CN111534563B - Cellular immunotherapy evaluation method and system - Google Patents
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Abstract
The invention relates to the technical field of biomedicine, in particular to a cell immunotherapy evaluation method and a cell immunotherapy evaluation system, wherein the cell immunotherapy evaluation method comprises the following steps: acquiring an image set of immune cells and tumor cells, wherein the image set comprises a plurality of frames of moving images; respectively carrying out feature marking on immune cells and tumor cells of an initial frame image in a plurality of frame moving images; performing fluorescence tracking imaging of the cells according to the feature markers and the plurality of frames of moving images; and analyzing the proportion of the apoptotic tumor according to the tracking imaging result. The cellular immunotherapy evaluation method and the cellular immunotherapy evaluation system can accurately monitor the curative effect of the cellular immunotherapy.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a cell immunotherapy evaluation method and system.
Background
Cellular immunotherapy of tumors, such as CAR-T, TCR-T, etc., is well-established and increasingly important in tumor therapy. One common feature of both technologies is that the recognition and attack ability of T cell receptors to specific cancer cell antigens is enhanced by means of genetic engineering. And thus are also collectively referred to as "T cell receptor redirection" techniques.
CAR-T and TCR-T are two latest immune cell technologies of the ACT technology for the current adoptive cell-back therapy, and are widely concerned and researched due to the fact that the CAR-T and the TCR-T can express specific receptor target recognition cells such as tumor cells, and the CAR-T and the TCR-T are changed into clinical application from the initial basic immune research. Based on synthetic biology, immunology and genetic modification technology, the synthesis of modified T cells with enhanced specific functions is possible. CD19 antigen-specific CAR-T cells have shown sustained disease remission in clinical trials for the treatment of B cell leukemia and lymphoma. Because of the excellent performance and wide application prospect of the CAR-T/TCR-T technology, the CAR-T/TCR-T technology enters the competitive stage of the current fierce pharmaceutical industry and is higher than the CAR-T/TCR-T technology in the traditional pharmaceutical industry.
However, in the prior art, preclinical efficacy evaluation of cellular immunotherapy has a major drawback, and many patients have poor efficacy in practical clinical application due to lack of preclinical efficacy evaluation. The existing evaluation system is only based on the sequencing result or the pathological result of the whole exon of the patient, and the real killing effect of the immune cells on the tumor is difficult to judge. The personalized PDX model of the patient based on the humanized mouse has extremely high manufacturing cost, great difficulty and long time consumption, and is difficult to be used as a rapid reagent tool for clinical application. In order to solve the above problems, the present invention provides a method and a system for evaluating a cellular immunotherapy.
Disclosure of Invention
The invention solves the technical problem of providing a method and a system for evaluating a cellular immunotherapy. The method and the system for evaluating the cellular immunotherapy can accurately monitor the curative effect of the cellular immunotherapy.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a method of evaluating cellular immunotherapy, comprising:
acquiring an image set of immune cells and tumor cells, wherein the image set comprises a plurality of frames of moving images;
respectively carrying out feature marking on immune cells and tumor cells of an initial frame image in a plurality of frame moving images;
performing fluorescence tracking imaging of the cells according to the feature markers and the plurality of frames of moving images;
and analyzing the proportion of the apoptotic tumor according to the tracking imaging result.
Preferably, the immune cells and the tumor cells are labeled by staining in advance, the immune cells are labeled by celltrace-treated staining, and the tumor cells are labeled by staining of caspase3/7 apoptosis probe.
Preferably, the method for separately labeling the immune cells and the tumor cells comprises the following steps:
taking immune cells and tumor cells in the initial frame image as targets, and respectively marking cell nuclei of the two cells as an identifier 1 and an identifier 2;
the shapes of cell membranes of all target cells are sketched, and the cell membranes are marked;
and fusing the images of the cell nucleus and the cell membrane to generate the characteristic labeled cells. The target cells are marked, namely the immune cells are marked as mark 1, and the tumor cells are marked as mark 2, so that each marked cell is ensured to be tracked in the subsequent cell tracking process, and the loss of the cells in the tracking process is prevented.
Further preferably, after the generating of the characteristic labeled cells, the method further comprises performing color channel decomposition on a color image of the labeled cells:
acquiring an initial frame image, wherein the initial frame image is an RGB image;
and (3) carrying out color channel decomposition on the immune cells and the tumor cells which are stained and marked in the image, and acquiring a color channel image corresponding to each marked cell. Because the colors of the two cell markers are different when the cells are marked with the color markers, the RGB values of the two cells are acquired for better marking and distinguishing the two cells, so that the tracking effect is more accurate.
