Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

November 2022

Research Paper

Lipidomic and metabolic proﬁling of plasma andplasma-derived extracellular vesicles by UHPLC-MS/MS
1,2
3
1
4
2,5
Daisuke Saigusa *, Takeshi Honda , Yuko Iwasaki , Koji Ueda , Eiji Hishinuma ,
2

6,7,8

Naomi Matsukawa , Akira Togashi

93, Noriyuki Matsutani , Nobuhiko Seki *Laboratory of Biomedical and Analytical Sciences, Faculty of Pharma-Science, Teikyo University,2‒11‒1 Kaga, Itabashi-ku, Tokyo 173‒8605, Japan2
Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University,
2‒1 Seiryo-machi, Aoba-ku, Sendai 980‒8573, Japan
3
Division of Medical Oncology, Department of Internal Medicine, Teikyo University School of Medicine,2‒11‒1 Kaga, Itabashi-ku, Tokyo 173‒8606, Japan
4
Project for Realization of Personalized Cancer Medicine, Cancer Precision Medicine Center,
Japanese Foundation for Cancer Research, 3‒8‒31 Ariake, Koto, Tokyo 135‒8550, Japan
5
Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University,
2‒1 Seiryo-machi, Aoba-ku, Sendai 980‒8573, Japan
6
Nippon Boehringer Ingelheim Co., Ltd., ThinkPark Tower, 2‒1‒1 Osaki, Shinagawa-ku, Tokyo, Japan
7
Molecular Proﬁling Research Center for Drug Discovery, National Institute ofAdvanced Industrial Science and Technology (AIST), 2‒3‒26 Aomi, Koto-ku, Tokyo 135‒0064, Japan
8
Japan Biological Informatics Consortium (JBIC), TIME24 Bldg. 10th Floor, 2‒4‒32 Aomi, Koto-ku, Tokyo 135‒8073, Japan
9
Dept. of Surgery, Teikyo University School of Medicine Mizonokuchi Hospital,
5‒1‒1 Futako, Takatsu-ku, Kawasaki 213‒8507, Japan
1

Abstract

Extracellular vesicles (EVs) are secreted from donor cells that bind to receptors on receiving cells to mediatesignal transduction. EVs contain nucleic acids, proteins, miRNAs and small molecules, including lipid species and othermetabolites. These molecules are likely functional mediators and have potential as candidate disease biomarkers and/or
therapeutic targets. However, EV-selective isolation and the optimal extraction of small molecules are essential to observe
the variation in EV molecules, and a more detailed classiﬁcation of lipid species is required for lipidomic proﬁling in EVs.
Here, we examined methanol precipitation to extract a wide polarity range of small molecules from isolated EVs of humanplasma by kit-based metabolic and lipidomic proﬁling using ultrahigh-performance liquid chromatography triple quadrupole tandem mass spectrometry and multivariate analyses. A total of 278 and 379 molecules were identiﬁed in EVs andplasma, respectively. The contents of triglycerides consisting of polysaturated fatty acids were signiﬁcantly higher in EVs
than in plasma, whereas the contents of cholesterol esters were lower in EVs. This method of EV lipidomic proﬁling may
be essential to reveal the function of EVs and utilize them for future biomarker discovery in the clinical ﬁeld.
Key words: extracellular vesicles, lipidome, metabolome, UHPLC-MS/MS* Corresponding authors
Daisuke Saigusa

Nobuhiko Seki

Laboratory of Biomedical and Analytical Sciences
Faculty of Pharma-Science, Teikyo University

Division of Medical Oncology, Department of Internal Medicine,
Teikyo University School of Medicine
2‒11‒1 Kaga, Itabashi-ku, Tokyo 173‒8606, Japan2‒11‒1 Kaga, Itabashi-ku, Tokyo 173‒8605, Japan
Tel: ＋81‒3‒3946‒8069

Tel: ＋81‒3‒3964‒1211
E-mail: nseki@med.teikyo-u.ac.jp

E-mail: saigusa.daisuke.vf@teikyo-u.ac.jp

Received: July 8, 2022. Accepted: October 13, 2022.
Epub November 15, 2022.
DOI: 10.24508/mms.2022.11.007

126Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

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Introduction

11)
MS-based analytical technologies . Therefore, the lipid-

The smaller size of secreted extracellular vesicles (EVs)are 50‒150 nm in diameter that are thought to reﬂect theomic proﬁling of not only general biospecimens but also
EVs is an interesting way to identify new potential bio-characteristics of the original cell, as their surface contains

markers that are diﬀerentially expressed with the progres-lipids and proteins derived from the cell membrane, whilesion of disease

their interior contains nucleic acids and proteins derived
from the cell, and to be involved in cell‒cell communica-

classes were found to be triglycerides (TGs), phosphatidylcholines (PCs) and sphingomyelins (SMs), which repre-

tion mechanisms in physiological and pathological tis-sented 53.5‒56.2%, 35.5‒39.4% and 3.5‒6.2% of all lipids

12,13)

1)sues . The EVs secreted from donor cells bind to receptors. For instance, the most abundant lipid

in EVs, respectively, and high abundances of exosomal
sphingosines and certain other lipids were strongly associ-on the surface of receiving cells, and the contents of EVs
taken up by the receiving cell mediate signal transduction .
For instance, it has been reported that the intravenous pre-2)

ated with hepatocellular carcinoma by means of MS-based
14)-UMP, palmitoleic acid,lipidomic proﬁling . In contrast, 3′administration of EVs derived from lung metastatic cancer

palmitaldehyde, and isobutyl decanoate were signiﬁcantly

cells to mice followed by the intravenous administration of

associated with oesophageal squamous cell carcinoma .

bone metastatic cancer cells increases lung metastasis, eventhough the cells themselves are not capable of lung metasta-

Therefore, both exosomal lipids and metabolites havepotential as biomarkers. In addition, the lipidomic charac-

15)

sis . The role of exosomal molecules in the central nervous

terization of EVs isolated from human plasma using vari-system in neurodegenerative diseases, such as Alzheimerʼsous MS techniques has previously been reported . Since

disease and Parkinsonʼs disease, has also been demonstrated

this study was performed on EVs in plasma or serum, it
was necessary to use EV-selective isolation and the optimal

3)

16)

4)

in a previous report . Therefore, exosomal components arelikely to act as functional mediators and could be candi-

extraction of small molecules as pretreatment methods toobserve the variations in molecules contained in EVs spe-

dates for disease biomarkers and/or therapeutic targets.
The metabolome is the group of small-molecule sub-ciﬁcally, and a more detailed classiﬁcation of lipid species,
such as the saturation of fatty acids, is required to under-

strates and products of metabolism, including a wide rangeof metabolites, such as amino acids, biogenic amines, ste-

stand the diﬀerence in lipidomic proﬁling between plasmarols, fatty acids, and lipid species, that drive essential cellu-

and EVs .

lar functions, such as energy production and storage andsignal transduction. The metabolome can derive fromIn this study, we ﬁrst obtained EV samples isolated fromplasma by means of an EV-selective extraction method

microorganisms, as well as from xenobiotic, dietary anddeveloped previously . Next, the extraction conditions forother exogenous sources, and directly reﬂects phenotypic
5,6)changes in the human body . Therefore, metabolic proﬁl-detecting a wide range of small molecules, including lipid

17)

18)

species and other components, in EVs were optimized todetermine the quantitative values of each molecule by ultra-

ing to characterize these molecules in circulating bioﬂuids
for disease prediction, progression, and prognosis and tohigh-performance liquid chromatography triple quadrupole
tandem mass spectrometry (UHPLC-MS/MS) using hierar-

investigate the role of small molecules in systems biology
7,8)
and disease expression . Various analytical platforms pro-chical clustering heatmaps, principal component analysis(PCA) and volcano plot analyses. We then investigated dif-vide information on the metabolic phenotypes of individu-

ferences in the quantitative values of the small molecules,

als or populations, which can be applied to personalized

focusing on the lipid species derived from plasma and its

medicine for drug development or to public healthcare for

EVs to clarify the characteristic lipid species by using aand tissues is a promising approach to identify biomarkers

9,10). In fact, metabolic and lipidomic
proﬁling by mass spectrometry (MS)-based technologies
biomarker discovery

PAI chart, correlation plots and volcano plot analysis.

has identiﬁed potential biomarkers that gave us functional

Materials and Methods

and/or new information on disease expression using several

Reagents
Acetonitrile, chloroform, isopropanol, and methanol

biospecimens. Moreover, biomarker discovery targeting thediﬀerences in lipid species in disease has become a grow-were purchased from Kanto Chemical Co., Inc. (Tokyo

ing scientiﬁc ﬁeld with the recent development ofJapan) for the LC/MS analyses. Formic acid, ammonium
127Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

November 2022

formate (1 mol/L), phenyl isothiocyanate (PITC) and Mag-8)

method and conditions were as previously described .

