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Medical Mass Spectrometry Vol. 7 No. 1

Review

Metabolic proﬁling by integrated analysis with LC-MS and MSI
1, 2
Daisuke Saigusa *1

Abstract

Laboratory of Biomedical and Analytical Sciences, Faculty of Pharma-Science, Teikyo University,2‒11‒1 Kaga, Itabashi-ku, Tokyo 173‒8605, Japan
2
Tohoku Medical Megabank Organization, Tohoku University,
2‒1 Seiryōmachi, Aoba-ku, Sendai, Miyagi 980‒0872, JapanThe metabolome is a group of small molecules that reﬂect phenotypic changes; this set of molecules is a poten-

tial source of biomarkers to predict the risk of disease development and progression. Therefore, it is necessary to establish acomprehensive and precise method to analyze the metabolome in biological samples. Mass spectrometry (MS) is one of themost suitable technologies to detect those molecules for metabolic proﬁling. In particular, liquid chromatography (LC)-MSbased methodology has been applied to various disease models and clinical samples, and biomarker candidates have been
identiﬁed in previous studies. However, it is necessary to clarify their functions by in vivo research, and their molecularmechanisms in cells and/or tissues should be considered before the markers are deemed clinically applicable. MS imaging(MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus can clarify the
distribution of biomarker candidates on tissue to evaluate the results by means of LC-MS-based analysis. Here, we describe
the outline of the protocols for biomarker discovery by LC-MS and explain the utilization of integrated analyses using LCMS and MSI to evaluate the function of biomarker candidates.
Key words: metabolome, biomarker, mass spectrometry, LC-MS, MSI

1.

Introduction

Therefore, the development of novel analytical technology

It is expected that next-generation medicine will provide
us with advanced and accurate medical technologies to pre-is required to understand the eﬀects of not only genetic but

dict and prevent disease processes for early diagnosis and

The metabolome is a group of small molecules that

also environmental factors on biological processes.

1)

personalized medicine . While genetic diagnosis has madedirectly reﬂect the phenotypic changes derived fromremarkable progress in the clinic, the identiﬁcation of risk

genetic and environmental factors . Therefore, metabolic

factors (biomarkers) to anticipate and prevent diseasedevelopment remains a challenge because multiple environ-proﬁling can be utilized to understand the origins andmental factors, including lifestyle, habits, diet, stress andhave already been established for metabolic proﬁling in2)

gut microbiota, are inﬂuenced by disease manifestations .

biology , and recent technological improvements in the*Corresponding author

system sensitivity, molecular coverage and analyticalrobustness of liquid chromatography-mass spectrometry

3)

4, 5)

mechanisms of disease processes. Several technologies

6)

Daisuke Saigusa(LC-MS) have made it possible to detect and annotate awide variety of metabolomes and have contributed particu-

Laboratory of Biomedical and Analytical Sciences, Faculty of
Pharma-Science, Teikyo University, 2‒11‒1 Kaga, Itabashi-ku,
Tokyo 173‒8605, Japan
Tel: ＋81‒3‒3946‒8069

larly to research on biomarker discovery using disease

Received: February 10, 2023. Accepted: March 29, 2023.

conducted using bioﬂuids, such as plasma, serum, urine andEpub May 16, 2023.

cerebrospinal ﬂuid . Although metabolic proﬁling by
LC-MS can obtain quantitative information, the tissue must

7, 8)

models and clinical samples derived from humans.

Metabolic proﬁling for biomarker discovery is generally

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

9)

DOI: 10.24508/mms.2023.06.009
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Medical Mass Spectrometry Vol. 7 No. 1

be homogenized as part of the preparation process before

a wide range of molecules in the metabolome, small mole-metabolic proﬁling, and information on the localization ofcules are often extracted from plasma and tissue samples

10)

the molecules is lost .

using deproteinization methods with organic solvents, such
16, 17). However, while deproas methanol and acetonitrile

Mass spectrometry imaging (MSI) is a promising
approach that allows label-free detection and mapping ofteinization has advantages in extracting hydrophilic mole-the metabolome on biological sample surfaces and provides

cules, including amino acids, organic acids and sugars, it islocalized information by visualizing molecules on tissueless eﬃcient in extracting hydrophobic molecules, such as11, 12). Thus, MSI enables the spatial localization ofmolecules. This approach has been utilized to facilitate bio-

lipid species. Therefore, it is recommended to select themarker discovery, and it has contributed greatly to validat10, 13). Therefore, combining LC-MS
ing molecular functions

which are suitable for extracting hydrophobic molecules toand MSI is expected to provide additional reliability andComprehensive analysis of lipid species is known as lipi21, 22), and a number of disease-related lipid mediadomics

sections

18, 19)Folch method or the Bligh and Dyer method, both of20)

target lipid species in biomarker discovery research .

