Cell classification and function evaluation method and system based on nucleolus morphology

Abstract

The invention relates to the field of cell classification, in particular to a cell classification and function evaluation method and system based on nucleolus morphology. The method provided by the invention comprises the following steps: S101, acquiring microscopic image data of a biological sample; s102, acquiring a first region of interest, a second region of interest and a third region of interest; s103, extracting kernel feature data; s104, carrying out first classification on cells in the biological sample; s105, carrying out second classification on the C-type cells in the biological sample; and S106, calculating the number and / or proportion of the A-type cells and the B-type cells in the biological sample so as to evaluate the functional state of the tissues or organs corresponding to the biological sample. The method can accurately and sensitively reflect and evaluate the functional state of the tissue or organ corresponding to the biological sample, and is especially suitable for the biological sample which is lack of a specific marker and has strong heterogeneity.

Description

Technical Field

[0001] The present invention relates to the field of cell classification, and in particular to a method and system for cell classification and function evaluation based on nucleolus morphology. Background Art

[0002] Traditional disease characterization methods rely on specific disease markers and are often limited by factors such as high tissue heterogeneity and low marker expression levels. In particular, when screening drug effectiveness and molecular targets, existing methods can often only evaluate toxicity indicators by detecting cell activity or cell apoptosis levels, which is difficult to reflect changes in the response of key subpopulations of cells to drugs.

[0003] As an important subcellular structure in cells, the nucleolus undertakes multiple core functions such as rRNA transcription, ribosome biogenesis, cell stress and response, and cell cycle regulation. The nucleolus plays a key role in the cell growth process and is closely related to cell growth, proliferation, differentiation, and dysfunction. In recent years, studies have shown that the morphological changes of the nucleolus can reflect the health status of cells, especially closely related to the occurrence and development of cancer, immune diseases, and cardiovascular diseases.

[0004] Although the prior art has certain evaluation criteria for nucleolar morphological changes, such as the nucleolus of cancer cells may become larger, it lacks clear judgment criteria, and the evaluation of nucleolar morphological results is highly subjective. For more complex biological samples (such as tissue samples with ambiguous nucleolar phenotypes and high heterogeneity), the identification accuracy is low and the repeatability is poor. For example, Chinese patent application CN112239455A discloses an RNA fluorescent probe that uses nucleolar morphological changes to quickly distinguish cancer from normal tissues. The interpretation of the staining results of the nucleolus not only still relies on manual work, but can only distinguish breast tissue from normal tissue, and it is difficult to characterize the specific pathological state corresponding to the biological sample. Chinese patent application CN111886630A discloses an automated method for analyzing biological tissues and cells, which is based on three-dimensional images. While the imaging is difficult and complex, it requires the combination of multiple geometric morphological features of the nucleolus to achieve cell classification, and it is difficult to sensitively reflect the change trend of the nucleolus, and thus it is difficult to apply to application scenarios such as early diagnosis of diseases and high-throughput drug screening. Summary of the invention

[0005] In a first aspect, the present invention provides a method for cell classification and function assessment based on nucleolar morphology, comprising the following steps: S101 acquires microscopic image data of biological samples; In some embodiments, the microscopic image data includes an optical microscope image with an objective lens of 40 times or more and / or super-resolution microscopic image data.

[0006] In some embodiments, the biological sample comprises a tissue or a cell culture comprising at least one cell.

[0007] In some embodiments, the biological sample is labeled with nucleolar markers, and the nucleolar markers include at least a dense fiber component marker and a granular component marker.

[0008] In some embodiments, the dense fiber component marker comprises fibrinogen (FBL).

[0009] In some embodiments, the particle component marker comprises nucleophosmin 1 (NPM1).

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

[0011] In some embodiments, the nuclear marker comprises 4',6-diamidino-2-phenylindole (DAPI).

[0012] S102: Acquire a first region of interest, a second region of interest, and a third region of interest in the microscopic image data; In some embodiments, the first region of interest is a cell nucleus region delineated by a fluorescent channel corresponding to the cell nucleus marker.

[0013] In some embodiments, the second region of interest is a nucleolus region delineated by a fluorescent channel corresponding to the dense fiber component marker.

[0014] In some embodiments, the third region of interest is a nucleolus region delineated using a fluorescent channel corresponding to the particle component marker.

[0015] S103 extracting nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; S104, performing a first classification on cells in the biological sample based on the line scan data; In some embodiments, the line scan data includes fluorescence intensity distribution curves of the second region of interest and the third region of interest.

[0016] In some embodiments, the first classification method includes: When the fluorescence intensity distribution curve of the third region of interest surrounds the fluorescence intensity distribution curve of the second region of interest, classifying the cell as a type A cell; When the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, the cell is classified as a type C cell.

[0017] In some embodiments, the spillover assessment method comprises: Analyze whether the fluorescence intensity change trends of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest at the first non-overlapping area and the last non-overlapping area are the same in the non-overlapping area formed outside the fluorescence intensity distribution curve of the third region of interest; if not, it overflows; if so, it does not overflow.