Capture™ Exosome Isolation Kit PS were purchased from

Analytical conditions of UHPLC-MS/MS

FUJIFILM Wako Pure Chemical Corporation (Osaka,
Japan). Puriﬁed water was obtained from a Milli-Q Gradi-Thesystem consisted of dual pumps(ACQUITY UPLC H-Class, Waters, Wilmslow, Manches-

ent system (Millipore, Billerica, MA, USA). Ethanol waspurchased from Nacalai Tesque (Kyoto, Japan). Pooled nor-

UHPLC

ter, UK) and an autosampler with a column compartment(ACQUITY UPLC I-Class), and multiple reaction monitor-

mal human plasma (NorPla) and Na EDTA (IPLA-N, Lot:

ing (MRM) was performed with the Xevo TQ-XS system(Waters). The optimal UHPLC-MS/MS and ﬂow injection®

26393) were purchased from Innovative Research, Inc.
(Novi, MI, USA).

analysis (FIA)-MS/MS modes with all ionization parame-

EV extraction and transmission electron microscopy(TEM) analysis

ters, ion transfer voltages/temperatures and the detection ofm/z pairs of precursor and product ions in MRM mode for

EVs were isolated from NorPla (200 μL) by the Tim4-af-

628 molecules were automatically set using the method in

ﬁnity method, and the MagCapture™ Exosome Isolation
Kit PS was used according to the manufacturerʼs instruc-the kit. The representative 106 molecules, including aminoacids, amino acid-related metabolites, bile acids, biogenic

18)

tions (NorEVs) . In brief, 0.6 mg of streptavidin magnetic
beads bound with 1 μg of biotinylated mouse Tim4-Fc wasamines, cresol, fatty acids, hormones, indole derivatives,
nucleobases and vitamins, were detected in the samples via
UHPLC-MSMS using an analytical column, and 521 lipid

added to 10 K supernatant supplemented with 2 mM CaCl2,
and the mixture was rotated overnight at 4°
C. The beads

species (such as acylcarnitines (ACs), ceramides (Cers),
hexosylceramides (HexCers), dihydroceramides, choles-

were washed three times with 1 mL of washing buﬀer(20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 0.0005% Tween

terol esters (CEs), lysophosphatidylcholines (LPCs), PCs,20, 2 mM CaCl2), and the bound sEVs were eluted with elution buﬀer (20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 2 mM

SMs, diacylglycerols (DGs), TGs, and one hexose (mixtureof sugars) were detected in samples via ﬂow injection anal-

EDTA) (TBS). The isolated EVs in the buﬀer (10 μL) weredried on freshly glow-discharged 200 mesh carbon-coatedysis (FIA)-MS/MS. The number of carbon and double

Cu TEM grids (NISSIN EM, Tokyo, Japan), negatively

two acyls, “aa,” or including ether, “ae,” and our method

stained with TI blue (NISSIN EM), and observed using an
H-7650 TEM (Hitachi High-Tech Corp., Tokyo, Japan)

characterized one acyl chain and its TG properties. The

operated at 80 kV. The image of the visualized EVs wasbond and double bond numbers.

bonds of PCs were the sum of two fatty acids separated byother two chains were described by the summed carbon

captured by the software.

