-glutamine, MEM-NEAA, and β-mercaptoethanol (all from Invitrogen, Carlsbad, CA, United States) which was supplemented with 10 µg/mL basic fibroblast growth factor (bFGF; Millipore-Sigma, Burlington, MA, United States). For viral infection, iPSCs were plated at a density of 95,000 cells/cm2 . Ultrapure lentiviral titers (Alstem, Richmond, CA, United States) were used at the following concentrations: Tet-O-Ngn2-puro: 0.1 µL/50,000 cells; Tet-O-FUW-EGFP: 0.05 µL/50,000 cells; FUdeltaGW-rtTA: 0.11 µL/50,000 cells. Ngn-2 expression was induced by addition of doxycycline on iN day 1 at a concentration of 2 µg/mL. On iN day 2, puromycin was added at 20 µg/mL, and this concentration was maintained thereafter. On iN day 4, cells were plated on Matrigel (Corning, NY, United States)-coated 100 mm dishes (650,000 cells) and cultured in Neurobasal media (Gibco, Carlsbad, CA, United States) containing Glutamax, 20% dextrose, MEM-NEAA, B27 and 10 ng/mL BDNF, CNTF, GDNF (PeproTech, Rocky Hill, NY, United States). Conditioned media (CM) was collected on iN D21-33, pooled, and processed in batches of 30 mL. First, CM was centrifuged at 300 × g and 4 ◦C for 10 min to pellet floating cells. Next, the upper 97% was recovered, and centrifuged at 2,000 × g and 4◦C to pellet apoptotic bodies. Then, the upper 97% was recovered, diluted in neurobasal media, and centrifuged at 10,000 × g and 4◦C for 30 min to pellet microvesicles and cell debris. Finally, the upper

CTL, control; HPC, high pathology control; AD, Alzheimer's Disease; Cognitive Diagnosis: 1 (no cognitive impairment), 2 (mild cognitive impairment), 4 (Alzheimer's Disease Dementia); NIA-Reagan: 1 (high likelihood of AD), 2 (intermediate likelihood of AD), 3 (low likelihood of AD); CERAD score: 1 (definite AD), 2 (probable AD), 3 (possible AD), 4 (no AD); Braak stage: 0 (no NFT pathology), I and II (NFT pathology confined to the enthorinal region), III and IV (involvement of limbic regions), V and VI (moderate to severe neocortical involvement). ∗Used for miRNA high content arrays †age-range is given to preserve anonymity of participants. ‡This brain was used only for the pooled analysis and not for assessment of individual brains. Gray represents brain samples from the control group, pink represents HPC, and orange represents AD.

97% was recovered and subjected to centrifugation at 100,000 × g and 4◦C for 70 min to pellet exosomes.

#### RNA Isolation and miRNA Analysis From Brain Tissue

Gray matter from frozen cortex was dissected on dry ice. Total RNA was extracted from ∼20 mg gray matter using a miRNeasy Advanced Kit (Qiagen, Louisville, KY, United States) according to the manufacturer's guidance. Briefly, samples were suspended in 200 µL ice cold RIPA buffer followed by addition of 120 µL kit-provided RPL buffer. Tissue was homogenized by gently pipetting samples up and down. Forty µL kit-provided RPP buffer was added to samples and vortexed for ∼30 s to facilitate precipitation of proteins. Samples were then centrifuged at 12,000 × g for 3 min to pellet the precipitate and debris, and approximately 230 (µL of supernatant was transferred to a new 1.5 mL tube. An equal volume of isopropanol was added and mixed by pipetting before transferring the entire sample to an RNeasy UCP MinElute column. Columns containing sample were centrifuged at 12,000 (x g for 1 minute. Flowthrough was discarded and columns were washed according to the manufacturer''s protocol. RNA was eluted with two rounds of 20 (L RNase-free water. RNA yield and quality were quantified on a nanodrop. Fifty ng/µL aliquots of RNA were prepared and stored at −20◦C.

One hundred and fifty ng of RNA was used for reverse transcription (RT) using a miSCRIPT RT II kit (Qiagen, Louisville, KY, United States), where samples were incubated at 37◦C for 60 min followed by inactivation at 95◦C for 5 min. RT efficiency was determined by measuring levels of miRTC, a RT control.

### RNA Isolation and miRNA Analysis From iN Cells and iN-Derived Exosomes

iN cells were harvested and centrifuged at 300 × g for 10 min to pellet cells. Cell pellets were washed with PBS and lysed with RPL buffer. Exosomes from iN CM were isolated by differential centrifugation as described above,

(Continued)

NCI, no cognitive impairment; AD-MCI, mild cognitive impairment due to Alzheimer's Disease; AD, Alzheimer's Disease. †Age-range is given to preserve anonymity of participants.

washed with PBS, and lysed in RPL buffer. Subsequent steps were followed identically to the isolation of miRNA from brain tissue.

#### Isolation of Exosomes and Enrichment of Neuronal Exosomes

To verify that our methods could reliably detect miRNA expression in an exosomal fraction of human plasma, and to optimize preparative and analytical methods prior to processing precious clinical samples, we first tested experimental conditions using human plasma (each in technical duplicate) from two healthy volunteers.

