JP2000046723A - Method for inspecting function of platelet - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automate inspection and save labor, and at the same time to improve likelihood ratio and accuracy by calculating a shape parameter for each platelet whose image is picked up, and by judging the function of the platelet (the activation state of the platelet and the number of platelets) based on the data group. SOLUTION: Continuous visible light and pulse near infrared rays are applied to a sheath flow cell for accommodating sample liquid containing a stained platelet so that the continuous visible light and the pulse near infrared rays cross each other. Then, forward scattered light and side fluorescence are received by a photodetector (photodiode 17) and the photodetector (photomultiplier 11), respectively, and a forward scattering intensity signal S1 and a side fluorescence intensity signal S2 are outputted to a particle analysis part 100a of an analysis part 100. At the particle analysis part 100a, a two-dimensional scattergram of forward scattering intensity and side fluorescence intensity is created, and a platelet region is analyzed. Also, at an operation part 100d, the number of platelets per fixed volume is calculated according to the flow rate information of the sample liquid being inputted in advance, and a measurement result is outputted from an output part 100e.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for determining the function of platelets using a flow-type particle image analyzer for capturing a particle image.

[0002]

2. Description of the Related Art Platelet counts and swirling tests are important tests that determine the value of a product in shipping platelet products. Platelet counting has already been automated by a hemocytometer, but swirling tests are a type of platelet function and remain a visual inspection. At present, the determination is made by holding the blood bag over a light source such as a fluorescent lamp, and confirming swirling in which platelets flow spirally. Platelets with normal functions are disc-shaped, and swirling phenomena due to light scattering are confirmed.However, platelets showing reduced function tend to be activated and become spherical, so swirling phenomena are hardly observed. Based on what you can't see.

A method (apparatus) for measuring the platelet activity state in a platelet preparation, which focuses on the swirling phenomenon based on the form of platelets, has been proposed. This method measures the ratio of normal platelets in a blood bag with a non-puncture needle, but does not directly measure the platelet morphology, but indirectly determines the platelet morphology from changes in the light scattering intensity and transmitted light intensity of the entire platelet aggregate Things. Japanese Patent Laid-Open No. 60-1 which is the first method
The method for measuring platelet activity disclosed in 13153 focuses on the fluctuation range of scattered light accompanied by small fluctuations from platelets flowing in a specific direction by flowing a platelet preparation stored in a blood bag. is there. That is, the blood bag is divided into two parts, upper and lower, through a flow path having a predetermined thickness, and the blood bag is vibrated so that the platelet product moves back and forth in the flow path. Of the scattered light emitted from the platelets in a specific direction by detecting the fluctuation amplitude of the scattered light. The detected value is compared with a reference value of a fluctuation amplitude separately detected for fresh platelets under the same conditions.

The second method, Japanese Patent Laid-Open No. Hei 9-318624
The apparatus for measuring platelet activity disclosed in the above publication focused on the transmittance of a platelet preparation in a blood bag. That is, the blood bag is placed in a horizontal state, and is sandwiched between flat plates, so that the blood bag is divided into two storage sections via a platelet preparation layer for measurement, which is a flow path of a predetermined thickness, and the blood bag is in the orthogonal direction in a stationary state. Irradiate a light beam, obtain a static transmission intensity by measuring the transmitted light of the measurement platelet product layer, under horizontal vibration at a predetermined flow rate of the blood bag, by the same operation as above,
Obtain the dynamic transmission intensity. The rate of change in light transmission intensity, which is the ratio of the phase difference between the dynamic transmission intensity and the static light transmission intensity and the static light transmission intensity, is calculated. It is intended to be able to calculate the ratio.