Data processing
Sample preparation procedure for analysis of the EV
lipidome and metabolome(Waters). Quantiﬁed values (μmol/L) were calculated

The ﬁnal EV extract (NorEVs) was in TBS (100 μL), andaccording to the manufacturerʼs protocol using MetIDQ

50% or 80% methanol in water or 100% methanol (500 μL)
was added to NorEV samples (n＝3 each). Each sample was

Oxygen software (Biocrates Life Sciences AG). The data
sheet from a ﬁle in Excel 2018 was exported from the soft-

mixed for 30 s, sonicated for 5 min, and evaporated underware and highlighted in 4 colours according to the kit crite-

vacuum for 20 min. The residue was frozen at −80°
C andria: purple:＜limit of detection; yellow: IS out of range;lyophilized for 18 h. Then, the sample was dissolved (20 μL,
water/acetonitrile, 50/50, v/v%), applied to a 96-well plate,light blue:＜lower limit of quantiﬁcation or＞upper limit of

The data were collected by MassLynks 4.2 software

quantiﬁcation; and green: quantiﬁed. The values coloured®

and analyzed by the MxP Quant 500 kit (Biocrates Life

green and light blue were selected. The lipid species andSciences AG, Innsbruck, Austria). NorPla (10 μL) was also
applied to the plate. A blank, calibration standard and Bio-other metabolites that satisﬁed speciﬁc criteria wereselected: the species that were detected in all triplicate sam-crates quality control were applied to the plate as optimalples in NorPla or NorEVs with a coeﬃcient of variation(CV) less than 30%. The extracted molecules were sub-internal standards (ISs). All of the preparation procedures
followed the kit protocol, and the detailed UHPLC-MS/MS

jected to further multivariate analysis by MetaboAnalyst
128

Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

November 2022

5.0 (https://www.metaboanalyst.ca/). The statistical analy-

lites in each group increased with the volume of plasma,sis, creation of a PAI chart, and correlation analyses wereand 600 μL of plasma seemed suitable for analysis (data not

performed by GraphPad Prism 8.4.3.

shown). Therefore, more than 200 μL of plasma should
be prepared for a second assay of the concentrations ofResults and Discussion

targeted exosomal metabolites in future studies.

EV extraction and TEM analysis
Comparison of the exosomal molecular proﬁles in
EVs isolated from human plasma and extracted with
diﬀerent concentrations of methanol

The MagCapture™ exosome isolation kit was recently
developed to select EVs based on binding with PS, including PS on the membrane of the EV surface, and utilized for18)
the analysis of EV-speciﬁc functions . We ﬁrst observed

A total of 264, 278, or 264 small molecules were stably
detected from isolated EVs by UHPLC-MS/MS with highthe size of EVs by TEM, and most EVs were under 50 nm,
which excluded large EVs (Fig. 1), thereby achieving con-accuracy of quantiﬁcation based on the kit criteria. Moresistency with the size of EVs in previous publications.
Therefore, the metabolites extracted from EVs were esti-

than 250 molecules were lipid species, and 9 were othermetabolites. All detected molecules are listed in Supple-

mated for small EVs.

mental Table S1.

Several sample preparation procedures have been estab-We then analysed the curated data with MetaboAnalyst

lished using organic solutions, such as methanol, ethanol,
acetonitrile, isopropanol and chloroform/methanol. There-

for hierarchical cluster heatmap analysis. Two large clusters

fore, we ﬁrst proﬁled the exosomal small molecules by anisolation kit with wide polarity coverage by means of meth-

LPCs could be observed in the heatmap (Fig. 2). For

of TGs (TG group 1 and TG group 2) and clusters of PCs or
instance, a higher concentration of TGs (groups 1 and 2)

19)anol precipitation . We then prepared 50, 80 and 100%

was detected in the EVs extracted by using a solution of

methanol (500 μL, each) because the sample EV extract

80% methanol, whereas a lower concentration was detected

solution was an existing buﬀer solution (100 μL), whereasusing 50% or 100% methanol. TGs may have hydrophobic,

the estimated content in the organic solution was 42, 68,
and 80% methanol. We proposed that research on the meth-hydrophilic or both types of acyl chains in the compound

odology for isolating EVs and extracting exosomal molecules is essential, and more careful design for method opti-(PUFAs), such as TG (20:5_34:1), TG (18:1_32:3) and TG

mization, especially using organic solutions, is required forto hydrophilic acyl chains.

