Extracellular vesicles (EVs) are membrane vesicles released by all cells. EVs contain RNA and protein cargo from their cell of origin and are a promising class of biomarkers in biofluids such as plasma (Shah et al., 2018). Since EVs are much less abundant than free proteins and lipoproteins in plasma, enriching them without the co-isolation of these impurities remains highly challenging (Sódar et al., 2016; Smolarz et al., 2019). This is particularly the case for separating EVs and lipoproteins, as these two classes of particles have overlapping size ranges (Simonsen, 2017). Developing EV isolation methods is also particularly challenging due to the inability of most methods, such as the commonly used Nanoparticle Tracking Analysis (NTA) to differentiate between EVs and similarly sized lipoproteins (Johnsen et al., 2019). Thus, it is difficult to compare EV isolation methods without suitable techniques to quantify both EV yield and lipoprotein content (Hartjes et al., 2019).

We have previously developed a framework for comparing EV isolation methods by measuring three tetraspanin transmembrane proteins present on EVs (CD9, CD63, and CD81) and albumin (Ter-Ovanesyan et al., 2021). We used the tetraspanins as a way to compare EV yields across different isolation methods and albumin (the most abundant free protein in plasma) as a way to measure protein contamination. To measure these proteins, we used Single Molecule Array (Simoa) technology, a digital ELISA method that results in high sensitivity and a wide dynamic range (Rissin et al., 2010).

In this work, we developed a Simoa assay for ApoB-100, the major protein component of several lipoproteins. ApoB-100 is present on low-density lipoproteins (LDL), intermediate-density lipoproteins (IDL), and very low-density lipoproteins (VLDL) (German et al., 2006; Sniderman et al., 2019). By combining the new ApoB-100 assay with our previously developed CD9, CD63, CD81, and albumin assays, we could quantify EVs, lipoproteins, and free proteins for each sample on the same platform. We then used these assays to further improve EV isolation methods from plasma, enabling us to separate EVs from lipoproteins and free proteins at purity levels beyond those of previously described methods. We envision these methods to enable applications requiring high EV purity, such as EV proteomics for biomarker discovery from human biofluids.

To measure lipoproteins, we developed a Simoa assay against ApoB-100, the protein component of lipoproteins LDL, IDL, and VLDL. Simoa is a digital ELISA method where individual immuno-complexes are trapped on magnetic beads that are loaded into microwells that fit, at most, one bead per well. Counting the ‘on wells’ thus translates to counting individual protein molecules, providing much higher sensitivity than traditional ELISA (Rissin et al., 2010). First, we compared a variety of capture and detector antibodies using calibration curves of the purified ApoB-100 protein standard. We then chose the antibody pair with the highest signal-to-background ratio (Figure 1A) and validated this assay with dilution linearity experiments in three individual plasma samples (Figure 1B). We also performed spike and recovery experiments using a purified protein standard (Table 1). We then combined this Simoa assay for ApoB-100 with our previously developed CD9, CD63, CD81, and albumin assays (Ter-Ovanesyan et al., 2021; Norman et al., 2021) to simultaneously measure EVs, free proteins, and lipoproteins on the same platform.

Figure 1

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Validation of Simoa ApoB-100 assay.

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We first investigated whether EVs can be separated from ApoB-100-containing lipoproteins using existing techniques. We tested EV separation based on size using size exclusion chromatography (SEC) columns with three different resins (Sepharose CL-2B, CL-4B, and CL-6B). We collected 0.5 ml fractions after performing SEC and used Simoa to measure CD9, CD63, CD81, albumin, and ApoB-100 in each fraction (Figure 2A). As in our previous work (Ter-Ovanesyan et al., 2021), we calculated relative EV yields between different EV isolation methods (Figure 2B). To calculate EV yield, we first determined the ratio of each tetraspanin between conditions and then took the average of the three tetraspanin ratios. To then calculate EV purity, we calculated the ratio of EV yield relative to albumin or ApoB-100 levels. We found that although the ratio of EVs relative to ApoB-100 was higher in the resin with the largest pore size, Sepharose CL-2B, this was at the expense of EV yield relative to the other two resins (Figure 2C).

