Cell characterization and identification classification systems, methods, and applications based on drug sensitivity

Abstract

This application relates to the field of biotechnology, and more particularly to a cell identification, classification, and characterization system, method, and application based on drug susceptibility. The cell characterization system includes a cell mechanical force detection device, which includes a base and a micropillar array consisting of one or more micropillars mounted on the base that are deformable under the action of cell mechanical forces from cells or multicellular aggregates, the top and / or upper part of the micropillar and / or the upper part of the columnar surface having a light-reflecting layer, and the cells, multicellular aggregates exhibit corresponding drug susceptibility to a specific drug. The cell identification and classification system includes an information acquisition unit, a preprocessing unit, a learning unit, and an identification unit, and this application distinguishes cells or multicellular aggregates with different drug resistance levels based on cell mechanical forces, completes identification in a short time, thereby achieving highly accurate drug screening with an accuracy rate of 98% or higher.

Description

Technical Field

[0001] This application relates to the field of biotechnology, and particularly to a cell characterization, identification and classification system, method and use based on drug sensitivity.

Background Art

[0002] Cells in the human body can be classified into cells with different degrees of drug resistance and non-resistant cells according to the degree of sensitivity to drugs, and further divided into drug-resistant tumor cells and non-resistant tumor cells.

[0003] Drug resistance, also known as antimicrobial resistance, refers to the ability of microorganisms, parasites and tumor cells to show resistance to the action of drugs. Once drug resistance occurs, the effect of the drug is significantly reduced. According to its cause of occurrence, drug resistance is classified into acquired resistance and natural resistance. Pathogens in nature (for example, certain strains of bacteria) may have natural resistance. When antibiotics are used for a long time, most sensitive strains are killed one by one, resistant strains multiply in large numbers and replace the sensitive strains, and the drug resistance rate of bacteria to the drug continuously increases.

[0004] Currently, the methods for evaluating the drug resistance of cells are based on animal experiments or cell cultures, which are time-consuming and costly, difficult to meet the urgent drug treatment needs of patients, and also difficult to discover and select primary resistant cells. In cancer patients, drugs effective against tumors often have a killing effect on normal cells, causing serious and even destructive damage to other organs of the patient during cancer treatment. Therefore, the conventional drug sensitivity cell characterization, identification and classification systems and methods have disadvantages such as insufficient sensitivity, limited specificity, time-consuming, cumbersome operation, strong subjectivity, high cost, and inability to comprehensively reflect cell characteristics.

Summary of the Invention

Problems to be Solved by the Invention

[0005] In view of the above-mentioned shortcomings and inconveniences of the prior art, this application provides a cell characterization system and method based on drug sensitivity. This system and method can acquire minute changes in biological and physical information such as cellular mechanical forces, and can be used to characterize the type and state of cells statically, during the culture process, under external stimuli, and after external stimuli.

[0006] Accordingly, this application further provides a system for characterizing and identifying cells based on drug sensitivity. This system can identify cell types, such as cells with different resistance levels, in a short time, at low cost, and with high throughput, thereby enabling accurate drug selection.

[0007] Accordingly, the present invention further provides applications in methods for detecting interactions between cells and multicellular aggregates, including a drug sensitivity-based cell characterization system and identification / classification system. These applications can be used for the precise selection of drugs in different methods, enabling the selection of drugs with different resistance levels, such as those effective against tumors but harmless to normal cells. [Means for solving the problem]

[0008] To achieve the above objectives, the main technical means employed in this application are as follows:

[0009] In a first embodiment, the present application provides a method for identifying the degree of drug resistance of cells, the method being: A step of obtaining cytophysical information including cellular mechanical force and / or cellular stiffness, The process includes the step of identifying cells with different drug resistance levels and non-resistant cells based on the cellular physical information.

[0010] In this application, "cellular drug resistance" refers to the degree of drug sensitivity exhibited by a cell or multicellular aggregate to a particular drug. Therefore, in this type of characterization, the term "cell" should be broadly interpreted as a single cell or a group of cells (cell aggregate) composed of two or more cells. In the definition of this application, a multicellular aggregate refers to a group of cells where cells are the basic structural and functional units of an organism, and where cells normally reproduce or differentiate to form a group of cells where two or more cells aggregate. Multicellular aggregates include cell groups cultured in vitro or in vivo, such as tumor multimers, and include organoids and active tissues. Cells with different resistance levels in this application refer to cells that have different degrees of resistance to a certain drug; for example, tumor cells can be divided into multiple subtypes based on differences in resistance levels.

[0011] In a preferred embodiment of the present application, the cellular mechanical force includes one or more of the magnitude, direction, distribution, and frequency of the cellular mechanical force, and preferably includes a temporal variation of one or more of the magnitude, direction, distribution, and frequency of the cellular mechanical force. The aforementioned cell stiffness includes the magnitude of cell stiffness at a specific location within the cell, and preferably includes the magnitude, spatial distribution, and temporal changes of stiffness of different layers at a specific location within the cell. Preferably, the cytophysical information includes pre-drug action, during action, and / or post-drug action. Preferably, the cytophysical information includes cytomechanical forces and / or cell stiffness obtained in at least one of the following: interactions between cells and multicellular aggregates, interactions between cells, interactions between multicellular aggregates, the action of a substance on cells and / or multicellular aggregates, and the action of other physical, biological or chemical factors on cells and / or multicellular aggregates.

[0012] In some embodiments, the cytophysical information includes one or more combinations of the following: cell number, cellular mechanical force, cell stiffness, and cell morphology.

[0013] In some embodiments, the method for identifying the drug resistance of cells further includes the step of quantifying the drug resistance of cells with different levels of drug resistance. This allows for not only distinguishing between resistant and non-resistant cells, but also further classifying the resistance levels of resistant cells.

[0014] In some embodiments, the method for identifying the drug resistance of the cells further includes the step of applying different types and intensities of stimuli in the process of obtaining the cell physical information, Preferably, the stimulus is one or more combinations of physical stimuli, chemical stimuli, and biological stimuli. Preferably, the physical, chemical, and biological stimuli include a combination of one or more types of stimuli from among drugs, mechanical force, hardness, biochemical factors, electric fields, flow fields, chemotactic induction, and radiation. Here, directional induction refers to inducing cells to grow and change in the direction of a line drawn on the substrate by drawing a line in a specific direction. In the preferred scheme, if the external stimulus is a specific microenvironment, the differences in cellular mechanical force information between different classifications of cells can be amplified, making identification easier and improving the efficiency and accuracy of identification. For example, in a soft environment, it is difficult to accurately distinguish between two different cell subtypes, but after applying a hardness stimulus in a hard environment, the differences between cell subtypes can be amplified, thereby allowing for more accurate identification.

[0015] In some other embodiments, the method for identifying the drug resistance of the cells further includes the step of comparing and analyzing the results identified based on the cytophysical information with biochemical and / or optical characterization results, wherein the biochemical and / or optical characterization includes at least one of protein staining, histochemical staining image characterization and single-cell sequencing.

[0016] In a preferred embodiment of the present invention, the method for identifying the drug resistance of cells includes the step of characterizing using a cell characterization system, the cell characterization system includes a cell mechanical force detection device for obtaining cell mechanical force, and the cells have different drug resistance levels to specific drugs. The cell mechanical force detection device includes a base and a micropillar array consisting of one or more micropillars that are deformable under the action of cell mechanical forces and are installed on the base. The micropillar includes a bottom end connected to a base, sides, a columnar body enclosed by the sides, and a top end that is detached from the base and opposite the bottom end, and has a light-reflecting layer on the micropillar, and optionally the base and the columnar body of the micropillar have light-transmitting portions, and the top end of the micropillar has a light-reflecting layer.

[0017] Specifically, the multicellular aggregate of the present invention is attached to a cellular mechanical force detection device in several ways, and two of these specific attachment methods are as follows.

[0018] First coupling method: A culture medium is placed on a micropillar of a cell mechanical force detection device, and cells are transplanted into the medium on the micropillar and cultured to obtain a multicellular aggregate. In another embodiment, if the cell mechanical force information is output in a visualized format, this coupling method allows for real-time monitoring of the cell culture process and can be applied to the effects of chemical, biological, and physical external stimuli such as culture medium and drugs on cell growth.

[0019] Second binding method: The cultured multicellular aggregate is directly attached to the micropillar of the cell mechanical force detection device for detection.

[0020] In a more preferred embodiment, the cell characterization system further includes an optical signal generator and an optical signal detector, The optical signal generating device has a light source, and the light rays emitted from the light source are irradiated onto the light reflection layer through the incident light path. The optical signal detection device is used to detect the light beam reflected from the optical reflection layer, and the light beam reflected from the optical reflection layer enters the optical signal detection device through the reflection optical path. Optionally, the light beam intensity acquired by the optical signal detection device and the cell mechanical force are linearly correlated within a certain range.

[0021] The cell characteristic evaluation system further includes an optical signal analysis device, which forms the optical signal analysis result as visualization information or data. Optionally, the optical signal analysis device is used to calculate the cell mechanical force of each micropillar based on the attenuation of the reflected light intensity, and to determine the type, state, and behavior of the detected cells based on a preset model.

[0022] In a second aspect, the present application provides a cell identification and classification system based on drug sensitivity, which includes an information acquisition unit and a preprocessing unit. The information acquisition unit is used to acquire cell physical information, and the cell physical information includes cell mechanical force and / or cell hardness. The preprocessing unit is used to preprocess the cell physical information to form structured cell information.

[0023] Preferably, the cell mechanical force includes one or more of the magnitude, direction, distribution, and frequency of the cell mechanical force, and preferably includes the temporal change of one or more of the magnitude, direction, distribution, and frequency of the cell mechanical force. The cell hardness includes the magnitude of the cell hardness at a specific position within the cell, and preferably includes the magnitude, spatial distribution, and temporal change of the hardness of different layers at a specific position within the cell. Preferably, the cell physical information includes before, during, and / or after the action of a specific drug.

[0024] In a more preferred embodiment, the cell physical information includes a combination of one or more of the number of cells, cell mechanical force, cell hardness, and cell morphology.

[0025] In a preferred solution, the cell identification and classification system further includes a learning unit and an identification unit, The learning unit is used to construct a cell feature model using supervised, unsupervised, or semi-supervised machine learning with the structured cell information as input data. The identification unit is used to apply the cell feature model to the classification or clustering of cells based on drug sensitivity, thereby enabling the identification of cell types based on drug sensitivity.

[0026] In a third embodiment, the inventor provides a method for obtaining cytophysical information based on drug sensitivity, wherein the cytophysical information includes cellular mechanical force and / or cellular stiffness, and comprises the following steps:

[0027] A step of taking multiple primitive cells and obtaining cellular physical information from each primitive cell. A step of performing a specific drug screening on the aforementioned primitive cells to obtain cells with different levels of drug resistance, A step of obtaining cytophysical information of cells with different drug resistance levels, A step of comparing the cellular mechanical force information of cells with different drug resistance levels with the cellular mechanical force information of the corresponding primitive cells, designating cells whose decrease in cellular mechanical force during and / or after drug treatment is less than a first preset threshold as resistant cells, and cells whose decrease is greater than or equal to a second preset threshold as non-resistant cells, or The steps include comparing the cell stiffness of cells with different levels of drug resistance with the corresponding cell stiffness of the primitive cells, designating cells whose reduction in cell stiffness during and / or after drug treatment is less than a third predetermined threshold as resistant cells, and cells whose reduction is greater than or equal to a fourth predetermined threshold as non-resistant cells. The step includes obtaining cytophysical information of resistant and non-resistant cells to the aforementioned specific drug.

[0028] In a fourth embodiment, the inventor provides a method for identifying and classifying cells based on drug sensitivity, wherein the cell physical information of the cells is obtained and used for identification and classification, and the cell physical information includes cell mechanical force information. Optionally, the cellular mechanical force information includes at least one of the cellular mechanical force information obtained before, under, and after the action of an external factor. Selectively classify cells based on different levels of drug resistance. Optionally, the cell classification includes normal cells and pathological cells, activated cells and inactivated cells. In random selection, the cellular mechanical forces of EGFR-mutated resistant cells are large and uniformly distributed throughout the cell, while the cellular mechanical forces of non-resistant cells are small and concentrated in the peripheral regions of the cell.

[0029] Some of the proposed improvements to the above-mentioned cell identification and classification method based on drug sensitivity are: A step of obtaining cellular physical information of a cell based on drug sensitivity, wherein the cellular physical information includes cellular mechanical force information and cell stiffness information at a point within the cell, the cellular mechanical force information includes the magnitude, direction, frequency, distribution and temporal dynamic change of the cellular mechanical force at a specific location in the cell or multicellular aggregate, and the cell stiffness information includes the magnitude, spatial distribution and temporal change of the stiffness of different layers at a specific location within the cell. A step of preprocessing the cell physical information to form structured cell information, wherein the structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature. The process includes the steps of: constructing a cell feature model using supervised, unsupervised, or semi-supervised machine learning with the structured cell information as input data; and applying the cell feature model to classify or cluster cells based on drug sensitivity of an unknown type or state.

[0030] In a fifth embodiment, the inventors provide an identification method described in the first embodiment of the present application, a drug-sensitivity-based cell identification and classification system described in the second embodiment of the present application, a method for obtaining the cellular mechanical forces of cells and / or multicellular aggregates based on drug sensitivity described in the third embodiment of the present application, and an application of the drug-sensitivity-based cell identification and classification method described in the fourth embodiment of the present application, the application of which includes high-precision drug screening, resistant cell screening and drug discovery.

[0031] Preferably, the above applications are To obtain the cellular mechanical forces of two or more cells before, during, and after drug action, classify cells and / or multicellular aggregates, predict drug efficacy, and achieve highly accurate screening of therapeutic agents. or This includes obtaining the cellular mechanical forces of a single cell before, during, and after the action of different drugs, classifying cells in relation to different drugs, screening for the most effective drug or drug combination, and achieving high-precision screening of therapeutic agents.

[0032] Selectively, physical, biological, and / or chemical stimuli are applied to test cells, and information on cellular mechanical forces and / or dynamic changes before and after different conditions and stimuli is obtained, respectively, to identify stimuli and microenvironmental conditions that can expand different cell types. The system selects which different therapeutic agents are applied to tumor cells and normal cells, respectively, and acquires information on the cellular mechanical forces of each. It then identifies therapeutic agents and their concentrations that are non-lethal or have little effect on normal cells, but are effective against tumor cells. [Effects of the Invention]

[0033] Unlike the prior art, the beneficial effects of this invention are as follows:

[0034] First, this invention can distinguish cells or multicellular aggregates with different resistance levels based on cellular mechanical forces, enabling the identification of resistance or non-resistance to different drugs in a short time, at low cost, and with high throughput, thereby achieving accurate drug selection with an accuracy rate of over 98%. This invention can rapidly determine the relationship between drugs and cells or multicellular aggregates under different scenes and conditions, and the cell samples are reusable. In addition to measuring the active mechanical forces of cells, it can also measure the hardness (rigidity) at a specific location within the cell, and by combining and analyzing both, the accuracy rate in predefined application scenes can be improved, making it applicable to scenes of more subtle changes, further expanding the information on changes in cells and multicellular aggregates, and making them easier to characterize.