[0018] S105 performing a second classification on the type C cells in the biological sample; In some embodiments, the second classification method includes: The ratio of the area of ​​the second region of interest to the area of ​​the third region of interest in the C-type cells is analyzed to determine whether it is abnormal, so as to perform a second classification on the C-type cells in the biological sample.

[0019] In some embodiments, if the ratio of the area of ​​the second region of interest to the third region of interest in the C-type cells is abnormal, the classification of the C-type cells is adjusted to B-type cells; if it is not abnormal, the classification of the C-type cells is adjusted to A-type cells.

[0020] In some embodiments, when the ratio of the area of ​​the second region of interest to the area of ​​the third region of interest shows an increasing trend, it is considered abnormal.

[0021] S106 Calculate the number and / or ratio of the type A cells to the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

[0022] In some embodiments, the biological sample includes a biological sample treated with a drug and a biological sample not treated with a drug.

[0023] In some embodiments, the method further comprises: S107 analyzing an average diameter of spots formed by the dense fiber component marker in the second region of interest in the B-type cells, and when the average diameter is less than a first threshold, the biological sample is evaluated as being in a malignant proliferation state.

[0024] In some embodiments, the first threshold comprises 0.5-2 μm.

[0025] In a second aspect, the present invention provides a cell classification and function evaluation system based on nucleolus morphology, the system comprising: An image acquisition module 102, configured to acquire microscopic image data of a biological sample; In some embodiments, the microscopic image data includes an optical microscope image with an objective lens of 40 times or more and / or super-resolution microscopic image data.

[0026] In some embodiments, the biological sample comprises a tissue or a cell culture comprising at least one cell.

[0027] In some embodiments, the biological sample is labeled with nucleolar markers, and the nucleolar markers include at least a dense fiber component marker and a granular component marker.

[0028] In some embodiments, the dense fiber component marker comprises fibrinogen (FBL).

[0029] In some embodiments, the particle component marker comprises nucleophosmin 1 (NPM1).

[0030] In some embodiments, the biological sample is further labeled with a nuclear marker.

[0031] In some embodiments, the nuclear marker comprises 4',6-diamidino-2-phenylindole (DAPI).

[0032] The first processing module is configured to acquire a first region of interest, a second region of interest and a third region of interest in the microscopic image data.

[0033] In some embodiments, the first region of interest is a cell nucleus region delineated by a fluorescent channel corresponding to a cell nucleus marker.

[0034] In some embodiments, the second region of interest is a nucleolus region delineated by a fluorescent channel corresponding to a dense fiber component marker.

[0035] In some embodiments, the third region of interest is a nucleolus region delineated using a fluorescent channel corresponding to a particle component marker.

[0036] A second processing module is configured to extract nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; A first classification module is configured to perform a first classification on cells in the biological sample based on the line scan data; In some embodiments, the line scan data includes fluorescence intensity distribution curves of the second region of interest and the third region of interest.

[0037] In some embodiments, the first classification method includes: When the fluorescence intensity distribution curve of the third region of interest surrounds the fluorescence intensity distribution curve of the second region of interest, classifying the cell as a type A cell; When the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, the cell is classified as a type C cell.

[0038] In some embodiments, the spillover assessment method comprises: Analyze whether the fluorescence intensity change trends of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest at the first non-overlapping area and the last non-overlapping area are the same in the non-overlapping area formed outside the fluorescence intensity distribution curve of the third region of interest; if not, it overflows; if so, it does not overflow.

[0039] A second classification module 110 is configured to perform a second classification on the type C cells in the biological sample; In some embodiments, the second classification method includes: The ratio of the area of ​​the second region of interest to the area of ​​the third region of interest in the C-type cells is analyzed to determine whether it is abnormal, so as to perform a second classification on the C-type cells in the biological sample.

[0040] In some embodiments, if the ratio of the area of ​​the second region of interest to the third region of interest in the C-type cells is abnormal, the classification of the C-type cells is adjusted to B-type cells; if it is not abnormal, the classification of the C-type cells is adjusted to A-type cells.

[0041] In some embodiments, when the ratio of the area of ​​the second region of interest to the area of ​​the third region of interest shows an increasing trend, it is considered abnormal.

[0042] The analysis module is configured to calculate the number and / or ratio of the type A cells and the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

[0043] In some embodiments, the biological sample includes a biological sample treated with a drug and a biological sample not treated with a drug.

[0044] In some embodiments, the analysis module is further configured to analyze an average diameter of spots formed by the dense fiber component marker in the B-type cells, and when the average diameter is less than a first threshold, the biological sample is assessed to be in a malignant proliferation state.

[0045] In some embodiments, the first threshold comprises 0.5-2 μm.

[0046] Compared with the prior art, the beneficial effects of the present invention include at least the following aspects: Conventional single nucleolus geometric morphology characteristic index is difficult to sensitively reflect the change trend of nucleoli, so the existing technology usually needs to combine multiple nucleolus geometric morphology characteristics to classify cells in biological samples. Even so, the existing methods of cell classification based on nucleolus morphology can usually only achieve qualitative analysis.

