Available via license: CC BY-NC 2.0
Content may be subject to copyright.
Histochem Cell Biol (2008) 130:421–434
DOI 10.1007/s00418-008-0469-9
123
REVIEW
MALDI imaging mass spectrometry for direct tissue analysis:
a new frontier for molecular histology
Axel Walch · Sandra Rauser · Sören-Oliver Deininger ·
Heinz HöXer
Accepted: 24 June 2008 / Published online: 11 July 2008
© Springer-Verlag 2008
Abstract Matrix-assisted laser desorption/ionization
(MALDI) imaging mass spectrometry (IMS) is a powerful
tool for investigating the distribution of proteins and small
molecules within biological systems through the in situ
analysis of tissue sections. MALDI-IMS can determine the
distribution of hundreds of unknown compounds in a single
measurement and enables the acquisition of cellular expres-
sion proWles while maintaining the cellular and molecular
integrity. In recent years, a great many advances in the
practice of imaging mass spectrometry have taken place,
making the technique more sensitive, robust, and ultimately
useful. In this review, we focus on the current state of the
art of MALDI-IMS, describe basic technological develop-
ments for MALDI-IMS of animal and human tissues, and
discuss some recent applications in basic research and in
clinical settings.
Keywords MALDI imaging mass spectrometry · Tissue ·
In situ-proteomics · Pathology
Introduction
In biomedical research, the discovery of new biomarkers
and new drugs demands analytical techniques with high
sensitivity together with increased throughput. The possi-
bility to localize or to follow changes in organisms at the
molecular level by imaging component distributions of spe-
ciWc tissues is of prime importance to unravel biochemical
pathways and develop new treatments and drugs (Rohner
et al. 2005). In recent years, several proteomic methodolo-
gies have been developed that now make it possible to iden-
tify, characterize, and comparatively quantify the relative
level of expression of hundreds of proteins that are co-
expressed in a given cell type or tissue. These advances
have resulted from the integration of diverse scientiWc dis-
ciplines including molecular and cellular biology, protein/
peptide chemistry, bioinformatics, analytical and bioanalyt-
ical chemistry, and the use of instrumental and software
tools. Mass spectrometry has become an indispensable tool
for proteomic studies (Lavoie and Paiement 2008; Chau-
rand et al. 2004a; Aebersold and Goodlett 2001; Godovac-
Zimmermann and Brown 2001; Lahm and Langen 2000;
McDonald and Yates 2000; Pandey and Mann 2000; Roe-
pstorV 1997). Desorption and ionization techniques such as
matrix-assisted laser desorption ionization mass spectrome-
try (MALDI MS) (Hillenkamp et al. 1991; Karas and Hil-
lenkamp 1988) and electrospray ionization mass
spectrometry (ESI MS) (Fenn et al. 1989) have revolu-
tionized the analysis of proteins. These improvements oVer
levels of mass accuracy and sensitivity never before
achieved for the detection and identiWcation of proteins.
MALDI MS is an ideal tool to investigate complex protein
mixtures. It utilizes a matrix, a small acidic aromatic mole-
cule that absorbs energy at the wavelength of the irradiating
laser. The analyte molecule is mixed with the matrix, deposited
A. Walch (&) · S. Rauser · H. HöXer
Institute of Pathology, Helmholtz Zentrum München,
Deutsches Forschungszentrum für Gesundheit und Umwelt
(GmbH), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
e-mail: axel.walch@helmholtz-muenchen.de
S.-O. Deininger
Bruker Daltonik GmbH, Bremen, Germany
H. HöXer
Institute of Pathology, Technische Universität München,
Munich, Germany
422 Histochem Cell Biol (2008) 130:421–434
123
on a target plate or a conductive glass slide and allowed to
dry. During the drying process, matrix-analyte co-crystals
form. These crystals are then submitted to very short laser
pulses (typically UV laser light), resulting in the desorption
and ionization of the analyte molecule. The ions mass-to-
charge (m/z) are measured in a time-of-Xight (TOF) mass
analyzer (Chaurand et al. 2005).
To date, there are several technologies available to ana-
lyze proteins in a tissue specimen. The most commonly used
technology is the separation and visualization of proteins by
2-dimensional (2D) gel electrophoresis and subsequent iden-
tiWcation by mass spectrometry and database searching
(Lahm and Langen 2000; Godovac-Zimmermann and
Brown 2001). One of the drawbacks of the 2-D gel techno-
logy is that sample preparation removes the direct relation-
ship between morphological tissue regions and a speciWc
protein. One solution to this problem is to purify cells from
thin tissue sections by microdissection techniques prior to
protein extraction. Although such an approach has been suc-
cessful (Curran et al. 2000), the extraction of a suYcient
quantity of material is very labor intensive and requires a
large amount of microdissected cells. A major advantage of
direct MALDI analysis is to avoid time-consuming extrac-
tion, puriWcation or separation steps, which have the poten-
tial for producing artefacts. One of the recent applications of
MALDI MS is its use to proWle and image proteins directly
from tissue sections (Caprioli et al. 1997; Stoeckli et al.
2001; Todd et al. 2001; Chaurand and Caprioli 2002; Chau-
rand et al. 2002, 2004b, 2005; Cornett et al. 2007).
MALDI imaging mass spectrometry (MALDI-IMS) is a
new technology that allows for simultaneous mapping of
hundreds of peptides and proteins present in thin tissue sec-
tions with a lateral resolution of approximately 30–50 m.
Matrix is Wrst uniformly deposited over the surface of the
section, utilizing procedures optimized to minimize protein
migration. Proteins are then desorbed from discrete spots or
pixels upon irradiation of the sample in an ordered array or
raster of the surface. Each pixel thus is keyed to a full mass
spectrum consisting of signals from protonated species of
molecules desorbed from that tissue region. A plot of the
intensity of any one signal produces a map of the relative
amount of that compound over the entire imaged surface.
This technology provides a powerful discovery tool for the
investigation of biological processes because the identities
of proteins observed do not need to be known in advance.
MALDI-IMS under various forms has already been suc-
cessfully used to characterize the expression of proteins and
other organic biological compounds in numerous normal
and diseased tissues (Chaurand et al. 2005). For example,
protein organization in mouse colon (Chaurand et al. 1999),
brain (Stoeckli et al. 2001; Todd et al. 2001; Chaurand
et al. 2002, 2004b) and epididymis (Chaurand et al. 2003)
as well as phospholipid organization in mammalian lens
tissue (Rujoi et al. 2004) has been studied. Variations in
protein expression have been investigated in the cases of
Parkinson’s (Pierson et al. 2004) and Alzheimer’s (Stoeckli
et al. 2002) diseases. Several forms of cancers have also
been investigated including gliomas (Stoeckli et al. 2001;
Chaurand et al. 2004b; Schwartz et al. 2004), breast cancer
(Palmer-Toy et al. 2000; Xu et al. 2002), prostate cancer
(Masumori et al. 2001, Schwamborn et al. 2007), colon
cancer (Chaurand et al. 2001) and lung cancer (Bhattach-
arya et al. 2003; Yanagisawa et al. 2003). In this later study
(Yanagisawa et al. 2003), protein patterns have been shown
to be predictive of diagnosis and prognosis. Methodologies
aimed at detecting and mapping pharmaceutical com-
pounds by direct MALDI MS analysis of sections from
dosed tissues have also been described (Hsieh et al. 2007;
Khatib-Shahidi et al. 2006; Reyzer et al. 2003).