[0035] Next, the cell mechanical force detection device included in the cell characterization system provided in this application can detect cell mechanical force using reflected light, and has the characteristics of high throughput and low cost compared to existing cell mechanical force detection devices. Compared with existing TFM and conventional micropillar arrays, the technology of this application eliminates reliance on expensive confocal microscopes and greatly simplifies the operation flow. This is because it does not require high-resolution imaging by microscope, and cells can be monitored at high throughput simply by monitoring the intensity of reflected light, resulting in low cost. The cell mechanical force detection device deforms a micropillar with respect to cell mechanical force, converts the cell mechanical force into an optical signal for detection, and has the characteristics of high accuracy and sensitivity. The optical intensity and the magnitude of the cell mechanical force are linearly correlated, and qualitative and quantitative analysis of cell mechanical force can be performed. It has high single-cell resolution, allowing each cell to be monitored in real time, and can be combined with other single-cell analysis techniques to measure the heterogeneity of the drug response of cells. Real-time monitoring: Since it does not require fluorescence and avoids the phototoxic effect of lasers on cells, it is suitable for long-term monitoring and can be used to study the long-term response of cells to drugs. High sensitivity: The reflected signal amplifies the deformation signal of the micropillar, improving the sensitivity of deformation monitoring.

[0036] Furthermore, by utilizing the specular reflection principle to detect the attenuation of reflected light, this invention can actually magnify the deformation signal of micropillars, and experimental verification has shown that the same signal can be observed under a 5x objective lens. By incorporating a special reading system, deformation of micro / nanopillars can be effectively detected without relying on high-magnification optical objective lenses, thereby significantly reducing system costs and effectively improving throughput.

[0037] Furthermore, the cellular mechanical force detection device of this invention can mimic the cellular microenvironment. It can mimic the components and morphology of the extracellular matrix, enabling it to address a wider range of technological needs. Moreover, the cellular mechanical force detection device of this invention can detect cellular mechanical forces in multilayer cells, tumor multimers, etc., and can be applied to scenarios requiring characterization of multicellular aggregates in drug selection, regenerative medicine, gene editing, precision medicine, organogenesis, and disease modeling.

[0038] Furthermore, because the cell mechanical force detection device of this application has a magnetic metal reflective layer and magnetic material, the micropillar can achieve a more flexible motion method and more precise motion control. In addition to measuring the active mechanical force of cells, it can also measure the hardness (rigidity) of a specific location inside the cell. By combining and analyzing both, the accuracy in predetermined application scenes can be improved, and it can be applied to scenes of more subtle changes, further expanding the information on changes in cells and multicellular aggregates and making them easier to characterize. The cell mechanical force detection device employs side waveguide illumination and evanescent wave illumination modes to improve the signal-to-noise ratio and optical effect.

[0039] Simultaneously, the present invention can amplify the differences between different cell subtypes by employing appropriate microenvironments and stimuli, for example, by increasing stiffness to amplify the differences between resistant and non-resistant cells, thereby facilitating cell classification by mechanical force. Furthermore, the present invention can utilize conductive and fluorescent materials for operations such as electrical stimulation or photolabeling, and can be combined with microfluidic technology to perform sample sorting. The measurement range of the present invention includes various types of multicellular aggregates (e.g., tumor globules) and biological tissues, and can acquire spatial omics information, thereby comprehensively and objectively reflecting the characteristics of cells or multicellular aggregates (e.g., tumor globules, biological tissues).

[0040] As described above, this invention more directly elucidates behavioral changes in cells or multicellular aggregates under drug action, thereby deepening our understanding of the mechanisms of resistance development and helping to accurately determine the degree of drug resistance in cells. It possesses higher sensitivity and specificity, and can more accurately reveal subtle changes in cells under drug action. Distinguishing resistant cells by cellular mechanical force also has the advantages of being easy to operate and low-cost. [Brief explanation of the drawing]

[0041] [Figure 1] This is a schematic diagram of the structure of a type of cellular mechanical force detection device according to the first embodiment of this application. [Figure 2] This is a top view image of a scanning electron microscope (SEM) of a micropillar (actual object) of a cell mechanical force detection device in the first embodiment of this application. [Figure 3] This is a side view of the cell mechanical force detection device according to the first embodiment of this application. [Figure 4] This is a schematic diagram of the structure of a type of cellular mechanical force detection system related to the ninth embodiment of this application. [Figure 5] This is a schematic diagram of the structure of a type of cellular mechanical force detection system related to the tenth embodiment of this application. [Figure 6] This is a scanning electron microscope image of a micropillar (polydimethylsiloxane) having a light-reflecting layer (gold) at its apex, as shown in a specific embodiment of this application. [Figure 7] Figure 6 shows the elemental properties of the top region of the micropillar. [Figure 8] Figure 6 shows the elemental properties of the lateral region (excluding the top region) of the micropillar. [Figure 9] This is a fluorescence imaging diagram showing cells, as depicted in the seventh embodiment of this application, adhering to a pre-defined pattern composed of micropillar groups having fibronectin at their apex. [Figure 10] This is a cell force distribution map derived from light reflection signals measured on micropillars with fibronectin at their apex. [Figure 11] This is a schematic diagram of a test in which the OKT3 antibody is used as a substance having cell adhesion properties in a specific example of this application. [Figure 12] The two images in the upper part of Figure 12 are fluorescence imaging diagrams showing cells adhering to the tops of micropillars, one with OKT3 antibody and the other with fibronectin, respectively. The two images in the lower part of Figure 12 are magnitude distribution diagrams of cellular mechanical forces, calculated from light reflection signals measured on the micropillars. [Figure 13] This is a comparison chart of the mechanical magnitudes measured on surfaces coated with OKT3 antibody and fibronectin, respectively. [Figure 14] This diagram shows the cellular dynamic changes that occurred after T cells were implanted on the surface of the OKT3 antibody (top of the micropillar). [Figure 15] This is a schematic diagram of the structure of a cell mechanical force detection device that has a cell restriction mechanism. [Figure 16] This is a schematic diagram of a cell mechanical force detection device that has another type of cell restriction mechanism. [Figure 17] This is a drawing of a cellular mechanical force detection device that uses a silicon thin film as a cell restriction mechanism, as described in a specific embodiment of this application. [Figure 18] This is a fluorescence microscope image of a device for detecting cellular mechanical forces using a silicon thin film as a cell-limiting mechanism under light reflection. [Figure 19] This is an enlarged view of Figure 18. [Figure 20] This is a fluorescence microscope image showing the cellular mechanical force being monitored by the cellular mechanical force detection system of the eleventh embodiment. [Figure 21] This is a schematic diagram of the structure of a cell mechanical force detection system provided by the twelfth embodiment of this application. [Figure 22] This is an image of the light reflection signal from the cell mechanical force detection device acquired by the optical signal detection device. [Figure 23] This diagram shows the visualization effect of the mechanical magnitude and distribution processed by the optical signal analyzer of the twelfth embodiment. [Figure 24] This is a schematic diagram of the structure of a device for detecting cellular mechanical forces in a microfluidic environment before and after fluid release. [Figure 25]This is a comparison diagram of a bright-field microscope image of a micropillar before fluid dissection, a reflected light signal distribution diagram, and a diagram showing the superposition effect of both. [Figure 26] This is a comparison diagram of a bright-field microscope image of a micropillar after fluid dissection, a reflected light signal distribution diagram, and a superposition effect diagram of the two. Here, the superposition effect diagram refers to the effect diagram formed by superimposing the bright-field microscope image of the micropillar and the reflected light signal distribution diagram. [Figure 27] These are the intensity values ​​of the light reflection signal before and after fluid dispersion. [Figure 28] This is the linear interval between the attenuation of the light reflection signal and the apex displacement of the micropillar. [Figure 29] This is a schematic diagram of the structure of the micropillar of the cell mechanical force detection device shown in a specific embodiment of this application, before and after it comes into contact with a cell. [Figure 30] This is a reflected light signal distribution map acquired by an optical signal detection device. [Figure 31] This is a monitoring chart of the cell migration process. [Figure 32] This is a diagram showing the distribution of reflected light signals during cell migration. [Figure 33] This is a fluorescence imaging image of a mixed system of healthy cells and non-small cell lung cancer cells. [Figure 34] This is a light reflection signal distribution map of the cell mechanical force detection device acquired by the optical signal detection device. [Figure 35] This is a visualization of the mechanical magnitude and distribution processed by an optical signal analyzer. [Figure 36] Figure 35 shows a magnified view of the cellular force distribution of representative single cells, comparing healthy cells and non-small cell lung cancer cells. [Figure 37] This is a comparative diagram of the cell morphology of healthy cells and non-small cell lung cancer cells. [Figure 38] This is a comparison chart of the strength of the reflected signal after mixing healthy cells, non-small cell lung cancer cells, and these two types of cells in different proportions. [Figure 39] Figure 35 is a clustering analysis diagram obtained after structuring the data and then processing it based on the structured cell information. [Figure 40]This is a schematic diagram of the operational flow of the cell vitality detection method shown in a specific embodiment of this application. [Figure 41] This is a comparative diagram of cell vitality, reflecting the cell vitality measured by the MTT method and the cell mechanical force measured by the apparatus, system, or method of this application, after A549 cells, as shown in the specific examples of this application, have been treated with different doses of 5FU for 24 hours. [Figure 42] This is a comparative diagram of cell vitality, reflecting the cell vitality measured by the MTT method and the cell mechanical force measured by the apparatus, system, or method of this application, after A549 cells, as shown in the specific examples of this application, have been treated with different doses of 5FU for different durations. [Figure 43] This diagram shows the cellular mechanical forces of cells with different levels of drug resistance in a specific embodiment of this application, illustrating the dynamic changes during the drug treatment process. [Figure 44] This shows the distribution and magnitude of cellular mechanical forces under different pH conditions in specific embodiments of this application. [Figure 45] This is a visualized cellular mechanical force distribution diagram of lung cancer cells with different drug resistance levels in a specific embodiment of this application. [Figure 46] This is a visualized diagram of the cellular mechanical force distribution in non-resistant lung cancer cells. [Figure 47] This is a distribution map of the cellular mechanical force intensity of resistant and non-resistant lung cancer cells. The horizontal axis represents the radius of the cell center, and the vertical axis represents the average cellular mechanical force intensity around the circumference corresponding to the radius of the horizontal axis. [Figure 48] This is a visualized cellular mechanical force distribution diagram of breast cancer cells with different drug resistance levels in a specific embodiment of this application. [Figure 49] This is a visualized cellular mechanical force distribution diagram of non-resistant breast cancer cells in a specific embodiment of this application. [Figure 50] This is a distribution map of the cellular mechanical force intensity of resistant and non-resistant breast cancer cells in a specific embodiment of this application, where the horizontal coordinate is the radius of the cell center and the vertical coordinate is the average cellular mechanical force intensity of the circumference corresponding to the radius of the horizontal coordinate. [Figure 51]This is a schematic diagram of a method for selecting EGFR-TKI-resistant cells in HCC827 cells based on the cell mechanical force intensity distribution in the 26th embodiment of this application. [Figure 52] This is a schematic diagram illustrating the process of standardizing displacement information at a certain point location in the 31st embodiment of this application. [Figure 53] This figure shows the results of applying the cell feature model established in the extended embodiment of the 32nd embodiment of this application to the identification of unknown cells or unknown cell characteristic evaluation types. [Figure 54] This figure shows the results of applying the cell feature model established in the extended embodiment of the 32nd embodiment of this application to the identification of unknown cells or unknown cell characteristic evaluation types. [Modes for carrying out the invention]

[0042] To better explain and facilitate understanding of the present application, exemplary embodiments are described in more detail below. While exemplary embodiments are shown below, it should be understood that the application is not limited to these embodiments and can be implemented in various forms. Rather, these embodiments are provided to allow for a clearer and more thorough understanding of the application and to fully communicate its scope to those skilled in the art. In this invention, hardness is a core indicator describing the mechanical properties of a cell, referring to its ability to resist local deformation against external forces. This property manifests not only at a microscopic level (for example, flexibility or stiffness measured by indentations made by an atomic force microscope probe), but also fundamentally determines the macroscopic stability of the entire cell, that is, it is an expression of "rigidity" that maintains its own shape and resists overall bending and twisting.

[0043] First Example This is a type of cellular mechanical force detection device; please refer to Figure 1. Figure 1 is a schematic diagram of the structure of a type of cellular mechanical force detection device, and the cellular mechanical force detection device shown in the figure includes a translucent base 11 and a micropillar 12 mounted on the base 11 that is deformable under the action of cellular mechanical force, the top of the micropillar 12 is coated with a light-reflecting layer 13, the thickness of which is 5 nm (in some other embodiments, the thickness of the light-reflecting layer 13 may be between 5 nm and 20 nm—the thickness of the coating is related to the coating material, and assuming the same type of coating material is applied, the selection of the coating thickness is limited to ensuring the light-transmitting effect, ensuring the stability of the micropillar column, and ensuring that the connection with the micropillar column does not detach). The column of the micropillar 12 can transmit light, and the group of arrows pointing in opposite directions in the figure indicate incident and reflected light rays. (Note: Although the term "coating" is used in this embodiment, this only indicates that the light-reflecting layer 13 in this embodiment can be manufactured by a coating method, and does not necessarily limit the manufacturing of the light-reflecting layer 13 to a coating method.) Figure 2 and Figure 3 Please refer to Figure 2. Figure 2 is a scanning electron microscope (SEM) image of the micropillar 12 (actual object) of the cell mechanical force detection device in this embodiment. 2 is Top view of the cell mechanical force detection device, Figure 3 This is a side view of the cell mechanical force detection device. Figure 2 and Figure 3 As can be seen, the microstructure of the micropillars of the cell mechanical force detection device is orderly and uniform, its dimensions are controllable, and the mechanical values ​​measured by the cell mechanical force detection device based on this embodiment are more precise than those measured by existing cell mechanical force detection devices.

[0044] When using the cellular mechanical force detection device 1 of this embodiment, the number of micropillars 12 will be one or more. 4 Please refer to the diagram. 4 This is a schematic diagram of the structure of a cell mechanical force detection system related to the ninth embodiment of the present application. 4This can be used to understand this embodiment. The system shown in Figure 3 includes, in addition to the cell mechanical force detection device 1 described in this embodiment, an optical signal generator 2 and an optical signal detection device 3 having a light source installed below the base 11. Light rays emitted from the light source are irradiated from the translucent base 11 of the cell mechanical force detection device 1 to the light reflection layer of the micropillar 12 via the incident light path. The optical signal detection device 3 is used to detect light rays reflected from the light reflection layer 13 at the top of the micropillar. The light rays reflected from the light reflection layer 13 pass through the reflection light path, are acted upon by the spectrometer 5, and then enter the optical signal detection device 3. After acquiring the reflected light signal, the optical signal analyzer 4 can compare and analyze the reflected light rays before and after the cell mechanical force acts between the cell mechanical force detection device 1 and the test cell to obtain cell mechanical force information. When the micropillar 12 is not subjected to force, the micropillar should remain upright, thereby reflecting the exploration light to the maximum extent. When the micropillar 12 comes into contact with a cell, under the action of cellular mechanical forces, the micropillar 12 bends, and the light reflection level decreases. Therefore, the greater the cellular mechanical force, the smaller the resulting light reflection signal. Thus, by observing the intensity of the light reflection signal, the magnitude of the cellular mechanical force at that point can be easily calculated.

[0045] Furthermore, the measurement light source in the technical solution of this embodiment can be an infrared laser of a constant intensity. Conventional micropillar measurements require the acquisition of high-resolution images, and using a laser in this process is likely to cause phototoxicity of cells or fluorescence quenching of the sample. In this technical solution, only the reflected signal needs to be measured, so the effect of an infrared laser within a certain light intensity is basically negligible on cells, and therefore it is suitable for long-term monitoring of cells.

