Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

Abstract

The protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

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Longitudinal stability
of urinary extracellular
vesicle protein patterns
within and between individuals
Leyla A. Erozenci1,2, Sander R. Piersma1, Thang V. Pham1, Irene V. Bijnsdorp1,2* &
Connie R. Jimenez1*
The protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-
invasive biomarker source. However, little is known about the consistency and variability of urinary
EV proteins within and between individuals over a longer time-period. Here, we evaluated the
stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using
data-independent-acquisition mass spectrometry. The 1802 identied proteins had a high correlation
amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more
than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded
person-specic proles. The core EV-protein-interaction network of 516 proteins detected in all
measured samples revealed sub-clusters involved in the biological processes of G-protein signaling,
cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specic
expression patterns were detected in the urinary EV proteome. Our ndings indicate that the urinary
EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period,
further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.
Urine is a biouid that has raised great interest as a biomarker source for the detection of disease, since it can
be collected in large volumes, in a non-invasive manner. Extracellular vesicles (EVs) are small vesicles that are
secreted by most cell types of the human body into biouids, including blood and urine1. Circulating EVs are
biomarker-rich organelles as they, at least in part, represent their cell-of-origin2. Importantly their molecular
cargo (DNA, RNA, miRNA, protein) is protected by the EV bilayer membrane from degradation by enzymes
present in biouids3.Advances in mass spectrometry-based proteomics over the past decade have allowed for
in-depth analysis of proteins in clinical samples, including urinary EVs4,5. e proteome cargo of urinary EVs
has been the subject of several biomarker studies6. Previous studies on the whole urinary proteome reported a
high level of variation within individuals over time (intra-individual variation), as well as between individuals
(inter-individual variation)7–12; which hampers biomarker applications. On the contrary, it was reported that the
urinary EV proteome of few donors was stable in a short time period13,14. e stability of urinary EV proteome
was never investigated in a comprehensive manner with multiple individuals and longitudinal sampling over
an extended period of time.
In the present study, we assessed the stability of the urinary EV proteome using a highly reproducible next
generation proteomics approach based on liquid chromatography on-line coupled to data-independent acquisi-
tion (DIA)-MS15–17. is mass spectrometry approach combines the benets of global discovery proteomics with
the quantitative precision of targeted mass spectrometry with dynamic range over 3 order of magnitude17,18, and
hence provides a pathway for large-scale clinical proteomics19. To this end, we measured a longitudinal cohort
of eight dierent healthy subjects (four females, four males) of whom urine samples were collected at 9 dierent
timepoints over 6months (see Fig.1a for a schematic overview). e stability of the urinary EV proteome over
time, as well as the consistency between individuals was examined. Moreover, biological functions and core
expression networks of EV proteins identied in all subjects were investigated, and gender-specic processes
were explored.
OPEN
Department of Medical Oncology, OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC,
Location VUMC, Amsterdam, The Netherlands. Department of Urology, Amsterdam UMC, Location VUMC,
Amsterdam, The Netherlands. *email: iv.bijnsdorp@amsterdamumc.nl; c.jimenez@amsterdamumc.nl
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Materials and methods
Urine collection and informed consent. e study was approved by the Amsterdam University Med-
ical Center (Amsterdam UMC, Location VUMC) local Medical Ethical Committee (METC reference num-
ber #2018.657). Urine was collected aer a signed informed consent was obtained from each participant. All
methods and experiments were performed in accordance with the relevant guidelines and regulations, which
is in accordance with the Declaration of Helsinki. e workow for collecting and processing urine samples is
depicted in Fig.1a. Urine (50–150mL) was collected and aliquoted into sterile polypropylene tubes and centri-
fuged for 10min at 500 × g at 4°C. Subsequently the supernatant was centrifuged for 20min at 2000 × g at 4°C
and divided in 50mL aliquots which were immediately frozen at − 80°C until processing for proteomics.
