A data processing method for polymer detection using multi-channel fluorescence correlation spectroscopy

Abstract

The present invention discloses a multi-channel fluorescence correlation spectroscopy (FCS) polymer detection data processing method, which relates to the field of biological detection and spectral analysis technology. The method comprises the following steps: calculating the optical path difference of each pair of channels one by one and correcting it based on a calibrated data set; filtering out pseudo-correlation signals; obtaining a noise-filtered timestamp data set; labeling the target polymer molecules as donor probes and acceptor probes based on the noise-filtered timestamp data set; calculating the temporal correlation between the donor and the acceptor; obtaining a timestamp pair set of FRET events; performing FCS analysis on the timestamp pair set of FRET events, calculating the diffusion time and comparing it with the known monomer diffusion time, determining whether the FRET event is a true polymer signal, and outputting a set of polymer event detection results. The present invention effectively reduces the time deviation caused by physical distance by adjusting the physical optical path differences between different channels.

Description

Technical Field

[0001] The present invention relates to the fields of biological detection and spectral analysis, and in particular to a method for processing polymer detection data of multi-channel fluorescence correlation spectroscopy. Background Art

[0002] In recent decades, multichannel fluorescence correlation spectroscopy (FCS) has made rapid progress in physics and molecular biology. Early FCS techniques often relied on single-channel detection, measuring the diffusion behavior and concentration changes of molecules by analyzing the autocorrelation function of fluorescence intensity. With advances in photodetector and time-to-digital converter technology, FCS can now simultaneously acquire fluorescence signals from multiple detection channels. Combined with fluorescence resonance energy transfer analysis, FCS has been widely used to study protein interactions, polymer formation, and dynamic conformations.

[0003] Existing data processing methods for multimer detection suffer from numerous shortcomings. Transmission delays caused by optical path length differences are often ignored or compensated for through rough estimates, failing to accurately measure and correct for the physical differences in optical fibers across channels. Filtering of spurious correlation signals often relies on simple statistical methods, lacking mechanisms to eliminate instrument jitter and environmental interference, leading to increased false positive rates in multimer detection. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a polymer detection data processing method of multi-channel fluorescence correlation spectroscopy to solve the problems of inaccurate correction of optical path delay and interference of pseudo-correlation signals in existing polymer detection data processing methods.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy, comprising collecting and calibrating photon arrival timestamp data to obtain a calibrated data set;

[0008] Based on the calibrated data set, the optical path difference of each pair of channels is calculated and corrected one by one, and the false correlation signal is filtered to obtain a noise-filtered timestamp data set;

[0009] Based on the noise-filtered timestamp dataset, the target polymer molecules are labeled as donor probes and acceptor probes, and the temporal correlation between the donor and the acceptor is calculated to obtain a set of timestamp pairs of FRET events.

[0010] Perform FCS analysis on the timestamp pair set of FRET events, calculate the diffusion time, and compare it with the known monomer diffusion time to determine whether the FRET event is a true multimer signal, and output the multimer event detection result set.

[0011] As a preferred embodiment of the method for processing polymer detection data in multi-channel fluorescence correlation spectroscopy of the present invention, the method further comprises: obtaining a calibrated data set by collecting photon arrival timestamp data using a high-speed time-to-digital converter and saving the data in the form of a time series to form an original timestamp data set;

[0012] Set the emission time, calculate the deviation between the photon arrival timestamp data and the emission time, obtain the average time offset, and build a channel time offset compensation table;

[0013] The original timestamp data set and channel time offset compensation table are loaded and calibrated using LabVIEW software to obtain the calibrated data set.

[0014] As a preferred solution of the polymer detection data processing method of multi-channel fluorescence correlation spectroscopy described in the present invention, the calculation of the optical path difference of each pair of channels one by one and correction thereof refers to using a laser rangefinder to measure the physical length of the optical fiber of each channel, calculating the transmission delay time difference of the optical fiber length and correcting it using a programmable delay generator, and outputting an optical path compensation timestamp data set.

[0015] As a preferred embodiment of the method for processing polymer detection data by multi-channel fluorescence correlation spectroscopy of the present invention, obtaining the noise-filtered timestamp data set comprises loading the optical path compensation timestamp data set using MATLAB software and storing it as a variable, reading the photon arrival timestamp data from the variable and performing pairing, and calculating the time difference between each pair of photon arrival timestamp data;

[0016] The instrument jitter threshold is set, and the time difference between each pair of photons arriving at the timestamp data is compared with the instrument jitter threshold to obtain the marker comparison result. The marker comparison result is used to eliminate the pseudo-correlation signal and obtain the noise-filtered timestamp data set.

[0017] As a preferred embodiment of the multimer detection data processing method of the multi-channel fluorescence correlation spectroscopy of the present invention, wherein: the labeling of the target multimer molecule as a donor probe and an acceptor probe refers to selecting Cy3 as the donor probe, Cy5 as the acceptor probe, and selecting a target multimer molecule solution;

[0018] The storage solution of the Cy3 donor probe and the Cy5 acceptor probe is extracted and added to the target polymer molecule solution to form a mixed solution. After the mixed solution is incubated and dialyzed, the absorbance value of the mixed solution is measured using a UV-visible spectrophotometer and labeled to form labeled target polymer molecules.

[0019] As a preferred embodiment of the multi-channel fluorescence correlation spectroscopy data processing method of the present invention, obtaining a timestamp pair set of FRET events refers to setting up a multi-channel fluorescence correlation spectroscopy device, a FRET donor channel, and a FRET acceptor channel, dripping a labeled target multi-polymer molecule solution into a sample well on a glass slide, and collecting fluorescence signal timestamps of the FRET donor channel and the FRET acceptor channel to form a donor channel fluorescence signal timestamp dataset and an acceptor channel fluorescence signal timestamp dataset;

[0020] Set a time difference threshold, use PicoQuantTimeHarp software to pair the fluorescence signal timestamps of the FRET donor channel and the FRET acceptor channel, calculate the time difference between the fluorescence signal timestamps of each pair of FRET donor channel and FRET acceptor channel, and mark it as a potential FRET event when the time difference between the fluorescence signal timestamps of each pair of FRET donor channel and FRET acceptor channel is less than the time difference threshold;

[0021] Setting the photon intensity standards for the FRET donor channel and the FRET acceptor channel, and marking potential FRET events that meet the photon intensity standards as valid FRET events;

[0022] The potential FRET events and the effective FRET events are integrated to form a FRET event set, the energy transfer efficiency between the donor and acceptor photon intensities is calculated, the calculation results are organized into an energy transfer efficiency list, and the energy transfer efficiency list and the FRET event set are combined into a timestamp pair set of FRET events.

