A high-throughput methodology for the efficient isolation of highly pure extracellular vesicles from skeletal muscle myoblasts

doi:10.21203/rs.3.rs-2041930/v1

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A high-throughput methodology for the efficient isolation of highly pure extracellular vesicles from skeletal muscle myoblasts

https://doi.org/10.21203/rs.3.rs-2041930/v1

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Background: Skeletal muscle extracellular vesicles (SM-EVs) regulate gene expression events in myogenic differentiation. Optimising effective SM-EV isolation methods offering high levels of purity will be important to accurately define their composition and functionality. Size-exclusion chromatography (SEC) applied in combination with ultrafiltration (UF) has the potential to increase sample throughput, scalability and selectivity. However, an optimal UF+SEC methodology has not been tested for the isolation of myotube derived EVs. Our aim was to compare two different UF protocols and define an optimal window of SEC fractions to maximise SM-EVs recovery and sample purity.

Methods: C2C12 myotube conditioned medium was pre-concentrated using Amicon® Ultra 15 or Vivaspin®20, 100KDa UF columns and processed by SEC (IZON, qEV 70nm). The resulting thirty fractions obtained were individually analysed to identify an optimal fraction window for EV recovery.

Results: EV markers Alix and TSG101 could be detected up to fraction 13, while CD9 and Annexin A2 only up to fraction 6. ApoA1+ lipoprotein contaminants were detected from fraction 6 onwards for both protocols. Amicon and Vivaspin UF preconcentration protocols led to qualitative and quantitative variations in EV marker profiles and purity. Eliminating lipoprotein co-isolation by reducing the SEC fraction window resulted in a net loss of particles, but increased measures of sample purity and had only a negligible impact on the presence of EV marker proteins.

Conclusion: In conclusion, this study developed optimal UF+SEC protocols for the isolation of SM-EVs based on sample purity (fractions 1-5) and total abundance (fractions 2-10). The resulting protocols will be valuable in isolating highly pure SM-EV preparations for biomarker studies.

Skeletal muscle

extracellular vesicles

isolation

myoblast

ultrafiltration

size-exclusion chromatography

Extracellular vesicles (EVs) are a diverse family of nanosized particles delimited by a lipid bilayer. Their cargo includes proteins (e.g. oncogenic regulators and transcription factors), nucleic acids (e.g. mRNA, microRNA, Y RNAs), lipids and metabolites that are delivered to recipient cells after activation of host cells surface-receptors or by fusion with the recipient plasma membrane (1), leading to phenotypic changes (2,3). EVs can be broadly separated into three subpopulations based on their cellular origin and biogenesis. These include exosomes (size range ~30-150nm), microvesicles (MVs) (size range ~100-1000nm) and apoptotic bodies (size range ~0.5-2µm). Due to their overlapping sizes and bio-compositions, effectively differentiating between the three groups is challenging and it is recommended that they are collectively termed EVs (4). These particles are produced by practically every cell type in the human body and have diverse and often complex functions in a range of physiological and pathophysiological processes such as tissue homeostasis, regeneration and inflammation (5–9). This has led to growing interest in their application in healthcare as diagnostic biomarkers of pathologies including cancers, bone disease or autoimmune diseases (10–12). However, the roles of EVs in skeletal muscle development, regeneration and ageing are less well defined due to a lack of an optimised isolation method offering a highly pure and high throughput means of isolation.

Skeletal muscle (SM) is primarily characterised by its mechanical functions in processes such as energy metabolism and locomotion. SM is formed through the activation and differentiation of resident satellite cells to myoblasts, their subsequent fusion to form myotubes and their aggregation into myofibers. Over the past two decades, the secretory role of this tissue and the contribution of myokines in autocrine, paracrine and endocrine processes has been well described (13). However, the contribution of EVs in these processes remains less well defined. Skeletal muscle EVs (SM-EVs) were first isolated from undifferentiated C2C12 cells by Guescini et al. in 2010, who predicted their role in the modulation of IGF-1 signalling and intra- and inter-organ communication through proteomic analysis. However, these results were not validated experimentally (14). Myoblast and myotube derived SM-EVs have different protein profiles, with myotube derived EVs promoting myoblast differentiation and the upregulation of myogenin (15,16). SM-EVs from differentiated C2C12 myotubes have been visualised budding from the plasma membrane, with the endosomal sorting protein Alix identified as a key regulator in their biogenesis (17). Myoblast-derived SM-EV are enriched in tRNA-, Y RNA, miRNAs, such as miR-451a, miR-144, miR-223 or miR-142a, and protein coding genes (18). The content and function of heterogenous SM-EV fractions are also dependent on the local physiological environment, with SM-EVs derived from myotubes under an induced inflammatory environment found to inhibit myogenesis and induce atrophy in C2C12 cells (19). While, SM-EVs recovered from both myoblasts and myotubes had effects on mitochondria and energy metabolism (20,21). Finally, murine hindlimb SM fibre derived EVs contained high levels of some miRNAs, such as miR-133a, which could have downstream effects on myogenic and osteogenic activities through the targeting of Smarcd1 and Runx2, respectively (22).

