In vivo flow cytometry detection methods, systems and devices for cells

Abstract

The present application relates to an in vivo flow detection method, system and device for cells. The method comprises: obtaining a liquid to be detected and a flow pipe, wherein the liquid to be detected includes a plurality of cells to be detected; in response to the liquid to be detected flowing through the in vivo flow pipe, a line scan is performed on the liquid to be detected based on the spectral focusing principle and a pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in vivo flow pipe; if the integrity of the in vivo flow pipe represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of multiple cells to be detected and the number of each cell to be detected. The use of this method can reduce the problem of missed detection in the in vivo flow detection process of cells and improve the accuracy of the in vivo flow cell detection results.

Description

Technical Field

[0001] The present application relates to the fields of biomedicine and optical imaging / detection technology, and in particular to an in vivo flow cytometry detection method, system and device for cells. Background Art

[0002] Flow cytometry is a technique that conducts rapid qualitative and quantitative analysis or sorting of single-row cells or biological particles in a fast linear flow state one by one, with multiple parameters. It has the characteristics of fast detection speed, multiple measurement parameters, large amount of data collection, comprehensive analysis, high sorting purity, and flexible methods. It has been widely used in various fields such as microbiology, immunology, virology, cancer biology, stem cell biology, and metabolic engineering. However, the existing in vitro flow detection technology relies on blood drawing and cannot meet the needs of non-invasive dynamic monitoring of living cells in biomedical research.

[0003] Traditional flow cytometry mainly relies on fluorescent labeling for cell phenotyping, which is an indirect measurement of intracellular molecules and surface antigens. It usually requires time-consuming preparation schemes and it is difficult to label small biological molecules (such as metabolites) in living cells. Detection technology based on Raman spectroscopy has the advantages of no labeling and simple sample preparation. However, spontaneous Raman scattering is a weak process, with only one inelastic scattered photon for every ten million elastic scattered photons, which makes the integration time for obtaining a spectrum too long, seriously restricting the detection flux of cell flow cytometry, and is not suitable for in vivo flow cytometry. Different from spontaneous Raman scattering, stimulated Raman scattering significantly enhances the interaction between laser and molecules, greatly improves the signal excitation efficiency, greatly improves the acquisition speed of Raman spectral information, and also improves the detection flux of Raman flow cytometry.

[0004] However, the current cell flow cytometry technology based on Raman spectroscopy is implemented in an in vitro environment and cannot be directly applied to in vivo detection and achieve good results. For example, there is currently a technology that uses the principle of collinear excitation of a narrowband pump beam and a broadband Stokes beam to quickly obtain stimulated Raman spectra to achieve in vitro flow cytometry detection. Its shortcomings are: low laser energy utilization rate, the laser fails to effectively penetrate the surface skin of the organism and directly reach the detection target pipeline, and the laser focus is focused on the middle position in the flow pipeline, that is, the cell detection site is fixed, and when the cells flowing in the flow pipeline do not pass through the laser focus, it will lead to missed detection. For example, another technology that uses the principle of narrow-band pump light and Stokes light wavelength tuning to quickly acquire four-color stimulated Raman images for in vitro flow detection has the following shortcomings: the laser energy utilization rate is low, the laser fails to efficiently penetrate the surface skin of the organism and reach the target detection pipeline directly, and because its essence relies on the morphology of the image to identify and distinguish cells, when the cells flowing through the detection point are adhered or the distance between cells is too small, it will cause large errors in the typing results. Summary of the invention

[0005] Based on this, it is necessary to provide an in vivo flow cytometry detection method, system and device for cells that can improve the accuracy of cell detection results in response to the above technical problems.

[0006] In a first aspect, the present application provides an in vivo flow cytometry detection method for cells, comprising:

[0007] Obtaining a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel;

[0008] In response to the liquid to be detected flowing through the in-body flow pipe, the liquid to be detected is line-scanned based on the spectral focusing principle and the pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow pipe;

[0009] If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected.

[0010] In one embodiment, the analyzing the first stimulated Raman spectrum to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected includes:

[0011] Performing line scanning on the standard solution according to the detection scanning line to obtain a stimulated Raman spectrum of the standard solution;

[0012] Performing intensity correction on the first stimulated Raman spectrum using the stimulated Raman spectrum of the standard solution to obtain a second stimulated Raman spectrum;

[0013] Performing spectral analysis on the second stimulated Raman spectrum according to the relative relationship between the sampling points on the detection scan line and the spectral frequency information to obtain a third stimulated Raman spectrum;

[0014] Dimensionality reduction and cluster analysis are performed on the third stimulated Raman spectrum to obtain a scatter plot of the liquid to be detected, wherein the scatter plot represents the types of the multiple cells to be detected and the number of each type of cells to be detected.

[0015] In one embodiment, the method further comprises:

[0016] According to the scatter plot, the stimulated Raman spectrum of each cell to be detected is averaged to obtain the average spectrum and the distribution range of the spectrum of each cell to be detected.

[0017] In one embodiment, the method further comprises:

[0018] Performing line scanning on the standard according to the detection scanning line to obtain a stimulated Raman spectrum of the standard, wherein the standard represents an experimental solution of a known cell type;

[0019] The stimulated Raman spectrum of the standard solution is used to perform intensity correction on the stimulated Raman spectrum of the standard product to obtain the relative relationship between the sampling points on the detection scanning line and the spectral frequency information.

[0020] In one embodiment, the method further comprises:

[0021] Performing pulse conversion on the first stimulated Raman spectrum to obtain a time-series pulse signal of the first stimulated Raman spectrum;

[0022] Whether the timing pulse signal is a valid pulse signal is determined according to a preset pulse threshold.

[0023] In one embodiment, the method further comprises:

[0024] Acquire a plurality of the first stimulated Raman spectra within a preset time;

[0025] Based on the plurality of the first stimulated Raman spectra, counting the number of valid pulse signals within the preset time;

[0026] According to the preset time and the quantity, the detection flux per unit time is obtained.

[0027] In one embodiment, the method further comprises:

[0028] Obtaining the pulse strength of each valid pulse signal within the preset time;

[0029] Determine the relative intensity of the spectrum corresponding to the first stimulated Raman spectrum according to the pulse intensity;

[0030] The number of first stimulated Raman spectra whose relative spectral intensity is less than or equal to a first preset threshold is counted.

[0031] In one embodiment, the method further comprises:

[0032] Obtain the valley value between two adjacent valid pulse signals;

[0033] The trough value is compared with a second preset threshold value. If the trough value is greater than or equal to the second preset threshold value, it is determined that the two adjacent valid pulse signals are a cluster.

