High-Purity Enrichment of Extracellular Vesicles from Diverse Sources by Conventional and Image-Based Fluorescence Activated Cell Sorters for Robust Downstream Applications

High-Purity Enrichment of Extracellular Vesicles from Diverse Sources by Conventional and Image-Based Fluorescence Activated Cell Sorters for Robust Downstream Applications | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Jochen Behrends , View ORCID Profile Anne Rissiek , View ORCID Profile Christopher Urbschat , View ORCID Profile Santra Brenna , Hela Uplegger , View ORCID Profile Bente Siebels , View ORCID Profile Cecile L Maire , View ORCID Profile Katrin Lamszus , Anke Diemert , View ORCID Profile Franz Ricklefs , View ORCID Profile Tim Magnus , View ORCID Profile Petra Arck , View ORCID Profile Berta Puig doi: https://doi.org/10.1101/2025.10.12.681862 Isabel Graf 1 Laboratory for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabel Graf Amanda Salviano-Silva 3 Laboratory for Brain Tumor Biology, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amanda Salviano-Silva Jochen Behrends 4 Core Facility Fluorescence Cytometry, Research Center Borstel, Leibniz Lung Center , Borstel, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jochen Behrends Anne Rissiek 5 Cytometry and Cell Sorting Core Facility, Dean’s Office for Research, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne Rissiek Christopher Urbschat 1 Laboratory for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Urbschat Santra Brenna 6 Neurology Department, ERSI group, University Center Hamburg Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Santra Brenna Hela Uplegger 6 Neurology Department, ERSI group, University Center Hamburg Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bente Siebels 7 Section Mass Spectrometry and Proteomics, Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center Hamburg-Eppendorf (UKE) , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bente Siebels Cecile L Maire 3 Laboratory for Brain Tumor Biology, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cecile L Maire Katrin Lamszus 3 Laboratory for Brain Tumor Biology, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katrin Lamszus Anke Diemert 8 Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Franz Ricklefs 3 Laboratory for Brain Tumor Biology, Department of Neurosurgery, University Medical Center Hamburg-Eppendorf , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Franz Ricklefs Tim Magnus 6 Neurology Department, ERSI group, University Center Hamburg Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tim Magnus Petra Arck 1 Laboratory for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Petra Arck Berta Puig 6 Neurology Department, ERSI group, University Center Hamburg Eppendorf , Hamburg, Germany 2 Hamburg Center for Translational Immunology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Berta Puig For correspondence: b.puig-martorell{at}uke.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Separating and enriching specific extracellular vesicle (EV) subpopulations from the broader EV pool present in tissues and blood is crucial for understanding their role in physiological and pathological conditions. However, high-purity enrichment of specific EV-subpopulations remains challenging due to the lack of suitable techniques. Initial studies have shown that Fluorescence-Activated Cell Sorting (FACS) has great potential for enriching EV subpopulations, despite the technical challenges posed by their small size. Yet, existing protocols have been inconsistent, and proper validation using state-of-the-art sorters has been inadequate. Here, we introduce an EV sorting workflow that overcomes technical challenges and allows for the analysis of EVs from various species, tissue sources and cell culture. We used two fluorescence cell sorters, the BD FACSAria Fusion and the BD FACSDiscover S8, to sort EVs with different fluorescent labels. The successful sorting of EVs was validated using high-sensitivity imaging flow cytometry, transmission electron microscopy, and liquid chromatography tandem mass spectrometry. We defined the optimal parameters for nozzle sizes, flow rates, sample dilutions, and sorting modes, enabling the enrichment of EV populations of interest to nearby 100% purity, including low-frequency EV populations of under 10%, while preserving compatibility with downstream analyses. The workflow presented here provides a powerful tool for both, basic science and translational applications. INTRODUCTION Extracellular vesicles (EVs) are secreted nanoparticles involved in cellular communication. They are increasingly gaining recognition as valuable biomarkers and tools for drug delivery. Every cell releases EVs, resulting in a highly diverse and heterogeneous EV population based on their cellular origins and biomolecular cargo 1 , 2 . Studying their cargo provides insights into the functional condition of the parent cells and makes EVs a promising target for minimally invasive tissue analysis from biofluids 3 , 4 . Developing the capacity to separate and analyse different EV subtypes, is crucial to acquire a deeper understanding of EV biology 5 . This is of particular relevance for clinical diagnostics and therapeutics 6 , as only particular EV populations expressing certain markers or derived from defined cell types are of interest 5 . Yet, enriching specific EV-subpopulations remains challenging, especially when working with complex EV sources such as tissue or biofluids. Most of the standard methods used for EV isolation –such as differential centrifugation, density gradient ultracentrifugation, size-exclusion chromatography or tangential flow separation - are based on size and density and do not enable the discrimination of EV populations according to their cell of origin, e.g. brain-derived or placenta-derived EVs, present in biofluids. Additionally, recent methodological advances in single-EV analysis such as Nano-Flow Cytometry or advanced imaging techniques such as Raman Tweezers Spectroscopy (RTS), Atomic Force Microscopy (AFM) or Cryo-Electron Microscopy (Cryo-EM) do not allow for untargeted cargo discovery of the selected EV populations 7 . Immunoprecipitation techniques, such as Magnetic Activated Cell Sorting (MACS) 8 , allows separation of distinct EV populations. However these techniques often lacks specificity 9 and risks contamination with MACS beads and bovine serum albumin (BSA), which can interfere with downstream analyses. Furthermore, immunoprecipitation workflows cannot separate more than one population of interest at a time. Fluorescence-Activated Cell Sorting (FACS) shows great potential for enriching specific EV populations 10 , 11 (also referred to as FAVS ‘Fluorescence Activated Vesicle Sorting’). As FACS instruments (hereafter sorter) are naturally built for cell sorting, a primary challenge in using sorters for EVs is their considerably smaller size compared to cells. Accordingly, optimal technical adjustments needed to enable effective EV sorting must be established. Pioneer studies have shown the feasibility of this approach 10 - 15 , however sorting of EVs is rarely performed and it is far from being standardized 16 . Reasons include limited reporting of necessary parameters, lack of proper validation or use of sorters that have since been discontinued. Moreover, the behavior of EVs within the sorter, and the potential impact of the sorting process on their physicochemical properties, considering the heterogeneous origins of EVs, have not yet been investigated. Therefore, there is an urgent need to develop a FACS-based EV-sorting workflow using the newer generation of sorters without requiring physical customisation of the flow cytometer. This would enable the implementation in core facilities with the in-house instruments, ensuring a broad applicability. In this study, we have established specific parameters and a reproducible workflow that facilitates EV enrichment using both, conventional and new generation image-based spectral sorters. We demonstrate enrichment of EVs isolated from diverse sources of increasing complexity, including conditioned media, blood and tissue as well as different labelling, including membrane and intraluminal dyes, fluorescent-tagged antibodies or EVs endogenously expressing fluorescent reporter proteins (e.g. td-Tomato) which may differently affect the biophysical properties of EVs 17 , 18 Our approach allows EV sorting independently of source and type of labelling while preserving their purity and stability enabling downstream applications such as transmission electron microscopy (TEM) and proteomic analysis. The latter is crucial to address key questions within the EV field, including whether different cell-specific EV populations transport distinct cargoes that might change in pathological states, and how the targeted uptake and delivery of this cargo to specific cell compartments occurs 21 - 24 . In summary, we present for the first time a novel, robust and reproducible FACS-based approach for the high purity enrichment of EVs from multiple sources with different fluorescent labels, enabling downstream analysis, offering a strong potential to accelerate biomarker discovery in clinical settings. RESULTS AND DISCUSSION Technical adjustments enabling the use of FACS instruments for EV sorting The experimental workflow we employed to establish the sort of EVs is illustrated in Figure 1A . We used two droplet sorters with different laser power, electronics and detection systems: (I) the BD FACSAria™ Fusion (hereafter Fusion) as a conventional state-of-the-art sorter (II) the BD FACSDiscover™ S8 (hereafter S8) as a new generation, image-based spectral, droplet sorter. A key challenge of using sorters for EV enrichment is the considerable size difference between cells and EVs. Hence, one objective of this study was to identify the technical requirements that enable FACS instruments to perform EV sorting successfully and effectively. The aim is to establish a narrowly focused core sample stream that facilitates the analysis of a singular, non-swarmed EV. For EVs to be accurately charged and correctly allocated into the collection tube, it must transit from the interrogation point to the breakoff point at a precisely controlled time. To achieve this, it is necessary to maintain an optimal level of pressure to prevent structural damage. The parameters to adjust include (i) sample dilution; (ii) core stream diameter and sorting flow rate; (iii) nozzle size, sheath pressure and drop drive frequency; and (iv) sort precision mode. Table 1 outlines the physical principles guiding feature adjustments essential for EV sorting, their possible settings, and our chosen parameters. To reduce the necessity of extensive testing for all the possible combinations of features with different sample types and their replicates, we relied on previous work and on theoretical assumptions to find the best parameters for EV sorting. View this table: View inline View popup Table 1. Technical features and physical requirements allowing successful EV sorting. Download figure Open in new tab Figure 1. Study overview and technical capacities of FACS instruments for sorting EVs. A) Study overview and experimental set-up. DG: density gradient; DUC: differential ultracentrifugation; IFC: imaging flow cytometry; LC-MS/MS: liquid chromatography/tandem mass spectrometry. B) FSC-SSC Megamix beads (i) and NIST-beads (ii) in the size-range of EVs are successfully detected in the Fusion. c) FSC-SSC Megamix beads (i) and NIST-beads (ii) in the size-range of EVs are successfully detected in the S8. High sample dilutions are necessary to ensure single-particle analysis and avoid coincidence (swarmed particles). In this regard, the study by Kormelink et al. 25 which employed serial dilutions of 100 nm reference beads, liposomes and EVs, already demostrated that high sample dilution is critical not only to prevent coincidence events, but also to minimize excessive light scattering and fluorescent intensities. We thus decided to dilute the samples to a degree that allowed to clearly resolve and separate the populations of interest with low levels of swarmed particles resulting in the average detection of 10,000 events/min for the Fusion and 2,000 events/min for the S8 ( Table 1 ). Regarding flow rate, we set it up to the minimum value (1) for both sorters as the goal was to maintain a very narrow core sample stream to allow only single EVs to pass through the laser beam. Previous studies have used different nozzle sizes for EV sort, such as 70 µm 13 , 14 , and 140 µm 25 . However, these studies did not rigorously report the preservation of EV integrity and stability after sort, nor did they provide adequate validation. We also performed experimental trials with a 70 µm nozzle with no success in terms of EV detection after sort and enrichment. Therefore, based on our experience and in accordance with the findings of Higginbotham et al. 10 who successfully detected EVs by dSTORM microscopy after sorting, we selected a 100 µm nozzle to keep an optimal balance between a small droplet size -increasing the likelihood of single EV sorting-while avoiding excessive high pressure that could compromise EV integrity. Lastly, the sort precision mode ‘purity’ was chosen in each sorter meaning that purity was chosen over yield. Further details of the sorting procedure are outlined in a step-by-step list provided in the methods section. Before