CN109357975B - A method to measure the effective diffusion coefficient of biomolecules - Google Patents
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Abstract
The invention provides a method for measuring an effective diffusion coefficient of a biomolecule, which comprises the following steps; a1, planning an initial marking region ROI0 of the biological molecules with diffusion coefficients to be measured; planning a diffusion process image collection region ROI 1; a2, fluorescence labeling or other optical labeling of biomolecules in the region ROI 0; a3, diffusing the biological molecules in the ROI0, ensuring that the diffusion area of the biological molecules is smaller than the ROI1, and collecting dynamic images of the diffusion process of the biological molecules in the ROI 1; a4, checking whether the collected dynamic image is valid; a5, performing dimension reduction on the image data, and generating plane coordinate data of a biomolecule diffusion process according to the image data; a6, deducing the effective diffusion coefficient of the diffusion process of the biological molecules according to the generated plane coordinate data by a formula; the invention has few application premises assumptions and a wide application range.
Description
Technical Field
The invention relates to the technical field of experimental measurement, in particular to a method for measuring an effective diffusion coefficient of a biomolecule.
Background
The biological system utilizes passive diffusion to carry out logistics management on almost all scales, and the diffusion process is important for all levels of biological processes, such as metabolic logistics, formation and growth of tissue patterns in the development process, recruitment of lymphocytes in the immune reaction process, vegetation coverage patterns in the ecological field and the like. Biomolecules generally move in a complex in vivo environment, rather than freely diffuse, and are influenced by multi-scale factors such as tortuosity, degradation, transient binding, and other kinetic processes, so that an important parameter for measuring the propagation rate of biomolecules in an in vivo environment is effective diffusion coefficient (effective diffusion coeffient), which is different from free diffusion coefficient (free diffusion coeffient).
However, it is very challenging to accurately measure the effective diffusion coefficient of biomolecules that diffuse in the complex biological environment in vivo. Currently, Fluorescence Recovery (FRAP) or mirror image FRAP (ifrap) after photobleaching is commonly used to measure effective diffusivity. However, FRAP or iFRAP methods suffer from serious application drawbacks due to the need for rigorous assumptions, which are often not met in biological systems. First, current FRAP or iFRAP analysis relies strictly on the distribution of diffusing molecules throughout the target region, requiring not only that it be in a steady state, but also that the target region be homogeneous or that the distribution of diffusing molecules conform to some particular function. Therefore, FRAP or iFRAP is impractical for most highly dynamic biological processes and may lead to misleading conclusions or explanations. Second, FRAP or iFRAP analysis methods are strongly influenced by the kinetics of various reactions involved in biomolecules, such as receptor interaction, reversible binding, endocytosis and exocytosis, degradation. Ignoring these influencing factors may result in a rather inaccurate measurement result. Third, in current FRAP/iFRAP analysis methods, the sample geographic geometry is overly simplified, thus introducing large errors. Thus, the use of conventional techniques has led to many misleading scientific conclusions or explanations and applications, and the emergence of new technologies is urgently needed.
Disclosure of Invention
The invention provides a method for measuring the effective diffusion coefficient of a biomolecule, which has few assumptions on application and wide application range.
The invention adopts the following technical scheme.
A method of measuring the effective diffusion coefficient of a biomolecule, the method of measuring comprising the steps of;
a1, planning an initial marking region ROI0 of the biological molecules of which the diffusion coefficients need to be measured, wherein the ROI0 region is a distribution region of the marked biological molecules to be detected at an initial time point; planning an image collection area ROI1 in the diffusion process of the biological molecules, wherein the ROI1 range is larger than the area of the biological molecules after diffusion in an image collection time window in the diffusion process of the biological molecules to be detected; the range of the ROI1 is greater than ROI 0;
a2, fluorescence labeling or other optical labeling of biomolecules in the region ROI 0;
a3, diffusing the biological molecules in the ROI0, ensuring that the diffusion area of the biological molecules is smaller than the ROI1, and collecting dynamic images of the diffusion process of the biological molecules in the ROI 1;
a4, checking whether the collected dynamic image is effective, if the diffusion area of the marked biological molecule is always in the imaging range of the dynamic image in the image collection process, judging the collected dynamic image to be effective;
a5, performing dimension reduction on the image data, and generating plane coordinate data of a biomolecule diffusion process according to the image data;
and A6, deducing the effective diffusion coefficient of the diffusion process of the biological molecules according to the generated plane coordinate data by a formula.