Despite evidence suggesting a diverse range of distinct regulatory functions for myoblast and myotube SM-EVs, the majority of studies to date have typically isolated heterogeneous EV fractions using protocols such as ultracentrifugation or precipitation that have often not accounted for the presence and contribution of co-isolated components such as lipoproteins and RNA binding proteins (e.g. AGO2) routinely identified in EV studies due to the lack of an optimal isolation method (23–25). As such, optimising effective SM-EV isolation methods with high levels of purity will be important if we are to accurately define their composition and functional effects in physiological and pathophysiological systems. At present, Alpha-sarcoglycan (αSGCA) and miR-206are the only described potential biomarkers of SM-EVs. Previous studies have identified the presence of αSGCA-EVs in human plasma, containing high concentrations of miRNA species identified in previous C2C12 studies and human primary cells (15,26). Within recovered SM-EV fractions, αSGCA represented only 1-5% of the total population analysed. While miR-206 has been identified in SM-EV in local communication and in response to exercise (27–29), there is a lack of information on the specificity of its association with SM-EVs (or other co-isolated RNA binding proteins) and its application as a SM-EV marker. No other markers have currently been identified to detect the presence of SM-EVs in cell culture medium, plasma or other biofluids. As such, accurately identifying and differentiating SM-EVs in complex physiological systems remains difficult and would benefit from the application of defined in vitro systems in which the efficiency of isolation methods can first be evaluated. To this end, in vitro systems can offer a controlled environment for the optimisation of EV isolation protocols and the identification of additional prospective markers that can be subsequently validated using in vivo models. For example, in vitro systems have provided consolidated strategies to understand of basic skeletal muscle physiology and have been widely employed to investigate muscle function, regeneration and ageing (30–37).

To date, multiple methods have been applied for the isolation of EVs, with each presenting advantages and disadvantages related to sample purity and compatibility with downstream applications for sample characterisation and the identification of EV-specific markers (e.g. mass spectrometry). When focusing on the skeletal muscle field, the majority of in vitro studies have applied dUC (14,15,41,42,16–20,38–40) or commercial isolation kits (21,39,43,44) for this purpose. In vivo, the majority of studies have applied dUC (26,45–48), polyethylene glycol (PEG) (49) or commercial isolation kits (27,29,50) with blood plasma as the main reported source of SM-EVs. Throughout the literature dUC remains the most widely applied EV isolation technique (51), likely due to the fact it is relatively simple and cost-effective. However, there are several disadvantages to this technique, such as its highly variable yield (10-80%) and low purity (52,53). Precipitation methods, commercial isolation kits or PEG based methods, are compatible with cell culture medium and biofluids and they have the potential to deliver high yields for therapeutic applications. However, the co-isolation of polymeric molecules (i.e. proteins or other particles) can limit options for downstream analysis and confound interpretation of the results (54–56). Isolation protocols offering enhanced specificity, (e.g. immunoaffinity capture) are available but provide only low yields and rely on detailed knowledge of specific ligands on EV outer membrane (57). These techniques are becoming increasingly challenged by emergence of physically milder isolation methods including size-exclusion chromatography (SEC), which can enhance sample purity while maintaining throughput and scale. SEC represents an increasingly utilised method – with a 30% marked increase of the number of publications associated with that method since 2016 (58–60) for the isolation of EV-enriched fractions or in combination with mass spectrometry for the isolation and profiling of EVs from plasma (53,61,62). However, SEC frequently needs to be combined with an ultrafiltration (UF) step to reduce sample volumes and increase overall purity, throughput and recovery (63–66). Different UF systems are available that differ in membrane composition and molecular weight cut-off (MWCO). Membranes can be manufactured from materials including regenerated cellulose (Amicon) and polyethersulfone (PES) (Vivaspin). The influence of the membrane and MWCO are likely to have a significant effect on EV recovery but have been only minimally examined in the scientific literature in non-skeletal muscle sources (66,67). Within the literature, SEC has been only minimally applied for the isolation of SM-EVs, with the only two examples being to examine the stability of several muscle derived miRNAs in C2C12 cells (68) and in blood samples to evaluate changes in circulating EVs and miRNAs associated with exercise (69,70). However, in these examples, the authors provided no conclusive validation of EV containing fractions in accordance with published guidelines (4,71) and no study has sought to optimise a combined UF+SEC protocol for the high-throughput isolation pure SM-EV preparations. The optimisation of this system would be of considerable value in diagnostic and therapeutic applications, where the rapid and efficient separation of EVs from free proteins/lipoproteins separation is integral for defining biological outcomes.

The selection of choice of UF column and SEC fraction windows varies throughout the literature using different EVs sources (9,67,72–75). It is becoming clear that these parameters need to be optimised for each cell type and starting fluid in order to maximise EV recovery and sample purity. Consequently, this study sought to optimise a simple, high-throughput and universally applicable protocol for the isolation of highly pure EV fractions from skeletal muscle cells. To achieve this, we aimed to compare SM-EVs recovery using two widely utilised UF columns and define an optimal fraction window using SEC that maximises EVs purity and reduces lipoprotein co-isolation that could negatively impact biomarker studies.

CELL CULTURE

C2C12 murine skeletal muscle myoblast (ATCC® CRL-1772™) cells were grown using standard growth medium (GM) composed of high glucose Dulbecco’s Modified Eagle’s Medium (DMEM) (D6429, Merck KGaA, Darmstadt, Germany), 20% fetal bovine serum (FBS) (P40-37500, Pan Biotech, UK), and 1% Penicillin/Steptomyocin (P/S) (11548876, FisherScientific, UK). Cells were cultured in T175 flasks (12034917, FisherScientific, UK) and incubated in a 5% CO₂ humidified atmosphere at 37°C until 80% confluence was attained. After reaching 80% confluence, the growth media was replaced with differentiation media composed of DMEM High Glucose, 2% EVs-depleted horse serum (HS) (30021200, Pan Biotech, UK) and 1% P/S. HS was depleted of EVs by spinning at 120,000 xg for 16 hours. After 48 hours differentiation (Figure 1A) conditioned media (CM) was collected and centrifuged at 2,000 xg for 20 minutes to remove cells and debris. CM was stored at -80°C up to 3 months for EV isolation.

EV ISOLATION

CM was concentrated using Amicon^® Ultra 15 (100 kDa) (UFC910024, Merck KGaA, Darmstadt, Germany) or Vivaspin®20 (100 kDa) (GE28-9323-63, Merck KGaA, Darmstadt, Germany) UF columns according to the manufacturer’s recommendations. Concentrated sample was submitted to SEC columns (qEVoriginal/70nm, IZON SCIENCE LTD, New Zealand), collecting 30 independent fractions of 500 µL using an Automatic Fraction Collector (AFC) (IZON SCIENCE LTD, New Zealand). In instances where 500 µL fractions were combined to maximise EV recovery, UF was reapplied to reduce total sample volumes as outlined in Figure 1B.

