Time sequence fluorescence tracing system based on coupling detection lipid probe

Abstract

The invention relates to the technical field of cytobiology and biomedicine detection, and discloses a time sequence fluorescence tracing system based on a coupling detection lipid probe. Comprising an imaging detection module which is used for adding FM lipophilic styrene fluorescent dye in a culture environment, forming a membrane probe to observe the morphological change of a membrane and detect the formation of vesicles, and completing the complete time sequence tracing of the cell endocytosis process by adopting a content dyeing method; the deep learning module is used for carrying out deep learning on the obtained time sequence image, establishing a living cell imaging screening and image analysis system, and identifying protein molecules related to the target external vesicles; carrying out image description on the generation, transportation and fusion processes of the vesicles generated by the proteins in different stages before fusion of the outer vesicles and the cell membranes and after fusion and shearing; and the knock-down operation module is used for exploring the generation mode of the vesicle contents in the early endosome in combination with knockout and knock-down operations of the specific drug compound.

Description

Technical Field

[0001] This application relates to the fields of cell biology and biomedical detection technology, and in particular to a time-series fluorescence tracing system based on coupled detection lipid probes. Background Technology

[0002] In cell biology and drug delivery research, vesicle circulation (such as endocytosis and membrane fusion) is a key factor in elucidating drug delivery efficiency and intracellular signal transduction. Traditional techniques for studying vesicle circulation often employ single detection methods.

[0003] 1. Content staining method: It only focuses on the fluorescent labeling of substances inside vesicles (such as proteins and small molecule drugs), and cannot simultaneously track the dynamic changes in cell membrane structure, making it difficult to reveal the coupling relationship between membrane structural deformation such as vesicle formation and membrane fusion and content transport.

[0004] 2. pH indicator method: It indirectly reflects the characteristics of the pathway by relying on the change of pH value during endocytosis, but the acidity and alkalinity fluorescence signal is strongly interfered with, which can easily contaminate the image data, and it cannot specifically analyze the mechanism of action of membrane protein molecules at different stages.

[0005] 3. Single image analysis: It lacks in-depth mining of time-series images, making it difficult to quantify the dynamic behavior of membrane proteins (such as transferrin receptor and epidermal growth factor receptor) at key nodes such as before, during, and after vesicle fusion, and even more difficult to combine with gene manipulation to explore the function of regulatory factors.

[0006] The core deficiency of existing technologies lies in the lack of a coupled detection system for membrane structure probes and contents tracing, resulting in the absence of a complete temporal correlation of "membrane morphology changes - vesicle generation - contents transport - protein molecule action" during endocytosis. At the same time, the lack of intelligent analysis of temporal images and functional verification at the gene level makes it difficult to achieve quantitative analysis of key mechanisms of vesicle circulation.

[0007] Therefore, there is an urgent need to design a technical solution to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0008] This application provides a time-series fluorescence tracing system based on coupled lipid probes, aiming to solve the problem that existing technologies have not established a coupled detection system for membrane structure probes and contents tracing, resulting in the lack of a complete temporal correlation of "membrane morphology changes - vesicle generation - contents transport - protein molecule interaction" during endocytosis; at the same time, the lack of intelligent analysis of temporal images and functional verification at the gene level makes it difficult to achieve quantitative analysis of key mechanisms of vesicle circulation.

[0009] In a first aspect, this application provides a time-series fluorescence tracing system based on a coupled lipid probe, comprising:

[0010] The imaging detection module is used to add FM lipophilic styrene fluorescent dye to the culture environment. The lipophilicity of FM lipophilic styrene fluorescent dye is used to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe changes in membrane morphology and detect the formation of vesicles. Then, the contents staining method is used to complete the complete time-series tracing of the cell endocytosis process. The non-specific staining of the cell surface membrane is removed by washing and then used for imaging detection.

[0011] The deep learning module is used to perform deep learning on the acquired time-series images, establish a screening and image analysis system based on live cell imaging, identify protein molecules related to target extravesicles, and provide image descriptions of the vesicle generation, transport and fusion processes of these proteins at different stages before, during and after the extravesicles fuse with the cell membrane.

[0012] The knockdown module is used to combine knockout and knockdown operations of specific drug complexes to explore the generation mode of vesicle contents in early endosomal tissues, as well as the continuous cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in generation mode, membrane fusion mode, function and key regulatory factors, and the system is not coupled with pH indicator mode.

[0013] In some embodiments, the method of using the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation includes: adding FM lipophilic styrene fluorescent dye at a preset concentration in a cell culture environment, allowing the dye molecules to embed into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction; utilizing the characteristic that the dye emits strong fluorescence in a lipid environment but does not emit fluorescence in an aqueous medium; acquiring morphological images of the cell membrane and endocytic vesicles in real time using a fluorescence microscope; and identifying the initiation site of vesicle formation, growth process, and detachment nodes from the cell membrane based on the dynamic changes in the fluorescence signal of the membrane structure.

[0014] In some embodiments, the method of staining contents to complete the full temporal tracing of the cellular endocytosis process includes: fluorescently labeling the endocytic contents; introducing the contents fluorescent labeling reagent simultaneously with or before the addition of FM dye; establishing a correspondence between the dynamic changes in membrane structure and the transport trajectory of contents by synchronously acquiring time-series images of membrane probe fluorescence signals and contents fluorescence signals; and realizing the temporal fluorescence signal correlation recording of the entire process of endocytosis initiation, vesicle encapsulation of contents, and contents entering the intracellular endosome pathway; wherein the endocytic contents include protein drugs, small molecule conjugates, and intravesical biomolecules.

[0015] In some embodiments, the non-specific staining of the cell surface membrane is removed by washing before imaging detection, including: after the FM dye binds to the cell membrane and completes vesicle internalization, the cells are washed multiple times with dye-free cell culture medium or buffer to remove uninternalized free dye molecules on the cell membrane surface; the non-specific fluorescence signal on the cell surface after washing is determined to be below a preset detection limit by using a fluorescence signal intensity threshold; and then the cells are subjected to real-time live cell imaging to ensure that only the fluorescence signal internalized into the vesicle membrane structure is retained for subsequent analysis.

[0016] In some embodiments, the step of performing deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system to identify target extravesicular vesicle-related protein molecules includes: inputting the acquired time-series fluorescence images into a pre-trained convolutional neural network model, identifying the spatiotemporal distribution characteristics of fluorescence signals in the images through multi-layer feature extraction layers, combining a preset protein molecule fluorescence labeling feature library, performing pixel-level localization and trajectory tracking of protein molecules related to extravesicles in the images, and outputting dynamic distribution and abundance change data of target protein molecules during endocytosis; wherein, the protein molecule fluorescence labeling feature library includes protein localization tags, fluorescence wavelength characteristics, and dynamic behavior patterns.

[0017] In some embodiments, the image description of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion with the cell membrane includes: dividing the endocytosis process into pre-fusion, fusion, and post-fusion stages based on the timestamps of the time-series images; extracting the fluorescence signal features of the target protein molecules in each stage; and establishing a membrane structure deformation model, transport path trajectory diagram, and a visual description of molecular interactions at the fusion interface for vesicle generation in each stage. The fluorescence signal features include aggregation sites on the membrane structure, signal intensity change curves, and spatial positional relationships with the vesicle boundary.

