CN112525806B - Flow cell detection device, preparation method and system - Google Patents
Flow cell detection device, preparation method and system Download PDFInfo
- Publication number
- CN112525806B CN112525806B CN202011120179.6A CN202011120179A CN112525806B CN 112525806 B CN112525806 B CN 112525806B CN 202011120179 A CN202011120179 A CN 202011120179A CN 112525806 B CN112525806 B CN 112525806B
- Authority
- CN
- China
- Prior art keywords
- cell
- channel
- target
- refractive index
- detection device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 178
- 238000002360 preparation method Methods 0.000 title abstract description 10
- 230000006835 compression Effects 0.000 claims abstract description 57
- 238000007906 compression Methods 0.000 claims abstract description 57
- 239000002184 metal Substances 0.000 claims abstract description 45
- 229910052751 metal Inorganic materials 0.000 claims abstract description 45
- 230000005284 excitation Effects 0.000 claims abstract description 38
- 238000011084 recovery Methods 0.000 claims abstract description 22
- 239000000758 substrate Substances 0.000 claims abstract description 21
- 239000010410 layer Substances 0.000 claims description 61
- 238000009826 distribution Methods 0.000 claims description 48
- 238000000684 flow cytometry Methods 0.000 claims description 30
- 238000003384 imaging method Methods 0.000 claims description 28
- 238000004458 analytical method Methods 0.000 claims description 21
- 238000012360 testing method Methods 0.000 claims description 19
- 230000003287 optical effect Effects 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 16
- 238000001228 spectrum Methods 0.000 claims description 14
- 238000005286 illumination Methods 0.000 claims description 12
- 239000002344 surface layer Substances 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 8
- 230000010287 polarization Effects 0.000 claims description 7
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 210000004027 cell Anatomy 0.000 description 209
- 239000010408 film Substances 0.000 description 34
- 238000000034 method Methods 0.000 description 32
- 239000000243 solution Substances 0.000 description 23
- 238000010586 diagram Methods 0.000 description 21
- 230000008569 process Effects 0.000 description 19
- 239000000523 sample Substances 0.000 description 15
- 238000005259 measurement Methods 0.000 description 8
- 229920002120 photoresistant polymer Polymers 0.000 description 8
- 230000008859 change Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 239000010931 gold Substances 0.000 description 7
- 238000007654 immersion Methods 0.000 description 7
- 239000003921 oil Substances 0.000 description 7
- 238000007493 shaping process Methods 0.000 description 6
- 210000000170 cell membrane Anatomy 0.000 description 5
- 239000004205 dimethyl polysiloxane Substances 0.000 description 5
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 5
- 229910052737 gold Inorganic materials 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 229920000435 poly(dimethylsiloxane) Polymers 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 239000010409 thin film Substances 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000007747 plating Methods 0.000 description 4
- 238000012706 support-vector machine Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000004113 cell culture Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000005684 electric field Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 2
- 229920001486 SU-8 photoresist Polymers 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 2
- 239000011651 chromium Substances 0.000 description 2
- 239000006059 cover glass Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000008103 glucose Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000007769 metal material Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000001259 photo etching Methods 0.000 description 2
- 102000004169 proteins and genes Human genes 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 238000001448 refractive index detection Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000003746 surface roughness Effects 0.000 description 2
- 239000010936 titanium Substances 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 208000035143 Bacterial infection Diseases 0.000 description 1
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 1
- 206010059866 Drug resistance Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- -1 Polydimethylsiloxane Polymers 0.000 description 1
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 206010066901 Treatment failure Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 208000022362 bacterial infectious disease Diseases 0.000 description 1
- 239000000872 buffer Substances 0.000 description 1
- 239000007853 buffer solution Substances 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 229910052804 chromium Inorganic materials 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 230000005059 dormancy Effects 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000003574 free electron Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000002198 surface plasmon resonance spectroscopy Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 229910052719 titanium Inorganic materials 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 210000004881 tumor cell Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N15/1434—Optical arrangements
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a flow cell detection device, a preparation method and a system, wherein the device comprises: the surface plasmon excitation chip is used for exciting surface plasmons under the irradiation of polarized light and comprises a transparent substrate and a metal film layer attached to the surface of the substrate; the cell channel comprises an inflow channel, a compression channel and a recovery channel, and is bonded with one surface of the chip, to which the metal film layer is attached, to form a watertight closed space. Wherein, the metal film layer is attached to the covering area of the compression channel. When the device is in a working state, the cell to be tested carried by the solution flows in from the inflow channel, enters the compression channel, passes through the target detection area in a state of being in contact with the metal film layer, and flows out from the recovery channel. The device can effectively realize the rapid detection of the refractive index of the surface of the single cell to be detected.
Description
Technical Field
The invention relates to the technical field of photoelectric detection, in particular to a flow cell detection device, a preparation method and a system.
Background
As a basic unit of organism structure and function, cell research is important to reveal chemical nature and law in life process. Due to the limitation of research means, the past life science research mainly measures the comprehensive characteristics of a large number of cells, however, obvious microscopic heterogeneity exists among different individuals of the same cell, the experimental result based on the comprehensive characteristics of a large number of cells hardly reflects the life activity rule on the single cell level, and the realization of single cell analysis becomes one of the development directions of measurement technologies. Meanwhile, microscopic heterogeneity of the same cell is important in relation to the nature and regularity of vital activities, such as: heterogeneity between tumor cells is a critical factor in causing cancer mortality, treatment failure, and drug resistance, and therefore, a large number of single cells of the same species need to be analyzed separately.
Based on the need for high throughput single cell analysis, different analytical methods have been developed to analyze individual characteristics of cells, such as: genomic sequencing, PCR, mass spectrometry, microscopic imaging, and the like. Single cell characterization is a systematic problem, and developing an analysis of its different characteristics helps to further enhance cell awareness and reveal life process laws. The refractive index profile of the cell surface layer is a physical quantity that characterizes the cell: the refractive index of the cell surface layer is related to the factors such as the water content, the protein concentration and the like, so that the refractive index can effectively analyze the composition components of the cell under different physiological states; meanwhile, the refractive index of the cells changes in the processes of bacterial infection, cell dormancy and the like. However, since the cell surface layer substances in different states are similar in composition, the refractive index change is small, and signals from the cell surface layer are often mixed with signals from the inside of the cell, and are difficult to extract separately. Therefore, the refractive index of the surface layer of a single cell is difficult to measure.
Disclosure of Invention
The invention provides a flow cell detection device, a preparation method and a system, which can rapidly and effectively measure the surface refractive index of single cells.