The method to isolate neuron-derived exosomes from plasma has been validated and described in detail before (Guix et al., 2018), and is a slightly modified version of the procedure pioneered by the Goetzl/Kapogiannis group (Kapogiannis et al., 2015). Five µL thrombin (System Bioscience, Palo Alto, CA, United States) was added to 500 µL aliquots of plasma to induce clot formation and allow the removal of fibrin and related proteins. Reactions were mixed by inversion and incubated for 30 min at room temperature before dilution with 495 µL Ca2+- and Mg2+-free Dulbecco's Phosphate-Buffered Saline (DPBS) (Sigma-Aldrich, St. Louis, MO, United States) containing 3x phosphatase (Thermo Fisher, Carlsbad, CA, United States) and protease (Roche, Branchburg, NJ, United States) inhibitors. Thereafter, the samples were centrifuged at 6000 × g for 20 min at 4 ◦C and the supernatant transferred to a new tube. Next, 252 µL ExoQuick (System Bioscience, Palo Alto, CA, United States) was added to the supernatants, samples mixed by inversion and left to stand at 4◦C for 1 h. Vesicles present in the serum were recovered by centrifugation at 1500 × g for 20 min at 4◦C and resuspended by vortexing in 500 µL MilliQ water containing 3 × phosphatase and protease inhibitors. ExoQuick pellets were then mixed overnight at 4◦C on a vertical rotating mixer.

To isolate neuronal exosomes from the suspensions of total plasma exosomes, 4 µg biotinylated anti-L1CAM antibody (eBioscience, San Diego, CA, United States) in 42 µL 3% bovine serum albumin in DPBS (BSA/DPBS) was added to the

resuspended vesicles and incubated on a rotating mixer for 1 h at 4◦C. Thereafter, 15 µL of pre-washed Streptavidin-Plus UltraLink Resin (Thermo Fisher, Danvers, MA, United States) in 25 µL 3% BSA/DPBS was added to each sample and incubated for 4 h at 4◦C with rotation. Neuronal exosomes bound to the antibody/resin complex were recovered by centrifugation at 200 × g for 10 min at 4◦C, and washed once with 3% BSA/DPBS before elution in 200 µL 0.1 M glycine (pH 3.0). Resin was removed by centrifugation at 4500 × g for 5 min at 4◦C. Thereafter, 195 µL supernatant was neutralized with 15 µL of 1 M TrisHCl (pH 8.0) and exosomes were lysed by adding 360 µL M-PER (Thermo Fisher, Carlsbad, CA, United States), 25 µL 3% BSA/DPBS containing 1× phosphatase and protease inhibitors, and 2 freeze-thaw cycles before downstream analysis.

RNA isolation from neural exosomes was performed identically to brain samples, with the following modifications: total exosomes (Exoquick pellets), neural exosomes (L1CAM immunoisolates), or irrelevant/non-specific exosomes (46-4 immunoisolates) were mixed with 240 µL RPL buffer, followed by 80 µL RPP buffer. All subsequent steps were performed as for RNA isolation from brain tissue.

Bioanalyzer analysis showed that samples were enriched with small RNA species following elution from RNeasy UCP MinElute columns.

We cannot completely rule out that some plasma RNA outside of exosomes might be isolated along with exosomal RNA in our protocol. However, this seems unlikely, since free-floating RNA is rapidly degraded in plasma, and RNA protected in RNAbinding proteins or lipids would not allow isolation by L1CAM immunoprecipitation.

#### miRNA Quantitative PCR Array

The miRNA expression profile of RNA extracted from brain tissue was analyzed using a miScript miRNA PCR Array Human miRBase Profiler HC Plate 1, MIHS-3401Z (Qiagen, Hilden, Germany). Each array contains 372 unique miRNA assays, 3 snoRNA and 3 snRNA housekeeping genes, duplicate RT primer assays to evaluate RT efficiency, and duplicate PCR primer assays to evaluate the efficiency of the qPCR reaction (for a full list of miRNAs detectable using miScript arrays see: https://b2b.qiagen.com/~/media/genetable/mi/hs/34/ mihs-3401z). Six µL RNA (initial concentration = 50 ng/µL) was prepared in 20 µL reverse-transcription reactions (final concentration = 15 ng/µL) using miScript HiSpec Buffer where mature miRNAs are polyadenylated by Poly(A)polymerase and reverse transcribed into cDNA using oligo-dT primers. The oligo-dT primers have a 30 degenerate anchor and a universal tag sequence on the 50 end, allowing amplification of mature miRNA in real-time PCR. Each reverse-transcription reaction was further diluted with 90 µL RNAse-free water (4.5 fold dilution) before being used as template for real-time PCR analysis on the miScript miRNA HC PCR Array. The array contained miRNA-specific miScript primers, the miScript SYBR Green kit with the miScript universal primer (reverse primer), and QuantiTect SYBR Green PCR Master Mix. miRNA cDNA was quantified using a ViiA 7 Real-Time PCR System (Applied Biosystems, Waltham, MA, United States). The thermal cycling protocol was as follows: 95◦C for 10 min (PCR activation step), 45 amplification cycles at 94◦C for 15 s (denaturation), at 55◦C for 30 s (annealing), and at 70◦C for 30 s (extension), followed by fluorescence data collection. A no-template control (NTC) of RNase-free water was co-purified and profiled like the samples to measure background.