[0034] In a second aspect, the present application provides an in vivo flow detection system for cells, the system comprising a first optical path, a second optical path, a combined optical path and a signal processing module;

[0035] The first optical path is used to output a Stokes beam; the first optical path includes a galvanometer optical path tuning module, the galvanometer optical path tuning module includes a resonant galvanometer, and the resonant galvanometer is used to move according to a preset rule to generate an optical path difference;

[0036] The second optical path is used to output a pump beam and to make the pump beam coincide with the Stokes beam in time;

[0037] The combined optical path is used to overlap the Stokes beam and the pump beam in space, and when the resonant galvanometer moves, based on the spectral focusing principle and the pre-set detection scanning line, a line scan is performed on the liquid to be detected flowing through the in-body flow pipe to obtain a first stimulated Raman spectrum of the liquid to be detected, wherein the liquid to be detected includes a plurality of cells to be detected;

[0038] The signal processing module is used to obtain a synchronous bright field image corresponding to the first stimulated Raman spectrum; if the integrity of the in vivo flow pipeline represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected, and the synchronous bright field image is used to characterize the integrity of the scanned in vivo flow pipeline.

[0039] In a third aspect, the present application provides an in vivo flow detection device for cells, the device comprising:

[0040] An acquisition module, used for acquiring a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel;

[0041] a scanning module, configured to perform line scanning on the liquid to be detected based on a spectral focusing principle and a pre-set detection scanning line in response to the liquid to be detected flowing through the in-body flow-type pipeline, to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow-type pipeline;

[0042] The analysis module is used to analyze the first stimulated Raman spectrum image to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected if the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value.

[0043] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method provided in the first aspect or any one embodiment of the first aspect is implemented.

[0044] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0045] Obtaining a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel;

[0046] In response to the liquid to be detected flowing through the in-body flow pipe, the liquid to be detected is line-scanned based on the spectral focusing principle and the pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow pipe;

[0047] If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected.

[0048] In a sixth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0049] Obtaining a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel;

[0050] In response to the liquid to be detected flowing through the in-body flow pipe, the liquid to be detected is line-scanned based on the spectral focusing principle and the pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow pipe;

[0051] If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected.

[0052] The above-mentioned in vivo flow cytometry detection method, system and device of cells first perform line scanning on the liquid to be detected based on the spectral focusing principle and the pre-set detection scanning line to obtain the first stimulated Raman spectrum and the synchronous bright field image, and then analyze the first stimulated Raman spectrum to obtain the types of multiple cells to be detected and the number of each type of cells to be detected, and characterize the integrity of the scanned in vivo flow pipeline according to the synchronous bright field image. Since the first stimulated Raman spectrum is obtained by line scanning, the problem of missed detection in the process of flow detection of cells is reduced, and the integrity of the in vivo flow pipeline is obtained by synchronous bright field image to determine whether there is movement during the detection process, thereby improving the accuracy of the in vivo flow cell detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 is a structural framework diagram of an in vivo flow detection system for cells in one embodiment;

[0055] Figure 2 A schematic diagram of a scanning path of an in vivo flow detection system for cells in one embodiment;

[0056] Figure 3 is a partial structural schematic diagram of another combined optical path in one embodiment;

[0057] Figure 4 is a partial structural schematic diagram of another combined optical path in one embodiment;

[0058] Figure 5 is a partial structural schematic diagram of another combined optical path in one embodiment;

[0059] Figure 6 is a partial structural schematic diagram of another combined optical path in one embodiment;

[0060] Figure 7 A schematic diagram of the process of an in vivo flow cytometry detection method for cells in one embodiment;

[0061] Figure 8is a schematic diagram of imaging results of ear blood vessels in one embodiment;

[0062] Fig. 9 is a schematic diagram of a first stimulated Raman spectrum in one embodiment;

[0063] Fig.10 is a schematic diagram of a synchronous bright field signal image corresponding to a first stimulated Raman spectrum in one embodiment;

[0064] Fig.11 is a schematic diagram of a synchronous bright field image corresponding to a first stimulated Raman spectrum in one embodiment;

[0065] Fig.12 A scatter plot of cells of a liquid to be detected in one embodiment;

[0066] Fig.13 A schematic diagram showing the average expression of stimulated Raman spectra of each type of cells in one embodiment;

[0067] Fig.14 is a timing pulse diagram in one embodiment;

[0068] Fig.15 A schematic diagram of the detection flux result in one embodiment;

[0069] Fig.16 is a distribution diagram of relative intensity of a spectrum of a first stimulated Raman spectrum in one embodiment;

[0070] Fig.17 is a structural block diagram of an in vivo flow detection device for cells in one embodiment;

[0071] Fig.18 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0073] Figure 1 FIG. 1 is a structural framework diagram of an in vivo flow detection system for cells in an embodiment, as shown in FIG. Figure 1 As shown, the in vivo flow detection system of the cell includes a first optical path 110, a second optical path 120, a merging optical path 130 and a signal processing module.

[0074] The first optical path 110 may include a first optical power continuous adjustment module, a laser amplitude modulation module, a first reflector (M) 113 , a laser polarization state conversion module and a galvanometer optical path tuning module. The first optical path 110 is used to output a Stokes beam.

[0075] In an example, the first optical power continuous adjustment module may include a first half-wave plate (HWP) 1111 and a first polarization beam splitter prism (PBS) 1112; the laser amplitude modulation module may include a first convex lens (Lens) 1121, an acousto-optic modulator (AOM) 1122 and a second convex lens (Lens) 1123; the laser polarization state transformation module may include a second polarization beam splitter prism (PBS) 1141, a first quarter-wave plate (QWP) 1142 and a second half-wave plate (HWP) 1143; the galvanometer optical path tuning module may include a resonant galvanometer (RG) 1151, a third convex lens (Lens) 1152 and a second reflector (M) 1153.

[0076] In one example, the first optical power continuous adjustment module can control the optical power of the laser passing through the first polarization beam splitter prism (PBS) 1112 by continuously rotating the first half-wave plate (HWP) 1111, thereby achieving the purpose of controlling the laser power acting on the liquid to be detected.

[0077] The function of the first convex lens (Lens) 1121 in the laser amplitude modulation module is to converge the laser incident to the acousto-optic modulator (AOM) 1122 so that the diameter of the laser beam incident to the acousto-optic modulator (AOM) 1122 satisfies the matching relationship with the effective aperture. At the same time, since the acousto-optic modulator (AOM) 1122 has a high requirement for the modulation speed, the use of a converging lens can also make the laser beam thinner and more energy concentrated, thereby reducing the ultrasonic transit time, and the function of the second convex lens (Lens) 1123 is to expand and collimate the laser beam after passing through the acousto-optic modulator (AOM) 1122 so that the laser can propagate in the subsequent optical path. The acousto-optic modulator (AOM) 1122 uses the acousto-optic principle to modulate the laser amplitude, and its modulation signal is derived from the external output signal of the lock-in amplifier (LIA).

[0078] The first reflecting mirror (M) 113 may change the propagation path of the Stokes beam.