14)clarify the functions of biomarker candidates . In this23)

tors have been identiﬁed in previous studies . However, it

review, we ﬁrst describe the typical protocol, including
sample preparation; data acquisition by LC-MS-based metabolic proﬁling; data processing; and multivariate analysis

is important to consider targeting lipid species according totheir polarity range by means of diﬀerent extraction meth-for biomarker discovery. Then, we illustrate the integrated
use of LC-MS and MSI by summarizing previous studies

ods. Fig. 2(a) illustrates the total ion current/chromatogram(TIC) from lipidomic proﬁling by LC-MS using methanolthat have used this approach for disease biomarker discov-

deproteinization and the Folch method for extraction fromery.

human plasma based on the previously established method.

2.

The most abundant and signiﬁcantly diﬀerent molecules

Biomarker Discovery by LC-MS

indicated by the S-plot of orthogonal partial least square
discriminant analysis (OPLS-DA) in the deproteinization

Recent improvements in methodology for sample prepa-

ration, analytical methods and data processing have enabled

method are shown in Fig. 2(b). The identiﬁed lipid species
that were signiﬁcantly more abundant with the deprotein-researchers to realize biomarker discovery using disease
15)
models and clinical samples . The typical protocol of met-

ization method were lysophosphatidylcholines (LPCs),

abolic proﬁling for biomarker discovery is shown in Fig. 1.

lipid species that are relatively hydrophilic due to a groupof phosphates in their molecular structure, whereas they

2.1.

Sample preparation

were detected at lower levels with the Folch method. In
contrast, the hydrophobic lipid species annotated as tri-

First, since biomarker discovery generally aims to detect

Fig. 1.

Typical protocol of metabolic proﬁling by LC-MS for biomarker discovery.
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glycerides (TGs) and cholesteryl esters (CEs) were signiﬁ-

32)

cantly more abundant with the Folch method because theysample every 2 h .
Methods of LC-MS-based metabolic proﬁling targetingwere extracted with high eﬃciency by chloroform. In fact,
similar results were observed in the tissue lipidomics analy-hydrophobic molecules have been developed using C18
33)
columns in previous studies . However, functional metab-sis (data not shown). Thus, appropriate sample preparation

olites contain many types of hydrophilic molecules, such as

methods for biomarker discovery targeting lipid speciesamino acids, organic acids, nucleic acids and sugars, andshould be carefully considered.
Recently, kit-based metabolic proﬁling has become possi-

hydrophilic interaction chromatography (HILIC) columns
have been utilized to retain those hydrophilic molecules by
31, 33‒35). Ten injections of the QC
means of LC-MS analysis

ble with ultrahigh-performance liquid chromatography triple quadrupole mass spectrometry (UHPLC-MS/MS),as the conditioning column were necessary to maintain sta-

which is performed in strict accordance with a standard

ble analysis using the HILIC column until data acquisition

operating procedure involving detailed documentation for

was completed .

sample preparation, instrument setup, system suitability
testing, and data analysis; this technique can quantify sev-

Recently, mixed-mode columns that can retain bothhydrophilic and hydrophobic molecules have been devel-. For example, UHPLC-MS/

oped and demonstrated to be useful for biomarker discov36, 37). Shotgun metabolic proﬁling by FIA analysis withery

24‒26)

eral hundred metabolites

31)

MS can detect representative metabolites such as aminoacids, amino acid‒related metabolites, bile acids, biogenicout columns has been conducted for large-scale analysis for
38)
high-throughput metabolic proﬁling . However, there areamines, cresol, fatty acids, hormones, indole derivatives,

still disadvantages for the accuracy of quantitative values,

nucleobases and vitamins; for this reason, the technique is27, 28). Howwidely applied in biomarker discovery projects

and careful evaluation is needed in future studies.

ever, the lipid species detected by the kit (such as acylcarni-

2.3.

tines, ceramides, CEs, diacylglycerols, dihydroceramides,
and glycerophospholipids including LPCs and phosphati-

Data processing

dylcholines (PCs), glycosylceramides, sphingolipids, andMetabolic proﬁling is generally classiﬁed into global
metabolomics (G-Met), which consists of untargeted metab-

