Automated Analysis

Quantitative expression analysis in tissue has a long and checkered history. Pathologists have devised numerous "semi-quantitative" grading systems that have waxed and waned in popularity. As the information capacity of computers increased, morphometric quantitative analysis became possible. Like the cDNA microarrays, the tissue microarray format lends itself to more quantitative analysis. However, tissue microarrays present some special problems that require dedicated readers, or at least dedicated software. An automated analysis protocol must not only be able to select the region of interest, but also normalize it so that the expression level read from any given disk can be compared with other disks. A related problem is that of subcellular localization. Comparisons of nuclear or membranous staining are quite different than total cytoplasmic staining. Although there are now a number of automated devices for reading tissue microarrays (Aperio, Bioimagene, ACIS, Biogenex, Applied Imaging) in our lab we use AQUA, a dedicated, automated TMA analysis software, written by Dr. Robert Camp. This system is completely described in a paper in Nature Medicine (Camp et al, 2002) and produced commercially by HistoRx in New Haven, CT.

Briefly, the concept behind AQUA is to use molecular, rather than feature based compartmentalization. Subcellular compartments are defined by molecular interactions using one set of fluorophores, then the protein of interest is quantified using another fluorophore within the previously defined compartments. Often Cy5, a fluorophore in the far red, is used since there is minimal tissue auto-fluorescence at this emission wavelength. Ultimately, the goal of the AQUA technology is to generate a protein concentration within a molecularly defined subcellular or architectural compartment. A “concentration” is simply a numerator over a denominator. The denominator is actually the total area of interest or what you’re trying to measure within and we call that the “compartment” and the numerator is our “target” of interest. Perhaps this is best illustrated with the example of measurement of estrogen receptor in breast cancer. Starting with either tissue microarrays or whole sections the cytokeratin staining provides a region of interest from which AQUA creates a mask. Then by DAPI staining is done to define pixels that will represent the nuclear compartment (as opposed to round things or spheres which would use contrast generation as used by other methods). Next estrogen receptor levels are measured in the pixels that are DAPI positive. Then the estrogen receptor intensity is divided by the area of the compartment to generate an AQUA score (this is a slight simplification for full details on compartmentalization, see (Camp et al., 2002; Gustavson et al., 2009)). This score can then be standardized directly to absolute amounts of protein. This is achieved by quantitative measurement of absolute recombinant estrogen receptor by Western blot, with simultaneous measurement of ER concentrations in a series of standard cell lines. Those cell lines are then produced in a tissue microarray (as described in Moeder et al (Moeder et al., 2009)) and read using the AQUA technology. This analysis allows production of a standard curve of AQUA score vs absolution protein concentration, read in our lab as pg/ug of total protein.

Figure 1Figure 1: Immunofluorescent Images Used in Automated Quantitative Analysis of Tissue Microarrays. 

The images shown in Figure 1 are of a breast cancer tissue microarray core immunofluorescently stained with a rabbit pan-cytokeratin antibody (Figure 1A), DAPI (Figure 1B) and an estrogen receptor antibody (Figure 1C) allowing for differential fluorescent tagging of each. In this example, keratin defines a tumor mask, DAPI defines a nuclear compartment and estrogen receptor is measured quantitatively within the pixels in the keratin mask within the DAPI compartment. This objective and continuous scoring technology has revealed numerous associations with outcome not previously discernable to pathologists using nominal "by-eye" scoring methods.

The antibodies used for immunofluorescence were rabbit pan-cytokeratin antibody from DAKO (Glostrup, Denmark) Estrogen Receptor antibody (mAb clone 1D5, DAKO) and DAPI, allowing for differential fluorescent tagging of each. A. Cytokeratin staining (Cy2, green) of the breast cancer TMA core shows strong staining of epithelial tissue, which is used to define a binary mask for the tumor region to separate it from the surrounding stroma. B. Top right: DAPI (blue) stains all nuclei in the specimen within both tumor and stromal regions. This is used to define the subcellular compartment of 'nuclei'. C. Estrogen Receptor (ER) staining (Cy5, red) shows nuclear staining. Cy5 is used as for the staining of the target of interest since it is outside the auto-fluorescence spectrum of tissue. D. This three-color overlay image illustrates the separation of epithelial tumor (green regions) from the stroma, which stained only with DAPI. The overlay of the ER staining onto the cytokeratin and DAPI images shows that ER stains nuclei only within the breast tumor region and not the stromal nuclei, resulting in a magenta color.


 

  • Camp, R.L., G.G. Chung, and D.L. Rimm. 2002. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med. 8:1323-7.
  • Gustavson, M.D., B. Bourke-Martin, D.M. Reilly, M. Cregger, C. Williams, G. Tedeschi, R. Pinard, and J. Christiansen. 2009. Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated Quantitative Analysis. Appl Immunohistochem Mol Morphol. 17:329-37.
  • Moeder, C.B., J.M. Giltnane, S.P. Moulis, and D.L. Rimm. 2009. Quantitative, fluorescence-based in-situ assessment of protein expression. Methods Mol Biol. 520:163-75.