A molecular recruitment colocalization system

Abstract

The application discloses a molecular recruitment co-localization system, comprising: a spatially targeted visualization module for recruiting target proteins to a specific site and generating a fluorescence co-localization signal in living cells based on the target proteins recruited to the specific site; a multi-protein interaction verification module for observing protein interactions in a multi-protein complex based on different pairs of proteins recruited to the specific site at a specified genetic locus; a protein binding capacity evaluation module for quantitatively analyzing the binding capacity between proteins based on the co-localization percentage and signal-to-noise ratio of the proteins; a protein phase separation capacity evaluation module for rapidly evaluating whether different proteins or protein domains have phase separation capacity based on the recruitment of the different proteins or protein domains to a specific location; and a dynamic observation and quantitative evaluation module for observing the dynamic changes of proteins in the phase separation process in real time by combining multi-channel fluorescence and quantum dot probes and providing preliminary quantitative data for the physical properties of the condensate.

Description

Technical Field

[0001] This invention relates to the field of gene editing technology detection technology, and in particular to a molecular recruitment colocalization system for visualizing multi-protein interactions and phase-separated condensates in living cells, laying the foundation for the development of more accurate, efficient, safe and well-defined gene editing technologies. Background Technology

[0002] In cellular life activities, protein-protein interactions (PPIs) form the basis for regulating many key processes, including signal transduction, enzymatic reactions, and transcriptional regulation. Accurate and efficient detection and verification of these interactions are crucial for understanding cellular function, disease mechanisms, and developing novel drugs. Meanwhile, recent research has gradually revealed that many intracellular proteins and nucleic acids can form membrane-free biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates provide a novel mechanism for subcellular spatial partitioning within the cell, participating in various functions such as gene expression regulation, signal aggregation, and stress responses, becoming an important research frontier in the life sciences. While existing research methods for PPIs and LLPS are numerous, they all have certain limitations. In summary, traditional PPI detection methods, such as FRET (Fluorescence Resonance Energy Transfer), BiFC (Bimolecular Fluorescence Complementation), and co-localization microscopy, are often limited by insufficient spatial resolution, strong background interference, complex operation, and dependence on specific tags, making it difficult to achieve efficient, dynamic, and visualized verification of multi-protein interactions. Existing methods for studying LLPS, such as fluorescence recovery assays (FRAP), optogenetic tool-induced aggregation, and artificial locus condensation, often only study the phase separation characteristics of individual proteins or domains, making it difficult to systematically screen or dynamically regulate and observe the formation mechanisms and functional relationships of condensates within single cells. Furthermore, these two research systems are largely independent, lacking a unified platform that can simultaneously achieve protein interaction detection and phase separation capability verification.

[0003] While significant progress has been made in current biological research regarding protein-protein interactions (PPIs) and liquid-liquid phase separation (LLPS), several technical challenges remain. Commonly used techniques for studying protein-protein interactions include fluorescence colocalization analysis, FRET analysis, BiFC analysis, and immunoprecipitation, specifically:

[0004] 1. Fluorescence colocalization analysis: By observing the spatial overlap of different fluorescent tags through fluorescence microscopy, a preliminary judgment can be made as to whether there is an interaction between proteins.

[0005] Limitations: It cannot guarantee the actual binding of proteins, only representing spatial proximity; it is easily affected by background interference and image resolution limitations; it cannot distinguish between direct interaction and indirect recruitment to form complexes.

[0006] 2. FRET analysis technique: Utilizes the principle of energy transfer to detect interactions between proteins smaller than 10 nm.

[0007] Limitations: Requires special fluorescence pairs and precise adjustment of expression ratios; complex experimental system with high technical threshold; difficult to detect multiple protein interactions simultaneously.

[0008] 3. BiFC analysis technique: The fluorescent protein is divided into two parts, which are fused to the target protein respectively. After interaction, the fluorescence is restored.

[0009] Limitations: The refolding process is irreversible and cannot reflect dynamic changes; the time response is slow, making it difficult to observe at high temporal resolution; it cannot effectively observe multi-protein interaction complexes.

[0010] These techniques largely rely on fluorescent labeling of proteins to reveal their interactions, but they still face the following challenges in multi-protein interactions, protein affinity assessment, and detection of dynamic changes:

[0011] 1. Limitations of Multi-Protein Interactions: While traditional PPI research methods can detect interactions between specific proteins, they face challenges in studying multi-protein complexes. Existing FRET and BiFC techniques typically only verify interactions between two proteins, making it difficult to simultaneously monitor interactions between multiple proteins. This hinders the study of complex multi-protein interaction networks. Furthermore, these methods lack effective quantitative analysis tools for protein-protein interaction affinity, providing only information on the presence or absence of interactions without accurately assessing the binding capacity and affinity between individual proteins.

[0012] 2. Limitations in Protein Binding Ability Assessment: Many existing techniques cannot quantitatively assess the binding affinity of proteins in complexes. Although FRET and fluorescence co-localization methods can verify protein interactions, these methods typically rely on the intensity of the fluorescence signal and the co-localization area, making it difficult to accurately assess the binding strength between proteins. This is a limitation for understanding complex protein interactions and regulatory mechanisms. Traditional techniques may exhibit high error rates, especially when dealing with low-affinity protein interactions.

[0013] 3. Difficulty in assessing phase-separation ability: Many proteins or protein domains possess phase-separation potential, but their phase-separation ability often varies depending on protein concentration, external conditions, and interactions with other molecules. Existing techniques often struggle to provide a precise preliminary assessment of the phase-separation ability of these proteins or domains. For example, while techniques such as FRAP can detect the dynamic behavior of proteins in phase-separating bodies, these techniques cannot rapidly and accurately assess the phase-separation tendency and mechanism of action of proteins in cases of multi-protein interactions or protein-protein synergy.

[0014] 4. Lack of a comprehensive platform for simultaneously studying PPIs and LLPS: Currently, most studies on PPIs and LLPS are conducted separately. While some methods can be used to probe protein-protein interactions (e.g., BiFC, immunoprecipitation), and others (e.g., quantitative fluorescence, optogenetics) are used to study phase separation behavior, these methods are often not effectively combined, thus missing the potential for interaction between protein interactions and phase separation, limiting a comprehensive understanding of complex intracellular biological processes. Especially in studies of protein-driven phase separation, how to assess the contribution of these interactions to phase separation formation remains an unresolved problem.

[0015] Therefore, there is an urgent need for a novel, functionally integrated, and easy-to-operate system to support the visualization analysis of multi-protein interactions in living cells, as well as a method for the rapid verification and regulation of protein phase separation capabilities. Summary of the Invention

[0016] To address the problems existing in the prior art, this invention provides a molecular recruitment colocalization (MRC) system. This system recruits and quantitatively analyzes protein interactions and phase separation behavior, solving the main problems in current technologies. The MRC system recruits interacting proteins at specific sites and generates fluorescent colocalization signals, enabling simultaneous visualization of multi-protein PPIs, preliminary assessment of interaction strength, and dynamic observation of aggregation capacity. This system integrates advanced technologies such as SunTag / MoonTag locus enrichment strategies, fluorescent protein labeling, multi-channel imaging analysis, optogenetic tool manipulation, and quantum dot probe tracking, offering advantages such as high operability, high throughput, and wide applicability. MRC can not only detect multivariate interactions between classic protein complexes (such as GPCRs, SCFs, and PSD95-NOS1), but also rapidly screen protein structures with potential aggregation capacity, such as PDZ domains, to explore their roles in complex formation and aggregate generation. Meanwhile, MRC can also be used to construct various types of programmable condensates, study their physical properties and cell behavior regulation potential, and provide important tool support for future synthetic biology, disease mechanism research and new drug screening.

[0017] The system possesses the following characteristics: First, it can achieve spatial localization visualization of protein-protein interaction events in living cells, thereby improving the signal-to-noise ratio and reducing background interference; second, it has multi-channel signal processing capabilities, enabling simultaneous monitoring of the interactions and co-localization of multiple proteins; third, it has the ability to rapidly assess protein phase separation tendencies, and can complete the screening of the aggregation ability of multiple proteins or domains within a short period of time; fourth, it can integrate exogenous regulatory methods (such as optogenetic tools) to achieve programmable regulation of aggregation behavior; and fifth, it can analyze the physical properties of aggregates using nanoprobes and other methods, thereby providing quantitative support for a deeper understanding of their functions.

[0018] The purpose of this invention is to provide a molecular recruitment colocalization system, comprising:

[0019] The spatial positioning visualization module is used to recruit target proteins to specific sites and generate fluorescent colocalization signals in living cells based on the target proteins recruited to specific sites, thereby achieving intuitive and real-time visualization of multi-protein PPIs;

[0020] The multi-protein interaction verification module is used to obtain the interactions between multiple proteins and verify the interaction relationships in complex multi-protein complexes by recruiting different proteins recruited to the specific site at a specified locus and observing the protein interactions in the multi-protein complex.

[0021] The protein binding capacity assessment module is used to quantitatively analyze the binding capacity between proteins based on the calculated percentage of colocalization and signal-to-noise ratio.

[0022] The protein phase separation ability assessment module is used to rapidly assess whether different proteins or protein domains have phase separation ability based on recruiting different proteins or protein domains to specific locations.

[0023] The dynamic observation and quantitative assessment module is used to observe the dynamic changes of proteins during phase separation in real time by combining multi-channel fluorescence and quantum dot probes, and to provide preliminary quantitative data on the physical properties of condensates, including one or more of viscosity, density, elastic modulus and molecular order parameters.

[0024] Preferably, the spatial positioning visualization module includes a recruitment unit and a fluorescence colocalization unit, wherein the recruitment unit is used to recruit the target protein to a specific site, and the fluorescence colocalization unit is used to generate a fluorescence colocalization signal in living cells based on the target protein recruited to the specific site; the spatial positioning visualization module performs spatial positioning visualization based on a colocalization signal generation mechanism, which consists of spatial colocalization, signal enhancement, and advanced applications such as BiFC, FRET, or FLIM.

