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All CyteSeer analyses start by breaking down each image into core biological component masks for relevant cells, tissues and sub-cellular regions. It then aggregates measurements as needed for experiments with many slides, wells, plates etc.

1) First, CyteSeer identifies all of the nuclei available from the nuclear images. A nuclear mask for each cell is established where the mask contains all of the pixel locations automatically identified as nuclear for a given cell.

2) Next, CyteSeer analyzes a second image – a lipid droplet image for example –such that the lipid droplets are assigned to the lipid droplet mask. A rich set of data parameters are then calculated on a “per cell basis”.

3) Third, CyteSeer tries to define the cell boundaries. This varies from assay to assay. It can be done by cell border, cell membrane or – as in this case – by assigning which droplets belong to which cell.

As CyteSeer identifies each cell and sub-cellular region, it makes measurements based on the identified masks. Each algorithm in CyteSeer identifies these regions and aggregates the resulting measurements in different ways. For example, many of the available algorithms include the same nuclear segmentation and resulting ploidy measurements but differ in how they segment and measure the cytoplasm. The protein expression algorithm is a general protein signal tracking tool whereas the membrane segmentation algorithm is an excellent tool for tracking cell membrane activity like those found in the beta-catenin and cadherin assays. The RNA Spots algorithm is excellent for finding Fluorescence In Situ Hybridization (FISH) spots whereas the Lipid Droplet algorithm is optimized for finding lipid droplets.

Depending on the algorithm CyteSeer will create masks and measurements from each image. If there is a nuclear image (Ni) and a protein image (Pi), the CyteSeer protein expression algorithm will create a nuclear mask (Nm) and a protein mask (Pm). A lipid droplet experiment might also have a nuclear image (Ni) but will have a lipid droplet image (Li) and lipid droplet mask (Lm) instead of a protein image (Pi) and protein mask (Pm). A beta-catenin membrane experiment would also have a nuclear image (Ni) and nuclear mask (Nm) but would now have a membrane image (Mi) and membrane mask (Mm).

CyteSeer's image segmentation algorithms process each image to identify each cell. The result of the processing are one or more "masks" for every cell that usually correlate to a relevant biological structure. The example in this graphic shows an image of an individual cell stained for two fluorescent labels. The deep blue is a DAPI stained nucleus and the green specifically labels lipid droplets. The image on the right has an overlay of a red outline of the CyteSeer created nuclear mask and the associated CyteSeer generated unique cell ID.

All CyteSeer measurements are derived from the masks. When CyteSeer reports the nuclear area of a cell, it is simply calculating the area of the nuclear mask.

Every pixel under this nuclear mask has an intensity value associated with it that depends on the microscope's camera. An 8-bit camera can have an intensity value between 0 and 255 for every pixel of the camera's sensor chip. CyteSeer reports the DNA Content of a cell by calculating the sum of every pixel under the nuclear mask's intensity value. This is the Total Integrated Intensity of Nuclear Mask (Tii Ni Nm). In these examples, the nuclear mask is identified by the all the red pixels and DNA content is defined by the sum of all blue DAPI stained intensity collected from the associated original image for each pixel in the camera.

Quantifying the Total Integrated Intensity (Tii) of the Lipid uses a similar concept but this mask is depicted in yellow and the Total Integrated Intensity Lipid Mask (Tii Li Lm) is the sum of all the individual pixel intensities of the green from the original image under the yellow lipid mask generated by CyteSeer.

CyteSeer can now compare how the masks overlap with each other to get colocalization measurements. In this example, we might ask, "How much of the lipid droplet signal from the original image is colocalized with the nucleus?" CyteSeer reports this is as the Total Integrated Intensity of Lipid Image over the Nuclear Mask (Tii Li Nm). The Total Integrated Intensity Lipid Image Lipid Mask (Tii Li Lm) would be the sum of all the green from this example image under the yellow lipid mask whereas the Total Integrated Intensity Lipid Image Nuclear Mask (Tii Li Nm) would restrict itself to just the sum of green overlapping the blue DNA stain.

Now that the CyteSeer algorithm has the correct images and masks for every cell available, the measurements derived from these images and masks fall into a few general categories:

Intensity: How bright are the pixels that make up this mask? CyteSeer ploidy analysis using DNA content uses CyteSeer's Total Integrated Intensity of the Nuclear Image over the Nuclear Mask (TII Ni Nm). This is a sum of the intensity (brightness) of each pixel in the nuclear image for the mask CyteSeer has created for that nucleus. Average pixel intensity (API) and maximum pixel intensity (MPI) also fall into this category

Size: How many pixels are part of the mask? For the nucleus, area would be a count of the pixels in the nuclear mask. Measurements like diameter, width and height fall into this category.

Count: An algorithm may report count as well. The "Lipid Droplet Count" refers to how many individual lipid droplets CyteSeer found for a given cell.

Comparisons & Colocalizations: Many of the measurements describe how one or more of these measurements or masks compare with one another. These are built up from concepts described above. Where Total Integrated Intensity of the Nuclear Image over the Nuclear Mask is the sum of pixel brightness for the nucleus, there is also a measurement of Total Integrated Intensity of the Protein Image over the Nuclear Mask (Tii Pi Nm). This measures the total amount of protein signal colocalized with the what CyteSeer has determined to be the nuclear mask.

TII, MPI, API for Each Part of the Cell Mask: Though the list measurements in CyteSeer is long, many of them are simply combinations of Total Integrated Intensity, Average Pixel Intensity and Average Pixel Intensity over the available masks.