employing these settings for EV sorting, we tested the resolution of the sorter to ensure sufficient sensitivity to detect particles as small as EVs. We used FSC-SSC-Megamix and NIST beads with a range size of 100 nm – 900 nm and 50 nm - 90 nm, respectively. As shown in Figure 1B and 1C both sorters demonstrated the ability to reliably detect particles within the EV size range and to distinguish beads of varying sizes. Importantly, the smallest bead size (50nm), was clearly distinguishable from the PBS background. However, It must be taken into account that, due to the disparity in refractive index between the EVs and the beads, this method provides only an estimate of the sorter’s ability 26 . Within our experimental setup, the EV samples were characterized both prior to and after sorting with Imaging Flow Cytometry (IFC), using the Cytek Amnis ImageStream Mk II to assess purity and yield. IFC is a highly sensitive flow cytometry-based imaging technique for single EV analysis 19 , 20 . It allows discrimination of false-positive fluorescent signals, which may display a sideward-scatter and fluorescence profile similar to EVs but cannot be identified as EVs in brightfield images ( Supplementary Figure 1A-C ). After sorting, we performed IFC again to confirm the genuineness of the sorted EV and validate their purity and yield. The IFC gating strategy applied within our experimental set-up to exclude doublets, swarmed EVs and false-positive particles with inadequate brightfield image, is depicted in Supplementary Figure 1D . High purity sorting of cell culture-derived EVs The handling of cell culture-derived EVs is generally considered as straightforward since conditioned media is a relatively uncontaminated EV source, compared to biofluids such as blood 27 . Moreover, cell culture conditions are highly controllable allowing for standardization and scalability. Therefore, we first validated our sorting approach using EVs from glioblastoma cell lines, which are commonly used and have a high translational relevance for studying brain tumor biology 28 . EVs were obtained by differential ultracentrifugation of conditioned media from the human glioma-derived cell line (NCH1681 line) genetically modified to express palm-tdTomato, and from the murine glioma cell line (Mut3) transduced to express BFP2 (Blue Fluorescent Protein 2, from now onwards BFP2) ( Figure 2A ). EVs were characterized by Nanoparticle Tracking Analysis (NTA), Transmission Electron Microscopy (TEM) and IFC. tdTomato + EVs had a mean size of 153.4nm (+/-6.6 nm) and BFP2 + EVs had a mean size of 175.7nm (+/-5.2 nm) (Supplementary Figure 2A) . TEM showed the expected cup-shape morphology (Supplementary Figure 2B) and IFC showed the presence of tetraspanins (referred to as ‘TSPN’: CD9, CD81 and CD63 combined, as bona fide EV markers) confirming successful EV isolation (Supplementary Figure 2C) . To demonstrate that applying our sorting-workflow enables high purity separation of a defined EV population, we prepared an input mixture consisting of approximately 80% tdTomato + EVs and 20% BFP2 + EVs for sorting with the Fusion and approximately 65% tdTomato + EVs and 35% BFP2 + EVs for sorting with the S8, as determined by IFC ( Figure 2B ). Download figure Open in new tab Figure 2. Sorting of glioblastoma cell culture-derived EVs. A) Experimental set up. B) IFC analysis of the sorting input for the Fusion (left) and the S8 (right). C) Gating strategy for sorting tdTomato + EVs and BFP2 + EVs using the Fusion. D) IFC analysis of the sorted fraction when sorted for BFP2 + EVs with the Fusion. E) IFC analysis of the sorted fraction when sorted for tdTomato + EVs with the Fusion. F) Gating strategy for sorting tdTomato + EVs and BFP2 + EVs using the S8. G) IFC analysis of the sorted fraction when sorted for BFP2 + EVs with the S8. H) IFC analysis of the sorted fraction when sorted for tdTomato + EVs with the S8. As cultured media from glioma stem-cells was highly enriched in EVs (Supplementary Figure 2A) , high dilutions were needed for clearly resolving the EV population of interest in the gating as shown by the sorting gate count of 65-85 events/sec (4,000-5,000 events/min) for both sorters ( Figures 2C,F ). Serial dilutions were tested (Supplementary Figure 3A ). For sorting with the Fusion, swarmed EVs and unspecific signals were excluded by gating exclusively for events with a low sideward-scatter signal ( Figure 2C ). This resulted in a successful enrichment of BFP2 + EVs up to 91% and of tdTomato + EVs up to 98% as confirmed by IFC ( Figure 2D,E ). The S8 offers a variety of imaging features such as Diffusivity, Eccentricity, Total Intensity and Radial Moment 29 . Thus, for sorting with the S8, imaging features were tested to minimize contamination with background signals and swarmed particles, as visualization of the detected signals bears great potential to exclude undesired particles. We tested the imaging features for each type of sample and selected the ones which allowed the best separation from background signals and swarmed particles. However, for the particular sorting of cell culture-derived EVs these features did not allow clear separation of EVs from undesired signals, as shown for the parameters Diffusivity and Eccentricity in Figure 2F (Diffusivity being the measure of local concentration of a parameters intensity and Eccentricity defined as the ratio of the shortest to the longest axis of the identified particle). This may be attributable to the low contamination rates of cell culture-derived EVs. Therefore, positive fluorescence signals without further gating were selected for sorting ( Figure 2F ). This resulted in a high-purity enrichment, achieving 100% of positive output fractions for BFP2 + and tdTomato + EVs. It is important to mention that with both sorters the absolute EV recovery was low, according to IFC. The trade-off for high-purity sorting is a reduction in yield (Supplementary Figure 4A) . Therefore, we recommend extended sorting periods (longer than the 2-3 h used herein) to achieve a comparable initial input concentration. Corresponding controls as suggested by the MIFlowCyt-EV guidelines 30 can be found in Supplementary Figure 5 . To ensure reproducibility of our results, we provide the position of the reference beads relative to the applied gates ( Supplementary Figure 6 ). In summary, the emission from endogenous fluorescent reporter proteins like tdTomato and BFP2 resulted in a clear distinguishable signal that could be separated from the background. Both sorting strategies achieved a high-purity enrichment (more than 90% of the population of interest) with very low contamination rates. This enrichment was achieved regardless of the input population