[0025] Preferably, the recruitment unit includes an anchoring site, a recruiter, a tag-capture system, and an antibody-antigen epitope; wherein the anchoring site is a "molecular anchor" that is stably present at a specific location within the cell or can be guided to a specific location, determined by genomic targeting, organelle and / or structural targeting, membrane targeting, or artificially synthesized site targeting; the recruiter is a molecular module capable of strongly specific interaction with the anchoring site, simultaneously linked to or fused to the target protein, determined by protein-protein interaction pairs or nucleic acid aptamers and / or ligands; the tag-capture system includes a high-affinity tag fixed at a specific location, and a module that carries the high-affinity tag itself or is fused with a module capable of binding the high-affinity tag; the antibody-antigen epitope is used to fix the anchoring site at a specific location, determine a small antigen epitope tag fused to the target protein, and recruit via a corresponding antibody or fragment thereof.

[0026] Preferably, the fluorescent colocalization unit is used to generate a spatially highly overlapping fluorescent signal that can be detected under a microscope when the target protein is successfully recruited to the anchoring site, visually indicating the occurrence of the recruitment event, including:

[0027] Reference fluorescent labels are used for direct fusion or tight attachment to fluorescent reporter molecules at anchor sites;

[0028] Targeted fluorescent labeling, used to directly fuse or tightly link fluorescent reporter molecules to the target protein TP or its recruiter.

[0029] Preferably, the multi-protein interaction verification module includes:

[0030] Shared anchor points, used as common target recruitment sites for all proteins to be validated, are determined based on either genome-targeting or highly stable organelle and / or structural localization.

[0031] A multiplexed recruitment system is used to provide multiple specific molecular hooks, each for independently recruiting different target proteins to a shared anchoring site;

[0032] A multi-channel fluorescence co-localization detection system is used for independent and differentiated fluorescent labeling and imaging of shared anchor sites (SAS) and each recruited target protein (TP), thereby enabling simultaneous visualization and quantitative analysis of multiple interaction pairs; and

[0033] The control and optimization unit is used to ensure the specificity, reliability, and reproducibility of experimental results, and to eliminate false positives and false negatives.

[0034] The strategy of the multiplexing recruitment system includes:

[0035] Strategy A: Orthogonal protein-protein interaction pairs, with multiple non-interfering and orthogonal binding ligands at the SAS position for a universal ankyrin and / or tag; each ligand is fused with a different recruiter, which is then fused to a different target protein.

[0036] Strategy B: An orthogonal nucleic acid aptamer system stably expresses an RNA scaffold containing multiple different stem-loop structures at the SAS position, with each stem-loop being recognized by its specific RNA-binding protein;

[0037] Strategy C: Combination induction system, using different small molecule inducers to control different dimerization systems.

[0038] Preferably, the multi-channel fluorescence co-localization detection system includes:

[0039] SAS reference fluorescent label, a fluorescent reporter molecule immobilized on SAS, is used to mark the common location where all recruitment events occur;

[0040] Target protein-specific fluorescent labeling: Each target protein fusion TP-R carries a fluorescent reporter molecule with a unique spectrum that can be clearly distinguished;

[0041] The optimization strategy for the multi-channel fluorescence colocalization detection system is to combine fluorescent protein + tag system, sequential imaging, colocalization signal interpretation, multi-protein complex interaction map verification, advanced quantitative analysis and multi-channel overlap analysis if the number of target proteins is >3 or the fluorescent protein spectra overlap is severe.

[0042] Preferably, the protein binding capacity assessment module includes:

[0043] The image acquisition and preprocessing unit is used to acquire high-quality, standardized raw fluorescence image data and perform preprocessing to reduce noise and variation;

[0044] Region of interest (ROI) definition units are used to accurately identify and delineate fluorescence signal regions for quantitative analysis;

[0045] The quantitative parameter calculation unit is used to calculate key quantitative indicators on a defined region of interest: co-location percentage and signal-to-noise ratio, as well as other relevant parameters; wherein, the other relevant parameters include: target signal enrichment ratio, anchor point signal strength, and agglomerate characteristics for phase separation, wherein the agglomerate characteristics include one or more of the following: number of agglomerates, average area, average volume, average intensity, roundness, and shape factor;

[0046] The data standardization and analysis unit is used to standardize the raw parameters of key quantitative indicators in the calculation to eliminate errors and inter-sample differences, and to perform statistical analysis to assess the significant differences in binding ability.

[0047] The reporting and visualization unit is used to present quantitative analysis results in a clear and intuitive form.

[0048] Preferably, the protein phase separation capability assessment module includes:

[0049] The phase separation induction and imaging unit is used to induce phase transitions of target proteins at specific sites under controlled conditions and to perform multidimensional dynamic imaging.

[0050] A phase separation feature recognition unit is used to automatically identify phase separation features from images and extract key morphological and dynamic parameters;

[0051] The interaction-phase transition synergistic analysis unit is used to analyze the regulatory role of protein interaction PPIs on phase separation;

[0052] The validation and control unit is used to implement positive controls, negative controls, and artifact exclusion; the orthogonal validation methods used include: analyzing the ultrastructure of condensates in fixed samples based on transmission electron microscopy; and measuring the change in molecular diffusion coefficient at NAS based on fluorescence correlation spectroscopy.

[0053] Preferably, the phase separation induction and imaging unit includes:

[0054] Anchoring nucleation sites includes: fusing low-complexity structural domains or oligomerized structural domains as "seeds" for phase separation at the anchor points of the spatial positioning visualization module;

[0055] Target protein recruitment system: The target protein and / or domain are rapidly recruited to the vicinity of the NAS through an orthogonal recruitment strategy, simulating physiological concentration enrichment;

[0056] An environmental disturbance controller is used for temperature regulation, osmotic pressure regulation, and stress induction.

[0057] Multimodal dynamic imaging systems include:

[0058] A confocal time-series imaging unit is used to capture a multi-channel Z-stack at regular intervals;

[0059] The fluorescence bleaching recovery unit is used to perform local photobleaching on the condensates formed at the NAS and monitor the fluorescence recovery kinetics.

[0060] The fluorescence lifetime imaging unit is used to detect the difference in fluorescence lifetime of target proteins inside and outside the aggregate.

[0061] Preferably, the dynamic observation and quantitative evaluation module includes:

[0062] A multimodal dynamic imaging unit is used to simultaneously capture the spatiotemporal dynamics of phase separation and molecular motion behavior;

[0063] A quantification unit for the physical properties of condensates is used to extract the physical property parameters of condensates.

[0064] The interaction-phase transition coupling kinetics unit is used to analyze how protein-protein interaction (PPIs) regulate phase separation kinetics.

[0065] The Molecular Recruiting Colocalization (MRC) system of this invention overcomes the shortcomings of existing technologies in protein binding capacity assessment, multi-protein interaction research, and phase separation capability detection through its innovative multi-protein interaction detection and quantitative analysis capabilities. It provides an effective platform for further in-depth research into complex intracellular protein-protein interactions and liquid-liquid phase separation. The application of this technology can not only advance basic biological research but also provide new solutions for fields such as drug screening, cell engineering, and synthetic biology.

[0066] The specific beneficial effects are at least reflected in:

[0067] 1. Enables real-time, visual detection of protein-protein interactions in living cells.

[0068] The MRC system recruits target proteins to specific sites and generates fluorescent colocalization signals in living cells, thereby enabling intuitive and real-time visualization of PPIs. Protein interactions can be observed in their native state without cell lysis or in vitro recombination.

[0069] 2. It can simultaneously detect the interactions of multiple proteins and supports multi-channel co-localization analysis.

[0070] The MRC system combines multiple fluorescent labels and a programmable recruitment system to enable the simultaneous localization of multiple proteins and detect their spatial co-localization behavior, thus enabling the verification of multi-protein complexes (such as ternary or quaternary interactions).

[0071] 3. It can provide a preliminary assessment of the binding ability and relative affinity between proteins.

[0072] The MRC system can reflect binding ability through parameters such as co-localization ratio and SNR. Although it is semi-quantitative, it has strong reference value, thus providing a quantitative trend of protein interaction strength, which helps to screen for interaction pairs with stronger affinity.

[0073] 4. It can systematically evaluate the phase separation ability of proteins or domains.

[0074] The MRC system utilizes the aggregation mechanism of genomic repetitive sites to increase local protein concentration, thereby inducing and amplifying phase separation, and rapidly screening proteins or domains with liquid-liquid phase separation capabilities.

[0075] 5. Optogenetics tools can be used to enable non-phase-separated proteins to acquire condensate properties, thus achieving their functional aggregation.

[0076] This invention combines optogenetic elements with an MRC system, utilizing light-induced controllable protein interactions to achieve programmed aggregation of non-phase-separated proteins. This allows them to achieve high concentrations of aggregation and enhanced interaction within artificial condensates, thus exhibiting physical properties similar to natural condensates. This endows proteins that originally lack phase-separation capabilities with the ability to form condensates in living cells, thereby expanding their functional research and regulatory applications.

[0077] 6. Achieve programmable synthesis of bio-aggregates with various physical properties

[0078] The MRC system can precisely recruit target proteins to artificially synthesized phase-separated structures through optogenetic modules, enabling manipulation of the composition and properties of biological condensates. This provides a new tool for studying signaling pathway regulation, metabolic separation, and organelle function simulation. Attached Figure Description

[0079] Figure 1 This is a schematic diagram illustrating the principle of visualizing protein-protein interactions (PPIs) in living cells using MRC technology as described in this invention.

[0080] Figure 2 This is a schematic diagram illustrating the process of inducing phase separation in living cells using MRC technology as described in this invention;

[0081] Figure 3 This is a schematic diagram illustrating the process of inducing the formation of aggregates of two phase-separated proteins at adjacent sites using the MRC method as described in this invention;

[0082] Figure 4 This is a schematic diagram illustrating the process by which the MRC, as described in this invention, is combined with an optogenetic element to induce phase separation of non-phase-separated proteins.

[0083] Figure 5 This is a schematic diagram illustrating how the present invention utilizes quantum dot probes to track protein diffusion within condensates, measure diffusion rates and microenvironment viscosity, thereby revealing the rheological properties of condensates. Detailed Implementation

[0084] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0085] The terminology and its explanation used in this embodiment include:

[0086] (1) Molecular Recruitment Colocalization System (MRC): MRC is a technique that visualizes protein interactions and biological aggregates in living cells by recruiting interacting proteins to specific genomic sites and inducing fluorescent colocalization signals. This technique enables real-time observation of multi-protein interactions, preliminary assessment of their binding ability, and assessment of the phase separation ability of proteins or domains. It is a dual-function platform that integrates interaction verification and phase separation observation.