[0018] In some embodiments, the combination of knockout and knockdown of specific drug complexes to explore the generation of vesicle contents in early endosomes and their sustained cycling effects on receptors such as transferrin receptor and epidermal growth factor receptor includes: knocking out or knocking down genes related to specific drug complexes in cells using gene editing or RNA interference techniques to prepare cell models with differential gene expression; performing FM dye labeling and content staining under the same experimental conditions; comparing the time-series fluorescence images of the knockout group, knockdown group, and wild-type cells; analyzing the differences in the appearance time, distribution pattern, and receptor protein cycling pathway of fluorescence signals of vesicle contents in early endosomes; and quantifying the regulatory effects of specific gene products on vesicle generation, membrane fusion efficiency, and sustained receptor cycling.

[0019] Secondly, this application provides a time-series fluorescence tracing method based on coupled lipid probe detection, applicable to the time-series fluorescence tracing system based on coupled lipid probe detection provided in any embodiment of this application, the method comprising:

[0020] FM lipophilic styrene fluorescent dye was added to the culture environment. The lipophilicity of FM lipophilic styrene fluorescent dye was used to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe changes in membrane morphology and detect vesicle formation. Then, the contents staining method was used to complete the complete time-series tracing of the cell endocytosis process. Non-specific staining of the cell surface membrane was removed by washing and then imaged.

[0021] Deep learning was used to acquire time-series images to establish a live-cell imaging screening and image analysis system, identify protein molecules related to target extravesicles, and provide image descriptions of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion of extravesicles with the cell membrane.

[0022] By combining knockout and knockdown of specific drug complexes, this study explores the generation of vesicle contents in early endosomes and their sustained cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in their generation, membrane fusion, function, and key regulatory factors. Furthermore, the system is not coupled with a pH indicator approach.

[0023] Thirdly, embodiments of this application provide a computer device, the computer device including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method provided in any embodiment of this application.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the method provided in any embodiment of this application.

[0025] This application couples FM lipophilic dyes with cell contents staining to simultaneously capture changes in cell membrane morphology and the transport trajectory of cell contents, achieving dynamic recording of the entire endocytic process from vesicle formation to endosome sorting, thus overcoming the limitations of traditional single-staining methods in dynamically analyzing membrane structures. Deep learning is used to extract features from time-series images, identifying the spatiotemporal distribution and behavioral patterns of exovesicle-related proteins at different stages. For the first time, a visual description of protein molecules before, during, and after vesicle fusion is achieved, providing crucial data for elucidating membrane protein cycling mechanisms. By combining knockout / downgrade operations with specific drug complexes, the differences in fluorescence signals under different gene expression states are compared, quantifying the effects of key regulatory factors on vesicle formation, membrane fusion, and receptor cycling, providing experimental evidence for optimizing drug delivery systems. The use of pH indicators is explicitly excluded to avoid strong fluorescence signal contamination, improving the accuracy and reliability of image analysis.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic block diagram of a time-series fluorescence tracer system based on coupled lipid probes provided in an embodiment of this application;

[0029] Figure 2 This is a schematic flowchart of the steps of a time-series fluorescence tracing method based on coupled detection lipid probes provided in an embodiment of this application;

[0030] Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0032] 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, 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.

[0033] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0034] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0035] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0037] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0038] This invention relates to the field of cell biology and biomedical detection technology, specifically to a time-series fluorescence tracing system based on coupled lipid probes, used for real-time dynamic tracking of cell vesicle circulation processes and drug delivery efficiency, and is particularly suitable for quantitative analysis of membrane structure changes, vesicle generation, transport and fusion processes and related protein molecule action mechanisms in the live cell endocytic pathway.

[0039] In cell biology and drug delivery research, vesicle circulation (such as endocytosis and membrane fusion) is a key factor in elucidating drug delivery efficiency and intracellular signal transduction. Traditional techniques for studying vesicle circulation often employ single detection methods.

[0040] Content staining method: It only focuses on the fluorescent labeling of substances inside vesicles (such as proteins and small molecule drugs), and cannot simultaneously track the dynamic changes in cell membrane structure, making it difficult to reveal the coupling relationship between membrane structural deformation such as vesicle formation and membrane fusion and content transport.

[0041] pH indicator method: It indirectly reflects the characteristics of the pathway by relying on the change of pH value during endocytosis, but the acidity and alkalinity fluorescence signal is strongly interfered with, which can easily contaminate the image data, and it cannot specifically analyze the mechanism of action of membrane protein molecules at different stages.

[0042] Single image analysis lacks in-depth mining of time-series images, making it difficult to quantify the dynamic behavior of membrane proteins (such as transferrin receptor and epidermal growth factor receptor) at key nodes such as before, during, and after vesicle fusion, and even more difficult to combine with gene manipulation to explore the function of regulatory factors.

[0043] The core deficiency of existing technologies lies in the lack of a coupled detection system between membrane structure probes and content tracing, resulting in the absence of a complete temporal correlation during endocytosis involving "membrane morphology changes - vesicle formation - content transport - protein molecule interaction." Furthermore, the lack of intelligent analysis of temporal images and functional verification at the gene level hinders the quantitative analysis of key mechanisms in vesicle circulation. Although existing technologies employ individual fluorescence staining or image analysis methods, none involve a systematic approach combining "lipid probe coupling detection + deep learning temporal analysis + gene manipulation functional verification," nor do they propose targeted designs to avoid interference from pH indicators. Therefore, a novel tracing system is urgently needed that can integrate dynamic information on membrane structure and content, accurately analyze protein molecule interaction stages, and achieve quantitative analysis.

[0044] To solve the above problem, please refer to Figure 1 This application provides a time-series fluorescence tracing system based on coupled lipid probes, comprising: an imaging detection module for adding FM lipophilic styrene fluorescent dye to the culture environment, utilizing the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer lobes in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation; and then using a content staining method to complete the complete time-series tracing of the cell endocytosis process, wherein non-specific staining of the cell surface membrane is removed by washing before imaging detection; and a deep learning module for performing deep learning on the acquired time-series images to establish a live cell-based... The imaging screening and image analysis system identifies protein molecules related to target extravesicles and provides image descriptions of vesicle generation, transport, and fusion processes at different stages before, during, and after vesicle fusion with the cell membrane. A knockdown module is used to combine knockdown and knock-off operations with specific drug complexes to explore the generation of vesicle contents in early endosomes and their sustained cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of vesicle roles in generation, membrane fusion, function, and key regulatory factors. Furthermore, the system is not coupled with a pH indicator approach.

[0045] Specifically, this invention provides a systematic tracing platform that integrates membrane structure probe coupling detection, temporal image deep learning analysis, and gene function verification. Through the synergistic collaboration of three modules, it achieves precise analysis of the entire process of "membrane morphology changes - vesicle dynamics - content transport - protein molecule interaction" during endocytosis of living cells. This solves the shortcomings of single detection methods in traditional technologies (such as the separation of membrane structure and content information, the coarse temporal image analysis, and the lack of gene-level functional verification), and provides a quantitative tool for the study of cell vesicle circulation mechanisms and the optimization of drug delivery systems.