This review article provides an overview of several ani-
mal and human tissue imaging applications performed by
MALDI-IMS, including sample preparation, matrix selec-
tion and application, histological staining in MALDI ana-
lysis, tissue proWling, imaging, and data analysis. Several
applications represent direct translation of this technology
to basic research and in clinical applications.
In situ analysis of proteins from tissue sections
Instrumentation
To use a mass spectrometer as an imaging instrument, it is
essential for the spectrometer to be equipped with an auto-
matic rastering function, automatic data acquisition system,
and visualization software. Recently, several manufacturers
have released novel instruments having these features.
Almost all manufacturers have developed in-house software
for their instruments and have included a driver for instru-
ment and image reconstruction. Instruments used for imaging
mass spectrometry can be classiWed according to how ions are
generated from the sample: either by irradiation by pulsed
laser or bombardment by energetic particles. Laser based sys-
tems include MALDI and laser desorption ionization instru-
ments. Secondary ion mass spectrometry (SIMS) systems use
particle bombardment with a continuous beam of highly-
focused, energetic ions. In general, these imaging instruments
were based mostly on MALDI-TOF, MALDI-TOF/TOF, or
TOF-SIMS. MALDI-TOF imaging instruments are high
throughput, and TOF-SIMS imaging instruments can provide
submicrometer spatial resolution of »500 nm (Altelaar et al.
2006). Both instruments have powerful advantages in ima-
ging mass spectrometry. There are several published articles
discussing and comparing MALDI and SIMS imaging
(Heeren et al. 2005; McDonnell and Heeren 2007; Shimma
et al. 2008). This review focuses on MALDI imaging.
Histochem Cell Biol (2008) 130:421–434 423
123
Tissue sample acquisition and preparation
The representativity of an imaging mass spectrometry
experiment, in terms of both chemical representativity and
spatial integrity, can only be as good as the worst step in the
entire process of sample acquisition, preparation, imaging
mass spectrometry, and interpretation. In all sample-han-
dling steps throughout acquisition and preparation, the spa-
tial and chemical integrity must be maintained, within a
length scale determined by the spatial resolution of the
mass spectrometry technique used (McDonnell et al. 2007).
Preparation methods for MALDI-IMS tissue imaging must
be carefully performed to maintain the spatial arrangement
of compounds and avoid delocalization and degradation of
the analytes. Experimental parameters that should be con-
sidered include treatment of tissue immediately after sam-
ple procurement, sectioning, sample transfer to the MALDI
target plate or conductive glass slide, matrix application,
and tissue storage after sectioning (Caldwell and Caprioli
2005). Careful handling of tissue samples, including freez-
ing the tissue in liquid nitrogen immediately after procure-
ment, is essential to preserve the native condition of the
tissue. Typically, the interior of tissue samples is exposed
using cryosectioning techniques. Embedding in optimal
cutting temperature (OCT) polymer or Tissue-Tek
®
should
be avoided as it can smear across the sample, thus compro-
mising the chemical integrity. Embedding in gelatine (Alt-
elaar et al. 2005) and agarose (Kruse and Sweedler 2003)
has been used to facilitate handling of small or fragile sam-
ples (e.g., biopsies) but most often cryosectioning is per-
formed directly with the frozen tissue samples (Kruse and
Sweedler 2003; Schwartz et al. 2003). Although thickness
is not critical, 10–20 m thick sections are optimal for han-
dling and analyzing in the high vacuum environment of the
mass spectrometer. The thickness of the tissue sections
used is normally 10–20 m: thin sections can be very
fragile and diYcult to manipulate, whereas thicker sections
need longer to dry and, because they are insulating samples,
can adversely aVect the performance of the mass analyzer
(Caldwell and Caprioli 2005). The sample plate of choice is
a transparent conductive slide such as indium-tin-oxide-
coated glass slides because they allow the optical micro-
scopic image acquisition and mass spectrometry to be
performed on the same sample (Chaurand et al. 2004b;
Altelaar et al. 2005).
The signiWcant potential of tissue-based proteomic bio-
marker studies can be restricted by diYculties in accessing
tissue samples in optimal fresh-frozen form. While archival
formalin-Wxed tissue collections with attached clinical and
outcome data represent a valuable alternate resource, the use
of formalin as a Wxative which induces protein cross-linking,
has generally been assumed to render them unsuitable for
proteomic studies. Formalin Wxation of tissues, generally
followed by paraYn embedding, is the standard and well-
established processing method employed by pathologists.
Analysis of formalin-Wxed, paraYn-embedded (FFPE) tis-
sues from biopsy libraries has been, so far, a challenge for
proteomics biomarker studies. To date, paraYn-embedded,
formalin-Wxed tissues are limited candidates for MALDI MS
tissue proWling because these treatments may hamper
eYcient ionization of the proteins and peptides. There are
recent methodological reports demonstrating the principal
feasibility of MALDI-IMS on FFPE tissues (Stauber et al.
2008; Lemaire et al. 2007a; Wisztorski et al. 2007).
Selection and application of MALDI matrices
The multiple roles played by the matrix in the desorption
and ionization of large analytes has lead to the optimum
choice of matrices for diVerent molecular classes being
empirically determined (McDonnell et al. 2005). For tissue
analysis, it has been reported that sinapinic acid provides
the best signals for higher molecular weight proteins,
whereas -cyano-4-hydroxycinnamic acid is more suitable
for lower molecular weight peptides (Schwartz et al. 2003).
For higher spatial resolution analysis, in which deposition
of the matrix solution has been carefully controlled to mini-
mize spatial relocation, sinapinic acid has been recom-
mended (Kruse and Sweedler 2003).
Matrix application protocols include manual methods
such as spraying using an airbrush or TLC sprayer or dip-
ping the tissue sections into matrix containing solutions
(Schwartz et al. 2003). An intrinsic disadvantage of these
manual procedures is poor reproducibility; higher repro-
ducibility can be achieved using automated sample prepara-
tion devices. These devices fall into two classes: spotting
devices and spraying devices. Matrix spotters (e.g., Labcyte
Portrait, Shimadzu ChiP or Leaptec TMiD) apply small
droplets of matrix solution in a grid onto the tissue. Under-
neath each droplet the spatial information is lost, so the lateral
resolution is deWned by the spot-to-spot distance. This
distance can actually be signiWcantly larger than the actual
droplet size, because the requirement for spotting onto the
same position repeatedly to achieve suYcient matrix coat-
ing causes signiWcant loss of resolution due to small posi-
tioning inaccuracies. In addition, capillary forces cause
some droplets moving on the tissue oV their landing site.
Another reason for reduced spotting resolution is crystalli-
zing matrix at the nozzle of, e.g., piezoelectric dispensers
which aVects the angle of droplet impact. Robot designs
without a nozzle can eYciently eliminate the clogging
problem, though not the previously listed eVects. Here the
droplet is ejected from an open reservoir by a sonic impulse
(Labcyte) (Aerni et al. 2006). Spotting robots can also be
used to add chemical reagents onto the tissue in a controlled
way to perform chemical modiWcations or digestions on the
424 Histochem Cell Biol (2008) 130:421–434
123
tissue, as reported with the CHiP (Shimadzu) (Groseclose
et al. 2007). However, spotting robots suVer with regard to
preparation time, which increases quadratically with the tis-
sue area and targeted resolution, and lateral resolution limi-
tations. Spotters are limited to provide for image
resolutions in the 200–500 m range. Because smaller
droplets do not extract the analyte as eYcient as large ones,
the spectra quality rapidly drops if preparations for higher
image resolutions are used. SigniWcantly higher resolutions
can be achieved by spray preparation of the matrix onto the
tissue. Two spraying principles are currently utilized in
commercial devices: pneumatic spray and vibrational
spray. The ImagePrep device (Bruker Daltonics) utilizes
vibrational vaporization of the matrix with a piezo-electric
spray head. The spray head moves a pinhole sheet next to
the matrix reservoir to eject small droplets with an average
diameter of 20 m. These droplets deposit onto the tissue
and incubate in a controlled atmosphere chamber. The
entire process of spraying and drying is monitored by an
optical scattering-light sensor that allows the evaluation of
the tissue wetness, matrix thickness and drying rate. There-
fore, ImagePrep allows high resolutions of up to 25 m
with a good spectra quality by push button operation, being
a signiWcant advantage for routine operation. Another spray
robot, the TM-sprayer (Leaptec), uses a heated capillary
with a pneumatic spray that moves in predeWned patterns
over the tissue sample. This makes the spray more repro-
ducible than a manual pneumatic spray. Parameters such as
the drying rate and the wetness of the tissue are not moni-
tored during the preparation but have to be tuned in
advance.