[0047] The present invention finds that the relative position and distribution of dense fiber component markers and granular component markers in the nucleolus will change with the functional state of the tissue or organ corresponding to the biological sample, and the line scan data of the second region of interest and the third region of interest delineated based on the dense fiber component marker and the granular component marker (i.e., the distribution change of fluorescence intensity) can sensitively reflect the change trend of the nucleolus (for example, from regular to irregular), and thus can accurately classify the cell subpopulations in the biological sample through the first classification method.

[0048] Furthermore, the present invention also found that although the nucleolar morphology of non-tumor biological samples and tumor biological samples presents different characteristics, when the nucleolar function is "abnormal", the change trend of the ratio of the area of ​​the second region of interest to the third region of interest is similar. Therefore, based on this change trend, the present invention performs a second classification on the A-type cells and C-type cells classified in the first classification on the basis of the first classification, so as to obtain a more accurate number and / or ratio of A-type cells and B-type cells, and then more effectively perform quantitative and qualitative analysis of cells, so as to accurately and sensitively reflect and evaluate the functional state of the tissue or organ corresponding to the biological sample, and is particularly suitable for biological samples with strong heterogeneity that lack specific markers.

[0049] The method and system provided by the present invention effectively overcome the limitation of traditional methods in dependence on markers, and can be combined with machine learning technology to improve the analysis speed while ensuring the accuracy and efficiency of cell classification. Therefore, in the early stages of the disease, it can accurately and sensitively reflect the changes in nucleolus-related phenotypes to predict cell functions (such as hyperactivity of cell proliferation or cell failure) and characterize changes in tissue health status (such as vascular intimal hyperplasia, tumors or degeneration, etc.) and the pathological state of diseased tissues, thereby providing support for early diagnosis of the disease.

[0050] In addition, since the method and system provided by the present invention can accurately classify cell subpopulations in biological samples with high repeatability, quantitative analysis can be performed based on the cell classification results of the present invention, which is suitable for application scenarios such as high-throughput drug screening and drug effectiveness evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale. Obviously, the drawings described below are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without paying creative labor.

[0052] Figure 1 is a flow chart of the method of embodiment 1; Figure 2 This is a schematic diagram of the modules of the system of Embodiment 2; Figure 3 This is a graph showing the results of testing rat vascular smooth muscle cell culture samples in Example 3; Figure 4 This is a graph showing the results of detecting a culture sample of a neuroblastoma cell line SK-N-SH in Example 4; Figure 5 This is a diagram showing the results of detecting neuroblastoma tumor tissue in Example 5.

[0053] 100 is a cell classification and function evaluation system, 102 is an image acquisition module, 104 is a first processing module, 106 is a second processing module, 108 is a first classification module, 110 is a second classification module, and 112 is an analysis module. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0055] Herein, suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings by themselves. Therefore, "module", "component" or "unit" can be used mixedly.

[0056] In this document, the directions or positional relationships indicated by the terms "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", etc. are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0057] In this document, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Herein "and / or" includes any and all combinations of one or more of the associated listed items.

[0059] Herein, "plurality" means two or more than two, ie, it includes two, three, four, five, etc.

[0060] It should be noted that, in this article, the terms "include", "comprises" or any other variations 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 also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0061] 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, and even more typically + / - 0.5% of the stated value.

[0062] In this specification, some embodiments may be disclosed in a format of being in a range. It should be understood that this description of "being in a range" is only for convenience and brevity, and should not be interpreted as a rigid limitation on the disclosed range. Therefore, the description of the range should be considered to have specifically disclosed all possible sub-ranges and independent numerical values ​​within this range. For example, the description of the range 1-6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as individual numbers within this range, such as 1, 2, 3, 4, 5 and 6. Regardless of the breadth of the range, the above rules apply.

[0063] Embodiment 1 like Figure 1 As shown, this embodiment provides a method for cell classification and function evaluation based on nucleolus morphology, comprising the following steps: S101 acquires microscopic image data of biological samples; In some embodiments, the microscopic image data includes an optical microscope image with an objective lens of 40 times or more and / or super-resolution microscopic image data.

[0064] In some embodiments, immunofluorescence images of biological samples can be collected in batches by a high-throughput imaging system to obtain the microscopic image data. In some embodiments, the imaging requirements include: using an objective lens of 40 times or more to image a glass bottom or other ultra-thin bottom surface (e.g., 0.17-0.19 mm) of a well plate.

[0065] In some embodiments, the biological sample includes a tissue or cell culture containing at least one cell. In some embodiments, the biological sample can be any solid or fluid sample obtained from any organism, excreted by any organism, or secreted by any organism. In some embodiments, the organism includes a healthy or apparently healthy human subject or a human patient affected by a condition or disease (e.g., cancer) to be diagnosed or studied. In some embodiments, the biological sample can be a biological fluid obtained from blood, plasma, serum, urine, bile, ascites, saliva, cerebrospinal fluid, aqueous humor, or vitreous humor, or any body secretion, exudate, exudate (e.g., fluid obtained from an abscess or any other site of infection or inflammation), or fluid obtained from a joint (e.g., a normal joint or a joint affected by a disease). In some embodiments, the biological sample can also be a sample obtained from any organ or tissue (including a biopsy or autopsy specimen, such as a tumor biopsy). In some embodiments, the biological sample can include cells (whether primary cells or cultured cells) or a medium regulated by any cell, tissue, or organ.

