A biological sample analysis method and system based on nuclear pore distribution patterns

Abstract

The present application relates to the field of tumor detection, and in particular to a biological sample analysis method and system based on nuclear pore distribution patterns. The method provided by the present application comprises: S101, obtaining microscopic image data of a biological sample; S102, obtaining a first region of interest, a second region of interest and a third region of interest; S103, drawing a nuclear membrane edge curve; S104, extracting nuclear pore distribution-nuclear membrane curvature distribution data of cells in the biological sample; S105, identifying the nuclear pore distribution pattern of the cells in the biological sample; S106, evaluating the state of the tissue or organ corresponding to the biological sample. Based on the identified nuclear pore distribution pattern of individual cells in the biological sample, the present application can evaluate the state of the tissue or organ corresponding to the biological sample according to the overall nuclear pore distribution pattern of the cells in the biological sample, such as whether it is a tumor sample, whether it has a tendency to cancer, etc., which is of great significance for early diagnosis of tumors.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of tumor detection, in particular to a biological sample analysis method and system based on nuclear pore distribution pattern. BACKGROUND

[0002] Neuroblastoma originates from primitive sympathetic ganglion cells of the neural crest during embryonic period, and is the most common extracranial solid tumor in childhood. The diagnosis of neuroblastoma requires a combination of multi-modal methods, and the main diagnostic basis is histopathological examination (tumor biopsy) or bone marrow puncture / biopsy to find characteristic tumor cells. Imaging examination (such as ultrasound, computed tomography CT, magnetic resonance imaging MRI) can provide information such as tumor site, size, invasion range and metastasis. Biochemical detection mainly targets the significant increase of catecholamine metabolites in urine, such as vanillylmandelic acid (VMA) and homovanillic acid (HVA), and needs to be combined with serum markers (such as NSE, LDH). Although imaging and biochemical detection are crucial for assessing tumor burden and late prognosis, relying solely on these methods is insufficient for early diagnosis of neuroblastoma. Currently, large-scale VMA / HVA screening in infancy has been suspended due to problems such as over-diagnosis and failure to reduce mortality, so the early diagnosis of neuroblastoma still faces great challenges.

[0003] In higher eukaryotes, the nucleus is the largest organelle in the cell. The nucleus mainly contains the genetic material of the cell, i.e. DNA or chromatin. The nucleus is not only a repository of genetic information, but also a control center containing the genome. Although existing technologies have certain evaluation standards for morphological changes of the nucleus, such as the possibility of morphological changes of the nucleus being a marker of cancer, they lack clear judgment criteria, and the evaluation of the results of the nucleus morphology is highly subjective and mainly depends on the interpretation of pathologists. Chinese patent application CN116568401A discloses a method for grading the morphology of tumor cells, which needs to induce the nuclear deformation of tumor cells by specific nanocolumns, and evaluate the malignancy of tumors through the deformation mode (i.e. nuclear morphological changes) of the nucleus of tumor cells under specific mechanical stimulation. Therefore, this method is only suitable for analyzing tumor cells. SUMMARY

[0004] In a first aspect, the present application provides a biological sample analysis method based on nuclear pore distribution pattern, comprising the following steps:

[0005] S101, obtaining microscopic image data of a biological sample;

[0006] In some embodiments, the microscopic image data includes fluorescence microscope images and / or super-resolution microscopic image data of an objective lens with a magnification of 40 times or more.

[0007] In some embodiments, the biological sample comprises at least one cell.

[0008] In some embodiments, the biological sample comprises ≥20 cells.

[0009] In some embodiments, the biological sample comprises tissue and / or cell culture.

[0010] In some embodiments, the biological sample is labeled with a nuclear pore marker and a nuclear membrane marker.

[0011] In some embodiments, the nuclear pore marker comprises nuclear pore protein Nup98.

[0012] In some embodiments, the nuclear membrane marker comprises nuclear lamina protein Lamin A / C.

[0013] In some embodiments, the biological sample is further labeled with a cell nucleus marker.

[0014] In some embodiments, the cell nucleus marker comprises 4',6-diamidino-2-phenylindole (DAPI).

[0015] S102, obtaining a first region of interest, a second region of interest and a third region of interest in the microscopic image data.

[0016] In some embodiments, the first region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the cell nucleus marker.

[0017] In some embodiments, the second region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the nuclear pore marker.

[0018] In some embodiments, the third region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the nuclear membrane marker.

[0019] S103, based on the third region of interest, circumscribing a nuclear membrane edge curve of a cell in the biological sample;

[0020] In some embodiments, the circumscribing method comprises: selecting a first number of points on the edge of the third region of interest and connecting them into a closed curve.

[0021] In some embodiments, the first number is at least 20.

[0022] In some embodiments, the first number of points are connected by a smooth curve.

[0023] S104, based on the nuclear membrane edge curve, extracting nuclear pore distribution-nuclear membrane curvature distribution data of the cell;

[0024] In some embodiments, the nuclear pore distribution-nuclear membrane curvature distribution data comprises point curvature data at the nuclear membrane edge curve and fluorescence intensity data of the nuclear pore marker.

[0025] In some embodiments, starting from an arbitrary point on the nuclear membrane edge curve, the point curvature value and the fluorescence intensity value of the nuclear pore marker at each position of the nuclear membrane edge curve are sequentially obtained in a predetermined direction (e.g., along the clockwise direction or the counterclockwise direction), and then the point curvature data at the nuclear membrane edge curve and the fluorescence intensity data of the nuclear pore marker can be obtained.

[0026] In some embodiments, the nuclear pore distribution-nuclear membrane curvature distribution data comprises a first form and / or a second form.

[0027] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form, the point curvature data is a point curvature distribution curve at the nuclear membrane edge curve, and the fluorescence intensity data of the nuclear pore marker is a fluorescence intensity distribution curve of the nuclear pore marker at the nuclear membrane edge curve.

[0028] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the point curvature data is normalized point curvature data, and the fluorescence intensity data of the nuclear pore marker is normalized fluorescence intensity data of the nuclear pore marker.

[0029] S105, based on the nuclear pore distribution-nuclear membrane curvature distribution data, identifying a nuclear pore distribution pattern of the cells in the biological sample;

[0030] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form, a first identification method is used to identify the nuclear pore distribution pattern.

[0031] In some embodiments, the first identification method comprises:

[0032] When the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak reverse change trend, the nuclear pore distribution pattern is identified as negative correlation.

[0033] In some embodiments, when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak same direction change trend, the nuclear pore distribution pattern is identified as positive correlation.

[0034] In some embodiments, when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker neither present a peak-to-peak reverse change trend nor present a peak-to-peak same direction change trend, the nuclear pore distribution pattern is identified as no correlation.

[0035] In some embodiments, when the nuclear pore distribution-nuclear envelope curvature distribution data is in the second form, the nuclear pore distribution pattern is identified using a second identification method.

[0036] In some embodiments, the second identification method comprises:

[0037] dividing the normalized point curvature data into n point curvature equidistantly increasing intervals;

[0038] when the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as negative correlation.

[0039] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as positive correlation.

[0040] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows neither a downward trend nor an upward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as no correlation.

[0041] In some embodiments, n can be an integer ≥10, for example, 15.

[0042] In some embodiments, S106 can be: obtaining the overall nuclear pore distribution pattern of the biological sample according to the nuclear pore distribution pattern of the cells in the biological sample, and then evaluating the state of the tissue or organ corresponding to the biological sample;

[0043] In some embodiments, when the positive correlation cell rate of the biological sample is greater than the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is evaluated as positive correlation.

[0044] In some embodiments, when the positive correlation cell rate of the biological sample is less than or equal to the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is evaluated as negative correlation.

[0045] In some embodiments, when the nuclear pore distribution-nuclear envelope curvature distribution data is in the second form, the overall nuclear pore distribution pattern of the biological sample can be identified based on the second identified method. That is, when the nuclear pore distribution-nuclear envelope curvature distribution data is in the second form, step S106 can be replaced by:

[0046] dividing the normalized point curvature data of one or more cells in the biological sample into n point curvature equidistantly increasing intervals;

[0047] When the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n-point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as negative correlation.

[0048] When the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n-point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as positive correlation.

[0049] In some embodiments, when the overall nuclear pore distribution pattern of the biological sample is positive correlation, the biological sample is evaluated as a non-tumor sample, and when the overall nuclear pore distribution pattern of the biological sample is negative correlation, the biological sample is evaluated as a tumor sample.

[0050] In some embodiments, the S106 can further include:

[0051] According to the nuclear pore distribution pattern of the cells in the biological sample, the positive correlation cell rate and / or the negative correlation cell rate of the biological sample are obtained, and then the state of the tissue or organ corresponding to the biological sample is evaluated;

[0052] In some embodiments, when the positive correlation cell rate of the biological sample is not lower than a first preset value, the biological sample is evaluated as a non-tumor sample.

[0053] In some embodiments, the non-tumor sample can be a normal tissue sample and / or a paracancerous tissue sample.

[0054] In some embodiments, the first preset value can be 50%-60%.

[0055] In some embodiments, when the negative correlation cell rate of the biological sample is not lower than a second preset value, the biological sample is evaluated as a tumor sample.

[0056] In some embodiments, the second preset value can be 75%-90%.

[0057] In some embodiments, the S106 can further include: comparing the difference in the overall nuclear pore distribution pattern, the positive correlation cell rate and / or the negative correlation cell rate between the biological sample and a reference sample, to evaluate the change trend of the state of the tissue or organ corresponding to the biological sample.

[0058] In a second aspect, the present application provides a biological sample analysis system based on nuclear pore distribution pattern, which comprises:

[0059] An image acquisition module configured to acquire microscopic image data of a biological sample;

[0060] In some embodiments, the microscopic image data comprises fluorescence microscope images and / or super-resolution microscopic image data with an objective magnification of 40x or more.

[0061] In some embodiments, the biological sample comprises at least one cell.

[0062] In some embodiments, the biological sample comprises >20 cells.

[0063] In some embodiments, the biological sample comprises a tissue and / or a cell culture.

[0064] In some embodiments, the biological sample is labeled with a nuclear pore marker and a nuclear membrane marker.

[0065] In some embodiments, the nuclear pore marker comprises nuclear pore protein Nup98.

[0066] In some embodiments, the nuclear membrane marker comprises nuclear lamina protein Lamin A / C.

