Skip to main content
PMC home page
eLife logoLink to eLife
. 2023 May 30;12:e86394. doi: 10.7554/eLife.86394

Improved isolation of extracellular vesicles by removal of both free proteins and lipoproteins

Dmitry Ter-Ovanesyan 1,†, Tal Gilboa 1,2,†, Bogdan Budnik 1, Adele Nikitina 1, Sara Whiteman 1, Roey Lazarovits 1, Wendy Trieu 1, David Kalish 1, George M Church 1,3, David R Walt 1,2,3,✉
Editors: Marcio L Rodrigues4, Suzanne R Pfeffer5
PMCID: PMC10229111  PMID: 37252755

Abstract

Extracellular vesicles (EVs) are released by all cells into biofluids such as plasma. The separation of EVs from highly abundant free proteins and similarly sized lipoproteins remains technically challenging. We developed a digital ELISA assay based on Single Molecule Array (Simoa) technology for ApoB-100, the protein component of several lipoproteins. Combining this ApoB-100 assay with previously developed Simoa assays for albumin and three tetraspanin proteins found on EVs (Ter-Ovanesyan, Norman et al., 2021), we were able to measure the separation of EVs from both lipoproteins and free proteins. We used these five assays to compare EV separation from lipoproteins using size exclusion chromatography with resins containing different pore sizes. We also developed improved methods for EV isolation based on combining several types of chromatography resins in the same column. We present a simple approach to quantitatively measure the main impurities of EV isolation in plasma and apply this approach to develop novel methods for enriching EVs from human plasma. These methods will enable applications where high-purity EVs are required to both understand EV biology and profile EVs for biomarker discovery.

Research organism: Human

Introduction

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.

Results

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. Validation of ApoB-100 Simoa assay.

Figure 1.

Open in a new tab

Simoa ApoB-100 assay was validated using: (A) Calibration curve using purified ApoB-100 protein. (B) Dilutions of human plasma (from three different individuals) to confirm dilution linearity of endogenous ApoB-100. Error bars represent the standard deviation from two technical replicates.

Figure 1—source data 1. Validation of Simoa ApoB-100 assay.

Table 1. Spike and recovery for ApoB-100 assay.

Percent recovery of different concentrations of purified ApoB-100 spike added to plasma and measured using the ApoB-100 Simoa assay.

Spike concentration(ng/ml) Average recovery (%)
Spike 1 5 94.7
Spike 2 10 87.2
Spike 3 50 90.8
Spike 4 500 90.6
Open in a new tab

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).

Figure 2. Size exclusion chromatography (SEC) of plasma using different resins.

(A) Levels of CD9, CD63, CD81, ApoB-100, and albumin were measured by Simoa after SEC of 1 ml plasma in each fraction using either Sepharose CL-2B, Sepharose CL-4B, or Sepharose CL-6B resin. (B) Extracellular vesicle (EV) yield is calculated in fractions 7–10 for Sepharose CL-2B, Sepharose CL-4B, or Sepharose CL-6B by averaging the ratios of CD9, CD63, and CD81. (C) Purity of EVs with respect to lipoproteins or free proteins is calculated by dividing relative EV yield (the average of the ratios of CD9, CD63, and CD81) by levels of ApoB-100 (top) or albumin (bottom). Error bars represent the standard deviation of four columns measured on different days with two technical replicates each.

Figure 2—source data 1. Simoa data (protein concentrations) for fractions of SEC columns with different resins.
Figure 2—source data 2. Simoa data (protein concentrations) for SEC column with different number of washes.

Figure 2.

Figure 2—figure supplement 1. In-column phosphate-buffered saline (PBS) washes improve extracellular vesicle (EV) recovery.

Figure 2—figure supplement 1.

(A) Levels of CD9, CD63, CD81, and albumin were measured by Simoa after EV isolation from 1 ml plasma with size exclusion chromatography (SEC) using 0, 1, 2, or 3 in-column 10 ml PBS washes. Error bars represent the standard deviation from two technical replicates. (B) Percent recovery of EVs using average of ratios of CD9, CD63, and CD81 in SEC isolation relative to plasma.
Open in a new tab

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.

Figure 3. Separation of extracellular vesicles (EVs), lipoproteins, and free proteins from plasma using density gradient centrifugation.

Levels of CD9, CD63, CD81, albumin, and ApoB-100 were measured by Simoa in individual 1 ml fractions (collected from the top) after density gradient centrifugation of plasma using an iodixanol gradient. Error bars represent the standard deviation of two replicates of each measurement.

Figure 3—source data 1. Simoa data (protein concentrations) for different density gradient centrifugation fractions.

Figure 3.

Figure 3—figure supplement 1. Comparison of density gradient centrifugation to size exclusion chromatography (SEC).

Figure 3—figure supplement 1.

Levels of CD9, CD63, CD81, ApoB-100 albumin were measured (in duplicate and then averaged) by Simoa to compare extracellular vesicle (EV) isolation from 1 ml plasma using density gradient (DG) centrifugation, SEC, or density gradient centrifugation followed by size exclusion chromatography (DG-SEC). For the DG and DG-SEC condition, fraction 10 was analyzed. Simoa measurements were used to quantify relative EV recovery (A), EV/ApoB-100 ratio (B), and EV/albumin ratio (C).
Open in a new tab

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.

Figure 4. Comparison of novel columns for extracellular vesicle (EV) isolation from plasma using electron microscopy and Simoa.