[0023] As a preferred embodiment of the multi-channel fluorescence correlation spectroscopy data processing method of the present invention, the FCS analysis of the timestamp pair set of FRET events refers to using Zeiss ConfoCor software to arrange the timestamp sequence in the timestamp pair set of FRET events in chronological order to form an autocorrelation curve and perform fitting processing, extracting the diffusion time from the fitted autocorrelation curve and integrating it into a diffusion time list.

[0024] As a preferred embodiment of the method for processing polymer detection data by multi-channel fluorescence correlation spectroscopy of the present invention, the outputting of a set of polymer event detection results comprises comparing the diffusion time in the diffusion time list with the known monomer diffusion time, calculating the difference between the diffusion time and the monomer diffusion time, and obtaining a diffusion time comparison table;

[0025] The diffusion time comparison table and energy transfer efficiency list are used to determine the efficiency of the FRET event and output a set of multimer event detection results.

[0026] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the multi-channel fluorescence correlation spectroscopy polymer detection data processing method as described in the first aspect of the present invention is implemented.

[0027] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the multi-channel fluorescence correlation spectroscopy polymer detection data processing method as described in the first aspect of the present invention is implemented.

[0028] The present invention ensures that data from each channel can be compared on the same timescale. A hardware time delay compensator adjusts for physical optical path differences between channels, effectively reducing time deviations caused by physical distance. Furthermore, the instrument's jitter threshold is used to eliminate spurious correlation signals, reducing background noise interference and significantly improving signal fidelity and reliability, enabling researchers to more accurately identify true FRET events. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 Schematic diagram of the multi-channel fluorescence correlation spectroscopy detection method in Example 1.

[0031] Figure 2 This is a flow chart of channel optical path difference correction and false signal filtering in Example 1.

[0032] Figure 3 Schematic diagram of the optical path compensation mechanism in Example 1.

[0033] Figure 4Flowchart of FCS diffusion time analysis and multimer determination in Example 1. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0036] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0037] Reference Figures 1 to 4 This embodiment provides a method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy, comprising the following steps:

[0038] S1. Collect and calibrate photon arrival timestamp data to obtain a calibrated data set.

[0039] The following steps are included:

[0040] S1.1. Connect the micro-atomic clock device to the signal input of the photodetector, ensuring that each photodetector can receive the unified time reference signal provided by the micro-atomic clock (which must be controlled below 100 picoseconds). After completing the connection, turn on the micro-atomic clock and perform initial calibration. During the calibration process, verify the output frequency of the micro-atomic clock using the built-in reference clock signal, such as stabilizing at around 10 MHz. Connect a high-speed time-to-digital converter (TDC) to the output of each photodetector. The specific operations are as follows:

[0041] Use a high-bandwidth coaxial cable to connect the photodetector and the high-speed time-to-digital converter, and set the sampling rate of the high-speed time-to-digital converter to, for example, 10 GHz.

[0042] S1.2. Prepare a standard single-photon source (a quantum dot-based single-photon emitter is preferred). Place the standard single-photon source at the entrance of the optical path and evenly distribute the emitted pulse signal to each detection channel using an optical lens assembly. Simultaneously with the pulse signal emission, trigger the high-speed time-to-digital converters (HTDCs) in all channels to synchronously record the photon arrival timestamps. The recording process is as follows: When a photon emitted by the quantum dot is captured by a photodetector, it converts the optical signal into an electrical signal and transmits it to the HTDC. The HTDC then timestamps each electrical signal based on the time reference of a micro-atomic clock. For example, if channel A records the arrival time of the first photon at 0.1 ns and the second at 1.3 ns, the generated timestamp sequence is {t_A1 = 0.1 ns, t_A2 = 1.3 ns, ...}. Channel B might record {t_B1 = 0.3 ns, t_B2 = 1.5 ns, ...}.

[0043] After recording is completed, the photon arrival timestamps generated by the high-speed time-to-digital converter of each channel are transmitted to the computer through the data acquisition card and saved in the form of a time series. For example, the timestamp sequence of channel A is stored as {t_A1, t_A2, t_A3, ...}, and that of channel B is stored as {t_B1, t_B2, t_B3, ...}. Each sequence is labeled with the channel number and the recording start time (based on the reference time of the micro-atomic clock, for example, 2025-03-10, 10:00:00.000000000ns). The storage format uses high-precision floating-point numbers to ensure that the timestamp accuracy is not lost due to data conversion.

[0044] S1.2. Use the transmitted periodic pulse signal as a reference and set the transmission time. For example, set the transmission time interval of the pulse signal to 1000ns, generate a reference time series {t_ref1 = 0ns, t_ref2 = 1000ns, t_ref3 = 2000ns, ...}, and save it as a temporary file, such as Reference Time Series.

[0045] Calculate the deviation between the photon arrival timestamp and the emission time for each channel. Specifically, compare the photon arrival timestamps of channel A and channel B with the reference time series one by one. For example, if the emission time of the first pulse signal is t_ref1 = 0ns, the photon arrival timestamp recorded by channel A is t_A1 = 0.1ns, and the deviation is 0.1ns - 0ns = 0.1ns. The photon arrival timestamp recorded by channel B is t_B1 = 0.3ns, and the deviation is 0.3ns - 0ns = 0.3ns.