[0067] In some embodiments, the biological sample is further labeled with a cell nucleus marker.

[0068] In some embodiments, the cell nucleus marker comprises 4', 6-diamidino-2-phenylindole (DAPI).

[0069] a first processing module configured to obtain a first region of interest, a second region of interest and a third region of interest in the microscopic image data.

[0070] In some embodiments, the first region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the cell nucleus marker.

[0071] In some embodiments, the second region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the nuclear pore marker.

[0072] In some embodiments, the third region of interest is a cell nucleus region circumscribed by a fluorescence channel corresponding to the nuclear membrane marker.

[0073] a second processing module configured to delineate a nuclear membrane edge curve of a cell in the biological sample based on the third region of interest;

[0074] In some embodiments, the method of delineating comprises selecting a first number of points at the edge of the third region of interest and connecting them into a closed curve.

[0075] In some embodiments, the first number is at least 20.

[0076] In some embodiments, the first number of points are connected by a smooth curve.

[0077] a third processing module configured to extract nuclear pore distribution-nuclear membrane curvature distribution data of the cell based on the nuclear membrane edge curve;

[0078] In some embodiments, the nuclear pore distribution-nuclear membrane curvature distribution data comprises point curvature data at the nuclear membrane edge curve and fluorescence intensity data of the nuclear pore marker.

[0079] In some embodiments, the method of extracting the nuclear pore distribution-nuclear membrane curvature distribution data comprises: taking an arbitrary point on the nuclear membrane edge curve as a starting point, sequentially obtaining point curvature values and fluorescence intensity values of the nuclear pore marker at each position of the nuclear membrane edge curve in a clockwise direction or a counterclockwise direction, and then obtaining the point curvature data at the nuclear membrane edge curve and the fluorescence intensity data of the nuclear pore marker, i.e., the nuclear pore distribution-nuclear membrane curvature distribution data.

[0080] a fourth processing module configured to process the nuclear pore distribution-nuclear membrane curvature distribution data into a first form and / or a second form.

[0081] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form, the point curvature data is a point curvature distribution curve at the nuclear membrane edge curve, and the fluorescence intensity data of the nuclear pore marker is a fluorescence intensity distribution curve of the nuclear pore marker at the nuclear membrane edge curve.

[0082] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the point curvature data is normalized point curvature data, and the fluorescence intensity data of the nuclear pore marker is normalized fluorescence intensity data of the nuclear pore marker.

[0083] a first identification module configured to perform first identification on the nuclear pore distribution-nuclear membrane curvature distribution data in the first form to identify a nuclear pore distribution pattern of the cell of the biological sample.

[0084] In some embodiments, the method of the first identification comprises: when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak reverse change trend, the nuclear pore distribution pattern is identified as negative correlation.

[0085] In some embodiments, when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak same direction change trend, the nuclear pore distribution pattern is identified as positive correlation.

[0086] In some embodiments, when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker do not present a peak-to-peak reverse trend or a peak-to-peak same trend, the nuclear pore distribution pattern is identified as irrelevant.

[0087] In some embodiments, the system can further comprise a second identification module configured to perform a second identification on the second form of nuclear pore distribution-nuclear membrane curvature distribution data to identify the nuclear pore distribution pattern of the cells of the biological sample.

[0088] In some embodiments, the method of the second identification comprises dividing the normalized point curvature data into n point curvature equidistantly increasing intervals;

[0089] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker presents a downward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as negative correlation;

[0090] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker presents an upward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as positive correlation.

[0091] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker neither presents a downward trend nor an upward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as irrelevant.

[0092] In some embodiments, n can be an integer greater than or equal to 10, for example, 15.

[0093] An evaluation module configured to evaluate the state of the tissue or organ corresponding to the biological sample.

[0094] In some embodiments, the evaluation module comprises a first evaluation unit configured to obtain the overall nuclear pore distribution pattern of the biological sample based on the nuclear pore distribution pattern of the cells identified by the first identification module and / or the second identification module.

[0095] In some embodiments, when the positive correlation cell rate of the biological sample is greater than the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is positive correlation.

[0096] In some embodiments, when the positive correlation cell rate of the biological sample is less than or equal to the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is negative correlation.

[0097] In some embodiments, the evaluation module further comprises a second evaluation unit configured to perform a second identification according to a second form of nuclear pore distribution-nuclear membrane curvature distribution data of one or more cells in the biological sample, i.e., dividing the normalized point curvature data of one or more cells in the biological sample into n point curvature equidistantly increasing intervals;

[0098] When the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as negative correlation.

[0099] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as positive correlation.

[0100] In some embodiments, the evaluation method comprises: when the overall nuclear pore distribution pattern of the biological sample is positive correlation, the biological sample is evaluated as a non-tumor sample; and when the overall nuclear pore distribution pattern of the biological sample is negative correlation, the biological sample is evaluated as a tumor sample.

[0101] In some embodiments, the first evaluation unit is further configured to obtain a positive correlation cell rate and / or a negative correlation cell rate of the biological sample according to the nuclear pore distribution pattern of the cells in the biological sample, and further evaluate the state of the tissue or organ corresponding to the biological sample.

[0102] In some embodiments, the evaluation method can further comprise: when the positive correlation cell rate of the biological sample is not lower than a first preset value, the biological sample is evaluated as a non-tumor sample.

[0103] In some embodiments, the non-tumor sample can be a normal tissue sample and / or a paracancerous tissue sample.

[0104] In some embodiments, the first preset value can be 50%-60%.

[0105] In some embodiments, when the negative correlation cell rate of the biological sample is not lower than a second preset value, the biological sample is evaluated as a tumor sample.

[0106] In some embodiments, the second preset value can be 75%-90%.

[0107] In some embodiments, the evaluation module can further comprise a third evaluation unit configured to compare the difference in the overall nuclear pore distribution pattern, the positive correlation cell rate and / or the negative correlation cell rate of the biological sample and a reference sample, and evaluate the change trend of the state of the tissue or organ corresponding to the biological sample.

[0108] Compared with the prior art, the beneficial effects of the present application at least include the following aspects:

[0109] The nuclear membrane is a double lipid bilayer membrane surrounding the cell nucleus, separating the cell nucleus from the cytoplasm. The prior art (for example, CN117252808A) usually uses immunofluorescence staining of lamin A / C to locate the nuclear membrane, and then analyzes whether the morphology of the cell nucleus is abnormal by analyzing whether the cell nucleus is close to an oval shape and / or whether the nuclear membrane appears to be concave. However, this method is essentially still based on the traditional understanding of morphological changes of the cell nucleus in the prior art, and is not suitable for analyzing complex biological samples, let alone capturing the state changes of the corresponding tissues or organs of the biological samples (for example, whether they have a tendency to "cancer").

[0110] The nuclear pore complex (referred to as the nuclear pore) is anchored to the nuclear membrane of the cell nucleus and is the only channel for mediating the nuclear-cytoplasmic transport of biological macromolecules. Under the nuclear membrane and the nuclear pore complex of the higher animal cell nucleus is the nuclear lamina, which is a reticular structure mainly composed of proteins. On the inner side of the nuclear lamina is the chromatin and the nucleolus. The nuclear membrane, the nuclear pore complex and the nuclear lamina constitute the peripheral structure of the cell nucleus, providing a relatively stable environment for the metabolism of genetic material in the chromatin and the nucleolus, while regulating the material transport between the inside and outside of the cell nucleus, playing an important role in cell proliferation, differentiation, individual development and cell aging. The assembly and positioning of the nuclear pore are regulated by various factors. The nuclear pore is disassembled during mitosis with the nuclear membrane, and is reassembled on the nuclear membrane after mitosis ends. With the expansion of the nuclear membrane, new nuclear pores continuously gather on the nuclear membrane during the interphase. However, the distribution pattern of the nuclear pore on the nuclear membrane (which can also be understood as the distribution pattern of the nuclear pore) is not clear.

[0111] Unlike conventional ideas, the present application, on the basis of simultaneously labeling the cells in the biological sample with the nuclear pore marker and the nuclear membrane marker, also extracts the point curvature data at the nuclear membrane edge curve (i.e., at the same continuous spatial position) and the fluorescence intensity data of the nuclear pore marker based on the nuclear membrane edge curve drawn by the nuclear membrane marker. The setting of the nuclear membrane edge curve enables the present application to simultaneously analyze the two different types of data mentioned above.

[0112] Unexpectedly, the present application can identify the nuclear pore distribution pattern (e.g. positive correlation, negative correlation) of individual cells in a biological sample by analyzing the correlation between the point curvature data at the nuclear envelope edge curve and the fluorescence intensity data of the nuclear pore marker (nuclear pore fluorescence intensity-nuclear envelope curvature correlation), which specifically includes: the nuclear pore distribution pattern of tumor cells (e.g. neuroblastoma cells) is negative correlation, i.e. the nuclear pore fluorescence intensity of tumor cells and the nuclear envelope curvature present a typical negative correlation; the nuclear pore distribution pattern of normal cells is positive correlation, i.e. the nuclear pore fluorescence intensity of normal cells and the nuclear envelope curvature present a typical positive correlation.

[0113] Based on the identified nuclear pore distribution pattern of individual cells in a biological sample, the present application can also evaluate the state of the tissue or organ corresponding to the biological sample according to the overall nuclear pore distribution pattern of the cells in the biological sample, such as whether it is a tumor sample, whether it has a tendency to be cancerous, etc. Therefore, the present application can perform tumor detection evaluation on biological samples, which is of great significance for early diagnosis of tumors. In addition, the biological sample analysis method and system based on the nuclear pore distribution pattern provided by the present application can help reveal new pathological mechanisms and provide new directions and research foundations for the discovery and development of new drug treatments or treatment strategies. BRIEF DESCRIPTION OF DRAWINGS

[0114] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. In all the drawings, similar elements or parts are generally identified by similar reference signs. In the drawings, each element or part is not necessarily drawn according to the actual proportion. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0115] Figure 1 Flow chart of the method of embodiment one;

[0116] Figure 2 Module schematic diagram of the system of embodiment two;

[0117] Figure 3 Schematic diagram of the nuclear envelope edge curve;

[0118] Figure 4 Schematic diagram of the nuclear pore fluorescence intensity-nuclear envelope curvature distribution curve of tumor cells;

[0119] Figure 5 Nuclear pore fluorescence intensity-nuclear envelope curvature distribution rose map of neuroblastoma cell culture sample;

[0120] Figure 6Representative images of tumor-adjacent tissue sections from neuroblastoma patients;

[0121] Figure 7 Nuclear pore fluorescence intensity-nuclear membrane curvature distribution curve of tumor cells in tumor tissue from neuroblastoma patients (a) and nuclear pore fluorescence intensity-nuclear membrane curvature distribution rose map of tumor tissue samples from neuroblastoma patients (b); Figure 7 Figure 7 b);

[0122] Figure 8 Nuclear pore fluorescence intensity-nuclear membrane curvature distribution curve of normal cells in tumor-adjacent tissue from neuroblastoma patients (a) and nuclear pore fluorescence intensity-nuclear membrane curvature distribution rose map of tumor-adjacent tissue samples from neuroblastoma patients (b); Figure 8 Figure 8 b);

[0123] Figure 9 Statistical chart for analysis of nuclear pore distribution patterns of cells in tumor tissue and tumor-adjacent tissue;

[0124] Figure 10 Schematic diagram of typical peak-to-peak reverse change trend;

[0125] Figure 11 Schematic diagram of typical peak-to-peak same direction change trend.