(A) Schematic of the columns being compared: size exclusion chromatography (SEC) column comprised of 10 ml Sepharose CL-6B, dual-mode chromatography (DMC) columns comprised of 10 ml Sepharose CL-6B SEC resin atop 2 ml Fractogel cation exchange resin, Tri-Mode Chromatography (TMC) columns comprised of 10 ml Sepharose CL-6B SEC resin atop 2 ml 2:1 ratio of 2 ml Fractogel cation exchange resin to Capto Core 700 multimodal chromatography resin. (B) Electron microscopy of EVs isolated from plasma using SEC (left), DMC (middle), or TMC (right) columns. EVs indicated with red arrows (among background of lipoproteins). (C) EV recovery is calculated for EV isolation from plasma for SEC (fractions 7–10), DMC (fractions 9–12), or TMC (fractions 9–12). Simoa measurements in the designated fractions for CD9, CD63, and CD81 are taken as a ratio relative to measurements of these proteins from diluted plasma and these three ratios are then averaged to calculate recovery. (D) Purity of EVs with respect to free proteins is determined by dividing relative EV yield (the average of the ratios of CD9, CD63, and CD81) by relative levels of albumin in each condition. (E) Purity of EVs with respect to lipoproteins is determined by dividing relative EV yield (the average of the ratios of CD9, CD63, and CD81) by relative levels of ApoB-100 in each condition. Error bars represent the standard deviation of four column measured on different days with two technical replicates each.

Figure 4—source data 1. Simoa data (protein concentrations) for TMC columns with different ratios of resins in the bottom layer.
Figure 4—source data 2. Simoa data (protein concentrations) for different fractions of SEC and DMC columns.
Figure 4—source data 3. Simoa data (protein concentrations) comparing SEC (fractions 7-10), DMC and TMC columns (fractions 9-12).

Figure 4.

Figure 4—figure supplement 1. Comparison of resin volumes and ratios for Tri-Mode Chromatography (TMC) column.

Figure 4—figure supplement 1.

Levels of CD9, CD63, CD81, ApoB-100, and albumin were measured (in duplicate and then averaged) by Simoa in extracellular vesicle (EV) samples isolated from 1 ml plasma to compare TMC columns with different volumes and ratios of Fractogel cation exchange resin to Capto Core 700 resin. All conditions describe the bottom layer under a 10 ml Sepharose CL-6B top layer. The 1, 2, or 4 ml volume of the bottom later indicates the volume of the solid resin mixture of Fractogel cation exchange resin and Capto Core 700 resin. The following fractions were collected for each: 8–11 for 1 ml bottom layer, 9–12 for 2 ml bottom layer, and 11–14 for 4 ml bottom layer.

Figure 4—figure supplement 2. Analysis of markers in individual fractions of size exclusion chromatography (SEC) and dual-mode chromatography (DMC).

Figure 4—figure supplement 2.

Levels of CD9, CD63, CD81, ApoB-100, and albumin were measured by Simoa in fractions 7–10 for SEC with 10 ml Sepharose CL-6B column and fractions 7–14 for DMC using a column with 2 ml Fractogel cation exchange bottom layer and 10 ml Sepharose CL-6B top layer. Error bars represent the standard deviation from two technical replicates.

Figure 4—figure supplement 3. Comparison of marker levels in size exclusion chromatography (SEC), dual-mode chromatography (DMC), and Tri-Mode Chromatography (TMC).

Figure 4—figure supplement 3.

Levels of CD9, CD63, CD81, ApoB-100, and albumin were measured by Simoa in extracellular vesicle (EV) samples isolated from 1 ml plasma using SEC (fractions 7–10), DMC (fractions 9–12), or TMC columns (fractions 9–12). Error bars represent the standard deviation of four column measured on different days with two technical replicates each.
Open in a new tab

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. Development and validation of automated device for running size exclusion chromatography (SEC) columns in parallel.

Figure 5.

Open in a new tab

(A) CAD image of semi-automated SEC stand designed to hold eight columns at once with sliding collection tube holder that allows liquid to drip either into 2 ml collection tubes, or to waste. (B) Photograph of stand connected to a Tecan Cavro syringe pump controlled by a Raspberry Pi. (C) Simoa comparison of CD9, CD63, CD81, ApoB-100, and albumin when SEC was performed on 16 samples of 1 ml plasma using either manual SEC (8 samples) or SEC on the automated device (8 samples). Each point is the average of two Simoa measurements (technical replicates).

Figure 5—source data 1. Simoa data (protein concentrations) comparing manual and automated SEC EV isolation (fractions 7-10).

Discussion

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.

Methods

Key resources table.

Reagent type (species) or resource Designation Source or reference Identifiers Additional information
Biological
sample (human)		 Plasma BioIVT Cat#
HUMANPLK2PNN		 Pooled gender, K2EDTA
Antibody anti-CD9 (rabbit monoclonal) Abcam Cat# ab263024 Simoa capture (0.031 μg per assay)
Antibody anti-CD9 (mouse monoclonal) Abcam Cat# ab58989 Simoa detector (0.06 μg per assay)
Antibody anti-CD63 (mouse monoclonal) R&D Systems Cat# MAB5048 Simoa capture (0.031 μg per assay)
Antibody anti-CD63 (mouse monoclonal) BD Cat# 556019
RRID: AB_396297 		Simoa detector (0.0435 μg per assay)
Antibody anti-CD81 (mouse monoclonal) Abcam Cat# ab79559 Simoa capture (0.031 μg per assay)
Antibody anti-CD81 (mouse monoclonal) BioLegend Cat# 349502
RRID: AB_10643417 		Simoa detector (0.0435 μg per assay)
Antibody Human Serum Albumin
DuoSet ELISA		 R&D Systems Cat# DY1455 Simoa capture (0.031 μg per assay) and detector
(0.002 μg per assay)
Antibody anti-ApoB (mouse
monoclonal)		 R&D Systems Cat# mab4124
RRID:AB_2057095 		Simoa capture (0.031 μg per assay)
Antibody anti-ApoB (mouse
monoclonal)		 R&D Systems Cat# mab41242 Simoa detector (0.08 μg per assay)
Peptide,
recombinant protein		 CD9 Abcam Cat# ab152262
Peptide,
recombinant protein		 CD63 Origene Cat# TP301733
Peptide, recombinant protein CD81 Origene Cat# TP317508
Peptide, recombinant protein Albumin Abcam Cat# ab201876
Other Purified ApoB-100 Standard Origene Cat# BA1030 Protein standard for Simoa
Other Sepharose CL-2B Cytiva Cat# 17014001 Resin for SEC
Other Sepharose CL-4B Cytiva Cat# 17015001 Resin for SEC
Other Sepharose CL-6B Cytiva Cat# 17016001 Resin for SEC
Other Fractogel EMD SO3- (M) MilliporeSigma Cat# 1168820100 Resin for DMC/TMC
Other Capto Core 700 multimodal chromatography resin Cytiva Cat# 17548102 Resin for TMC
Other Econo-Pac Chromatography Columns Bio-Rad Cat # 7321010 Empty columns
Open in a new tab

Human samples

Pooled human plasma (collected in K2 EDTA tubes) was ordered from BioIVT. Plasma was thawed at room temperature and centrifuged at 2000 × g for 10 min. The supernatant was filtered through a 0.45-μm Corning Costar Spin-X centrifuge tube (MilliporeSigma) at 2000 × g for 10 min. For all direct comparison experiments, plasma was first pooled and 1 ml used per EV isolation.

