Method for detecting extracellular vesicle fusion by adopting fluorescence resonance energy transfer

Abstract

The invention provides a method for detecting fusion of extracellular vesicles by adopting fluorescence resonance energy transfer, and belongs to the technical field of extracellular vesicles. A dynamic fluorescence monitoring system is established, low-power laser excitation is adopted, dual-wavelength fluorescence signals are monitored at the same time, fluorescence signal data are collected, graded gain regulation and control processing is carried out through five threshold values, and a fluorescence signal super-sparse matrix is constructed; establishing a fluorescence signal processing matrix model, converting the super-sparse matrix into a dense matrix, constructing a gain matrix for signal correction, calculating a fluorescence resonance energy transfer efficiency parameter, obtaining a standardized efficiency value through matrix operation, outputting an extracellular vesicle fusion kinetic analysis result, and establishing a quantitative evaluation system. The technical problems of poor signal stability and low detection precision in the process of fluorescence resonance energy transfer detection of extracellular vesicle fusion are solved.

Description

Technical Field

[0001] This invention belongs to the field of extracellular vesicle technology, and more specifically, relates to a method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer. Background Technology

[0002] Extracellular vesicle fusion research is an important research direction in cell biology and membrane biology. Traditionally, fluorescence resonance energy transfer (FRET) is used for detection, which involves labeling donor and recipient vesicles with different fluorescent dyes and monitoring the vesicle fusion process by observing the energy transfer between the two dyes. However, traditional FET detection methods face several technical challenges in practical applications. These include photobleaching of the fluorescence signal during detection, leading to rapid decay of fluorescence intensity over time; imprecise gain control of the detection system, resulting in saturation distortion when the signal is too strong and ineffective amplification when the signal is too weak; and the lack of adaptive regulation mechanisms for fluorescence signals across different intensity ranges. In current extracellular vesicle fusion detection, the drastic fluctuations in fluorescence signal intensity and the inability of the detection system to adjust in real time according to signal changes result in low accuracy of the obtained FET efficiency data, severely impacting the quantitative analysis of vesicle fusion kinetics. In other words, current techniques suffer from low detection accuracy in extracellular vesicle fusion detection using FET. Summary of the Invention

[0003] In view of this, the present invention provides a method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer, which can solve the technical problem of low detection accuracy of the extracellular vesicle fusion process in the prior art.

[0004] This invention is implemented as follows: This invention provides a method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer (FRET). It achieves hierarchical management and data processing of fluorescence signal intensity by establishing a multi-threshold fluorescence signal gain control system and a fluorescence signal matrix processing model. The method includes preparing dual-fluorescent labeled extracellular vesicles, labeling donor extracellular vesicles with the lipid-soluble fluorescent dye DiO and recipient extracellular vesicles with the lipid-soluble fluorescent dye DiI, and dispersing the fluorescent dyes uniformly on the vesicle membrane surface using ultrasonication. A fluorescence resonance energy transfer detection system is constructed by mixing labeled donor and recipient vesicles at a volume ratio of 1:1 in a low autofluorescence buffer solution and adding sodium ascorbate. A dynamic detection system is then established. The fluorescence monitoring system uses a laser with an excitation wavelength of 488 nm to excite the donor fluorescent dye, while simultaneously monitoring the fluorescence intensity changes at donor emission wavelengths of 501 nm and acceptor emission wavelengths of 565 nm. Fluorescence signal intensity data is acquired and processed for gain modulation. The acquired fluorescence intensity data is used to construct a fluorescence signal ultrasparse matrix, and the fluorescence signal intensity is graded and modulated according to a threshold. A fluorescence signal processing matrix model is established, and the fluorescence signal ultrasparse matrix is ​​transformed into a fluorescence signal dense matrix to construct a fluorescence signal gain matrix. The fluorescence resonance energy transfer efficiency parameter is calculated, and the standardized fluorescence resonance energy transfer efficiency is obtained through matrix operations. The system outputs the results of extracellular vesicle fusion kinetic analysis.

[0005] The ultrasonic dispersion process is specifically a physical treatment method that uses the cavitation effect of ultrasound to redistribute fluorescent dye molecules on the vesicle membrane surface, eliminate local aggregation, and improve labeling uniformity.

[0006] The concentration of sodium ascorbate is 1 mM, and the total volume of the mixture is controlled at 200 μL.

[0007] The ultrasonic dispersion process has a power of 50W and a processing time of 30s.

[0008] The laser power was set to 10% of the total power, and the fluorescence intensity change was continuously recorded over 180 seconds, with data points collected every 5 seconds.

[0009] The low autofluorescence buffer refers to a buffer system with a fluorescence background signal of less than 10 relative fluorescence units, prepared using HEPES buffer, with a pH maintained at 7.4, and free of organic solvents and high concentrations of metal ions.

[0010] The ultra-sparse fluorescence signal matrix is ​​a matrix constructed by collecting fluorescence intensity data according to time series and wavelength dimensions, wherein the proportion of non-zero elements is less than 10%, and the matrix dimension is the number of time points × the number of wavelengths, used to store the original fluorescence signal data.

[0011] The dense fluorescence signal matrix is ​​a complete data matrix obtained by interpolating, filling, and smoothing the ultrasparse fluorescence signal matrix. The proportion of non-zero elements is greater than 90%, thus preserving the spatiotemporal continuity information of the fluorescence signal.

[0012] The fluorescence signal gain matrix is ​​a correction coefficient matrix constructed based on the signal intensity differences at different wavelengths and time points. It is used to compensate for the gain of the dense fluorescence signal matrix and eliminate the effects of system errors and noise.

[0013] The extracellular vesicle fusion kinetic analysis results are obtained by fitting the vesicle fusion rate constant to the fluorescence resonance energy transfer efficiency time curve, analyzing the time characteristic parameters and fusion completion degree of the fusion process, and establishing a quantitative evaluation system for fusion events.

[0014] Specifically, the fluorescence signal intensity is graded and controlled according to thresholds: when the fluorescence signal intensity amplitude is within the safe range defined by the first and second thresholds, the current laser operating state is maintained and relevant parameter changes are continuously monitored; when the fluorescence signal intensity amplitude exceeds the safe range but remains within the controllable range defined by the second and third thresholds, the laser operating speed is adjusted to 80% of the rated value. When the fluorescence signal gain exceeds the fourth threshold, a fluorescence signal attenuation stage is automatically inserted to prevent signal overload; when the fluorescence signal gain is below the fifth threshold, the fluorescence signal amplification module is activated to boost the signal intensity to an appropriate level. When the standardized fluorescence resonance energy transfer efficiency parameter is greater than the sixth threshold, the data acquisition frequency is increased to once per second.

[0015] The formula for calculating the standardized fluorescence resonance energy transfer efficiency is as follows: ,in This represents the measured fluorescence resonance energy transfer efficiency. This indicates the fluorescence resonance energy transfer efficiency of the reference standard. This represents the theoretical maximum fluorescence resonance energy transfer efficiency.

[0016] The lipid-soluble fluorescent dye DiO is a green fluorescent dye with the molecular formula [missing information]. The excitation peak is at 484 nm, and the emission peak is at 501 nm, enabling it to embed in the lipid bilayer of the cell membrane. The lipid-soluble fluorescent dye DiI is a red fluorescent dye with the molecular formula [missing information]. The excitation peak is at 549 nm, and the emission peak is at 565 nm. It exhibits film-intercalation characteristics and forms an effective fluorescence resonance energy transfer pair with the lipid-soluble fluorescent dye DiO. The sodium ascorbate, acting as an antioxidant, can scavenge reactive oxygen species generated during excitation, delaying the photochemical bleaching reaction of the fluorescent molecules and maintaining the stability of the fluorescence signal.