[0126] 100 is a biological sample analysis system, 102 is an image acquisition module, 104 is a first processing module, 106 is a second processing module, 108 is a third processing module, 110 is a fourth processing module, 112 is a first identification module, 114 is a second identification module, 116 is an evaluation module, 202 is a first evaluation unit, 204 is a second evaluation unit, and 206 is a third evaluation unit. DETAILED DESCRIPTION

[0127] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0128] Herein, the suffix such as "module", "component" or "unit" used for representing an element is only for the convenience of description of the present application, and has no specific meaning by itself. Therefore, "module", "component" or "unit" can be mixedly used.

[0129] ​​In this document, the terms "upper", "lower", "inner", "outer", "front", "back", "one end", "the other end", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for the purpose of description and cannot be understood as indicating or implying relative importance.

[0130] As used herein, "and / or" includes any and all combinations of one or more of the associated items.

[0131] As used herein, "plurality" means two or more, that is, it includes two, three, four, five, etc.

[0132] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article, or device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or device including the element.

[0133] As used in this specification, the term "about", typically means + / - 5% of the stated value, more typically + / - 4% of the stated value, more typically + / - 3% of the stated value, more typically + / - 2% of the stated value, even more typically + / - 1% of the stated value, even more typically + / - 0.5% of the stated value.

[0134] In this specification, certain embodiments can be disclosed in a format that is a range. It is to be understood that such a "range" format is merely used for convenience and brevity and should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges within that range as if each numerical value and sub-range is explicitly recited. For example, a range of 1-6 should be interpreted to include not only the explicitly recited limits of 1 and 6, but also the individual numbers 2, 3, 4, 5, and the sub-ranges 1-3, 2-4, 3-5, and 2-6, etc. This same logic should be applied to ranges of any magnitude.

[0135] Example One

[0136] As Figure 1As shown, the present embodiments provide a method for biological sample analysis based on nuclear pore distribution patterns, comprising the following steps:

[0137] S101, obtaining microscopic image data of a biological sample;

[0138] In some embodiments, the microscopic image data comprises fluorescence microscope images and / or super-resolution microscope image data using an objective lens of 40x or more.

[0139] In some embodiments, the microscopic image data can be obtained by batch acquisition of immunofluorescence images of the biological sample using a high-throughput imaging system.

[0140] In some embodiments, the imaging requirements include imaging of well plates with glass bottoms or other ultra-thin bottom surfaces (e.g., 0.17-0.19 mm) using an objective lens of 40x or more.

[0141] In some embodiments, the biological sample comprises at least one cell.

[0142] In some embodiments, the biological sample comprises >20 cells.

[0143] In some embodiments, the biological sample can be obtained from any organism. In some embodiments, the organism comprises a healthy or apparently healthy subject or a subject affected by a condition or disease (e.g., cancer) to be diagnosed or studied. In some embodiments, the subject is a human.

[0144] In some embodiments, the biological sample comprises a tissue and / or a cell culture. In some embodiments, the biological sample can be from a cell or a group of cells or a quantity of tissue from a subject. In some embodiments, the biological sample can be a sample obtained from any organ or tissue (including a tissue biopsy (e.g., a tumor biopsy), a post-mortem sample, a surgically removed tissue). In some embodiments, the biological sample can comprise cells (whether primary cells or cultured cells) or a medium conditioned by any cell, tissue or organ.

[0145] In some embodiments, the biological sample can comprise tumor cells and / or cells with the potential to develop into tumor cells.

[0146] In some embodiments, the biological sample can not contain tumor cells.

[0147] In some embodiments, the biological sample comprises neuroblastoma cells.

[0148] In some embodiments, "tumor" refers to any neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. "Cancer" and "cancerous" refer to or describe the physiological condition in a subject that is typically partially characterized by unregulated cell growth.

[0149] In some embodiments, the biological sample is labeled with a nuclear pore marker and a nuclear membrane marker.

[0150] In some embodiments, the nuclear pore marker comprises nuclear pore protein Nup98.

[0151] In some embodiments, the nuclear membrane marker comprises lamin protein Lamin A / C.

[0152] In some embodiments, the biological sample is further labeled with a cell nucleus marker.

[0153] In some embodiments, the cell nucleus marker comprises 4', 6-diamidino-2- phenylindole (DAPI).

[0154] In some embodiments, the above markers in a cell can be labeled by immunofluorescence staining or other suitable methods.

[0155] S102, obtaining a first region of interest, a second region of interest and a third region of interest in the microscopic image data.

[0156] In some embodiments, the first region of interest is a cell nucleus region circled by a fluorescence channel corresponding to the cell nucleus marker.

[0157] In some embodiments, the second region of interest is a cell nucleus region circled by a fluorescence channel corresponding to the nuclear pore marker.

[0158] In some embodiments, the third region of interest is a cell nucleus region circled by a fluorescence channel corresponding to the nuclear membrane marker.

[0159] S103, based on the third region of interest, circulate a nuclear membrane edge curve of a cell in the biological sample;

[0160] In some embodiments, the method of circulating the nuclear membrane edge curve comprises selecting a first number of points at the edge of the third region of interest (i.e. the edge of the cell nucleus) and connecting them into a closed curve.

[0161] In some embodiments, the first number is at least 20.

[0162] In some embodiments, the first number of points are connected by a smooth curve.

[0163] In some embodiments, the nuclear envelope edge curve can be set to 2 pixels.

[0164] In some embodiments, to obtain a nuclear envelope edge curve closer to the edge of an actual cell nucleus, the first number can be set to no less than 30.

[0165] In some embodiments, the cells do not include cells that contact the micrograph boundary and the cell nucleus overlap each other.

[0166] S104, based on the nuclear envelope edge curve, extracting nuclear pore distribution-nuclear membrane curvature distribution data of the cell;

[0167] In some embodiments, the nuclear pore distribution-nuclear membrane curvature distribution data includes point curvature data at the nuclear envelope edge curve and fluorescence intensity data of the nuclear pore marker.

[0168] The prior art generally uses immunofluorescence staining of lamin A / C to locate the nuclear envelope, and then analyzes whether the morphology of the cell nucleus is abnormal (e.g., aging) by analyzing whether the cell nucleus is close to an ellipse and / or whether the nuclear envelope appears concave (e.g., determining the concave-convex direction of the nuclear envelope based on the curvature of the inflection point of the nuclear envelope). However, this method is essentially still based on the traditional understanding of the morphological changes of the cell nucleus in the prior art, and is not suitable for analyzing complex biological samples, let alone capturing the state changes of the corresponding tissue or organ of the biological sample (e.g., whether it has a "cancerous" tendency).

[0169] Unlike conventional ideas, the present embodiment, on the basis of simultaneously labeling cells in a biological sample with a nuclear pore marker and a nuclear envelope marker, extracts point curvature data at the nuclear envelope edge curve and fluorescence intensity data of the nuclear pore marker (i.e., nuclear pore distribution-nuclear membrane curvature distribution data) based on the nuclear envelope edge curve drawn by the nuclear envelope marker. The setting of the nuclear envelope edge curve of the present embodiment can obtain point curvature data and fluorescence intensity data of the nuclear pore marker at the same continuous spatial position (i.e., at the nuclear envelope edge curve). Therefore, the present embodiment can analyze the above two different types of data.

[0170] In some embodiments, an arbitrary point on the nuclear envelope edge curve can be taken as a starting point, and in a predetermined direction (e.g., along the clockwise direction or the counterclockwise direction), the (nuclear membrane) point curvature value and the fluorescence intensity value of the nuclear pore marker at each position of the nuclear envelope edge curve can be obtained in turn, and thus the point curvature data and the fluorescence intensity data of the nuclear pore marker at the nuclear envelope edge curve (i.e., at the continuous spatial position) can be obtained.

[0171] In some embodiments, the nuclear pore distribution-nuclear envelope curvature distribution data can be in a first form and / or a second form.

[0172] In some embodiments, the first form is a distribution curve form, i.e., the point curvature data can be a point curvature distribution curve at the nuclear envelope edge curve, and the fluorescence intensity data of the nuclear pore marker can be a fluorescence intensity distribution curve of the nuclear pore marker at the nuclear envelope edge curve.

[0173] In some embodiments, the distribution curve is a fitted curve.

[0174] In some embodiments, the fitted curve refers to a continuous curve obtained by modeling a set of discrete data points with a mathematical function to reflect the overall trend of the data (e.g., the point curvature data at the nuclear envelope edge curve and the fluorescence intensity data of the nuclear pore marker).

[0175] In some embodiments, the point curvature distribution curve at the nuclear envelope edge curve and the fluorescence intensity distribution curve of the nuclear pore marker at the nuclear envelope edge curve are fitted based on an eight-order polynomial function.

[0176] In some embodiments, the second form is a normalized data form, i.e., the point curvature data can be normalized point curvature data, and the fluorescence intensity data of the nuclear pore marker can be normalized fluorescence intensity data of the nuclear pore marker.

[0177] In some embodiments, the normalization method can be Min-Max normalization.