[0046] FM dye labeling involves adding lipophilic styrene fluorescent dye to a cell culture environment (such as DMEM medium containing 10% fetal bovine serum) and incubating cells at a preset concentration (such as 5-10 μM) for 5-15 minutes. The dye embeds into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction. Utilizing its property of "luminescence in lipid environments and non-luminescence in aqueous media," the dynamic contours of the cell membrane and endocytic vesicles are displayed in real time under a fluorescence microscope (such as a confocal microscope).

[0047] Vesicle formation detection visualizes the vesicle formation process by monitoring the local indentation, constriction, and detachment of fluorescence signals in membrane structures, identifying vesicle initiation sites (such as areas of concentrated fluorescence signals on the cell membrane), growth trajectories (changes in fluorescence intensity during progressive indentation of the membrane structure), and detachment nodes (instantaneous separation of fluorescence signals when the membrane breaks down).

[0048] Content labeling involves fluorescently labeling endocytic contents (including protein drugs, small molecule conjugates, vesicle biomolecules, etc.) with Alexa Fluor series dyes and fluorescent protein tags. The labeling reagent is introduced simultaneously with or before FM dye labeling (with an interval of ≤5 minutes) to ensure that the contents and membrane probe signals are collected synchronously.

[0049] Timing signal acquisition: Using a multi-channel fluorescence microscope, fluorescence images of the membrane probe (excitation wavelength 488nm, emission wavelength 500-550nm) and contents (excitation / emission wavelengths set according to the labeling dye) are synchronously recorded at preset time intervals (e.g., 1 frame per second). A time-series correlation database of "membrane structure deformation - contents encapsulation - endogenous transport" is established (e.g., ...). Figure 1 As shown, the horizontal axis represents time, and the vertical axis represents the intracellular spatial location. Different color channels distinguish between membrane and contents signals.

[0050] After FM dye incubation, the cells were washed three times with dye-free incubation medium (37°C, 5% CO2) (5 minutes each time, centrifuged at 1000 rpm to remove free dye). The fluorescence signal intensity threshold was used to ensure that only the specific fluorescence signal internalized into the vesicle membrane structure was retained, avoiding background interference.

[0051] The acquired time-series fluorescence images (resolution ≥ 600×600 pixels, TIFF format) are input into a pre-trained convolutional neural network (CNN) model. The model architecture includes 5-7 convolutional layers (extracting spatial features) and 2 recurrent layers (capturing time-series features). The preprocessing steps include background noise filtering (median filtering), fluorescence channel separation (membrane signal / content signal / protein signal) and normalization (pixel values ​​are normalized to [0,1]).

[0052] Combining a pre-defined protein molecule fluorescence label feature library (including protein localization tags such as HA and Myc, fluorescence wavelength features such as 561nm excitation / 580nm emission, and dynamic behavior patterns such as membrane surface aggregation and vesicle boundary enrichment), the model uses multi-layer feature extraction layers (such as ResNet residual blocks) to identify the pixel-level localization (output coordinate matrix) and trajectory tracking (generate a sequence of position coordinates at continuous time points) of target proteins (such as transferrin receptor and epidermal growth factor receptor) in the image, and simultaneously outputs the fluorescence intensity change curves of each protein molecule (reflecting abundance dynamics).

[0053] Pre-fusion stage (vesicles not yet in contact with the cell membrane, timestamps t1-t2): Analyze the aggregation sites of target proteins on the cell membrane (e.g., clathrin-mediated endocytosis sites) and regions of sudden signal intensity increases, and establish a membrane structural deformation model for vesicle formation (e.g., indentation depth-time curve). During fusion (vesicle membrane in contact with the cell membrane, timestamps t2-t3): Extract the spatial distribution characteristics of protein molecules at the fusion interface (e.g., signal density within 50 nm of the fusion pore), and construct a dynamic trajectory map of fusion pore formation and expansion. Post-shearing stage (vesicles detach from the cell membrane, timestamps t3-t4): Track the transport pathways of protein molecules entering the cell with the vesicles (e.g., the appearance time of early endosome localization signals), and visualize the redistribution patterns of protein molecules after membrane fusion (e.g., the path curve of receptor circulation to the cell membrane).

[0054] Cell model preparation includes: Gene knockout: Using the CRISPR-Cas9 system, sgRNAs targeting genes related to specific drug complexes (such as endocytosis regulators Rab5 and Clathrin) were designed. After transfection, stable knockout monoclonal cell lines were obtained through puromycin selection. Gene knockdown: Using siRNA interference technology, siRNAs targeting the target gene (concentration 20-50 nM) were introduced into cells via liposome transfection reagent. After 48 hours, gene expression levels were detected (qPCR verification knockdown efficiency ≥70%) to prepare knockdown cell models.

[0055] Comparative experiments and data analysis were conducted by labeling wild-type, knockout, and knockdown cells with FM dye and staining their contents under the same experimental conditions (such as the same culture parameters, dye concentration, and imaging time), and simultaneously acquiring time-series fluorescence images.

[0056] Key differences in analytical indicators include: Vesicle content production: comparing the first appearance time (reflecting vesicle formation efficiency) and distribution density (reflecting the amount of content encapsulated) of fluorescent signals in early endosomes (labeled as EEA1-positive structures). Receptor circulation: tracing the complete pathway time of transferrin receptor (TfR) from the cell membrane to early endosomes and back to the membrane, quantifying the difference in circulation cycle between the knockout / knockdown group and the wild type (e.g., the proportion of cycle lengthening or shortening). Output quantitative results: generating vesicle formation rate tables, membrane fusion efficiency curves, and receptor circulation pathway heatmaps for different cell models, clarifying the regulatory role of specific gene products on key steps of vesicle circulation (e.g., Rab5 knockout leads to a 40% decrease in membrane fusion efficiency).

[0057] The time-series image data output by the imaging detection module is transmitted to the deep learning module in real time for feature analysis. The analysis results are fed back to the knockdown operation module to guide the design of comparative experiments for gene models, ultimately forming a closed loop of "detection-analysis-verification" to ensure systematic research from molecular behavior observation to functional mechanism analysis.

[0058] This system breaks through the limitations of traditional single-staining methods. By coupling the signal of FM dye with the staining of contents, it achieves for the first time the spatiotemporal correlation tracing of membrane structure and content transport, solving the technical challenge of "asynchronous analysis of vesicle generation and content encapsulation." Based on the timestamp division of time-series images, it accurately captures the dynamic differences of protein molecules before, during, and after vesicle fusion (such as the instantaneous enrichment of protein molecules at the membrane interface during fusion), providing microscopic evidence for elucidating molecular mechanisms of action. Combining gene editing and image analysis, it directly links the dynamic behavior of protein molecules with gene function (such as knockdown of Clathrin leading to a 60% reduction in vesicle initiation sites), providing quantitative evidence for targeted optimization of drug delivery systems.