[0079] The laser polarization state conversion module is used to split the Stokes beam incident on the second polarization beam splitter (PBS) 1141 into a P-linear polarization state and an S-linear polarization state. The polarization conditions of the P-linear polarization state and the S-linear polarization state are spatially orthogonal. The P-linear polarization state light propagates along the optical path and passes through the first quarter wave plate (QWP) 1142, and then the polarization state changes to a circular polarization state. After passing through the galvanometer optical path tuning module, the Stokes beam first passes through the first quarter wave plate (QWP) 1142 and then passes through the second polarization beam splitter (PBS) 1141 along the original optical path. At this time, the polarization state of the Stokes beam changes to an S state and propagates to the second half-wave plate (HWP) 1143 in a direction perpendicular to the original optical path. Then, the polarization state of the Stokes beam is consistent with the pump beam, so as to achieve the purpose of matching the polarization states of the two excitation lights.

[0080] The resonant galvanometer (RG) 1151 in the galvanometer optical path tuning module moves according to a preset rule to generate an optical path difference. Figure 2 A schematic diagram of a scanning path of an in vivo flow cytometry detection system for cells in one embodiment. Figure 2 The small squares in the middle are sampling points. The first small square in the X direction represents the resonant galvanometer (RG) 1151 moving from top to bottom, and the second small square in the X direction represents the resonant galvanometer (RG) 1151 moving from bottom to top. The two sampling points are one movement cycle of the resonant galvanometer (RG) 1151.

[0081] The distance between the resonant galvanometer (RG) 1151 and the third convex lens (Lens) 1152 in the galvanometer optical path tuning module may be the focal length f of the third convex lens (Lens) 1152, and the distance between the third convex lens (Lens) 1152 and the second reflector (M) 1153 may also be the focal length f of the third convex lens (Lens) 1152. The incident Stokes light beam needs to be incident on the edge of the resonant galvanometer (RG) 1151. When the resonant galvanometer (RG) 1151 moves from the uppermost end to the lowermost end, it will bring about a corresponding optical path difference. In this process, the Stokes light is reflected by the resonant galvanometer (RG) 1151 and then incident on the third convex lens (Lens) 1152. After passing through the third convex lens (Lens) 1152, it is vertically incident on the second reflector (M) 1153. The second reflector (M) 1153 reflects the Stokes light, and the Stokes light returns in the reverse direction along the original path.

[0082] The second optical path 120 may include a second optical power continuous adjustment module, a beam expansion module 1221 , a delay line (Delayline) 1231 and a third reflector (M) 1241 .

[0083] In one example, the second optical power continuous adjustment module may include a third half-wave plate (HWP) 1211 and a third polarization beam splitter (PBS) 1212; the beam expansion module 1221 may include two fourth convex lenses (Lens) for beam expansion; the delay line (Delay line) 1231 may include two fourth reflectors (M); and the second optical path 120 is used to output a pump beam and temporally overlap the pump beam with the Stokes beam.

[0084] In one example, the second optical power continuous adjustment module can control the optical power of the pump light beam passing through the third polarization beam splitter prism (PBS) 1212 by continuously rotating the third half-wave plate (HWP) 1211, thereby achieving the purpose of controlling the laser power acting on the liquid to be detected.

[0085] The delay line 1231 is a precision displacement platform that can perform high-precision displacement and finely adjust the optical path of the pump light. It can precisely fine-tune the optical path of the pump light so that it coincides with the Stokes light in time.

[0086] The third reflector (M) 1241 is used to change the optical propagation path of the pump beam.

[0087] The merging optical path 130 may include a dichroic mirror (DM) 131, a chirped glass rod (SF57) 132, a reflector module 133, a two-dimensional scanning galvanometer module (Scan unit) 134, a collimation module 135, a microscope objective (OBJ) 136, a stage 137, a condenser (C) 138, a fifth reflector (M) 139 and a filter (Filter) 140; the reflector module 133 may be composed of two sixth reflectors (M); the two-dimensional scanning galvanometer module (Scan unit) 134 is composed of two rotatable seventh reflectors (M); the collimation module 135 is composed of two fifth convex lenses (Lens).

[0088] In one example, a dichroic mirror (DM) 131 is used to spatially coincide the Stokes beam with the pump beam.

[0089] The chirped glass rod (SF57) 132 is used to linearly chirp the Stokes beam and the pump beam that overlap in time and space, so that the spectrum distribution in the time spectrum becomes wider, that is, time dispersion is generated.

[0090] The reflector module 133 is used to change the path of the combined light path to meet the layout requirements of the system.

[0091] The two-dimensional scanning galvanometer module (Scan unit) 134 controls the deflection angle of the second rotatable mirror (M) through the analog voltage signal output by the data acquisition card, so that the second rotatable mirror (M) can complete the scanning in the X direction and the Y direction according to the specified scanning path. Figure 2 shown.

[0092] The microscope objective lens (OBJ) 136 is used to focus the laser light entering the objective lens, and the focus of the objective lens can stimulate stimulated Raman spectroscopy signals.

[0093] The stage 137 is used to place living animals.

[0094] The condenser (C) 138 can be used to collect the light after excitation.

[0095] The fifth reflector (M) 139 is used to change the path of the excited light, so as to facilitate the subsequent processing of the spectrum and meet the installation requirements of the system.

[0096] The filter 140 is used to filter out the Stokes light in the excited light.

[0097] In one example, the condenser (C) 138 can collect the light after excitation by transmission or reflection, so as to further broaden the detection range of the in vivo flow detection system for detecting cells.

[0098] In one example, Figure 1 The partially merged optical path structure in the dashed box is to place the in vivo flow channel of the cell solution to be detected to receive the stimulated Raman scattering signal ( Figure 1 The microscope objective lens (OBJ) 136 excites the signal, and the condenser lens (C) 138 receives the signal), which is referred to as transmission detection. Figure 1 Part of the combined optical path structure in the dotted box is replaced by a reflection method (the structure in the dotted box includes: collimation module 135, microscope objective (OBJ) 136, stage 137, condenser (C) 138, fifth reflector (M) 139 and filter (Filter) 140). The structure of collecting laser light by reflection can specifically include the following:

[0099] like Figure 3 As shown, Figure 3 A partial structural schematic diagram of another combined optical path provided by an embodiment. Figure 3 The structure shown can be replaced by Figure 1 The portion shown in the dashed box. Figure 3The replaceable parts include a collimating module 135 , a fourth polarization beam splitter prism (PBS) 310 , a second quarter wave plate (QWP) 320 , a microscope objective lens (OBJ) 136 , a stage 137 and a filter 140 . Figure 3 The microscope objective lens (OBJ) 136 is used to receive the excited signal, and then the excited signal is transmitted to the filter (Filter) 140 through the second quarter wave plate (QWP) 320 and the fourth polarization beam splitter (PBS) 310. Therefore, Figure 3 A reflective detection is provided, for example, blood vessels in animals that need reflective detection can be detected, and the detection range of the system is expanded. The specific principle is as follows: the combined light is vertically incident on the fourth polarization beam splitter (PBS) 310, and the vertical polarization direction linear polarized light (P light) is emitted. The P light is incident on the second quarter wave plate (QWP) 320, and the emitted light is converted into left-handed circularly polarized light (or right-handed circularly polarized light). The circularly polarized light then passes through the microscope objective (OBJ) 136, and the microscope objective (OBJ) 136 excites the stimulated Raman signal of the detection cell. Due to the reflection effect, the light that has acted on the liquid to be detected will return along the excitation light path and become right-handed circularly polarized light (or left-handed circularly polarized light) and enter the second quarter wave plate (QWP) 320, and is converted into horizontal polarization direction linear polarized light (S light). The S light passes through the fourth polarization beam splitter (PBS) 310 and is reflected to the filter (Filter) 140.