[0046] Second Example This is a type of cellular mechanical force detection device, and the difference from the first embodiment is that the micropillar 12 not only has a light-reflecting layer 13 on its top end face, but also has a light-reflecting layer 13 on the upper half of the columnar surface of the micropillar 12 (i.e., the curved surface connecting both end faces of the column). In fact, in other embodiments, the method of installing the light-reflecting layer 13 on the lower half of the side columnar surface of the micropillar 12 is not adopted because it is less effective in practice. However, by installing the light-reflecting layer 13 on the upper half of the side surface of the micropillar 12, the detection effect that the present invention aims to achieve can be basically realized. That is, in some other embodiments, the light-reflecting layer 13 can even be laid at any local position on the upper half side columnar surface or at a local position on the top, and it is not necessarily required to lay it on the entire upper half columnar surface or the entire top end face. In either case, the expected purpose can be achieved, but there may be differences in the data acquired and the effectiveness of post-processing.

[0047] Furthermore, the definitions of "columnar face" and "end face" of the micropillar appear in the first and second embodiments of this application. That is, an independent column as we normally understand it should have two end faces and a curved surface (columnar face) connecting the two end faces, but the micropillar in this application has only a top end face due to the presence of a base, and the other end is fixedly connected to the base or integrally molded with the base. However, in some other embodiments, the top end face may be a curved surface that is smoothly connected integrally with the column face, and there is not necessarily an intersection line or clear boundary as shown in the first or second embodiment. In this case, the installation position of the light-reflecting layer 13 is also understood to be the upper half of the column and is not limited to the "end face" or "columnar face".

[0048] Third Example It is a type of cellular mechanical force detection device, as shown in the figure. 5 Please refer to the diagram. 5This is a schematic diagram of the structure of a type of cellular mechanical force detection system in the tenth embodiment of the present application, and is used to explain the cellular mechanical force detection device 1 in this embodiment. The difference between this embodiment and the first and second embodiments is that there is no requirement for the light transmission performance of the base 11 and the micropillar 12 columns of the micropillar array; that is, they can be light-transmitting, opaque, or semi-transmitting. In this case, it is only necessary to change the positions of the optical signal generator 2 and the optical signal detection device 3, and both are installed above the base 11. In this way, each time the micropillar bends, the optical signal received by the optical signal detection device 3 changes compared to when the micropillar 12 is upright and undeformed. By analyzing the changes in the optical signal before and after, the relative magnitude of the cellular mechanical force can be obtained in the same way, and after calibration with a standard value, the absolute magnitude value of the cellular mechanical force can be obtained.

[0049] Fourth Embodiment This is a type of cellular mechanical force detection device. The difference between this embodiment and the first to third embodiments is that an anti-reflective layer is installed in areas other than the light-reflecting layer 13 on the surface of the micropillar 12. This design reduces interference of reflected light signals that may be caused by the columnar surface, improves the signal-to-noise ratio, and makes the detection results more accurate.

[0050] In some embodiments, the light-reflecting layer 13 may be a single layer of gold foil. In other embodiments, the light-reflecting layer 13 may be a metal layer or other reflective material having other light-reflecting properties. Different materials may result in differences in reflective effect, difficulty of manufacturing the reflective layer, and cost, and can be considered and selected based on specific conditions in actual operation.

[0051] In the first to fourth embodiments, the cross-sectional shape of the micropillar 12 is circular. In other embodiments, the cross-sectional shape of the micropillar 12 may be elliptical or polygonal. In various different specific embodiments of the present application, different cross-sections can achieve different purposes. For example, a circular cross-section has isotropic characteristics, i.e., the mechanical properties of the micropillar itself are not sensitive to direction. When the cross-section is elliptical, it is anisotropic, i.e., the mechanical properties of the micropillar itself are sensitive to direction, thereby allowing control of sensitivity to force fields in different directions and allowing adjustment of cell orientation to a certain extent (the geometric form of most cells is actually asymmetric, and cell orientation in this application refers to the morphological asymmetry, polarity, or directionality that characterizes the cell. For example, when fitting the shape of a cell projection using an ellipse, the major axis of the ellipse can be considered the direction the cell has). When the cross-section is elliptical, the cross-section has a major axis and a minor axis, and it is much easier to push the micropillar along the minor axis than along the major axis, and the deformation under relative force conditions is also greater. In some stretching embodiments, when cells are seeded onto such micropillars, an anisotropic mechanical interaction exists between the cells and the micropillars, resulting in the cells growing along one side. When applied to fluids, it can be used to measure the direction of the fluid.

[0052] In the first to fourth embodiments, the dimensions of the micropillar array are: column height 10 nm to 500 μm, column spacing 10 nm to 50 μm, and column top surface diameter 50 nm to 50 μm. Micropillars within this dimensional range can satisfy the basic usage conditions for micropillars used as sensors, namely they are at least deformable and do not fall over. On this basis, by adjusting the different micropillar array dimensions, the following functions can be further realized. For example, by adjusting the aspect ratio of the micropillar (which can be understood as the ratio of height to cross-sectional diameter / side length / major axis on the layered surface of the micropillar), a certain micropillar deformation performance adjustment function can be realized, thereby better mimicking the internal organ tissue environment (e.g., bone tissue and nerve tissue of different hardnesses).

[0053] Furthermore, the overall dimensions of the array or the number of micropillars 12 on a base 11 of a certain area also affect the ligand density, that is, the number of points on the surface where cells can adhere can be found. The sparser the array of micropillars 12, the smaller the number of adhesion points that cells can find, which has a greater impact on cell behavior.

[0054] The size of the cross-sectional area of ​​a micropillar also affects cell adhesion behavior. This is because a certain area is required to form a focal adhesion through cell adhesion. In the case of nanomicropillars, the cross-sectional area of ​​the micropillar is small, which affects the formation of focal adhesion.

[0055] In short, by combining the inherent properties of the material with specific micropillar array dimensions, it is possible to achieve cell support effects, chip stability, and measurement accuracy that better meet the demands. By adjusting the distribution of the micropillar array, it is also possible to control and influence the cell adhesion state to a certain extent.

[0056] In the first to fourth embodiments, the material of the micropillar 12 is polydimethylsiloxane (PDMS). In some other major embodiments of the present invention, the material of the micropillar 12 may be other polymer materials, such as silicon-based polymers, photoresist polymer materials, conductive polymer materials, and temperature-sensitive polymer materials. The reason that the major embodiments of the present invention mainly employ polymer materials is that current polymer materials have deformation properties suitable for the application of the present invention. However, the implementation of the present invention is not limited to polymer materials for the material of the micropillar, and can be extended to all materials with fully corresponding deformability, and any of them can realize the inventive concept of the present invention. Simply put, the condition that the material of the micropillar must satisfy is that it has a certain force-receiving deformation property, and in some embodiments it needs to have a certain light transmittance, but the latter is not a requirement for all embodiments. When manufacturing micropillars using materials with limited light transmittance, the inventive concept of the present invention can be realized in the same way by simply appropriately positioning the optical signal generator and optical signal detection device.

[0057] Overall, the hardness (deformability) of the micropillar 12 can be adjusted based on actual demand through multiple technological dimensions such as dimensions (mainly aspect ratio), selection of material type, control of the degree of crosslinking of polymer materials, and chemical or physical surface treatment.

[0058] figure 6~8 Please refer to the diagram. 6~8 This is a scanning electron microscope image of a micropillar (polydimethylsiloxane) with a light-reflecting layer (gold) at its apex, where, Figure 6 This is a scanning electron microscope image of a micropillar. Figure 7 Figure 1 shows the elemental properties of the top region of the micropillar, and Figure 8 shows the elemental properties of the side region of the micropillar (excluding the top region). 6~8 By characterizing the material composition of the micropillar using scanning electron microscope images, it can be confirmed that the element Au is present at the top of the micropillar and the element Si is present at other positions on the micropillar.

[0059] Fifth Example This is a type of cellular mechanical force detection device, and the difference between this embodiment and the first to fourth embodiments is that a substance having cell adhesion properties is provided on the apical end faces of some micropillars 12 of the micropillar array. In this embodiment, collagen from the extracellular matrix molecules is used, while in other embodiments, collagen may be included, and the binding of one or more types of extracellular matrix molecules such as fibronectin, vitronectin, laminin, and elastinogen may also be employed. In some other embodiments, other types of substances having cell adhesion properties may be provided on the apical end faces of all or some of the micropillars of the micropillar 12 array, for example, an extracellular matrix mimic (such as a peptide containing an RGD adhesion sequence), or a substance having a cell adhesion promoting mechanism (such as polylysine), or a substance that interacts with cell surface receptors.

[0060] Providing a substance with such cell adhesion properties on the apical end face of the micropillar 12 effectively promotes the attachment of cells to the micropillar 12, thereby enabling the regulation of cell attachment, proliferation, migration, state, differentiation, etc. Furthermore, if a substance with cell adhesion properties (e.g., an extracellular matrix protein such as fibronectin) is provided on the apical end face of a portion of the micropillars in a pre-defined area of ​​the micropillar array, these micropillars can form a certain shape. As a result, cells tend to adhere to micropillars of a specific position and shape, thereby enabling high-transmission mechanical measurements when controlling the size, shape, and tropism characteristics of cells.

[0061] In the identification method to which the cellular mechanical force detection device of this embodiment is applied, the cell adhesion material adheres cells or multicellular aggregates, making it possible to easily detect cellular mechanical forces.

[0062] Sixth Embodiment This is a type of cellular mechanical force detection device. The difference between this embodiment and the fifth embodiment is that, unlike the fifth embodiment where a substance having cell adhesion properties is provided on the apical end face of a portion of the micropillar array's micropillar 12, in this embodiment, a substance having cell adhesion inhibitory properties (e.g., F-127) is further provided on the columnar surface (end face or side surface) of the micropillar 12 in the portion where the substance having cell adhesion properties is not provided on the apical end face. As a result, cells tend to adhere to micropillars of a more specific position and shape, thereby enabling high-passage mechanical measurements when controlling the size, shape, and tropism characteristics of cells.

[0063] Seventh Example This is a type of cellular mechanical force detection device. The difference between this embodiment and the first to fourth embodiments is that micropillars, each having a cell adhesion substance on its apical end face, form a pre-set pattern. Specifically, a cell adhesion molecular layer of a particular pattern can be printed using microprinting technology, promoting cell adhesion to these areas. The so-called pre-set pattern may be in the shape of a triangle, quadrilateral, polygon, circle, ellipse, etc. The effects of the pre-set pattern include: firstly, controlling cell-to-cell contact with the pattern composed of these cell adhesion substances, easily achieving high-transmission data acquisition; secondly, achieving dimensionality reduction in data processing by unifying cell shapes, thereby reducing the difficulty of analysis; and thirdly, achieving the objective of controlling cell size, shape, tropism, differentiation state, etc., by limiting the cell adhesion area, and further, adjusting the cellular mechanical state by controlling actin filaments, even achieving the requirements of certain specific technical demands.

[0064] In another embodiment similar to this embodiment, the unprinted, pre-set patterned areas can be treated with a substance that inhibits cell adhesion (e.g., BSA (bovine serum albumin) or F127 (high molecular weight nonionic surfactant)) to suppress cell adhesion in these areas, thereby enabling directed adhesion, control of cell morphology, or mimicry of a specific cellular microenvironment.

[0065] In some other embodiments, fibronectin (FN) is selected as an example of a substance having cell adhesion properties, but this does not limit the embodiments of the present invention. Polydimethylsiloxane microstamps having convex square and rectangular patterns on their surfaces are used, respectively. Fibronectin is adhered to the surface of the microstamp, and the fibronectin on the convex portion of the stamp is transferred to the magnetic metal reflective layer at the top of the micropillar by a micro-contact printing method. Subsequently, the micropillar is immersed in F-127 solution to provide a cell adhesion inhibitory effect to the areas where fibronectin is not present. Finally, after thoroughly washing the micropillar with physiological saline, fibroblasts stained with cell membrane dye are seeded on the surface of the micropillar, and fluorescence imaging is performed on the cells (Figure). 9 (As shown in Figure 1). Simultaneously, high-resolution measurements are taken of the intracellular force field (Figure 2). 10 (As shown in the figure). 9 and Figure 10 Please refer to the following. Figure 9 This is a fluorescence imaging image showing cells adhering to a pre-defined pattern composed of micropillars with fibronectin at their apex, indicating that the cell adhesion area is restricted to the region containing fibronectin. Based on this, it is possible to restrict the cell attachment area using a pre-defined pattern and to perform mechanical monitoring of cells when controlling cell size, shape, tropism, differentiation state, etc. Figure 10 This is a distribution map of the magnitude of cellular mechanical forces, calculated from light reflection signals measured on a micropillar.

[0066] In some other embodiments, the substance having cell adhesion properties is exemplified by OKT3 antibody (i.e., a substance that interacts with cell surface receptors) or fibronectin (FN), but this does not limit the embodiments of the present invention. 11~14 Please refer to the following. Figure 11 This is a schematic diagram of an experiment in which the OKT3 antibody was used as a substance with cell adhesion properties. Figure 12 The upper part of the image is a fluorescence imaging diagram showing cells attached to the tops of micropillars, each equipped with OKT3 antibody and fibronectin. Figure 12 The lower part of the image is a distribution map of light reflection signals (which reflect the magnitude of cellular mechanical forces) measured on a micropillar. Figure 13 The graphs show a comparison of the mechanical magnitudes measured on surfaces coated with OKT3 antibody and fibronectin, respectively. Figure 14 This diagram shows the cellular dynamic changes that occurred after T cells were seeded on the surface of the OKT3 antibody (the top of the micropillar). Specifically, the above test involves coating the top of a partial micropillar of the same or different cell force detection device with OKT3 antibody or fibronectin, and then seeding T cells onto the surface of the cell mechanical force detection device that has a cell adhesion substance. 11~14 As can be seen, a cell mechanical force detection device coated with a substance that interacts with cell surface receptors (e.g., OKT3 antibody) or fibronectin (FN) on the surface of a micropillar can be used to monitor the effects and interactions of mechanical forces exerted by the substance on cells in real time.

[0067] Eighth Example This is a type of cell mechanical force detection device, and the difference between this embodiment and the first to seventh embodiments is that the cell mechanical force detection device further includes a cell restriction mechanism, the cell restriction mechanism includes one or more restriction surfaces 16, the restriction surface 16 is perpendicular to the plane on which the base 11 is located, is connected to the base 11 or is integrally molded with the base 11, is a flat or curved surface, and the height of the restriction surface 16 is greater than the micropillar 12, surrounding a preset number of micropillars 12.

[0068] The function of the cell restriction mechanism installed in this embodiment is single-cell isolation detection, that is, it avoids contact or adhesion between cells during detection and restricts cell morphology, thereby facilitating high-flow testing. Based on different requirements, the number of restricting surfaces 16 or the enclosed shape in the cell position restriction mechanism may vary. For example, the restricting surface 16 included in the cell position restriction mechanism may be a single cylindrical surface, three planes that are contiguous to form a triangular cross-sectional shape and surround a certain number of micropillars, four planes that are perpendicular to each other and contiguous to form a rectangular shape and surround a certain number of micropillars, N planes that are contiguous to surround an N-sided shape, or a single curved surface with a nearly circular cross-section. In other words, the cross-sectional shape formed by the restricting surface 16 is a controllable closed shape, and its area (or the number of micropillars that can be accommodated in that space) is also controllable.

[0069] In actual embodiments, depending on the manufacturing process, the cell restriction mechanism may further manifest in the following forms: Large, Figure 15 Please refer to the diagram. 15 This is a schematic diagram of the structure of a cell mechanical force detection device having a cell restriction mechanism. In the diagram, the cell restriction mechanism and the base 11 are integrally molded, that is, the material forming the cell restriction mechanism has multiple recessed spaces 15, the walls of the recessed spaces 15 are the restriction surfaces 16, the depth of the recessed spaces 15 is the height of the restriction surfaces 16, the bottom of the recessed spaces 15 is the base 11, and there are multiple micropillars 12 inside each recessed space 15.

