1
Introduction: The Roles of
Metals in Biology
From the perspective of a chemist, biology
confers a rich variety
of roles on a number of metal ions. It is widely agreed that a large
fraction of the genomic output of living things contains metal or
metalloid ions, although estimates of this fraction vary widely and
depend upon which metal ions are considered.
1−3
Moreover, recent
reports suggest that, at least in some cases, there are many uncharacterized
metalloproteins.
4
With inclusion of the
s-block metals such as Na, K, Mg, and Ca, the proportion likely approaches
100%; recent estimates from the protein data bank indicate that the
prevalence of heavier metal ions of atomic number above 20 within
proteins is around 22%,
5
with Zn2+ proteins alone constituting about 11%. Living organisms have an
inherent and very rich physical structure, with relevant length scales
ranging from the nanometer scale for subcellular structure to hundreds
of micrometers and above for tissue, organ, or organism-level organization.
The ability to derive the spatial distribution of elements on this
diversity of length scales is a key to understanding their function.
Metals play essential and central roles in the most important and
chemically challenging processes required for life, with active site
structures and mechanisms that, at the time of their discovery, have
usually not yet been duplicated in the chemical laboratory. Furthermore,
diseases of metal dysregulation can cause disruption in the distribution
of metals.
6
For example, Menke’s
disease and Occipital Horn Syndrome,
7
and
Wilson’s disease,
8
involve disruption
in copper uptake and excretion, respectively, through mutation in
the ATP7A and ATP7B Cu transporters.
9
The
mechanisms of action of toxic elements such as mercury and arsenic
are also of interest, as are essential nonmetal trace elements, such
as selenium. Likewise, an increasing number of pharmaceuticals include
metals or heavier elements; such chemotherapeutic drugs include the
platinum derivatives cisplatin and carboplatin,
10
some promising new ruthenium drugs,
11
and arsenic trioxide, which has been used to treat promyelocytic
leukemia.
12
Understanding the localization,
speciation, and distribution of these at various length scales is
of significant interest.
A wide variety of heavier elements
can be probed by X-ray spectroscopic
methods; these are shown graphically in Figure 1. X-ray fluorescence imaging is a
powerful technique that can be
used to determine elemental and chemical species distributions at
a range of spatial resolutions within samples of biological tissues.
Most modern applications require the use of synchrotron radiation
as a tunable and high spectral brightness source of X-rays. The method
uses a microfocused X-ray beam to excite X-ray fluorescence from specific
elements within a sample. Because the method depends upon atomic physics,
it is highly specific and enables a wide range of chemical elements
to be investigated. A significant advantage over more conventional
methods is the ability to measure intact biological samples without
significant treatment with exogenous reagents. The technique is capable
of determining metal and nonmetal distributions on a variety of length
scales, with information on chemical speciation also potentially available.
Figure 2 shows examples of rapid-scan X-ray
fluorescence imaging at two contrasting length scales: rapid-scan
imaging
13
of a section of a human brain
taken from an individual suffering from multiple sclerosis and showing
elemental profiles for Fe, Cu, and Zn;
14
and a high-resolution image showing mercury and other elements in
a section of retina from a zebrafish larva treated with methylmercury
chloride.
15
We will discuss both the state
of the art in terms of experimental methods and some recent applications
of the methods. This Review considers X-ray fluorescence imaging with
incident X-ray energies in the hard X-ray regime, which we define
as 2 keV and above. We review technologies for producing microfocused
X-ray beams and for detecting X-ray fluorescence, as well as methods
that confer chemical selectivity or three-dimensional visualization.
We discuss applications in key areas with a view to providing examples
of how the technique can provide information on biological systems.
We also discuss synergy with other methods, which have overlapping
or complementary capabilities. Our goal is to provide useful and pertinent
information to encourage and enable further use of this powerful method
in chemical and biochemical studies of living organisms.
Figure 1
Periodic table
of the elements showing elements of biological interest
that can be probed using X-ray fluorescence imaging. Elements are
divided into three categories, those that are physiologically important,
those that are pharmacologically active, and those that are toxic
or of environmental concern. Those elements that can be detected using
hard X-ray XFI are shown in bold color, while those that cannot are
indicated by a lighter shade of the same color. Some elements will
fall into more than one category; for example, selenium is both essential
and toxic in excess with a relatively narrow margin separating adequate
supply and the onset of toxic symptoms. Similarly, bromine is used
pharmacologically but is also a physiologically important element.
Figure 2
Examples of biological X-ray fluorescence imaging
(XFI), illustrating
the different accessible length-scales. Panel a shows a coronal section
of human brain taken post-mortem from an individual suffering from
multiple sclerosis,
14
with normalized levels
of iron, zinc, and copper indicated as shades of red, green, and blue.
Panel b shows a thin section of larval zebrafish retina imaged at
high resolution showing preferential accumulation of mercury in the
photoreceptor cells.
15
Several acronyms are used for X-ray fluorescence imaging;
some
commonly in use are X-ray fluorescence (XRF), synchrotron X-ray fluorescence
(SXRF), X-ray fluorescence imaging (XFI), synchrotron X-ray fluorescence
imaging (SXFI), synchrotron radiation-induced X-ray emission (SRIXE),
rapid scan X-ray fluorescence (RSXRF), as well as X-ray fluorescence
microscopy and X-ray fluorescence mapping (both of which are commonly
abbreviated XFM). All of these correspond to essentially the same
experiment. Purists might argue that most of what we discuss here
is predominantly mapping and not imaging; however, given that we will
cover both two-dimensional and three-dimensional applications, the
acronym used here is X-ray fluorescence imaging (XFI).
2
Experimental Methods and Strategies
In essence, the technique
of X-ray fluorescence imaging uses a
small or microfocused X-ray beam incident upon a sample, an energy-dispersive
detector to monitor the X-ray fluorescence, and sample translation
so that the X-ray beam sequentially interrogates different parts of
the sample to develop an image of the sample. Essentially all modern
applications of XFI in biological systems employ synchrotron radiation
as a high spectral brightness source of X-rays. Synchrotron radiation
from a state-of-the-art third-generation source is of many orders
of magnitude greater spectral brightness than even the most powerful
rotating anode X-ray sources; the low concentrations of metals in
biological tissues mean that XFI is only practical on these samples
using synchrotron light. A detailed discussion of the origins and
properties of synchrotron light is outside the scope of this Review,
and has been reviewed by others.
16
A schematic
of the experimental apparatus is shown in Figure 3. Below we review different aspects
of the experiment, with
an emphasis on those components that are likely to be selected or
adjustable by the experimenter.
Figure 3
Highly simplified and schematic plan view
of the apparatus for
conducting X-ray fluorescence imaging experiments. The X-ray source
is typically synchrotron light, used in conjunction with upstream
X-ray optics (not shown) and in most cases an X-ray double crystal
monochromator.
2.1
Micro-Focus
Optics
There are five
important methods of generating a small or microfocused X-ray beam:
simple apertures, Kirkpatrick–Baez (K–B) mirrors, capillary
optics, Fresnel zone plates, and compound refractive lenses. Excellent
reviews of these methods have been reported relatively recently,
17−20
and therefore we will discuss each method only briefly.
2.1.1
Apertures
The simplest method of
generating small X-ray beams, apertures, is most suitable when relatively
large beams are required. Because they do not focus X-rays, they are
often neglected in reviews, but much outstanding work has been conducted
using simple apertures. An aperture usually consists of a precision
slit assembly or a laser drilled aperture or pinhole of fixed size
(e.g., 20–100 μm), usually fabricated from a high atomic
number material such as tungsten or tantalum. In general, it is good
practice to locate the aperture close to the sample, especially if
upstream focusing optics are used, as noncolinearity of the X-ray
beams and scattering from the edges of the aperture may result in
different size projection, sometimes called beam blow-up, at the sample.
These effects, especially the latter, can be reduced by means of a
scatter slit or second aperture downstream of the first.
2.1.2
Specular Optics
Two of the common
methods of X-ray microfocusing depend upon X-ray reflection: K–B
mirrors and capillary optics. Both require that the incident angle
θ between the optic surface and the incident X-rays is less
than the X-ray critical angle θc. Outside of the
total external reflection condition (i.e., θ > θc), the reflectivity of all materials
is negligible.
The value of θc is dependent both upon the X-ray
energy and upon the atomic number and density of the material used
for the reflective optics. X-ray mirrors are often made of silicon
and coated with a material of high atomic number (such as nickel,
platinum, gold, or rhodium), as high atomic number coatings give higher
θc values, which for hard X-rays and common coatings
is generally between 0.1 and 10 mrad. An alternative approach is to
use multilayer mirrors. Here, alternating layers of high and low refractive
index materials are used to achieve high reflectivity at angles greater
than θc through constructive interference at multiple
surface interfaces.
2.1.3
Kirkpatrick–Baez
(K–B) Mirrors
Probably the most commonly used method
of obtaining microfocused
X-ray beams are K–B mirrors.
21
These
comprise two spherical or cylindrical mirrors in a crossed configuration
21
with adjustable bend radii to achieve optimum
focus at different wavelengths. A schematic diagram of a K–B
pair is shown in Figure 4. In most cases, the
beam sizes achievable with K–B mirrors are of the order of
1–5 μm, but in some cases sub-100 nm spot sizes have
been achieved.
22
The focal length of most
modern devices is such that several centimeters are typically available
between the physical end of the optic and the sample. Recently, side-by-side
K–B mirror configurations, sometimes called Montel mirrors,
have been discussed.
23,24
These provide a more compact
system that is capable of higher demagnification than traditional
sequential K–B mirrors, but at the time of writing they are
not in routine use.
Figure 4
Schematic representation of a two-mirror Kirkpatrick–Baez
system. The lines indicate the flight path of rays from the source
to the sample. The angle of incidence θ is shown on the first
mirror. The two mirrors focus in orthogonal directions.
2.1.4
Capillary Optics
In most cases,
capillary optics are made of glass, although metal devices have been
explored. There are two basic types of capillary optics: polycapillaries
and monocapillaries. Both function by total external X-ray reflection
from the inner walls of the glass (Figure 5). Polycapillaries have multiple small
channels that guide X-rays
by multiple internal reflections along the channel’s inner
wall, and focal spot sizes in the range 10–50 μm are
achievable.
25
Polycapillaries have the
major advantage of being able to collect larger solid angles than
monocapillaries. Monocapillaries can be further divided into condensing
capillaries and single bounce ellipsoidal capillaries. The latter
focus the X-ray with a single reflection from its inner surface with
the advantage of superior efficiency. The focal length of glass devices
typically provides several millimeters between the tip of the optic
and the sample. Tapered metal capillaries have been explored
26
and used in a research setting;
27,28
these have the advantage of greater efficiency and superior performance
especially at low X-ray energies,
29
and
can produce beams sizes of around 3–15 μm. The main disadvantage
of these devices is that the focal length is very short, so that the
sample needs to be located at about 0.1 mm from the beam exit of the
capillary. Polycapillaries fitted to detectors have been used in the
construction of confocal X-ray fluorescence imaging systems, which
we will discuss below (section 2.7).
Figure 5
Capillary microfocus
optics. (a) Schematic diagram of a multiple-bounce
tapered monocapillary microfocus optic. (b) The beam intensity profile
of a tapered metal capillary optic,
26
measured
using a cadmium tungstate scintillator placed at the focal point of
the device.
2.1.5
Compound
Refractive Lenses
X-ray
refractive lenses, analogous to the glass lenses commonly used for
visible light, were considered impractical for many years due to the
weak relative refraction and strong absorption typical of X-rays.
However, lenses fabricated from low atomic number materials such as
Be, B, C, and Al have been used to minimize X-ray attenuation due
to absorption. Because of the low refractive index, focusing refractive
X-ray lenses must have a concave shape. To achieve reasonable focal
lengths, many individual lenses can be stacked, forming a compound
refractive lens and yielding working distances of the order of 1 m.
While they remain a promising alternative, especially for high energy
applications,
30
at the time of writing
compound refractive lenses are little used for microfocus optics in
X-ray fluorescence imaging applications, and we will not consider
them further.
2.1.6
Fresnel Zone Plates
Effectively
lenses for monochromatic X-rays, Fresnel zone plates consist of a
series of concentric rings where the rings become narrower at larger
radii until the last and narrowest ring is reached (Figure 6). The radii of the zone
plate edges r are given by eq 1:
1
where n is the zone number,
λ is the X-ray wavelength, and F is the focal
length. We consider here applications in which Fresnel zone plates
are used to focus a larger beam onto a small spot. The focusing ability
is based upon constructive interference of the X-ray wavefront, modified
by passage through the zone plate, either in phase or more commonly
by attenuation due to the zones. In most cases, the working distance
from the zone plate to the sample is of the order of centimeters,
and the spot sizes achievable are defined by both the X-ray energy
(wavelength λ) and the outermost zone width Δr = r
N
– r
N–1, where N is the number of zones. To avoid chromatic blurring, N must be less than
the inverse of the X-ray wavelength resolution
λ/Δλ. As with all grating optics, Fresnel zone plates
give different diffraction orders (Figure 6B), with the higher orders having focal
points that are closer to
the optic, zero order corresponding to a straight-through beam path,
and negative orders giving diverging beams. Only the first order beam
is used, and the other orders are predominantly rejected by using
an order-sorting aperture, which is placed between the focus of the
first-order beam (the location of the sample) and the zone plate.
Fresnel zone plates were first applied in soft X-ray transmission
microscopy
31
for which spot sizes below
50 nm can be obtained. In the hard X-ray regime, beam spots of the
order of 100–300 nm can often be achieved. Fresnel zone plates
differ from the other commonly used micro focus optics considered
here (K–B mirrors and capillaries) in that their focal length
is dependent on the X-ray energy. This can cause problems when performing
μ-XAS experiments in which energy is scanned (section 2.6) because ideally the sample
should be translated
as a function of energy so as to maintain its position at the X-ray
focus; the alignment need only be slightly imperfect for the beam
to move across the sample during the course of the μ-XAS scan.
If the energy range being scanned in μ-XAS is not large, then
it may be better to maintain a stationary sample position to avoid
such problems.
Figure 6
Fresnel zone plate microfocus optics. (A) Simplified schematic
diagram of a Fresnel zone plate microfocus optic. The X-rays from
the source are depicted as parallel rays, and following the zone plate
converge on a focus at the sample. (B) Positioning of order sorting
aperture and beam stop in a typical Fresnel zone plate-based XFI experiment.
The order sorting aperture serves to reject the light from diffraction
orders other than 1, with the sample ideally located at the order
1 beam focus.
The different methods
used for generating microscopic X-ray beams
in XFI experiments are summarized and compared in Table 1.
Table 1
Comparison of Optics for Generating
Micro-Beams for Hard X-ray XFIa
apertures
Kirkpatrick–Baez
mirrors
capillaries
Fresnel zone
plates
max X-ray energy
N/A
30 keV
20–40 keV
30 keV
typical resolution
50–100 μm
2–20 μm
1–20 μm
150–300 nm
best resolution
ca. 20 μm
40 nm
1 μm
60 nm
focal length
N/A
several cm
0.1–5 mm
several cm
disadvantages
inefficient (low photon
flux), poor resolution
short focal length
energy-dependent focal length
a
Only commonly used optics are considered;
values given are those for an X-ray energy of 10 keV.
2.2
X-ray
Fluorescence Detection
When
the energy E of an X-ray is above an absorption edge
of an element, it can excite core electrons, creating a photoelectron
and a core hole. The core hole is rapidly filled by decay of an outer
electron with either the loss of an Auger electron or the emission
of an X-ray fluorescence photon. The division between these two modes
of decay is specified by the fluorescence yield,
32
and as it is the latter that concerns us here we will not
discuss electron yield further. Figure 7 shows
a simplified schematic diagram of the underlying physics together
with the fluorescence emission spectra of a selenium-containing sample
illuminated with X-rays above and below the Se K-edge.
Figure 7
Physics of X-ray absorption
and X-ray fluorescence. (A) Schematic
diagram of the physics. The incident X-ray photon excites a core–electron
as a photoelectron creating a core hole in the process. This core
hole is filled by dipole-allowed decay of an inner electron with concomitant
emission of an X-ray fluorescence photon. (B) Experimental X-ray emission
spectra collected with incident X-ray energy above and below the selenium
K-absorption edge of a biological sample containing high levels of
selenium. The spectrum with the incident X-ray energy below the Se
K-edge shows only scattered X-rays, whereas peaks due to the Se Kα and Se Kβ X-ray
fluorescence are clearly
visible when the incident X-ray energy is above the Se K-edge.
The X-ray fluorescence emission
lines of the elements occur at
characteristic energies, which are listed in the so-called “orange
book”, otherwise known as the X-ray data booklet.
33
The specific X-ray fluorescence lines for K
and L edges are summarized in Figure 8. They
obey the familiar dipole selection rules common to most electronic
spectra with Δl = ±1, where l is the azimuthal quantum number. Thus, for a K-edge,
the most intense
fluorescence lines are the Kα1 and Kα2, which arise from the decay of 2p3/2 and 2p1/2
electrons, respectively, to fill the 1s core hole. Similarly, the
lines of secondary intensity, the Kβ1 and Kβ3, arise from decay of 3p3/2 and 3p1/2
electrons,
respectively. Because the K-edges of heavy elements such as third
row transition elements may be higher in energy than the working range
of most XFI beamlines, in these cases the L-edges are used. Here,
the most intense fluorescence lines arise from decay of 3d electrons
to fill the 2p hole; for example, for LIII edges, the Lα1 and Lα2 arise from 3d5/2
→ 2p3/2 and 3d3/2 → 2p3/2 transitions, respectively. Figure 9 shows
a typical X-ray fluorescence emission spectrum from a biological sample.
In an XFI experiment, conventionally the Kα1 and
Kα2 lines cannot be resolved by the detector (section 2.3) and are labeled as an average
Kα peak, likewise for the Kβ peak. All of the other,
more minor, lines from the K-shell elements can typically be neglected
(Figure 9). The Kα is almost
always the fluorescence line employed for experimental measurements
because it is the most intense; the Kβ may need to
be taken into account in cases where it overlaps with the fluorescence
of another element. For L-shell excitation with an energy above all
three L-edges, the primary fluorescence appears as a characteristic
pair of intense peaks, the Lα12 and the Lβ1, the most intense lines from the LIII
and LII edges, respectively. In principle, the X-ray fluorescence lines
from all elements in a sample with absorption edge energies below
that of the incident beam are observable, but in practice experimental
factors such as attenuation of lower energy X-rays and low fluorescence
yields for the lighter elements mean that only fluorescence from elements
heavier than silicon is typically observable, unless the element is
present in great abundance. With biological samples the X-ray emission
spectrum is often dominated not by X-ray fluorescence but by scattered
X-rays. X-ray scattering can be divided into elastic or Rayleigh scattering,
and inelastic or Compton scattering. The former occurs at the same
energy as the incident X-ray beam, while the latter occurs at lower
energies and shows a broad asymmetric energy profile with a low-energy
tail that can extend into regions of the emission spectrum where X-ray
fluorescence of interest occurs (Figure 9).
The Compton scattering energy decreases as (1 – cos ϑ),
where ϑ is the observation angle relative to the incident beam,
which creates challenges for detectors that accept a large solid angle,
such as array detectors.
Figure 8
Origins and nomenclature of the major and minor
X-ray fluorescence
emission lines. The bold lines indicate the most intense fluorescence
transitions, with the boldest lines indicating those most suitable
for X-ray fluorescence imaging experiments.
Figure 9
Biological X-ray fluorescence emission spectrum. The spectrum shown
is that of a sample of human cerebral cortex from an individual poisoned
through skin exposure to dimethylmercury, plotted with counts per
second (CPS) on the ordinate. Two copies of the trace are shown, one
full-scale, and the other (labeled) with the vertical scale expanded
by a factor of 20. The spectrum was collected using a Canberra LeGe
detector using analog electronics with a Gaussian shaping amplifier
employing a shaping time of 2 μs. The incident X-ray energy
was 13 200 eV. Data were collected on SSRL 9-3.
To select for specific characteristic fluorescence
lines, almost
all X-ray fluorescence imaging is achieved using solid-state energy
dispersive detectors, which allow the intensity of all photons emanating
from the sample to be plotted as a function of the photon energy.
Figure 10 shows the results of an X-ray fluorescence
imaging measurement on a biological sample, the tip of a leaf from
a selenium hyperaccumulating plant, showing the levels of different
elements in the sample together with the fluorescence emission spectrum.
Figure 10
Example
of elemental mapping using X-ray fluorescence imaging.
