Maximum Likelihood FRET Imaging

描述（由申请人提供）：荧光共振能量转移（FRET）成像是一种使用染料对的显微镜技术。它检测蛋白质的结合，反过来，被用来推断许多活动，如通过细胞周期的染色体动力学。当一种染料（供体）放弃一种能量状态时，它不是将能量转化为光子，而是将能量转移到附近的受体染料分子。然后，受体分子发出一个光子，被照相机探测到。当这种情况发生时，供体和受体之间的距离必须在纳米以内，并且很可能与同一种蛋白质结合。这样，蛋白质的结合就可以被监控。这项研究的长期目标是提供一个有利可图的商业软件产品，使FRET成像直接，廉价和准确。FRET需要一个图像处理算法来观察生物样本中FRET活动的区域，因此化学键正在发生。目前广泛使用的算法具有数学近似，通常不准确，因此会引起可靠性问题。这些数学近似背后的主要物理原因是荧光染料对在其激发和发射光谱中具有严重的串扰和漏光。矛盾的是，这种串扰和渗透是FRET发生的必要条件，因此不可能避免它们。在数学上，未知数比联立方程要多，因此这是一个根本无法解决的数学问题。本文提出的软件克服了这些数学问题。它基于最大似然估计，消除了联立方程的需要，而是提供了基于最大似然准则的解决方案。本研究的具体目的是：(1)开发一个原型软件算法。(2)展示提供精确图像的可行性。这一目标将通过处理生物数据集和来自已知FRET指数标本的测试数据来实现。

DESCRIPTION (provided by applicant): Fluorescence resonance energy transfer (FRET) imaging is a microscope technique that uses a dye pair. It detects bonding of proteins, and in turn, is used to deduce many activities such as chromosome dynamics through the cell cycle. When one dye (the donor), gives up an energy state, instead of converting the energy into a photon, it transfers the energy to a nearby acceptor dye molecule. The acceptor molecule then emits a photon which is detected by the camera. When this happens, the donor and acceptor must be within nanometers of one another and are probably bound to the same protein. This way, protein bondings are monitored. The long-term objective of this research is to provide a profitable commercial software product that makes FRET imaging straightforward, inexpensive and accurate. FRET requires an image processing algorithm to see the regions in a biological sample where the FRET activity, and thus the chemical bonds, are happening. The algorithms that are used widely today have mathematical approximations that are often not accurate and therefore cause reliability questions. The main physical cause that underlies these mathematical approximations is that the fluorescent dye pairs have severe cross-talk and bleed-through among their excitation and emission spectra. Paradoxically, this cross-talk and bleed-through are necessary for FRET to occur, so it is impossible to avoid them. Mathematically, there are more unknowns than simultaneous equations, and therefore there is a fundamentally unsolvable mathematical problem. The software proposed herein overcomes these mathematical problems. It is based upon Maximum Likelihood Estimation, which eliminates the need for simultaneous equations, and instead provides a solution based upon the maximum-likelihood criterion. The specific aims of this research are: (1) Develop a prototype software algorithm. (2) Show feasibility of providing accurate images. This aim will be accomplished by processing biological data sets and test data from a specimen of known FRET indices.