The formula derivation process in step a6 is as follows;
the region ROI1 is a target region for dynamic image collection;
setting a as the proportion of molecules to be detected in a target area distributed outside cells; when the value of a is a known or measured value by other methods;
the derivation process A is;
setting I (t) to represent the light intensity corresponding to the diffusion molecule marked on the position x of a certain pixel point in the observation visual field at a certain observation time point t during dynamic image collection, wherein the light intensity is in direct proportion to the number of molecules; x is located in the large field of view ROI1[ a, b ] of the imaging observation]The origin of the coordinates is generally selected as the geometric center of ROI 1; x is the number of2Is the square of the position coordinate; the sum sign represents the sum of the products of the light intensity and the square of the position coordinates at all positions, and is approximated as an integral if the data points are sufficiently continuous; t is the time point of sampling, and the moment of optical marking is taken as the starting point t0(ii) a F1(t) is obtained by multiplying the fluorescence intensity of each position of the target area by the square sum of the positions;
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 1:
the denominator of this equation is the total fluorescence intensity within ROI1 as a correction for the optical signal, see fig. 1;
fitting the experimental data according to the function expressed by the formula 2;
equation 2:
k is derived by fitting0And D alpha/k0. Wherein k is0And a is a constant, DIs the effective diffusion coefficient.
Preferably, when the value of a is unknown,
the derivation process B is;
let s be a certain time point in the observation imaging time window [0, t ], and [ -l, l ] be the region of the initial marker ROI0 or any region containing the initial marker region but small enough relative to ROI1, which is designated as ROI 2; λ is a constant, see figure 1,
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 3:
fitting a function curve expressed by formula 3 to calculate a value a; k is obtained through derivation process A0And D alpha/k0D can be calculated according to the numerical value of (2).
Preferably, derivation process B does not perform a direct fit calculation for D in equation 3.
The reason is that D in formula 3 appears in an exponential function, so if D is fitted according to the formula, the deviation will be very large, so formula 3 is not suitable for direct fitting calculation of D
In step A2, the fluorescence labeling method comprises labeling the biomolecule with a photo-convertible or photo-excitable fluorophore; the fluorophore comprises a phototranscript.
In step a5, when the image collection region ROI1 has a left-right symmetric spatial symmetry, two-dimensional data of the biomolecule diffusion process is projected to the symmetry axis based on the image data to form one-dimensional data.
In step a3, when collecting the dynamic image, the detector is used to collect the optical signal, and the superimposed optical tangential column is used to collect the optical signal, so as to eliminate the influence of the movement of the fluorescence labeling molecule in the Z-axis direction on the dynamic data, and achieve the dimension reduction effect of data collection.
In step a4, it is determined whether the labeled biomolecules are always located in the imaging range of the dynamic image during the image collection process by calculating whether the total labeled optical signal remains constant over time in the ROI1 during the imaging process, and if the total labeled optical signal remains constant over time, it indicates that the diffusing molecules are neither degraded nor diffused out of the boundary within the measurement time window.
In the dynamic image collection process of step a3, it is necessary to ensure that the sample to which the biomolecule belongs does not move or deform integrally, so as to prevent the obtained dynamic distribution data of the biomolecule from deviating.
Compared with the prior art, the invention has the advantages that:
a. the application premise is less, the distribution of diffusion molecules is not required to be in a stable state, and the dynamic process can be analyzed; nor is it required that the target region be homogeneous or that the distribution of diffusing molecules conform to a particular function.
b. The effects of various reaction kinetics, such as interaction with receptors, reversible binding, endocytosis and exocytosis, degradation, etc., can be taken into account, and the experimental timescale at the time of measurement can be adjusted according to the actual timescale differences that occur with various kinetic processes.
c. The method is not only suitable for the steady state condition, but also suitable for the measurement of highly dynamic cells, tissues and organs.
d. The measurement method has wide application range, and is not only suitable for relatively inert diffusion molecules, but also suitable for biological diffusion molecules in a complex organism internal environment.
e. The geometric shapes of irregular organisms and objects to be detected can be comprehensively considered, and the specific imaging and data analysis strategies of the experiment can be adjusted.
