A review of imaging techniques for systems biology
© Kherlopian et al; licensee BioMed Central Ltd. 2008
Received: 01 April 2008
Accepted: 12 August 2008
Published: 12 August 2008
This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.
Systems biology [1–8] attempts to model the dynamics and structure of complete biological systems. To accomplish this goal, it enlists concepts and expertise from a wide array of fields such as mathematics, physics, engineering, and computer science in addition to the biological sciences. The "building blocks" of systems biology models are knowledge and data produced within experimental biology, and mathematical modeling provides the "cement" that links these "building blocks." Systems biology extensively uses computational technology and numerical techniques to simulate complex biological networks. The goal is not only to describe biology on a single component level, but also to understand system processes, mechanisms, and principles. The insight gained from simulation results can then be used to design in vivo and in vitro experiments, and in turn further develop models in an ever more refined description of physical and biological reality.
There are two approaches or "avenues" describing the interplay between experimental and theoretical biology. The traditional approach has been for experimental results to drive model creation. An alternative approach is to generate models based on first principles and then test model-inspired hypotheses by new experiments. The ideal situation is to traverse both "avenues," and so central to the methodology of systems biology is the notion of an iterative and strategic interplay between experimentation and modeling [1, 3, 5, 6, 8–11].
Role of imaging
Since the time of Galileo, imaging has been the "eyes of science." Modern imaging technologies allow for visualization of multi-dimensional and multi-parameter data. Imaging is increasingly used to measure physical parameters such as concentration, tissue properties, and surface area  and to glean temporal insight on biological function. Molecular probes can also be employed to allow for both therapeutic and diagnostic applications [13–16]. As the spatial resolution and acquisition frequency of imaging techniques increase, using imaging to monitor substrate and protein dynamics in real time may be more readily achieved. Data acquired by imaging can provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks [12, 17]. Imaging can also be a suitable means to test computational models already developed.
Digital image processing techniques such as segmentation and registration contribute to model creation and validation strategy. Segmentation can help outline and identify particular regions in an imaged volume where there is biological activity of interest taking place. Registration can assist in the alignment of imaged volumes and areas acquired at different times. Segmentation and registration used together can generate time series data for validating systems biology models. After segmentation and registration, volume and surface rendering can be employed for data visualization . Implementing systems biology models in conjunction with imaging provides a way to refine understanding of biological systems . Eventually, as imaging tools become more widely used, and as more biological processes are understood, systems biology models can be developed that will have true predictive capabilities. To reach this end biology will be propelled by computational models, and imaging science will guide their formulation and validation.
Major efforts are underway to apply systems biology methods to oncology [19, 20]. Increasingly sophisticated and accessible genomics, proteomics, and metabolomics high throughput experiments provide a basis for new types of oncology research . The number of published results based on gene expression microarray data alone has increased by a factor of 1700% over the last decade . These advances in experimental systems biology coupled with new analysis techniques and quantitative imaging software tools are helping to generate a more complete picture of many cancer related signaling pathways [21–23].
The actual development of cancer is a complex process, requiring the accumulation of multiple independent mutations each governing different pathways of cell growth and the cell cycle [21, 24]. Genome-wide experiments have shown many signaling pathways to be interrelated and with many transcription factors serving as co-regulators in other signaling pathways [21, 24, 25]. This integrated nature of cancer pathways leads to difficulty in targeting specific pathway components. Efforts are underway to create comprehensive models of the cell cycle that can be used to better understand both the dynamics of cancer and to enable the design of targeted therapeutics [21, 24, 26].
Advances in molecular imaging can help to satisfy the post-genomic era need for the study of complete biological pathways, and this can potentially accelerate the achievement of a systems level understanding of biological complexity [27, 28]. Molecular imaging enables the determination of both the temporal and the spatial distributions of biological processes throughout an intact living subject. With this approach, it is possible to obtain more meaningful results than can be achieved by comparable in vitro methods .
With the advance of molecular imaging techniques, properly tagged molecules can be visualized leading to insights on cell function, membrane binding sites, and the effectiveness of particular therapies [30–33]. For example, integrating imaging and modeling has led to successful monitoring of immune system functionality via T cell activity  and the development of bacteriophages for cancer targeting . It is this type of integration of imaging and modeling that can enable new advances in oncology and other fields in the biomedical sciences.
Imaging on multiple scales
Comparison of imaging technology for systems biology
Contrast Agents and Molecular Probes
15 – 1000 nm
Fluorescent proteins, dyes, rhodamine amide, quantum dots
Visualization of cell structures
Atomic Force Microscopy
10 – 20 nm
Mapping cell surface
Discerning protein structure
12 – 50 μm
Lung and bone tumor imaging
4 – 100 μm
Mins – Hrs
Gadolinium, dysprosium, iron oxide particles
Secs – Mins
Oxygenated hemoglobin (HbO2) deoxygenated hemoglobin (Hb)
Functional imaging of brain activity
N-acetylaspartate (NAA), creatine, choline, citrate
Detection of metabolites
1 – 2 mm
Fluorodeoxyglucose (FDG), 18F, 11C, 15O
In STED (Stimulated Emission Depletion) microscopy [36, 37], two pulsed lasers are used in tandem to break the diffraction barrier. The first laser pulse has a wavelength that excites fluorophores, and is immediately followed by a second laser pulse that depletes fluorescence. The fluorescence depletion is achieved as the wavelength of the second laser is tuned to be longer than the fluorescence emission. Absorption of a photon from the second laser induces electrons to drop to a lower energy level (stimulated emission) preventing typical fluorescence. The difference in area of the two focused beams leaves only a very small area from where fluorescence is detected. This area is smaller than a diffraction-limited spot. Using STED, images have been captured with a resolution of ~30 nm . In another study using rhomadine amide and a photoswitching technique, a resolution of 15 nm was achieved . In [39, 40], an optical trapping system was used to make angstrom resolution measurements of pair based stepping of RNA polymerase, and thus has established an important resolution benchmark in molecular biology.
Electron microscopy has offered a resolution of ~5 nm for imaging biological tissue . However, to prepare a sample to be imaged by an electron microscope is a rigorous process that does not allow for imaging of live samples . One common sample preparation technique is cryofixation, which is a high pressure and deep freezing technique that results in contrast in electron microscopy . Even though the samples are no longer viable, electron microscopy has provided invaluable insights on the structural details of organelles and membranes .