Urinary EV isolation. Urinary EVs isolation was performed as previously reported using the Vn96 peptide
capture method that precipitates EVs via binding to HSPs at the EV surface20–22. 50mL of urine for each sample
was thawed O/N at 4°C. Briey, urine samples were centrifuged at 2000 × g for 20min to remove THP-polymers
from the urine that have been formed by cold conditions. Subsequently, 0.05% Nonidet P40 Substitute (NP40,
Sigma- Aldrich, Zwijndrecht, e Netherlands) was added to the urine. 50mL urine per sample was concen-
trated to 1ml using a 100kDa cuto lters (Amicon Ultra, Millipore, Amsterdam, e Netherlands). To prevent
degradation by proteases, protease inhibitor cocktail was added (PIC, #10,276,200, Roche). To remove small
debris and large protein complexes, urine was centrifuged at 16,000 × g for 15min at 4°C in a tabletop centri-
fuge. Subsequently 40µl of the VN-96 peptide (Microvesicle Enrichment kit, New England Peptide, #W1073-2,
USA) was added, and incubated on a rotation wheel for 1h at RT. EVs were isolated aer centrifugation for
15min at 16,000 × g. e pellet was washed with PBS and centrifuged again for 15min at 16,000 × g to obtain the
nal sample. All the centrifugation steps were performed at 4°C. e urinary-EV pellets were dissolved in LDS
sample buer (containing 10% Dithiothreitol, Life Technologies, Carlsbad, CA, cat No:NP0008) for proteomics
experiments.
Spectral library generation and DDA-LC-MS/MS measurement. For library generation for DIA-
measurement, aliquots were taken from all samples to make two pools, one consisting of samples from males
and one from females that were used as input for EV isolation. e pooled EV samples were loaded on gradient
gels from Invitrogen (NuPAGE 4–12% Bis–Tris gel, 1mm × 10 wells). e gels were stained with Coomassie bril-
liant blue G-250 (Pierce, Rockford, IL) and in-gel digested as previously described23. In brief, gels were washed
twice in 50mM ammonium bicarbonate (ABC) and dehydrated twice in 50mM ABC/50% acetonitrile (ACN).
Cysteine bonds were reduced by incubation with 10mM DTT/50mM ABC at 56°C for 1h and alkylated with
50mM iodoacetamide/50mM ABC at room temperature (RT) for 45min. Aer washing sequentially with ABC
and ABC/50% ACN, the whole gel was sliced in 10 bands for each lane. Gel parts were sliced into cubes of 1mm3,
which were incubated overnight with 6.25ng/mL trypsin (Promega, sequence grade V5111). Peptides were
extracted once in 1% formic acid and twice in 5% formic acid/50% ACN. Subsequently, the extract volume was
reduced to 50µL in a vacuum centrifuge. e sample was ltered using a 0.45µm lter to remove gel particles
and contaminants prior to LC–MS analysis.
Extracted peptides were separated on a 75µm × 42cm custom packed Reprosil C18 aqua column (1.9µm,
120Å) in a 90min. gradient (2–32% Acetonitrile + 0.5% Acetic acid at 300nl/min) using a U3000 RSLC high
pressure nanoLC (Dionex). Eluting peptides were measured on-line by a Q Exactive mass spectrometer (ermo
Fisher, Bremen, Germany) operating in data-dependent acquisition mode. Intact peptide ions were detected at
a resolution of 70,000 (at m/z 200) and fragment ions at a resolution of 17,500 (at m/z 200); the MS mass range
was 350–1,400Da. AGC Target settings for MS were 3E6 charges and for MS/MS 1E6 charges. Peptides were
selected for Higher-energy C-trap dissociation fragmentation at an underll ratio of 1% and a quadrupole isola-
tion window of 1.6Da, peptides were fragmented at a normalized collision energy of 28.
Processing of individual samples for DIA-LC-MS/MS. For proteomics analysis of individual urine EV
samples, each sample was loaded on gradient gels (Invitrogen, NuPAGE 4–12% Bis–Tris gel, 1mm × 10 wells),
Figure1. Schematic workow of the study and overview of the whole urinary EV proteome. (a) Schematic
diagram of the collection of urine samples on the upper panel, and DIA-MS workow on the lower panel.
Urine was collected from 8 individuals at 9 timepoints over the course of 6months (total 72 samples) and was
pre-cleared from dead and apoptotic cells using centrifugation and stored at − 80°C. 50ml urine per donor
was concentrated to 1ml with ultraltration (100-kDa cut-o) and urinary EVs were subsequently isolated
using the Vn96-peptide-anity kit20. For the high-depth spectral library generation, gender-specic urinary
EV pools (n = 2) were subjected to 10-band gel fractionation followed by DDA-MS. Individual samples (n = 72)
were measured in single shot DIA-MS mode, followed by data extraction and quantication using intensities,
and extensive data analyses. (b) Total number of proteins identied (upper panel) per individual sample and
distribution of normalized protein intensities (lower panel) for each sample (n = 67), showing a highly similar
protein identication number between the samples and individuals. (c) Data presence plot, showing a high
data presence amongst all samples. e expression levels of the total urinary EV proteome (1802 proteins) is
indicated amongst all samples (n = 67). e proteins were ranked according to data presence and average log2-
intensity. e missing values (29,913 out of total 120,734 data points) are gray. (d) Expression levels of selected
EV-related protein markers for each sample with Heat-shock proteins (upper panel), tetraspanins (middle
panel), and TSG101 and PDCD6IP (lower panel) showing a good consistency in the level of these proteins in
time.