abundance. However, the output yield was low, therefore longer sorting times are required to obtain higher yields. High-purity sorting of EVs from complex biofluids Next, we increased the complexity of the EV-target group to be sorted. We chose blood-circulating EVs as these are the most commonly investigated source among the biofluid-derived EVs 31 . Serum is a challenging source for EV analysis because of the substantial contamination with lipoproteins and platelet-derived particles. EVs were isolated from serum samples using differential ultracentrifugation ( Figure 3A ). EV isolation was validated using NTA, TEM and IFC as before. The mode size of the EVs to be sorted was 124.2 nm (+/-11.4 nm) and TEM images showed the characteristic cup-shape EV morphology (Supplementary Figure 7A and 7B) . The presence of established TSPN as well as the absence of serum contaminants was evaluated with IFC proving successful EV enrichment ( Supplementary Figure 7C and 7D) . Subsequently EVs were labelled with fluorophore-conjugated antibodies: CD34(-AF647), an endothelial marker identifying our population of interest, and CD9(-PE) a marker for EVs ( Figure 3A ). Since human blood-circulating EV populations of interest are usually present at very low levels, one sample was stained for CD9 + EVs and spiked with low amounts of CD34 + EVs from a second sample, resulting in 7% CD34 + EVs for sorting with the Fusion and 12% CD34 + EVs for sorting with the S8, as input to be loaded into the sorters ( Figure 3B ). Download figure Open in new tab Figure 3. Sorting of human blood-derived EVs. A) Experimental set up. B) IFC analysis of the sorting input for the sort with the Fusion (left) and the S8 (right). C) Gating strategy for sorting CD34 + EVs and CD9 + EVs using the Fusion d) IFC analysis of the sorted fraction when sorted for CD34 + EVs with the Fusion. E) IFC analysis of the sorted fraction when sorted for CD9 + EVs with the Fusion. F) Gating strategy for sorting CD34 + EVs and CD9 + EVs using the S8. G) IFC analysis of the sorted fraction when sorted for CD34 + EVs with the S8. H) IFC analysis of the sorted fraction when sorted for CD9 + EVs with the S8. For both sorters high sample dilutions of 6 – 9 events/sec (361 to 596 events/min) were used to clearly distinguish and detect non-swarmed events in the sorting gates ( Figure 3C, 3F ). Serial dilutions are available in Supplementary Figure 3B . To sort with the Fusion, the first gate applied was intended to distinguish fluorescently labelled EVs from background. This was followed by subsequent gating for the fluorescent populations of interest ( Figure 3C ). Our sorting strategy resulted in an enrichment of nearly 90% for CD34 + EVs and nearly 100% when sorted for CD9 + EVs as validated by IFC ( Figure 3D, 3E ). For sorting with the S8, the “Total Intensity” feature - which integrates all intensity values of all pixels for one parameter – was gated against the SSC-A ( Figure 3F ). This allowed us to identify the events displaying high intensity values, effectively distinguishing the target population form the background contaminants ( Supplementary Figure 7E ). Other imaging features previously mentioned such as Eccentricity, were also considered, however the best visual separation was achieved when focusing on the Total Intensity feature. With this gating strategy we achieved an enrichment of both sorted fractions reaching up to 100% ( Figure 3 G, H ). The evaluation of yield after sorting revealed that every 15 th EV of interest was sorted (Supplementary Figure 4B) . Controls according to MIFlowCyt-EV guidelines 30 and the position of the reference beads relative to the gates to ensure reproducibility are provided in Supplementary Figures 8 - 10 . Despite the challenges associated with blood-circulating EVs, high enrichment rates up to 100% were achieved when labelling the EVs with antibody-coupled fluorophores even if the population of interest to be sorted represented less than 10% of the overall EV population. S8 imaging features employed minimized successfully background signals and swarmed particles. This opens a unique opportunity to analyze rare EV populations in blood for diagnostic and therapeutic purposes. High-purity sorting of EVs originating from tissue Directly isolating EVs from tissue enables the study of a highly diverse EV population that retains, probably partially, the EV corona, integrates information from cell-to-cell interactions, and captures the temporal patterns of disease progression, difficult to replicate under cell culture conditions. Therefore, in the past few years isolation of EVs directly from tissues is gaining attention in the field 32 . Because EV properties could be different in this context, we aimed to challenge our sorting-workflow with a next level of complexity and test whether tissue-derived EVs could also be sorted. Brain-derived EVs (BDEVs) were isolated from wild-type (WT) mouse brain through differential gradient ultracentrifugation following published protocols 33 . BDEVs were afterwards labelled with mCling, a dye that is retained at the plasma membrane and endocytic membranes 34 , or with CFSE, a cell permeable dye that binds covalently to intracellular proteins very commonly used for flow cytometry experiments ( Figure 4A ). The mCling + and CFSE + EVs to be sorted had a mode size of 183.9nm (+/-23.0 nm) and 146.4 nm (+/-17.8 nm) respectively ( Supplementary Figure 11A ). Their characteristic cup-shaped morphology and the presence of TSPN was confirmed via TEM and IFC, respectively ( Supplementary Figure 11B-C ). The input for both sorters consisted of approximately 30% mCling-Atto-647-labelled EVs and 70% CFSE-labelled EVs ( Figure 4B ). Download figure Open in new tab Figure 4. Sorting of tissue brain-derived EVs. Experimental set up. B) IFC analysis of the sorting input for the sort with the Fusion (left) and the S8 (right). C) Gating strategy for sorting CFSE + EVs and mCling + EVs using the Fusion. D) IFC analysis of the sorted fraction when sorted for mCling + EVs with the Fusion. E) IFC analysis of the sorted fraction when sorted for CFSE + EVs with the Fusion. F) Gating strategy for sorting mCling + EVs and CFSE + EVs using the S8. G) IFC analysis of the sorted fraction when sorted for mCling + EVs with the S8. H) IFC analysis of the sorted fraction when sorted for CFSE + EVs with the S8. For sorting, samples were diluted to 15 - 160 events/sec (905 - 9772 events/min) depending on the sorter to clearly resolve the events. Serial dilutions were tested and are available in Supplementary Figure 3C . For sorting with the Fusion, swarmed particles and non-EV-components were excluded by first gating for low sideward scatter (SSC-A), before gating for the fluorescent