Further, platelets in a whole blood sample are identified,
A method has been proposed in which the platelet count is measured simultaneously with the platelet activity state. Japanese Patent Application Laid-Open No. 9-1 which is a third method
The method disclosed in 96914 focuses on the refractive index obtained by Mie scattering theoretical analysis of two types of light scattering intensity for individual platelets in an aqueous suspension. That is, in a device equipped with a laser light source, a flow cell that surrounds platelets in a suspension with a sheath liquid, and a single optical measurement channel including two optical detectors, a part of the incident laser light when the platelets pass through the flow cell When scattering light, two optical parameters of high-angle scattering and low-angle scattering intensity were detected, and based on the refractive index obtained by Mie scattering theory analysis, it was attempted to distinguish between activated platelets and non-activated platelets. Things.

On the other hand, as a method of directly measuring the morphology of platelets, there is a method using a microscope. In the fourth method using a microscope, a scanning electron microscope is often used because it is difficult to capture platelet morphology with an optical microscope. Observation of the morphology with an electron microscope requires pretreatment of the blood with a strong fixative such as glutaraldehyde.

[0007]

In view of the above prior art, the first and second methods (apparatuses) are methods for measuring the ratio of normal platelets in a blood bag by using a non-puncture needle. Instead of measuring, the platelet morphology is obtained indirectly from changes in light scattering intensity and transmitted light intensity of the entire platelet aggregate. For this reason, there is a concern about the effect on the measured value depending on the material of the blood bag and the platelet concentration, and the first method requires reference data for fresh platelets, and the second method requires correlation data with the disc state of normal platelets. Therefore, calibration for inputting those data into the apparatus in advance is always required. At the same time, the function of measuring the number of platelets, which is essential for the examination of platelet preparations, cannot be added, and it is not possible to cope with the automation of the examination. In the above third method, the platelet count and platelet activity can be measured simultaneously, but the platelet activity is not measured based on the change in platelet morphology, but the principle is to calculate the platelet component concentration (density) from the refractive index by diagonal scattering measurement. The platelet activity is measured by conversion. It is also disclosed that the platelet activity does not depend on platelet morphology, but it is generally known that the refractive index changes depending on the surface state and shape of the particles, and there is a problem in the accuracy and precision of the platelet activity state. There is. The above three platelet activity measurement methods (apparatus)
Does not directly measure platelet activity based on platelet morphology.

The fourth method is a method of directly measuring platelet morphology, and it is known that platelet is activated by pretreatment to change the morphology. In addition, there is a problem in accuracy / accuracy such as not being able to obtain a large amount of analysis particles, and automation is very difficult.

In the present invention, a flow step of converting a platelet-containing sample solution into a sample flow in a flow cell surrounded by a sheath liquid, an imaging step of sequentially imaging the sample flow, and an analysis step of processing and analyzing the imaged image And a display step of displaying an analysis result, wherein the analysis step includes a calculation step of calculating a shape parameter for each imaged platelet, and determines a platelet function based on the value of the obtained shape parameter data group. Another object of the present invention is to provide an automated method for platelet preparation and labor saving by adding a platelet count measurement function.

[0010]

A method for testing platelet function according to the present invention comprises a flow step of converting a platelet-containing sample solution into a sample flow surrounded by a sheath liquid in a flow cell, and an imaging step of sequentially imaging the sample flow. And, comprises an analysis step of processing and analyzing the captured image, and a display step of displaying the analysis result,
The analysis step includes a calculation step of calculating a shape parameter for each imaged platelet, and a determination step of determining platelet function based on the value of the obtained shape parameter data group. Is what you do. Here, the platelet-containing sample liquid refers to whole blood collected, a platelet suspension separated from whole blood, a platelet preparation in which platelets collected by blood component collection are suspended in plasma, and the like. The platelet function test indicates the activation state of platelets and the number of platelets.

Further, it is preferable that the shape parameter calculated in the calculation step is a circularity reflecting the roundness.

Further, it is preferable that the statistical values calculated from the values of the data group of the shape parameters in the calculation step are an average value, a standard deviation, and a coefficient of variation.

[0013] Further, a detection step of detecting light from individual platelets in the sample stream, and analyzing the detected detection signal,
It is preferable to include a calculation step of calculating the platelet count.