[0178] Unexpectedly, the present embodiment finds that by analyzing the correlation between the point curvature data at the nuclear envelope edge curve and the fluorescence intensity data of the nuclear pore marker, the nuclear pore distribution pattern of individual cells in the biological sample can be obtained, and then the state of the tissue or organ corresponding to the biological sample, such as whether it is a tumor sample (e.g., a neuroblastoma sample), whether it has a tendency to be cancerous, etc., can be evaluated according to the overall nuclear pore distribution pattern of the cells in the biological sample.

[0179] S105, based on the nuclear pore distribution-nuclear envelope curvature distribution data, identifying the nuclear pore distribution pattern of the cells in the biological sample;

[0180] In some embodiments, when the nuclear pore distribution-nuclear envelope curvature distribution data is in the first form, a first identification method is used to identify the nuclear pore distribution pattern.

[0181] In some embodiments, the first identification method comprises:

[0182] When the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak reverse change trend, the nuclear pore distribution pattern is identified as a negative correlation.

[0183] In some embodiments, the peak-to-peak reverse change trend refers to, at the continuous spatial positions (which can also be understood as "positions") provided by the nuclear envelope edge curve, when the point curvature distribution curve reaches a local maximum value, the fluorescence intensity distribution curve of the nuclear pore marker reaches a local minimum value at the same or adjacent position corresponding to the local maximum value; when the point curvature distribution curve reaches a local minimum value, the fluorescence intensity distribution curve of the nuclear pore marker reaches a local maximum value at the same or adjacent position corresponding to the local minimum value. In other words, the above identification method can identify that the point curvature at the nuclear envelope edge curve and the fluorescence intensity of the nuclear pore marker have a negative correlation in the same spatial position dimension. A typical schematic diagram of the peak-to-peak reverse change trend is shown in FIG. 2. Figure 10 (Green curve: fluorescence intensity distribution curve of nuclear pore marker, black curve: point curvature distribution curve).

[0184] In other words, the peak-to-peak reverse change trend can also be understood as: at the continuous spatial positions provided by the nuclear envelope edge curve, when the point curvature distribution curve presents a "peak", the fluorescence intensity distribution curve of the nuclear pore marker presents a "valley" at the same or adjacent position corresponding to the "peak"; when the point curvature distribution curve presents a "valley", the fluorescence intensity distribution curve of the nuclear pore marker presents a "peak" at the same or adjacent position corresponding to the "valley".

[0185] In some embodiments, the peak-to-peak reverse change trend can also be understood as: at the continuous spatial positions provided by the nuclear envelope edge curve, the segment of the point curvature distribution curve in which the point curvature presents a change trend of first increasing to a peak value and then decreasing (i.e., in this segment, the point curvature presents a change trend of first increasing to a peak value and then decreasing) is the same or similar to the segment of the fluorescence intensity distribution curve of the nuclear pore marker in which the fluorescence intensity presents a change trend of first decreasing to a valley value and then increasing (i.e., in this segment, the fluorescence intensity presents a change trend of first decreasing to a valley value and then increasing) (i.e., the segment of the point curvature distribution curve in which the point curvature presents a change trend of first increasing to a peak value and then decreasing is substantially different from the segment of the fluorescence intensity distribution curve of the nuclear pore marker in which the fluorescence intensity presents a change trend of first increasing to a peak value and then decreasing).

[0186] In some embodiments, "peak" refers to a point on a distribution curve at which the curve changes direction from up to down along the mountain-shaped part as the curve rises and falls in the vertical direction.

[0187] In some embodiments, "valley" refers to a point on a distribution curve at which the curve changes direction from down to up along the valley-shaped part as the curve falls and rises in the vertical direction.

[0188] In some embodiments, the nuclear pore distribution pattern is identified as positive correlation when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker present a peak-to-peak co-directional variation trend.

[0189] In some embodiments, the peak-to-peak co-directional variation trend refers to, at the continuous spatial positions (which can also be understood as "locations") provided by the nuclear envelope edge curve, when the point curvature distribution curve reaches a local minimum value, the fluorescence intensity distribution curve of the nuclear pore marker reaches a local minimum value at the same or adjacent position corresponding to the local minimum value; when the point curvature distribution curve reaches a local maximum value, the fluorescence intensity distribution curve of the nuclear pore marker reaches a local maximum value at the same or adjacent position corresponding to the local maximum value. In other words, the above identification method can identify that the point curvature at the nuclear envelope edge curve and the fluorescence intensity of the nuclear pore marker have a positive correlation in the same spatial position dimension. A typical peak-to-peak co-directional variation trend is shown in the following figure: Figure 11 (Green curve: fluorescence intensity distribution curve of nuclear pore marker, black curve: point curvature distribution curve).

[0190] In other words, the peak-to-peak co-directional variation trend can also be understood as: at the continuous spatial positions provided by the nuclear envelope edge curve, when the point curvature distribution curve appears a "peak", the fluorescence intensity distribution curve of the nuclear pore marker appears a "peak" at the same or adjacent position corresponding to the "peak"; when the point curvature distribution curve appears a "valley", the fluorescence intensity distribution curve of the nuclear pore marker appears a "valley" at the same or adjacent position corresponding to the "valley".

[0191] In some embodiments, the peak-to-peak co-directional variation trend can also be understood as: at the continuous spatial positions provided by the nuclear envelope edge curve, the section of the point curvature distribution curve in which the point curvature appears a "peak" variation trend (i.e., in this section, the point curvature presents a trend of first increasing to a peak value and then decreasing) is the same or similar to the section of the fluorescence intensity distribution curve of the nuclear pore marker in which the fluorescence intensity appears a "peak" variation trend (i.e., in this section, the fluorescence intensity presents a trend of first increasing to a peak value and then decreasing) (i.e., the section of the point curvature distribution curve in which the point curvature appears a "peak" variation trend is substantially the same as the section of the fluorescence intensity distribution curve of the nuclear pore marker in which the fluorescence intensity appears a "peak" variation trend).

[0192] In some embodiments, the nuclear pore distribution pattern is identified as non-correlation when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker neither present a peak-to-peak reverse variation trend nor present a peak-to-peak co-directional variation trend.

[0193] In fact, the present embodiment finds that the nuclear pore distribution pattern of cells is less common, and therefore, the nuclear pore distribution pattern of cells can also be considered as negative correlation or positive correlation according to actual needs.

[0194] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, a second identification method is used to identify the nuclear pore distribution pattern.

[0195] In some embodiments, the second identification method comprises:

[0196] dividing the normalized point curvature data into n point curvature equidistantly increasing intervals;

[0197] In some embodiments, n can be an integer ≥ 10, for example, 15.

[0198] In some embodiments, equidistantly increasing intervals refer to dividing a given numerical range into intervals of fixed length, so that the length of each adjacent point curvature equidistantly increasing interval is equal, and the point curvature value increases in the equidistantly increasing interval. For example, dividing the normalized point curvature data into 15 point curvature equidistant intervals, the 15 normalized point curvature equidistant intervals can be: [0, 1 / 15), [1 / 15, 2 / 15), [2 / 15, 3 / 15), [3 / 15, 4 / 15), [4 / 15, 5 / 15), [5 / 15, 6 / 15), [6 / 15, 7 / 15), [7 / 15, 8 / 15), [8 / 15, 9 / 15), [9 / 15, 10 / 15), [10 / 15, 11 / 15), [11 / 15, 12 / 15), [12 / 15, 13 / 15), [13 / 15, 14 / 15), [14 / 15, 1].

[0199] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as negative correlation. In other words, since the point curvature value increases in the n point curvature equidistantly increasing intervals, the distribution density of the fluorescence intensity of the nuclear pore marker decreases as the n point curvature equidistantly increasing intervals increase, indicating that the fluorescence intensity of the nuclear pore marker has a higher distribution density in the low curvature region of the n point curvature equidistantly increasing intervals, i.e. the point curvature at the nuclear membrane edge curve and the fluorescence intensity of the nuclear pore marker have a negative correlation relationship in the same spatial position dimension.

[0200] In some embodiments, the distribution density of the fluorescence intensity of the nuclear pore marker in the n point curvature equidistant increasing intervals presents a downward trend, which means that in the n point curvature equidistant increasing intervals, the distribution density of the fluorescence intensity of the nuclear pore marker corresponding to the first point curvature equidistant increasing interval is greater than the distribution density of the fluorescence intensity of the nuclear pore marker corresponding to the n point curvature equidistant increasing interval.

[0201] In some embodiments, the distribution density of the fluorescence intensity of the nuclear pore marker can be calculated according to the average value of the normalized fluorescence intensity data of the nuclear pore marker corresponding to the point curvature in a single point curvature equidistant increasing interval.

[0202] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker in the n point curvature equidistant increasing intervals presents an upward trend, the nuclear pore distribution pattern is identified as positive correlation. In other words, as the point curvature value increases in the n point curvature equidistant increasing intervals, the distribution density of the fluorescence intensity of the nuclear pore marker increases with the increase of the n point curvature equidistant increasing intervals, indicating that the fluorescence intensity of the nuclear pore marker has a higher distribution density in the high curvature region of the n point curvature equidistant increasing intervals, i.e. the point curvature at the nuclear membrane edge curve has a positive correlation with the fluorescence intensity of the nuclear pore marker in the same spatial position dimension.

[0203] In some embodiments, the distribution density of the fluorescence intensity of the nuclear pore marker in the n point curvature equidistant increasing intervals presents an upward trend, which means that in the n point curvature equidistant increasing intervals, the distribution density of the fluorescence intensity of the nuclear pore marker corresponding to the first point curvature equidistant increasing interval is less than the distribution density of the fluorescence intensity of the nuclear pore marker corresponding to the n point curvature equidistant increasing interval.

[0204] Similarly, when the distribution density of the fluorescence intensity of the nuclear pore marker in the n point curvature equidistant increasing intervals neither presents a downward trend nor an upward trend, the nuclear pore distribution pattern is identified as irrelevant.

[0205] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form and the second form, the first identification method and the second identification method can be used to identify the nuclear pore distribution pattern, respectively.

[0206] S106, according to the nuclear pore distribution pattern of the cells in the biological sample, obtaining the overall nuclear pore distribution pattern of the biological sample, and further evaluating the state of the tissue or organ corresponding to the biological sample;

[0207] In some embodiments, the overall nuclear pore distribution pattern of the biological sample means that after the identification of the nuclear pore distribution pattern of one or more cells in the biological sample, the identified cells present the nuclear pore distribution pattern at the overall level.

[0208] In some embodiments, when the positive correlation cell rate of the biological sample is greater than the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is evaluated as positive correlation.

