Centrifugal system, cell processing method and device

Abstract

The present application relates to a centrifugal system, a cell processing method and an apparatus. The centrifugal system includes: a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber; the sample chamber is used to place a preset reagent; the mixing and resuspension chamber is connected to the sample chamber and has a first channel that allows the liquid in the sample chamber to enter the mixing and resuspension chamber at a preset centrifugal speed, and is used to mix the preset reagent entering from the first channel and the sample liquid to be tested input from the input channel at the mixing centrifugal speed to obtain a mixed liquid, and separate the mixed liquid to obtain a cell precipitate and a waste liquid; the collection chamber is used to collect the cell precipitate; the waste liquid chamber is used to collect the waste liquid. The centrifugal structure can realize automated centrifugal separation while reducing damage to cell morphology and interference with other cells.

Description

Technical Field

[0001] The present application relates to the field of biomedical technology, and in particular to a centrifugal system, a cell processing method and a device. Background Art

[0002] Cystoscopy is the gold standard for bladder cancer diagnosis and recurrence monitoring, but cystoscopy is invasive and increases the risk of other urinary complications. Traditional imaging modalities (ultrasound, computed tomography, etc.) often detect tumors in the middle or late stages, resulting in poor treatment response and prognosis. Early diagnosis and recurrence monitoring of urinary transitional cell carcinoma have become urgent challenges.

[0003] Fluorescence in situ hybridization (FISH), a method for studying molecular genetics, has been used in recent years to detect chromosome number and structural aberrations. FISH is used to examine chromosomes in exfoliated cells, but this method suffers from low sensitivity and can easily alter cell morphology during the detection process, resulting in low accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a centrifugal system, a cell processing method and a device that can improve the accuracy of cell detection in order to address the above technical problems.

[0005] In a first aspect, the present application provides a centrifugal system, comprising: a sample chamber, a mixing and resuspension chamber, a collection chamber, and a waste liquid chamber;

[0006] The sample chamber is used to place preset reagents;

[0007] The mixing and resuspension chamber is connected to the sample chamber with a first channel for allowing the liquid in the sample chamber to enter the mixing and resuspension chamber at a preset centrifugal speed, and is used to mix the preset reagent entering the first channel and the sample liquid to be tested input from the input channel at the mixing centrifugal speed to obtain a mixed liquid, and separate the mixed liquid into a cell precipitate and a waste liquid;

[0008] The collection chamber is used to collect the cell sediment; the waste liquid chamber is used to collect the waste liquid.

[0009] In one embodiment, the preset reagent includes a first reagent and a second reagent; the sample chamber includes a first sample chamber and a second sample chamber, and the mixing centrifugal speed includes a first mixing centrifugal speed and a second mixing centrifugal speed; the sample liquid to be tested enters the mixing and resuspension chamber before the preset reagent; the mixing and resuspension chamber includes a first mixing and resuspension chamber and a second mixing and resuspension chamber; wherein,

[0010] The first mixing and resuspension chamber is used to mix and centrifuge the sample liquid to be tested at the first mixing centrifugal speed, or to mix and centrifuge the sample liquid to be tested and the first reagent to obtain a first mixed liquid, so that the waste liquid chamber collects the waste liquid of the first mixed liquid through the second channel, and collects the first cell pellet of the first mixed liquid into the second mixing and resuspension chamber through the second channel;

[0011] The second mixing and resuspension chamber is used to mix the first cell pellet with the second reagent in the second sample chamber at the second mixing centrifugal speed to obtain a second mixed liquid, so that the collection chamber collects the second cell pellet in the second mixed liquid, and the waste liquid of the second mixed liquid is collected through the waste liquid chamber.

[0012] In one embodiment, the predetermined centrifugal speed comprises a first centrifugal speed, and the system further comprises:

[0013] The first sample chamber is used to store the first reagent;

[0014] The first channel is used to introduce the first reagent into the first mixing and resuspension chamber under the action of the first centrifugal speed after the sample liquid to be detected is input through the input channel.

[0015] In one embodiment, the preset centrifugal speed includes a second centrifugal speed, and the second sample chamber includes a plurality of sample sub-chambers and third channels corresponding to each sample sub-chamber;

[0016] The plurality of sample subchambers are used to store the second reagent;

[0017] The third channel is used to introduce the second reagent into the second mixing and resuspension chamber under the action of the second centrifugal speed.

[0018] In one embodiment, the collection chamber is a multi-layer filter membrane structure; the pore size of the multi-layer filter membrane structure decreases from top to bottom, and the pore size of the bottom filter membrane of the multi-layer filter membrane structure is smaller than the diameter of the desired cells; the multi-layer filter membrane structure includes a first filter membrane, a second filter membrane, and a third filter membrane; the first filter membrane is used to retain a first impurity of a first diameter; the second filter membrane is used to retain a second impurity of a second diameter; and the third filter membrane is used to collect the cell precipitate and filter out a third impurity of a third diameter;

[0019] The first diameter is larger than the second diameter, and the second diameter is larger than the third diameter.

[0020] In one embodiment, the waste liquid chamber includes a first waste liquid chamber and a second waste liquid chamber;

[0021] A fourth channel is connected between the first waste liquid chamber and the first mixing and resuspension chamber, and is used to allow the waste liquid in the first mixing and resuspension chamber to enter the waste liquid chamber at a third centrifugal speed;

[0022] The second waste liquid chamber is used to collect the third impurities with the third diameter.

[0023] In one embodiment, a fifth channel is connected between the collection chamber and the second mixing and resuspension chamber, which allows the second mixed liquid to enter the collection chamber under the action of a fourth centrifugal speed.

[0024] In a second aspect, the present application further provides a cell processing method, which is applied to the centrifugal system as described in the first aspect; the method comprises:

[0025] Injecting a sample liquid to be tested from an input channel into a first mixing and resuspension chamber for pretreatment to obtain a pretreated sample liquid to be tested; injecting a first reagent into the first mixing and resuspension chamber through a first channel under the action of a first centrifugal speed; mixing and centrifuging the first reagent and the pretreated sample liquid to be tested under the action of the first mixing centrifugal speed to obtain a first mixed liquid; when the centrifugal speed of the centrifugal system matches a preset speed threshold, activating a fourth channel, sucking waste liquid of the first mixed liquid into the first waste liquid chamber through the fourth channel, and collecting a first cell precipitate of the first mixed liquid into a second mixing and resuspension chamber;

[0026] Under the action of the second centrifugal speed, the second reagent is injected into the second mixing and resuspension chamber, and the first cell pellet and the second reagent are mixed and centrifuged under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, the second mixed liquid is injected into the collection chamber through the fifth channel, the second cell pellet of the second mixed liquid is collected through the collection chamber, and the second waste liquid of the second mixed liquid is collected through the waste liquid chamber.

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

[0028] The second cell precipitate is rinsed with a cell buffer to obtain a cell suspension of the sample to be tested; the cell suspension is imaged using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift;

[0029] performing single-cell image segmentation processing on the coherent Raman scattering image using an image segmentation network model to obtain coherent Raman scattering images of multiple single cells;

[0030] performing single-cell feature extraction on the coherent Raman scattering image of each single cell in the plurality of single-cell coherent Raman scattering images using a feature extraction model to obtain a single-cell feature map corresponding to the coherent Raman scattering image of each single cell;

[0031] Combining the single cell feature maps according to the target object to obtain a single cell feature map group corresponding to the target object;

[0032] A preset classification model is used to perform feature analysis on the single-cell feature map group to obtain the abnormality probability of the target object; based on the abnormality probability and a preset abnormality probability threshold, the abnormality of the target object is determined.

