WO2020069640A1 - 一种测量生物分子有效扩散系数的方法 - Google Patents

一种测量生物分子有效扩散系数的方法

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WO2020069640A1
WO2020069640A1 PCT/CN2018/124270 CN2018124270W WO2020069640A1 WO 2020069640 A1 WO2020069640 A1 WO 2020069640A1 CN 2018124270 W CN2018124270 W CN 2018124270W WO 2020069640 A1 WO2020069640 A1 WO 2020069640A1
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diffusion
biomolecules
biomolecule
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roi1
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PCT/CN2018/124270
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French (fr)
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倪世伟
张西
杨宇丰
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福州大学
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Publication of WO2020069640A1 publication Critical patent/WO2020069640A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N13/00—Investigating surface or boundary effects, e.g. wetting power; Investigating diffusion effects; Analysing materials by determining surface, boundary, or diffusion effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N13/00—Investigating surface or boundary effects, e.g. wetting power; Investigating diffusion effects; Analysing materials by determining surface, boundary, or diffusion effects
    • G01N2013/003—Diffusion; diffusivity between liquids

Definitions

  • the invention relates to the technical field of experimental measurement, in particular to a method for measuring the effective diffusion coefficient of biomolecules.
  • Biomolecules use passive diffusion for logistics management at almost all scales.
  • the diffusion process is critical for biological processes at all levels, such as metabolic logistics, tissue pattern formation and growth during development, lymphocyte recruitment during the immune response, and ecology
  • Biomolecules usually move in a complex in vivo environment rather than freely diffuse, and are affected by multi-scale factors such as tortuosity, degradation, instantaneous binding, and other dynamic processes. Therefore, an important parameter to measure the propagation rate of biomolecules in the biological environment is effective Diffusion coefficient diffusion coefficient), which is different from free diffusion coefficient coefficient).
  • FRAP fluorescence recovery after photobleaching
  • iFRAP mirror image FRAP
  • FRAP or iFRAP methods have serious application defects due to the need for strict assumptions, because these assumptions are usually not met in biological systems.
  • the current FRAP or iFRAP analysis strictly depends on the distribution of diffuse molecules throughout the target area. Not only is it required to be in a steady state, but also the target area is homogeneous or the distribution of diffuse molecules conforms to a certain function. Therefore, FRAP or iFRAP is impractical for most highly dynamic biological processes and may lead to misleading conclusions or explanations.
  • FRAP or iFRAP analysis methods are strongly influenced by various reaction kinetics involved in biomolecules, such as interaction with receptors, reversible binding, endocytosis and exocytosis, and degradation. Ignoring these influencing factors may lead to quite inaccurate measurement results.
  • the present invention proposes a method for measuring the effective diffusion coefficient of biomolecules, which has few premise assumptions and a wide range of applications.
  • the present invention adopts the following technical solutions.
  • a method for measuring the effective diffusion coefficient of biomolecules includes the following steps;
  • A1 Plan the initial labeling area ROI0 of the biomolecules whose diffusion coefficients need to be measured.
  • the ROI0 area is the distribution area of the labeled biomolecules to be tested at the initial time point;
  • the image collection area ROI1 of the planning biomolecule diffusion process is larger than the biomarker to be tested.
  • the molecular diffusion process performs the area after biomolecule diffusion within the image collection time window; the range of the ROI1 is greater than ROI0;
  • A2. Perform fluorescent labeling or other optical labeling on the biomolecules in the region ROI0;
  • A5. Perform dimensionality reduction on the image data and generate plane coordinate data of the biomolecule diffusion process based on the image data;
  • the effective diffusion coefficient of the biomolecule diffusion process is derived by the formula.