Atomic force microscopes do not acquire information optically, but rather by recording intermolecular forces between a probe tip and a surface. The primary information acquisition component of an atomic force microscope is a cantilever with a nanometer-scale silicon tip. The tip is brought in close proximity with the sample and the deflection of the cantilever due to Van der Waals forces is recorded to generate a contour map of the sample surface [45, 46]. In comparison with an electron microscope, the sample does not need any special treatment that would actually destroy the sample and prevent its reuse. However, using contact or tapping mode of an atomic force microscope, which impinges on the sample surface to acquire measurements such as strain, can mechanically damage cells and tissue. Other probe microscopy techniques include scanning tunneling microscopy and near-field scanning optical microscopy .
Fluorescence microscopy is often used in systems biology and there is a strong push for the development of high throughput methods. In the application of genome-wide RNAi screens to document the phenotype for each suppressed gene [48, 49], there can be millions of images from a single screen which can amount to several terabytes of data . It is systems biology modeling that relieves the bottleneck of processing this large amount of RNAi screen image data by providing an efficient means of classification. With high throughput microscopy there is much more data generated than can be annotated or evaluated manually, and so developing a fine-tuned and efficient classification model is paramount for unlocking the potential of high throughput methods.
As seen in Figure 2, fluorescent proteins can be used to visualize many functional and structural aspects of cells. Using multi-photon methods as well can provide insight on cell structural and biochemical changes . Multi-photon methods have a wide array of applications including in vivo brain imaging in animals (Figure 3) [52–54], where cortical micro-architecture has been investigated with single cell resolution . Electron microscopes have been used to elucidate macromolecule structure . As for cancer applications, atomic force microscopes have been used to monitor the super-coiled state of DNA, which is preferential to the binding of the tumor suppressing protein p53 . Also in regard to detecting levels of p53 in cells, fluorescence microscopy has been used to determine the effectiveness of oncolytic adenoviruses. Specially designed oncolytic adenoviruses target cancerous tissues and are programmed to replicate if the cellular p53 level is low. Viral oncolytic therapy is an intense research area for cancer treatment and as microscopy techniques advance so will the ability to assess the effectiveness of viral vectors for tumor ablation [57, 58].
Advantages and limitations
It has been long held that the wave nature of light imposes a seemingly fundamental limit on the resolving power of a microscope. The limitation was approximately half the wavelength of visible light or 200 nm. Recently, there has been over a 10-fold resolution improvement with advances in microscopy [35, 38]. However, optical techniques have limited penetration as light readily scatters in tissue. This can be partially ameliorated by using more powerful lasers, but this in turn can lead to increased photobleaching effects which can limit the amount of time that an experiment can run.
Overview of microscopy image analysis software
Image Analysis Software
Clemex Vision PE
Image-Pro Bundled Solutions
Image-Pro 3D Suite
Dewinter Caliper Pro
Dewinter Material Plus
Dewinter Foundry Plus
Dewinter Micro Measurement Pro
MBF BioScience MicroBrightField
C, D, E
Pixcavator Image Analyzer
GSA Bansemer & Scheel GbR
GSA Image Analyser
A, D, E
ImageJ for Microscopy*
Scion Imaging Software*
(*) Open source/freeware software packages
A = Automated microscope
B = Planar microscope
C = Confocal microscope
D = Functional microscope
E = Digital microscope
Ultrasound imaging entails moving a hand held probe over the patient and using a water-based gel to ensure good acoustic coupling. The probe contains one or more acoustic transducers and sends pulses of sound into the patient. Whenever a sound wave encounters a material with different acoustical impedance, part of the sound wave is reflected which the probe detects as an echo. The time it takes for the echo to travel back to the probe is measured and used to calculate the depth of the tissue interface causing the echo. The greater the difference between acoustic impedances, the larger the echo is. A computer is then used to interpret these echo waveforms to construct an image .
In tumor and angiogenesis models, surface expression of α v β 3 has been demonstrated to be a strong ligand for targeting endothelial cells in angiogenic vessels . In order to image this surface expression, microbubbles were conjugated with peptides that bind to α v β 3 . These microbubbles have been shown to have a binding preference to the endothelial surface of Fibroblast Growth Factor (FGF) stimulated neovessels. The extent of neovascularization in a matrigel model matched the image enhancement in ultrasound images to a large extent. Thus, ultrasound imaging served to help validate this experimental model for angiogenesis .
Advantages and limitations
The signal-to-noise ratio for ultrasound images is much lower with nanoparticles than those using microbubbles. Although microbubbles are restricted to the vascular space, this can be an advantage since it minimizes potential signal interference from nonvascular cells . As with other molecular imaging techniques, there is an inverse relationship between sensitivity and resolution for contrast enhanced ultrasound. The relative rate of unbound tracer clearance is also an important issue that determines temporal resolution. In this regard, with clearance time within minutes, microbubble tracers are ideal .
Intrinsic differences in X-ray absorption among water, bone, fat, and air provide contrast in Computed Tomography (CT). In CT, a low energy X-ray source and a detector rotate around the subject, acquiring volumetric data. The detectors are typically Charged Coupled Devices (CCD) and act to phototransduce incoming X-rays . For animal studies, microCT machines can be used which typically operate with higher energy X-rays when compared to human scanners. The increase in energy improves resolution, but exposes the specimen to more ionizing radiation which has adverse health effects.
CT has relatively low soft tissue contrast for tumors and surrounding tissue, but with iodinated contrast agents organs and tumors can be detected . As a result, incorporating iodine into new probes for CT imaging may be necessary. Furthermore, to detect a tumor or other target there must be sufficient site-specific accumulation of probes to result in attenuation of X-rays. With differential attenuation of X-rays, the target can be more readily delineated .
CT can be used to image lung tumors and bone metastasis, given its fast imaging time and high spatial resolution. High throughput techniques using microCT have been used for phenotyping large numbers of transgenic mice and detecting macroscopic abnormalities . In , the co-registration of microCT images containing tumor structural details with bioluminescence images allowed for the study of cell trafficking, tumor growth, and response to therapy in vivo. This image analysis method could potentially be used for assessing hematological reconstitution following bone marrow transplantation.