[0070] B, Figure 16 Please refer to the diagram. 16 This is a schematic diagram of a cell mechanical force detection device that has a cell restriction mechanism. In the diagram, the restriction surface 16 is a structure that is bonded to the base 11.

[0071] Ninth Example This is a type of cellular mechanical force detection device, and the difference between this embodiment and the eighth embodiment is that the cell restriction mechanism in this embodiment is a silicon thin film. Specifically, see Figure 17~19 Please refer to the diagram. 17This is a diagram of a cellular mechanical force detection device that employs a silicon thin film as a cell restriction mechanism. The silicon thin film is perforated with a laser and then bonded to a base, with micropillars evenly distributed within each hole. The silicon thin film restricts the morphology and movement of cells, while simultaneously controlling intercellular contact or adhesion. 18 Figure 1 is a fluorescence microscope image of a cell mechanical force detection device that employs a silicon thin film as a cell restriction mechanism under light reflection. Figure 18 shows 9. This is an enlarged view. In some embodiments, the size of each pore in the silicon thin film can be set to match the size of a single cell, making it suitable for single cell attachment, thereby limiting cell contact, cell morphology, and their range of movement.

[0072] Tenth Example This is a type of cellular mechanical force detection system, comprising a cellular mechanical force detection device 1, an optical signal generator 2, and an optical signal detection device 3 according to the first or second embodiment. Both the optical signal generator 2 and the optical signal detection device 3 are located below the base 11 in the cellular mechanical force detection device 1. The optical signal generator 2 has a light source, and the light emitted from the light source is irradiated onto the light reflection layer 13 of the micropillar 12 via the incident light path (successively passing through a translucent base and a translucent micropillar column), causing reflection, and the reflected light enters the optical signal detection device 3 via the reflection light path (successively passing through a translucent micropillar column and a translucent base). The optical signal detection device 3 can acquire reflected light signals before and after contact between the micropillar 12 and the cell. In some other embodiments, this type of cellular mechanical force detection system further includes an optical signal analyzer 4, which can obtain cellular mechanical force information by comparing, analyzing, and calculating the reflected light signals before and after contact between the micropillar 12 and the cell, including the magnitude, direction, and changes within a certain time range of the cellular mechanical force.

[0073] Eleventh Example A type of cellular mechanical force detection system; please refer to Figure 4. Figure 4 is a schematic diagram of the structure of a type of cellular mechanical force detection system according to the eleventh embodiment of the present application, and includes the cellular mechanical force detection device 1 of the third embodiment, and further includes an optical signal generator 2 and an optical signal detection device 3. Both the optical signal generator 2 and the optical signal detection device 3 are located above the base 11 of the cellular mechanical force detection device 1. The optical signal generator 2 has a light source, and the light rays emitted from the light source are irradiated onto the light reflection layer 13 via the incident light path, causing reflection, and the optical signal detection device 3 can acquire reflected light signals before and after the micropillar 12 comes into contact with the cell.

[0074] In the embodiments of this application, the optical signal detection device may be a microscope, a charge-coupled element CCD, a complementary metal-oxide-semiconductor CMOS, a photomultiplier tube PMT and a photoelectric converter PT, a film, or other optical signal detection elements having similar functions, and this application is not specifically limited. In some embodiments of this application, when a microscope is used as the optical signal detection device, it is not necessary to install a separate optical signal generator. The cell mechanical force detection device of this application can be placed directly on the microscope stage, the light source of the microscope can be used as the optical signal generator, and the objective lens of the microscope (a 5x objective lens is sufficient, and it is not necessary to rely on a high-magnification optical objective lens) can be used as the optical signal detection device. When other optical signal detection devices, such as a charge-coupled element CCD, are used, it is necessary to install a separate optical signal generator. In the embodiments of this application, the optical signal generator may be an LED, a halogen lamp, a laser (e.g., an infrared laser), or other light source, or a device having these light sources, and this application is not specifically limited.

[0075] The following describes in detail the visualization process for using a type of cellular mechanical force detection system related to this embodiment for monitoring cellular mechanical forces.

[0076] figure 20 Please refer to the diagram. 20This is a fluorescence microscope image used to monitor cellular mechanical force using the cellular mechanical force detection system of this embodiment. Specifically, cells (for example, fibroblasts are used in this embodiment) are placed on the micropillar of the cellular mechanical force detection device, and the optical signal detection device (for example, a microscope is used in this embodiment) converts the cellular mechanical force information into an optical signal to form an image, which can be used for visualization observation and real-time feedback of changes in cellular mechanical force.

[0077] Twelfth Example A type of cellular mechanical force detection system Please refer to Figure 4. Figure 4 is a schematic diagram of the structure of a type of cellular mechanical force detection system according to the twelfth embodiment of the present application, and includes the cellular mechanical force detection device 1 of the third embodiment, and further includes an optical signal generator 2, an optical signal detection device 3, and an optical signal analyzer 4. Both the optical signal generator 2 and the optical signal detection device 3 are located above the base 11 of the cellular mechanical force detection device 1. The optical signal generator 2 has a light source, and the light rays emitted from the light source are irradiated onto the light reflection layer 13 via the incident light path, causing reflection. The spectrometer 5 may be a semitransparent semi-reflective or other equivalent optical element, the main purpose of which is to simplify the design of the optical path. The optical signal detection device 3 can acquire reflected light signals before and after the micropillar 12 and the cell come into contact. The optical signal analyzer 4 can obtain cellular mechanical force information, including the magnitude, direction, and changes within a certain time range of the cellular mechanical force, by comparing, analyzing, and calculating the reflected light signals before and after the micropillar 12 and the cell come into contact.

[0078] In the embodiments of the present invention, the optical signal analyzer may be optical image analysis software ImageJ, Matlab, Fluoview, Python, or other optical image analysis elements with similar functionality, or a combination of these analysis software, and the present invention is not specifically limited.

[0079] The following describes in detail the detection and analysis process of a type of cellular mechanical force detection system related to this embodiment.

[0080] figure 21~23 Please refer to the following. Figure 21 This is a schematic diagram of the structure of a cellular mechanical force detection system. Figure 22 This is an image of the light reflection signal from the cell mechanical force detector acquired by the optical signal detection device. Figure 23 This is a diagram illustrating the visualization effect of mechanical magnitude and distribution. 21 As shown, a magnetic metal reflective layer is provided at the top of each micropillar on the cell mechanical force detection device, and an anti-reflective layer is provided on the sides. When no cells are present, light rays are shone onto the micropillars from below, are completely reflected, and are fully received by the optical signal detection device (e.g., a CCD camera). However, when cells attach to the micropillars, the cellular forces generated by the movement of the cells cause the micropillars to tilt, and the reflected signal decreases. After analyzing the light reflected signal, the intensity of the cellular force can be calculated.

[0081] Furthermore, an optical signal detection device (e.g., a CCD camera) is used to collect images of the light reflection signals from the cell mechanical force detection device and magnified images of the local cell adhesion region (Figure). 22 (As shown in Figure) Next, the optical signal analyzer is used to analyze the signal. 22 Further processing of the image creates a more intuitive visualization of the mechanical magnitude and distribution. 23 Convert to.

[0082] The specific processing steps are as follows. First, see Figure 22 Based on this, a bright-field reflection signal diagram (I, focused on cells) is obtained. Subsequently, after performing a Fourier transform on the image, the high-frequency signals are filtered, and an inverse Fourier transform is performed to calculate and obtain the micropillar reflection signal image (I0) in the case of no displacement. Next, the I and I0 images are further processed to convert the reflection signal diagram into a more intuitive cell mechanics diagram (subtracting the I signal value from the I0 signal value), and then standardize it to obtain a more intuitive cell mechanical force intensity diagram j.

[0083] Thirteenth Example Methods for calculating mechanical forces, and the interrelationship between mechanics and light reflection signals. This embodiment combines the cell mechanical force detection system of any of the 10th to 12th embodiments or the cell mechanical force detection method of the 14th embodiment to explain the calculation method of mechanical force in the embodiment of the present invention, and verifies the interrelationship between mechanics and light reflection signals using fluid as an external force.

[0084] figure 24~28 Please refer to the diagram. 24 This is a schematic diagram of the structure of a cellular mechanical force detection device in a microfluidic environment before fluid switching. 25 and 26 This is a comparison diagram of bright-field microscope images of micropillars before and after fluid switching, a reflected light signal distribution diagram, and a diagram showing the superposition effect of both. Figure 27 This is a diagram showing the light reflection signal intensity before and after fluid switching. Figure 28 This is a linear relationship diagram between the light reflection signal and the micropillar shift. Here, the superposition effect diagram refers to an effect diagram formed by superimposing a bright-field microscope image of a micropillar with a reflected light signal distribution diagram.

[0085] First, the diagram 24~28 As shown, the cellular mechanical force detection device is integrated into the microfluidic channel, and as the external flow velocity increases, the micropillars are displaced, and at the same time, the angle of the reflective layer on the surface of the micropillars changes (Figure 24~26 ). Figure 27 As can be seen, the light reflection signal changes from strong to weak before and after fluid opening and closing. Specifically, by changing the degree of displacement of the micropillars using the strength of the flow velocity, the displacement of the top of the micropillar relative to the bottom can be photographed using a confocal microscope, and the mechanical force acting on each micropillar can be calculated using the following formula.

[0086] JPEG2026511270000047.jpg1041 In the formula, F represents the mechanical force that deflects the micropillar by an angle of δ, E represents Young's modulus, and k bend θ represents the ideal spring constant of an isolated nanopillar, D represents the diameter of the micropillar, and L represents the height of the micropillar.

[0087] Simultaneously, the light reflection signal at the top of the micropillar was recorded, and the light reflection signal and the micropillar displacement were used to create a figure. 28 By creating this, a linear relationship diagram and linear range can be obtained between the light reflection signal (reflecting cellular mechanical forces) and the micropillar displacement.

[0088] Fourteenth Example A method for detecting a type of cellular mechanical force A method for detecting a type of cellular mechanical force, comprising the following steps:

[0089] A light ray is emitted using the optical signal generator 2 in the cell mechanical force detection system of any of the tenth to twelfth embodiments.

[0090] In any of the tenth to twelfth embodiments of the cell mechanical force detection system, the optical signal detection device 3 is used to detect light rays after they have acted upon the cell mechanical force detection device 1. The optical signal detection device 3 can acquire reflected light signals before and after contact between the micropillar 12 and the cell in the cell mechanical force detection device 1. In some other embodiments, the optical signal analyzer 4 in the cell mechanical force detection system can obtain cell mechanical force information, including the magnitude, direction, and changes within a certain time range of the cell mechanical force, by comparing, analyzing, and calculating the reflected light signals before and after contact between the micropillar 12 and the cell.

[0091] figure Figures 29-32 Please refer to the following. Figures 29 and 30 This is a schematic diagram of the structure before and after contact between the micropillar, a device for detecting cellular mechanical forces, and a cell. Figure 30 This is the reflected light signal acquired by the optical signal detection device (CCD electronic photosensitive element), and a clear attenuation of the reflected signal can be seen in regions where the force field around the cell is large. Figure 31 This is a monitoring diagram of the cell migration process (the cell membrane is stained, then excited with a fluorescent light source, and the migration process is recorded with a CCD electronic photosensitive element). Figure 32 This is a map showing the distribution of reflected light signals during cell migration (a CCD electronic photosensitive element records the changes in reflected light signals during the migration process). Figure 31 and Figure 32As can be seen, the reflected light signal is clearly attenuated in the area where the cell applies force. Figure 32 The graph shows the reflected light signal for real-time monitoring during cell migration, and the reflected light signal provides real-time feedback of the mechanical forces during migration. By monitoring the reflected light signal during cell migration using an optical signal detection device, it is possible to provide real-time feedback of changes in the mechanical forces of cells during cell migration.

[0092] Fifteenth Example Method for detecting a type of cell mechanical force A method for manufacturing a type of cellular mechanical force detection device, comprising the following steps: laying a light-reflecting layer 13 on the top or upper half-columnar surface of a micropillar 12 to obtain a micropillar 12 having a reflective layer on the top or upper half-columnar surface.

[0093] Sixteenth Example Method for detecting a type of cell mechanical force A method for manufacturing a type of cellular mechanical force detection device, comprising the following steps: Plating an anti-reflective layer uniformly over the entire micropillar 12; removing the anti-reflective layer from the top or upper half-pillar surface; and laying a light-reflecting layer 13 on the top or upper half-pillar surface of the micropillar 12.

[0094] Seventeenth Example Method for detecting a type of cell mechanical force The difference between this embodiment and the fifteenth and sixteenth embodiments is that the step "laying a light-reflecting layer 13 on the top or upper half-columnar surface of the micropillar 12" specifically involves: uniformly sputtering a reflective metal onto the top or upper half-columnar surface of the micropillar to obtain a micropillar having a metallic light-reflecting layer on the top or upper half-columnar surface. The cellular mechanical force detection devices of the first to seventeenth embodiments of this application can be used for the detection of multicellular aggregates. Here, the micropillar can be deformed by the action of the cellular mechanical forces of the multicellular aggregate. The cellular mechanical forces of the multicellular aggregate include cellular mechanical forces, mechanical forces generated between cells and the extracellular matrix, and characterization of multiple cellular mechanical forces.

[0095] Eighteenth Example A type of cell identification and classification system and a method for identifying cell types based on their drug sensitivity (including visual qualitative identification, as well as accurate qualitative and quantitative identification). This embodiment specifically provides cell mechanical force information obtained by any of the cell mechanical force detection systems of the 10th to 12th embodiments or the cell mechanical force detection method of the 14th embodiment, and applies it to a cell identification method.

[0096] figure Figures 33-39 Please refer to the following. Figure 33 This is a fluorescence imaging image of a mixed system of healthy cells and non-small cell lung cancer cells. Figure 34 This is a light reflection signal distribution map of the cell mechanical force detection device acquired by the optical signal detection device. Figure 35 This is a visualization diagram of the mechanical magnitude and distribution. Figure 36 teeth Figure 35 This is a magnified view of the typical single-cell force distribution of healthy cells and non-small cell lung cancer cells. Figure 37 This is a comparative diagram of the cell morphology of healthy cells and non-small cell lung cancer cells. Figure 38 This is a comparative chart of the reflected signal intensity after mixing healthy cells, non-small cell lung cancer cells, and these two types of cells in different proportions. Figure 39 teeth Figure 35 This is a clustering analysis diagram obtained based on structured cell information processing after the data has been structured.

[0097] Specifically, this embodiment uses healthy cells (Normal) and lung non-small cell cancer cell lines (Cancer) as detection targets. The cell membranes of the healthy cells and lung cancer cells are pre-stained using two different fluorescent dyes (Dil & DIO), mixed in a certain ratio, and then added to the same cell mechanical force detection device (in some other embodiments, it may be added to different, independent cell mechanical force detection devices).

[0098] Furthermore, an image of the light reflection signal from the cell mechanical force detector is collected using an optical signal detection device (a microscope is used in this embodiment) (Figure 34As shown in Figure 1, the high-resolution force field distributions within the two types of cells are directly rendered by an optical signal detector and converted into readable light intensity attenuation signals (reflecting cell force intensity), which are then displayed in the image (Figure 2). 35 (As shown in [image]). Figure 35 Based on the difference in the degree of light attenuation of the two types of cells displayed, the two types of cells can be intuitively distinguished by observing them with the naked eye (qualitative analysis).

