Method for optimizing live cell clathrin imaging parameters based on srrf nanoscope and related apparatus

Abstract

The application discloses a kind of live cell clathrin imaging parameter optimization method and related device based on SRRF nanoscope, it is related to biomedical imaging field, the method includes under live cell culture condition, when using TIRF microscope to the time series imaging of live cell sample expressing fluorescent label, original image sequence is obtained;It is input NanoJ-SRRF processing system, a plurality of core reconstruction parameters are systematically combined, and several super-resolution reconstruction images are generated;For the super-resolution image reconstructed by each combination parameter, comprehensive quantitative evaluation is carried out using multimodal evaluation system, and multimodal quantitative quality evaluation result is obtained;Based on this result, the parameter combination that meets the preset resolution, structure fidelity index and the minimum visual artifact is screened out, and the optimal parameter set for the imaging of live cell clathrin coated pit is determined.The application can reduce reconstruction artifact, realize high-fidelity nanoscale observation of live cell CCP dynamics.

Description

Technical Field

[0001] This application relates to the field of biomedical imaging, and in particular to a method and related apparatus for optimizing imaging parameters of clathrin in live cells based on an SRRF nanomicroscope. Background Technology

[0002] In the field of biomedical imaging, the study of living cells is crucial, and observing the dynamic changes of specific proteins within living cells is one of the key steps in gaining a deeper understanding of cellular physiological processes. Clathrins (CCPs), as key proteins involved in a variety of important physiological functions within cells, have always been a hot topic in the field of kinetic studies.

[0003] Traditional microscopy imaging techniques, limited by resolution, struggle to achieve precise nanoscale observation of CCPs within living cells, significantly hindering in-depth research into CCP dynamics. While conventional fluorescence microscopy can image cells, it cannot overcome the optical diffraction limit, failing to clearly reveal the details and dynamic changes of CCPs at the nanoscale. The emergence of super-resolution microscopy offers hope for solving this problem. Among them, the SRRF nanomicroscopy enables high-speed in vivo imaging at nanoscale resolution, is compatible with conventional fluorescence microscopy, and significantly lowers the technical barrier to super-resolution imaging. This method exhibits low phototoxicity and good compatibility, making it ideal for long-term, super-resolution dynamic imaging of living cells. However, when using the SRRF nanomicroscopy to image clathrin in living cells, reconstruction artifacts arise. These artifacts interfere with the accurate assessment of the true dynamics of CCPs, leading to biased observations and affecting the accuracy and reliability of the research. Summary of the Invention

[0004] The purpose of this application is to provide a method and related device for optimizing imaging parameters of live-cell clathrin based on SRRF nanomicroscopy, which can reduce reconstruction artifacts and achieve high-fidelity nanoscale observation of the dynamics of live-cell CCPs.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy, including:

[0007] Under live cell culture conditions, time-series imaging of live cell samples expressing fluorescent labels was performed using a TIRF microscope to obtain the original image sequence;

[0008] The original image sequence is input into the NanoJ-SRRF processing system, and the core reconstruction parameters are systematically combined to generate a super-resolution reconstructed image with systematically combined parameters. The systematically combined parameters include: ring radius, radial magnification, number of ring axes, temporal analysis mode, radial constraint mode, weighting strategy, and correction method.

[0009] For super-resolution reconstructed images with systematic combined parameters, a multimodal evaluation system including SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis is used for comprehensive quantitative evaluation to obtain multimodal quantitative quality evaluation results.

[0010] Based on the multimodal quantitative quality assessment results, the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts is selected and determined as the optimal parameter set for imaging clathrin-coated pits in live cells.

[0011] Optionally, the live cell culture conditions are 37°C and 5% CO2 environment; the TIRF microscope uses a 100x oil immersion lens with a numerical aperture of 1.4; the fluorescent label is CLTA-GFP; and the time-series imaging frame rate is 20 frames / second.

[0012] Optionally, the value of the ring radius ranges from 0.1 to 3.0; the value of the radial magnification ranges from 1, 5, and 10; the value of the number of ring axes ranges from 2, 6, and 8; the time analysis modes include TRA, TRPPM, and TRAC; the radial constraint modes include positive constraint removal, renormalization, and gradient smoothing; the weighting strategies include intensity weighting and gradient weighting; and the correction methods include minimizing SRRF correction mode and fast linearization correction mode.