NANOPARTICLE TRACKING ANALYSIS (NTA)

Size distribution and particle concentration for each sample were measured using the NanoSight LM10 (Malvern Panalytical, UK). EV fractions were diluted using DPBS (15326239, FisherScientific, UK), capturing measurements of 20-100 particles per frame. 6 videos with a duration of 30 seconds each were captured of each sample. Settings for videos and data acquisition were constant with camera level set between 13 and 15 and screen gain between 1 and 2. All runs were analysed with the same threshold. Data was analysed using Nanosight NTA 3.2 software (Malvern Panalytical, UK).

BICINCHONINIC ACID (BCA) PROTEIN ASSAY

BCA assay was applied to determine protein concentration for each fraction used. Pierce™ BCA Protein Assay Kit (23227, ThermoFisher Scientifics, UK) was used according to the supplier’s instructions. 25 µL of each sample, neat or diluted (1:2) was loaded in a 96-well plate, followed by 200 µL of the BCA/copper complex solution from the kit. The absorbance was measured at 562 nm in the microplate reader Thermo Scientific Varioskan Flash (ThermoFisher Scientifics, UK) using Skanlt software (SkanIt Software 2.4.5 RE).

WESTERN BLOT (WB)

Samples were prepared using Sample buffer (SB4X) (1610747, BioRad, UK), 25% of the total sample, and Lysis buffer (LB) [0.5% Triton X-100, EDTA 1X and protease inhibitors (10085973, FisherScientific, UK)]. Samples were boiled to encourage protein denaturation for 5 minutes at 98°C. Different proteins present in the samples were separated in precast polyacrylamide gels (4561083, BioRad, UK), loading 5μg protein. Precision Plus Protein™ Dual Color Standards were applied for estimation of molecular weight (1610374, BioRad, UK). Protein bands were transferred to Polyvinylidene fluoride (PVDF) membranes (11544996, FisherScientific, UK) that were blocked in EveryBlot blocking buffer (12010020, BioRad, UK) and washed in Tris buffer solution with 0.1% of Tween20 (Merck™ 655204-100ML, FisherScientifics, UK) (TBST). Membranes were incubated with primary antibodies (Table 1) overnight in a rocker at 4°C. The following day, membranes were washed three times and incubated with the appropriate secondary antibody for 1 hour. Proteins were detected on membranes using the ChemiDoc XRS+ system (1708265, BioRad, UK) and Image Lab software (Life Science Research, BioRad, UK). Image J (National Institutes of Health, USA) was applied for WB band quantification.

Table 1: Western Blot antibody guide for targeted proteins. We included information about dilutions, source, supplier and codes and secondary antibody dilutions used in the results represented in this report.

Primary Antibody	Source	Dilution	Supplier	Secondary antibody	Dilution
Anti-Alix	Rabbit	1:1000	Santa Cruz (sc-53540)	Anti-Rabbit	1:3000
Anti-Annexin A2	Rabbit	1:2000	Abcam (ab41803)	Anti-Rabbit	1:3000
Anti- TSG101	Rabbit	1:1000	Abcam (ab30871)	Anti-Rabbit	1:3000
Anti-CD9	Rabbit	1:1000	Abcam (ab92726)	Anti-Rabbit	1:2000
Anti-CD63	Rabbit	1:1000	Abcam (ab216130)	Anti-Rabbit	1:3000
Anti-Calnexin	Mouse	1:1000	Abcam (ab22595)	Anti-Mouse	1:3000
Anti-ApoA1	Rabbit	1:1000	Abcam (ab20453)	Anti-Rabbit	1:3000
Anti-ApoB	Rabbit	1:1000	Novus Biological (NB200-527)	Anti-Rabbit	1:3000

NANO FLOW CYTOMETRY (nFCM)

Nano flow cytometry (nFCM) was performed using a NanoAnalyzer U30 instrument (NanoFCM Inc., Nottingham, UK). The nFCM instrument was equipped with photon-counting avalanche photodiodes (APDs) and two dual 488/640 nm lasers that were applied for simultaneous detection of side scatter (SSC) and fluorescence-based detection of individual particles. All samples were diluted to 1.87x10¹⁰ particles/mL and stained with CD9-FITC (1:200) (124809, BioLegend, UK), CD63-APC (1:100) (ab233056, abcam, UK) or CD81-APC (1:100) (104909, BioLegend, UK) for 30 min at room temperature. Optical alignment was tested and calibrated using fluorescent 250 nm silica nanoparticles. Further calibration measurements were taken prior to analysis using 250 nm silica nanoparticles of known concentration (for EV concentration calculation) and the proprietary four-modal silica nanosphere cocktail generated by NanoFCM containing nanosphere populations of 68, 91, 113 and 155 nm diameter (for EVs size calculation). nFCM analysed samples by gating particles ranging from 40 to 200nm. Samples were measured at least 3 times and data was processed by nFCM Professional Suite v1.8 software, using a gating for subpopulations separated by fluorescent intensities with size distribution and concentration available for each sub-population.

ZETA POTENTIAL MEASUREMENTS

Zetasizer Nano ZS (Malvern Panalytical, UK) was used to perform Z-potential measurements. DTS1070 folded capillary cells were washed with isopropanol and ionized water and then dried before applying the sample. Once cleaned, samples were submitted to the capillary cell and device (Malvern Panalytical, UK). Measurement time was 60s at room temperature in Monomodal mode at 50mV.