◂
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similar as described above. e gels were stained with Coomassie brilliant blue G-250 (Pierce, Rockford, IL),
reduced by 10mM DTT/50mM ABC at 56°C for 1h and alkylated with 50mM iodoacetamide/50mM ABC
at room temperature (RT) for 45min. Aer washing sequentially with ABC and ABC/50% ACN, the whole gel
lanes were sliced in 3 bands per sample. Gel parts were sliced into cubes of 1 mm3, which were incubated over-
night with 6.25ng/mL trypsin (Promega, sequence grade V5111). Peptides were extracted once in 1% formic
acid and twice in 5% formic acid/50% ACN. e extracts of three fractions were pooled per biological sample.
Volume was reduced to 100µl in a vacuum centrifuge to evaporate acetonitrile and samples were desalted using a
10mg OASIS HLB column (Waters, Milford). aer washing in 0.1% TFA. Samples were eluted in 80% ACN/0.1%
TFA and were dried in a vacuum centrifuge. Peptides were redissolved in 20µl loading solvent (4% ACN in 0.5%
TFA) for LC-MS analysis.
DIA-LC-MS/MS measurement. Peptides were separated by an Ultimate 3000 nanoLC system (Dionex
LC-Packings, Amsterdam, e Netherlands), equipped with a 50cm × 75µm ID nanoViper fused silica column
packed with 1.9µm 120Å Pepmap Acclaim C18 particles (ermo Fisher, Bremen, Germany). Aer injection,
peptides were trapped at 3μl/min on a 10mm × 100 μm ID trap column packed with 3μm 120 Å Pepmap
Acclaim C18 at 0% buer B (buer A: 0.1% formic acid in ultrapure water; buer B: 80% ACN + 0.1% formic
acid in ultrapure water) and separated at 300nl/min in a curved 10–52% buer B gradient in 120min (140min
inject-to-inject). Eluting peptides were ionized at a potential of + 2 kVa into a Q Exactive mass spectrometer
(ermo Fisher, Bremen, Germany). Data was measured using a data-independent acquisition (DIA) protocol.
e DIA-MS method consisted of an MS1 scan from 350 to 1400m/z at 120,000 resolution (AGC target of 3E6
and 60ms injection time). For MS2, 24 variable size DIA segments were acquired at 30,000 resolution (AGC
target 3E6 and auto for injection time). e DIA-MS method starting at 350m/z included one window of 35m/z,
20 windows of 25m/z, 2 windows of 60m/z and one window of 418m/z, which ended at 1400m/z. Normalized
collision energy was set at 28. e spectra were recorded in centroid mode with a default charge of 3 + and a rst
mass of 200m/z.
Raw data processing. DDA Raw les were processed using MaxQuant 1.6.4.0. MS/MS spectra were
searched against a Swissprot FASTA le downloaded Feb 2019. e precursor and fragment mass tolerance
were set to 4.5 and 20 p.p.m., respectively. Peptides with minimum of seven amino-acid length were considered
with both the peptides and proteins ltered to a false discovery rate (FDR) of 1%. Enzyme specicity was set
to trypsin and up to two missed cleavages were allowed. Cysteine carbamidomethylation was searched as a
xed modication, whereas protein N-terminal acetylation and methionine oxidation were searched as variable
modications. DIA raw les were searched in Spectronaut version 13.10 (Biognosys, Schlieren, Switzerland)
with default settings. e MaxQuant msms.txt le from the DDA search result was imported into Spectronaut to
generate a project-specic spectral library. Modications were the same as for the MaxQuant DDA search. e
search result was exported at the fragment ion level for MaxLFQ protein quantication24. e mass spectrometry
proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE25,26 partner repository
with the dataset identier PXD022983.