[0013] Optionally, the optimal parameter set is as follows: loop radius is 1.0, time analysis mode is TRPPM, radial magnification is 5, number of loop axes is 6, weighting strategy is intensity weighting strategy, correction method is enabled by minimizing SRRF correction mode, and radial constraint mode is enabled by removing positive constraints and disabling gradient smoothing.

[0014] Optionally, the preset resolution specification is a full width at half maximum (FWHM) ≤ 220nm.

[0015] Optionally, the structural fidelity index is ≥0.92.

[0016] Optionally, the minimum manifestation of the visual artifact is that there are no significant abnormal hot spots in the SQUIRREL error heatmap.

[0017] Secondly, this application provides a live-cell clathrin imaging parameter optimization system based on an SRRF nanomicroscope, used to implement the aforementioned live-cell clathrin imaging parameter optimization method based on an SRRF nanomicroscope, comprising:

[0018] The TIRF microscope imaging module is used to perform time-series imaging of live cell samples expressing fluorescent labels under live cell culture conditions using a TIRF microscope to acquire raw image sequences.

[0019] The NanoJ-SRRF image processing module is used to input the original image sequence into the NanoJ-SRRF processing system, systematically combine the core reconstruction parameters, and generate a super-resolution reconstructed image with systematically combined parameters. The systematically combined parameters include: ring radius, radial magnification, number of ring axes, time analysis mode, radial constraint mode, weighting strategy, and correction method.

[0020] The multimodal quality assessment module is used to comprehensively and quantitatively evaluate super-resolution reconstructed images with systematic combined parameters using a multimodal assessment system that includes SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis, to obtain multimodal quantitative quality assessment results.

[0021] The optimal parameter decision module is used to select the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts based on the multimodal quantitative quality assessment results, and determine it as the optimal parameter set for imaging of clathrin-coated pits in live cells.

[0022] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy as described above.

[0023] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing live-cell clathrin imaging parameters based on an SRRF nanomicroscope as described above.

[0024] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0025] This application provides a method and related apparatus for optimizing imaging parameters of clathrin in live cells based on SRRF nanomicroscopy. Under live cell culture conditions, TIRF microscopy is used to perform time-series imaging of live cell samples expressing fluorescent labels, acquiring raw image sequences. This imaging method better meets the observation environment requirements of live cells. The raw image sequences are input into the NanoJ-SRRF processing system, where multiple core reconstruction parameters, including ring radius, radial magnification, ring axis number, time analysis mode, radial constraint mode, weighting strategy, and correction method, are systematically combined to generate several super-resolution reconstructed images. Through full or partial combined scanning, the imaging potential under different parameter combinations can be fully explored. For the super-resolution images reconstructed by each parameter combination, a multimodal evaluation system including SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis is used for comprehensive quantitative evaluation, obtaining multimodal quantitative quality assessment results. A multimodal evaluation system considers image quality from different dimensions. For example, error mapping reflects the error between the reconstructed image and the real image, resolution scaling indicators assess resolution, and full width at half maximum (FWHM) and line contour analysis help analyze image details. This comprehensive evaluation makes the judgment of image quality more accurate. Based on the results of the multimodal quantitative quality evaluation, the parameter combination that simultaneously meets the preset resolution and structural fidelity indicators while minimizing visual artifacts is selected as the optimal parameter set for imaging clathrin-coated pits in live cells. This rigorous selection criterion ensures that the final parameter set achieves the best balance between resolution, structural fidelity, and artifact reduction, thereby enabling high-fidelity nanoscale observation of the dynamics of live-cell CCPs and effectively reducing reconstruction artifacts. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a method for optimizing live-cell clathrin imaging parameters based on an SRRF nanomicroscope, provided as an embodiment of this application;

[0028] Figure 2 A schematic diagram showing the resolution comparison of CLTA images reconstructed in HeLa cells using NanoJ-SRRF with different ring radii, provided as an embodiment of this application;

[0029] Figure 3A schematic diagram comparing error maps of CLTA super-resolution images with different ring radii in NanoJ-SRRF, provided as an embodiment of this application;

[0030] Figure 4 A schematic diagram comparing the resolution of CLTA images reconstructed in HeLa cells using NanoJ-SRRF with different radial magnification factors and number of in-ring axes, provided as an embodiment of this application;

[0031] Figure 5 A schematic diagram comparing the error of CLTA super-resolution images reconstructed using NanoJ-SRRF with different radial magnification factors and number of axes within the ring, provided for an embodiment of this application;