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Simoa data (protein concentrations) for fractions of SEC columns with different resins.

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Simoa data (protein concentrations) for SEC column with different number of washes.

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Because separating EVs from lipoproteins based on size alone was not fruitful, we attempted to separate EVs from lipoproteins based on other properties. We first investigated the separation of EVs from lipoproteins and albumin using density gradient ultracentrifugation (DGC). We loaded 1 ml of plasma on an iodixanol gradient and performed ultracentrifugation for 16 hr. We then collected 1 ml fractions and used Simoa to measure tetraspanins, albumin, and ApoB-100 in each fraction. We found that we could readily separate EVs from ApoB-100-containing lipoproteins (Figure 3) using DGC, although the EV yield was lower than in SEC (Figure 3—figure supplement 1A). As DGC is time-consuming and low throughput, we explored other EV isolation methods that would be more suitable for processing clinical samples.

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Simoa data (protein concentrations) for different density gradient centrifugation fractions.

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We next considered whether SEC could be modified to maximize EV yield while removing both free proteins and lipoproteins. First, we wanted to assess the absolute recovery of EVs by SEC using Sepharose CL-6B, the resin with the highest EV yield (Figure 2—figure supplement 1). We took advantage of Simoa’s wide dynamic range and specificity to measure tetraspanin levels in diluted plasma and compared these levels after EV purification from the same batch of plasma by SEC. We also evaluated how various other parameters affected EV recovery by SEC. We found that performing at least one 10 ml phosphate-buffered saline (PBS) wash in-column, instead of simply washing the resin in bulk before making the column, increased the EV yield significantly (Figure 2—figure supplement 1A). One potential reason for this result could be that in-column washes are more effective at removing the ethanol in which the resin is supplied. After performing an in-column wash, we were able to achieve >50% EV yield using SEC with Sepharose CL-6B (Figure 2—figure supplement 1B), as measured by comparing tetraspanin levels in SEC fractions 7–10 relative to diluted plasma.

We then decided to take advantage of the property that ApoB-100 is positively charged (Olsson et al., 1991), while EVs are generally negatively charged (Brownlee et al., 2014) to separate EVs from lipoproteins. It has previously been demonstrated that dual-mode chromatography (DMC) columns with a bottom layer of cation exchange resin below Sepharose CL-4B can be used to isolate EVs (Van Deun et al., 2020). Since Sepharose CL-6B yields more EVs than Sepharose CL-4B (Figure 2C), we constructed DMC columns with a 2-ml cation exchange resin bottom layer and a top layer of 10 ml Sepharose CL-6B. Inspired by the DMC approach of combining different resins in the same column, we also developed a new type of column, Tri-Mode mixed-mode Chromatography (TMC), where we further added Capto Core 700 to the cation exchange resin in the bottom layer. Capto Core 700 is a multimodal chromatography resin that contains porous beads with an inner core layer functionalized with octylamine groups that bind and trap proteins (Blom et al., 2014). Thus, we reasoned that having this resin in the bottom layer would ‘catch’ free proteins that co-isolate with EVs during SEC (Figure 4A). By changing the volumes and ratios of the two resins, the depletion of both albumin and ApoB-100 could be tuned to increase purity, although at the cost of EV yield (Figure 4—figure supplement 1). To balance EV yield and purity, we settled on a 2:1 ratio of solid cation exchange resin to multimodal resin in the 2 ml bottom layer.

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Simoa data (protein concentrations) for TMC columns with different ratios of resins in the bottom layer.

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Simoa data (protein concentrations) for different fractions of SEC and DMC columns.

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Figure 4—source data 3

Simoa data (protein concentrations) comparing SEC (fractions 7-10), DMC and TMC columns (fractions 9-12).