[0100] like Figure 4 As shown, Figure 4 A partial structural schematic diagram of another combined optical path is provided. Figure 4 The partial structure of the combined light path shown includes: collimation module 135, semi-transparent mirror 410, microscope objective (OBJ) 136, stage 137 and filter 140. The specific principle is as follows: after the combined light passes through the collimation module 135, it is incident into the microscope objective (OBJ) 136 through the semi-transparent mirror 410, and stimulates the stimulated Raman scattering signal of the cells to be detected. Due to the reflection effect, the scattered light part after the reaction with the cells will return along the excitation light path, pass through the microscope objective (OBJ) 136 and then to the semi-transparent mirror 410. The semi-transparent mirror 410 reflects through the filter 140 and then is incident on the subsequent signal processing module to process the laser signal.

[0101] like Figure 5 and Figure 6 As shown, Figure 5 and Figure 6 A partial structural schematic diagram of another combined optical path is provided, wherein: Figure 5 is a side view of the structure. Figure 6The top view of the structure is as follows: the combined light enters the objective lens and excites the stimulated Raman scattering signal of the cells to be detected. Due to reflection, part of the scattered light after interacting with the cells will return along the excitation light path, but most of it will be scattered around. In order to make as much reflected light as possible incident on the photodiode detector (PD), the PD and the filter are made into a large circular shape with a hole in the middle to allow the excitation light to pass through.

[0102] The merging optical path 130 is used to overlap the Stokes beam and the pump beam in space and perform line scanning on the liquid to be detected according to a preset detection scanning line when the resonant galvanometer (RG) 1151 moves, so as to obtain a first stimulated Raman spectrum of the liquid to be detected.

[0103] The signal processing module may include a photodiode detector (PD) 141, a lock-in amplifier (LIA) 142, and a workstation 143. The signal processing module is used to analyze the first stimulated Raman spectrum to obtain the types of multiple cells to be detected and the number of each type of cells to be detected.

[0104] In one example, the Stokes beam and the pump beam are emitted by a dual-path laser.

[0105] In one example, the specific process of the in vivo flow detection system of cells in the embodiment of the present application obtaining the stimulated Raman spectrum of the liquid to be detected is as follows: the microscope objective (OBJ) 136 applies the laser focus to a position somewhere on the detection scanning line of the liquid to be detected. At this time, the resonant galvanometer (RG) 1151 moves from top to bottom to complete a stimulated Raman spectrum acquisition, that is, the stimulated Raman spectrum of that part of the liquid to be detected is obtained. Next, the two-dimensional scanning galvanometer module (Scan unit) 134 controls the focus to move to the next position. When the focus arrives, the resonant galvanometer (RG) 1151 moves from bottom to top again to complete the next stimulated Raman spectrum acquisition. The stimulated Raman spectra obtained by the resonant galvanometer (RG) 1151 moving from top to bottom and from bottom to top are the same except that the timing is opposite. Therefore, when the focus is pressed Figure 2 After the line scan of the liquid to be detected is completed in the path sequence shown, the first stimulated Raman spectrum of the liquid to be detected can be obtained by reconstruction.

[0106] In an exemplary embodiment, the in vivo flow cytometry detection system of the cell is applied to ex vivo flow cytometry detection or in vivo flow cytometry detection, thereby realizing in vivo or ex vivo flow cytometric detection of hyperspectral stimulated Raman scattering.

[0107] In an exemplary embodiment, Figure 7 As shown, a method for in vivo flow cytometry detection of cells is provided, and the method is applied to Figure 1Taking the system shown in FIG. 1 as an example, the method may include the following steps 701 to 703. Among them:

[0108] Step 701, obtaining the liquid to be detected and the flow pipeline.

[0109] The liquid to be detected may be any cell liquid, the liquid to be detected may include a variety of cells to be detected, and the flow channel is an in vivo flow channel.

[0110] In one example, the liquid to be detected may include an in vivo flow cytometry liquid. For example, the in vivo flow cytometry liquid may include but is not limited to in vivo blood. The liquid to be detected may also include an ex vivo cell liquid, and the liquid to be detected may be detected through an in vivo flow channel.

[0111] In one example, the in vivo flow cytometric liquid can be blood in an animal, etc.; a mixture of cells in vitro, such as a mixture of red blood cells and cancer cells, etc. The embodiments of the present application are only used to illustrate that there is no limitation on the liquid to be detected and the cells to be detected included in the liquid to be detected.

[0112] In one example, the flow pipe may include an ex vivo flow pipe and an in vivo flow pipe. Among them, the ex vivo flow pipe can be used to detect cell fluid that is not in the body. The in vivo flow pipe may include a biological blood vessel or other pipe. The specific type of the flow pipe is not limited here.

[0113] Step 702, in response to the liquid to be detected flowing through the in-body flow pipe, the liquid to be detected is line-scanned based on the spectral focusing principle and the pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum.

[0114] The synchronized bright field image is used to characterize the integrity of the scanned in vivo flow pipeline.

[0115] In one example, the pre-set detection scan line is obtained by placing the in vivo flow channel of the cell liquid to be detected at the test site, such as Figure 1 The stage under the microscope objective lens (OBJ) 136 controls the two-dimensional scanning galvanometer module (Scan unit) 134 to make the focus along the Figure 2 The detection scanning line is composed of several sampling points and should cover the cross section of the liquid pipeline to be detected. Figure 8 As shown, Figure 8 is a schematic diagram of imaging results of ear blood vessels in one embodiment, Figure 8The results of imaging the mouse ear blood vessels can be clearly seen in the figure. White blood cells flow through both sides of the blood vessel wall. The black dotted line with squares represents the detection scan line set for the relatively "straight" part of the blood vessel. Exemplarily, the synchronous bright field image is introduced, and the synchronous bright field image of the mouse ear blood vessels is reconstructed by using the changes in light and dark brightness during the process of the detection scan line scanning the pipeline of the cells to be detected. The synchronous bright field image is used to characterize the integrity of the detected mouse ear blood vessels, which can further improve the accuracy of in vivo cell detection.

[0116] In one example, the liquid to be detected is scanned on the detection scanning line, and the following is obtained: Fig. 9 The schematic diagram of the first stimulated Raman spectrum is shown in FIG. Fig. 9 The bright stripes in the figure are the spectrum of the liquid to be detected. The horizontal axis (X axis) represents the Raman frequency shift; the vertical axis (Y axis) represents the flow time, and the vertical direction (Z axis) represents the intensity, that is, the light and dark in the figure. Line scanning of the liquid to be detected can effectively reduce the occurrence of missed cell detection.