[0033] In one embodiment, the single-cell feature map includes cell morphological characteristics and cell metabolic characteristics; the cell morphological characteristics include at least one or more of cell area, cell eccentricity, cell fitted circle diameter, cell major axis length, cell minor axis length, cell circumference, cell true area / fitted circle area, cell nuclear area, cytoplasm area, and cell nuclear-cytoplasm ratio; the cell metabolic characteristics include at least one or more of cell lipid droplet number, cell lipid droplet area, lipid droplet ratio of cell area, distance from lipid droplet to cell center, average distance from lipid droplet to cell center of gravity, and cell lipid droplet grayscale.

[0034] In one embodiment, the cell suspension is subjected to coherent Raman scattering imaging using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift, including:

[0035] Inputting one or more groups of excitation light into a coherent Raman scattering imaging system, wherein each group of excitation light has a different Raman shift and each group of excitation light includes a first excitation light and a second excitation light; the coherent Raman scattering imaging system includes at least: an optical path module and a signal acquisition module;

[0036] For each set of excitation light, the first excitation light and the second excitation light are collinearly processed by the optical path module to obtain a combined optical path, and the combined optical path is used to scan the cell suspension to obtain a coherent Raman scattering signal; the coherent Raman scattering signal is processed by the signal acquisition module to obtain a coherent Raman scattering image.

[0037] In a third aspect, the present application further provides a cell processing device, comprising: the device is used in a centrifugal system, the device comprising:

[0038] a first processing module, configured to inject a sample liquid to be detected from an input channel into a first mixing and resuspension chamber for pretreatment to obtain a pretreated sample liquid to be detected; inject a first reagent into the first mixing and resuspension chamber through the first channel under the action of a first centrifugal speed; mix and centrifuge the first reagent and the pretreated sample liquid to be detected under the action of the first mixing centrifugal speed to obtain a first mixed liquid; and activate a fourth channel when the centrifugal speed of the centrifugal system matches a preset speed threshold, and suck waste liquid of the first mixed liquid into the first waste liquid chamber through the fourth channel, and collect a first cell precipitate of the first mixed liquid into a second mixing and resuspension chamber;

[0039] The second processing module is used to inject the second reagent into the second mixing and resuspension chamber under the action of the second centrifugal speed, and mix and centrifuge the first cell pellet and the second reagent under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, inject the second mixed liquid into the collection chamber through the fifth channel, collect the second cell pellet of the second mixed liquid through the collection chamber, and collect the second waste liquid of the second mixed liquid through the waste liquid chamber.

[0040] 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 the processor implements the method provided in the second aspect when executing the computer program.

[0041] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method provided in the second aspect above.

[0042] In a sixth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps in the method provided in the second aspect above.

[0043] The above-mentioned centrifugal system, cell processing method and device, by providing a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber, centrifuges and mixes the sample liquid to be tested in the mixing and resuspension chamber by adding preset reagents, and separates the generated waste liquid and cell sediment to obtain the required cell sediment, thereby realizing automated centrifugal separation, enriching the sample and removing impurities, while reducing damage to cell morphology and improving the accuracy of subsequent detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 any creative work.

[0045] Figure 1 A schematic structural diagram of a centrifugal system in one embodiment;

[0046] Figure 2 A schematic structural diagram of a centrifugal system in one embodiment;

[0047] Figure 3 A schematic structural diagram of a centrifugal system in one embodiment;

[0048] Figure 4 A schematic structural diagram of a multi-layer filter membrane structure in one embodiment;

[0049] Figure 5 Schematic diagram of a cell processing method according to one embodiment;

[0050] Figure 6 Schematic diagram of a cell processing method according to one embodiment;

[0051] Figure 7 1 is a schematic structural diagram of a coherent Raman scattering imaging system in one embodiment;

[0052] Figure 8 Schematic diagram of the structure of an image segmentation network model in one embodiment;

[0053] Figure 9 is a schematic diagram of a coherent Raman scattering image of a single cell in one embodiment;

[0054] Figure 10 A schematic diagram of cell processing in one embodiment;

[0055] Figure 11 is a schematic diagram of signal intensity at different Raman shifts in one embodiment;

[0056] Figure 12 is a structural block diagram of a cell processing device in one embodiment;

[0057] Figure 13 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0059] In an exemplary embodiment, Figure 1 and Figure 2 As shown, Figure 1 Schematic diagram of the structure of a centrifugal system in an embodiment, the centrifugal system includes: a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber.

[0060] Sample chamber, used to place preset reagents.

[0061] The mixing and resuspension chamber is connected to the sample chamber and has a first channel that allows the liquid in the sample chamber to enter the mixing and resuspension chamber at a preset centrifugal speed. The mixing and resuspension chamber is used to mix the preset reagent entering from the first channel and the sample liquid to be tested input from the input channel at the mixing centrifugal speed to obtain a mixed liquid, and separate the mixed liquid to obtain cell precipitation and waste liquid.

[0062] The collection chamber is used to collect cell pellets. The waste liquid chamber is used to collect waste liquid.

[0063] The preset reagent may be a reagent for decomposing impurities in the sample liquid to be tested, and the preset reagent may be one or more. The sample liquid to be tested may be urine, and the preset reagent may be a reagent for decomposing other impurities in urine, such as a red blood cell lysis solution. The sample chamber may include multiple subchambers for storing different types of preset reagents; the sample chamber may also include a single chamber for sequentially placing different preset reagents, without limitation herein.

[0064] Specifically, if Figure 1 As shown, the sample chamber can be connected to the mixing and resuspension chamber, and the preset reagent in the sample chamber can be injected into the mixing and resuspension chamber through the first channel, mixed with the sample liquid to be tested in the mixing and resuspension chamber, and obtain a mixed liquid. The mixed liquid is separated in the first mixing and resuspension chamber to obtain a cell pellet and a waste liquid. The separation method can be set to a corresponding centrifugal speed in the centrifugal system to separate the waste liquid and the cell pellet in the mixed liquid. The specific separation method is not limited here.

[0065] Specifically, the cell precipitate obtained after the mixed liquid is separated can be collected by a collection chamber or other container so that the collected cell precipitate can be subsequently processed. The waste liquid after the mixed liquid is separated can be collected by a waste liquid chamber. The collection method can be to introduce the waste liquid into the waste liquid chamber through a siphon channel, or the waste liquid after the cell precipitate is filtered through the collection chamber by the mixed liquid. Figure 1 and Figure 2 As shown, Figure 2This is a schematic diagram of the structure of a centrifugal system in one embodiment. The specific method of collecting the cell sediment into the collection chamber or collecting the waste liquid into the waste liquid chamber is not limited here and can be adjusted according to the actual application scenario.

[0066] Specifically, a relatively easy-to-puncture material can be designed at the bottom of the sample chamber. For example, aluminum foil can be provided with spikes at corresponding locations on the foil, and springs can be placed between the plane where the spikes are located and the bottom of the sample chamber. Under the influence of centrifugal force, the springs can deform, puncturing the foil with the spikes, allowing the preset reagents in the sample chamber to flow into the mixing and resuspension chamber.

[0067] For example, in this embodiment, Figure 1 and Figure 2 The centrifugal system in Figure 1 The cell pellet in the collection chamber is directly introduced into Figure 2 The mixing and resuspension chamber in Figure 1 The cell pellet is then processed. For example, Figure 1 The system is used as a device for preliminary treatment of the sample solution to be tested, and the cell sediment after preliminary treatment is directly introduced into the device for further treatment of the cell sediment. Figure 3 As shown, Figure 3 Schematic diagram of the structure of a centrifugal system in one embodiment.