The invention also has the advantages that:
a. the accuracy of measuring the effective diffusion coefficient of the biomolecule is improved;
b. the measuring method has wide application range, is suitable for relatively inert diffusion molecules and biological diffusion molecules in the environment of a complex organism; the method is not only suitable for the steady state condition, but also suitable for the measurement of highly dynamic cells, tissues and organs;
C. the method can be manufactured into an application module to be directly embedded into all the existing operating systems for high-end microscope imaging, and has stronger practicability and better social benefit.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of dynamic propagation of light-labeled biomolecules after diffusion in an example
FIG. 2 is a schematic diagram showing the distribution of Drosophila larval eye occurrence discs Dpp:: Dendra2 before (left) and after (right) light conversion in the examples;
FIG. 3 is a schematic diagram showing dynamic image data of red fluorescence dynamic transmission after light conversion of Dendra2 in analyzing Dpp in fruit fly eye dish;
FIG. 4 is a graph showing Dpp in Drosophila's eye plates, Dendra2, light-converted, red fluorescence dynamic propagation dynamic data F1(t)、F2(t) a fit legend;
Detailed Description
As shown in the attached drawings, a method for measuring an effective diffusion coefficient of a biomolecule is characterized in that: the measuring method comprises the following steps;
a1, planning an initial marking region ROI0 of the biological molecules of which the diffusion coefficients need to be measured, wherein the ROI0 region is a distribution region of the marked biological molecules to be detected at an initial time point; planning an image collection area ROI1 in the diffusion process of the biological molecules, wherein the ROI1 range is larger than the area of the biological molecules after diffusion in an image collection time window in the diffusion process of the biological molecules to be detected; the range of the ROI1 is greater than ROI 0;
a2, fluorescence labeling or other optical labeling of biomolecules in the region ROI 0;
a3, diffusing the biological molecules in the ROI0, ensuring that the diffusion area of the biological molecules is smaller than the ROI1, and collecting dynamic images of the diffusion process of the biological molecules in the ROI 1;
a4, checking whether the collected dynamic image is effective, if the diffusion area of the marked biological molecule is always in the imaging range of the dynamic image in the image collection process, judging the collected dynamic image to be effective;
a5, performing dimension reduction on the image data, and generating plane coordinate data of a biomolecule diffusion process according to the image data;
and A6, deducing the effective diffusion coefficient of the diffusion process of the biological molecules according to the generated plane coordinate data by a formula.
The formula derivation process in step a6 is as follows;
the region ROI1 is a target region for dynamic image collection;
setting a as the proportion of molecules to be detected in a target area distributed outside cells; when the value of a is a known or measured value by other methods;
the derivation process A is;
setting I (t) to represent the light intensity corresponding to the diffusion molecule marked on the position x of a certain pixel point in the observation visual field at a certain observation time point t during dynamic image collection, wherein the light intensity is in direct proportion to the number of molecules; x is located in the large field of view ROI1[ a, b ] of the imaging observation]The origin of the coordinates is generally selected as the geometric center of ROI 1; x is the number of2Is the square of the position coordinate; the sum sign represents the sum of the products of the light intensity and the square of the position coordinates at all positions, and is approximated as an integral if the data points are sufficiently continuous; t is the time point of sampling, and the moment of optical marking is taken as the starting point t0(ii) a F1(t) is obtained by multiplying the fluorescence intensity of each position of the target area by the square sum of the positions;
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 1:
the denominator of this equation is the total fluorescence intensity within ROI1 as a correction for the optical signal, see fig. 1;
fitting the experimental data according to the function expressed by the formula 2;
equation 2:
k is derived by fitting0And D alpha/k0. Wherein k is0And a is a constant and D is the effective diffusion coefficient.