structure. In fact, TGs that have polysaturated fatty acids(20:2_34:2), were signiﬁcantly higher in concentration due

the future study of biomarker discovery targeting lipids orSimilarly, a higher concentration of TG (group 1) clusters

metabolites.
We prepared 200 μL samples of plasma in this study due

consisting of PUFAs, such as 18:2 and 18:3 of TG, could beobserved by using 50% methanol. In contrast, a lower con-to the limited sample volume (maximum 200 μL, each) of
the MagCapture™ exosome isolation kit. We then exam-

centration of TG (group 2) clusters consisting of saturated
fatty acids (SFAs), such as 14:0, 16:0, 18:0, or monounsatu-

ined the volume of plasma for the detection of metabolites
from exosomal samples. The total concentration of metabo-

rated fatty acids (MUFAs), such as 16:1 or 18:1 of TG,
could be observed by using 50% methanol. Therefore, thereduction in TG levels by extraction with 50% methanol
was due to the lower extraction capacity for the hydrophobic type of fatty acids, SFAs and MUFAs of TGs.
Although a higher concentration of PCs could beobserved in the heatmap by extraction with 50% or 100%
methanol than with 80% methanol due to the hydrophilicityof the phosphate group, there are many characteristic clusters of PCs that depend on the fatty acid saturation, such as

Fig. 1.

TGs. However, a higher concentration of LPCs was

Transmission electron microscopy images of EVs
derived from plasma.

detected by extraction with 100% methanol. This result wasdue to the capacity of the extracting solution, which had a

Scale bar＝100 or 200 nm.

129Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

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Fig. 2.

Hierarchical clustering heatmaps of molecules extracted by using 50, 80 or 100% methanol from EVs isolated fromhuman plasma.

Quantile and Pareto scaling were used for sample normalization and data scaling. The distance was measured by Pearson and clustered by average. The relatively higher and lower rates are shown in deeper red and blue, respectively. The three classes, 50, 80 and
100% methanol (n＝3, each), are shown in green, blue and red, respectively.

higher capability of dissolving LPC into 100% methanol,
instead of a lower dissolving rate of TGs in 100% metha-

extraction eﬃciency among the three groups. The scorenol. We then researched the XlogP3-AA (logP) values ofof the variation and characteristic distribution among thethree groups (Fig. 3). Notably, the characteristic compo-

plot of PCA indicated that PC1 and PC2 explained 88.8%

metabolites on PubChem (https://pubchem.ncbi.nlm.nih.
gov/) to estimate the relationship between the solvent prop-

nents extracted with 50% and 80% methanol were similarerty and the hydrophobicity. Lower logP values of LPC (5.6

to each other and diﬀerent from those extracted with 100%

to 12.1) were observed, and PCs, including PUFAs, had

methanol.

similar values. However, the logP values of TG were

Therefore, we ﬁrst compared the molecules between theclearly higher than those of LPC and PC. Although the

80% methanol and 100% methanol extracts by volcano

present results show one aspect of choosing lipid extraction

plot, and 57 molecules were selected with a fold change

conditions, the ability to identify the detailed diﬀerence
between isomers of lipid species is limited. Therefore, fur-(FC) threshold＞1.5 and a P value threshold＜0.05 in Fig.
4(a, top panel) and are listed in Supplemental Table S2. The
statistical signiﬁcance was calculated using the MetaboAn-

ther studies using in silico prediction methods are needed toestimate such diﬀerences. All molecular lists in the hierar-

alyst algorithm , and the molecules that were not quanti20)

chical cluster heatmap (expanding version of Fig. 2) areﬁed or not detected in one group exhibited larger variation,

shown in Supplemental Figure S1.

log2 (FC)＜−2 and 2＜log2 (FC). Several species of TGs

We next investigated PCA and volcano plot analysis towere signiﬁcantly higher on the volcano plot after

observe which molecules contributed to the diﬀerences inextraction with 80% methanol, while multiple species of130

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Fig. 3.

Score plot of principal component analysis among three groups: 50, 80 or 100% methanol extraction.
The green, blue and red dots represent 50, 80 and 100% methanol extraction, respectively.

lower values of logP than Cers.