TRANSMISSION ELECTRON MICROSCOPY (TEM)

EVs were prepared on continuous carbon (Cu 300 mesh) TEM grids. Samples were fixed in glutaraldehyde 3% and, once dry, washed twice with milliQ water. To visualise EVs, slides were negatively stained with 1% Uranyl Acetate and air dried. Images were recorded on an FEI Tecnai G2 12 Biotwin TEM (ThermoFisher, USA) operating at 100 kV, using a Gatan SIS Megaview iV camera (EMSIS GmbH, Germany).

STATISTICAL ANALYSIS

Graphs were prepared using Origin Lab (OriginLab Corporation, USA), with values presented as mean ± standard deviations (SDs) with 95% confidence intervals. Pearson’s correlation-r was used for correlations within samples. Student’s t-tests and analysis of variance (ANOVA) with Bonferroni post-hoc were performed using GraphPad Prism 6 (GraphPad Software, San Diego, USA). Differences were considered statistically significant at *; p < 0.05, **; p < 0.01 or ***; p < 0.001.

EV RECOVERY

Total protein concentrations began to elevate considerably between fraction 7 and 8 for both UF protocols (3-fold changes for both methods respectively, p<0.05). A decline in particle concentration intersected with an increased total protein concentration between fractions 8 and 10 when using Amicon and Vivaspin filters, respectively (Figure 2A,B, Supplementary Figure 1). Peak of particle concentration was found within the first 14 fractions. Fraction 5 seemed to be the peak for Amicon+ SEC isolation protocol, although when using Vivapsin filters, the highest particle concentration was detected between fractions 5 and 6. Particle size distributions, reflecting the mode size±SD, were 76.30±3.00 vs. 93.43±3.4 nm and mean size±SD: 158.15±19.33 vs. 163.41±30.12 nm, for Amicon and Vivaspin, respectively (Figure 2C, Supplementary Figure 1). Broader size distribution of particles was encountered in Vivaspin fractions, especially between fractions 2 to 7 (Figure 2B,C, Supplementary figure 1). 72% and 85% in Amicon and Vivaspin columns, respectively of the total particle measurement corresponded to the recorded between fractions 1 to 10 (Figure 2A,B, Supplementary figure 1). Qualitative detection of positive (Alix, Annexin 2, TSG101 and CD9) and negative EVs makers (Calnexin, ApoA1 and ApoB) by WB showed marker profile differences within all fraction’s windows analysed. CD9 and Annexin A2 were detected in fractions 2 to 4, but with some presence in fraction 6, following Amicon pre-concentrated samples, although, in fractions 3 to 6 and 2 to 6 for Vivaspin pre-concentration samples. Alix was present from fraction 3 after Amicon UF and from fraction 4 following Vivaspin UF. TSG101 showed positive signal between fractions 6 to 14 when using Amicon but in fractions 5 to 14 after Vivaspin filtration (Figure 2D). Presence of EV tetraspanins CD63 and CD81 were analysed by ExoELISA assay, showing an increased signal from fraction 4 (Supplementary figure 2A,B) confirming the enrichment of EVs within that range of fractions for both isolation protocols. High- (ApoA1) and low-density (ApoB) lipoprotein markers were used to provide an indication of the relative purity of each fraction. Of the negative markers analysed, Calnexin and ApoB could not be detected in any samples analysed. ApoA1 was visibly increased from fraction 6 after Vivaspin and Amicon pre-concentration (Figures 2D,E). When evaluating particle-to-protein (PTP) ratio, as an indirect measure of sample purity, fractions 5, 6 and 7 in Vivaspin pre-concentrated samples fractions had a significantly higher value compared to the samples obtained following Amicon pre-concentration (Figure 2F).

MAXIMISING EV RECOVERY: ANALYSIS OF EV-ENRICHED FRACTIONS (2 TO 10) FOLLOWING UF PRE-CONCENTRATION

Based on the outcomes of Figure 2, an initial fraction window of 2-10 was selected for both UF columns to provide comparable yield of EV enriched material while minimising - but not fully eliminating - lipoprotein contamination at this stage. Within this combined fraction window, Vivaspin columns recovered 15% more particles than Amicon columns, with particle concentrations of 8.42x10⁸ and 7.17x10⁸ particles/mL, respectively (Figures 3A,C). Particles recovered after Amicon pre-concentration had a wider size distribution compared to Vivaspin UF+SEC protocol (Figure 3A), but larger particles were isolated using Amicon columns (mean size ± SD: 153.96 ± 12.31 nm; mode size ± SD: 99.83 ± 11.85 nm; p>0.05) when compared to Vivaspin (mean size ± SD: 119.76 ± 15.30 nm; mode size ± SD: 90.00 ± 11.27 nm; p>0.05) (Figure 3A,B). Total protein concentration was significantly (p<0.05) reduced for Amicon preparations (Figure 3B). This trend was reflected in the PTP ratios, where higher purity was recorded following Amicon pre-concentration (129.0 vs 60.1). TEM analysis of samples provided visual evidence of individual SM-EVs for both isolation protocols, which could be visually identified by the presence of a cup shaped morphology (Figure 3C and Supplementary figure 3). WB analysis revealed qualitative differences in marker presence between the two UF+SEC protocols, with TSG101 and CD63 most abundant in Vivaspin fractions and CD9 and Alix most abundant in the Amicon fractions. Calnexin was absent from all UF+SEC preparations and only visible in the cell lysate control. ApoA1 was detected in both UF+SEC preparations, but more predominantly in Vivaspin preparations (Figure 3D). Data provided by nFCM measurements showed that in particles sized 40 to 200 nm, CD9 was the most abundant tetraspanin in both isolations (Figure 3E), but the qualitative differences showed for this marker by WB (Figure 3D) were not supported. nFCM measurements also revealed significantly (p<0.001) higher CD63⁺ and CD81⁺ particles were detected in the samples obtained after Amicon pre-concentration when compared to Vivaspin (Figure 3E). Finally, zeta potential measurements identified values of -14.68mV and -10mV following Amicon and Vivaspin UF respectively, with no significant differences between them, but more negative values were detected in Amicon preparations (p>0.05) (Figure 3F).