In a first aspect, embodiments of the present disclosure provide a flow cytometric device, the device comprising:
the surface plasmon excitation chip is used for exciting surface plasmons under the irradiation of polarized light, and comprises a transparent substrate and a metal film layer attached to the surface of the substrate;
the cell channel comprises an inflow channel, a compression channel and a recovery channel, wherein the inflow channel, the compression channel and the recovery channel are sequentially communicated, the cell channel is bonded with one surface of the chip, to which the metal film layer is attached, to form a watertight closed space, and the metal film layer is attached to the coverage area of the compression channel;
when the device is in a working state, the to-be-detected cells carried by the solution flow in from the inflow channel, enter the compression channel, pass through the target detection area in a state of keeping contact with the metal film layer, and flow out from the recovery channel, wherein the refractive index of the solution is smaller than that of the substrate.
Further, the flow cytometry apparatus further comprises: and the driver is connected with the cell channel and used for driving the cells to be tested to enter from the inflow channel, pass through the compression channel and flow out from the recovery channel.
Further, the height and width of the compression channel are both smaller than or equal to the size of the cell to be measured.
Further, the difference between the height of the compression channel and the size of the test cell is between 0 and 10 microns, and the difference between the width of the compression channel and the size of the test cell is between 0 and 10 microns.
In a second aspect, embodiments of the present disclosure provide a method for preparing a flow cytometric device, the method comprising: forming a cell channel matched with the transparent substrate, wherein the cell channel comprises an inflow channel, a compression channel and a recovery channel, and the inflow channel, the compression channel and the recovery channel are sequentially communicated; preparing a metal film layer on the surface of the transparent substrate to form a surface plasmon excitation chip, wherein the chip is used for exciting the surface plasmon under the irradiation of polarized light; and bonding the cell channel with one surface of the surface plasmon excitation chip, on which the metal film layer is prepared, to form a flow type cell detection device, wherein the metal film layer is attached to a covering area of the compression channel on the chip, and the compression channel is used for enabling a cell to be detected to pass through a target detection area in a state of keeping contact with the metal film layer.
In a third aspect, embodiments of the present disclosure provide a flow cytometric detection system comprising: an optical detection device and a flow cytometry detection apparatus according to the first aspect. The flow cell detection device is used for driving cells to be detected to pass through the target detection area one by one in a mode of keeping contact with the metal film layer on the surface plasmon excitation chip. The optical detection device includes: the system comprises an illumination subsystem, an objective lens, an imaging subsystem and a data processing device, wherein the illumination subsystem is used for collimating and regulating polarization of light emitted by a light source to generate polarized light to be incident to the objective lens; the objective lens is used for making the polarized light incident to a target detection area on the flow cell detection device, and exciting surface plasmons on the surface of a metal film layer of the target detection area to interact with the passing cells to be detected, wherein the target detection area is positioned in the coverage area of the compression channel; an imaging subsystem for imaging the reflected light formed by the polarized light on the detection device to a target surface of a photoelectric detector and collecting a target image sequence formed by the reflected light at a target position through the photoelectric detector; and the data processing device is used for obtaining the surface refractive index and the morphology information of the cell to be detected based on the target image sequence.
Further, the optical detection apparatus further includes: and the carrying subsystem comprises a clamp and a moving mechanism, wherein the clamp is connected with the moving mechanism, the clamp is used for fixing the flow cell detection device, and the moving mechanism is used for carrying the flow cell detection device to move so as to adjust the position of the target detection area and focus the objective lens.
Further, the objective lens is used for parallelly entering polarized light generated by the illumination subsystem into the target detection area, and the target position is the conjugate imaging plane of the rear focal plane of the objective lens. The data processing device is used for: acquiring brightness distribution data of each target image in the target image sequence; and determining surface refractive index distribution and profile information of the cells to be detected based on the brightness distribution data of each target image and a first preset corresponding relation, wherein the first preset corresponding relation is a corresponding relation between the brightness of the reflected light and the refractive index of the sample.
Further, the objective lens is used for focusing the parallel polarized light generated by the illumination subsystem to the target detection area, and the target position is the Fourier plane of the conjugate imaging plane of the back focal plane of the objective lens. The data processing device is used for: obtaining a target reflected light space frequency domain spectrum corresponding to the cell to be detected aiming at each target image in the target image sequence; and determining the surface refractive index and the surface fluctuation distribution of the cell to be detected based on the target reflected light space frequency domain spectrum corresponding to each target image in the target image sequence and a second preset corresponding relation, wherein the second preset corresponding relation is a corresponding relation between the reflected light space frequency domain spectrum obtained based on a transmission theoretical model and the sample refractive index and the target thickness.
Further, the data processing device is further configured to: and obtaining an analysis result of the cell to be detected based on the surface refractive index of the cell to be detected and a pre-trained cell analysis model, wherein the cell analysis model is a machine learning model.
According to the flow cytometry detection device provided by the embodiment of the specification, the surface plasmon excitation chip and the cell channel are bonded, so that the cells to be detected flow in the cell channel under the loading of the solution, and in the process of flowing through the compression channel, the cells pass through the target detection area one by one in a state of being in contact with the metal film layer. By matching with an optical detection system, incident polarized light enters a target detection area to excite surface plasmons, and the surface plasmons interact with cells to be detected which pass through the target detection area one by one, so that the detection of the refractive index of the surface of a single cell to be detected is realized. In the detection process, a great amount of time is not required to be spent in advance for the wall-attached growth on the measurement chip, namely, longer cell culture time is not required, and the switching of different cells is not required to be realized by continuously changing the position of the measurement chip, so that the detection time consumption is reduced, the rapid detection of the refractive index of the surface of a single cell to be detected is facilitated, and the high-flux single cell detection is further realized. In addition, the flow cytometry detection device can be repeatedly used for detecting different kinds of cells, and different detection chips are not required to be prepared for different cells, so that the detection cost is reduced. In addition, the flow cytometry detection system provided in the embodiments of the present disclosure can effectively implement rapid detection of the refractive index of a single cell to be detected by using the flow cytometry detection device described above.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic structural diagram of a flow cytometry detection apparatus according to a first aspect of the present disclosure;
FIG. 2 is a process flow diagram of a method for manufacturing a flow cytometry apparatus according to a second aspect of embodiments of the present disclosure;
FIG. 3 is a schematic diagram of an exemplary flow cytometry system provided in a third aspect of embodiments of the present disclosure;
FIG. 4 is a schematic diagram of another exemplary flow cytometric detection system provided in accordance with a third aspect of embodiments of the present disclosure;
FIG. 5 is a schematic view of two imaging methods according to a third aspect of the embodiments of the present disclosure;
FIG. 6 is a spatial frequency domain image of reflected light corresponding to an exemplary sample provided in accordance with a third aspect of embodiments of the present disclosure;
fig. 7 is a schematic diagram of a detection principle of a point area detection manner according to a third aspect of the embodiment of the present disclosure.