Data analysis. All analysis was done using log2-transformed intensities in R version 4.0.2 (https:// www.R-
proje ct. org/). All the clustering analyses were performed using R package ComplexHeatmaps version 2.5.527. For
the data presence heatmap, proteins ordered on the number of identied data points (high-to-low) and average
log2-normalized intensity (high-to-low) over the 67 individual samples was plotted. For intra- and inter-individ-
ual sample clustering, the Spearman correlation of the normalized intensities was calculated for whole urinary
EV proteome per sample, and the correlation coecient was plotted. For protein clustering of top 10% most
variable proteome, the protein abundancies were normalized to zero mean and unit variance for each individual
protein. Subsequently, the Spearman distance measure was used for clustering. R package ggplot2 version 3.3.2
(https:// ggplo t2. tidyv erse. org) was used for all plots other than heatmaps and networks. Intra-individual CV was
calculated amongst all timepoints per person; while inter-individual coecient of variation (CV) was calculated
amongst all 67 individual samples. Gene Ontology (GO) term analyses of the core urinary EV proteome were
performed using ClueGO version 2.5.728. Redundant GO terms were manually collapsed into one parent term
and were presented in the GO barplots. Protein networks for the core urinary EV proteome were created using
STRING version 1129 and visualization was further modied using Cytoscape version 3.8.030. Signicantly con-
nected protein clusters were extracted using ClusterONE version 1.031, and further annotated in detail for impli-
cated biological processes using BINGO version 3.0.332. Dierential statistical analysis to compare the protein
expression between female and male samples was performed using R package Limma version 3.45.1433. Gene Set
Enrichment Analysis (GSEA) was performed using R package fgsea version 1.15.234.
Results
Longitudinal urinary EV proteome proling in healthy subjects. To investigate the temporal stabil-
ity of the urinary EV proteome within and between individuals, as well as its biology, we collected urine from
8 healthy individuals (4 males and 4 females) at 9 timepoints spanning 6months (see Fig.1a for a schematic
overview). Urinary EVs were isolated using the Vn96 peptide capture method that enables reproducible high-
throughput proling20–22. e characterization and validity of VN96 peptide-based EV capture method has been
addressed in previous studies for cell culture supernatants, blood and urine20–22,35–37 [Erozenci etal. 2021, sub-
mitted manuscript 2, Submission ID a4f6d1e6-ce2f.-483a-927b-3da8b2083095]. ese studies showed that the
EV fraction captured is in the size range (30 to 100nm), enriched for exosome markers and is largely comparable
to EVs isolated by ultracentrifugation. For urinary EV proling by DIA-MS, we generated a project-specic
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spectral library using two gender-specic pools of urinary EVs. ese pools were analyzed by in-depth proteom-
ics based on 10 band gel-fractionated samples coupled to shot gun proteomics (DDA-MS) (Fig.1a). e nal
spectral library consisted of 3166 proteins. Subsequently, all 72 individual samples were measured using single-
shot DIA-MS (Fig.1a). e total urinary EV proteome consisted of 1802 proteins. Five samples were excluded
from further downstream analysis, because the number of identied proteins was below 2 standard deviations of
the mean (indicated in Supplementary Fig.1). e whole urinary EV proteome of 1802 proteins were identied
with a mean of ~ 1355 proteins per urinary EV sample (ranging between 905 and 1587) and with > 75% of data
points present across all 67 samples (Fig.1b,c). Selected common exosome markers such as tetraspanins (CD9,
CD63, CD81), TSG101, PDCD6IP (ALIX) and heat-shock proteins (HSPs) (HSP90AA1, HPS90AB1, HSPA8)
were detected in all individuals at almost all timepoints at median-to-high abundance (Fig.1d). Closer inspec-
tion of these EV markers revealed that day-to-day variation within the same donor was low, especially for HSPs
(median CV = 0.58), TSG101 and PDCP6IP (CV 0.74 and 0.70) and showed comparable abundance levels in
dierent individuals (Fig.1d). e level of CD9, CD63 and CD81 exosome markers was more variable compared
to the HSPs, most notably between dierent individuals (median CV = 1.15) (Fig.1d), with donor “Male5” show-
ing the highest expression, suggesting that inter-individual dierences in EV subpopulations might be present
in the urine.