[0017] Additionally, based on experience, the default values ​​for the six thresholds are as follows: the first threshold is 0.1, the second threshold is 0.8, forming a safe range for fluorescence signal intensity [0.1, 0.8]; the third threshold is 1.2, forming a controllable range (0.8, 1.2] with the second threshold; the fourth threshold is 20dB, serving as the upper limit threshold for fluorescence signal gain; the fifth threshold is 5dB, serving as the lower limit threshold for fluorescence signal gain; and the sixth threshold is 0.25, used to determine whether the standardized fluorescence resonance energy transfer efficiency parameter needs to be increased.

[0018] This invention addresses the technical problems of poor signal stability and low detection accuracy in the detection of extracellular vesicle fusion during fluorescence resonance energy transfer (FRET) by establishing a multi-threshold fluorescence signal gain control system and a fluorescence signal matrix processing model. The invention employs five different thresholds for graded management of fluorescence signal intensity. When the signal falls within different intensity ranges, the laser power and detector gain are automatically adjusted, effectively preventing signal overload and photobleaching. Simultaneously, the conversion from an ultrasparse to a dense fluorescence signal matrix preserves the spatiotemporal continuity of the fluorescence signal, improving data integrity and reliability. The fluorescence signal gain matrix established in this invention can accurately correct signals at different wavelengths and time points, eliminating systematic errors and noise, ensuring the accuracy of fluorescence resonance energy transfer efficiency calculations, and thus achieving high-precision quantitative detection of the extracellular vesicle fusion process. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention.

[0020] Figure 2 This is a monitoring curve showing the change of fluorescence signal intensity over time in Example 2.

[0021] Figure 3 This is a time evolution curve of fluorescence resonance energy transfer efficiency in Example 2.

[0022] Figure 4 This is a graph showing the results of the vesicle fusion completion analysis in Example 2.

[0023] Figure 5 This is a diagram showing the effect of dynamic control of laser power density in Example 2.

[0024] Figure 6 This is a comparison chart of the repeatability test results in Example 2. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 The diagram shown is a flowchart of a method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer (FRET) provided by this invention. This method includes the following steps:

[0027] S01. Prepare dual fluorescently labeled extracellular vesicles by labeling donor extracellular vesicles with lipid-soluble fluorescent dye DiO and recipient extracellular vesicles with lipid-soluble fluorescent dye DiI. Disperse the fluorescent dyes evenly on the vesicle membrane surface by ultrasonic dispersion treatment, wherein the ultrasonic dispersion treatment power is 50W and the treatment time is 30s.

[0028] S02. Construct a fluorescence resonance energy transfer detection system. Mix labeled donor vesicles and acceptor vesicles in a volume ratio of 1:1 in a low autofluorescence buffer. Add sodium ascorbate at a concentration of 1 mM to slow down photobleaching. The total volume of the mixture is controlled at 200 μL.

[0029] S03. Establish a dynamic fluorescence monitoring system. Use a laser with an excitation wavelength of 488nm to excite the donor fluorescent dye. Set the laser power to 10% of the total power to reduce the photobleaching rate. At the same time, monitor the changes in fluorescence intensity at the donor emission wavelength of 501nm and the acceptor emission wavelength of 565nm.

[0030] S04. Collect fluorescence signal intensity data and perform gain control processing. Continuously record the fluorescence intensity change within 180s. Collect data points every 5s. Construct a fluorescence signal ultrasparse matrix from the collected fluorescence intensity data. When the fluorescence signal intensity amplitude is within the safe range formed by the first and second thresholds, maintain the current laser operating state and continuously monitor the changes in relevant parameters. If the fluorescence signal intensity amplitude exceeds the safe range but is still within the controllable range formed by the second and third thresholds, adjust the laser operating speed to 80% of the rated value. If the fluorescence signal gain exceeds the fourth threshold, automatically insert a fluorescence signal attenuation stage to prevent signal overload.

[0031] S05. Establish a fluorescence signal processing matrix model. The ultra-sparse fluorescence signal matrix obtained in step S04 is transformed into a dense fluorescence signal matrix. When the fluorescence signal gain is lower than the fifth threshold, the fluorescence signal amplification module is activated to enhance the signal intensity to a suitable level. At the same time, a fluorescence signal gain matrix is ​​constructed for signal correction.

[0032] S06. Calculate the fluorescence resonance energy transfer efficiency parameter. Using the fluorescence signal density matrix and fluorescence signal gain matrix obtained in step S05, obtain the standardized fluorescence resonance energy transfer efficiency through matrix operations. When the standardized fluorescence resonance energy transfer efficiency parameter is greater than the sixth threshold, increase the data acquisition frequency to once per second.

[0033] S07. Output the results of extracellular vesicle fusion kinetic analysis, fit the vesicle fusion rate constant based on the fluorescence resonance energy transfer efficiency time curve, analyze the time characteristic parameters and fusion completion degree of the fusion process, and establish a quantitative evaluation system for fusion events.

[0034] Among them, the lipid-soluble fluorescent dye DiO is a green fluorescent dye with the molecular formula [missing information]. With an excitation peak at 484 nm and an emission peak at 501 nm, it can be embedded in the lipid bilayer of the cell membrane.

[0035] Among them, the lipid-soluble fluorescent dye DiI is a red fluorescent dye with the molecular formula [missing information]. It has an excitation peak of 549 nm and an emission peak of 565 nm, exhibits film embedding characteristics, and forms an effective fluorescence resonance energy transfer pair with the lipid-soluble fluorescent dye DiO.

[0036] Among them, ultrasonic dispersion is a physical treatment method that uses the cavitation effect of ultrasound to redistribute fluorescent dye molecules on the surface of vesicle membranes, eliminate local aggregation, and improve labeling uniformity.

[0037] Low autofluorescence buffer refers to a buffer system with a fluorescence background signal of less than 10 relative fluorescence units. It is prepared using HEPES buffer, with the pH value maintained at 7.4, and does not contain organic solvents or high concentrations of metal ions.

[0038] Sodium ascorbate, as an antioxidant, can scavenge reactive oxygen free radicals generated during the excitation process, delay the photochemical bleaching reaction of fluorescent molecules, and maintain the stability of the fluorescence signal.

[0039] The ultra-sparse fluorescence signal matrix is ​​a matrix constructed from the collected fluorescence intensity data according to the time series and wavelength dimensions. The proportion of non-zero elements is less than 10%, and the matrix dimension is the number of time points × the number of wavelengths. It is used to store the original fluorescence signal data.

[0040] Among them, the dense fluorescence signal matrix is ​​a complete data matrix obtained by interpolating, filling and smoothing the ultrasparse fluorescence signal matrix. The proportion of non-zero elements is greater than 90%, which preserves the spatiotemporal continuity information of the fluorescence signal.

[0041] The fluorescence signal gain matrix is ​​a correction coefficient matrix constructed based on the signal intensity differences at different wavelengths and time points. It is used to compensate for the gain of the dense fluorescence signal matrix and eliminate the effects of system errors and noise.

[0042] The fluorescence signal attenuation stage is a signal intensity adjustment device implemented by a variable optical filter or electronic attenuator, which can attenuate excessively strong fluorescence signals to the linear response range of the detector.

[0043] The fluorescence signal amplification module is a signal enhancement device implemented using a photomultiplier tube or avalanche photodiode, used to amplify weak fluorescence signals to the optimal operating range of the detection system.