Detailed Description
In order to better understand the technical solutions provided by the embodiments of the present specification, the following detailed description of the technical solutions of the embodiments of the present specification is made through the accompanying drawings and the specific embodiments, and it should be understood that the specific features of the embodiments of the present specification are detailed descriptions of the technical solutions of the embodiments of the present specification, and not limit the technical solutions of the present specification, and the technical features of the embodiments of the present specification may be combined with each other without conflict.
In a first aspect, embodiments of the present disclosure provide a flow cytometer, as shown in fig. 1, the flow cytometer 10 includes: surface plasmon excitation chip 110 and cell channel 120.
The surface plasmon excitation chip 110 includes a transparent substrate 101, and a metal film layer 102 attached to the surface of the substrate 101 for exciting surface plasmons under irradiation of polarized light. In a specific implementation process, the surface plasmon excitation chip 110 may be formed by plating a metal film with a specified thickness on a high refractive index cover glass substrate.
It is understood that surface plasmons (Surface Plasmon Resonance, SPR) are electromagnetic oscillations formed by a free electron and photon interaction at a metal-dielectric interface. The electromagnetic field of SPR decays in an exponential form along the normal direction at both sides of the interface of two materials, has extremely strong field distribution in a space range far smaller than the wavelength, is very sensitive to the change of the surrounding refractive index, and can realize high-sensitivity detection. Whereas SPR is an evanescent field that decays exponentially along a metal interface, its field strength density is very high and is extremely sensitive to the distribution of objects within its field coverage and its refractive index variations.
In an alternative embodiment, the metal film layer 102 attached to the surface of the substrate 101 includes an adhesion layer and an excitation layer, and the adhesion layer is attached to the surface of the transparent substrate for adhering the excitation layer. The excitation layer is used to excite SPR at the excitation layer-medium interface. For example, the metal material used for the adhesion layer may be titanium (Ti) or chromium (Cr); the metal material used for the excitation layer may be gold (Au) as the SPR excitation metal. The thickness of the adhesion layer and the excitation layer may be set according to practical needs, for example, the thickness of the adhesion layer may be 2 to 5nm, and the thickness of the excitation layer may be 45 to 55nm.
The left side view of fig. 1 is a front view of the flow cytometer 10, the upper right side dashed box is a top view of the dashed box in the front view, and the oval-shaped and dot-filled region represents the measurement cell 100, and the shape and size of the cell are only schematic. The cell channel 120 includes an inflow channel 121, a compression channel 122, and a recovery channel 123, and the inflow channel 121, the compression channel 122, and the recovery channel 123 are sequentially communicated. Cell channel 120 is bonded to the surface of surface plasmon excitation chip 110 to which metal film layer 102 is attached, thereby forming a watertight sealed space. The height and width of the compression channel 122 are both smaller than or equal to the size of the test cell 100, and the difference between the height and width of the compression channel 122 and the size of the test cell 100 is within a preset range, so as to ensure that the test cell 100 can pass through the target detection area in the compression channel 122 in a compressed state in a close contact with the lower metal film layer 102 one by one. For example, the height and width of the compression channels 122 may be in the range of 1 to 50 microns. The size of the test cell refers to the maximum size of the test cell in an uncompressed state, for example, if the test cell is oval, the length of the major axis is the length; the preset range is a deviation range of the height and the height of the compression channel and the corresponding size of the cells to be tested under the condition that the single cells to be tested can pass through the compression channel in a compression state in an actual application scene. For example, the difference between the height of the compression channel 122 and the size of the test cell 100 may be between 0 and 10 microns, and the difference between the width of the compression channel 122 and the size of the test cell 100 may be between 0 and 10 microns.
The compression channels 122 have a metal film layer attached to the covered area of the chip surface. In this embodiment, the metal film may cover the entire excitation chip surface; alternatively, for convenience of bonding, only the area range corresponding to the compression channel may be covered, so as to ensure that the metal film layer is attached to the target detection area 1221 located in the compression channel 122, so as to implement detection of the cells to be detected passing through the target detection area 1221; alternatively, the partial region may be covered with the region range corresponding to the compression passage and the vicinity of the region range.
When the flow cytometer 10 is in an operating state, for example, when the flow cytometer is applied to surface refractive index detection of a cell to be measured, the cell to be measured is carried with a solution, so that the cell to be measured 100 flows in from the inflow channel 121, enters the compression channel 122, passes through the target detection region while being kept in contact with the metal film layer in the compression channel, and flows out from the recovery channel 123. Specific refractive index detection processes are detailed in the flow cytometry systems below. Of course, in order to drive the directional flow of the test cells in the cell channel, an external driver is required. The refractive index of the solution for mounting cells is smaller than that of the chip substrate 101.
In an alternative embodiment, to facilitate use of the flow cytometric device, the flow cytometric device 10 may further comprise: the driver 130, the driver 130 is connected to the cell channel 120, for driving the cell to be tested to enter from the inflow channel 121, to flow out from the recovery channel 123 via the compression channel 122.
In this embodiment, the driver 130 may be a pneumatic driver. In one embodiment, the driver may employ a positive pressure driver having a pressure greater than the pressure in the cell channel, connected to the inflow channel, and configured to drive the directional flow of the test cells in the cell channel by positive pressure. In another embodiment, the driver can adopt a negative pressure driver, the air pressure of the negative pressure driver is smaller than the air pressure in the cell channel, the negative pressure driver is connected with the recovery channel, and the negative pressure drives the cells to be tested to directionally flow in the cell channel.
It should be noted that, the flow cytometry apparatus 10 may further include other components besides the above components, for example, a connection pipeline 140, such as a pipeline connected to the inflow channel and a pipeline connected to the recovery channel, and the like, and may specifically refer to a conveying pipeline of an existing microfluidic chip, which is not described in detail herein.