The sample imaged is a leaf tip of the selenium hyperaccumulator Astragalus bisulcatus,
grown hydroponically with
selenate-containing media. The X-ray emission spectrum is shown in
the lower part of the figure, with the Kα lines of different
elements detected indicated. The Ar fluorescence arises from the air
in the experiment. The optical micrograph is shown in opt, plus XFI maps of Ca, Zn,
and Se, together with a map for scattered
X-rays. Ca can be seen to be concentrated in the leaf hairs (trichomes),
Zn in the spongy mesophyll cell layer within the leaf, and the Se
is relatively uniformly distributed throughout the leaf. We note the
relative intensities of the lower and higher energy fluorescence lines,
such as Ca and Se, do not reflect the absolute relative levels of
these elements because of air and window attenuation of the low-energy
X-rays. Data were collected on SSRL 9-3, using a glass capillary microfocus
optic, a silicon drift (Vortex) detector, and a Gaussian shaping amplifier
with a shaping time of 0.5 μs.
2.3
Solid-State Energy Dispersive X-ray Detectors
A variety of solid-state detectors
34,35
are commercially
available, each with their advantages and disadvantages; we will discuss
a few different types. The properties sought are good efficiency over
a wide energy range, good energy resolution, and high count rate capability.
The literature on detectors tends to be specialized, and at the time
of writing we know of no reviews that are readily accessible to researchers
in chemistry or the life sciences. We therefore briefly summarize
below the different types of detector currently available, their modes
of operation, and their advantages and drawbacks.
2.3.1
Germanium
Detectors
The first of
the commonly used energy dispersive detectors that we will consider
are the Ge detectors (Figure 11). These essentially
consist of large reverse-bias diodes, typically arranged so that X-ray
photons enter through a thin ion-implanted p+ contact, with the n+
contact at the opposite end of the device (Figure 11). The passage of an X-ray photon
into the detector causes
a cloud of electron–hole pairs to form within the diode, the
number of which is proportional to the X-ray energy, assuming that
the X-ray photon and any scattered or fluorescent daughter photons
are all absorbed within the diode. The sweeping voltage causes the
electrons and holes, respectively, to drift toward the front and the
rear of the detector, giving a current pulse that is proportional
to the X-ray energy. Ge detectors give excellent energy resolution
and good efficiency at high X-ray energies, although escape peaks
can be problematic for incident X-ray energies above the energy of
the Ge K-edge [E(Ge K) = 11.1 keV]. Escape peaks
arise from X-ray fluorescence from the Ge of the detector itself;
if these fluorescence photons are absorbed by the detector, then there
will be no effect, but if they “escape” outside the
detector, then the energy registered is that of the incident photon
displaced to lower energy by the Ge fluorescence energy. The result
is that for X-rays incident on the detector with energy E
in > E(Ge K), there is a reproduction
of all structures above E(Ge K) displaced to lower
energy by the fluorescence energy (primarily the Kα), as illustrated in Figure 12.
The presence
of escape peaks for incident energies typical of XFI experiments can
often obscure the fluorescence lines of lighter elements in the sample;
for example, in Figure 12 sulfur fluorescence
would be difficult to observe because it would be overlaid by the
escape peaks. A small inconvenience of Ge detectors is that they must
be cooled to liquid nitrogen temperatures. This is because at room
temperature the thermal excitation of the valence electrons can be
sufficient to allow them to cross the band gap and reach the conduction
band. In an appropriately cooled detector, the charge is transferred
to a field-effect transistor (FET) to convert the current into a voltage
as part of the preamplifier.
Figure 11
Germanium detector. (a) Canberra LeGe germanium
detector, and (b)
a schematic diagram showing the basis of its function. The beryllium
window, the preamplifier, and the liquid nitrogen cryostat are indicated
on (a).
Figure 12
X-ray fluorescence emission spectrum
showing Ge detector escape
peaks. The data set is the same as that of Figure 9 but replotted with a wider energy
range to show the escape
peaks, plotted with counts per second (CPS) on the ordinate. X-rays
impinging the detector with energies above the Ge K-edge (indicated)
generate additional escape peaks due to loss of Ge X-ray fluorescence
from the detector. The escape peaks corresponding to both the Ge Kα
and the Kβ fluorescence lines are seen as echoes of the scatter
peaks displaced to low energy by the Ge Kα and Kβ fluorescence
energies, respectively. Data were collected on SSRL 9-3.
2.3.2
Lithium Drifted Silicon
Detectors
Lithium drifted silicon (SiLi) detectors are alternatives
to germanium
detectors that do not suffer from problems due to escape peaks obscuring
low-energy fluorescence lines of interest. These devices also require
cooling with liquid nitrogen. They are less efficient at high X-ray
energies than the Ge detector, but more efficient than the silicon
drift detector, considered below, as the typical diode thickness is
3–5 mm. In general, these devices are not much used for modern
XFI experiments as silicon drift detectors are preferred.
2.3.3
Silicon Drift Detectors
Silicon
drift detectors offer many of the most desirable features, providing
high count rate ability with excellent energy resolution in a system
that does not require liquid nitrogen cooling. Reliable and relatively
affordable silicon drift detectors have now been available for a number
of years, and these systems are increasingly considered the detector
of choice in XFI experiments. Silicon drift detectors have a concentric
ring structure composed of p+ silicon implanted on one side of an
n-type silicon chip. These concentric rings are designed to generate
an electric field gradient across the device (Figure 13) with a small centrally positioned
contact that serves as
the anode of the device. A homogeneous p+ junction implanted on the
reverse side of the detector acts as an X-ray entrance window. As
with other types of solid-state detector, electron–hole pairs
are generated by the incoming X-ray photons, and these drift toward
the anode under the influence of the electric field. At the anode
the charge is transferred to the FET. Silicon drift detectors do require
cooling but not to the liquid nitrogen temperatures needed for Ge
detectors, and in the case of single detectors or small arrays of
discrete detectors, Peltier cooling is sufficient. Silicon drift detectors
do not suffer from escape peak problems because the low energy silicon
fluorescence photon is so readily absorbed that it has negligible
probability of escaping from the device. Because the overall thickness
of the device is usually of the order of 450 μm, the efficiency
at high X-ray energies is low, which can be a major disadvantage of
such devices. Thicker devices that are more efficient at high X-ray
energies have recently been created by stacking individual silicon
drift detectors,
36
and these are a promising
new technology, combining high count rate ability and excellent energy
resolution with no escape peak issues. We note in passing that silicon
drift detectors are also suitable for specialized experiments such
as low temperature X-ray magnetic circular dichroism.
37
This is because the Hall effect from the applied magnetic
field that will divert the current in a Ge detector will not do so
in a drift detector because in the latter case the current flow is
transverse to the detector axis.
Figure 13
Silicon drift detector. Part (a) shows
a SII Vortex 4-element array
detector, with the beryllium windows for the four detectors seen on
the right side of the photograph, part (b) shows a schematic diagram
of a single silicon drift detector element showing the surface of
the detector with the drift rings, and part (c) shows a side-on view
of a single silicon drift detector element with the current flow indicated.
2.3.4
Solid-State
Detector Data Readout and Preprocessing
The data from a solid-state
detector are typically processed by
analog electronics or by a digital equivalent. With both analog and
digital systems, a charge preamplifier with an FET generates a pulse
train that feeds the subsequent processing electronics. The preamplifier
is generally an integral part of the detector and is not normally
adjusted by the user. With analog electronics, the preamplifier pulse
train is fed to a shaping amplifier, operating with user-selected
shaping time (see discussion in Detector Dead
Time Effects), which in turn is used to feed either a set of
single channel analyzers adjusted to discriminate voltages corresponding
to the fluorescence lines of interest or an analog-to-digital converter
(ADC), which would be used to record the whole fluorescence emission
spectrum. With the digital alternative, the preamplifier pulse train
is fed to a digital signal processing unit containing a high-speed
ADC and firmware that analyzes the shape of the pulse train to effectively
give the entire fluorescence emission spectrum. The function of processing
electronics is summarized in a simplified schematic manner in Figure 14.
Figure 14
Schematic diagram of energy dispersive solid-state detector
signal
processing. The preamplifier pulse train shows sharp step-like voltage
increases corresponding to the arrival of photons at the detector,
superimposed upon a slower exponential decay. The size of the preamplifier
voltage steps is proportional to the energy of the photons. The preamplifier
pulse train can then be analyzed either by older analog electronics,
using a shaping amplifier (a typical oscilloscope type display is
depicted, triggering on the voltage rise), the output of which is
sent to an analog to digital converter, or directly by modern digital
signal processing. The latter involves a fast analog to digital converter,
and real-time shape analysis of the preamplifier pulse train.
With an analog system, the output
of the shaping amplifier can
be passed to an ADC and to a multichannel analyzer (MCA) in which
pulses of different voltages are accumulated as a histogram of the
emission spectrum of the samples, which is commonly referred to as
the MCA spectrum. If a sufficiently fast ADC is available, then the
entire MCA spectrum can be recorded to computer for each data point.
Alternatively, a number of single channel analyzers (SCAs) can be
used to register shaping amplifier pulses within voltage ranges corresponding
to fluorescence lines of interest. The MCA is, in any case, useful
to determine the SCA voltage limits, and is often used in conjunction
with a delay amplifier and coincident and anticoincident gating of
the ADC to visualize this process, which is commonly called windowing.
In general, the digital systems have the significant advantage
of providing the complete emission spectrum (or MCA spectrum) much
more readily than the analog systems. In the recent past, analog systems
could still operate at higher count rates for a given energy resolution
than the digital alternative, but recently the gap between the digital
and analog systems has closed as the digital systems have increased
in speed. The Maia array detector
38−40
is discussed below (in Array Detectors), but we will briefly consider
the electronics of this system here. This large array detector system
has very impressive performance with real-time low-latency pulse processing
employing high-density application-specific integrated circuits. The
data processing pipeline is implemented using a large field programmable
gate array (FPGA) processing subsystem employing fine-grained parallelism.
39,40
Another advantage of the new digital systems is also exemplified
by the Maia electronics, which is the compact nature of the hardware.
Thus, analog electronics for Maia would require close to 10 full height
racks, with considerable quantities of cable while the Maia digital
processing system is attached to the detector, with an overall system
size of only 140 × 310 × 42 mm3.
39,40
Some of the newer commercial digital processing systems such as
the Xspress 3 system from Quantum Detectors (Harwell, Oxford, UK)
or the Falcon X (XIA, Hayward, CA) can now easily exceed analog systems
in performance. At the time of writing, and in most cases, the optimum
readout electronics for XFI are considered to be the digital option.
2.3.5
Detector Dead Time Effects
All
solid-state detectors suffer from electronic dead time effects. If
the time taken to process a photon is τ, the detector will not
register the arrival of another photon within τ. Moreover, the
dead time is extended by a further τ following the arrival of
the second photon. This gives rise to what is known as a paralyzing
or extending dead time,
34,41
for which the measured
count rate r is related to the incident count rate
ρ by
2
where τ̅
is the average value
of τ over the entire spectrum. The effects of electronic dead
time are illustrated in Figure 15. While dead
time correction is often possible,
42
it
is generally considered desirable to operate solid-state detectors
at count rates that are sufficiently low that the exponential term
approximates to unity, in what is called the pseudolinear regime.
In general, there is a trade-off between dead time effects and energy
resolution. At shorter pulse shaping times (or the digital equivalent,
often called the peaking time), the detector will be able to operate
faster (at higher count-rates) but with a poorer energy resolution.
With longer shaping times, the detector resolution can approach the
theoretical optimum as discussed below, but in this case count rates
are limited.
Figure 15
Solid-state detector electronic dead time effects. The
detector
employed for the measurements was a Canberra LeGe with a spectroscopy
amplifier Gaussian shaping time of 0.125 μs. The sample was
1 wt % cupric oxide in a polyvinylpolypyrrolidone pressed disk with
incident count rates adjusted by driving a slit blade through the
beam incident on the sample. Data were collected on SSRL 2-3.
2.3.6
Pile-Up
Pile-up events occur when
pulses originating from two photons arrive closer in time than the
pulse resolution of the system.
34
There
are two types of pile-up that are of interest in XFI detection. The
first, more common type of pile-up, is where the second pulse arrives
on the tail of the first, potentially resulting in incorrect pulse
amplitude determination and broadening or distorting the energy peak
in the output spectrum. The second type of pile-up happens when photons
arrive sufficiently close in time that the two pulses appear as a
single pulse with amplitude the sum of the two individual amplitudes.
This is registered just as if it were arising from a single photon
with the sum of the energy of the two photons, and gives rise to an
apparent high energy peak called a pile-up peak. The process by which
pile-up events are discriminated is called pile-up rejection or detection,
and a variety of electronic
34
and digital
43
means are employed. In most cases, these methods
are sufficiently effective that users of modern spectrometer systems
do not need to be concerned about their effects. With analog detector
electronics, pile-up events are rejected with loss of signal, but
modern digital signal processing electronics can in principle provide
effective estimation of nearly overlapping pulses resulting in fewer
rejected events.
2.3.7
Energy Resolution
As discussed
above, the energy resolution of a solid-state detector is a function
of the selected operating conditions. Under conditions where dead
time effects are not prevalent, the measured peak shape is usually
best approximated as a Gaussian, with detector energy resolution usually
expressed as a full width at half-maximum (fwhm) or half-width at
half-maximum (hwhm), relating to the width of the peak at half of
the maximal measured intensity. The energy resolution is a function
of the amplifier pulse shaping time or the digital peaking time (which
are essentially functionally equivalent but numerically different),
the operating count-rate, and the level of detector saturation (dead
time). Because the use of a short shaping time will not include all
of the preamplifier pulse, and the use of a very long shaping time
will include too much signal noise, careful matching of the preamplifier
output is required to obtain optimum resolution. For a well-optimized
detector system such as a modern germanium or silicon drift detector,
operating at around 10 keV, an energy resolution close to the theoretical
best possible resolution of about 150 eV fwhm should be achievable
at count rates with negligible dead time. Thus, for the Ge detector
example shown in Figure 9, a relatively long
amplifier shaping time of 2 μs was used with count rates limited
to nonsaturating values, resulting in an energy resolution of about
160 eV fwhm. In most cases, however, the selected amplifier shaping
times will be shorter to allow collection of high count-rate data.
Thus, a 0.125 μs shaping time will allow high count rates (Figure 15) with energy
resolutions in the range of 350–400
eV fwhm and count-rates in the vicinity of 100 000 counts per
second. An intermediate example is shown in Figure 10, in which a 0.5 μs shaping time
provides an energy
resolution of 200 eV fwhm with a silicon drift detector. Using the
most modern digital signal processing hardware, considerably better
energy resolution at high count-rates is possible, with little degradation
in resolution even at the highest count rates.
2.3.8
Array Detectors
One solution to
the dead time problem of limited count rates is the use of array detectors.
42
A number of individual detectors are combined,
each with similar (ideally) independent electronic dead time to multiply
both the overall count rate and the solid angle of fluorescence detected
by the number of detectors within the array. Arrays can be composed
of monolithic devices or arrays of discrete detectors packed into
a single housing. In general, the larger arrays are found on beamlines
dedicated to X-ray absorption spectroscopy, as these have larger X-ray
beam sizes and can typically provide much greater photon fluxes at
the sample, leading to correspondingly greater detector dead time
problems. Nevertheless, it is common to find small arrays such as
the four-element silicon drift detector array Vortex-ME4 (Hitachi
High-Technology Science America, Inc., Northridge, CA), which has
an active area of 170 mm2. Monolithic detectors are also
available, typically germanium devices, which can accept good solid
angles with little dead area between the pixels. The Maia detector
38−40
is a sizable monolithic array that is currently in use at the X-ray
fluorescence microscopy beamline at the Australian Synchrotron and
most recently at the Cornell High Energy Synchrotron Source. The system
consists of a 20 × 20 pixel silicon monolith detector, with the middle square of 4
× 4
pixels removed to make a central hole, yielding 384 potentially active
pixels in the monolithic array. The illuminating X-ray beam is projected
through the central hole and onto the sample, which must be about
10 mm away. The chief advantage of this geometry is that the detector
accepts more solid angle, and the chief disadvantage is that it must
also accept substantial scattered radiation. Siddons et al.
40
have pointed out that this is not overwhelming,
but can be useful in providing an estimate of sample density. Because
each pixel is 1 × 1 mm2, the system accepts a very
large solid angle and has an integrated pipelined, parallel processor
with embedded data analysis. The individual array pixels have a maximum
count rate of about 30 000 counts per second, and the resolution
is worse than competing systems, requiring sophisticated data analysis
methods; nevertheless, the system is very impressive, with high sensitivity
and rapid scan ability.
2.3.9
Other Detector Systems
Although
what follows does not relate to solid-state detectors, we briefly
discuss them here as they present alternative strategies that may
have applications for specific systems. Considerably higher energy
resolutions are available through the use of crystal optics, with
commercial detectors including a log-spiral bent Laue detector,
44
available from FMB Oxford, UK. These systems
use crystal optics to provide high energy resolution for fluorescence
in a narrow range, and must be used with another detector (e.g., a
nondispersive detector) employed to register the X-rays. The major
advantage of these systems is improved discrimination of nearby fluorescence
lines and superior background rejection, both of which arise from
the high energy resolution. Moreover, in principle there are no inherent
count-rate limitations due to detector dead time effects. The disadvantage
is that fluorescence from only one element at a time is registered,
and while these systems could be used in combination with a solid-state
detector, they will likely be restricted to niche applications. Similar
arguments apply to commercial crystal-based electron microscopy wavelength
dispersive detectors (e.g., Oxford FMB INCAWave spectrometers) that
can be found on a number of beamlines capable of XFI measurements.
These systems typically have a variety of different crystals and use
a Johannson geometry Rowland circle affording excellent energy resolution,
corresponding to about 30 eV fwhm, but probing a low solid angle.
2.4
Sample Scanning Systems and Experimental Strategies
With synchrotron light sources the X-ray beams are static, and
the sample must be moved relative to the incident light. The stage
system selected will depend upon the required spatial resolution and
speed of movement, and can include several types. Stepper motor driven
stages can have a large range of travel, be scanned reasonably rapidly,
and move with a typical precision of the order of 1 μm. So-called
DC-servo stages combine a direct current motor plus gear reducer to
a coupled position sensor in a closed feedback loop. They have the
advantage of being able to move very rapidly and precisely and have
excellent ranges. Piezoelectric stages are capable of the most precise
movement, depending upon deformation of piezoelectric materials under
the influence of an applied electric field; their main disadvantage
is that the range of motion is typically quite small.
Irrespective
of the type of stage used, two different types of data collection
strategy are employed in modern systems: point-by-point and continuous
or fly scan. With the point-by-point data acquisition strategy, the
sample is moved to a location, and, possibly following a settling
time, the data are acquired on a stationary sample. The time taken
to scan samples with this strategy is often considerable; for example,
a typical midscale resolution X-ray fluorescence mapping data set
might be 250 pixels ×250 pixels, with a collection time of 0.2
s per point, with an additional 0.1 s per point required to move the
stage and perhaps allow for settling. A single energy image collected
in this way would take more than 5 h. Continuous scan is a much faster
strategy; here, the sample is moved continuously with data being collected
while the sample is in motion. The simplest such strategy is to scan
the sample in one direction and then rapidly return to the start of
the next raster, but more efficient still is a bidirectional mode
in which left-to-right and right-to-left raster scans are interlaced.
Effective dwell times can be reduced to the milliseconds range, and
a scan that would take many hours in point-by-point mode can be rapidly
completed in a fraction of the time, albeit with reduced signal-to-noise
corresponding to shorter effective count times. Continuous scan mode
leads to some inevitable motion blurring of the detail in the image,
and is most effective for samples in which signal counts are not statistically
limited by dilution of the elements of interest. In dilute cases,
the point-by-point mode may be the most effective because accumulation
of sufficient counts for the fluorescence signal of interest can require
averaging. Point-by-point also potentially yields the best spatial
resolution as no motion blurring should be present. Very large samples
can be imaged using continuous scanning, with examples including paintings,
45
ancient manuscripts,
46
and large fossils.
47,48
In some experiments, individual
images of samples at a number of
different X-ray excitation energies are used to determine chemical
information. This method is discussed below in section 2.6. In this case, we effectively
scan in three dimensions,
one being the X-ray energy and the other two orthogonal axes of sample
motion. In many XFI setups, the X-ray monochromator will take considerably
longer to complete an energy move than the sample stage does to move
between pixels. In this case, the most effective scanning method is
generally to scan an individual raster at successive X-ray energies,
and then go to the next raster on completion of the sequence of energies.