Histological staining of tissues and MALDI-IMS
For the interpretation of MALDI-IMS results it is an abso-
lute necessity to correlate the MALDI image with the histo-
logical information. Advanced MALDI-IMS software
allows superimposing the MALDI images over a macro-
scopic or microscopic optical image of the sample taken
before the MALDI measurement. While that primary mac-
roscopic optical image is suYcient to recognize the outline
of the tissue and to deWne the measurement area, it is usu-
ally not possible to see histological features in that image in
contrast to microscopic images. For a histomorphological
interpretation it is necessary to use stained tissue sections.
Two approaches have been used to correlate histology with
the MALDI-IMS result so far.
Histological staining of a consecutive section
In this approach, one section is used for the MALDI ima-
ging, and another section is used to do the histological stain-
ing. This approach allows the use of any staining protocol,
especially to use a Hematoxylin–Eosin (H&E) staining. This
staining technique is a preferred histological staining
because it yields a high degree of information on the sample.
The main diYculty with this approach is the fact that the
MALDI image is derived from a diVerent section than the
histological image. It may be suYcient to diVerentiate rather
large tissue features, such as large invasive tumor regions
from large connective tissue regions. To some degree it
remains guesswork if the features seen in the histology are
properly matched with the molecular information.
Histological staining of the section prior
to the MALDI-IMS
This approach has been reported to integrate histology and
MALDI-IMS (Chaurand et al. 2004b), and it does allow an
unambiguous correlation of histomorphology and MALDI-
IMS. One of the commonly used staining procedures in
clinical pathology employs H&E, although unfortunately
the quality of the mass spectra from these sections is sig-
niWcantly compromised relative to that obtained from
unstained sections. To circumvent these limitations, proto-
cols were developed to permit histomorphology and protein
proWling to be performed on the very same tissue section. A
series of commonly used histological dyes was tested for
compatibility with mass spectrometric analysis. It was
found that cresyl violet and methylene blue, as well as sev-
eral other dyes, do not compromise overall mass spectra
quality and allow speciWc regions of tissues to be easily
analyzed. This approach has two disadvantages, however,
Wrst, it subjects the sample to additional handling steps
prior the measurement. Second, and more severe is the fact
that the choice of stains is limited to MALDI compatible
ones. These stains (e.g., methylene blue or DAPI) are suY-
cient to localize cells in the histology, but they do not yield
the same information as an H&E stain, so crucial informa-
tion on the sample may be missed and it is still necessary to
do a full H&E staining on a consecutive section.
Recently a new approach has been reported: The staining
of the sample after the MALDI measurement (Schwamborn
et al. 2007
). This allows the use of H&E staining and an
unambiguous correlation with the MALDI-IMS results.
However, a prerequisite for this approach is the integrity of
tissue under the MALDI measurement, shown in Fig. 1.
Further MALDI-IMS experiments shown in the Figs. 2, 3,
and 4 of this review are performed by staining of the sam-
ple after the MALDI measurement.
Data processing, visualization and statistical analysis
In MALDI-IMS very information-rich datasets with hun-
dreds of mass signals can be obtained. This is clearly an
advantage, but it may become a limitation when a large
Histochem Cell Biol (2008) 130:421–434 425
123
number of datasets have to be evaluated peak-by-peak,
because the evaluation can be very time consuming. Many
tools have been developed to visualize and analyze this vast
dataset intelligently and eYciently. Using diVerent data
processing strategies for visualization, it enables fast mass
spectrometric imaging of large surfaces at high-spatial reso-
lution and thus aids in the understanding of various dis-
eases and disorders (Klinkert et al. 2007, Villmann et al.
2008). This includes many basic but widely used features,
such as region-of-interest analysis, choice of intensity
scales for image display (color palette, linear/logarithmic),
image overlay, binning of spectral and image data for
improved signal-to-noise, intensity proWles (variation of
intensity with time or space), and chemical libraries for
analyte identiWcation (McDonnell and Heeren 2007). In
addition to these facilities, imaging mass spectrometrists
have continued to develop a broad array of tools. These
have improved the quality of information extracted from
the large data sets, reduced the dimensionality to more
practical levels and allowed diVerent imaging data sets to
be aligned and compared. Several available software pro-
grams address data management, including determination
of statistical signiWcance and relative abundance between
particular protein species. Moreover, the need for such soft-
ware amid the growing number of proteomics laboratories
will ensure that such programming will be standardized,
continually upgraded, and user friendly. For clinical stu-
dies, large numbers of patients need to be studied, each hav-
ing unique aspects to their disease. Indeed, the base premise
of individualized medicine is founded on this exciting but
enormously complex molecular diversity. It is evident that
innovative biocomputational studies are an absolute neces-
sity in identifying individualized molecular patterns to aid
in diagnosis and prognosis. Examples of data analysis algo-
rithms have been published and consist of four steps. Pro-
teins are selected that are diVerentially expressed among
histomorphological deWned groups. This selection is based
on the Kruskal–Wallis test, Fisher’s exact test (dichotomiz-
ing the expression level as present or not), the Student’s t
test, signiWcance analysis of microarrays, weighted gene
analysis, and the modiWed info score methods. The cutoV
points for each method are P < 0.0001, P < 0.0001,
P < 0.0001, 3.5, 2, and 0, respectively. Proteins are
included in the Wnal list if they meet at least three of these
six selection criteria (Caldwell and Caprioli 2005). One
technique to reduce the complexity of the information in
multidimensional datasets in MALDI-IMS is the principal
component analysis (PCA) (Van de Plas et al. 2007). This
is a transformation of the original coordinate system deW-
ned by peak intensities to a coordinate system that better
explains the variance in the dataset. The new coordinates
are called principal components and are ordered in decreas-
ing number of variance. By removing those PC’s that do
not contain a lot of information the dimensionality of the
dataset can be reduced to a large extent. The principal com-
ponent scores can then be used to reconstruct a few images
that contain most of the information.