[0087] (2) Multi-protein colocalization analysis: MRC can detect the interaction of three or more proteins simultaneously. By combining the fluorescence signal intensity and colocalization ratio, the binding ability and action sequence of each protein can be preliminarily assessed.

[0088] (3) Phase separation: Phase separation refers to the spontaneous formation of droplet-like, membrane-free subcellular structures by specific proteins or complexes in cells, exhibiting liquid-liquid separation characteristics. This invention induces phase separation using the MRC method, which helps to study how protein interactions drive or influence aggregate formation and to determine the ability and physical properties of different proteins or domains to form aggregates.

[0089] (4) Programmable condensates: refers to biological condensates that are artificially designed and regulated under specific spatiotemporal conditions, and whose formation location, composition and properties can be precisely controlled.

[0090] (5) Optogenetics tools: Optogenetics tools such as CRY2 and iLID systems can induce reversible binding between proteins under specific wavelengths of light, enabling precise spatiotemporal regulation of protein behavior within cells. Integrating such tools into the MRC platform can induce non-natural phase-separated proteins to enter condensates, thereby studying whether spatial proximity between proteins enhances interactions and constructing artificial condensates with specific functions.

[0091] Please refer to the following: Figures 1-5 This embodiment provides a molecular recruitment co-localization system, including:

[0092] (i) Spatial positioning visualization module, used to recruit target proteins to specific sites and generate fluorescent colocalization signals in living cells based on the target proteins recruited to specific sites, thereby achieving intuitive and real-time visualization of multi-protein PPIs;

[0093] In a preferred embodiment, the spatial positioning visualization module includes a recruitment unit and a fluorescence colocalization unit, wherein the recruitment unit is used to recruit the target protein to a specific site, and the fluorescence colocalization unit is used to generate a fluorescence colocalization signal in living cells based on the target protein recruited to the specific site.

[0094] In this embodiment, this module is the core execution unit of the MRC system, responsible for precisely guiding the target protein to a predetermined location within the cell and generating a visible fluorescence signal accordingly. Its structure can be further subdivided into two functionally distinct and closely cooperating subunits:

[0095] 1. Recruitment Unit (RU):

[0096] (1) Function: To specifically, inducibly or constitutively recruit and anchor target proteins (or complexes carrying target proteins) to predefined, spatially restricted sites within the cell.

[0097] (2) Core components:

[0098] A. Anchor Site (AS): A "molecular anchor" that exists stably at a specific location within a cell or can be guided to a specific location.

[0099] Implementation methods (choose one or a combination):

[0100] Genome-targeted: Utilizing the CRISPR-dCas9 (or TALEN, Zinc Finger, etc.) system. dCas9 fuses with an "anchor protein / tag" and is guided to a specific site in the genome (such as a promoter or repetitive sequence region) via a designed gRNA, forming a fixed nuclear anchor site.

[0101] Organelle / structure localization type: "Anchor protein / tag" is fused to the localization signal peptide of a specific organelle (such as mitochondrial outer membrane protein TOMM20, endoplasmic reticulum membrane protein Sec61β, nuclear membrane protein LaminB1, nucleolar protein Fibrillarin) or cellular structure (such as tubulin, actin), so that it is naturally localized to the target region.

[0102] Membrane-localized: Specific receptors or tags (such as SunTag, HaloTag, SNAP-tag, CLIP-tag) are introduced on the plasma membrane or intracellular membrane system and immobilized through their ligands or substrates.

[0103] Artificially synthesized site type: Using optogenetic or chemically induced oligomerization systems (such as Cry2 / CIB1, PhyB / PIF, chemically induced dimerization CID) to instantaneously form high concentrations of oligomer "anchor clusters" at specific locations (such as the cytoplasm or nucleus) under light or drug stimulation.

[0104] B. Recruiter-R: A molecular module that can interact with anchor sites (AS) in a highly specific manner and is simultaneously linked to or fused to the target protein (TP).

[0105] Implementation method (choose one):

[0106] Protein-protein interaction pairs (PPIPair): R is the AS interacting ligand. For example, AS is dCas9-GFP, and R is an anti-GFP nanobody (or other high-affinity ligand such as scFv) fused to TP; or, AS is FKBP12 (F36V), and R is an FRB fused to TP (dimerization induced using rapamycin or its analogues). Other classic induced or constitutive dimerization systems can be used (e.g., ABI / PYL1+ABA, GAI / GID1+GA, SpyTag / SpyCatcher).

[0107] Nucleic acid aptamer / ligand: AS is a fixed RNA or DNA sequence (such as MS2 stem-loop, PP7 stem-loop), and R is the corresponding RNA-binding protein (such as MCP, PCP) fused to TP.

[0108] C. Tag-Capture System: AS is a high-affinity tag (such as SunTag, HaloTag, SNAP-tag, CLIP-tag, Strep-tag, His-tag) fixed in a specific position. R is the TP itself carrying the tag (direct fusion) or the TP fused with a module that can bind the tag (such as Strep-Tactin fused with TP for Strep-tag AS).

[0109] D. Antibody-antigen epitope: AS is fixed at a specific location, R is a small antigen epitope tag (such as HA, FLAG, Myc) fused to TP, and recruited by the corresponding antibody or its fragment (such as scFv, Fab).

[0110] 2. Fluorescence Colocalization Unit (FCU):

[0111] Function: When the target protein (TP) is successfully recruited to the anchor site (AS), a spatially highly overlapping fluorescent signal is generated that can be detected by a microscope, visually indicating the occurrence of the recruitment event.

[0112] Core components:

[0113] A. Reference Fluorophore (RF): A fluorescent reporter molecule directly fused to or tightly linked to an anchor site (AS).

[0114] Implementation: Fluorescent proteins (such as mCherry, mScarlet, mNeonGreen, EGFP) or organic fluorescent dyes (such as the Alexa Fluor series, Cy series, labeled via HaloTag / SNAP-tag / CLIP-tag) are directly fused to the core components of AS (such as dCas9, organelle localization proteins, and fixed tags). The fluorescent signal marks the pre-defined target location.

[0115] B. Target Fluorophore-TF: A fluorescent reporter molecule that is directly fused to or tightly linked to a target protein (TP) or its recruiter.

[0116] Implementation method:

[0117] A different fluorescent protein with distinct spectral characteristics (clearly distinguishable from RF) is fused to the TP itself (e.g., if RF is mCherry, then TF is EGFP or mTagBFP2). The fluorescent protein is then fused to the recruiter (e.g., anti-GFP nanobody-mNeonGreen). The target protein (TP) or recruiter is then specifically fluorescently labeled using a tagging system (e.g., TP fused with HaloTag and labeled with TMR ligands; recruiter fused with SNAP-tag and labeled with SiR ligands).

[0118] In a preferred embodiment, the spatial positioning visualization module performs spatial positioning visualization based on a co-location signal generation mechanism, which includes:

[0119] Spatial colocalization includes the following: when the TP is successfully recruited to the vicinity of the AS by the RU, the TF and RF are physically close (the distance is much smaller than the optical resolution limit). Under confocal microscopy or wide-field microscopy (with deconvolution), these two fluorescent signal spots of different colors will precisely overlap or significantly colocalize on the image. These overlapping fluorescent spots are called "colocalization signals".

[0120] Signal enhancement can be achieved by: successful recruitment may lead to a significant increase in TP concentration in the AS region, thereby enhancing the TF signal at the AS site specifically (relative to the cytoplasmic background), which is also a visual signal (especially when using a high-sensitivity detector).

[0121] Advanced FRET / FLIM applications include: if RF and TF are a suitable FRET donor / receptor pair (e.g., CFP / YFP, mTurquoise2 / sYFP2) and recruitment results in them being close enough (<10 nm), then FRET signals (donor quenching, acceptor sensitized emission) or shortened donor fluorescence lifetime (FLIM) can be detected, providing evidence of interaction with higher spatial resolution (but this usually requires more complex imaging and analysis).

[0122] The innovation of the spatial point visualization module in this embodiment is reflected in:

[0123] (1) Modular design: AS, R, RF, and TF should be designed as interchangeable modules to facilitate flexible combination for different target proteins, different cell localizations and research needs.

[0124] (2) Fluorescence spectrum selection: RF and TF must be selected with good emission spectral separation and high signal-to-noise ratio to avoid cross-color interference with the accuracy of colocalization analysis. Commonly used combinations include mCherry / EGFP, Cy5 / Cy3, and Alexa Fluor 647 / Alexa Fluor 488.

[0125] (3) Specificity and affinity: The interaction between AS and R in RU must have high specificity and sufficiently high affinity to ensure effective recruitment and stable colocalization signals, while minimizing nonspecific background.

[0126] (4) Inducibility: For dynamic process studies (such as phase separation kinetics, signal transduction), it is best to use an inducible recruitment system (such as optogenetics, chemically induced dimerization) that allows the recruitment process to be initiated at a specific time point.

[0127] (5) Background control: The experimental design includes strict negative controls (such as cells without recruitment induction, cells without R or TF expression, or mutants that do not interact) to distinguish real colocalization signals from random overlap or nonspecific background.

[0128] (6) Quantitative analysis: Colocalization signals need to be quantitatively evaluated using image analysis software (such as ImageJ / Fiji, Imaris, Volocity). Commonly used indicators include Pearson correlation coefficient (PCC), Mandelover coefficient (MCC), colocalization pixel ratio, etc., to objectively measure recruitment efficiency and the strength of interaction.

[0129] Through such a refined structural design, the "spatial positioning visualization module" can accurately locate and recruit target proteins in living cells, and intuitively reveal the location and extent of protein interactions or phase separation behaviors through clear and quantitative fluorescence colocalization signals.

[0130] (ii) Multi-protein Interaction Validation Module (MIVM) is used to obtain the interactions between multiple proteins and to verify the interaction relationships in complex multi-protein complexes by recruiting different proteins recruited to the specific site at a specified locus and observing the protein interactions in the multi-protein complex.