29
This method, summarized in Figure 16, minimizes the time difference between energies,
and allows accumulation of a complete fractional map at all energies
in case of equipment or software failure or sample deterioration during
the experiment.
Figure 16
Sample scanning strategy for a multienergy image. Individual
rasters
are scanned at each energy, building up a complete multienergy map
with as little time separation between the individual rasters as possible.
In the example, four energies (E
1–E
4) are in the process of being recorded, with
the active raster at E
3.
During oversampling, the sample is scanned with
a step-size that
is finer than the beam-size, which can yield a small amount of additional
detail when compared to using a step-size that is the same as or coarser
than the beam-size. The beam-size is usually expressed as a full-width
half-maximum, determined by scanning a wire or slit blade across the
beam, and can be thought of as having a distorted Gaussian-type profile
(e.g., Figure 5), sometimes broadened in one
dimension. Assuming a Gaussian beam profile, sharp features within
an image that are spatially separated by the full-width half-maximum
of the beam spot should in principal be resolvable by using oversampling.
2.4.1
Sample Geometry
The geometry of
an experimental setup is also an important factor. The two common
sample geometries relate to the angle of the surface of a flat sample
with respect to the incident beam. Probably the most common is the
45° geometry (Figure 17) in which the
sample is inclined at 45° to the incident X-ray beam. The fluorescence
detector is oriented at 90° to the incident beam in the horizontal
plane, at which angle the X-ray scatter is minimized, allowing a degree
of background suppression while minimizing detector saturation. The
disadvantage of this geometry is that the non-normal incident X-ray
beam is spread out horizontally on the sample by a factor of 21/2 with potential parallax
problems. In addition, for imperfectly
flat samples, more prominent “edge effects” may result
in which physical edges are either preferentially illuminated or cast
into shadow, depending upon the orientation with respect to the beam.
An alternative geometry is normal incidence in which the sample is
oriented at 90° to the incident beam (Figure 17). This has the advantages of being
conceptually simpler with
optimal horizontal beam size. Edge artifacts and parallax problems
may be less prominent, but may still be present as the detector will
be at an angle relative to the sample. The major disadvantage of this
geometry is that the detector is usually positioned at an angle to
the beam at which more scattered X-rays are sampled and the Compton
scatter is shifted to lower energy. In some cases, the choice of setup
geometry is constrained by the experiment or physical limitations
of the equipment, such as with the Maia detector, which is designed
to be used only at normal incidence. Each of these experimental configurations
has its own advantages and limitations, and ideally the experimenter
should be able to choose between them. However, changing configurations
is not always convenient and can consume precious beamtime from the
assigned allotment so that experimenters are not often presented with
this choice.
Figure 17
Plan view schematic diagrams of different sample geometries
for
X-ray fluorescence imaging. The geometry shown in (a) has the plane
of the sample oriented at 45° to the incident beam, while that
shown in (b) has the sample plane oriented at normal incidence to
the illuminating X-ray beam.
2.4.2
Microscope
Another component common
to nearly all XFI experiments is an optical microscope (Figure 3, Figure 17). This
is used
to check alignment of samples with respect to the beam prior to scanning
the sample, and some experimental setups have more than one microscope,
each suited to a particular experimental aspect. In the 90° orientation
(Figure 17), a microscope downstream of the
sample can be used, but care must be taken that X-rays do not strike
the lens both because commercial optical glass contains many elements
of interest (e.g., high levels of zinc) and because the X-rays will
damage the lens by induction of color centers. In some cases, a mirrored
pellicle made of a low X-ray cross-section material (such as silicon
with a thin aluminum coating on one side) can be used to obtain a
straight-on view of the front of the sample with no parallax errors
(Figure 17). At many beamlines, the sample
is fiducialized at an off-line microscope before the sample is brought
to the beamline; this enables the regions of interest to be chosen
ahead of time.
2.5
Quantitative Analysis of
Two-Dimensional Images
Quantitative two-dimensional maps
of flat samples such as tissue
sections are often shown in terms of what is sometimes called areal
density, typically with the somewhat unorthodox units of μg/cm2, representing the
total amount of an element of interest
(in μg) within the sample expressed as a function of surface
area, and neglecting thickness. Calibration of the raw X-ray fluorescence
data to quantitative units of this type is done by using measurements
of standards of known composition and relating the measured fluorescence
peak areas to the peak areas of the standard. Quantification of fluorescence
data is often done by simple binning of counts, within an electronic
window, as shown in Figure 10. This may be
done if the fluorescence emission spectrum is not available digitally;
for example, if the beamline employs only analog electronics, then
single channel analyzers can be used to discriminate amplifier pulses
within a specific voltage range. In many cases, this binning method
can produce reasonably accurate results, although if the energy dispersive
data are available, then a peak-fitting approach is usually more accurate.
Well-established methods for peak-fitting are available with sophisticated
background corrections;
49
these approaches
have major advantages over binning if partly overlapping fluorescence
peaks are present in the spectrum. In many cases, the distribution
in intensities of peaks (e.g., Figure 9) is
sufficiently broad that the results are best displayed on a logarithmic
scale. Figure 18 shows an example of peak fitting
and illustrates the potential benefits of this method. For the case
of dilute samples in which the fluorescence energy of primary interest
is reasonably close in energy to the incident X-ray energy, the low-energy
tail of the inelastic scattering peak can provide a substantial background.
This occurs, for example, in measuring selenium in tissue samples
using an energy just below the bromine K-edge (e.g., 13 450
eV) (see section 2.13). In such cases, and
when a full energy dispersive spectrum is not available on a per-pixel
basis, adequate background removal can be achieved by careful measurement
of both scatter and fluorescence signal and estimating and subtracting
the overlap.
50
In some cases, standards
are available that contain known quantities of the element or elements
of interest, but in other cases elements with nearby fluorescence
lines have been used. In the quantification of Hg, for example, certified
standards for gold and thallium, the elements adjacent to Hg in the
periodic table, were used because standards containing elemental mercury
showed a gradual decrease in signal over time, presumably due to loss
of elemental mercury vapor.
51
In the case
of very thin samples, such as tissue sections, which may be 0.2–10
μm thick, there is usually no need for correction for thickness
in the hard X-ray regime. In the case of thicker samples, such as
whole small organisms, a correction for thickness should be applied.
Correcting for thickness is simple in principle, and depends upon
the X-ray attenuation by both sample and experiment (e.g., by windows,
flight paths, etc.) of incident and fluorescent X-rays. For an X-ray
beam penetrating into a sample of depth t, with incident
X-ray intensity I
0, the X-ray intensity
at t is I, given by
3
where μ is known as
the X-ray absorption
coefficient. Neglecting the effects of absorption edges, μ varies
with X-ray energy E approximately as 1/E
3, or more specifically for an element μ ≈
ρZ
4/ME
3, where ρ is density, Z is the atomic number,
and M is the atomic mass. The X-ray absorption coefficient
is the product of the X-ray cross-section σ and the density
ρ and is related to the absorbance A by
4
McMaster has tabulated the X-ray
cross-section
for most of the periodic table, from atomic number Z = 1 to 94, with only a handful
of exceptions, all of which are heavy
elements (i.e., Z = 84, 85, 87, 88, 89, 91, and 93).
52
For an X-ray fluorescence imaging experiment,
the attenuation of both the incident X-ray beam at energy E and the fluorescent X-ray
beam at energy E
f should be considered.
Figure 18
Example of quantification of X-ray fluorescence
data using peak
fitting. The data used in this example are those shown in Figure 9; here, the points
represent experimental data,
while the continuous (black) line shows the best fit. Selected individual
fluorescence contributions are shown as filled colored areas. Partly
overlapping fluorescence lines such as the Zn Kβ and Hg Lα
(indicated in the figure) would be difficult to properly quantify
without peak-fitting, especially under lower energy resolution–higher
count rate conditions. The elastic scatter, inelastic scatter, and
background contributions to the fit are not shown, and some of the
lower intensity fluorescence peaks are below the bottom of the plot
due to the log scale.
Pickering et al.
50
have described
a
simple method that can be used for samples of irregular thickness,
such as plant tissues. This uses the measured transmittance of the
sample and assumes that the absorption of the plant material can be
approximated as water (i.e., using σ and ρ for water).
Thus, if A
e is the experimentally measured
absorbance, and A and A
b are the absorbances of the sample and the background (from X-ray
windows, flight paths, etc.), respectively, then the sample absorbance
can be obtained by the difference A = A
e – A
b = σρt, and the sample thickness t easily be
obtained as
5
In many biological
cases, approximating the sample as being composed
essentially of water is either an excellent approximation, or the
materials present are composed of other abundant light elements (C,
N, O, H) in which case they will have X-ray absorption characteristics
similar to those of water. Calculation of molar quantities and concentrations
from the areal densities and the per-pixel sample thickness t is then a relatively
simple matter.
Even with a
constant incident X-ray energy E,
different elements will have different fluorescence energies E
f, and for each of these the penetration depth
and signal at the detector will be different. For a planar sample
inclined at an angle θ to an incident X-ray beam of intensity I
0 with a detector at an angle φ to the
plane of the sample, in the limit of a small solid angle β accepted
by the detector, and neglecting absorption by X-ray windows, atmosphere,
etc., then the fluorescence F from a homogeneous
sample of thickness t will be simply given by
6
where ω is the fluorescence yield, and
α incorporates the X-ray absorption coefficients at the incident
X-ray energy μi and at the fluorescence energy μf:
7
The sample
thickness t
f required to obtain a fraction f of the maximum
possible signal (that obtained with an infinitely thick sample) is
given by:
8
Figure 19a and b shows
a series of fluorescence curves for a selection of biologically relevant
elements plotted as a function of sample thickness t, calculated using eq 6 assuming
a water matrix
and a 45° geometry (Figure 17a; θ
= φ = 45°). Figure 19c shows curves
of t
f against atomic number Z for the K-edge fluorescence of midrange elements, showing
that for
lighter elements the surface produces most of the signal. Thus, for
sulfur and selenium, in a dilute aqueous sample, 90% of the maximum
possible signal will come from the first 40 μm and the first
3 mm, respectively.
Figure 19
Fluorescence signal depth attenuation. Curves are calculated
for
selected biologically relevant elements, but omitting the solid angle
term (β/4π) in eq 6; (a) shows the
fluorescence signal without accounting for fluorescence yield ω,
32
and (b) shows the same data including fluorescence
yield but on a logarithmic scale. Curves of the required thickness t
f for 10% increments between 90% and 10% of
the maximum possible signal are shown in (c) plotted against the atomic
number Z of the fluorescing element (eq 8). In all cases, the absorption was approximated
by that of
water, with an incident X-ray energy of 13 450 eV, and both
θ and φ set to 45°.
In an elegant application of quantitative methodology, De
Samber
et al.
53
have used the differential attenuation
of the calcium Kα12 and Kβ13 fluorescence
lines to correct three-dimensional data collected using confocal XFI
(section 2.7) of the small freshwater crustacean Daphnia magna. These workers exploited
the rapidly
changing X-ray cross-section in the vicinity of the calcium fluorescence
energy (the Kα12 and Kβ13 are at
3690.5 and 4012.7 eV, respectively
33
) plus
the intensity ratio of the measured intensities F Kα12 and Kβ13 to estimate the
X-ray attenuation of the fluorescence for all elements in the sample.
They define the ratio R = F
Kα12
/F
Kβ13
, which holds a value R
0 without
any attenuation, and which is expected to be 11.54 for a K-edge,
33
and a parameter k, which is
defined as the ratio of the fluorescence energies k = E
Kα12
/E
Kβ13
. For an effective depth from which
the signal originates d, De Samber et al. give the
following approximate expression:
9
These workers use this relationship to estimate
that, for their samples, the Ca Kα12 signal is attenuated
to about 17% and the Zn Kα12 signal to about 87%.
53
Recently, a novel approach to quantification
has been reported
by Kosior et al.
54
These workers used magnified
phase-contrast imaging to provide an indication of projected mass
for lyophilized cells. This information was used in combination with
XFI to provide quantitative estimates of subcellular concentration
for a variety of trace elements.
54
2.6
Chemically Selective Spectroscopic Imaging
Chemically
selective XFI is an extension of the X-ray fluorescence
microprobe method that exploits the chemically sensitive differences
in X-ray absorption near-edge spectra to generate images or maps of
specific chemical types.
50
The method was
first suggested and tested for transmittance-based imaging in a basic
form by Kinney and co-workers,
55,56
but more than a decade
passed before the first real application using X-ray fluorescence.
50
The methods rely on the chemical sensitivity
of near-edge spectra. Near-edge spectra, sometimes called the X-ray
absorption near-edge structure (or XANES), are often rich in structure,
5
which arises from X-ray excited transitions of
the core electron to unoccupied levels near to the valence levels.
Intense features are usually electric dipole allowed (Δl = ±1), although weak features
due to electric quadrupole
transitions can also be observed,
57,58
and can be
used as fingerprints of overall chemical type by simple comparison
with spectra of standard compounds. In the case of transmittance measurements,
such as scanning transmission X-ray microscopy (STXM), the signal
is sufficiently strong that very short dwell times can facilitate
a data set, which comprises an image at every energy point in the
near-edge spectrum, from which can be derived a near-edge spectrum
at every pixel.
With X-ray fluorescence on dilute biological
levels of metals, longer count times mean that usually such a complete
data set cannot be collected in a tractable amount of time. Two basic
methods can be used to derive similar information. The first is micro-X-ray
absorption spectroscopy (μ-XAS), in which a conventional fluorescence
image is taken, and then the sample is moved to locate the microfocused
X-ray beam on selected points of interest and a spectrum recorded.
In μ-XAS the extended X-ray absorption fine structure (EXAFS)
portion of the spectrum can also be examined, provided that signal-to-noise
and tolerance to radiation damage are sufficient, to give local structural
information.
5
Figure 20 shows an example of μ-XAS combined with an elemental
image using an incident X-ray energy well above the absorption edge
of the elements of interest in the sample. The spectra show that different
chemical forms are present in different locations. There are two major
disadvantages to this method. First, only a small number of specific
locations can be interrogated by recording the μ-XAS, which
gives rise to the possibility of missed detail, and second that the
X-ray radiation dose to the sample is considerable because of the
longer duration required to scan the μ-XAS spectra. This in
turn increases the risks of radiation damage with significant chemical
change to the sample (section 2.11).
59
Figure 20
Example μ-XAS experiment on selenium in a red lentil
seed
(Lens culinaris). The central image
shows the X-ray microprobe results with Ca, Se, and Zn, indicated
as red, green, and blue, respectively. Selected locations on the image,
as indicated, were then interrogated by using μ-XAS, with the
different spectra indicating the presence of chemical differences
between the different parts of the lentil seed. Data were collected
on SSRL 9-3 using a glass monocapillary with a 6 μm exit (focal
length 2 mm).
The second method of
chemically selective imaging requires collection
of multiple images at different energies. As we have discussed above,
elemental imaging normally employs an incident X-ray energy that is
well above the absorption edges of the elements to be studied. Chemically
selective X-ray fluorescence imaging uses multiple incident energies
across the absorption edge of the element of interest. The method
exploits the aforementioned sensitivity of near-edge spectra to chemical
species with incident X-ray energies carefully chosen to confer chemical
selectivity. It requires prior near-edge analysis or some knowledge
of the probable composition of the system so that appropriate energies
can be chosen; typically X-ray absorption spectroscopy is carried
out first on a bulk sample to establish candidate energies. When using
an energy-dispersive detector with good background rejection, the
number of necessary incident energies is minimally equal to the number
of species to be mapped.
50
For energies E and i components, we can write an expression
for the fluorescence, F(E).
10
where k
s is a
quantification constant of proportionality derived from measurements
of standards of known concentration, m
i
is the molar quantity of component i per pixel, and I
i
(E) is the normalized intensity of component i at energy E, derived from measured
spectra.
50
This equation can be solved for the molar quantities m
i
. As an example we consider
this method for the fern Pteris vittata.
28
This plant, the Chinese brake fern,
can take up arsenic from the soil in the form of the oxy-anion arsenate
(a mixture of [H2AsO4]− and
[HAsO4]2– at neutral pH), which is relatively
nontoxic, but then transforms it into the much more toxic oxy-anion
arsenite (essentially 100% [As(OH)3] at neutral pH) and
hyperaccumulates it in its tissues.
28,60,61
Figure 21a shows spectra of
aqueous solutions of arsenite and arsenate at physiological pH, and
the X-ray fluorescence imaging data at two incident X-ray energies
(E
1 and E
2 in Figure 21) together with the transmittance
data. The transmittance is essentially invariant between the two energies
because the bulk of the X-ray absorbance is not specific to the relatively
dilute arsenic but from the other components of the tissue. The bulk
of the arsenic present is stored in the tissues as arsenite and as
a result the total fluorescence at the two energies looks very similar,
but solving eq 5 gives images that clearly show
the arsenate to be present at very low levels in the tissues and additionally
to be confined to transport vessels seen as faint lines in the center
of the leaf (Figure 21).
Figure 21
Chemically specific
imaging of the fern Pteris vittata.
The X-ray absorption spectra of standard solutions of arsenite
and arsenate are compared in (a), and the incident X-ray energies
selected for imaging are indicated as E
1 and E
2. The tip of one pinna (leaf)
was selected for imaging (b), and the raw imaging data are shown in
(d)–(f) with a micrograph (c), X-ray absorbance (d), and X-ray
fluorescence at E
1 (e) and at E
2 (f). Using eqs 4 and 5, the analyzed data in (g)–(i) are obtained,
with the concentration of arsenite (g) and arsenate (h), with maxima
of 40 and 2 μM, respectively. The thickness (i) has a maximum
value of 0.2 mm. Adapted with permission from ref (28). Copyright 2006 American
Chemical Society.
Since the first report
of the methods for quantitative analysis,
50
chemically specific fluorescence imaging has
been employed to follow chemical localizations of trace elements in
a number of biological and environmental systems.
26,50,62
Other workers have reported attempts at
contrast resolution by measuring total quantities of an element with
incident X-ray energies above and at a near-edge peak of one component,
but in these cases quantitative analysis was lacking.
63−65
In some systems, energy dispersive fluorescence detection is not
as useful because of the relatively small separation between scatter
and fluorescence. For example, at the lower energies of the sulfur
K-edge, the separation between elastic scatter and S Kα fluorescence is almost an
order of magnitude smaller than with the
hard X-ray case, being only about 160 eV, which is sufficiently small
that the fluorescence and scatter cannot be adequately resolved by
conventional solid-state dispersive detectors.
29
In this case, a slightly more complex approach can be used
in which additional images at incident X-ray energies above and below
the absorption edge of the element of interest are collected. We note
that even though discrete fluorescence peaks may not be readily resolved,
there is still important additional information from dispersive detection
systems, including the fluorescence of elements of lower atomic numbers,
such as phosphorus. We can assume that the total measured signal T(E) at incident
X-ray energy E can be expressed as the sum of two components, F(E), the fluorescence
signal of interest, and background, B(E):
11
We note that our treatment here contains no
corrections for energy-dependent processes, which might affect the
fluorescence intensity, such as depth of penetration of the beam into
the sample, although these would be comparatively simple to include,
and would be specific to the case where only one absorption edge falls
within the energy range of the experiment. The background B(E) consists of contributions
from both
elastic and inelastic scattered X-rays, and in the case of nonenergy
dispersive detectors, X-ray fluorescence arising from absorption edges
other than the one of interest. B(E) is expected to vary smoothly as a function of
energy and can be
approximated as a polynomial function of the X-ray energy, E. A series of equations
(eq 10) will
be expressed, with different values of I
i
(E), for each incident energy used.
All forms contribute in an essentially chemically insensitive manner
to images at energies that are well above the absorption edge, whereas
below the edge, the intensity will equal the function at that energy B(E). However,
close to or at the absorption
edge itself, the near-edge structure shows considerable variation,
and the intensity at a given energy will be different for each component.
The equations (eq 10) can be solved by matrix
inversion to yield m
i
, the molar amount of the element present as species i at each pixel (Figure 22),
which can then
easily be converted to give the fraction f
i
of the element that is present as each species:
12
Figure 22
Chemically specific spectroscopic X-ray fluorescence imaging at
the sulfur K-edge. The upper part shows sulfur K-edge spectra of standard
solutions used to analyze the data providing criteria for selecting
incident energies (see markers) E and the values
for I(E). The inset shows an expanded
energy scale in the region of disulfides and thiols/sulfides, illustrating
the spectroscopic discrimination between these forms. The upper row
of images corresponds to raw intensity data for background [F(E
1)], total sulfur [F(E
6)], and the spectroscopic
peaks of disulfides, sulfides, sulfoxides, and sulfate [F(E
2)–F(E
5), respectively]. Processed images showing
mole fractions m
i
of
the different chemical species are shown in the lower part of the
figure. Adapted with permission from ref (29). Copyright 2009 American Chemical Society.