[0066] It should be understood that the same biological sample may be a mixture of different cell subpopulations with strong heterogeneity. For example, the same biological sample may contain cells with both "cancerous" and "non-cancerous" cell phenotypes. Therefore, it is very challenging to correctly classify each single cell in the biological sample.

[0067] In some embodiments, the biological sample is labeled with nucleolar markers, and the nucleolar markers include at least a dense fiber component marker and a granular component marker.

[0068] In some embodiments, the dense fiber component marker comprises fibrinogen (FBL).

[0069] In some embodiments, the particle component marker comprises nucleophosmin 1 (NPM1).

[0070] In this embodiment, FBL and NPM1 are used to simultaneously label the nucleolus, wherein FBL is used as a dense fiber component marker (the middle layer of the nucleolus) and NPM1 is used as a granular component protein marker (the outer layer of the nucleolus) to label the middle and outer layers of the nucleolus of the cells in the biological sample.

[0071] In some embodiments, the biological sample is further labeled with a nuclear marker.

[0072] In some embodiments, the nuclear marker comprises 4',6-diamidino-2-phenylindole (DAPI).

[0073] In some embodiments, the above markers in cells can be labeled by immunofluorescence staining or other suitable methods.

[0074] S102: Acquire a first region of interest, a second region of interest, and a third region of interest in the microscopic image data.

[0075] In some embodiments, the first region of interest is a cell nucleus region delineated by a fluorescent channel corresponding to the cell nucleus marker.

[0076] In some embodiments, the second region of interest is a nucleolus region delineated by a fluorescent channel corresponding to the dense fiber component marker.

[0077] In some embodiments, the third region of interest is a nucleolus region delineated using a fluorescent channel corresponding to the particle component marker.

[0078] S103 extracting nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; S104, performing a first classification on cells in the biological sample based on the line scan data; In some embodiments, the line scan data includes fluorescence intensity distribution curves of the second region of interest and the third region of interest. Line scan is an image analysis method for measuring changes in fluorescence intensity in a specific region. For example, line scan can be used to measure the fluorescence intensity of a specific region by drawing a line and analyzing the fluorescence value along the line to quantitatively analyze the fluorescence intensity distribution of the specific region.

[0079] The prior art generally uses line scanning to determine the relative position of the protein to be analyzed in a specific area, for example, a specific marker is selected as a reference, and the co-localization degree of the protein to be analyzed and the selected marker is evaluated by line scanning. It should be understood that in this application scenario, the relative position and distribution of the selected marker in the specific area are assumed to be unchanged, so the relative position of the protein to be analyzed can be determined.

[0080] This embodiment uses dense fiber component markers and granular component markers to simultaneously mark the nucleolus, which can not only locate the position of the nucleolus more accurately, but also this embodiment finds that, unlike conventional thinking, the relative position and distribution of dense fiber component markers and granular component markers in the nucleolus will change with the functional state of the tissue or organ corresponding to the biological sample, and the line scan data of the second region of interest and the third region of interest delineated based on the dense fiber component marker and the granular component marker (i.e., the distribution change of fluorescence intensity) can sensitively reflect the change trend of the nucleolus (for example, from regular to irregular), thereby helping to achieve preliminary classification of cells in the biological sample.

[0081] It should be emphasized that a conventional single nucleolus geometric morphological characteristic index is difficult to sensitively reflect the change trend of the nucleolus (for example, from regular to irregular), so the prior art usually requires combining multiple geometric morphological characteristics of the nucleolus to classify cells in biological samples. This embodiment adopts the first classification method to quickly and accurately determine the change trend of the nucleolus, and then classify the cells into type A cells and type C cells.

[0082] In some embodiments, the first classification method includes: When the fluorescence intensity distribution curve of the third region of interest surrounds the fluorescence intensity distribution curve of the second region of interest, classifying the cell as a type A cell; When the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, the cell is classified as a type C cell.

[0083] In some embodiments, the outside of the fluorescence intensity distribution curve of the third region of interest refers to a side of the fluorescence intensity distribution curve having a higher fluorescence intensity than that of the third region of interest.

[0084] In some embodiments, "spillover" means that part of the segment of the fluorescence intensity distribution curve of the second region of interest is distributed outside the fluorescence intensity distribution curve of the third region of interest, and forms one or more non-overlapping areas with the fluorescence intensity distribution curve of the third region of interest, and the fluorescence intensity change trend of the fluorescence intensity of the segment of the fluorescence intensity distribution curve of the second region of interest corresponding to the non-overlapping area is different from that of the fluorescence intensity distribution curve of the third region of interest.