TGs) include several isobaric and isomeric compounds thatcannot be separated by ﬂow injection analysis (FIA)-MS/

olomics for comprehensive analysis of thousands of molecules, and targeted metabolomics (T-Met) or widely tar-MS. These isomers are detected together, and low-abun-

geted metabolomics (WT-Met) for quantiﬁed analysis ofdance species are detected due to ion suppression, whichtens to hundreds of molecular species .
High-resolution MS (HRMS), such as time-of-ﬂight

7)

particularly compromises the accurate quantiﬁcation of
26)

lipid species . Therefore, it is necessary to ﬁrst select the(TOF) or Fourier transform (FT) MS, is generally used for
G-Met and is suitable for biomarker discovery because it

optimal pretreatment method for metabolic proﬁling.

provides a large quantity of spectral data and has the poten6, 33‒35)

Data acquisition by LC-MS-based metabolic
proﬁling. However, data
tial to detect unknown compounds
analysis of G-Met consumes much time due to complicatedIn the process of data acquisition, the run order should be
29, 30).
considered by metabolic proﬁling by LC-MS analysis

data preprocessing, such as chromatogram alignment, peak

The quality control (QC) standard was ﬁrst prepared byexpertise in data analysis for MS spectra is often required

mixing all the samples. Then, this QC sample is subjected
to LC-MS analysis 5 to 10 times under the appropriate con-to identify molecules using databases.
In contrast, T-Met and WT-Met were analyzed with anditions for the step, which depends on the type of analytical

2.2.

7)

detection, and data curation to eliminate artifacts . Indeed,column . After the QC injection, the actual samples are

MS/MS system. The data preprocessing is generally simple
because the spectra have been obtained by chemical stan-

injected in randomized order, with the QC being repeateddards of known metabolites, and these techniques can beonce in an optimized number of injections to correct for thetrends of intensity variation among individual molecules orused as a beginning step in biomarker discovery .
Although there is a possibility that signiﬁcant unknown bio-the overall median value, which may be inﬂuenced by themarkers will be missed, the quantitative information pro-

31)

6)

31)

run order . Previous studies recommend injecting the QC

vided by these techniques also provides a major advantage
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Fig. 2.

Medical Mass Spectrometry Vol. 7 No. 1

Example of the diﬀerence in detected molecules between the deproteinization and Folch extraction methods by lipidomic proﬁling by LCMS analysis.
(a) Total ion current/chromatogram (TIC) using methanol deproteinization (Dep.) and the Folch extraction method for humanplasma lipidomic proﬁling. The sample preparation procedures are shown below, and the analytical method was performed by thepreviously established method (ref. 26). Surrounded by dots square at 5 and 20 min indicates the detection ranges for lysophosphatidylcholines (LPCs) and cholesterylesters (CEs)/triglycerides (TGs), respectively. NL: normalized(b) S-plot of orthogonal partial least square discriminant analysis (OPLS-DA). The left bottom surrounding dotted square shows
36 signiﬁcantly lower features in Folch with p(corr)[1]＜−0.9, p[1]＜−0.02, p value＜0.05, and fold change＞2.0. The right uppersurrounding dotted square shows 91 signiﬁcantly higher features in Folch with p(corr)[1]＞0.9, p[1]＞0.02, p value＜0.05, and foldchange＞2.0.
(c) Histograms of molecules signiﬁcantly diﬀerent between the two extraction methods.

in later functional analysis.

trends as the ﬁrst step to perform metabolic proﬁling insuch a large-scale data set without bias. In particular, princi-

2.4.

pal component analysis (PCA), in which data obtained in
multiple dimensions are represented in fewer new dimen-

Multivariate analysis for biomarker discovery

Multivariate analysis is widely used for large amounts of39)

metabolome information . Although the analysis of eachdetected individual metabolite has the potential to be a bio-sions designated PC1, PC2, PC3, ..., PCn by reducing amarker, multivariate analysis is often utilized to capture

dimensions, is an essential method to understand the trends

data set that originally contained hundreds to thousands of6

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40)47, 48)in the metabolic proﬁles of interest . Therefore, PCA is
the ﬁrst option for multivariate analysis in biomarker dis-to biomarker discovery

covery, as it often yields characteristic distributions on aof disease with higher sensitivity and speciﬁcity without

score plot or PCA.

being aﬀected by the artifacts described above.