[0209] In some embodiments, when the positive correlation cell rate of the biological sample is less than or equal to the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is evaluated as negative correlation.

[0210] In some embodiments, the positive correlation cell rate refers to the proportion of cells with positive correlation nuclear pore distribution in the biological sample to all the cells that have been identified in the biological sample.

[0211] In some embodiments, the negative correlation cell rate refers to the proportion of cells with negative correlation nuclear pore distribution in the biological sample to all the cells that have been identified in the biological sample.

[0212] In some embodiments, the sum of the negative correlation cell rate and the positive correlation cell rate is ≤ 100%.

[0213] It should be noted that in some application scenarios (for example, only the evaluation of whether the biological sample is a tumor sample is required), the state of the tissue or organ corresponding to the biological sample can be directly evaluated based on the overall nuclear pore distribution pattern of the biological sample, that is, when the overall nuclear pore distribution pattern of the biological sample is positive correlation, the biological sample is evaluated as a non-tumor sample, and when the overall nuclear pore distribution pattern of the biological sample is negative correlation, the biological sample is evaluated as a tumor sample.

[0214] In some embodiments, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the overall nuclear pore distribution pattern of the biological sample can also be identified based on the second identified method. That is, when the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, step S106 can be replaced by:

[0215] dividing the normalized point curvature data of one or more cells in the biological sample into n point curvature equidistantly increasing intervals;

[0216] When the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as negative correlation.

[0217] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as positive correlation.

[0218] In some embodiments, the distribution density of the fluorescence intensity of the nuclear pore marker can be calculated according to the average value of the normalized fluorescence intensity data of the nuclear pore marker corresponding to the point curvature of the one or more cells in the biological sample in the point curvature equidistantly increasing interval.

[0219] In other words, the second identification method provided by the present embodiment is also applicable to identifying the overall nuclear pore distribution pattern of the biological sample, i.e., taking the normalized point curvature data and the normalized fluorescence intensity data of the nuclear pore marker of the one or more cells in the biological sample as a whole, identifying the overall nuclear pore distribution pattern of the biological sample by judging whether the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend (or a downward trend) in the n point curvature equidistantly increasing intervals.

[0220] In some embodiments, to more accurately evaluate the biological sample, the S106 can further include:

[0221] According to the nuclear pore distribution pattern of the cells in the biological sample, the positive correlation cell rate and / or the negative correlation cell rate of the biological sample are obtained, and then the state of the tissue or organ corresponding to the biological sample is evaluated;

[0222] In some embodiments, when the positive correlation cell rate of the biological sample is not lower than a first preset value, the biological sample is evaluated as a non-tumor sample.

[0223] In some embodiments, the non-tumor sample can be a normal tissue sample and / or a paracancerous tissue sample.

[0224] In some embodiments, the first preset value can be 50%-60%.

[0225] In some embodiments, when the negative correlation cell rate of the biological sample is not lower than a second preset value, the biological sample is evaluated as a tumor sample.

[0226] In some embodiments, the second preset value can be 75%-90%.

[0227] After analyzing the nuclear pore distribution patterns of the cells in the tumor cell culture sample, the tumor tissue section sample and the paracancerous tissue section sample, it is found that the overall nuclear pore distribution pattern of the paracancerous tissue section sample is obviously different from that of the tumor cell culture sample and the tumor tissue section sample: the overall nuclear pore distribution pattern (nuclear pore fluorescence intensity-nuclear membrane curvature correlation) of the tumor cell culture sample and the tumor tissue section sample is negatively correlated (the negative correlation cell rate reaches 75% or even 90%), while the overall nuclear pore distribution pattern of the paracancerous tissue section sample is positively correlated (the positive correlation cell rate reaches 50% or more, for example, 60%).

[0228] In some embodiments, the S106 can further comprise comparing the overall nuclear pore distribution pattern, the positive correlation cell rate and / or the negative correlation cell rate of the biological sample with that of a reference sample to evaluate the trend of the state of the tissue or organ corresponding to the biological sample, thereby providing support for early diagnosis of diseases (e.g., cancer).

[0229] In some embodiments, the reference sample can be a selected reference sample and / or a reference sample derived from a subject. In some embodiments, when the negative correlation cell rate of the biological sample shows an increasing trend compared with the reference sample, the biological sample is evaluated as having a tendency of canceration (or deterioration). In some embodiments, when the positive correlation cell rate of the biological sample shows an increasing trend compared with the reference sample, the biological sample is evaluated as having a tendency of improvement. In some embodiments, when the positive correlation cell rate of the biological sample shows a decreasing trend compared with the reference sample, the biological sample is evaluated as having a tendency of canceration (or deterioration). In some embodiments, when the positive correlation cell rate of the biological sample shows an increasing trend compared with the reference sample, the biological sample is evaluated as having a tendency of improvement. In some embodiments, when the overall nuclear pore distribution pattern of the biological sample turns to negative correlation compared with the reference sample, the biological sample is evaluated as having a tendency of canceration (or deterioration). In some embodiments, when the overall nuclear pore distribution pattern of the biological sample turns to positive correlation compared with the reference sample, the biological sample is evaluated as having a tendency of improvement.

[0230] In some embodiments, "tendency", such as "tendency of canceration", refers to the reasonable medical probability of an event (e.g., cancer occurrence or recurrence). The term "tendency" also includes the frequency of such an event that can occur before, after or during continuous treatment.

[0231] In summary, the present embodiments can accurately identify the nuclear pore distribution pattern of individual cells in a biological sample and the overall nuclear pore distribution pattern of the biological sample, thereby achieving accurate evaluation of the state of the tissue or organ corresponding to the biological sample (e.g., early tumor detection, postoperative detection of advanced tumor).

[0232] Embodiment Two

[0233] As shown in Figure 2 The present embodiments provide a nuclear pore distribution pattern-based biological sample analysis system 100 corresponding to the method of Embodiment One, which comprises:

[0234] An image acquisition module 102 configured to acquire microscopic image data of a biological sample;

[0235] In some embodiments, the microscopy image data comprises fluorescence microscopy images and / or super-resolution microscopy image data at 40x magnification or greater.

[0236] In some embodiments, the microscopy image data can be obtained by batch acquisition of immunofluorescence pictures of the biological sample using a high-throughput imaging system.

[0237] In some embodiments, the imaging requirements comprise imaging of a well plate with a glass bottom or other ultra-thin bottom surface (e.g. 0.17-0.19 mm) using a 40x magnification objective.

[0238] In some embodiments, the biological sample comprises at least one cell.

[0239] In some embodiments, the biological sample comprises > 20 cells.

[0240] In some embodiments, the biological sample comprises a tissue and / or a cell culture.

[0241] In some embodiments, the biological sample can comprise tumor cells and / or cells with the potential to develop into tumor cells.

[0242] In some embodiments, the biological sample can not comprise tumor cells.

[0243] In some embodiments, the biological sample comprises neuroblastoma cells.

[0244] In some embodiments, the biological sample is labeled with a nuclear pore marker and a nuclear membrane marker.

[0245] In some embodiments, the nuclear pore marker comprises nuclear pore protein Nup98.

[0246] In some embodiments, the nuclear membrane marker comprises nuclear lamina protein Lamin A / C.

[0247] In some embodiments, the biological sample is further labeled with a nuclear marker.

[0248] In some embodiments, the nuclear marker comprises 4', 6-diamidino-2-phenylindole (DAPI).

[0249] In some embodiments, the above markers can be labeled in the cells by immunofluorescence staining or other suitable methods.

[0250] The first processing module 104 is configured to obtain a first region of interest, a second region of interest and a third region of interest in the microscopy image data.

[0251] In some embodiments, the first region of interest is a nucleus region circled by a fluorescence channel corresponding to the nucleus marker.

[0252] In some embodiments, the second region of interest is a nucleus region circled by a fluorescence channel corresponding to the nuclear pore marker.

[0253] In some embodiments, the third region of interest is a nucleus region circled by a fluorescence channel corresponding to the nuclear membrane marker.

[0254] The second processing module 106 is configured to delineate a nuclear membrane edge curve of a cell in the biological sample based on the third region of interest.

[0255] In some embodiments, the method of delineating the nuclear membrane edge curve comprises selecting a first number of points on the edge of the third region of interest (i.e. the edge of the nucleus) and connecting them into a closed curve.

[0256] In some embodiments, the first number is at least 20.

[0257] In some embodiments, the first number of points are connected by a smooth curve.

[0258] In some embodiments, the nuclear membrane edge curve can be set to 2 pixels.

[0259] In some embodiments, to obtain a nuclear membrane edge curve closer to the actual edge of the nucleus, the first number can be set to no less than 30.

[0260] The third processing module 108 is configured to extract nuclear pore distribution-nuclear membrane curvature distribution data of the cell based on the nuclear membrane edge curve.

[0261] In some embodiments, the nuclear pore distribution-nuclear membrane curvature distribution data comprises point curvature data at the nuclear membrane edge curve and fluorescence intensity data of the nuclear pore marker.

[0262] In some embodiments, the method of extracting the nuclear pore distribution-nuclear membrane curvature distribution data comprises taking an arbitrary point on the nuclear membrane edge curve as a starting point, sequentially obtaining point curvature values and nuclear pore marker fluorescence intensity values at each position of the nuclear membrane edge curve in a clockwise direction or a counterclockwise direction, and then obtaining point curvature data and nuclear pore marker fluorescence intensity data at the nuclear membrane edge curve (i.e. continuous spatial positions), i.e. nuclear pore distribution-nuclear membrane curvature distribution data.

[0263] The fourth processing module 110 is configured to process the nuclear pore distribution-nuclear membrane curvature distribution data into a first form and / or a second form.

[0264] In some embodiments, the first form is a distribution curve form, i.e., the point curvature data can be a point curvature distribution curve at the nuclear envelope curve, and the fluorescent intensity data of the nuclear pore marker can be a fluorescent intensity distribution curve of the nuclear pore marker at the nuclear envelope curve.

[0265] In some embodiments, the distribution curve is a fitted curve.

[0266] In some embodiments, the point curvature distribution curve at the nuclear envelope curve and the fluorescent intensity distribution curve of the nuclear pore marker at the nuclear envelope curve are fitted based on an eight-order polynomial function.