We used the 3 snoRNAs and 3 snRNAs in addition to miR-16 as endogenous normalization controls in the arrays. MicroRNA expression was quantified as delta Ct values, where Ct = threshold cycle, delta Ct = (Ct target miRNA minus the average Ct of the 7 normalization controls). Relative miRNA expression was calculated as ddCt (AD vs. CTRL) = mean dCtAD – mean dCtCTRL or ddCt (AD vs. HPC) = mean dCtAD – mean dHPC. Higher values indicate higher expression. Signals with a ddCt ≥ | 0.58|, which corresponds to a fold change of ≥| 1.5|, were considered as differentially regulated. The assay showed good technical reproducibility when repeated three times on three different days (**Supplementary Figure S2**).

#### Quantitative PCR of Individual miRNAs

QPCR of individual miRNAs was carried out as described for the miRNA array with the following modifications: cDNA was diluted 1:100 with RNAse free water in primer specific qPCR for each miR. For qPCR validation in brains, miR-16 and SNORD95 was used as endogenous normalization controls, whereas miR-16 alone was used as normalization control for qPCR of miRNAs in neural exosomes. miR-16 has been evaluated as an endogenous normalization control in plasma, and similar plasma levels were found in AD and controls (Bhatnagar et al., 2014). Moreover, several previous studies found miR-16 levels to be consistent across AD and control brains (Hebert et al., 2008; Banzhaf-Strathmann et al., 2014). A list of all primers used for qRT-PCR is presented in **Supplementary Table S1**.

#### Statistics

Statistical analyses were carried out using GraphPad Prism, version 8 (La Jolla, CA, United States). The Shapiro–Wilk test was used to determine normal distribution of data. Significance was determined by one-way ANOVA followed by Tukey's post hoc test for normally distributed data. Otherwise, Kruskal–Wallis test followed by Dunn's post hoc test was used. Mean age of brain and plasma donors was highly similar between diagnostic groups, and the age-distribution did not differ significantly. Thus, we did not control for age in our analyses. P-values are reported without correction for multiple testing because of the exploratory nature of this study. The significance threshold was set to a twosided p < 0.05.

## RESULTS

#### miRNAs Are Altered in AD Brains

To determine whether miRNAs are dysregulated in AD brain, we pooled RNA isolated from the brains of five individuals who died with AD, five individuals who had AD pathology but were without cognitive impairment at the time of death, and

FIGURE 1 | miRNAs are differentially expressed in AD compared to control and HPC brains. (A) Workflow of miRNA candidate discovery. RNA was isolated from cortical brain tissue of individuals who were cognitively intact (n = 5), AD (n = 5) or HPC (n = 5) (see Table 1 for demographic data). RNA from the 5 control, 5 AD or 5 HPC brains were pooled and used as template for cDNA synthesis. cDNA from pooled control, AD or HPC were used as templates for qRT-PCR. (B) Comparative analysis of profiled miRNAs between disease groups. miRNAs that are upregulated (red) and downregulated (blue) by >2-fold are indicated. Each point represents log10(2-11Ct) average values from each cDNA pool run in triplicate (3 plates). (C) miRNAs that were significantly altered by >2-fold. A total of 16 miRNAs were dysregulated in AD brains compared to control or HPC brains. Six of those miRNAs were consistently altered specifically in AD brains (miR-608, -219a-5p, 1304-5p, -182-5p, -566, -591). Six additional miRNAs were upregulated >2-fold in AD brains compared to control, but <2-fold compared to HPC brains (miR-32-5p, -519e-5p, 132-3p, 212-3p, -662, -138-3p). Red text = upregulated miRNAs; blue text = downregulated miRNAs.

five individuals who were without cognitive impairment normal and free of AD pathology (**Table 1**). RNA was extracted from cortical gray matter, pooled, reverse transcribed into cDNA, and cDNA was used for miRNA assays (**Figure 1A**). Since it is often difficult to distinguish miRNAs that are altered due to disease from normal aging, comparative analysis of profiled miRNAs was performed between AD brains relative to control, and relative to HPC. Of the 372 miRNAs that were measured, 12 miRNAs were changed >2-fold in AD brains compared to controls (4 miRNAs were up- and 8 mIRNAs were downregulated, respectively) (**Table 3** and **Figure 1B**). Compared to HPC brains, 10 miRNAs were changed >2-fold (4 up- and 6 downregulated, respectively) (**Table 3** and **Figure 1B**). Six miRNAs were up (miR-608, miR-219a-5p) or downregulated (miR-566, -182-5p, -591, -1304-5p), specifically in AD brains vs. both HPCs and controls (**Figure 1C** and **Table 3**). Compared to controls, 8 miRNAs were increased in HPC brains no miRNAs were decreased (**Figure 1B**, right panel).