Advantages and limitations
A key advantage of CT is its high spatial resolution, 12 – 50 μm [29, 70], which is needed to visualize fine anatomical details. CT can also be combined with functional imaging technologies that provide dynamic and metabolic information. The radiation dose of CT, however, is not negligible and this limits repeated imaging in human studies due to health risks .
MRI/MicroMRI, fMRI, and MRS
Magnetic Resonance Imaging (MRI) is achieved by placing a subject in a strong magnetic field, typically 1.5 or 3 Tesla for human scanners, which aligns the hydrogen nuclei spins in a direction parallel to the field. A Radio Frequency (RF) pulse is applied to the sample which causes the spins to acquire enough energy to tilt and precess, where an RF receiver can record the resulting signal . After the removal of the RF pulse, the spins realign parallel to the main magnetic field with a time constant of T1 which is tissue dependent. Signal strength decreases in time with a loss of phase coherence of the spins. This decrease occurs at a time constant T2 which is always less than T1. Magnetic gradients are used to localize spins in space, enabling an image to be formed. The difference in spin density among different tissues in a heterogeneous specimen enables the excellent tissue contrast of MRI . MicroMRI follows the same principles, but a much higher magnetic field strength is used for animal studies. Increasing magnetic field strength improves resolution, but can disturb the visual system and lead to peripheral nerve stimulation.
Functional Magnetic Resonance Imaging (fMRI) is a modality used to image brain activity in response to specified stimuli. When a stimulus solicits a response from a certain area of the brain, metabolism in that region increases. Metabolic demand leads to an increase in blood flow and more oxygenated hemoglobin in the region. As the supply of oxygenated hemoglobin exceeds the metabolic demand, the concentration of oxygenated hemoglobin increases. The balance between oxygenated and deoxygenated hemoglobin is altered leading to a change in image contrast. To detect a change, the image is compared with baseline measurements. Typical cortical activation leads to a 1 – 5% increase in image intensity .
The range of microMRI applications spans from purely experimental to preclinical. MicroMRI technology has been used to track stem cells, monitor immune cell proliferation, and describe embryological development . It has also been used to obtain three-dimensional high resolution representations of bone structure . MicroMRI has advanced to the point at which individual cells, and their organelles, can be imaged with spatial resolution of <4 microns. Images of a paramecium and a spirogyra alga were acquired utilizing a magnetic field of 9 Tesla, phase encoding in all three axes (which improves signal to noise), and Carr-Purcell echo refocusing (incorporation of multiple 90 degree spin echo pulses into the sequence to minimize signal loss due to sample inhomogeneity) .
Contrast agents have been developed with greater affinity for cellular and molecular targets. These include iron oxide particles (which have been used to label individual T cells), manganese ions (which act as a paramagnetic surrogate of calcium), and caged compounds. The latter involves chelated gadolinium surrounded by an enzyme substrate, which physically obstructs water molecules from approaching the gadolinium. When an enzyme cleaves the substrate, water is able to approach the gadolinium. This in turn reduces T1 and increases contrast. The caged-compound technique has been used to demonstrate regionalized in vivo gene expression in frog embryos whereas manganese ions have been used to trace neuronal pathways .
fMRI is used to study the functions of the living brain in a non-invasive manner. It has been shown with fMRI that different cognitive functions, such as attention, perception, imagery, language, and memory, elicit specific cognitive activation patterns in different regions of the brain. One common clinical use of fMRI is in the treatment of patients with brain tumors, and a primary treatment goal is to preserve functional brain tissue. fMRI is used to determine the functionality of brain tissue surrounding the tumor so that potentially harmful therapy can be directed away from critical areas .
Due to the ability of MRS to identify the presence of molecules within voxels, many studies have been devoted to using it to help diagnose cancer and characterize neoplastic tissue. Currently, MRS has been successfully employed in regard to brain, breast, and prostate cancer through identification of various biochemical markers of neoplasm in the imaged volume [78, 79]. 1H has been the element of choice because of its large abundance, but studies involving 31P and 13C appear promising. The latter has been used as an effective dynamic marker of metabolic processes through a hyperpolarization technique .
Advantages and limitations
The two chief advantages of MRI are its excellent tissue contrast and lack of ionizing radiation . Improved signal-to-noise ratio and resolution can be obtained via a small receiver coil radius and high magnetic field strength. However, high magnetic field strength is problematic in human applications because of arising physiological effects such as nausea and visual abnormalities. Also, higher field strength leads to other technical challenges including an increase in the operating frequency, which potentially generates artifacts.
The main advantage of fMRI is its ability to non-invasively image brain. Since image contrast is achieved through the levels of oxygenated and deoxygenated hemoglobin, no external contrast agent is needed. However, due to the faster temporal resolution needed to acquire images of dynamic brain activity, spatial resolution is reduced.
Due to the ability of MRS to reveal the presence of particular biomedical molecules and compounds within an in vivo sample, it seems ideally poised for use in systems biology research. However, certain challenges must be overcome such as large voxel size, long sampling times, and questionable quantitative accuracy of assessing molecular concentrations .
There are many radioactive tracers for PET that are used in different preclinical and clinical applications . The tracers that target specific tumors are essential for systems biology studies due to the information provided regarding metabolic activity [83, 84]. Examples of small targeting ligands include 11C-labelled N-methylspiperone and 18F-labelled spiperone for targeting dopamine receptors on pituitary adenomas .
PET is useful in systems biology studies related to bone metabolism  and metastasis. Bone metastasis is common for several cancers, including prostate, breast, and lung . 15O-labelled water can be extracted from the blood and used to assess tumor blood perfusion. Tumors are in constant need of nutrients from the blood, and tumor neovascularization provides a crucial lifeline for rapidly dividing tumor cells. The uptake of tracer into tissues is proportional to delivery, and so is a measure of perfusion .
PET can be used for measuring therapeutic effects on disease processes. Specific metabolic enzymes that are selectively expressed in prostate cancer cells constitute such a target. In , genes that were differentially expressed between early stage and late stage prostate cancer were studied. L-lactate dehydrogenase-A catalyzes the formation of pyruvate from S-lactase and was expressed at a high level in the late stage cancer cells. PET tracers based on this process would serve to validate this finding and may allow for the identification of prostate cancer metastasis .