[0267] In some embodiments, the second form is a normalized data form, i.e., the point curvature data can be normalized point curvature data, and the fluorescent intensity data of the nuclear pore marker can be normalized fluorescent intensity data of the nuclear pore marker.

[0268] In some embodiments, the normalization method can be Min-Max normalization.

[0269] The first identification module 112 is configured to perform first identification on the nuclear pore distribution-nuclear envelope curvature distribution data in the first form to identify the nuclear pore distribution pattern of the cells of the biological sample.

[0270] In some embodiments, the first identification method includes: when the point curvature distribution curve and the fluorescent intensity distribution curve of the nuclear pore marker present a peak-to-peak reverse change trend, the nuclear pore distribution pattern is identified as negative correlation.

[0271] In some embodiments, when the point curvature distribution curve and the fluorescent intensity distribution curve of the nuclear pore marker present a peak-to-peak same direction change trend, the nuclear pore distribution pattern is identified as positive correlation.

[0272] In some embodiments, when the point curvature distribution curve and the fluorescent intensity distribution curve of the nuclear pore marker neither present a peak-to-peak reverse change trend nor present a peak-to-peak same direction change trend, the nuclear pore distribution pattern is identified as no correlation.

[0273] In some embodiments, the biological sample analysis system 100 can further include a second identification module 114 configured to perform second identification on the nuclear pore distribution-nuclear envelope curvature distribution data in the second form to identify the nuclear pore distribution pattern of the cells of the biological sample.

[0274] In some embodiments, the second identification method includes: dividing the normalized point curvature data into n point curvature equidistant increasing intervals;

[0275] In some embodiments, n can be an integer ≥ 10, for example 15.

[0276] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n-point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as negative correlation;

[0277] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n-point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as positive correlation.

[0278] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows neither a downward trend nor an upward trend in the n-point curvature equidistantly increasing intervals, the nuclear pore distribution pattern is identified as no correlation.

[0279] The evaluation module 116 is configured to evaluate the state of the tissue or organ corresponding to the biological sample.

[0280] In some embodiments, the evaluation module 116 further comprises a first evaluation unit 202 configured to obtain the overall nuclear pore distribution pattern of the biological sample based on the nuclear pore distribution pattern of the cells identified by the first identification module 112 and / or the second identification module 114.

[0281] In some embodiments, when the positive correlation cell rate of the biological sample is greater than the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is positive correlation.

[0282] In some embodiments, when the positive correlation cell rate of the biological sample is less than or equal to the negative correlation cell rate, the overall nuclear pore distribution pattern of the biological sample is negative correlation.

[0283] In some embodiments, the evaluation module 116 further comprises a second evaluation unit 204 configured to perform a second identification according to the second form of nuclear pore distribution-nuclear membrane curvature distribution data of one or more cells in the biological sample, i.e., dividing the normalized point curvature data of one or more cells in the biological sample into n-point curvature equidistantly increasing intervals;

[0284] When the distribution density of the fluorescence intensity of the nuclear pore marker shows a downward trend in the n-point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as negative correlation;

[0285] In some embodiments, when the distribution density of the fluorescence intensity of the nuclear pore marker shows an upward trend in the n-point curvature equidistantly increasing intervals, the overall nuclear pore distribution pattern of the biological sample is identified as positive correlation.

[0286] In some embodiments, the method of evaluation comprises: when the overall nuclear pore distribution pattern of the biological sample is positively correlated, the biological sample is evaluated as a non-tumor sample; and when the overall nuclear pore distribution pattern of the biological sample is negatively correlated, the biological sample is evaluated as a tumor sample.

[0287] In some embodiments, the first evaluation unit 202 is further configured to obtain a positively correlated cell rate and / or a negatively correlated cell rate of the biological sample according to the nuclear pore distribution pattern of cells in the biological sample, and to evaluate the state of the tissue or organ corresponding to the biological sample.

[0288] In some embodiments, the method of evaluation can further comprise: when the positively correlated cell rate of the biological sample is not lower than a first preset value, the biological sample is evaluated as a non-tumor sample.

[0289] In some embodiments, the non-tumor sample can be a normal tissue sample and / or a paracancerous tissue sample.

[0290] In some embodiments, the first preset value can be 50%-60%.

[0291] In some embodiments, when the negatively correlated cell rate of the biological sample is not lower than a second preset value, the biological sample is evaluated as a tumor sample.

[0292] In some embodiments, the second preset value can be 75%-90%.

[0293] In some embodiments, the evaluation module 116 can further comprise a third evaluation unit 206 configured to compare the difference in the overall nuclear pore distribution pattern, the positively correlated cell rate and / or the negatively correlated cell rate between the biological sample and a reference sample, and to evaluate the trend of change in the state of the tissue or organ corresponding to the biological sample.

[0294] In some embodiments, the reference sample can be a selected reference sample and / or a reference sample derived from the subject.

[0295] In some embodiments, the biological sample is assessed as having a tendency to cancerize (or worsen) when the negatively associated cell rate of the biological sample is trending higher compared to the reference sample. In some embodiments, the biological sample is assessed as having a tendency to improve when the positively associated cell rate of the biological sample is trending higher compared to the reference sample. In some embodiments, the biological sample is assessed as having a tendency to cancerize (or worsen) when the positively associated cell rate of the biological sample is trending lower compared to the reference sample. In some embodiments, the biological sample is assessed as having a tendency to improve when the positively associated cell rate of the biological sample is trending higher compared to the reference sample. In some embodiments, the biological sample is assessed as having a tendency to cancerize (or worsen) when the overall nuclear pore distribution pattern of the biological sample is trending negative compared to the reference sample. In some embodiments, the biological sample is assessed as having a tendency to improve when the overall nuclear pore distribution pattern of the biological sample is trending positive compared to the reference sample.

[0296] Example Three

[0297] The method of Example One is applied to detect neuroblastoma cell cultures.

[0298] Neuroblastoma cultures under normal culture conditions are immunofluorescently stained as follows: Neuroblastoma cell cultures grown in confocal dishes are removed from the incubator; washed with PBS at room temperature for 3 times, 2 min each; fixed with 4% paraformaldehyde in PBS solution at room temperature for 30 min; washed with PBS at room temperature for 3 times, 2 min each; permeabilized with 0.3% Triton-X 100 solution at room temperature for 30 min; washed with PBS at room temperature for 3 times, 2 min each; blocked with 10% goat serum in PBS solution at room temperature for 1 h; aspirate the goat serum and add primary antibody (Nup98 1 : 1000 dilution, Lamin A / C 1 : 1000 dilution), incubate overnight at 4 °C; the next day aspirate the primary antibody, wash with PBS at room temperature for 3 times, 2 min each; add secondary antibody (1 : 1000 dilution), incubate at room temperature for 2 h in the dark; wash with PBS at room temperature for 3 times, 2 min each; add DAPI solution, incubate at room temperature for 20 min in the dark to stain the nuclei; wash with PBS at room temperature for 3 times, 2 min each; store at 4 °C in the dark until ready for imaging.

[0299] The fluorescence microscope images were obtained using a confocal laser scanning microscope and processed using ImageJ software. The nuclei and background in the fluorescence microscope images were identified, and the touching images and overlapping nuclei were discarded. The kappa plug-in was opened, and at least 30 points were uniformly selected on the nuclear envelope edge according to Lamin A / C (a nuclear membrane marker), and a smooth curve was used to connect to obtain the nuclear envelope edge curve. The nuclear envelope edge curve was manually adjusted to adapt to the nuclear envelope edge, and the edge region threshold was set to 2 pixels Figure 3 ). The obtained nuclear envelope edge curve was subjected to curvature fitting calculation (for example, according to formula I) to obtain the point curvature at the nuclear envelope edge curve and the fluorescence intensity of Nup98 (a nuclear pore marker).

[0300] (I);

[0301] wherein y' and y" represent the first derivative and the second derivative, respectively.

[0302] The obtained point curvature at the nuclear envelope edge curve and the fluorescence intensity of Nup98 were exported. To avoid extreme curvature and measurement errors, an eight-order polynomial was used to curve fit the "Nup98 fluorescence intensity-position" data (as shown by the green points in Figure 4 ) and the "point curvature-position data" (as shown by the gray points in Figure 4 ) at the nuclear envelope edge curve to obtain a nuclear pore fluorescence intensity-nuclear membrane curvature distribution curve. As shown in Figure 4 , the Nup98 fluorescence intensity distribution curve (as shown by the green curve in Figure 4 ) and the point curvature distribution curve (as shown by the black curve in Figure 4 ) of the neuroblastoma cells showed a peak-to-peak reverse (as indicated by the red dashed box in Figure 4 ) trend, i.e., the point curvature at the nuclear envelope edge curve and the fluorescence intensity of Nup98 had a negative correlation in the same spatial position dimension, and the nuclear pore distribution pattern of the neuroblastoma cells was negatively correlated.

[0303] This embodiment further identified the nuclear pore distribution pattern of each tumor cell in the neuroblastoma cell culture. The results are shown in Table 1. Among the 32 identified cells, more than 80% of the cells had a negatively correlated nuclear pore distribution pattern (i.e., the negative correlation cell rate was ≥80%, which was confirmed by Spearman correlation analysis), i.e., the overall nuclear pore distribution pattern of the neuroblastoma cell culture sample was negatively correlated.

[0304] wherein the Spearman correlation analysis used in the present embodiment calculates the Spearman correlation coefficient and the significance of the correlation between the point curvature and the Nup98 fluorescence intensity in each cell (e.g. according to equations II, III, IV). When the Spearman correlation coefficient is negative and <0.05, the correlation is considered to be negative, when the Spearman correlation coefficient is positive and <0.05, the correlation is considered to be positive, when ≥0.05, the correlation is considered to be non-significant.

[0305] (II);

[0306] wherein, is the Spearman correlation coefficient; xi is the point curvature value of the i-th discrete point, yi is the Nup98 fluorescence intensity value of the i-th discrete point, R ( xi ) represents the rank of the i-th value (i.e. the point curvature) in the variable x among all x, x ( R ) represents the rank of the i-th value (i.e. the Nup98 fluorescence intensity) in the variable y among all y; yi represents the average of all ranks of the variable y represents the average of all ranks of the variable is the total number of discrete points analyzed on the nuclear envelope curve of a single cell. x y (III);

[0307] (IV); wherein,

[0308] is the test statistic calculated from the Spearman correlation coefficient, is the Spearman correlation coefficient, is the total number of discrete points analyzed on the nuclear envelope curve of a single cell;

[0309] is the probability of the Spearman correlation, represents the probability that a random variable subject to a t-distribution and having degrees of freedom is greater than . Table 1

[0310]

[0311]

[0312] ​​​​The point curvature and the fluorescence intensity of Nup98 at the points on the nuclear membrane edge curve of the 32 cells are normalized by min-max normalization. The normalized point curvature is divided into 15 equal intervals, and the relative fluorescence intensity of Nup98 in the 15 equal intervals is calculated and plotted as a rose diagram. Figure 5 ).