. This method has the potential toidentify biomarker candidates indicating the characteristics

On the other hand, metabolic proﬁles of human speci-

Metabolic proﬁling has also been conducted with urine,mens are often aﬀected by not only environmental factors

feces, cells and tissues. Although a large variation in molecular concentration could be observed in urine among indi-such as diet and lifestyle but also artifacts such as time ofblood collection and storage procedures and periods, making it impossible to observe a clear PC axis that capturesviduals, it has been found that reliable urine results can beachieved with appropriate dilution according to a preanaly-

trends of data and distributions that show characteristics on

sis of creatinine values . In addition, the eﬀect and rela-a score plot of PCA for extracting disease biomarkers .
Discriminant analysis methods such as PLS-DA and41)

tionship with gut microbiota can be analyzed by metabolic
50)
proﬁling of feces . On the other hand, some previous stud-OPLS-DA, which add group parameters as a new dimen-

ies have identiﬁed markers directly related to diseases bysion, can be used to observe the trends of data and extract
the contributing factor(s). In fact, volcano plots and S-plots

using cells and tissues derived from patient specimens

provide a variable importance projection score, a criterion

ples because most of the samples were collected fromfor evaluating signiﬁcance, and are utilized for extracting
metabolites as biomarker candidates that contribute to dif-patients in hospitals.

49)

51, 52);
however, it was extremely diﬃcult to obtain control sam-Thus, biological specimens for metabolic proﬁling are40, 42).
ferences between the two selected groups
Although license-based software and programs havebecoming more diverse, and it is necessary to have morealready been developed by the producers of MS systems tocomplicated and large data sets to extract biomarkers andperform multivariate analysis, the freely available online

understand their biological function with a simpler andsoftware MetaboAnalyst (https://www.metaboanalyst.ca/)

more precise strategy for metabolic proﬁling in the future.

has become increasingly popular in recent years due to thesimple process of loading data prepared in text or csv for-3.

Analysis of Metabolic Proﬁles by Combined LC-MS and MS Imaging

3.1.

Impact of metabolic proﬁling in tissue

43, 44)mat, and it can be used for most multivariate analyses

bioinformatics knowledge and techniques to treat more.

In fact, there are several options for multivariate analysis
that contain complicated parameters for metabolic proﬁlabolic changes in groups or biological specimens to select

Although several biomarker candidates have been identiﬁed by LC-MS-based T-Met, sensitivity and speciﬁcity arean optimal multivariate analysis.

not satisfactory for application in the clinical ﬁeld due toAn overview of typical protocols for metabolic proﬁling
by LC-MS has been described above, but there are still

the metabolites, such as amino acids and lipid molecular

ing; however, it is essential to consider the variation in met-

53, 54)points to be noted depending on the type of biological spec-. In contrast,
species, being altered by multiple diseases
most metabolites annotated by G-Met are often less known

imens. In general, plasma and serum are used for biomarker

or unknown in relation to disease; however, G-Met some-7)

discovery using metabolomics techniques . However,
metabolome analysis in a large-scale cohort revealed thattimes has extremely high potential to identify biomarkers
even if the metabolites were unknown at the time of identi-

blood-derived metabolites show large variation due to envi-

ﬁcation .

ronmental factors, such as age, gender, body mass index,.

For instance, Kikuchi et al. reported phenyl sulfate (PS),which is derived from the intestinal microbiota and pro-

Therefore, it must be considered that identiﬁed molecules

duced via biosynthetic enzymes in the liver, as an unknown

take into account the possibility of false positives and/or
negatives in blood-based biomarker discovery.

molecule that accumulates in plasma from a rat model of

Recently, there have been some studies on metabolic proﬁling using extracellular vesicles derived from blood biopsy;

They demonstrated that accumulation of PS in the kidney
was suppressed in transgenic rats with the excretion trans-

these studies have reported the possibility of a new approach

porter SLCO4C1 in proximal tubular cells. They examined

45, 46)

food and lifestyle habits, as well as genetic factors

55)

55)

diabetic kidney disease by treatment with streptozotocin .