Advantages and limitations
PET is a highly sensitive, minimally-invasive technology that is ideally suited for pre-clinical and clinical imaging of cancer biology. By using radioactive tracers, three-dimensional images can be reconstructed to show the concentration and locations of metabolic molecules of interest . Since the study of cancer cells in their normal environment within intact living subjects is essential, PET is ideally suited for monitoring molecular events early in the course of a disease, as well as during pharmacological or radiation therapy. Furthermore, it can be used to acquire prognostic information and to image for disease recurrence [2, 82].
PET spatial resolution is comparatively poor, and is limited by pixel sampling rate, the source size, and blurring in the phosphor screens of the detector rings. Another limitation of PET is that radioisotopes with very short half lives must be immediately injected after production. Due to the same decay type of the different radioactive tracers, it is only possible to trace one molecular species in a given imaging experiment or clinical scan .
Achieving contrast is essential to imaging technology and is often made possible by contrast agents or molecular probes. As mentioned above, fluorescent proteins have played a key role in microscopy studies providing insight on cell structure. Microbubbles have greatly enhanced the use of ultrasound both in imaging and therapeutic applications. For CT, iodine has been instrumental in differentiating tissue types. In MRI based technologies, manipulation of hydrogen spins has allowed for excellent soft tissue contrast and functional imaging of the brain. FDG and other radioactively labeled tracers have enabled targeting of cancer and imaging of metabolic activity with PET. Below, two promising molecular probes are profiled, quantum dots and nanoshells, which may yield a new array of imaging applications.
Quantum dots (QD) are a class of polymer-encapsulated and bioconjugated probes that can fluoresce at multiple wavelengths spanning the visible spectrum. Larger quantum dots emit red light while smaller ones emit blue light. Quantum dots themselves are comprised of a semiconductor core, encased in another semiconductor material that has a larger spectral band gap. This construction enables fluorescence upon excitation. Quantum dots can be packaged in amphiphilic polymers and conjugated with targeting ligands for imaging applications . Under harsh conditions such as wide pH range (1–14), varied salt conditions (0.01 to 1 M), and a strong corrosive environment (1.0 M hydrochloric acid), quantum dots demonstrate extraordinary resiliency and sustained functionality .
Ligands on quantum dots can be tailored to target specific cancer lines. Quantum dots fashioned to target prostate cancer, QD-PSMA (Prostate Specific Membrane Antigen), showed active emission in the presence of C4-2 prostate cancer cells while other quantum dots did not . Quantum dots can also be used to passively target tumors since leaky tumor vasculatures retain more quantum dots than surrounding healthy tissue. Thus, by both active binding and passive diffusion, more quantum dots will be present near cancerous tissue . With the targeting capabilities of quantum dots there is potential for use as a delivery vehicle for therapeutic compounds. Delivery schemes can be based on the release of a therapeutic compound triggered by ligand binding [13, 14, 16, 90]. As an example of a drug delivery application, in  quantum dots with cadmium sulfide were used as chemically removable caps inside mesoporous silica nanospheres to prevent the premature release of drug molecules. Targeted release of drug molecules was mediated by disulfide bond-reducing agents. Quantum dots could also be used in photodynamic therapy by which there is an energy transfer from the quantum dots to target cells, leading to the generation of reactive oxygen species, and thus potentially inducing apoptosis [92, 93]. One limitation of such a therapy in vivo would be reliable and localized energy transfer to ensure the destruction of specific cells.
As applied to bacteriophage development, quantum dots can be multi-purpose by validating the design model as well as showing the effectiveness of tumor targeting . One design model for bacteriophages with quantum dots is based upon the characteristics of quantum dots themselves. These are namely durability due to the co-polymer shell and flexibility due to the possibility of several different ligands. Experiments have been conducted with quantum dot embedded bacteriophages in both in vitro and in vivo with the goal of destroying cancerous tissues. Iteratively designing and creating bacteriophages is an example where quantum dots provide both the effective targeting means, but also the validation of the design model due to visualization of ligand binding .
Advantages and limitations
Information acquired by using quantum dots are constrained by the physical limits of fluorescence microscopy, since that is the imaging technique typically used when detecting emissions from quantum dots. There have been some studies using quantum dots in electron microscopy, which has an order of magnitude higher resolution than light microscopy . The quantum dots themselves experience "blinking," as in each quantum dot randomly switches from on to off. Fortunately, the fluorescence of a bound quantum dot is stronger than of an unbound quantum dot. Still, the randomness of "blinking" imposes some limitations on applications requiring single molecule detection as well as on applications requiring quantification of total fluorescence . Using two different color quantum dots, single molecule imaging has been achieved by co-localization on target molecules .
Advantages and limitations
Optical imaging with nanoshells offers the potential for non-invasive, high resolution in vivo imaging at relatively low cost . Scattering based optical imaging technologies rely on inherent changes in indices of refraction. Strategies that depend only on the intrinsic optical contrast within tissue have proved clinically valuable in some screening applications. However, such techniques are not sensitive enough to resolve an image based on disease biomarkers . In cancer, when early detection is critical to reducing morbidity and mortality, the use of molecule specific contrast agents provides the ability to optically sense and image abnormalities long before pathologic changes occur at the anatomic level . In the future, nanoshells may provide excellent contrast for other imaging modalities such as CT .
In this review we have assessed a range of imaging techniques in systems biology spanning from microscopy to clinical imaging. In addition to the techniques reviewed, there are multiple other technologies that have lead to significant contributions to a systems level understanding of biological processes. Two such techniques are optical coherence tomography [97, 98] and hyperspectral imaging [99, 100]. With the refinement of current technologies and the development of new techniques, additional information will be available to help dissect biological systems.
Beyond improvements in resolution, a grand challenge remains for the imaging technology development community: to enable dynamic imaging of both biological system components and of their respective connections. For example, the ability to resolve and monitor an entire mammalian cortical circuit in vivo has yet to be realized. Electrophysiology has been increasing complemented by fMRI over the last 15 years, but with fMRI information on neural activity is provided as an indirect measure and on the scale of hundreds of thousands or millions of neurons. Two-photon imaging has provided for single cell resolution, but functionally visualizing hundreds of synapses performing computations is limited by axonal labeling of neuronal populations and also by overall temporal acquisition frequency. As a result, innovations in methods for visualizing neural circuitry and for deciphering spike times will be necessary to further advance systems neuroscience with imaging. In a broader set of application areas, using imaging to simultaneously monitor components of a molecular network will be useful in further understanding cellular processes, such as apoptosis which is critical for the development of new cancer treatments.