REFINING FRACTION WINDOW TO EXCLUDE LIPOPROTEINS

In Figure 1, we highlighted an increased presence of lipoproteins beyond fraction 6 after pre-concentrating with both UF protocols. For this part of this research, fractions 1 to 5 obtained after using Amicon filters for both concentration steps were compared to Vivaspin pooled fractions 2 to 10 previously analysed (Figure 3) to observe the effects of eliminating fractions enriched in lipoproteins on EV recovery. Total particle concentration was reduced to 4.85x10⁸ particles/mL for samples pre-concentrated using Amicon filters (Figure 4A,B). This finding appeared to be visually supported by the TEM analysis in which fewer particles could be observed per frame (Figure 4C and Supplementary figure 3). Average particle sizes of 139.67 ± 12.31 nm were recorded for pooled Amicon fractions 1 to 5, while mean sizes ± SD of 153.96 ± 12.31 nm (mode size ± SD: 92.48 ± 0.97 nm) were recorded for Vivaspin preparations, however without any significant differences (p>0.05) (Figures 4A,B). PTP ratio increased from 129 (Figure 4B) to 140 (p>0.05) when combining fractions 1 to 5 compared to fractions 2 to 10 following Amicon UF and Vivaspin preparation PTP ratio was again lower (Figure 4B). The presence of EV specific markers was qualitatively examined by WB (Figure 4D), revealing that the overall trend in marker expression was not dissimilar to data presented in previous results (Figure 3D). The WB data highlighted an increased presence in CD9 and Alix for Amicon pooled fractions 1-5, but not TSG101 and CD63 (Figure 4D). Quantitative comparison of EV markers revealed significant increases in CD81⁺ particles were maintained when reducing the fraction window following Amicon pre-concentration relative to Vivaspin fractions 2-10 (p<0.001) (Figure 4E). However, significant differences in CD63 could no longer be observed when compared with Vivaspin fractions 2-10 (Figures 4D,E). Zeta potential measurements were -11.13mV and -10.00 in Amicon and Vivaspin preparations respectively, observing a decrease in this value for Amicon preparations containing fractions 1 to 5 compared to the Amicon 2 to 10 preparation (p>0.05) (Figure 4F).

SELECTION OF PROTOCOL DEPENDING ON DOWNSTREAM APPLICATIONS

We will now summarise the results to demonstrate how UF + SEC can be applied to enhance EV purity (fractions 1-5) or total particle recovery (fractions 2-10). Protein and particle concentrations were significantly higher (p<0.05) in Vivaspin 2-10 preparations. However, PTP ratios were 57% higher for Amicon fractions 1-5 final products (p>0.05) (Figure 5A). When comparing the outcomes of reducing the Amicon fraction window (fractions 1-5), a reduction of the 32% was observed in particle content. While only a reduction of 8% in total protein concentration and PTP ratio was observed in this case (p>0.05) (Figure 5A). No significant differences were recorded for the combined presence of tetraspanin proteins CD9, CD63 and CD81 as recorded using nFCM for total (46.3% total recovery for Amicon 1-5 and 53.7% total recovery for Vivaspin 2-10) or individual (23.3% for CD9, 9.8% for CD63 and 13.2% for CD81 in Amicon 1-5 preparations vs 29.4% for CD9, 11.3% for CD63 and 13% for CD81 in Vivaspin preparations) EV tetraspanins recovery, as determined using nFCM (p<0.001) (Figure 5B). We demonstrated almost complete elimination of lipoprotein co-isolates (1.4%) in the Amicon 1-5 preparations, in contrast to Vivaspin 2-10 preparations (63.4%) (Figure 5C). Lastly, the table provided (Figure 5D) gives a summary of outcomes of interest, which is designed to enable other researchers to select the SEC + UF protocol most suitable for their own applications. Results suggested that in order to maximise purity, combination fractions 1-5 after Amicon pre-concentration would be the most suitable approach, but that Vivaspin fractions 2-10 could be utilised to maximise EV recovery at the expense of sample purity. From here, differences in EV marker presence have been highlighted. TSG101 and CD63 were consistently increased for Vivaspin preparations, while CD9 was the predominant marker for Amicon 1-5 preparations. Finally, greater presence of CD81⁺ particles was encountered in Amicon 1-5 preparations but no differences were observed regarding the CD63⁺ population (Figure 5D, Supplementary Figure 3).

Circulating EVs are typically found at concentrations of 10¹⁰ EVs per mL of blood (76). Although αSGCA has previously been used to identify SM-EVs from blood plasma (26) these particles make up only around 1 to 5% of the total population, and a lack of broader SM-EV specific markers limits current understanding of the precise functions of these nanoparticles in physiological and pathophysiological states. The ability to identify additional SM-EV markers and map their biological functions is confounded by the fact that no optimal method has been developed for the isolation of SM-EVs. To date, studies have applied multiple isolation methods that often lack specificity, such as dUC (14,15,41,42,16–20,38–40), PEG or commercial isolation kits (21,39,43,44). While neutral on a functional level, any EV isolation protocol can affect sample quality, introducing impurities or a variety of co-isolated particles (lipoproteins and protein/RNA complexes) (77–79). Not only does this have a confounding effect on therapeutic and biomarker studies, it can also risk the elimination or aggregation of EVs, thereby masking potentially valuable biomarkers (80–82). These factors make distinguishing the precise origin of EVs in highly complex biofluids, such as blood, extremely challenging and the breadth of SM-EV migration throughout the body remains controversial (83). Additionally, the inclusion of non-EV materials can also add to challenges in the reproducibility and regulation of prospective therapeutic strategies employing these biologically potent nanoparticles (84). In biomarker studies, increases in the presence of circulating small EVs as identified following acute exercise could be derived from a range of tissues that is not limited to skeletal muscle but also includes endothelial, cardiac, hepatic and adipose tissues (28). Furthermore, EVs are outnumbered in the circulation by lipoproteins by a factor of one billion, which due to their overlapping diameters (LDLs: 20 – 200 nm) and densities (HDLs: 1.06 – 1.21 g/mL), can lead to considerable inaccuracies if not depleted in the isolation protocol (85). HDLs are also carriers of RNAs, which unless depleted in the EV preparation can lead to potentially false impressions that miRNA biomarkers are associated with EVs (86). This not only poses considerable issues in the accurate identification of SM-EVs but also risks the potential mislabelling or overrepresentation of EVs as delivery vehicles for established and emerging myokines and exerkines (87,88). As such, there exists a requirement to define optimal isolation protocols for the purification and profiling of SM-EVs in defined in vitro systems before biomarker studies can be effectively progressed in complex biofluids such as blood plasma. It is imperative that isolation protocols are developed to eliminate lipoprotein contaminants and enable the comprehensive and accurate characterisation of SM-EVs. In the present study we optimised an isolation methodology combining SEC+UF for the recovery of pure EV fractions from a C2C12 cell line. Using a simple unicellular single skeletal muscle cell system, we were able to validate a specific fraction window for the collection or EVs and elimination of lipoprotein contaminants. Lastly, we were able to identify the impact of often indiscriminately applied UF column concentration on SM-EV recovery.