[0014]

FIG. 1 shows the configuration of an embodiment of the method for testing platelet function according to the present invention. In this embodiment, a platelet-containing sample solution is supplied into a square sheath flow cell 3. This platelet-containing sample solution may be previously stained with a staining solution, for example, auramine O or the like.

As shown in FIG. 1, there are provided two light sources, a continuous light source 1 for detecting scattered light and fluorescence in the sheath flow cell 3 and a pulse light source 2 for picking up a cell image. I have. The light source 1 is a visible light source, and the light source 2 is a near-infrared light source. An Ar laser emitting visible light having a center wavelength of 488 nm is used as the light source 1, and a pulse laser diode emitting near-infrared light having a center wavelength of 780 nm and a spectral width of 7 nm is used as the light source 2. You are using As the sheath flow cell, a conventionally known cell can be used. However, a flow cell in which the directions of discoid (flat) platelets in the flow are aligned in a certain direction is not preferable, and it is better to allow the flow to flow randomly in the flow direction. . When the platelets are aligned, only the shape of the platelets is imaged from one direction, so that the accurate form of the platelets is not reflected. Therefore, it is impossible to discriminate between discotic platelets and spherical platelets.

Continuous visible light L from the two light sources 1 and 2
1 and the pulsed near-infrared light L2 are supplied to the sheath flow cell 3 (FIG. 1).
In this case, the sample flows in a direction perpendicular to the paper surface).

When cells flow into the illuminated area, forward scattered light from the cells is collected by a condenser lens 5 and received by a first photodetector 7 comprising a photodiode via a band-pass filter 6 to receive a signal. It is output as S1 (the beam stopper is not shown). Lateral fluorescence generated from the fluorescently stained cells is received as a signal S2 which is received and multiplied by a second photodetector 11 composed of a photomultiplier tube via a collimating lens 8, a dichroic mirror 9, and a condenser lens 10, and is output. To

FIG. 2 shows the configuration of a signal processing system according to this embodiment. The forward scattered intensity signal S1 and side fluorescence intensity signal S2 output by the first and second photodetectors 7, 11 are analyzed by an analysis unit. The data is input to 100 particle analysis units 100a.

In the particle analyzer 100a, a two-dimensional scattergram of the forward scattering intensity and the side fluorescence intensity is created based on the input signal, and the platelet region is analyzed. Next, the arithmetic unit 100d calculates the number of platelets per fixed volume based on the flow rate information of the platelet-containing sample liquid input in advance, and outputs the measurement result to the output unit 100e. The input signals S1 and S2 are information obtained corresponding to each cell, and are analyzed by using a two-dimensional scattergram of the forward scattered intensity and the side fluorescence intensity.
More accurate platelet count can be measured.

The imaging control unit 100c supplies the pulse light source 2 with a light emission trigger signal Ts for imaging the cell at the same time or after a predetermined time when the signal S1 is input to the analysis unit 100 from the first detector 7. .

The pulse light source 2 is a light source of a type that emits light only momentarily (about 25 nanoseconds) by the light emission trigger signal Ts. Even if the flow rate of the sample flow is as high as several m / sec, the flowing cells are not shaken. Images can be taken.

As shown in FIG. 1, the pulsed light L2 is guided to a collimator lens 12 by a kaleidoscope (which can be a multimode optical fiber) 2a, which is an example of a coherence reducing means, passes through a cold mirror 13, and passes through a condenser. The light is narrowed down by the lens 14 and is irradiated on the sample flow.

By irradiating the light via the optical fiber 2a, the coherency of the pulse light L2 is reduced, and a cell image can be captured with high contrast. The optical fiber 2a has a core diameter of 800 μm and a length of 2 m (MKH-800 manufactured by Sumitomo Electronics Co., Ltd.).

The pulse light transmitted through the sample flow is converted into parallel light by the collimating lens 8 and reflected by the dichroic mirror 9, and forms an image on the light receiving surface of the video camera 17 via the band pass filter 16 and the condenser lens 15. Then, a monochrome transmitted light image of the entire cell is captured.