The further development of imaging technologies will continue to be important in the advancement of systems biology. Imaging can provide a wide array of data that can be used to build and validate models. The information acquired with imaging can be readily incorporated into models as biochemical concentrations, functional activity, and anatomical coordinates. In addition, imaging provides data for new discoveries and diagnostic information. Oncology and other areas in the biomedical sciences will benefit greatly from imaging and systems biology approaches.
The authors thank Lance Kam, Samuel Sia, and Jonathan Victor as well as Noah Lee, George Xu, and Allison Bell for useful discussions. The authors also greatly appreciate the imaging efforts of Brian Gillette, Jones Tsai, Wei-Ning Lee, X. Sherry Liu, Yinpeng Jin, and Bhranti Shah.
- Dhar PK, Zhu H, Mishra SK: Computational approach to systems biology: from fraction to integration and beyond. IEEE Trans Nanobioscience. 2004, 3: 144-152.PubMedGoogle Scholar
- Gambhir SS: Molecular imaging of cancer with positron emission tomography. Nature Reviews Cancer. 2002, 2: 683-693.PubMedGoogle Scholar
- Ideker T, Galitski T, Hood L: A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet. 2001, 2: 343-372.PubMedGoogle Scholar
- Kitano H: Foundations of Systems Biology. 2001, 320-Cambridge, MA, The MIT PressGoogle Scholar
- Kitano H: Systems biology: a brief overview. Science. 2002, 295: 1662-1664.PubMedGoogle Scholar
- Kitano H: Computational systems biology. Nature. 2002, 420: 206-210.PubMedGoogle Scholar
- Massoud TF, Gambhir SS: Molecular imaging in living subjects: seeing fundamental biological processes in a new light. Genes Dev. 2003, 17: 545-580.PubMedGoogle Scholar
- Wolkenhauer O, Kitano H, Cho KH: Systems biology. IEEE Control Systems Magazine. 2003, 23: 38-48.Google Scholar
- Van Brunt J: Systems Biology Completes the Circle. Signals, The Online Magazine of Biotechnology Industry Analysis. 2003, Walnut Creek, California , Recombinant CapitolGoogle Scholar
- Yun AJ, Lee PY, Gerber AN: Integrating systems biology and medical imaging: understanding disease distribution in the lung model. Ajr. 2006, 186 (4): 925-930.PubMedGoogle Scholar
- Hood L, Heath JR, Phelps ME, Lin B: Systems Biology and New Technologies Enable Predictive and Preventative Medicine. Science. 2004, 306 (5696): 640-643.PubMedGoogle Scholar
- Eils R, Athale C: Computational imaging in cell biology. J Cell Biol. 2003, 161 (3): 477-481.PubMed CentralPubMedGoogle Scholar
- Alivisatos AP, Gu W, Larabell C: Quantum Dots as Cellular Probes. Annu Rev Biomed Eng. 2005, 7: 55-76.PubMedGoogle Scholar
- Gao X, Cui Y, Levenson RM, Chung LW, Nie S: In vivo cancer targeting and imaging with semiconductor quantum dots. Nat Biotechnol. 2004, 22 (8): 969-976.PubMedGoogle Scholar
- Loo CH, Min-Ho L, Hirsch LR, West JL, Halas NJ, Drezek RA: Proc SPIE - Int Soc Opt Eng. 2004, 1-4. Nanoshell bioconjugates for integrated imaging and therapy of cancer San Jose, CA, USA , SPIE-Int. Soc. Opt. Eng, 1, Proc. SPIE - Int. Soc. Opt. Eng. (USA)Google Scholar
- Michalet X, Pinaud FF, Bentolila LA, Tsay JM, Doose S, Li JJ, Sundaresan G, Wu AM, Gambhir SS, Weiss S: Quantum dots for live cells, in vivo imaging, and diagnostics. Science. 2005, 307 (5709): 538-544.PubMed CentralPubMedGoogle Scholar
- Chen X, Murphy RF: Location proteomics: determining the optimal grouping of proteins according to their subcellular location patterns as determined from fluorescence microscope images. Signals, Systems and Computers. 2004, 1: 50-54.Google Scholar
- Hasty J, McMillen D, Collins JJ: Engineered gene circuits. Nature. 2002, 420 (6912): 224-230.PubMedGoogle Scholar
- Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell. 2002, 1 (2): 203-209.PubMedGoogle Scholar
- Ferrando AA, Neuberg DS, Staunton J, Loh ML, Huard C, Raimondi SC, Behm FG, Pui CH, Downing JR, Gilliland DG, Lander ES, Golub TR, Look AT: Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell. 2002, 1 (1): 75-87.PubMedGoogle Scholar
- Hanahan D, Weinberg RA: The Hallmarks of Cancer. Cell. 2000, 100: 57-70.PubMedGoogle Scholar
- Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP: GenePattern 2.0. Nature Genetics. 2006, 38 (5): 500 -5501.PubMedGoogle Scholar
- Gould J, Getz G, Monti S, Reich M, Mesirov JP: Comparative gene marker selection suite. Bioinformatics. 2006, 22 (15): 1924-1925.PubMedGoogle Scholar
- Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA, Marks JR, Dressman HK, West M, Nevins JR: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006, 439 (7074): 353-357.PubMedGoogle Scholar
- Cam H, Balciunaite E, Blais A, Spektor A, Scarpulla RC, Young R, Kluger Y, ynlacht BD: A Common Set of Gene Regulatory Networks Links Metabolism and Growth Inhibition. Molecular Cell. 2004, 16: 399-411.PubMedGoogle Scholar
- Sterr CJ: Principles of Tumor Suppression. Cell. 2004, 116: 235-246.Google Scholar
- Khalil IG, Hill C: Systems biology for cancer. Curr Opin Oncol. 2005, 17 (1): 44-48.PubMedGoogle Scholar