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We compared EV purification from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column. We found that, although lipoproteins were still present, DMC and TMC led to a higher purity of EVs relative to lipoproteins (Figure 4B). We then used Simoa to quantify the relative levels of EVs, lipoproteins, and free proteins using SEC, DMC, and TMC columns. We collected fractions 9–12 (instead of fractions 7–10 as for SEC) for DMC and TMC to account for the extra 2 ml of resin in the column (Figure 4—figure supplement 2). We found that DMC and TMC columns significantly depleted ApoB-100, but also led to some loss in EV yield, particularly CD9, compared to SEC columns (Figure 4—figure supplement 3). Calculating the relative yields of each tetraspanin and averaging the three tetraspanin ratios to calculate EV yield, we found that DMC and TMC columns had a lower EV yield than SEC but significantly higher EV/ApoB-100 ratios. Although DMC columns had higher ratios of EVs to ApoB-100 compared to SEC, the ratio of EVs to albumin remained the same. The TMC column, on the other hand, had a higher ratio of both EVs to ApoB-100 and EVs to albumin compared to the SEC column (Figure 4C–E).

To assess the utility of highly pure EVs isolated with TMC, we performed mass spectrometry-based proteomic analysis. Performing mass spectrometry on EVs from plasma is challenging because levels of both free proteins and lipoproteins are several orders of magnitude higher than those of EV proteins (Smolarz et al., 2019). Using TMC, we were able to detect 780 proteins from EVs isolated from only 1 ml of plasma (Supplementary file 1). These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume.

Single-use chromatography columns, whether SEC to maximize EV yield or TMC to maximize EV purity, represent an attractive platform for isolating EVs from clinical samples as they are inexpensive and take a short time to run (Monguió-Tortajada et al., 2019). The throughput of columns, however, is limited. Columns are usually run one at a time, and although it is possible to run more than one column at the same time, this becomes challenging if done manually. To increase the throughput and reproducibility of column-based EV isolation, we built a semi-automated stand for running eight columns in parallel. Using a syringe pump run by a Raspberry Pi, we were able to dispense liquid to all columns in parallel (Figure 5A, B). We tested the reproducibility of our device using Simoa and found high concordance between eight SEC columns run by the device and eight SEC columns run manually for EV isolation from plasma (Figure 5C). Although this device was built as a proof of principle to run eight columns in parallel, we envision building a similar device that could run many more columns simultaneously in the future.

Figure 5

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Simoa data (protein concentrations) comparing manual and automated SEC EV isolation (fractions 7-10).

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In this work, we developed methods to enrich EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays. First, we developed and validated a Simoa assay for ApoB-100. We then combined this assay with previously developed Simoa assays for the tetraspanins CD9, CD63, and CD81, as well as albumin (Ter-Ovanesyan et al., 2021; Norman et al., 2021). With these assays in place, we were able to quantify EVs, free proteins, and lipoproteins from the same sample on one experimental platform. Using this approach, we assessed different ways of separating EVs from lipoproteins with the aim of developing improved EV isolation methods.

Plasma contains several types of lipoproteins with varying protein and lipid compositions. Although there is not a single present on all lipoproteins, we chose to measure ApoB-100, as it is a protein component of several lipoproteins (such as LDL, IDL, and VLDL) that overlap in size with EVs (Simonsen, 2017; Johnsen et al., 2019). We evaluated the possibility that SEC using resins with three different pore sizes might separate EVs from ApoB-100-containing lipoproteins using our platform. We previously used our tetraspanin and albumin Simoa assays to directly compare EV yield and free protein contamination for different SEC resins (Ter-Ovanesyan et al., 2021). Here, we used a similar approach to compare EV yield and lipoprotein contamination by including the ApoB-100 assay and found that we were unable to effectively separate tetraspanins from ApoB-100 by SEC. We also used our Simoa assays to evaluate DGC and showed that this technique enables good separation of tetraspanins from ApoB-100 and albumin; however, since DGC requires an ultracentrifuge, is low throughput, and time-intensive, it is not suitable for clinical samples.