  • step A6 The formula derivation process in step A6 is as follows;
  • Region ROI1 is the target region for dynamic image collection
  • a be the ratio of the molecules to be tested distributed outside the cell in the target area; when a is the value known or measured by other methods;
  • the derivation process A is;
  • I (t) represent the light intensity corresponding to the diffusing molecules marked on the position x of a pixel in the observation field at an observation time point t during motion image collection, and the light intensity is proportional to the number of molecules;
  • x is in the large field of view of the imaging observation
  • x 2 is the square of the position coordinates; the plus sign indicates the intensity of all positions and the square of the position coordinates
  • t is the sampling time point, and the instant of the optical mark is taken as the starting point t 0 ;
  • Is F1 (t)
  • the denominator of this formula is the total fluorescence intensity in ROI1, as the correction of the optical signal, see Figure 1;
  • the derivation process B is;
  • Equation 3 The function curve expressed by Equation 3 can be fitted to calculate the value of a; then the value of k 0 and D ⁇ / k 0 obtained through the derivation process A can be used to calculate D.
  • the derivation process B does not perform a direct fitting calculation on D with Equation 3.
  • the method of fluorescent labeling includes labeling biomolecules with a fluorescent group that can be photo-transduced or photo-excitable; the fluorescent group includes a photo-transducer.
  • step A5 when the image collection area ROI1 has left-right symmetric spatial symmetry, the two-dimensional data of the biomolecule diffusion process is projected to the axis of symmetry based on the image data to form one-dimensional data.
  • step A3 when performing dynamic image collection, a detector is used to collect the optical signal, and the superimposed light-cut cylinder is used to collect the optical signal to eliminate the influence of the movement of the fluorescent label molecule in the Z-axis direction on the dynamic data To achieve the dimensionality reduction effect of data collection.
  • step A4 it is determined whether the diffusion area of the labeled biomolecule in the image collection process is always within the imaging range of the dynamic image by calculating whether the total labeled optical signal in the ROI1 remains constant over time during the imaging process.
  • the labeled optical signal remains constant with time, indicating that the diffusing molecules have neither degraded nor diffused out of bounds within the measurement time window.
  • step A3 it is necessary to ensure that the sample to which the biomolecule belongs does not move or deform in its entirety, so as to prevent deviation of the obtained dynamic distribution data of the biomolecule.
  • the distribution of diffuse molecules is not required to be stable and dynamic processes can be analyzed.
  • the target area is not homogeneous or the distribution of diffuse molecules conforms to a certain function.
  • reaction kinetics can be considered, such as interaction with receptors, reversible binding, endocytosis and exocytosis, degradation, etc.
  • the measurement time can be adjusted according to the actual time scale difference of various kinetic processes Experimental time scale.
  • the measurement method has a wide range of applications, not only for relatively inert diffusion molecules, but also for biological diffusion molecules in a complex in vivo environment.
  • the geometry of irregular organisms and objects to be measured can be comprehensively considered, and the specific imaging and data analysis strategies of the experiment can be adjusted.
  • the measurement method has a wide range of applications, not only for relatively inert diffusion molecules, but also for biological diffusion molecules in a complex biological environment; not only for steady-state conditions, but also for highly dynamic cells , Measurement of tissues and organs;
  • This method can be made as an application module to be directly embedded in all current high-end microscope imaging operating systems, which has strong practicality and better social benefits.
  • FIG. 1 is a schematic diagram of diffusion dynamic propagation of biomolecules after light-to-labeling in an embodiment
  • Dpp is a schematic diagram of the distribution of Dpp :: Dendra2 before and after light rotation (left) and after light rotation (right) in the embodiment of Drosophila larval eye discs;
  • Figure 3 is a schematic diagram of the analysis of the dynamic image data of red fluorescence dynamic propagation after Dpp :: Dendra2 is turned into light in the eye disc of Drosophila;
  • Figure 4 shows the F 1 (t) and F 2 (t) fitting legends of the dynamic data of the red fluorescence dynamic propagation of Dpp :: Dendra2 in the eye disc of Drosophila;
  • a method for measuring the effective diffusion coefficient of biomolecules is characterized in that the measurement method includes the following steps;
  • A1 Plan the initial labeling area ROI0 of the biomolecules whose diffusion coefficients need to be measured.
  • the ROI0 area is the distribution area of the labeled biomolecules to be tested at the initial time point;
  • the image collection area ROI1 of the planning biomolecule diffusion process is larger than the biomarker to be tested.