signal ( Figure 4C ). This resulted in an enrichment of up to 74% for the less abundant mCling + EVs population and 96% for the more abundant CFSE + EVs population as validated with IFC ( Figure 4D-E ). For sorting with the S8, the imaging features ‘Eccentricity’ and ‘Diffusivity’ were applied to exclude background signals and contaminants ( Figure 4E ). This gating strategy resulted in an enrichment of mCling + EVs population up to 87% and CFSE + EVs population up to 100% as validated with IFC ( Figure 4F-G ). Also here, other imaging features were tested, however the best visual separation from background signals was achieved with Eccentricity and Diffusivity (Supplementary Figure 11D) . The yield analysis after sorting showed that nearly all the EVs of interest were successfully sorted. Controls according to MIFlowCyt-EV guidelines 30 and the position of the reference beads relative to the gates are shown in Supplementary Figures 12 – 14 . We here show that even EVs subjected to complex isolation protocols labelled with intraluminal and intramembrane dyes and biologically more diverse than cell culture-derived EVs can be successfully sorted, providing a valuable tool for investigating EV biology in this context. Post-sorted EVs maintain their integrity and stability enabling downstream analysis As shown in Figure 5A TEM confirmed the presence of intact cell culture-derived EVs after sorting. Interestingly, as an additional side result, we observed that successful TEM performance required highly positively charged grids as EVs were not detectable when using negatively charged grids. We hypothesize that the charge applied to the EVs during sorting interfered with the grid charges, thereby reducing their binding capacity. Download figure Open in new tab Figure 5. Effective EV population recovery after sorting. A) TEM of BFP2+ EVs after sort. B) Amount of detected proteins of CD34+ EVs before and after sort. C) Common EV markers as well as contaminants identified among the proteins after sort (blue=detected, white= not detected). D) Gene ontology analysis of overall human serum-derived EVs before sort. E) Gene ontology analysis of CD34+ EVs after sort with Fusion. F) Gene ontology analysis of CD34+ EVs after sort with S8 As a proof-of-principle to demonstrate that it is possible to perform downstream experiments after EV sorting, sorted human blood-circulating EVs and sorted BDEVs samples were analyzed using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Proteomic analysis of all sorted CD34 + EVs, which were initially isolated from 250µl serum, was sufficient to detect approximately 2,000 proteins ( Figure 5B , Supplementary Table 1 ). The majority of proteins overlapped with those detected in the initial EV population before sorting, while some proteins were uniquely detected in the enriched fraction. Among the identified proteins, typical EV markers were present ( Figure 5C ). However, proteins such as albumin or apolipoproteins also remained. Although they are considered EV contaminants, recent studies are challenging this knowledge and reveal that they may have a functional role by being part of the EV corona 35 . Therefore, it is unlikely that they can be removed during the sorting process 36 , 37 . Gene ontology analysis conducted before the sort ( Figure 5D ) and after the sort ( Figure 5E-F ), including all identified proteins, showed presence of EV-related terms across all three GO categories, such as ‘Extracellular exosome’, ‘RNA-binding,’ and ‘Metabolic process’. Likewise, proteomics of CFSE + EVs confirmed successful isolation, identifying EV-specific markers (Supplementary Figure 15) . Lastly, NTA measures confirmed no selection bias towards EVs of a specific size (Supplementary Figure 16) . However, it must be noted that the post-sorting concentrations were relatively low, leading to a decreased sensitivity of EV size detection by the NTA. To stress the robust reproducibility of our sorting strategy, several sorting experiments were independently performed on different days and using samples prepared in different batches, obtaining similar results ( Supplementary Figure 17 ). CONCLUSION We provide for the first time a protocol for successful sorting of EVs using conventional and spectral FACS instruments regardless of the EVs’ species, source, size, isolation and fluorophore-labelling method. This workflow enables high-purity enrichment of EVs, including low-abundance populations (<10%), preserving stability and integrity for downstream applications. The strategy employed, among others using the S8 imaging features, minimizes swarming effects and contaminants while maintaining adequate yield. More advanced gating may in the future facilitate the discrimination of even rarer EV subsets. Importantly, our analyses excluded sorting of undesired specific EV subpopulations and demonstrated that IFC is a valuable tool for validating EV sorting. Furthermore, we present the first quantitative proteomic study of sorted human blood-derived EVs and BDEVs. The ability to enrich specific EV populations and perform downstream analysis provides a powerful tool for both basic research and translational applications. MATERIAL AND METHODS Step-by-step setup for EV sorting with BD FACSAria Fusion and BD FACSDiscover S8 1-Setup of the instrument Fusion: Perform startup procedure and clean the nozzle for approximately 15 minutes using Contrad 70, FACS Clean, and a final rinse with distilled water Center the stream of sorted droplets relative to the collection tube to minimize the risk of sorted droplets adhering to the tube wall. Set collection chamber and sample tube holder temperature to 4°C. S8: Perform startup procedure twice, first run the extended fluidics startup, second run the daily fluidics startup procedure. Run the sample line backflush twice, to minimize background. Set collection chamber and sample tube holder temperature to 6°C. 2-Instrument calibration Fusion: Calibration beads (BD CS&T Beads) to optimize the time delay, essential for sorting small particles 7.5µm BD FACS Accudrop Beads to optimize the drop delay, repeating as often as necessary until the drop delay stabilizes. S8: Calibration beads (BD FACSDiscover Setup Beads) enable the software to automatically characterize, track, and report performance measurements in conjunction with onboard LEDs. BD Cell View Calibration Beads enable the software to automatically calibrate the imaging system. 7.5µm BD FACS Accudrop Beads to optimize the drop delay, repeating as often as necessary until the drop delay stabilizes. 