CORE B- HEART BIOLOGY CORE

在我们的实验条件下， 在光学显微镜的分辨率极限中确定“共定位”。在这个PPG中，“共定位”术语 这意味着两个分子可以共享相同的微环境和/或可以是相同集合的元素 由光学系统分辨的大分子复合物。例如，两种蛋白质形成部分 两种不同的分子复合物可能会显示积极的“共定位”测试由于缺乏的光学分辨率 来检测每个分子复合体。尽管如此，阳性检测将表明这两种蛋白质位于近端 在光学分辨率内。因此，在“共定域”确定中，关键是实现最大的x，y，z 分辨率在本PPG中，我们将使用实验室开发的方法来实现最高分辨率 用光学显微镜就能得到。我们已经最大限度地提高了光学线性度和记录的光量。 检测器结合图像恢复分析，以恢复由光学缺陷损失的光。 系统这种方法使我们能够在x，y平面上达到约100-200 nm的分辨率（见图1和图2）。1-3）。 定义“共定位”和随机“共定位”。在用两个荧光团标记的细胞中，一个红色（R） 和一个绿色（G），用红色荧光团标记的高于阈值的体素的数量是nR， 用绿色荧光团标记的数字是nG。共定位体素的数量，即包含 来自两个荧光团的高于阈值的强度信号是nco 10 c。G与R的“共定位”百分比为： 由100*（eq. 1）;并且R与G的“共定位”的百分比由100*^^（等式1）给出。2）。在 n* nc 通过这些测量，我们将估计测量的“共定位”可能偶然发生的概率， 根据（（面积_1 ×面积_2）/总面积A2）× 100计算%随机“共定位”，其中面积_1和面积_2是 高于阈值的面积（eq. 3）。 “共定位”方法通常基于用户设置强度阈值的能力，并且具有主要的 缺乏一个数学模型来调整两幅图像的阈值，从而定义了 成对像素重叠。最重要的是，像素重叠方法具有作为二进制测试的局限性， 确定两个图像中的两个成对像素是否具有高于强度阈值的强度 不考虑两种染色的蛋白质是否具有高度相关的强度染色的景观， 如果它们是一个共同的复合体的元素，这是可以预期的。相关测量的应用 最近由Li等人描述。（2004）7。 在下面的章节中，我将讨论并应用已开发的算法来量化蛋白质-蛋白质的程度。 通过两种方法，强度相关分析和强度阈值关联 “共定位“分析。 强度相关分析。在本分析中，我们获得高分辨率图像，以比较 来自两种不同的双染色细胞的配对图像的等效x，y坐标中的像素强度的相关性 proteins.预测是，如果两种蛋白质是同一大分子复合物的元素， 染色景观的两个图像应具有x、y像素与像素的正相关性。相反，如果两个 蛋白质定位在不同的区室中，结果将是负相关。最后，如果蛋白质在 两个图像以漫射非结构化图案（随机）标记，相关性将趋于0。 该方法基于这样的原理：对于任何一组值，与平均值的差之和等于零， 也就是说，? N（A-a）=0，其中a是具有N个Al值的分布的平均值。 像素，A是每个像素的强度。如果我们在两个数组1和2中有两组值，每个数组有N个像素 对于具有强度A-1和6的随机分布的阵列1和2，它们的差的乘积的和将 也趋于零，因此IN（A-a）（S1-Jb）~0。另一方面，如果两个强度正相关，则乘积 将倾向于是正值（^（Ara）（Brb}>G），并且如果它们负相关，则乘积将倾向于 负值（LN（Ara）（Brb）<0）。

In our experimental conditions, we are determining "colocalization" in the resolution limit of light microscopy. In this PPG, the "colocalization" term will imply that two molecules may share the same microenvironment and/or can be elements of the same set of macromolecular complexes which are resolved by the optical system. Forexample, two proteins forming part of two different molecular complexes may show positive "colocalization" tests due to the lack of the optical resolution to detect each molecular complex. Still, the positive test will indicate that the two proteins are located proximally within the optical resolution. Thus, in "colocalization" determinations it is critical to achieve the maximal x,y,z resolution. In this PPG, we will use developed methodologies in the laboratory to achieve the highest resolution attainable with optical microscopy. We have maximized the optical linearity and the quantity of light recorded by the detectors in conjunction with image restoration analysis to recover the light lost by the optical imperfections of the system. This methodology has allowed us to reach a resolution in the x,y plane of about 100-200 nm (see Figs. 1-3). Definition of "colocalization" and random "colocalization". In a cell labeled with two fluorophores, one red (R) and one green (G), the number of voxels above threshold that are labeled with the red fluorophore is nRand the number labeled with the green fluorophore is nG. The number of colocalized voxels, i.e. those voxels containing intensity signals above threshold from both fluorophores is ncoioc. The percentage of "colocalization" of G with R is given by 100*^^- (eq. 1); and the percentage of "colocalization" of R with G is given by 100*^^ (eq. 2). In n* nc these measurements we will estimate the probability that the measured "colocalization" could occur by chance by calculating % random "colocalization" from ((area_1x area_2)/total areaA2) x100, where area_1 and area_2 are the areas above threshold (eq. 3). The "colocalization" method is generally based on the user ability to set the intensity threshold, and has the major draw back of the lack of a mathematical model to adjust the threshold of both images that will define the degree of paired pixel overlap. Most importantly the pixel overlap method has the limitation of being a binary test that determines whether the two paired pixels in two images have or not intensities above the intensity threshold and does not consider whether the two stained proteins have a landscape of intensity staining with a high degree of correlation, as it would be expected if they are elements of a common complex. The application of a correlation measurement was recently described by Li et al. (2004)7. In the following sections, I will discuss and apply developed algorithms to quantify the degree of protein-protein association by two methods, the INTENSITY CORRELATION ANALYSIS and the INTENSITY THRESHOLD "COLOCALIZATION"ANALYSIS. Intensity correlation analysis. In the present analysis, we acquire high resolution images to compare the correlation of pixel intensities in equivalent x,y coordinates of paired images from cells double stained for two different proteins. The prediction is that if two proteins are elements of the same macromolecular complex, the intensity staining landscape of the two images should have a x,y pixel to pixel positive correlation. On the contrary, if the two proteins are localized in distinct compartments the result will be a negative correlation. Finally, if the proteins in the two images are labeled in a diffuse non structured pattern (random), the correlation will tend to 0. This method is based on the principle that for any set of values the sum of the differences from the mean equal zero, i.e., ?N(A-a)=0, where a is the mean of the distribution with N values of Al. In the experiment N is the number of pixels, and A is the intensity for each pixel. If we have two set of values in two arrays 1 and 2 with N pixels per array having a random distribution of intensities A-,and 6, for arrays 1 and 2 , the sum of the product of their differences will also tend to zero, thus IN(A-a)(S,-Jb)~0. On the other hand, if the two intensities are positively correlated, the product will tend to be a positive value (^(Ara)(Brb}>G) and if they are negatively correlated the product will tend to a negative value (LN(Ara)(Brb)<0).