Simoa assays

Simoa assays for CD63, CD81, and albumin were performed as previously described (Ter-Ovanesyan et al., 2021; Norman et al., 2021). Due to antibody availability, CD9 ab263024 (Abcam) was used as a capture antibody instead of ab195422 (Abcam). For ApoB-100, mab4124 (R&D Systems) was used as the capture antibody, mab41242 (R&D Systems) was used as the detector antibody, and purified ApoB-100 BA1030 (Origene) was used as a standard. For SEC, onboard dilution was performed with 4× dilution for each of the assays, with an additional 4× off-board dilution for CD9 and 10× off-board dilution for ApoB-100. For measuring protein levels in total plasma, each protein was measured with 4× onboard dilution and three additional off-board dilutions: for CD9 – 40×, 80×, and 160×; for CD63 and CD81 – 3×, 9×, and 27×; for albumin – 100×, 3000×, and 9000×; and for ApoB-100 – 100×, 300×, and 900× dilution. All samples were measured in duplicate using the HD-X analyzer (Quanterix). Tetraspanins were measured with a two-step assay, while albumin and ApoB-100 were measured with a three-step assay. Average Enzyme per Bead (AEB) values were calculated by the HD-X software.

Calculation of EV yield and purity

EV yield was calculated for EV isolation from plasma for SEC (fractions 7–10), DMC (fractions 9–12), or TMC (fractions 9–12). Levels of the three tetraspanins CD9, CD63, and CD81 in the designated EV fractions and their levels in total plasma were measured using Simoa. The yield of each tetraspanin was calculated by dividing its level in the EV fraction by its level in total plasma. The total EV yield was then calculated as the average of the three ratios. Relative EV yield for comparing multiple conditions was calculated by dividing the EV yield of each condition by the highest EV yield of all the conditions. The purity of EVs with respect to free proteins or lipoproteins was determined by dividing the relative EV yield by relative levels of albumin or ApoB-100 in the EV fractions.

Validation of ApoB-100 Simoa assay

Antibodies were first cross-tested using serial dilutions of purified protein standard. The antibody pair with the highest signal-to-background ratio was chosen. The assay was validated using dilution linearity and spike and recovery experiments. Plasma samples were diluted serially in the assay-specific buffer, a dilution factor in the middle of the linear range was chosen to be the dilution factor for the spike and recovery test. Three protein concentrations of purified ApoB-100 were spiked into the diluted plasma from the top calibrator used in the calibration curve. All recoveries fell in the range of 85–100% (Table 1). The assay validation was conducted using commercially available plasma samples (BioIVT).

Preparation of columns

Sepharose CL-2B, Sepharose CL-4B, and Sepharose CL-6B resins (Cytiva) were washed with PBS in a glass bottle. The volume of resin was washed three times with an equal volume of PBS before use. Econo-Pac Chromatography columns (Bio-Rad) were packed with resin and a frit was inserted into the column above the resin. For all columns in Figures 4 and 5, each column was washed with 10 ml PBS (twice 5 ml at a time) prior to loading of sample. For SEC columns, resin was added until the bed volume (resin without liquid) reached 10 ml. For DMC columns, Fractogel EMD SO3- (M) (MilliporeSigma) was added as a bottom layer with 2 ml bed volume, and 10 ml of Sepharose CL-6B bed volume was added as a top layer. For TMC columns, a 2:1 by volume (of dry resin) mixture was prepared of Fractogel EMD SO3- (M) (MilliporeSigma) and Capto Core 700 (Cytiva) and 2 ml bed volume bottom layer was added to the column before 10 ml of Sepharose CL-6B bed volume was added as a top layer.

Collection of column fractions

Sample (1 ml plasma) was loaded once PBS from wash had finished going through the column. Once the sample fully entered the column, 0.5 ml fractions were collected. PBS was then added in volumes equal to those being collected for one fraction (0.5 ml) or a set of four fractions (2 ml). In experiments where just the EV fractions were collected, fractions 7–10 were collected for SEC and fractions 9–12 were collected for DMC and TMC.

Density gradient centrifugation

DGC was performed as previously described (Norman et al., 2021). Four layers of OptiPrep (iodixanol) were prepared and stacked in a 13.2-ml polypropylene tubes (Beckman Coulter) from bottom to top: 3 ml 40%, 3 ml 20%, 3 ml 10%, and 2 ml 5%. OptiPrep (MilliporeSigma) was diluted in a solution of 0.25 M sucrose (MilliporeSigma) and pH 7.4 Tris–EDTA (MilliporeSigma). Sample (1 ml plasma) was loaded on top of the gradient and centrifuged at 100,000 RCF in an SW 41 Ti swinging bucket rotor for 18 hr at 4°C using a Beckman Coulter Optima XPN-80 ultracentrifuge. After centrifugation, fractions were removed from the top 1 ml at a time. For the DGC, SEC, and DGC–SEC comparisons, fraction 10 was analyzed (directly for the DGC condition, or then run through an SEC column for DGC–SEC condition).

Negative staining and transmission electron microscopy (TEM) imaging

Carbon-coated grids (CF-400CU, Electron Microscopy Sciences) were glow discharged, and 5 μl of the sample was absorbed to the grid for 1 min. Excess sample was blotted with a Whatman paper. The grid was then stained with 5 μl 1% uranyl acetate for 15 s and excess stain was blotted. Samples were imaged on a JEOL 1200EX – 80 kV transmission electron microscope with an AMT 2k CCD camera.