[0046] Continuing to compare subsequent pulses, for example, if the emission time of the second pulse signal is t_ref2 = 1000ns, Channel A's t_A2 = 1.3ns, resulting in a deviation of 1.3ns - 1000ns = -998.7ns (considering a relative deviation of 0.3ns). Channel B's t_B2 = 1.5ns, resulting in a deviation of 1.5ns - 1000ns = -998.5ns (a relative deviation of 0.5ns). To improve accuracy, select at least multiple pulse signals (e.g., 1000) for statistical analysis, and calculate the time offset after recording each deviation. For example, the sum of 1000 deviations for Channel A is 100ns (i.e., 1000 × 0.1ns), resulting in an average time offset of 0.1ns. The sum of 1000 deviations for Channel B is 300ns (i.e., 1000 × 0.3ns), resulting in an average time offset of 0.3ns.

[0047] S1.3. Construct a channel time offset compensation table based on the calculated average time offset. The specific format is a text file, such as {channel A: +0.1ns, channel B: +0.3ns}, and save it as the channel time offset compensation table.

[0048] Start LabVIEW on the computer and open the data processing interface. Use the file read function to load the inter-channel time offset compensation table and the original timestamp data set. Create a processing script in LabVIEW, then read the values ​​in the channel time offset compensation table and iterate through each photon arrival timestamp in channel A, subtracting 0.1ns one by one. For example, t_A1 is corrected from 0.1ns to 0ns, t_A2 is corrected from 1.3ns to 1.2ns, and t_A3 is corrected from 2.5ns to 2.4ns. Subtract 0.3ns from each photon arrival timestamp in channel B. For example, t_B1 is corrected from 0.3ns to 0ns, t_B2 is corrected from 1.5ns to 1.2ns, and t_B3 is corrected from 2.7ns to 2.4ns. (The basis of calibration is to align the photon arrival timestamp with the theoretical emission time. For example, if the theoretical time t_ref1 = 0ns, the calibrated t_A1 and t_B1 are both 0ns, eliminating the deviation). After calibration is completed, a complete calibrated data set is generated. For example, channel A is {t_A1'=0ns, t_A2'=1.2ns, t_A3'=2.4ns, ...}, and channel B is {t_B1'=0ns, t_B2'=1.2ns, t_B3'=2.4ns, ...}.

[0049] S2. Based on the calibrated data set, calculate and correct the optical path difference of each pair of channels one by one, and filter the pseudo-correlation signal to obtain a noise-filtered timestamp data set.

[0050] The following steps are included:

[0051] S2.1. Based on the calibrated data set, measure the physical length of the optical fiber in each channel. Specifically, use a high-precision laser rangefinder to measure the length of the fiber segment by segment along the fiber path. For example, if the total length of the fiber from the photodetector to the beam splitter is 1.05 meters for Channel A and 1.00 meters for Channel B, the length difference is 0.05 meters. During measurement, ensure that the optical fiber is straight and free of bends, and maintain a stable ambient temperature of approximately 25°C to minimize thermal expansion and contraction. Record the measurement results on a paper spreadsheet, noting the channel number and measurement date.

[0052] Based on the measured fiber lengths of 1.05 meters for Channel A and 1.00 meters for Channel B, the transmission delay caused by this fiber length difference was calculated. Specifically, considering the speed of light in optical fiber, which is 300 million meters per second and a refractive index of 1.5, the speed of light in optical fiber is 200 million meters per second. Given the 0.05-meter difference in the fiber lengths between Channels A and B, the time required for light to traverse this length difference was calculated: 0.05 meters divided by 200 million meters per second equals 0.00000000025 seconds (or 0.25 nanoseconds).

[0053] S2.2. Next, the transmission delay caused by differences in optical path length needs to be corrected. Specifically, a hardware time delay compensator, using a programmable delay generator (such as the Stanford Research DG645), is connected to the front end of each channel's high-speed time-to-digital converter. The specific installation steps are as follows: Disconnect the photodetector output signal line of Channel A and connect it to the input of the programmable delay generator. Then, connect the output of the programmable delay generator to the input of the high-speed time-to-digital converter, while Channel B remains connected as before. Start the programmable delay generator and enter the control interface. Use the digital knob or serial port command to set the delay value of Channel A to 0.25 nanoseconds. After setting, lock the device parameters and power on.

[0054] Start a standard single-photon source, maintaining an emission frequency of approximately 1 MHz and a pulse interval of 1000 nanoseconds. Recording begins at the start of the calibrated timestamp dataset (e.g., 2025-03-10, 10:00:00.000000000ns). After the photon signal is transmitted via optical fiber to the photodetector, the signal in channel A is delayed by 0.25 nanoseconds using a programmable delay generator. The high-speed time-to-digital converter then records the photon arrival timestamps, for example, t_A1'' is recorded as 0.25 nanoseconds and t_A2'' as 1000.25 nanoseconds. Channel B is recorded directly, for example, t_B1'' is recorded as 0 nanoseconds and t_B2'' is recorded as 1000 nanoseconds. Continue recording for 10 seconds, generating approximately 10,000 photon arrival timestamps to ensure sufficient data. During acquisition, use an oscilloscope to monitor the programmable delay generator's output waveform in real time. Connect the oscilloscope probes to the programmable delay generator output of Channel A and the photodetector output of Channel B, respectively. Set the time resolution to 10 picoseconds per division and observe whether the propagation delay difference between the two channels remains stable at 0.25 nanoseconds. If the propagation delay difference exceeds 20 picoseconds, for example, reaching 0.27 nanoseconds, pause the acquisition, fine-tune the delay value to 0.25 nanoseconds, and restart. After acquisition, transfer the corrected propagation delay difference to the computer via the data acquisition card and save it as a temporary file. For example, the file name for Channel A is "Optical Path Compensation Timestamp Dataset, Channel A," with the content {t_A1'' = 0.25 ns, t_A2'' = 1000.25 ns, ...}.