- Nelson SJ, Graves E, Pirzkall A, Li X, Antiniw Chan A, Vigneron DB, McKnight TR: In vivo molecular imaging for planning radiation therapy of gliomas: an application of 1H MRSI. Journal of Magnetic Resonance Imaging. 2002, 16 (4): 464-476.PubMedGoogle Scholar
- Weissleder R: Scaling down imaging: molecular mapping of cancer in mice. Nature reviews. 2002, 2 (1): 11-18.PubMedGoogle Scholar
- Burstein D: MRI for development of disease-modifying osteoarthritis drugs. NMR Biomed. 2006, 19 (6): 669-680.PubMedGoogle Scholar
- Price P: The role of PET scanning in determining pharmacoselective doses in oncology drug development. Ernst Schering Res Found Workshop. 2007, 185-193.Google Scholar
- Sarker D, Workman P: Pharmacodynamic biomarkers for molecular cancer therapeutics. Adv Cancer Res. 2007, 96: 213-268.PubMedGoogle Scholar
- Cho CR, Labow M, Reinhardt M, van Oostrum J, Peitsch MC: The application of systems biology to drug discovery. Curr Opin Chem Biol. 2006, 10 (4): 294-302.PubMedGoogle Scholar
- Chen L, Zurita AJ, Ardelt PU, Giordano RJ, Arap W, Pasqualini R: Design and validation of a bifunctional ligand display system for receptor targeting. Chem Biol. 2004, 11 (8): 1081-1091.PubMedGoogle Scholar
- Garini Y, Vermolen BJ, Young IT: From micro to nano: recent advances in high-resolution microscopy. Curr Opin Biotechnol. 2005, 16 (1): 3-12.PubMedGoogle Scholar
- Hell SWJW: Breaking the diffraction resolution limit by stimulated emission. OptLett. 1994, 19 ((11):): 780-782.Google Scholar
- Klar TA, Jakobs S, Dyba M, Egner A, Hell SW: Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97 (15): 8206-8210.PubMed CentralPubMedGoogle Scholar
- Folling J, Belov V, Riedel D, Schonle A, Egner A, Eggeling C, Bossi M, Hell SW: Fluorescence nanoscopy with optical sectioning by two-photon induced molecular switching using continuous-wave lasers. Chemphyschem. 2008, 9 (2): 321-326.PubMedGoogle Scholar
- Abbondanzieri EA, Greenleaf WJ, Shaevitz JW, Landick R, Block SM: Direct observation of base-pair stepping by RNA polymerase. Nature. 2005, 438 (7067): 460-465.PubMed CentralPubMedGoogle Scholar
- Shaevitz JW, Abbondanzieri EA, Landick R, Block SM: Backtracking by single RNA polymerase molecules observed at near-base-pair resolution. Nature. 2003, 426 (6967): 684-687.PubMed CentralPubMedGoogle Scholar
- Murphy GE, Jensen GJ: Electron cryotomography. BioTechniques. 2007, 43 (4): 413, 415, 417 passim-PubMedGoogle Scholar
- Muscariello L, Rosso F, Marino G, Giordano A, Barbarisi M, Cafiero G, Barbarisi A: A critical overview of ESEM applications in the biological field. J Cell Physiol. 2005, 205 (3): 328-334.PubMedGoogle Scholar
- Koster AJ, Klumperman J: Electron microscopy in cell biology: integrating structure and function. Nat Rev Mol Cell Biol. 2003, Suppl: SS6-10.PubMedGoogle Scholar
- Stan RV: Structure of caveolae. Biochim Biophys Acta. 2005, 1746 (3): 334-348.PubMedGoogle Scholar
- Hansma PK, Schitter G, Fantner GE, Prater C: Applied physics. High-speed atomic force microscopy. Science. 2006, 314 (5799): 601-602.PubMedGoogle Scholar
- Gaboriaud F, Dufrêne YF: Atomic force microscopy of microbial cells: Application to nanomechanical properties, surface forces and molecular recognition forces. Colloids Surf B Biointerfaces. 2007, 54: 10-19.PubMedGoogle Scholar
- Horber JK, Miles MJ: Scanning probe evolution in biology. Science. 2003, 302 (5647): 1002-1005.PubMedGoogle Scholar
- Clark J, Ding S: Generation of RNAi Libraries for High-Throughput Screens. Journal of biomedicine & biotechnology. 2006, 2006 (4): 45716-Google Scholar
- Bargmann CI: High-throughput reverse genetics: RNAi screens in Caenorhabditis elegans. Genome biology. 2001, 2 (2): REVIEWS1005-PubMed CentralPubMedGoogle Scholar
- Pepperkok R, Ellenberg J: High-throughput fluorescence microscopy for systems biology. Nature reviews. 2006, 7 (9): 690-696.PubMedGoogle Scholar
- Konig K: Multiphoton microscopy in life sciences. J Microsc. 2000, 200 ( Pt 2): 83-104.PubMedGoogle Scholar
- Hillman EM: Optical brain imaging in vivo: techniques and applications from animal to man. J Biomed Opt. 2007, 12 (5): 051402-PubMed CentralPubMedGoogle Scholar
- Hillman EM, Devor A, Bouchard MB, Dunn AK, Krauss GW, Skoch J, Bacskai BJ, Dale AM, Boas DA: Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation. Neuroimage. 2007, 35 (1): 89-104.PubMed CentralPubMedGoogle Scholar
- Spires TL, Meyer-Luehmann M, Stern EA, McLean PJ, Skoch J, Nguyen PT, Bacskai BJ, Hyman BT: Dendritic spine abnormalities in amyloid precursor protein transgenic mice demonstrated by gene transfer and intravital multiphoton microscopy. J Neurosci. 2005, 25 (31): 7278-7287.PubMed CentralPubMedGoogle Scholar
- Ohki K, Chung S, Ch'ng YH, Kara P, Reid RC: Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature. 2005, 433 (7026): 597-603.PubMedGoogle Scholar
- Hansma HG: Surface biology of DNA by atomic force microscopy. Annu Rev Phys Chem. 2001, 52: 71-92.PubMedGoogle Scholar
- Woo Y, Adusumilli PS, Fong Y: Advances in oncolytic viral therapy. Curr Opin Investig Drugs. 2006, 7 (6): 549-559.PubMedGoogle Scholar