Mass spectrometry

EVs were isolated from 1 ml plasma using TMC columns with a 2-ml bed volume bottom layer of 2:1 of Fractogel EMD SO3- (M) (MilliporeSigma) to Capto Core 700 (Cytiva) and 10-ml bed volume top layer of Sepharose CL-6B (Cytiva). EVs were concentrated using Amicon Ultra-2 centrifugal 10 kD filter (MilliporeSigma). After concentration, EV protein was precipitated by adding 9 volumes of 100% ethanol to 1 volume of EVs, vortexing and leaving at −20°C for 30 min. Sample was then centrifuged at 16,000 × g for 15 min at 4°C. Supernatant was removed and pellet was left to air dry for 10 min. Sample was then sent to Bruker for proteomics analysis. Sample was resuspended in 50 mM triethylammonium bicarbonate (Thermo Fisher Scientific) and digested for 2 hr at 50°C using Trypsin Platinum (Promega) using 1:50 Trypsin to sample ratio by mass. After evaporating solution in a Vacufuge (Eppendorf) ar 45°C, sample was resolubilized in 10 μl 0.1% formic acid (Thermo Fisher Scientific). Next, 1.5 μl of sample was injected into C18 tips (Evosep) and eluted into a 25-cm length 150 μm internal diameter PepSep analytical column packed with 1.5 μm C18 beads (Dr. Maisch). Sample was eluted into a Bruker timsTOF HT. A gradient from 3% of to 28% of 0.1% formic acid in acetonitrile at 63 min was then increased to 85% until 80 min. Data were analyzed using Spectronaut 17 (Biognosys) software for data-independent acquisition (DIA). A false discovery rate of 1% was used at both the peptide and protein levels. Keratin proteins (likely contaminants) were manually removed from the list.

SEC stand and automated device

Experiments in Figure 5 were performed using the automated SEC stand connected to a Cavro XLP 6000 syringe pump (Tecan) controlled by a Raspberry Pi 4 model B. CAD files and instructions for assembly are provided in the Supplementary Information. Code for Raspberry Pi is deposited at: https://github.com/Wyss/automated-chromatography/, (Kalish and Tat, 2023). All other SEC experiments were performed using custom SEC stand described previously (Ter-Ovanesyan et al., 2021).

Acknowledgements

The authors thank John Wilson for suggestions regarding the TMC column, Allen Tat for help with programming the software for the Raspberry Pi, Jan Van Deun for helpful discussions, and Matt Willets, Diego Assis, and Elizabeth Gordon at Bruker for help with mass spectrometry. This work was funded by Chan Zuckerberg Initiative, Good Ventures, and the Wyss Institute. Tal Gilboa is an awardee of the Weizmann Institute of Science Women’s Postdoctoral Career Development Award.

Funding Statement

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Contributor Information

David R Walt, Email: dwalt@bwh.harvard.edu.

Marcio L Rodrigues, Instituto Carlos Chagas - Fiocruz PR, Brazil.

Suzanne R Pfeffer, Stanford University, United States.

Funding Information

This paper was supported by the following grants:

  • Chan Zuckerberg Initiative NeuroDegeneration Challenge Network Grant 2018-191864 to Dmitry Ter-Ovanesyan.

  • Good Ventures Foundation to Dmitry Ter-Ovanesyan.

  • Wyss Institute to Dmitry Ter-Ovanesyan.

  • Weizmann Institute of Science Women’s Postdoctoral Career Development Award to Tal Gilboa.

Additional information

Competing interests

DT has filed IP on methods for EV isolation and analysis.

TG has filed IP on methods for EV isolation and analysis.

BB has filed IP on methods for EV isolation and analysis.

No competing interests declared.

No competing interests declared.

DK has filed IP on methods for EV isolation and analysis.

GMC has filed IP on methods for EV isolation and analysis.

DRW has filed IP on methods for EV isolation and analysis.

Author contributions

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

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

Investigation, Methodology, Writing – review and editing.

Investigation, Methodology, Writing – review and editing.

Investigation, Methodology, Writing – review and editing.

Investigation, Methodology, Writing – review and editing.

Investigation, Methodology, Writing – review and editing.

Resources, Software, Methodology, Writing – review and editing.

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

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

Additional files

Supplementary file 1. Protein list identified by mass spectrometry of extracellular vesicles (EVs) isolated from plasma using Tri-Mode Chromatography (TMC).
elife-86394-supp1.xlsx (71.1KB, xlsx)
MDAR checklist

Data availability

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.