[0313] As shown in FIG. 8, in the neuroblastoma cell culture sample, the relative fluorescence intensity of Nup98 of the neuroblastoma cells (indicated by the numbers in the circle of FIG. 8) increases (indicated by the red arrow of FIG. 8) and decreases with the point curvature equal interval (indicated by the numbers outside the circle of FIG. 8, where “1” represents the first point curvature equal interval, and the corresponding normalized point curvature is [0, 1 / 15]). Figure 5 Figure 5 Figure 5 Figure 5 That is, the results of FIG. 8 show that in the tumor sample (such as the neuroblastoma cell culture sample), the nuclear pores tend to be distributed in the low curvature region, that is, the point curvature at the nuclear membrane edge curve and the fluorescence intensity of Nup98 have a negative correlation in the same spatial position dimension, and the overall nuclear pore distribution pattern of the tumor sample (such as the neuroblastoma cell culture sample) is negative correlation.

[0314] That is, the results of FIG. 8 show that in the tumor sample (such as the neuroblastoma cell culture sample), the nuclear pores tend to be distributed in the low curvature region, that is, the point curvature at the nuclear membrane edge curve and the fluorescence intensity of Nup98 have a negative correlation in the same spatial position dimension, and the overall nuclear pore distribution pattern of the tumor sample (such as the neuroblastoma cell culture sample) is negative correlation. Figure 5 The above results all show that the nuclear pore distribution pattern of the tumor cells is negative correlation, that is, the nuclear pore fluorescence intensity of the neuroblastoma cells and the nuclear membrane curvature show a typical negative correlation, and the overall nuclear pore distribution pattern of the tumor sample (such as the neuroblastoma cell culture sample) is negative correlation.

[0315] Example Four

[0316] The method of Example One is applied to detect tumor tissue and paracancerous tissue samples from neuroblastoma patients.

[0317]

[0318] ​​​​Immunofluorescence staining was performed on paraffin sections of tumor-adjacent tissue from neuroblastoma patients as follows: The tumor-adjacent tissue paraffin sections were preheated in a 60°C oven for 30 min, and the preheated sections were then placed in xylene I and II solutions for 10 min each. Subsequently, the sections were placed in gradient alcohol (100 v / v%, 95 v / v%, 90 v / v%, 80 v / v%, and 70 v / v%) for 10 min each. The sections were washed with PBS at room temperature for 3 times, each for 2 min. The sodium citrate-EDTA repair solution containing the glass slides was heated to boiling for 15 min, and then cooled to room temperature for antigen repair. The sections were washed with PBS at room temperature for 3 times, each for 2 min. Immunofluorescence blocking solution was added, and the sections were blocked at room temperature for 60 min. The sections were washed with PBS at room temperature for 3 times, each for 2 min. The primary antibody (Nup981: 1:1000 dilution, Lamin A / C 1:1000 dilution) was added, and the sections were incubated overnight in a refrigerator at 4°C. The next day, the primary antibody was removed, and the sections were washed with PBS at room temperature for 3 times, each for 2 min. The secondary antibody (1:1000 dilution) was added, and the sections were incubated at room temperature in the dark for 2 h. The sections were washed with PBS at room temperature for 3 times, each for 2 min. DAPI solution was added, and the sections were incubated at room temperature in the dark for 20 min for nuclear staining. Mounting medium was added to the sections, and the sections were covered with coverslips. The staining results are shown in FIG. 7, wherein the clustered structure in the central region is tumor tissue (the upper right panel of FIG. 7 is a magnified view of the tumor tissue), and the edge region is the adjacent tissue (the lower right panel of FIG. 7 is a magnified view of the adjacent tissue). Figure 6 Figure 6 Figure 6

[0319] The fluorescence microscope images were obtained using a confocal laser scanning microscope, and the fluorescence microscope images were processed using ImageJ software. The nuclei and background in the fluorescence microscope images were identified, and the contact image boundaries and overlapping nuclei were discarded. The kappa plug-in was opened, and at least 30 points were selected uniformly on the nuclear envelope edge according to the lamin A / C (nuclear membrane marker), and a smooth curve was used to connect to obtain the sketched nuclear envelope edge curve. The nuclear envelope edge curve was manually adjusted to adapt to the nuclear envelope edge, and the edge region threshold was set to 2 pixels. The kappa plug-in was used to perform curvature fitting calculation on the obtained nuclear envelope edge curve to obtain the point curvature at the nuclear envelope edge curve and the fluorescence intensity of Nup98.

[0320] The obtained point curvature at the nuclear envelope edge curve and the fluorescence intensity of Nup98 were exported. The "Nup98 fluorescence intensity-position" data (as shown by the green points in FIG. 8a) and the "point curvature-position data" (as shown by the gray points in FIG. 8a) at the nuclear envelope edge curve were fitted using an eight-degree polynomial to obtain the nuclear pore fluorescence intensity-nuclear membrane curvature distribution curve. As shown in FIG. 8b, the fluorescence intensity of Nup98 at the nuclear envelope edge curve is positively correlated with the point curvature at the nuclear envelope edge curve. Figure 7 Figure 7 Figure 7 ​​​​​As shown in Figure a, the Nup98 fluorescence intensity distribution curve of neuroblastoma cells in tumor tissue derived from neuroblastoma patients (e.g., Figure 7 (as shown by the green curve in a) and the point curvature distribution curve (as shown in...) Figure 7 The black curve in a shows a peak-to-peak reversal (as shown in a). Figure 7 The trend of change (indicated by the red dashed box in a) shows that the curvature of the point at the edge of the nuclear membrane is negatively correlated with the fluorescence intensity of Nup98 in the same spatial dimension, and the nuclear pore distribution pattern of neuroblastoma cells is negatively correlated.

[0321] This embodiment further identified the nuclear pore distribution patterns of various cells in tumor tissues derived from neuroblastoma patients. The results are shown in Table 2. Among the 30 cells, more than 90% of the cells showed negative correlations in their nuclear pore distribution patterns (i.e., the rate of negatively correlated cells ≥ 90%, a result confirmed by Spearman correlation analysis), indicating that the overall nuclear pore distribution patterns of tumor tissue samples derived from neuroblastoma patients were negatively correlated.

[0322] Table 2

[0323]

[0324] In this embodiment, the point curvature at the nuclear membrane edge curve of the above 30 cells and the fluorescence intensity data of Nup98 were normalized using min-max. The normalized point curvature was divided into 15 equal parts (i.e., 15 equally spaced intervals of point curvature), and the relative fluorescence intensity of Nup98 in the 15 equally spaced intervals of point curvature was calculated and a rose diagram was drawn. Figure 7 b).

[0325] like Figure 7 As shown in b, the relative fluorescence intensity of Nup98 in neuroblastoma cells (e.g., in tumor tissue samples derived from neuroblastoma patients) Figure 7 The numbers within the circle of b indicate an interval where the curvature of the point increases at equal intervals (e.g., Figure 7 The numbers outside the circle indicated by 'b' indicate that "1" represents the first interval of equidistantly increasing curvature, with the corresponding normalized curvature rising and falling within the range of [0, 1 / 15). The relative fluorescence intensity of Nup98 at the first interval of equidistantly increasing curvature (i.e., "1") is greater than that at the last interval of equidistantly increasing curvature (i.e., "15").

[0326] In other words, Figure 8The results of b show that in tumor samples (e.g., tumor tissue samples derived from neuroblastoma patients), nuclear pores tend to be distributed in low curvature regions, i.e., there is a negative correlation between the point curvature at the nuclear membrane edge curve and the fluorescence intensity of Nup98 in the same spatial position dimension, and the overall nuclear pore distribution pattern of tumor samples (e.g., tumor tissue samples derived from neuroblastoma patients) is negatively correlated.

[0327] The above results show that the nuclear pore distribution pattern of tumor cells is negatively correlated, i.e., the nuclear pore fluorescence intensity of neuroblastoma cells and the nuclear membrane curvature show a typical negative correlation, and the overall nuclear pore distribution pattern of tumor samples (e.g., tumor tissue samples derived from neuroblastoma patients) is negatively correlated.

[0328] This embodiment further analyzes the nuclear pore distribution pattern of normal cells in the cancer-adjacent tissue derived from the neuroblastoma patient.

[0329] The point curvature at the nuclear membrane edge curve and the fluorescence intensity data of Nup98 are exported. The "Nup98 fluorescence intensity-position" data (as shown by the green dots in Figure 8 a) and the "point curvature-position data" (as shown by the gray dots in Figure 8 a) are curve fitted using an eight-degree polynomial to obtain a nuclear pore fluorescence intensity-nuclear membrane curvature distribution curve. As shown in Figure 8 a, the Nup98 fluorescence intensity distribution curve (as shown by the green curve in Figure 8 a) and the point curvature distribution curve (as shown by the black curve in Figure 8 a) of the normal cells in the cancer-adjacent tissue derived from the neuroblastoma patient show a peak-to-peak co-directional (as indicated by the red dashed box in Figure 9 a) trend, i.e., there is a positive correlation between the point curvature at the nuclear membrane edge curve and the fluorescence intensity of Nup98 in the same spatial position dimension, and the nuclear pore distribution pattern of normal cells is positively correlated.

[0330] The above results show that, unlike tumor cells, the nuclear pore distribution pattern of normal cells in the cancer-adjacent tissue derived from the neuroblastoma patient is positively correlated, i.e., the nuclear pore fluorescence intensity of normal cells and the nuclear membrane curvature show a typical positive correlation.

[0331] This embodiment further identifies the nuclear pore distribution pattern of each cell in the cancer-adjacent tissue derived from the neuroblastoma patient. The results are shown in Table 3. Among the 21 cells, more than 60% of the cells have a positively correlated nuclear pore distribution pattern (positively correlated cell rate ≥ 60%, which is confirmed by Spearman correlation analysis), and the overall nuclear pore distribution pattern of the cancer-adjacent tissue sample derived from the neuroblastoma patient is positively correlated.