In the obtained monochrome image, cells are relatively well photographed. Therefore, the image signal Vs from the video camera 17 is passed to the image processing unit 100b shown in FIG. According to the image processing procedure shown in FIG.
In b, a cell image is cut out, binarized and stored as a digital image (step S1). First, background correction is performed to correct the intensity unevenness (shading) of the irradiation light with respect to the sample liquid flow (step S2).
Specifically, this is performed by comparing and calculating image data obtained by irradiating light when the cell does not pass through the flow cell 3 and image data of an actual cell imaging screen. This processing is generally well-known as image processing.

Next, contour emphasis processing is performed as preprocessing for accurately extracting the contour of the cell image (step S).
3). Specifically, Laplacian enhancement processing is performed.

Next, the image data is binarized at a certain appropriate threshold level (step S4). Next, 2
It is determined whether or not the valued cell image is an edge point, and information indicating in which direction an edge point adjacent to the focused edge point is located, that is, a chain code is generated (step S5). . Next, perform an edge trace of the cell image while referring to this chain code,
The total number of pixels, the total number of edges, and the number of oblique edges of each cell image are obtained (step S6).

The above-described image processing is repeatedly performed on a plurality of screens to be imaged, and the cell image cut out to a predetermined size is stored in the image memory of the image processing unit 100b (step S7).

When the imaging is completed (step S8), the next
In this way, circle equivalent diameter and circle
The degree is calculated (step S9). The equivalent circle diameter is a cell
Assuming a circle with the same area as the projected area of the image,
It is a diameter and is represented by equation (1). Roundness is a circle
Is a parameter representing the degree of addition and subtraction, for example, defined by equation (2)
The circularity is 1 when the cell image is circular.
As the cell image becomes slender, the circularity decreases.
Become. First, the total number of pixels required for each cell image,
From the total number of edges and the number of oblique edges, the projected area of each cell image
Find S and perimeter L. Further, the area S and the perimeter L
Is used to determine the circle equivalent diameter D and the circularity C. D = 2 (S / π)^1/2……… (1)  C = 2 (πS)^1/2/L...(2)

After the characteristic parameters are calculated for each cell image in this way, next, necessary scattergrams and histograms are created based on a command from a keyboard or a mouse (steps S10 and S1).
1). Next, the average circularity and the standard deviation are calculated using the histogram and the scattergram data (step S1).
2) The platelet function is determined based on the data (step S13), and the measurement result is displayed (step S14).
The cell images cut out in step S7 are collectively displayed on the output unit 100e for each size (step S15).
The input unit 200 shown in FIG. 2 includes a keyboard and a mouse, and performs various commands and setting of conditions to the analyzing unit 100. The output unit 100e includes a CRT or a printer, and outputs a calculation result obtained by the calculation unit 100d.

FIGS. 4 and 5 show a two-dimensional scattergram of a circle equivalent diameter and circularity, a circularity histogram and a circle equivalent diameter histogram for each sample when the platelet preparation was actually measured. The vertical axis of the graph indicates the degree of circularity, and the horizontal axis indicates the circle-equivalent diameter. The measurement results of the samples with swirling (samples 2 and 3) are shown in graphs (a) and (b) of FIG. 4, and the measurement results of the samples without swirling (samples 6 and 7) are shown in graph (c) of FIG. ) And (d). In the sample with swirling (samples 2 and 3), the distribution width of the circularity histogram spreads uniformly and is not biased. On the other hand, in the samples without swirling (samples 6 and 7), the circularity histogram has a peak at a position where the circularity is 1 and is biased. As described above, from the histogram, it is possible to easily discriminate visually swirling (shape in shape: normal platelets) and non-swirling (shape in shape: platelets with reduced function) visually.