[0068] Specifically, the centrifugal system can be used in a centrifuge, which provides the required centrifugal speed for the centrifugal system, thereby reducing application costs.

[0069] In summary, this embodiment sets up a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber, and the sample liquid to be tested is centrifuged and mixed in the mixing and resuspension chamber by adding preset reagents, and the generated waste liquid and cell sediment are separated to obtain the required cell sediment, thereby realizing automated centrifugal separation, reducing damage to cell morphology, and improving the accuracy of subsequent detection.

[0070] In an exemplary embodiment, the mixing and resuspension chamber includes a first mixing and resuspension chamber and a second mixing and resuspension chamber; wherein the first mixing and resuspension chamber is used to mix and centrifuge the sample liquid to be tested at a first mixing centrifugal speed, or to mix and centrifuge the sample liquid to be tested with a first reagent to obtain a first mixed liquid, so that the waste liquid chamber collects the waste liquid of the first mixed liquid through the waste liquid chamber, and collects the first cell precipitate of the first mixed liquid into the second mixing and resuspension chamber through the second channel; the second mixing and resuspension chamber is used to mix the first cell precipitate with the second reagent in the second sample chamber at a second mixing centrifugal speed to obtain a second mixed liquid, so that the collecting chamber collects the second cell precipitate in the second mixed liquid, and collects the waste liquid of the second mixed liquid through the waste liquid chamber.

[0071] Wherein, the preset reagent includes a first reagent and a second reagent, and the types of the first reagent and the second reagent can be different or the same. The sample chamber includes a first sample chamber and a second sample chamber, and the first sample chamber and the second sample chamber can both be used to place the preset reagent. The first sample chamber and the second sample chamber only represent different positions in the centrifugal system. The mixing centrifugal speed includes a first mixing centrifugal speed and a second mixing centrifugal speed, and the first mixing centrifugal speed is less than the second mixing centrifugal speed. The sample liquid to be tested needs to enter the mixing and resuspension chamber before the preset reagent. The second cell precipitate is the cells required in the extracted sample liquid to be tested.

[0072] Specifically, if Figure 3 The centrifugal system shown includes a mixing and resuspension chamber including a first mixing and resuspension chamber 31 and a second mixing and resuspension chamber 32. The first mixing and resuspension chamber 31 and the second resuspension chamber are located at different positions of the centrifugal structure. The first mixing and resuspension chamber 31 can receive the sample liquid to be tested injected through the input channel, and then mix and centrifuge the sample liquid to be tested at a mixed centrifugal speed to complete the pretreatment of the sample liquid to be tested, so that the waste liquid chamber can collect the waste liquid generated through the channel. Under the action of the first centrifugal speed, the first reagent can be injected into the first mixing and resuspension chamber 31 through the first channel. Under the action of the first mixing centrifugal speed, the first mixing and resuspension chamber 31 mixes and centrifuges the first reagent with the sample liquid to be tested; the waste liquid chamber can collect the waste liquid generated through the set channel; the cell sediment can pass through the second channel to collect the first cell sediment of the first mixed liquid into the second mixing and resuspension chamber 32.

[0073] The second mixing and resuspension chamber 32 is configured to receive the first cell pellet and, when the centrifugal speed reaches a predetermined centrifugal speed, receive the second reagent from the second sample chamber. At the second mixing centrifugal speed, the second reagent is mixed and centrifuged with the first cell pellet to produce a second mixed liquid. The second mixed liquid can flow through the channel into the collection chamber. The collection chamber retains the second cell pellet and discharges the remaining waste liquid into the waste liquid chamber.

[0074] In an exemplary embodiment, the centrifugal system further includes: a first sample chamber 34 for storing a first reagent; and a first channel for introducing the first reagent into the first mixing and resuspension chamber under the action of a first centrifugal speed after the sample liquid to be tested is input through the input channel 33.

[0075] Specifically, the first sample chamber 34 can be used to store a first reagent, which can be a red blood cell lysate. The first channel can establish a connection between the first sample chamber 34 and the first mixing and resuspension chamber under the action of the first centrifugal speed, inject the first reagent in the first sample chamber 34 into the first mixing and resuspension chamber, and perform a preliminary treatment on the first reagent and the sample liquid to be tested. For example, Figure 3As shown, the bottom of the first sample chamber 34 can be made of aluminum foil, with spikes positioned at corresponding locations on the aluminum foil. A spring is designed to connect the spikes to the bottom of the first sample chamber 34. Under the action of a first centrifugal speed, the spikes can pierce the aluminum foil, establishing a first channel between the first sample chamber 34 and the first mixing and resuspension chamber, allowing the first reagent to be introduced from the first sample chamber 34 into the first mixing and resuspension chamber.

[0076] In an exemplary embodiment, the second sample chamber 35 includes a plurality of sample subchambers and third channels corresponding to each sample subchamber; the plurality of sample subchambers are used to store the second reagent; and the third channel is used to introduce the second reagent into the second mixing and resuspension chamber under the action of the second centrifugal speed.

[0077] Specifically, the second sample chamber 35 may include a plurality of sub-sample chambers, each of which may store a second reagent. The second reagent may be one or more different reagents. Under the action of the second centrifugal speed, the third channel establishes a connection between the second sample chamber 35 and the third channel, and introduces the reagent in each sub-sample chamber into the second mixing and resuspension chamber in sequence. The centrifuge can puncture the aluminum foil of each sub-sample chamber in sequence by designing different second centrifugal speeds, so that the second reagent in each sub-sample chamber is introduced into the second mixing and resuspension chamber in sequence. For example, Figure 3 As shown, Figure 3 The second sample chamber 35 includes two sub-sample chambers, each of which can hold NaHCO3 (sodium bicarbonate) and PBS (phosphate buffered saline), respectively. Each sub-sample chamber can hold up to one reagent, depending on the specific application requirements, but this is not limited here. Aluminum foil is placed at the bottom of the second sample chamber 35, and spikes are placed below the corresponding positions of the aluminum foil. Under the influence of the second centrifugal speed, the spikes sequentially pierce the aluminum foil of the NaHCO3 sub-sample chamber and the aluminum foil of the PBS sub-sample chamber, forming a third channel. The second reagent is sequentially introduced from each sub-sample chamber into the second mixing and resuspension chamber.

[0078] In an exemplary embodiment, the collection chamber 36 is a multi-layer filter membrane structure; the pore size of the multi-layer filter membrane structure decreases from top to bottom, and the pore size of the bottom filter membrane of the multi-layer filter membrane structure is smaller than the diameter of the required cells; the multi-layer filter membrane structure includes a first filter membrane, a second filter membrane and a third filter membrane; the first filter membrane is used to retain the first impurities of the first diameter; the second filter membrane is used to retain the second protein flocs; the third filter membrane is used to collect the second cell precipitate and filter the impurities of the third diameter.

[0079] The first diameter is larger than the second diameter, the second diameter is larger than the third diameter, and the second cell pellet is smaller than the second diameter but larger than the third diameter. The first impurity and the second impurity are protein flocs of different diameters. The third impurity may be bacteria or other impurities within the third diameter range.

[0080] In one example, if Figure 4 As shown, Figure 4 The figure is a schematic diagram of the structure of a multi-layer filter membrane structure in one embodiment. The collection chamber 36 is a multi-layer filter membrane structure. The multi-layer filter membrane structure is fixed to the tube wall of the centrifugal system and is detachable. The pore size of each layer of the multi-layer filter membrane structure is set according to the diameter of the impurities to be filtered and the diameter of the desired cells. Impurities larger than the diameter of the desired cells are filtered before reaching the bottom filter membrane. In other words, the pore size of the bottom filter membrane should be smaller than the diameter of the desired cells and larger than the diameter of the impurities to be filtered. The desired cells are collected on the bottom filter membrane, providing a basis for subsequent detection of cell types and further improving the accuracy of cell detection.