  • the molecular diffusion process performs the area after biomolecule diffusion within the image collection time window; the range of the ROI1 is greater than ROI0;
  • A2. Perform fluorescent labeling or other optical labeling on the biomolecules in the region ROI0;
  • A5. Perform dimensionality reduction on the image data and generate plane coordinate data of the biomolecule diffusion process based on the image data;
  • the effective diffusion coefficient of the biomolecule diffusion process is derived by the formula.
  • step A6 The formula derivation process in step A6 is as follows;
  • Region ROI1 is the target region for dynamic image collection
  • a be the ratio of the molecules to be tested distributed outside the cell in the target area; when a is the value known or measured by other methods;
  • the derivation process A is;
  • I (t) represent the light intensity corresponding to the diffusing molecules marked on the position x of a pixel in the observation field at an observation time point t during motion image collection, and the light intensity is proportional to the number of molecules;
  • x is in the large field of view of the imaging observation
  • x 2 is the square of the position coordinates; the plus sign indicates the intensity of all positions and the square of the position coordinates
  • t is the sampling time point, and the instant of the optical mark is taken as the starting point t 0 ;
  • Is F1 (t)
  • the denominator of this formula is the total fluorescence intensity in ROI1, as the correction of the optical signal, see Figure 1;
  • the derivation process B is;
  • Equation 3 The function curve expressed by Equation 3 can be fitted to calculate the value of a; then the value of k 0 and D ⁇ / k 0 obtained through the derivation process A can be used to calculate D.
  • the derivation process B does not perform a direct fitting calculation on D with Equation 3.
  • the method of fluorescent labeling includes labeling biomolecules with a fluorescent group that can be photo-transduced or photo-excitable; the fluorescent group includes a photo-transducer.
  • step A5 when the image collection area ROI1 has left-right symmetric spatial symmetry, the two-dimensional data of the biomolecule diffusion process is projected to the axis of symmetry based on the image data to form one-dimensional data.
  • step A3 when performing dynamic image collection, a detector is used to collect the optical signal, and the superimposed light-cut cylinder is used to collect the optical signal to eliminate the influence of the movement of the fluorescent label molecule in the Z-axis direction on the dynamic data, To achieve the dimensionality reduction effect of data collection.
  • step A4 it is determined whether the diffusion area of the labeled biomolecule in the image collection process is always within the imaging range of the dynamic image by calculating whether the total labeled optical signal in the ROI1 remains constant over time during the imaging process.
  • the labeled optical signal remains constant with time, indicating that the diffusing molecules have neither degraded nor diffused out of bounds within the measurement time window.
  • step A3 it is necessary to ensure that the sample to which the biomolecule belongs does not move or deform in its entirety, so as to prevent deviation of the obtained dynamic distribution data of the biomolecule.
  • the eye organs (eye-forming disks) of Dpp were dissected and cultured in vitro.
  • the in vitro culture was carried out according to standard procedures.
  • the culture medium was Life's Schneider ’s insect culture medium, Hyclone 1% calf serum was added, and the culture vessel was Nest brand confocal microscopy special culture dish.
  • the confocal Z stack is composed of five confocal optical slices (about 12 ⁇ m thick in total), and real-time imaging at 20-second intervals takes 5 to 10 minutes.
  • the ROI1 is designated as a square of 30-40 ⁇ 30-40 ⁇ m 2 , and the light-transmitting region ROI0 is located at the center thereof.
  • this quality assurance link can ensure that the Dpp :: Dendra2 molecule in ROI1 does not diffuse out of bounds and is not observed Suffered from degradation within the time window.
  • ROI2 can choose to overlap with ROI0 or a rectangle with the same width as ROI0 and the same length as ROI1. See Figure 3 (Analysis of the analysis of dynamic image data of red fluorescence dynamic propagation after Dpp :: Dendra2 is turned into light in the eye disc of Drosophila).
  • the images are acquired at the same time interval.
  • Select two specific ROIs ROI1 and ROI2 on each image.
  • the change in fluorescence distribution in ROI1 is reduced from two dimensions to one dimension, and then the fluorescence intensity at each position is multiplied by the square of the position and the sum is F 1 (t).