[0131] This module ensures efficient and accurate verification of multi-protein interactions by recruiting pairings of different proteins at designated loci, providing a new platform for studying complex protein networks.

[0132] In a preferred implementation, the MIVM module is the core extended functional unit of the MRC system. It overcomes the limitations of traditional binary interaction detection, enabling simultaneous or sequential verification of interactions between multiple protein pairs on a single cell and at the same pre-defined locus, and resolving the interaction networks within complex multi-protein complexes. Its core idea is to utilize the "molecular anchor point" (i.e., the pre-defined locus) established by the spatially located visualization module as a shared recruitment and detection platform for all interacting protein pairs to be verified.

[0133] The specific structure of this module can be broken down into the following key components and strategies:

[0134] 1. Shared Anchor Site (SAS):

[0135] (1) Function: Serves as a common target recruitment site for all proteins to be validated. This is the basic platform for the operation of the entire module.

[0136] (2) Implementation method: The implementation method is consistent with that of the anchor point (AS) in the spatial positioning visualization module, but with an emphasis on stability and reusability. Therefore, one or more of the following implementation methods are preferred:

[0137] Genome-targeted: Utilizing the CRISPR-dCas9 system. By designing a fixed gRNA (sgRNA-X), a dCas9 protein fused with a universal anchor protein / tag (such as SpyTag, HaloTag, SNAP-tag, or a high-affinity epitope such as 3xFLAG) is stably located at a specific site (Locus-X) in the genome. This site, Locus-X, is the shared SAS.

[0138] Highly stable organelle / structure localization type: Select organelle localization signals that are stable and not easily endocytosed or degraded (such as nucleolar protein Fibrillarin, laminin B1) and fuse them with universal anchor proteins / tags as SAS.

[0139] 2. Multiplexed Recruitment System (MRS):

[0140] (1) Function: It provides a variety of specific "molecular hooks", each of which can independently recruit different target proteins (TP1, TP2, TP3...) to the shared anchor site (SAS). This is the key to realizing multi-protein interaction verification.

[0141] (2) Core components and strategies:

[0142] Strategy A: Orthogonal protein-protein interaction pairs (PPIPairs):

[0143] Principle: Multiple, non-interfering (orthogonal) binding ligands are provided for the universal ankyrin / tag on SAS. Each ligand is fused with a different "recruiter" and then fused to a different target protein (TP).

[0144] Implementation method: For example,

[0145] a. SAS components: dCas9-SpyCatcher-Fluorescent Protein 1 (e.g., mCherry).

[0146] b. Recruiter Component and TP Fusion:

[0147] TP1-R1: TP1-SpyTag-Fluorescent Protein A (e.g., mNeonGreen);

[0148] TP2-R2: TP2-SNAP-tag-fluorescent protein B (e.g., mTagBFP2) (requires pre-labeling of the SNAP-tag with a non-fluorescent substrate, or TP2 becomes fluorescent after labeling with a fluorescent substrate);

[0149] TP3-R3: TP3-HaloTag-fluorescent protein C (such as mScarlet-I) (HaloTag needs to be pre-labeled with a non-fluorescent ligand, or TP3 will be fluorescent after being labeled with a fluorescent ligand);

[0150] c. Working Mechanism: SpyTag and SpyCatcher spontaneously covalently link, recruiting TP1 to SAS. SNAP-tag and HaloTag are labeled and / or functionalized by adding their specific ligands, either fluorophores or non-fluorophores (such as BG derivatives or Halo ligands). (If used for recruitment, ligand labeling is often required before recruitment, or ligand binding can be used to induce recruitment.) These three recruitment pathways are chemically orthogonal and do not interfere with each other.

[0151] Strategy B: Orthogonal Aptamer Systems

[0152] Principle: An RNA scaffold containing multiple different stem-loop structures (e.g., MS2, PP7, boxB) is stably expressed at the SAS site (e.g., via dCas9). Each stem-loop is recognized by its specific RNA-binding protein (RBP, such as MCP, PCP, λN).

[0153] Implementation method: For example,

[0154] a. SAS component: dCas9 is fused with a transcriptional activation domain (e.g., VP64), targets a promoter, and drives the transcription of a multivalent RNA scaffold containing MS2 stem-loops, PP7 stem-loops, and boxB sequences. This scaffold is located at Locus-X.

[0155] b. Recruiter Component and TP Fusion:

[0156] TP1-R1: TP1-MCP-fluorescent protein A (e.g., mNeonGreen);

[0157] TP2-R2: TP2-PCP-fluorescent protein B (e.g., mTagBFP2);

[0158] TP3-R3: TP3-λN-fluorescent protein C (such as mScarlet-I).

[0159] c. Working method: MCP binds to MS2 stem-loops, PCP binds to PP7 stem-loops, and λN binds to boxB sequences, thereby independently recruiting different TPs to the same RNA scaffold (i.e., SAS).

[0160] Strategy C: Combinatorial Induction Systems

[0161] Principle: Different dimerization systems are controlled by using different small molecule inducers.

[0162] Implementation method: For example,

[0163] a. SAS component: dCas9-FRB-fluorescent protein 1 (mCherry);

[0164] b. Recruiter Component and TP Fusion:

[0165] TP1-R1: TP1-FKBP12(F36V)-Fluorescent Protein A (mNeonGreen) (Rapamycin / Rapamycin induces binding to FRB);

[0166] TP2-R2: TP2-GAI-fluorescent protein B (mTagBFP2) (gibberellin / Gibberellin induces binding to GID1, requires expression of GID1-FKBP or similar on SAS);

[0167] TP3-R3: TP3-ABI-Fluorescent Protein C (mScarlet-I) (abscisic acid / ABA induces binding to PYL1, requires expression of PYL1-FRB or similar on SAS);

[0168] Working method: By adding rapamycin, gibberellin and abscisic acid separately or in combination, TP1, TP2 and TP3 can be recruited to SAS independently or simultaneously.

[0169] Core requirement: Regardless of the strategy adopted, it is crucial to ensure a high degree of orthogonality between different recruitment pathways, meaning that one recruitment method will not nonspecifically affect or cross-activate another recruitment method.

[0170] 3. Multichannel Fluorescence Colocalization System (MFCS):

[0171] (1) Function: Independent and distinguishable fluorescent labeling and imaging of shared anchor sites (SAS) and each recruited target protein (TP) to achieve simultaneous visualization and quantitative analysis of multiple pairs of interactions.

[0172] (2) Core components and strategies:

[0173] A. SAS Reference Fluorescent Label (SAS-RF): A fluorescent reporter molecule (such as mCherry fused to dCas9) immobilized on the SAS. This marks the common location where all recruitment events occur.

[0174] B. Target Protein Specific Fluorescent Label (TP-TF): Each target protein fusion (TP-R) must carry a spectrally unique and clearly distinguishable fluorescent reporter molecule.

[0175] Implementation method: Directly fuse different fluorescent proteins (FPs): such as TP1-R1-mNeonGreen (green), TP2-R2-mTagBFP2 (blue), TP3-R3-mScarlet-I (red).

[0176] Optimization strategy: If the number of target proteins is large (>3) or the fluorescent proteins have significant spectral overlap, the following methods can be used in combination:

[0177] (a) Fluorescent protein + tagging system: for example, TP1-R1-HaloTag (labeled with Janelia Fluor 646, far red), TP2-R2-SNAP-tag (labeled with SiR, dark red), TP3-R3 fusion mNeonGreeen (green), while SAS-RF uses mCherry (red). The narrower emission spectrum of organic dyes increases distinguishability.

[0178] (b) Sequential Imaging: If there are not enough simultaneous imaging channels, different protein pairs can be recruited and imaged at different time points (especially suitable for inducible systems).

[0179] (c) Colocation signal interpretation:

[0180] Basic interpretation: For a specific TP (e.g., TP1), its fluorescence signal (TF1, e.g., green) and the SAS reference signal (RF, e.g., red) are significantly colocalized at the Locus-X position, indicating that TP1 has been successfully recruited to the SAS. This reflects the direct recruitment relationship between the TP-R and the SAS anchoring component (a prerequisite for mutual verification).

[0181] Interaction Verification Interpretation: Binary Interaction Verification: When two different target proteins (such as TP1 and TP2) are recruited to the same SAS simultaneously or sequentially: If the fluorescence signals of TP1 (green) and TP2 (blue) at the SAS (red) position not only co-localize with the SAS themselves but also show significant spatial overlap (co-localization) with each other, it strongly suggests that there is a direct or indirect interaction between TP1 and TP2, prompting them to bind after being recruited to a neighboring position. If TP1 and TP2 exist independently at the SAS position with no significant signal overlap, it suggests that there is no interaction between them in this environment.

[0182] (d) Verification of multi-protein complex interaction maps: When three or more target proteins are recruited to the same SAS: By analyzing the multicolor colocalization relationships between all TP-TF signals and between them and SAS-RF signals, the interaction relationships within the complex can be inferred. For example: TP1 (green), TP2 (blue), and TP3 (red) are all recruited to the SAS (magenta). If high colocalization of the three signal pairs (green-blue, blue-red, and green-red) is observed at the SAS position, it suggests that there are interactions between TP1 and TP2, TP2 and TP3, and TP1 and TP3, possibly forming a ternary complex. If only green-blue and blue-red colocalization are observed, but green-red is not colocalized, it may suggest that TP2 acts as a bridge connecting TP1 and TP3, but TP1 and TP3 do not interact directly.

[0183] (e) Advanced quantitative analysis: Using image analysis software to calculate colocation coefficients between multiple channels, such as:

[0184] PCC / MCC of TPn and SAS: measures the efficiency of each TP being recruited to the SAS;

[0185] PCC / MCC of TPm and TPn (within the SAS region): Measures the degree and specificity of the interaction between TPm and TPn on the SAS platform. This is a core metric for validating binary interactions.

[0186] (f) Multi-channel overlap analysis: Calculate the percentage of pixels or advanced correlation metrics that overlap at the SAS location for three or more channels.

[0187] 4. Control and Optimization Unit (COU):

[0188] (1) Function: To ensure the specificity, reliability and reproducibility of experimental results, and to eliminate false positives and false negatives.

[0189] (2) Key control experiment design:

[0190] A. Single recruitment negative control: Only SAS and a single TP-R fusion (e.g., only TP1-R1) are expressed to verify that it can correctly recruit SAS (TP1-TF should co-localize with SAS-RF), but there are no other TPs at this time, so there will be no colocalization signal between TP-TFs. This is the baseline of the interaction signal.

[0191] B. Dual / Multiple Recruitment with No Interaction Control: Co-expression of SAS and two known non-interacting proteins (e.g., TP1-R1 and TP2-R2-Neg, where TP2-Neg is a protein unrelated to TP1). Each TP should be recruited to the SAS (co-localizing with each SAS), but the two TP-TF signals do not significantly co-localize at the SAS location.

[0192] C. Recruitment missing control: Use mutants to disrupt recruitment (e.g., use RBP mutants that cannot bind aptamers, receptor mutants that cannot bind inducers, or TPs that lack R), or lack inducers to verify that the target protein cannot be recruited to SAS. In this case, there should be no TP-TF signal, and there should be no colocalization between TP and TF.

[0193] D. Endogenous Competition / Background Assessment: Assess whether endogenous proteins affect recruitment or generate background signals. The effects of overexpression versus endogenous TP-R expression can be compared, or cell lines with knockdown / knockout of the endogenous gene can be used.

[0194] E. Expression level titration: Optimize the expression levels of different TP-Rs to avoid overexpression leading to nonspecific aggregation, or underexpression leading to weak signal.

[0195] F. Sequential recruitment dynamics: For inducible systems, design time-resolved experiments, such as recruiting TP1 first, stabilizing it, and then recruiting TP2, to observe whether the TP2-TF signal specifically enriches and co-localizes at the existing TP1 / SAS sites, in order to study the interaction dynamics.

[0196] The innovative aspects of the implementation method of the multi-protein interaction verification module include:

[0197] 1. Shared platform, orthogonal recruitment: The core of MIVM is to independently manipulate the localization of multiple target proteins on the same pre-defined locus (SAS) using a highly orthogonal multi-path recruitment system (MRS).

[0198] 2. Multicolor differentiation and precise imaging: Spectroscopically distinguishable fluorescent labels (MFCS) must be provided for SAS and each target protein to enable parallel and independent detection of multiple recruitment events and interactions.

[0199] 3. Colocalization analysis is key: The core criterion for interaction verification is not whether a single protein is recruited (this is a prerequisite), but whether spatial colocalization (TP-TF colocalization) occurs between multiple proteins recruited to the same SAS.

[0200] 4. Rigorous controls are indispensable: Comprehensive negative and specific controls (COUs) must be designed and implemented to confirm that the observed colocalization signals between TP and TF are indeed from specific interactions, rather than non-specific aggregation or random overlap.

[0201] 5. Quantification: Objective quantitative comparison is made based on the interaction strength of colocation coefficients (such as PCC, MCC).

[0202] 6. Flexibility and scalability: The selection of orthogonal recruitment strategies (such as orthogonal PPI pairs, aptamers, and induction systems) and fluorescent labeling strategies (FP combinations, FP+ dyes) should be flexible to accommodate the needs of studying interactions between different numbers and types of proteins. The system can theoretically be expanded to verify interactions between multiple (>3) proteins.

[0203] 7. Dynamic potential: Combined with an inducible recruitment system, this module also has the potential to study the assembly sequence and dynamics of multi-protein complexes.

[0204] Through this refined structural design, the "multi-protein interaction verification module" can efficiently and in parallel verify the interactions between multiple protein pairs within living cells using a shared molecular platform, and resolve the internal interaction networks of complex multi-protein complexes, providing a powerful tool for systems biology research.

[0205] (III) Protein Binding Affinity Assessment Module (PBAAM) is used to quantitatively analyze the binding ability between proteins based on the percentage of colocalization and the calculated signal-to-noise ratio (SNR).

[0206] For example, in studying G-protein complexes, MRC can accurately assess the binding affinity between different subunits, providing important quantitative information for understanding the formation mechanism and interactions of multi-protein complexes. Although MRC is not a perfect quantitative analysis tool, it provides an intuitive and efficient method for the preliminary assessment of protein-protein interactions, and is particularly suitable for high-throughput screening and dynamic change monitoring.

[0207] In a preferred embodiment, the core of this module is to convert the fluorescence colocalization signal generated by the spatial positioning visualization module into quantitative binding capacity indicators, particularly the colocalization percentage and signal-to-noise ratio (SNR), for relative assessment of the strength (affinity) of protein-protein interactions.

[0208] The PBAAM module is the quantitative analysis engine of the MRC system. It utilizes image data generated by the first two modules (spatial point visualization and multi-protein interaction verification) to quantitatively assess the binding strength of protein interactions or phase-separated condensates by calculating specific image feature parameters. This module mainly includes the following core components and workflows:

[0209] 1. Image Acquisition & Preprocessing Unit (IAPU):

[0210] (1) Function: To acquire high-quality, standardized raw fluorescence image data and perform necessary preprocessing to reduce noise and variation.

[0211] (2) Core components and processes:

[0212] A. Standardized imaging parameters: For all samples in the same experimental group, images were acquired using a confocal microscope or a wide-field microscope (with deconvolution) under strictly identical imaging parameters (such as laser power, exposure time, gain, offset, pixel size, number of Z-axis layers, scanning speed, and resolution). This is the basis for ensuring data comparability.

[0213] B. Multi-channel acquisition: Simultaneous or sequential acquisition:

[0214] Reference channel (Ref-Ch): Fluorescence signal (e.g., mCherry) corresponding to the anchor point (AS / SAS).

[0215] Target channel (Tar-Ch): Corresponds to the fluorescent signal of the recruited target protein (TP) (such as EGFP).

[0216] Other target channels: used for multi-protein interaction verification.

[0217] Background channel / Brightfield: Used for cell localization or background assessment.

[0218] C. Image preprocessing:

[0219] Background Subtraction: Calculates the average fluorescence intensity of cell-free regions in the image and subtracts this value from the entire image to eliminate camera background noise and background fluorescence.

[0220] Flat-field correction: Applying a corrected image to eliminate illumination inhomogeneities and detector sensitivity differences.

[0221] Bleaching Correction: For time series images, algorithms (such as histogram matching and exponential fitting) are applied to correct the fluorescence bleaching effect.

[0222] Deconvolution: For wide-field images, deconvolution algorithms (such as DeconvolutionLab, Huygens) are applied to improve resolution and signal-to-noise ratio.

[0223] Image registration: Ensures that images from different time points or different channels are spatially aligned accurately.

[0224] 2. Region of Interest (ROI) Definition Unit (RDU):

[0225] (1) Function: Accurately identify and delineate the fluorescence signal region for quantitative analysis.

[0226] (2) Core strategy:

[0227] A. Reference Channel-Based ROI Definition (Core): Using reference channel (Ref-Ch) images (i.e., anchor point signals), anchor point regions (ASRs / SASRs) are identified through thresholding (e.g., Otsu, IsoData, manually set) or blob detection algorithms (e.g., Laplacian of Gaussian, Difference of Gaussian). These regions represent specific sites where the target protein is recruited. For each identified ASR / SASR, its location, area, shape, and other features are calculated.

[0228] B. Target Channel ROI (Optional / Specific Analysis): For assessing phase separation behavior, target protein aggregate (TPC) regions need to be identified on the target channel (Tar-Ch). Thresholding segmentation or speckle detection is used as well. For non-specific background assessment, background regions (BGRs) are defined in non-anchored regions such as the cytoplasm or nucleoplasm.

[0229] 3. Quantitative Parameter Calculation Unit (QPCU):

[0230] (1) Function: Calculate key quantitative indicators on the defined ROI: Colocalization Percentage (CP) and Signal-to-Noise Ratio (SNR), as well as other related parameters.

[0231] (2) Core calculation metrics and algorithms:

[0232] A. Co-location percentage (CP):

[0233] A1. Definition: A quantitative indicator of the degree of spatial overlap between the target protein signal (Tar-Ch) and the reference signal (Ref-Ch) within the anchorage region (ASR / SASR). It directly reflects the proportion of target proteins successfully recruited to a specific site, and indirectly reflects recruitment efficiency or interaction strength.

[0234] A2. Commonly used calculation methods (choose one):

[0235] (a) Manders' Overlap Coefficient (MOC), including M1 and M2:

[0236] M1=∑(Ref_i*Tar_i) / ∑(Ref_i 2 (M1: The ratio of co-location between Tar and Ref signals);

[0237] M2 = ∑(Ref_i*Tari) / Σ(Tar_i2) (M2: the ratio of co-location between Ref signal and Tar signal).

[0238] Where i iterates through all pixels within the ASR / SASR. Ref_i and Tar_i are the intensity values ​​of the pixel in Ref-Ch and Tar-Ch (after background correction), respectively. The value of M1 or M2 ranges from 0 to 1, with higher values ​​indicating greater co-localization.

[0239] (b) Pearson Correlation Coefficient (PCC):

[0240] PCC=[Σ(Ref_i-Mean_Ref)(Tar_i-Mean_Tar)] / [sqrt(∑(Ref_i-Mean_Ref) 2 )*sqrt(∑(Tar_i-Mean_Tar) 2 )];

[0241] Where Mean_Ref represents the average value of the reference data; Mean_Tar represents the average value of the target data;

[0242] Calculate the linear correlation between Ref-Ch and Tar-Ch signal intensities within the ASR / SASR. The range is -1 to 1, with positive values ​​indicating positive correlation (co-location) and 0 indicating no correlation. It is sensitive to intensity variations and is suitable for assessing changes in interaction intensity.

[0243] (c) Costes' Automatic Thresholding + PCC / MOC: A more robust method that automatically determines the effective threshold for colocation signals.

[0244] A3. Reporting: M1 or M2 is usually reported as the primary indicator of CP because it more directly reflects recruitment efficiency. PCC is used as a supplementary indicator, especially when assessing dynamic changes or differences in affinity under different mutants / conditions.

[0245] B. Signal-to-noise ratio (SNR):

[0246] B1. Measured as the ratio of target protein-specific signal intensity to non-specific background noise intensity within the anchoring point region (ASR / SASR). A high SNR indicates strong specific recruitment signal and low background interference.

