CORE B- HEART BIOLOGY CORE

在我们的实验条件下，我们是 确定光学显微镜分辨率极限中的“共焦”。在这部PPG中，“协同定位”一词 将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 由光学系统分解的大分子络合物。例如，形成部分的两种蛋白质 由于缺乏光学分辨率，两种不同的分子络合物可能会显示出阳性的“共局域”测试 来检测每个分子复合体。尽管如此，阳性测试仍将表明这两种蛋白质位于近端 在光学分辨率内。因此，在“共定位”的确定中，实现最大x，y，z是至关重要的 决议。在这个PPG中，我们将使用实验室中开发的方法来实现最高分辨率 用光学显微镜可以达到的。我们已经最大化了光学线性度和记录的光量 探测器与图像恢复分析相结合，以恢复因光学缺陷而丢失的光 系统。这种方法使我们能够在x，y平面上达到大约100-200 nm的分辨率(见图1-3)。 “共址”和随机“共址”的定义。在用两个荧光团标记的细胞中，一个是红色的(R) 和一个绿色(G)，用红色荧光团标记的阈值以上的体素数量为nRd， 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ncoioc。G和R的“共定位”百分比为 由100*^^-(等式1)；R与G的“共局部化”百分比由100*^^(等式)给出。2)。在……里面 N*NC 我们将通过这些测量来估计测量的“协同定位”偶然发生的概率。 从((Area_1x Area_2)/总AreaA2)x100计算%随机“共定位”，其中Area_1和Area_2是 高于阈值的面积(等效数3)。 共局定位的方法一般是基于用户的能力来设置强度阈值，并且具有较大的 弥补了缺乏数学模型来调整两幅图像的阈值，这将定义程度 成对的像素重叠。最重要的是，像素重叠方法的限制是二进制测试，即 确定两个图像中的两个成对像素是否具有高于亮度阈值的亮度，并且 不考虑这两个染色蛋白是否具有高度相关的强度染色景观， 如果它们是共同复合体的元素，那是可以预料到的。一种相关性度量方法的应用 最近由Li等人描述。(2004)7. 在接下来的几节中，我将讨论和应用开发的算法来量化蛋白质-蛋白质的程度 利用强度相关分析和强度阈值两种方法进行关联 “COLOCALI化”分析。 强度相关分析。在目前的分析中，我们获得了高分辨率的图像来比较 来自两个不同细胞的双重染色的细胞图像在等效x，y坐标下的像素强度的相关性 蛋白质。预测是，如果两个蛋白质是同一大分子复合体的元素，那么强度 两幅图像的染色景观应具有x、y像素与像素的正相关关系。相反，如果两个人 蛋白质定位于不同的隔间，结果将是负相关。最后，如果细胞内的蛋白质 两个图像被标记为漫射的非结构化模式(随机)，相关性将趋于0。 该方法基于这样的原理：对于任何一组值，与平均值之和等于零， 即？n(A-a)=0，其中a是N值为Al的分布的平均值。在实验中，N是数 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 对于阵列1和阵列2，具有强度A-和6的随机分布，它们的差值的乘积之和为 也趋于零，因此在(A-a)(S，-Jb)~0。另一方面，如果这两个强度是正相关的，乘积 将趋于正值(^(Ara)(brb}&gt；G))，如果它们呈负相关，则产品将趋于 负值(LN(Ara)(Brb)&lt；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).