To assess the robustness of the findings obtained using miRNA array analysis, we used miR-specific qRT-PCR to measure levels of the 12 miRNAs that were found to be most changed in our array experiments. In our initial qRT-PCR we used exactly the same pools as employed for the array analysis. The changes detected by array analysis of pooled brain RNA (**Table 3**) were largely confirmed by qRT-PCR analysis of individual miRs (**Table 4**, columns 1 and 2). Nine of 12 miRs showed the same directional and comparable magnitude changes by both

TABLE 3 | miRNAs significantly changed by ≥2-fold in AD.

miRs with a >2 fold change in AD compared to CTL and HPC are displayed in bold font. miRs with decreased expression in AD brain are shown blue, whereas those with a increased expression are displayed in red.

#### TABLE 4 | qPCR validation of miRs.

fnins-13-01208 November 23, 2019 Time: 16:4 # 8

miRs with decreased expression in AD brain are shown blue, whereas those with a increased expression are displayed in red.

arrays and qRT-PCR, but miR-1304, miR-138-1-3p, and miR-519e-5p changed in opposite directions (compare **Tables 3**, **4**). Thereafter, we undertook qRT-PCR analysis of RNA preparations from each of the 15 individual brains used for the initial pools. When values were averaged for each disease group (i.e., for the 5 AD, the 5 HPC and the 5 controls) the direction of change detected by qRT-PCR and array analysis were in complete agreement (compare **Tables 3**, **4**, columns 3 and 4). We then went on to use qRT-PCR to measure the levels of the same 12 miRs using RNA isolated from a total of 27 brains (11 AD, 7 HPC, and 9 cognitively intact controls). Here the results were similar to those obtained using array analysis of pooled samples (**Table 3**) and of the 15 individual samples (**Table 4**, compare columns 3 and 4 vs. columns 5 and 6). Two of these 12 miRNAs (miR-182-5p, miR-32- 5p) were significantly changed from controls. miR-132-3p was significantly different between disease groups (p < 0.05), but did not reach statistical significance in pair-wise comparisons (control vs. AD, p = 0.130, HPC vs. AD, p = 0.094). miR-219a-5p showed a strong trend for change between disease groups (p = 0.051) (**Figure 2**). It is important to note that levels of miR-9, a neuron-specific miR, was unchanged between disease groups (p = 0.769). The latter finding indicates that dysregulation of miR-182-5p, miR-32-5p, and miR-132-3p are disease-specific.

#### miRNAs Can Be Detected in Neural Exosomes Derived From Blood

To determine whether miRNAs can be reliably detected in neural Exosomes, we initially measured reference miRNAs in exosomes isolated from human iPSC-derived cortical neurons (iNs) (Guix et al., 2018). Ubiquitously expressed miR-16 (Chevillet et al., 2014) was detected with cycle threshold (Ct) values of 27 and 31 in iN cell lysates and their exosomes, respectively. Neuron-specific miR-9 (Coolen et al., 2013) was readily detected in both iN cell lysates (Ct = 25) and exosome (Ct = 32), whereas miR-451a, a peripherally expressed miRNA was barely detectable with ct values near the upper limit of reliable quantitation. These data are consistent with the relative enrichment of neuronal miRs in neurons and neuron-derived exosomes, and support the use of miR-9 and miR-451a as markers to assess the neuronal origin of exosomes (**Figure 3A**).

Next, we examined whether we could discern a difference in the levels of miR-9 and miR451-a in total plasma exosomes vs. neural exosomes isolated from plasma using L1CAM (**Figure 4B**). Exosomes were isolated from the plasma of two healthy donors. RNA was extracted from total ExoQuick pellets or L1CAM-precipitated neural exosomes and used for qPCR (**Figure 3B**). MiR-9 was low in total exosomes (Ct = 34- 37), while miR-451a was higher (Ct = 26-31). In contrast, L1CAM-isolated exosomes had high levels of miR-9 (Ct = 27- 33), suggesting the L1CAM IP effectively enriched for neural exosomes (**Figure 3C**). The fact that we observed comparable levels of miR-451a in both total and L1CAM exosomes suggest our neural exosome preparation contained both neural and peripheral exosomes. Values for the house-keeping miR-16 were similar (Ct = 30–33) in both L1CAM and total exosomes. Notably, immunoprecipitation with the non-specific mAb, 46-4, did not lead to an enrichment of the neural miR-9. Together, these results confirm that measurement of miR-9 can be used to assess neural enrichment.

## Levels of miR-132 and miR-212 Are Decreased in Neural Exosomes in Alzheimer's Disease

Diagnosis of AD based on clinical criteria alone is imperfect and typically 20–30% of individuals diagnosed as having AD have other neurological disorders, and considerable numbers

2

disease groups. Fold change was determined by 2−11Ct relative to miR-16. Significance was determined by Kruskal–Wallis test followed by Dunn's post hoc test.