[0055] S2.3. Filter out spurious correlation signals using the optical path compensated timestamp dataset. Spurious correlation signals in multi-channel fluorescence correlation spectroscopy experiments are false timestamp overlaps caused by instrument noise, environmental interference, or non-target photon events, rather than actual photon signals. For example, instrument jitter or background light may cause channel A and channel B to record photon signals simultaneously within a very short period of time (e.g., less than 0.5 nanoseconds). These specific events are not due to fluorescent molecules in the sample but rather random interference. These photon signals can interfere with subsequent analysis and therefore require filtering.

[0056] Set the instrument jitter threshold based on the time resolution of the high-speed time-to-digital converter and the response time jitter of the photodetector (typically in the range of 200 to 300 picoseconds). This threshold can be adjusted based on actual conditions. Start MATLAB and open the work interface. Select Import Data from the menu bar to load the optical path compensation timestamp dataset and store it as variables ChannelA and ChannelB (Channel refers to the channel). Write a processing script. First, pair the photon arrival timestamps of Channel A and Channel B in chronological order. Specifically, create a loop to read the photon arrival timestamps from variables ChannelA and ChannelB one by one. For example, the first pair is t_A1'' = 0.25 ns and t_B1'' = 0 ns, and the second pair is t_A2'' = 1.45 ns and t_B2'' = 1.2 ns.

[0057] For each channel pair, calculate the time difference between the photon arrival timestamps by subtracting the smaller value from the larger value. For example, t_A1'' - t_B1'' yields 0.25 ns, and t_A2'' - t_B2'' yields 1.45 ns - 1.2 ns = 0.25 ns. Compare the time difference between the photon arrival timestamps to the instrument jitter threshold. If the time difference between the photon arrival timestamps is less than 0.5 ns, for example, 0.25 ns, it is considered a false correlation signal. (This is because true photon events can have larger time intervals due to molecular motion or optical path differences, while false correlation signals are more likely caused by instrument jitter or random noise.) False correlation signal timestamp pairs are marked as invalid. Create an array, Status, and assign each timestamp pair a value of 0 (false signal) or 1 (reserved), for example, {t_A1'', t_B1'', 0.25 ns, 0} and {t_A2'', t_B2'', 0.25 ns, 0}. If the time difference between the photon arrival timestamps is greater than or equal to 0.5 nanoseconds, for example, if t_A3'' = 2.65ns and t_B3'' = 2.05ns, the time difference is 0.6ns, then the flag is set to 1, indicating retention. The flag comparison result is saved to the file timestamp status table.

[0058] S2.4. Eliminate pseudo-correlation signals based on the tag comparison results and continue processing in MATLAB. First, load the timestamp status table, read the Status array, and then create new arrays ValidA and ValidB to store and retain the photon arrival timestamps. Specifically, traverse each pair of photon arrival timestamps. If the Status array is 0, for example, {t_A1'', t_B1'', 0.25ns, 0}, skip it. If it is 1, for example, {t_A3'', t_B3'', 0.6ns, 1}, then append t_A3''=2.65ns to ValidA and t_B3''=2.05ns to ValidB. Divide the traversed photon arrival timestamps into segments of 1000 pairs and execute them segment by segment. For example, 800 pairs marked as 0 in the first 1000 pairs are eliminated, and 200 pairs are retained and saved to temporary files to filter the intermediate data set channel A_001 and filter the intermediate data set channel B_001.

[0059] After processing each segment, check the array length. For example, length(ValidA) should be 200 to ensure correctness. After processing all segments, merge the temporary files to generate a complete noise-filtered timestamp dataset. For example, for channel A, {t_A_valid = 2.65ns, ...}, and for channel B, {t_B_valid = 2.05ns, ...}. If a segment retains an unusually small number of array pairs, for example, fewer than 50, backtrack to check if the original arrays were corrupted. Save the final results to the noise-filtered timestamp dataset channel A and the noise-filtered timestamp dataset channel B.

[0060] S3. Based on the noise-filtered timestamp dataset, label the target multimer molecule with a donor probe and an acceptor probe, and calculate the temporal correlation between the donor and the acceptor to obtain a timestamp pair set of FRET events.

[0061] S3.1. Using the noise-filtered timestamp dataset, prepare to label the target multimers using fluorescence resonance energy transfer (FRET). Specifically, select Cy3 as the FRET donor probe (emission wavelength range: 550-600 nm) and Cy5 as the FRET acceptor probe (emission wavelength range: 650-700 nm). Before labeling, prepare a target multimer solution and place it in a centrifuge tube. Adjust the solution concentration to 10 μM in phosphate-buffered saline (pH approximately 7.4). Remove the Cy3 probe stock solution from the refrigerator and thaw it at room temperature for 30 minutes. Use a micropipette to draw 5 μL of the Cy3 solution and add it to the target multimer solution. Gently vortex for 10 seconds to ensure even distribution. Next, take the Cy5 probe stock solution, thaw it, and draw 5 μL of it into the target multimer solution. Vortex again for 10 seconds to form a mixed solution.

[0062] S3.2. Incubate the mixed solution in a constant temperature water bath (e.g., for 2 hours) to promote chemical binding of the Cy3 and Cy5 probes to the target polymer molecules. After incubation, dialyze the mixed solution using a dialysis bag. Perform the dialysis process at approximately 4°C using phosphate-buffered saline as the dialysate, changing the dialysate every 2 hours.

[0063] After dialysis, the absorbance of the mixed solution at the Cy3 absorption peak (552 nm) and the Cy5 absorption peak (650 nm) was measured using a UV-Vis spectrophotometer. The specific measurement process was to start the UV-Vis spectrophotometer (Shimadzu UV-1800) and connect it to the power supply. Press the Power button. After the photometer self-test is complete, remove two quartz cuvettes: one for the blank control and the other for the sample. Fill the blank cuvette with 5 ml of phosphate-buffered saline (the same pH as the dialysate, approximately 7.4) and place it in the instrument's sample reservoir. Close the sample cover and press the AutoZero button on the instrument control panel to return the absorbance of the Cy3 and Cy5 absorption peaks to zero.