While the PCA reduces the dimensionality of the dataset,
it does not classify the spectra. An unsupervised classiWca-
tion can be achieved by hierarchical clustering (McCombie
et al. 2005). Here the spectra are pair-wise clustered accord-
ing to similarity until a dendrogram is obtained that contains
Fig. 1 Integrity of histomor-
phology after MALDI measure-
ment of tissue. a Frozen sample
of a human pancreatic tissue on a
cryostat steel plate. b Tissue sec-
tion (10 m) mounted onto a
conductive glass slide for MAL-
DI-IMS. c Tissue section after
MALDI measurement: MALDI
matrix was coated on the tissue
section before measurement. d
Subsequent to MALDI measure-
ment the matrix is removed and
the very same section is stained
using standard H&E. e The his-
tomorphology after MALDI-
IMS is well-preserved as shown
on higher magniWcation. Scale
bars 4 mm (b–d) and 400 m (e)
426 Histochem Cell Biol (2008) 130:421–434
123
all spectra. Each branch of the dendrogram can be consi-
dered a class of spectra. It is now possible for a tissue expert
to select those dendrogram nodes that reXect certain histo-
logical features. The XexImaging software, e.g., provides a
interactive dendrogram browser in which dendrogram nodes
can be selected and the spectra that belong to that node are
displayed on the image. Several branches can be selected
with diVerent colors to reconstruct a full image. This makes
the evaluation of complex datasets much faster than evalua-
ting hundreds of individual mass signals. An example of
hierarchical clustering of a mouse kidney dataset is shown in
Fig. 5. Further examples illustrate the applications of various
software tools for data analysis (Figs. 2, 3, 4, 6).
Protein identiWcation
IdentiWcation of diVerentially expressed peptides and pro-
teins in tissues enhances the understanding of the biological
processes underlying disease. After processing of the raw
mass spectra, and determination of statistical signiWcance, a
list of spectral features (m/z species) is generated. These m/
z species represent peptides and proteins with expressions
that are signiWcantly modulated by the disease. The identiW-
cation strategy may vary for a particular protein because of
sample complexity, protein abundance, or molecular
weight (Fig. 7). There are two approaches to protein identi-
Wcation. The top–down approach involves ionization and
gas phase fragmentation of the protein of interest inside the
mass spectrometer (Reid and McLuckey 2002), whereas the
bottom–up approach utilizes MS to identify peptides
obtained from protease digestion of that protein, often in a
mixture of other proteolytic fragments (Wysocki et al.
2005). The resulting mass spectra are searched against the-
oretical protein/peptide databases for corresponding
sequence patterns. Searches are performed using conven-
tional algorithms such as MASCOT and Sequest (Hirosawa
et al. 1993; Link et al. 1999).
Recent work has shown that digestion and identiWcation
of proteins may be coupled with direct tissue analysis
(Groseclose et al. 2007). This technique involves automati-
Fig. 2 Comparison of non-can-
cerous (exocrine pancreas) and
cancerous (invasive ductal pan-
creatic cancer) tissue by MAL-
DI-IMS. a Histological image of
the tissue section after MALDI-
IMS. b Selected mass species
correlates with non-cancerous
(m/z 14,836) tissue area. c Se-
lected mass species correlates
with cancerous (m/z 13,777) tis-
sue area. d Average spectra in
the mass range of 12–16.5 kDa
obtained from the non-cancerous
(green) and the cancerous (red)
region displaying the two mass-
es visualized in (b) and (c). e
Corresponding Pseudo-Gel view
of (d). Scanning resolution
160 m. Scale bars 2mm
Histochem Cell Biol (2008) 130:421–434 427
123
cally depositing a spotted array of enzymatic solution onto
the tissue at room temperature. After hydrolysis, MALDI
matrix is deposited onto the array for subsequent MALDI
MS analysis. The in situ identiWcation of proteins requires
less time than conventional protein identiWcation strategies
and the ability to compare a given protein image to the
image of its subsequent peptides increases identiWcation
conWdence. This process is best used for validation, i.e.,
where the presence of one or more speciWc proteins is
needed to be established (Herring et al. 2007).
Fig. 3 MALDI-IMS of a tissue section of rat pituitary gland. a Optical
microscopic image of a H&E stained tissues section. The staining was
done after the MALDI measurement of the tissue section. b–d Visual-
ized selected m/z species representing features to pars distalis (m/z
6,651; green), pars intermedia (m/z 2,897; red) and pars neuralis (m/z
9,685; yellow). e Merge of a–d. f MALDI-TOF MS spectra obtained
from this case from pars distalis (green), pars intermedia (red) and pars
neuralis (yellow) showing molecular diVerences between the histological
regions. Scanning resolution 50 m. Scale bars 1mm
Fig. 4 Mass species representing molecular features of preinvasive
and invasive lesions of the breast. a Optical microscopic image of a
H&E stained tissues section showing several carcinomata in situ re-
gions (outlined in green). The staining was done after the MALDI
measurement of the tissue section. This allows an unambiguous corre-
lation with the MALDI imaging results. b Visualization of ion density
images of two selected masses (m/z = 9,750 shown in yellow,
m/z = 4,519 shown in blue) c Overlay of H&E staining and molecula
r
image. The distribution of these two masses suggests a diVerent clonal
evolution of the preinvasive lesions. These two masses are also present
in the invasive cancer cells surrounding some carcinomata in situ (righ
t
site). Scanning resolution 80 m. Scale bars 1mm
428 Histochem Cell Biol (2008) 130:421–434
123
Finally, immunohistochemistry can be employed to
verify protein identiWcation, assuming appropriate antibodies
for the protein exist.
Application of MALDI-IMS to tissue analysis
A majority of MALDI-IMS studies have been dedicated to
the study of proteins contained in human and animal tissue
sections (Cornett et al. 2007). Applications in the Weld of
pathology hold particular interest for many because of the
potential beneWt for clinical diagnoses and treatment. These
applications are complemented by the Weld of MALDI-IMS
for the detection of drugs and metabolites in tissues. Other
studies have focused on proteomic events occurring in nor-
mally developing tissues.
Diagnostic and prognostic assessments
in clinical pathology
To date, proWling and imaging by MALDI-IMS has been
applied to multiple diseased tissues, including human non-
small-cell lung tumors (Yanagisawa et al. 2003), gliomas
(Schwartz et al. 2005), breast cancer (Cornett et al. 2006)
and ovarian tumors (Lemaire et al. 2007b) and continues to
be a main focus of MALDI-IMS studies (Chaurand et al.
2004a; Caprioli 2005; Caldwell and Caprioli 2005; Sköld
et al. 2006; Stoeckli et al. 2007; Meistermann et al. 2006;
Cornett et al. 2006; Herring et al. 2007; Corbin et al. 2008).