References

  1. Blom H, Åkerblom A, Kon T, Shaker S, van der Pol L, Lundgren M. Efficient chromatographic reduction of Ovalbumin for egg-based influenza virus purification. Vaccine. 2014;32:3721–3724. doi: 10.1016/j.vaccine.2014.04.033. [DOI] [PubMed] [Google Scholar]
  2. Brownlee Z, Lynn KD, Thorpe PE, Schroit AJ. A novel "salting-out" procedure for the isolation of tumor-derived Exosomes. Journal of Immunological Methods. 2014;407:120–126. doi: 10.1016/j.jim.2014.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. German JB, Smilowitz JT, Zivkovic AM. Lipoproteins: When size really matters. Current Opinion in Colloid & Interface Science. 2006;11:171–183. doi: 10.1016/j.cocis.2005.11.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Hartjes TA, Mytnyk S, Jenster GW, van Steijn V, van Royen ME. Extracellular Vesicle Quantification and characterization: Common methods and emerging approaches. Bioengineering. 2019;6:7. doi: 10.3390/bioengineering6010007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Johnsen KB, Gudbergsson JM, Andresen TL, Simonsen JB. What is the blood concentration of extracellular Vesicles? implications for the use of extracellular Vesicles as blood-borne biomarkers of cancer. Biochimica et Biophysica Acta. Reviews on Cancer. 2019;1871:109–116. doi: 10.1016/j.bbcan.2018.11.006. [DOI] [PubMed] [Google Scholar]
  6. Kalish D, Tat A. Automated chromatography. c84e5a3Github. 2023 https://github.com/Wyss/automated-chromatography/
  7. Karimi N, Cvjetkovic A, Jang SC, Crescitelli R, Hosseinpour Feizi MA, Nieuwland R, Lötvall J, Lässer C. Detailed analysis of the plasma extracellular Vesicle Proteome after separation from lipoproteins. Cellular and Molecular Life Sciences. 2018;75:2873–2886. doi: 10.1007/s00018-018-2773-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Monguió-Tortajada M, Gálvez-Montón C, Bayes-Genis A, Roura S, Borràs FE. Extracellular Vesicle isolation methods: Rising impact of size-exclusion chromatography. Cellular and Molecular Life Sciences. 2019;76:2369–2382. doi: 10.1007/s00018-019-03071-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Norman M, Ter-Ovanesyan D, Trieu W, Lazarovits R, Kowal EJK, Lee JH, Chen-Plotkin AS, Regev A, Church GM, Walt DR. L1Cam is not associated with extracellular Vesicles in human cerebrospinal fluid or plasma. Nature Methods. 2021;18:631–634. doi: 10.1038/s41592-021-01174-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Olsson U, Camejo G, Olofsson SO, Bondjers G. Molecular parameters that control the Association of low density lipoprotein Apo B-100 with chondroitin sulphate. Biochimica et Biophysica Acta. 1991;1097:37–44. doi: 10.1016/0925-4439(91)90021-z. [DOI] [PubMed] [Google Scholar]
  11. Rissin DM, Kan CW, Campbell TG, Howes SC, Fournier DR, Song L, Piech T, Patel PP, Chang L, Rivnak AJ, Ferrell EP, Randall JD, Provuncher GK, Walt DR, Duffy DC. Single-molecule enzyme-linked immunosorbent assay Detects serum proteins at Subfemtomolar concentrations. Nature Biotechnology. 2010;28:595–599. doi: 10.1038/nbt.1641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Shah R, Patel T, Freedman JE. Circulating extracellular Vesicles in human disease. The New England Journal of Medicine. 2018;379:2180–2181. doi: 10.1056/NEJMc1813170. [DOI] [PubMed] [Google Scholar]
  13. Simonsen JB. What are we looking at? extracellular Vesicles, lipoproteins, or both. Circulation Research. 2017;121:920–922. doi: 10.1161/CIRCRESAHA.117.311767. [DOI] [PubMed] [Google Scholar]
  14. Smolarz M, Pietrowska M, Matysiak N, Mielańczyk Ł, Widłak P. Proteome profiling of Exosomes purified from a small amount of human serum: The problem of Co-purified serum components. Proteomes. 2019;7:18. doi: 10.3390/proteomes7020018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Sniderman AD, Thanassoulis G, Glavinovic T, Navar AM, Pencina M, Catapano A, Ference BA. Apolipoprotein B particles and cardiovascular disease: A narrative review. JAMA Cardiology. 2019;4:1287–1295. doi: 10.1001/jamacardio.2019.3780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Sódar BW, Kittel Á, Pálóczi K, Vukman KV, Osteikoetxea X, Szabó-Taylor K, Németh A, Sperlágh B, Baranyai T, Giricz Z, Wiener Z, Turiák L, Drahos L, Pállinger É, Vékey K, Ferdinandy P, Falus A, Buzás EI. Low-density lipoprotein mimics blood plasma-derived Exosomes and Microvesicles during isolation and detection. Scientific Reports. 2016;6:24316. doi: 10.1038/srep24316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ter-Ovanesyan D, Norman M, Lazarovits R, Trieu W, Lee J-H, Church GM, Walt DR. Framework for rapid comparison of extracellular Vesicle isolation methods. eLife. 2021;10:e70725. doi: 10.7554/eLife.70725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Van Deun J, Jo A, Li H, Lin H-Y, Weissleder R, Im H, Lee H. Integrated dual-mode chromatography to enrich extracellular Vesicles from plasma. Advanced Biosystems. 2020;4:e1900310. doi: 10.1002/adbi.201900310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Zhang X, Borg EGF, Liaci AM, Vos HR, Stoorvogel W. A novel three step protocol to isolate extracellular Vesicles from plasma or cell culture medium with both high yield and purity. Journal of Extracellular Vesicles. 2020;9:1791450. doi: 10.1080/20013078.2020.1791450. [DOI] [PMC free article] [PubMed] [Google Scholar]

Editor's evaluation

Marcio L Rodrigues 1

This study presents a valuable contribution to how we isolate and analyze EVs. The proposed approaches are supported by solid experimental evidence. This work will be of interest to cell biologists working not only with mammalian EVs but also microbial, parasitic, and plant vesicles.

Decision letter

Editor: Marcio L Rodrigues1
Reviewed by: James Byrd2, Qing Zhou3

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Improved Isolation of Extracellular Vesicles by Removal of Both Free Proteins and Lipoproteins" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Suzanne Pfeffer as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: James Byrd (Reviewer #2); Qing Zhou (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Please observe that, due to the methodological nature of your study, it is extremely important to make sure that you consider the experimental suggestions provided by reviewers 1-3;

2) We expect this manuscript to be of use to a wide audience of biologists since the EV community is growing rapidly. Please also consider the suggestions for improving the manuscript's presentation and discussion depth.

Reviewer #1 (Recommendations for the authors):

The paper can be very useful, but can be improved by addressing a few comments:

1. 'We then decided to take advantage of the property that ApoB-100 is positively charged (12), while EVs are generally negatively charged (13) to separate EVs from lipoproteins.'

LDL particles, as particles, are negatively charged. See, for example:

• La Belle M, Blanche PJ, Krauss RM. Charge properties of low-density lipoprotein subclasses. J Lipid Res. 1997 Apr;38(4):690-700. PMID: 9144084.