Preferably, the fluorescence tracking imaging of the cell according to the feature marker and the plurality of frames of moving images is specifically:
and detecting and identifying each marked cell through a deep learning network, positioning to the position of each target cell, and generating a tracking path.
Further preferably, the method for detecting and identifying each labeled cell is,
after each cell finishes the feature marking, simultaneously generating the corresponding RGB value, the cell membrane shape and the cell nucleus shape feature, taking the feature value as a feature point set,
the cells are detected and identified by matching the feature point set,
if the RGB value of the target cell changes, the target cell is not detected, and the subsequent step of detecting the target cell is automatically cancelled. In the process of cell tracking, compared data are the shape of cell nucleus, the shape of cell membrane and the specific RGB value of each cell after dyeing, and the cell tracking is carried out on the three aspects, so that the tracking accuracy is ensured.
Preferably, the analyzing the proportion of the apoptotic tumor according to the three-dimensional imaging result specifically comprises: counting the number of the two marked cells, and judging the number ratio of the two cells.
A cellular immunotherapy evaluation system, comprising:
an image acquisition module: the image acquisition module is used for acquiring an image set of immune cells and tumor cells, and the image set comprises a plurality of frames of moving images;
a feature marking module: the characteristic marking module is used for respectively carrying out characteristic marking on immune cells and tumor cells of an initial frame image in a plurality of frame moving images;
a tracking imaging module: the tracking imaging module is used for carrying out fluorescence tracking imaging on the cells according to the feature markers and a plurality of frames of moving images;
an analysis module: the analysis module is used for analyzing the proportion of the apoptotic tumor according to the tracking imaging result.
A computer readable storage medium having stored thereon computer program instructions adapted to be loaded by a processor and to execute a method of cellular immunotherapy evaluation.
A mobile terminal is characterized by comprising a processor and a memory, wherein the processor is used for executing a program stored in the memory so as to realize a cellular immunotherapy evaluation method.
Compared with the prior art, the invention has the beneficial effects that: the cellular immunotherapy evaluation method and the cellular immunotherapy evaluation system can accurately monitor the curative effect of the cellular immunotherapy, are used for preclinical tests of personalized cellular immunotherapy of patients, explore the optimal clinical curative effect to the maximum extent, save the economic cost and the time cost, and have great scientific research and clinical application values. The method specifically comprises the following steps: the method is characterized in that cells are firstly subjected to staining marking and characteristic marking, so that immune cells and tumor cells can be distinguished, the movement paths of the two cells are tracked due to the marking, but when the cells are apoptotic, the cells are not tracked any more due to the staining change, namely the change of RGB values, so that the number of the tumor cells which are not apoptotic can be tracked finally, the apoptotic tumor proportion is determined, each cell can be tracked more accurately through the automatic monitoring of the system, and the optimal clinical curative effect is searched to a great extent.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic flow chart of a method for evaluating cellular immunotherapy according to the present invention;
FIG. 2 is a block diagram of an evaluation system of the seed cell immunotherapy according to the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic drawings and illustrate only the basic flow diagram of the invention, and therefore they show only the flow associated with the invention.
Example 1
As shown in fig. 1, the present invention is a method for evaluating a cellular immunotherapy, specifically comprising:
s1, acquiring an image set of immune cells and tumor cells, wherein the image set comprises a plurality of frame moving images;
s2, respectively carrying out feature marking on immune cells and tumor cells of an initial frame image in a plurality of frame moving images;
s3, carrying out fluorescence tracking imaging on the cells according to the feature marks and a plurality of frames of moving images;
and S4, analyzing the apoptosis tumor proportion according to the tracking imaging result.
Before observing and monitoring the process of the immune cell therapy, the following steps are required:
the method comprises the steps of culturing a formed three-dimensional tumor primary 3D cell model in an isolated environment through a special three-dimensional culture mode, culturing immune cells through CAR-T technology, then carrying out staining marking on the immune cells and the tumor cells in advance, wherein the immune cells are subjected to the staining marking through celltrace-treated staining, and the tumor cells are subjected to the staining marking through a caspase3/7 apoptosis probe. After the above treatment process, the following steps are carried out for monitoring:
step S1: acquiring an image set of immune cells and tumor cells, wherein the image set comprises a plurality of frames of moving images; and (3) acquiring a motion video of the immune cells and the tumor cells, segmenting each frame of the video, and storing the video in an image set, wherein the first frame of the video serves as an initial frame and is processed in the step 2.