[0081] For example, as shown in Table 1, Table 1 shows the diameters of common cells in one embodiment.

[0082] Table 1

[0083]

[0084] The multilayer filter membrane structure consists of three membrane layers. The first membrane has a pore size of 80 μm and is used to filter out a first impurity of a first diameter. The second membrane has a pore size of 40 μm and is used to filter out a second impurity of a second diameter. The third membrane has a pore size of 3 μm and is used to filter out bacteria and collect the desired cells. It should be understood that the above values ​​are for example only and do not limit the specific pore sizes of the multilayer filter membrane structure.

[0085] In an exemplary embodiment, the waste liquid chamber includes a first waste liquid chamber 37 and a second waste liquid chamber 38; the first waste liquid chamber 37 is connected to the first mixing and resuspension chamber via a fourth channel 39 for allowing the waste liquid from the first mixing and resuspension chamber to enter the waste liquid chamber at a third centrifugal speed; the second waste liquid chamber 38 is used to collect third impurities of a third diameter.

[0086] Specifically, if Figure 3 As shown, the fourth channel 39 can be a siphon channel, which is composed of an inner and outer channel. The inner siphon channel is used to remove the waste liquid generated when the sample liquid to be tested is pretreated, and the outer siphon channel is used to remove the waste liquid of the first mixed liquid. Aluminum foil is used to seal the end of the fourth channel 39 connected to the first waste liquid chamber 37, and a spike is set at the lower end of the aluminum foil. When the centrifugal speed is the third centrifugal speed, the spike can pierce the aluminum foil, and the connection between the first waste liquid chamber 37 and the first mixing and resuspension chamber is established through the fourth channel 39. When the centrifugal speed drops to 0, the siphon channel is activated, and the waste liquid of the first mixed liquid in the first mixing and resuspension chamber can be siphoned to the first waste liquid chamber 37, thereby separating the waste liquid and cell sediment of the first mixed liquid.

[0087] Specifically, the second waste liquid chamber 38 can collect waste liquid after the second mixed liquid passes through the collection chamber 36 .

[0088] In an exemplary embodiment, a fifth channel is connected between the collection chamber 36 and the second mixing and resuspension chamber 32 for allowing the second mixed liquid to enter the collection chamber 36 under the action of the fourth centrifugal speed.

[0089] Specifically, if Figure 3 As shown, the bottom of the second mixing and resuspension chamber 32 can be set as aluminum foil, and a spike is set at the lower end of the corresponding position of the aluminum foil. The spike can pierce the aluminum foil under the action of the fourth centrifugal speed. At the same time, the fifth channel can establish a connection between the second mixing and resuspension chamber and the collection chamber, and introduce the second mixed liquid in the second mixing and resuspension chamber 32 into the collection chamber 36 to achieve enrichment of the second cell precipitation in the collection chamber 36.

[0090] Specifically, the speed values ​​of the first centrifugal speed, the second centrifugal speed, the third centrifugal speed and the fourth centrifugal speed increase successively. The specific speed values ​​can be set according to the actual application scenario and are not limited here, so as to realize the automatic flow of the liquid in one chamber into the next chamber in sequence.

[0091] In an exemplary embodiment, Figure 5 As shown, Figure 5 A schematic flow chart of a cell processing method provided in one embodiment is applied to a centrifugal system as described in the above embodiment. The cell processing method includes:

[0092] Step 501: inject the sample liquid to be tested from the input channel into the first mixing and resuspension chamber for pretreatment to obtain the pretreated sample liquid to be tested; under the action of the first centrifugal speed, inject the first reagent through the first channel into the first mixing and resuspension chamber; under the action of the first mixing centrifugal speed, mix and centrifuge the first reagent and the pretreated sample liquid to be tested to obtain a first mixed liquid; when the centrifugal speed of the centrifugal system matches the preset speed threshold, activate the fourth channel, and suck the waste liquid of the first mixed liquid into the first waste liquid chamber through the fourth channel, and collect the first cell precipitate of the first mixed liquid into the second mixing and resuspension chamber.

[0093] Step 502: Under the action of the second centrifugal speed, the second reagent is injected into the second mixing and resuspension chamber, and the first cell pellet and the second reagent are mixed and centrifuged under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, the second mixed liquid is injected into the collection chamber through the fifth channel, the second cell pellet of the second mixed liquid is collected through the collection chamber, and the second waste liquid of the second mixed liquid is collected through the waste liquid chamber.

[0094] The preset speed threshold may be 0, and when the centrifugal speed drops to 0, it may match the preset speed threshold.

[0095] In one example, the method of treating the cells is applied to Figure 3 In the centrifugal system shown, a urine sample is used as the sample to be tested, and the specific processing flow is as follows:

[0096] (1) 30 ml of urine sample was injected into the first mixing and resuspension chamber, 1.5 ml of red blood cell lysis solution was injected into the first sample chamber, 0.1 ml of NaHCO3 was injected into the first sub-sample chamber, and 1 ml of PBS was injected into the second sub-sample chamber.

[0097] (2) Centrifugal structure: Centrifuge at 3000 rpm for 10 minutes.

[0098] (3) Centrifuge at 3500 rpm for 5 seconds, compress the spring, and puncture the aluminum foil at the bottom of the inner fourth channel.

[0099] (4) Reduced to 0 rpm, the siphon channel opens, and the waste liquid of the urine sample enters the first waste liquid chamber through the siphon effect.

[0100] (5) Centrifuge at 3700 rpm for 5 seconds. The spring is compressed, puncturing the aluminum foil of the first sample chamber, and the red blood cell lysis chamber flows into the first mixing and resuspension chamber.

[0101] (6) Repeat the mixing process for 15 min (centrifugation at 1000 rpm for 10 s followed by centrifugation at 2500 rpm for 10 s, repeated) to lyse the red blood cells in the urine sample.

[0102] (7) Centrifuge at 3000 rpm for 10 min. Centrifuge at 3900 rpm for 5 s. Compress the spring and puncture the aluminum foil in the fourth channel.

[0103] (8) Reduced to 0 rpm, the fourth channel is opened, and the waste liquid of the first mixed liquid enters the first waste liquid chamber through siphon action.

[0104] (9) Centrifuge at 4100 rpm for 1 min. The spring is compressed, piercing the aluminum foil at the bottom of the first mixing and resuspension chamber, and the sample sediment (first cell pellet) flows into the second mixing and resuspension chamber.

[0105] (10) Perform a rotation process (centrifuge at 400 rpm for 10 s and then reduce to 0 rpm), puncture the aluminum foil at the bottom of the first subsample chamber, and release NaHCO3 (sodium bicarbonate) into the second mixing and resuspension chamber.

[0106] (11) Repeat the mixing process for 15 min to digest the crystals in the urine sample.

[0107] (12) Perform a rotation process to puncture the second sub-sample chamber and release PBS (phosphate buffered saline) into the second mixing and resuspension chamber.

[0108] (13) Repeat the mixing process for 1 min to obtain a second mixed liquid.

[0109] (14) Centrifuge at 4500 rpm for 3 min, puncture the aluminum foil at the bottom of the second mixing and resuspension chamber, and accelerate the mixed sample to flow through the multi-layer filter membrane under the action of centrifugal force. Desquamated cells, protein flocs, and other substances are collected by the multi-layer filter membrane structure, and the waste liquid (containing digested red blood cells, digested crystals, and bacteria) enters the second waste liquid chamber.