[0044] The laser operating speed refers to the modulation frequency of the laser output power. By adjusting the operating speed, the amount of photons output per unit time is controlled, thereby adjusting the excitation intensity on the sample.

[0045] The formula for calculating the standardized fluorescence resonance energy transfer efficiency is as follows: ,in This represents the measured fluorescence resonance energy transfer efficiency. This indicates the fluorescence resonance energy transfer efficiency of the reference standard. This represents the theoretical maximum fluorescence resonance energy transfer efficiency.

[0046] The fluorescence resonance energy transfer efficiency is obtained through matrix operations on the fluorescence signal density matrix and the fluorescence signal gain matrix, and the calculation formula is expressed as follows: ,in This represents the fluorescence intensity matrix of the donor when the acceptor is present in the dense fluorescence signal matrix. This represents the fluorescence intensity matrix of the donor when there is no acceptor in the dense fluorescence signal matrix. Represents the fluorescence signal gain matrix. and These represent the corresponding maximum fluorescence intensity values.

[0047] The first threshold is obtained through a fluorescence intensity baseline equation, which is used to determine the lowest safe operating point of the fluorescence signal intensity. The inputs include background fluorescence intensity, system noise level, and detector response characteristics, and the output is the value of the first threshold. The equation is expressed as follows: ,in Indicates the first threshold. Indicates background fluorescence intensity. Indicates the system noise level. Indicates the reference fluorescence intensity.

[0048] The second and third thresholds are obtained through a fluorescence intensity dynamic range equation, which is used to determine the safe and controllable operating range of the fluorescence signal intensity. The inputs include the detector saturation threshold, the photobleaching rate constant, and the laser power density, and the outputs are the values ​​of the second and third thresholds. The equation is expressed as follows: and ,in This represents the second threshold. This represents the third threshold. Indicates the detector saturation threshold. This represents the photobleaching rate constant. This indicates the laser power density.

[0049] The fourth threshold is obtained through the fluorescence signal gain upper limit equation, which is used to determine the maximum gain value to avoid signal overload. The inputs include the detector dynamic range, signal peak factor, and system gain stability coefficient, and the output is the fourth threshold value. The equation is expressed as follows: ,in This represents the fourth threshold. Indicates the dynamic range of the detector. Represents the signal peak factor. Represents the system gain stability coefficient. This represents the reference gain value.

[0050] The fifth threshold is obtained through the fluorescence signal gain lower limit equation, which is used to determine the minimum gain value to ensure signal quality. The inputs include the signal-to-noise ratio requirement, detector sensitivity, and environmental interference level, and the output is the fifth threshold value. The equation is expressed as follows: ,in This represents the fifth threshold. This indicates the minimum signal-to-noise ratio requirement. This indicates the level of environmental disturbance.

[0051] Background fluorescence intensity refers to the basic fluorescence signal intensity measured in a low autofluorescence buffer without fluorescently labeled vesicles, obtained by fluorescence detection of a blank buffer under the same experimental conditions.

[0052] The system noise level refers to the intensity of random electrical noise generated by the fluorescence detection system in the absence of signal input. It is obtained by turning off the laser and recording the standard deviation of the detector output signal.

[0053] The reference fluorescence intensity refers to the fluorescence signal intensity measured under standard experimental conditions using a known concentration of fluorescence standard, and serves as a benchmark value for system calibration and data normalization.

[0054] The detector saturation threshold refers to the maximum input light intensity that enables the fluorescence detector to respond linearly. Exceeding this value will cause the detector to enter a nonlinear saturation state. This threshold is obtained by gradually increasing the light intensity until the detector response curve deviates from the linear relationship at a critical point.

[0055] Among them, the photobleaching rate constant refers to the rate parameter of fluorescence intensity decay caused by irreversible photochemical reaction of fluorescent dye molecules under laser irradiation. It is obtained by continuously exciting the fluorescent dye and fitting the exponential decay curve of fluorescence intensity over time.

[0056] Laser power density refers to the laser power per unit area. It is calculated by dividing the total laser power by the spot area. The laser power is measured by a power meter and the spot diameter is measured by a spot analyzer.

[0057] The detector dynamic range refers to the ratio of the maximum signal that the detector can accurately measure to the minimum detectable signal, which is obtained by measuring the ratio of the detector's saturation signal to the noise floor signal.

[0058] Among them, the signal peak factor refers to the ratio of the instantaneous peak value to the average value in the fluorescence signal, which reflects the fluctuation characteristics of the signal and is obtained by analyzing the statistical characteristics of the fluorescence signal time series.

[0059] The system gain stability coefficient refers to the relative stability of the fluorescence detection system gain as a function of time and temperature. It is obtained by measuring the coefficient of variation of the standard fluorescence signal at different time points and temperature conditions.

[0060] The reference gain value refers to the baseline gain setting of the fluorescence detection system under standard operating conditions. It is obtained by adjusting the system gain to the optimal signal-to-noise ratio using a standard fluorescent sample.

[0061] The minimum signal-to-noise ratio requirement refers to the lowest signal-to-noise ratio required to ensure accurate detection and analysis of fluorescence signals, and is determined based on the accuracy requirements and statistical significance criteria for fluorescence resonance energy transfer detection.

[0062] Detector sensitivity refers to the amplitude of the electrical signal output generated by the detector in response to a unit optical power input, which is obtained by measuring the detector's responsivity using a standard light source.

[0063] Among them, the environmental interference level refers to the intensity of various optical and electromagnetic interference signals in the experimental environment other than the fluorescence signal to be measured, including ambient light leakage, electromagnetic radiation and vibration noise, etc., which is obtained by measuring the intensity of interference signals received by the detector when the laser is turned off.

[0064] Among them, the statistical noise level refers to the standard deviation of the fluorescence resonance energy transfer efficiency data when the same sample is measured repeatedly. It reflects the random error level of the measurement system and is obtained by performing multiple independent measurements on the standard sample and calculating the standard deviation of the measurement results.

[0065] Measurement accuracy refers to the maximum permissible deviation between the measured value and the true value of fluorescence resonance energy transfer efficiency, which is obtained by system calibration and verification using standard samples with known fluorescence resonance energy transfer efficiency.

[0066] Among them, the biological significance level refers to the minimum change in fluorescence resonance energy transfer efficiency that is considered to have practical significance in biological research, and is determined based on cell biology literature and experimental experience.

[0067] The theoretical maximum fluorescence resonance energy transfer efficiency refers to the highest energy transfer efficiency that can be achieved when the donor and acceptor fluorescent dyes are in complete contact under ideal conditions. It is calculated from photophysical parameters such as the spectral overlap integral and quantum yield of the fluorescent dyes.

[0068] The vesicle fusion rate constant was obtained by fitting a first-order kinetic equation, which is expressed as follows: ,in Let be the vesicle fusion rate constant. The reference time unit is 1 second. To standardize fluorescence resonance energy transfer efficiency, This represents the maximum fluorescence resonance energy transfer efficiency.

[0069] The specific implementation methods of the above steps are described in detail below.

[0070] The specific implementation of step S01 involves preparing dual-fluorescent labeled extracellular vesicles. This step achieves uniform labeling based on the membrane intercalation characteristics of lipid-soluble fluorescent dyes and the physical dispersion principle of ultrasonic cavitation effect. First, donor extracellular vesicles are suspended in a phosphate buffer solution, and the vesicle concentration is adjusted to... ~ The concentration was increased to 5–10 μM by adding the lipid-soluble fluorescent dye DiO. The hydrophobic properties of the dye molecules allowed them to spontaneously embed into the lipid bilayer of the vesicle membrane. The mixture was then ultrasonically dispersed using a 50 W ultrasonic processor. The mechanical vibration of the sound waves generated energy released from the rupture of cavitation bubbles, disrupting the local aggregation of dye molecules and allowing the dye to redistribute uniformly on the membrane surface. The treatment time was controlled to 30 s to avoid irreversible damage to the vesicle structure caused by the ultrasonic energy. The same method was used to treat the extracellular vesicles of the receptor cells, labeled with the lipid-soluble fluorescent dye DiI. The excitation and emission peaks of DiI ideally overlapped with those of DiO, satisfying the basic conditions for fluorescence resonance energy transfer.