Molecular protein signatures represent a unique data set
with which to classify and correlate clinically relevant
information and outcomes with changing molecular events
ongoing in the progression and treatment of disease. The
general approach taken by these studies is comparative pro-
teomic analyses whereby mass spectral features (m/z peaks)
are correlated with a variety of patient data, such as thera-
peutic regimen and overall outcome. In some cases, the goal
of the experiment is to identify speciWc molecular changes
associated with progression of disease. For example, mole-
cular signatures from glioma or non-small-cell lung cancer
can be used to distinguish tumor grade and were found to be
correlated with patient survival (Yanagisawa et al. 2003;
Schwartz et al. 2005). IdentiWcation of speciWc cellular
changes within heterogeneous tissue can be particularly
challenging from the perspective of matrix spot size, place-
ment and lateral resolution (Cornett et al. 2007). From work
already published, it seems likely that the integration of this
technology into protocols for disease diagnosis as well as
outcome prediction will soon take place. As protein expres-
sion data becomes available from various tissue types, this
approach will provide a common disease-wide approach
that can be applied to many speciWc problems. One can
envision that, e.g., the use of MALDI-IMS technology to
evaluate a “tumour of unknown primary.” Current data sug-
Fig. 5 Hierarchical Clustering
of a mouse kidney dataset
achieved by MALDI-IMS. a
Full dendrogram of all spectra in
a mouse kidney dataset. b Opti-
cal image of the mouse kidney
analysed by MALDI-IMS. c and
d Reconstruction of selected
dendrogram branches and corre-
sponding images. The three
main branches reXect the renal
cortex (blue), medulla (green)
and pelvis (red) c The medulla
branch separates into two dis-
tinct areas, while cortex branch
further diVerentiates into fat and
connective tissue of the renal
capsule and hylus and the actual
cortex (d)
Histochem Cell Biol (2008) 130:421–434 429
123
gests that MALDI MS might be superior to immunohisto-
chemical stains in identifying the site of origin for such
tumors (Chaurand et al. 2004a). The potential capability of
MALDI-IMS to measure response to therapeutic agents in
tumor and surrounding tissues is a particularly exciting
application of this technology. The original protein proWle
obtained from the primary tumor could be used to inXuence
the selection of therapeutic agents. Levels of drugs such as
chemotherapeutic agents could be measured directly from a
tissue biopsy to assess adequacy of delivery to a particular
organ site. The ability of drugs and other bioreactive mole-
cules to adequately penetrate larger tumors has long been
known to be problematic and could be more adequately
assessed by this technology. In addition, alterations in spe-
ciWc molecular pathways directly modulated or indirectly
aVected by the agent could be evaluated. Studies of this type
that establish proof of principle have been reported (Reyzer
et al. 2003). Similar methods could be envisioned to moni-
Fig. 6 Histology directed
MALDI-IMS proWling of gastric
mucosa and esophageal squa-
mous epithelium. a Overall aver-
age spectra in the mass range of
4.3–7.4 kDa obtained from eight
cases of human gastric mucosa
(blue) and eleven cases of esoph-
ageal squamous epithelium
(green). b Virtual gel view of
individual spectra displayed for
mass range of 4.3–7.4 kDa. Sta-
tistical analysis revealed in this
experiment 63 diVerentially ex-
pressed m/z species at signiWcant
levels
430 Histochem Cell Biol (2008) 130:421–434
123
tor patients treated with conservative therapy for relapse.
Together with genomics and perhaps additional molecular
information describing the state of lipids and metabolites,
proteomics provides an entry into individualized medicine,
where each patient’s disease is unique at the molecular level
and will be treated accordingly (Chaurand et al. 2004a).
Small-molecule imaging and drug metabolism
During drug discovery often the question is raised as to
whether the drug can reach the site of action which helps
researchers better assess the potential value of that com-
pound as a pharmaceutical product and toxicological out-
comes (Hsieh et al. 2007). MALDI-IMS opens the
possibility to directly determine the distribution of pharma-
ceuticals in tissue sections which might unravel their dispo-
sition or biotransformation pathway for new drug
development. Many compounds that are biologically or
pharmacologically relevant are less than 1 kDa in size and
thus fall into the broad category of small molecules (Cor-
nett et al. 2007). These include both exogenous and endog-
enous molecules, such as pharmaceutical compounds and
their metabolites, drugs of abuse, environmental toxins,
endogenous metabolites and lipids. Compared to the stan-
dard method in this Weld, the whole-body autoradiography
(WBA), MALDI imaging oVers some advantages but also
some drawbacks. In WBA, a radioactive labeled drug is
administered to the animal. Thin sections of the animals are
then exposed to photographic Wlm which detects the radio-
activity. The advantages of MALDI-IMS in contrast to
WBA are that it is not needed to use radiolabeled drugs.
This is a big advantage especially in early stages of drug
development since there large numbers of compounds need
to be screened and preparing radiolabeled drugs is tedious.
The second advantage of MALDI-IMS is that it oVers the
possibility to diVerentiate between the drug and its metabo-
lites, while in WBA the radioactivity may come from either
drug or metabolite. On the other hand, WBA will always
yield a result and has a very low detection limit. In
MALDI-IMS, there is always a possibility that the ion of
interest is suppressed in desorption. The detection limit of
MALDI-IMS is strongly dependent on the drug, some
drugs may not even be seen. This is why MALDI-IMS of
drugs has found its main application in drug safety studies,
where high doses of drugs are used. Drug imaging diVers
from other MALDI imaging applications such a way that
the compound to be detected is known in advance. The
mass range in which the drugs are measured is usually
crowded by a large number of signals that come from the
MALDI matrix as well as from lipids and naturally occur-
ring substances. It is therefore necessary to ensure the
necessary speciWcity of detection of a particular, probably
small signal in between many other signals. This has been
so far mainly achieved by using a MS/MS measurement.
The ion of interest is selected and fragmented. The quantiW-
cation and reconstruction of the MALDI image is then done
on a speciWc fragment of the drug. Interfering signals of
similar mass in MS mode usually yield diVerent MS/MS
signals, so the necessary speciWcity of the measurement is
achieved. Recently MALDI-IMS became available on
instruments, that allow the measurement of mass signals
with very high resolution and precision, namely Fourier-
transform mass spectrometers (Cornett et al. 2008). In these
instruments the necessary speciWcity of the drug imaging
experiment can be reached by measuring the precise mass
of the drug. Matrix and background ions that would inter-
fere in instruments with lower resolution can be separated
in the MS mode alone. Applications of small-molecule
MALDI-IMS focused on pharmaceutically active com-
pounds and their metabolites (Khatib-Shahidi et al. 2006;
Hsieh et al. 2006, 2007), and considerable eVort continues
in this Weld (Reyzer and Caprioli 2007). For example, one
study reported the localization of the drug olanzapine and
two-Wrst-pass metabolites in whole-rat sections (Khatib-
Shahidi et al. 2006). The results of this study correlated
well with both autoradiography and LC-MS/MS quantita-
tive results, illustrating the power of MALDI-IMS for
metabolite proWling. More recently, there has been consid-
erable interest in adding quantitative capabilities to the
MALDI-IMS experiment (Signor et al. 2007; Corr et al.
2006). The technology also opens up the exciting possibi-
lity of correlating drug distribution with concomitant
protein changes (Reyzer et al. 2004).
Developmental biology
Protein imaging and proWling using MALDI-IMS has
become a powerful method for analyzing changes in global
Fig. 7 WorkXow of protein identiWcation
Histochem Cell Biol (2008) 130:421–434 431
123
protein expression patterns in cells and tissues as a function
of developmental processes (Cornett et al. 2007). Proteo-
mic studies using MALDI-IMS can yield insight into some
complex biological processes. MALDI-IMS analyses of
epididymal sections have been used to determine the local-
ized composition of the epididymal Xuid from caput to
cauda (Chaurand et al. 2003), conWrming that diVerent pro-
tein compositions are found along the length of the epididy-
mis. Other recent examples include the direct imaging of
secretory peptides from frog skin (Brand et al. 2006) and
bovine and rabbit ocular lens proteins (Han and Schey
2006; Grey and Schey 2008), as well as neuropeptides from
rat pituitary (Altelaar et al. 2007), decapod crustacean neu-
ronal tissue (DeKeyser et al. 2007), protein proWles during
embryo implantation (Burnum et al. 2008) or mouse pros-
tate development (Chaurand et al. 2008).
Imaging of phospholipids
Lipidomics is one new frontier in biomarker research. Not
only are lipids the main components in membranes that
deWne the contours of the cell and its organelles, but they
are also used for storage. Lipids form stable noncovalent
complexes with proteins as well as with many drugs. Lipids
are a storage depot for drugs and certain types of organic
molecules. Analysis of lipids is challenging because of the
wide molecular diversity of this class of compounds and
their relative insolubility in aqueous systems. Direct tissue
imaging by MALDI-IMS is a powerful tool as it gives a
more complete and accurate structural picture and can trace
and follow where drugs localize in tissue with minimal ana-
tomical disruption and a minimum of manipulations. The
use of ion mobility has also been reported in conjunction
with MALDI MS as a means to fractionate lipids (Woods
and Jackson 2006; Jackson et al. 2005a, b; Cornett et al.