The determination of the molar
amount m
i
is dependent
on measurements at all energies, rather
than just on the measurement corresponding to the maximum of intensity
of that component. This allows the separation of species whose spectra
have quite a degree of overlap but which still show enough distinction
that energies can be chosen to confer chemical sensitivity (e.g.,
Figure 22, inset). Because this method requires
prior knowledge of the chemical forms present in a sample and their
spectra, it is typically preceded by careful bulk spectroscopic measurements.
29
Chemically specific imaging is one of the most
important and powerful capabilities of X-ray fluorescence imaging,
and while this is still a fairly specialized method, its use is increasing.
There are also a number of recent reports in which quantitative analysis
has been attempted using difference spectra. Although such numerically
trivial approaches lack the rigor of the treatment described here,
useful information can still be obtained.
66
In some cases, and especially at very low X-ray energies,
67
it may be possible to take images using a large
number of energies so that each pixel is represented by what amounts
to a complete near-edge spectrum. In this case, the number of data
values considerably exceeds the number of species so that the problem
is overdetermined and matrix inversion of eq 10 cannot be used to give m
i
values. Early applications used least-squares fitting of individual
pixels to standard compound spectra, but more recently single value
decomposition (SVD) has become established as the best method for
efficiently providing the least-squares solution (see section 2.10 for a discussion
of SVD).
68
An interesting analysis variant has been reported by Lerotic
et al.
69
that can be used in the case where
standard compounds are lacking. These workers used principal component
analysis (see section 2.10) in conjunction
with cluster analysis to classify the pixels in an image according
to spectroscopic similarity and to extract representative cluster-averaged
spectra.
69
2.7
Three-Dimensional
Methods
2.7.1
X-ray Fluorescence Tomography
X-ray
fluorescence tomography
70,71
has been employed increasingly
in recent years,
72−74
primarily in an elemental mapping mode to develop
three-dimensional images of elements within samples. A schematic of
a basic fluorescence tomography experiment is shown in Figure 23, with a micro or
nanofocused X-ray beam penetrating
the sample, which is raster scanned along the direction x and rotated about the angle
φ. In some cases, an alternative
and nearly equivalent data acquisition strategy can be to scan the
detector and keep x stationary.
74
The X-ray fluorescence output is monitored as a function
of x and φ, and the resulting sinogram (Figure 23) is subjected to tomographic reconstruction
75
to visualize a cross-sectional slice of the
object. The sample can then be translated vertically along the z direction (Figure
23) and the procedure
repeated to yield another slice of the object. These can be stacked
and combined for three-dimensional viewing as elegantly demonstrated
by de Jong et al.,
76
who conducted tomographic
elemental reconstruction of an air-dried freshwater diatom cell (Cyclotella meneghiniana)
with 400 nm spatial resolution
(Figure 24) following a total data acquisition
time of 36 h. Diatoms are encapsulated by a siliceous frustule. The
frustule is often exquisitely structured, and while this is best viewed
in the scanning electron microscope it is also visible using light
microscopy. The microstructure of the frustule was not observed by
de Jong et al.,
76
and while they do not
comment on this, we suspect that it was likely due to the use of an
air-dried sample rather than radiation damage, necessitated by the
pioneering nature of the work. However, as with any X-ray exposure
intensive method, the possibility of radiation damage is a significant
concern, and this is discussed in section 2.11. In the interests of clarity, we note
that coordinate systems alternative
to that shown in Figure 23 are often employed;
for example, the z-direction is often defined as
the direction of the X-ray beam with x being the
orthogonal horizontal axis and y being vertical.
Figure 23
Schematic
of a typical X-ray fluorescence tomography experiment.
Parts (a) and (b) show schematic side and plan views, respectively.
The sample is positioned on a stage capable of both rotary and Cartesian
motion. It is scanned through x and rotated about
φ to develop the sinogram (c), which can be subjected to tomographic
reconstruction of a slice. A number of sinograms at different values
of z can be collected, and their tomographic reconstructions
were stacked together to make a three-dimensional rendition of the
sample.
Figure 24
X-ray fluorescence tomography of an air-dried
single-celled freshwater
diatom Cyclotella meneghiniana. The
silicacious frustule has been removed to show inner detail. Adapted
with permission from ref (76). Copyright 2010 National Academy of Sciences.
Just as with two-dimensional chemically specific
imaging, chemically
specific fluorescence tomography is a practical experiment, and at
least one report has been published.
77
An
example is shown in Figure 25, of a seedling
of the two-grooved milk vetch (Astragalus bisulcatus) germinated in 5 mM selenate
solution. Unlike adult plants grown
in the absence of selenate,
26
seedlings
tolerate high concentrations of selenate without any apparent ill
effects. The tomography in this example clearly shows the selenate
in the transport vessels of the seedling with organic selenium forms
in the tissues. We note that μ-XAS in which an XAS spectrum
of a specific voxel can be acquired is not practical with fluorescence
tomography, although this has been reported for transmittance tomography.
72,78
Tomographic reconstruction artifacts develop if inadequate numbers
of angles φ are collected, and these appear as radial lines
(spokes) in tomographic reconstructions. We note that these are present
to a small degree in Figure 25. Artifacts due
to attenuation of the incident and fluorescent X-rays are not likely
to be important for small samples such as the diatom examined by de
Jong et al.,
76
but for large samples correction
for attenuation of both the incident and the fluorescent X-ray beams
may be important. For samples that have significant internal variation
in X-ray cross section, this will be a function both of X-ray energy
and of the orientation of the sample within the experiment, so that
different corrections may be needed for different values of x, φ, and y (Figure 23),
making absorption correction a very convoluted problem.
Not surprisingly, applications to date have not attempted rigorous
corrections of this type.
Figure 25
Chemically specific fluorescence tomographic
reconstruction of
a seedling from the selenium hyperaccumulating plant Astragalus bisulcatus. Absorbance
(a) and fluorescence
X-ray maps at low (b) and high energies (c) (12 661.08 and
12 667.28 eV, respectively) together with thickness (d) and
chemically specific maps (amounts) for Se-methyl-selenocysteine (e),
and selenate (f). The radicle (r) and cotyledons (ct) are shown in
(a). Sinograms for absorbance (g) and fluorescence at low (h) and
high (i) energies are also shown. The dotted lines in (a)–(c)
show the z-value used for the tomography. Tomographic
reconstruction of the data in (a)–(i) is shown in (k)–(n),
the tomogram for Se-methyl-selenocysteine (k), selenate (l), and transmittance
(m). The tricolor plot in (n) compares the localizations of Se-methyl-selenocysteine
(red), selenate (green), and calcium (blue). The circle visible in
the tomographic reconstruction of transmittance (m) corresponds to
the sample support, a clear plastic metal-free drinking straw. The
faint radial structures visible in the figure are artifacts of the
tomographic reconstruction. Data were collected on SSRL 9-3.
2.7.2
Confocal
X-ray Fluorescence Detection
This technique can give three-dimensional
information that is very
similar to that from fluorescence tomography and is illustrated schematically
in Figure 26. It has two advantages, that μ-XAS
spectra of specific voxels can readily be recorded, and that correction
for X-ray attenuation is comparatively simple. Its main disadvantage
is that the spatial resolution is often limited instrumentally in
one plane. The method uses two different focusing optics, one upstream
of the sample to generate the microfocused X-ray beam incident on
the sample (as with all other applications addressed herein) and another
placed between the sample and the detector to restrict the X-ray light
accepted by the detector to a narrow volume where the two foci intersect
(Figure 26). Typically this would be a polycapillary
optic (section 2.1) or alternatively a microchannel
array, which can give a tighter focus yielding a narrower probe volume
(Figure 26), although with the disadvantage
that the distance between the optic and the sample is small.
79
The sample can be scanned, as indicated in Figure 26, to interrogate a plane and the
detector translated
toward or away from the sample to interrogate a different plane. Figure 27 shows an
example of a three-dimensional confocal
reconstruction of a portion of a head of a zebrafish larvae following
treatment with methylmercury chloride. Confocal X-ray fluorescence
has been elegantly applied to probe the elemental composition of different
tissue types in the water flea Daphnia magna.
53
Figure 26
Confocal X-ray fluorescence experiment.
A schematic diagram of
the confocal experiment is shown in plan-view, with a blow-up of the
sample area (inset), illustrating the confocal layer within the sample
that is probed by the combined focal regions of the incident X-ray
beam and the detector polycapillary.
Figure 27
Confocal X-ray fluorescence imaging of a methylmercury-chloride
treated zebrafish larvae. The sample is viewed from approximately
midway between the dorsal and ventral surfaces down to the ventral
surface. Both volume-rendered (a) and iso-surface-rendered (b) images
are shown, with zinc depicted in green, selenium in red, and mercury
in blue. All axis scales are in micrometer relative to an arbitrary
zero point. The eye-lens (el), brain (br), and retina (re) are marked
on (a). Data were collected on APS 20ID.
2.8
Fluorescence Imaging at High X-ray Energies
Most of the XFI of interest to us in this Review is that conducted
using X-ray energies in the middle to hard X-ray regime, with incident
X-ray energies between 5 and 15 keV. The upper bound of this energy
range is partly governed by the normal working range of many beamlines
with silicon double crystal monchromators and partly because it is
comfortably above the absorption edges of the majority of biologically
important elements, including all of the first transition row elements
and the p-block elements through bromine. The use of higher energies,
in the vicinity of 33 keV, is common in synchrotron radiation-based
biomedical imaging, often using iodine-containing commercial intravenous
radiological contrast agents, all of which are substituted 2,4,6-tri-iodobenzoates
(e.g., diatrizoate or 3,5-bis(acetylamino)-2,4,6-triiodobenzoic acid)
combined with K-edge subtraction (KES imaging) in which transmittance
images above and below the iodine K-edge are subtracted to improve
contrast.
80
This method has been recently
applied at lower X-ray energies, such as the Sr K-edge
81
(16.1 keV), but as expected penetration depth
is a restriction.
81
The use of high X-ray
energies is beneficial relative to lower energy experiments because
the radiation dose is substantially decreased due to the much lower
X-ray absorption cross sections at high energies, which means that
measurements on live vertebrate animals become possible. The use of
iodine X-ray fluorescence to improve sensitivity at low levels has
recently been explored,
82,83
and a combined small
animal K-edge subtraction and fluorescence imaging system has been
developed.
83
The major problem at these
higher energies and with large objects is that the X-ray inelastic
(Compton) scattering tends to be very broad and extends by several
keV, overlapping and adding a substantial background to the iodine
Kα fluorescence. To alleviate this problem, Zhang
et al.
82
developed fluorescence subtraction
imaging (FSI) in which incident beam energies just above and below
the iodine K-edge (e.g., 33.25 and 33.10 keV) are used to obtain a
Compton background image that can be subtracted.
82
We note that the simplest method of removing this Compton
background would be to use an incident energy that is more than 10
keV above the iodine K-edge, and a similar approach has been employed
in Cd K-edge XFI, discussed in section 3.4.
84,85
In cases where the main method of data acquisition is KES, the use
of higher energies may add more than acceptable time to data acquisition.
Alternatively, beam energies of ∼43 keV may be outside the
working range of the monochromator, and application of FSI may be
the best strategy. Finally, the advantages of high energies are in
lower radiation dose and high penetration, which are reversed at low
energies. Below 2.5 keV, nearly all of the beam will be absorbed within
the first 50 μm of a sample, and the radiation dose and the
potential for sample damage are correspondingly multiplied (section 2.11).
2.9
Fluorescence Imaging at
Low X-ray Energies
Challenges for fluorescence imaging at
low incident X-ray energies
(below 5 keV) include several factors not present at higher X-ray
energies. (i) Fluorescence yield (as opposed to electron yield) is
much lower at low energies; (ii) the depth of penetration is very
low, making sample preparation more problematic; and (iii) when using
the near-edge, the energy separation between scatter and fluorescence
peaks is less, leading to more overlap of these peaks. Conversely,
the longer core-hole lifetimes at lower energies mean that near-edge
spectra are sharper and better resolved, and so are more sensitive
to chemical environment. Thus, the sensitivity of the sulfur K near-edge
spectra allows ready discrimination of a number of individual chemical
types, as we have discussed above (section 2.6). There are only a handful of XFI beamlines
worldwide that have
been specifically designed to operate in the range 2–5 keV;
the ideal beamline in this range would offer all of high spatial resolution
(e.g., <100 nm), high spectral brightness, and high energy resolution.
In most cases, experiments are conducted in a helium filled flight
path with thin polymer windows, because of the high attenuation of
the X-ray beam by air and other materials, although a small air flight
path has been used in some cases.
29
Recent
advances at scanning transmission X-ray microscopy (STXM) beamlines
have added a fluorescence detector capability, effectively turning
them into beamlines for XFI, although in many cases conducting experiments
in the vicinity of 2 keV places STXM beamlines near their upper operational
bound, and occasionally there are difficulties associated with this.
These beamlines typically operate in a vacuum, and the difficulties
associated with using helium are further compounded. This particularly
rich spectroscopic region shows significant promise as an area of
research growth in the coming years.
2.10
Other
Data Analysis Methods
2.10.1
Correlation Plots
The simple expedient
of plotting the levels of two different elements for each pixel in
an XFI data set can often provide important chemical information.
Figure 28 shows an example for the XFI of insect
larvae (Chironomus dilutus) showing
correlated iron and nickel localization in the gut, but not with zinc
that is localized in the tissues.
86
In
some cases, application of a mask in which pixels within a certain
correlation range are specifically selected or suppressed can be used
to reveal more detail.
Figure 28
Use of correlation plots to show colocalization
of two elements,
nickel and iron, in the gut of a larval Chironomus
dilutus, a species of nonbiting midge.
86
The linear appearance of the nickel versus iron
plot indicates correlation and colocalization of the two elements.
A correlation plot of zinc versus iron is also shown, illustrating
the appearance when there is little or no colocalization of two elements.
Data were collected on SSRL 10-2.
2.10.2
Principal Component Analysis
XFI
data typically consist of several thousand individual pixels, each
with an intensity corresponding to fluorescence measurements at a
specific physical position on the sample. If the entire output of
an energy dispersive detector is used, each of these pixels has an
associated emission spectrum, itself consisting of between 1024 and
4096 energy points, and if an array detector is used (section 2.3), these data are
accordingly multiplied by the
number of array elements. Thus, the size of the data set can be considerable;
it is not uncommon for gigabytes of data to be accumulated, and a
high resolution image collected using the Maia detector can approach
terabytes in size. However, this substantial volume of data contains
fluorescence contributions from only a limited number of elements,
probably numbering less than 20, together with the inelastic and elastic
scatter, and background. Because of this, principal component analysis
(PCA) can be used to carry out data reduction. The method employs
single value decomposition (SVD) with data arranged as a two-dimensional
matrix, A, each column containing an energy dispersive
spectrum of n points at a particular pixel. Spatial
correlation is not important in the SVD calculation so that these
spectra are arranged by a pixel index number, up to a total m, and not as two- or
three-dimensional spatially arranged
data. These can be written using a single-value decomposition in which A is written
as the product of an m × n matrix U, an m × m diagonal matrix V, and the transpose
of an m × m orthogonal matrix W. The matrix U contains columns of orthonormal eigenvectors,
called the principal components, and V is a diagonal
matrix that contains the eigenvalues.
13
The
principal components (eigenvectors) thus
obtained are not physical component spectra, but instead are mathematical
constructs. As discussed by Vogt et al.,
87
the eigenvectors can be remapped as images to yield what are called
the eigenimages,
87
and these can be arranged
in order of significance in the data set, with the data set as a whole
being represented by a comparatively small number of eigenvectors
or eigenimages. The photon noise in the image is not correlated between
pixels and does not contribute to the important principal components.
Moreover, fitting the eigenvectors can provide images of elemental
components without the computational burden of peak-fitting the emission
spectra of single pixels.
87
2.10.3
Soft Clustering Methods
We have
discussed in section 2.6 the “hard”
cluster analysis of Lerotic et al.
69
This
approach assigns pixels to groups based on a measure of similarity
between pixels. Recently, Ward et al.
88
have described methods using a soft clustering approach employing
multiple Gaussian fits to elemental correlation plots, and assignment
of pixel-probability to groups (each represented by a Gaussian) to
effectively relax group membership criteria. As a test of the method,
Ward et al. compared rigid cluster analysis and soft clustering with
a four Gaussian fit for Fe and Zn levels in XFI data of the malaria
parasite Plasmodium falciparum, and
found that soft cluster analysis could readily discriminate between
the parasite, the parasite’s food vacuole, the host erythrocyte,
and the background.
88
In comparison, hard
cluster analysis failed to discriminate these regions.
88
The method was found to accurately segment images
into biologically relevant subregions, and is as accurate as manual
image segmentation, but considerably faster. As pointed out by Ward
et al.,
88
as the speed of XFI data acquisition
increases, automated methods such as soft cluster analysis will become
increasingly useful.
2.11
Radiation Damage and Prevention
Sample damage due to ionizing radiation such as X-rays is a recurring
theme in studies of biological samples. The worst case is undoubtedly
that of living samples, with vertebrate cells and tissues representing
the extreme in sensitivity. The only report of XFI of living vertebrates
is that of Korbas et al.,
89
who reported
XFI of intact living zebrafish larvae maintained under anesthesia.
Invertebrates such as insects and plants are comparatively resistant
and can survive extended exposure with no apparent short-term effects.
In the case of aqueous samples, the radiation chemistry of water can
be a major source of problems, especially with long dwell times.
59
The use of microfocused X-ray beams gives a
very high dose, although this is countered by the short exposure times
generally used in XFI experiments, especially in rapid scan experiments.
For three-dimensional imaging (either tomography or confocal, section 2.7), preventing
dehydration, heating, or beam-damage
during longer data acquisition times can be a particular challenge.
With fixed samples, three common embedding alternatives are acrylic,
wax, and epoxy. Epoxy is traditionally used for electron microscopy
and is appropriately used for very thin sections of tissue, suitable
for the highest XFI resolutions. For more commonly used XFI resolutions,
acrylic and wax are the most appropriate choices, and of these we
have found that acrylic is the most susceptible to beam damage, showing
discoloration and deformation more rapidly on exposure to beam than
the old-fashioned wax embedding material. Low temperatures are often
employed to prevent beam damage, and a number of commercial stages
designed for other spectroscopic or microscopic applications, such
as the Linkam FTIR600 (Linkam Scientific Instruments, Guildford, Surrey,
UK), have been adapted for XFI experiments.
2.12
Sample
Preparation
Sample preparation
is one of the most important factors in an XFI experiment, but has
often been neglected in reviews. There are many issues to be considered,
depending on the capabilities and physical setup of the beamline,
the type of sample being analyzed, and the type of insight being sought.
As with most sample preparations for analysis using synchrotron X-ray
methods, minimal pretreatment is preferred. Chemical pretreatment
of samples has the potential to alter the distribution of elements
as well as their chemical speciation, while the ubiquitous use of
phosphate buffered formalin for tissue fixation is also a confounding
factor for phosphorus imaging and speciation. Early work by Bush et
al.
90
reported that trace elements were
not influenced by formalin fixation; however, this has been refuted
in later work by Gellein et al. where longer durations were considered.
91
Using ICP-MS, Gellein et al. have demonstrated
that leaching of most elements, such as Mg, Mn, Fe, Cu, Zn, As, Cd,
and Hg, from biological tissue occurs,
91
although the most significant changes are largely associated with
long-term storage. Leaching of trace Cr and Ni from tissues stored
in formalin, however, was minimal.
91,92
Because
of problems encountered with irregular surfaces or other issues such
as edge effects, the surface to be mapped is typically flat or reasonably
uniform, although corrections can be applied to account for irregularities
or structured surfaces. Edge effects (discussed earlier in section 2.4) can arise
in thick samples or those with irregular
or rough surfaces and can result in an increased fluorescence signal
from edges or decreased signal in an area that is shadowed from the
detector or incident beam, both of which will alter the apparent fluorescence
intensity of the sample. Similar edge effects can arise in sectioned
material, such as the inadvertent introduction of small wrinkles and
folds during collection of cryo-sectioned tissue onto a mounting medium.