3-Setup parameters for sorting For nozzle size, sheath pressure, sorting flow rate and event rates, please refer to Table 1 . Sorting mode mask: Purity 38 – only drops free of contaminating particles will be sorted. Depending on the presence of contaminants, up to two drops may be sorted. An empty drop may be sorted if it is adjacent to a drop containing an EV of interest. This mode prioritizes sample purity at the expense of yield. Threshold (mode) used for Fusion: WT mouse brain-derived EVs: APC, FITC (OR) Human and mouse glioblastoma-derived EVs: BV421, PE (OR) Human blood-derived EVs: PE, APC (OR) All thresholds were set at 200. Threshold (mode) used S8: iv) Human and mouse glioblastoma-derived EVs: Threshold SSC (Imaging), 1,009 v) Human blood-derived EVs: Threshold SSC (Imaging), 5,075 vi) WT mouse brain-derived EVs: Threshold SSC (Imaging), 7,110 4-Preparing the instruments for measurement Fusion: Clean the nozzle for approximately 15 minutes using Contrad 70, FACS Clean, and a final rinse with distilled water. S8: vi) Two times backflush; run a sample with rinse for 15min with highest flow rate; two times backflush, run a sample with water for 15min with highest flow rate; reduce flow rate to 1 and run the water sample for 10min; two times backflush. Both Fusion and S8: Between the samples, backflush twice to avoid contamination from previous samples, followed by running 1min rinse and 1min water, followed by a final backflush. 5-Set-up of gates including controls We refer to the main part of the manuscript where gating is described in detail. 6-Sample measurement To stabilize the stream/pressure after cleaning procedures, the samples were measured at least 1min on the instruments before recording the data. Data was collected for 1min with the flow rate of 1 (for both instruments). EV isolation Isolation of human and mouse glioblastoma-derived EVs The human glioma cell line NCH1681 (IDH1 R132H/WT ) 39 and the mouse glioma line Mut3 (hGFAP-cre; Nf1flox/+; Trp53−/+) 40 were used. NCH1681 was transduced with lentivirus (CSCW2 lentiviral backbone) encoding for palm-tdTomato, while mut3 cells were transduced with mTagBFP2. Cells not expressing tdTomato or BFP2 were removed by flow cytometry using a BD Aria Fusion cell sorter. Cells were kept growing for up to 10 days in serum-free Neurobasal Medium (NBM) with B-27 Supplement, FGF-2, EGF and Heparin. Conditioned media was collected and centrifuged at 300xg for 7 min. The cell-depleted supernatant was cleared from remaining debris by centrifuging at 2,000xg for 10 min, and then followed to ultracentrifugation at 100,000xg for 70 min (4°C) to purify small/medium EVs. EV pellets were carefully resuspended in 0.2µm filtered phosphate buffer saline (fPBS) and maintained refrigerated during the whole protocol. Isolation of human blood-circulating EVs Venous blood samples of healthy pregnant individuals (3 rd trimester) and non-pregnant individuals were obtained via peripheral venipuncture with a butterfly needle collection system (21 gauge). Samples were rested for minimum 60 min to allow RBC clotting. The RBC clot was subsequently pelleted by centrifugation at 2,000 g for 15 min and serum was aspirated. Serum samples were stored at −80 °C in 250µl aliquots and were not thawed before usage. Detailed information on preanalytical values, blood collection and processing as well as quality controls are reported according to the ‘MIBlood-EV - Standardized Reporting Tool for Blood EV Research’ as suggested by the Guidelines of the International Society of Extracellular Vesicles ( Supplementary document 1 ). Next, to pellet cell fragments, larger particles and other debris the serum samples were diluted 1:2 with fPBS and centrifuged at 10,000xg for 30 minutes. The supernatant was transferred into an 8,9 ml Beckmann Coulter ultracentrifugation tube and the tube was filled up with fPBS for ultracentrifugation at 100,00x g for 70 min using a Type 70.1 Ti Fixed-Angle Titanium Rotor. Subsequently the pelleted medium and small EVs were resuspended in fPBS for further processing. Samples of healthy pregnant individuals were obtained from the PRINCE study (Prenatal Identification of Children’s Health), an ongoing prospective longitudinal pregnancy cohort study. Healthy non-pregnant individuals were recruited at the University Medical Center Hamburg-Eppendorf, Germany. All study subjects signed informed consent forms. The PRINCE study protocol was approved by the ethics committee of the Hamburg Chamber of Physicians under the license number PV 3694 and was conducted according to the Declaration of Helsinki for Medical Research involving Human Subjects. Isolation of tissue brain-derived EVs Isolation of BDEVs was performed following published methods 33 with some modifications. Briefly, frozen mouse brain devoid of bulbi and cerebellum was gently cut with a scalpel, in RPMI-1640 medium (Gibco) containing 2mg/mL collagenase D (Roche) and 40U/mL of DNase I (Roche). This whole procedure was performed on ice. Samples were then incubated at 37°C for 30 min with manual agitation every 5 min. Afterwards, protease inhibitors (Protease Inhibitors Cocktail, Roche) were added to stop the action of the collagenase D. Homogenization of the sample was achieved with pipetting up and down approximately 10x with a 1,000 µL pipet with a cut tip. The sample was placed on a 70µm cell strainer (Fisher Scientific) on top of a 50mL falcon and filtered by gravity. 1mL of fresh RPMI was added on top of the strainer to wash. The filtrated sample was then subjected to serial centrifugations at 4°C (every time collecting the supernatant and discarding the pellet): 300xg for 10 min, 2,000xg for 20 min and a final centrifugation of 16,500xg for another 20 min. The resulting pellet of the latter was also collected, resuspended in 500µL of fPBS containing protease inhibitors and kept at 4°C as “large/medium EVs”, while the supernatant underwent further ultracentrifugation at 118,000xg for 150 min at 4°C (SW40Ti rotor, Optima Ultracentrifuge, Beckman Coulter). After this ultracentrifugation, the supernatant was discarded and the pellet containing “small EVs” was resuspended in 500µL of fPBS containing protease inhibitors. L/m EVs and sEVs preparations were mixed (final volume of 1mL), mixed with Optiprep (Axis Shield) to reach a concentration of 45%. Layers of 30% and 10% of Optiprep diluted as described in the Crescitelli et al. protocol were layered on top. Samples were then centrifuged at 186,000xg for 150 min at 4°C in the same rotor as before and the white band at the interface between 10% and 30% of the cushion gradient was collected as the isolated EV fraction (2 mL). This fraction was further diluted in cold fPBS and centrifuged again at 118,000xg for 150 min at 4°C to finally collect a pellet enriched in BDEVs. This pellet was resuspended in 100µL of fPBS containing protease inhibitors and kept at -80°C in