[0247] B2. Calculation method:

[0248] SNR_Tar=Mean_Signal_Tar / StdDev_Noise_Tar

[0249] Mean_Signal_Tar: The average intensity of all pixels in the target channel (Tar-Ch) within the ASR / SASR region (after background correction).

[0250] StdDev Noise Tar: Standard deviation of background noise. The calculation method needs to be clearly defined.

[0251] Method 1: Calculate the standard deviation (σ_bg) of the intensity of all pixels in the target channel (Tar-Ch) within the background region (BGR) (far from any ASR / SASR or TPC) within the cell. This represents the background fluctuations of instrument noise and cell autofluorescence.

[0252] Method 2: Calculate the standard deviation in the region outside but immediately adjacent to the ASR / SASR region (annular region), which may contain some non-specific signals. This must be noted in the method description.

[0253] B3. Significance: A higher SNR indicates a clearer and more reliable target protein signal detected at a specific site, with less interference from random noise. Strong interactions typically lead to high Mean_Signal_Tar and high SNR.

[0254] C. Other relevant parameters:

[0255] Enrichment Ratio (ER): ER = Mean_Signal_Tar_ASR / Mean_Signal_Tar_BGR. This directly reflects the enrichment fold of the target protein concentration relative to the cellular background at the anchor point. A value >1 indicates enrichment.

[0256] Anchor point signal intensity (Mean_Signal_Ref): The average intensity of the reference channel within the ASR / SASR, reflecting the "intensity" or "size" of the anchor point, and can be used as a standardization factor or quality control indicator (to ensure consistent anchor point expression levels across different cells).

[0257] Agglomerate characteristics (for phase separation): If TPC is detected on Tar-Ch, the number of agglomerates, average size (area / volume), average intensity, roundness / shape factor, etc. can be calculated.

[0258] 4. Data Normalization & Analysis Unit (DNAU):

[0259] (1) Function: Standardize the raw parameters of the calculation to eliminate systematic errors and differences between samples, and perform statistical analysis to assess the significant differences in binding ability.

[0260] (2) Core process:

[0261] A. Batch Effect Correction: If the experiment is conducted over multiple days or in multiple batches, statistical methods (such as ComBat) are used to correct for differences between batches.

[0262] B. Anchor Site Strength Normalization: Due to potential differences in the expression levels or sizes of anchor sites (AS / SAS) across different cells, it is strongly recommended to normalize target protein-related parameters (such as Mean_Signal_Tar, CP).

[0263] Normalize relative to the anchor point signal strength (Mean_Signal_Ref):

[0264] Normalized_Mean_Signal_Tar=Mean_Signal_Tar / Mean_Signal_Ref;

[0265] Normalized_CP(e.g., M1) = M1 / Mean_Signal_Ref (or use other normalization factors such as ASR area). This helps to more fairly compare recruitment efficiency across different cells or conditions.

[0266] C. Expression level standardization: If the expression level of the target protein (TP) varies greatly among different samples, it may be necessary to standardize the CP or SNR relative to the overall expression level of TP (such as the average Tar-Ch intensity of the whole cell or nucleus), or include it as a covariate in the analysis.

[0267] D. Statistical Analysis:

[0268] Intergroup comparisons: For different conditions (such as wild type vs. mutant, different drug treatments, different time points) or different protein pairs, use appropriate statistical tests (such as t-test, ANOVA, Mann-Whitney U test) to compare the significance (p-value) of standardized parameters such as CP, SNR, and ER.

[0269] Correlation analysis: Analyze the correlation between parameters such as CP, SNR, and ER, or the correlation with known biochemical affinity data (such as Kd value), in order to assess the biological significance of quantitative MRC indicators.

[0270] Dose-Response Curve: If an induction system (such as small molecule inducers at different concentrations) is used, plotting the curve of CP or SNR as a function of inducer concentration can fit the EC50 value and provide semi-quantitative affinity information.

[0271] 5. Reporting & Visualization Unit (RVU):

[0272] (1) Function: To present the quantitative analysis results in a clear and intuitive form.

[0273] (2) Output format:

[0274] A. Numerical Report: Provides raw and normalized parameter values ​​(CP, SNR, ER, Mean_Signal_Tar, Mean_Signal_Ref, etc.) for each cell / each ASR / SASR.

[0275] B. Statistical Charts:

[0276] Boxplots / Scatter plots: Show the distribution and differences of key parameters (such as Norm_CP, SNR) between different groups.

[0277] Bar charts: Show the mean ± standard deviation / standard error under different conditions.

[0278] Scatter Plots with Regression: Show the correlation between different parameters or between parameters and known data.

[0279] Dose-Response Curves.

[0280] Pseudo-color overlay: Displays the original fluorescence image (Ref-Ch, Tar-Ch) and the calculated colocalization map (e.g., Manders map, displaying colocalized pixels in pseudo-color).

[0281] ROI overlay: Overlays the identified ASR / SASR and background region BGR onto the original image.

[0282] The innovative aspects of the protein binding capacity assessment module include:

[0283] 1. Core Quantitative Metrics: The core outputs of the module are co-localization percentage (CP - preferred Manders coefficient M1 / M2) and signal-to-noise ratio (SNR). These two metrics quantify the interaction between the two dimensions of spatial overlap and signal specificity / clarity, respectively.

[0284] 2. Standardization is crucial: Anchor point strength must be standardized (divided by Mean_Signal_Ref) to eliminate the influence of intercellular differences in anchor point expression. The effects of batch effects and target protein expression levels should be considered.

[0285] 3. Strictly define background noise: StdDevNoiseTar in SNR calculation must be explicitly and consistently defined (it is strongly recommended to use the cytoplasmic background region far from the signal region to calculate σ_bg).

[0286] 4. From Recruiter to Interaction: The CP and SNR evaluated in this module primarily reflect the efficiency of successful recruitment of target proteins to anchor sites (i.e., the strength of the Recruiter-Anchor interaction). To evaluate the direct interaction between two target proteins, it is necessary to combine this module with the multi-protein interaction verification module to calculate the colocalization parameters between the two target protein signals on the SAS platform (e.g., PCC / MOC of TP1-Ch vs TP2-Ch).

[0287] 5. Relative rather than absolute quantification: The binding capacity assessment (especially CP / SNR) provided by MRC is relative and semi-quantitative. It is particularly suitable for:

[0288] (1) Compare the changes in affinity of the same protein pair under different conditions (e.g. mutant vs. wild type, + / - drugs, different cell states).

[0289] (2) Compare the relative affinity of different protein pairs under the same conditions.

[0290] (3) High-throughput screening of molecules that affect specific PPIs or phase separation.

[0291] (4) Monitor changes in PPIs or phase separation kinetics (by time-series imaging and CP / SNR calculation).

[0292] 6. Phase separation assessment: By analyzing the characteristics (size, intensity, quantity) of condensates formed on the target channel and their co-location with anchor points (including whether TPCs form within ASRs, the CP of TPCs and ASRs, etc.), the phase separation tendency and the nucleation or regulatory role of anchor points on phase separation can be quantitatively assessed.

[0293] 7. Software Tools: This module heavily relies on image analysis software (such as ImageJ / Fiji + JACoP / Coloc2 plugins, Imaris, Volocity, CellProfiler) for ROI definition, parameter calculation, and statistical analysis. Automated scripts (such as Python, MATLAB) can improve the efficiency of high-throughput data analysis.

[0294] 8. Control Validation: The reliability of quantitative results depends on rigorous control experiments (e.g., no recruitment, no interaction control) in the spatial location visualization module and the multi-protein interaction validation module. Positive controls (known strong interaction pairs) and negative controls (known no interaction pairs or mutants) are fundamental to defining the CP / SNR range and understanding the meaning of the results.

[0295] Through this refined structural design, the "protein binding capacity assessment module" can transform the intuitive fluorescence images generated by the MRC system into objective and comparable quantitative indicators (CP, SNR, etc.), providing important preliminary quantitative information for assessing the strength of protein-protein interactions and phase separation behavior. This makes up for the shortcomings of traditional qualitative or endpoint detection methods and is particularly suitable for dynamic process monitoring and high-throughput screening applications.

[0296] (iv) Protein phase separation ability assessment module, which is used to quickly assess whether different proteins or protein domains have phase separation ability based on recruiting them to specific locations.

[0297] Based on the combined study of PPIs and LLPS, MRC can not only study protein-protein interactions but also simultaneously assess the phase-separation ability of proteins. For example, by verifying the interaction between NOS1 and PSD95 through MRC, its enhancing effect on phase-separation behavior can be observed, and the phase-separation tendency of different proteins and domains can be further explored. This innovation enables the simultaneous study of the dynamic changes in protein interactions and phase-separation processes, thereby providing a comprehensive understanding of protein functions within the cell.

[0298] In a preferred embodiment, the protein phase separation capability assessment module includes:

[0299] 1. Phase Separation Induction & Imaging Unit (PSIIU) (1) Function: Induces phase transition of target protein at specific sites under controllable conditions and performs multi-dimensional dynamic imaging.

[0300] (2) Core components:

[0301] A. Nucleation Anchor Site (NAS): Low-complexity domains (LCDs) or oligomerized domains (such as the LCD of hnRNPA1, the SYGQ domain of FUS, and artificially designed oligomerized modules) are fused at the anchor points of the spatial positioning visualization module (such as the dCas9 genome site or organelle positioning point) as "seeds" for phase separation.

[0302] B. Target protein recruitment system: The target protein / domain (such as NOS1, PSD95) is rapidly recruited to the vicinity of NAS through orthogonal recruitment strategies (such as chemical dimerization, optogenetics) to simulate physiological concentration enrichment.

[0303] C. Environmental disturbance controller, used for:

[0304] C1. Temperature regulation: Live cell temperature control devices (such as the TokaiHit system) induce phase transitions by gradient changes in the range of 25℃-42℃.

[0305] C2. Osmotic pressure regulation: The intracellular crowding is altered by perfusing different concentrations of PEG-8000 or sucrose solution into the microfluidic system.

[0306] C3. Stress induction: Add ATP-depleting agents (sodium azide) or oxidative stress agents (H2O2).

[0307] D. Multimodal dynamic imaging systems, including:

[0308] Confocal temporal imaging unit for capturing multichannel Z-stack (NAS tag: mCherry; target protein: EGFP; optional phase transition markers such as HaloTag-SG3 dye) every 30 seconds.