Highly stable personal urinary EV proteomes with larger inter-individual variation. To inves-
tigate the intra- and inter-individual stability of urinary EV proteome, an unsupervised correlation analysis was
performed on the whole urinary EV proteome of 1802 proteins (Fig.2a). Unsurprisingly, the highest correla-
tions were observed within individuals, even over a longer period of time (6months) (average intra-individual
r = 0.77) (Fig.2a), indicating a high stability of personal urinary EV proteomes. e correlation of protein pro-
les between individuals was lower than within individuals (average inter-individual r = 0.54) (Fig.2a), indicat-
ing that each individual has their own level of abundance of the proteins that are present in urinary EVs. e
lowest correlation was observed between donor “Male 5” to the other individuals (minimum r = 0.38), which
also exhibited slightly dierent levels of EV markers as compared to the other subjects (Fig.1d).
To investigate the potential eect of protein variation in normal urinary EV proteome, we focused on the most
variable and most stable proteins. e protein variation was calculated on the total urinary EV proteome (1802
proteins) amongst all data points of 8 individuals. Unsupervised hierarchical cluster analysis of the top 10% most
variable proteins separated the samples mostly related to individual protein proles (Fig.2b). is indicates that
the most variable proteins determined the majority of the personal urinary EV proteomes (Fig.2b). Importantly,
37 of the top 100 ExoCarta38 exosome-associated proteins were identied in the top 10% most stable proteome
of urinary EVs; including HSPs, multiple RAB proteins, ACTB, ANXA2, and several members of the 14–3-3
protein family, indicating the stability of the proteins within the urinary EVs over time and between individuals.
Examples for the stable Exocarta38 proteins, the top 15 most stable proteins and the top 15 most variable proteins
are provided in Supplementary Fig.2a–c.
e composition of the personal urinary EV proteomes were found to be highly similar in time with > 90%
of the whole urinary EV proteome of 1802 proteins present in more than 1 timepoint per person (Fig.2c). Fur-
thermore, of the 1314 protein groups (i.e. excluding the protein isoforms), only a small number of proteins were
unique to one individual, ranging from 3 to 45 proteins, with 90% of the identied proteome (1174 proteins)
overlapping in any 2 or more individuals (Fig.2d, Supplementary Fig.3); revealing that the composition of uri-
nary EV proteome is similar and comparable between dierent individuals. To examine EV consistency within
and between individuals, we analyzed the core urinary EV proteome of our dataset, dened as the 516 proteins
that were common to all 8 individuals at all timepoints measured (Fig.2d, Supplementary Fig.3). No dierence
was observed between the day-to-day variation of the donors (Fig.2e). e median CV of the core proteome of 8
individuals over the 6-month period was 0.604 (ranging from 0.34 to 1.43) (Fig.2f). e donor-donor variation
was signicantly higher than the day-to-day variation of the proteome with a median of 0.79 (ranging from 0.402
to 4.35; p < 2.2e−16) (Fig.2f ). However, this larger donor-donor variation was mostly based on a small subset of
‘outlier’ proteins that also had a high intra-individual CV (which represents less than 20% of the core proteome).
Hence, most of the proteins within the urinary EVs are highly stable between days over a long period of time and
also between dierent individuals. No relation between protein intensity and variation was observed, indicating
that high abundant proteins do not necessarily have the lowest CV or vice versa, which is in agreement to previ-
ous observations reporting about variation of the entire human urinary proteome using DDA-MS (Fig.2g)7,12.
Together, this analysis shows that the majority of the urinary EV proteome is stable and highly comparable
within and between individuals.
Biological functions of the core urinary EV proteome. To investigate the functions of urinary EV
proteins, we focused on the proteins that were consistently identied in all individuals (the core urinary EV
proteome, Fig.2d, Supplementary Fig.3). Gene ontology (GO) analysis showed that almost all detected proteins
were associated with the cellular component term extracellular exosome and vesicle, underlining the EV nature
of our samples (Fig.3a). In addition, the majority of the core urinary EV proteins were found to be involved in
biological processes that are vesicle-related such as vesicle-mediated transport and exocytosis (Fig.3b).
In order to have a deeper understanding of the implicated general GO terms, we annotated in detail the bio-
logical pathways as well as functional protein clusters on the core urinary EV proteome. Cluster analysis using
ClusterONE31 identied signicantly connected protein complexes including EV-, immune- and metabolism-
related proteins within the large urinary EV protein–protein interaction network (Fig.3c; for details see Sup-
plementary Fig.4). A vesicle-linked cluster containing EV-related proteins such as ALIX, TSG101, HSPs, RABs
and members of ESCRT-III (endosomal sorting complex required for transport III) was one of the signicantly
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enriched subnetworks (p = 2.540E−4) in urinary EVs (Fig.3d); indicating that urinary EVs contain several mem-
bers of the EV biogenesis machinery. In addition, proteins involved in the regulation of cytoskeleton organiza-
tion and signal transduction, in particular small GTPase-mediated and RAS signaling, were connected with the
EV-associated network cluster (Fig.3d), suggesting the presence of a signal transduction network in the core
urinary EV proteome intertwined with the vesicle-linked protein cluster.