[0117] In one example, a synchronous bright field image is introduced, and an image is reconstructed by using the changes in brightness when the detection scan line passes through the pipeline of the cells to be detected. For example, the mouse ear is detected, such as Fig.10 As shown, Fig.10 This is the synchronous bright field signal image corresponding to the first stimulated Raman spectrum obtained by scanning the in vivo flow channel (i.e., the blood vessels in the mouse ear) when testing the mouse ear. The horizontal axis represents the acquisition time of the resonant galvanometer, the vertical axis represents the flow time, and the vertical axis represents the intensity. Since the laser is blocked in the area where the cells flow through, the display becomes darker, while the area where the cells do not flow through becomes brighter. Its image display state is similar to the following Fig. 9 The bright and dark states of the first stimulated Raman spectrum shown are opposite. The bright field signal is reconstructed to obtain a synchronous bright field image, such as Fig.11 As shown, Fig.11 is the synchronous bright field image corresponding to the first stimulated Raman spectrum during in vivo flow cytometry. For example, when performing flow cytometry on the ear blood vessels of mice, line scanning is performed 10,000 times, that is, 10,000 first stimulated Raman spectrum images and their corresponding synchronous bright field signal images are obtained. At this time, these 10,000 synchronous bright field signal images are reconstructed to obtain the following: Fig.11 As shown in the figure, Fig.11 The upper dashed box represents the upper wall of the blood vessel scanned by the line scan, and the lower dashed box represents the lower wall of the blood vessel scanned by the line scan. Fig.11 The mouse's vascular wall and its status can be clearly displayed, indicating that the mouse's blood vessels did not undergo significant displacement during in vivo flow cytometry. Since in vivo flow cytometry takes a long time, the mouse may wake up from anesthesia prematurely or have body convulsions due to unconditioned reflexes, resulting in movement of the detected blood vessel relative to the microscope objective. Fig.11 Need and Fig. 9 If the analysis results are synchronized, Fig.11 If there is an obvious fault or disconnection somewhere in the blood vessel wall in the displayed image, it means that the mouse moved at a certain moment during the in vivo flow cytometry detection. The impact is that the set detection scan line does not completely cover the blood vessel diameter. Then the accuracy of the data of the in vivo flow cytometry result (such as cell typing, detection flux) will be seriously affected and need to be re-tested.

[0118] In an exemplary embodiment, the present application embodiment detects the liquid to be detected based on the spectral focusing principle. For example, the spectral focusing principle uses a resonant galvanometer (RG) 1151 and utilizes the optical path difference caused by the reciprocating motion of the resonant galvanometer (RG) 1151 to quickly obtain a stimulated Raman spectrum. Figure 2 When the sampling point is Figure 2 When in the small and medium squares, the resonant galvanometer (RG) 1151 moves from top to bottom or from bottom to top once, realizing the rapid acquisition of the first stimulated Raman spectrum based on the principle of spectral focusing, improving the utilization rate of the laser energy of the cell flow detection system, and the laser can effectively pass through the surface skin of the living body and directly reach the detection target pipeline, realizing in vivo cell detection. Rapid acquisition of stimulated Raman spectra based on the principle of spectral focusing In order to improve the detection flux of the liquid to be detected, the detection flux will affect the missed detection rate of the cells to be detected. During in vivo flow detection, the blood flow rate is fast, and the detection flux is small, so in vivo flow detection cannot be achieved.

[0119] Step 703: If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of multiple cells to be detected and the number of each type of cells to be detected.

[0120] In one example, the integrity of the in vivo flow channel represented by the synchronous bright field image is evaluated. The integrity can reflect whether the in vivo flow channel moves during the detection process, such as Fig.11 As shown, Fig.11 The images of the in-vivo flow pipeline shown are images at a higher degree of integrity.

[0121] In one example, the first stimulated Raman spectrum is analyzed in combination with machine learning feature extraction and classification algorithms, and the different stimulated Raman spectral features of different cells are used to achieve label-free, rapid and high-accuracy classification of the cells to be detected and obtain the specific number of each cell. Since the hemoglobin in the cell expresses both the stimulated Raman scattering signal of the protein and the transient absorption signal of heme, the strength of the transient absorption signal has nothing to do with the Raman signal. Therefore, when the stimulated Raman spectrum of hemoglobin is collected, the contribution of the transient absorption signal will result in broadband spectral features. Since mature red blood cells in mammals do not contain nuclei, but only hemoglobin, while white blood cells and prostate cancer cells contain nuclei but not hemoglobin. At the same time, prostate cancer cells are rich in lipid droplets, which will appear at 2850cm in the Raman spectrum. -1 Therefore, by using broadband spectral characteristics and the expression of lipid droplet signals in prostate cancer cells, red blood cells, white blood cells and prostate cancer cells can be accurately distinguished, achieving in vivo cell detection.

[0122] In an exemplary embodiment, step 703 includes: performing line scanning on the standard solution according to the detection scanning line to obtain a stimulated Raman spectrum of the standard solution; performing intensity correction on the first stimulated Raman spectrum using the stimulated Raman spectrum of the standard solution to obtain a second stimulated Raman spectrum; performing spectral analysis on the second stimulated Raman spectrum according to the relative relationship between the sampling points on the detection scanning line and the spectral frequency information to obtain a third stimulated Raman spectrum; performing dimensionality reduction and cluster analysis on the third stimulated Raman spectrum to obtain a scatter plot of the liquid to be detected, wherein the scatter plot represents the types of multiple cells to be detected and the number of each type of cells to be detected.

[0123] The stimulated Raman spectrum of the standard solution can be obtained by referring to the process of obtaining the first stimulated Raman spectrum, and the liquid to be detected in the microscope objective lens (OBJ) 136 can be replaced by the standard solution.

[0124] Among them, the specific method of using the stimulated Raman spectrum of the standard solution to perform intensity correction on the first stimulated Raman spectrum may include, according to the intensity value of the spectrum in the first stimulated Raman spectrum, the minimum intensity value of the spectrum in the first stimulated Raman spectrum, the intensity value of the spectrum in the stimulated Raman spectrum of the standard solution, the minimum intensity value of the spectrum in the stimulated Raman spectrum of the standard solution and the correction formula. The specific correction formula is as follows:

[0125]

[0126] Wherein, I represents the intensity value of the spectrum in the first stimulated Raman spectrum, represents the minimum intensity value of the spectrum in the first stimulated Raman spectrum, represents the intensity value of the spectrum in the stimulated Raman spectrum of the standard solution; Represents the minimum intensity value of the spectrum in the stimulated Raman spectrum of the standard solution; Represents a constant.

[0127] In one example, according to the sampling point and spectral frequency information, Fig. 9 The first stimulated Raman spectrum shown is analyzed to obtain the spectral intensity corresponding to the spectral frequency information, and the spectrum analysis is completed to achieve a one-to-one correspondence between the spectral frequency signal and the spectral intensity.