LPCs were signiﬁcantly lower, as mentioned above.
We next compared the molecules between the 50% metha-

In general, lipid species are extracted with methanol,

nol and 100% methanol extracts by volcano plot in Fig. 4 (b,chloroform, and other organic solvents to cover as many

middle panel), and 41 molecules were signiﬁcantly higher or

lipid classes as possible , and the complexity of choosing

lower in the 80% methanol extract (Supplemental Table S2).

the mixture ratio of organic solvents for extracting small

In particular, the diﬀerence in TGs reﬂected the diﬀerence inmolecules was previously reported by Bligh and Dyer .

fatty acid molecular species contained in TGs, as described

However, the ﬁrst molecular proﬁle screening in EVs

above. However, there were no signiﬁcant molecular species

should cover the wide range of polarities not only of lipidof PCs and LPCs, whereas the levels of HexCers and Cers

species but also of polar metabolites. We therefore selected

were signiﬁcantly higher in 50% methanol. Furthermore, wemethanol precipitation based on previous publications

21)

22)

23,24),
and a total of 296 metabolites were detected in the EV sam-compared the molecules between the 50% methanol andples by kit analysis for widely targeted molecules. In fact,
the most abundant lipid species measured using the metabo-

80% methanol extracts by volcano plot in Fig. 4 (c, bottom
panel), and 42 molecules were signiﬁcantly higher or lower

lome kit was TGs, and the total concentration of TGs in the
80% methanol extract was signiﬁcantly higher than the oth-in 80% methanol than in 100% methanol (Supplemental
Table S2). TGs were signiﬁcantly lower in 50% methanol.
Although some species of PCs and LPCs were signiﬁcantly
higher in 50% methanol than in 80% methanol, they had rel-ers. The concentration of PCs was also higher in EVs, andatively low abundances in the EVs. Notably, the abundances

better than 100% methanol. On the other hand, LPCs wereof multiple species of HexCers were signiﬁcantly higher in
50% methanol. The higher hydrophilicity might be attribut-

present at lower concentrations in 80% methanol than inable to the hexose group(s), which were estimated to havetheir metabolites are essential lipid mediators involved inboth 50% methanol and 80% methanol could extract PCs

the other methanol concentrations; nevertheless, LPCs and131Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

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Fig. 4.

Volcano plots of detected molecules between (a) 80% methanol and 100% methanol extraction; (b) 50% methanoland 100% methanol extraction; (c) 50% methanol and 80% methanol extraction.
The red and blue dots with their compound names indicate signiﬁcantly higher and lower contents, respectively, between the twogroups.

132

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Fig. 5.

PAI chart of the concentrations of classiﬁed molecules as contributions to the in total concentration (μmol/L).
(a) Molecules in EVs isolated from normal human plasma (NorEVs, total concentration: 1,107 μmol/L); (b) molecules in plasma(NorPla, total concentration: 12,059 μmol/L); (c) lipid species extracted from NorPla data (total concentration: 2,422 μmol/L)

25,26)

disease expression and progression

plasma and EVs have been reported; however, these molecules could not be detected by previous GC-MS/MS analy-.

Diﬀerences in exosomal and plasma lipidomes andmetabolomes

sis owing to the very low abundances of amino acids in27)

EVs, whereas their abundances are higher in plasma . In

A total of 379 molecules were stably detected in NorPla,

the present study, although most amino acids could not be

whereas 278 molecules were detected in NorEVs by kit
proﬁling using UHPLC-MS/MS (Supplemental Table S3).

detected in all samples, even those derived from 600 μL

The total concentrations of small molecules in NorPla andplasma, corresponding to the method in the previous study,

NorEVs were 12,059±459 μmol/L and 1,107±54 μmol/L,some speciﬁc amino acids, such as cysteine, could be
detected by UHPLC-MS/MS. Cysteine and cystine are key

respectively, and the metabolites in NorEVs were found tomolecules in energy metabolism and are related to the elim-

be approximately ten times lower than those in NorPla. We

ination of oxidative stress in organisms . Exosomal aminothen discriminated the abundances of small molecules

acid metabolism is still not well understood. We therefore
focused on the variation in lipid species contained in Nor-28)

according to the primary classes in the kit, and the results
are displayed in a PAI chart (Fig. 5). The lipid species rep-

EVs and NorPla. A total of 304 lipid species were selected,

shown in Fig. 5 (a, upper panel). Notably, TGs and PCs

and their total concentration in NorPla was 2,422±33 μmol/
L, which was found to be twice the concentration in Nor-

were highly abundant in EVs, which was consistent with aEVs. The PAI chart of lipid species in NorPla is shown inresented a higher percentage in NorEVs than in NorPla, as16)

previous publication .