For thin sections, this may mean there are areas within a map of a
10 μm thick section that are significantly thicker, giving rise
to significantly higher fluorescence and therefore greater apparent
areal densities of elements within the defect region. Inspection of
the X-ray scatter map can be a useful tool for identifying such edge
and surface irregularities, and in some instances it may be possible
to use the scatter map to normalize data channels during postprocessing.
Szczerbowska-Boruchowska has reported a detailed investigation of
sample thickness effects for quantitiative XFI as well as composition
details for reference human tissues.
93
Depending on the necessities of the experiment, samples may be
windowless, with the surface of the sample exposed, or else they may
be sealed, either to prevent dehydration during data acquisition or
to prevent contamination. Windowless is typically preferred for imaging
of light elements, for which the window material can significantly
attenuate the fluorescence signal and also contribute to X-ray scatter.
For soft X-ray imaging, such as that done using incident X-ray energies
in the vicinity of the sulfur K-edge, enclosure of the sample and
detector within a He-filled container is necessary to minimize attenuation
of the sample fluorescence at these energies. A proper purge with
He is absolutely required following sample changes with such a setup;
otherwise, the attenuation will drift for a period at the beginning
of the scan as the system equilibrates. In some cases, samples for
soft X-ray experiments can be maintained in air, but in this case
the bulk of the apparatus is contained in a helium chamber with a
very small air gap (ca. 0.1 mm) between a thin polymer X-ray window
and the sample surface to minimize air absorption.
29
For similar reasons, window material containing high atomic
number elements should be avoided as this can also attenuate the fluorescence
signal from the sample arising from lower atomic number elements.
2.12.1
Sample Preparation/Fixation
X-ray
fluorescence imaging of cells and tissues demands careful consideration
of sample preparation protocol, because measurements are typically
done on ex vivo samples as opposed to living systems. To avoid misinterpretation
of the ex vivo elemental distributions, which may not reflect the
in vivo state, care must be taken during such steps as anesthetization,
the time between death and tissue fixation (sometimes called that
agonal period), storage and preservation protocols (such as chemical
fixation or cryo-preservation at LN2 temperatures), the
method of tissue preparation, and the analysis conditions (analysis
of fully hydrated tissue at cryogenic temperatures or air-dried and
dehydrated samples at room temperature).
The use of aqueous
environments to culture cells, or animal models that involve small
organisms (Caenorhabditis elegans,
zebrafish, etc.), simplifies the choices somewhat. Fixation can be
achieved by passive diffusion with cell cultures or organisms in liquid
fixation or anesthetic media.
94−96
However, despite the simplicity
with which chemical fixation can be completed for cell cultures and
small organisms, elemental distributions particularly for elements
such as Cl, K, and Ca that are present as readily diffusible ions
are best obtained by cryo-preservation and analysis of frozen hydrated
samples.
97
Increasing organism complexity
(e.g., mouse or rat models) brings
increasing complexity to sample preparation and a greater chance for
altering elemental concentrations and distributions relative to the
in vivo state. In larger animals, simple diffusion of a chemical fixative
is not possible, and if chemical fixation is to be performed it should
be done via perfusion-fixation.
98
Perfusion
with saline is also often performed
99,100
and has the
advantage of removing blood from the tissue, which can be a major
source of iron contamination. There may be significant drawbacks with
this method, however. It has been established that elements such as
K and Cl that are present as ions are mobilized by aqueous fixation
media and are rapidly redistributed and leached from biological samples.
97,101
The process of perfusion also disrupts blood supply to tissues,
creating ischemic conditions (insufficient oxygen and energy supply),
and although no XFI studies have systematically investigated the changes
in elemental distribution as a consequence of perfusion, it is known
that disrupted oxygen supply drastically alters elemental distributions
in cultured cells.
96
It is also well established
that many cell types, particularly brain cells, release intracellular
molecular stores into the extracellular space during perfusion, which
could lead to confounding results. The possibility for altered elemental
distribution during perfusion or perfusion-fixation of animals should
always be kept in mind.
An obvious but often overlooked fact
is that buffers and fixation
media must be kept free of exogenous contaminants that may be inadvertently
deposited in tissues, although to our knowledge few laboratories check
perfusion buffers for possible contamination from trace elements such
as metal ions.
101
Elevated levels of metals
(i.e., Fe, Cu, Zn) are often observed in formalin-fixed tissues,
101,102
which have been demonstrated to result from metal contamination
in the perfusion, fixation, or buffer media.
101
Furthermore, contamination is highly unlikely to be uniform across
biological samples, giving rise to regional differences in contamination
due to different binding affinities of metals for different tissue
types. For example, contamination of brain fixation media with Fe,
Cu, and Zn results in the greatest increase in Fe and Zn levels in
the gray matter, and the greatest increase in Cu levels in the white
matter.
101
Therefore, as significant molecular
differences exist between different tissue types, and possibly between
the same tissue type when effected by different physiological or disease
processes, it is not necessarily valid to assume that consistent sample
preparation across all samples will yield consistent or similar effects
in all samples.
In addition to alterations in elemental distribution
as a consequence
of sample preparation, chemical speciation may also change. Specifically,
the results of XFI employing multiple energies (section 2.6) may be significantly
impacted by sample preparation.
Drastic alterations in the speciation of sulfur have been observed
to occur as a consequence of formalin-fixation (relative to cryo-preserved
tissue), and subtle alterations also occur as a consequence of air-drying
tissue sections.
103
As imaging of living
cells or tissues is often impractical with XFI, the possibility for
some degree of chemical alteration from the in vivo state should always
be considered, highlighting the importance for a thorough understanding
of the likely chemical alterations, which may occur with the specific
sample preparation protocol employed.
Although analysis of frozen-hydrated
cryo-preserved samples yields
both elemental distribution and chemical speciation closest to the
in vivo state,
97,103,104
this method requires specialized sampling accessories compatible
with analysis at cryogenic temperatures. Implementation of such an
approach is not compatible with all studies (for example, post-mortem
autopsy human tissue), nor with physical or experimental limitations
that may be imposed by the beamline at which the experiments will
be carried out. Biological samples may also be present in some form
of matrix, such as embedded in paraffin or methacrylate (acrylic).
Such embedding media are highly compatible with XFI, as these matrixes
contain low Z elements that do not contribute to the energy ranges
generally considered for such experiments. However, as discussed above,
the possibility for altered chemical speciation, elemental redistribution,
leaching, or contamination from the long list of reagents or preparation
steps used in paraffin and methacrylate embedding processes should
always be considered.
2.12.2
Sample Thickness
Samples for biological
applications are usually relatively thin and of uniform thickness.
For cell cultures, for example, monolayers are the preferred choice
as these typically will be on the order of 10–20 μm thick
(per cell) with the resulting image not being complicated by signal
from overlapping cells. Additionally for cell cultures, nonmotile
cells (in the event of live cell imaging) or cells that have been
fixed are necessary as the potentially long scan times provide ample
opportunity for cells to move within the beam.
Larger biological
specimens typically are sectioned to the desired thickness. These
sections are often preferred for quantification as their thickness,
and self-absorption issues can be neglected (see section 2.5).
93
Further, as samples
can be mounted at an angle of 45° to the incident beam, loss
of spatial resolution is minimized or negligible if the sample thickness
is comparable to or less than the spatial resolution of the experiment.
However, for dilute levels of trace elements, the trade-off is reduced
sample volume and therefore lower fluorescence signal (assuming complete
penetration of the incident beam as well as negligible loss of fluorescence
signal escaping from the sample), which results in low signal-to-noise
and necessitates longer dwell times.
2.12.3
Sample
Substrate
Samples, especially
sections, must be mounted on some type of support such as a coverslip,
slide, or other reasonably X-ray transparent material. Careful choice
of metal-free, sulfur-free, or halogen-free mounting material is required,
although many commercial vendors will not be able to ensure their
products meet these requirements. Therefore, screening of mounting
materials prior to experiments is always recommended. As a general
rule, samples should be mounted on a material that is thinner than
the sample to be mapped, as this will present minimal background scatter
(e.g., 10 μm thick tissue on 6 μm polypropylene). In practice,
however, good success has been achieved with thicker mounting materials,
such as 20 μm thick tissue mounted on 200 μm plastic coverslips,
provided the coverslip is comprised of low atomic number material
(i.e., carbon, oxygen, hydrogen). Plastic materials often have the
advantage of being relatively inexpensive, durable, and able to accommodate
a wide range of sample types and sizes. Plastic supports supplied
by vendors can have variable levels of trace impurities, such as one
instance in the authors’ experience of commercial “metal-free”
microscope mounting materials that contained substantial levels of
cobalt which varied from batch to batch. Conventional glass materials
used for optical microscopy are incompatible choices for XFI measurements
because of their high content of zinc and other elements; additionally,
substantial silicon fluorescence from glass slides may saturate detectors.
Electron microscopy grids can also be employed; however, careful consideration
must be given to the proximity of the area to be mapped with the grid,
as the gridbars may also contain elements of interest to the experiment.
Silicon nitride (Si3N4) windows are a close
to ideal mounting material from an imaging perspective, are compatible
with cell cultures, and allow a range of spectroscopic measurements
to be performed on the same sample.
105
However,
the use of silicon nitride windows is often limited by their small
size (generally 2 × 2 mm2 or 5 × 5 mm2), their fragile nature, and higher cost.
2.13
Pitfalls and Problems
2.13.1
Overlapping Fluorescence
Lines and Contamination
One of the advantages of XFI is that
in most cases elements cannot
be confused, as the technique depends on basic atomic physics. A common
question among newcomers to the technique is whether chemically similar
metal ions, such as Cd2+ and Hg2+, can be confused.
In this, and in almost all other cases, there is no possibility of
confusion, as the fluorescence lines of cadmium and mercury are very
well separated in energy. However, the fluorescence lines of a few
pairs of elements overlap sufficiently that additional measurements
are needed to distinguish them. A very well-known and sometimes encountered
example is that of arsenic and lead; the centroid of the As Kα12 fluorescence is 10 532
eV, which is insufficiently
separated from the Pb Lα12 at 10 518 eV to
be distinguished [note that solid-state detectors cannot typically
resolve α1 and α2 fluorescence lines (section 2.3) and so we use the intensity weighted
centroid
of these lines here]. Both arsenic and lead may be present in a sample,
and in addition lead X-ray fluorescence contamination is sometimes
seen due to lead’s widespread use in beamline radiation shielding.
In the case of discriminating As from Pb, simply changing the X-ray
monochromator to move the incident X-ray energy below the lead LIII edge at 13 035
eV but above the As K-edge at 11 867
eV would eliminate Pb Lα12 fluorescence without removing
As Kα12 fluorescence, effectively allowing determination
of As in the presence of Pb. Determination of Pb in the presence of
As can also be accomplished by moving the monochromator energy to
above the Pb LII edge at 15 202 eV, which would
excite the intense primary LII fluorescence, the Lβ1 at 12 614 eV; unfortunately confusion
of the
Lβ1 with the Se Kβ at 12 496
eV is also possible, although the Se Kα12 at 11 209
eV would clearly indicate the presence of Se. Studies of samples containing
As, Se, and Pb require some care to unambiguously determine the different
elements present. In many cases, simply changing the monochromator
energy will not be sufficient to eliminate overlaps in fluorescence
because of the energy ranges of the beamline. Thus, hard X-ray beamlines
that typically operate at 10–15 keV may not be able to operate
at energies much below 5 keV. For example, XFI investigation of organo-tin
toxicology might be somewhat problematic on a hard X-ray beamline
because the monochromator would excite all of the L-edges, with the
tin LII primary fluorescence, Sn Lβ1 (3663
eV), substantially overlapping with the Ca Kα12 (3691
eV). Another noteworthy case is the proximity of the Zn Kβ (9572 eV) to the Hg Lα12
(9980 eV); while these
are easily resolvable, Zn can be so abundant in biological samples
that the tail of the Zn Kβ can add a background to
the Hg Lα12 and confuse inattentive researchers,
especially if simple binning is used.
Components of the beamline
can give rise to contaminating signals that may confuse the analysis.
Examples include iron, lead, copper, bromine, and antimony. As discussed
above, lead is found in shielding and is second only to iron, which
is present in a very large number of beamline components, including
most vacuum-compatible X-ray windows fabricated from beryllium, the
element of choice due to its low X-ray cross section. The authors
have also encountered copper contamination arising from exposed wiring
internal to a new array detector, which was subsequently returned
to the manufacturer and the problem eliminated. Bromine at first seems
a somewhat unlikely contaminant, but it is often present in plastics
such as acrylics, because ethyl 2-bromoisobutyrate and ethyl 2-bromopropionate
are used as polymerization initiators.
106
Similarly, antimony compounds such as antimony trioxide (Sb2O3) or antimony triacetate
are used as condensation
catalysts in the production of poly ethylene terepthallate (PET).
107
PET is most commonly found in plastic bottles
used to contain drinks such as bottled water or carbonated beverages,
and we note in passing that antimony leaching from PET is a potential
source of exposure to compounds of this highly toxic element.
108
PET is often used in plastic coverslips as
sample supports in XFI, and if used for this purpose care must be
taken to obtain antimony-free PET as the Sb Lα12 (3604
eV) and Ca Kα12 (3691 eV) overlap enough to make
determination of biological calcium difficult.
2.13.2
X-ray Fluorescence Self-Absorption
The phenomenon known
as fluorescence self-absorption is an attenuation
of the X-ray fluorescence due to high X-ray absorption coefficient,
and is only a problem for concentrated samples that are also thick.
The common description of this phenomenon as “self-absorption”
is somewhat inaccurate, and it could also be referred to as fluorescence
thickness effects. Although thickness effects, including self-absorption,
are discussed at length in section 2.5, we
mention self-absorption briefly here because it represents a potential
pitfall. With adequately dilute or thin samples, the X-ray fluorescence
from the sample is proportional to the absorption coefficient μ(E), eq 3, which is
the condition desired.
In most cases, X-ray fluorescence self-absorption can be avoided with
good experimental practice, especially with biological samples that
are usually in the dilute limit. In some cases, however, self-absorption
may be unavoidable, such as calcium from bone in sections of whole
animals, and comparison with optically thin standards must be done
with care.
3
Applications in the Study
of Plant Physiology
Plants were used in many of the early
XFI studies of living systems.
While plants are interesting in their own right, early XFI studies
were enabled by the fact that plants are highly resistant to radiation
damage from the X-ray beam so that they often can be observed in a
living state. Moreover, no protocols are required for their study,
minimizing paperwork and eliminating ethical review, which is mandatory
for animal and human tissue work. Applications of XFI to metal ion
plant physiology have recently been reviewed at length by Sarret et
al.,
109
and for this reason we will only
discuss selected topics to provide examples. For a more comprehensive
review, the reader is referred to Sarret et al.
109
3.1
Hyperaccumulating Plants
Of particular
note in the field of plant metal ion physiology are those plants known
as hyperaccumulators, which can assimilate very high levels of metals
or metalloids from their environment. They are of interest with respect
to the bioremediation method called phytoremediation,
110,111
in which plants are used to sequester or modify an environmental
contaminant, thereby alleviating an environmental problem without
the need for excavating large amounts of soil or rock. The levels
of accumulation can be quite extreme; for example, the New Caledonian
tree Sebertia acuminata can have vivid
blue-green latex due to its startlingly high nickel concentration
of about 2 M.
112,113
Other examples include the nickel
hyperaccumulating penny-cress Thlaspi goesingense,
114,115
the arsenic hyperaccumulating brake fern Pteris vitata,
28,60,61,116
and the selenium hyperaccumulating
two-grooved milk vetch Astragalus bisulcatus.
117
Some tropical trees accumulate selenium
in their tissues to a surprising extent, including the brazil nut
tree (Bertholletia excelsa), which
concentrates l-selenomethionine in the nuts,
118
and the Coco de Mono or Monkey Pot tree (Lecythis ollaria).
119−121
The nuts of the Coco
do Mono tree, also known as Paradise Nuts, have a very high l-selenocystathionine
content,
122,123
and are a noteworthy
source of human toxic exposure to selenium, possibly due to their
agreeable taste. Kerdel-Vegas has described a number of case histories
of people who consumed Coco de Mono nuts, nearly all of whom were
warned by locals of dire consequences but nonetheless ate the nuts,
with the unpleasant symptoms of selenium intoxication as their reward.
120
Because the first and one of the best-studied
hyperaccumulator plants is A. bisulcatus, we will initially discuss XFI of selenium
hyperaccumuators.
3.2
Selenium Hyperaccumulators and Accumulators
Selenium
hyperaccumulation was first noted by Moxon in the 1930s,
124
while investigating the cause of the livestock
disease called the “blind staggers” or “locoism”.
The blind staggers are now thought of as several unrelated diseases
in which an unsteady gait and seeming blindness are apparent. In the
North American plains, a common cause may be selenium intoxication
from ingestion of selenium hyperaccumulating range plants, including
certain Astragali (in particular A. bisulcatus), Aster, Stanleya, and Oonopsis.
124
While selenium intoxication by feeding
animals A. bisulcatus does produce
the symptoms of the blind staggers,
125
O’Toole
et al. have criticized the hypothesis that the bind staggers are due
to selenium, but they also comment that it is not yet discredited.
126
The two-grooved milk vetch A.
bisulcatus can take up selenium from the soil;
127
it is accumulated as a mixture of Se-methyl-seleno-l-cysteine and γ-glutamyl-Se-methyl-seleno-l-cysteine.
128
These compounds are generated from seleno-l-cysteine by a specific selenocysteine
methyltransferase.
129
A. bisulcatus is also known as the poison-vetch and by cattle ranchers as “locoweed”
due to its association with the blind staggers. The first XFI study
of any hyperaccumulating plant was of A. bisulcatus, by Pickering et al.,
50
who showed that
the plant stored different forms of selenium in leaves of different
ages. Newly grown or young leaves were found to contain high levels
of Se-methyl-seleno-l-cysteine, while older mature leaves
contained predominantly selenate. Leaves of intermediate age were
found to contain a mixture of these species. Pickering et al. also
reported that the overall selenium content of the leaves decreased
with their age.
50
Some Astragali are well-known for their malodorous nature, which is due to loss
of volatile selenium compounds, among which dimethyldiselenide is
prominent.
130
Pickering et al. have also
reported that there is correspondence between sulfur and selenium
chemical forms in old and young A. bisulcatus leaves.
131
It has long been thought that
selenium hyperaccumulation might be a defense mechanism aimed at insect
herbivores, and it seems likely that the production of volatile selenium
compounds such as dimethyldiselenide
130
plays a role in this. The mechanism by which dimethyldiselenide
is generated from Se-methyl-seleno-l-cysteine is still unknown,
but Pickering et al. noted that both are relatively nontoxic as compared
to selenate, the other major selenium species present. These workers
also commented that the young leaves in which these less toxic forms
predominate are those that the plant can least afford to lose, but
that the foul smell of the volatile selenium species might serve to
redirect herbivorous insects to the older, less pungent, but more
toxic leaves. Freeman et al. have expanded this early work, and also
studied a different selenium hyperaccumulator, a mustard known as
prince’s plume (Stanleya pinnata).
132
They used XFI with improved spatial
resolution to show that organic selenium was concentrated in the trichomes
(leaf hairs) of plants thought to be A. bisulcatus. These workers also harvested large
numbers of trichomes, and employed
conventional analysis to show that this was Se-methyl-seleno-l-cysteine and γ-glutamyl-Se-methyl-seleno-l-cysteine.
132
Se-methyl-seleno-l-cysteine was found
to be present in the vascular tissues of a S. pinnata young leaf petiole as well as
in guttation fluid.
132,133
These results contrast with the results of Pickering and co-workers
26
(also including unpublished data), who did not
find organic selenium in the trichomes but did observe selenium within
cells, and found high levels of calcium in the trichomes (Figure 10). The two groups
used different culture methods;
Freeman et al. used a commercial horticultural growing mix,
132
while Pickering et al. used hydroponic growth
media.
50,131
The plants were also harvested from different
locations; Freeman et al. obtained theirs from Fort Collins, CO, and
Pickering et al. from Big Hollow, WY, and from Saskatoon, Saskatchewan,
Canada. Moreover, Freeman et al. did not observe calcium in the trichomes
of plants identified as A. bisulcatus,
132
but did for the trichomes of the
nonhyperaccumulator Indian mustard, Brassica juncia. Given that large numbers of related
vetches of similar appearance
exist, it seems plausible that one group or other misidentified A. bisulcatus, and
that two discrete species were
in fact studied. At present, more work is required to resolve this
discrepancy. More recently, XFI has been used to examine Astragalus roots
134,135
and two Se-resistant
herbivorous moths, one of which was parasitized by a wasp.