of individuals that have AD are misdiagnosed as having other neurologic disorders (Monsell et al., 2015). To avoid use of samples with uncertain diagnose we employed samples from individuals whose clinical diagnosis was confirmed by CSF Aβ42 and tau. Using these criteria 63 subjects from the UCSD Shiley-Marcos Alzheimer's Disease Research Center were identified (31 cognitively intact biomarker-negative controls, 16 CSF biomarker-positive MCI, and 16 CSF biomarker-positive AD) and their plasma used to isolate neural exosomes. RNA was extracted from neural exosomes, reversed transcribed and analyzed by qRT-PCR for five of the 12 miRNAs found to be altered in AD brains plus two miRNAs used to assess neuronal origin (miR-9 and miR-451a). The five miRNAs (miR-132-3p, miR-182-5p, miR-32-5p, miR-219a-5p, and miR-591) analyzed were chosen because they were consistently elevated in AD vs. control brain by >1.5-fold (**Tables 3**, **4**). In accord with our initial experiments using L1CAM-isolated exosomes (**Figure 3**), neuron-specific miR-9 was readily detected in all EV preps (Ct ≤ 35) and at higher levels than the peripherally derived miR-451a (Ct ≤ 40). Similar to brain samples, fold-changes of miR-9 and miR-451a were comparable between disease groups when normalized to miR-16 (**Figures 4A,B**).

showed a strong trend for change between the three disease groups [X

miRNA-219a-5p could not be reliably detected in neural exosomes, and miR-32-5p, miR-182-5p, and miR-591 were unchanged between disease groups (**Supplementary Figure S3**). In contrast, miR-132 was significantly decreased (**Figure 4C**). miR-132, a miRNA that was regulated between AD, HPC, and control brains in our study, as well as in prior studies (Wong et al., 2013; Salta et al., 2016; Pichler et al., 2017), was decreased by ∼9-fold (log2 fold decrease of 3.15) in plasma-derived neural exosomes in AD vs. controls, and ∼5.4 fold in AD vs. AD-MCI (**Figure 4C**). Based on this result and the fact that miRNAs within the same cluster are often co-regulated, we sought to test whether miR-212, a miRNA in tandem to miR-132, was also altered in AD. Strikingly, the levels of miR-212 were reduced 4-fold in AD samples compared to controls (**Figure 4D**). Interestingly, higher CSF Tau/Aβ1-42 levels showed modest, but statistically significant association with lower levels of both miR-132 and miR-212 in plasma neural exosomes. Collectively, these results suggest that concerted dysregulation of miR-132 and miR-212 occurs in AD and that measuring the levels of these miRNAs in neurally derived exosome may offer a window on changes occurring in AD brain.

(2) = 5.906, p = 0.051]. (F) miR-212-3p levels were not significantly changed between

Receiver operating characteristic (ROC) curves is a common tool used to assess the diagnostic utility of novel biomarkers. ROC analysis (**Figures 5A,B**) revealed that miR-132 levels separated controls from AD-MCI with an AUC of 0.58 (95% CI: 0.38– 0.78), and controls from AD dementia with an AUC of 0.77 (95% CI: 0.61–0.93). miR-212 showed better discrimination than miR-132 between both AD-MCI and controls, and AD and controls (**Figure 5**). ROC analysis of miR-212 levels separated controls from AD-MCI with an AUC of 0.68 (95% CI: 0.5–0.86), and controls from AD dementia with an AUC of 0.84 (95% CI: 0.72–0.96) (**Figures 5C,D**). miR-212 achieves a sensitivity (the ability to predict AD cases) of 92.2% (95% CI: 68.5–99.6%), and a specificity (the ability to exclude controls) of 69.0% (95% CI: 50.8–82.7%) at the best cut-off point determined by Youden's J statistics. Overall, our ROC analyses indicate that measurement of miR-212 in neurally derived plasma exosomes showed sufficient diagnostic sensitivity and specificity to pursue its use as a potential screening assay for AD.

miRNAs from plasma exosomes. RNAs were isolated from total plasma exosomes (Exoquick pellets), or neural exosomes immunoprecipitated (IP) from total plasma exosomes with anti-L1CAM antibody. RNA was reverse transcribed with Qiagen's HiFlex buffer to generate cDNAs, and subsequently used as template for qPCR using miRNA-specific primers for miRNA detection. C(t) values for miR-9, miR-451a, and miR-16 from (C) plasma-derived total- and L1CAM (L1) immunoprecipitated exosomes. As a negative control, an irrelevant antibody, 46-4 was used for immunoisolation. Total and IP'ed exosomes were isolated in duplicate (#1 and #2) from 2 healthy control donors (Plasma 1 and plasma 2). Low Ct values indicate high levels of miRNA, and high Ct values indicate low levels of miRNA. The reliable CT limit is ∼40.

#### DISCUSSION

Despite considerable progress in our understanding of AD significant gaps remain and there is a desperate need for disease biomarkers that can offer insight about pathogenesis and which may serve as theragnostic indicators. To address this void, we exploited two exciting areas of AD research, namely miRNAs and neural exosomes. miRNAs are small regulatory molecules that post-transcriptionally repress gene expression and thereby regulate diverse biological processes, including neuronal differentiation, plasticity, survival, and regeneration (Kosik, 2006; Sambandan et al., 2017). Several neuronal miRNAs have been directly linked to AD, but there has been controversy as to which miRNAs are most important (Herrera-Espejo et al., 2019; Swarbrick et al., 2019).