2007; McLean et al. 2007). In this technique, ions are Wrst
separated by ion mobility and then analyzed by TOF mass
spectrometry. The desorbed lipid ions fall on a trend line (a
function of charge and collision cross-section) that is sepa-
rate from those of oligonucleosides, peptides, proteins, and
drugs and metabolites having the same nominal mass. This
allows ions originating from lipids to be distinguished from
other small molecules. Another study reported label-free
imaging mass spectrometry to study lipid–lipid interactions
in a model membrane system (Zheng et al. 2007).
Conclusions
The emergence of MALDI-IMS has provided an enormous
impulse to the Weld of imaging mass spectrometry. Direct
tissue analysis by MALDI-IMS is an important technology
for assessing the localization of molecular species and for
revealing the underlying molecular signatures indicative of
disease. The molecular species identiWed in these experi-
ments can provide insight into mechanisms and etiology of
disease. It is now commercially available and is widely
used to record the spatial distributions of numerous peptide
and protein ions, in parallel and without a label, from tissue
sections and cells. It has been applied to study the changes
in protein expression levels and distributions associated
with a range of pathologies. Furthermore, the same tech-
niques are used to trace the distribution of pharmaceuticals
and their metabolites within organs and complete animals,
as well as to investigate the resulting changes in the organ’s
proteome. The potential of MALDI imaging has driven
many developments covering all aspects of the experiment.
Advances in data acquisition now allow fast MALDI
imaging with high-spatial resolution. Developments in sample
preparation have also continued unabated. Many of the
developments discussed in this review can be combined,
the results can then be analyzed using multivariate tech-
niques or combined with information from other imaging
techniques using cross correlation analysis (Sinha et al.
2008). These results can then be compared with the results
of global proteomics/lipidomics strategies applied to spa-
tially resolved samples, either through voxelization, laser-
capture microdissection, or any other sample preparation
protocol. It is this potential, which is slowly being fulWlled,
to obtain extensive spatially resolved biomolecular infor-
mation, an absolute necessity for a thorough picture of the
molecular processes underpinning biological and patho-
logical processes or to track the distribution of pharmaceu-
ticals that is attracting ever more interest.
Acknowledgments We thank Ulrike Buchholz, Claudia-Mareike
PXüger, Eleonore Samson, and Andreas Voss for excellent technical
assistance in performing MALDI-IMS.
References
Aebersold R, Goodlett DR (2001) Mass spectrometry in proteomics.
Chem Rev 101(2):269–295
Aerni HR, Cornett DS, Caprioli RM (2006) Automated acoustic matrix
deposition for MALDI sample preparation. Anal Chem
78(3):827–834
Altelaar AF, van Minnen J, Jiménez CR, Heeren RM, Piersma SR
(2005) Direct molecular imaging of Lymnaea stagnalis nervous
tissue at subcellular spatial resolution by mass spectrometry. Anal
Chem 77(3):735–741
Altelaar AF, Klinkert I, Jalink K, de Lange RP, Adan RA, Heeren RM,
Piersma SR (2006) Gold-enhanced biomolecular surface imaging
of cells and tissue by SIMS and MALDI mass spectrometry. Anal
Chem 78(3):734–742
Altelaar AF, Luxembourg SL, McDonnell LA, Piersma SR, Heeren
RM (2007) Imaging mass spectrometry at cellular length scales.
Nat Protoc 2(5):1185–1196
Bhattacharya SH, Gal AA, Murray KK (2003) Laser capture microdis-
section MALDI for direct analysis of archival tissue. J Proteome
Res 2(1):95–98
432 Histochem Cell Biol (2008) 130:421–434
123
Brand GD, Krause FC, Silva LP, Leite JR, Melo JA, Prates MV, Pes-
quero JB, Santos EL, Nakaie CR, Costa-Neto CM, Bloch C Jr
(2006) Bradykinin-related peptides from Phyllomedusa hypo-
chondrialis. Peptides 27(9):2137–2146
Burnum KE, Tranguch S, Mi D, Daikoku T, Dey SK, Caprioli RM
(2008) Imaging mass spectrometry reveals unique protein proWles
during embryo implantation. Endocrinology. 2008 Apr 10
Caldwell RL, Caprioli RM (2005) Tissue proWling by mass spectrom-
etry: a review of methodology and applications. Mol Cell Proteo-
mics 4(4):394–401
Caprioli RM (2005) Deciphering protein molecular signatures in can-
cer tissues to aid in diagnosis, prognosis, and therapy. Cancer Res
65(23):10642–10645
Caprioli RM, Farmer TB, Gile J (1997) Molecular imaging of biolog-
ical samples: localization of peptides and proteins using MALDI-
TOF MS. Anal Chem 69(23):4751–4760
Chaurand P, Caprioli RM (2002) Direct proWling and imaging of pep-
tides and proteins from mammalian cells and tissue sections by
mass spectrometry. Electrophoresis 23(18):3125–3135
Chaurand P, Stoeckli M, Caprioli RM (1999) Direct proWling of pro-
teins in biological tissue sections by MALDI mass spectrometry.
Anal Chem 71(23):5263–5270
Chaurand P, DaGue BB, Pearsall RS, Threadgill DW, Caprioli RM
(2001) ProWling proteins from azoxymethane-induced colon
tumors at the molecular level by matrix-assisted laser desorption/
ionization mass spectrometry. Proteomics 1(10):1320–1326
Chaurand P, Schwartz SA, Caprioli RM (2002) Imaging mass spec-
trometry: a new tool to investigate the spatial organization of pep-
tides and proteins in mammalian tissue sections. Curr Opin Chem
Biol 6(5):676–681
Chaurand P, Fouchécourt S, DaGue BB, Xu BJ, Reyzer ML, Orgebin-
Crist MC, Caprioli RM (2003) ProWling and imaging proteins in
the mouse epididymis by imaging mass spectrometry. Proteomics
3(11):2221–2239
Chaurand P, Sanders ME, Jensen RA, Caprioli RM (2004a) Proteo-
mics in diagnostic pathology: proWling and imaging proteins
directly in tissue sections. Am J Pathol 165(4):1057–1068
Chaurand P, Schwartz SA, Billheimer D, Xu BJ, Crecelius A, Caprioli
RM (2004b) Integrating histology and imaging mass spectrome-
try. Anal Chem 76(4):1145–1155
Chaurand P, Schwartz SA, Reyzer ML, Caprioli RM (2005) Imaging
mass spectrometry: principles and potentials. Toxicol Pathol
33(1):92–101
Chaurand P, Rahman MA, Hunt T, Mobley JA, Gu G, Latham JC,
Caprioli RM, Kasper S (2008) Monitoring mouse prostate devel-
opment by proWling and imaging mass spectrometry. Mol Cell
Proteomics 7(2):411–423
Corbin BD, Seeley EH, Raab A, Feldmann J, Miller MR, Torres VJ,
Anderson KL, Dattilo BM, Dunman PM, Gerads R, Caprioli RM,
Nacken W, Chazin WJ, Skaar EP (2008) Metal chelation and inhi-
bition of bacterial growth in tissue abscesses. Science
319(5865):962–965
Cornett DS, Mobley JA, Dias EC, Andersson M, Arteaga CL, Sanders
ME, Caprioli RM (2006) A novel histology-directed strategy for
MALDI-MS tissue proWling that improves throughput and cellu-
lar speciWcity in human breast cancer. Mol Cell Proteomics
5(10):1975–1983
Cornett DS, Reyzer ML, Chaurand P, Caprioli RM (2007) MALDI
imaging mass spectrometry: molecular snapshots of biochemical
systems. Nat Methods 4(10):828–833
Cornett DS, Frappier SL, Caprioli RM (2008) MALDI-FTICR imag-
ing mass spectrometry of drugs and metabolites in tissue. Anal
Chem [Epub ahead of print]
Corr JJ, Kovarik P, Schneider BB, Hendrikse J, Loboda A, Covey TR
(2006) Design considerations for high speed quantitative mass
spectrometry with MALDI ionization. J Am Soc Mass Spectrom
17(8):1129–1141
Curran S, McKay JA, McLeod HL, Murray GI (2000) Laser capture
microscopy. Mol Pathol 53(2):64–68
DeKeyser SS, Kutz-Naber KK, Schmidt JJ, Barrett-Wilt GA, Li L
(2007) Imaging mass spectrometry of neuropeptides in decapod
crustacean neuronal tissues. J Proteome Res 6(5):1782–1791
Fenn JB, Mann M, Meng CK, Wong SF, Whitehouse CM (1989) Elec-
trospray ionization for mass spectrometry of large biomolecules.