[0064] Use a micropipette to transfer 1 mL of the mixed solution to a sample cuvette (wipe the outside of the sample cuvette to remove fingerprints or water stains, and ensure no bubbles are formed). Place the sample cuvette in the instrument's sample well and close the sample cover. Select Wavelength mode on the instrument control panel and press Enter. After the instrument stabilizes, record the displayed absorbance value, for example, 0.45. Then, adjust the wavelength to 650 nm and repeat the process, recording the absorbance value, for example, 0.42.

[0065] S3.3. Label the recorded absorbance values. Labeling is successful when the Cy3 and Cy5 probes effectively bind to the target polymer molecule and the ratio of Cy3 and Cy5 probes is close to the same, such as 1:1. Specifically, when the absorbance values ​​of the Cy3 absorption peak and the Cy5 absorption peak are both greater than the same value (such as 0.2, indicating sufficient probe concentration), and the absorbance ratio of the Cy3 and Cy5 probes is within the same numerical range. For example, if the absorbance value of the Cy3 absorption peak is 0.45 and the Cy5 absorption peak is 0.42, the absorbance ratio is 0.45 / 0.42≈1.07, indicating that the binding amounts of Cy3 and Cy5 are balanced, indicating successful labeling.

[0066] When the absorbance values ​​of Cy3 and Cy5 probes are too low, for example, the absorbance value of the Cy3 absorption peak is 0.1 and the absorbance value of the Cy5 absorption peak is 0.08, it indicates that the probes are not effectively bound, which may be due to insufficient incubation time or inaccessible molecular sites. If the absorbance ratio deviates too much, for example, the absorbance value of the Cy3 absorption peak is 0.6 and the absorbance value of the Cy5 absorption peak is 0.2, and the ratio is 3, it indicates that Cy3 is excessively bound and Cy5 is insufficient, and the labeling ratio is unbalanced. If the absorbance of the dialysate is high, for example, the Cy3 absorption peak is 0.15, it indicates that a large amount of free Cy3 has not been removed and the labeling purity is insufficient (when the above three specific situations occur, the absorbance value is marked as failure, and the probe concentration needs to be readjusted or the incubation time needs to be extended).

[0067] In order to ensure that the labeling position meets the FRET distance requirement (i.e., less than 10 nanometers), the known target polymer molecular structure data, that is, the three-dimensional spatial structure information of the target polymer molecule, is obtained from experiments through technologies such as nuclear magnetic resonance (NMR) or cryo-electron microscopy (Cryo-EM).

[0068] Taking multimeric proteins as an example, the three-dimensional spatial structure information is stored in the Protein Data Bank (PDB), including the amino acid sequence, spatial coordinates and secondary structure information of each subunit in the multimer. Two specific sites close to the active site of the molecule are selected for labeling (the two specific points refer to the two chemical reaction active sites on the target multimeric molecule that are suitable for labeling FRET donors and acceptors, that is, specific amino acid residues. For example, in a dimeric protein, the specific sites will select a pair of lysine residues close to the molecular interface because the side chain amino group of the lysine residue easily reacts with the N-hydroxysuccinimide ester of the probe to form a covalent bond).

[0069] Confirm the distance between the labeled sites through structural analysis to ensure that the FRET distance requirements are met. The specific confirmation process is to obtain the crystal structure file of the target multimer molecule from the public protein database and download it using PDB (Protein Data Bank). Then, use PyMOL molecular visualization software to open the crystal structure file of the target multimer molecule, select the Measurement tool, click the Distance option under the Wizard menu, and locate two specific sites, such as the Lys-45 nitrogen atom of subunit A and the Lys-62 nitrogen atom of subunit B. After clicking on the two specific points, PyMOL molecular visualization software automatically calculates and displays the spatial distance between the specific points, for example, 8.2 nanometers. Repeat the measurement three times and take the average value, for example 8 nanometers, to confirm that it is less than 10 nanometers and meets the FRET distance requirement.

[0070] S3.4. Configure the multi-channel fluorescence correlation spectroscopy device to collect fluorescence signal timestamps. Specifically, start the multi-channel fluorescence correlation spectroscopy device, turn on the power switch, set channel A as the FRET donor channel, and connect a 550 to 600 nm bandpass filter. Set channel B as the FRET acceptor channel and connect a 650 to 700 nm bandpass filter. Use an optical microscope to adjust the laser light source. The laser light source refers to the light source used to excite fluorescent probes (FRET donor Cy3 and acceptor Cy5) on target polymer molecules in a multi-channel fluorescence correlation spectroscopy device. Use a YAG frequency-doubled laser with an excitation wavelength of 532 nanometers. The specific adjustment process is to turn on the On button of the YAG frequency-doubled laser, wait for about 5 minutes of preheating (when the output wavelength is displayed as 532 nanometers, it indicates stability), set the power to 50 microwatts, guide the laser beam into the optical microscope, adjust the mirrors in the light path (such as mirrors M1 and M2), turn the fine-tuning screw on the mirror frame, allow the laser beam to pass through the beam splitter and then enter the objective lens, then use the eyepiece to observe the light spot while rotating the screw until the light spot is centered, place the slide (with the sample added) on the stage, move the stage handle up and down, observe the size of the light spot below the objective lens, and reduce the light spot to the minimum (about 1 micron in diameter) using the focusing knob of the optical microscope.

[0071] S3.5. Once the multi-channel fluorescence correlation spectroscopy instrument is configured, pipette the labeled target polymer solution into the sample well on the glass slide. Cover with a coverslip to prevent air bubbles. Secure the glass slide to the microscope stage and adjust the stage position so that the center of the sample well aligns with the laser focus. Start the high-speed time-to-digital converter (HTDC) and set the sampling rate (e.g., 10 GHz). Press the synchronized acquisition button on the instrument control panel to simultaneously trigger the photodetectors of channels A and B to begin recording fluorescence signal timestamps. Set the acquisition time to 10 minutes. After acquisition, the HTDC saves the fluorescence signal timestamps for channels A and B, as the donor channel A fluorescence signal timestamp dataset and the acceptor channel B fluorescence signal timestamp dataset (both fluorescence signal timestamp datasets contain photon count data recorded by the HTDC). Channel A is recorded as {t_A_valid1, t_A_valid2, ...}, and channel B is recorded as {t_B_valid1, t_B_valid2, ...}.