Preferably, when the value of a is unknown,
the derivation process B is;
let s be a certain time point in the observation imaging time window [0, t ], and [ -l, l ] be the region of the initial marker ROI0 or any region containing the initial marker region but small enough relative to ROI1, which is designated as ROI 2; λ is a constant, see figure 1,
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 3:
fitting a function curve expressed by formula 3 to calculate a value a; k is obtained through derivation process A0And D alpha/k0D can be calculated according to the numerical value of (2).
Preferably, derivation process B does not perform a direct fit calculation for D in equation 3.
The reason is that D in formula 3 appears in an exponential function, so if D is fitted according to the formula, the deviation will be very large, so formula 3 is not suitable for direct fitting calculation of D
In step A2, the fluorescence labeling method comprises labeling the biomolecule with a photo-convertible or photo-excitable fluorophore; the fluorophore comprises a phototranscript.
In step a5, when the image collection region ROI1 has a left-right symmetric spatial symmetry, two-dimensional data of the biomolecule diffusion process is projected to the symmetry axis based on the image data to form one-dimensional data.
In step a3, when collecting the dynamic image, the detector is used to collect the optical signal, and the superimposed optical tangential column is used to collect the optical signal, so as to eliminate the influence of the movement of the fluorescence labeling molecule in the Z-axis direction on the dynamic data, and achieve the dimension reduction effect of data collection.
In step a4, it is determined whether the labeled biomolecules are always located in the imaging range of the dynamic image during the image collection process by calculating whether the total labeled optical signal remains constant over time in the ROI1 during the imaging process, and if the total labeled optical signal remains constant over time, it indicates that the diffusing molecules are neither degraded nor diffused out of the boundary within the measurement time window.
In the dynamic image collection process of step a3, it is necessary to ensure that the sample to which the biomolecule belongs does not move or deform integrally, so as to prevent the obtained dynamic distribution data of the biomolecule from deviating.
Example (b):
eye organs (eyes forming discs) of Drosophila larvae Dendra2 were dissected and cultured in vitro as shown in FIGS. 1-4.
The in vitro culture was performed according to the standard procedure, the culture medium was Schneider's insect culture medium from Life, Hyclone 1% calf serum was added, and the culture dish was a Nest brand confocal microscopy dedicated culture dish.
Performing a light conversion-dynamic imaging experiment:
real-time imaging experiments were performed using an SP5 inverted Leica confocal microscope. Dendra2 was photo-converted at approximately 800nm by a two-photon pulsed laser (chameleon): laser power 25% -30%, MP gain 100%, MP offset 12% -60%, scanning excitation 2-3 times within 2 seconds. Dendra2 emitted red fluorescence after phototranslation, see FIG. 2 (Drosophila larvae eye development disc Dpp:: Dendra2 profile before and after phototranslation). The light transition region ROI0 was chosen to be a 10X 10 μm2 square on the tissue plane. Images were collected using a 40 × 1.25 oil immersion lens: laser lines 543nm 15% output (for red fluorescence imaging) and 488nm laser output were below 0.5% (green fluorescence imaged before light conversion if necessary), confocal scanning imaging was performed at 0.30 μm x 0.30 μm per pixel. The confocal Z-stack consists of five confocal optical sections (approximately 12 μm thick in total) imaged in real time at 20 second intervals for 5-10 minutes.