[0085] In some embodiments, the segment of the fluorescence intensity distribution curve of the second region of interest corresponding to the overflowed non-overlapping area generally presents a "peak"-shaped change trend (i.e., in this segment, the fluorescence intensity first increases to a peak value and then decreases), while the segment of the fluorescence intensity distribution curve of the corresponding third region of interest generally presents a non-"peak"-shaped change trend (for example, in this segment, the fluorescence intensity first decreases and then increases, only increases, or only decreases, etc.).

[0086] In some embodiments, a "peak" refers to a point on a fluorescence intensity distribution curve where the curve changes direction from top to bottom along a mountain-shaped portion as the curve rises and falls in a vertical direction.

[0087] In some embodiments, the spillover assessment method comprises: Analyze whether the fluorescence intensity change trends of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest at the first non-overlapping area and the last non-overlapping area are the same in the non-overlapping area formed outside the fluorescence intensity distribution curve of the third region of interest; if not, it overflows; if so, it does not overflow.

[0088] In some embodiments, "the fluorescence intensity distribution curve of the third region of interest is in a state of surrounding the fluorescence intensity distribution curve of the second region of interest (it can also be understood that the fluorescence intensity distribution curve of the third region of interest basically surrounds the fluorescence intensity distribution curve of the second region of interest)" can be: the fluorescence intensity distribution curve of the third region of interest completely surrounds the fluorescence intensity distribution curve of the second region of interest; it can also be: the fluorescence intensity distribution curve of the second region of interest overlaps with the fluorescence intensity distribution curve of the third region of interest for the most part, the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest have a non-overlapping area, but the fluorescence intensity change trend of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest corresponding to the non-overlapping area is the same (that is, both are "peak" shaped) and / or the fluorescence intensity of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest corresponding to the non-overlapping area are basically the same.

[0089] In some embodiments, when the fluorescence intensity distribution curve of the second ROI does not have an overflow non-overlapping area outside the fluorescence intensity distribution curve of the third ROI, it can be evaluated that the fluorescence intensity distribution curve of the third ROI substantially surrounds the fluorescence intensity distribution curve of the second ROI.

[0090] In some embodiments, a trained cell classification model may be used to perform a first classification (and a subsequent second classification) on cells in the biological sample.

[0091] In some embodiments, the cell classification model can be obtained by training a corresponding model using a nucleolus morphology feature set associated with the cell type, such as using cell data of known categories for annotation learning. The morphological differences of nucleoli in different cell types can be analyzed by linear relationship using machine learning, thereby improving the accuracy of classification.

[0092] In some embodiments, the cell classification model can perform image recognition on the microscopic image data to determine the cell type (eg, vascular smooth muscle cells, neuroblastoma cells, etc.) of the cells in the biological sample and their corresponding nucleolar features.

[0093] To further ensure the accuracy of cell classification, the C-type cells may be subjected to a second classification: In some embodiments, the method may further include: S105 performing a second classification on the type C cells in the biological sample; In some embodiments, the second classification method includes: The ratio of the area of ​​the second region of interest to the area of ​​the third region of interest in the C-type cells is analyzed to determine whether it is abnormal, so as to perform a second classification on the C-type cells in the biological sample.

[0094] In some embodiments, if the ratio of the area of ​​the second region of interest to the third region of interest in the C-type cells is abnormal, the classification of the C-type cells is adjusted to B-type cells; if it is not abnormal, the classification of the C-type cells is adjusted to A-type cells.

[0095] In some embodiments, when the ratio of the area of ​​the second region of interest to the area of ​​the third region of interest shows an increasing trend, it is considered abnormal.

[0096] This embodiment found that when the nucleolar function is "abnormal" (for example, the nucleolar function is impaired), the area of ​​the second region of interest and the third region of interest increases, and the ratio shows an increasing trend. This increasing trend can be used to accurately perform a second classification of type C cells, thereby obtaining a more accurate number and / or ratio of type A cells and type B cells, and effectively performing quantitative and qualitative analysis of cells to accurately and sensitively reflect and evaluate the functional state of the tissue or organ corresponding to the biological sample, which is particularly suitable for biological samples with strong heterogeneity that lack specific markers.

[0097] In some embodiments, type A cells are cells with normal nucleolar function relative to the type of cells themselves.

[0098] In some embodiments, a B-type cell is a cell that has abnormal nucleolar function relative to the cell type itself.

[0099] In some embodiments, type C cells refer to cells whose nucleolar function may be abnormal relative to the type of cells themselves.

[0100] In other words, in some special application scenarios (for example, when the accuracy requirements for the classification results are relatively low or when there are higher requirements for the classification speed), the subsequent S106 can be performed directly based on the results of the first classification (i.e., class C cells are regarded as class B cells) without performing step S105.