[0099] Furthermore, using an optical signal analyzer, Figure 35 Further processing of the reflected light signal. Specifically, this embodiment uses an optical signal analyzer (ImageJ and Python analysis software are used in this embodiment; other image analysis software may be used in other embodiments) to obtain a diagram of the cell force field. 35 This involves collecting information on multiple points in each cell, including information on the magnitude of cellular mechanical forces, thereby obtaining data on the magnitude of cellular mechanical forces at multiple points in multiple cells. The obtained cellular mechanical force magnitude information is preprocessed to form structured cell information, and the results of comparing the cell morphology of healthy cells and lung non-small cell cancer cells are obtained by analyzing the structured cell information (Figure). 37 As shown in [image], you will obtain [result].

[0100] Structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature (for example, cell adhesion area and cell roundness in this embodiment). In this case, structured cell information can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features, where P=2, meaning the cell features are the magnitude of the cellular mechanical force and the intracellular distribution of the cellular mechanical force.

[0101] Furthermore, using the above structured cell information as input data, a cell feature model was constructed using supervised machine learning (pre-staining and comparing two cell lines using different cell membrane dyes (Dil & DIO)), and the cell feature model was trained using a large amount of structured cell information, as shown in Figure. 39After obtaining a clustering analysis diagram as shown, the obtained cell feature model is applied to classify and identify cells of unknown types or states. This allows for the clustering and classification of normal healthy cells and cancer cells using an optical signal analyzer (ImageJ and Python analysis software are used in this embodiment; other clustering analysis software can be used in other embodiments) with structured cell feature data (magnitude of cellular mechanical force, distribution of cellular mechanical force within the cell) as input data, thereby enabling the identification of unknown cell types.

[0102] figure 37 This indicates that there are no statistically significant and clear differences in the morphology of different cells (including cell adhesion area and cell roundness). Figure 38 This shows a clear difference in the reflection signal intensity (reflecting cellular strength) between normal cells and tumor cells, and that the reflection signal intensity and mixing ratio after mixing normal cells and tumor cells in a certain proportion exhibit a constant linear relationship. This allows us to identify other characteristics of the cells (e.g., Figure 37 Compared to morphological information such as cell adhesion area and cell roundness, the cellular mechanical characteristics measured by the cellular mechanical force detection device of this invention allow for a more intuitive and accurate identification of the state and type of cells (quantitative and qualitative analysis).

[0103] Furthermore, Figure 36 and Figure 38 The data shows that tumor cells exhibit higher mechanical force magnitudes and more non-uniform distributions than normal cells. By visualizing cellular mechanical forces using imaging techniques, clear differences in the force field characteristics of different cells can be intuitively observed with the naked eye. Furthermore, after structuring the force field magnitudes at each point of different cells using image analysis software, a comprehensive analysis is performed to produce the figure. 37 Cell morphology information, Figure 38 The intensity of the reflected signal (reflecting cellular strength) and Figure 39 A clustering analysis diagram is obtained. This invention enables accurate identification of cell types by comprehensively analyzing the force field structure information of each cell point, thereby performing clustering classification and quantitative analysis of different cells (for example, healthy cells and non-small cell lung cancer cells in this embodiment).

[0104] As described above, the cell mechanical force detection device of this application not only allows for intuitive visual differentiation and qualitative analysis, but also enables more intuitive and accurate identification of cell states and types based on measured cell mechanical characteristics (quantitative and qualitative analysis), demonstrating that cell types can be better distinguished using the cell force field as a marker.

[0105] The cell identification method described above is applicable to the identification of resistant and non-resistant cells.

[0106] Nineteenth Example A method for detecting cellular vitality This embodiment specifically provides cell mechanical force information obtained by any of the cell mechanical force detection systems of the 10th to 12th embodiments or the cell mechanical force detection method of the 14th embodiment, and applies it to monitoring cell vitality.

[0107] figure Figures 40-42 Please refer to the diagram. 40 This is a schematic diagram of the operation flow for the cell vitality detection method. Figure 41 This is a comparative diagram of cell vitality measured by the MTT method and cell mechanical force measured by the apparatus, system, or method of the present application after treating A549 cells with different doses of 5FU for 24 hours. Figure 42 This is a comparative diagram of cell vitality measured by the MTT method and cell mechanical force measured by the apparatus, system, or method of the present application after treating A549 cells with different doses of 5FU for different durations.

[0108] Specifically, in this example, non-small cell lung cancer cells A549 were cultured on multiple cell mechanical force detection devices, treated with 5-fluorouracil (5-FU), a cell proliferation inhibitor, at different doses, and the cell mechanical forces at different time points were monitored using the cell mechanical force detection system of any of Examples 10 to 12 or the cell mechanical force detection method of Example 14. Cell proliferation and cytotoxicity at different time points were monitored using the CCK-8 kit, and cell vitality measured by the MTT method was used as a control group, as shown in Figure 41 and Figure 42Obtain the data.

[0109] figure 41 and Figure 42 As shown, after measurement using the conventional MTT measurement method and the apparatus, system, or method of the present application, both the cell vitality measured by the MTT measurement method and the cell vitality reflected by the cell mechanical force tend to decrease gradually in a dose-dependent manner, meaning that there is a positive correlation between cell mechanical force and cell vitality.

[0110] Furthermore, Figure 41 As shown, after treatment with different doses of 5FU for 24 hours, the mechanical force can reflect a greater decrease in cell vitality compared to the control group DMSO, allowing for a more intuitive assessment of cell vitality. 42 As shown, when treated with 5FU for 12 hours, the change in cell vitality measured by the MTT method is not clear. However, by measuring mechanical force, it is possible to observe a decrease in cellular mechanical force at an earlier time point than when a decrease in cellular metabolic activity is detected by the MTT method. Specifically, a clear downward trend appears at 6 hours with a treatment dose of 0.5 μM, and at 3 hours with a treatment dose of 1 μM, which allows for a more sensitive representation of the decrease in cell vitality.

[0111] As described above, this embodiment demonstrates that directly detecting cellular mechanical force using a cellular mechanical force detection device is a highly sensitive and effective method for evaluating the drug response activity of cells.

[0112] Twentieth Example A method for obtaining the cellular mechanical force of cells with different resistance levels. This embodiment obtains cell mechanical force information of different resistance levels using a cell mechanical force detection device or detection system of any of the embodiments described above, or a cell mechanical force detection method of any of the above-described schemes. Specifically, it includes the following steps.

[0113] S1 A small number of primitive cells are taken, and the cellular mechanical force information of each primitive cell is obtained. The spatial position of each cell in the cellular mechanical force detection device is recorded and used as a label.

[0114] Cells with different resistance levels are obtained by drug selection from S2 primitive cells. Drugs can be added directly to the cell mechanical force device, or the cell mechanical force device can be placed in a culture medium containing the drug. Drugs can be added periodically and quantitatively, and changes in cell mechanical force can be monitored in real time using an optical signal detector and optical signal analyzer (visual monitoring can be performed using a microscope as an optical signal detector), although this is not strictly necessary. (Figure) 43 As shown, this is a dynamic change in cellular mechanical forces during the drug treatment process; resistant cells fluctuate within a stable range, while susceptible cells decrease first, then increase, and then suddenly decrease again.

[0115] Using S3 spatial position information, the resistance information of cells with different resistance levels is matched one-to-one with the primitive cells before drug selection in step S1. The cellular mechanical force information of cells with different resistance levels is compared with the cellular mechanical force information of the corresponding primitive cells. Cells whose decrease in cellular mechanical force during and / or after drug treatment is less than the first preset threshold are classified as resistant cells, and cells whose decrease is greater than or equal to the second preset threshold are classified as non-resistant cells. The first preset threshold (which can be, for example, 3% to 50%) and the second preset threshold are determined according to the cells and drugs actually measured.

[0116] In their research, the inventors discovered that the stronger the resistance, the less damage the cells suffered from the drug, the better their cellular vitality, and the stronger their cellular mechanical force, while the weaker the resistance, the greater the change in cellular mechanical force. In step S2, by monitoring the change in cellular mechanical force, the cellular mechanical force of cells with different resistance levels can be obtained and verified by methods such as fluorescent labeling. In step S2, the method for selecting cells with different resistance levels can be performed by first obtaining cells with known corresponding resistance levels from the corresponding tissues of patients with different resistance levels (e.g., patients with high resistance and patients with low resistance), and then obtaining preliminary cellular mechanical force information for cells with different resistance levels and those with low resistance levels using a cellular mechanical force detection device. Based on this foundation, by obtaining cellular information of the test cells in step S2 and performing comparative analysis, the resistance level of each cell can be distinguished. The accuracy rate for distinguishing cells with different resistance levels using this method is over 98%.

[0117] In some other embodiments, a method for obtaining cytophysical information based on drug sensitivity, wherein the cytophysical information includes cell stiffness, comprises the following steps:

[0118] S1 A small number of primitive cells are taken, cellular physical information of each primitive cell is obtained, and the spatial position of each primitive cell in a cellular mechanical force detection device is labeled.

[0119] S2 Following the above procedure, primitive cells are selected for specific drugs to obtain cells with different resistance levels.

[0120] In step S3, cytophysical information of cells with different resistance levels is acquired, and the resistance information of cells with different resistance levels is correlated one-to-one with the primitive cells before drug selection in step S1 of this embodiment using labeled spatial position information. The cell stiffness of cells with different resistance levels is compared with the cell stiffness of the corresponding primitive cells, and cells whose decrease in cell stiffness during and / or after drug treatment is less than a third preset threshold are designated as resistant cells, and cells whose decrease is greater than or equal to a fourth preset threshold are designated as non-resistant cells. The above-mentioned specific thresholds are determined according to the cells and drugs actually measured. Cytophysical information of resistant and non-resistant cells for a specific drug is acquired.

[0121] The cellular mechanical forces of a multicellular aggregate can be obtained using a similar method.

[0122] In some specific embodiments, the system further includes a magnetic material device, where a magnetosensitive material is placed on a micropillar, and the magnetic device acts on the magnetosensitive material. The aforementioned magnetic device acts on the magnetosensitive material to cause the micropillar to move or oscillate in a specific direction, deform, and penetrate cells or multicellular aggregates, which are used to measure cell stiffness. Optionally, a cell mechanical force device further includes an antimagnetic material. Optionally, the magnetic device statically or dynamically controls the movement and softness of the micropillar using the magnetosensitive material. Optionally, the magnetosensitive material is a magnetic metal reflective layer or a magnetic reflective layer placed on the surface of the micropillar. Optionally, the magnetosensitive material includes one or more combinations of iron, cobalt, nickel, and ferrite. Based on this, cell stiffness information can be further obtained. Cell information, including cell mechanical force information and cell stiffness information, is used for identifying cells based on drug sensitivity of unknown classifications.

[0123] Example 21: Method for identifying cells with different resistance levels This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the above embodiments, and acquires cell mechanical force information of different tolerance levels.

[0124] Specifically, the method provides a way to distinguish between different resistance levels and non-resistant cells in lung tumors based on EGFR-TKIs, the step of which is to determine whether a cell is of a different resistance level based on whether or not cellular mechanical forces accumulate around the cell membrane.

[0125] In some specific embodiments, the steps may be as follows: S1 After obtaining tumor tissue from lung cancer patients, the tissue is shredded, and the tissue is broken down using collagenase and dispase to obtain primary tumor cells. S2 Cellular mechanical force information is acquired from the obtained primary tumor cells using the cell mechanical force detection device described in one of the above examples. Here, the cell mechanical force information can be collected as an image of the light reflection signal from the cell mechanical force detection device using an optical signal detection device (a microscope is used in this example). The high-resolution force field distribution within the two types of cells is directly rendered by the optical signal detection device and converted into a readable light intensity attenuation signal (reflecting the cell force intensity) and displayed in the image. Based on the difference in the degree of light attenuation of the two types of cells displayed in the image, the two types of cells can be intuitively distinguished by observation with the naked eye (qualitative analysis). The cell mechanical force distribution of primary tumor cells from EGFR-TKI drug-sensitive patients and patients with different degrees of EGFR-TKI resistance is compared. The cell mechanical force of tumor cells with different degrees of resistance is distributed throughout the cell, while the mechanical force of drug-sensitive tumor cells is concentrated around the cell membrane.

[0126] Here, the cellular mechanical force information can be further processed by an optical signal analyzer on the light reflection signal in the optical signal detection device. Specifically, in this embodiment, information is collected from a diagram of the cellular force field acquired by an optical signal analyzer (in this embodiment, ImageJ and Python analysis software are used, but in other embodiments, other image analysis software may be used), and this includes collecting information on the magnitude of cellular mechanical forces at multiple points in each cell, thereby obtaining multipoint cellular mechanical force magnitude data in multiple cells. The acquired cellular mechanical force magnitude information is preprocessed to form structured cell information, and a comparative diagram of the cell morphology of cells with different resistance levels and non-resistance levels is obtained by analyzing the structured cell information.

[0127] Structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature (for example, cell adhesion area and cell roundness in this embodiment). In this case, structured cell information can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. In this embodiment, P=2, meaning the cell features are: cell mechanical force magnitude and the distribution of cell mechanical force within the cell. Using the above structured cell information as input data, a cell feature model is constructed using supervised machine learning (pre-staining and comparing two types of cell lines using different cell membrane dyes (Dil&DIO)). The cell feature model is trained using structured cell information from a large number of cells, and after obtaining a clustering analysis diagram, the obtained cell feature model is applied to identify different resistance and non-resistance levels of the tumor cells being tested. This method allows for the automatic identification of cells with different resistance and non-resistance levels in a short time.

[0128] Example 22: Method for identifying cells with different resistance levels This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, and acquires cell mechanical force information of different resistance levels and distinguishes cells of different resistance levels based on the cell mechanical force information.

[0129] Specifically, this includes: S1 A cell mechanical force detection device is used to visualize cell information of cells and multicellular aggregates with different resistance levels based on a specific drug, including visualization information such as the magnitude and distribution of changes in cell mechanical force. Here, resistance information with different resistance levels can be obtained from known resistant patients or verified by other methods.

[0130] S2: Visualization information is used for the visualization and identification of test cells and multicellular aggregates.

[0131] Example 23: Method for identifying cells with different resistance levels This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, and acquires cell mechanical force information of different resistance levels and distinguishes cells of different resistance levels based on the cell mechanical force information.

[0132] Specifically, this includes: S1 A cell mechanical force detection device is used to acquire cell information of cells with different resistance levels based on a specific drug, and of multicellular aggregates. The cell information includes cell mechanical force information at a specific point within the cell acquired by the cell mechanical force detection device, and the cell mechanical force information includes the magnitude of the cell mechanical force at that point. Specifically, a light signal detection device (or in combination with a light signal analyzer) is used to collect cell information from multiple cells on the cell mechanical force detection device, and this includes collecting cell mechanical force magnitude information for multiple points on each cell, thereby acquiring multipoint cell mechanical force magnitude data from multiple cells. S2 Preprocessing is performed on the cell information of cells with different resistance levels obtained to form structured cell information. The structured cell information includes the number of cells, the number of cell features, and the characteristic information of each cell feature. At this time, the structured cell information can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. In this embodiment, P=1, meaning the cell feature is the magnitude of the cell's mechanical force. S3 uses structured cell information as input data, constructs cell feature models using supervised, unsupervised, or semi-supervised machine learning, and applies these cell feature models to classify or cluster cells with different resistance levels, thereby enabling the identification of cells with different resistance levels and non-resistance cells.