- Ramachandra M, Rahman A, Zou A, Vaillancourt M, Howe JA, Antelman D, Sugarman B, Demers GW, Engler H, Johnson D, Shabram P: Re-engineering adenovirus regulatory pathways to enhance oncolytic specificity and efficacy. Nature biotechnology. 2001, 19 (11): 1035-1041.PubMedGoogle Scholar
- Djidel S, Gansel JK, Campbell HI, Greenaway AH: High-speed, 3-dimensional, telecentric imaging. Optics Express. 2006, 14 (18): 8269-8277.PubMedGoogle Scholar
- Rosen J, Brooker G: Non-scanning motionless fluorescence three-dimensional holographic microscopy. Nature Photonics. 2008, 2: 190-195.Google Scholar
- Jensen JA: Medical ultrasound imaging. Prog Biophys Mol Biol. 2007, 93 (1-3): 153-165.PubMedGoogle Scholar
- Lee WN, Ingrassia CM, Fung-Kee-Fung SD, Costa KD, Holmes JW, Konofagou EE: Theoretical quality assessment of myocardial elastography with in vivo validation. IEEE Trans Ultrason Ferroelectr Freq Control. 2007, 54 (11): 2233-2245.PubMedGoogle Scholar
- Christiansen JP, Lindner JR: Molecular and Cellular Imaging with Targeted Contrast Ultrasound. Proceedings of the IEEE. 2005, 93 (4): 809-818.Google Scholar
- Thomasson DM, Gharib A, Li KCP: A Primer on Molecular Biology for Imagers. Academic Radiology. 2004, 11 (10): 1159-1168.PubMedGoogle Scholar
- Lanza GM, Wicklilne SA: Targeted ultrasonic contrast agents for molecular imaging and therapy. Prog Cardiovasc Dis. 2001, 44 (1): 13-31.PubMedGoogle Scholar
- Kalender WA: X-ray computed tomography. Phys Med Biol. 2006, 51 (13): R29-43.PubMedGoogle Scholar
- Huang J HX Dimitris M, Devarata B: 3D Tumor Shape Reconstruction from 2D Bioluminescence Images and Registration with CT Images. Microscopic Image Analysis with Applications in Biology Workshop Proceedings. 2006, 99-105.Google Scholar
- Liu XS, Sajda P, Saha PK, Wehrli FW, Guo XE: Quantification of the roles of trabecular microarchitecture and trabecular type in determining the elastic modulus of human trabecular bone. J Bone Miner Res. 2006, 21 (10): 1608-1617.PubMed CentralPubMedGoogle Scholar
- Guarino V, Causa F, Taddei P, di Foggia M, Ciapetti G, Martini D, Fagnano C, Baldini N, Ambrosio L: Polylactic acid fibre-reinforced polycaprolactone scaffolds for bone tissue engineering. Biomaterials. 2008Google Scholar
- Meng Y, Shaw CC, Liu X, Altunbas MC, Wang T, Chen L, Tu SJ, Kappadath SC, Lai CJ: Comparison of two detector systems for cone beam CT small animal imaging - a preliminary study. Proceedings - Society of Photo-Optical Instrumentation Engineers. 2006, 6142: nihpa21188-Google Scholar
- Hornak JP: The Basics of MRI. http://www.cis.rit.edu/htbooks/mri/
- Parrish TB, Gitelman DR, LaBar KS, Mesulam MM: Impact of signal-to-noise on functional MRI. Magnetic Resonance in Medicine. 2000, 44 (6): 925-932.PubMedGoogle Scholar
- Arias-Mendoza F, Brown TR: In vivo measurement of phosphorous markers of disease. Disease Markers. 2003, 19 (2-3): 49-68.PubMedGoogle Scholar
- Pautler RG, Fraser SE: The year(s) of the contrast agent - micro-MRI in the new millennium. Curr Opin Immunol. 2003, 15 (4): 385-392.PubMedGoogle Scholar
- Wehrli FW, Leonard MB, Saha PK, Gomberg BR: Quantitative high-resolution magnetic resonance imaging reveals structural implications of renal osteodystrophy on trabecular and cortical bone. Journal of Magnetic Resonance Imaging. 2004, 20 (1): 83-89.PubMedGoogle Scholar
- Ciobanu L, Pennington CH: 3D micron-scale MRI of single biological cells. Solid State Nuclear Magnetic Resonance. 2004, 25 (1-3): 138-141.PubMedGoogle Scholar
- Atlas SW, Howard RS, Maldjian J, Alsop D, Detre JA, Listerud J, D'Esposito M, Judy KD, Zager E, Stecker M: Functional magnetic resonance imaging of regional brain activity in patients with intracerebral gliomas: findings and implications for clinical management. Neurosurgery. 1996, 38 (2): 329-338.PubMedGoogle Scholar
- Bolan PJ, Nelson MT, Yee D, Garwood M: Imaging in breast cancer: Magnetic resonance spectroscopy. Breast Cancer Research. 2005, 7 (4): 149-152.PubMed CentralPubMedGoogle Scholar
- Karam JA, Mason RP, Koeneman KS, Antich PP, Benaim EA, Hsieh JT: Molecular imaging in prostate cancer. Journal of Cellular Biochemistry. 2003, 90 (3): 473-483.PubMedGoogle Scholar
- Golman K, in 't Zandt R, Thaning M: Real-time metabolic imaging. Proc Natl Acad Sci U S A. 2006, 103 (30): 11270-11275.PubMed CentralPubMedGoogle Scholar
- He Q, Xu RZ, Shkarin P, Pizzorno G, Lee-French CH, Rothman DL, Shungu DC, Shim H: Magnetic resonance spectroscopic imaging of tumor metabolic markers for cancer diagnosis, metabolic phenotyping, and characterization of tumor microenvironment. Disease Markers. 2003, 19 (2-3): 69-94.PubMedGoogle Scholar
- Phelps ME, Chatziioannou A, Cherry S, Gambhir S: Molecular Imaging of Biological Processes from MicroPET in Mice to PET in Patients. IEEE International Symposium on Biomedical Imaging. 2002, 1: 1-9.Google Scholar
- Muhr C, Bergstrom M, Lundberg PO, Bergstrom K, Hartvig P, Lundqvist H, Antoni G, Langstrom B: Dopamine receptors in pituitary adenomas: PET visualization with 11C-N-methylspiperone. J Comput Assist Tomogr. 1996, 10 (2): 175-180.Google Scholar
- Anderson CJ: 64Cu-TETA-octreotide as a PET imaging agent for patients with neuroendocrine tumors. J Nucl Med. 2001, 42: 213-221.PubMedGoogle Scholar