According to the flow cytometry detection device provided by the embodiment of the specification, the surface plasmon excitation chip and the cell channel are bonded, so that the cells to be detected flow in the cell channel under the loading of the solution, and in the process of flowing through the compression channel, the cells pass through the target detection area one by one in a state of being in contact with the metal film layer. By matching with an optical detection system, incident polarized light enters a target detection area to excite surface plasmons, and the surface plasmons interact with cells to be detected which pass through the target detection area one by one, so that the detection of the refractive index of the surface of a single cell to be detected is realized. In the detection process, a great amount of time is not required to be spent in advance for the wall-attached growth on the measurement chip, namely, longer cell culture time is not required, and the switching of different cells is not required to be realized by continuously changing the position of the measurement chip, so that the detection time consumption is reduced, the rapid detection of the refractive index of the surface of a single cell to be detected is facilitated, and the high-flux single cell detection is further realized. In addition, the flow cytometry detection device can be repeatedly used for detecting different kinds of cells, and different detection chips are not required to be prepared for different cells, so that the detection cost is reduced.
In a second aspect, embodiments of the present disclosure provide a method for preparing a flow cytometer, which is used to prepare the flow cytometer according to the embodiment of the first aspect.
The preparation method provided in this embodiment may include: forming a cell channel matched with the transparent substrate, wherein the cell channel comprises an inflow channel, a compression channel and a recovery channel, and the inflow channel, the compression channel and the recovery channel are sequentially communicated; preparing a metal film layer on the surface of a transparent substrate to form a surface plasmon excitation chip, wherein the chip is used for exciting the surface plasmon under the irradiation of polarized light; and bonding the cell channel with one surface of the surface plasmon excitation chip, on which the metal film layer is prepared, to form the flow cell detection device, wherein the metal film layer is required to be attached to the coverage area of the compression channel on the chip during bonding. The compression channel is used for enabling the cells to be tested to pass through the target detection area one by one in a state of being kept in contact with the metal film layer. The specific structure of the flow cytometry apparatus may be described with reference to the embodiments of the apparatus provided in the first aspect, which is not described herein.
For example, fig. 2 shows a schematic diagram of an exemplary manufacturing process flow. In fig. 2, the left a-E diagram depicts the preparation flow of a cell channel made of PDMS (Polydimethylsiloxane) material, and the right F-I diagram depicts the preparation flow of a surface plasmon excitation chip, and the metal film is exemplified by a gold film. J graph shows the bonding of cell channels to surface plasmon excitation chips.
The preparation flow of the cell channel comprises the following steps: a layer of photoresist 1, such as SU-8 photoresist, is coated on the surface of a smooth substrate, such as a silicon wafer, wherein the thickness of the photoresist determines the height of a cell channel and is smaller than or equal to the height of a cell to be detected, and the height can be between 1 and 50 microns. It is understood that the smooth substrate is one intermediate for forming PDMS cell channels. The exposure operation is performed based on a Mask (Mask) shown in the figure A, and then a layer of photoresist 2, such as SU-8 photoresist, is coated on the exposed photoresist surface layer, wherein the thickness of the photoresist can be between 1 and 200 micrometers. The alignment and the secondary exposure operation (the arrow in the A diagram and the B diagram represents photoetching exposure) are carried out on the basis of the mask plate shown in the B diagram, then development (shown in the C diagram) and pouring of PDMS (shown in the D diagram) are carried out in sequence, and then the cell channel built by the PDMS is taken down and perforated to serve as a channel and conveying pipeline interface (shown in the E diagram).
The preparation process of the surface plasmon excitation chip comprises the following steps: coating a layer of photoresist 3 on the surface of a transparent substrate such as a cover glass, carrying out photoetching operation based on a mask plate shown in an F diagram, developing (shown in a G diagram), removing the photoresist at the position inside a compression channel, plating a gold (Au) film (shown in an H diagram) on the surface after the development operation is finished, and stripping the photoresist to obtain the surface plasmon excitation chip (shown in an I diagram).
Further, the formed cell channel is aligned with the surface plasmon excitation chip, mainly by aligning the compressed channel position with the gold film plating position, and bonding it to the gold film plating surface, the flow cytometry detection device can be obtained, as shown in the J-diagram of fig. 2.
In a third aspect, embodiments of the present disclosure provide a flow cytometric detection system comprising: the optical detection device and the flow cytometry detection device provided by the embodiment of the first aspect above.
The flow cell detection device is used for driving the cells to be detected to pass through the target detection area one by one in a mode of keeping contact with the metal film layer on the surface plasmon excitation chip. The specific structure of the flow cytometry apparatus may be described with reference to the embodiments of the apparatus provided in the first aspect, which is not described herein.
In this embodiment, the optical detection apparatus includes: an illumination subsystem, an objective lens, and an imaging subsystem.
The illumination subsystem is used for collimating and regulating polarization of light emitted by the light source, generating polarized light to be incident to the objective lens, and preferably, single-wavelength laser or narrow-band light can be used as the light source.
And the objective lens is used for making polarized light incident to a target detection area on the flow cell detection device, and exciting surface plasmons on the surface of the metal film layer of the target detection area to interact with the passing cells to be detected, wherein the target detection area is positioned in the coverage area of the compression channel on the surface plasmons excitation chip. When the cells to be tested pass through the compression channels one by one, the cells to be tested also cling to the lower metal film layer in a compressed state one by one and sequentially pass through the target detection area.
And the imaging subsystem is used for imaging the reflected light formed by the polarized light on the flow cytometry detection device to the target surface of the photoelectric detector and collecting a target image sequence formed by the reflected light at the target position through the photoelectric detector. The target image sequence comprises target images of a plurality of moments which are continuously read in a preset time period.
Further, in the embodiment of the present disclosure, the optical detection apparatus may further include a carrying subsystem for carrying the current-carrying type cell detection device and enabling high-precision movement for achieving switching of the target detection area and focusing of the objective lens. Specifically, the mounting subsystem may include a clamp for fixing the flow cytometer, and a moving mechanism for moving the flow cytometer to adjust the position of the target detection area and focus the objective lens. For example, the movement mechanism may employ an motorized three-dimensional translation stage.
In the implementation process, the optical detection device can adopt one of two detection modes according to the measurement range and the detection quantity of the imaging system.
First, surface plasmon imaging detection mode. At this time, the objective lens is used to make the p-polarized light generated by the probe light generating subsystem incident in parallel to the target detection area, correspondingly, the target position is the conjugate imaging plane of the back focal plane of the objective lens, and the collected target image is the imaging of the electric field distribution of the target detection area.
For example, the system light path diagram of the surface plasmon imaging detection mode may be as shown in fig. 3, and an exemplary detection process of the detection mode is described below.