Other signicantly enriched protein network clusters in the core urinary EV proteome were related to immu-
nity (p = 3.834E−8) and metabolism (p = 6.738E−5). e immune subnetwork included proteins involved in
innate and acute inammatory response, complement factors, and immunoglobulin family of proteins among
others (Supplementary Fig.4), suggesting that urinary vesicles may be derived at least in part from immune
cells. e most predominant metabolic subnetworks contained proteins involved in carbohydrate/alcohol and
protein metabolism, with a smaller cluster functioning in lipid metabolism (Fig.3c; Supplementary Fig.4).
Detailed examination revealed that the molecular function of these proteins is mainly enzymatic, with a remark-
able enrichment in glycolytic enzymes as each step of the reaction was represented in the core urinary proteome
together with several enzymes involved in pentose phosphate pathway (Fig.3e; Supplementary Fig.5). In addi-
tion, many enzymes required for the subsequent amino acid biogenesis from the intermediary metabolites were
identied in the core urinary EV proteome (Fig.3e; Supplementary Fig.5).
Taken together, our functional data mining of core urinary EV proteins showed an enrichment for EV bio-
genesis, metabolism and immune-related processes that are consistently identied in time and within all donors.
ese processes are frequently deregulated in diseases, underlining the potential for the use of urinary EVs for
the detection of disease.
Gender-distinct patterns are detected in urinary EVs. Besides intra- and inter-individual variation,
we also investigated whether gender dierences can be detected in the urinary EV proteome. A complete list of
dierentially expressed proteins in females and males are provided in Supplementary Table1. Comparison of
female- versus male-derived urinary EV proteomes by Gene Set Enrichment Analysis (GSEA) showed that the
androgen response pathway and spermatogenesis are enriched in males, whereas females show increased estro-
gen response processes (Fig.4a). Moreover, blood- and oxygen-related pathways such as hypoxia, coagulation,
angiogenesis and heme metabolism were enriched in females compared to males (Fig.4a), further suggesting
that reproductive system-based dierences are detectable in the urinary EV proteome.
Further inspection of the proteins underlying these signatures showed that multiple hemoglobin subunits
were signicantly increased in females when compared to males (Fig.4b,c; Supplementary Fig.6a); whereas in
males, prostate-associated protein KLK2 was present only in male-derived urinary EVs and prostate-implicated
proteins such as SPOCK1 and TMPRSS were upregulated in males (Fig.4b,c; Supplementary Fig.6a). Further-
more, we also inspected 3 male specic and 3 female specic proteins from the Human Protein Atlas (htt p:// www.
prote inatl as. org). All three prostate-secreted proteins (KLK3/PSA, TGM4, ACPP) were signicantly increased in
male-derived urinary EVs; whereas vagina/cervix-associated proteins SERPINB3 and FABP5 were signicantly
increased in females, and CNFN uniquely expressed in female urinary EVs (Fig.4b,c; Supplementary Fig.6a).
Finally, network visualization of the gender-enriched proteins revealed an inter-connected network mostly
within gender, and to a lesser degree between genders (Supplementary Fig.6b). Analysis of individual protein
clusters showed enrichment of immune-related processes and carbohydrate metabolism in female urinary EVs;
while enzymes involved in protein metabolism were identied in male-derived urinary EVs (Fig.4d). Interest-
ingly, in addition to prostate-secreted proteins (ACPP, TGM4, KLK2, KLK3/PSA), several vesicle-related proteins
such as RABs and VAMPs, as well as tetraspanins CD9 and CD63 were signicantly increased in male urinary
EVs, underlining the secretory function of the prostate gland in males (Fig.4d).
Together this analysis demonstrates that gender-specic proteins and functions can be detected in the urinary
EV proteome.
Discussion
Characterization of the uctuations in the normal urinary EV proteome provides crucial information for bio-
marker research. We demonstrate that the majority of the urinary EV proteins are stable in time, and shared
between dierent individuals. e core urinary EV protein networks are involved in EV-related functions,
metabolism and immunity, and gender-enriched processes are linked to hormonal and reproductive functions.