In order to better define the most relevant miRNAs that are dysregulated in AD we conducted unbiased profiling of frontal cortex from three distinct groups of age-matched elderly individuals. Unlike some previous studies which compared only AD vs. controls (Salta et al., 2016; Pichler et al., 2017) we also compared AD vs. HPCs to distinguish between miRNAs that were altered by active neurodegeneration vs. those that may change in the absence of disease, but in response to amyloid and tangles. To ensure the robustness of our data we analyzed the same 5 AD, 5 HPC and 5 control samples in several different ways and then examined these 15 samples alongside additional AD, HPC and control samples. Initially, we used miRNA array analysis in an unbiased effort to profile differences between pools of RNA from AD, HPCs, and controls. Thereafter, we used miRspecific qRT-PCR to investigate the levels of 12 miRNAs that were most altered in the array experiments using both disease group pools of RNA, and RNA from individual brains. This approach yielded similar results regardless of the method used or whether RNA was from pools or individual brains, and these changes were maintained when additional brains were analyzed. Although all 12 miRNAs showed consistent fold differences (>1.5 fold) between AD, HPCs, and controls, only three miRNAs (miR-182-5p, miR-32-5p, and miR-132-3p) stood out in terms of group-based differences.

When we turned to analyze neural exosomes, we assessed miR-182-5p, miR-32-5p, and miR-132-3p, plus two other miRNAs (miR-219a-5p and miR-591) which in brain tissue showed consistent fold changes. Based on our iPSC-neuron experiments, we also used miR-9 and miR-451a to assess the neuronal origin

followed by Dunn's post hoc test was used.

of our exosome preparations. miR-9 is one of the most highly expressed miRNAs in the vertebrate brain (Coolen et al., 2013) and here we show that miR-9 is enriched in human iPSCderived neurons and neuronal exosomes. As we had anticipated, the peripherally expressed miR-451a (Okamoto et al., 2018) was barely detected in human neurons or neuronal exosomes. Applying these markers to our L1CAM-isolated plasma exosomes we found that miR-9 was readily detected, whereas miR-451a was present at only low levels. It is important to note that L1CAM is not uniquely expressed in brain, but is also found in kidney cells. Therefore, we cannot completely rule out, that a small contribution may come from L1CAM-positive kidney exosomes. However, the relative enrichment of neural-toperipheral miRNAs in L1CAM-isolated exosomes indicates that the bulk of these exosomes are of neuronal origin. Consequently, the use of miR-9 should provide the field with a to tool to differentiate between neural and non-neuronal exosomes.

Of the four miRNAs most dysregulated in AD brain only miR-132 was significantly altered in AD neural exosomes. miR-219 could not be reliably detected, whereas, miR-32 and miR-182 were reliably detected, but their levels were similar across disease groups in neural exosomes. Why these miRNAs are dysregulated in brain, but unaltered in bona fide neural exosomes is not completely clear, but it is reasonable to assume that the miRNAs measured in brain tissue are derived from many different cell types, whereas our neural exosomes are predominantly neuronal in origin. In this regard, it is interesting to note that miR-132 is one of the most abundant brain-enriched miRNAs, whereas miR-182 is highly enriched in cells of myeloid linage (Pucella et al., 2015). Indeed, in future studies it will be important to determine if the four miRNAs we found to be most changed in AD brain are dysregulated in exosomes from microglia, astrocytes or oligodendrocytes.

Notwithstanding the importance of investigating miRNAs in exosomes from other brain cell types, it is intriguing that miR-132 is downregulated in both AD brain and neural exosomes. This finding is consistent with numerous other studies which have found miR-132 to be dysregulated in AD brain and with a host of studies which tie miR-132 down-regulation to AD pathogenesis (Lau et al., 2013; Wong et al., 2013; Hernandez-Rapp et al., 2016; Salta et al., 2016; Patrick et al., 2017; Pichler et al., 2017). For instance, reductions in miR-132 appears to occur before neuronal loss and in vitro miR-132 protects neurons against both Aβ and glutamate (Wong et al., 2013), and overexpression of miR-132 reduces tau pathology and caspase-3-dependent apoptosis in tau transgenic mice (El Fatimy et al., 2018). Since miRNAs within the same cluster are often co-regulated, we measured miR-212 in neural exosomes. The sequence of miR-212 is closely similar

to that of miR-132 and both are enriched in neurons and exert regulatory functions on neuronal survival, maturation, plasticity and memory (Wanet et al., 2012). Moreover, downregulation of the miR-132/212 cluster in the frontal cortex had been reported for patients with amnestic MCI and mild AD (Weinberg et al., 2015). Here, we found that miR-212 was strongly dysregulated in AD neural exosomes, and tended to be decreased in AD brain. Why the decrease in miR-212 was significant in neural exosomes, but not brain is unclear, but (as with miR-182) this may relate to the relative distribution of miRNAs in neurons vs. glia.

Importantly, ROC analysis indicated that measurement of miR-132-3p and miR-212-3p in neurally derived plasma exosomes showed good sensitivity and specificity to diagnose AD, but did not effectively separate individuals with AD-MCI from controls. The criteria used to identify dysregulated miRNAs in AD brain required that they were specifically altered in AD vs. both HPCs and controls. Given that at least some HPCs maybe on path to AD it is perhaps not surprising that miRNAs that are preferentially dysregulated in AD brain compared with HPC brain do not discriminate AD from AD-MCI when measured in neural exosomes. Future studies should investigate whether miRNAs that are dysregulated in HPC brain compared to control brain might discriminate AD-MCI from controls when quantified in neural exosomes. Nonetheless, quantification of miR-132 and miR-212 in neural exosomes should aid diagnosis of symptomatic patients. While this is not the population most in need of blood-based biomarkers, measurement of neural exosome miR-132 and miR-212 may have theragnostic potential and it would be particularly interesting to measure these markers longitudinally. Future studies should determine whether the observed changes in miR-132 and miR-212 can also be seen in total plasma exosomes or are specific for neural exosomes.