• Reynolds L, Mulik RS, Wen X, Dilip A, Corbin IR. Low-density lipoprotein-mediated delivery of docosahexaenoic acid selectively kills murine liver cancer cells. Nanomedicine (Lond). 2014 Jul;9(14):2123-41. doi: 10.2217/nnm.13.187. Epub 2014 Jan 7. PMID: 24397600; PMCID: PMC4156561.

The paper cited shows the charge of certain ApoB100 peptides!

LDL, VLDL, etc., contain many other components, not just ApoB100.

2. 'We compared EV isolation from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column and found that TMC led to EVs of the highest purity (Figure 4B).'

The majority of the particles present on TEM images are lipoproteins, more likely VLDL or Chylomicrons. One can notice more dense round shaped particles, different from 'doughnut-shaped' EVs. For example, Figure 4B TMC column TEM image has a single EV, while eleven lipoprotein particles are visible.

3. 'Using TMC, we were able to detect 780 proteins from EVs isolated from only 1 ml of plasma (Supplementary table 1). These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume.'

According to the dataset provided in the supplementary materials, ApoB100 remains the most abundant protein, as well as other lipoproteins, including the ones specific for High-density lipoproteins. Also, a lot of free proteins were identified. In my experience, 780 proteins after albumin depletion are not as high for raw plasma proteomics analysis.

4. 'In this work, we developed methods to separate EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays.'

This statement is not confirmed by proteomics analysis and TEM images. Authors can use the more accurate term 'enriched EVs' compared to lipoproteins. Also, depletion of chylomicrons and HDL were not shown.

Reviewer #2 (Recommendations for the authors):

I have a few suggestions for the authors' consideration:

It may be more accurate to describe the free proteins and lipoproteins as impurities (i.e., present at the outset, but not intended to be present in the final preparation), rather than contaminants (i.e., introduced inadvertently)

The introduction could be more laser-focused on plasma since that's the focus of the work, and the lipoprotein issue may be greatly magnified compared to in e..g, urine

A few more words about the principle of the Simoa assay could be useful to the reader

"These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume." Some comparison to the prior state of the art would bolster this conclusion.

Beyond the relative yields, can synthetic EV spike-ins be used to calculate the absolute yields?

Figure legend for Figure 2: The EV yield is calculated "by averaging the ratios of CD9, CD63, and CD81." I think this be explained in clearer terms. This is the average of what is taken in ratio to what? This comment applies to a variety of other places in the paper where ratios are mentioned but without precise definitions of the numerator and denominator.

"Simoa measurements in the designated fractions for CD9, CD63, and CD81 are taken as a ratio relative to measurements of these proteins from diluted plasma and these three rations are then averaged to calculate recovery." Here again, I think this can be explained with a clearer description of what is in the numerator and denominator of these ratios (also a typo, "rations")

It would be helpful if the authors wrote down the calculation they're referring to here: "by averaging the ratios of each of the tetraspanin levels between conditions" The numerator and denominator in the ratios are not as clear as they ideally could be.

A challenge is that after the removal of the main known impurities, whether additional proteins identified in proteomics experiments are in the vesicles or free proteins is not simple to distinguish. How does the relative quantitation of proteins after the application of the new column compare with plasma proteomics performed without enrichment for extracellular vesicles? Is the rank order of abundance similar?

The authors have shared all the relevant data, as well as additional resources that will be useful to the scientific community.

Reviewer #3 (Recommendations for the authors):

1) The "relative EV yields" was used to quantify the amount of EVs across different methods. The authors described it as "the average of the ratios of CD9, CD63, and CD8". Please describe how this is calculated exactly. The same for relative EV/ Albumin and relative EV/ApoB-100.

2) Please also calculate the relative EV yields, relative EV/ Albumin, and relative EV/ApoB-100 for the density gradient centrifugation experiment. Also, Figure 3 is a bit hard to follow due to the distance between the bars.

3) For Figure 5, it will be great to draw lines between 2 replicas to provide a sample-to-sample comparison.

4) Different fractions were selected for SEC (fractions 7-10), DMC (fractions 9-12), and TMC (fractions 9-12). Please provide the reason and the potential influence of the fairness of the comparison.

5) Figure 5—figure supplement 2, the decrease of EV yield cross SEC, DMC, and TMC is different for CD9, CD63, and CD8 (increasing in DMC). What is the reason?

6) The mechanism for the better performance of TMC needs to be discussed.

eLife. 2023 May 30;12:e86394. doi: 10.7554/eLife.86394.sa2

Author response

Essential revisions:

1) Please observe that, due to the methodological nature of your study, it is extremely important to make sure that you consider the experimental suggestions provided by reviewers 1-3;

2) We expect this manuscript to be of use to a wide audience of biologists since the EV community is growing rapidly. Please also consider the suggestions for improving the manuscript's presentation and discussion depth.

Thank you to the reviewers for their suggestions. We address the specific comments below.

Reviewer #1 (Recommendations for the authors):

The paper can be very useful, but can be improved by addressing a few comments:

1. 'We then decided to take advantage of the property that ApoB-100 is positively charged (12), while EVs are generally negatively charged (13) to separate EVs from lipoproteins.'

LDL particles, as particles, are negatively charged. See, for example:

• La Belle M, Blanche PJ, Krauss RM. Charge properties of low-density lipoprotein subclasses. J Lipid Res. 1997 Apr;38(4):690-700. PMID: 9144084.

• Reynolds L, Mulik RS, Wen X, Dilip A, Corbin IR. Low-density lipoprotein-mediated delivery of docosahexaenoic acid selectively kills murine liver cancer cells. Nanomedicine (Lond). 2014 Jul;9(14):2123-41. doi: 10.2217/nnm.13.187. Epub 2014 Jan 7. PMID: 24397600; PMCID: PMC4156561.

The paper cited shows the charge of certain ApoB100 peptides!

LDL, VLDL, etc., contain many other components, not just ApoB100.