Our results underscore the value of urinary EV proteins as promising source for biomarker discovery.
Figure2. Personal and inter-individual variation of the urinary EV proteome. (a) Unsupervised spearman
correlation analysis of 8 individuals (67 samples) based on total proteome (1802 proteins) with the correlation
coecient values from 0 (white) to 1 (red). (b) Hierarchical clustering of the top 10% most variable urinary EV
proteins (180 proteins) showing a clustering largely based on individual. (c) Number of proteins identied at
all timepoints (purple), at more than 1 timepoint (blue) or at a single timepoint (gray) per donor. Percentages
relative to the total proteins identied per person are annotated on the barplots. (d) Number of proteins
identied in all individuals (core proteome, 516 proteins), in more than 1 individual (total 1174 proteins), and
unique proteins per person (combined 113 proteins). (e) Distribution of personal CVs (intra-CVs) of each
individual for the core urinary EV proteome (516 proteins) which was dened as the proteins detected at all
timepoints in all 8 individuals. (f) Distribution of inter- and intra-CVs for the core urinary EV proteome (516
proteins). Inter-CV is calculated amongst 67 samples, and intra-CV is represented as the mean of the personal
CVs of 8 individuals. (g) Distribution of inter-CVs in relation to protein abundance for the core urinary
proteome (516 proteins), showing that the CV is independent from abundance.
◂
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Figure3. Enriched biological functions in the core urinary EV proteome. (a) Gene ontology (GO) term cellular
component (CC) analysis of the core urinary EV proteome. Barplots show top 5 GO:CC terms associated with the
core urinary EV proteome, analyzed using ClueGO28. (b) Gene ontology (GO) term biological process (BP) analysis
of the core urinary EV proteome. Barplots show top 5 GO:BP terms associated with the core urinary EV proteome,
analyzed using ClueGO28. (c) Schematic overview of the enriched biological functions in the core urinary EV protein
network (516 proteins), analyzed by clusterONE31 and BINGO32. For the detailed network, see Supplementary Fig.4.
(d) Protein interaction network of EV markers and associated signaling and localization protein clusters, predicted by
clusterONE31. (e) Protein interaction network of metabolic cluster. Enriched sub-networks predicted by clusterONE31
are indicated within the gure. PPP: Pentose-Phosphate Pathway.
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Figure4. Gender-based dierences in the urinary EV proteome. (a) Gene set enrichment analysis (GSEA, hallmarks) of the
dierential analysis between female- and male-derived urinary EVs was performed by ranking proteins based on the sign of their fold
change and p-value, with proteins signicantly overexpressed in females at the top of the list. Gene sets enriched in females are marked
pink, and gene sets enriched in males are marked blue. e size of the dot reects the signicance of the enrichment (false discovery
rate). (b) Volcano plot of the urinary EV protein expression levels between female and male samples. Colored dots indicate the 172
proteins that were signicantly dierent between female or male samples. A total of 89 proteins were upregulated in males (le side, in
blue), whereas 83 proteins were overexpressed in females (right side, pink). Proteins-of-interest that are shown in Fig.4C are annotated
in the plot. (c) Expression levels of selected female- (upper 2 panels) and male-specic (lower 2 panels) urinary EV proteins, showing
an enrichment of gender-specic proteins in the expected samples. (d) Protein interaction networks enriched in female (le) and male
(right) urinary EVs, predicted by clusterONE31.
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A reproducible and clinically-applicable EV isolation method is essential to investigate urinary EV-based
functions and biomarkers. Minor non-EV protein contaminants are not a concern as long as these do not obscure
the analysis of the EV proteome. e VN96 peptide-based EV isolation provides such a method. It interacts
with heat-shock proteins (HSPs) exposed on vesicle surface20. Being a charged peptide, it may co-isolate some
non-EV associated proteins and nucleic acids. We previously showed that EVs isolated using Vn96-based an-
ity capture are highly similar to those isolated by ultracentrifugation21,22. Comparison to EV fractions isolated
using either size focusing (i.e. size exclusion chromatography) or anity pull down (i.e. using anti-tetraspanin
immunobeads) remain to be done. In view of the high anity for HSPs to pull-down EVs, it is possible that
Vn96 introduces a bias for isolating specic EV populations under certain circumstances. For example, HSPs
are known to be upregulated in cancer39, and therefore the Vn96 method may be advantageous to enrich for
cancer EVs. However, in our benchmark study22, we did not see dierential enrichment of HSPs in the cancer
EVs isolated by the VN96 method as compared to ultracentrifugation.
e proteome of full urine may be aected by many factors that can be induced by dierences between indi-
viduals’ lifestyle such as hydration status, diet, exercise, age, gender and environmental factors among others10.