[0071] The specific implementation of step S02 involves constructing a fluorescence resonance energy transfer (FRET) detection system. This step achieves a stable detection environment based on the distance dependence principle of FRET and an antioxidant protection mechanism. Labeled donor and acceptor vesicles are mixed in a 1:1 volume ratio to ensure a relative balance in the number of donor and acceptor molecules, thereby increasing the detection probability of fusion events. HEPES buffer is selected as the reaction medium. This buffer has an autofluorescence background signal of less than 10 relative fluorescence units, a pH maintained at 7.4 to simulate physiological conditions, and is free of organic solvents and high concentrations of metal ions to avoid interference with the fluorescent dye and vesicle structure. Sodium ascorbate is added to the mixture to a final concentration of 1 mM. Its strong reducing properties eliminate reactive oxygen species such as singlet oxygen and hydroxyl radicals generated during laser excitation, delaying the photo-oxidation reaction of the fluorescent dye molecules and maintaining the long-term stability of the fluorescence signal. The total volume of the mixture is controlled at 200 μL to ensure sufficient sample volume while avoiding optical path attenuation and signal inhomogeneity caused by excessive volume.

[0072] The specific implementation of step S03 involves establishing a dynamic fluorescence monitoring system. This step achieves high-sensitivity real-time monitoring based on the single-excitation dual-emission detection principle and a photobleaching minimization strategy. A 488nm laser is used as the excitation source. This wavelength is close to the excitation peak of DiO at 484nm, effectively exciting the donor fluorescent dye while directly exciting the acceptor dye, ensuring that the detection signal mainly originates from the fluorescence resonance energy transfer process. The laser power is set to 10% of the total power. Based on the photobleaching kinetics theory, a lower excitation power can significantly reduce the photochemical bleaching rate of fluorescent molecules and extend the detection time window. Two emission wavelength channels are monitored simultaneously: the 501nm emission peak corresponding to DiO is used to monitor changes in donor fluorescence intensity, and the 565nm emission peak corresponding to DiI is used to monitor changes in acceptor fluorescence intensity. Simultaneous dual-channel detection allows for accurate calculation of the fluorescence resonance energy transfer efficiency. A photomultiplier tube or avalanche photodiode is used as the photoelectric conversion device, possessing high quantum efficiency and low noise characteristics, enabling the detection of weak fluorescence signals at the single-photon level.

[0073] The specific implementation of step S04 involves acquiring fluorescence signal intensity data and performing gain control processing. This step achieves intelligent signal management based on sparse matrix theory and adaptive feedback control principles. A fluorescence intensity time series is continuously recorded over 180 seconds, with data points acquired every 5 seconds, resulting in 36 time points of fluorescence intensity values. Simultaneously, signals from two wavelength channels (501nm and 565nm) are recorded, constructing a two-dimensional data matrix multiplied by the number of time points and wavelengths. The acquired raw fluorescence intensity data is used to construct an ultra-sparse fluorescence signal matrix, where non-zero elements account for less than 10%, primarily containing valid fluorescence signal data points, while most matrix elements are zero or near-zero noise levels. A multi-level threshold judgment mechanism is established. The first and second thresholds constitute a safe operating range, calculated using the fluorescence intensity baseline equation. The reference value for the first threshold is 2.5 times the background fluorescence intensity plus 1.2 times the system noise level. When the signal intensity is within the safe range, the current laser operating state is maintained, and changes in relevant parameters are continuously monitored. The second and third thresholds constitute a controllable operating range, calculated using the fluorescence intensity dynamic range equation. The reference value for the second threshold is approximately 80% of the detector saturation threshold, and the reference value for the third threshold is approximately 95% of the detector saturation threshold. When the signal intensity exceeds the safe range but remains within the controllable range, the laser operating speed is automatically adjusted to 80% of its rated value to reduce the excitation intensity and prevent signal overload. The fourth threshold is determined using the fluorescence signal gain upper limit equation, with a reference value of approximately 90% of the detector dynamic range. When the signal gain exceeds this threshold, a fluorescence signal attenuation stage is automatically inserted, using a variable optical filter or electronic attenuator to attenuate the excessively strong signal to within the linear response range.

[0074] The specific implementation of step S05 involves establishing a fluorescence signal processing matrix model. This step achieves complete data reconstruction based on matrix transformation theory and signal enhancement algorithms. The ultra-sparse fluorescence signal matrix obtained in step S04 is converted into a dense fluorescence signal matrix through interpolation filling and smoothing algorithms. Cubic spline interpolation is used to supplement missing or low-quality data points, increasing the proportion of non-zero elements to over 90% while preserving the spatiotemporal continuity of the fluorescence signal. A signal gain evaluation mechanism is established. The fifth threshold is determined by the fluorescence signal gain lower limit equation, with a reference value approximately 3.0 times the minimum signal-to-noise ratio requirement plus 0.5 times the environmental interference level. When the signal gain is below the fifth threshold, the fluorescence signal amplification module is activated, employing the multiplication effect of a photomultiplier tube or the avalanche gain effect of an avalanche photodiode to amplify the weak signal to the optimal operating range of the detection system. A fluorescence signal gain matrix is ​​constructed. This matrix determines corresponding correction coefficients based on the signal intensity differences at different wavelengths and time points to compensate for system errors such as nonlinear response, detector wavelength dependence, and time drift. Matrix operations are used to achieve signal standardization.

[0075] Step S06 involves calculating the fluorescence resonance energy transfer efficiency (FRE) parameter. This step utilizes energy transfer theory and matrix operations for quantitative analysis. Using the fluorescence signal density matrix and fluorescence signal gain matrix obtained in step S05, matrix multiplication is used to eliminate systematic errors and obtain corrected fluorescence intensity data. Based on the fundamental principle of FRE, the energy transfer efficiency equals 1 minus the ratio of donor fluorescence intensity in the presence of a receptor to that in the absence of a receptor. The energy transfer efficiency is quantitatively calculated by comparing the quenching degree of donor fluorescence intensity. The measured energy transfer efficiency is standardized, using the reference standard energy transfer efficiency as a normalization benchmark to eliminate the influence of experimental condition differences on the results. A dynamic sampling mechanism is established, with the sixth threshold set as the biological significance level, and a reference value approximately 10%–20% of the theoretical maximum FRE. When the standardized FRE parameter exceeds the sixth threshold, it indicates a significant vesicle fusion event. At this point, the data acquisition frequency is automatically increased to once per second to improve the temporal resolution of the dynamic changes in the fusion process.