[0032] Figure 6 A schematic diagram comparing the resolution of CLTA images reconstructed in HeLa cells using different temporal analyses, radial, weighted, and correction methods using NanoJ-SRRF, as provided in an embodiment of this application;

[0033] Figure 7 A schematic diagram comparing error maps of CLTA super-resolution images reconstructed using different temporal analyses, radial methods, weighting, and correction methods using NanoJ-SRRF, provided as an embodiment of this application;

[0034] Figure 8 A comparative schematic diagram illustrating the reconstruction of CLTA images in HeLa cells using different time-series analyses with NanoJ-SRRF, provided as an embodiment of this application;

[0035] Figure 9 This is a schematic diagram of the functional modules of a live-cell clathrin imaging parameter optimization system based on an SRRF nanomicroscope, provided in an embodiment of this application.

[0036] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy, including:

[0041] Step 101: Under live cell culture conditions, use a TIRF microscope to perform time-series imaging of live cell samples expressing fluorescent labels to obtain the original image sequence;

[0042] Step 102: Input the original image sequence into the NanoJ-SRRF processing system, systematically combine the core reconstruction parameters, and generate a super-resolution reconstructed image with systematically combined parameters;

[0043] Step 103: For the super-resolution reconstructed image with systematic combined parameters, a multimodal evaluation system including SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement and line profile analysis is used for comprehensive quantitative evaluation to obtain multimodal quantitative quality evaluation results.

[0044] Step 104: Based on the multimodal quantitative quality assessment results, select the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts, and determine it as the optimal parameter set for imaging clathrin-coated pits in live cells.

[0045] In some embodiments, when performing steps 101-104, the specific steps may be as follows:

[0046] The live cell culture conditions were 37°C and 5% CO2; images of HeLa cells expressing CLTA-GFP were acquired at 20fps using a TIRF microscope (100×, NA 1.4).

[0047] When performing a systematic full combination of parameters: the loop radius (0.1-3.0), radial magnification (1 / 5 / 10), number of loop axes (2 / 6 / 8), time analysis mode (TRA / TRPPM / TRAC), radial constraints (positive constraint removal / renormalization / gradient smoothing), weighting strategy (intensity / gradient weighting), and correction method (minimize SRRF mode / fast linearization) of NanoJ-SRRF are fully combined and reconstructed.

[0048] Specifically, the ring radius is optimized to 1.0, achieving a balance between resolution (218±37nm) and structural fidelity (RSP 0.935±0.018), avoiding filamentary artifacts of small radius (0.1-0.5) and fidelity reduction of large radius (2.0-3.0) (RSP<0.89).

[0049] The optimal parameter set is as follows: loop radius is 1.0, time analysis mode is TRPPM, radial magnification is 5, number of loop axes is 6, weighting strategy is intensity weighting strategy, correction method is enabled by minimizing SRRF correction mode, and radial constraint mode is enabled by removing positive constraints and disabling gradient smoothing.

[0050] Specifically, the preferred temporal analysis mode is TRPPM, which improves the resolution to 154±30nm while preserving the fidelity of the TRA mode (RSP / RSE comparable to the baseline), significantly outperforming the artifact expansion problem of the TRAC mode (RSP↓22%, RSE↑63%). Disabling gradient weighting and positive constraint removal avoids background artifacts (RSP decreases to 0.273±0.151) and fidelity loss (RSE increases to 229.90±31.76).

[0051] The preset resolution index is FWHM ≤ 220nm. The structure fidelity index is RSP ≥ 0.92. The minimum visual artifact is defined as the absence of significant abnormal hot spots in the SQUIRREL error heatmap.

[0052] This application also provides specific implementation examples:

[0053] like Figure 2 The image shows a comparison of the resolution of CLTA images of HeLa cells reconstructed by NanoJ-SRRF under different loop radii. Clathrin light chain A (CLTA) was expressed as a GFP-labeled protein via plasmid transfection. Time-series images (effective pixel size: 130 nm) were acquired using total internal reflection fluorescence microscopy (TIRF). Super-resolution images were generated using the NanoJ-SRRF plugin in ImageJ. Image resolution was qualitatively assessed using PlotProfile (peak counting method) or quantitatively analyzed using the full width at half maximum (FWHM) calculated by ImageJ. (A): Original image and its qualitative resolution. (BF): SRRF images reconstructed with loop radii of 0.1, 0.5, 1, 2, and 3, and their corresponding qualitative resolutions. (G): Quantitative resolution analysis of the FWHM between the original image and the SRRF image. Data are expressed as mean ± standard deviation (n = 4). *P < 0.05 compared to the original image; #P < 0.05 compared to the SRRF image with a loop radius of 1. Scale bar = 2.5 μm.