After the probe light is emitted from the light source 301, the probe light passes through the beam-expanding and shaping converging lens group 302, and the incident light is focused in p-polarization state on the back focal plane of the oil-immersed objective lens 305 through the polarizing plate 303 and the thin film beam splitter 304. The detection light may be monochromatic light or narrowband light, such as laser output monochromatic light, superluminescent diode SLD or LED monochromatic light, the beam-expanding and shaping converging lens group 302 may be composed of a plurality of lenses, and the polarizing plate 303 may be located at a middle position of the beam-expanding and shaping converging lens group.
Adjusting the illumination subsystem to focus the incident light at a location on the back focal plane of oil immersion objective 305; the position of the flow cell detection device 10 is adjusted by the carrying subsystem 309, so that the metal upper surface of the surface plasmon excitation chip is located at the working height of the objective lens, and meanwhile, the illumination position of the incident light corresponds to the target detection area in the flow cell detection device 10, and the generated total reflection evanescent wave vector is matched with the surface plasmon excitation element wave vector, so that the transmission surface plasmon is excited in the target detection area on the surface of the surface plasmon excitation chip.
The flow cytometry detection device 10 is controlled to start to work, that is, a solution carrying the cells to be tested is conveyed into the flow cytometry detection device 10, and the cells to be tested are driven to enter from the inflow channel, pass through the compression channel and then flow out from the recovery channel.
The position of the thin film beam splitter 304 is adjusted left and right in the direction of the arrow by a one-dimensional electric translation stage 306 of the illumination subsystem, and the parallel incidence angle of the incident light is changed to excite the strongest surface plasmons. The excited surface plasmons interact with the cells to be detected passing through the target detection area, and reflected light is collected by the same oil immersion objective 305 and then sequentially enters the photodetector 308 through the thin film beam splitter 304 and the tube mirror 307. The focal length and position of the tube mirror 307 are reasonably selected, so that the photoelectric detector 308 is positioned on the conjugate imaging plane of the back focal plane of the oil immersion objective 305, and at this time, the signal received by the photoelectric detector 308 is the imaging of the electric field distribution of the target detection area in the flow cell detection device 10, and can be further used for calculating the refractive index distribution information of the surface of the cell to be detected.
Second, a dot area detection method. The point area detection mode is to adopt a target detection area in a convergent light incident flow type cell detection device, at the moment, the target detection area is a focused light spot, reflected light is collected, and the reflected light is imaged by utilizing the Fourier transform function of the lens to obtain the reflectivity distribution of incident light with different angles. That is, the objective lens is used for focusing the parallel polarized light generated by the probe light generating subsystem to the target detection area, correspondingly, the target position is the fourier plane of the conjugate imaging plane of the rear focal plane of the objective lens, and the target image is the spatial frequency domain image of the reflected light.
For example, a system light path diagram of the point area detection mode may be as shown in fig. 4, and an exemplary detection process of the detection mode is described below.
The flow cytometry detection device 10 is controlled to start to work, that is, a solution carrying the cells to be tested is conveyed into the flow cytometry detection device 10, and the cells to be tested are driven to enter from the inflow channel, pass through the compression channel and then flow out from the recovery channel.
After the probe light is emitted from the light source 401, the probe light is subjected to beam expansion and shaping by the beam expansion and shaping lens group 402, and then sequentially passes through the polarization adjustment device 403 and the thin film beam splitter 404, so that the incident light is vertically incident to the oil immersion objective lens 405 in a parallel light state. The detection light may be monochromatic light, such as laser output monochromatic light, superluminescent diode SLD, or LED monochromatic light, and the beam-expanding, shaping, converging lens group 402 may be composed of a plurality of lenses. The polarization adjustment device 403 is used to adjust the polarization state of the incident light, and may use various polarization states, such as linear polarized light and radial polarized light, and radial polarized light may be preferably used in this embodiment. The position of the flow cytometry detection device 10 is adjusted by the carrying subsystem 409, so that the upper surface of the surface plasmon excitation chip metal is positioned at the working height of the objective lens, and the illumination position of the incident light corresponds to the target detection area in the flow cytometry detection device 10. The parallel incident light is converged by the oil immersion objective lens 405 to the target detection area in the flow cytometry detection device 10, so as to form an incident light set incident at different angles, and the incident light energy in a specific angle range excites surface plasmons. The specific angular range is related to the distribution of the medium on the surface of the target detection zone. In addition to the incident light in this particular angular range, other incident light is reflected by the surface plasmon excitation chip surface in flow cell detection apparatus 10.
After being collected by the same oil immersion objective 405, the reflected light sequentially passes through the thin film beam splitter 404, the tube mirror 406 and the optical lens 407 to be incident on the photoelectric detector 408, and the focal length and the position of the tube mirror 406 and the optical lens 407 are reasonably selected to enable the photoelectric detector 408 to be positioned on a Fourier plane of a conjugate imaging plane of a rear focal plane of the oil immersion objective 405, and at the moment, a signal received by the photoelectric detector 408 is a spatial frequency domain image of the reflected light. The spatial frequency domain image can be used for calculating refractive index information and morphology information of the surface of the cell to be detected.
Further, in order to facilitate rapid acquisition of detection data, the optical detection apparatus provided in this embodiment further includes a data processing device. The data processing device is connected with the photoelectric detector and is used for reading the target image sequence obtained by the photoelectric detector and obtaining characteristic information of the cells to be detected, such as the surface refractive index and the morphology information of the cells to be detected, based on the target image sequence.
Specifically, the data processing device may include a chip having a data processing function such as a single chip microcomputer, a DSP, and an ARM, and may be, for example, a personal computer, a notebook computer, and the like.
In an alternative embodiment, the optical detection device adopts the surface plasmon imaging detection mode, and at this time, the target image sequence acquired by the photodetector is imaging of the electric field distribution of the target detection area. The data processing device is specifically used for: acquiring brightness distribution data of each target image in a target image sequence; and determining surface refractive index distribution and profile information of the cells to be detected based on the brightness distribution data of each target image and a first preset corresponding relation, wherein the first preset corresponding relation is a corresponding relation between the brightness of the reflected light and the refractive index of the sample.
Specifically, the data processing device reads an imaging image of the photoelectric detector, namely a target image, firstly judges whether the target detection area has cells to be detected at a plurality of different moments in a preset time period, and if the cells to be detected exist at the current moment, further calculates the surface refractive index of the cells to be detected according to the target image read at the current moment, so as to count the surface refractive index information of the cells to be detected. The preset time period and the sampling interval can be set according to actual needs and multiple tests.