SEC has been shown to be a promising high-throughput and adaptable method for the isolation of EVs from a range of biofluids such as plasma and urine, as well as cell culture medium (89–91). Studies have demonstrated preserved biophysical and functional properties for EVs isolated using SEC when compared to dUC, with altered biodistributions and less accumulation in off-target tissues such as the lungs when administered in animal models (66). Studies have also evidenced a reduced protein corona following SEC isolation that could mask the origin of these particles and influence the inflammatory profile of resident cells such as monocyte-derived dendritic cells and enhance regenerative processes if applied therapeutically (92,93). The SEC technique is also simple, making it amenable to non-specialists, and can be adapted to suite different budgets, with the option to build columns in-house or purchase prefabricated sepharose-based columns from a range of commercial suppliers (e.g. IZON, Cell Guidance Systems, STEMCELL Technologies, Galen Laboratory Supplies). Furthermore, SEC recovers multiple fractions that can independently be tested for the presence of EVs and lipoproteins to optimise a final recovery window, which can be tailored depending on application and individual requirements. This is reflected in the outcomes of the present study, where we have identified distinct fraction windows able to enhance EV purity (fractions 1-5 after Amicon UF) and recovery (fractions 2-10 for both isolation protocols). Identifying an optimal fraction window for EV collection is a major consideration when applying SEC, as this can differ depending on the source material (e.g. cell culture medium, plasma, urine, etc.) and cell/tissue type (74). To date, relatively few studies have applied SEC to obtain SM-EVs from skeletal muscle myoblasts. In these studies, the selection of EV enriched fractions was based on absorbance at 280nm, representing total protein content of the samples, where they observed a peak from fractions 4 to 9. Grouping these fractions they encountered particles with a modal size of 125 nm and the presence of EV markers ALIX, TSG101, and CD81 (68). They also described the presence of some SM and exercise related miRNAs (miR-1, miR133a, miR-206, miR16 and let-7a) recovered in the resulting SM-EV preparations. Soluble proteins, such as enzymes, were separated, however, no mention of other co-isolates, such as lipoproteins, was reported. Our study reflected similar outcomes in separation, particle size and marker positivity, although revealed the presence of ApoA1⁺lipoproteins within fractions 6-10 in the final preparations. ApoA1 is a marker of HDL and, as indicated earlier, are relevant in miRNAs circulation and signalling (86) and, as such, it remains to be determined whether miRNAs identified in the previous studies are truly associated with EVs or the result of co-isolated particulates. PEG or commercial isolation kits based on precipitation (ExoQuick and Total Exosome Isolation Kit) have also been applied in studies seeking to isolate and characterise SM-EVs. While interesting observations have been recorded for SM-EVs derived from human primary skeletal cells cultured ex vivo, such as presenting myogenic differentiation and regeneration activities, and the presence of some myogenic factors including EGFR, IGFBP1, and TGF-β1 (21,43,94), (21,43), (94). For this reason, the application of one step precipitation methodologies is broadly discouraged within the literature (4,79,89,95).

Lipoprotein contamination in EV preparations represents a considerable limitation if we wish to accurately determine the physiological functions of SM-EVs and develop EV diagnostics for the predication of pathological outcomes related to skeletal muscle ageing and degeneration. The metabolism of lipoproteins has long been shown to be influenced by endurance training and training-induced adaptations in SM (96). It has long been appreciated that exercise training has a notable effect on the relative numbers of lipoproteins within the circulation (97). Nonetheless, the majority of exercise studies often do not seek to differentiate variation in EVs from those of lipoproteins. So, it is possible that variations in circulating nanoparticles reported in exercise studies could partly be explained basic variations in non-EV components and not specifically EVs (98). Indeed, while multiple studies have demonstrated interesting variations in the presence of circulating nanoparticles in human and animal acute exercise models, they have often utilised non-specific isolation methods such as dUC without testing for the presence of lipoproteins (99,100). dUC was the first isolation method adopted for SM-EVs and remains the most broadly applied (14,15,40,16–20,24,38,39). dUC has been applied to isolate SM-EVs for downstream proteomic analysis (44), in which its comparison against a commercial isolation kit showed improvements in SM-EV yield and purity. In this instance, polymer based isolation, combined with clean-up steps using 100kDa Amicon filters improved the isolation of functional EVs. However, the influence of lipoproteins was once again not reported regularly, likely underestimating their role and the influence in SM-EV functionality. In an ex vivo study, dUC was applied to isolate SM-EVs from murine skeletal muscle explants where different lipid and miRNA profiles were found between obese and healthy mice, principally correlating with lipid metabolism outside the mitochondria. SM-EVs from obese individuals presented altered lipid profiles and miRNAs targeting fatty acid metabolism pathways (101). However, as with the previous study, lipoprotein recovery was not assessed even though lipoproteins have been implicated in the transport of miRNAs and this could lead to erroneous reporting of EV in RNA dynamics (102,103). Similar limitations can also be observed in SM-EVs obtained from in vivo studies. In such studies, plasma has been the predominant source used (70,104–106). Results reflected an increase in SM-EV production after activity, increase of some exercise related miRNAs (miR-1, 133a, 133b, 206, and 486) (70) and changes in the tetraspanin profile (104–106). However, just one of the studies (106) accounted for the presence of lipoproteins in EV samples. In contrast to our results, Warnier et al. claimed that ApoB appeared from fraction 8 onwards, corresponding to low density lipoprotein particles. While ApoA1⁺ particles appeared from fractions 9 onwards, contrasting with our own data. Inherent differences in the content and viscosity of blood plasma and cell culture medium, as well as acknowledged variations in the abundance of lipoproteins would likely account for the observed inconsistencies in ApoA1⁺ fractions between our own study and that of Warnier et al. This finding highlights the need for EV isolation methods to be optimised depending on the starting material.