Fig. 5 (c, bottom right panel).

In contrast, amino acids and their related biogenic

Interestingly, CEs were more abundant in NorPla thanamines, mainly lactic acid and sugars, were present at
higher concentrations in NorPla, as shown in Fig. 5 (b, bot-

NorEVs, whereas TGs and PCs were more abundant in both
NorPla and NorEVs. Free cholesterol in the blood is esteri-

tom left panel). These diﬀerences in metabolites between

ﬁed on the surface of HDL by the action of lecithin choles133

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Fig. 6.

Correlation of concentration between plasma and EV molecules.
(a) Lysophosphatidylcholines, LPCs; (b) phosphatidylcholines, PCs; (c) ceramides, Cers; (d) hexosylceramides, HexCers;(e) sphingomyelins, SMs; (f) diacylglycerols, DGs; (g) triacylglycerols, TGs.
The Pearson correlation coeﬃcient (r) and P value are shown for each lipid class plot.
30)
stress-induced major depressive disorder , and the adiponectin/T-cadherin system enhances EV biogenesis and29)

terol or free cholesterol in the blood . The CEs are mainly
produced by cholesterol acyltransferase and are highly
abundant in the very low-density lipoproteins (VLDL) and31)

secretion, leading to a decrease in cellular ceramides . In

low-density lipoproteins (LDL) in the blood. On the otherfact, EV secretion was increased by overexpression of thehand, large amounts of TGs are contained or accumulated in

ceramide synthase neutral sphingomyelinase . Therefore,chylomicrons in blood, whereas TGs are lower in HDL and
LDL. Therefore, the content of TGs and CEs directly indi-exosomal ceramide has an important role in mediating cellsignals from donor cells. In contrast, the exosomal concen-cates the ratio of lipoproteins in blood and EV diﬀerences.
Previously, it has been demonstrated that ceramide-en-

tration of Cer was lower than that in plasma in the present

riched EVs in the blood plasma are increased in mice withto proﬁle the molecules in EVs , and the concentrations of32)

study. In general, ten to hundreds of samples were prepared
33)

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Fig. 7.

Volcano plots of lipid species detected in EVs and plasma.
The red and blue dots with their compound names indicate signiﬁcantly higher and lower contents, respectively, between the twogroups.

EV lipid classes were approximately 2‒12 times lower than

the function of EV TGs is needed.

16)

those in plasma . Although the number of plasma EVs

On the other hand, CE (20:0), SMs, Cers, and HexCers

derived from healthy and unhealthy individuals should be
examined to conﬁrm the Cer concentration in future stud-were more abundant in NorPla, whereas PCs with SFA or
MUFA were found to be signiﬁcantly less abundant in Nor-ies, the value of EV/plasma Cer may have potential as a

Pla than in NorEVs. The results indicated that the composi-biomarker.

tion of exosomal lipid species was diﬀerent from that of the

NorEVs and NorPla in each lipid class. The concentrations

original plasma and corresponded to the previous exosomal
16)
lipidomic analysis . Indeed, although LPCs were signiﬁ-of the lipid classes (number of species) in NorEVs forcantly lower in NorEVs than in NorPla, we excluded the

LPCs (11), PCs (68), Cers (14), HexCers (11), SMs (13),

LPCs from the comparative analysis to prevent an artefact

DGs (4) and TGs (139) were highly correlated (r＞0.973)
with those in NorPla (Fig. 6). Interestingly, the concentra-

due to the lower extraction eﬃciency of 80% methanol fortion of TGs in EVs was similar to that in the original

shown in supplemental Figure S3.