  • F 2 (t) was fitted using the change in fluorescence intensity in ROI2 at different times.
  • the effective diffusion coefficient D can be calculated by fitting D ⁇ and ⁇ according to the two formulas and dividing. See Figure 4 for representative fitting results. According to this procedure, 8 samples were measured and averaged.
  • the effective diffusion coefficient of Dpp molecules in the eye disc of Drosophila larvae was 0.48 ⁇ m 2 / s.

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Abstract

一种测量生物分子有效扩散系数的方法，包括以下步骤； A1、规划需测量扩散系数的生物分子的初始标记区域ROI0；规划扩散过程影像收集区域ROI1； A2、对区域ROI0内的生物分子进行荧光标记或其他光学标记； A3、使ROI0内的生物分子扩散，并确保生物分子的扩散区域小于区域ROI1，对区域ROI1内的生物分子扩散过程进行动态影像收集； A4、校验所收集的动态影像是否有效； A5、对影像数据进行降维，根据影像数据生成生物分子扩散过程的平面坐标数据； A6、根据生成的平面坐标数据，以公式推导出生物分子扩散过程的有效扩散系数；其对应用的前提假设少，且应用范围较广。

Description

一种测量生物分子有效扩散系数的方法 技术领域
本发明涉及实验测量技术领域，尤其是一种测量生物分子有效扩散系数的方法。
 背景技术
生物***几乎在所有尺度上都利用被动扩散进行物流管理，扩散过程对于各级生物学过程至关重要，比如代谢物流，发育过程中组织模式形成与生长，免疫反应过程中淋巴细胞的募集以及生态领域的植被覆盖模式等。生物分子通常在复杂的体内环境中移动，而不是自由扩散，受到多尺度因素影响，例如曲折度、降解、瞬时结合以及其他动力学过程，因此衡量生物分子在生物体内环境传播速率的重要参数为有效扩散系数（effective diffusion coefficient），区别于自由扩散系数 (free diffusion coefficient)。
 技术问题
然而，精确测量在体内复杂的生物环境中扩散的生物分子的有效扩散系数是非常具有挑战性的。目前，光漂白后的荧光恢复（FRAP）或镜像FRAP（iFRAP）通常用于测量有效扩散率。然而，FRAP或iFRAP方法由于需要严格的假设而存在着严重的应用缺陷，因为这些假设通常在生物***中不能满足。首先，当前的FRAP或iFRAP分析严格依赖于整个目标区域的扩散分子的分布，不仅要求其处于稳态，而且要求目标区域是同质的或者扩散分子的分布符合某种特定的函数。因此，FRAP或iFRAP对于大部分高度动态的生物学过程是不切实际的，并且可能引起误导性的结论或解释。其次，FRAP或iFRAP分析方法受到生物分子所涉及的各种反应动力学的强烈影响，例如与受体相互作用，可逆结合、细胞内吞和胞吐作用、降解。忽视这些影响因素可能导致相当不准确的测量结果。第三，在当前的FRAP / iFRAP分析方法中，样本地理几何结构过于简化，因而引发很大的误差。因此，使用传统技术导致了目前很多误导性的科学结论或解释以及应用，亟需新技术的出现。
 技术解决方案
本发明提出一种测量生物分子有效扩散系数的方法，对应用的前提假设少，且应用范围较广。
本发明采用以下技术方案。
一种测量生物分子有效扩散系数的方法，所述测量方法包括以下步骤；
A1、规划需测量扩散系数的生物分子的初始标记区域ROI0，ROI0区域为标记的待测生物分子在初始时间点的分布区域；规划生物分子的扩散过程影像收集区域ROI1，ROI1范围大于待测生物分子扩散过程进行影像收集时间窗口内生物分子扩散后区域；所述ROI1的范围大于ROI0；