[0128] In an exemplary embodiment, the in vivo flow cytometric detection method of cells further includes: performing line scanning on a standard according to a detection scanning line to obtain a stimulated Raman spectrum of the standard, wherein the standard represents an experimental solution of a known cell type; performing intensity correction on the stimulated Raman spectrum of the standard using the stimulated Raman spectrum of the standard solution to obtain the relative relationship between the sampling points on the detection scanning line and the spectral frequency information.

[0129] The stimulated Raman spectrum of the standard can be obtained by placing the standard at the microscope sample test position and using the detection scanning line to perform the following steps on the standard: Figure 2 The line scan path shown is used for line scanning.

[0130] In one example, the resolved third stimulated Raman spectrum is taken as a set and the set is analyzed by the spectrum analysis method in the related art (the t-distributed Stochastic Neighbor Embedding (t-SNE) method can be used for dimensionality reduction, and the k-means clustering algorithm (k-means) can be used for clustering according to the obtained scattered point distribution), and the following is obtained: Fig.12 The scatter plot of cells in the liquid to be detected is shown. For example, the ear blood vessels of mice are detected based on the method provided in the present application, and the scatter plot of cells to be detected in the ear blood vessels of mice is detected. Fig.12 The circle in the figure is the scatter plot of red blood cells, the triangle is the scatter plot of prostate cancer cells, and the square is the scatter plot of white blood cells. The scatter plot represents the type of cells detected by flow cytometry this time, and the number of scatter plots of different cell types is counted to obtain the number of each type of cell. The difference in stimulated Raman spectra of different cells is used to classify and count the cells to be detected, thereby improving the accuracy of cell detection.

[0131] In the above-mentioned in vivo flow cytometric detection method of cells, firstly, a line scan is performed on the liquid to be detected according to a preset detection scanning line to obtain a first stimulated Raman spectrum, and then the first stimulated Raman spectrum is analyzed to obtain multiple types of cells to be detected and the number of each type of cells to be detected. Since the first stimulated Raman spectrum is obtained by line scanning, the problem of missed detection in the process of cell flow cytometric detection is reduced, thereby improving the accuracy of the in vivo flow cytometric detection results of cells.

[0132] In an exemplary embodiment, the in vivo flow cytometry detection method of cells further includes: according to the scatter plot, averaging the stimulated Raman spectrum of each cell to be detected to obtain an average spectrum and a distribution range of the spectrum of each cell to be detected.

[0133] In one example, the stimulated Raman spectra of different types of cells in the obtained scatter plot are respectively averaged to obtain the average spectrum and spectral distribution range of each type of stimulated Raman spectrum. Fig.13 As shown, Fig.13 It is a schematic diagram of the average expression of the stimulated Raman spectrum of each type of cell. The horizontal axis represents the Raman frequency shift, and the vertical axis represents the Raman signal intensity. For example, the ear blood vessels of mice are detected based on the method provided in the present application, and the stimulated Raman spectra of various cells in the ear blood vessels of mice detected are averaged and expressed. The stimulated Raman spectra and spectral distribution ranges of red blood cells, prostate cancer cells and white blood cells are detected by detecting the ear blood vessels of mice. The thick solid line represents the average spectrum of all spectra classified as red blood cells after clustering, and the shadow between the two thin solid lines above and below the thick solid line represents the distribution range of all spectra classified as red blood cells; the thick dotted line represents the average spectrum of all spectra classified as prostate cancer cells after clustering, and the shadow between the two thin dotted lines above and below the thick dotted line represents the distribution range of all spectra classified as prostate cancer cells; the thick double-dotted line represents the average spectrum of all spectra classified as white blood cells after clustering, and the shadow between the two thin double-dotted lines above and below the thick double-dotted line represents the distribution range of all spectra classified as white blood cells. The average expression of different types of stimulated Raman spectra can be compared with the standard stimulated Raman spectra of the cells to be tested, which can verify the correctness of the flow cytometry detection results and reflect the types and distribution of different cells to be tested.

[0134] In an exemplary embodiment, the in vivo flow cytometry detection method of cells further includes: performing pulse conversion on the first stimulated Raman spectrum to obtain a timing pulse signal of the first stimulated Raman spectrum; and determining whether the timing pulse signal is a valid pulse signal according to a preset pulse threshold.

[0135] Among them, the preset pulse threshold can be set as needed, for example, it can be set according to the 3σ of the pulse. The preset pulse threshold can be used to measure the number of spectra in the first stimulated Raman spectrum that meet the subsequent cell analysis. When the peak value of the timing pulse signal is greater than the pulse threshold, it indicates that the timing pulse signal is in line with the analysis of the cells to be detected, so the timing pulse signal can be determined as a valid pulse signal. When the peak value of the timing pulse signal is less than the pulse threshold, it indicates that the pulse signal does not meet the conditions for subsequent detection of the cells to be detected, so the pulse signal can be ignored.

[0136] In one example, the first stimulated Raman spectrum is converted into a time-sequential pulse, a bright stripe in the first stimulated Raman spectrum represents a pulse, and the brightness and darkness of the bright stripe represent the intensity of the pulse. Fig.14 As shown, we can get Fig. 9 The corresponding timing pulse diagram is shown. Fig.14 The horizontal axis represents the flow time, and the vertical axis represents the pulse signal intensity. The black curve represents the original pulse signal; the square curve represents the pulse signal after filtering and smoothing by the Butterworth filter; the dashed line represents the preset pulse threshold, which is determined by the 3σ (3 times the mean square error) level of the Butterworth filter output. When the pulse of the square curve is higher than the threshold represented by the dashed line, it is a valid count, and when the pulse of the square curve is lower than the threshold represented by the dashed line, no count is performed.

[0137] In an exemplary embodiment, the in vivo flow cytometry detection method of cells also includes: acquiring multiple first stimulated Raman spectra within a preset time; based on the multiple first stimulated Raman spectra, counting the number of effective pulse signals within the preset time; and obtaining the detection flux per unit time according to the preset time and the number.

[0138] The preset time can be set as needed, for example, the preset time can be set to within 2 seconds. For each first stimulated Raman spectrum acquired within the preset time, it can be determined whether each timing pulse signal is a valid pulse signal according to a preset pulse threshold, and the number of valid pulse signals can be counted.

[0139] Exemplarily, the first stimulated Raman spectrum may contain multiple bright stripes, that is, there will be multiple pulses with valid counts within 16ms. During each detection, the pipeline of the liquid to be detected is scanned tens of thousands of times, and the cross-section of the pipeline of the liquid to be detected is scanned 10,000 times, and 10,000 first stimulated Raman spectra will be obtained. This process takes a total of 160s. Therefore, the bright stripes that appear in 125 flow charts are used as statistics, and the detection flux within 2 seconds is counted, and then divided by 2 to obtain the detection flux per second. The unit of detection flux is eps, that is, event per second. Fig.15 As shown, Fig.15 is a schematic diagram of the detection flux result in one embodiment, Fig.15 It is a result diagram of the detection flux at a certain speed. For example, the flux of the mouse ear blood vessels is detected. The horizontal axis represents the detection flux and the vertical axis represents the frequency. When the flow speed of the cells to be detected changes, the flux will change. It should be understood that the above description is only for illustration and does not constitute a specific limitation.