Next, for each sample (samples 1 to 8), the average value and the standard deviation value, which are statistically calculated values of circularity, are shown in the table of FIG. The average value of the circularity with swirling is 0.77 to 0.86, and the average value is 0.79, the standard deviation is 0.10 to 0.31, and the average value is 0.23. . The average value of the circularity without swirling is 0.88 to 0.93, and the average value is 0.91.
, The standard deviation is 0.07 to 0.11, and the average value is 0.09. Thus, there is a significant difference between the average circularity and the standard deviation. The samples with swirling (disc shape: normal platelets) and those without swirl (spherical shapes: degraded platelets) In between, it is possible to determine.

Next, FIG. 7 shows a graph of the values of the average circularity and the standard deviation shown in FIG. 6 in a two-dimensional scattergram. In the graph of FIG. 7, a sample with swirling is indicated by a black square symbol, and a sample without swirling is indicated by a black circle symbol. As seen in FIG. 7, the average circularity is 0.85, and the standard deviation is 0.2.
, The region A is a sample with swirling (disc shape: normal platelets), and the region B is a sample without swirling (spherical shape: platelets with reduced function)
Can be determined. In this way, platelet function discrimination data is stored in advance and stored as a database,
By using the determination of platelet superiority, more accurate determination of platelet function can be performed.

Next, an example of a measurement result when a certain sample is measured will be described. The scattergram, histogram, average circularity, standard deviation, discrimination of platelet function, and platelet count shown above can be displayed collectively as shown in FIG. In the platelet function determination result, the platelet activation state is displayed as "-" because normal platelets are inactive, and "+" because platelets that have already become spheroidized and have a reduced function are activated. Is done. This gives the scattergram,
The measurement result including the determination result is useful at a glance.

The present invention has the following effects. The present invention provides a flow step of converting a platelet-containing sample solution into a sample flow by surrounding it with a sheath liquid in a flow cell, an imaging step of sequentially imaging the sample flow, an analysis step of processing and analyzing the imaged image, A display step of displaying the result, wherein the analysis step uses a calculation step of calculating a shape parameter for each of the captured platelets, and a method of determining platelet function based on the value of the obtained shape parameter data group. The swirling inspection, which has been a visual inspection until now, can be automated. In addition, by performing the test at the same time as the platelet count, which is an essential test, the shipping inspection of the preparation can be significantly labor-saving.

[Brief description of the drawings]

FIG. 1 is a configuration diagram showing an optical system of the present invention.

FIG. 2 is a configuration diagram showing a signal processing system of the present invention.

FIG. 3 is a flowchart illustrating an image processing system of the present invention.

FIG. 4 is a graph of a measurement result of a sample.

FIG. 5 is a graph of a measurement result of a sample.

FIG. 6 is a table showing circularity data of samples.

FIG. 7 is a graph of circularity data of a sample.

FIG. 8 shows a measurement result of a sample.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Continuous light source 2 Pulse light source 2a Optical fiber 3 Sheath flow cell 4 Condenser lens 5 Condensing lens 6 Band pass filter 7 First photodetector 8 Collimating lens 9 Dichroic mirror 10 Condenser lens 11 Second photodetector 12 Collimating lens 13 Cold mirror 14 Condenser lens 15 Condenser lens 16 Bandpass filter 17 Video camera

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification code FI Theme coat ゛ (Reference) G01N 33/49 G01N 33/49 X

Claims (4)

[Claims] 1. A flow step of converting a platelet-containing sample solution into a sample flow in a flow cell surrounded by a sheath liquid, an imaging step of sequentially imaging the sample flow, an analysis step of processing and analyzing the imaged image, A display step for displaying the analysis result is provided,
A platelet function test method comprising: an analysis step including a calculation step of calculating a shape parameter for each imaged platelet, and a determination step of determining a platelet function based on a value of the obtained shape parameter data group. 2. The platelet function test method according to claim 1, wherein the shape parameter calculated in the calculation step includes a parameter reflecting roundness. 3. The method according to claim 1, wherein the determining step calculates a statistical value from a value of the data group of the shape parameter and determines the platelet function based on the statistical value. 4. The method according to claim 1, further comprising a detecting step of detecting light from individual platelets in the sample stream, and a calculating step of calculating a platelet count by analyzing the detected detection signal. Platelet function test method.