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molecular biological techniques to demonstrate the function

ﬁling are shown in Fig. 3. In general, metabolites areof PS using animal models and showed that the inhibitor

extracted by homogenizing a certain amount of tissue with

suppressed PS accumulation in the kidney. Furthermore,an organic solvent (Fig. 3(a)). Since whole or partial tissue

they quantiﬁed PS in hundreds of human samples andis prepared for biomarker discovery, it is utilized to reveal

revealed that PS could be a prognostic marker for diabetic
renal dysfunction after 2 years of correlation with clinicalthe diﬀerences in metabolic changes associated with thelevel, presence or absence of disease onset between the dis-

information, indicating that environmental factors haveease model and control

potential as biomarkers for application in clinical practice.

tion of metabolites is completely lost during the sample

Furthermore, they clariﬁed the distribution and accumu-

preparation procedure. Therefore, it is necessary to apply

lation of PS contained in kidney sections by MSI andthe optimal method to preserve the localized information of
metabolites for the tissue, which has a complicated struc-56, 57)

showed by visualization that administration of azatyrosine,. However, the spatial distribu-

which is an inhibitor of PS synthetic enzymes, suppressed55)
the accumulation . A previous study showed the impor-ture such as the brain.

tance of validating molecular function using other metabolomics techniques, such as tissue metabolomics, to demon-

tribution analysis . Two typical analytical techniques are

In this case, a cryosection of tissue is often used for dis58)

shown in this review. One is a laser microdissection (LMD)

understand function with a high degree of conﬁdence .

technique, which can cut the small region of interest formolecular localization. The other is the MSI technique rep-

3.2.

resented by matrix-assisted laser desorption/ionization(MALDI)-MSI, which can visualize the detailed distribu-

strate the advancement of candidate disease biomarkers to
10)Strategies of metabolic proﬁling in tissue

The characteristics of strategies for tissue metabolic pro-

Fig. 3.

tion by reconstructing the spectral intensity and spatial

Strategies of metabolic proﬁling in tissue.

(a) Bead-homogenizing procedure of whole or partial tissue by organic solution(b) Laser microdissection (LMD) procedure of a tissue section(c) MALDI-MSI procedure of a tissue section8June 2023

Medical Mass Spectrometry Vol. 7 No. 1

62)

information.

sidered .

3.3.

3.4.

LMD for metabolic proﬁling

As shown in Fig. 3(b), LMD can capture the characteris-

MALDI-MSI for metabolic proﬁling

Recently, MSI technology has been improved and utilized in many studies to reveal the localization of metabo-tics of a wide range of tissues, such as the brain, kidney,
liver, and spleen, which are assumed to be widely distrib-

63)

lites in tissue sections . MALDI is the most widely usedtechnique for MSI, whereas other ionization technologies,

uted to some extent, whereas it cannot observe the cell59, 60). Wang et al. demonstrated the
localization of sphingosine 1-phosphate (S1P), which is one

such as desorption electrospray ionization, secondary ionof the lipid mediators in the red and white pulp on the
spleen, quantiﬁed by LC-MS/MS, showing the diﬀerence inanalysis, have been developed

level of distribution

mass spectrometry imaging or liquid extraction surface
64, 65). Although this review

does not describe much about the development of MSI
instruments, MSI is utilized in the research ﬁeld of bio-61)

S1P concentration in the LMD sections .
While the tissue distribution of metabolites in LMD sec-

marker discovery, and many biological molecules havetions highly corresponds with the results of partiallyhomogenized tissue analysis by LC-MS/MS, LMD works at

been identiﬁed with the development of MSI technology.

room temperature due to the combination with a micro-

shown in Fig. 4. After preparing the tissue section andscopic system. Therefore, the stability of temperature forobtaining the visualized image by the MSI protocol, as

target metabolites should be evaluated in advance by LMD
assay because some metabolites, such as S1P, have a phos-shown in Fig. 3(c), the localized spots were selected byphate group in the chemical structure, and the observed
time depends on the reduction in tissue at room tempera-

extracted by multivariate analysis, and a similar process tothe protocol of biomarker discovery by G-Met was used.

The typical strategy of biomarker discovery by MSI is

imaging software. Then, biomarker candidates were

Therefore, the combination of G-Met by LC-MS and distri-59)

ture . In some cases, a heat stabilizer technique is utilized
to suppress the decomposition of tissue metabolites; how-bution analysis by MSI using tissue samples has promoted
research that takes advantage of the beneﬁts of both meth-ever, the other eﬀects of heating specimens should be con-Fig. 4.

Typical strategy of biomarker discovery by MSI.