[0332] Table 3

[0333]

[0334] like Figure 8 As shown, the overall nuclear pore distribution pattern in tumor tissue exhibits a significant negative correlation, with the negatively correlated cell rate reaching 90%, while the positively correlated cell rate is only 7%. Conversely, the overall nuclear pore distribution pattern in adjacent normal tissue shows a significant positive correlation, with the positively correlated cell rate at 62%, while the negatively correlated cell rate is only 33%. Therefore, the differences in the overall nuclear pore distribution pattern, positively correlated cell rate, and / or negatively correlated cell rate of biological samples can be used for tumor diagnosis.

[0335] In this embodiment, the point curvature at the nuclear membrane edge curve of the above 21 cells and the fluorescence intensity data of Nup98 were normalized using min-max. The normalized point curvature was divided into 15 equal parts (i.e., 15 equally spaced intervals of point curvature), and the relative fluorescence intensity of Nup98 in the 15 equally spaced intervals of point curvature was calculated and a rose diagram was drawn. Figure 8 b).

[0336] like Figure 8 As shown in b, the relative fluorescence intensity of Nup98 in normal cells in adjacent normal tissues derived from neuroblastoma patients (e.g., ... Figure 8 The numbers within the circle of b indicate an interval where the curvature of the point increases at equal intervals (e.g., Figure 8 The numbers outside the circle indicated by 'b' indicate that "1" represents the first interval of equidistantly increasing curvature, with the corresponding normalized curvature increasing from [0, 1 / 15). The relative fluorescence intensity of Nup98 at the first interval of equidistantly increasing curvature (i.e., "1") is less than that at the last interval of equidistantly increasing curvature (i.e., "15").

[0337] In other words, ​ The results of b indicate that in non-tumor samples (e.g., adjacent normal tissue from neuroblastoma patients), nuclear pores tend to be distributed in low curvature regions. That is, the curvature of the point at the edge of the nuclear membrane is positively correlated with the fluorescence intensity of Nup98 in the same spatial dimension. The overall nuclear pore distribution pattern of non-tumor samples (e.g., adjacent normal tissue samples from neuroblastoma patients) is positively correlated.

[0338] The above results all indicate that the nuclear pore distribution pattern of normal cells is positively correlated, that is, the nuclear pore fluorescence intensity of normal cells shows a typical positive correlation with the nuclear membrane curvature, and the overall nuclear pore distribution pattern of non-tumor samples (such as adjacent normal tissue samples from neuroblastoma patients) is positively correlated.

[0339] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, also can be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of contribution to the prior art can be embodied in the form of software product, the computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk), including a plurality of instructions to make a computer terminal (may be a mobile phone, computer, server, or network equipment, etc.) executes the method described in various embodiments of the present application.

[0340] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, the above-mentioned specific embodiments are only illustrative, but not limited, those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which all belong to the protection of the present application.

Claims

1. A method for analyzing biological samples based on nuclear pore distribution patterns, characterized in that, Includes the following steps: S101. Acquire microscopic image data of a biological sample, wherein the biological sample includes at least one cell; the biological sample is labeled with nuclear pore markers and nuclear membrane markers; S102. Obtain the first region of interest, the second region of interest, and the third region of interest from the microscopic image data; the first region of interest is the nucleus region delineated using the fluorescence channel corresponding to the nuclear marker; the second region of interest is the nucleus region delineated using the fluorescence channel corresponding to the nuclear pore marker. The third region of interest is the cell nucleus region delineated using the fluorescence channel corresponding to the nuclear membrane marker; S103. Based on the third region of interest, delineate the nuclear membrane edge curve of the cells in the biological sample; the delineation method includes selecting a first number of points at the edge of the third region of interest and connecting them to form a closed curve; S104. Based on the nuclear membrane edge curve, extract the nuclear pore distribution-nuclear membrane curvature distribution data of the cell. The nuclear pore distribution-nuclear membrane curvature distribution data includes the point curvature data at the nuclear membrane edge curve and the fluorescence intensity data of the nuclear pore markers. The nuclear pore distribution-nuclear membrane curvature distribution data includes a first form and / or a second form. When the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form, the point curvature data is the point curvature distribution curve at the edge curve of the nuclear membrane, and the fluorescence intensity data of the nuclear pore marker is the fluorescence intensity distribution curve of the nuclear pore marker at the edge curve of the nuclear membrane; S105. Based on the nuclear pore distribution-nuclear membrane curvature distribution data, identify the nuclear pore distribution pattern of cells in the biological sample; When the nuclear pore distribution-nuclear membrane curvature distribution data is in a first form, the nuclear pore distribution pattern is identified using the first identification method. The first identification method includes: When the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker show opposite peak-to-peak trends, the nuclear pore distribution pattern is identified as negatively correlated; when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker show the same peak-to-peak trends, the nuclear pore distribution pattern is identified as positively correlated. S106. Based on the nuclear pore distribution pattern of the cells in the biological sample, obtain the overall nuclear pore distribution pattern of the biological sample, and then assess the state of the tissue or organ corresponding to the biological sample; S106 specifically includes: when the overall nuclear pore distribution pattern of the biological sample is positively correlated, the biological sample is assessed as a non-tumor sample, and when the overall nuclear pore distribution pattern of the biological sample is negatively correlated, the biological sample is assessed as a tumor sample.

2. The method as described in claim 1, characterized in that, When the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the point curvature data is normalized point curvature data, and the fluorescence intensity data of the nuclear pore markers is normalized fluorescence intensity data of the nuclear pore markers.

3. The method as described in claim 2, characterized in that, When the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the nuclear pore distribution pattern is identified using the second identification method; the second identification method includes: The normalized point curvature data is divided into n point curvature equal-distance increasing intervals; when the distribution density of the fluorescence intensity of the nuclear pore marker shows a decreasing trend in the n point curvature equal-distance increasing intervals, the nuclear pore distribution pattern is identified as negatively correlated; when the distribution density of the fluorescence intensity of the nuclear pore marker shows an increasing trend in the n point curvature equal-distance increasing intervals, the nuclear pore distribution pattern is identified as positively correlated.

4. The method as described in claim 1, characterized in that, S106 further includes: obtaining the positive and / or negative cell rates of the biological sample based on the nuclear pore distribution pattern of the cells in the biological sample, and then assessing the state of the tissue or organ corresponding to the biological sample.

5. A biological sample analysis system based on nuclear pore distribution patterns, characterized in that, include: The image acquisition module is configured to acquire microscopic image data of biological samples; The biological sample mentioned above includes at least one cell; The biological samples were labeled with nuclear pore markers and nuclear membrane markers; The first processing module is configured to acquire a first region of interest, a second region of interest, and a third region of interest from the microscopic image data; the first region of interest is a nuclear region delineated using the fluorescence channel corresponding to a nuclear marker; the second region of interest is a nuclear region delineated using the fluorescence channel corresponding to a nuclear pore marker. The third region of interest is the cell nucleus region delineated using the fluorescence channel corresponding to the nuclear membrane marker; The second processing module is configured to delineate the nuclear membrane edge curve of cells in the biological sample based on the third region of interest; the delineation method includes selecting a first number of points at the edge of the third region of interest and connecting them to form a closed curve; The third processing module is configured to extract nuclear pore distribution-nuclear membrane curvature distribution data of the cell based on the nuclear membrane edge curve; the nuclear pore distribution-nuclear membrane curvature distribution data includes point curvature data at the nuclear membrane edge curve and fluorescence intensity data of the nuclear pore markers; The fourth processing module is configured to process the nuclear pore distribution-nuclear membrane curvature distribution data into a first form and / or a second form; When the nuclear pore distribution-nuclear membrane curvature distribution data is in the first form, the point curvature data is the point curvature distribution curve at the edge curve of the nuclear membrane, and the fluorescence intensity data of the nuclear pore marker is the fluorescence intensity distribution curve of the nuclear pore marker at the edge curve of the nuclear membrane; The first identification module is configured to perform a first identification on the first form of nuclear pore distribution-nuclear membrane curvature distribution data to identify the nuclear pore distribution pattern of the cells in the biological sample; The first identification method includes: when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker show a peak-to-peak reversal trend, the nuclear pore distribution pattern is identified as negatively correlated; when the point curvature distribution curve and the fluorescence intensity distribution curve of the nuclear pore marker show a peak-to-peak unidirectional trend, the nuclear pore distribution pattern is identified as positively correlated. An evaluation module is configured to evaluate the state of the tissue or organ corresponding to the biological sample; the evaluation module includes a first evaluation unit configured to obtain the overall nuclear pore distribution pattern of the biological sample based on the nuclear pore distribution pattern of identified cells, so as to evaluate the state of the tissue or organ corresponding to the biological sample; the evaluation method includes: when the overall nuclear pore distribution pattern of the biological sample is positively correlated, the biological sample is evaluated as a non-tumor sample; when the overall nuclear pore distribution pattern of the biological sample is negatively correlated, the biological sample is evaluated as a tumor sample.

6. The system as described in claim 5, characterized in that, When the nuclear pore distribution-nuclear membrane curvature distribution data is in the second form, the point curvature data is normalized point curvature data, and the fluorescence intensity data of the nuclear pore markers is normalized fluorescence intensity data of the nuclear pore markers.

7. The system as described in claim 6, characterized in that, The system may further include: a second identification module configured to perform a second identification on the second form of nuclear pore distribution-nuclear membrane curvature distribution data to identify the nuclear pore distribution pattern of cells in the biological sample; the second identification method includes: The normalized point curvature data is divided into n point curvature equal-distance increasing intervals; when the distribution density of the fluorescence intensity of the nuclear pore marker shows a decreasing trend in the n point curvature equal-distance increasing intervals, the nuclear pore distribution pattern is identified as negatively correlated; when the distribution density of the fluorescence intensity of the nuclear pore marker shows an increasing trend in the n point curvature equal-distance increasing intervals, the nuclear pore distribution pattern is identified as positively correlated.

8. The system as described in claim 5, characterized in that, The first evaluation unit is further configured to obtain the positive and / or negative cell rates of the biological sample based on the nuclear pore distribution pattern of the cells in the biological sample, and then evaluate the state of the tissue or organ corresponding to the biological sample.

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