It will be appreciated that if there are no cells to be detected above a certain fixed area in the target detection area, i.e. the compression channel, then above the target detection area there is mainly a cell-carrying solution, such as PBS buffer or cell culture solution, whose refractive index is fixed, and the detected image is a background light distribution. If a cell to be detected flows over the target detection area, the detection image will change. For example, fig. 5 depicts two target images in the surface plasmon imaging detection mode, wherein the left image in fig. 5 is a target image obtained when no cell to be detected is present in the target detection area, and the right image in fig. 5 is a target image obtained when one cell to be detected is present in the target detection area. Obviously, the corresponding reflected light intensity of the same detection position is different when the cells exist or not, and the light intensity difference is related to the refractive index of the cells and the parameters of the detection system.
Therefore, for the same system, the inventor proposes that, before using the surface plasmon imaging detection mode, experimental calibration can be performed in advance to determine the first preset corresponding relationship. The specific calibration process comprises the following steps: and (3) introducing liquids with different known refractive indexes into a cell channel of the flow cell detection device, ensuring that other system parameters are the same, obtaining brightness corresponding to different refractive indexes by detecting brightness data in a read target image, and determining the corresponding relation between the brightness of the reflected light of the system under the fixed parameters and the refractive index of a sample as a first preset corresponding relation.
And after the data processing device acquires the target image at each sampling time within a preset time period, the brightness distribution data can be extracted from the target image, and the brightness distribution data of the target image acquired at each time can be converted into the refractive index distribution of cells above the target detection area at the time based on the first preset corresponding relation. Further, the obtained refractive index distribution may be counted, and the refractive index distribution corresponding to the same cell may be spliced or averaged, so that the surface refractive index distribution of the single cell may be obtained. In addition, as can be seen from comparing the left and right images in fig. 5, the contour information of the cells to be measured can be obtained from the brightness distribution data of the target image, and it can be understood that the point of the brightness difference from the background in the brightness distribution data corresponds to the contour of the cells to be measured.
In an alternative embodiment, the surface refractive index of the obtained test cells may be further used for cell analysis, for example, classification of cells. At this time, the data processing apparatus is further configured to: and obtaining an analysis result of the cell to be detected based on the surface refractive index distribution of the cell to be detected and a pre-trained cell analysis model. The cell analysis model may be a machine learning model, for example, a Support Vector Machine (SVM) or a neural network algorithm. Specifically, the purpose of cell analysis can be various, and model training can be performed according to actual needs.
For example, in an application scenario, the cell analysis model may be a clustering model, the same type of cells to be tested have different forms, the surface refractive index distribution of the different forms is different, one or more of feature quantities such as an average value, a maximum difference value, a root mean square difference value and the like of the surface refractive index of each cell can be calculated, the calculated feature quantities are input into the cell analysis model trained in advance, and a large number of the cells to be tested of the same type are clustered, so that the obtained cells of each type of clusters correspond to one form of the cells. In another application scenario, the cell analysis model may be a classification model, and at this time, the calculated feature quantity of a certain cell is input into a pre-trained cell analysis model, so that the class to which the cell belongs may be identified.
In an alternative embodiment, the optical detection device adopts the above-mentioned point area detection mode, and the target image collected by the photodetector is a spatial frequency domain image of the reflected light. The detection mode can obtain the surface refractive index distribution of the cells to be detected, and also can obtain the surface fluctuation distribution of the cells to be detected, which can be called as surface roughness, thereby being beneficial to obtaining richer single-cell detection data. At this time, the data processing apparatus is specifically configured to: aiming at each target image in the target image sequence, obtaining a target reflected light space frequency domain spectrum corresponding to the cell to be detected; and determining the surface refractive index and the surface fluctuation distribution of the cell to be detected based on the target reflected light space frequency domain spectrum corresponding to each target image in the target image sequence and a second preset corresponding relation, wherein the second preset corresponding relation is the corresponding relation between the reflected light space frequency domain spectrum obtained based on the transmission theoretical model and the sample refractive index and the target thickness.
In the spot area detection system, at a single moment, the image imaged by the photodetector is a circular bright spot that presents a dark ring (for radially polarized light) or two symmetrical dark arcs (for linearly polarized light). Taking linear polarized light as an example, the distance between the dark arc and the center of the circle and the light intensity distribution near the dark arc represent the excitation angle spectrum distribution of the surface plasmon at the moment, and the incident angle corresponding to the dark arc is the excitation angle of the surface plasmon. As shown in fig. 6, the left image is a reflected light spatial frequency domain image obtained when the medium above the target detection area is air, the middle image in fig. 6 is a reflected light spatial frequency domain image obtained when the medium above the target detection area is water, and the right image in fig. 6 is a reflected light spatial frequency domain image obtained when the medium above the target detection area is a glucose solution with a mass fraction of 10%. The three samples with refractive indexes of 1, 1.33 and 1.35, which are respectively obtained by using air, water and 10% glucose solution by mass fraction, are used for measurement, and the larger the refractive index of the sample is, the larger the corresponding radius of the circular arc is.
Further, in order to obtain a curve of the reflection intensity changing with the incident angle, a circle where the arc is located may be determined by using Hough transformation, and then a position of a minimum value of the intensity value on the circle (a point corresponding to a dark arc on the circle may be identified as the intensity minimum value), and an incident angle corresponding to the circle center is zero degrees. And taking an intensity value from the center of the circle to the radius where the point is located along the radial direction, namely a curve of the change of the reflection intensity along with the incident angle. Or, the dark arc can be basically symmetrical left and right by adjusting the light path, and the intensity value change curve from the center to the edge of the image can also be extracted along the radial direction of the center of the dark arc. In another embodiment, when radial polarized light is used for incidence, the intensity value and the corresponding incidence angle can be extracted along the radial direction by taking the center of a circle as a starting point, and a change curve of the reflection intensity along with the incidence angle can be obtained. Further, the smoothness of the curve can be increased by means of averaging multiple groups, and noise interference is reduced.
For each target image in the target image sequence, a change curve of the reflected light intensity along with the incident angle, namely, a target reflected light space frequency domain spectrum corresponding to the cell to be detected is obtained, and then a specific implementation process for determining the surface refractive index distribution and the surface relief distribution of the cell to be detected is described below.