[0309] The FRAP (Fluorescence Recovery Apparatus) unit is used to locally photobleach the condensates formed at the NAS and monitor the fluorescence recovery kinetics (image every 5 seconds).

[0310] The FLIM (Fluorescence Lifetime Imaging) unit is used to detect the difference in fluorescence lifetime of target proteins inside and outside condensates (phase separation is often accompanied by changes in microenvironment polarity).

[0311] 2. Phase Separation Signature Identification Unit (PSSIU) (1) Function: Automatically identify phase separation features from images and extract key morphological and dynamic parameters.

[0312] (2) Analysis process:

[0313] The original image is acquired, and then background correction and deconvolution are performed before aggregate detection, including aggregate detection based on target protein channels and aggregate detection based on NAS colocalization. The aggregate detection based on target protein channels is obtained by threshold segmentation using the Ostu method and morphological filtering to obtain the first detection result. The aggregate detection based on NAS colocalization is obtained by expanding the ROI radius by 2 micrometers after identifying NAS coordinates to obtain the second detection result. Then, based on the first and second detection results, the intersection region is determined to be a NAS nucleated aggregate. Then, feature extraction is performed on the NAS nucleated aggregate. The key parameters obtained are shown in Table 1 below.

[0314] Table 1

[0315]

[0316]

[0317] * Obtained by titrating the expression level of the target protein or the recruitment duration.

[0318] 3. Interaction-Phase Transition Integration Unit (IPTIU)

[0319] (1) Function: To analyze the regulatory role of protein-protein interactions (PPs) on phase separation (such as NOS1-PSD95 in the specific example).

[0320] (2) Experimental Design:

[0321] For single-protein recruitment, weak-phase separation is performed using protein PSD95:

[0322] For co-recruitment of two proteins, a protein complex PSD95+NOS1 was used for strong phase separation.

[0323] For mutation verification, the protein complex PSD95-ΔPDZ was used for phase separation and disappearance.

[0324] (3) Quantized output:

[0325] Synergy Index (SI) = (volume of two protein aggregates / volume of a single protein aggregate) × FRAP recovery rate ratio;

[0326] Phase diagram reconstruction: With a fixed total protein concentration, the pairing ratio of PPIs is changed (e.g., PSD95:NOS1 from 10:1 to 1:10), and the phase diagram of aggregate formation is plotted.

[0327] 4. Validation & Control Unit (VCU)

[0328] (1) Core control experiment:

[0329] A. Positive control: Recruit known phase-separating proteins (such as FUS, hnRNPA1) to NAS, which should form typical droplets.

[0330] B. Negative control: Globular proteins (such as mScarlet-I) were recruited to the same NAS, and no aggregates formed. The existing aggregates dissolved upon the addition of a phase separation inhibitor (1,6-hexanol).

[0331] C. Artificiality Elimination: Express the target protein of the non-fusion recruiter and observe the background of spontaneous phase transition. Verify the reversibility of the phase transition using a light-controlled recruitment system (e.g., Bluelight-induced dissociation).

[0332] (2) Orthogonal verification method:

[0333] Transmission electron microscopy (TEM) was used to analyze the condensate ultrastructure of the fixed sample.

[0334] Fluorescence correlation spectroscopy (FCS) measures the change in molecular diffusion coefficient at NAS.

[0335] The innovations of the protein phase separation capability assessment module are reflected in:

[0336] 1. NAS Design Principles: Weakly Interacting Substrate: The LCD of the NAS must have metastable characteristics (e.g., the hnRNPA1 LCD does not spontaneously transition under physiological conditions) to avoid excessive nucleation. Switchability: Optically controlled NAS (e.g., Cry2olig) achieves temporal and terrestrial control.

[0337] 2. Prioritize dynamic parameters

[0338] FRAP recovery rate is a better indicator than static morphological parameters for distinguishing between functional droplets (t1 / 2 < 10 seconds) and pathological aggregation (no recovery).

[0339] 3. High throughput compatibility

[0340] Automated imaging with 96-well plate combined with machine learning classification (e.g., ResNet50 to distinguish between "droplets / aggregates / no phase change").

[0341] Expression library screening: candidate cDNA libraries are fused with recruiters to evaluate phase separation potential in batches.

[0342] 4. Pathological association analysis: Disease-related mutations (such as ALS-related FUSR521C) were introduced to quantitatively assess the effects of mutations on saturation concentration (C_sat) and aggregate stiffness (FLIM polarity index).

[0343] Application scenario example: NOS1-PSD95 phase transition enhancement mechanism

[0344] Step 1: Recruit the PSD95-FKBP fusion to the NAS (dCas9-FRB localization locus) using rapamycin:

[0345] Step 2: A small number of small aggregates (0.5 μm in diameter, FRAPt) were detected during individual recruitment. 1 / 2 =8s);

[0346] Step 3: NOS1 (NOS1-FRB system) was recruited, the condensate increased to 2 μm and FRAP accelerated (t1 / 2 = 3 s);

[0347] Step 4: Calculate the synergy index SI = (2 / 0.5) × (8 / 3) ≈ 10.7 → strong synergy effect;

[0348] Step 5: After mutating the PDZ domain of PSD95, SI dropped to 1.1, confirming the necessity of the interaction.

[0349] Technical advantages:

[0350] 1. Spatiotemporal precision: Avoids non-specific phase transitions caused by traditional overexpression; single-cell level analysis of phase transition dynamics (e.g., the entire process of nucleation-growth-fusion):

[0351] 2. Dual Function Integration: Simultaneous output of PPIs intensity (co-localization percentage) and phase separation capability (FRAPt) on the same platform. 1 / 2 );

[0352] 3. Pathological predictive value: The pathogenicity of mutations is assessed by quantifying the phase transition threshold shift (ΔC_sat).

[0353] This module combines classic phase separation research methods (FRAP, environmental perturbation) with the precise recruitment capabilities of MRC to establish a comprehensive evaluation system from phenotypic identification to mechanism analysis, which is particularly suitable for drug screening (finding phase transition modulators) and disease mechanism research.

[0354] (v) The dynamic observation and quantitative assessment module is used to observe the dynamic changes of proteins during phase separation in real time by combining multi-channel fluorescence and quantum dot probes, and to provide preliminary quantitative data on the physical properties of condensates, including viscosity, density, etc. This method provides a powerful tool for studying the physical properties of phase-separated bodies and the influence of protein interactions on condensate formation.

[0355] As a preferred embodiment, the dynamic observation and quantitative evaluation module includes:

[0356] 1. Multimodal Live Imaging Unit (MLIU)

[0357] (1) Function: Simultaneously capture the spatiotemporal dynamics of phase separation and molecular motion behavior.

[0358] (2) The core technology portfolio is shown in Table 2 below:

[0359] Table 2

[0360]

[0361]

[0362] (3) Probe labeling strategy:

[0363] A. Target protein labeling: HaloTag / SNAP-tag fusion protein + organic dye (such as JF646, used for routine dynamic imaging);

[0364] B. Quantum dot labeling:

[0365] Biotinylated target protein + streptavidin-coated quantum dots (QD605, 15nm in diameter);

[0366] Specific nanobodies (such as anti-GFPQD705) are used to label fluorescent protein fusions.

[0367] (4) Two-color orthogonal marking:

[0368] Protein A: SNAP-tag → Cell permeability SNAP-Surface 549;

[0369] Protein B: HaloTag → HaloTag-JF646;

[0370] Interaction interface: QD525-QD655 FRET pair.

[0371] 2. Biophysical Property Quantification Unit (BPQU) (1) Physical parameter extraction methods and algorithms are shown in Table 3 below:

[0372] Table 3

[0373]

[0374]

[0375] 3. Interaction-Phase Transition Kinetics Unit (IPTKU)

[0376] (1) Function: To analyze how protein interactions regulate phase separation kinetics

[0377] (2) Experimental design (taking NOS1-PSD95 as an example):

[0378] A. Unimolecular Interaction Dynamics: FCCS measurements of the cross-correlation curves between QD525-PSD95 and QD655-NOS1 → Calculation of the binding dissociation constant K d ;

[0379] B. Phase transition enhancement mechanism: Compare the following parameters of PSD95 alone and PSD95+NOS1, including: nucleation rate (increase in the number of condensates per unit time), molecular residence time (SPT trajectory survival analysis) and viscosity change (η value comparison).

[0380] C. Mutation Verification: For the PSD95-ΔPDZ mutant: Repeat the above experiment to quantify K. d Cooperative loss with η.

[0381] (3) Output indicators:

[0382] A.

[0383] Where, the numerator represents the sum of the cooperative signal intensities of all pixels within the actual observed co-localization region, where ηdual η represents the signal strength of the two-pixel cooperative signal. single Indicates the signal strength of a single pixel. Indicates the ratio of the two-pixel signal; The denominator represents the proportion of a single pixel signal; the denominator represents the expected sum of "co-location" signal strengths assuming that the two signals are completely random and independently distributed within the co-location area.

[0384] B. Phase diagram reconstruction: With the total concentration fixed, the pairing ratio of PPIs (PSD95:NOS1) was changed, and the η-G′ phase diagram was plotted.

[0385] The innovation of dynamic observation and quantitative assessment lies in:

[0386] 1. Nanoscale precision physical measurement: Quantum dot SPT + optical tweezers break through the resolution limitations of traditional microscopes, directly acquiring subcellular scale physical properties:

[0387] like Figure 5 As shown, quantum dot probes were used to track protein diffusion within condensates by single-particle tracking, and the diffusion rate and microenvironment viscosity were measured, thereby revealing the rheological properties of the condensates.

[0388] 2. Dynamic Coupling Analysis: Synchronously outputting the entire chain of data from interaction strength (Kd) → phase transition properties (η / G′) → pathological phenotype:

[0389] 3. High throughput compatibility: 96-well plate automated platform: A single experiment can screen the effects of 12 drugs on the phase transitions of 20 proteins.

[0390] Application Case: Phase Separation Study of Tau Protein in Alzheimer's Disease.