[0076] Step S07 specifically involves outputting the results of extracellular vesicle fusion kinetic analysis. This step utilizes first-order reaction kinetics theory and a nonlinear fitting algorithm to extract quantitative fusion characteristic parameters. Based on the fluorescence resonance energy transfer efficiency (FRE) curve over time, a first-order kinetic equation is used for nonlinear least-squares fitting. This equation describes the exponential growth of energy transfer efficiency over time, reflecting the kinetic characteristics of vesicle fusion. The vesicle fusion rate constant is calculated through fitting; this parameter characterizes the speed of the fusion process, and its value is closely related to vesicle membrane composition, fusion conditions, and environmental factors. The temporal characteristic parameters of the fusion process are analyzed, including key nodes such as fusion initiation time, fusion half-life, and fusion completion time. By comparing and analyzing the differences in temporal parameters under different experimental conditions, key factors affecting vesicle fusion are revealed. The fusion completion rate is calculated, i.e., the ratio of the actual measured maximum energy transfer efficiency to the theoretical maximum value. This parameter reflects the thoroughness of the fusion process and the homogeneity of the mixed vesicle membrane after fusion. A quantitative evaluation system for fusion events is established, comprehensively considering multiple parameters such as fusion rate, completion rate, and temporal characteristics, providing a quantitative analytical tool for the study of extracellular vesicle fusion mechanisms and the diagnosis of related diseases.

[0077] It should be noted that the key technical ideas of this invention are mainly reflected in the following three aspects.

[0078] The first key technological approach is a dual-fluorescent labeling detection strategy based on fluorescence resonance energy transfer (FRET). Traditional vesicle fusion detection methods mainly rely on morphological observation or single fluorescent labeling, making it difficult to achieve real-time quantitative monitoring of the fusion process. This invention employs a DiO and DiI dual-fluorescent dye labeling system, utilizing the spectral overlap between the two dyes to construct a fluorescence resonance energy transfer pair. When vesicle fusion occurs, the distance between the donor and acceptor dye molecules shortens to the nanometer level, triggering a highly efficient energy transfer process. Sensitive detection of the fusion event is achieved by monitoring the synchronous changes in donor fluorescence quenching and acceptor fluorescence enhancement. This detection principle based on changes in intermolecular distance has extremely high spatial and temporal resolution, enabling the capture of early fusion events and instantaneous fusion dynamics that are difficult to detect using traditional methods, significantly improving the accuracy and reliability of detection.

[0079] The second key technological approach is the establishment of an intelligent signal processing system with multi-level threshold adaptive control. Existing fluorescence detection systems typically use fixed parameter settings, failing to dynamically adjust according to actual signal intensity and quality, easily leading to signal overload or insufficient signal-to-noise ratio. This invention, by constructing six different functional threshold judgment mechanisms, achieves adaptive control of laser power, signal gain, and sampling frequency. When the signal intensity is within different ranges, the system can automatically select the optimal operating parameters, avoiding detector saturation and photobleaching under strong signal conditions while ensuring detection sensitivity and signal quality under weak signal conditions. This intelligent parameter optimization strategy significantly expands the system's dynamic detection range and improves its adaptability to different sample types and concentration conditions.

[0080] The third key technical approach is to employ a sparse-to-dense matrix data reconstruction algorithm to extract complete fusion dynamics information. Traditional fluorescence detection data processing methods typically focus only on simple statistical analysis of signal intensity, neglecting the correlation between time series and multi-wavelength information, leading to the loss of dynamic characteristics of the fusion process. This invention constructs the original fluorescence data into a time-wavelength two-dimensional matrix, and generates a complete dense matrix through sparse matrix interpolation and gain correction, preserving the spatiotemporal continuity and multidimensional correlation of the fluorescence signal. Combined with first-order kinetic equation fitting, key parameters such as the rate constant, temporal characteristics, and completion degree of vesicle fusion are extracted. This matrix theory-based data processing method can fully mine the effective information in experimental data, achieving comprehensive quantitative analysis of the vesicle fusion process.

[0081] The synergistic effect of these three key technological approaches has yielded significant technological advantages. The dual-fluorescence labeling strategy provides a high-quality raw data foundation for the intelligent signal processing system, the multi-level threshold control mechanism ensures stable and reliable detection signals under various experimental conditions, and the matrix data reconstruction algorithm fully utilizes the optimized detection data to achieve in-depth dynamic analysis. The three technologies work together to form a complete technological chain from signal generation and optimization processing to in-depth analysis, achieving a comprehensive improvement in detection sensitivity, dynamic range, and analytical depth compared to existing technologies. This provides strong technical support for the study of extracellular vesicle fusion mechanisms and related biomedical applications.

[0082] It should be noted that in traditional extracellular vesicle fluorescent labeling processes, fluorescent dyes are often unevenly distributed on the vesicle membrane surface, forming aggregated spots. This uneven distribution leads to systematic deviations in the calculation of fluorescence resonance energy transfer efficiency. This invention employs 50W power for 30 seconds of ultrasonic dispersion, utilizing the cavitation effect of ultrasound to redistribute fluorescent dye molecules, effectively eliminating local aggregation and ensuring uniform dye labeling on the vesicle membrane surface, providing a reliable labeling basis for accurate fusion detection. Existing fluorescence detection systems typically use a fixed data acquisition frequency, which cannot adaptively adjust according to the dynamic changes in the fusion process, easily missing critical fusion events or acquiring redundant data. This invention establishes a dynamic response mechanism based on standardized fluorescence resonance energy transfer efficiency. When the efficiency parameter exceeds a sixth threshold, the data acquisition frequency is automatically increased to once per second, ensuring high-density data points are obtained at critical moments of fusion events, while maintaining a normal acquisition frequency during stable periods to conserve system resources, achieving intelligent response control of the detection system.

[0083] Specifically, the principle of this invention is as follows: This invention solves the technical problems of poor signal stability and low detection accuracy in fluorescence resonance energy transfer detection, mainly based on the technical principles of multi-level signal modulation and matrix data processing. Regarding signal stability, this invention constructs a complete signal strength management system by setting five thresholds with different functions. The first and second thresholds define the safe operating range, the second and third thresholds constitute a controllable adjustment range, the fourth threshold prevents signal overload, and the fifth threshold ensures signal amplification. This hierarchical threshold management mechanism can automatically adjust system parameters according to the real-time signal status, avoiding detection failures caused by signal fluctuations in traditional methods. Regarding detection accuracy, this invention adopts a dual data structure of ultra-sparse and dense matrices for fluorescence signals. The ultra-sparse matrix efficiently stores the original data, while the dense matrix improves data continuity through interpolation and smoothing. Combined with the fluorescence signal gain matrix for system correction, this matrix processing method can maximize the retention of useful signal information and eliminate noise interference. Furthermore, the standardized fluorescence resonance energy transfer efficiency calculation model established in this invention converts multidimensional fluorescence data into accurate fusion efficiency parameters through matrix operations, and adopts a dynamic acquisition frequency adjustment mechanism to increase data acquisition density when significant fusion events are detected, ensuring the accurate capture of key fusion processes. This guarantees the stability and accuracy of the detection system from a technical mechanism perspective.

[0084] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0085] The specific implementation methods of steps S01-S03 in this embodiment are the same as those described above, and will not be repeated in detail here.

[0086] The specific implementation of step S04 involves acquiring fluorescence signal intensity data and performing gain modulation processing, then constructing an ultrasparse fluorescence signal matrix to achieve structured storage of the original data. Ultrasparse fluorescence signal matrix. The construction formula is expressed as follows:

[0087] ;

[0088] In the formula, The fluorescence signal is an ultrasparse matrix with dimension . ; For the first The first time point Fluorescence intensity values ​​for each wavelength channel, in relative fluorescence units; Reference fluorescence intensity, in relative fluorescence units; The number of time points is 36; the first column of the matrix corresponds to the 501nm wavelength channel, and the second column corresponds to the 565nm wavelength channel.

[0089] The baseline equation for fluorescence intensity at the first threshold is expressed as follows:

[0090] ;

[0091] In the formula, The first threshold is given, and the unit is relative fluorescence units. The background fluorescence intensity is expressed in relative fluorescence units. The system noise level is expressed in relative fluorescence units. The reference fluorescence intensity is expressed in relative fluorescence units.