Analyzing the dynamic imaging data, and calculating the effective diffusion coefficient of Dpp:
during image data processing, the ROI1 was designated 30-40X 30-40 μm2Is located in the center of the square, the light transfer region ROI0 is located in the center. The ROI1 was chosen to be large enough in both directions to ensure that the total red fluorescence signal intensity in ROI1 remained constant over the time window of monitoring, a warranty link that ensured that Dpp in ROI 1:, Dendra2 molecules did not diffuse out of bounds and also did not suffer degradation over the observation time window. The ROI2 may be selected to be a rectangle that overlaps ROI0 or that is the same width as ROI0 and the same length as ROI 1. See FIG. 3 (a schematic diagram of dynamic image data of red fluorescence transmission after light conversion of Dpp:: Dendra2 in Drosophila's eye plates). After the optical rotation, images are acquired at the same time interval. Two specific ROIs (ROI1 and ROI2) were selected on each image. The change in fluorescence distribution in ROI1 is reduced from two dimensions to one dimension, and the sum of the fluorescence intensity at each position multiplied by the square of the position is F1(t) of (d). Fitting F Using changes in fluorescence intensity in ROI2 at different times2(t) of (d). And fitting the two formulas to obtain D alpha and alpha division, and calculating the effective diffusion coefficient D. Representative fitting results are shown in fig. 4. 8 samples were measured according to this procedure and averaged to give an effective diffusion coefficient of 0.48 μm for the Dpp molecule in Drosophila larvae eye plates2/s。
Claims (7)
1. A method of measuring the effective diffusion coefficient of a biomolecule, comprising: the method comprises the following steps;
a1, planning an initial marking region ROI0 of the biological molecules of which the diffusion coefficients need to be measured, wherein the ROI0 region is a distribution region of the marked biological molecules to be detected at an initial time point; planning an image collection area ROI1 in the diffusion process of the biological molecules, wherein the ROI1 range is larger than the area of the biological molecules after diffusion in an image collection time window in the diffusion process of the biological molecules to be detected; the range of the ROI1 is greater than ROI 0;
a2, optically labeling the biomolecules in the region ROI0, the optical labeling being fluorescent labeling;
a3, diffusing the biological molecules in the ROI0, ensuring that the diffusion area of the biological molecules is smaller than the ROI1, and collecting dynamic images of the diffusion process of the biological molecules in the ROI 1;
a4, checking whether the collected dynamic image is effective, and judging the collected dynamic image to be effective when the diffusion area of the marked biological molecules in the image collection process is always positioned in the imaging range of the dynamic image;
a5, performing dimension reduction on the image data, and generating plane coordinate data of a biomolecule diffusion process according to the image data;
a6, deducing the effective diffusion coefficient of the diffusion process of the biological molecules according to the generated plane coordinate data by a formula;
the formula derivation process in step a6 is as follows;
the region ROI1 is a target region for dynamic image collection;
setting a as the proportion of molecules to be detected in a target area distributed outside cells; when the value of a is known or measured;
the derivation process A is;
setting I (t) to represent the light intensity corresponding to the diffusion molecule marked on the position x of a certain pixel point in the observation visual field at a certain observation time point t during dynamic image collection, wherein the light intensity is in direct proportion to the number of molecules; x is located in the large field of view ROI1[ a, b ] of the imaging observation]The origin of the coordinates is selected as the geometric center of ROI 1; x is the number of2Is the square of the position coordinate; the sum sign represents the sum of the products of the light intensity and the square of the position coordinates at all positions, and is approximated as an integral if the data points are sufficiently continuous; t is the time point of sampling, and the moment of optical marking is taken as the starting point t0(ii) a F1(t) is obtained by multiplying the fluorescence intensity of each position of the target area by the square sum of the positions;
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 1:
the denominator of this equation is the total fluorescence intensity within ROI1 as a correction for the optical signal;
fitting the experimental data according to the function expressed by the formula 2;
equation 2:
k is derived by fitting0And D alpha/k0(ii) a Wherein k is0And a is a constant and D is the effective diffusion coefficient;
when the value of a is not known,
the derivation process B is;
let s be a certain time point in the observation imaging time window [0, t ], and [ -l, l ] be the region of the initial marker ROI0 or any region containing the initial marker region but small enough relative to ROI1, which is designated as ROI 2; the lambda is a constant number which is,
obtaining a function dynamic curve from the data of the biomolecule diffusion experiment, and deriving the function dynamic curve
Equation 3:
fitting a function curve expressed by formula 3 to calculate a value a; k is obtained through derivation process A0And D alpha/k0D can be calculated according to the numerical value of (2).
2. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: derivation process B does not perform a direct fit calculation for D in equation 3.
3. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: in step A2, the fluorescence labeling method comprises labeling the biomolecule with a photo-convertible or photo-excitable fluorophore; the fluorophore comprises a phototranscript.
4. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: in step a5, when the image collection region ROI1 has a left-right symmetric spatial symmetry, two-dimensional data of the biomolecule diffusion process is projected to the symmetry axis based on the image data to form one-dimensional data.
5. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: in step a3, when collecting the dynamic image, the detector is used to collect the optical signal, and the superimposed optical tangential column is used to collect the optical signal, so as to eliminate the influence of the movement of the fluorescence labeling molecule in the Z-axis direction on the dynamic data, and achieve the dimension reduction effect of data collection.
6. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: in step a4, it is determined whether the labeled biomolecules are always located in the imaging range of the dynamic image during the image collection process by calculating whether the total labeled optical signal remains constant over time in the ROI1 during the imaging process, and if the total labeled optical signal remains constant over time, it indicates that the diffusing molecules are neither degraded nor diffused out of the boundary within the measurement time window.
7. The method of claim 1, wherein the effective diffusion coefficient of the biomolecule is measured by: in the dynamic image collection process of step a3, it is necessary to ensure that the sample to which the biomolecule belongs does not move or deform integrally, so as to prevent the obtained dynamic distribution data of the biomolecule from deviating.
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| DE19930598A1 (en) * | 1998-12-16 | 2000-07-13 | Univ Ruprecht Karls Heidelberg | Visualization and analysis of dynamic processes in dynamic biological system useful for determining the dynamics of GFP- labeled peroxisomes comprises following object over time and space and reconstructing spacial structure |
| JP2005043278A (en) * | 2003-07-24 | 2005-02-17 | Institute Of Physical & Chemical Research | Fluorescence correlation spectroscopy measuring apparatus and diffusion coefficient measuring method in heterogeneous sample |
| CN1953701A (en) * | 2004-03-11 | 2007-04-25 | 通用医院有限公司 | Method and system for tomographic imaging using fluorescent proteins |
| US20050221504A1 (en) * | 2004-04-01 | 2005-10-06 | Petruno Patrick T | Optoelectronic rapid diagnostic test system |
| JP2007316017A (en) * | 2006-05-29 | 2007-12-06 | Olympus Corp | Method of detecting interaction between biomolecule and compound and factor for variations |
| JP5508808B2 (en) * | 2009-10-15 | 2014-06-04 | オリンパス株式会社 | Image analysis method and image analysis apparatus |
| US10908403B2 (en) * | 2011-02-14 | 2021-02-02 | European Molecular Biology Laboratory (Embl) | Light-pad microscope for high-resolution 3D fluorescence imaging and 2D fluctuation spectroscopy |
| CN102183497B (en) * | 2011-02-15 | 2012-07-04 | 中国科学院化学研究所 | Measuring method for diffusion motion trail of single molecular chain in thin polymer film |
| US20150132775A1 (en) * | 2011-05-13 | 2015-05-14 | Apiconisis Ab | Method and device for determining concentration, crosstalk and displacement fluorescence cross correlation spectroscopy |
| KR20140137296A (en) * | 2013-05-16 | 2014-12-02 | 포항공과대학교 산학협력단 | Real-time analysis of membrane protein internalization using single particle tracking through super-resolution microscopy |
| CN105928836A (en) * | 2016-04-26 | 2016-09-07 | 中山大学 | Method and apparatus for measuring liquid diffusion coefficient of rock stratum based on 3D printing and SPT technology |
| KR101823990B1 (en) * | 2016-09-07 | 2018-01-31 | 포항공과대학교 산학협력단 | A Method for Quantitative Analysis of Transient Interactions of Membrane Proteins in a Single Living Cell |
| CN107121362A (en) * | 2017-04-17 | 2017-09-01 | 金华职业技术学院 | A kind of method for studying molecular migration motion |
| CN107703027B (en) * | 2017-09-26 | 2020-08-14 | 西安交通大学 | Method for measuring cytoplasm viscosity based on quantum dot three-dimensional tracing |
| CN108535229B (en) * | 2018-04-12 | 2020-10-27 | 中国科学院化学研究所 | Measurement method for obtaining diffusion and movement modes of single polymer molecules in limited melt |
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2018
- 2018-10-31 CN CN201811289392.2A patent/CN109357975B/en active Active
- 2018-12-27 WO PCT/CN2018/124270 patent/WO2020069640A1/en not_active Ceased
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| CN109357975A (en) | 2019-02-19 |
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