Experiment 1 (ex.1) or ex.2 was the representative comparison between wild type and knockout or nontumor and tumor analysis by MSI.
*OPLS-DA: orthogonal partial least square discriminant analysis#
PCA: principal component analysis

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Table 1.
Disease

Combination analysis of biomarker discovery by LCMS and MSI

Model/humanSpecimine

Molecule/s

LCMS

MSI

Ref.

clear cell renal cell
carcinoma

humankidneylipid species

LC-FTMSMALDI-LTQ-FTMS

14)

hepatocellular
carcinoma (HCC)

human

liver

triglycerides

LC-QTOF/MS

DESI-MSI

52)

diabetic kidney disease(DKD)

DKD model micekidneyphenylsulfate

LC-QTOF/MS

MALDI-TOF/MS

55)

ischemia‒reperfusion
injurymiddle cerebral artery
occlusionbrainamino acid, nucleotides,
tricarboxylate cycle
metabolites

LCMS-IT-TOF

MALDI-TOF/MS, QIT,TOF/TOF66)

glaucoma

optic nerve crush mice retina

L-acetylcarnitine,
phosphatidylcholine

LC-FTMS,LC-QTOF/MS

MALDI-TOF/MS

67)

diabetic

db/db mice

Whole body, brain,
liver, kidney, heart,
alanine, aspartate, andspleen, lung, muscle, glutamate metabolism
and pancreas

LC-FTMS

airﬂow-assisted desorption
electrospray ionization(AFADESI)-MSI

79)

acute liver failure

acetaminophen-induced acute liver
failure miceliver

oxidized
phosphatidylcholines

LC-FTMS,
LC-MS/MS

MALDI-MS/MS,
MALDI-MSI

80)

knee osteoarthritis

humantibial plateau tissue

N-glycans

LC-QTOF/MS

MALDI-TOF/TOF

81)

Niemann-Pick diseasetype C1 (NPC1)

NPC1 model micebrain

phosphoinositides

LC-ESI-QTOF/MS,
MALDI-TOF/TOF
LC-MS/MS83)

adenocarcinoma

human

lung

phosphatidylcholines,
sphingomyelins

LC-QTOF/MS

MALDI-TOF/MS

84)

cauda equina
compression (CEC)

CEC model rat

spinal cord, dorsal
root ganglia

lysophospholipids

LC-MS/MS

MALDI-TOF/MS

85)

sarciopenia

adenine-induced
chronic kidney disease skeletal muscle(CKD) model miceindoxyl sulfate

LC-MS/MS

MALDI-TOF/MS,
MALDI-QIT-TOF/MS

86)

CKD

adenine-induced
chronic kidney disease kidney(CKD) model miceindoxyl sulfate, p-cresyl
sulfateLC-MS/MS

MALDI-TOF/MS,
MALDI-QIT-TOF/MS

87)

preeclampsia (PE)sFlt-1-overexpressing
PE miceplacentacarnitine and acylcarnitine

LC-FTMS,LC-QTOF/MS

MALDI-TOF/MS

88)

traumatic brain injury(TBI)

TBI ratbrain

peroxidized
LC-FTMS
phosphatidylethanolamine

gas cluster ion beam
secondary ion mass
spectrometry

89)

ods

13, 14)

CTX and CPu were observed after reperfusion, demonstrating that the correlation between MSI and LC-MS data was. A summary of the combination studies based on aprevious publication is listed in Table 1.
Irie et al. analyzed rat transient middle cerebral artery

relatively high in the CTX and CPu.

occlusion (MCAO) brain tissue after ischemia‒reperfusion
to characterize the detailed metabolomic response to patho-

Sato et al. analyzed retinal samples from a mouse modelof glaucoma in which retinal ganglion cell (RGC) death
was induced by nerve crush (NC) treatment by G-Met anal-

66)

logical alterations . They compared the spatially resolved
metabolic state between ischemic and contralateral hemispheres of the MCAO brain, and coronally sliced tissues

ysis and identiﬁed molecules that were signiﬁcantly
increased or decreased on NC day 2, day 4, or day 7 com-were subjected to MSI. They also measured the metabolites

pared with their controls . The identiﬁed metabolites wereextracted from three diﬀerent cerebral regions, including

detected in RGCs, and histological sections were obtainedfor MSI. Two metabolites, L-acetylcarnitine and phospho-67)

the whole cortex (CTX), hippocampus and corpus striatum(CPu), by LC-MS, and signiﬁcant metabolic changes in thelipids, were increased not only preceding the peak of RGC
10

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death in the whole retina but also in the RGC layer.