We next investigated the correlation of concentration in

LPC extraction from NorEVs. The original volcano plot is
In this study, we discussed the quantiﬁcation of total

plasma (Fig. 6g), whereas PCs and the other lipid species
were 1.5‒8.0 times lower.

metabolites and lipids in plasma and EVs. Although thecontributions to the diﬀerence between NorEVs and NorPla

determination method followed the protocol of the MxPQuant 500 Kit, which was established based on the stan-by volcano plot analysis (Fig. 7). The values were mediandard operating procedure for method validation, the quanti-

normalized and palate scaled by MetaboAnalyst. TGs wereﬁed values of lipid species should be evaluated with othersigniﬁcantly higher in NorEVs than NorPla, notably, thosemethodologies . Therefore, for the major lipids and

TGs with PUFAs. EVs mediate cell signals to inhibit the34)
eﬀects of oxidative stress , and PUFAs react with antioxi-

metabolites, recovery rates need to be proven for plasma

We then investigated the lipid species with the greatest

36)

and EV samples by additive recovery studies in the future.

35)

dants to reduce oxidative stress . Therefore, EVs may

In summary, exosomal small molecules, including lipid

accumulate PUFAs in TGs to reduce the eﬀects. A detailed

species and metabolites derived from human plasma, have
25,27). However, the detailed variabeen analysed previously

examination focusing on the antioxidative eﬀect to reveal
135

Medical Mass Spectrometry “Lipidomics” Vol. 6 No. 2

November 2022

tion in lipid species remained unknown. We demonstrated

XO: RNA delivery by extracellular vesicles in mammali-

the diﬀerence in lipid species in TGs, focusing on SFAs,

an cells and its applications. Nat Rev Mol Cell Biol

MUFAs and PUFAs, and the present results are essential toreveal the function of EVs and utilize them for future bio-21(10): 585‒606, 2020.
3) Hoshino A, Costa-Silva B, Shen TL, Rodrigues G,

marker discovery in the clinical ﬁeld.

Hashimoto A, et al: Tumour exosome integrins determineorganotropic metastasis. Nature 527(7578): 329‒335,

Author Contributions

2015.

Conceptualization, D.S., A.T., No.M. and N.S.; method-4) Fan Y, Chen Z, Zhang M: Role of exosomes in the patho-

ology, D.S., Y.I., K.U., E.H. and Na.M.; software, D.S.,

genesis, diagnosis, and treatment of central nervous sys-

E.H., and T.H.; data curation, D.S., T.H., K.U., E.H. and
N.M.; writing̶original draft preparation, D.S.; writing̶

tem diseases. J Transl Med 20(1): 291, 2022.
5) Johnson CH, Patterson AD, Idle JR, Gonzalez FJ. Xenobi-

review and editing, D.S., T.H, A.T., Na.M. A.T., No.M. andotic metabolomics: Major impact on the metabolome.

N.S.; visualization, D.S., Y.I.; supervision, A.T., No.M. andAnnu Rev Pharmacol Toxicol 52: 37‒56, 2012.

N.S; funding acquisition, D.S., K.U., A.T., No.M. and N.S.

6) Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics:

All authors have read and agreed to the published version

Beyond biomarkers and towards mechanisms. Nat Revof the manuscript.

Mol Cell Biol 17(7): 451‒459, 2016.
7) Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats

Acknowledgements

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This work was supported in part by the Tohoku Medical
Megabank Project from MEXT, the Japan Agency for Med-

8) Saigusa D, Matsukawa N, Hishinuma E, Koshiba S: Iden-

ical Research and Development (AMED; under grant num-tiﬁcation of biomarkers to diagnose diseases and ﬁnd ad-

bers JP20 km0105001 and JP21tm0124005), the Project for
Promoting Public Utilization of Advanced Research Infra-

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work for Research Equipment (MEXT). A part of this work

typing in health and disease. Cell 134(5): 714‒717, 2008.

was also supported by KAKENHI Grant Number10) Wishart DS: Emerging applications of metabolomics in

JP20H03374 [D.S.], AMED LEAP under Grant Number

drug discovery and precision medicine. Nat Rev Drug

J200001087 [D.S.], and a grant from Boehringer Ingelheim[K.U., N.S.]. We thank the members of the Central Labora-

Discov 15(7):473‒484, 2016.
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proﬁling of phosphoinositide regioisomers and their dis-cal, Taiho Pharmaceutical, Pﬁzer Japan Inc., Ono Pharmaceutical, Nippon Boehringer Ingelheim Co., Ltd., and Bris-

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