When applying SEC for the isolation of EVs, it is often necessary to combine it with a sample pre-concentration step. This becomes essential when applying the SEC method for the isolation of EV from larger sample volumes or if attempting to scale up EV therapeutics. Sample concentration can be simply achieved using a UF column (66,67,72,90,107–112). UF has also been applied independently of SEC for EV isolation (63,64,113). However, this will inevitably reduce the specificity of the technique and the purity of the EV preparation. The efficiency and specificity of UF is likely to be dependent on the composition of the UF membrane (e.g. cellulose, cellulose triacetate (CTA), polyethersulfone (PES) or modified nylon) and the MWCO applied (67). To the best of our knowledge, no study has looked at the effects of UF column pre-concentration on SM-EV recovery when combined with SEC. In the present study, we identified that different UF materials had an impact on EV recovery, as shown by variations in tetraspanin profiles and sample purity ratios (Figures 2 and 3). Samples obtained after Amicon pre-concentration were enriched in CD9, Alix and CD81, while TSG101 was more prominent in Vivaspin 2-10 preparations (Figures 3D,E). This is a highly relevant finding, since it suggests that the choice of UF column can have a considerable impact on the composition of the resulting SM-EV preparation, thereby impacting the outcomes of EV biomarker studies. While any adhesion of EVs to the UF membrane could be mitigated through the application of detergents (114), their effects on EV permeability and bioactivity would need to be further evaluated as they have previously been shown to have differential effects on EV subpopulations (115). In addition to differential membrane properties, UF devices typically have MWCOs ranging from 3kDA to 100kDa depending on the commercial supplier and device selected. Previous studies have typically utilised columns with a 10kDa MWCO in combination with SEC, demonstrating enrichment in EV-like particles in cell culture medium, blood plasma and urine (67,72). A study by Vergauwen et al. concluded that regenerated cellulose Amicon filters with 10kDa MWCO recovered significantly more EVs, with less than 40% recovery reported for PES Vivaspin 10kDa and other membrane types (CTA and modified nylon) (67). However, in this study it was not possible to differentiate the effects of filter membrane from those of the MWCO, and no validation of defined EV markers was presented. A study directly comparing UF MWCO on EV recovery reported that a 100kDa pore size was more effective at recovering EVs than 10kDa cutoff (107). However, it should be noted that EV characterisation was minimal and based only on size (60–140 nm) and the expression of the CD9, which cannot be used as a specific marker for EVs and has been shown to be differentially expressed depending on EV source (116). To the best of our knowledge, just one other paper has applied a UF pre-concentration from C2C12 myoblast cells for SM-EV isolation (68). In this instance, Coenen-Stass et al. applied PES membranes (Vivaspin filters) with a MWCO of 10kDa, observing SM-EV recovery and TSG101 enrichment. However, fraction selection based non-specifically only on total protein presence (as measured by absorbance), where no clear separation between the EV and non-EV components could be observed. Our research looked specifically at the effects of UF pre-concentration on the recovery of SM-EVs, comparing 100kDa regenerated cellulose (Amicon) and PES (Vivaspin) filter membranes. We applied 100kDa columns in the present study to eliminate major proteins present in the FBS and cell culture media (largest common protein identified in FBS being complement C3 at 187,135 Da) while retaining the EV fraction (117). This results in further purification of the resulting EV fraction and ensures our protocol is amenable to therapeutic EV production as well as diagnostic applications. This also makes the protocol applicable for the purification of EVs in more complex biofluids such as blood where proteins such as albumin are highly abundant. This aligns with precious findings by La Shu et al. who demonstrated that filters with 100kDa MWCO, independent of the material of the filter, were able to recover distinct and high-quality melanoma EV-like populations enriched in CD63 and TSG101, and reduce soluble factor contamination (e.g. cytoplasmic contaminants and cytokines) (109,118). In conclusion, this portion of our data emphasises the need for the validation of EV isolation protocols that apply UF devices to ensure the translatability and reproducibility of findings when applied to the increasing number of SM-EV studies.