[0101] S106 Calculate the number and / or ratio of the type A cells to the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

[0102] It should be emphasized that the present embodiment found that the above-mentioned increasing trend is applicable not only to non-tumor biological samples, but also to tumor biological samples, that is, although the nucleolar morphology of non-tumor biological samples and tumor biological samples presents different characteristics, when the nucleolar function is "abnormal", the change trend of the ratio of the area of ​​the second region of interest to the third region of interest is similar. Therefore, the method provided in the present embodiment does not need to rely on specific cell activity markers, and can achieve high-accuracy and high-precision cell classification only through nucleolar-related phenotypic characteristics, providing a new detection method for diseases that lack specific markers.

[0103] In addition, the method provided in this embodiment can accurately and sensitively reflect changes in nucleolus-related phenotypes in the early stages of the disease to predict cell functions (such as hyperactivity of cell proliferation or cell failure) and characterize changes in tissue health status (such as vascular intimal hyperplasia, tumors or degeneration, etc.) and the pathological state of diseased tissues, thereby providing support for early diagnosis of the disease.

[0104] In some embodiments, the biological sample includes a biological sample treated with a drug and a biological sample not treated with a drug. It should be understood that, unlike the existing cell classification methods based on nucleolar morphology, which usually can only achieve qualitative analysis, the method provided in this embodiment can accurately classify cell subpopulations in biological samples with high repeatability. Therefore, quantitative analysis can be performed based on the cell classification results of this embodiment, which is particularly reflected in that the method provided in this embodiment can also be used for high-throughput drug screening and drug effectiveness evaluation.

[0105] In some embodiments, the effectiveness of the drug (e.g., whether it has a tumor-inhibiting effect) can be determined by comparing the number and / or ratio of type A cells and type B cells in biological samples treated with drugs and biological samples not treated with drugs. In some embodiments, high-throughput drug screening can also be performed by comparing the number and / or ratio of type A cells and type B cells in biological samples treated with drugs of different concentration gradients and / or biological samples treated with different drugs.

[0106] In some embodiments, when the biological sample is a tumor sample (e.g., neuroblastoma), the method may further include: S107 analyzing the average diameter of the spots formed by the dense fiber component marker in the second region of interest in the B-type cells; when the average diameter is less than a first threshold, the biological sample is assessed to be in a malignant proliferation state.

[0107] In some embodiments, the first threshold comprises 0.5-2 μm.

[0108] Embodiment 2 like Figure 2As shown, this embodiment provides a cell classification and function evaluation system 100 based on nucleolus morphology corresponding to the method of embodiment 1, which includes: An image acquisition module 102, configured to acquire microscopic image data of a biological sample; In some embodiments, the microscopic image data includes an optical microscope image with an objective lens of 40 times or more and / or super-resolution microscopic image data.

[0109] In some embodiments, immunofluorescence images of biological samples can be collected in batches by a high-throughput imaging system to obtain the microscopic image data. In some embodiments, the imaging requirements include: using an objective lens of 40 times or more to image a glass bottom or other ultra-thin bottom surface (e.g., 0.17-0.19 mm) of a well plate.

[0110] In some embodiments, the biological sample comprises a tissue or a cell culture comprising at least one cell.

[0111] In some embodiments, the biological sample is labeled with nucleolar markers, and the nucleolar markers include at least a dense fiber component marker and a granular component marker.

[0112] In some embodiments, the dense fiber component marker comprises fibrinogen (FBL).

[0113] In some embodiments, the particle component marker comprises nucleophosmin 1 (NPM1).

[0114] In some embodiments, the biological sample is further labeled with a nuclear marker.

[0115] In some embodiments, the nuclear marker comprises 4',6-diamidino-2-phenylindole (DAPI).

[0116] 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 microscopic image data.

[0117] In some embodiments, the first region of interest is a cell nucleus region delineated by a fluorescent channel corresponding to a cell nucleus marker.

[0118] In some embodiments, the second region of interest is a nucleolus region delineated by a fluorescent channel corresponding to a dense fiber component marker.

[0119] In some embodiments, the third region of interest is a nucleolus region delineated using a fluorescent channel corresponding to a particle component marker.

[0120] A second processing module 106 is configured to extract nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; A first classification module 108, configured to perform a first classification on cells in the biological sample based on the line scan data; In some embodiments, the line scan data includes fluorescence intensity distribution curves of the second region of interest and the third region of interest.

[0121] In some embodiments, the first classification method includes: When the fluorescence intensity distribution curve of the third region of interest surrounds the fluorescence intensity distribution curve of the second region of interest, classifying the cell as a type A cell; When the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, the cell is classified as a type C cell.

[0122] In some embodiments, the spillover assessment method comprises: Analyze whether the fluorescence intensity change trends of the fluorescence intensity distribution curve of the second region of interest and the fluorescence intensity distribution curve of the third region of interest at the first non-overlapping area and the last non-overlapping area are the same in the non-overlapping area formed outside the fluorescence intensity distribution curve of the third region of interest; if not, it overflows; if so, it does not overflow.

[0123] A second classification module 110 is configured to perform a second classification on the type C cells in the biological sample; In some embodiments, the second classification method includes: The ratio of the area of ​​the second region of interest to the area of ​​the third region of interest in the C-type cells is analyzed to determine whether it is abnormal, so as to perform a second classification on the C-type cells in the biological sample.