In contrast to the full urinary proteome that is quite variable7–9,11,12, the urinary EV proteome may be highly
stable due to the protection against degradation provided by the lipid bilayer3. Previously, two small-scale studies
also described low level of variation of the EV proteome, though the depth of these studies was limited (ranging
between 500 and 1000 proteins)13,14. e high intra- and inter-individual stability of the urinary EV proteome in
our in-depth study demonstrates that urinary EV proteins are highly suitable for biomarker studies.
Enriched biological functions in urinary EVs included an EV-biogenesis-linked protein cluster containing
ESCRT components, multivesicular body proteins as well as other known EV-markers. ese proteins were highly
connected with signaling proteins such as signaling transducer G protein subunits and RAB proteins. is may
indicate the presence of activated kinases within urinary EVs. at phosphorylated proteins are present in EVs
has previously been demonstrated in dierent invitro studies40,41 and in human plasma42. Moreover, phospho-
proteome analyses have veried their activation status also in the urinary EVs43,44. Activated kinases within EVs
were previously shown to have a functional role invitro where they can inuence the behaviors of recipient cells
such as altering their hypoxia status45 and metastatic potential46,47. Whether or how these signaling proteins have
a functional role within the urinary EVs remains unclear.
A remarkable enrichment in glycolytic enzymes was present in urinary EVs with almost 50% of the mem-
bers of the glycolysis pathway present in the core urinary EV proteome. e presence of a metabolic cluster was
previously reported in urinary EVs, mainly focused on TCA cycle and respiratory chain proteins48,49. Of note,
EV biogenesis-associated and metabolic, especially glycolytic proteins were recently suggested to belong to two
distinct EV subpopulations, exosomes and exomeres, respectively50,51; suggesting that our urinary EV samples
might contain dierent types of vesicles. Nevertheless, a metabolic, especially glycolytic function appears to
be enriched in the urinary EV proteome. e glycolytic pathway is known to be a major driver of immune cell
function52, which is consistent with the inammatory protein network in the core urinary EV proteome. is
is in line with previous research that suggested a role for urinary EVs in host defense in the urinary tract53.
Although a part of this immune signature might be derived from immune cells; regardless of origin, detection
of inammatory markers in the urinary EV proteome might provide an opportunity for disease monitoring, or
immune-based therapy response monitoring.
Recently, we demonstrated the power of DIA-MS to generate robust, sensitive and reproducible data across
eleven dierent laboratories in nine countries on seven consecutive days in a 24/7 operation mode17. is mass
spectrometry approach will be highly suitable for future large-scale quantitative proteomics to study the urinary
EV proteome under a range of conditions and perturbations, and this approach may also provide a platform for
diagnostic applications.
In conclusion, the majority of the urinary EV proteome is stable in time, as well as between dierent individu-
als. erefore, the urinary EV proteome represents an attractive liquid biopsy to identify deregulated proteins in
disease and for diagnostic/prognostic applications. ese applications may not be limited to diseases of proximal
organs such as prostate or bladder cancer, but may also include early detection of distant diseases such as colo-
rectal, lung or breast cancer6,54,55.
Data availability
e mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the
PRIDE25,26 partner repository with the dataset identier PXD022983.
Received: 6 April 2021; Accepted: 29 June 2021
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Acknowledgements
is work was made possible through the “IMMPROVE” consortium (Innovative Measurements and Mark-
ers for Prostate Cancer Diagnosis and Prognosis using Extracellular Vesicles), which is sponsored by an Alpe
d’HuZes grant of the Dutch Cancer Society (grant #EMCR2015-8022). Furthermore, Cancer Center Amsterdam
and Netherlands Organization for Scientic Research (NWO Middelgroot, #91116017) are acknowledged for
support of the mass spectrometry infrastructure.
Author contributions
L.A.E, I.V.B and C.R.J. conceived the idea. L.A.E. collected the urine samples and performed the experiments.
S.R.P. performed the mass spectrometry measurement and DDA search. T.V.P. performed the DIA search and
data quantication. L.A.E., I.V.B. and C.R.J. analyzed and interpreted the data, wrote the manuscript and made
the gures. All authors reviewed and edited the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
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