[0059] Through the above implementation methods, this system enables the observation of structural dynamics at the cellular level and the analysis of mechanisms at the molecular level, providing an innovative interdisciplinary tool for cell biology research and biomedical detection.

[0060] In some embodiments, the method of using the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation includes: adding FM lipophilic styrene fluorescent dye at a preset concentration in a cell culture environment, allowing the dye molecules to embed into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction; utilizing the characteristic that the dye emits strong fluorescence in a lipid environment but does not emit fluorescence in an aqueous medium; acquiring morphological images of the cell membrane and endocytic vesicles in real time using a fluorescence microscope; and identifying the initiation site of vesicle formation, growth process, and detachment nodes from the cell membrane based on the dynamic changes in the fluorescence signal of the membrane structure.

[0061] By utilizing the lipophilic properties of FM lipophilic styrene fluorescent dye, it is embedded in the outer leaflets of the cell membrane phospholipid bilayer to form a membrane structure probe. The cell membrane morphology and the generation process of endocytic vesicles are observed in real time through dynamic changes in fluorescence signals, including vesicle initiation site identification, growth trajectory tracking, and detachment node determination.

[0062] Dye preparation and cell incubation include: Dye concentration: Adjust the FM dye concentration according to cell type (e.g., HeLa cells, primary macrophages), usually 5-15 μM (preferably 10 μM), dissolved in serum-free medium (to avoid interference from serum proteins with dye binding). Incubation conditions: Add the dye to the cell culture environment (37℃, 5% CO2), incubate for 5-20 minutes (optimized according to the endocytosis rate, e.g., 5 minutes for rapidly endocytic cells, 15 minutes for slowly endocytic cells), allowing the dye molecules to embed into the outer leaflets of the cell membrane through lipophilic interactions.

[0063] Fluorescence signal acquisition and imaging include: Microscope selection: using a confocal fluorescence microscope (e.g., Leica SP8) or a super-resolution microscope (e.g., STORM), with the excitation wavelength set according to the FM dye model (e.g., FM4-64 excitation wavelength 515nm, emission wavelength 540-600nm). Imaging parameters: acquiring images in real time at a frequency of 1-5 frames per second, with a resolution ≥600×600 pixels and a z-axis interlayer spacing of 0.5μm (to acquire three-dimensional membrane structure information), ensuring the capture of dynamic deformation of the membrane structure.

[0064] Vesicle formation feature identification includes: Initiation site: Identifying localized areas of fluorescence concentration on the cell membrane (regions with a sudden increase in grayscale value ≥20%), and using membrane curvature algorithms (such as curvature calculation based on the Laplacian operator) to determine the indentation site. Growth process: Tracking the increase in fluorescence depth (indentation depth change ≥10nm per frame) and area expansion (area increase of 5-10μm per second) in the indented region. 2This generates vesicle growth curves (indentation depth versus time relationship). Detachment node: Detects the instantaneous separation of fluorescence signals at the moment of membrane rupture (a sudden increase in fluorescence intensity difference between the two regions ≥30%), and uses an optical flow algorithm to confirm the moment of vesicle separation from the cell membrane.

[0065] In some embodiments, the method of staining contents to complete the full temporal tracing of the cellular endocytosis process includes: fluorescently labeling the endocytic contents; introducing the contents fluorescent labeling reagent simultaneously with or before the addition of FM dye; establishing a correspondence between the dynamic changes in membrane structure and the transport trajectory of contents by synchronously acquiring time-series images of membrane probe fluorescence signals and contents fluorescence signals; and realizing the temporal fluorescence signal correlation recording of the entire process of endocytosis initiation, vesicle encapsulation of contents, and contents entering the intracellular endosome pathway; wherein the endocytic contents include protein drugs, small molecule conjugates, and intravesical biomolecules.

[0066] By fluorescently labeling endocytic contents (protein drugs, small molecule conjugates, and vesicle biomolecules) and synchronously acquiring FM membrane probe signals, a spatiotemporal correspondence between membrane structure changes and contents transport is established, enabling the time-series fluorescence signal correlation recording of the entire endocytosis process (initiation, encapsulation, and endosome transport).

[0067] Labeling methods include: Protein drugs: Fusing fluorescent proteins (such as mCherry, GFP) to the N / C ends of proteins using genetic engineering, or chemically conjugating Cy3 / Cy5 dyes (to label lysine residues of proteins). Small molecule conjugates: Preparing fluorescent conjugates (labeling efficiency ≥80%) by reacting succinimide-activated fluorescent dyes (such as Alexa Fluor488 NHS ester) with the amino groups of small molecule drugs. Intravesical biomolecules: Labeling using specific antibodies (such as fluorescently labeled Rab5 antibodies to recognize early endosomes), or gene-encoded probes (such as pH-sensitive GFP to label endosome proteins).

[0068] The time window includes: the contents labeling reagent can be introduced 5 minutes before, simultaneously with, or within 10 minutes after the addition of FM dye to ensure that the signals of the two overlap in the early stage of vesicle internalization (e.g., the contents probe is added after 10 minutes of FM dye incubation and incubated together for 5 minutes).

[0069] Multi-channel imaging uses independent fluorescence channels to acquire membrane probe (channel 1, e.g., 488nm excitation) and content signals (channel 2, e.g., 561nm excitation) respectively, and generates a dual-channel time-series image sequence by synchronizing with timestamps (error ≤ 50ms).

[0070] Coordinate registration was performed using ImageJ plugins (such as MultiStackReg) to rigidly register the dual-channel images, ensuring that the membrane structure and the contents signal were spatially aligned (error ≤ 0.2 μm).

[0071] The trajectory is matched using a particle tracking algorithm (such as the LAPJV algorithm) to match the co-localization trajectory of the membrane vesicle fluorescence signal and the content signal (overlap time ≥ 3 frames, spatial distance ≤ 100 nm), and a time-series correlation table of "membrane indentation - content encapsulation - vesicle detachment" is generated (recording the start time, encapsulation amount and transport path of each vesicle encapsulating the content).

[0072] In some embodiments, the non-specific staining of the cell surface membrane is removed by washing before imaging detection, including: after the FM dye binds to the cell membrane and completes vesicle internalization, the cells are washed multiple times with dye-free cell culture medium or buffer to remove uninternalized free dye molecules on the cell membrane surface; the non-specific fluorescence signal on the cell surface after washing is determined to be below a preset detection limit by using a fluorescence signal intensity threshold; and then the cells are subjected to real-time live cell imaging to ensure that only the fluorescence signal internalized into the vesicle membrane structure is retained for subsequent analysis.

[0073] By repeatedly washing away uninternalized FM dye from the cell membrane surface, only the specific fluorescence signal internalized into the vesicle membrane structure is retained, avoiding background interference and ensuring the purity of the imaging signal.

[0074] Use preheated dye-free culture medium (e.g., DMEM without phenol red, 37°C) or PBS buffer (containing 1 mM Ca2+ / Mg2+, pH 7.4) for washing, and wash 3-5 times (each wash should be 5 times the culture volume).

[0075] The procedure includes: after FM dye incubation, discard the dye solution, immediately add washing solution, gently shake the culture dish for 10 seconds, centrifuge at 1000 rpm for 5 minutes (centrifugation can be omitted for adherent cells, and the washing solution can be directly discarded), and repeat 3 times.

[0076] The detection method includes: after washing, randomly selecting 10 fields of view under a fluorescence microscope, collecting the fluorescence intensity of the cell membrane region (ROI), and calculating the average gray value (G_mean) and background noise value (G_bg, gray value of the extracellular region).

[0077] The threshold criteria include determining that non-specific staining removal is achieved when G_mean / G_bg≤1.5 (i.e. the signal intensity on the membrane surface is only 1.5 times or less than the background); otherwise, the number of washing cycles is increased (re-testing is performed after each washing).

[0078] The culture medium was replaced with a live-cell imaging medium containing 1% low-melting-point agarose (to reduce cell movement interference), and a constant temperature environment of 37°C was maintained (the microscope was equipped with a heating stage). Imaging parameters used low-phototoxicity excitation light (such as an LED light source, power ≤10%), and exposure time ≤50ms / frame to avoid cell damage or fluorescence bleaching caused by prolonged light exposure.

[0079] In some embodiments, the step of performing deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system to identify target extravesicular vesicle-related protein molecules includes: inputting the acquired time-series fluorescence images into a pre-trained convolutional neural network model, identifying the spatiotemporal distribution characteristics of fluorescence signals in the images through multi-layer feature extraction layers, combining a preset protein molecule fluorescence labeling feature library, performing pixel-level localization and trajectory tracking of protein molecules related to extravesicles in the images, and outputting dynamic distribution and abundance change data of target protein molecules during endocytosis; wherein, the protein molecule fluorescence labeling feature library includes protein localization tags, fluorescence wavelength characteristics, and dynamic behavior patterns.

[0080] By utilizing pre-trained convolutional neural networks (CNNs) to analyze time-series fluorescence images and combining them with a protein molecule fluorescence label feature library, pixel-level localization, trajectory tracking, and dynamic abundance analysis of target proteins are achieved, providing quantitative data for the spatiotemporal distribution of protein molecules during vesicle circulation.

[0081] Training data is obtained by collecting live cell time-series images (≥1000 sets, each set containing more than 200 frames) containing fluorescently labeled target proteins (such as TfR, EGFR), and manually annotating the protein localization regions (pixel-level masks) and dynamic trajectories (coordinate sequences).

[0082] The model architecture uses U-Net++ or ResU-Net networks, including an encoder (5 convolutional layers + pooling) and a decoder (5 deconvolutional layers + skip connections), outputting a protein presence probability map for each pixel (a probability ≥ 0.7 is considered positive). Training parameters include: a loss function using Dice coefficient + cross-entropy, an optimizer Adam (learning rate 1e-4), and 50-100 training epochs (training stops when the Dice coefficient on the validation set ≥ 0.92).

[0083] The feature library includes: Location tags: subcellular protein location tags (e.g., membrane location tag "plasmamembrane", endosome tag "early endosome"). Wavelength features: excitation / emission wavelengths of fluorescent dyes (e.g., Alexa Fluor 555 excitation 555nm, emission 570nm). Behavioral patterns: dynamic behavioral tags (e.g., "membrane surface aggregation", "vesicle co-localization", "periodic cycling") and corresponding signal change patterns (e.g., a 30% surge in signal intensity during the fusion phase). The matching algorithm filters the protein location signals output by the model against the feature library using wavelength filtering (excluding signals from non-target dyes), and then uses the Dynamic Time Warping (DTW) algorithm to match behavioral patterns (similarity ≥ 0.8 is considered the target protein).

[0084] Trajectory tracking uses the TrackMate plugin to link protein localization points in consecutive frames, setting a maximum displacement threshold (e.g., 1 μm / frame) and a minimum trajectory length (5 frames) to generate a motion trajectory file for each protein molecule (including coordinates, time, and fluorescence intensity). Abundance analysis calculates the sum of protein fluorescence intensities in the target region (e.g., vesicle membrane, endosomal membrane) at each time point, generating an abundance change curve (time-intensity curve, resolution 0.1 sec / point).

[0085] In some embodiments, the image description of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion with the cell membrane includes: dividing the endocytosis process into pre-fusion, fusion, and post-fusion stages based on the timestamps of the time-series images; extracting the fluorescence signal features of the target protein molecules in each stage; and establishing a membrane structure deformation model, transport path trajectory diagram, and a visual description of molecular interactions at the fusion interface for vesicle generation in each stage. The fluorescence signal features include aggregation sites on the membrane structure, signal intensity change curves, and spatial positional relationships with the vesicle boundary.

[0086] Based on time-series image timestamps, the endocytosis process is divided into three stages: pre-fusion, during fusion, and post-cleavage. Fluorescence signal features (aggregation sites, intensity changes, and spatial locations) of target proteins in each stage are extracted to establish membrane deformation models, transport trajectory diagrams, and visualizations of molecular interactions.

[0087] The stage division criteria include: Pre-fusion stage (t1): From the appearance of the vesicle initiation site to the first contact between the vesicle membrane and the cell membrane (membrane distance ≤20nm), characterized by deepening of local membrane indentation and no content signal entering the indented area. Mid-fusion stage (t2): From membrane contact to vesicle neck narrowing (neck diameter ≤50nm), characterized by the beginning of content signal entering the vesicle and enrichment of the target protein at the fusion interface (signal intensity ≥2 times the background). Post-shearing stage (t3): From complete detachment of the vesicle from the cell membrane to entry into early endosomes, characterized by independent vesicle movement and protein signal transport with the vesicle and gradual dispersion.

[0088] Feature extraction and model building include: Pre-fusion stage: Aggregation sites: Identify protein aggregation hotspots on the cell membrane using density clustering algorithm (DBSCAN) (inter-spacing ≤100nm, number of points ≥5). Membrane deformation model: Fit the relationship between indentation depth (d) and time (t), such as d(t)=a*(1-e^(-bt)), where a is the maximum depth and b is the indentation rate constant. Fusion stage: Interface features: Measure the protein signal density at the fusion interface (number of signal pixels / interface area) and establish a density-time curve (peak appearance time corresponds to the maximum open state of the fusion pore). Interaction visualization: Display the intermolecular distance at the fusion interface (≤10nm is considered effective interaction) through fluorescence resonance energy transfer (FRET) efficiency mapping (if the protein is labeled with FRET pairs). Post-shearing stage: Transport trajectory: Fit the vesicle movement path using Gaussian mixture model (GMM) to distinguish between directional transport (velocity ≥0.5μm / s) and Brownian motion (velocity ≤0.2μm / s). Redistribution analysis: Calculate the ratio of protein fluorescence intensity at the endosome membrane to that at the cell membrane (R = I endosome / I membrane) to quantify receptor circulation efficiency (e.g., R = 0.8 indicates that 80% of the receptor enters the endosome).