[0110] (15) Reduce the speed to 0 rpm, restore the centrifuge tube, collect the required cell sample (second cell pellet) on the third filter membrane and take it out.

[0111] In an exemplary embodiment, Figure 6 As shown, Figure 6 This is a schematic flow chart of a cell processing method provided in one embodiment, wherein the cell processing method further comprises:

[0112] Step 601: Rinse the second cell precipitate with a cell buffer to obtain a cell suspension of the sample liquid to be tested.

[0113] The cell buffer may be PBS buffer.

[0114] Specifically, the second cell pellet was washed away with PBS. The collected cell suspension was concentrated to 1*10 7 cell / ml, and aspirate 3 μl of cell suspension onto a glass slide for the next imaging step.

[0115] Step 602: Perform coherent Raman scattering imaging on the cell suspension using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift.

[0116] Specifically, this embodiment can use a coherent Raman scattering imaging system to detect cells to be detected, such as Figure 7 As shown, Figure 7 This is a schematic diagram of the structure of a coherent Raman scattering imaging system, which uses a first excitation light and a second excitation light to illuminate the urine exfoliated cell specimen; in response to the first excitation light and the second excitation light irradiating the urine exfoliated cell specimen, the system receives reflected light having at least one Raman shift characteristic peak from the urine exfoliated cell specimen (cell suspension) to obtain the coherent Raman scattering image of the urine exfoliated cell specimen. The first excitation light and the second excitation light each have a predetermined frequency; both the first excitation light and the second excitation light are pulsed lasers; both the first excitation light and the second excitation light are pulsed lasers with a certain frequency difference, which can be 2800-3100 cm-1 , repetition frequency is 10~300MHz, and pulse width is 10fs~20ps.

[0117] Specifically, the coherent Raman scattering imaging system in this embodiment is a stimulated Raman scattering imaging system that collects stimulated Raman scattering (SRS) signals, where the SRS signals include stimulated Raman gain signals and stimulated Raman loss signals. In addition to stimulated Raman scattering imaging systems, coherent Raman scattering imaging systems also include coherent anti-Stokes Raman scattering (CARS) imaging systems. In imaging systems, CARS and SRS share the following characteristics: 1. Both require two beams of excitation light with a certain frequency difference to excite the sample and generate coherent Raman scattering signals. 2. Both require an optical path device consisting of components such as reflectors, dichroic mirrors, and scanning galvanometers to ensure that the two excitation light beams overlap in space and time, thereby achieving scanning imaging of the sample. However, for SRS signal acquisition, the signal light is collected by devices such as photodetectors, and the excitation light must be modulated and the collected signal demodulated to achieve the acquisition of stimulated Raman loss or stimulated Raman gain signals. The excitation light can be modulated using an electro-optical modulator, and the signal demodulated using a lock-in amplifier. The CERS signal can be collected and converted into an electrical signal using a photomultiplier tube (PMT). Meanwhile, methods such as polarization detection, pump and Stokes field time delay detection, and backward detection (CARS) can be used to suppress non-resonant background signals in the CERS signal.

[0118] Step 603: Use the image segmentation network model to perform single-cell image segmentation processing on the coherent Raman scattering image to obtain coherent Raman scattering images of multiple single cells.

[0119] The image segmentation network model can be a comprehensive neural network segmentation model that combines deep supervision and transfer learning. This deep supervision + transfer learning neural network segmentation model primarily has the following two features: 1. A pre-trained single-cell segmentation model. Currently available pre-trained models include Cellpose (a cell segmentation algorithm), Stardist (an open-source computer vision library), and DeepCell (deep cells). 2. A deep supervision mechanism that adds additional network branches between certain intermediate hidden layers in the pre-trained model. This network branch introduces additional supervisory information, helping the model better learn the feature representation of the input data and improving its performance and generalization capabilities.

[0120] Specifically, a neural network segmentation model based on deep supervision and transfer learning is used to perform single cell image segmentation on the coherent Raman scattering image under the at least one Raman shift to obtain coherent Raman scattering images of multiple single cells. For example, a Raman shift of 2930 cm is used. -1 The SRS images were used for single-cell segmentation, and the pre-trained Cellpose + deep supervision segmentation method was used to segment single-cell urine exfoliated cells. Compared with using only the Cellpose model, this segmentation method combined with deep supervision can significantly improve the segmentation effect of urine exfoliated cells.

[0121] like Figure 8 As shown, Figure 8 It is a structural diagram of an image segmentation network model. Figure 8 The Cellpose model is combined with a deep supervision model. This deep supervision network branch fuses partial feature maps from the output of the Cellpose model's intermediate hidden layer with the output of the Cellpose model as the final output, thereby performing supervised learning on both the Cellpose output and the output of the intermediate hidden layer. Each intermediate hidden layer in the Cellpose model consists of two residual blocks, each consisting of two convolutions with a kernel size of 3×3. The Cellpose model includes an encoder and a decoder. The encoder includes the first, second, third, and fourth downsampling layers; the decoder includes the first, second, third, and fourth upsampling layers. Feature maps are encoded in the upsampling and downsampling layers to generate corresponding feature maps. The downsampling layers are connected to the corresponding upsampling layers through an attention mechanism, so that the feature maps of the downsampling layers are sent to the corresponding upsampling layers. For example, the output feature map of the first downsampling layer can be sent to the fourth upsampling layer through the attention mechanism, and so on. The deep supervision model includes the first convolutional layer, the second convolutional layer, and the third convolutional layer. The first convolutional layer receives the output of the second upsampling layer and performs convolution processing on the output, which is then fed into the third convolutional layer. The second convolutional layer receives the output of the third upsampling layer and performs convolution processing on it to obtain an output, which is then fed into the third convolutional layer. In the third convolutional layer, the output of the Cellpose model, the output of the first convolutional layer, and the output of the second convolutional layer are combined to produce coherent Raman scattering images of multiple single cells. Specifically, the output of each decoder layer calculates the loss compared to the manually annotated mask, and the loss is back-propagated to optimize the model parameters, further improving the speed of model fitting.

[0122] like Figure 9 As shown, Figure 9 A schematic diagram of a coherent Raman scattering image of a single cell provided in one embodiment. Figure 9The top three figures are coherent Raman scattering images of single cells obtained using only the Cellpose model; Figure 9 The following three figures are coherent Raman scattering images of single cells obtained using the Cellpose+ deep supervision network model.

[0123] In one example, in a urine exfoliated cell specimen, the shapes and sizes of different cells vary greatly. Neither the traditional watershed segmentation algorithm nor the segmentation method using a transfer learning-based neural network can achieve ideal segmentation results. Therefore, this embodiment introduces a deep supervision mechanism based on the transfer learning-based neural network segmentation model. A network branch is added to certain intermediate hidden layers of the deep neural network to supervise the backbone network, which is beneficial for improving the performance and generalization ability of the model. This method can not only effectively segment small and round cells, but also effectively segment cells with more unusual shapes (irregular shapes and small roundness).

[0124] In urine exfoliated cell specimens, different cells vary greatly in shape and size. Neither the traditional watershed segmentation algorithm nor the transfer learning-based neural network segmentation method can achieve ideal segmentation results. Therefore, this embodiment introduces a deep supervision mechanism based on the transfer learning-based neural network segmentation model. This mechanism adds a network branch to certain intermediate hidden layers of the deep neural network to supervise the main network, which helps improve the model's performance and generalization capabilities.

[0125] Step 604: Using the feature extraction model, single-cell feature extraction is performed on the coherent Raman scattering image of each single cell in the multiple coherent Raman scattering images of single cells to obtain a single-cell feature map corresponding to the coherent Raman scattering image of each single cell.