#### DATA AVAILABILITY STATEMENT

The raw data from our array experiments can be accessed through https://www.ebi.ac.uk/arrayexpress/browse.html?query= under the accession number E-MTAB-8283. All other data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher. Religious Orders Study resources can be requested at the Rush Alzheimer's Disease Center Research Resource Sharing Hub at www.radc.rush.edu.

#### ETHICS STATEMENT

fnins-13-01208 November 23, 2019 Time: 16:4 # 13

The studies involving human participants were reviewed and approved by the Partners Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

#### AUTHOR CONTRIBUTIONS

DW conceived the project, designed and supervised the research, and wrote the manuscript. DC performed the exosome isolations, analyzed the brain tissue and exosomes for miR content, analyzed data, prepared figures, and wrote the manuscript. DM coded and decoded the specimen designations to ensure all experiments were done blind to the disease status of donors/patient, conducted statistical analysis, prepared figures, and wrote the manuscript. WL dissected the brain tissue and assisted with preparation of iPSC-derived neurons. DS provided the critical guidance. MM and DK provided expert guidance on the preparation of neural exosomes. DG and RR supplied archived samples and relevant clinical data. DB provided brain samples, and detailed clinical and postmortem data. All authors critically appraised the manuscript.

#### FUNDING

This work was supported by grants to DW from the Alzheimer's Drug Discovery Foundation and by an Alzheimer's Association Zenith Award. DM is a research fellow funded by the German Research Foundation (DFG ME 4858/1- 1). DW is an Alzheimer's Association Zenith Fellow. The

#### REFERENCES

Religious Orders Study was supported by NIA grants P30AG10161 and R01AG15819.

#### ACKNOWLEDGMENTS

We are grateful to brain donors and their families. We thank clinician colleagues for referring the patients, collecting blood, and performing lumbar punctures. We acknowledge the ARCND for providing access to the real-time PCR system. We also thank Dr. Tracy Young-Pearse for YZ1 human iPSC cells used to generate the iPSC neurons employed in this study.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins. 2019.01208/full#supplementary-material

FIGURE S1 | Identification of AD and AD-MCI subjects based on cognitive testing and best available CSF biomarkers used for subsequent exosome isolation. A total of 63 plasma samples were obtained from the UCSD biomarker collection. CSF from these subjects was analyzed and levels of Aβ42 and tau yielded clear segregation of the disease groups.

FIGURE S2 | Replicate plates within pooled disease groups show good correlation. Spearman correlation between replicates of different plates run on different days for the same sample/cDNA show high correlation with an R 2 > 0.99 for all.

FIGURE S3 | miR-32-5p, miR-182-5p, and miR-591 in plasma neural exosomes are unchanged between diagnostic groups. (A) miR-32-5p, (B) miR-182-5p, (C) miR-591, were measured in plasma neural Exosomes, but were not significantly changed (p > 0.05). Significance was determined by one-way ANOVA followed by Tukey's post hoc test for normally distributed data. Otherwise, Kruskal–Wallis test followed by Dunn's post hoc test was used.

TABLE S1 | miRNA list and primers.

pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement. 11, 600.e1–607.e1. doi: 10.1016/j.jalz.2014.06.008

**Conflict of Interest:** DW is an employee of Biogen Inc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Cha, Mengel, Mustapic, Liu, Selkoe, Kapogiannis, Galasko, Rissman, Bennett and Walsh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fnins-13-01208 November 23, 2019 Time: 16:4 # 14

# Exosome Production Is Key to Neuronal Endosomal Pathway Integrity in Neurodegenerative Diseases

#### Paul M. Mathews1,2,3 \* and Efrat Levy1,2,3,4 \*

1 Center for Dementia Research, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States, 2 Department of Psychiatry, New York University Langone Health, New York, NY, United States, 3 NYU Neuroscience Institute, New York University Langone Health, New York, NY, United States, 4 Department of Biochemistry and Molecular Pharmacology, New York University Langone Health, New York, NY, United States

#### Edited by:

Francesc Xavier Guix, Severo Ochoa Molecular Biology Center (CSIC-UAM), Spain

#### Reviewed by:

Archan Ganguly, University of California, San Diego, United States Melissa Calegaro Nassif, Major University, Chile

#### \*Correspondence:

Paul M. Mathews<EMAIL_ADDRESS>Efrat Levy<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