134,136
XFI of the adult moths, larvae, and wasps showed that each accumulated
organo selenium compounds, with no apparent ill effects. The studies
of roots showed that elemental selenium producing endosymbionts may
be able to affect selenium speciation in hyperaccumulators.
134,135
In contrast to the selenium hyperaccumulators, which have
special selenium biochemistry, selenium uptake by nonhyperaccumulating
species appears to be solely as a consequence of the similarities
between sulfur and selenium chemistry.
137
Thus, selenate uptake is thought to occur through sulfate transporters,
and selenate reduction is thought to occur via the normal route for
sulfate, involving the ATP sulfurylase/APS reductase pathway.
137
In nonhyperaccumulating plants, a predominant
form in tissues is often l-selenomethionine.
138
While a number of studies have been directed
at phytoremediation,
138
XFI has been used
to investigate the physiology of selenium uptake and enrichment in
putative biofortified food plants,
139,140
a particularly
interesting application may be ordinary crop plants
141
grown on naturally selenium-rich areas as a potential selenium
supplement for parts of the world that are selenium deficient.
142
3.3
Arsenic Hyperaccumulators
and Accumulators
Arsenic accumulation and hyperaccumulation
is rare among plants,
and is only known for a handful of ferns, of which Pteris vittata is the most noteworthy.
28,60,61
Pickering et al.
28
used chemically selective XFI to show that the chemical
form present in the transport vessels was arsenate (a mixture of [HAsO4]2– and [H2AsO4]−
at physiological pH), which was transformed to arsenite
(As(OH)3) in the tissues (Figure 29). They also clearly detected a small amount of
thiolate coordinated
As(III),
28
which had been previously observed
in bulk measurements.
60
Pickering et al.
hypothesized that this was an intermediate in the reduction of the
As(V) arsenate to arsenite.
28
By enhancing
the intensity threshold, Pickering et al. observed a line of arsenite
following the periphery of the arsenate-containing transport vessels,
28
which suggested transport of arsenite from the
photosynthetic tissues to the rest of the plant. The same species
of fern was subsequently studied using XFI by Hokura et al.,
143
who used large area imaging to view substantial
parts of the plant. Recently, a different hyperaccumuating fern, the
gold dust fern Pityrogramma calomelanos var. austroamericana, has been studied
using XFI by Kachenko et al.
144
These workers
also observed arsenate in the transport vessels, and detected thiolate
coordinated species in the tissues. The clear physiological picture
that is apparent from these studies is that arsenate is taken up from
the soil by the roots and transported through the vascular tissue
from the roots to the fronds (leaves), where it is reduced to arsenite
and stored at high concentrations. The arsenic-thiolate species surrounding
veins may be intermediates in this reduction. The life cycle of the
fern
145
can be described in terms of two
discrete generations; the familiar leafy fern is the diploid sporophyte
generation that generates haploid spores through meiosis. These spores
are contained in capsules known as sporangia, which in turn are localized
within spore dispersal regions called sori on the underside of mature
fronds.
145
Following dispersal and germination,
the spores develop into small free-living photosynthetic haploid gametophyte
plants,
145
only a few millimeters across
and one cell thick throughout most of their structure, which develop
sperm within antheridia and egg cells within archegonia. Fertilization
results in a new sporophyte plant, which grows directly through the
gametophyte.
145
In addition to examining
the sporophytes, Pickering et al. also examined the sporophyte sporangia
and gametophytes of Pteris vittata.
28
In both cases, the reproductive tissues, or
the areas in which reproductive tissues would develop, were found
to be free of arsenite. Figure 30 shows the
XFI data of the P. vittata sporangia
and a typical gametophyte. Thus, in the sporophyte sori and vicinity,
arsenite is excluded from the spores and the sporangia, but it is
preferentially concentrated within the paraphyses (Figure 30), sterile hair-like structures
adjacent to but
not directly associated with the sporangia. Arsenite also accumulates
within the pseudoindusium, the reflexed margin of the lamina that
partially covers and protects the sori. Exclusion of toxic arsenite
from the plant reproductive tissues may be needed to protect these
from genetic damage.
28
The images of the
gametophyte also give clues to the subcellular localization of arsenite.
Plant cells typically have a large centralized vacuole with the majority
of the cytoplasm contained in the periphery;
145
the images clearly show arsenite localized within the central cellular
vacuole. Arsenate levels are very low in the gametophyte and, as suggested
by Pickering et al., may be confined to the numerous plant cell Golgi
bodies, but further high-resolution imaging is needed to determine
this.
28
Figure 29
Chemically specific X-ray fluorescence
imaging of Pteris vittata, showing
the presence of arsenate
in the transport vessels, and biotransformation to arsenite in the
tissues.
28
The figure shows XFI of the
highlighted area in the optical micrograph, showing a central rachis
(stem) with two branching rachilla supporting the pinnae (leaves).
Arsenate is depicted in red, arsenite in green, and the derivative
of the transmittance-derived thickness t in blue.
The derivative of t is shown rather than t itself so as to better indicate the tissue
periphery.
Adapted with permission from ref (28). Copyright 2006 American Chemical Society.
Figure 30
X-ray fluorescence imaging of Pteris vittata reproductive tissues.
28
In the figure,
(a), (c), and (e) show optical micrographs, and (b), (d), and (f)
show XFI images with arsenate in red, arsenite in green, and thickness
(transmittance) in blue. The sporangia of the sporophyte generation
are shown at different resolutions in (a)–(d) and the gametophyte
in (e) and (f). The future site of archegonial development is shown
in (f) (ar), and several rhizoids (rh) are visible in (e). Adapted with permission
from ref (28). Copyright 2006 American
Chemical Society.
3.4
Cadmium
Hyperaccumulators and Accumulators
Cadmium is generally thought
of as a toxic element and has a demonstrated
functional role in only a single case, in a carbonic anhydrase of
the marine diatom Thalassiosira weissflogii.
146
Cadmium hyperaccumulators are included
here as a further example, primarily because they provide an example
of an element where relatively inconvenient X-ray energies are involved.
The cadmium K-edge at 26 714.0 eV is high for a conventional
XFI experiment (section 2.8), although still
within the reach of many beamlines, and the LIII and LII-edges at 3537.6 and 3728.0
eV, respectively, are close to
our lower bound in X-ray energy (section 2.9). The high X-ray energies of the K-edge
mean that short core-hole
lifetimes give broad near-edge features,
147
which in turn means that any chemical differences give changes in
the spectra that are quite subtle and difficult to exploit. Conversely,
the LIII and LII-edges are at low energies,
147
and so the problems we have previously discussed
associated with beam attenuation will be prominent. Moreover, the
Cd Lα12 and Lβ1 fluorescence lines
at 3133 and 3317 eV, respectively (the primary lines of the LIII and LII edges, respectively;
Figure 8), are inconveniently close to the Kα12 of biologically common potassium at
3313 eV. Cadmium is therefore
an element that poses some challenges for XFI, but despite this the
method has been used to provide important information on cadmium in
both hyperaccumulating and nonhyperaccumulating plants.
Early
work on the nonhyperaccumulator Brassica juncea identified that Cd was bound by sulfur
donors in the root and by
hard ligands such as oxygen or nitrogen in the xylem sap with transpiration-driven
mobilization into the leaves.
148
This work
also conclusively showed that Cd was localized in trichomes, but rather
than XFI these investigators employed a combination of
109
Cd autoradiography to probe localization and
Cd K-edge XAS to probe the chemical form.
148
Subsequent XFI combined with μ-diffraction of the nonhyperaccumulating
tobacco plant (Nicotiana tabacum L.)
showed that when Cd2+ was given in the presence of excess
Ca2+, Cd-containing crystalline deposits were exuded from
the tips of trichomes.
149
The accumulator
willow (Salix sp.) has been explored
as a potential cadmium phytoremediator; XFI has been used to show
accumulation of cadmium at the tips of the serrations in leaves, and
in the bark.
150,151
Tian et al.
152
have used both XFI and LA-ICP-MS (section 5.2) to compare hyperaccumulator and nonaccumulator
ecotypes
of Sedum alfredii. This study showed
that the hyperaccumulator S. alfredii ecotype accumulates cadmium in parenchymal cells,
especially in
the stems, but that cadmium was restricted to the vascular bundles
in the nonhyperaccumulating ecotype.
152
Tian et al. also showed a distinctive correlation between the levels
of cadmium and calcium, but not cadmium and zinc, in the hyperaccumulator,
152
and used EXAFS spectroscopy to show that cadmium
was bound by oxygen donors.
152
These workers
suggested that cadmium metabolism in the hyperaccumulating S. alfredii is associated
with calcium pathways,
and that the metal is bound with oxygen donors from malate.
152
Elegant studies using Cd K-edge XFI of the
cadmium hyperacculator Arabidopsis halleri spp. gemmifera with an incident X-ray
energy of 37 keV also clearly showed cadmium in the trichomes.
84,85
XFI indicated that Cd was localized in highly specific regions within
the trichome, bilaterally disposed, straddling the stem of the Y-shaped
trichomes, just below the branch.
85
Moreover,
the Cd was colocalized with manganese and zinc, but not with calcium,
and μ-XAS showed that, as in other species, Cd was bound to
hard ligands such as oxygen or nitrogen.
85
Pickering et al. have suggested what may be a general rule,
28
which is that the chemistry underlying hyperaccumulating
plants, or plants otherwise adapted to high levels of metal or metalloids,
lacks sulfur coordination but instead shows binding by oxygen or nitrogen.
153,154
This appears to be true even for chemical species that have an inherently
high affinity for thiolates, and chalcogenides in general, such as
the arsenite stored in P. vittata.
28
Conversely, sulfur is often involved when plants
that are poorly adapted to toxic metals are exposed.
155−157
4
Applications in the Health Sciences
XFI is finding increasing application in the field of health science.
Examples include characterization of disease and dyshomeostasis, as
well as molecular therapy and correlation with other imaging modalities,
with XFI providing fresh insights into such critical topics as heavy
metal toxicology, Alzheimer’s disease, amyotrophic lateral
sclerosis, stroke, and multiple sclerosis. We will review pertinent
examples taken from each of these fields.
4.1
Mercury
Toxicology
The compounds
of mercury in general are more toxic than those of any other nonradioactive
heavy element (Figure 31). Despite this extreme
toxicity, human and environmental exposure is surprisingly widespread,
with significant sources ranging from dental amalgam to predatory
marine fish. Moreover, despite widespread human exposure and health
concerns for many populations worldwide, there remain many unanswered
questions about the mechanism of action of mercury and its compounds.
As a further consequence of its high toxicity, mercury levels in biological
tissues are usually very low, making this a difficult element to study.
Added to this, the mercury LIII near-edge shows only subtle
spectroscopic variability with chemical form. These issues combine
to effectively make studies of mercury toxicology particularly challenging.
Two broad chemical forms are of interest, the inorganic form, exposure
to which might arise from industrial activities or from dental amalgam,
and the organometallic form, found in marine fish, which is known
to be predominantly monomethylmercury covalently bound to a single
aliphatic thiolate (probably l-cysteineate).
158
Methylmercury compounds are some of the most
dangerous neurotoxicants because they can cross both the blood–brain
barrier and the blood–placental barrier, accumulating in the
brain and resulting in severe damage. Indeed, while under some circumstances
the placenta can provide protection against other toxic metal species,
such as thallium,
159
the consequences of
in utero exposure are particularly disastrous for the developing fetus.
The toxic potential of mercury is demonstrated most graphically by
large-scale catastrophic acute mercury poisonings that have occurred
in Japan and in Iraq.
160
These tragic incidents
revealed the insidious and debilitating nature of mercury poisoning
with its creeping but deadly neurotoxicity, particularly in fetuses
and young children.
160−163
While adults can be severely impacted by methylmercury exposure,
effects on the developing fetus are devastating, and can result in
microcephaly, cerebropalsy, seizures, mental retardation, blindness,
paralysis, and other very severe consequences.
164
The extreme toxicity of methylmercury to vertebrate fetuses
was unknown until the Minamata mass-poisoning, where many severely
handicapped children were born to relatively healthy mothers.
163
Numerous additional consequences of mercury
poisoning include central nervous system defects, arrhythmias and
cardiomyopathies, and kidney damage. Mercury inhalation can result
in necrotizing bronchitis and pneumonitis that in turn can result
in respiratory failure.
160,161
Mercury can also act
as either an immunostimulant or an immunosuppressant, depending on
the nature of the exposure, leading to a number of pathologic consequences.
160,161
One of the most perplexing phenomena in the toxicology of methylmercury
species is that there can be significant latency between administration
and onset of toxic symptoms,
165
which in
humans can be as long as 150 days.
166
The
cause of this latency remains one of the major unanswered questions
in this field, and while different possible mechanisms have been suggested,
165
at present the cause remains unknown. The toxicology
of mercury and methylmercury has been studied by XFI of larval stage
zebrafish as an animal model and of human brain samples.
Figure 31
A comparison
of the relative toxicities of some heavy elements
that pose human health problems. Toxicities are expressed as the reciprocal
of values of the LD50 (rat, oral, mg/kg) taken from MSDS
forms (Sigma-Aldrich, Acros Organics) for a common toxic inorganic
form of each metal or metalloid. The compounds used were as follows:
Na2CrO4, NaAsO2, Na2SeO3, CdCl2, SnCl2, HgCl2, and
PbCl2.
Larval zebrafish (Danio rerio) are
increasingly used as a model organism to study embryonic development.
Their many advantages include high fecundity, ease of maintenance,
and well-characterized developmental stages. They are easily treated
with exogenous agents by simple addition to their habitat water. Zebrafish
are used increasingly in vertebrate toxicology
167
and by the pharmaceutical industry for de facto first-pass
drug screening.
168
Korbas et al. used XFI
of both anaesthetized larvae and prepared sections to show that fish
treated with methylmercury-l-cysteinate accumulate Hg preferentially
in the outer layers of eye lens (Figure 32).
89
This compound was selected in initial studies
because of its stability in aqueous solution, so that the larvae were
presented with essentially a single molecular entity. The comparison
of the XFI of anaesthetized and fixed sectioned specimens indicated
that no large-scale redistribution of the metals had been caused by
the sample preparation.
89
These workers
also observed differential mercury accumulation in the brain and various
other organs. Subsequent work studied the dynamics of methylmercury
accumulation and redistribution in the lens following embryonic and
larval exposure.
169
The accumulation of
mercury in the lens continued well after removal of the fish from
treatment solutions, thus significantly increasing the postexposure
loading of mercury in the lens. This indicated that mercury is redistributed
from the original target tissue to the eye lens, identifying the developing
lens as a major sink for methylmercury in early embryonic and larval
stages. Subsequent comparative XFI work revealed substantial differences
in the tissue-specific accumulation patterns of mercury in zebrafish
larvae exposed to four different mercury formulations in water.
51
For nonthiolate coordinated mercury compounds,
multiple species are expected to be present in solution, and Korbas
et al. calculated the expected species distributions for the four
reagents added to the fish culture water (HgCl2, CH3HgCl, Hg(l-cysteineate)2, and
CH3Hg-l-cysteineate). Thus, for 1 μM HgCl2 solutions, Korbas et al. calculated that
solutions would consist
of 90.0% Hg(OH)2, 9.5% Hg(OH)Cl, 0.3% HgCO3,
and 0.2% HgCl2. XFI showed that methylmercury species exhibited
higher mercury uptake and targeted different cells and tissues than
inorganic mercury, revealing a significant role of speciation in cellular
and molecular targeting and mercury sequestration. For methylmercury
species, the highest concentrations of mercury were observed in the
surface of the eye lens, independent of the formulation ligand. For
inorganic mercury species, the olfactory epithelium and kidney accumulated
the greatest amounts of mercury.
51
However,
with Hg(II)-bis-l-cysteineate, uptake was significantly decreased.
This work showed that the common differentiation between organic and
inorganic mercury is insufficient to determine the toxicity of various
mercury species. To determine whether physical chemical factors alone
could account for the chemical sensitivity observed with XFI, Korbas
et al. estimated diffusion coefficients and membrane permeabilities
for all of the significant solution species present for each treatment
group.
51
They concluded that a simple passive
diffusion model of the compounds across the lipid part of the cell
membranes cannot explain the differential accumulation of inorganic
and organic mercury observed by XFI, so that other processes such
as active transport must play a role. Most recently, high-resolution
XFI has been used to study the uptake and localization of methylmercury
species in larval zebrafish photoreceptors.
15
This work addressed a long-standing assumption, which is that the
visual disturbances that are a known symptom of organo-mercury exposure
were due to damage to the visual cortex of the brain. The XFI study
showed that photoreceptors take up methylmercury compounds directly,
indicating an additional possible mechanism by which methymercury
species might cause visual disturbances.
15
Figure 32
XFI of mercury in methylmercury-l-cysteinate treated zebrafish
larvae. Part (a) shows an intact tricaine-anesthetized larva showing
localization of Ca, Zn, and Hg plus an optical micrograph (opt); the developing fish
otoliths are prominent in the
Ca image. Part (b) shows a typical head section displaying the same
chemical elements, plus a histologically stained optical micrograph
together with a line rendition of the stained micrograph showing the
cornea (co), eye-lens (el), retina (re), optic nerve (on), and the
fore-brain (br) (diencaphalon). Intensity scales are shown with linear
scaling (lin), and with logarithmic scaling (log) to allow better visualization of
the lower intensities.
Part (c) shows a high-resolution image of the surface regions of the
eye lens indicating that the mercury is localized in a layer beneath
the lens epithelium (ep). Data were collected on SSRL 9-3 (a),
89
APS 20ID (b),
51
and
APS 2ID (c).
15
XFI has also been used in conjunction with XAS to study samples
of human brain following poisoning or environmental exposure.
170
Samples from individuals poisoned with high
levels of methylmercury species showed high levels of both mercury
and selenium colocalized in the gray matter, with a substantial fraction
of nano-particulate mercuric selenide (HgSe) together with inorganic
mercury and methylmercury bound to organic sulfur. Mercury exposure
did not perturb observed organic selenium levels. Individuals with
a lifetime of high fish consumption showed much lower HgSe levels
and dominant methylmercury-l-cysteineate. These results provide
the first evidence that HgSe formation may act as a detoxification
mechanism in humans, at least under conditions of acute exposure.
XFI has provided evidence that may challenge the perceived benign
nature of mercury amalgam dental restoratives. Loss of Hg and biotransformation
of mercury from amalgam fillings has been observed,
171
and Harris et al.
172
have used
XFI to show that Hg can be detected several millimeters away from
the site of the filling, associated with dental tubules, and that
the chemical form of Hg had also been altered.
172
Their work also identified potential routes of exposure
to Hg from fillings, as Hg was localized in the calculus (suggesting
it may at one time have been in a mobile form within the mouth) and
highly innervated areas of the tooth with circulating blood flow,
potentially affording a route to enter the bloodstream.
To date,
almost all of the studies on mercury toxicology to date
have employed acute mercury exposure, in which organisms experience
relatively high doses for a short time. However, most human and environmental
exposure is chronic in nature, involving much lower doses over considerably
longer exposure times. Whether the lessons learned from acute exposure
can be translated to the chronic case remains unclear. Irrespective
of this, the high sensitivity and specificity of XFI will ensure that
it will continue to play an essential role in understanding the risks
posed by this most problematic and enigmatic of the toxic elements.
4.2
Arsenic Toxicology
As compared to
mercury, arsenic is an element that is very well suited to study by
XFI, with a K-edge energy convenient for most beamlines and a rich
and highly structured spectroscopy. Despite its reputation as a deadly
poison, it is very much less toxic than mercury (Figure 31), which means that rather
higher levels can be
tolerated, with a consequence that it is much easier to study. Also,
in contrast to mercury, at least insofar as acute toxicity is concerned,
the mechanism of arsenic toxicity is relatively well understood, so
that there are fewer fundamental questions that need to be answered.
Arsenic, as the trivalent arsenite, is the most toxic environmentally
common form of the element. It is often written as [AsO2]−, although no such molecular
entity actually
exists,
173
and more correctly as As(OH)3 (at physiological pH). Similarly to mercury, As(III)
has
a high affinity for thiols; in acute toxicity, arsenite acts as a
respiratory poison by chelation of essential dithiols and in particular
reduced α-lipoic acid in the pyruvate dehydrogenase complex
to form a stable six-membered chelate ring. Arsenic-based chemical
warfare agents owe their lethality to the same mechanism.