It will be appreciated that when the cell to be measured passes over the target detection region, i.e., the detection point, the two medium layers are distributed over the detection position, assuming the lowest layer is the buffer solution layer, the refractive index is n1, the thickness is d, the upper layer is the cell layer, the refractive index is n2, and all the SPR field regions are covered. The refractive index of the solution is uniform and can be a known quantity, the thickness d of the solution changes to reflect the shape information of cell membranes, and n2 represents the refractive index distribution information of cell surface layers. When no cell exists above the detection point, the detection position presents uniform refractive index medium layer distribution, and can be approximately equal to the penetration depth of SPR, and the SPR excitation angle spectrum distribution is only influenced by the refractive index (n 1) of the medium at the lower layer.
Theoretically, the relation of the values of n1, d, n2 and the reflection spatial spectral distribution can be obtained from a transmission theoretical model. That is, the second preset corresponding relation can be obtained based on the medium distribution and the transmission theoretical model above the detection position, the refractive index of the sample is the refractive index n2 of the cell layer, the target thickness is the solution layer thickness d, and the cell membrane morphology information is reflected. It should be noted that the transmission theoretical model is an existing theoretical model, and represents the relationship between transmission and reflection in the multilayer medium and incident light, which is not described in detail herein.
And matching the target reflected light space frequency domain spectrum obtained by the experiment with the reflected light space frequency domain spectrum corresponding to different sample refractive indexes and target thicknesses in a second preset corresponding relation, and obtaining the surface refractive index n2 and the solution layer thickness d of the cell to be detected. For example, a least square method, a residual square sum or a root mean square error method can be used for fitting, so that the objective function value of theoretical data and experimental data in the second preset corresponding relation is the smallest, and the surface layer refractive index n2 of the cell to be detected and the solution layer thickness d can be matched. The objective function value is a function value for representing the degree of difference between theoretical data and experimental data, and the specific function is determined according to the adopted fitting method.
In the implementation process, based on continuously reading target images at a plurality of moments in a preset time period, the refractive index n2 of the cell surface layer and the thickness d of the solution layer at the moments can be respectively measured. The preset time period is a time period shown in fig. 7 when the cell surface passes over the detection point. Then, the refractive index n2 of the surface layer of the cell obtained at these successive moments in the preset time period and the thickness d of the solution layer are sequentially connected, so that the surface refractive index distribution curve of the cell to be measured and the topography information, that is, the fluctuation distribution of the cell surface, can be obtained respectively, as shown in fig. 7.
In an alternative embodiment, the obtained surface refractive index profile and morphology information of the test cells may be further used for cell classification. The characteristic extraction can be performed according to the surface refractive index distribution curve and the morphology information of the cells to be detected; and inputting the extracted characteristic information into a pre-trained cell analysis model, and identifying the category of the cell to be detected. The cell analysis model may be a machine learning model, for example, a support vector machine or a deep neural network may be used to identify the class to which the cell to be measured belongs.
Specifically, classification algorithms can be gradually adopted to study classification capability:
1) And extracting spatial features of different scales from the surface refractive index distribution curve and the morphology information of the cells to be detected by using a wavelet analysis feature method, and carrying out network training and classification research on the extracted features by combining a support vector machine method.
2) An end-to-end deep neural network algorithm is adopted: and extracting and classifying different spatial scale features from the surface refractive index distribution curve and the morphology information of the cells to be detected through a feature pyramid network (Feature Pyramid Network, FPN).
3) The introduction of an attention mechanism weights the spatial features, enhancing the ability of the algorithm to discover specific detailed structures and advanced features in the cell membrane morphology.
4) The repeated EfficientDet algorithm is adopted, and the characteristics extraction of different spatial scales of the deep network EfficientNet and the bidirectional characteristic fusion of a bidirectional characteristic pyramid (BiFPN) are combined to classify, so that the method is applied to the characteristic extraction and classification research based on the cell membrane morphology.
Furthermore, based on a statistical method, the overall statistical parameters corresponding to physical concepts such as 'cell membrane surface roughness' and 'protein distribution' can be adopted, and the statistical methods such as variance analysis and T test can be adopted for classification capability detection of results including expectation and variance.
In summary, the flow cytometry detection system provided in the embodiments of the present disclosure, which adopts the flow cytometry detection device provided in the first aspect, drives the cells to be detected to pass through the target detection area in the compression channel one by one in a state of keeping contact with the metal film layer, and on this basis, provides polarized light to be incident into the target detection area to excite the SPR at the metal film-medium interface, so that the SPR interacts with the cells to be detected passing through the compression channel one by one, and the reflected light detected by the photodetector is imaged, thereby obtaining the surface refractive index and the morphology information of the cells to be detected, and realizing rapid detection of the surface refractive index of the single cells to be detected, thereby being beneficial to realizing high-flux single cell detection.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While preferred embodiments of the present description have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the disclosure.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present specification without departing from the spirit or scope of the specification. Thus, if such modifications and variations of the present specification fall within the scope of the claims and the equivalents thereof, the present specification is also intended to include such modifications and variations.
Claims (5)
1. A flow cytometry system comprising: optical detection device and flow cytometry detection device, wherein,
the flow cytometry apparatus includes: the surface plasmon excitation chip is used for exciting surface plasmons under the irradiation of polarized light, and comprises a transparent substrate and a metal film layer attached to the surface of the substrate; the cell channel comprises an inflow channel, a compression channel and a recovery channel, wherein the inflow channel, the compression channel and the recovery channel are sequentially communicated, the cell channel is bonded with one surface of the chip, to which the metal film layer is attached, to form a watertight closed space, the metal film layer is attached to the coverage area of the compression channel, when the device is in a working state, a solution-carried cell to be tested flows in from the inflow channel, enters the compression channel, passes through a target detection area one by one in a manner of keeping contact with the metal film layer, and enables a solution layer between the lower surface of the cell to be tested and the metal film layer and the lower surface layer of the cell to be tested to cover all surface plasmon field areas, and then flows out by the recovery channel, wherein the refractive index of the solution is smaller than that of the substrate;
The optical detection device includes: an illumination subsystem, an objective lens, an imaging subsystem, a photodetector, and a data processing device, wherein,
the illumination subsystem is used for collimating and regulating polarization of light emitted by the light source, and polarized light is generated and enters the objective lens;
an objective lens, configured to focus the polarized light to a target detection area on the flow cell detection device, where a surface plasmon is excited on a surface of a metal film layer of the target detection area and interacts with a passing cell to be detected, where the target detection area is located in a coverage area of a compression channel and is a focused light spot;
the imaging subsystem is used for imaging the reflected light formed by the polarized light on the detection device to the target surface of the photoelectric detector, and collecting a target image sequence formed by the reflected light at a target position through the photoelectric detector, wherein the photoelectric detector is positioned on a Fourier plane of the rear focal plane conjugate imaging surface of the objective lens;
the data processing device is used for obtaining the surface refractive index and the morphology information of the cell to be detected based on the target image sequence, and comprises the following steps: obtaining a target reflected light space frequency domain spectrum corresponding to the cell to be detected aiming at each target image in the target image sequence; determining a surface refractive index distribution curve and surface relief distribution of the cell to be detected based on the target reflected light space frequency domain spectrum corresponding to each target image in the target image sequence and a second preset corresponding relation, wherein the second preset corresponding relation is a corresponding relation between the reflected light space frequency domain spectrum obtained based on a transmission theoretical model and a sample refractive index and a target thickness; extracting characteristics of the surface refractive index distribution curve and the surface fluctuation distribution of the cells to be detected; inputting the extracted characteristic information into a pre-trained cell analysis model, and identifying the category of the cell to be detected.