It is certainly true that ApoB-100 is not the only protein component of lipoproteins. We initially chose ApoB-100 as it was the main protein we detected by mass spectrometry after isolating EVs from plasma using SEC. This makes sense, as ApoB-100 is present in several lipoproteins that overlap in size with EVs (1). Regarding the question of whether ApoB-100 can be depleted based on cation exchange, it was not clear that this would work just based on the knowledge that there are positively charged peptides in ApoB-100, but we were inspired by previous work reporting the development of the dual mode chromatography columns (2). We were able to confirm the ability of cation exchange resin to deplete lipoproteins containing ApoB-100 with our ApoB-100 Simoa assay.

2. 'We compared EV isolation from plasma using SEC, DMC, and TMC columns. We first used electron microscopy to image EVs from each column and found that TMC led to EVs of the highest purity (Figure 4B).'

The majority of the particles present on TEM images are lipoproteins, more likely VLDL or Chylomicrons. One can notice more dense round shaped particles, different from 'doughnut-shaped' EVs. For example, Figure 4B TMC column TEM image has a single EV, while eleven lipoprotein particles are visible.

We agree that the majority of particles in by TEM are lipoproteins and not EVs. However, the TEM demonstrates that there is an increase in purity of EVs relative to lipoproteins for TMC/DMC compared to SEC using a technique other than Simoa. Despite the significant reduction of ApoB-100 in TMC, there is still ApoB-100 present. The ApoB-100 can be further reduced by increasing the amount of cation-exchange resin in the TMC column. We provide additional data in Figure 4-supplemental figure 1 showing how varying the volumes of the Fractogel cation exchange resin and the Capto Core 700 resin changes the levels of tetraspanins, albumin, and ApoB-100. Increasing the amount of cation-exchange resin reduces ApoB-100, but also leads to a decrease in tetraspanins.

3. 'Using TMC, we were able to detect 780 proteins from EVs isolated from only 1 ml of plasma (Supplementary table 1). These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume.'

According to the dataset provided in the supplementary materials, ApoB100 remains the most abundant protein, as well as other lipoproteins, including the ones specific for High-density lipoproteins. Also, a lot of free proteins were identified. In my experience, 780 proteins after albumin depletion are not as high for raw plasma proteomics analysis.

Both the electron microscopy (discussed above) and mass spectrometry analysis show that ApoB-100 is still present after TMC. We think it is likely impossible to fully separate EVs from lipoproteins and free proteins, but by increasing the ratio of EVs to lipoproteins and free proteins, as we have done with the TMC column, the depth of coverage for proteomics can be greatly increased. Our focus here was to provide a framework for quantitatively comparing EV isolation method yield and purity (with respect to both lipoproteins and free proteins); mass spectrometry is one application where having high purity EVs is important. The optimal EV isolation method will vary for different applications and will be highly dependent on the amount of starting material necessary for the technique being used. In mass spectrometry, different instruments have different requirements for the amount of input protein material necessary, so the optimal isolation technique will be instrument-dependent. We provide additional data in Figure 4-supplemental figure 1 (discussed above) showing how increasing the amount of cation-exchange resin allows for further depletion of ApoB-100. We did not, however, test all the different TMC variations using mass spectrometry. We are currently working on optimizing mass spectrometry for EVs from plasma, but this work is beyond the scope of this paper, as it also depends on many parameters downstream of EV isolation. Nonetheless, 780 proteins is high for a single-step technique from a starting volume of only 1 mL. For comparison, one study used a three step protocol (PEG precipitation followed by iohexol gradient fractionation and SEC) to enrich EVs from 1 mL plasma and reported detecting 250 proteins (3). Another group reported detecting 1187 proteins from plasma using ultracentrifugation, density gradient, and then SEC, but started with 40-80 mL plasma (4). We’ve added this information to the Discussion section.

4. 'In this work, we developed methods to separate EVs from both lipoproteins and free proteins in plasma based on our ability to measure proteins associated with these different components using ultrasensitive assays.'

This statement is not confirmed by proteomics analysis and TEM images. Authors can use the more accurate term 'enriched EVs' compared to lipoproteins. Also, depletion of chylomicrons and HDL were not shown.

Thank you for the suggestion. We modified the wording of this sentence and replaced “separate” with “enrich.” We did not specifically analyze cholymicrons or HDL in this study. As mentioned in the Discussion section, Simoa assays for protein components of lipoproteins other than ApoB-100 (such as Apo-A1 for HDL) could be developed and an identical analysis framework could be employed. In this study, we decided to focus on the most abundant protein component of lipoproteins found in our mass spectrometry analysis, ApoB-100.

Reviewer #2 (Recommendations for the authors):

I have a few suggestions for the authors' consideration:

It may be more accurate to describe the free proteins and lipoproteins as impurities (i.e., present at the outset, but not intended to be present in the final preparation), rather than contaminants (i.e., introduced inadvertently)

We’ve changed “contaminants” to “impurities.”

The introduction could be more laser-focused on plasma since that's the focus of the work, and the lipoprotein issue may be greatly magnified compared to in e..g, urine

We’ve added this clarification to the last paragraph of the introduction.

A few more words about the principle of the Simoa assay could be useful to the reader

We’ve added two sentences about Simoa to the Results section.

"These results demonstrate the advantage of using TMC for deep proteomics analysis using a small sample volume." Some comparison to the prior state of the art would bolster this conclusion.

We have added these comparisons (as mentioned in response to one of the comments from Reviewer #1) to the Discussion section.

Beyond the relative yields, can synthetic EV spike-ins be used to calculate the absolute yields?

EV spike-ins from cell lines can be used, but we don’t think this is a way to provide absolute quantification since existing EV analysis techniques are unable to provide absolute quantification of these cell-culture derived EVs (as there are non-EV particles in cell culture media as well). Additionally, using EV spike-ins assumes that these EVs have very similar properties to plasma EVs, which may not necessarily be the case.

Figure legend for Figure 2: The EV yield is calculated "by averaging the ratios of CD9, CD63, and CD81." I think this be explained in clearer terms. This is the average of what is taken in ratio to what? This comment applies to a variety of other places in the paper where ratios are mentioned but without precise definitions of the numerator and denominator.