[0072] S3.6. Use PicoQuantTimeHarp software to analyze the temporal correlation between the donor and acceptor fluorescence signal timestamp datasets. Specifically, open PicoQuantTimeHarp software, click the Analysis menu on the main interface, set up the FRET Event window, click the TimeWindow input box, select nanoseconds, and press Apply to confirm.

[0073] After setting a time difference threshold (based on the physical properties of FRET, which refers to the transfer of resonance energy from the donor to the acceptor when the distance between the donor and acceptor probes is less than 10 nanometers), the threshold reflects the close temporal correlation between the donor and acceptor emissions and is set to 1 nanosecond. PicoQuant TimeHarp software then automatically pairs the fluorescence signal timestamps of the donor and acceptor channels. The matching process involves reading the timestamp sequences of the fluorescence signal timestamp dataset for donor channel A and the fluorescence signal timestamp dataset for acceptor channel B. The software's TCSPC Correlation window automatically scans the two timestamp sequences in chronological order, searching for the closest corresponding points in time, starting from the beginning. For example, the software starts at t_A_valid1, traverses the fluorescence signal timestamps of channel B, finds the first t_B_valid value that is closest to t_A_valid1 (for example, t_B_valid1), and then moves to t_A_valid2 as a pair, repeating the traversal process until t_B_valid2 is found, and so on.

[0074] After pairing is complete, the time difference between the fluorescence signal timestamps of each pair of donor and acceptor channels is calculated. The calculation process is as follows: For each pair of fluorescence signal timestamps, such as t_A_valid1 and t_B_valid1, PicoQuantTimeHarp software determines which fluorescence signal timestamp is earliest, for example, t_B_valid1 is earlier than t_A_valid1. The software then counts from the earliest time value, incrementally increasing the time unit (in 100-picosecond steps) until it reaches the latest time value, while recording the total number of elapsed time steps. For example, if t_B_valid1 is 0 nanoseconds and t_A_valid1 is 0.3 nanoseconds, the software starts at 0 nanoseconds and increments by 0.1 nanoseconds three times (i.e., from 0 to 0.1, 0.1 to 0.2, and 0.2 to 0.3), resulting in a time difference of 0.3 nanoseconds. If t_A_valid1 is earlier than t_B_valid1, the software counts backwards and takes the absolute value.

[0075] When the time difference between the fluorescence signal timestamps of each pair of donor and acceptor channels is less than the time difference threshold (i.e., 1 nanosecond), PicoQuantTimeHarp software marks the pair of donor and acceptor fluorescence signal timestamps with a time difference of less than 1 nanosecond as a potential FRET event. A FRET event occurs during fluorescence resonance energy transfer (FRET), when the donor probe (Cy3) absorbs laser energy but does not directly emit fluorescence. Instead, it nonradiatively transfers energy to a nearby acceptor probe (Cy5) smaller than 10 nanometers, causing the acceptor to emit fluorescence. A FRET event manifests as a decrease in the fluorescence signal recorded by donor channel A (i.e., fluorescence quenching) and a simultaneous increase in the photon signal recorded by acceptor channel B. The time difference between the fluorescence signal timestamps of the donor and acceptor channels is extremely small, such as less than 1 nanosecond.

[0076] Subsequently, the PicoQuantTimeHarp software was used to extract the photon intensity (i.e., photon count) of the fluorescence signal timestamp of each pair of donor and acceptor channels from the donor fluorescence signal timestamp dataset and the acceptor fluorescence signal timestamp dataset using the timestamp index (e.g., the serial number of t_A_valid1). For example, 10 photons were found at t_A_valid1 and 25 photons were found at t_B_valid1, which were extracted as intensity values.

[0077] Set the photon intensity standards for the FRET donor and acceptor channels. Specifically, the FRET donor channel's photon intensity standard is set to the baseline photon intensity (the average fluorescence photon intensity of the donor and acceptor probes when no FRET event occurs, serving as a reference value). This baseline intensity is 50% of the typical photon counts recorded by the photodetector when the target polymer is labeled but no energy transfer occurs. The FRET acceptor channel's photon intensity standard is set to 150% of the baseline photon intensity. This is based on the physical properties of FRET: when a FRET event occurs, donor energy is transferred to the acceptor, resulting in a significant decrease in donor fluorescence (by more than half) and a significant increase in acceptor fluorescence (increasing to 1.5 times or more of the normal value). The 50% and 150% values ​​are estimated based on the typical range of FRET efficiencies (i.e., 0.3-0.8) to ensure that events with significant energy transfer are screened out, rather than subtle fluctuations or noise interference.

[0078] In PicoQuant TimeHarp software, the fluorescence signal timestamps for each pair of donor and acceptor channels were screened one by one. A valid FRET event was identified if the time difference was less than 1 nanosecond and met the intensity conditions (i.e., donor photon intensity was less than 50% of the baseline photon intensity, and acceptor photon intensity was greater than 150% of the baseline photon intensity). Valid FRET events and potential FRET events were combined into a FRET event set.

[0079] S3.7. Next, calculate the energy transfer efficiency (E) between the donor and acceptor photon intensities. Specifically, assuming IA is the donor photon intensity and IB is the acceptor photon intensity, the energy transfer efficiency is the acceptor photon intensity divided by the sum of the donor photon intensity and the acceptor photon intensity. Once the calculation is complete, save the results in a table named "Energy Transfer Efficiency List." Combine the FRET event set and the energy transfer list into a set of FRET event timestamp pairs by clicking the Export button in the PicoQuantTimeHarp software.

[0080] S4. Perform FCS analysis on the timestamp pair set of FRET events, calculate the diffusion time and compare it with the known monomer diffusion time to determine whether the FRET event is a true multimer signal, and output a multimer event detection result set.