Step S2: in a plurality of frames of moving images, respectively carrying out feature marking on immune cells and tumor cells of an initial frame of image, wherein the feature marking process comprises the following steps: taking immune cells and tumor cells in the initial frame image as targets, and respectively marking cell nuclei of the two cells as an identifier 1 and an identifier 2; the shapes of cell membranes of all target cells are sketched, and the cell membranes are marked; and fusing the images of the cell nucleus and the cell membrane to generate the characteristic labeled cells.
After the generation of the characteristic labeled cells, the method also comprises the following steps of carrying out color channel decomposition on a color image of the labeled cells: acquiring an initial frame image, wherein the initial frame image is an RGB image; and (3) carrying out color channel decomposition on the immune cells and the tumor cells which are stained and marked in the image, and acquiring a color channel image corresponding to each marked cell. The characteristic marking of the cells is completed through the processes. Note that the nucleus of the immune cell is labeled as label 1 and the nucleus of the tumor cell is labeled as label 2, so that two different cells can be distinguished.
Step S3: and carrying out fluorescence tracking imaging on the cells according to the feature markers and a plurality of frames of moving images. The fluorescence tracking imaging of the cells according to the feature markers and the plurality of frames of moving images is specifically as follows: and detecting and identifying each marked cell through a deep learning network, positioning to the position of each target cell, and generating a tracking path.
The method for detecting and identifying each marked cell comprises the steps of simultaneously generating corresponding RGB value and cell membrane shape and cell nucleus shape characteristics after each cell completes characteristic marking, using the characteristic values as a characteristic point set, detecting and identifying the cell by matching the characteristic point set, and automatically removing the subsequent step of detecting the target cell if the RGB value of the target cell changes. Since the color of the staining of the cell changes after the cell is apoptotic, the RGB value of the cell changes, and since the cell is tracked by the result of the marking of the cell nucleus, the marking of the cell membrane and the RGB value in the tracking process, the change of the RGB value in a small range can be tracked, and if the cell is tracked in a large range, the cell cannot be tracked, so that the cell is proved to be apoptotic, and the cell is not tracked any more.
Step S4: and analyzing the proportion of the apoptotic tumor according to the tracking imaging result. The analysis of the apoptosis tumor proportion according to the three-dimensional imaging result specifically comprises the following steps: counting the number of the two marked cells, and judging the number ratio of the two cells. When the system is in the characteristic marking, the system is stored in the system as an initial value by counting the number of the identifiers 1 and 2. By tracking the two cells in step 3, the number of cells of markers 1 and 2 can be detected, so that the apoptotic tumor ratio is: finally, the initial value of the number ratio of the markers 2 can be tracked, and the apoptotic tumor ratio can be calculated.
Example 2
As shown in fig. 2, the present invention provides a cellular immunotherapy evaluation system, including:
the image acquisition module 1: the image acquisition module is used for acquiring an image set of immune cells and tumor cells, and the image set comprises a plurality of frames of moving images;
the feature marking module 2: the characteristic marking module is used for respectively marking the immune cells and the tumor cells of the initial frame image in a plurality of frame moving images.
Tracking imaging module 3: the tracking imaging module is used for carrying out fluorescence tracking imaging on the cells according to the feature markers and the plurality of frames of moving images.
The analysis module 4: the analysis module is used for analyzing the proportion of the apoptotic tumor according to the tracking imaging result.
The image acquisition module 1: the method is used for acquiring an image set of immune cells and tumor cells, wherein the image set comprises a plurality of moving images. The motion video of immune cells and tumor cells is input in an image acquisition module, and the image acquisition module divides the video into each frame and stores the frame in an image set, wherein the first frame of image is used as an initial frame. And the image acquisition module transmits the processed image set to the feature marking module for processing.
The feature labeling module 2: the method is used for respectively carrying out feature labeling on immune cells and tumor cells of an initial frame image in a plurality of frame moving images, and the feature labeling process comprises the following steps: taking immune cells and tumor cells in the initial frame image as targets, and respectively marking cell nuclei of the two cells as an identifier 1 and an identifier 2; the shapes of cell membranes of all target cells are sketched, and the cell membranes are marked; and fusing the images of the cell nucleus and the cell membrane to generate the characteristic labeled cells.
After the generation of the characteristic labeled cells, the method also comprises the following steps of carrying out color channel decomposition on a color image of the labeled cells: acquiring an initial frame image, wherein the initial frame image is an RGB image; and (3) carrying out color channel decomposition on the immune cells and the tumor cells which are stained and marked in the image, and acquiring a color channel image corresponding to each marked cell. The characteristic marking of the cells is completed through the processes. Note that the nucleus of the immune cell is labeled as label 1 and the nucleus of the tumor cell is labeled as label 2, so that two different cells can be distinguished. The marked image is transmitted to the tracking imaging module by the feature marking module.