irradiation technique that allows ions to be detected with

Nagai et al. identiﬁed hepatocellular carcinoma (HCC)biomarkers in human liver samples by G-Met and demon-

stable intensity even when the spatial resolution is

strated their localization in cryosections using the desorp52)
tion electrospray ionization (DESI)-MSI technique . From

dopamine, and norepinephrine levels in the whole brain of
the mouse, obtained by the analysis of continuous cryosec-

their multivariate analysis, m/z 904.83 and m/z 874.79 weresigniﬁcantly high and low, respectively, in tumor samples

tion, and the technique was utilized to observe the dynam76)
ics of molecules by MALDI-MSI .

and were identiﬁed as TGs and clearly localized in thetumor or nontumor areas of the cryosection. Thus, the strat-

Other new technologies, such as desorption ionization
using a through-hole alumina membrane (DIUTHAME),

egy of combining LC-MS and MSI contributes to the under-

based on premanufactured nanostructured membranes to be77)
deposited on top of a tissue section , show promising fea-

75)

reduced . Sugiyama et al. generated an atlas of serotonin,standing of molecular function as a disease biomarker.

tures for easy and reproducible sample preparation andHowever, even if a biomarker was identiﬁed with
LC-MS-based metabolic proﬁling, some molecules could

have demonstrated a higher lateral resolution of 5 μm at
high mass resolution using atmospheric-pressure MSI of

not be detected by MSI due to the compatibility with the
optimal matrix for MALDI-MSI to promote ionization. In

biomolecules from native tissues from diﬀerent organisms
78)

addition, the absolute number of ionized molecules isin a previous publication .

reduced by physical means because the laser irradiation of
MALDI is focused on a small number of cells at 10‒50 μm

of candidate biomarker molecules identiﬁed by LC-MS haveThus, novel methods that enable highly precise evaluation

68)

spatial resolution .

been developed and are expected to be used in the future.

In these cases, chemical derivatization is utilized to overcome the detection limit for speciﬁc molecules and promis-

3.5.

Technical limitations of LC-MS and MSI

Although MS-based techniques are utilized for biomarker

ing techniques in visualizing minor and poorly ionizable
69)
molecules by MALDI-MSI . A target analyte is derivat-

discovery, there are still some technical limitations. For
instance, in the data preprocessing step, time-dependentized by introducing a molecular tag to enhance its ionization eﬃciency. The tag also increases the molecular weightvariation could sometimes be observed on the score plot of
PCA by LC-MS analysis. Normalization by the result of

to avoid the eﬀect of spectral noise. Recently, a method foron-tissue derivatization using chemical reagents, which areintermediate QC analysis is able to work to suppress the

directly deposited onto tissue, was developed. For instance,
Girardʼs reagent T and 2-picolylamine enhance the detectioneﬀect of the variation; however, it is impossible to apply.

Speciﬁcally, detection in the m/z region, which is not easily

sample analyses in the clinic and cohort. Moreover, even if
biomarker candidates have been extracted in several stud-aﬀected by increased ionic strength or ion suppression,
allows separation from background ions and high sensitiv-

ies, it is diﬃcult to annotate accurate molecular structuresby hand using database searching due to isomeric and iso-ity detection.

baric molecules, especially lipid species. Recently, software

Iwama et al. developed a novel on-tissue derivatization
method using Phos-tag, a zinc complex that speciﬁcallyusing fragment mass spectra has been developed . It is apromising approach and is utilized for the accurate identiﬁ-

binds to a phosphate monoester group, and made it possible

cation of molecular structures and estimating future

to image lysophosphatidic acid and S1P in the mouse brain
73)
by MSI . Uruno et al. utilized N-ethylmaleimide to avoid

improvements.

nonspeciﬁc reactions to the thiol residue of glutathione(GSH) by MALDI-MSI and demonstrated that GSH levels

and system limitations of data acquisition by MSI could bein various parts of the brain are increased in the Alzheimerʼs

irradiation of the MSI system, it is still diﬃcult to observe
the molecular distribution at the single-cell and organelle

70‒72)of corticosteroids and free fatty acids, respectively

the result on a large scale, such as over tens of hundred

90)

In contrast, technical limitations of sample preparation
observed. For instance, due to the size limitation of laser

disease model mouse brain, perhaps due to increased
74)
nuclear factor-erythroid 2-related factor 2 activity .
In addition, there have been many improvements inlevels. In addition, the stability of the laser control system
of MSI is essential for reproducible imaging and maintain-

recent years, such as the development of a two-step laser

ing the higher sensitivity detection of molecules. Although11June 2023

Medical Mass Spectrometry Vol. 7 No. 1

environments. Nature 491(7424): 384‒392, 2012.