[0124] In some embodiments, if the ratio of the area of ​​the second region of interest to the third region of interest in the C-type cells is abnormal, the classification of the C-type cells is adjusted to B-type cells; if it is not abnormal, the classification of the C-type cells is adjusted to A-type cells.

[0125] In some embodiments, when the ratio of the area of ​​the second region of interest to the area of ​​the third region of interest shows an increasing trend, it is considered abnormal.

[0126] In some embodiments, the first classification module 108 and the second classification module 110 may be based on a trained cell classification model.

[0127] In some embodiments, the cell classification model can be obtained by training a corresponding model using a nucleolus morphology feature set associated with the cell type, such as using cell data of known categories for annotation learning. The morphological differences of nucleoli in different cell types can be analyzed by linear relationship using machine learning, thereby improving the accuracy of classification.

[0128] The analysis module 112 is configured to calculate the number and / or ratio of the type A cells and the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

[0129] In some embodiments, the biological sample includes a biological sample treated with a drug and a biological sample not treated with a drug.

[0130] In some embodiments, the analysis module 112 is further configured to analyze the average diameter of the spots formed by the dense fiber component marker in the B-type cells. When the average diameter is less than a first threshold, the biological sample is evaluated to be in a malignant proliferation state.

[0131] In some embodiments, the first threshold comprises 0.5-2 μm.

[0132] Embodiment 3 The method of Example 1 was used to detect rat vascular smooth muscle cell cultures. Figure 3 Figure a shows super-resolution microscopic images and line scan results of rat vascular smooth muscle cells in the normal group and drug-treated group fluorescently labeled with FBL and NPM1. The results showed that in the fluorescence distribution curve of rat vascular smooth muscle cells in the normal group, the fluorescence distribution curve corresponding to NPM1 basically surrounded the fluorescence distribution curve corresponding to FBL, representing the clear outer and middle layer divisions of the normal nucleolus of rat vascular smooth muscle cells. The addition of drug intervention caused the fluorescence distribution curve corresponding to FBL to overflow to the outside of the fluorescence distribution curve corresponding to NPM1 (the typical situation is indicated by the arrow).

[0133] Figure 3 b shows the area of ​​vascular smooth muscle cells of rats in the normal group and drug-treated group fluorescently labeled with FBL and NPM1. The area of ​​FBL and NPM1 and the ratio of the area of ​​NPM1 to FBL of vascular smooth muscle cells of rats in the drug-treated group increased. Figure 3 Figure c shows that the method and system provided by the present invention can be combined with machine learning (deep learning), and through the high efficiency of machine learning algorithms, complex cell image data (microscopic image data) can be quickly processed and analyzed, thereby improving the accuracy and precision of cell classification. Figure 3As shown in c, the nucleoli of type A cells are regular, while the nucleoli of type B cells are irregular.

[0134] Figure 3 Figure d shows the changes in the number and ratio of type A cells and type B cells in rat vascular smooth muscle cell culture samples before and after drug intervention. The results showed that after drug intervention (such as Figure 3 In the rat vascular smooth muscle cell culture samples, the number and proportion of type A cells decreased, while the number and proportion of type B cells increased. The above results indicate that under the intervention of the drug, the nucleolar function is inhibited, leading to impaired cell function.

[0135] Embodiment 4 The method of Example 1 was used to detect the culture of the neuroblastoma cell line SK-N-SH. Figure 4 a shows super-resolution microscopic images of neuroblastoma cells in the normal group and drug-treated group that were fluorescently labeled with FBL and NPM1. Figure 4 b in the figure shows the corresponding line scan results. The results show that the fluorescence distribution curve corresponding to NPM1 of the neuroblastoma cells in the normal group basically surrounds the fluorescence distribution curve corresponding to FBL, representing the clear outer and middle layer partitions of the normal nucleolus of neuroblastoma cells. The addition of drug intervention causes the fluorescence distribution curve corresponding to FBL to overflow to the outside of the fluorescence distribution curve corresponding to NPM1 (the typical situation is indicated by the arrow).

[0136] Figure 4 Figure c shows the area of ​​neuroblastoma cells in the normal group and drug-treated group fluorescently labeled with FBL and NPM1. The area of ​​FBL and NPM1 and the ratio of the area of ​​NPM1 to FBL in the neuroblastoma cells of the drug-treated group increased.

[0137] Figure 4 Figure d shows the changes in the number and proportion of type A cells and type B cells in SK-N-SH culture samples before and after drug intervention. The results showed that after drug intervention, the number and proportion of type A cells in SK-N-SH culture samples decreased, and the number and proportion of type B cells (cells with "necklace"-like nucleoli) increased. The above results indicate that under the intervention of this drug, the function of the nucleolus is inhibited, which can inhibit neuroblastoma cells.