174
Chronic arsenic exposure, for which the mechanisms
of action are comparatively poorly understood, poses enormous human
health problems for communities in developing countries that consume
contaminated drinking water. Such exposure results in arsenicosis,
a syndrome featuring characteristic patterns of hypermelanosis and
hyperkeratosis, vascular and endocrine pathologies, several types
of cancer, and increased mortality.
175
While
the biochemical mechanisms underlying arsenicosis are still substantially
unknown, arsenic is a known carcinogen; there is extensive literature
that links human exposure to inorganic arsenic with a variety of cancers,
including lung cancers,
176
skin cancers,
177
and urinary bladder carcinoma.
178
Moreover, arsenic may act as a cocarcinogen
in urinary and bladder cancer with cigarette smoking
178,179
and in skin cancer with UV light exposure, even at low levels.
180,181
Several possible modes of action may be involved in arsenic carcinogenesis,
182
but the underlying mechanisms remain poorly
defined. Probably in excess of 100 million individuals worldwide have
severe health impacts from arsenicosis, with the crisis in Bangladesh
representing an extreme example. There are thus many areas of arsenic
toxicology in which XFI could make a significant impact, although
to date only a few XFI studies of arsenic in animal systems have been
reported, including some studies of insects,
62
with most vertebrate data limited to such samples as fur and feathers.
183
Insofar as studies relevant to human health
are concerned, Munro et al.
184
used XFI
and μ-XAS to investigate the fate of arsenite and arsenate in
cultures of human liver carcinoma cells (HepG2) and observed accumulation
in the euchromatin region of the cell nucleus following arsenite exposure,
which suggested arsenic targeting of either DNA or proteins involved
in DNA transcription. μ-XAS and XAS of arsenite-treated cells
showed predominance of arsenic sulfur-coordinated species, supporting
the hypothesis that the mechanism of action may concern nuclear proteins,
that this occurs through arsenic’s affinity for chalconide
donors, and that this nuclear interaction may be a key factor in arsenic-induced
toxicity.
Arsenic and selenium have long been known to have
intertwined toxicities,
185,186
wherein a lethal dose
of arsenic can be counteracted by an equal and otherwise lethal dose
of selenium.
186
The mechanism of this surprising
antagonism was found to be the formation of a novel arsenic selenium
species, the seleno-bis(S-glutathionyl) arsinium
ion,
187
which is formed in the liver
187
and possibly in erythrocytes
188
and excreted in the bile,
189
probably through the MRP2 transporter.
190
The organic selenium compound 1,4-phenylene bis(methylene)selenocyanate
and vitamin E have both been shown to block the cocarcinogen activity
of arsenic in mice with ultraviolet irradiation, although only vitamin
E had beneficial effects with ultraviolet irradiation in the absence
of arsenic.
191
XFI has been used to probe
this protective effect of selenium; Burns et al. found elevated levels
of arsenic were present in hair follicles and epidermis of mice treated
with both arsenite and ultraviolet light, yet when 1,4-phenylene bis(methylene)selenocyanate
was given, arsenic was entirely absent in these tissues, with only
diffuse low levels in the liver.
192
Arsenic
K near-edge μ-XAS of the liver was consistent with the presence
of the seleno-bis(S-glutathionyl) arsinium ion, suggesting
that the mechanism underlying the protective effect of selenium in
arsenic/ultraviolet cocarcinogenesis was through removal and biliary
excretion of arsenic.
192
An intriguing
example in which XFI provided critical information
is that of Phar Lap. Phar Lap was a successful and famous racehorse
that ran between 1929 and 1932, gaining acclaim for a spectacular
series of wins. He became a cultural icon of the Australian sporting
community, but while in California he died suddenly and painfully
following a spectacular win in Mexico. While poisoning was suspected,
necropsies were inconclusive, and Phar Lap’s death remained
a mystery for nearly eight decades. Phar Lap’s hide is preserved
on display at Melbourne Museum, and a recent XFI and μ-XAS study
used hair taken from the preserved hide to partly resolve the mystery.
193
Because of Phar Lap’s symptoms, arsenic
was suggested early as a possible poison. However, providing conclusive
evidence of this proved difficult because arsenic can be used in taxidermy
and in the 1930s routinely was administered as a tonic to race horses,
in the form of Fowler’s solution. Kempson and Dermot used XFI
to study Phar Lap’s hair, employing four samples that included
intact bulb and root tissue.
193
Longitudinal
scans indicated low arsenic levels within the hair but with an abrupt
rise in the subcutaneous region followed by decrease in level toward
the root tip, consistent with a large dose followed by a slow decay
due to metabolism and excretion.
193
Kempson
and Dermot also detected external arsenic that had likely been applied
during taxidermy, but at much lower levels than that contained within
the hair. The subcutaneous arsenic-rich regions of the hair were shown
by μ-XAS to contain As(III) coordinated by three thiolates,
193
which would be expected if the poison was administered
as arsenite or arsenic trioxide. The study showed that Phar Lap had
received a sudden and substantial dose of trivalent arsenic that was
likely responsible for his death, making the case for poisoning stronger,
although exactly who administered the poison will likely remain a
mystery.
193
4.3
Iatrogenic
Toxic Metals
In rare cases,
harmful effects from heavy metal ion intoxication have resulted from
medical treatment, which is known as iatrogenic, or physician induced,
exposure. We note in passing that mercury exposure from dental amalgam,
considered in section 4.1, constitutes a potential
iatrogenic exposure to a toxic metal, although the negative consequences
of this are still a matter of debate, so this may not be the best
example. One example in which XFI has provided definitive information
is that of the gadolinium contrast agents. Trivalent gadolinium magnetic
resonance imaging contrast agents have many excellent properties,
including enhancing water T1 relaxation through interaction
with the strongly paramagnetic Gd(III).
194
These Gd(III) agents are clinically presented in the form of chelation
complexes, and are in general very well tolerated with only a very
low incidence of anaphylaxis.
195
Indeed,
they are considered better tolerated than the very commonly used iodinated
X-ray CT contrast agents.
195
The Gd drugs
are typically eliminated in the urine with a half-life of only 80
min in healthy patients.
196
However, in
patients with kidney insufficiency, elimination is prolonged and with
chronic renal failure can be between 30 and 130 h.
196
In such patients, repeated administration of Gd contrast
agents may trigger nephrogenic systemic fibrosis (NSF).
197
NSF is a very serious syndrome characterized
by red skin areas or plaques that successively develop to painful
thickened skin with a wood-like texture developing over several weeks.
The fibrosis may occur many months after the use of the contrast agent
and typically starts at the extremities, may spread to the trunk,
and may also progressively affect skin, joints, eyes, and internal
organs.
198,199
XFI has been used to definitively identify
Gd deposition within biopsied tissue samples from NSF patients.
200,201
Moreover, George et al. have determined that the deposited Gd was
no longer bound to the original contrast agent chelator, and was instead
within a Na-, P-, and Ca-rich region,
201
which was spectroscopically consistent with a GdPO4-type
chemical form.
200,201
NSF is thus thought to be associated
with release of Gd(III) from the chelator, and recommendations as
to choice of contrast agent are now in place based on perceived in
vivo stability of the various chelators. Optimization of a high-affinity
Gd(III)-specific chelator using criteria similar to those developed
for other metals
202,203
might prove useful in this area.
4.4
Neurodegenerative Diseases
Recent
advances in understanding the role of trace metals in neurodegerative
diseases have been gained through the application of XFI. A good general
overview of XFI in neurodegenerative diseases has recently been provided
by Bourassa and Miller,
204
and the potential
of XFI has been eloquently discussed by Popescu, Nichol, and co-workers.
205,206
There remains much groundwork to be done in this area, including
determination of elemental distributions, concentrations, and chemical
species within cultured neurological cells and from ex vivo tissues.
Examples of the type of groundwork being carried out come from Ortega
and Carmona et al.,
207,208
who have mapped the elemental
distribution in dopaminergic cells and neuritic processes at high
spatial resolution (90 nm).
207,208
Their results have
identified iron-rich structures collocated with neurovesicles,
207
as well as elevated Cu, Zn, and Pb within neuritic
processes, which, excluding the latter, may be intrinsic components
related to the protein machinery involved in these outgrowths.
The infectious miss-folded form of the prion protein is well-known
as the cause of fatal neurodegenerative prion diseases,
209
including Creutzfeldt–Jakob disease
in humans and bovine spongiform encephalopathy, commonly known as
“mad cow disease” in cattle. The normal function of
the properly folded nonpathogenic form of the prion protein has been
the focus of debate. Prion protein is highly expressed in the brain
and central nervous system and has been proposed to have a role, at
least in part, in regulating trace metals.
210
Pushie and co-workers have used XFI to map the trace metal distribution
in the brains of transgenic mice expressing different levels of the
prion protein, comparing a prion knockout strain, wild-type, and a
prion overexpressing strain.
211
The levels
of iron, copper, and zinc were all found to be altered upon changing
prion expression level, although their overall localization within
the brain was not altered. Pushie et al. also used immunohistochemical
staining in combination with XFI to reveal that regions of the brain
showing pronounced differences in prion expression mirrored relative
changes in metal levels, although the changes in metal ion levels
were more pronounced and widespread than the differences in detectable
prion expression alone.
211
In particular,
XFI indicated dramatically elevated copper levels in periventricular
regions.
211
The extent to which metal ions
play a role in the development and pathology of neurodegenerative
prion diseases is at present unknown; however, the XFI results clearly
indicate a role for prion protein in the regulation of specific metals
within the central nervous system. This in turn raises the possibility
that prion levels might modulate the progression of diseases in which
altered metal homeostasis is thought to play a pathogenic role such
as Alzheimer’s, Parkinson’s, and Wilson’s diseases,
and disorders such as hemochromatosis.
Parkinson’s disease
(PD) is characterized by the loss of
dopaminergic neurons from the substantia nigra. Popescu et al.
13
have used XFI to characterize the iron accumulation
associated with PD in post mortem autopsy sections from human brain,
confirming earlier investigations by alternate methods such as magnetic
resonance imaging. Indeed, the major point of this work was to present
rapid scan XFI as a new method for investigation of neurodegenerative
disease, as the findings were essentially confirmatory in nature,
although they were at rather higher spatial resolution than previous
work. High-resolution XFI has recently been reported for small regions
of PD brain, targeting specific brain regions that are known to be
vulnerable.
212
This work compared the surviving
neurons in normal and PD brains from the substantia nigra and locus
coeruleus, and employed both XFI and conventional methods to show
that both copper and copper transporter protein 1 were substantially
reduced in PD, but that copper-containing superoxide dismutase was
not.
212
Alzheimer’s disease
(AD) is associated with the formation
of plaques that are known to accumulate trace metals; however, some
animal models of disease have shown significant differences in the
levels of detectable metals accumulated. The ability of XFI to directly
measure the total trace metal content associated with plaques can
clarify ambiguities that may arise through alternative metal detection
methods, such as the use of metal-specific fluorescent probes. Miller
and colleagues have pioneered synchrotron-based imaging investigations
of AD tissue, using a combination of Fourier transform infrared (FTIR)
and XFI.
213
Their XFI results have confirmed
the colocalization of elevated levels of Fe, Cu, and Zn within human
plaques associated with AD
214
and have
also identified significant differences in the extent of metal accumulation
between several mouse lines used in AD research.
215
Other examples of the use of XFI include investigation
of Zn distribution in the hippocampus in relation to memory loss and
delayed neurodegeneration associated with stroke,
216
epilepsy,
217−220
and mechanical brain injury.
221
4.5
Pharmacological Applications
The
ability to characterize elemental distributions and chemical forms
under conditions of dyshomeostasis as well as to track the uptake
of drug and probe molecules is particularly attractive in cancer research.
Typical approaches use metal-labeled drugs or other tagged molecules
that are anticipated to be preferentially taken up by the cancerous
cells, often by dint of some facet related to the cancerous cell’s
growth or nutrient requirements; the targeted location within the
tumor or tumor cells can be identified through tracking the tagging
element.
222
XFI has been used to identify
the localization of Gd and Pt within treated tumor cells,
223
where incorporation of these heavy elements
is ultimately anticipated to enhance neutron capture therapy and synchrotron
stereotactic radiotherapy treatments. XFI tomography has been used
to map the platinum distribution in DLD-1 colorectal cancer cell spheroids
that had been exposed to platinum-containing anticancer drugs.
224,225
This work showed for the first time that the anticancer agent can
accumulate in the central hypoxic and necrotic region of the tumor
spheroids, and that the charge on the complex can affect cellular
uptake and modulate tumor penetration.
224
Two Ru(III)-based drugs, NAMI-A and KP1019, are structurally similar
but possess vastly different anticancer properties. NAMI-A has been
shown to inhibit metastasis formation,
226
whereas KP1019 shows efficacy in inducing apoptosis in primary tumors.
227
Aitken et al.
228
have investigated the cellular fate of both Ru-based drugs via intact
single cell XFI and found that uptake of KP1019 appears to disrupt
Fe metabolism. The KP1019-treated cells showed ruthenium apparently
localized to the nucleus, which was consistent with its wide range
of applicability against primary tumors, whereas NAMI-A did not show
any significant Ru uptake into cells, consistent with its proposed
extracellular level of action.
228
Ruthenium
anticancer drugs usually contain N-heterocyclic ligands to Ru, such
as the bis-indazole coordination in the case of the drug KP1019, in
which the two heterocycles bind axially with the pseudo-octahedral
coordination being completed by four equatorial chloride ligands.
One important question has been whether the Ru remains coordinated
to the N-heterocycle in the cell. Recent XAS data
229
clearly indicate that substantial chemical modification
rapidly occurs in biological media, but these measurements are most
sensitive to the loss of coordinating chloride and sulfur donors,
so whether the heterocycle remains coordinated to the metal was not
clear. This problem has recently been addressed in novel experiments
using iodinated analogues of ruthenium anticancer drugs.
230
By quantitative monitoring of both the ruthenium
and the iodine X-ray fluorescence, colocalization of the two elements
was clearly demonstrated,
230
indicating
that the Ru–N bonds likely remain intact. These results are
expected to be important in future refinements of these promising
anticancer drugs.
The selenium-containing drug ebselen [2-phenyl-1,2-benzoselenazol-3-one]
is an antioxidant drug, which shows considerable promise in the treatment
of stroke, with demonstrated reduced infarct volumes in focal ischemia.
231
Ebselen has been postulated as an in situ mimic
of glutathione peroxidase.
232
XFI has been
applied to ebselen-treated rodent neuroblastoma cells to develop an
understanding of the mechanism of action of this novel drug.
233
Selenium was localized in a specific region
of the cell, which, through comparison with phosphorus and potassium
localizations, was postulated to correspond to the cellular endoplasmic
reticulum.
233
Most notably, an ebselen-induced
efflux of potassium, chloride, and calcium was observed in this study,
changes that are characteristic of the induction of oxidative stress.
Thus, almost paradoxically, the antioxidant effects of ebselen may
not be a direct consequence of any antioxidant abilities of the drug
itself, but rather may be due to induction of cellular antioxidant
defense mechanisms through induction of an oxidative stress response.
233
XFI has also been used to develop an
understanding of the mechanism
of action of some novel antibiotics to combat pathogenic Escherichia coli. The majority
of urinary tract infections
(UTI) are caused by what are known as uropathogenic E. coli.
234
Orally administered
gallium maltolate has been used to successfully treat UTI mice infected
with a uropathogenic strain of E. coli. XFI showed that Ga was localized in the transitional
epithelium
of the bladder, which is a potential site of E. coli.
235
These results indicate that Ga compounds
may prove to be effective in antimicrobial therapy for UTI caused
by uropathogenic E. coli.
As
a final topic in this section, we turn to arsenical-based chemotherapy.
Arsenic is best known as a poison, as we have discussed in section 4.2. Legitimate
use of arsenic compounds as drugs
has been extant since the advent of salvarsan in 1909.
236,237
In more recent years, arsenic trioxide has become established as
a highly effective treatment for acute promyelocytic leukemia, and
at the time of writing there are over 100 clinical trials of arsenic
compounds registered with the United States Food and Drug Administration.
238
As discussed above, Munro et al. have used
XFI to show that arsenite-treated human hepatoma cells present arsenic
localization in the nucleus,
184
and these
workers have also used a combination of XAS and XFI to show that [CH3As(GS)2] and
[(CH3)2As(GS)]
(where GS is glutathione bound through its cysteine thiolate) were
produced in the cells.
239
There remains
much uncertainty concerning the mechanism of action of arsenic drugs
in chemotherapy,
238
and, given the sensitivity
of arsenic XAS as a spectroscopic probe,
173
it seems very likely that applications of XFI will play an important
role in understanding the mechanism of action of existing drugs, and
in the development of new and more effective agents.
4.6
Selenium Biochemistry
Synchrotron
XFI and spectroscopy has also been used to redefine our understanding
of cellular selenium biochemistry, in part through characterization
of subcellular selenium localization and speciation.
240,241
A long-held belief has been that subcellular HSe– comprises the intracellular Se store;
however, no evidence for this
chemical form of Se has been found in human lung cancer A549 cells.
242
A novel finding using the same cell system
was that selenite treatment induced localized selenium deposits within
cells, and that these were associated with an approximately 2-fold
increase in colocalized copper.
243
Despite
the colocalization, no evidence was found for chemical bonding between
the two elements.
243
Bulk selenium EXAFS
indicated the presence of Se–S and Se–Se backscattering,
and μ-XAS of the highly localized selenium regions was mostly
elemental selenium. Weekley et al. point out that the selenium levels
were some 13-fold higher than the observed Cu levels, so that Se–Cu
might not be observable in the presence of significant other types
of bonding. Weekley et al.
243
were not
able to examine the Cu μ-XAS, so that while there is no evidence
for it, a chemical association between the two elements cannot yet
be ruled out. Weekley et al. also suggest that the copper accumulation
might be a response to the toxicity of the selenium,
243
although at the time of writing more research is required
to elucidate the molecular basis of this colocalization. Weekley et
al. have recently extended these cell culture studies with similar
experiments on kidney tissues from rats given selenite supplements.
244
In these tissues, a selenium and copper colocalization
was observed, and the bulk of the selenium was found to be in species
containing Se–Se bonding and no evidence for Cu–Se species
was found, just as with the previous cell culture experiments,
243
where Cu was predominantly bound to sulfur
and nitrogen. Moreover, the expression levels of superoxide dismutase
1 did not change, so upregulation of this enzyme has no role in the
observed copper accumulation.
244
Selenium
accumulation has been reported in neuromelanin deposits in the substantia
nigra of human brain.
245
In this study,
XFI indicated iron-rich microdeposits associated with pigmented neuromelanin,
with colocalized sulfur, calcium, manganese, copper, zinc, and selenium.
245
Bohic et al. suggest that the increased selenium
is likely to be associated with glutathione peroxidase.
245
The results are interpreted in the context
of neurological development, and, interestingly, these workers observe
changes in elemental composition and in the sulfur XAS spectra of
neuromelanin deposits with the age of the individual from which the
brain sample was taken.
245
However, post-mortem
chemical changes associated with sample preparation (formalin and
paraffin embedding) might distort the sulfur biochemistry (section 2.12).
XFI has been used to investigate the
roles of selenium in spermatogenesis with a dramatic increase in selenium
in late spermatids, with selenium concentrating at the luminal side
of elongating spermatids.
246
XFI of knockout
mice lacking mitochondrial glutathione peroxidase 4 and lacking selenoprotein
P showed no elevated selenium levels.
246
Thus, selenium transport was found to be associated with selenoprotein
P, and accumulation with mitochondrial glutathione peroxidase 4.
246
In the spermatozoa, selenium was found to be
colocalized with copper and iron, and primarily located in the mitochondria-rich
sperm midpiece. These workers also estimated concentrations of selenium
in the sperm cell head and the sperm cell midpiece to be 29 and 377
μM, respectively.
246
These levels
are considerably above what are normally considered as toxic levels
of selenium, illustrating the ability of the sperm cells to handle
these high levels.
246
4.7
Pathological Organisms
New insight
into the nutrient requirements of the erythrocyte pathogen, Plasmodium falciparum,
associated with malaria, has
been gained through elemental mapping of red blood cells during P. falciparum infection
and may point to new biochemical
avenues to combat malaria. Using a combination of XFI and ICP-MS,
Marvin et al.
247
have identified that the P. falciparum parasite induces massive Zn accumulation
within the host erythrocyte.
247
Zinc accumulation
occurs late in the pathogen’s lifecycle, associated with replication
of mitochondria. The accumulated Zn was integral for propagation of
the P. falciparum pathogen, as depletion
of free Zn through administration of a strong Zn2+ chelator
halted parasite growth.
Significant areas of growth are in cellular
and subcellular imaging. These areas will be particularly useful for
identifying target organs and cellular fates of trace element containing
drugs or probe molecules (i.e., cancer or tagged metabolites). For
the foreseeable future, these experiments will be of benefit to informing
better drug or target molecule design as well as improving treatments,
while the high radiation doses, on the other hand, generally preclude
large-scale experiments on live subjects. Using XFI imaging to identify
changes in localization, concentration, and chemical speciation is
readily obtainable, but a critical challenge for this field is extending
the wealth of insight and information to the underlying biochemical
processes and metabolic pathways from which they arise. As the field
matures, additional methods will need to be integrated to provide
the kinds of insight necessary for understanding the significance
of XFI imaging data. One such example is the application of peripheral
techniques to tissue or biological matrix XFI imaging, such as imaging
of electrophoresis gels, where components can be separated and their
trace element composition determined.
248−250
These data can then
be related back to elemental or chemical speciation maps to aid in
identification and characterization.
5
Synergy
with Other Methods
A number of other methods can provide
information that is complementary
to that available from XFI and vice versa. In this section, we will
discuss these techniques with an emphasis on the information provided
by XFI, and upon overlap and complementary information.
5.1
Magnetic Resonance Imaging
Magnetic
resonance imaging (MRI) is a widely used and very successful clinical
and research imaging technique. It has the significant advantage over
XFI of being noninvasive and available for large samples, such as
intact humans, with moderate spatial resolution. Metal ions can be
detected by MRI but are sensed only indirectly and only when in paramagnetic
oxidation states through their influence on water proton relaxation
times. Hopp et al.
251
and Zheng et al.
252
have used XFI to calibrate the iron levels
in MRI using susceptibility weighted imaging at 1.5 T and susceptibility
mapping at 3 T, respectively. Susceptibility mapping estimates of
iron correlate well with those determined by XFI
251,252
in both cadaver brain and ferritin phantoms. This calibration provides
an excellent example of translational research with direct impacts
relevant to methods for diagnosis of human diseases.
5.2
Mass Spectrometry Imaging
Mass spectrometry
imaging (MSI) is an increasingly used family of techniques that are
capable of mapping both metals and biomolecules. Laser-ablation inductively
coupled-plasma mass spectrometry (LA-ICP-MS) uses a laser beam focused
to micrometer size on a sample to ablate small particles from the
surface of the sample. These are then fed to an inductively coupled
plasma (ICP) source, which vaporizes, atomizes, and ionizes the ablated
particle. The ICP source in turn feeds a mass spectrometer, and the
ions are separated and detected according to their mass to charge
ratio m/z. LA-ICP-MS can provide
elemental maps with resolution and sensitivity similar to that of
XFI,
253
but with better access to the lighter
elements such as carbon.
254
The ideal sample
thickness for LA-ICP-MS is 20–30 μm,
253
and well-defined methods for quantification have been established.
254,255
In general, data collection times for LA-ICP-MS are rather longer
than for XFI; for example, data acquisition of a whole transverse
section of rat brain with LA-ICP-MS at a resolution of 120 μm
will take 3–4 h, whereas the same resolution XFI scan might
take less than 1 h. However, the LA-ICP-MS experiment has the significant
advantage that commercial instruments are available and access to
large specialized XFI synchrotron infrastructure is not needed. Whereas
LA-ICP-MS can provide elemental mapping information essentially similar
to that of XFI, other mass spectrometry methods can provide quite
different and highly complementary information. Matrix-assisted laser
desorption ionization (MALDI) mass spectrometry can spatially resolve
molecular weights in the range of m/z ∼1000 to ∼200 000, and, in some cases, higher.
256
Tissue section thicknesses of ∼10 μm
are compatible with XFI, but electrically conductive sample plates
are required, which may not be directly compatible with XFI. MALDI
MSI also requires deposition of a compatible matrix material to aid
desorption of molecules from the surface of the sample. Matrix deposition
is critical for reproducibility and spatial resolution, although smaller
matrix droplets reduce the detectable signal as there are fewer ions
generated. Prior to matrix deposition, MSI sample preparation often
includes several rinsing steps, which may include ethanol and acetic
acid to improve the quality of the MS data. In our experience, rinsing
tissue sections with deionized water can remove labile pools of some
elements, although their originally associated proteins may persist.
While these changes in elemental content may be detectable with XFI,
they are unlikely to be consequential to MSI as they likely represent
the loss of low molecular weight species or the loss of labile ions
from sites within their parent protein, which may still persist after
rinsing.
Spatial resolution in MALDI MSI can approach 10 μm,
although careful consideration must be given to the method of matrix
deposition, as well as the acquisition method, to ensure resulting
images contain spatially relevant information at these length scales.
There are also relatively fewer molecules available for desorption
below 10 μm, which significantly limits detectability. Oversampling
methods, where the step size of the sample position motor is smaller
than the laser spot size, have been successfully applied
257
in a manner analogous to that of XFI. However,
unlike XFI, there is less material available for ablation and for
subsequent detection from the surface in subsequent steps after the
initial spot. A microscope mode method of MALDI MSI has also been
developed, whereby snapshots of a large area of sample are collected
and mapped to a position sensitive detector, affording significantly
improved spatial resolution equivalent to ∼4 μm.
258
MSI requires generating ionizable species
that can be readily ablated
from the sample surface; however, some biomolecular species may not
be amenable to such methods. Further challenges are posed by the ion-suppression
effect, the possible presence of isobaric species, as well as detection
of low molecular weight species. Separation of isobaric species is
possible with ion mobility mass spectrometry,
259
whereby ions are separated according to their collision
cross section as well as their mass-to-charge ratio, making analysis
of complex mixtures readily accessible, as well as separation of low
molecular weight species. Alternate methods of detection for low molecular
weight species, such as drugs or probe molecules, can potentially
be overcome by subsequent collision-induced dissociation of the molecular
ion peak belonging to the suspected parent species, followed by confirmation
of the parent ion from its characteristic molecular fragments.
Combining molecular imaging techniques, such as MSI, with the additional
wealth of data afforded by XFI as well as XAS characterization of
chemical species within particular regions of interest is particularly
attractive and holds promise for more complete characterization of
molecular species within biological tissues at the cellular (and in
some cases subcellular) level. Mass spectrometric analysis consumes
sample material, requiring that MSI be performed on the same sample
following imaging and analysis with nondestructive methods such as
XFI, or be performed on adjacent sample sections.
5.3
Fluorescent Molecular Probes
Metal
ion fluorescent probes are small molecules that are specifically synthesized
to bind metal ions with a degree of selectivity, and, upon binding,
to change their optical fluorescent properties.
260
There are significant advantages to this approach, including
the convenience of optical microscopy, and the short time required
to acquire an image. To illustrate the relative strengths and limitations,
we will discuss three different examples: mercury, copper, and zinc.
5.3.1
Mercury
As we have already discussed
XFI applications to mercury toxicology in some detail (section 4.1), as an example
we will examine the comparative
strengths and limitations of mercury fluorescent probes. This area
has recently been reviewed by Nolan and Lippard;
261
the reader is referred to this work for a comprehensive
discussion. Mercury sensors can be categorized into molecules that
either turn on or turn off fluorescence upon mercury exposure,
262,263
including novel approaches in which mercury causes chemical modification
of the sensor.
264−266
The primary concerns with these approaches
are the chemical modifications of the mercury, the specificity of
the probe, and sensitivity. The turn-on probes developed by Chang
and co-workers have excellent reported specificity, but because the
in vivo levels of Hg2+ can be several orders of magnitude
lower than other species such as Zn2+, despite the response
of the probes for Hg2+ being many times that of other cations,
some confusion may be possible. Chemical modification of the endogenous
mercury that is inherent in these methods may also be a cause for
concern. Thus, XFI of both mercury and methylmercury compounds has
clearly shown that uptake, transport, and localization all depended
heavily upon the chemical form in which mercury is presented,
51
and in vivo association with a sensor molecule
might change the metal distribution. Insofar as sensitivity is concerned,
both XFI and fluorescent probes give sufficient response to estimate
mercury in tissue samples, although the sensitivity of XFI depends
strongly upon the source of X-rays and detector technology being employed.
5.3.2
Copper
Studies using copper fluorescent
probes side-by-side with XFI have been reported. Price et al.
267
have studied the Cu(I)-specific fluorescent
probe CS1.
268
Price et al. found that CS1
competed poorly with endogenous copper handling biomolecules, and
that increased fluorescence from CS1 was difficult to directly attribute
to coordination of the probe’s target, Cu(I).
267
Their results also highlight important caveats in the use
of fluorescent probes, which might not be evident without complementary
XFI characterization; in their case, trafficking of CS1 to lysosomes,
and its localization therein, could have ultimately altered some of
the subcellular distribution of Cu(I), which was significantly different
in controls as compared to the same compartment in treatments where
the fluorescent chelator was employed. Thus, when compared to the
use of fluorescent probes, XFI has the significant advantage that
chemical treatments or chemical modification of the target metal is
not required. Fluorescent probes may also specifically target a labile
pool of the metal ion of interest, or those occurrences of the metal
ion in which it may be competitively exchanged. In such cases, while
the use of fluorescent probes alone might be misleading, the combined
use of XFI and fluorescent probes can in principle provide highly
complementary information, with XFI probing total metal and the sensor
molecules only labile metals. The benefits of using both methods have
been discussed by Yang et al.,
269
who used
a combination of XFI in parallel with a different Cu(I)-specific fluorescent
probe CTAP-1, in addition to μ-XAS to probe chemical form. In
this case, the XFI and CTAP-1 images showed similarities, and Yang
et al. reported evidence for a kinetically labile copper pool, predominantly
localized in mitochondria and Golgi.
269
5.3.3
Zinc
As the second most abundant
transition metal in the mammalian body, zinc plays many crucial roles
in biological chemistry. Although the metal itself is incapable of
redox chemistry under physiological conditions, depleted or elevated
zinc levels are known to indirectly affect cell redox status, with
carefully balanced zinc homeostasis being crucial for cellular viability.
In addition, redox stability of Zn2+ is a likely reason
underlying many of the biological functions of the metal, particularly
redox independent cell signaling pathways. For these reasons, imaging
the distribution and alterations in local concentration of zinc is
of great interest. However, studies in this field may be problematic,
particularly in the field of neuroscience, due to the occurrence of
several Zn2+ pools having significant chemical differences.
Specifically, although the majority of zinc is tightly bound within
proteins, a sizable percentage of brain zinc exists as a loosely bound
or “chelatable” pool of zinc, particularly in the hippocampus.
This “chelatable” zinc has long been thought to play
crucial roles in neuronal signaling pathways, although the complete
role of this zinc pool remains to be elucidated.
270,271
“Chelatable” zinc has previously been imaged using
cytochemical methods (Timm’s staining),
272
and, more recently, a wide range of zinc chelating fluorophores
have been designed to study the chelatable zinc pool.
273,274
As reviewed recently, however, nonspecific binding of Zn2+ chelators to other divalent
ions (i.e., Ca2+), or to
protein bound Zn2+, has the potential to confound studies
of the chelatable Zn pool.
273,275−277
Therefore, a combined approach with XFI to determine the distribution
of total zinc, in combination with fluorescence probes and/or cyto-chemistry
to study chelatable zinc, has been valuable. In an elegant example
of this approach, traditional biochemical methods were used to demonstrate
the location of the ZnT3 protein and chelatable zinc pool in wild-type
and in ZnT3 knockout mice.
278,279
XFI was then used
to demonstrate that the decrease in chelatable zinc was not due to
altered speciation or coordination of zinc, but due to a net decrease
in total zinc in ZnT3 knockout mice.
280
This important contribution conclusively demonstrated a direct role
of ZnT3 as a Zn transport protein.
280
Overall, it is clear that a combination of techniques provides strong
mutual benefits for understanding and characterizing trace metals
in biological systems. Thus, XFI and μ-XAS can probe total elemental
distributions and provide chemical speciation information, while prudently
chosen fluorescent probes can be used to target and identify labile
or localized pools of the target metal.
5.4
Fourier
Transform Infrared
Fourier
transform infrared (FTIR) spectroscopy provides a wealth of molecular
information for biological samples, is nondestructive, and can be
performed on the same sample as XFI analyses if a suitable substrate
is used.
The FTIR spectrum results from the absorption of characteristic
wavelengths of light that correspond to the frequency of an oscillating
molecular dipole. FTIR spectroscopic maps or images are best viewed
as “functional group” maps or images, in which a variety
of organic functional groups present in lipids, proteins, carbohydrates,
and various metabolites can be distinguished. Detailed reviews of
FTIR data acquisition methods have recently been reported,
281−283
and we present only a brief discussion here. Collection of FTIR
spectroscopic data may be performed in two different modes, each with
specific advantages. FTIR mapping mode works in a way similar to many
XFI experiments (e.g., see section 2.4); a
small beam, typically defined with an aperture, is employed, and the
sample is moved in a step-by-step manner to spectroscopically map
an area. The second approach, known as FTIR imaging, does not use
an aperture, but incorporates wide-field sample illumination and detection
using a focal plane array detector to quickly acquire FTIR images
of substantial areas of the sample. In general, the point-by-point
mapping approach provides superior quality spectra over a larger range
than can be achieved with the imaging approach, but the focal plane
array detector allows substantial oversampling (e.g., pixel sizes
<1 μm) relatively rapidly, so that images can be acquired
in several minutes instead of hours or days for point-by-point mapping.
284
With both methods, synchrotron light is needed
to provide sufficient spectral brightness for diffraction-limited
spatial resolution, although globar sources and benchtop instruments
are well suited to imaging larger samples at spatial resolutions of
approximately double the diffraction limit, making them a competitive
alternative to synchrotron-based FTIR imaging for some samples.
The diffraction-limited spatial resolution of FTIR images is 2–10
μm, which is comparable to most XFI experiments. Moreover, the
sample substrates, sample thicknesses, and methods of sample preparation
can be similar for both methods. For example, cells grown on infrared-transparent
Si3N4 membranes, or on thin carbon-polymer-based
films (e.g., mylar), can be investigated with FTIR imaging as air-dried,
freeze-dried, or chemically fixed samples, and the same cells can
then be analyzed with XFI. Likewise, sections from hard tissues such
as bone or teeth, or from soft tissues such as brain or tumors, can
be mounted on Si3N4 or polymer films and analyzed
in transmission geometry by FTIR, or mounted on aluminum or gold-coated
slides and analyzed in transflection geometry with FTIR, prior to
XFI. Transflection measurements use a reflective substrate and measure
transmission spectra from the two passes through the sample, the first
through sample to the reflective layer, and the second on the return
out of the sample, with a very small reflection contribution from
the surface of the sample itself. We note that, as discussed by Bassan
et al.,
285
artifacts can occur when biological
samples are analyzed in transflection geometry so that such analysis
should done with care. In many cases, examination of exactly the same
sample by FTIR and XFI is not needed as serial sections of a sample
are often available, so that alternate sections can be mounted on
CaF2 or BaF2 for FTIR and on plastic or Si3N4 films for XFI.
One of the major applications
of complementary FTIR and XFI analyses
has been the field of neuroscience. This approach is well suited to
study spatially resolved cause and effect relationships between molecular
alterations such as protein misfolding and the formation of β-sheet
aggregates and metal (Fe, Cu, Zn) and ionic (Cl–, K+, Ca2+) homeostasis. Miller and
co-workers
have successfully applied a combined approach to study both metal
content and protein secondary structure within amyloid plaques in
Alzheimer’s disease,
286
metal content
and protein secondary structure within individual neuons in Parkinson’s
Disease and Amyotrophic lateral sclerosis,
287
and protein aggregation and elemental content in the hippocampus
after “epileptic”-like seizures.
288,289
Additional examples include the use of FTIR spectroscopic imaging
to identify crystalline creatine deposits in diseased brain tissue,
and corresponding elemental analysis of these deposits using XFI.
290
In addition to analysis of brain tissue,
a combined FTIR and XFI
approach has been used to monitor the molecular and elemental changes
induced in tumor spheroids by chemotherapeutic drugs.
291
Likewise, the use of FTIR in combination with
XFI and XAS to study molecular alterations induced by metal drug uptake
in cancer cells has been reviewed recently.
292
A significant advantage of this combined multimodal approach is
that greater insight is obtained regarding chemical alterations induced
in cells at the site of drug uptake/localization, and molecular alterations
that result as secondary consequences, which are not colocalized with
administered drugs. Other samples that have been successfully investigated
at the subcellular level using a combined FTIR and XFI approach have
previously been reviewed,
293
and include
cultured adipocytes and cardiomyoctes.
105
5.5
Novel X-ray Imaging Methods
A number
of new and novel imaging modalities of a complementary nature have
recently been reported. For the stable iron isotope 57Fe
(2% natural abundance), nuclear resonance is readily excited by synchrotron
light at 14 412 eV, and imaging with both coherent and incoherent
nuclear resonant scattering has recently been explored at a spatial
resolution of 5 μm.
294
The coherent
nuclear resonant scattering is more commonly known as synchrotron
Mössbauer spectroscopy (SMS);
295
it is measured in the forward direction (i.e., in the direction
of the beam), and yields information on the magnetic properties of
the iron in the sample. The incoherent nuclear resonant scattering
is measured at 90° to the incident beam, is probably less useful
than SMS imaging in a biological context, and yields information on
lattice dynamics. While pilot SMS imaging studies have explored only
very high concentrations, biological systems are likely to become
possible with new chopper technology. The major disadvantage of SMS
imaging is that, for all practical purposes, it cannot be used for
elements other than iron. However, given the widespread importance
of iron in biology, the technique is still likely to be important
in future studies.
Another method of potential importance that
to date has remained unexplored in an imaging modality is high-resolution
fluorescence spectroscopy.
296,297
Typically, these data
are collected using a Johann geometry bent-crystal analyzer apparatus,
often constructed as an array to increase the solid angle. Here, minor
fluorescence lines, such as the Kβ″, which shift depending
upon chemical environment, can be monitored. The major disadvantage
with this method is low sensitivity as the aforementioned minor fluorescence
lines are at best hundreds of times less intense than Kα1 and Kα2 lines, and it is
possible that this may
never become practical on the most challenging biological samples.
Although they are not strictly speaking X-ray fluorescence spectroscopy,
resonant inelastic X-ray scattering
298
and
X-ray Raman
299
experiments are conducted
with essentially the same bent-crystal analyzers as high-resolution
fluorescence, although a different geometrical arrangement of crystals
may be used for the different experiments. All of these are inherently
low sensitivity methods, and very high spectral brightness X-ray sources
are required for them to be viable. Despite caution about the practicability
of these methods for many biological systems, the authors are aware
of at least three such bent-crystal analyzers that are, or will be,
available on XFI compatible beamlines, and it seems likely that this
area will be explored in future applications.
6
Concluding Remarks: Refinements and Future Directions
Future
applications of XFI will require an increase in the capabilities
of beamlines, scanning systems, and detectors. The capabilities of
synchrotron radiation sources to provide improved spectral brightness
continue to advance. Modern synchrotron light sources are also considerably
more stable than in previous years, and are also able to operate essentially
continuously, in what is called top-up mode, without routine interruptions
in beam delivery to refill the electron storage ring. As the sources
improve, the technique of XFI will undoubtedly improve to keep pace.
Faster and more efficient detector systems with good energy resolution,
together with faster readout electronics, will be increasingly important.
Also important will be improved speed of data acquisition and sample
scanning systems. As we have discussed, biological samples are inherently
delicate, and improved cryogenic protection for larger samples is
likely to be important. The multienergy methods discussed in section 2.6 may also
improve to the ideal of collecting a
complete μ-XAS spectrum for every pixel within a tractable amount
of time, and as throughput and data acquisition improve then so will
analytical methods and automation. The ability to change spatial resolution
on a sample will also likely be an important future capability. At
present, if an experimenter wishes to use XFI to interrogate a region
of interest within a sample at substantially higher resolution, it
may require a different beamline and sometimes even necessitate relocating
to a different synchrotron light source. The advantages of being able
to dynamically change spatial resolutions on one experimental station
would be significant. At the time of writing, the authors know of
only one beamline for which this is planned (the BioXAS Imaging beamline
at the Canadian Light Source). Finally, improved overlap of XFI with
other methods and alternate imaging modalities are likely to continue
to see growth and increasing scrutiny as these methods are employed
for their mutual benefit (section 5).