Pearson's and Manders: The Pearson's and Manders colocalization coefficients are generally very effective and described below in further detail.

A routine aspect of every analysis verifying that CyteSeer has correctly identified the region of interest. The figure above describes how to choose the right nuclear size parameter needed to create an accurate nuclear mask from the nuclear image.

Upper Left: Nuclear size set too small. CyteSeer misidentifies a single nuclei as three independent nuclei.
Upper Right: Nuclear Size set correctly. CyteSeer correctly identifies the single nuclei.

Lower Left: Nuclear size set too large. CyteSeer misidentifies two nearby nuclei as a single nuclei.
Lower Right: Nuclear size set correctly. CyteSeer correctly identifies the two nearby nuclei.

The figure above describes how to choose the right sensitivity parameter needed to create an accurate mask from a given signals' source microscope image.

Upper Left: Original unprocessed microscope image
Upper Right: Edge protein mask overlay from CyteSeer Image Viewer with CyteSeer protein sensitivity set to 100% (default setting).

Lower Left: Edge protein mask overlay from CyteSeer Image Viewer with CyteSeer protein sensitivity set to 25%. Note how the protein mask does not select as much of the signal.
Lower Right: Edge protein mask overlay from CyteSeer Image Viewer with CyteSeer protein sensitivity set to 200%. Note how the protein mask selects too much of the signal.

Though the list of available measurements for any given algorithm is long, the appropriate plan of attack should focus on what you are trying to measure biologically. Are you tracking a protein from the cytoplasm to the cell membrane? Are you counting the number of spots in a cell? Are you tracking how much two signals are colocalized?

CyteSeer has measurements for these and a much broader set of scenarios. The key technical challenge is to balance how well CyteSeer automatically distinguishes signals from each other, how clear the biological change is and how accurate the statistics need to be for a given experiment. Generally, this is an iterative process carried out over a limited set of controls specifically looking at how the image masks and statistics vary over a variety of settings and measurements.

Pearson's coefficient (Manders et al. 1992):
The linear equation describing the relationship between the intensities in two images is calculated by linear regression. The slope of this linear approximation provides the rate of association of two fluorochromes. In contrast, the Pearson's coefficient provides an estimate of the goodness of this approximation. Its value can range from 1 to -1, with 1 standing for complete positive correlation and -1 for a negative correlation, with zero standing for no correlation.

Overlap coefficient, k1& k2 (Manders et al. 1992):
Introduced by Manders and colleagues, those three coefficients rely on Pearson's coefficient. The overlap coefficient is calculated as the Pearson's coefficient with the mean intensity value of both channels being taken out of the expression. k1 and k2 are defined as two components of the overlap coefficient, the former being related to the first channel total intensity, the later being related to the second channel total intensity.

M1 & M2 coefficients (Manders et al. 1992):
Manders' overlap coefficient is based on the Pearson's correlation coefficient with average intensity values being taken out of the mathematical [removed]Manders 1992). This new coefficient will vary from 0 to 1, the former corresponding to non-overlapping images and the latter reflecting 100% colocalization between both images. M1 is defined as the ratio of the "summed intensities of pixels from the green image for which the intensity in the red channel is above zero" to the "total intensity in the green channel" and M2 is defined conversely for red. Therefore, M1 (or M2) is a good indicator of the proportion of the green signal coincident with a signal in the red channel over its total intensity, which may even apply if the intensities in both channels are really different from one another.

Formulas:

In the following, channel A and channel B grey values of pixel/voxel i will be noted Ai & Bi respectively and the corresponding average intensities over the full image a & b.

Pearson's coefficient:
rP= (Si ((Ai-a)x(Bi-b)))/(Si (Ai-a)²x Si (Bi-b)²)

Overlap coefficient:
Same as previous except that mean value is not subtracted:
r= (Si (AixBi))/(Si (Ai-a)²x Si (Bi-b)²)

k1 and k2 coefficients:
r²=k1xk2 with k1= (Si (AixBi))/ (Si (Ai)²) & k2= (Si (AixBi))/ (Si (Bi)²)

M1 & M2 coefficient:
k1= (Si (Ai, coloc))/ (Si Ai) & k2= (Si (Bi, coloc))/ (Si Bi)
With Ai, coloc being Ai if Bi>0 and 0 if Bi=0; and Bi, coloc being Bi if Ai>0 and 0 if Ai=0.

References:
Manders, E., Stap, J., Brakenhoff, G., van Driel, R., and Aten, J. (1992). Dynamics of three-dimensional replication patterns during the S-phase, analysed by double labelling of DNA and confocal microscopy. J Cell Sci 103, 857-862. Costes SV, Daelemans D., Cho EH, Dobbin Z, Pavlakis G, Lockett S. (2004). Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys J. 86, 3993-4003.

Li, Q., Lau, A., Morris, T. J., Guo, L., Fordyce, C. B., and Stanley, E. F. (2004). A Syntaxin 1, Galphao, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization. J. Neurosci. 24, 4070-4081.

Van Steensel, B., van Binnendijk, E., Hornsby, C., van der Voort, H., Krozowski, Z., de Kloet, E., and van Driel, R. (1996). Partial colocalization of glucocorticoid and mineralocorticoid receptors in discrete compartments in nuclei of rat hippocampus neurons. J Cell Sci 109, 787-792.

Bolte, S., Cordelieres, F. P. (2006). A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy. 224, 3, 213-232

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