aliquots to be used for further experiments. Validation of EV isolation All purified EVs were characterized according to the most recent guidelines for studies with extracellular vesicles 41 . EVs were evaluated regarding their concentration and size distribution using NTA, their characteristic cup-shape morphology by TEM, and for the presence of classical EV markers the tetraspanins CD9, CD81 and CD63 by Imaging Flow Cytometry (IFC). Imaging Flow Cytometry – sample preparation Human and mouse glioblastoma-derived EVs For the validation of successful EV isolation IFC of human-lineage EVs (positive for palm-tdTomato; acquired in Channel 03), samples were stained for 45 min at room temperature (RT) in the dark in fPBS containing 8% exosome-depleted FBS (Invitrogen, cat. no. A2720801), with a cocktail of the following anti-human antibodies conjugated with FITC: anti-CD9 (Biolegend, clone HI9a, 1ng/μL), anti-CD81 (Biolegend, clone 5A6, 40ng/μL), and anti-CD63 (Biolegend, clone H5C6, 40ng/μL). For IFC of murine samples (positive for BFP2; acquired in Channel 07), EVs were similarly stained, with the following anti-mouse antibodies conjugated with AlexaFluor 647: anti-CD9 (Biolegend, clone MZ3, 10ng/μL), anti-CD81 (Biolegend, clone Eat2, 10ng/μL), and anti-CD63 (Biolegend, clone NVG-2, 10ng/μL).EV-cocktail solutions are washed using a 300 kDa filter (Nanosep, 4,000g for 7 min at 4°C) and lastly resuspended in fPBS. For the validation of EV compositions before and after sort, human-lineage EVs (positive for palm-tdTomato) and murine samples (positive for BFP2) were measured without any further processing. Human blood-circulating EVs For the validation of successful EV isolation, EVs were treated with human IgG (ChromPure, Jackson ImmunoResearch) in order to block unspecific FcγRII/III binding for 30 minutes in the dark at 4°C together with exosome depleted fetal calf serum (FCS, gilbeco). The antibody cocktail was prepared including the following anti-human antibodies: Pacific Blue anti-CD9 (BioRad, clone MM2/57, 0.05 mg/ml) diluted 1:10, Pacific Blue anti-CD63 antibody (Biolegend, clone H5C6, 200µg/ml), Pacific Blue anti-CD81 antibody (Biolegend, clone TAPA-1, 300µg/ml). The antibody cocktail was filtered using 300 kDa filter (Nanosep) at 7,000xg for 10 min at 4°C and subsequently the EVs were stained for 45 min in the dark at 4°C. Subsequently the EVs were washed using the 300 kDa filter mentioned before at 7,000xg for 10 minutes at 4°C and resuspended in fPBS. Quality of EV preparation was assessed using the same staining procedure by staining with the following anti-human antibodies: FITC anti-CD41 (Biolegend, clone HIP8, 20µg/nl), FITC anti-ApoB (Biolegend, clone A-6, 200 µg/ml) and PE anti-CD9 (Biolegend, clone H19a, 20 µg/ml). For the EV sort same staining procedure was applied, except that the FCS was omitted, as this would otherwise lead to high albumin levels in the downstream mass spectrometric analysis. The following anti-human antibodies were used for the sort: AF647 anti-CD34 (Bioss, 1µg/µl, 4H11clone) diluted 1:10 and PE anti-CD9 (Biolegend, clone H19a, 20µg/nl) diluted 1:10. For the validation of EV compositions before and after sort the stained EVs were measured without any further processing. Tissue brain-derived EVs For the validation of successful EV isolation, prior to antibody staining, EVs were incubated in normal rat serum and anti-mouse CD16/32 (TruStain fcX, Biolegend) to block unspecific FcγRII/III binding for 30 min in the dark at 4°C. The antibody cocktail was prepared including the following anti-mouse antibodies: APC anti-CD9 (Biolegend, clone MZ3, 0.2 mg/ml) diluted 1:10, APC anti-CD63(Biolegend, clone NVG-2, 0.2 mg/ml) and APC anti-CD81 (Biolegend, clone Eat-2, 0.2 mg/ml). The antibody cocktail was filtered using 300 kDa filter (Nanosep) at 7,000xg for 10 min at 4°C and subsequently the EVs were stained for 45 min in the dark at 4°C. Subsequently the EVs were washed using the 300 kDa filter mentioned before at 7,000xg for 10 min at 4°C and resuspended in fPBS. For the sort, EVs were stained with mCling-Atto 647, a dye that is retained at the plasma membrane and endocytic membranes and with CFSE, a cell permeable dye that binds covalently to intracellular proteins. For mCling labelling, EVs were incubated with mCling (Synaptic Systems, final conc. 0.4µM) on ice for 5 minutes in the dark. The labelling reaction was stopped by adding 1% BSA. For CFSE labelling, EVs were incubated with CFSE (Thermo Fisher Scientific, final conc. 0.5µM) for 2 h at 37 °C in the dark. Following dye incubation, the EV suspensions were transferred to ultracentrifugation tubes containing 11 ml fPBS and ultracentrifuged at 118,000xg for 150 min at 4 °C. The EV pellets were subsequently resuspended in fPBS. Imaging Flow Cytometry – technical settings For IFC we used the Cytek Amnis ImageStream Mk II. All samples were measured at 60x magnification and with low flow rate. The following channels were used for acquisition: FITC and CFSE – channel 02, PE and palm-tdTomato – channel 03, BFP2 – channel 07, mCling and AF647 – channel 11. All samples were analyzed using IDEAS software version 6.2 (Amnis, Luminex Corporation). Nanoparticle Tracking analysis The concentration and size of EVs was determined by nanoparticle tracking analysis (NTA), using a LM10 instrument (Nanosight, Amesbury, UK). Human and mouse glioblastoma-derived EVs before sort were diluted in fPBS (1:300), and five movies of 1 min each were recorded on camera level 15, and then analyzed with detection threshold 4 and screen gain 2. Human blood-circulating EVs before sort were diluted in fPBS (1:200) and five movies of 1 min each were recorded on camera level 14, and then analyzed with detection threshold 5 and screen gain 2. Brain tissue-derived EVs were diluted in fPBS (1:1000) and ten movies of 0.5 min each were recorded on camera level 16, and then analyzed with detection threshold 6 and screen gain 2. For NTA after the sort EVs of one sample type were measured with the same settings without any dilutions. The analysis was performed by NTA 3.0 software. The concentrations were extrapolated per ml and the size values are presented as mode values. Negative Staining and Transmission Electron Microscopy For high resolution analysis of extracellular vesicles (EVs), a volume of 30 µl of EVs was fixed with 16% paraformaldehyde (PFA) to achieve a final concentration of 4%. Subsequently, formvar-carbon coated 200 mesh copper grid (#ECF200-Cu-50, Science Services) were positively charged using GLOQUBE PLUS. Next, 5 µl of this fixed EV solution was applied to the grid and allowed to rest in a dry environment for 20 min. Following this, the samples were rinsed in PBS three times, with each rinse lasting two