[0126] Feature extraction models include, but are not limited to, threshold segmentation algorithms, blob detection algorithms, and deep neural network algorithms. Single-cell feature maps include cellular morphological and metabolic features. Cell morphological features include, but are not limited to, one or more of: cell area, cell eccentricity, cell diameter of a perfect circle, cell major axis length, cell minor axis length, cell perimeter, true cell area / fitted circle area, nuclear area, cytoplasmic area, and nuclear-cytoplasmic ratio. Cell metabolic features include, but are not limited to, one or more of: number of lipid droplets, lipid droplet area, lipid droplet-to-cell area ratio, distance from lipid droplet to cell center, average distance from lipid droplet to cell center of gravity, and lipid droplet grayscale. Morphological features are primarily extracted from binary images obtained through single-cell segmentation, while metabolic features require coherent Raman scattering images.

[0127] Specifically, single-cell feature extraction is performed on the coherent Raman scattering image of each single cell in the multiple coherent Raman scattering images of single cells to obtain a single-cell image feature map corresponding to the coherent Raman scattering image of the single cell. To extract the lipid droplet features, lipid droplet segmentation is performed on the coherent Raman scattering image of each single cell in the multiple coherent Raman scattering images of single cells to obtain a coherent Raman scattering image of lipid droplets within each single cell region in the coherent Raman scattering image of the single cell. Lipid droplet segmentation can be performed using algorithms such as threshold segmentation and spot detection methods, or using deep learning models such as YOLO.

[0128] like Figure 10 As shown, Figure 10 Schematic diagram of cell processing in one embodiment. Specifically, based on the Raman shift of 2930 cm -1 The SRS image is used for single cell segmentation (the single cell segmentation model is the Cellpose+ deep supervision model) to obtain a binary image of the single cell. Based on the binary image of the single cell, the morphological characteristics of each cell are extracted, such as cell area, eccentricity, cell perimeter, etc. Based on the binary image of the single cell, the Raman shift of 2850cm -1 and Raman shift 2930 cm -1 The SRS images at the 3D position are used to extract the metabolic characteristics of single cells, such as the number of lipid droplets, lipid droplet area, lipid droplet area ratio, etc. The specific methods are as follows: 1. Based on the binary image of the single cell, the Raman shift of the single cell is obtained at 2850 cm -1 and Raman shift 2930 cm -1 2. Based on the Raman shift of the single cell at 2850 cm -1 and a Raman shift of 2930 cm -1 The SRS image at the position is used to calculate the cell image of the lipid channel and the cell image of the protein channel using matrix operations, as shown in Figure 11 As shown, Figure 11 Schematic diagram of signal intensity at different Raman shifts in one embodiment. The solid line in the figure represents the Raman shift of 2850 cm -1 The signal image, the dotted line in the figure represents the Raman shift of 2930cm -1 3. Based on the cell image of the lipid channel and the cell image of the protein channel, metabolic characteristics such as the cell lipid / protein content ratio, the protein signal intensity in the cytoplasmic region, and the lipid signal intensity in the cytoplasmic region can be extracted. In particular, in the embodiment of the present invention, based on the Raman shift of 2850cm -1 The SRS image is segmented using the YOLOv5 model.

[0129] For example, the expression of matrix operation can be:

[0130]

[0131] Among them, P1 represents the lipid channel signal, P2 represents the protein channel signal, and S1 represents the 2850cm -1 signal, S2 represents 2930cm -1 signal, A1, A2, S1, and S2 represent different coefficients respectively.

[0132] Step 605: Combine the single-cell feature maps according to the target object to obtain a single-cell feature map group corresponding to the target object.

[0133] Among them, the target object is the person to be tested.

[0134] Specifically, the single cell image feature maps from the same target object are combined to obtain the target object's image feature map. The target object's image feature map is input into a pre-trained machine learning classifier to determine whether the target object has cancer.

[0135] Step 606: Use a preset classification model to perform feature analysis on the single-cell feature map group to obtain the abnormality probability of the target object.

[0136] Among them, the preset classification models include but are not limited to one or more of support vector machine classifiers, linear discriminant classifiers, K-neighbor classifiers, logistic regression classifiers, random forest decision tree classifiers, gradient boosting classifiers, virtual neural network classifiers, deep learning convolutional neural network classifiers, contrastive learning classifiers, and multiple instance learning classifiers.

[0137] Specifically, a principal component analysis algorithm is used to reduce the dimensionality of the target object's image feature map, obtaining 10 principal components, namely PC1 to PC10. These 10 principal components are then input into a multi-instance learning-based distributed gradient boosting library (eXtreme Gradient Boosting, XGboost) classifier for classification to determine whether the target object has cancer.

[0138] Step 607: Determine the abnormality of the target object based on the abnormality probability and a preset abnormality probability threshold.

[0139] Specifically, this embodiment can use leave-one-out cross-validation to obtain a preset abnormality probability threshold. For example, 80 samples can be designed. Specifically, among all the data, each time the model is trained, a group of data will be left as a test set, and the remaining data will be used as a training set. Then, each piece of data will be used as a test set. The performance of the model is evaluated based on the results of each test set to obtain the preset abnormality probability threshold.

[0140] Specifically, when the abnormal probability is greater than the preset abnormal probability threshold, the target object is determined to be abnormal. When the abnormal probability is less than the preset abnormal probability threshold, the target object is considered normal. The case classification method achieved a sensitivity of 88.46%, a specificity of 88.88%, an accuracy of 86.67%, and an area under the curve (AUC) of 0.955.

[0141] In an exemplary embodiment, coherent Raman scattering imaging is performed on the cell suspension using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift, including:

[0142] One or more groups of excitation light are input into the coherent Raman scattering imaging system respectively; for each group of excitation light, the first excitation light and the second excitation light are collinearly processed by the optical path module to obtain a combined optical path, and the combined optical path is used to scan the cell suspension to obtain a coherent Raman scattering signal; the coherent Raman scattering signal is processed by the signal acquisition module to obtain a coherent Raman scattering image.

[0143] Each set of excitation light has a different Raman shift, and each set includes a first excitation light and a second excitation light. The first excitation light can be pump light, and the second excitation light can be Stokes light. The Raman shift is determined by the frequency difference between the first and second excitation lights. The coherent Raman scattering imaging system includes at least an optical path module and a signal acquisition module. The optical path module includes at least a lens, a dichroic mirror, a two-dimensional galvanometer assembly, and an objective lens.

[0144] Specifically, if Figure 7 As shown, first, a picosecond laser with a high repetition rate emits a first excitation light and a second excitation light, wherein the first excitation light includes a pump light ( Figure 7 The second excitation light includes Stokes light ( Figure 7 The wavelength of the emitted pump light can be tuned within the range of 700-960 nm (796.8 nm for imaging in this embodiment, corresponding to the Raman shift characteristic peak of 2850 cm -1), the Stokes wavelength is fixed at 1031 nm. The first and second excitation lights are then transmitted through a lens and a reflector, respectively, into a dichroic mirror, allowing the pump and Stokes beams to overlap in space and time, resulting in a combined optical path. The combined optical path passes through a scanning two-dimensional galvanometer assembly and is then transmitted to the objective lens. Finally, the combined beam is focused by the objective lens onto a cell suspension (urine exfoliated cell specimen), inducing resonance of specific molecules in the cell suspension to generate stimulated Raman loss and gain signals, resulting in a coherent Raman scattering signal. The coherent Raman scattering signal is collected by another water immersion objective (condenser) of the same model. The signal acquisition module converts the coherent Raman scattering signal into an electrical signal and stores it as a coherent Raman scattering image in a computer device within the signal acquisition module. For example, depending on the signal being collected, for example, an electro-optical modulator can be used to modulate the SRS signal, and a lock-in amplifier can be used to demodulate the signal to obtain an SRS image. For CARS signals, a photomultiplier tube (PMT) can be used to collect and convert them into electrical signals. Meanwhile, methods such as polarization detection, pump and Stokes field time-delay detection, and backward detection CARS can be used to suppress non-resonant background signals in the coherent anti-Stokes Raman scattering signal. It should be understood that the signal acquisition module can be adjusted accordingly based on the signal, and the signal acquisition module is not specifically limited herein.