Science 246(4926):64–71
Godovac-Zimmermann J, Brown LR (2001) Perspectives for mass
spectrometry and functional proteomics. Mass Spectrom Rev
20(1):1–57
Grey AC, Schey KL (2008) Distribution of bovine and rabbit lens al-
pha-crystallin products by MALDI imaging mass spectrometry.
Mol Vis 14:171–179
Groseclose MR, Andersson M, Hardesty WM, Caprioli RM (2007)
IdentiWcation of proteins directly from tissue: in situ tryptic diges-
tions coupled with imaging mass spectrometry. J Mass Spectrom
42(2):254–262
Han J, Schey KL (2006) MALDI tissue imaging of ocular lens alpha-
crystallin. Invest Ophthalmol Vis Sci 47(7):2990–2996
Heeren RM (2005) Proteome imaging: a closer look at life’s organiza-
tion. Proteomics 5(17):4316–4326
Herring KD, Oppenheimer SR, Caprioli RM (2007) Direct tissue anal-
ysis by matrix-assisted laser desorption ionization mass spec-
trometry: application to kidney biology. Semin Nephrol
27(6):597–608
Hillenkamp F, Karas M, Beavis RC, Chait BT (1991) Matrix-assisted
laser desorption/ionization mass spectrometry of biopolymers.
Anal Chem 63(24):1193A–1203A
Hirosawa M, Hoshida M, Ishikawa M, Toya T (1993) MASCOT: mul-
tiple alignment system for protein sequences based on three-way
dynamic programming. Comput Appl Biosci 9:161–167
Hsieh Y, Casale R, Fukuda E, Chen J, Knemeyer I, Wingate J, Morri-
son R, Korfmacher W (2006) Matrix-assisted laser desorption/
ionization imaging mass spectrometry for direct measurement of
clozapine in rat brain tissue. Rapid Commun Mass Spectrom
20(6):965–972
Hsieh Y, Chen J, Korfmacher WA (2007) Mapping pharmaceuticals in
tissues using MALDI imaging mass spectrometry. J Pharmacol
Toxicol Methods 55(2):193–200
Jackson SN, Wang HY, Woods AS (2005a) Direct proWling of lipid
distribution in brain tissue using MALDI-TOFMS. Anal Chem
77(14):4523–4527
Jackson SN, Wang HY, Woods AS, Ugarov M, Egan T, Schultz JA
(2005b) Direct tissue analysis of phospholipids in rat brain using
MALDI-TOFMS and MALDI-ion mobility-TOFMS. J Am Soc
Mass Spectrom 16(2):133–138
Karas M, Hillenkamp F (1988) Laser desorption ionization of proteins
with molecular masses exceeding 10, 000 daltons. Anal Chem
60(20):2299–2301
Khatib-Shahidi S, Andersson M, Herman JL, Gillespie TA, Caprioli
RM (2006) Direct molecular analysis of whole-body animal tis-
sue sections by imaging MALDI mass spectrometry. Anal Chem
78(18):6448–6456
Klinkert I, McDonnell LA, Luxembourg SL, Altelaar AF, Amstalden
ER, Piersma SR, Heeren RM (2007) Tools and strategies for visu-
alization of large image data sets in high-resolution imaging mass
spectrometry. Rev Sci Instrum 78(5):053716
Kruse R, Sweedler JV (2003) Spatial proWling invertebrate ganglia us-
ing MALDI MS. J Am Soc Mass Spectrom 14(7):752–759
Lahm HW, Langen H (2000) Mass spectrometry: a tool for the identi-
Wcation of proteins separated by gels. Electrophoresis
21(11):2105–2114
Histochem Cell Biol (2008) 130:421–434 433
123
Lavoie C, Paiement J (2008) Topology of molecular machines of the
endoplasmic reticulum: a compilation of proteomics and cyto-
logical data. Histochem Cell Biol 129(2):117–128
Lemaire R, Desmons A, Tabet JC, Day R, Salzet M, Fournier I (2007a)
Direct analysis and MALDI imaging of formalin-Wxed, paraYn-
embedded tissue sections. J Proteome Res 6(4):1295–1305
Lemaire R, Menguellet SA, Stauber J, Marchaudon V, Lucot JP, Coll-
inet P, Farine MO, Vinatier D, Day R, Ducoroy P, Salzet M, Four-
nier I (2007b) SpeciWc MALDI imaging and proWling for
biomarker hunting and validation: fragment of the 11S protea-
some activator complex, Reg alpha fragment, is a new potential
ovary cancer biomarker. J Proteome Res 6(11):4127–4134
Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik
BM, Yates JR 3rd (1999) Direct analysis of protein complexes us-
ing mass spectrometry. Nat Biotechnol 17:676–682
Masumori N, Thomas TZ, Chaurand P, Case T, Paul M, Kasper S,
Caprioli RM, Tsukamoto T, Shappell SB, Matusik RJ (2001) A
probasin-large T antigen transgenic mouse line develops prostate
adenocarcinoma and neuroendocrine carcinoma with metastatic
potential. Cancer Res 61(5):2239–2249
McCombie G, Staab D, Stoeckli M, Knochenmuss R (2005) Spatial
and spectral correlations in MALDI mass spectrometry images by
clustering and multivariate analysis. Anal Chem 77(19):6118–
6124
McDonald WH, Yates JR 3rd (2000) Proteomic tools for cell biology.
TraYc 1(10):747–754
McDonnell LA, Heeren RM (2007) Imaging mass spectrometry. Mass
Spectrom Rev 26(4):606–643
McDonnell LA, Piersma SR, MaartenAltelaar AF, Mize TH, Luxem-
bourg SL, Verhaert PD, van Minnen J, Heeren RM (2005) Sub-
cellular imaging mass spectrometry of brain tissue. J Mass
Spectrom 40(2):160–168
McLean JA, Ridenour WB, Caprioli RM (2007) ProWling and imaging
of tissues by imaging ion mobility-mass spectrometry. J Mass
Spectrom 42(8):1099–1105
Meistermann H, Norris JL, Aerni HR, Cornett DS, Friedlein A, Erskine
AR, Augustin A, De Vera Mudry MC, Ruepp S, Suter L, Langen
H, Caprioli RM, Ducret A (2006) Biomarker discovery by imag-
ing mass spectrometry: transthyretin is a biomarker for gentami-
cin-induced nephrotoxicity in rat. Mol Cell Proteomics
5(10):1876–1886
Palmer-Toy DE, Sarracino DA, Sgroi D, LeVangie R, Leopold PE
(2000) Direct acquisition of matrix-assisted laser desorption/ion-
ization time-of-Xight mass spectra from laser capture microdis-
sected tissues. Clin Chem 46(9):1513–1516
Pandey A, Mann M (2000) Proteomics to study genes and genomes.