the molecular localization could be observed at 0.6 μm of
spatial resolution, which reached the single-cell level of3) Holmes E, Wilson ID, Nicholson JK: Metabolic pheno-

detection by a t-MALDI 2 MSI system , higher abundanttyping in health and disease. Cell. 134(5): 714‒717, 2008.

molecules could be detected on the tissue section, and more
improvement is still needed to see lower abundant mole-

4) Trifonova OP, Maslov DL, Balashova EE, Lokhov PG:

75)

Current state and future perspectives on personalized me-

cules. In addition, when the spatial resolution reaches the
single-cell level, higher quality tissue sections are required

tabolomics. Metabolites 13(1): 67, 2023.
5) Rinschen MM, Ivanisevic J, Giera M, Siuzdak G: Identiﬁ-

for MSI analysis. A conductive adhesive ﬁlm has beendeveloped for cutting high-quality tissue sections and has

cation of bioactive metabolites using activity metabolom-

been applied for MALDI-MSI . Therefore, future technological improvements are needed for MSI analysis for bio-6) Azad RK, Shulaev V: Metabolomics technology and bio-

ics. Nat Rev Mol Cell Biol 20(6): 353‒367, 2019.

58)

informatics for precision medicine. Brief Bioinform 20(6):1957‒1971, 2019.

marker discovery.

7) Saigusa D, Matsukawa N, Hishinuma E, Koshiba S:

4.

Conclusion

Identiﬁcation of biomarkers to diagnose diseases and ﬁnd

Metabolic proﬁling is a promising approach for future
precision medicine to identify disease-speciﬁc and sensitive

adverse drug reactions by metabolomics. Drug Metabbiomarkers. MS-based methodologies have already been

8) Chen CJ, Lee DY, Yu J, Lin YN, Lin TM: Recent advanc-developed, and the technique seems almost mature, espe-

es in LC-MS-based metabolomics for clinical biomarker

cially in terms of the comprehensibility and sensitivity of
LC-MS. However, the optimal sample preparation proce-discovery. Mass Spectrom Rev e21785. 2022.

Pharmacokinet 37: 100373, 2021.

9) Johnson CH, Ivanisevic J, Siuzdak G: Metabolomics:

dure must be selected according to the type of targeted molecule. In addition, the annotated biomarker should be evalu-

Beyond biomarkers and toward mechanisms. Nat Rev

ated by another methodology to clarify the biological
function. In this case, the combination of LC-MS and MSI10) Saoi M, Britz-McKibbin P: New advances in tissue me-

is utilized for metabolic proﬁling for biomarker discovery11) Spengler B: Mass spectrometry imaging of biomolecular

Mol Cell Biol 17(7): 451‒459, 2016.
tabolomics: A review. Metabolites 11(10): 672, 2021.
information. Anal Chem 87(1): 64‒82, 2015.

using tissue samples, and we expect that further signiﬁcanttechnological improvements will be made in MSI method-12) Shimma S, Sugiura Y, Hayasaka T, Zaima N, Matsumoto

ology in future studies.

M, et al: Mass imaging and identiﬁcation of biomoleculeswith MALDI-QIT-TOF-based system. Anal Chem 80(3):Acknowledgments

878‒885, 2008.

This work was supported in part by KAKENHI Grant

13) Scott AJ, Jones JW, Orschell CM, MacVittie TJ, Kane

Number JP20H03374 [D.S. and AMED LEAP under Grant

MA, et al: Mass spectrometry imaging enriches biomark-Number J200001087 [D.S.]. A part of this work was also

er discovery approaches with candidate mapping. Healthsupported by the Tohoku Medical Megabank Project from
MEXT, the Japan Agency for Medical Research and Devel-

Phys 106(1): 120‒128, 2014.
14) Martín-Saiz L, Abad-García B, Solano-Iturri JD, Mosteiro

opment (AMED; under grant numbers JP20 km0105001

L, Martín-Allende J, et al: Using the synergy between

and JP21tm0124005).

HPLC-MS and MALDI-MS imaging to explore the lipidomics of clear cell renal cell carcinoma. Anal Chem

Conﬂict of Interest

95(4): 2285‒2293, 2023.
15) Zhou J, Zhong L: Applications of liquid chromatogra-

The author declares no conﬂict of interest.

phy-mass spectrometry based metabolomics in predictive

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