[0054] like Figure 3As shown, the error maps of CLTA super-resolution images reconstructed by NanoJ-SRRF with different ring radii are compared. The error maps are generated by NanoJ-SQUIRREL. The SQUIRREL error maps show the local differences between the super-resolution image and the reference image (original image): the error values ​​are lower in the purple area and higher in the yellow area. The resolution scaling Pearson correlation coefficient (RSP) and resolution scaling error (RSE) are quality evaluation indicators of super-resolution images. (AF) shows the SQUIRREL error maps corresponding to the super-resolution images reconstructed by NanoJ-SRRF with different ring radii (0.1, 0.5, 1, 2, and 3). (G) Statistical analysis of resolution scaling Pearson correlation coefficient (RSP) and resolution scaling error (RSE). Data are expressed as mean ± standard deviation (n=4). *P<0.05 compared with the SQUIRREL error map of ring radius 1.0. Scale bar = 2.5μm.

[0055] like Figure 4 The image shows a resolution comparison of HeLa cell CLTA images reconstructed by NanoJ-SRRF under different radial magnification and ring axis number parameters. Clathrin light chain A (CLTA) was GFP-labeled and expressed via plasmid transfection. Time-series images (effective pixel size: 130 nm) were acquired using total internal reflection fluorescence microscopy. Super-resolution images were generated using the NanoJ-SRRF plugin in ImageJ. Image resolution was qualitatively assessed by profile analysis (peak counting) or quantitatively analyzed by full width at half maximum (FWHM) calculated by ImageJ. (AF) shows the super-resolution images and their qualitative resolution generated by NanoJ-SRRF with different radial magnifications (1, 5, 10) and ring axis numbers (2, 6, 8); (G) shows the quantitative analysis of the original image and SRRF image based on FWHM resolution. Data are expressed as mean ± standard deviation (n = 4). *P < 0.05 compared to the original image. Scale bar = 2.5 μm.

[0056] like Figure 5As shown, the error maps of CLTA super-resolution images reconstructed by NanoJ-SRRF under different radial magnification and ring axis numbers are compared. The error maps were generated by NanoJ-SQUIRREL. The SQUIRREL error maps show the local differences between the super-resolution image and the reference image (original image): the error values ​​are lower in the purple areas and higher in the yellow areas. The resolution scaling Pearson correlation coefficient (RSP) and resolution scaling error (RSE) are quality evaluation indicators of super-resolution images. (AF) shows the SQUIRREL error maps corresponding to the super-resolution images generated by NanoJ-SRRF with different radial magnification (1, 5, 10) and ring axis numbers (2, 6, 8). (G) Statistical analysis of resolution scaling Pearson correlation coefficient (RSP), (H) Statistical analysis of resolution scaling error (RSE). Data are expressed as mean ± standard deviation (n=4). Scale bar = 2.5μm.

[0057] like Figure 6 The image shows a resolution comparison of HeLa cell CLTA images reconstructed using NanoJ-SRRF under different time-series analyses, radial parameters, weighting, and correction methods. Clathrin light chain A (CLTA) was GFP-labeled and expressed via plasmid transfection. Time-series images (effective pixel size: 130 nm) were acquired using total internal reflection fluorescence microscopy. Super-resolution images were generated using the NanoJ-SRRF plugin in ImageJ. Image resolution was qualitatively assessed through profile analysis (peak counting) or quantitatively analyzed using the full width at half maximum (FWHM) calculated by ImageJ. (A) SRRF image and its qualitative resolution using the Temporal Radial Mean (TRA) method; (B) SRRF image and its qualitative resolution using the Temporal Radial Pairwise Product Mean (TRPPM) method; (C) SRRF image and its qualitative resolution using the Temporal Radial Self-Corrective (TRAS) method; (D) SRRF image and its qualitative resolution after removing positive constraints; (E) SRRF image and its qualitative resolution after renormalization; (F) SRRF image and its qualitative resolution after gradient smoothing; (G) SRRF image and its qualitative resolution after gradient weighting; (H) SRRF image and its qualitative resolution after fast linearization; (I) Quantitative analysis of the original image and SRRF image based on FWHM resolution. Data are expressed as mean ± standard deviation (n = 4). * P < 0.05 compared to the original image. # P < 0.05 compared to the SRRF image with a loop radius of 1. Scale bar = 2.5 μm.