We used our assays to develop novel methods for separating EVs from lipoproteins. A previous study described DMC columns that deplete lipoproteins by combining SEC using Sepharose CL-4B with a second bottom layer of cation exchange resin (Van Deun et al., 2020). We modified DMC to include the higher yield Sepharose CL-6B resin and demonstrated depletion of most of the ApoB-100, although at the cost of some EV depletion. To improve the ratio of EVs to ApoB-100 and albumin, we developed a new method that combines a top layer of Sepharose CL-6B with a bottom layer of both cation exchange resin and a multimodal chromatography resin called Capto Core 700. These ‘Tri-Mode mixed-mode Chromatography’, or TMC columns, produced EV preparations of higher purity relative to SEC columns in terms of both their lower albumin and lipoprotein content. As the multimodal chromatography resin binds free proteins but not EVs, the TMC columns reduce the co-elution of free protein with EVs.

This work presents a framework for quantitatively comparing EV isolation methods. There is not a single optimal way to isolate EVs because the purification method must be matched to the application; therefore, it is crucial to have effective ways of comparing both the yield and purity of different isolation methods. We developed TMC columns for applications where EVs of very high purity are needed and optimized these columns for EV purification from plasma using our Simoa assays. We envision these columns will be particularly useful for EV biomarker discovery using proteomics, where EV contamination with lipoproteins and free proteins prevents deep coverage (Smolarz et al., 2019). A recent study using a three-step protocol (polyethylene glycol precipitation followed by iohexol gradient fractionation and SEC) to enrich EVs from 1 ml plasma reported the detection of 250 proteins (Zhang et al., 2020). Another study reported the detection of 1187 proteins from plasma using ultracentrifugation, density gradient, and then SEC; the starting volume in that study was 40–80 ml plasma (Karimi et al., 2018). Using TMC columns, we were able to measure the plasma EV proteome using an easy, single-step isolation protocol and detect 780 proteins using a 1-ml sample. By also building an automated device for running columns in parallel, we demonstrate a path toward using column-based methods for clinical samples. Future iterations of the device will further increase the sample throughput. Taken together, the methods we developed will contribute to the realization of EV profiling in molecular diagnostics.

All data generated or analyzed during this study are included in the manuscript and supporting files; mass spectrometry data Source Data files have been provided.

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Author details

1. Dmitry Ter-Ovanesyan

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Tal Gilboa

   Competing interests

   DT has filed IP on methods for EV isolation and analysis

   "This ORCID iD identifies the author of this article:" 0000-0002-1134-0073

2. Tal Gilboa

   1. Wyss Institute for Biologically Inspired Engineering, Boston, United States
   2. Department of Pathology, Brigham and Women’s Hospital, Boston, United States

   Contribution

   Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Dmitry Ter-Ovanesyan

   Competing interests

   TG has filed IP on methods for EV isolation and analysis

   "This ORCID iD identifies the author of this article:" 0000-0001-8172-7786

3. Bogdan Budnik

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   BB has filed IP on methods for EV isolation and analysis

4. Adele Nikitina

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Sara Whiteman

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Roey Lazarovits

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4635-9700

7. Wendy Trieu

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. David Kalish

   Wyss Institute for Biologically Inspired Engineering, Boston, United States

   Contribution

   Resources, Software, Methodology, Writing – review and editing

   Competing interests

   DK has filed IP on methods for EV isolation and analysis

9. George M Church

   1. Wyss Institute for Biologically Inspired Engineering, Boston, United States
   2. Harvard Medical School, Boston, United States

   Contribution

   Supervision, Funding acquisition, Project administration, Writing – review and editing

   Competing interests

   GMC has filed IP on methods for EV isolation and analysis

10. David R Walt

    1. Wyss Institute for Biologically Inspired Engineering, Boston, United States
    2. Department of Pathology, Brigham and Women’s Hospital, Boston, United States
    3. Harvard Medical School, Boston, United States

    Contribution

    Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    dwalt@bwh.harvard.edu

    Competing interests

    DRW has filed IP on methods for EV isolation and analysis

    "This ORCID iD identifies the author of this article:" 0000-0002-5524-7348

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