Nature 405(6788):837–846
Pierson J, Norris JL, Aerni HR, Svenningsson P, Caprioli RM, Andrén
PE (2004) Molecular proWling of experimental Parkinson’s dis-
ease: direct analysis of peptides and proteins on brain tissue sec-
tions by MALDI mass spectrometry. J Proteome Res 3(2):289–295
Reid GE, McLuckey SA (2002) ‘Top down’ protein characterization
via tandem mass spectrometry. J Mass Spectrom 37:663–675
Reyzer ML, Caprioli RM (2007) MALDI-MS-based imaging of small
molecules and proteins in tissues. Curr Opin Chem Biol
11(1):29–35
Reyzer ML, Hsieh Y, Ng K, Korfmacher WA, Caprioli RM (2003) Di-
rect analysis of drug candidates in tissue by matrix-assisted laser
desorption/ionization mass spectrometry. J Mass Spectrom
38(10):1081–1092
Reyzer ML, Caldwell RL, Dugger TC, Forbes JT, Ritter CA, Guix M,
Arteaga CL, Caprioli RM (2004) Early changes in protein expres-
sion detected by mass spectrometry predict tumor response to
molecular therapeutics. Cancer Res 64(24):9093–9100
RoepstorV P (1997) Mass spectrometry in protein studies from genome
to function. Curr Opin Biotechnol 8(1):6–13
Rohner TC, Staab D, Stoeckli M (2005) MALDI mass spectrometric
imaging of biological tissue sections. Mech Ageing Dev
126(1):177–185
Rujoi M, Estrada R, Yappert MC (2004) In situ MALDI-TOF MS
regional analysis of neutral phospholipids in lens tissue. Anal
Chem 76(6):1657–1663
Schwamborn K, Krieg RC, Reska M, Jakse G, Knuechel R, Wellmann
A (2007) Identifying prostate carcinoma by MALDI-Imaging. Int
J Mol Med 20(2):155–159
Schwartz SA, Reyzer ML, Caprioli RM (2003) Direct tissue analysis
using matrix-assisted laser desorption/ionization mass spectrom-
etry: practical aspects of sample preparation. J Mass Spectrom
38(7):699–708
Schwartz SA, Weil RJ, Johnson MD, Toms SA, Caprioli RM (2004)
Protein proWling in brain tumors using mass spectrometry: feasi-
bility of a new technique for the analysis of protein expression.
Clin Cancer Res 10(3):981–987
Schwartz SA, Weil RJ, Thompson RC, Shyr Y, Moore JH, Toms SA,
Johnson MD, Caprioli RM (2005) Proteomic-based prognosis of
brain tumor patients using direct-tissue matrix-assisted laser
desorption ionization mass spectrometry. Cancer Res
65(17):7674–7681
Shimma S, Sugiura Y, Hayasaka T, Zaima N, Matsumoto M, Setou M
(2008) Mass imaging and identiWcation of biomolecules with
MALDI-QIT-TOF-based system. Anal Chem 80(3):878–885
Signor L, Varesio E, Staack RF, Starke V, Richter WF, Hopfgartner G
(2007) Analysis of erlotinib and its metabolites in rat tissue sec-
tions by MALDI quadrupole time-of-Xight mass spectrometry.
J Mass Spectrom 42(7):900–909
Sinha TK, Khatib-Shahidi S, Yankeelov TE, Mapara K, Ehtesham M,
Cornett DS, Dawant BM, Caprioli RM, Gore JC (2008) Integrat-
ing spatially resolved three-dimensional MALDI IMS with in
vivo magnetic resonance imaging. Nat Methods 5(1):57–59
Sköld K, Svensson M, Nilsson A, Zhang X, Nydahl K, Caprioli RM,
Svenningsson P, Andrén PE (2006) Decreased striatal levels of
PEP-19 following MPTP lesion in the mouse. J Proteome Res
5(2):262–269
Stauber J, Lemaire R, Franck J, Bonnel D, Croix D, Day R, Wisztorski
M, Fournier I, Salzet M (2008) MALDI imaging of formalin-Wxed
paraYn-embedded tissues: application to model animals of Par-
kinson disease for biomarker hunting. J Proteome Res 7(3):969–
978
Stoeckli M, Chaurand P, Hallahan DE, Caprioli RM (2001) Imaging
mass spectrometry: a new technology for the analysis of protein
expression in mammalian tissues. Nat Med 7(4):493–496
Stoeckli M, Staab D, Staufenbiel M, Wiederhold KH, Signor L (2002)
Molecular imaging of amyloid beta peptides in mouse brain sec-
tions using mass spectrometry. Anal Biochem 311(1):33–39
Stoeckli M, Staab D, Schweitzer A, Gardiner J, Seebach D (2007)
Imaging of a beta-peptide distribution in whole-body mice sec-
tions by MALDI mass spectrometry. J Am Soc Mass Spectrom
18(11):1921–1924
Todd PJ, SchaaV TG, Chaurand P, Caprioli RM (2001) Organic ion
imaging of biological tissue with secondary ion mass spectrome-
try and matrix-assisted laser desorption/ionization. J Mass Spec-
trom 36(4):355–369
Van de Plas R, Ojeda F, Dewil M, Van Den Bosch L, De Moor B,
Waelkens E (2007) Prospective exploration of biochemical tissue
composition via imaging mass spectrometry guided by principal
component analysis. Pac Symp Biocomput 2007:458–469
Villmann T, Schleif FM, Kostrzewa M, Walch A, Hammer B (2008)
ClassiWcation of mass-spectrometric data in clinical proteomics
using learning vector quantization methods. Brief Bioinform
9(2):129–143
Wisztorski M, Lemaire R, Stauber J, Menguelet SA, Croix D, Mathé
OJ, Day R, Salzet M, Fournier I (2007) New developments in
434 Histochem Cell Biol (2008) 130:421–434
123
MALDI imaging for pathology proteomic studies. Curr Pharm
Des 13(32):3317–3324
Woods AS, Jackson SN (2006) Brain tissue lipidomics: direct probing
using matrix-assisted laser desorption/ionization mass spectrom-
etry. AAPS J 8(2):E391–E395
Wysocki VH, Resing KA, Zhang Q, Cheng G (2005) Mass spectrom-
etry of peptides and proteins. Methods 35:211–222
Xu BJ, Caprioli RM, Sanders ME, Jensen RA (2002) Direct analysis of
laser capture microdissected cells by MALDI mass spectrometry.
J Am Soc Mass Spectrom 13(11):1292–1297
Yanagisawa K, Shyr Y, Xu BJ, Massion PP, Larsen PH, White BC,
Roberts JR, Edgerton M, Gonzalez A, Nadaf S, Moore JH, Capr-
ioli RM, Carbone DP (2003) Proteomic patterns of tumour sub-
sets in non-small-cell lung cancer. Lancet 362(9382):433–439
Zheng L, McQuaw CM, Ewing AG, Winograd N (2007) Sphingomy-
elin/phosphatidylcholine and cholesterol interactions studied by
imaging mass spectrometry. J Am Chem Soc 129(51):15730–
15731