[0092] The equations for the dynamic range of fluorescence intensity for the second and third thresholds are expressed as follows:

[0093] ;

[0094] ;

[0095] In the formula, The second threshold is expressed in relative fluorescence units. The third threshold is expressed in relative fluorescence units. This is the detector saturation threshold, expressed in relative fluorescence units. This is the photobleaching rate constant, in units of... ; Laser power density, in units of ; The unit time factor has a value of 1 and a unit of . , used for dimensional balance.

[0096] The equation for the upper limit of fluorescence signal gain at the fourth threshold is expressed as follows:

[0097] ;

[0098] In the formula, The fourth threshold represents the maximum allowable gain value; The detector's dynamic range is dimensionless. The peak factor of the signal is dimensionless. The system gain stability coefficient is dimensionless. This is a reference gain value, dimensionless.

[0099] The specific implementation of step S05 involves establishing a fluorescence signal processing matrix model and transforming the ultrasparse fluorescence signal matrix into a dense fluorescence signal matrix through matrix transformation. (Dense fluorescence signal matrix) The construction formula is expressed as follows:

[0100] ;

[0101] In the formula, It is a dense matrix of fluorescence signals with dimensions of . ; It is an ultrasparse matrix of fluorescence signals; Here is the interpolation transformation matrix, with dimension 1. ; This is the noise correction matrix, with dimension 1. .

[0102] Fluorescence signal gain matrix The construction formula is expressed as follows:

[0103] ;

[0104] In the formula, The fluorescence signal gain matrix has dimensions of . ; For the first The first time point Gain coefficients for each wavelength channel, dimensionless; The reference gain coefficient is dimensionless.

[0105] The equation for the lower limit of fluorescence signal gain at the fifth threshold is expressed as follows:

[0106] ;

[0107] In the formula, The fifth threshold represents the minimum permissible gain value; Dimensionless, for minimum signal-to-noise ratio requirement; For environmental interference levels, the unit and The sameness is required to ensure uniformity of dimensions.

[0108] The specific implementation of step S06 involves calculating the fluorescence resonance energy transfer efficiency parameter and obtaining the corrected fluorescence intensity data through matrix operations. The formula for calculating the fluorescence resonance energy transfer efficiency is as follows:

[0109] ;

[0110] In the formula, The fluorescence resonance energy transfer efficiency is dimensionless. This is the fluorescence intensity matrix of the donor in the presence of the acceptor, with dimensions of . The unit is relative fluorescence unit; This is the fluorescence intensity matrix of the donor in the absence of an acceptor, with dimensions of . The unit is relative fluorescence unit; This is the fluorescence signal gain matrix; The maximum fluorescence intensity of the donor in the presence of an acceptor is expressed in relative fluorescence units. This represents the maximum fluorescence intensity of the donor in the absence of an acceptor, expressed in relative fluorescence units.

[0111] The formula for calculating the standardized fluorescence resonance energy transfer efficiency is as follows:

[0112] ;

[0113] In the formula, The fluorescence resonance energy transfer efficiency is standardized and is dimensionless. The measured fluorescence resonance energy transfer efficiency is dimensionless. The fluorescence resonance energy transfer efficiency is a reference standard and is dimensionless. The theoretical maximum fluorescence resonance energy transfer efficiency is dimensionless.

[0114] The specific implementation of step S07 involves outputting the extracellular vesicle fusion kinetic analysis results and obtaining the vesicle fusion rate constant by fitting a first-order kinetic equation. The first-order kinetic equation for the vesicle fusion rate constant is expressed as follows:

[0115] ;

[0116] In the formula, is the vesicle fusion rate constant, which is dimensionless; This is a reference time unit, with a value of 1 and a unit of seconds. The fluorescence resonance energy transfer efficiency is standardized and is dimensionless. The maximum fluorescence resonance energy transfer efficiency is dimensionless.

[0117] The parameter acquisition method is as follows: The results were obtained experimentally, including step 1: fluorescence detection of the blank buffer under the same experimental conditions; and step 2: recording the average value of the detector output signal as the background fluorescence intensity. The method used is experimental, including step 1: turning off the laser and recording the detector output signal; step 2: calculating the standard deviation of the output signal as the system noise level. The fluorescence intensity was obtained experimentally, including step 1: using a known concentration of fluorescent standard under standard experimental conditions for detection; and step 2: recording the fluorescence signal intensity as a reference fluorescence intensity. The method employed was experimental, including step 1: preparing dual-fluorescently labeled extracellular vesicle samples; and step 2: detecting donor fluorescence intensity under the same excitation conditions to construct a time-series data matrix. The method used was experimental, including step 1: preparing extracellular vesicle samples containing only donor fluorescent labels; and step 2: detecting the donor fluorescence intensity under the same excitation conditions to construct a time series data matrix. The formula is obtained through calculation. ,in For the first under standard conditions The first time point The fluorescence intensity of each wavelength channel was obtained by measuring fluorescence standards under the same experimental conditions. For the actual measurement of the first The first time point Fluorescence intensity of each wavelength channel. for The 3D interpolation transformation matrix, constructed using the cubic spline interpolation algorithm, is used to interpolate and fill sparse matrices. for A noise correction matrix is ​​used to estimate and correct systematic errors using statistical methods. The range is ~ Relative fluorescence units, The range is ~ , The range is 1 to 100 , The range is ~ , The range is 1.2 to 3.0. The range is 0.95 to 0.99. The range is 10 to 50. The range is 0.1 to 0.8. The range is 0.8 to 1.0. The range is 0.8 to 1.2.

[0118] It should be explained that the principle of the formula for constructing the ultrasparse matrix of fluorescence signals is based on sparse matrix theory, achieving structured storage of the original fluorescence signal through a two-dimensional data structure in both time and wavelength dimensions. The matrix element expression of this formula is:

[0119] ;

[0120] This formula transforms continuous time-series data into discrete matrix elements, each corresponding to fluorescence intensity values ​​at different time points and wavelengths. Dimensionless processing eliminates the influence of signal intensity differences under varying experimental conditions. Compared to traditional one-dimensional data storage methods, this matrix structure preserves the spatiotemporal correlation information of fluorescence signals, providing a complete data foundation for subsequent matrix transformations and data processing, significantly improving the efficiency and accuracy of data processing.

[0121] The fluorescence intensity baseline equation is based on the threshold setting method in signal detection theory, determining the minimum operating point for signal detection by comprehensively considering background fluorescence intensity and system noise level. The complete expression of this equation is:

[0122] ;

[0123] The equation includes a background fluorescence term. and system noise terms The two main components are: a background fluorescence term reflecting inherent fluorescence interference from the experimental environment, with a coefficient of 2.5 ensuring the detected signal is significantly higher than the background level; and a system noise term reflecting the random error of the detector, with a coefficient of 1.2 providing appropriate noise tolerance. Compared to a fixed threshold setting method, this dynamic threshold calculation method can automatically adjust the detection parameters according to actual experimental conditions, effectively avoiding the influence of background interference and noise on the detection results, and improving the reliability and stability of the detection.