[0081] S4.1. FCS analysis refers to fluorescence correlation spectroscopy analysis. Diffusion time refers to the average residence time of target polymer molecules in the donor and acceptor from entry to exit within the multi-channel fluorescence correlation spectroscopy device.

[0082] Zeiss ConfoCor software is used to chronologically arrange the timestamp sequences in a collection of FRET event timestamp pairs, for example, {t_A_valid1, t_A_valid2, ...} for channel A. Zeiss ConfoCor software then sets a series of delay time points (starting from very short intervals, such as 1 microsecond, to longer intervals, such as 10 milliseconds). For each delay time (τ), the software examines the correlation between the donor and acceptor photon intensities. This is done by taking the photon intensity at a specific moment (e.g., the signal intensity at t_A_valid1) and then comparing the photon intensity at τ later (e.g., t_A_valid1 + τ) to see if the fluctuations in the two photon intensities are similar. If the same multimer molecule remains within the detection volume, the photon intensities will remain consistent for a short period (i.e., the photon intensity fluctuations are consistent). However, if the multimer molecule leaves the detection volume over a longer period, the two photon intensities will become uncorrelated (i.e., the photon intensity fluctuations are random). ZeissConfoCor software repeatedly compares all time-stamp sequences and calculates the average correlation strength of photon intensities at different time points within the same channel, denoted as G(τ). G represents the average correlation strength between photon intensities at different time points. As τ increases, G(τ) decreases from high values ​​(short-term high correlation) to low values ​​(long-term low correlation), forming a descending curve, the autocorrelation curve (the horizontal axis of the autocorrelation curve is the delay time τ, and the vertical axis is G(τ)).

[0083] S4.2. Fit the autocorrelation curve and extract the diffusion time. (The goal of fitting is to find a theoretical curve whose shape matches the actual autocorrelation curve as closely as possible.) Specifically, the ZeissConfoCor software starts at the far left of the autocorrelation curve (i.e., the highest value when τ is close to 0) and observes the rate and shape of the autocorrelation curve's decline. Experiment with different diffusion time values, adjusting them from small to large, for example, first trying 0.1 milliseconds, then 0.2 milliseconds, until a diffusion time value is found that best matches the theoretical curve's downward trend with the autocorrelation curve. Specifically, the ZeissConfoCor software considers the time it takes for the theoretical curve to fall from its highest point to halfway, which is the diffusion time (τ_D). For example, if the theoretical curve falls to halfway at 0.5 milliseconds, the ZeissConfoCor software records τ_D as 0.5 milliseconds.

[0084] After the fit is complete, ZeissConfoCor displays an overlay of the theoretical curve and the autocorrelation curve in the results window. If the curves overlap sufficiently (e.g., more than 90%), the fit is successful. ZeissConfoCor extracts the diffusion time τ_D from the fit result, e.g., τ_D1 = 0.5 milliseconds, and adds the diffusion time to the diffusion time list. This process of fitting the curve and extracting the diffusion time is repeated, analyzing each pair of timestamp sequences one by one. For example, the second pair generates τ_D2, ultimately forming a complete list {τ_D1, τ_D2, ...}, which is saved to the diffusion time list.

[0085] S4.3. The known monomer diffusion time (τ_D_mono) is the average time required for a target molecule to diffuse through a fluorescence correlation spectroscopy instrument in its monomeric state (i.e., as a single molecule, not forming a multimer). A monomer refers to a single molecular unit of a target multimer, such as a protein dimer or oligomer, when the target is not aggregated into a large complex.

[0086] Compare the diffusion time list to the known monomer diffusion times. Specifically, use the Zeiss ConfoCor software to read each value from the diffusion time list one by one. For example, compare the monomer diffusion time τ_D_mono = 0.1 ms (assuming it's 0.1 ms). The comparison method is to calculate the difference between the diffusion time and the monomer diffusion time and subtract τ_D_mono from τ_D_mono, resulting in 0.5 ms - 0.1 ms = 0.4 ms. If the difference is significantly greater than the monomer diffusion time (for example, more than twice the monomer diffusion time, i.e., 0.2 ms), mark it as a significant difference, indicating that the molecule is a multimer. If the difference is close to zero (for example, less than 0.05 ms), mark it as close to a monomer. Record the comparison results in a temporary table, for example, {τ_D_mono, 0.4 ms, significant difference}, and save it as a diffusion time comparison table.

[0087] S4.4. Use the diffusion time comparison table and energy transfer efficiency table to determine the efficiency of FRET events and obtain a set of multimer event detection results. First, a diffusion time difference threshold of 0.2 milliseconds must be set. This threshold is based on the physical property differences between the target multimer and monomer. Specifically, the diffusion time (τ_D) is proportional to the size of the multimer and inversely correlated with the diffusion coefficient (D) of the multimer. The diffusion coefficient is related to the molecular radius (r) of the multimer through the Stokes-Einstein relationship. Assuming the multimer is a dimer, the volume of the dimer is approximately twice that of the monomer. An increase in the molecular radius by approximately 1.26 times also increases the diffusion time by approximately 1.26 times. For example, for a monomer diffusion time of τ_D_mono = 0.1 millisecond, the estimated diffusion time for the dimer is 0.126 milliseconds. However, in actual experiments, the effects of multimer shape, solution viscosity, and detection volume may amplify these differences. To ensure differentiation between monomers and polymers, the diffusion time difference threshold was set to 0.2 milliseconds, taking into account instrument resolution (approximately 0.01 milliseconds) and noise fluctuations. This value, approximately twice the duration of a monomer, effectively screens out significantly larger polymers while avoiding misclassification of monomers or noise as polymers.