[0138] Embodiment 5 The method of Example 1 was used to detect neuroblastoma tumor tissue. The cell proliferation marker Ki67 was used to divide the tumor tissue into three cell proliferation levels (~0%, 5.743%, 38.227%). Figure 5As shown in the figure, it can be seen that the diameter of FBL spots is significantly reduced in highly proliferating neuroblastoma cells. The above results show that the malignancy of tumors can be judged based on the diameter of FBL spots.

[0139] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a computer terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0140] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for cell classification and function evaluation based on nucleolar morphology, characterized in that: The following steps are involved: S101 Acquire microscopic image data of a biological sample, wherein the biological sample includes a tissue or a cell culture containing at least one cell; the biological sample is labeled with a nucleolar marker, and the nucleolar marker includes at least a dense fiber component marker and a granular component marker; S102 acquiring a first region of interest, a second region of interest, and a third region of interest in the microscopic image data; wherein the first region of interest is a cell nucleus region delineated by a fluorescence channel corresponding to a cell nucleus marker; the second region of interest is a nucleolus region delineated by a fluorescence channel corresponding to a dense fiber component marker; and the third region of interest is a nucleolus region delineated by a fluorescence channel corresponding to a particle component marker; S103 extracting nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; S104, based on the line scan data, performing a first classification on the cells in the biological sample; the line scan data includes the fluorescence intensity distribution curves of the second region of interest and the third region of interest; the first classification method includes: when the fluorescence intensity distribution curve of the third region of interest surrounds the fluorescence intensity distribution curve of the second region of interest, classifying the cells as type A cells; when the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, classifying the cells as type C cells; S105 performing a second classification on the C-type cells in the biological sample, wherein the second classification method comprises: analyzing whether the ratio of the area of ​​the second region of interest to the area of ​​the third region of interest in the C-type cells is abnormal; if abnormal, adjusting the classification of the C-type cells to B-type cells; if normal, adjusting the classification of the C-type cells to A-type cells; S106 Calculate the number and / or ratio of the type A cells to the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

2. The method according to claim 1, characterized in that The dense fiber component marker includes fibrillin, and the granular component marker includes nucleophosmin 1.

3. The method according to claim 1, characterized in that The cell nuclear marker includes 4',6-diamidino-2-phenylindole.

4. The method according to claim 1, characterized in that When the ratio of the area of ​​the second region of interest to the third region of interest shows an increasing trend, it is considered abnormal.

5. The method according to claim 1, characterized in that The biological samples include biological samples treated with drugs and biological samples not treated with drugs.

6. The method according to claim 1, wherein: The method further includes S107 analyzing an average diameter of spots formed by the dense fiber component marker in the second region of interest in the B-type cells, and when the average diameter is less than a first threshold, the biological sample is evaluated as being in a malignant proliferation state.

7. A cell classification and function evaluation system based on nucleolar morphology, characterized in that: The system comprises: An image acquisition module, configured to acquire microscopic image data of a biological sample; wherein the biological sample comprises a tissue or a cell culture comprising at least one cell; the biological sample is labeled with a nucleolar marker, and the nucleolar marker comprises at least a dense fiber component marker and a granular component marker; A first processing module is configured to obtain a first region of interest, a second region of interest, and a third region of interest in the microscopic image data; wherein the first region of interest is a cell nucleus region delineated by a fluorescence channel corresponding to a cell nucleus marker; the second region of interest is a nucleolus region delineated by a fluorescence channel corresponding to a dense fiber component marker; and the third region of interest is a nucleolus region delineated by a fluorescence channel corresponding to a particle component marker; A second processing module is configured to extract nucleolus feature data of the second region of interest and the third region of interest, wherein the nucleolus feature data includes line scan data and region area data; A first classification module is configured to perform a first classification on the cells in the biological sample based on the line scan data; the line scan data includes fluorescence intensity distribution curves of the second region of interest and the third region of interest; the first classification method includes: when the fluorescence intensity distribution curve of the third region of interest is in a surrounding state with the fluorescence intensity distribution curve of the second region of interest, classifying the cells as type A cells; when the fluorescence intensity distribution curve of the second region of interest overflows to the outside of the fluorescence intensity distribution curve of the third region of interest, classifying the cells as type C cells; A second classification module is configured to perform a second classification on the C-type cells in the biological sample; the second classification method comprises: analyzing whether the ratio of the area of ​​the second region of interest to the third region of interest in the C-type cells is abnormal; if abnormal, adjusting the classification of the C-type cells to B-type cells; if not abnormal, adjusting the classification of the C-type cells to A-type cells; The analysis module is configured to calculate the number and / or ratio of the type A cells and the type B cells in the biological sample, and then evaluate the functional state of the tissue or organ corresponding to the biological sample.

8. The system according to claim 7, characterized in that The dense fiber component marker includes fibrillin, and the granular component marker includes nucleophosmin 1.

9. The system according to claim 7, characterized in that When the ratio of the area of ​​the second region of interest to the third region of interest shows an increasing trend, it is considered abnormal.

10. The system according to claim 7, characterized in that The biological samples include biological samples treated with drugs and biological samples not treated with drugs.

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