min. After rinsing, the samples were stained on ice with a 2% methylcellulose-uranyl acetate solution for a duration of 10 min. The excess solution was then removed by gently looping the grid onto a filter paper. The EVs were subsequently analyzed and imaged using a Transmission Electron Microscope (Jeol JEM2100Plus), equipped with a XAROSA CMOS camera (EMSIS, Germany). Proteomic Analysis using Liquid Chromatography Tandem Mass Spectrometry Samples were dissolved in lysis buffer containing 100 mM triethyl ammonium bicarbonate (TEAB) and 1% w/v sodium deoxycholate (SDC) buffer, boiled at 95 °C for 5 min and sonicated with a probe sonicator. Disulfide bonds were reduced in 10 mM dithiothreitol for 30 min at 56 °C and alkylated in presence of 20 mM iodoacetamide for 30 min at 37 °C in the dark. Then, the samples were dissolved to a concentration of 70% acetonitrile (ACN) and carboxylate modified magnetic E3 and E7 speed beads (Cytvia Sera-Mag™, Marlborough, USA) at 1:1 ratio in LC-MS grade water were added in a 10:1 (beads/protein) ratio adapted from the SP3-protocol workflow 42 . Samples were shaken at 1400 rpm for 18 min at room temperature. Tubes were placed on a magnetic rack and the supernatant was removed. Magnetic beads were washed two times with 100% ACN and two times with 70% ethanol on the magnetic rack. After resuspension in 50 mM ammonium bicarbonate, digestion with 100 ng trypsin was performed (sequencing grade, Promega) (enzyme:protein) ratio at 37 °C overnight while shaking at 1400 rpm. Tryptic peptides were bound to the beads by adding 95% ACN and shaken at 1400 rpm for 10 min at room temperature. Tubes were placed on the magnetic rack, the supernatant was removed, and the beads were washed two times with 100% ACN. Elution was performed with 2% DMSO in 1% formic acid. The supernatant was dried in a vacuum centrifuge and stored at -20°C until further use. Chromatographic separation of peptides was achieved with a two-buffer system (buffer A: 0.1% FA in H 2 O, buffer B: 0.1% FA in 80% ACN) on a UHPLC (VanquishTM neo UHPLC system, Thermo Fisher). Attached to the UHPLC was a peptide trap cartridge (300 µm x 5 mm, C18, PepMap™ Neo Trap Cartridge, Thermo Fisher) for online desalting and purification, followed by a 25 cm C18 reversed-phase column (75 µm x 250 mm, 120 Å pore size, 1.7 µm particle size, Aurora Ultimate, IonOptics). Peptides were separated at a flow rate of 0.4 µL/min using a 70 min method with linearly increasing ACN concentration from 3% to 34% ACN over 60 min. MS/MS measurements were performed on a quadrupole-orbitrap hybrid mass spectrometer (Exploris 480, Thermo Fisher Scientific). Eluting peptides were ionized using a nano-electrospray ionization source (nano-ESI) with a spray voltage of 1,800 and analysed in data independent acquisition (DIA) mode. For each MS1 scan, ions were accumulated until 3 x 10 6 ions (AGC Target) was reached in automatic mode. Fourier-transformation based mass analysis of the data from the orbitrap mass analyzer was performed covering a mass range of m/z 400 – 800 with a resolution of 120,000 at m/z 200. Within a precursor mass range of m/z 400-800 fragmentation in DIA-mode with m/z 12 isolation windows and m/z 1 window overlaps was performed. Fragmentation was performed at normalized collision energy of 30% using higher energy collisional dissociation (HCD). Orbitrap resolution was set to 60,000 with a first mass of m/z 120. LC-MS/MS data were searched with the CHIMERYS DIA algorithm integrated into the Proteome Discoverer software (v3.1.0.638, Thermo Fisher Scientific) against a reviewed human or murine Swissprot database using Inferys 3.0 fragmentation as prediction model. Carbamidomethylation was set as a fixed modification for cysteine residues. The oxidation of methionine was allowed as variable modification. A maximum number of one missing tryptic cleavage was set. Peptides between 7 and 30 amino acids were considered. A strict cutoff (FDR < 0.01) was set for peptide identification. Quantification was performed by CHIMERYS based on fragment ions. Gene ontology analysis was performed using the STRING database 43 . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 44 partner repository with the dataset identifier PXD069156. Author contributions Conceptualization: I.G., B.P., J.B., A.R, A.S-S.; Methodology: I.G., J.B., A.R; Investigation: I.G., B.P, J.B., A.R, A.S-S., S.B., H.U., B.S., C.U.; Visualization and presentation of data: I.G., B.P.; Project funding acquisition: A.D., P.A., F.L.R., T.M. B.P.; Project administration: I.G.; Supervision: B.P.; Writing—original draft: I.G., B.P.; Writing—review & editing: all authors. Acknowledgments We thank for the financial support by grants from the German Research Foundation (Deutsche Forschungsgesellschaft DFG) to P.A. (CRC 1713: 91232/1-1713), F.L.R. (RI2616/6-1) and SFB 1328, FOR 2879 to T.M.; from the Federal Ministry of Research, Technology and Space to P.A.; from the German Center for Child and Adolescent Health, Hamburg site to A.D., P.A; from Schilling Foundation, T. Von Zastrow Foundation and Fielmann Foundation to T.M.; from Werner-Otto Stiftung to B.P.; I.G. was supported by the Else Kröner-Fresenius-Stiftung iPRIME Scholarship, the Jung Fellowship of the Jung-Stiftung für Wissenschaft und Forschung, Hamburg, the Ernst-Beinder Fellowship and is a fellow of the ‘Studienstiftung des deutschen Volkes’. We are grateful to Dr. Christel Herold-Mende (Heidelberg University Hospital, Heidelberg, Germany) for providing NCH1681 cell line, as well as Dr. Sean Lawler (Brown University, USA) for the mut3 mouse glioma line. We are also grateful to Dr. Xandra Breakefield lab (MGH, Harvard, Boston, USA) for providing us with the palm-tdTomato and Samir El Andaloussi palm-mTagBFP2 plasmids and Dr. Kristoffer Riecken (University Medical Center Hamburg Eppendorf, Hamburg, Germany), for help with the virus production and transduction. We thank the Core Facility Mass Spectrometric Proteomics as part of the Technology Platform Mass Spectrometry (TPMS) at University of Hamburg (UHH) and University Medical Center Hamburg-Eppendorf (UKE) for support with mass spectrometric measurements and analysis funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 518551069. The flow cytometer and sorter used in this study were provided by the FACS Core Unit of the University Medical Center Hamburg-Eppendorf. We further acknowledge the support of the staff of the Neurosurgery Department, the Neurology Department and the Laboratory of Experimental Feto-Maternal Medicine. Funder Information Declared Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64 German Center for Child and Adolescent Health Schilling Foundation T. 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