"Simoa measurements in the designated fractions for CD9, CD63, and CD81 are taken as a ratio relative to measurements of these proteins from diluted plasma and these three rations are then averaged to calculate recovery." Here again, I think this can be explained with a clearer description of what is in the numerator and denominator of these ratios (also a typo, "rations")

It would be helpful if the authors wrote down the calculation they're referring to here: "by averaging the ratios of each of the tetraspanin levels between conditions" The numerator and denominator in the ratios are not as clear as they ideally could be.

We have added a paragraph about the EV yield and purity calculations to the Methods section and also expanded the explanation in the Results section.

A challenge is that after the removal of the main known impurities, whether additional proteins identified in proteomics experiments are in the vesicles or free proteins is not simple to distinguish. How does the relative quantitation of proteins after the application of the new column compare with plasma proteomics performed without enrichment for extracellular vesicles? Is the rank order of abundance similar?

We agree that distinguishing whether a given protein in an EV preparation is truly in EVs or stuck to the outside remains a major challenge in the field. One way to approach this challenge is with proteinase protection assays, but significant optimization is required to ensure both sufficient proteinase activity and full inactivation of proteinase before analyzing the protected EV contents. Although comparing the proteomes of total plasma and plasma EVs was beyond the scope of this study, such a comparison (using data from several published studies) has been performed before (5). In that comparison, fewer than half of the proteins detected between total plasma and plasma EVs were shared, and for those proteins that were shared, the correlation of protein abundances was relatively poor.

The authors have shared all the relevant data, as well as additional resources that will be useful to the scientific community.

Reviewer #3 (Recommendations for the authors):

1) The "relative EV yields" was used to quantify the amount of EVs across different methods. The authors described it as "the average of the ratios of CD9, CD63, and CD8". Please describe how this is calculated exactly. The same for relative EV/ Albumin and relative EV/ApoB-100.

We have expanded the explanation of how we calculate EV yield in the Results section and added a paragraph on this to the Methods section.

2) Please also calculate the relative EV yields, relative EV/ Albumin, and relative EV/ApoB-100 for the density gradient centrifugation experiment. Also, Figure 3 is a bit hard to follow due to the distance between the bars.

We have replotted Figure 3 to display each marker separately. We have also redone the density gradient centrifugation experiment to include SEC from the same sample of plasma, as well as a condition combining density gradient centrifugation and SEC. We have added Figure 3—figure supplement 1 to compare relative EV yields, EV/albumin and EV/ApoB-100 for these conditions.

3) For Figure 5, it will be great to draw lines between 2 replicas to provide a sample-to-sample comparison.

For the experiment in Figure 5, we started with 16 samples (1 mL each) of the same pooled plasma. We ran eight of these samples using the automated SEC device and the other eight samples performing SEC manually. Each plotted point for a given marker is an average of two Simoa measurements (technical replicates). We reworded description of this experiment in the Results section and the figure caption to clarify this.

4) Different fractions were selected for SEC (fractions 7-10), DMC (fractions 9-12), and TMC (fractions 9-12). Please provide the reason and the potential influence of the fairness of the comparison.

As the SEC column has 10 mL Sepharose CL-6B resin, and the DMC and TMC columns have 2 mL additional resin below the 10 mL Sepharose CL-6B resin, the difference in column volume leads to a change in where the EVs elute. We decided that taking four 0.5 mL fractions for each column would be best for directly comparing them. To decide which fractions to take, we analyzed the tetraspanins, albumin, and ApoB-100 in each individual fraction (as indicated in Figure 4-supplement 2).

5) Figure 5—figure supplement 2, the decrease of EV yield cross SEC, DMC, and TMC is different for CD9, CD63, and CD8 (increasing in DMC). What is the reason?

We find a difference in yield for the different tetraspanins between SEC and DMC/TMC. One possibility could be a difference in charge between different tetraspanins or subpopulations of EVs containing different tetraspanins.

6) The mechanism for the better performance of TMC needs to be discussed.

The TMC has improved performance in terms of free protein depletion relative to the SEC because the Capto Core 700 multimodal resin irreversibly binds free proteins that co-elute in the EV fractions. The TMC column has improved performance relative to SEC in terms of lipoprotein depletion for the same reason as the DMC column. The cation exchange resin binds and retains positively charged ApoB-100-containing lipoproteins. We have expanded the explanation of why TMC provides EVs of higher purity than SEC in the Discussion section.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. Validation of Simoa ApoB-100 assay.
    elife-86394-fig1-data1.xlsx (10.9KB, xlsx)
    Figure 2—source data 1. Simoa data (protein concentrations) for fractions of SEC columns with different resins.
    elife-86394-fig2-data1.xlsx (21.2KB, xlsx)
    Figure 2—source data 2. Simoa data (protein concentrations) for SEC column with different number of washes.
    elife-86394-fig2-data2.xlsx (9.5KB, xlsx)
    Figure 3—source data 1. Simoa data (protein concentrations) for different density gradient centrifugation fractions.
    elife-86394-fig3-data1.xlsx (11KB, xlsx)
    Figure 4—source data 1. Simoa data (protein concentrations) for TMC columns with different ratios of resins in the bottom layer.
    elife-86394-fig4-data1.xlsx (10.2KB, xlsx)
    Figure 4—source data 2. Simoa data (protein concentrations) for different fractions of SEC and DMC columns.
    elife-86394-fig4-data2.xlsx (10.8KB, xlsx)
    Figure 4—source data 3. Simoa data (protein concentrations) comparing SEC (fractions 7-10), DMC and TMC columns (fractions 9-12).
    elife-86394-fig4-data3.xlsx (10.6KB, xlsx)
    Figure 5—source data 1. Simoa data (protein concentrations) comparing manual and automated SEC EV isolation (fractions 7-10).
    elife-86394-fig5-data1.xlsx (11.4KB, xlsx)
    Supplementary file 1. Protein list identified by mass spectrometry of extracellular vesicles (EVs) isolated from plasma using Tri-Mode Chromatography (TMC).
    elife-86394-supp1.xlsx (71.1KB, xlsx)
    MDAR checklist
    elife-86394-mdarchecklist1.pdf (383.9KB, pdf)

    Data Availability Statement

    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.

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

    RESOURCES