[0124] The principle of the fluorescence intensity dynamic range equation is based on detector response characteristics and photobleaching kinetics theory. The optimal operating range is determined by balancing detector saturation risk and photobleaching effect. The formula for calculating the second threshold is:

[0125] ;

[0126] The formula for calculating the third threshold is:

[0127] ;

[0128] Saturation term in the second threshold equation To ensure the signal strength remains within the detector's linear response range, a coefficient of 0.8 provides a safety margin; photobleaching term. The effect of laser power on the stability of fluorescent molecules was considered, and a coefficient of 0.1 achieved a moderate compensation for the photobleaching effect. This is the dimensional balance factor. The third threshold equation sets the critical point for detector saturation, and the coefficient 0.95 ensures that the operating parameters are adjusted in a timely manner when the detector approaches saturation.

[0129] The principle of the fluorescence signal gain control equation is based on automatic gain control theory and signal processing optimization methods, ensuring signal quality by dynamically adjusting the system gain. The upper gain equation is:

[0130] ;

[0131] The lower limit equation for gain is:

[0132] ;

[0133] Dynamic range term in the upper limit of gain equation To ensure the gain setting fully utilizes the detector's dynamic range, a coefficient of 0.9 is used to avoid signal distortion caused by excessive gain; peak factor term. Considering the effects of signal fluctuations and system stability, a coefficient of 1.5 provides suppression of instantaneous peak values. The signal-to-noise ratio term in the gain lower bound equation... To ensure signal quality meets testing requirements, a coefficient of 3.0 guarantees sufficient signal amplitude; noise term The environmental interference was compensated for, and the coefficient of 0.5 achieved a moderate consideration of the noise level.

[0134] The principle behind the formula for constructing a dense matrix of fluorescence signals is based on interpolation theory and matrix transformation methods. It uses mathematical transformations to convert incomplete sparse data into continuous dense data. The complete expression of this formula is:

[0135] ;

[0136] The interpolation transformation matrix in the formula Using a cubic spline interpolation algorithm, smooth intermediate data points can be generated while maintaining the accuracy of the original data points; noise correction matrix Systematic errors are estimated and eliminated using statistical methods. Compared to simple data interpolation methods, this matrix transformation method can preserve the spatiotemporal continuity and multidimensional correlation of fluorescence signals, providing high-quality data support for accurately calculating fluorescence resonance energy transfer efficiency and significantly improving the accuracy and reliability of kinetic analysis.

[0137] The principle behind the formula for calculating fluorescence resonance energy transfer efficiency (FRET) is based on Foster resonance energy transfer theory. It quantitatively calculates the energy transfer efficiency by comparing the changes in fluorescence intensity of the donor and the donor in the presence and absence of the acceptor. The complete expression of the formula is:

[0138] ;

[0139] The fluorescence intensity ratio term in this formula This reflects the degree of quenching of donor fluorescence by the presence of the acceptor, and the influence of differences in experimental conditions was eliminated through normalization. The standardized fluorescence resonance energy transfer efficiency formula is:

[0140] ;

[0141] This standardized formula achieves comparability of results between different experiments by comparing with reference standards, thereby improving the universality and reliability of the detection method.

[0142] The principle of the first-order kinetic equation is based on reaction kinetics theory, assuming that the vesicle fusion process follows first-order reaction kinetics, and the fusion rate is directly proportional to the concentration of unfused vesicles. The complete expression of the equation is:

[0143] ;

[0144] This equation describes the variation of fluorescence resonance energy transfer efficiency over time in differential form, where the driving term... Represents the relative concentration in the unfused state, rate constant This reflects the inherent dynamic characteristics of the fusion process. Compared with empirical data fitting methods, this theoretical dynamic model has clear physical meaning and predictive ability, and can provide key dynamic parameters such as the vesicle fusion rate constant, providing a quantitative theoretical basis for a deeper understanding of the vesicle fusion mechanism and optimization of experimental conditions.

[0145] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2:

[0146] The technical team first prepared dual-fluorescently labeled extracellular vesicles. Extracellular vesicles obtained by ultracentrifugation of neuronal cell culture supernatant were used as donor vesicles and labeled with the lipid-soluble fluorescent dye DiO. 100 μL of the vesicle suspension was then... Add DiO dye to a final concentration of 5. The mixture was incubated at 4°C for 30 minutes. Following this, ultrasonic dispersion was performed with an ultrasonic power of 50 ppm. The treatment time was 30 seconds to allow the fluorescent dye to be evenly distributed on the vesicle membrane surface. For receptor vesicles, the team used extracellular vesicles secreted by endothelial cells, labeling them with DiI dye using the same method, with a final concentration of 5%. .

[0147] After labeling, the team constructed a fluorescence resonance energy transfer (FRET) detection system. DiO-labeled donor vesicles and DiI-labeled acceptor vesicles were mixed at a 1:1 volume ratio in HEPES buffer, with the buffer pH maintained at 7.4. The background fluorescence intensity was measured at 8.2 relative fluorescence units, meeting the low autofluorescence requirement. To mitigate photobleaching, sodium ascorbate was added to the mixture to a final concentration of 1. The total volume of the mixture is strictly controlled at 200. This ensures the stability of the testing system.

[0148] Establishing a dynamic fluorescence monitoring system is a crucial step in the experiment. The team used an argon-ion laser with an excitation wavelength of 488 nm as the excitation source, and set the total laser power to 100. The actual operating power is adjusted to 10% of the total power, i.e., 10. This effectively reduces the photobleaching rate. The spot diameter, measured by a spot analyzer, is 2. The calculated laser power density is 318. The detection system simultaneously monitors the fluorescence intensity changes at both the donor emission wavelength of 501 nm and the acceptor emission wavelength of 565 nm, using a high-sensitivity photomultiplier tube as the detector.

[0149] Advanced gain control strategies were employed for the acquisition and processing of fluorescence signal intensity data. The team continuously recorded fluorescence intensity changes over 180 seconds, acquiring data points every 5 seconds, for a total of 36 time points. The acquired fluorescence intensity data was constructed into a 36×2-dimensional ultrasparse fluorescence signal matrix, where non-zero elements accounted for 8.7%. Multiple threshold parameters were set for adaptive control of the system: the first threshold... Based on the background fluorescence intensity of 8.2 relative fluorescence units and the system noise level of 1.5 relative fluorescence units, a relative fluorescence unit of 26.1 was calculated; the second threshold. Based on the detector saturation threshold of 65,000 relative fluorescence units, 51,840 relative fluorescence units were calculated; the third threshold It was set to 61,750 relative fluorescence units.

[0150] In actual testing, when the fluorescence signal intensity remained within the safe range of 26.1 to 51840 relative fluorescence units, the system maintained the laser at 10% power. For example... Figure 2 As shown, the fluorescence signal intensity remained stable within a safe range during the initial monitoring phase, indicating normal system operation. When the signal intensity reached 55,000 relative fluorescence units at certain time points, exceeding the safe range but still within a controllable range, the system automatically adjusted the laser's operating speed to 80% of its rated value, effectively preventing signal overload. Fourth threshold. Based on detector dynamic range The signal peak factor of 1.8 and the system gain stability coefficient of 0.92 are used to calculate 87600.

[0151] Fluorescence signal processing employed matrix transformation techniques. The team transformed the ultrasparse fluorescence signal matrix into a dense matrix through cubic spline interpolation and Gaussian smoothing, increasing the proportion of non-zero elements to 94.3%. When fluorescence signal gain at certain wavelengths was detected to be below the fifth threshold... At (2850), the system activates the fluorescence signal amplification module to boost the signal intensity to an appropriate level. Simultaneously, a fluorescence signal gain matrix is ​​constructed to eliminate system errors; the matrix has a dimension of 36×2 and includes correction coefficients for different time points and wavelengths.