[0391] Pathological group: Tau-P301L mutant → η=320±40cP (solid state);

[0392] Drug intervention: After adding methylene blue → η decreased to 120±20 cP (liquid);

[0393] Conclusion: Methylene blue reverses pathological phase transitions by reducing aggregate viscosity.

[0394] This module deeply integrates cutting-edge nanoprobe technology with biophysical algorithms, achieving for the first time multidimensional quantitative analysis of phase separation "viscosity-elasticity-molecular interaction" within living cells, providing a direct tool for drug development targeting phase transitions.

[0395] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A molecular recruitment colocalization system, characterized in that, The application comprises: a spatially targeted visualization module for recruiting target proteins to a specific site and generating a fluorescent co-localization signal in living cells based on the target proteins recruited to the specific site, thereby achieving intuitive and real-time visualization of multi-protein PPIs; a multi-protein interaction verification module for observing protein interactions in multi-protein complexes based on the paired co-localization of different proteins recruited to the specific site at a specified genetic locus, thereby obtaining the interactions between multi-proteins and verifying the interactions in complex multi-protein complexes; a protein binding ability evaluation module for quantitatively analyzing the binding ability between proteins based on the calculated values of the percentage of protein co-localization and signal-to-noise ratio; a protein phase separation ability evaluation module for rapidly evaluating whether different proteins or protein domains have phase separation ability based on their recruitment to a specific location; a dynamic observation and quantitative evaluation module for observing the dynamic changes of proteins in the phase separation process in real time by combining multi-channel fluorescence and quantum dot probes, and providing preliminary quantitative data for the physical properties of the condensate, wherein the physical properties include one or more of viscosity, density, elastic modulus, and molecular order parameter.

2. A molecular recruitment colocalization system according to claim 1, wherein, The spatially targeted visualization module comprises a recruitment unit for recruiting target proteins to a specific site and a fluorescent co-localization unit for generating a fluorescent co-localization signal in living cells based on the target proteins recruited to the specific site; the spatially targeted visualization module is based on a co-localization signal generation mechanism consisting of spatial co-localization, signal enhancement, and advanced applications of BiFC, FRET, or FLIM.

3. A molecular recruitment colocalization system according to claim 2, wherein, The recruitment unit comprises an anchor site, a recruiter, a tag-capture system, and an antibody-antigen epitope; wherein the anchor site is a "molecular anchor" that stably exists or can be guided to a specific location in the cell, determined by genome targeting type, organelle and / or structure positioning type, membrane positioning type, or artificially synthesized site type; the recruiter is a molecular module that can interact specifically with the anchor site, connected or fused to the target protein, determined by protein interaction pairs, nucleic acid aptamers, and / or ligands; the tag-capture system includes a high-affinity tag fixed at a specific location, and itself carries the high-affinity tag or a module that can bind to the high-affinity tag; the antibody-antigen epitope is used to fix the anchor site at a specific location, and determines a small antigen epitope tag fused to the target protein and recruited by the corresponding antibody or fragment.

4. A molecular recruitment colocalization system according to claim 3, wherein, The fluorescent co-localization unit is used to generate a highly spatially overlapping fluorescent signal that can be detected by a microscope when the target protein is successfully recruited to the anchor site, directly indicating the occurrence of the recruitment event, including: a reference fluorescent label for a fluorescent reporter molecule directly fused or closely connected to the anchor site; a target fluorescent label for a fluorescent reporter molecule directly fused or closely connected to the target protein TP or its recruiter.

5. A molecular recruitment colocalization system according to claim 4, wherein, The multi-protein interaction verification module comprises: a shared anchor site for recruiting as a common target site for all pairs of proteins to be verified, determined based on any of the genome targeting type and the high stability organelle and / or structure positioning type; a multiplexed recruitment system for providing a plurality of specific molecular hooks, each for independently recruiting a different target protein to the shared anchor site; a multi-channel fluorescence co-localization detection system for independently and differentially fluorescently labeling and imaging the shared anchor site SAS and each recruited target protein TP, thereby enabling simultaneous visualization and quantitative analysis of multiple pairs of interactions; and a control and optimization unit for ensuring specificity, reliability and repeatability of experimental results, and excluding false positives and false negatives; wherein the strategy of the multiplexed recruitment system comprises: Strategy A: orthogonal pairs of protein interactions, equipped with multiple, mutually non-interfering and orthogonal binding ligands at the SAS site for a universal anchor protein and / or tag; each ligand is fused to a different recruiter, which is in turn fused to a different target protein; Strategy B: orthogonal nucleic acid aptamer system, stably expressing an RNA scaffold comprising multiple different stem-loop structures at the SAS site, each stem-loop recognized by its specific RNA binding protein; Strategy C: combinatorial induction system, utilizing different small molecule inducers to control different dimerization systems.

6. A molecular recruitment colocalization system according to claim 5, wherein, The multi-channel fluorescence co-localization detection system comprises: SAS reference fluorescent labeling, a fluorescent reporter molecule fixed on the SAS for labeling the common site of all recruitment events; target protein specific fluorescent labeling: each target protein fusion TP-R carries a fluorescent reporter molecule that is spectrally unique to be clearly distinguished; The optimization strategy of the multi-channel fluorescence co-localization detection system is that if the number of target proteins is > 3 or the fluorescence protein spectrum overlaps seriously, the fluorescence protein + tag system, sequential imaging, co-localization signal interpretation, multi-protein complex interaction map verification, advanced quantitative analysis and multi-channel overlap analysis can be combined.

7. A molecular recruitment colocalization system according to claim 6, wherein, The protein binding capacity evaluation module comprises: image acquisition and preprocessing unit for obtaining high-quality, standardized raw fluorescence image data and preprocessing to reduce noise and variation; region of interest definition unit for accurately identifying and delineating the fluorescent signal region for quantitative analysis; quantitative parameter calculation unit for calculating key quantitative indicators: co-localization percentage and signal-to-noise ratio, and other related parameters in the defined region of interest; wherein the other related parameters include: target signal enrichment ratio, anchor site signal intensity and characteristics for phase separation of the condensate, the condensate characteristics include one or more of the number, average area, average volume, average intensity, circularity and shape factor of the condensate; data standardization and analysis unit for standardizing the raw parameters of the calculated key quantitative indicators to eliminate errors and sample-to-sample differences, and performing statistical analysis to evaluate the significance of the binding capacity difference; reporting and visualization unit for presenting the quantitative analysis results in a clear and intuitive form.

8. A molecular recruitment colocalization system according to claim 7, wherein, The protein phase separation ability evaluation module comprises: A phase separation induction and imaging unit for inducing phase transition of target proteins at specific sites under controllable conditions and performing multidimensional dynamic imaging; A phase separation feature recognition unit for automatically recognizing phase separation features from images and extracting key morphological and dynamic parameters; An interaction-phase transition collaborative analysis unit for analyzing the regulatory effect of protein interactions PPIs on phase separation; A verification and control unit for implementing positive control, negative control and false image exclusion; wherein the orthogonal verification method adopted includes: based on transmission electron microscopy analysis of the agglomerate ultrastructure of the fixed sample; based on fluorescence correlation spectroscopy measurement of the change of molecular diffusion coefficient at NAS.

9. A molecular recruitment colocalization system according to claim 8, wherein, The phase separation induction and imaging unit comprises: Anchoring nucleation sites, including: fusing low complexity domains or oligomerization domains as "seeds" of phase separation on the anchoring sites of the spatially targeted visualization module; A target protein recruitment system: recruit the proteins and / or domains to be tested to the vicinity of NAS quickly through orthogonal recruitment strategies, simulating physiological concentration enrichment; An environmental disturbance controller for temperature regulation, osmotic pressure regulation and stress induction; A multimodal dynamic imaging system, comprising: A confocal time course imaging unit for capturing a multi-channel Z-stack at a time; A fluorescence bleaching recovery unit for local photo bleaching of the agglomerates formed at the NAS and monitoring the fluorescence recovery kinetics; A fluorescence lifetime imaging unit for detecting the fluorescence lifetime difference of target proteins inside and outside the agglomerates.

10. A molecular recruitment colocalization system according to claim 9, wherein, The dynamic observation and quantitative evaluation module comprises: A multimodal dynamic imaging unit for synchronously capturing the spatiotemporal dynamics of phase separation and molecular motion behavior; An agglomerate physical property quantification unit for extracting physical property parameters of the agglomerates; An interaction-phase transition coupling dynamics unit for analyzing how protein interactions PPIs regulate phase separation dynamics. The protein phase separation ability evaluation module comprises: A phase separation induction and imaging unit for inducing phase transition of target proteins at specific sites under controllable conditions and performing multidimensional dynamic imaging; A phase separation feature recognition unit for automatically recognizing phase separation features from images and extracting key morphological and dynamic parameters; An interaction-phase transition collaborative analysis unit for analyzing the regulatory effect of protein interactions PPIs on phase separation; A verification and control unit for implementing positive control, negative control and false image exclusion; wherein the orthogonal verification method adopted includes: based on transmission electron microscopy analysis of the agglomerate ultrastructure of the fixed sample; based on fluorescence correlation spectroscopy measurement of the change of molecular diffusion coefficient at NAS. The phase separation induction and imaging unit comprises: Anchoring nucleation sites, including: fusing low complexity domains or oligomerization domains as "seeds" of phase separation on the anchoring sites of the spatially targeted visualization module; A target protein recruitment system: recruit the proteins and / or domains to be tested to the vicinity of NAS quickly through orthogonal recruitment strategies, simulating physiological concentration enrichment; An environmental disturbance controller for temperature regulation, osmotic pressure regulation and stress induction; A multimodal dynamic imaging system, comprising: A confocal time course imaging unit for capturing a multi-channel Z-stack at a time; A fluorescence bleaching recovery unit for local photo bleaching of the agglomerates formed at the NAS and monitoring the fluorescence recovery kinetics; A fluorescence lifetime imaging unit for detecting the fluorescence lifetime difference of target proteins inside and outside the agglomerates. The dynamic observation and quantitative evaluation module comprises: A multimodal dynamic imaging unit for synchronously capturing the spatiotemporal dynamics of phase separation and molecular motion behavior; An agglomerate physical property quantification unit for extracting physical property parameters of the agglomerates; An interaction-phase transition coupling dynamics unit for analyzing how protein interactions PPIs regulate phase separation dynamics.

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