[0133] In some other embodiments, the method for identifying cells of different resistance levels further includes the direction of the cellular mechanical force at the point in question. In some other embodiments, the method for identifying cells of different resistance levels further includes the change in the magnitude or direction of the cellular mechanical force at the point in question within a certain time interval. In some other embodiments, the method for identifying cells of different resistance levels further includes cellular morphological information. In some other embodiments, cellular mechanical force information of known cells of different resistance levels and non-resistant cells is obtained by the method of Embodiment 18. In some other embodiments, obtaining cellular mechanical force information of different cells of different resistance levels and non-resistant cells involves: obtaining known cells of different resistance levels and non-resistant cells in advance from corresponding tissues of patients of different resistance levels and non-resistant cells, and then obtaining preliminary cellular mechanical force information of different cells of different resistance levels and non-resistant cells using a cellular mechanical force detection device.

[0134] Based on the cell mechanical force detection device of this invention, it has been demonstrated that not only can qualitative analysis be performed by intuitively distinguishing cells with the naked eye, but that cell states and types can be identified more intuitively and accurately (quantitative and qualitative analysis) based on the measured cell mechanical characteristics, and that cell types can be better distinguished using the cell force field as a marker.

[0135] Example 24: Method for identifying cells with different resistance levels under pH influence This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, and acquires cell mechanical force information and distinguishes between cells with different resistance levels and non-resistance levels based on the cell mechanical force information, specifically including the following: S1 After obtaining tumor tissue from lung cancer patients, the tissue is shredded and then broken down into single-cell primitive cells using collagenase and dispase.

[0136] S2 Multiple primitive cells are placed on the cell mechanical force detection device described above, and cultured together with the cell mechanical force detection device in a normal culture environment (pH 7.4, 37°C, 5% CO2). After the cells adhere to the cell mechanical force detection device, primitive cell mechanical force information is obtained for each cell.

[0137] The S3 culture medium is replaced with an alkaline culture medium (pH 8.1, 5% CO2), and the primitive changes in the mechanical forces of each cell are recorded in real time.

[0138] S4 The culture medium is then replaced with normal culture medium, and 10 μM of the drug EGFR-TKI is added to kill the cells. Cellular mechanical force information of dead cells (non-resistant cells) and non-dead cells (cells with different resistance levels) is recorded in real time. If the cell's mechanical force disappears, it indicates that the cell has entered apoptosis or has already died. The time point of cell apoptosis is recorded and the resistance level of each cell is estimated.

[0139] The drug treatment experiments in S5 and S4 allowed us to determine the resistance level of each cell. By comparing this with the cell mechanical force data obtained in S3, we can see that cells with higher resistance show a more dramatic change in cell mechanical force when the pH increases, while non-resistant cells show less change in cell mechanical force.

[0140] S6 Based on the above cellular mechanical force information, different tolerance levels can be identified for unknown cell types based on the dynamic changes in cellular mechanical forces during the transition from acidic to alkaline conditions in the culture medium during the culture process in steps S1-S5.

[0141] This embodiment allows for the amplification of changes in cellular mechanical forces due to changes in pH values, facilitating the identification of different types of cells and multicellular aggregates. (Figure) 44 As shown: Distribution and magnitude of cellular mechanical forces under different pH levels.

[0142] Example 25: Method for identifying cells with different resistance levels under temperature changes. This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, and acquires cell mechanical force information and distinguishes cells with different resistance levels based on the cell mechanical force information, specifically including the following: S1 After obtaining tumor tissue from lung cancer patients, the tissue is shredded and then broken down into single-cell primitive cells using collagenase and dispase.

[0143] S2 Multiple primitive cells are placed on the cell mechanical force detection device described above, and cultured together with the cell mechanical force detection device in a normal culture environment (37°C). After the cells adhere to the cell mechanical force detection device, primitive cell mechanical force information is obtained for each cell.

[0144] S3 After raising the culture environment temperature to 45°C, the changes in the cellular mechanical force of each cell are monitored and recorded in real time.

[0145] S4 The temperature is then returned to the normal culture temperature (37°C), and 10 μM of the drug EGFR-TKI is added to kill the cells. Cellular mechanical force information of dead cells (non-resistant cells) and non-dead cells (cells with different resistance levels) is recorded in real time. If the cellular mechanical force of a cell disappears, it indicates that the cell has entered apoptosis or has already died. The time point of cell apoptosis is recorded and the resistance level of each cell is estimated.

[0146] The drug treatment experiments in S5 and S4 allowed us to determine the resistance level of each cell. By comparing this with the cell mechanical force data obtained in S3, we can see that cells with higher resistance show more dramatic changes in cell mechanical force when the temperature rises, while non-resistant cells show less change in cell mechanical force.

[0147] This embodiment can amplify the changes in cellular mechanical forces due to temperature changes, facilitating the identification of different types of cells and multicellular aggregates.

[0148] Example 26: Method for identifying different resistance levels and non-resistant cells of the lung cancer cell line HCC827 This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, acquires cell mechanical force information, and identifies the lung cancer cell line HCC827 as a cell with the corresponding resistance level based on the cell mechanical force information. S1 After extracting tumor tissue from lung cancer patients with different resistance levels and non-resistance levels, the tissue is shredded, and the tissue is broken down using collagenase and dispase to obtain primary tumor cells. Primary tumor cells from resistant and non-resistant patients were cultured on a cell mechanical force detection device, and their cell mechanical force distribution was observed. It was found that the cell mechanical force distribution of tumor cells from patients with different resistance levels was uniformly distributed throughout the cell, while the mechanical force distribution of primary tumor cells from non-resistant patients was mainly concentrated around the cell membrane. (Figure) 45 and Figure 46 As shown in Figure 1, an optical detection device is used to obtain a visualization image of the cellular mechanical force distribution. 45 and Figure 46 Further analysis of the distribution map, 47 The distribution maps of cellular mechanical force strength for lung cancer cells with different resistance levels and non-resistance levels are obtained, where the horizontal axis represents the radius of the cell center and the vertical axis represents the average cellular mechanical force strength around the circumference corresponding to the radius on the horizontal axis. S3 If an unknown lung cancer cell line HCC827 with a different resistance level is cultured on the micropillar of the cell mechanical force detection device in any of the above examples, and it is identified that the information such as the cell's mechanical force distribution matches that of cells with a different resistance level, it can be distinguished as a cell with the corresponding resistance level.

[0149] In some specific examples, in obtaining cellular information of cells with different resistance levels in step S2, in order to quickly identify cells with different resistance levels, the lung cancer cell line HCC827 is cultured on a cell mechanical force detector, an EGFR-TKI drug is added, and changes in cell mechanical force are monitored in a timely manner. The culture medium is placed on top of a micropillar, and the EGFR-TKI drug is added (in some specific examples, the drug can be added periodically and quantitatively, for example, once every three days). Real-time monitoring is possible using an optical signal detector and optical signal analyzer (visual monitoring can be performed using a microscope as an optical signal detector). A photoactivatable fluorescent dye is added, the photoactivatable dye is excited by UV laser light, causing the cells to emit red fluorescence. All cells are removed from the cell mechanical force detector using trypsin, the fluorescent cells are separated using a flow cytometer, and the separated cells are cultured in a culture medium containing the EGFR-TKI drug. If they can survive completely, it can be confirmed that the selected cells have EGFR-TKI resistance characteristics. (Figure) 51 As shown.

[0150] figure 47 This is a distribution map of the cellular mechanical force intensity of resistant and non-resistant lung cancer cells. The horizontal axis represents the radius of the cell center, and the vertical axis represents the average cellular mechanical force intensity around the circumference corresponding to the radius on the horizontal axis.

[0151] figure 48~50 Please refer to the diagram. 48 This is a visualization of the distribution of cellular mechanical forces in breast cancer cells with different resistance levels. Figure 49 This is a visualization of the cellular mechanical force distribution of non-resistant breast cancer cells, and the figure shows 50 This is a distribution map of mechanical force strength for resistant and non-resistant breast cancer cells. The horizontal axis represents the radius of the cell center, and the vertical axis represents the average cellular mechanical force strength around the circumference corresponding to the radius on the horizontal axis.

[0152] As can be seen from the figure, there are very clear differences in the cellular mechanical forces between resistant and non-resistant breast cancer and lung cancer cells, and these can be quickly qualitatively determined by microscopic visualization. 45 ~Figure 50 The cellular information forms structured information, which is used to identify the resistance level of unknown cells.

[0153] According to the method of this embodiment, a single tumor tissue typically contains tumor cells with multiple different molecular and characterizable features, known as intratumor heterogeneity, which is significantly related to different degrees of resistance to anticancer drugs. By utilizing the distribution of cellular mechanical forces, it is possible to quickly determine whether a patient's primary tumor cells contain tumor cells with different resistance levels, and this can be used to predict the effect of the anticancer drug on the patient. Furthermore, the selected tumor cells with different resistance levels can be used to continue testing with other anticancer drugs, enabling personalized medicine.

[0154] Example 27: Method for identifying different resistance levels of lung cancer cell globules A549 This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, acquires cell mechanical force information, and identifies lung cancer cell spheres as having different resistance levels based on the cell mechanical force information: S1 Primary tumor cells from patients with different resistance levels and non-resistance levels are cultured on a cell mechanical force detection device, the distribution of cell mechanical forces is observed, a machine learning model is trained via a cell mechanical dynamics database to form a cell characteristic model, and the obtained cell characteristic model is used to automatically identify cells with different resistance levels from unknown lung cancer cell spheres.

[0155] S2 After extracting tumor tissue from lung cancer patients, the tissue is shredded and then broken down using collagenase and dispase to obtain primary tumor cells. The tumor cells are cultured in an ultra-low attachment multiple well plate to grow into multicellular spheres. In step S3, multicellular spheres of lung cancer cell line A549 are cultured on a cell mechanical force detection device, and EGFR-TKI drugs are added. Changes in cell mechanical force are monitored in a timely manner, and if the computer (including the cell feature model from step S1) identifies that the mechanical force distribution of the cells is close to that of non-resistant multicellular spheres (large change in mechanical force), a photoactivatable fluorescent dye is added, and the photoactivatable dye is detected by UV laser light. The multicellular spheres are excited with a dye to cause red fluorescence, and then all multicellular spheres are removed from the cell mechanical force detector using trypsin and broken down into single cells. Fluorescent cells are then selected using a flow cytometer, and the selected cells are cultured again into multicellular spheres. After culturing in a culture medium containing an EGFR-TKI drug, if they can survive completely, it can be confirmed that the selected multicellular spheres are EGFR-TKI resistant cells. The selected EGFR-TKI resistant multicellular spheres are then cultured and enlarged, and then cultured in a porous plate containing a cell mechanical force detector or a general porous plate. Drug selection is then performed using a compound library to find drugs that reverse the mechanical distribution of resistant cells or kill EGFR-TKI resistant multicellular spheres.

[0156] As can be seen from the method described above, the resulting cell feature model can distinguish between multicellular spheres with different resistance levels in a short amount of time.

[0157] Example 28: Method for identifying cells with different resistance levels This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, acquires cell mechanical force information, and identifies the corresponding resistance level of unknown cells based on the cell mechanical force information.

[0158] This embodiment provides a method for detecting cellular mechanical forces using a mechanical force microscope to identify cells with different resistance levels and non-resistance levels, the steps being: S1 Polydimethylsiloxane (PDMS) solution is applied to the bottom of the culture dish, the PDMS surface is treated with silane, fluorescent nanobeads are added on top of it, and fibronectin is applied on top of the nanobeads. S2 EGFR-TKI-resistant cells with different resistance levels are cultured in culture dishes prepared in the above steps. Fluorescent nanobeads are imaged using a fluorescence microscope, and cell morphology is imaged using bright-field imaging. One image is taken every 15 minutes, and the cells are monitored for 24 hours. The positional changes of the nanobeads are analyzed to calculate the strength and distribution of the cellular mechanical force of cells with different EGFR-TKI resistance levels, thereby obtaining information on the cellular mechanical force of cells with different EGFR-TKI resistance levels. In step S3, the same procedure as in step S2 is performed on unknown cells to obtain cellular mechanical force information of cells with different resistance levels, construct a cellular characteristic model, and automatically identify the resistance level of the unknown cells.

[0159] Example 29: A method for selecting a type of precision therapeutic agent. This embodiment provides a cell mechanical force detection device or detection system or a cell mechanical force detection method in any of the embodiments described above, and acquires cell mechanical force information and precisely selects non-resistant drugs based on the cell mechanical force information. The specific steps are: S1 Using the method of the eighteenth example, cellular mechanical force information of cells with different resistance levels corresponding to multiple specific drugs is obtained. In step S2, the test cells are placed on a cell mechanical force detection device (in some other specific implementation methods, the culture medium is placed on a micropillar, and the cells are transplanted and cultured in the medium), cell mechanical force information of the test cells is obtained, and by comparing it with the cell mechanical force information from step S1, the resistance status of the test cells to multiple specific drugs is rapidly determined, enabling precise selection.

[0160] As an option, to enable AR automatic identification: S1 The method of the eighteenth embodiment is used to obtain cellular information from cells with different resistance levels corresponding to multiple specific drugs. The cellular information includes cellular mechanical force information at a point within the cell obtained based on a cellular mechanical force detection device, and the cellular mechanical force information includes the magnitude of the cellular mechanical force at that point. Specifically, cellular information is collected from multiple cells on the cellular mechanical force detection device using an optical signal detection device (or in combination with an optical signal analyzer), and this includes collecting cellular mechanical force magnitude information for multiple points within each cell, thereby obtaining multipoint cellular mechanical force magnitude data from multiple cells. S2 Preprocessing is performed on the cell information of cells with different resistance levels and non-resistance levels corresponding to different drugs obtained to form structured cell information. The structured cell information includes the number of cells, the number of cell features, and the characteristic information of each cell feature. At this time, the structured cell information can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. In this embodiment, P=1, meaning the cell feature is the magnitude of the cell's mechanical force. S3 uses structured cell information as input data, constructs cell feature models using supervised, unsupervised, or semi-supervised machine learning, applies the cell feature models to classify or cluster the test cells, achieves automatic identification of corresponding resistance levels to different drugs, selects drugs with corresponding resistance levels for the test cells, and achieves precise identification.

[0161] In some other embodiments, the method for identifying cells with different resistance and non-resistance further includes the direction of the cellular mechanical force at the point in question. In some other embodiments, the method for identifying cells with different resistance and non-resistance further includes the change in the magnitude or direction of the cellular mechanical force at the point in question within a certain time interval. In some other embodiments, the method for identifying cells with different resistance and non-resistance further includes cellular morphological information. In some other embodiments, cellular mechanical force information of known cells with different resistance and non-resistance is obtained by the method of Embodiment 18. In some other embodiments, obtaining cellular mechanical force information of cells with different resistance and non-resistance involves: obtaining known cells with different resistance and non-resistance from corresponding tissues of patients with different resistance and non-resistance in advance, and then obtaining preliminary cellular mechanical force information of cells with different resistance and non-resistance using a cellular mechanical force detection device.

[0162] Example 30: Method for identifying cells based on drug sensitivity (using only cell mechanical force magnitude; some data are labeled, some data are unlabeled) The following steps are included: S1. Cell information is obtained from multiple cells of different resistance and non-resistance levels, where some cells are of multiple known cell types or known cell states, and the remaining cells are of unknown cell types or unknown cell states. The cell information is the magnitude of the cellular mechanical force at a point in the cell, obtained based on a cell mechanics sensor. Specifically, this step includes: collecting information from the above multiple cells using a cell mechanics sensor, including collecting information on the magnitude of the cellular mechanical force at multiple points in each cell, thereby obtaining multipoint cellular mechanical force magnitude data in multiple cells. S2 Preprocessing is performed on the acquired cell mechanical force magnitude information to form structured cell information. The structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature. At this time, the structured cell information JPEG2026511270000048.jpg59 can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. In this embodiment, P=1, meaning the cell feature is the magnitude of the cell's mechanical force. S3 Using the above structured cell information as input data, a cell feature model is constructed using semi-supervised machine learning. After training the cell feature model with a large amount of structured cell information (including both labeled and unlabeled cells), the cell feature model is applied to the classification / clustering of cells of unknown types or states.