[0140] In one example, the detection throughput can be the ability of a flow cytometry system to detect the maximum number of cells per second, and can also estimate the time required to detect the liquid to be detected.

[0141] In one example, the present application uses a resonant galvanometer (RG) 1151, and adds a third convex lens (Lens) 1152 and a second reflector (M) 1153, so that a 4f optical system is formed between the resonant galvanometer (RG) 1151 and the third convex lens (Lens) 1152 and the second reflector (M) 1153, and utilizes the optical path difference caused by the reciprocating motion of the resonant galvanometer (RG) 1151 to achieve microsecond-level rapid acquisition of stimulated Raman spectra, thereby meeting the detection flux required for in vivo flow cytometry detection of cells.

[0142] In an exemplary embodiment, the in vivo flow cytometric detection method of cells also includes: obtaining the pulse intensity of each effective pulse signal within a preset time; determining the spectral relative intensity corresponding to the first stimulated Raman spectrum according to the pulse intensity; and counting the number of first stimulated Raman spectrums whose spectral relative intensity is less than or equal to a first preset threshold.

[0143] In one example, the relative intensity of the spectrum is obtained by normalizing the spectrum intensity in the first stimulated Raman spectrum to obtain a distribution diagram of the relative intensity of the spectrum in the first stimulated Raman spectrum. In addition, the mean square error σ of the spectrum intensity is calculated, and the stability of the spectrum acquisition and the accuracy of the classification of the cells to be detected are judged according to the layout within 3σ in the distribution diagram. If the proportion of the relative intensity of the spectrum less than 3σ is too small, it indicates that the stability of the spectrum acquisition is poor, and it will also affect the accuracy of the classification of the cells to be detected. Fig.16 As shown, Fig.16The diagram is a distribution diagram of relative intensity of a spectrum of a first stimulated Raman spectrum in an embodiment, wherein the abscissa represents the percentage of normalized maximum intensity and spectrum intensity, and the ordinate represents frequency.

[0144] In one example, the multiple bright stripes in the first stimulated Raman spectrum have different brightness conditions, reflecting Fig.14 The pulse intensity is different in the time series pulse diagram. The difference in pulse intensity reflects the difference in the intensity of the detected spectrum signal. Fig.16 The statistical results can determine the stability of this spectrum collection and predict the accuracy of subsequent classification of cells to be tested (that is, if the proportion of spectral relative intensity less than 3σ is too low, the stability of this spectrum collection is poor; at the same time, if the proportion of all spectra with low relative intensity is too large, the accuracy of subsequent classification of cells to be tested will be reduced; and if the proportion of spectral relative intensity less than 3σ is high, the stability of this spectrum collection is good, and if the proportion of all spectra with high relative intensity is large, the accuracy of subsequent typing results of cells to be tested will be improved). If Fig.16 When the proportion of spectral relative intensities less than 3σ is too small and the proportion of all spectral relative intensities too low is too large, in order to ensure the accuracy of the classification results of the cells to be tested later, the steps of the detection method should be checked to see if there are any operational errors. At the same time, the system parameters can be readjusted, such as laser power, in vivo flow tube size, etc., so that the proportion of spectral relative intensities less than 3σ is increased, and the proportion of all spectral relative intensities too low is moderate, so as to ensure the accuracy of subsequent cell detection.

[0145] In an exemplary embodiment, the in vivo flow cytometry detection method of cells also includes: obtaining a trough value between two adjacent valid pulse signals; comparing the trough value with a second preset threshold, and if the trough value is greater than or equal to the second preset threshold, determining that the two adjacent valid pulse signals are a cluster.

[0146] In one example, in the detection of an ideal cell to be detected, one effective pulse peak is equal to one cell, and also equal to one spectrum, that is, the number of pulses, the number of cells and the number of spectra are unified at this time. However, a small number of cells will form a "cluster" due to adhesion. It is possible that two cells adhere together to form a "cluster", and so on. Exemplarily, when two cells adhere together to form a "cluster", two pulses will be detected, but the actual count should be counted as 1, representing a "cluster". For the detected pulse, whether it is a "cluster" is judged as follows: obtain the trough value between two adjacent valid pulse signals; compare the trough value with the second preset threshold value. If the trough value is greater than or equal to the second preset threshold value, the two adjacent valid pulse signals are determined to be a cluster, which further improves the accuracy of the classification and statistics of the cells to be detected.

[0147] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0148] Based on the same inventive concept, the embodiment of the present application also provides an in vivo flow detection device for cells for implementing the in vivo flow detection method for cells involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the in vivo flow detection device for one or more cells provided below can refer to the limitations of the in vivo flow detection method for cells above, and will not be repeated here.

[0149] In an exemplary embodiment, Fig.17 As shown, a structural block diagram of an in vivo flow detection device for cells is provided, the device 150 comprises: an acquisition module 1501, a scanning module 1502 and an analysis module 1503, wherein:

[0150] An acquisition module 1501 is used to acquire a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel;

[0151] A scanning module 1502 is used for performing line scanning on the liquid to be detected based on the spectral focusing principle and a pre-set detection scanning line in response to the liquid to be detected flowing through the in vivo flow pipe, to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in vivo flow pipe;

[0152] The analysis module 1503 is used to analyze the first stimulated Raman spectrum image to obtain the types of multiple cells to be detected and the number of each type of cells to be detected if the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value.

[0153] In one embodiment, the analysis module 1503 is used to perform line scanning on the standard solution according to the detection scanning line to obtain a stimulated Raman spectrum of the standard solution;

[0154] Performing intensity correction on the first stimulated Raman spectrum using the stimulated Raman spectrum of the standard solution to obtain a second stimulated Raman spectrum;

[0155] Performing spectral analysis on the second stimulated Raman spectrum according to the relative relationship between the sampling points on the detection scanning line and the spectral frequency information to obtain a third stimulated Raman spectrum;

[0156] Dimensionality reduction and cluster analysis are performed on the third stimulated Raman spectrum to obtain a scatter plot of the liquid to be detected, wherein the scatter plot represents the types of multiple cells to be detected and the number of each type of cells to be detected.

[0157] In one embodiment, the analysis module 1503 is further used to average the stimulated Raman spectrum of each cell to be detected according to the scatter plot, so as to obtain the average spectrum and the distribution range of the spectrum of each cell to be detected.

[0158] In one embodiment, the analysis module 1503 is further used to perform line scanning on the standard according to the detection scanning line to obtain a stimulated Raman spectrum of the standard, wherein the standard represents an experimental solution of a known cell type;

[0159] The stimulated Raman spectrum of the standard solution is used to perform intensity correction on the stimulated Raman spectrum of the standard product to obtain the relative relationship between the sampling points on the detection scanning line and the spectral frequency information.

