Aequorin Classification Essay


In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

Supplementary information:Supplementary data are available at Bioinformatics online.


In the last several decades, numerous biomedical imaging techniques were developed, ranging from the whole organism level (millimeter resolution) down to the single molecule level (nanometer resolution) (Murphy, 2001; Tsien, 2003). Some of the most widely used biological imaging methods include confocal or two-photon laser scanning microscopy (LSM) (Pawley, 2006), scanning or transmission electron microscopy (EM) (Bozzola and Russell, 1999), etc. Novel imaging techniques such as PALM (Betzig et al., 2006), STORM (Rust et al., 2006), STED (Hell, 2003) that far surpass the resolution of conventional optical microscopes currently can pinpoint the location of individual proteins that are only several nanometers apart. Along with the dramatic advances of many related techniques such as image signal digitization and storage, biological tissue labeling [e.g. green fluorescent proteins (GFP) and enhanced GFP (EGFP) (Heim et al., 1995; Shimomura et al., 1962), Dronpa (Ando et al., 2004), Brainbow combinatorial labeling (Livet et al., 2007)], the number of biological images (e.g. cellular and molecular images, as well as medical images) acquired in digital forms is growing rapidly. Large bioimage databases such as Allen Brain Atlas (Lein et al., 2007) and the Cell Centered Database CCDB; (Martone et al., 2002) are becoming available. These image data could involve (1) two-dimensional (2D) or 3D spatial information, (2) multiple colors which may correspond to various molecular reporters, (3) 4D spatio-temporal information for developing tissues or moving cells, (4) various co-localized biological signals such as mRNA expression levels of different genes (Lein et al., 2007; Long et al., 2007b; Peng et al., 2007) or (5) other screening experiments related to RNA interference (RNAi), chemical compounds, etc. (Echeverri and Perrimon, 2006; Moffat et al., 2006; Sepp et al., 2008). Analyzing these images is critical for biologists to seek answers to many biological problems, such as differentiating cancer cell phenotypes (Long et al., 2007a), categorization of neurons (Jefferis et al., 2007), etc.

The deluge of complicated biological and biomedical images poses significant challenges for the image computing community. As a natural extension of the existing biomedical image analysis field, an emerging new engineering area is to develop and use various image data analysis and informatics techniques to extract, compare, search and manage the biological knowledge of the respective images. This new field can be called bioimage informatics. However, due to the great complexity and information content in bioimages, such as the very high density of cells (e.g. astrocytes, microglia, neurons) intertwined together (Fig. 1A), or very rapid microtubule growing process in a 4D movie of live cells, it is very challenging to directly apply existing medical image analysis methods to these bioimage informatics problems. Special techniques such as those developed in the FARSIGHT project (Roysam, 2008) will be necessary to analyze these complicated image objects (Fig. 1B). In addition, usually a single biological image stack has a large size (several hundreds of megabytes or even several gigabytes) and several color channels. The objects of interest in such an image, for instance the 3D structures of neurons, could have dramatic variations of morphology and intensity variations from image to image. It is yet not uncommon that thousands of images need to be automatically analyzed in a high-throughput way, in terms of the number of hours or days, but not months or years of manual work. All these difficulties make it necessary to develop novel bioimage informatics algorithms and systems, especially from three aspects: image processing and mining, image database and visualization.

Fig. 1.

(A) Maximum projection of a 5-channel confocal 3D image of a 100 μm thick section of rat hippocampus. Red: GFAP-labeled astrocytes; green: EBA-labeled blood vessels; yellow: Iba1-labeled microglia; cyan: CyQuant-labeled cell nuclei; purple: NeuroTrace-labeled Nissl substance; scale bar=50 μm. (B) 3D rendering (with a similar color scheme) of the segmented and classified cells produced using the FARSIGHT techniques for (A). Image courtesy of Badrinath Roysam (Bjornsson et al., 2008)

Fig. 1.

(A) Maximum projection of a 5-channel confocal 3D image of a 100 μm thick section of rat hippocampus. Red: GFAP-labeled astrocytes; green: EBA-labeled blood vessels; yellow: Iba1-labeled microglia; cyan: CyQuant-labeled cell nuclei; purple: NeuroTrace-labeled Nissl substance; scale bar=50 μm. (B) 3D rendering (with a similar color scheme) of the segmented and classified cells produced using the FARSIGHT techniques for (A). Image courtesy of Badrinath Roysam (Bjornsson et al., 2008)

Many studies of bioimage informatics are either underway or have been done over the last few years. Several very successful workshops (e.g. were organized to discuss the latest developments of this field. The goal of this essay is to briefly review the advance of bioimage informatics from the angles of applications, key techniques, available tools and resources. First, in Section 2 several application studies on the high-through biology, model organisms, etc., are introduced. Further, in Section 3 the desired computational techniques, including bioimage feature identification, segmentation, registration, annotation, mining, indexing, retrieval and visualization, are discussed. In Sections 4 and 5 the available tools and resources are summarized. While in this short article, it is difficult to include all the important work, and to explain the details of the discussed applications and computing methods (such as their biological objectives, challenges and findings), I hope that the presented facts and links can be helpful for both researchers in this field and general audiences who may have interests in learning the basic ideas of bioimage informatics.


Just like many other engineering fields, bioimage informatics is application-driven, as one can see from the following non-exclusive instances.

2.1 High-throughput and high-content analysis of cellular phenotypes

Large-scale screening of cellular phenotypes, at whole-cell or sub-cellular levels, is of importance for determination of gene functions, delineating cellular pathways, drug discovery and even cancer diagnosis. The CellProfiler system (Carpenter et al., 2006; Lamprecht et al., 2007) was developed to screen cellular images rapidly and gather information such as number of cells, size and other morphological features of cells, per-cell protein levels, cell cycle distribution, etc. This system has been used to detect various cell phenotypes, such as Drosophila Kc167 cells, whose images are often textured and clumpy, and human HT29 cells, which are smooth and elliptical. Intelligent human–computer interface and content-based image retrieval relevance feedback were also used to enable high-content screening of Drosophila (fruit fly) neurons (Hong, 2006; Lin et al., 2007). Analysis of the morphological signatures of cells was used to study signaling pathways related to cell protrusion, adhesion and tension (Bakal et al., 2007).

For high-resolution intracellular analysis, 3D protein location patterns associated with a number of subcellular organelles and components such as nucleus, nucleolus, mitochondria, cytoskeleton, etc., can be described and classified using fluorescence image features, such as Haralick textures features and Zernike moments (Murphy et al., 2003). Spatial patterns may also be considered in clustering analysis and used for prediction of breast cancers (Long et al., 2007a). More systematic descriptions, such as generative models for subcellular locations of proteins, can provide information for systems biology study (Zhao and Murphy, 2007).

2.2 Atlas building for model organisms

Bioimage informatics methods were used to study widely used model organisms, such as mouse (Dorr et al., 2008; Lein et al., 2007; Ng et al., 2007), fruit fly (Luengo Hendriks et al., 2006, Luengo Hendriks et al., 2006, Peng and Myers, 2004, Peng and Myers, 2004; H. Peng et al., unpublished data), Caenorhabditiselegans (Liu et al., 2008; Long et al., 2007b), zebrafish (Megason et al., 2007), etc. One very important aspect is to build various digital atlases of these organisms, and further integrate the respective anatomical and ontological knowledge into databases.

Allen Brain Atlas (Lein et al., 2007) integrates the genome-wide RNA in situ hybridization (ISH) gene expression information of 20 000 mouse genes. Besides a manually generated reference atlas, the Anatomic Gene Expression Atlas (AGEA) is an interactive 3D atlas of the adult mouse brain based on ISH gene expression images. AGEA is based on approximately 4000 coronal gene sets, which allows anatomic specification and browsing based on 3D spatial coordinates and expression threshold control. With the pixel resolution at ∼25 μm, Allen Brain Atlas provides very useful information for studies close to the cellular level.

Single-cell analysis for an entire animal is useful for understanding the cell functions, such as the neuronal circuit mapping based on 3D cellular images of a brain. This task is possible if the cells have unique identities, indicated by the stereotypy of their 3D locations, 3D morphology, birth orders (lineages), gene expression patterns or other functional properties. Several systems do have these distinct properties. In C.elegans, each cell has a unique lineage and identity. A recent development is the building of the single-cell atlas for the L1 stage of C.elegans (Long et al., 2007b). It is based on a series of bioimage-processing and mining techniques including C.elegans worm body straightening (Peng et al., 2008a), nuclei segmentation (Long et al., 2007c), annotation and cell identification (Long et al., 2008; Peng et al., 2008b) and atlas modeling. With this atlas, systematic and high-throughput analysis of gene expression at the truly single-cell level, instead of clusters of cells, becomes feasible (Liu et al., 2008). Several other pieces of similar work are underway for different systems, e.g. a fruit fly adult brain (H. Peng et al.

Aequorin is a calcium-activatedphotoprotein isolated from the hydrozoanAequorea victoria.[1] Though the bioluminescence was studied decades before, the protein was originally isolated from the animal by Osamu Shimomura.[2] In the animals, the protein occurs together with the Green fluorescent protein to produce green light by resonant energy transfer, while aequorin by itself generates blue light.

Discussions of "jellyfish DNA" to make "glowing" animals often refer to transgenic animals which express the Green fluorescent protein, not aequorin, although both originally derived from the same animal.


Work on aequorin began with E. Newton Harvey in 1921.[3] Though Harvey was unable to demonstrate a classical luciferase-luciferin reaction, he showed that water could produce light from dried photocytes and that light could be produced even in the absence of oxygen. Later, Osamu Shimomura began work into the bioluminescence of Aequorea in 1961. This involved tedious harvesting of tens of thousands of jellyfish from the docks in Friday Harbor, Washington.[1] It was determined that light could be produced from extracts with seawater, and more specifically, with calcium.[2] It was also noted during the extraction the animal creates green light due to the presence of the green fluorescent protein, which changes the native blue light of aequorin to green.[4]

While the main focus of his work was on the bioluminescence,[5] Shimomura and two others, Martin Chalfie and Roger Tsien, were awarded the Nobel Prize in 2008 for their work on green fluorescent proteins.


Aequorin is a holoprotein composed of two distinct units, the apoprotein that is called apoaequorin, which has an approximate molecular weight of 21 kDa, and the prosthetic groupcoelenterazine, the luciferin.[6] This is to say, apoaequorin is the enzyme produced in the photocytes of the animal, and coelenterazine is the substrate whose oxidation the enzyme catalyzes. When coelenterazine is bound, it is called aequorin. Notably, the protein contains three EF hand motifs that function as binding sites for Ca2+ ions.[7] The protein is a member of the superfamily of the calcium-binding proteins of which there are some 66 subfamilies.[8]

The crystal structure revealed that aequorin binds coelenterazine and oxygen in the form of a peroxide, coelenterazine-2-hydroperoxide.[9] The binding site for the first two calcium atoms show a 20X greater affinity for calcium than the third site.[10] However, earlier claims that only two EF-hands bind calcium,[11] were questioned when later structures indicated that all three site indeed can bind calcium.[12] Thus, titration studies show that all three calcium-binding sites are active but only two ions are needed to trigger the enzymatic reaction.[13]

Other studies have shown the presence of an internal cysteine bond that maintains the structure of aequorin.[14] This has also explained the need for a thiol reagent like beta mercaptoethanol in the regeneration of the protein since such reagents weaken the sulfhydryl bonds between cysteine residues, expediting the regeneration of the aequorin.

Chemical characterization of aequorin indicates the protein is somewhat resilient to harsh treatments. Aequorin is heat resistant.[15] Held at 95⁰C for 2 minutes the protein lost only 25% activity. Denaturants 6M-urea or 4M-guanidine hydrochloride did not destroy the protein.


Aequorin is presumably encoded in the genome of Aequorea. At least four copies of the gene were recovered as cDNA from the animal.[16][17] Because the genome has not been sequenced, it is unclear if the cDNA variants can account for all of the isoforms of the protein.[18]

Mechanism of action[edit]

Early studies of the bioluminescence of Aequorea by E. Newton Harvey had noted that the bioluminescence appears as a ring around the bell, and occurs even in the absence of air.[19] This was remarkable because most bioluminescence reactions appeared to require oxygen, and led to the idea that the animals somehow store oxygen.[20] It was later discovered that the apoprotein can stably bind coelenterazine and oxygen is required for the regeneration to the active form of aequorin.[21] However, in the presence of calcium ions, the protein undergoes a conformational change and through oxidation converts its prosthetic group, coelenterazine, into excited coelenteramide and CO2.[22] As the excited coelenteramide relaxes to the ground state, blue light (wavelength of 465 nm) is emitted. Before coelenteramide is exchanged out, the entire protein is still fluorescent blue.[23][24] Because of the connection between bioluminescence and fluorescence, this property was ultimately important in the discovery of the luciferin coelenterazine.[25]


Since the emitted light can be easily detected with a luminometer, aequorin has become a useful tool in molecular biology for the measurement of intracellular Ca2+ levels.[26] The early successful purification of aequorin led to the first experiments involving the injection of the protein into the tissues of living animals to visualize the physiological release of calcium in the muscle fibers of a barnacle.[27] Since then, the protein has been widely used as reported in many model biological systems, including zebrafish,[28]rats, mice, and cultured cells.[29][30][31][32]

Cultured cells expressing the aequorin gene can effectively synthesize apoaequorin: however, recombinant expression yields only the apoprotein, therefore it is necessary to add coelenterazine into the culture medium of the cells to obtain a functional protein and thus use its blue light emission to measure Ca2+ concentration. Coelenterazine is a hydrophobic molecule, and therefore is easily taken up across plant and fungal cell walls, as well as the plasma membrane of higher eukaryotes, making aequorin suitable as a (Ca2+ reporter) in plants, fungi, and mammalian cells.[33][34]

Aequorin has a number of advantages over other Ca2+ indicators: because the protein is large, it has a low leakage rate from cells compared to lipophilic dyes such as DiI. It lacks phenomena of intracellular compartmentalization or sequestration as is often seen for Voltage-sensitive dyes, and does not disrupt cell functions or embryo development. Moreover, the light emitted by the oxidation of coelenterazine does not depend on any optical excitation, so problems with auto-fluorescence are eliminated.[35] The primary limitation of aequorin is that the prosthetic group coelenterazine is irreversibly consumed to produce light, and requires continuous addition of coelenterazine into the media. Such issues led to developments of other genetically encoded calcium sensors including the calmodulin-based sensor cameleon,[36] developed by Roger Tsien and the troponin-based sensor, TN-XXL, developed by Oliver Griesbeck.[37]

Apoaequorin is an ingredient in "Prevagen", which is marketed by Quincy Bioscience as a memory supplement. The US Federal Trade Commission (FTC) has charged the maker of false advertising, because they claim marketing statements are not supported by scientific studies. Quincy says it will fight the charges.[38][39][40] A clinical trial run by researchers employed by Quincy Bioscience “found no overall benefit compared to a placebo for its primary endpoints involving memory and cognition” though the company’s advertising cites a few subgroup analyses that did show slight improvements.[41]


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