A2、对区域ROI0内的生物分子进行荧光标记或其他光学标记；
A3、使ROI0内的生物分子扩散，并确保生物分子的扩散区域小于区域ROI1，对区域ROI1内的生物分子扩散过程进行动态影像收集；
A4、校验所收集的动态影像是否有效，如被标记的生物分子在影像收集过程的扩散区域始终位于动态影像的成像范围内，则判定所收集的动态影像为有效的动态影像；
A5、对影像数据进行降维，根据影像数据生成生物分子扩散过程的平面坐标数据；
A6、根据生成的平面坐标数据，以公式推导出生物分子扩散过程的有效扩散系数。
在步骤A6中的公式推导过程如下；
区域ROI1为动态影像收集的目标区域；
设a为目标区域待测分子分布于细胞外的比例；当a值为已知或是通过其他方法测得的值时；
推导过程A为；
设I(t) 代表动态影像收集时某观测时间点t时观测视野中某个像素点位置 x 上标记的扩散分子对应的光强，该光强正比于分子数；x 位于成像观测的大视野ROI1［a,b］的区间内的对应位置座标，座标的原点一般选择为ROI1的几何中心；x 2为位置座标的平方; 加和号表示对所有位置的光强与位置座标平方的乘积求和，若数据点足够连续则近似为积分；t为采样的时间点，取光学标记的瞬间作为起始点t 0；目标区域每个位置的荧光强度乘以位置的平方求和得到的即为F1(t)；
则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
公式1：
Figure 159451dest_path_image001
；
该公式的分母为ROI1内的总荧光强度，作为光学信号的校正，参见附图1；
此实验数据再次按公式2所表述的函数进行拟合；
 
公式2：
Figure 766013dest_path_image002
；
通过拟合得出 k 0 以及 D α / k 0。其中 k 0以及a 为常数，D为有效扩散系数。
优选地，当a值未知时，
推导过程B为；
设s 为观测成像时间窗口[0,t] 中的某个时间点， [-l, l] 为初始标记的区域ROI0或是任意一个包含初始标记区但相对于ROI1足够小的区域，该区域记为ROI2；λ为常数，参见附图1，
则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
公式3：
Figure 106514dest_path_image003
；
由公式3所表述的函数曲线可拟合计算出a值；再经由推导过程A所得 k 0以及 D α / k 0的数值，即可计算出D。
优选地，推导过程B不以公式3对D进行直接拟合计算。
原因在于公式3的D出现在指数函数中，故如果按该公式拟合出来的D，偏差将会非常大，故公式3不宜对D进行直接拟合计算
在步骤A2中，荧光标记的方法包括使用可光转或是可光激发的荧光基团对生物分子进行标记；所述荧光基团包括光转蛋白。
在步骤A5中，当影像收集区域ROI1具有左右对称的空间对称性时，根据影像数据把生物分子扩散过程的二维数据投射至对称轴处形成一维数据。
在步骤A3中，进行动态影像收集时，使用检测器收集光信号，并且采用叠加的光切柱体进行光信号采集，以消除荧光标记分子在Z轴方向上的移动对动态数据造成的影响，达到数据采集的降维效果。
在步骤A4中，通过计算成像过程中在ROI1内总的标记光学信号是否随时间保持恒定，来确定被标记的生物分子在影像收集过程的扩散区域是否始终位于动态影像的成像范围内，如果总的标记光学信号随时间保持恒定，则说明扩散分子在测量时间窗口内既未发生降解，也没有扩散出界。
在步骤A3的动态影像收集过程中，需保证生物分子所属的样品没有发生整体的移动或形变，以防止得出的生物分子动态分布数据出现偏差。
与现有技术相比，本发明的优势在于：
a. 应用前提假设少，不要求扩散分子的分布处于稳态，可分析动态过程；也不要求目标区域是同质的或者扩散分子的分布符合某种特定的函数。
b. 可以考虑各种反应动力学的影响，例如与受体相互作用、可逆结合、细胞内吞和胞吐作用、降解等过程，可根据各种动力学过程发生的实际时间尺度差异调整测量时的实验时间尺度。
c. 不仅适用于稳态的情况，同样适用于高度动态的细胞、组织、器官的测量。