[0145] In an exemplary embodiment, a preset classification model is used to perform feature analysis on the single cell feature map group to obtain the abnormality probability of the target object, including:

[0146] The single-cell feature map group is subjected to dimensionality reduction processing to obtain a single-cell feature map group after dimensionality reduction processing, and the single-cell feature map group after dimensionality reduction processing is input into a preset classification model to obtain the abnormality probability of the target object.

[0147] Alternatively, clustering is performed on the single-cell feature map group to obtain a clustered single-cell feature map group, and the clustered single-cell feature map group is input into a preset classification model to obtain an outlier value corresponding to the target.

[0148] Alternatively, the single-cell feature map group is subjected to dimensionality reduction and clustering processing to obtain a processed single-cell feature map group, and the processed single-cell feature map group is input into a preset classification model to obtain an outlier value corresponding to the target.

[0149] Dimensionality reduction algorithms include, but are not limited to, principal component analysis, linear discriminant analysis, multidimensional scaling, isometric mapping, locally linear embedding, Laplace eigenmaps, locality preserving projections, and t-distributed stochastic neighbor embedding (t-SNE). Clustering algorithms include, but are not limited to, k-means clustering, hierarchical clustering, self-organizing map clustering, fuzzy clustering, Gaussian mixture model clustering, and expectation-maximization.

[0150] Specifically, before inputting the image feature map of the target object into a pre-trained machine learning classifier, the image feature map of the target object may be subjected to dimensionality reduction to remove noise or redundant information in the image feature map of the target object.

[0151] Specifically, before inputting the image feature map of the target object into a pre-trained machine learning classifier, a cluster analysis can also be performed on the image feature map of the target object to obtain the number of different types of cells in the target object and identify cell populations that are important for determining whether the target object has cancer.

[0152] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed 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 portion of steps or stages in other steps.

[0153] Based on the same inventive concept, embodiments of the present application further provide a cell processing device for implementing the aforementioned cell processing method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the one or more cell processing device embodiments provided below can be found in the above-described limitations of the cell processing method and will not be further elaborated here.

[0154] In an exemplary embodiment, Figure 12 As shown, a schematic structural diagram of a cell processing device is provided. The cell processing device 120 includes: a first processing module 121 and a second processing module 122, wherein:

[0155] The first processing module 121 is used to inject the sample liquid to be detected from the input channel into the first mixing and resuspension chamber for pretreatment to obtain the pretreated sample liquid to be detected; under the action of the first centrifugal speed, the first reagent is injected into the first mixing and resuspension chamber through the first channel; under the action of the first mixing centrifugal speed, the first reagent and the pretreated sample liquid to be detected are mixed and centrifuged to obtain a first mixed liquid; when the centrifugal speed of the centrifugal system matches the preset speed threshold, the fourth channel is activated, and the waste liquid of the first mixed liquid is sucked into the first waste liquid chamber through the fourth channel, and the first cell precipitate of the first mixed liquid is collected into the second mixing and resuspension chamber.

[0156] The second processing module 122 is used to inject the second reagent into the second mixing and resuspension chamber under the action of the second centrifugal speed, and mix and centrifuge the first cell pellet and the second reagent under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, the second mixed liquid is injected into the collection chamber through the fifth channel, the second cell pellet of the second mixed liquid is collected through the collection chamber, and the second waste liquid of the second mixed liquid is collected through the waste liquid chamber.

[0157] In one example, the second processing module 122 is further configured to rinse the second cell precipitate with a cell buffer to obtain a cell suspension of the sample solution to be tested;

[0158] performing coherent Raman scattering imaging on the cell suspension using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift;

[0159] performing single-cell image segmentation processing on the coherent Raman scattering image using an image segmentation network model to obtain coherent Raman scattering images of multiple single cells;

[0160] performing single-cell feature extraction on the coherent Raman scattering image of each single cell in the plurality of single-cell coherent Raman scattering images using a feature extraction model to obtain a single-cell feature map corresponding to the coherent Raman scattering image of each single cell;

[0161] Combining the single cell feature maps according to the target object to obtain a single cell feature map group corresponding to the target object;

[0162] A preset classification model is used to perform feature analysis on the single-cell feature map group to obtain the abnormality probability of the target object; based on the abnormality probability and a preset abnormality probability threshold, the abnormality of the target object is determined.

[0163] In one example, the single-cell feature map includes cell morphological features and / or cell metabolic features; the cell morphological features include at least one or more of cell area, cell eccentricity, cell fitted circle diameter, cell major axis length, cell minor axis length, cell circumference, cell true area / fitted circle area, cell nuclear area, cytoplasm area, and cell nuclear-cytoplasm ratio; the cell metabolic features include at least one or more of cell lipid droplet number, cell lipid droplet area, lipid droplet ratio of cell area, distance from lipid droplet to cell center, average distance from lipid droplet to cell center of gravity, and cell lipid droplet grayscale.

[0164] In one example, the second processing module 122 is further configured to input one or more groups of excitation light into a coherent Raman scattering imaging system, wherein each group of excitation light has a different Raman shift and each group of excitation light includes a first excitation light and a second excitation light; the coherent Raman scattering imaging system includes at least: an optical path module and a signal acquisition module;

[0165] For each set of excitation light, the first excitation light and the second excitation light are collinearly processed by the optical path module to obtain a combined optical path, and the combined optical path is used to scan the cell suspension to obtain a coherent Raman scattering signal; the coherent Raman scattering signal is processed by the signal acquisition module to obtain a coherent Raman scattering image.

[0166] Each module in the cell processing device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0167] 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. Figure 13 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via 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 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 via a network connection. The computer program is executed by the processor to implement a cell processing method.

[0168] Those skilled in the art will understand that Figure 13 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0169] In one embodiment, a processor-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.

[0170] 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.

[0171] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 above-mentioned embodiments. In particular, any reference to memory, database, or other media 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), magnetic 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. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0172] The technical features of the above embodiments can 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.

[0173] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for treating cells, characterized in that: Applied to a centrifugal system, the centrifugal system is applied to a centrifuge, the centrifuge provides the required centrifugal speed for the centrifugal system, the centrifugal system comprises: a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber; the mixing and resuspension chamber is connected to the sample chamber with a first channel for allowing the liquid in the sample chamber to enter the mixing and resuspension chamber at a preset centrifugal speed, and is used to mix the preset reagent entering from the first channel and the sample liquid to be detected input from the input channel at the mixing centrifugal speed to obtain a mixed liquid, and separate the mixed liquid to obtain a cell precipitate and a waste liquid, wherein the sample liquid to be detected is a urine sample; the preset reagent comprises a first reagent and a second reagent; the mixing and resuspension chamber comprises a first mixing and resuspension chamber and a second mixing and resuspension chamber; The method comprises: injecting the sample liquid to be tested from the input channel into the first mixing and resuspension chamber for pretreatment to obtain the pretreated sample liquid to be tested; injecting the first reagent into the first mixing and resuspension chamber through the first channel under the action of a first centrifugal speed; mixing and centrifuging the first reagent and the pretreated sample liquid to be tested under the action of the first mixing centrifugal speed to obtain a first mixed liquid; when the centrifugal speed of the centrifugal system matches a preset speed threshold, activating the fourth channel, sucking waste liquid of the first mixed liquid into the waste liquid chamber through the fourth channel, and collecting the first cell precipitate of the first mixed liquid into the second mixing and resuspension chamber; Under the action of the second centrifugal speed, the second reagent is injected into the second mixing and resuspension chamber, and the first cell pellet and the second reagent are mixed and centrifuged under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, the second mixed liquid is injected into the collection chamber through the fifth channel, the second cell pellet of the second mixed liquid is collected through the collection chamber, and the second waste liquid of the second mixed liquid is collected through the waste liquid chamber.