[0089] Generate pseudo-color images for each stage (blue marks aggregation sites in the pre-fusion stage, red highlights the interface in the fusion stage, and green traces the trajectory in the post-cutting stage), overlay them on bright-field cell images, and add timelines and key event markers (such as the "vesicle detachment" time point).

[0090] In some embodiments, the combination of knockout and knockdown of specific drug complexes to explore the generation of vesicle contents in early endosomes and their sustained cycling effects on receptors such as transferrin receptor and epidermal growth factor receptor includes: knocking out or knocking down genes related to specific drug complexes in cells using gene editing or RNA interference techniques to prepare cell models with differential gene expression; performing FM dye labeling and content staining under the same experimental conditions; comparing the time-series fluorescence images of the knockout group, knockdown group, and wild-type cells; analyzing the differences in the appearance time, distribution pattern, and receptor protein cycling pathway of fluorescence signals of vesicle contents in early endosomes; and quantifying the regulatory effects of specific gene products on vesicle generation, membrane fusion efficiency, and sustained receptor cycling.

[0091] By knocking out (CRISPR-Cas9) or knocking down (siRNA) genes related to specific drug complexes, gene-differential cell models are prepared. By comparing time-series images, the differences in vesicle content production and receptor circulation are analyzed, and the regulatory effect of gene products on vesicle circulation is quantified.

[0092] Gene knockout was achieved by designing sgRNA (targeting gene coding regions, such as exon 2 of the Rab5 gene), co-transfecting cells with Cas9 protein (liposome transfection reagent, sgRNA concentration 100 nM), and sorting single cells into 96-well plates after 48 hours. After 2 weeks of culture, the knockout efficiency (target protein expression level ≤5%) was verified by Western blot.

[0093] Gene knockdown was achieved by synthesizing siRNA (3 targeting different sequences, such as Clathrin heavy chain siRNA), transfecting with RNAiMAX reagent (final siRNA concentration 50 nM), and detecting mRNA levels by qPCR 48 hours later (siRNA sequences with knockdown efficiency ≥70% were used for experiments).

[0094] The comparative experimental design included: grouping: wild-type (WT), knockout group (KO), and knockdown group (KD), with 3 biological replicates (independent cell lines) in each group. Detection conditions: identical culture conditions (e.g., 10% FBS, 37℃), identical staining parameters (FM dye concentration 10 μM, content probe concentration 20 nM), and identical imaging time (2 hours of time-series images acquired for each group).

[0095] Vesicle content formation includes: the time to first appearance of the content signal in the early endosome (EEA1-positive structure) (T_first, the mean of 120 seconds for the WT group, and a delay of 180 seconds in the KO group indicates inhibited formation). Content distribution pattern: the coefficient of variation (CV = standard deviation / mean) of the signal intensity in the endosome is calculated; an increase in CV indicates non-uniform content encapsulation.

[0096] The receptor cycle includes: the transferrin receptor (TfR) cycle: the time interval from the disappearance to the reappearance of cell membrane signals (15 minutes for the WT group, and 25 minutes for the KD group, indicating a slowed cycle). The three-dimensional path of TfR from the membrane to the endosome and back to the membrane is plotted using trajectory analysis software (such as CellTracer), and the path length and number of turns are calculated (an increased number of turns indicates decreased transport efficiency).

[0097] The quantitative validation method used one-way ANOVA to compare differences between groups, with P < 0.05 considered significant. The expression levels of related proteins (such as clathrin and dynamin) in knockout / knockdown cells were detected by Western blot to establish the correlation between gene expression levels and vesicle circulation parameters (such as Pearson correlation coefficient ≥ 0.8).

[0098] This application couples FM lipophilic dyes with cell contents staining to simultaneously capture changes in cell membrane morphology and the transport trajectory of cell contents, achieving dynamic recording of the entire endocytic process from vesicle formation to endosome sorting, thus overcoming the limitations of traditional single-staining methods in dynamically analyzing membrane structures. Deep learning is used to extract features from time-series images, identifying the spatiotemporal distribution and behavioral patterns of exovesicle-related proteins at different stages. For the first time, a visual description of protein molecules before, during, and after vesicle fusion is achieved, providing crucial data for elucidating membrane protein cycling mechanisms. By combining knockout / downgrade operations with specific drug complexes, the differences in fluorescence signals under different gene expression states are compared, quantifying the effects of key regulatory factors on vesicle formation, membrane fusion, and receptor cycling, providing experimental evidence for optimizing drug delivery systems. The use of pH indicators is explicitly excluded to avoid strong fluorescence signal contamination, improving the accuracy and reliability of image analysis.

[0099] It should be noted that the acquisition of any information mentioned in the system is in accordance with relevant regulations and with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0100] Please see Figure 2 , Figure 2 This is a schematic flowchart of a time-series fluorescence tracing method based on coupled-detection lipid probes provided in one embodiment of this application. The execution device of the method is a computer device deployed in the time-series fluorescence tracing system based on coupled-detection lipid probes provided in any embodiment of this application.

[0101] like Figure 2 As shown, the provided method includes steps S101 to S103. The computer device can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc. This is used to implement steps S101 to S103 and their corresponding embodiments.

[0102] It should be noted that the acquisition of any information mentioned in the provided methods is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0103] Step S101. Add FM lipophilic styrene fluorescent dye to the culture environment. Utilize the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe membrane morphological changes and detect vesicle formation. Then, use the contents staining method to complete the complete temporal tracing of the cell endocytosis process. Among them, the non-specific staining of the cell surface membrane is removed by washing and then imaged for detection.

[0104] Step S102. Perform deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system, identify protein molecules related to the target extravesicles, and provide image descriptions of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after the extravesicles fuse with the cell membrane.

[0105] Step S103. By combining knockout and knockdown operations of specific drug complexes, explore the generation mode of vesicle contents in early endosomal tissues, as well as the continuous cyclic effect on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in generation mode, membrane fusion mode, function and key regulatory factors. The system is not coupled with pH indicator mode.

[0106] In some embodiments, the method of using the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation includes: adding FM lipophilic styrene fluorescent dye at a preset concentration in a cell culture environment, allowing the dye molecules to embed into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction; utilizing the characteristic that the dye emits strong fluorescence in a lipid environment but does not emit fluorescence in an aqueous medium; acquiring morphological images of the cell membrane and endocytic vesicles in real time using a fluorescence microscope; and identifying the initiation site of vesicle formation, growth process, and detachment nodes from the cell membrane based on the dynamic changes in the fluorescence signal of the membrane structure.

[0107] In some embodiments, the method of staining contents to complete the full temporal tracing of the cellular endocytosis process includes: fluorescently labeling the endocytic contents; introducing the contents fluorescent labeling reagent simultaneously with or before the addition of FM dye; establishing a correspondence between the dynamic changes in membrane structure and the transport trajectory of contents by synchronously acquiring time-series images of membrane probe fluorescence signals and contents fluorescence signals; and realizing the temporal fluorescence signal correlation recording of the entire process of endocytosis initiation, vesicle encapsulation of contents, and contents entering the intracellular endosome pathway; wherein the endocytic contents include protein drugs, small molecule conjugates, and intravesical biomolecules.