[0163] In some other implementations similar to this embodiment, optimization or improvement can be made in the following way: For a single cell, further information processing is performed on the acquired multipoint cell mechanical force magnitude information, for example: the average value of the cell mechanical force magnitude per unit area, the distribution of the cell mechanical force magnitude within the cell, and other equal-dimensional information are calculated and added as new cell features to the two-dimensional feature matrix in step S2, i.e., the contents of P are expanded, and subsequent machine learning can determine which features can better distinguish between resistant and non-resistant cells.

[0164] Example 31 A method for identifying cells based on a type of drug sensitivity (magnitude and direction of cellular mechanical forces, label-free). This embodiment provides a method for identifying unlabeled cells based on the magnitude and direction of cellular mechanical forces, and includes the following steps: S1, a step of acquiring cell information, wherein the cell information includes the magnitude and direction of the cellular mechanical force at a certain point in the cell, acquired based on a cell dynamics sensor, specifically: a cell dynamics sensor (in this embodiment, a nano-micro-column sensor) is used to collect cell information from multiple cells, which includes collecting information on the magnitude and direction of the cellular mechanical force at multiple points in each cell, thereby acquiring multipoint cellular mechanical force magnitude and direction data in multiple cells, S2, a step in which the information on the magnitude and direction of the mechanical force on the cell is preprocessed to form structured cell information, wherein the structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature, and the structured cell information at this time is JPEG2026511270000049.jpg59 can be considered as a two-dimensional feature matrix, where N is the cell number and P is the cell feature number. In this embodiment, P=2, meaning the cell features are: magnitude of the cell mechanical force, direction of the cell mechanical force, step, S3, a step in which the structured cell information is used as input data, a cell feature model is constructed using unsupervised machine learning, and the cell feature model is applied to clustering of cells of an unknown type or state.

[0165] Specifically, step S2 in this embodiment processes the cell information as follows: obtain cellular mechanical force vector data (magnitude and direction) for a total of n points within a cell, and these point positions are: ( JPEG2026511270000050.jpg721) is a two-dimensional coordinate system. JPEG2026511270000051.jpg921 and It corresponds to JPEG2026511270000052.jpg1021, and within that, t0 and t n These correspond to the initial and displaced point positions of the nano-columns, respectively. JPEG2026511270000053.jpg521 shows the direction of force at each coordinate point. Furthermore, each coordinate point has a single piece of reference information, namely the magnitude of the force d. In this way, the cell axis direction and center point coordinates can be estimated from the existing data, and each cell can be organized into a single vector of homologous length based on this.

[0166] For example, based on each vertex within each cell, the center point can be calculated as follows: JPEG2026511270000054.jpg1021. The cell axis is calculated using the two points that are furthest apart. JPEG2026511270000055.jpg1421 and Locate JPEG2026511270000056.jpg1421 and obtain the cell axis using the following formula: JPEG2026511270000057.jpg1580.

[0167] figure 52 Please refer to the diagram. 52 This is a schematic diagram of the standardization process of displacement information at a given point location in the fourth embodiment of the present application, where each point in the diagram represents a single point location, and the depth of color at each point location represents the range from shallow to deep, and the magnitude of the force represents the range from small to large. After obtaining the cell axis of each cell, further quantification processing can be performed on each point location: the displacement vector of each point and the angle between the cell axis are calculated as θ. Each point location can further be associated with the magnitude of the force (standard), for example, the magnitude of the force d can be considered as a weight, thereby making each point location a single standard The image is processed as JPEG2026511270000058.jpg1021. In this way, the cellular information of each cell is a single vector. It can be organized as JPEG2026511270000059.jpg821.

[0168] In some other implementations, implementations similar to this embodiment can be optimized or improved by the following method: For a single cell, further information processing is performed on the acquired multi-point cell mechanical force magnitude or cell mechanical force direction information, for example, by calculation: the average value of the cell mechanical force magnitude per unit area, the distribution of cell mechanical force magnitude within the cell, the distribution of cell mechanical force vectors within the cell, and other equal-dimensional information, which are added as new cell features to the two-dimensional feature matrix in step S2, i.e., the contents of P are expanded, and subsequent machine learning is used to determine which features can better distinguish cells of different types or states. Thirty-second embodiment A method for identifying cells based on a type of drug sensitivity (magnitude and direction of cellular mechanical forces, labeled), comprising the following steps: S1, obtain cellular information of cells of multiple known cell types or known cellular states, the cellular information being the magnitude and direction of cellular mechanical forces at a point within the cell obtained based on a cellular mechanical sensor, specifically: collect information on the multiple types of cells using a cellular mechanical sensor, which includes collecting information on the magnitude of cellular mechanical forces at multiple points in each cell, thereby obtaining data on the magnitude of cellular mechanical forces at multiple points in multiple cells. S2, Preprocessing is performed on the acquired information on the magnitude of the cell mechanical force to form structured cell information. The structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature. At this time, the structured cell information JPEG2026511270000060.jpg59 can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. Here, P=2, meaning the cell features are: the magnitude of the cellular mechanical force and the direction of the cellular mechanical force. S3, using the aforementioned structured cell information as input data, a cell feature model is constructed using supervised machine learning, and the cell feature model is trained using a large amount of structured cell information. Subsequently, the cell feature model is applied to classify cells of an unknown type or state. For example, in this embodiment, the Random Forest (RF) algorithm is used to extract significant features and estimate model parameters, and then it is applied to new cells to estimate labels corresponding to the new cells, i.e., the cell feature model is applied to classify cells of an unknown type or state. On the other hand, in other embodiments, it is also possible to complete the corresponding model construction and training work by employing machine learning algorithms / concepts such as Support Vector Machines (SVM) or deep learning.

[0169] figure 52 and figure 53 Please refer to the figure. 52 and figure53 Figures A and B show the results of using a cell feature model constructed in an extended embodiment of the fifth embodiment of the present application to identify unknown cells or unknown cell characteristic evaluation types. In Figure A, different rows represent different cell types, and different columns represent different samples. The black dots in the figure are the top 50 significant features extracted using a random forest algorithm, or they can also be called significant point locations. Here, a point location refers to a specific location within a single cell, and the cellular mechanical force information obtained from different locations is different. On the other hand, Figure B shows a remarkable identification effect for three different cell types using the top 50 significant features (significant point locations). Significant features learned based on labeled data can be used for dimensionality reduction visualization of the data. Subsequently, it is also possible to classify and identify cells based on the dimensionality-reduced data via a clustering algorithm.

[0170] In several other embodiments similar to this embodiment, optimization or improvement can be performed in the following manner: For a single cell, further information processing is performed on the acquired multi-point information on the magnitude or direction of cellular mechanical forces, for example: calculating dimensional information such as the average value of the magnitude of cellular mechanical force per unit area, the distribution of the magnitude of cellular mechanical force within the cell, and the distribution of cellular mechanical force vectors within the cell, and adding this as new cell features to the two-dimensional feature matrix in step S2, i.e., expanding the contents of P, and then, through subsequent machine learning, it can be determined which features can better distinguish cells of different types or states.

[0171] The 33rd example: A method for identifying cells based on a type of drug sensitivity (magnitude and direction of cellular mechanical forces, some data labeled, some data unlabeled), comprising the following steps: S1, Cellular information is acquired from multiple cells, some of which are of multiple known cell types or known cellular states, and the remaining cells are of unknown cell types or unknown cellular states. The cellular information is the magnitude of the cellular mechanical force at a point within the cell, acquired based on a cell dynamics sensor. Specifically: Information is collected from the multiple types of cells using a cell dynamics sensor, which includes collecting information on the magnitude of the cellular mechanical force at multiple points in each cell, thereby obtaining data on the magnitude of the cellular mechanical force at multiple points in multiple cells. S2, Preprocessing is performed on the acquired information on the magnitude of the cell mechanical force to form structured cell information. The structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature. At this time, the structured cell information JPEG2026511270000061.jpg59 can be considered as a two-dimensional feature matrix, where N is the number of cells and P is the number of cell features. Here, P=2, meaning the cell features are: the magnitude of the cellular mechanical force and the direction of the cellular mechanical force. S3, using the aforementioned structured cell information as input data, a cell feature model is constructed using semi-supervised machine learning, and the cell feature model is trained using structured cell information from a large number of cells. Subsequently, the cell feature model is applied to classify cells of unknown types or states.

[0172] In several other embodiments similar to this embodiment, optimization or improvement can be performed in the following manner: For a single cell, further information processing is performed on the acquired multi-point information on the magnitude or direction of cellular mechanical forces, for example: calculating information in dimensions such as the average value of the magnitude of cellular mechanical force per unit area, the distribution of the magnitude of cellular mechanical force within the cell, and the distribution of cellular mechanical force vectors within the cell; adding this as new cellular features to the two-dimensional feature matrix in step S2, i.e., expanding the contents of P; and then, through subsequent machine learning, it can be determined which features better distinguish cells of different types or states.

[0173] Example 34 A method for identifying cells based on a certain drug sensitivity (the instantaneous value of the cell mechanical force vector, the change situation of the cell mechanical force vector within a certain time interval, without labels), including the following steps: S1. Obtain cell information. The cell information is the instantaneous value of the vector of the cell mechanical force at a certain point inside the cell obtained based on a cell mechanics sensor and the change situation of the cell mechanical force vector at this point within a certain time interval. Specifically, it includes using a cell mechanics sensor to collect cell information for a plurality of cells, which involves collecting information on the magnitude and direction of the cell mechanical force at multiple points for each cell, thereby obtaining data on the magnitude and direction of the cell mechanical force at multiple points in a plurality of cells. S2. Perform preprocessing on the obtained information on the magnitude and direction of the cell mechanical force to form structured cell information. The structured cell information includes the number of cells, cell feature numbers, and feature information for each cell feature. At this time, the structured cell information can be regarded as a two-dimensional matrix of a feature matrix (Feature matrix) of JPEG2026511270000062.jpg59, where N is the number of cells and P is the cell feature number.

[0174] S3. Use the above-mentioned structured cell information as input data, utilize unsupervised machine learning to construct a cell feature model, and apply the cell feature model to the clustering of cells of unknown type or unknown state.

[0175] [[ID=十一年]] In this embodiment, what is acquired is not only the instantaneous value of the cell mechanical force vector at a certain point position inside the cell, but also the interval situation within a certain time interval thereof. Therefore, the obtained mechanical data of a single cell can actually be analogous to an image (instantaneous value) or a video (change situation within the time dimension). Thus, if the current cell-covered underlying dot matrix can be made analogous to the pixels in the image, and the information recorded at each point position (such as the magnitude and direction of the force, and other multiple cell characteristics) can be made analogous to the color corresponding to the pixel, then in subsequent machine learning, machine learning algorithms in the field of image and video data processing can be referred to. For example, machine learning widely applied to image recognition, more precisely, convolutional neural network (CNN) in deep learning can be adopted to perform data modeling analysis.

[0176] The thirty-fifth embodiment: A method for identifying cells with different drug resistance degrees (instantaneous value of the cell mechanical force vector, change situation of the cell mechanical force vector within a certain time interval, with labels), including the following steps: S1. Obtain cell information of multiple known cell types or known cell states. The cell information is the instantaneous value of the cell mechanical force vector at a certain point inside the cell obtained based on a cell mechanics sensor and the situation in which the cell mechanical force vector at that point changes within a certain time interval. Specifically, it includes: using a cell mechanics sensor to collect cell information for multiple cells, which includes collecting information on the magnitude and direction of the cell mechanical force at multiple points of each cell, thereby obtaining data on the magnitude and direction of the cell mechanical force at multiple points in multiple cells. S2. Perform preprocessing on the obtained information on the magnitude and direction of the cell mechanical force to form structured cell information. The structured cell information includes the number of cells, the number of cell characteristics, and the characteristic information of each cell characteristic. At this time, the structured cell information can be regarded as a two-dimensional matrix of a feature matrix (Feature matrix), where N is the number of cells and P is the number of cell characteristics.

[0177] S3, the aforementioned structured cell information is used as input data, a cell feature model is constructed using supervised machine learning, and the cell feature model is trained using structured cell information from a large number of cells. Subsequently, the cell feature model is applied to classify (i.e., identify; in this application, "identification" should be understood as identification in a broad sense, and includes not only "classification," i.e., determining the type or state of a cell, but also "clustering," i.e., clustering cells that are of the same or similar type or state, which may have the same or similar properties, even though the specific state or type of the cell is unknown). For example, in this embodiment, the Random Forest (RF) algorithm is employed to extract significant features and estimate model parameters, and then it is applied to new cells to estimate labels corresponding to the new cells, i.e., the cell feature model is applied to classify cells of an unknown type or state. On the other hand, in other embodiments, it is also possible to complete the corresponding model construction and training work by employing machine learning algorithms / concepts such as Support Vector Machines (SVM) or deep learning.

[0178] In this embodiment, the acquired data includes not only the instantaneous value of the cellular mechanical force vector at a specific point within the cell, but also the interstitial conditions within a certain time interval. Therefore, the acquired single-cell mechanical data can be made to resemble an image (instantaneous value) or a video (multiple instantaneous values ​​within a certain time dimension). This allows the underlying dot matrix covered by the current cell to be likened to pixels in an image, and the information recorded at each point (various cellular characteristics such as force magnitude and direction) to be likened to the color corresponding to the pixel. This allows subsequent machine learning to refer to machine learning algorithms in the fields of image and video data processing. For example, data modeling analysis can be performed using machine learning widely applied to image recognition, or more precisely, using a Convolutional Neural Network (CNN) in deep learning.

[0179] Thirty-sixth embodiment A method for measuring physical properties of a substance and a cell / multicellular aggregate, including cellular hardness, by a type of magnetic force. This embodiment specifically provides a biophysical property evaluation apparatus, including: a base, and a micropillar array consisting of a plurality of micropillars mounted on the base that can undergo a change of state upon magnetic force, the change of state including at least one of tension, activity in each direction, and deformation, the micropillar including a bottom end connected to the base, sides, a columnar body enclosed by the sides, and a top end far from the base and opposite the bottom end, a light-reflecting layer is provided on the top end and / or sides of the micropillar, and a magnetic material is provided at any position within the top end, sides, and columnar body of the micropillar, preferably a magnetic light-reflecting layer is provided on the top end of the micropillar. In this application, magnetic material refers to a material that reacts in some way to a magnetic field, and includes, but is not limited to, ferromagnetic materials, paramagnetic materials, diamagnetic materials, ferrimagnetic materials, antiferromagnetic materials, and superparamagnetic materials. The magnetic light-reflecting layer is a magnetic metal-reflecting layer.

[0180] When the characterization apparatus provided in this application characterizes cells or multicellular aggregates, the micropillars can penetrate the cells or multicellular aggregates. The penetrating force includes, but is not limited to, the gravity of the cells or multicellular aggregates, the adhesive force generated on the cells or multicellular aggregates by a substance having a mechanism that promotes cell adhesion, and applying pressure to the micropillars or cells or multicellular aggregates. In particular, after the micropillars penetrate the cells or multicellular aggregates, the magnetic metallic light-reflecting layer is subjected to magnetic force and pulled towards the micropillars, causing at least one type of activity or deformation in each direction.