[0058] like Figure 7As shown, error maps of NanoJ-SRRF reconstructed CLTA super-resolution images are compared under different time-series analysis, radial parameter, weighting, and correction methods. The error maps were generated by NanoJ-SQUIRREL. The SQUIRREL error maps show the local differences between the super-resolution image and the reference image (original image): purple areas have lower error values, while yellow areas have higher error values. Resolution scaling Pearson correlation coefficient (RSP) and resolution scaling error (RSE) are quality evaluation indicators for super-resolution images. (A) Squirrel error map of SRRF image reconstructed by Temporal Radial Mean (TRA); (B) Squirrel error map of SRRF image reconstructed by Temporal Radial Pairwise Product Mean (TRPPM); (C) Squirrel error map of SRRF image reconstructed by Temporal Radial Self-Corrective (TRAS); (D) Squirrel error map of SRRF image reconstructed by removing positive constraints; (E) Squirrel error map of SRRF image reconstructed by renormalization; (F) Squirrel error map of SRRF image reconstructed by gradient smoothing; (G) Squirrel error map of SRRF image reconstructed by gradient weighting; (H) Squirrel error map of SRRF image reconstructed by fast linearization; (I) Statistical analysis of SRRF image resolution; (I) Statistical analysis of resolution scaling Pearson correlation coefficient (RSP); (J) Statistical analysis of resolution scaling error (RSE). Data are expressed as mean ± standard deviation (n = 4). * P < 0.05 compared to SRRF images using TPA.

[0059] like Figure 8 The image shows a comparison of CLTA images reconstructed from HeLa cells using NanoJ-SRRF under different time-series analysis methods. Clathrin light chain A (CLTA) was GFP-labeled and expressed via plasmid transfection. Time-series images (effective pixel size: 130 nm) were acquired using total internal reflection fluorescence microscopy. Super-resolution images were generated using the NanoJ-SRRF plugin in ImageJ. Image resolution was qualitatively evaluated using ImageJ profile analysis (peak counting). (A) SRRF image and its qualitative resolution using the temporal radial mean (TRA) method; (B) SRRF image and its qualitative resolution using the temporal radial pairwise product mean (TRPPM) method; (C) SRRF image and its qualitative resolution using the temporal radial self-calibration (TRAS) method. Scale bar = 2.5 μm.

[0060] Example 2

[0061] like Figure 9As shown, this embodiment provides a live-cell clathrin imaging parameter optimization system based on an SRRF nanomicroscope, used to implement the aforementioned live-cell clathrin imaging parameter optimization method based on an SRRF nanomicroscope, including:

[0062] The TIRF microscope imaging module 901 is used to perform time-series imaging of live cell samples expressing fluorescent labels under live cell culture conditions using a TIRF microscope to acquire raw image sequences.

[0063] The NanoJ-SRRF image processing module 902 is used to input the original image sequence into the NanoJ-SRRF processing system, systematically combine the core reconstruction parameters, and generate a super-resolution reconstructed image with systematically combined parameters. The systematically combined parameters include: ring radius, radial magnification, number of ring axes, time analysis mode, radial constraint mode, weighting strategy, and correction method.

[0064] The multimodal quality assessment module 903 is used to comprehensively and quantitatively assess super-resolution reconstructed images with systematic combined parameters using a multimodal assessment system that includes SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis, to obtain multimodal quantitative quality assessment results.

[0065] The optimal parameter decision module 904 is used to select the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts based on the multimodal quantitative quality assessment results, and determine it as the optimal parameter set for imaging of clathrin-coated small pits in live cells.

[0066] Example 3

[0067] This embodiment provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 10As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores optimal parameter sets. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy.

[0068] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0069] In some embodiments, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0070] Example 4

[0071] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0072] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0073] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0074] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0075] In summary, this application has the following technical effects:

[0076] This application establishes an optimized parameter set for dense subcellular structures by systematically combining NanoJ-SRRF parameters (including ring radius, radial magnification, number of ring axes, temporal analysis mode, radial constraints, weighting strategy, and correction method) and integrating a multimodal quantitative evaluation system (SQUIRREL error mapping, RSP, RSE, FWHM, and line profile analysis): ring radius = 1.0, temporal analysis mode = TRPPM, radial magnification = 5, number of ring axes = 6, intensity weighting, minimizing the SRRF mode, enabling positive constraints, and disabling gradient smoothing. This method resolves the inherent contradiction between resolution and fidelity in SRRF imaging, significantly reduces reconstruction artifacts (such as background artifacts caused by gradient weighting), and achieves high-fidelity nanoscale observation of the dynamics of live-cell CCPs (resolution 218±37nm, RSP 0.935±0.018). It is suitable for studying dynamic processes such as endocytosis.