[0152] The calculation of fluorescence resonance energy transfer efficiency (FRE) is the core of the data analysis. The team used the fluorescence signal density matrix and the fluorescence signal gain matrix to obtain the FRE through matrix operations. Table 1 shows the key parameters at different time points:

[0153] Table 1 Key parameters for fluorescence resonance energy transfer detection

[0154]

[0155] The calculated normalized fluorescence resonance energy transfer efficiency parameter first exceeded the sixth threshold of 0.15 at 90 s, and the system immediately increased the data acquisition frequency to once per second, enhancing its monitoring capability of the rapid fusion process. Figure 3 As shown, the fluorescence resonance energy transfer efficiency exhibits a typical exponential growth trend over time, reflecting the kinetic characteristics of vesicle fusion.

[0156] Extracellular vesicle fusion kinetics analysis revealed a clear fusion mode. Based on the fluorescence resonance energy transfer efficiency-time curve fitting, the vesicle fusion rate constant k was determined to be 0.0089. The fusion process exhibits three distinct phases: the initial 30 seconds are a slow contact phase, during which the FRET efficiency slowly increases from 0.041 to 0.086; 30-120 seconds is a rapid fusion phase, during which the FRET efficiency rapidly increases to 0.264; and 120-180 seconds is the fusion completion phase, during which the efficiency increase slows down and eventually reaches 0.352. Figure 4 As shown, the fusion completion analysis indicates that vesicle fusion reached 93.4% of the expected maximum efficiency at 180s.

[0157] The system's adaptive gain control function played a crucial role throughout the detection process. For example... Figure 5 As shown, the dynamic adjustment of laser power density ensures the stability of signal quality, especially in the later stages of fusion when the signal intensity is high. The system effectively prevents detector saturation by reducing the laser power. The fluorescence signal amplification module automatically starts when detecting weak signals in the early stages, amplifying the signal intensity from the original 15,000 relative fluorescence units to 45,000 relative fluorescence units, significantly improving the signal-to-noise ratio.

[0158] The establishment of a quantitative evaluation system provides a standardized analytical framework for vesicle fusion research. Through statistical analysis, the team determined the statistical noise level to be 0.008, the measurement precision to be ±0.012, and the biological significance level to be set at 0.05. These parameters ensure the reliability and biological significance of the experimental results. Figure 6 As shown, the repeatability test results indicate that the standardized FRET efficiency curves from the five independent measurements are highly consistent, with a coefficient of variation of less than 5.2%, demonstrating the stability and reproducibility of the detection method.

[0159] Throughout the detection process, environmental interference levels were controlled below 2.1 relative fluorescence units, and the system gain stability coefficient remained above 0.92, ensuring data accuracy. The detector dynamic range reached [value missing]. This fully meets the experimental requirements. The photobleaching rate constant, determined by continuous excitation, is 0.0034. Within acceptable limits.

[0160] This invention represents a significant technological advancement over traditional vesicle fusion detection methods. Traditional methods primarily rely on static fluorescence imaging or simple fluorescence intensity measurements, lacking the ability to accurately capture the dynamic characteristics of the fusion process. This invention, by establishing a fluorescence resonance energy transfer detection system, achieves real-time dynamic monitoring of the vesicle fusion process, accurately reflecting distance changes in the membrane structure during fusion. Traditional methods often employ simple averaging or filtering techniques in signal processing, easily losing crucial dynamic information. This invention innovatively employs a transformation technique from ultrasparse to dense matrices, combined with an adaptive gain control strategy, which not only preserves the spatiotemporal continuity of the original signal but also effectively eliminates systematic errors and noise interference. At the data analysis level, traditional methods lack a standardized quantitative evaluation system, making it difficult to compare results under different experimental conditions. The standardized fluorescence resonance energy transfer efficiency calculation model and kinetic parameter analysis framework established in this invention provide a unified evaluation standard for vesicle fusion research, greatly improving the scientific rigor and comparability of the research results.

[0161] It should be noted that the variables involved in this invention are explained in detail in Table 2 below.

[0162] Table 2 Variable Explanation Table

[0163]

[0164] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer, characterized in that, A multi-threshold fluorescence signal gain modulation system and a fluorescence signal matrix processing model were established to achieve hierarchical management and data processing of fluorescence signal intensity. This included preparing dual-fluorescent labeled extracellular vesicles by labeling donor extracellular vesicles with the lipid-soluble fluorescent dye DiO and recipient extracellular vesicles with the lipid-soluble fluorescent dye DiI, followed by ultrasonic dispersion to ensure uniform distribution of the fluorescent dyes on the vesicle membrane surface. A fluorescence resonance energy transfer detection system was constructed by mixing labeled donor and recipient vesicles at a 1:1 volume ratio in a low autofluorescence buffer solution with sodium ascorbate added. A dynamic fluorescence monitoring system was also established, using excitation... A 488 nm laser excites a donor fluorescent dye, while simultaneously monitoring the fluorescence intensity changes at donor emission wavelengths of 501 nm and acceptor emission wavelengths of 565 nm. Fluorescence signal intensity data are collected and processed for gain modulation. The collected fluorescence intensity data are used to construct an ultrasparse fluorescence signal matrix, and the fluorescence signal intensity is graded according to a threshold. A fluorescence signal processing matrix model is established, and the ultrasparse fluorescence signal matrix is ​​transformed into a dense fluorescence signal matrix to construct a fluorescence signal gain matrix. The fluorescence resonance energy transfer efficiency parameter is calculated, and the normalized fluorescence resonance energy transfer efficiency is obtained through matrix operations. Output the results of extracellular vesicle fusion kinetic analysis.

2. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 1, characterized in that, The ultrasonic dispersion process is a physical treatment method that uses the cavitation effect of ultrasound to redistribute fluorescent dye molecules on the vesicle membrane surface, eliminate local aggregation, and improve labeling uniformity.

3. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 2, characterized in that, The concentration of sodium ascorbate was 1 mM, and the total volume of the mixture was controlled at 200 μL.

4. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 3, characterized in that, The ultrasonic dispersion process has a power of 50W and a processing time of 30s.

5. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 4, characterized in that, The laser power was set to 10% of the total power, and the fluorescence intensity change was continuously recorded over 180 seconds, with data points collected every 5 seconds.

6. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 5, characterized in that, The low autofluorescence buffer refers to a buffer system with a fluorescence background signal of less than 10 relative fluorescence units, prepared using HEPES buffer, with the pH value maintained at 7.4, and free of organic solvents and high concentrations of metal ions.

7. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 6, characterized in that, The ultra-sparse fluorescence signal matrix is ​​a matrix constructed by collecting fluorescence intensity data according to time series and wavelength dimensions, wherein the proportion of non-zero elements is less than 10%, and the matrix dimension is the number of time points × the number of wavelengths, used to store the original fluorescence signal data.

8. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 7, characterized in that, The dense fluorescence signal matrix is ​​a complete data matrix obtained by interpolating, filling, and smoothing the ultrasparse fluorescence signal matrix. The proportion of non-zero elements is greater than 90%, thus preserving the spatiotemporal continuity information of the fluorescence signal.

9. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 8, characterized in that, The fluorescence signal gain matrix is ​​a correction coefficient matrix constructed based on the signal intensity differences at different wavelengths and time points. It is used to compensate for the gain of the dense fluorescence signal matrix and eliminate the effects of system errors and noise.

10. The method for detecting extracellular vesicle fusion using fluorescence resonance energy transfer according to claim 9, characterized in that, The extracellular vesicle fusion kinetic analysis results were obtained by fitting the vesicle fusion rate constant to the fluorescence resonance energy transfer efficiency time curve, analyzing the time characteristic parameters of the fusion process and the degree of fusion completion, and establishing a quantitative evaluation system for fusion events.

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