d. 测量方法的应用范围广，不仅适用于相对而言比较惰性的扩散分子，也适用于处于复杂生物体内环境中的生物扩散分子。
e. 可综合考虑不规则的生物体、待测物体的几何形状，调整实验的具体成像以及数据分析策略。
 有益效果
本发明的优点还在于：
a.提高了测量生物分子有效扩散系数的精准度；
b.测量方法的应用范围广，不仅适用于相对而言比较惰性的扩散分子，也适用于处于复杂生物体内环境中的生物扩散分子；不仅适用于稳态的情况，同样适用于高度动态的细胞、组织、器官的测量；
C.本方法可以制作为应用模块，以直接嵌入目前所有高端显微镜成像的操作***里，具有较强的实用性以及较好的社会效益。
 附图说明
下面结合附图和具体实施方式对本发明进一步详细的说明：
附图1是实施例中，生物分子光转标记后扩散动态传播示意图
附图2是实施例中，果蝇幼虫眼发生盘Dpp::Dendra2在光转前（左）以及光转后（右）的分布示意图；
附图3是分析果蝇眼盘中Dpp::Dendra2被光转后红色荧光动态传播动态影像数据的示意图；
附图4是果蝇眼盘中Dpp::Dendra2被光转后红色荧光动态传播动态数据的F 1(t) 、F 2(t)拟合图例；
本发明的实施方式
如附图所示，一种测量生物分子有效扩散系数的方法，其特征在于：所述测量方法包括以下步骤；
A1、规划需测量扩散系数的生物分子的初始标记区域ROI0，ROI0区域为标记的待测生物分子在初始时间点的分布区域；规划生物分子的扩散过程影像收集区域ROI1，ROI1范围大于待测生物分子扩散过程进行影像收集时间窗口内生物分子扩散后区域；所述ROI1的范围大于ROI0；
A2、对区域ROI0内的生物分子进行荧光标记或其他光学标记；
A3、使ROI0内的生物分子扩散，并确保生物分子的扩散区域小于区域ROI1，对区域ROI1内的生物分子扩散过程进行动态影像收集；
A4、校验所收集的动态影像是否有效，如被标记的生物分子在影像收集过程的扩散区域始终位于动态影像的成像范围内，则判定所收集的动态影像为有效的动态影像；
A5、对影像数据进行降维，根据影像数据生成生物分子扩散过程的平面坐标数据；
A6、根据生成的平面坐标数据，以公式推导出生物分子扩散过程的有效扩散系数。
在步骤A6中的公式推导过程如下；
区域ROI1为动态影像收集的目标区域；
设a为目标区域待测分子分布于细胞外的比例；当a值为已知或是通过其他方法测得的值时；
推导过程A为；
设I(t) 代表动态影像收集时某观测时间点t时观测视野中某个像素点位置 x 上标记的扩散分子对应的光强，该光强正比于分子数；x 位于成像观测的大视野ROI1［a,b］的区间内的对应位置座标，座标的原点一般选择为ROI1的几何中心；x 2为位置座标的平方; 加和号表示对所有位置的光强与位置座标平方的乘积求和，若数据点足够连续则近似为积分；t为采样的时间点，取光学标记的瞬间作为起始点t 0；目标区域每个位置的荧光强度乘以位置的平方求和得到的即为F1(t)；
则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
公式1：
Figure 798527dest_path_image001
  ；
该公式的分母为ROI1内的总荧光强度，作为光学信号的校正，参见附图1；
此实验数据再次按公式2所表述的函数进行拟合；
 
公式2：
Figure 473222dest_path_image002
；
通过拟合得出 k 0以及 D α / k 0。其中 k 0以及a 为常数，D为有效扩散系数。
优选地，当a值未知时，
推导过程B为；
设s 为观测成像时间窗口[0,t] 中的某个时间点， [-l, l] 为初始标记的区域ROI0或是任意一个包含初始标记区但相对于ROI1足够小的区域，该区域记为ROI2；λ为常数，参见附图1，
则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
公式3：
Figure 137552dest_path_image004
；
由公式3所表述的函数曲线可拟合计算出a值；再经由推导过程A所得 k 0以及 D α / k 0的数值，即可计算出D。
优选地，推导过程B不以公式3对D进行直接拟合计算。
原因在于公式3的D出现在指数函数中，故如果按该公式拟合出来的D，偏差将会非常大，故公式3不宜对D进行直接拟合计算