[0108] In some embodiments, the non-specific staining of the cell surface membrane is removed by washing before imaging detection, including: after the FM dye binds to the cell membrane and completes vesicle internalization, the cells are washed multiple times with dye-free cell culture medium or buffer to remove uninternalized free dye molecules on the cell membrane surface; the non-specific fluorescence signal on the cell surface after washing is determined to be below a preset detection limit by using a fluorescence signal intensity threshold; and then the cells are subjected to real-time live cell imaging to ensure that only the fluorescence signal internalized into the vesicle membrane structure is retained for subsequent analysis.

[0109] In some embodiments, the step of performing deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system to identify target extravesicular vesicle-related protein molecules includes: inputting the acquired time-series fluorescence images into a pre-trained convolutional neural network model, identifying the spatiotemporal distribution characteristics of fluorescence signals in the images through multi-layer feature extraction layers, combining a preset protein molecule fluorescence labeling feature library, performing pixel-level localization and trajectory tracking of protein molecules related to extravesicles in the images, and outputting dynamic distribution and abundance change data of target protein molecules during endocytosis; wherein, the protein molecule fluorescence labeling feature library includes protein localization tags, fluorescence wavelength characteristics, and dynamic behavior patterns.

[0110] In some embodiments, the image description of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion with the cell membrane includes: dividing the endocytosis process into pre-fusion, fusion, and post-fusion stages based on the timestamps of the time-series images; extracting the fluorescence signal features of the target protein molecules in each stage; and establishing a membrane structure deformation model, transport path trajectory diagram, and a visual description of molecular interactions at the fusion interface for vesicle generation in each stage. The fluorescence signal features include aggregation sites on the membrane structure, signal intensity change curves, and spatial positional relationships with the vesicle boundary.

[0111] In some embodiments, the combination of knockout and knockdown of specific drug complexes to explore the generation of vesicle contents in early endosomes and their sustained cycling effects on receptors such as transferrin receptor and epidermal growth factor receptor includes: knocking out or knocking down genes related to specific drug complexes in cells using gene editing or RNA interference techniques to prepare cell models with differential gene expression; performing FM dye labeling and content staining under the same experimental conditions; comparing the time-series fluorescence images of the knockout group, knockdown group, and wild-type cells; analyzing the differences in the appearance time, distribution pattern, and receptor protein cycling pathway of fluorescence signals of vesicle contents in early endosomes; and quantifying the regulatory effects of specific gene products on vesicle generation, membrane fusion efficiency, and sustained receptor cycling.

[0112] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the time-series fluorescence tracing method and each step based on the coupled lipid probe described above can be referred to the corresponding process in the embodiments of the time-series fluorescence tracing system based on the coupled lipid probe described above, and will not be repeated here.

[0113] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0114] The storage medium may store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any embodiment of a time-series fluorescence tracing method based on coupled-probe lipid probes.

[0115] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0116] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When executed by a processor, the computer program enables the processor to perform any time-series fluorescence tracing system method based on coupled lipid probe detection.

[0117] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 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 terminal 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.

[0118] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0119] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0120] FM lipophilic styrene fluorescent dye was added to the culture environment. The lipophilicity of FM lipophilic styrene fluorescent dye was used to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe changes in membrane morphology and detect vesicle formation. Then, the contents staining method was used to complete the complete time-series tracing of the cell endocytosis process. Non-specific staining of the cell surface membrane was removed by washing and then imaged for detection.

[0121] Deep learning was used to acquire time-series images to establish a live-cell imaging screening and image analysis system to identify protein molecules related to target extravesicles. The generation, transport and fusion processes of these proteins at different stages before, during and after fusion of extravesicles with the cell membrane were described by images.

[0122] By combining knockout and knockdown of specific drug complexes, this study explores the generation of vesicle contents in early endosomes and their sustained cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in their generation, membrane fusion, function, and key regulatory factors. Furthermore, the system is not coupled with a pH indicator approach.

[0123] In some embodiments, the method of using the lipophilicity of FM lipophilic styrene fluorescent dye to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation includes: adding FM lipophilic styrene fluorescent dye at a preset concentration in a cell culture environment, allowing the dye molecules to embed into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction; utilizing the characteristic that the dye emits strong fluorescence in a lipid environment but does not emit fluorescence in an aqueous medium; acquiring morphological images of the cell membrane and endocytic vesicles in real time using a fluorescence microscope; and identifying the initiation site of vesicle formation, growth process, and detachment nodes from the cell membrane based on the dynamic changes in the fluorescence signal of the membrane structure.

[0124] In some embodiments, the method of staining contents to complete the full temporal tracing of the cellular endocytosis process includes: fluorescently labeling the endocytic contents; introducing the contents fluorescent labeling reagent simultaneously with or before the addition of FM dye; establishing a correspondence between the dynamic changes in membrane structure and the transport trajectory of contents by synchronously acquiring time-series images of membrane probe fluorescence signals and contents fluorescence signals; and realizing the temporal fluorescence signal correlation recording of the entire process of endocytosis initiation, vesicle encapsulation of contents, and contents entering the intracellular endosome pathway; wherein the endocytic contents include protein drugs, small molecule conjugates, and intravesical biomolecules.

[0125] In some embodiments, the non-specific staining of the cell surface membrane is removed by washing before imaging detection, including: after the FM dye binds to the cell membrane and completes vesicle internalization, the cells are washed multiple times with dye-free cell culture medium or buffer to remove uninternalized free dye molecules on the cell membrane surface; the non-specific fluorescence signal on the cell surface after washing is determined to be below a preset detection limit by using a fluorescence signal intensity threshold; and then the cells are subjected to real-time live cell imaging to ensure that only the fluorescence signal internalized into the vesicle membrane structure is retained for subsequent analysis.

[0126] In some embodiments, the step of performing deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system to identify target extravesicular vesicle-related protein molecules includes: inputting the acquired time-series fluorescence images into a pre-trained convolutional neural network model, identifying the spatiotemporal distribution characteristics of fluorescence signals in the images through multi-layer feature extraction layers, combining a preset protein molecule fluorescence labeling feature library, performing pixel-level localization and trajectory tracking of protein molecules related to extravesicles in the images, and outputting dynamic distribution and abundance change data of target protein molecules during endocytosis; wherein, the protein molecule fluorescence labeling feature library includes protein localization tags, fluorescence wavelength characteristics, and dynamic behavior patterns.

[0127] In some embodiments, the image description of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion with the cell membrane includes: dividing the endocytosis process into pre-fusion, fusion, and post-fusion stages based on the timestamps of the time-series images; extracting the fluorescence signal features of the target protein molecules in each stage; and establishing a membrane structure deformation model, transport path trajectory diagram, and a visual description of molecular interactions at the fusion interface for vesicle generation in each stage. The fluorescence signal features include aggregation sites on the membrane structure, signal intensity change curves, and spatial positional relationships with the vesicle boundary.

[0128] In some embodiments, the combination of knockout and knockdown of specific drug complexes to explore the generation of vesicle contents in early endosomes and their sustained cycling effects on receptors such as transferrin receptor and epidermal growth factor receptor includes: knocking out or knocking down genes related to specific drug complexes in cells using gene editing or RNA interference techniques to prepare cell models with differential gene expression; performing FM dye labeling and content staining under the same experimental conditions; comparing the time-series fluorescence images of the knockout group, knockdown group, and wild-type cells; analyzing the differences in the appearance time, distribution pattern, and receptor protein cycling pathway of fluorescence signals of vesicle contents in early endosomes; and quantifying the regulatory effects of specific gene products on vesicle generation, membrane fusion efficiency, and sustained receptor cycling.

[0129] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the processor described above can be referred to the corresponding process in the method embodiments of the above embodiments, and will not be repeated here.

[0130] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the time-series fluorescence tracing method based on coupled detection lipid probes provided in the above embodiments of this application.

[0131] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A time-series fluorescence tracer system based on coupled lipid probes, characterized in that, include: The imaging detection module is used to add FM lipophilic styrene fluorescent dye to the culture environment. The lipophilicity of FM lipophilic styrene fluorescent dye is used to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe changes in membrane morphology and detect the formation of vesicles. Then, the contents staining method is used to complete the complete time-series tracing of the cell endocytosis process. The non-specific staining of the cell surface membrane is removed by washing and then used for imaging detection. The deep learning module is used to perform deep learning on the acquired time-series images, establish a screening and image analysis system based on live cell imaging, identify protein molecules related to target extravesicles, and provide image descriptions of the vesicle generation, transport and fusion processes of these proteins at different stages before, during and after the extravesicles fuse with the cell membrane. The knockdown module is used to combine knockout and knockdown operations of specific drug complexes to explore the generation mode of vesicle contents in early endosomal tissues, as well as the continuous cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in generation mode, membrane fusion mode, function and key regulatory factors, and the system is not coupled with pH indicator mode.

2. The system according to claim 1, characterized in that, The method utilizes the lipophilicity of FM styrene fluorescent dye to bind with the outer lobules in the phosphate structure of the cell membrane to form a membrane probe for observing membrane morphological changes and detecting vesicle formation, including: In a cell culture environment, FM lipophilic styrene fluorescent dye is added at a preset concentration, allowing the dye molecules to embed into the outer leaflets of the cell membrane phospholipid bilayer through lipophilic interaction. Taking advantage of the dye's characteristic of emitting strong fluorescence in a lipid environment but not in an aqueous medium, real-time morphological images of the cell membrane and endocytic vesicles are acquired using a fluorescence microscope. Based on the dynamic changes in the fluorescence signal of the membrane structure, the initiation site of vesicle formation, the growth process, and the nodes at which vesicles detach from the cell membrane are identified.

3. The system according to claim 1, characterized in that, The method of using contents staining to complete the time-series tracing of the cellular endocytosis process includes: Fluorescent labeling of endocytic contents is performed by introducing fluorescent labeling reagents into the contents simultaneously or sequentially with the addition of FM dye. By synchronously acquiring time-series images of membrane probe fluorescence signals and contents fluorescence signals, a correspondence between dynamic changes in membrane structure and contents transport trajectory is established, enabling the time-series fluorescence signal correlation recording of the entire process of endocytosis initiation, vesicle encapsulation of contents, and contents entering the endosome pathway. The endocytic contents include protein drugs, small molecule conjugates, and vesicle biomolecules.

4. The system according to claim 1, characterized in that, The non-specific staining of the cell surface membrane is removed by washing before imaging detection, including: After the FM dye binds to the cell membrane and completes vesicle internalization, the cells are washed multiple times with dye-free cell culture medium or buffer to remove uninternalized free dye molecules from the cell membrane surface. The non-specific fluorescence signal on the cell surface after washing is judged to be below the preset detection limit by the fluorescence signal intensity threshold. Then, the cells are subjected to real-time live cell imaging to ensure that only the fluorescence signal internalized into the vesicle membrane structure is retained for subsequent analysis.

5. The system according to claim 1, characterized in that, The process of performing deep learning on the acquired time-series images to establish a live-cell imaging screening and image analysis system to identify protein molecules related to target extravesicles includes: The acquired time-series fluorescence images are input into a pre-trained convolutional neural network model. Through multiple feature extraction layers, the spatiotemporal distribution characteristics of fluorescence signals in the images are identified. Combined with a pre-set protein molecule fluorescence label feature library, the protein molecules related to external vesicles in the images are located at the pixel level and tracked. The dynamic distribution and abundance change data of the target protein molecules during endocytosis are output. The protein molecule fluorescent label feature library includes protein localization tags, fluorescence wavelength features, and dynamic behavior patterns.

6. The system according to claim 1, characterized in that, The image description of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion with the cell membrane includes: Based on the timestamps of the time-series images, the endocytosis process was divided into a pre-fusion stage, a fusion stage, and a post-shearing stage. Fluorescence signal features of target protein molecules were extracted in each stage, and membrane structure deformation models, transport path trajectories, and molecular interaction visualizations of vesicles generated in each stage were established. Fluorescence signal characteristics include aggregation sites on the membrane structure, signal intensity variation curves, and spatial relationship with vesicle boundaries.

7. The system according to claim 1, characterized in that, The aforementioned knockout and knockdown operations combining specific drug complexes explore the early production of vesicle contents in endosomes and their sustained cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor, including: By knocking out or down-knocking down genes related to specific drug complexes in cells using gene editing or RNA interference techniques, cell models with differential gene expression are prepared. Under the same experimental conditions, FM dye labeling and content staining are performed. The time-series fluorescence images of the knockout group, knockdown group and wild-type cells are compared to analyze the differences in the appearance time, distribution pattern and receptor protein circulation pathway of fluorescence signals in early endosomes. The regulatory effect of specific gene products on vesicle formation, membrane fusion efficiency and receptor continuous circulation is quantified.

8. A time-series fluorescence tracing method based on coupled lipid probes, characterized in that, The method, applied to the time-series fluorescence tracing system based on coupled lipid probes according to any one of claims 1-7, comprises: FM lipophilic styrene fluorescent dye was added to the culture environment. The lipophilicity of FM lipophilic styrene fluorescent dye was used to combine with the outer leaflets in the phosphate structure of the cell membrane to form a membrane probe to observe changes in membrane morphology and detect vesicle formation. Then, the contents staining method was used to complete the complete time-series tracing of the cell endocytosis process. Non-specific staining of the cell surface membrane was removed by washing and then imaged. Deep learning was used to acquire time-series images to establish a live-cell imaging screening and image analysis system, identify protein molecules related to target extravesicles, and provide image descriptions of the vesicle generation, transport, and fusion processes of these proteins at different stages before, during, and after fusion of extravesicles with the cell membrane. By combining knockout and knockdown of specific drug complexes, this study explores the generation of vesicle contents in early endosomes and their sustained cyclic effects on receptors such as transferrin receptor and epidermal growth factor receptor. This lays the foundation for quantitative analysis of the role of vesicles in their generation, membrane fusion, function, and key regulatory factors. Furthermore, the system is not coupled with a pH indicator approach.

9. A computer device, characterized in that, The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the method as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the method as described in claim 8.

Images