[0181] In some specific embodiments, an anti-reflective layer is provided on the sides of the micropillar and / or the surface of the base. This provides a significant improvement in the signal-to-noise ratio for biological characterization, making the measurement results more accurate. In some specific embodiments, the biophysical characterization apparatus further includes a plurality of cell-restricting structures mounted on the base. In some specific embodiments, a cell-interacting substance is provided on at least one of the base, micropillar, and restricting surface. In some specific embodiments, the cell-interacting substance is provided on the apex of the micropillar, and micropillars with multiple cell-interacting substances form a first predetermined pattern, and / or an anti-cell-adhesion substance is provided on the apex of the micropillar, and micropillars with multiple anti-cell-adhesion substances form a second predetermined pattern. The first predetermined pattern may be identical or different from the second predetermined pattern, and such naming in this application is solely for the purpose of distinguishing and identifying the patterns formed as a whole by micropillars with cell-interacting substances and anti-cell-adhesion substances, respectively. In some specific embodiments, a fluorescent substance is provided on the extension from the apex to the base of the micropillar. The extension includes the sides of the micropillar and the solid portion within the columnar body enclosed by the sides. By installing it in this manner, the objective of more intuitively calibrating cells or multicellular aggregates, amplifying the signal, and improving the signal-to-noise ratio can be achieved. At the same time, if the cells or multicellular aggregates to be characterized are stained with a fluorescent substance of a different color, the hardness of the cells or multicellular aggregates can be determined by the depth of penetration.

[0182] This embodiment specifically provides a single biophysical characterization system, which includes: one or more multimodal biophysical characterization devices according to the first aspect of the present application; an optical signal emitter, the optical signal emitter is used to emit a predetermined ray; an optical signal detector, the optical signal detector is used to detect a ray reflected from a light reflection layer; and a magnetic field generator, the magnetic field generator is used to generate a magnetic field and create a magnetic effect with a magnetic material; the ray emitted by the optical signal emitter passes through an incident optical path and irradiates the light reflection layer; and the ray reflected by the light reflection layer enters the optical signal detector via a reflected optical path.

[0183] In some specific embodiments, the biophysical characterization system further includes an optical signal analyzer, which is used to analyze the optical signal. In some specific embodiments, the biophysical characterization system further includes a data processing device, which is used to process the optical signal to obtain results and spatial distribution of cellular mechanical force and / or cellular hardness. In some specific embodiments, the data processing device may also provide predictive information on cell behavior and differentiation direction based on the results and spatial distribution of cellular mechanical force and / or cellular hardness. In some specific embodiments, the characterization system further includes a pressure application device, which is used to apply pressure to the cells. The pressure application device includes, but is not limited to, weights, automated pressure devices, probes, etc. In some specific embodiments, the optical signal emitter has a light source, which includes a first light source installed at the bottom of the base and / or a second light source installed on the side of the base, and the rays emitted from the first and / or second light sources reach the micropillar. In some specific embodiments, the first and second light sources are not limited to the order or type of installation of the light sources, but are merely used to distinguish light sources at different installation locations. Preferably, the second light source is a waveguide illumination light source. In some specific embodiments, the biophysical characterization system further includes a device that induces motion or deformation, which is used to apply mechanical force to the multimodal biophysical characterization system. The device that induces motion or deformation includes, but is not limited to, a tensioning device or a filling / exhausting device, which is used to apply appropriate mechanical tensile force to the base to control the horizontal stretching motion or deformation of the base, and a filling / exhausting device is used to create motion or deformation on a curved surface in the base.

[0184] In some specific embodiments, the base and / or micropillars are made of conductive material. The purpose of using conductive material for the base and / or micropillars is to enable electrical stimulation during the characterization process of cells or multicellular aggregates, as well as to achieve the purpose of receiving electrical signals from cells. In some specific embodiments, the biophysical characterization system is composed of two or more biophysical characterization devices to form a bimodal or polyhedral structure, where the outside of the bimodal or polyhedral structure is the base and the inside surrounds the three-dimensional accommodating cavity of cells. In some specific embodiments, when the bimodal or polyhedral structure is composed of two or more multimodal biophysical characterization devices, the cell restriction structure is adapted. In particular, before, during, or after the physical characterization of cells or multicellular aggregates, the sample to be measured may be subjected to stimuli of different types and intensities, including but not limited to mechanical, electrical, and optical stimuli. Furthermore, operations including but not limited to labeling, fixation, ablation, dissection, extraction, and sorting may be performed on the sample to be measured at a specific location or region, and comparative analysis may be performed in conjunction with other characterization methods, including but not limited to protein staining, histochemical staining, and single-cell sequencing.

[0185] In several specific embodiments, the micropillars are configured to deform simultaneously by cellular mechanical forces, which can be used to acquire these forces. Specific structural functions and applications are shown in the first to thirty-sixth embodiments. In this case, the magnetic metal reflective layer, acting as a light reflective layer, can also acquire the deformation of the micropillars by cellular mechanical forces under the action of a light source, converting the cellular mechanical force information into an optical signal for detection of cellular mechanical forces. That is, if the light reflective layer is a magnetic metal reflective layer, it can be used not only for detecting cellular mechanical forces but also for simultaneously detecting cell stiffness under the action of a magnetic field generator. Simultaneous detection of mechanical force and stiffness can provide a more comprehensive acquisition of overall cellular information and improve the accuracy of predictions and judgments related to cells / multicellular aggregates, such as cell or multicellular aggregate typing and growth state. It can be used to determine or predict interactions between drugs and cells / multicellular aggregates, to type for drug sensitivity, and to predict cell behavior through changes and dynamic trends in the mechanical force and stiffness of cells or multicellular aggregates, including but not limited to state, differentiation, proliferation, migration, and apoptosis.

[0186] Overall, this embodiment enables multimodal, high-resolution, high-throughput, high-sensitivity, low-cost, and low-damage physical characterization of single cells or multicellular aggregates. It allows for the application of different types and intensities of stimuli (e.g., mechanical, electrical, or optical stimulation) to the sample being measured, better mimicking the in vivo microenvironment, and performing operations (e.g., labeling, fixation, ablation, dissection, extraction, or sorting) on ​​samples at specific locations or regions, and combining these with other characterization methods (e.g., protein staining, histochemical staining, or single-cell sequencing) for comparative analysis. The multimodal biophysical characterization apparatus provided in this application is suitable for fields such as synthetic biology, diagnostics, drug discovery, early tumor screening, cell therapy, and precision medicine.

[0187] Finally, it should be noted that the above embodiments are solely for the purpose of illustrating, and not limiting, the technical solutions of the present application, and while a detailed description of the present application has been given with reference to the above embodiments, it should be understood that modifications can still be made to the technical solutions described in the above embodiments, or that some or all of the technical features therein can be replaced by equivalent substitutions, and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying cell drug resistance, A step of obtaining cytophysical information including cellular mechanical force and / or cellular stiffness, A method for identifying cellular drug resistance, characterized by comprising the step of identifying cells with different drug resistance levels and non-resistant cells based on the cellular physical information.

2. The cellular mechanical force includes one or more of the magnitude, direction, distribution, and frequency of the cellular mechanical force, and preferably includes one or more of the temporal changes in the magnitude, direction, distribution, and frequency of the cellular mechanical force. The aforementioned cell stiffness includes the magnitude of cell stiffness at a specific location within the cell, and preferably includes the magnitude, spatial distribution, and temporal changes of stiffness of different layers at a specific location within the cell. Preferably, the cytophysical information includes pre-drug action, during action, and / or post-drug action. Preferably, the identification method according to claim 1, wherein the cytophysical information includes cytomechanical forces and / or cell stiffness obtained in at least one of the following: interactions between cells and multicellular aggregates, interactions between cells and multicellular aggregates, the action of a substance on cells and / or multicellular aggregates, and the action of other physical, biological or chemical factors on cells and / or multicellular aggregates.

3. The identification method according to claim 2, characterized in that the cytophysical information includes one or more combinations of the following: cell number, cellular mechanical force, cell stiffness, and cell morphology.

4. The identification method according to claim 1, further comprising the step of quantifying the drug resistance of cells with different drug resistance levels.

5. The process of obtaining the aforementioned cellular physical information further includes the step of applying stimuli of different types and intensities, The stimulus is optionally one or more combinations of physical stimuli, chemical stimuli, and biological stimuli. The identification method according to claim 1, characterized in that, optionally, the physical stimulus, chemical stimulus, and biological stimulus include a combination of one or more stimulus types from among drugs, mechanical force, hardness, biochemical factors, electric fields, flow fields, chemotactic induction, and radiation.

6. The identification method according to claim 1, further comprising the step of comparing and analyzing the results identified based on the cellular physical information with biochemical and / or optical characterization results, wherein the biochemical and / or optical characterization includes at least one of protein staining, histochemical staining image characterization and single-cell sequencing.

7. The step includes characterization using a cell characterization system, the cell characterization system includes a cell mechanical force detection device for obtaining cell mechanical force, and the cells have different drug resistance levels to specific drugs. The cell mechanical force detection device includes a base and a micropillar array consisting of one or more micropillars that are deformable under the action of cell mechanical forces and are installed on the base. The identification method according to claim 1, characterized in that the micropillar includes a bottom end connected to a base, a side, a columnar body enclosed by the side, and a top end that is separated from the base and facing the bottom end, and the micropillar has a light-reflecting layer, and optionally the base and the columnar body of the micropillar have light-transmitting portions, and the top end of the micropillar has a light-reflecting layer.

8. The cell characterization system further includes a light signal generator and a light signal detection device, The optical signal generating device has a light source, and the light rays emitted from the light source are irradiated onto the light reflection layer through the incident light path. The optical signal detection device is used to detect light rays reflected from the light reflection layer, and the light rays reflected from the light reflection layer enter the optical signal detection device via the reflected light path. The identification method according to claim 7, characterized in that, optionally, the light intensity acquired by the optical signal detection device and the cellular mechanical force have a linear correlation within a certain range.

9. The cell characterization system further includes an optical signal analyzer, which forms the optical signal analysis results as visualization information or data. The identification method according to claim 8, wherein, optionally, the optical signal analyzer is used to calculate the cellular mechanical force of each micropillar based on the attenuation of reflected light intensity and to determine the type, state, and behavior of the detected cells based on a preset model.

10. A cell identification and classification system based on drug sensitivity, comprising an information acquisition unit and a pre-processing unit, The information acquisition unit is used to acquire cellular physical information, and the cellular physical information includes cellular mechanical force and / or cellular stiffness. A cell identification and classification system characterized in that the preprocessing unit is used to preprocess the cell physical information and form structured cell information.

11. The cellular mechanical force includes one or more of the magnitude, direction, distribution, and frequency of the cellular mechanical force, and preferably includes one or more of the temporal changes in the magnitude, direction, distribution, and frequency of the cellular mechanical force. The aforementioned cell stiffness includes the magnitude of cell stiffness at a specific location within the cell, and preferably includes the magnitude, spatial distribution, and temporal changes of stiffness in different layers at a specific location within the cell. Preferably, the cell identification and classification system according to claim 10 is characterized in that the cytophysical information includes pre-drug action, during action, and / or post-drug action.

12. The cell identification and classification system according to claim 10, characterized in that the cytophysical information includes one or more combinations of the following: cell number, cellular mechanical force, cell stiffness, and cell morphology.

13. The cell identification and classification system further includes a learning unit and an identification unit, The learning unit is used to construct a cell feature model using supervised, unsupervised, or semi-supervised machine learning with the structured cell information as input data. The cell identification and classification system according to claim 10, characterized in that the identification unit is used to apply the cell characteristic model to the classification or clustering of cells based on drug sensitivity, and to realize the identification of cell types based on drug sensitivity.

14. A method for obtaining cellular physical information based on drug sensitivity, wherein the cellular physical information includes cellular mechanical force and / or cellular stiffness. A step of taking multiple primitive cells and obtaining cellular physical information from each primitive cell. A step of performing a specific drug screening on the aforementioned primitive cells to obtain cells with different levels of drug resistance, A step of obtaining cytophysical information of cells with different drug resistance levels, The steps include comparing the cellular mechanical force information of cells with different drug resistance levels with the cellular mechanical force information of corresponding primitive cells, designating cells whose decrease in cellular mechanical force during and / or after drug treatment is less than a first preset threshold as resistant cells, and cells whose decrease is greater than or equal to a second preset threshold as non-resistant cells, or The steps include comparing the cell stiffness of cells with different levels of drug resistance with the corresponding cell stiffness of the primitive cells, designating cells whose reduction in cell stiffness during and / or after drug treatment is less than a third predetermined threshold as resistant cells, and cells whose reduction is greater than or equal to a fourth predetermined threshold as non-resistant cells. A method for obtaining cytophysical information based on drug sensitivity, characterized by comprising the step of obtaining cytophysical information of resistant and non-resistant cells of the aforementioned specific drug.

15. A method for identifying and classifying cells based on drug sensitivity, wherein the cell physical information of the cells is acquired and used for identification and classification, and the cell physical information includes cell mechanical force information. Optionally, the cellular mechanical force information includes at least one of the cellular mechanical force information obtained before, under, and after the action of an external factor. Selectively classify cells based on different levels of drug resistance. Optionally, the cell classification includes normal cells and pathological cells, activated cells and inactivated cells. A method for identifying and classifying cells based on drug sensitivity, characterized in that, at random selection, the cellular mechanical force of EGFR-mutated resistant cells is large and uniformly distributed throughout the cell, while the cellular mechanical force of non-resistant cells is small and concentrated in the peripheral region of the cell.

16. A step of obtaining cellular physical information of a cell based on drug sensitivity, wherein the cellular physical information includes cellular mechanical force information and cell stiffness information at a point within the cell, the cellular mechanical force information includes the magnitude, direction, frequency, distribution, and dynamic changes over time of the cellular mechanical force at a specific location in the cell or multicellular aggregate, and the cell stiffness information includes the magnitude, spatial distribution, and temporal changes of the stiffness of different layers at a specific location within the cell. A step of preprocessing the cell physical information to form structured cell information, wherein the structured cell information includes the number of cells, the number of cell features, and characteristic information for each cell feature. The method for identifying and classifying cells based on drug sensitivity according to claim 15, comprising the steps of: constructing a cell feature model using supervised, unsupervised, or semi-supervised machine learning with the structured cell information as input data; and applying the cell feature model to the classification or clustering of cells based on drug sensitivity of an unknown type or state.

17. An application of the identification method according to any one of claims 1 to 9, the drug-sensitive cell identification and classification system according to any one of claims 10 to 13, the drug-sensitive cell and / or multicellular aggregate cell-mechanical force acquisition method according to claim 14, and the drug-sensitive cell identification and classification method according to claim 15 or 16, wherein the application includes high-precision drug screening, resistant cell screening, and drug discovery.

18. To obtain the cellular mechanical forces of two or more cells before, during, and after drug action, classify cells and / or multicellular aggregates, predict drug efficacy, and achieve highly accurate screening of therapeutic agents. or This includes obtaining the cellular mechanical forces of a single cell before, during, and after the action of different drugs, classifying cells in relation to different drugs, screening for the most effective drug or drug combination, and achieving high-precision screening of therapeutic agents. Selectively, physical, biological, and / or chemical stimuli are applied to test cells, and information on cellular mechanical forces and / or dynamic changes before and after different conditions and stimuli is obtained, respectively, to identify stimuli and microenvironmental conditions that can expand different cell types. The application according to claim 17, characterized in that different therapeutic agents are applied to tumor cells and normal cells at the discretion of selection, cellular mechanical force information is obtained for each, and therapeutic agents and their concentrations are identified that have no killing effect or only a small effect on normal cells, but are effective on tumor cells.

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