在步骤A2中，荧光标记的方法包括使用可光转或是可光激发的荧光基团对生物分子进行标记；所述荧光基团包括光转蛋白。
在步骤A5中，当影像收集区域ROI1具有左右对称的空间对称性时，根据影像数据把生物分子扩散过程的二维数据投射至对称轴处形成一维数据。
在步骤A3中，进行动态影像收集时，使用检测器收集光信号，并且采用叠加的光切柱体进行光信号采集，以消除荧光标记分子在Z轴方向上的移动对动态数据造成的影响，达到数据采集的降维效果。
在步骤A4中，通过计算成像过程中在ROI1内总的标记光学信号是否随时间保持恒定，来确定被标记的生物分子在影像收集过程的扩散区域是否始终位于动态影像的成像范围内，如果总的标记光学信号随时间保持恒定，则说明扩散分子在测量时间窗口内既未发生降解，也没有扩散出界。
在步骤A3的动态影像收集过程中，需保证生物分子所属的样品没有发生整体的移动或形变，以防止得出的生物分子动态分布数据出现偏差。
实施例：
如附图1-4所示，解剖Dpp::Dendra2果蝇幼虫的眼睛器官（眼睛形成盘），进行体外培养。
体外培养根据标准流程进行，培养基为Life公司Schneider’s 昆虫培养基，加入Hyclone 1%小牛血清，培养器皿为Nest品牌共聚焦镜检专用培养皿。
进行光转-动态成像实验：
利用SP5倒置Leica共聚焦显微镜进行实时成像实验。通过双光子脉冲激光（变色龙）在大约800nm处对Dendra2进行光转换：激光功率25％-30％， MP增益100％， MP偏移12％-60％，2秒之内扫描激发2-3次。光转后Dendra2可发射红色荧光，参见图2（果蝇幼虫眼发生盘Dpp::Dendra2在光转前以及光转后的分布图）。光转区域ROI0选为在组织平面上10×10μm2正方形。使用40×1.25油浸镜头收集图像：激光线543nm 15％输出（用于红色荧光成像）和488nm激光输出低于0.5％（如果需要，在光转换之前成像绿色荧光），每像素0.30μm×0.30μm进行共聚焦扫描成像。共聚焦Z叠层由五个共焦光学切片（总共约12μm厚）组成，以20秒的间隔实时成像获5～10分钟。
分析动态成像数据，计算Dpp的有效扩散系数：
在图像数据处理过程中，指定ROI1为30-40×30-40μm 2的正方形，光转区域ROI0位于其中心。选择ROI1在两个方向上足够大以保证在监测的时间窗口内ROI1中的总红色荧光信号强度保持恒定，这个质保环节可保证在ROI1中的Dpp::Dendra2分子没有扩散出界，也未在观测时间窗口内遭受降解。ROI2可选择与ROI0重叠或是与ROI0同宽、与ROI1同长的长方形。参见图3 （分析果蝇眼盘中Dpp::Dendra2被光转后红色荧光动态传播动态影像数据的示意图）。在光转之后，按照同样的时间间隔获取图像。在每个图像上选取两个特定的ROI ( ROI1和ROI2)。将ROI1中的荧光分布的变化从二维降成一维，然后对每个位置上荧光强度乘以位置的平方求和得到的即为F 1(t)。利用不同时间中ROI2中荧光强度的变化拟合F 2(t)。根据两个公式拟合出Dα和α相除即可计算出有效扩散系数D。代表性拟合结果参见图4。根据如此流程测量8个样品，进行平均，得到Dpp分子在果蝇幼虫眼盘的有效扩散系数为0.48 μm 2/s。

Claims (9)

  1. 一种测量生物分子有效扩散系数的方法，其特征在于：所述测量方法包括以下步骤；
    A1、规划需测量扩散系数的生物分子的初始标记区域ROI0，ROI0区域为标记的待测生物分子在初始时间点的分布区域；规划生物分子的扩散过程影像收集区域ROI1，ROI1范围大于待测生物分子扩散过程进行影像收集时间窗口内生物分子扩散后区域；所述ROI1的范围大于ROI0；
    A2、对区域ROI0内的生物分子进行荧光标记或其他光学标记；
    A3、使ROI0内的生物分子扩散，并确保生物分子的扩散区域小于区域ROI1，对区域ROI1内的生物分子扩散过程进行动态影像收集；
    A4、校验所收集的动态影像是否有效，如被标记的生物分子在影像收集过程的扩散区域始终位于动态影像的成像范围内，则判定所收集的动态影像为有效的动态影像；
    A5、对影像数据进行降维，根据影像数据生成生物分子扩散过程的平面坐标数据；