2. The method according to claim 1, characterized in that The method further comprises: Washing the second cell precipitate with a cell buffer to obtain a cell suspension of the sample to be tested; performing coherent Raman scattering imaging on the cell suspension using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift; performing single-cell image segmentation processing on the coherent Raman scattering image using an image segmentation network model to obtain coherent Raman scattering images of multiple single cells; performing single-cell feature extraction on the coherent Raman scattering image of each single cell in the plurality of single-cell coherent Raman scattering images using a feature extraction model to obtain a single-cell feature map corresponding to the coherent Raman scattering image of each single cell; Combining the single cell feature maps according to the target object to obtain a single cell feature map group corresponding to the target object; A preset classification model is used to perform feature analysis on the single-cell feature map group to obtain the abnormality probability of the target object; based on the abnormality probability and a preset abnormality probability threshold, the abnormality of the target object is determined.

3. The method according to claim 2, characterized in that The single-cell feature map includes cell morphological characteristics and / or cell metabolic characteristics; the cell morphological characteristics include at least one or more of cell area, cell eccentricity, cell fitted circle diameter, cell major axis length, cell minor axis length, cell circumference, cell true area / fitted circle area, cell nuclear area, cytoplasm area, and cell nuclear-cytoplasm ratio; the cell metabolic characteristics include at least one or more of cell lipid droplet number, cell lipid droplet area, lipid droplet ratio of cell area, distance from lipid droplet to cell center, average distance from lipid droplet to cell center of gravity, and cell lipid droplet grayscale.

4. The method according to claim 3, characterized in that The cell morphological features are determined by extracting a binary image of single cell segmentation.

5. The method according to claim 3, characterized in that The cell metabolism characteristics are obtained by extracting coherent Raman scattering images.

6. The method according to claim 2, characterized in that The method of using one or more Raman shifts to perform coherent Raman scattering imaging on the cell suspension to obtain a coherent Raman scattering image corresponding to each Raman shift comprises: Inputting one or more groups of excitation light into a coherent Raman scattering imaging system, wherein each group of excitation light has a different Raman shift and each group of excitation light includes a first excitation light and a second excitation light; the coherent Raman scattering imaging system includes at least: an optical path module and a signal acquisition module; For each set of excitation light, the first excitation light and the second excitation light are collinearly processed by the optical path module to obtain a combined optical path, and the combined optical path is used to scan the cell suspension to obtain a coherent Raman scattering signal; the coherent Raman scattering signal is processed by the signal acquisition module to obtain a coherent Raman scattering image.

7. The method according to claim 2, characterized in that The method of using a preset classification model to perform feature analysis on the single cell feature map group to obtain the abnormality probability of the target object includes: Performing dimensionality reduction processing on the single-cell feature map group to obtain a single-cell feature map group after dimensionality reduction processing, inputting the single-cell feature map group after dimensionality reduction processing into a preset classification model to obtain the abnormality probability of the target object; or, Performing clustering processing on the single cell feature map group to obtain a clustered single cell feature map group, inputting the clustered single cell feature map group into a preset classification model to obtain the abnormality probability of the target object; or, The single-cell feature map group is subjected to dimensionality reduction and clustering processing to obtain a processed single-cell feature map group, and the processed single-cell feature map group is input into a preset classification model to obtain the abnormality probability of the target object.

8. A cell processing device, characterized in that: Applied to a centrifugal system, the centrifugal system is applied to a centrifuge, the centrifuge provides the required centrifugal speed for the centrifuge, the centrifugal system comprises: a sample chamber, a mixing and resuspension chamber, a collection chamber and a waste liquid chamber; the mixing and resuspension chamber is connected to the sample chamber with a first channel for allowing the liquid in the sample chamber to enter the mixing and resuspension chamber at a preset centrifugal speed, and is used to mix a preset reagent entering from the first channel and a sample liquid to be detected input from an input channel at the mixing centrifugal speed to obtain a mixed liquid, and separate the mixed liquid to obtain a cell precipitate and a waste liquid, wherein the sample liquid to be detected is a urine sample; the preset reagent comprises a first reagent and a second reagent; the mixing and resuspension chamber comprises a first mixing and resuspension chamber and a second mixing and resuspension chamber; The device comprises: a first processing module, configured to inject the sample liquid to be detected from the input channel into the first mixing and resuspension chamber for pretreatment to obtain the pretreated sample liquid to be detected; inject the first reagent into the first mixing and resuspension chamber through the first channel under the action of a first centrifugal speed; mix and centrifuge the first reagent and the pretreated sample liquid to be detected under the action of the first mixing centrifugal speed to obtain a first mixed liquid; when the centrifugal speed of the centrifugal system matches a preset speed threshold, activate the fourth channel, aspirate the waste liquid of the first mixed liquid into the first waste liquid chamber through the fourth channel, and collect the first cell precipitate of the first mixed liquid into the second mixing and resuspension chamber; The second processing module is used to inject the second reagent into the second mixing and resuspension chamber under the action of the second centrifugal speed, and mix and centrifuge the first cell pellet and the second reagent under the action of the second mixing centrifugal speed to obtain a second mixed liquid; when the centrifugal speed of the centrifugal system reaches a fourth centrifugal speed, inject the second mixed liquid into the collection chamber through the fifth channel, collect the second cell pellet of the second mixed liquid through the collection chamber, and collect the second waste liquid of the second mixed liquid through the waste liquid chamber.

9. The device according to claim 8, characterized in that The second processing module is further configured to rinse the second cell precipitate with a cell buffer to obtain a cell suspension of the sample solution to be detected; performing coherent Raman scattering imaging on the cell suspension using one or more Raman shifts to obtain a coherent Raman scattering image corresponding to each Raman shift; performing single-cell image segmentation processing on the coherent Raman scattering image using an image segmentation network model to obtain coherent Raman scattering images of multiple single cells; performing single-cell feature extraction on the coherent Raman scattering image of each single cell in the plurality of single-cell coherent Raman scattering images using a feature extraction model to obtain a single-cell feature map corresponding to the coherent Raman scattering image of each single cell; Combining the single cell feature maps according to the target object to obtain a single cell feature map group corresponding to the target object; A preset classification model is used to perform feature analysis on the single-cell feature map group to obtain the abnormality probability of the target object; based on the abnormality probability and a preset abnormality probability threshold, the abnormality of the target object is determined.

10. The device according to claim 9, characterized in that The second processing module is further configured to input one or more groups of excitation light into the coherent Raman scattering imaging system, wherein the Raman shift of each group of excitation light is different, and each group of excitation light includes a first excitation light and a second excitation light; the coherent Raman scattering imaging system includes at least: an optical path module and a signal acquisition module; For each set of excitation light, the first excitation light and the second excitation light are collinearly processed by the optical path module to obtain a combined optical path, and the combined optical path is used to scan the cell suspension to obtain a coherent Raman scattering signal; the coherent Raman scattering signal is processed by the signal acquisition module to obtain a coherent Raman scattering image.

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