[0088] Zeiss ConfoCor software iterates through all diffusion times and energy transfer efficiencies in the diffusion time list and energy transfer efficiency list. For example, for the first pair {τ_D1 = 0.5 ms, E1 = 0.6}, it checks if the difference between the diffusion time and the monomer diffusion time is 0.4 ms and greater than 0.2 ms (the diffusion time difference threshold), and if the energy transfer efficiency is 0.6 and greater than 0.5, it is considered a true multimer signal. Diffusion times close to 0.1 ms or energy transfer efficiencies below 0.5 are rejected. After processing is complete, click the Export ValidatedEvents button to save the result set as a multimer event detection result, which contains a set of timestamp pairs, diffusion times, and energy transfer efficiencies, for example, {{t_A_valid1, t_B_valid1, τ_D1, E1}, ...}.

[0089] This embodiment also provides a computer device suitable for the case of a method for processing polymer detection data of multi-channel fluorescence correlation spectroscopy, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for processing polymer detection data of multi-channel fluorescence correlation spectroscopy proposed in the above embodiment.

[0090] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0091] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for processing polymer detection data by multi-channel fluorescence correlation spectroscopy as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0092] In summary, this invention effectively reduces time deviations caused by physical distance by comparing data from each channel on the same timescale and using a hardware time delay compensator to adjust for physical optical path differences between channels. Furthermore, by using an instrument jitter threshold to eliminate spurious correlation signals, it also reduces background noise interference, significantly improving signal fidelity and reliability, enabling researchers to more accurately identify true FRET events.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy, characterized by: include, Collect and calibrate the photon arrival timestamp data to obtain a calibrated data set; Based on the calibrated data set, the optical path difference of each pair of channels is calculated one by one and corrected, and the false correlation signal is filtered to obtain a noise-filtered timestamp data set, wherein the optical path difference of each pair of channels is calculated one by one and corrected, and the physical length of the optical fiber of each channel is measured using a laser rangefinder, the transmission delay time difference of the optical fiber length is calculated and corrected using a programmable delay generator, and the optical path compensation timestamp data set is output; Based on the noise-filtered timestamp dataset, the target polymer molecules are labeled as donor probes and acceptor probes, and the temporal correlation between the donor and the acceptor is calculated to obtain a set of timestamp pairs of FRET events. Perform FCS analysis on the timestamp pair set of FRET events, calculate the diffusion time, and compare it with the known monomer diffusion time to determine whether the FRET event is a true multimer signal, and output the multimer event detection result set.

2. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 1, wherein: Obtaining the calibrated data set refers to collecting photon arrival timestamp data using a high-speed time-to-digital converter and saving it in the form of a time series to form an original timestamp data set; Set the emission time, calculate the deviation between the photon arrival timestamp data and the emission time, obtain the average time offset, and build a channel time offset compensation table; The original timestamp data set and channel time offset compensation table are loaded and calibrated using LabVIEW software to obtain the calibrated data set.

3. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 2, wherein: Obtaining the noise-filtered timestamp data set refers to using MATLAB software to load the optical path compensation timestamp data set and store it as a variable, reading the photon arrival timestamp data from the variable and pairing them, and calculating the time difference between each pair of photon arrival timestamp data; The instrument jitter threshold is set, and the time difference between each pair of photons arriving at the timestamp data is compared with the instrument jitter threshold to obtain the marker comparison result. The marker comparison result is used to eliminate the pseudo-correlation signal and obtain the noise-filtered timestamp data set.

4. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 3, wherein: The labeling of the target polymer molecules as donor probes and acceptor probes refers to selecting Cy3 as the donor probe, Cy5 as the acceptor probe, and selecting a target polymer molecule solution; The storage solution of the Cy3 donor probe and the Cy5 acceptor probe is extracted and added to the target polymer molecule solution to form a mixed solution. The mixed solution is incubated and dialyzed. The absorbance value of the mixed solution is measured using a UV-visible spectrophotometer and labeled to form labeled target polymer molecules.

5. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 4, wherein: Obtaining a timestamp pair set of FRET events refers to setting up a multi-channel fluorescence correlation spectroscopy device, a FRET donor channel, and a FRET acceptor channel, dripping a labeled target polymer molecule solution onto a sample well on a glass slide, and collecting fluorescence signal timestamps of the FRET donor channel and the FRET acceptor channel to form a donor channel fluorescence signal timestamp dataset and an acceptor channel fluorescence signal timestamp dataset; Set a time difference threshold, use PicoQuantTimeHarp software to pair the fluorescence signal timestamps of the FRET donor channel and the FRET acceptor channel, calculate the time difference between the fluorescence signal timestamps of each pair of FRET donor channel and FRET acceptor channel, and mark it as a potential FRET event when the time difference between the fluorescence signal timestamps of each pair of FRET donor channel and FRET acceptor channel is less than the time difference threshold; Setting the photon intensity standards for the FRET donor channel and the FRET acceptor channel, and marking potential FRET events that meet the photon intensity standards as valid FRET events; The potential FRET events and the effective FRET events are integrated to form a FRET event set, the energy transfer efficiency between the donor and acceptor photon intensities is calculated, the calculated energy transfer efficiency is organized into an energy transfer efficiency list, and the energy transfer efficiency list and the FRET event set are combined into a timestamp pair set of FRET events.

6. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 5, wherein: The FCS analysis of the FRET event timestamp pair set refers to using Zeiss ConfoCor software to arrange the timestamp sequence in the FRET event timestamp pair set in chronological order to form an autocorrelation curve and perform fitting processing, and extracting the diffusion time from the fitted autocorrelation curve and integrating it into a diffusion time list.

7. The method for processing polymer detection data using multi-channel fluorescence correlation spectroscopy according to claim 6, wherein: Outputting a set of multimeric event detection results refers to comparing the diffusion time in the diffusion time list with the known monomer diffusion time, calculating the difference between the diffusion time and the monomer diffusion time, and obtaining a diffusion time comparison table; The diffusion time comparison table and energy transfer efficiency list are used to determine the efficiency of the FRET event and output a set of multimer event detection results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-channel fluorescence correlation spectroscopy polymer detection data processing method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for processing polymer detection data of multi-channel fluorescence correlation spectroscopy according to any one of claims 1 to 7 are implemented.

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