The tracking imaging module 3: the fluorescent tracking imaging of the cells is carried out according to the characteristic markers and a plurality of frames of moving images; the fluorescence tracking imaging of the cells according to the feature markers and the plurality of frames of moving images is specifically as follows: and detecting and identifying each marked cell through a deep learning network, positioning to the position of each target cell, and generating a tracking path.
The method for detecting and identifying each marked cell comprises the steps of simultaneously generating corresponding RGB value and cell membrane shape and cell nucleus shape characteristics after each cell completes characteristic marking, using the characteristic values as a characteristic point set, detecting and identifying the cell by matching the characteristic point set, and automatically removing the subsequent step of detecting the target cell if the RGB value of the target cell changes. Since the color of the staining of the cell changes after the cell is apoptotic, the RGB value of the cell changes, and since the cell is tracked by the result of the marking of the cell nucleus, the marking of the cell membrane and the RGB value in the tracking process, the change of the RGB value in a small range can be tracked, and if the cell is tracked in a large range, the cell cannot be tracked, so that the cell is proved to be apoptotic, and the cell is not tracked any more. The tracking imaging module transmits the tracking result to the analysis module.
The analysis module 4: used for analyzing the proportion of the apoptosis tumor according to the tracking imaging result. After the analysis module obtains the result transmitted by the tracking imaging, the proportion of the apoptotic tumor is as follows: finally, the initial value of the number ratio of the markers 2 can be tracked, and the apoptotic tumor ratio can be calculated.
A computer readable storage medium having stored thereon computer program instructions adapted to be loaded by a processor and to execute a method of cellular immunotherapy evaluation.
A mobile terminal is characterized by comprising a processor and a memory, wherein the processor is used for executing a program stored in the memory so as to realize a cellular immunotherapy evaluation method.
The above detailed description is specific to possible embodiments of the present invention, and the above embodiments are not intended to limit the scope of the present invention, and all equivalent implementations or modifications that do not depart from the scope of the present invention should be included in the present claims.
Claims (1)
1. The application of a cellular immunotherapy evaluation system in preparation of a mobile terminal for evaluating cellular immunotherapy is characterized in that a three-dimensional tumor primary 3D cell model is formed by three-dimensional culture mode in an isolated environment, after immune cells are cultured by CAR-T technology, the immune cells and the tumor cells are subjected to staining marking in advance, the immune cells are subjected to cell-treated staining marking, and the tumor cells are subjected to staining marking by caspase3/7 apoptosis probe; after the treatment process is carried out, monitoring is carried out;
the cellular immunotherapy evaluation system includes:
an image acquisition module: the image acquisition module is used for acquiring an image set of immune cells and tumor cells, and the image set comprises a plurality of frames of moving images;
a feature marking module: the characteristic marking module is used for respectively carrying out characteristic marking on immune cells and tumor cells of an initial frame image in a plurality of frame moving images; the method for respectively carrying out characteristic marking on the immune cells and the tumor cells comprises the following steps: taking immune cells and tumor cells in the initial frame image as targets, and respectively marking cell nuclei of the two cells as an identifier 1 and an identifier 2; the shapes of cell membranes of all target cells are sketched, and the cell membranes are marked; fusing the images of the cell nucleus and the cell membrane to generate a characteristic labeled cell; after the generation of the characteristic labeled cells, the method also comprises the following steps of carrying out color channel decomposition on a color image of the labeled cells: acquiring an initial frame image, wherein the initial frame image is an RGB image; carrying out color channel decomposition on the immune cells and tumor cells which are stained and marked in the image to obtain a color channel image corresponding to each marked cell;
a tracking imaging module: the tracking imaging module is used for carrying out fluorescence tracking imaging on the cells according to the feature markers and a plurality of frames of moving images; the fluorescence tracking imaging of the cells according to the feature markers and the plurality of frames of moving images is specifically as follows: detecting and identifying each marked cell through a deep learning network, positioning the marked cell to the position of each target cell, and generating a tracking path; the method for detecting and identifying each marked cell comprises the following steps: after each cell finishes the feature marking, simultaneously generating a corresponding RGB value, a cell membrane shape and a cell nucleus shape feature of each cell, taking the feature value as a feature point set, detecting and identifying the cell by matching the feature point set, and automatically removing the subsequent step of detecting the target cell if the RGB value of the target cell changes and the target cell cannot be detected;
an analysis module: the analysis module is used for analyzing the proportion of the apoptotic tumor according to the tracking imaging result; the specific analysis of the apoptosis tumor proportion according to the tracking imaging result is as follows: counting the number of the two marked cells, and judging the number ratio of the two cells.
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