Received: 29 July 2019 Accepted: 29 November 2019 Published: 12 December 2019

#### Citation:

Mathews PM and Levy E (2019) Exosome Production Is Key to Neuronal Endosomal Pathway Integrity in Neurodegenerative Diseases. Front. Neurosci. 13:1347. doi: 10.3389/fnins.2019.01347 Dysfunction of the endosomal–lysosomal system is a prominent pathogenic factor in Alzheimer's disease (AD) and other neurodevelopmental and neurodegenerative disorders. We and others have extensively characterized the neuronal endosomal pathway pathology that results from either triplication of the amyloid-β precursor protein (APP) gene in Down syndrome (DS) or from expression of the apolipoprotein E ε4 allele (APOE4), the greatest genetic risk factor for late-onset AD. More recently brain exosomes, extracellular vesicles that are generated within and released from endosomal compartments, have been shown to be altered in DS and by APOE4 expression. In this review, we discuss the emerging data arguing for an interdependence between exosome production and endosomal pathway integrity in the brain. In vitro and in vivo studies indicate that altered trafficking through the endosomal pathway or compromised cargo turnover within lysosomes can affect the production, secretion, and content of exosomes. Conversely, exosome biogenesis can affect the endosomal–lysosomal system. Indeed, we propose that efficient exosome release helps to modulate flux through the neuronal endosomal pathway by decompressing potential "traffic jams." Exosome secretion may have the added benefit of unburdening the neuron's lysosomal system by delivering endosomal–lysosomal material into the extracellular space, where other cell types may contribute to the degradation of neuronal debris. Thus, maintaining robust neuronal exosome production may prevent or mitigate endosomal and lysosomal abnormalities linked to aging and neurodegenerative diseases. While the current evidence suggests that the exosomal system in the brain can be modulated both by membrane lipid composition and the expression of key proteins that contribute to the formation and secretion of exosomes, how exosomal pathway-regulatory elements sense and respond to perturbations in the endosomal pathway is not well understood. Based upon findings from the extensively studied DS and APOE4 models, we propose that enhanced neuronal exosome secretion can be a protective response, reducing

pathological disruption of the endosomal–lysosomal system in disease-vulnerable neurons. Developing therapeutic approaches that help to maintain or enhance neuronal exosome biogenesis and release may be beneficial in a range of disorders of the central nervous system.

Keywords: endosome, lysosome, extracellular vesicle, multi-vesicular body, neurodegeneration, apolipoprotein E, Alzheimer's disease, Down syndrome

#### INTRODUCTION

In Alzheimer's disease (AD), early alterations of the endosomal system in neurons is followed by extensive disruption of autophagic and lysosomal compartments (Colacurcio et al., 2018). Neuronal endosomal pathway disruption has been suggested to result from multiple pathological insults in AD, including, for example, intravesicular amyloid-β (Aβ) (Willen et al., 2017). Our laboratories and collaborators have focused on the apparently Aβ-independent endosomal pathway changes that are seen in Down syndrome (DS) (Cataldo et al., 2000, 2003, 2008) and as a result of the expression of the apolipoprotein E ε4 allele (ApoE4) (Nuriel et al., 2017). In DS, which leads to early onset AD, the elevated levels of the β-site cleaved carboxylterminal fragment (βCTF) of the amyloid-β precursor protein (APP) that result from the triplication and overexpression of the APP gene are sufficient to cause early endosomal changes (Cataldo et al., 2003; Salehi et al., 2006; Jiang et al., 2010, 2016; Nixon, 2017). Expression of APOE4, the most important genetic risk factor for late-onset AD (Corder et al., 1993; Farrer et al., 1997; Bu, 2009; Liu et al., 2013), leads to morphologically similar neuronal endosomal pathway changes in humans as well as in mouse models (Cataldo et al., 2000; Nuriel et al., 2017; Zhao et al., 2017). More recently, research focus in these models of AD-related endosomal disruption has extended to exosomes, membrane-bound vesicles that are generated within the endosomal pathway and secreted into the extracellular space (Kreimer et al., 2015; van der Pol et al., 2015). A widely held opinion is that extracellular vesicles (EVs), including exosomes, can be harmful within the brain. These stable vesicles may allow toxic material to be transported between cells and brain regions, and may promote the accumulation of this material in the extracellular space (Valdinocci et al., 2017; Perez et al., 2019).

Nevertheless they may also be beneficial, discarding potentially toxic material that a neuron has targeted for degradation, as well as through the transport of neuroprotective proteins. Indeed, exosome secretion was originally described as a process that can complement and supplement lysosomal and proteasomal degradation for the removal of obsolete membrane and cytosolic materials (Johnstone et al., 1987). While the in vivo functions of exosomes in the brain are likely to be a mixture of beneficial and potentially pathological effects, in this review, we have chosen to emphasize the beneficial role that exosome production may play in supporting neuronal endosomal– lysosomal function. Emerging evidence now links endosomal pathway function and the generation and secretion of exosomes into the brain extracellular space. Perturbations of the neuronal endosomal–lysosomal pathway, which can alter endosomal pathway flux and lead to inefficient degradation in lysosomes, appear to affect exosome secretion. Additionally, it appears that disease-driven deficiencies in exosomal production can negatively affect flux and catabolism through the endosomal– lysosomal pathway. Thus, our hypothesis is that exosome production plays a key role in maintaining neuronal endosomal pathway integrity and that disruption of these integrated systems can contribute to neurodegenerative diseases.

### DISCUSSION

#### Extracellular Vesicles in the Brain