[0077] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy, characterized in that, include: Under live cell culture conditions, time-series imaging of live cell samples expressing fluorescent labels was performed using a TIRF microscope to obtain the original image sequence; the live cell culture conditions were 37℃ and 5% CO2 environment; the TIRF microscope used was a 100x oil immersion microscope with a numerical aperture of 1.4; the fluorescent label was CLTA-GFP; the frame rate of the time-series imaging was 20 frames / second; The original image sequence is input into the NanoJ-SRRF processing system, and the core reconstruction parameters are systematically combined to generate a super-resolution reconstructed image with systematically combined parameters. These systematically combined parameters include: loop radius, radial magnification, number of loop axes, temporal analysis mode, radial constraint mode, weighting strategy, and correction method. The loop radius ranges from 0.1 to 3.0; the radial magnification ranges from 1, 5, and 10; the number of loop axes ranges from 2, 6, and 8; the temporal analysis modes include TRA, TRPPM, and TRAC; the radial constraint modes include positive constraint removal, renormalization, and gradient smoothing; the weighting strategies include intensity weighting and gradient weighting; and the correction methods include minimizing the SRRF correction mode and fast linearization correction mode. For super-resolution reconstructed images with systematic combined parameters, a multimodal evaluation system including SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis is used for comprehensive quantitative evaluation to obtain multimodal quantitative quality evaluation results. Based on the multimodal quantitative quality assessment results, the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts is selected and determined as the optimal parameter set for imaging clathrin-coated pits in live cells.

2. The method for optimizing live-cell meshin imaging parameters based on SRRF nanomicroscopy according to claim 1, characterized in that, The optimal parameter set is as follows: loop radius is 1.0, time analysis mode is TRPPM, radial magnification is 5, number of loop axes is 6, weighting strategy is intensity weighting strategy, correction method is enabled by minimizing SRRF correction mode, and radial constraint mode is enabled by removing positive constraints and disabling gradient smoothing.

3. The method for optimizing live-cell meshin imaging parameters based on SRRF nanomicroscopy according to claim 2, characterized in that, The preset resolution specification is a full width at half maximum (FWHM) of ≤220nm.

4. The method for optimizing live-cell meshin imaging parameters based on SRRF nanomicroscopy according to claim 3, characterized in that, The structural fidelity index is ≥0.

92.

5. The method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy according to claim 4, characterized in that, The minimum manifestation of visual artifacts is the absence of significant abnormal hot spots in the SQUIRREL error heatmap.

6. A live-cell clathrin imaging parameter optimization system based on an SRRF nanomicroscope, used to implement the live-cell clathrin imaging parameter optimization method based on an SRRF nanomicroscope as described in any one of claims 1-5, characterized in that, include: The TIRF microscope imaging module is used to perform time-series imaging of live cell samples expressing fluorescent labels under live cell culture conditions using a TIRF microscope to acquire raw image sequences. The NanoJ-SRRF image processing module is used to input the original image sequence into the NanoJ-SRRF processing system, systematically combine the core reconstruction parameters, and generate a super-resolution reconstructed image with systematically combined parameters. The systematically combined parameters include: ring radius, radial magnification, number of ring axes, time analysis mode, radial constraint mode, weighting strategy, and correction method. The multimodal quality assessment module is used to comprehensively and quantitatively evaluate super-resolution reconstructed images with systematic combined parameters using a multimodal assessment system that includes SQUIRREL error mapping, resolution scaling Pearson coefficient, resolution scaling error, full width at half maximum (FWHM) measurement, and line profile analysis, to obtain multimodal quantitative quality assessment results. The optimal parameter decision module is used to select the parameter combination that can simultaneously meet the preset resolution index, structural fidelity index and minimize visual artifacts based on the multimodal quantitative quality assessment results, and determine it as the optimal parameter set for imaging of clathrin-coated pits in live cells.

7. A computer device, comprising: The memory, the processor, and the computer program stored in the memory and executable on the processor are characterized in that the processor executes the computer program to implement the method for optimizing live-cell clathrin imaging parameters based on SRRF nanomicroscopy according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a method for optimizing live-cell clathrin imaging parameters based on an SRRF nanomicroscope, as described in any one of claims 1-5.

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