    A6、根据生成的平面坐标数据，以公式推导出生物分子扩散过程的有效扩散系数。
  2. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A6中的公式推导过程如下；
    区域ROI1为动态影像收集的目标区域；
    设a为目标区域待测分子分布于细胞外的比例；当a值为已知或是通过其他方法测得的值时；
    推导过程A为；
     设I(t) 代表动态影像收集时某观测时间点t时观测视野中某个像素点位置 x 上标记的扩散分子对应的光强，该光强正比于分子数；x 位于成像观测的大视野ROI1［a,b］的区间内的对应位置座标，座标的原点一般选择为ROI1的几何中心；x 2为位置座标的平方; 加和号表示对所有位置的光强与位置座标平方的乘积求和，若数据点足够连续则近似为积分；t为采样的时间点，取光学标记的瞬间作为起始点t 0；目标区域每个位置的荧光强度乘以位置的平方求和得到的即为F1(t)；
    则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
    公式1：
    Figure 66176dest_path_image001
    ；
    该公式的分母为ROI1内的总荧光强度，作为光学信号的校正，参见附图1；
    此实验数据再次按公式2所表述的函数进行拟合；
     
    公式2：
    Figure 511064dest_path_image002
    ；
    通过拟合得出 k 0以及 Dα/ k 0；其中 k 0以及a 为常数，D为有效扩散系数。
  3. 根据权利要求2所述的一种测量生物分子有效扩散系数的方法，其特征在于：当a值未知时，
    推导过程B为；
    设s 为观测成像时间窗口[0,t] 中的某个时间点， [-l, l] 为初始标记的区域ROI0或是任意一个包含初始标记区但相对于ROI1足够小的区域，该区域记为ROI2；λ为常数，参见附图1，
    则由生物分子扩散实验数据获得函数动态曲线，由函数动态曲线推导得出
    公式3：
    Figure 51766dest_path_image003
    ；
    由公式3所表述的函数曲线可拟合计算出a值；再经由推导过程A所得 k 0以及Dα/ k 0的数值，即可计算出D。
  4. 根据权利要求3所述的一种测量生物分子有效扩散系数的方法，其特征在于：推导过程B不以公式3对D进行直接拟合计算。
  5. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A2中，荧光标记的方法包括使用可光转或是可光激发的荧光基团对生物分子进行标记；所述荧光基团包括光转蛋白。
  6. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A5中，当影像收集区域ROI1具有左右对称的空间对称性时，根据影像数据把生物分子扩散过程的二维数据投射至对称轴处形成一维数据。
  7. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A3中，进行动态影像收集时，使用检测器收集光信号，并且采用叠加的光切柱体进行光信号采集，以消除荧光标记分子在Z轴方向上的移动对动态数据造成的影响，达到数据采集的降维效果。
  8. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A4中，通过计算成像过程中在ROI1内总的标记光学信号是否随时间保持恒定，来确定被标记的生物分子在影像收集过程的扩散区域是否始终位于动态影像的成像范围内，如果总的标记光学信号随时间保持恒定，则说明扩散分子在测量时间窗口内既未发生降解，也没有扩散出界。
  9. 根据权利要求1所述的一种测量生物分子有效扩散系数的方法，其特征在于：在步骤A3的动态影像收集过程中，需保证生物分子所属的样品没有发生整体的移动或形变，以防止得出的生物分子动态分布数据出现偏差。
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