- Blake GM, Park-Holohan SJ, Cook GJ, Fogelman I: Quantitative studies of bone with the use of 18F-fluoride and 99mTc-methylene diphosphonate. Semin Nucl Med. 2001, 31: 28-49.PubMedGoogle Scholar
- Piert M, Zittel TT, Becker GA, Jahn M, Stahlschmidt A, Maier G, Machulla HJ, Bares R: Assessment of porcine bone metabolism by dynamic [18F]fluoride ion PET:Correlation with Bone Histomorphometry. J Nucl Med. 2001, 42: 1091-1100.PubMedGoogle Scholar
- Carmeliet P, Jain RK: Angiogenesis in cancer and other diseases. Nature. 2000, 407: 249-257.PubMedGoogle Scholar
- Azzazy HM, Mansour MM, Kazmierczak SC: From diagnostics to therapy: prospects of quantum dots. Clinical biochemistry. 2007, 40 (13-14): 917-927.PubMedGoogle Scholar
- Shah BS, Clark PA, Moioli EK, Stroscio MA, Mao JJ: Labeling of mesenchymal stem cells by bioconjugated quantum dots. Nano Lett. 2007, 7 (10): 3071-3079.PubMed CentralPubMedGoogle Scholar
- Giepmans BN, Deerinck TJ, Smarr BL, Jones YZ, Ellisman MH: Correlated light and electron microscopic imaging of multiple endogenous proteins using Quantum dots. Nat Methods. 2005, 2 (10): 743-749.PubMedGoogle Scholar
- Lai CY, Trewyn BG, Jeftinija DM, Jeftinija K, Xu S, Jeftinija S, Lin VS: A mesoporous silica nanosphere-based carrier system with chemically removable CdS nanoparticle caps for stimuli-responsive controlled release of neurotransmitters and drug molecules. Journal of the American Chemical Society. 2003, 125 (15): 4451-4459.PubMedGoogle Scholar
- Bakalova R, Ohba H, Zhelev Z, Ishikawa M, Baba Y: Quantum dots as photosensitizers?. Nature biotechnology. 2004, 22 (11): 1360-1361.PubMedGoogle Scholar
- Samia AC, Dayal S, Burda C: Quantum dot-based energy transfer: perspectives and potential for applications in photodynamic therapy. Photochem Photobiol. 2006, 82 (3): 617-625.PubMedGoogle Scholar
- Agrawal A, Deo R, Wang GD, Wang MD, Nie S: Nanometer-scale mapping and single-molecule detection with color-coded nanoparticle probes. Proceedings of the National Academy of Sciences of the United States of America. 2008, 105 (9): 3298-3303.PubMed CentralPubMedGoogle Scholar
- Loo C, Hirsch L, Lee MH, Chang E, West J, Halas N, Drezek R: Gold nanoshell bioconjugates for molecular imaging in living cells. Optics Letters. 2005, 30 (9): 1012-1014. Optical Society of America, Washington, DC 20036-1023, United StatesGoogle Scholar
- Rabin O, Perez JM, Grimm J, Wojtkiewicz G, Weissleder R: An X-ray computed tomography imaging agent based on long-circulating bismuth sulphide nanoparticles. Nature Materials. 2006, 5: 118-122.PubMedGoogle Scholar
- Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, Hee MR, Flotte T, Gregory K, Puliafito CA: Optical coherence tomography. Science. 1991, 254 (5035): 1178-1181.PubMed CentralPubMedGoogle Scholar
- Shields CL, Materin MA, Shields JA: Review of optical coherence tomography for intraocular tumors. Current opinion in ophthalmology. 2005, 16 (3): 141-154.PubMedGoogle Scholar
- Siddiqi AM, Li H, Faruque F, Williams W, Lai K, Hughson M, Bigler S, Beach J, Johnson W: Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells. Cancer. 2008, 114 (1): 13-21.PubMedGoogle Scholar
- Vo-Dinh T, Stokes DL, Wabuyele MB, Martin ME, Song JM, Jagannathan R, Michaud E, Lee RJ, Pan X: A hyperspectral imaging system for in vivo optical diagnostics. Hyperspectral imaging basic principles, instrumental systems, and applications of biomedical interest. IEEE Eng Med Biol Mag. 2004, 23 (5): 40-49.PubMedGoogle Scholar
- Liu H, Kishi T, Roseberry AG, Cai X, Lee CE, Montez JM, Friedman JM, Elmquist JK: Transgenic mice expressing green fluorescent protein under the control of the melanocortin-4 receptor promoter. J Neurosci. 2003, 23 (18): 7143-7154.PubMedGoogle Scholar
- Rice Nanophotonics. http://cohesiondev.rice.edu/engineering/nanoigert
- Nanospectra. http://www.nanospectra.com/images/tumor.jpg
- Leonenko ZV, Finot E, Cramb DT: Atomic force microscopy to study interacting forces in phospholipid bilayers containing general anesthetics. Methods Mol Biol. 2007, 400: 601-609.PubMedGoogle Scholar
- Kriegeskorte N, Bandettini P: Analyzing for information, not activation, to exploit high-resolution fMRI. NeuroImage. 2007, 38 (4): 649-662.PubMed CentralPubMedGoogle Scholar
- Strobel K, van den Hoff J, Pietzsch J: Localized proton magnetic resonance spectroscopy of lipids in adipose tissue at high spatial resolution in mice in vivo. Journal of lipid research. 2008, 49 (2): 473-480.PubMedGoogle Scholar
- Gabbay V, Hess DA, Liu S, Babb JS, Klein RG, Gonen O: Lateralized caudate metabolic abnormalities in adolescent major depressive disorder: a proton MR spectroscopy study. The American journal of psychiatry. 2007, 164 (12): 1881-1889.PubMed CentralPubMedGoogle Scholar
- Park SJ, Leslie Rogers W, Huh S, Kagan H, Honscheid K, Burdette D, Chesi E, Lacasta C, Llosa G, Mikuz M, Studen A, Weilhammer P, Clinthorne NH: A prototype of very high resolution small animal PET scanner using silicon pad detectors. Nucl Instrum Methods Phys Res A. 2007, 570 (3): 543-555.PubMed CentralPubMedGoogle Scholar
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