2. The system of claim 1, wherein the optical detection device further comprises: and the carrying subsystem comprises a clamp and a moving mechanism, wherein the clamp is connected with the moving mechanism, the clamp is used for fixing the flow cell detection device, and the moving mechanism is used for carrying the flow cell detection device to move so as to adjust the position of the target detection area and focus the objective lens.
3. The system of claim 1, wherein the flow cell detection device further comprises:
and the driver is connected with the cell channel and used for driving the cells to be tested to enter from the inflow channel, pass through the compression channel and flow out from the recovery channel.
4. The system of claim 1, wherein the compression channel has a height and a width that are each less than or equal to the size of the test cell.
5. The system of claim 4, wherein the difference between the height of the compression channel and the size of the test cell is between 0 and 10 microns and the difference between the width of the compression channel and the size of the test cell is between 0 and 10 microns.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011120179.6A CN112525806B (en) | 2020-10-19 | 2020-10-19 | Flow cell detection device, preparation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011120179.6A CN112525806B (en) | 2020-10-19 | 2020-10-19 | Flow cell detection device, preparation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112525806A CN112525806A (en) | 2021-03-19 |
| CN112525806B true CN112525806B (en) | 2023-04-25 |
Family
ID=74979222
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011120179.6A Active CN112525806B (en) | 2020-10-19 | 2020-10-19 | Flow cell detection device, preparation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112525806B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113058668B (en) * | 2021-03-20 | 2022-05-27 | 山东大学 | Artificial surface plasmon micro-fluidic detection chip structure based on capacitive metamaterial structure and preparation and detection methods thereof |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104251810A (en) * | 2013-12-18 | 2014-12-31 | 中国科学院电子学研究所 | System for simultaneous representation of single cell Young's modulus and cell membrane specific capacitance |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419167B (en) * | 2008-12-04 | 2010-12-29 | 浙江大学 | High capture rate and high-sensitivity microflow control SPR bio-sensing method and device |
| CN102654457B (en) * | 2012-05-16 | 2014-06-04 | 北京大学 | Refractive index sensor and detection method thereof |
| CN104792746B (en) * | 2014-12-09 | 2018-06-26 | 中国科学院微电子研究所 | A kind of detection method being imaged using surface phasmon scattering to nano-substance |
| CN109738395A (en) * | 2019-01-11 | 2019-05-10 | 重庆大学 | Using the SPR detection device and SPR detection method of optical dispersion |
-
2020
- 2020-10-19 CN CN202011120179.6A patent/CN112525806B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104251810A (en) * | 2013-12-18 | 2014-12-31 | 中国科学院电子学研究所 | System for simultaneous representation of single cell Young's modulus and cell membrane specific capacitance |
Non-Patent Citations (3)
| Title |
|---|
| High resolution surface plasmon resonance imaging for single cells;Alexander W Peterson等;《BMC Cell Biology》;20141201;文献第9页第5段，第10页第2-3段及图1 * |
| 一种基于表面等离子共振的液体折射率测量系统;陈强华等;《光学学报》;20150510;第35卷(第5期);第166-172页 * |
| 基于光学表面波的折射率传感成像技术;张崇磊等;《光学学报》;20181113;第39卷(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112525806A (en) | 2021-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10379027B2 (en) | Method for identifying blood particles using a photodetector | |
| JP6273276B2 (en) | Image analysis and measurement of biological samples | |
| US10900885B2 (en) | Flow cytometry using hydrodynamically planar flow | |
| JP7079203B2 (en) | High-precision five-part differentiation using digital holographic microscopy and intact peripheral blood leukocytes (5-part Differential) | |
| US9372143B2 (en) | Scanning image flow cytometer | |
| JP2022078344A (en) | Image analysis and measurement of biological samples | |
| US9013692B2 (en) | Flow cytometer apparatus for three dimensional difraction imaging and related methods | |
| JP4016403B2 (en) | Analysis method and analyzer | |
| US5650847A (en) | Method and device for determination of parameters of individual microparticles | |
| US8107071B2 (en) | Method for detecting molecular analysis light, and apparatus and sample plate for use with the same | |
| CN104155266B (en) | A kind of multi-channel parallel detects surface plasma resonance biosensor and preparation thereof and detection method | |
| CA2604661A1 (en) | Method and applications to enhance and image optical signals from biological objects | |
| JP2013531787A (en) | Holographic fluctuation microscope apparatus and method for determining particle motility and / or cell dispersion | |
| US20040027659A1 (en) | Sample holder | |
| CA2591200A1 (en) | Systems, illumination subsystems, and methods for increasing fluorescence emitted by a fluorophore | |
| US20220299421A1 (en) | Systems, devices and methods for three-dimensional imaging of moving particles | |
| US7692791B2 (en) | Target substance-detecting apparatus and target substance-detecting method | |
| CN112525806B (en) | Flow cell detection device, preparation method and system | |
| US10677732B2 (en) | Detection chip, detection kit, detection system, and method for detecting detection target substance | |
| JP2000121551A (en) | Concentration measuring device | |
| WO2021063950A1 (en) | Method for classifying monitoring results from an analytical sensor system arranged to monitor molecular interactions | |
| CN110631992A (en) | Optical tweezers longitudinal positioning feedback device and method based on fluorescence coupling emergence | |
| CN210953775U (en) | Optical tweezers longitudinal positioning feedback device based on fluorescence coupling emergence | |
| US20030206291A1 (en) | Optical configuration and method for differential refractive index measurements | |
| US20240077479A1 (en) | Detection system and method for the migrating cell |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |