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Retinal Epithelium Cells: A Case Study - Biomatics.org

Retinal Epithelium Cells: A Case Study

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The structural and functional integrity of the retinal pigment epithelium (RPE) is fundamental for maintaining the function of the neuroretina. These specialized cells form a polarized monolayer that acts as the retinal–blood barrier, separating two distinct environments with highly specialized functions: photoreceptors of the neuroretina at the apical side and Bruch’s membrane/highly vascularized choriocapillaris at the basal side. The polarized nature of the RPE is essential for the health of these two regions, not only in nutrient and waste transport but also in the synthesis and directional secretion of proteins required in maintaining retinal homoeostasis and function. Although multiple malfunctions within the RPE cells have been associated with development of age-related macular degeneration (AMD), the leading cause of legal blindness, clear causative processes have not yet been conclusively characterized at the molecular and cellular level.

One of the main functions of the RPE is in the delivery of nutrients from the choroid to the photoreceptor cells, whilst transporting metabolic end products, ions and excess water in the opposite direction [4–6]. This function alone renders RPE a critical role in maintaining the retinal homoeostasis. However, the RPE also carries out other essential functions in visual cycle, phagocytosis of spent photoreceptor outer segments [7, 8], light absorption [7], and the expression and secretion of retinal proteins [9]. Failure of the RPE to conduct any of these processes efficiently can lead to retinal degeneration, and bring about diseases such as AMD – the leading cause of legal blindness in the Western world [10]. Although multiple malfunctions within the specialized cells in the retina, most importantly in the supportive RPE, have been associated with development of this disease of multifactorial origin, clear causative processes have not yet been conclusively established.


Protein Database, Human Retinal Pigment Epithelium


The retinal pigment epithelium (RPE) is a single cell layer adjacent to the rod and cone photoreceptors that plays key roles in retinal physiology and the biochemistry of vision. RPE cells were isolated from normal adult human donor eyes, subcellular fractions were prepared, and proteins were fractionated by electrophoresis. Following ingel proteolysis, proteins were identified by peptide sequencing using liquid chromatography tandem electrospray mass spectrometry and/or by peptide mass mapping using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Preliminary analyses have identified 278 proteins and provide a starting point for building a database of the human RPE proteome.


Emerging Role for Epithelial Polarity Proteins of the Crumbs Family as Potential Tumor Suppressors

Defects in apical-basal polarity regulation are associated with tissue overgrowth and tumorogenesis, yet the molecular mechanisms linking epithelial polarity regulators to hyperplasia or neoplasia remain elusive. In addition, exploration of the expression and function of the full complement of proteins required for the polarized architecture of epithelial cells in the context of cancer is awaited. This paper provides an overview of recent studies performed on Drosophila and vertebrates showing that apical polarity proteins of the Crumbs family act to repress tissue growth and epithelial to mesenchymal transition. Thus, these proteins emerge as potential tumor suppressors. Interestingly, analysis of the molecular function of Crumbs proteins reveals a function for these polarity regulators in junctional complexes stability and control of signaling pathways regulating proliferation and apoptosis. Thereby, these studies provide a molecular basis explaining how regulation of epithelial polarity is coupled to tumorogenesis.



Retinal pigment epithelial cells exhibit unique expression and localization of plasma membrane syntaxins which may contribute to their trafficking phenotype.


The SNARE membrane fusion machinery controls the fusion of transport vesicles with the apical and basolateral plasma-membrane domains of epithelial cells and is implicated in the specificity of polarized trafficking. To test the hypothesis that differential expression and localization of SNAREs may be a mechanism that contributes to cell-type-specific polarity of different proteins, we studied the expression and distribution of plasma-membrane SNAREs in the retinal pigment epithelium (RPE), an epithelium in which the targeting and steady-state polarity of several plasma membrane proteins differs from most other epithelia. We show here that retinal pigment epithelial cells both in vitro and in vivo differ significantly from MDCK cells and other epithelial cells in their complement of expressed t-SNAREs that are known - or suggested - to be involved in plasma membrane trafficking. Retinal pigment epithelial cells lack expression of the normally apical-specific syntaxin 3. Instead, they express syntaxins 1A and 1B, which are normally restricted to neurons and neuroendocrine cells, on their apical plasma membrane. The polarity of syntaxin 2 is reversed in retinal pigment epithelial cells, and it localizes to a narrow band on the lateral plasma membrane adjacent to the tight junctions. In addition, syntaxin 4 and the v-SNARE endobrevin/VAMP-8 localize to this sub-tight junctional domain, which suggests that this is a region of preferred vesicle exocytosis. Altogether, these data suggest that the unique polarity of many retinal pigment epithelial proteins results from differential expression and distribution of SNAREs at the plasma membrane. We propose that regulation of the expression and subcellular localization of plasma membrane SNAREs may be a general mechanism that contributes to the establishment of distinct sorting phenotypes among epithelial cell types.




Retinal G protein coupled receptor

From Wikipedia, the free encyclopedia
RPE-retinal G protein-coupled receptor is a protein that in humans is encoded by the RGR gene.[1][2]

Defects in this gene are a cause of retinitis pigmentosa. The gene is a member of the rhodopsin-like receptor subfamily of GPCR. Like other opsins which bind retinaldehyde, it contains a conserved lysine residue in the seventh transmembrane domain. The protein presumably acts as a photoisomerase to catalyze the conversion of all-trans-retinal to 11-cis-retinal, similar to retinochrome in invertebrates. The reverse isomerization occurs with rhodopsin in retinal photoreceptor cells. The protein is exclusively expressed in tissue adjacent to retinal photoreceptor cells, the retinal pigment epithelium and Mueller cells. This gene may be associated with autosomal recessive and autosomal dominant retinitis pigmentosa (arRP and adRP, respectively). Alternative splicing results in multiple transcript variants encoding different isoforms.[2]


The Crumbs complex: from epithelial-cell polarity to retinal degeneration

Recent work indicates that, in addition to the core components (which are always found together), other proteins can associate with the complex, depending on the type and developmental stage of the cell, thus providing the Crumbs complex with functional diversity and flexibility. Here, we summarise current knowledge on the composition of the Crumbs complex, its function in flies and vertebrates, and its possible participation in the development of human diseases. ...transient components of the Crumbs complex can link it to other protein complexes (Fig. 3A), including phosphoinositide signalling networks and the actin cytoskeleton.


Click on image to view larger version.

  Fig. 1.
Fig. 1.

Schematic diagram of the core proteins of the Drosophila Crumbs complex and the proposed structure of the complex. Four core components – Crb, Sdt, PATJ and Lin-7 – and their known domains are drawn to scale. The Sdt-B1 isoform is shown to represent the Sdt protein. AG, A-globulin.



Physical modeling of cell geometric order in an epithelial tissue

Fig. 1.

Fig. 1.

Drosophila eye geometry. (A) Adherens junction (AJ) cross-section schematic of an ommatidium, with the “core” of cone and primary cells and the “frame” of secondary and tertiary/bristle cells. (B) Side view of an ommatidium with photoreceptor cells (R) below the AJ and the lens (L) above it. The dimensions of the ommatidium are 20 μm across and 20 μm deep, as marked. In contrast, the depth of the AJ is 50 nm. (C) Double-stained confocal fluorescence image at the AJ plane of a pupal retina (age, 48 h). Antibody staining highlights E-cadherin (green) and N-cadherin (red); where the two proteins are colocalized the color appears to be orange. Note the extreme regularity of the structure.




On-centres and off-centres of the retina

The retina does not simply send a picture to the brain. The retina spatially encodes (compresses) the image to fit the limited capacity of the optic nerve. Compression is necessary because there are 100 times more photoreceptor cells than ganglion cells as mentioned above. The retina does so by "decorrelating" the incoming images in a manner to be described below. These operations are carried out by the centre surround structures as implemented by the bipolar and ganglion cells.

There are two types of centre surround structures in the retina—on-centres and off-centres. On-centres have a positively weighted centre and a negatively weighted surround. Off-centres are just the opposite. Positive weighting is more commonly known as excitatory and negative weighting is more commonly known as inhibitory.

These centre surround structures are not physical in the sense that one cannot see them by staining samples of tissue and examining the retina's anatomy. The centre surround structures are logical (i.e., mathematically abstract) in the sense that they depend on the connection strengths between ganglion and bipolar cells. It is believed that the connection strengths between cells is caused by the number and types of ion channels embedded in the synapses between the ganglion and bipolar cells. See Receptive field for figures and more information on centre surround structures.

The centre surround structures are mathematically equivalent to the edge detection algorithms used by computer programmers to extract or enhance the edges in a digital photograph. Thus the retina performs operations on the image to enhance the edges of objects within its visual field. For example, in a picture of a dog, a cat and a car, it is the edges of these objects that contain the most information. In order for higher functions in the brain (or in a computer for that matter) to extract and classify objects such as a dog and a cat, the retina is the first step to separating out the various objects within the scene.

As an example, the following matrix is at the heart of the computer algorithm that implements edge detection. This matrix is the computer equivalent to the centre surround structure. In this example, each box (element) within this matrix would be connected to one photoreceptor. The photoreceptor in the centre is the current receptor being processed. The centre photoreceptor is multiplied by the +1 weight factor. The surrounding photoreceptors are the "nearest neighbors" to the centre and are multiplied by the -1/8 value. The sum of all nine of these elements is finally calculated. This summation is repeated for every photoreceptor in the image by shifting left to the end of a row and then down to the next line.

-1/8 -1/8 -1/8
-1/8 +1 -1/8
-1/8 -1/8 -1/8

The total sum of this matrix is zero if all the inputs from the nine photoreceptors are the same value. The zero result indicates the image was uniform (non-changing) within this small patch. Negative or positive sums mean something was varying (changing) within this small patch of nine photoreceptors.

The above matrix is only an approximation to what really happens inside the retina. The differences are:

  1. The above example is called "balanced". The term balanced means that the sum of the negative weights is equal to the sum of the positive weights so that they cancel out perfectly. Retinal ganglion cells are almost never perfectly balanced.
  2. The table is square while the centre surround structures in the retina are circular.
  3. Neurons operate on spike trains traveling down nerve cell axons. Computers operate on a single Floating point number that is essentially constant from each input pixel. (The computer pixel is basically the equivalent of a biological photoreceptor.)
  4. The retina performs all these calculations in parallel while the computer operates on each pixel one at a time. There are no repeated summations and shifting as there would be in a computer.
  5. Finally, the horizontal and amacrine cells play a significant role in this process but that is not represented here.

Here is an example of an input image and how edge detection would modify it. Edge-detection-2.jpg

Once the image is spatially encoded by the centre surround structures, the signal is sent out the optical nerve (via the axons of the ganglion cells) through the optic chiasm to the LGN (lateral geniculate nucleus). The exact function of the LGN is unknown at this time. The output of the LGN is then sent to the back of the brain. Specifically the output of the LGN "radiates" out to the V1 Primary visual cortex.



Hexagonal sampling


A multidimensional signal is a function of M independent variables where  M \ge 2. Real world signals, which are generally continuous time signals, have to be discretized (sampled) in order to ensure that digital systems can be used to process the signals. It is during this process of discretization where sampling comes into picture. Although there are many ways of obtaining a discrete representation of a continuous time signal, periodic sampling is by far the simplest scheme. Theoretically, sampling can be performed with respect to any set of points. But practically, sampling is carried out with respect to a set of points that have a certain algebraic structure. Such structures are called lattices.[1] Mathematically, the process of sampling an N-dimensional signal can be written as:-

w(\hat{t}) =  w(V.\hat{n})

where  \hat{t} is continuous domain M-dimensional vector (M-D) that is being sampled, \hat{n} is an M-dimensional integer vector corresponding to indices of a sample, and V is an N X N Sampling Matrix.




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