[0160] In one embodiment, the analysis module 1503 is further used to perform pulse conversion on the first stimulated Raman spectrum to obtain a time-series pulse signal of the first stimulated Raman spectrum;

[0161] Whether the timing pulse signal is a valid pulse signal is determined according to a preset pulse threshold.

[0162] In one embodiment, the analysis module 1503 is further configured to obtain a plurality of first stimulated Raman spectra within a preset time;

[0163] Based on the plurality of first stimulated Raman spectra, counting the number of effective pulse signals within a preset time;

[0164] According to the preset time and quantity, the detection flux per unit time is obtained.

[0165] In one embodiment, the analysis module 1503 is further used to obtain the pulse strength of each valid pulse signal within a preset time;

[0166] Determine the relative intensity of the spectrum corresponding to the first stimulated Raman spectrum according to the pulse intensity;

[0167] The number of first stimulated Raman spectra whose relative spectral intensity is less than or equal to a first preset threshold is counted.

[0168] In one embodiment, the analysis module 1503 is further used to obtain a valley value between two adjacent valid pulse signals;

[0169] The trough value is compared with a second preset threshold value. If the trough value is greater than or equal to the second preset threshold value, it is determined that two adjacent valid pulse signals are a cluster.

[0170] Each module in the above-mentioned in vivo flow detection device for cells can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0171] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.18 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data of stimulated Raman spectra of cells to be detected. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for in vivo flow detection of cells is implemented.

[0172] Those skilled in the art will understand that Fig.18 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0173] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0174] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0175] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0176] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0177] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for in vivo flow cytometry detection of cells, characterized in that: include: Obtaining a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel; In response to the liquid to be detected flowing through the in-body flow pipe, the liquid to be detected is line-scanned based on the spectral focusing principle and the pre-set detection scanning line to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow pipe; If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, analyzing the first stimulated Raman spectrum to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected; The method further includes: performing pulse conversion on the first stimulated Raman spectrum to obtain a time sequence pulse signal of the first stimulated Raman spectrum; and determining whether the time sequence pulse signal is a valid pulse signal according to a preset pulse threshold.

2. The method according to claim 1, characterized in that The analyzing the first stimulated Raman spectrum to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected includes: Performing line scanning on the standard solution according to the detection scanning line to obtain a stimulated Raman spectrum of the standard solution; Performing intensity correction on the first stimulated Raman spectrum using the stimulated Raman spectrum of the standard solution to obtain a second stimulated Raman spectrum; Performing spectral analysis on the second stimulated Raman spectrum according to the relative relationship between the sampling points on the detection scan line and the spectral frequency information to obtain a third stimulated Raman spectrum; Dimensionality reduction and cluster analysis are performed on the third stimulated Raman spectrum to obtain a scatter plot of the liquid to be detected, wherein the scatter plot represents the types of the multiple cells to be detected and the number of each type of cells to be detected.

3. The method according to claim 2, characterized in that The method further comprises: According to the scatter plot, the stimulated Raman spectrum of each cell to be detected is averaged to obtain the average spectrum and the distribution range of the spectrum of each cell to be detected.

4. The method according to claim 2, characterized in that: The method further comprises: Performing line scanning on the standard according to the detection scanning line to obtain a stimulated Raman spectrum of the standard, wherein the standard represents an experimental solution of a known cell type; The stimulated Raman spectrum of the standard solution is used to perform intensity correction on the stimulated Raman spectrum of the standard product to obtain the relative relationship between the sampling points on the detection scanning line and the spectral frequency information.

5. The method according to claim 1, characterized in that The method further comprises: Acquire a plurality of the first stimulated Raman spectra within a preset time; Based on the plurality of the first stimulated Raman spectra, counting the number of valid pulse signals within the preset time; According to the preset time and the quantity, the detection flux per unit time is obtained.

6. The method according to claim 5, characterized in that The method further comprises: Obtaining the pulse strength of each valid pulse signal within the preset time; Determine the relative intensity of the spectrum corresponding to the first stimulated Raman spectrum according to the pulse intensity; The number of first stimulated Raman spectra whose relative spectral intensity is less than or equal to a first preset threshold is counted.

7. The method according to claim 1, characterized in that The method further comprises: Obtain the valley value between two adjacent valid pulse signals; The trough value is compared with a second preset threshold value. If the trough value is greater than or equal to the second preset threshold value, it is determined that the two adjacent valid pulse signals are a cluster.

8. An in vivo flow cytometry detection system for cells, characterized in that: The system comprises a first optical path, a second optical path, a combined optical path and a signal processing module; The first optical path is used to output a Stokes beam; the first optical path includes a galvanometer optical path tuning module, the galvanometer optical path tuning module includes a resonant galvanometer, and the resonant galvanometer is used to move according to a preset rule to generate an optical path difference; The second optical path is used to output a pump beam and to make the pump beam coincide with the Stokes beam in time; The combined optical path is used to overlap the Stokes beam and the pump beam in space, and when the resonant galvanometer moves, based on the spectral focusing principle and the pre-set detection scanning line, a line scan is performed on the liquid to be detected flowing through the in-body flow pipe to obtain a first stimulated Raman spectrum of the liquid to be detected, wherein the liquid to be detected includes a plurality of cells to be detected; The signal processing module is used to obtain a synchronous bright field image corresponding to the first stimulated Raman spectrum; If the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value, the first stimulated Raman spectrum is analyzed to obtain the types of the multiple cells to be detected and the number of each type of cells to be detected, and the synchronous bright field image is used to represent the integrity of the scanned in vivo flow channel; The signal processing module is further used to perform pulse conversion on the first stimulated Raman spectrum to obtain a time-series pulse signal of the first stimulated Raman spectrum; Whether the timing pulse signal is a valid pulse signal is determined according to a preset pulse threshold.

9. An in vivo flow detection device for cells, characterized in that: The device comprises: An acquisition module, used for acquiring a liquid to be detected and a flow channel, wherein the liquid to be detected includes a plurality of cells to be detected, and the flow channel is an in vivo flow channel; a scanning module, configured to perform line scanning on the liquid to be detected based on a spectral focusing principle and a pre-set detection scanning line in response to the liquid to be detected flowing through the in-body flow-type pipeline, to obtain a first stimulated Raman spectrum of the liquid to be detected and a synchronous bright field image corresponding to the first stimulated Raman spectrum, wherein the synchronous bright field image is used to characterize the integrity of the scanned in-body flow-type pipeline; an analysis module, configured to analyze the first stimulated Raman spectrum image to obtain the types of the plurality of cells to be detected and the number of each type of cells to be detected if the integrity of the in vivo flow channel represented by the synchronous bright field image is greater than a preset integrity value; The analysis module is further used to perform pulse conversion on the first stimulated Raman spectrum to obtain a timing pulse signal of the first stimulated Raman spectrum; and determine whether the timing pulse signal is a valid pulse signal according to a preset pulse threshold.

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