Outcomes from the present study have identified an optimal isolation protocol (Amicon, fractions 1-5) for the isolation of SM-EVs with enhanced purity and the absence of lipoprotein contaminants (ApoA1⁺ and ApoB⁺ positive particles). Size distributions of particles recovered using both UF filters aligned with those previously documented for small SM-EVs isolated by SEC (50-300 nm) [53], [55], [87] and other cell types, likely representing a mixture of small EVs comprising of exosomes and microvesicles [61], [63], [75], [76]. The elimination of lipoproteins through the reduction of the Amicon fraction window (1-5) (Figures 4 and 5) did not appear to compromise the presence of EV material. Although the total number of particles was reduced (from 8.42x10⁸ to 4.85x10⁸ particles/mL, p<0.05), sample purity as indicated by the PTP ratio increased (from 129 to 140, p>0.05) and lipoprotein contamination was practically eliminated (1.4%, Figures 2 and 5). Other authors have previously described how combining fewer fractions could increase overall sample purity, at the expense of EV yield (119). Although, sample purity in this example was based on the presence of total free protein and no specific assessment of lipoproteins or other contaminants was included. No obvious variations in the presence of tetraspanin positive EVs were encountered by reducing the fraction window to eliminate lipoprotein content, with changes in tetraspanin profiles resulting only from the type of UF column used (Figures 3, 4 and Supplementary figure 2). Of note, variations can also be observed in the profiles of tetraspanins depending on the sensitivity and specificity of the method of analysis used, with flow cytometry based methods only able to quantify externally expressed epitopes of transmembranal tetraspanin proteins included in the present study. Furthermore, the tetraspanin protein CD63 has no defined molecular weight (28-75 KDa) due to the fact that CD63 undergoes several post-translational modifications and we currently have a limited understanding of how these modifications impact the expression of CD63 on EVs (120). Lastly, in Figure 4 we provide a general overview of our study outcomes, with protocols for enhancing the purity (red stream) and total recovery (blue stream) of EV preparations depending on the objectives of a given research question. We also propose that alternative assessments of SM-EV purity are considered in addition to simple protein and particle measurements, with calculations of tetraspanin enrichment and lipoprotein inclusion providing a more informative indication of sample purity until additional SM-EV specific markers can be identified.

This study optimised a SEC + UF protocol for the high-throughput and efficient recovery of SM-EV preparations. Outcomes can be applied to generate samples of enhanced purity for the in-depth study of SM-EV composition. The resulting protocol provides users with an opportunity to identify additional SM-EV biomarkers that can be applied to monitor SM-EV activity and biodistribution in models of increasing complexity. The application of this optimised isolation method provides the user with the ability to isolate EVs from lipoproteins that could compromise downstream profiling and biofunctional outcomes and lead to an enhanced understanding of the roles of SM-EVs when combined with existing circulating SM-EV markers such as SGCA and the validate the presence of potential luminal miRNA markers (including miR-1, miR-133a, miR-133b and miR-206) that have been implicated as important regulators of tissue regeneration (27) (Figure 6).

AFC

ANOVA

αSGCA

ATCC

BCA

BSA

DMEM

DNA

DPBS

dUC

EDTA

EVs

FBS

FCM

IGF-1

mRNA

miRNA

MVB

MWCO

NTA

P/S

PAGE

PBS

PEG

PES

PTP

PVDF

RNA

SEC

TBS

TBST

TEM

v/v

w/w

Automatic Fraction Collector

Analysis of variance

Alpha-sarcoglycan

American Type Culture Collection

Bicinchoninic acid

Bovine serum albumin

Conditioned medium

Differentiation medium

Dulbecco's Modified Eagle Medium

Deoxyribose nucleic acid

Dulbecco's Phosphate buffered saline

Differential ultracentrifugation

Ethylenediaminetetraacetic

Extracellular vesicles

Foetal bovine serum

Flow cytometry

Growth medium

Horse serum

Insulin like growth factor 1

Lysis buffer

Messenger Ribose nucleic acid

Micro Ribose nucleic acid

Microvesicle

Multivesicular bodies

Molecular weight cut-off

Nanoparticle Tracking Analysis

Penicillin/streptomycin

Polyacrylamide gel electrophoresis

Phosphate buffered saline

Polyetilenglycol

Polyethersulfone

Particle to protein

Polyvinylidene fluoride

Ribose nucleic acid

Standard deviation

Size-exclusion chromatography

Skeletal muscle

Tris buffered saline

Tris buffered saline and Tween20

Transmission electron microscopy

Ultrafiltration

Volume by volume

Weight by weight

Western blot

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable

CONSENT FOR PUBLICATION

Not applicable

AVAILABILITY OF DATA AND MATERIALS

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

COMPETING INTERESTS

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

FUNDING

This work was supported by the Academy of Medical Sciences, Wellcome Trust, Government Department of Business, Energy and Industrial Strategy, British Heart Foundation, Diabetes UK (SBF004\1090) and the EPSRC/MRC Doctoral Training Centre in Regenerative Medicine.

AUTHOR CONTRIBUTIONS

Experimental work, data analysis and manuscript preparation were performed by MFR. BA collaborated in the data analysis. SW collaborated in experimental work. ARJ was implicated in manuscript editing. OGD contributed to study conception and manuscript editing. MPL contributed to study conception. The first draft of the manuscript was written by MFR and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

ACKNOWLEDGEMENTS

The authors would like to acknowledge Dr. Ben Peacock, Dr. Alice Law and Dr. Dimitri Aubert for the nFCM results acquisition and Dr. Julie Turner and Dr Zubair Ahmed Nizamudeen for his collaboration in the TEM images obtained and analysed.

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A high-throughput methodology for the efficient isolation of highly pure extracellular vesicles from skeletal muscle myoblasts

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Abstract

Figures

Background

Materials And Methods

CELL CULTURE

EV ISOLATION

NANOPARTICLE TRACKING ANALYSIS (NTA)

BICINCHONINIC ACID (BCA) PROTEIN ASSAY

WESTERN BLOT (WB)

NANO FLOW CYTOMETRY (nFCM)

ZETA POTENTIAL MEASUREMENTS

TRANSMISSION ELECTRON MICROSCOPY (TEM)

STATISTICAL ANALYSIS

Results

EV RECOVERY

MAXIMISING EV RECOVERY: ANALYSIS OF EV-ENRICHED FRACTIONS (2 TO 10) FOLLOWING UF PRE-CONCENTRATION

REFINING FRACTION WINDOW TO EXCLUDE LIPOPROTEINS

SELECTION OF PROTOCOL DEPENDING ON DOWNSTREAM APPLICATIONS

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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