Method of controlling cell localisation probes

Abstract

The application provides a control method of a cell positioning probe, including speed control of the probe, the speed control being achieved by obtaining an image of the probe, extracting a region S including a needle tip, and counting a gray scale histogram of the region S i , i=1, 2,..., n, and N is an integer not less than 2; if the peak value A and H1 are a first threshold value, the probe is controlled to move to the cell at a first speed v1; if the peak value A and H1 divide the region S into a needle tip region and a needle arm region, a difference ΔR of the definition of the needle tip region and the needle arm region is obtained; if the difference ΔR is less than R1, R1 is a second threshold value, the probe is controlled to move to the cell at a second speed v2; if the difference ΔR is greater than or equal to R1, the probe is controlled to move to the cell at a third speed v3, v3 The application has the advantages of high probe moving efficiency and accurate judgment.

Description

Technical Field

[0001] This invention relates to cell analysis, and more particularly to a method for controlling cell localization probes. Background Technology

[0002] In single-cell mass spectrometry analysis systems, controlling the probe's positioning and ensuring precise contact with the cell is essential. This requires controlling the probe's descent from above the cell surface to the cell surface and stopping upon contact. However, excessively rapid descent often leads to tip breakage, while slow descent significantly reduces system efficiency. Therefore, controlling the probe's movement speed is crucial. Furthermore, to prevent abnormal analytical results due to excessive or insufficient contact between the probe tip and the cell in the Z-direction (perpendicular to the stage), precise detection of Z-direction contact is of significant research importance.

[0003] With the development of micromanipulation techniques in recent years, studying probe speed and detecting probe-cell contact has become crucial in the field. However, traditional probe contact detection methods typically require additional auxiliary equipment such as force sensors and microscope lenses, increasing system cost and complexity. Furthermore, while there are limited tip contact detection algorithms, they suffer from low control precision and poor applicability. Most researchers locate cells by controlling the probe at a constant speed, which is inefficient, leads to tip breakage, and generates abnormal data.

[0004] Existing methods primarily employ contact force sensors and binocular vision to detect cell contact, achieving contact detection between the needle tip and the substrate. However, this increases the complexity of the microinjection system and control algorithms, leading to higher costs. Some researchers determine contact by detecting the needle tip's sliding distance, but this algorithm fails when the substrate is non-smooth, resulting in poor applicability. Additionally, some researchers use a fixed descent distance based on prior data; however, this method suffers from low control precision and often leads to reduced system success rates due to failure to make contact with cells. Summary of the Invention

[0005] To address the shortcomings of the existing technical solutions, the present invention provides a method for controlling a cell localization probe.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for controlling a cell localization probe, the method comprising speed control of the cell localization probe, wherein the speed control method is as follows:

[0008] Obtain an image of the cell localization probe, extract the region S including the probe tip, and statistically analyze the grayscale histogram of region S;

[0009] Perform multi-Gaussian fitting on the gray-level histogram to obtain the peak value A corresponding to each peak. i , i = 1, 2, ..., n, N is an integer not less than 2;

[0010] If the peak and H1 is a first threshold that controls the cell localization probe to move toward the cell at a first speed v1.

[0011] If the peak value and The region S is divided into a needle tip region and a needle arm region, and the difference in sharpness ΔR between the needle tip region and the needle arm region is obtained.

[0012] If the difference ΔR < R1, where R1 is the second threshold, the cell localization probe is controlled to move toward the cell at a second speed v2.

[0013] If the difference ΔR ≥ R1, the cell localization probe is controlled to move toward the cell at a third speed v3, where v3 < v2 < v1.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] 1. Enables rapid, efficient, and high-precision real-time detection of the contact between the probe tip and the bottom surface;

[0016] 2. Using only a single image, different operating speeds can be scientifically divided and controlled, improving the average speed of cell retrieval and increasing retrieval efficiency;

[0017] 3. It has a wide range of applications, solves all the defects in the current needle tip Z-direction contact detection algorithm, and lays a good foundation for microscopic manipulation;

[0018] It does not depend on external conditions and improves work efficiency and stability. Attached Figure Description

[0019] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are merely illustrative of the technical solutions of this invention and are not intended to limit the scope of protection of this invention. In the drawings:

[0020] Figure 1 This is a schematic flowchart of a method for controlling a cell localization probe according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of region S according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of multi-Gaussian fitting according to an embodiment of the present invention;

[0023] Figure 4This is a schematic diagram of the sum of peak values ​​of multi-Gaussian fitting according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of the difference in sharpness according to an embodiment of the present invention. Detailed Implementation

[0025] Figure 1-5 The following description illustrates optional embodiments of the invention to teach those skilled in the art how to implement and reproduce the invention. Some conventional aspects have been simplified or omitted to explain the technical solutions of the invention. Those skilled in the art should understand that variations or substitutions derived from these embodiments will be within the scope of the invention. Those skilled in the art should understand that the following features can be combined in various ways to form multiple variations of the invention. Therefore, the invention is not limited to the following optional embodiments, but is defined only by the claims and their equivalents.

[0026] Example 1:

[0027] Figure 1 A flowchart illustrating the control method of the cell localization probe in Embodiment 1 of the present invention is shown, as follows: Figure 1 As shown, the control method for the cell localization probe includes speed control of the cell localization probe, and the speed control method is as follows:

[0028] Obtain an image of the cell localization probe and extract the region S including the probe tip, such as... Figure 2 The image shows the grayscale histogram of the statistical region S.

[0029] Perform multi-Gaussian fitting on the grayscale histogram, such as... Figure 3 As shown, the peak value A corresponding to each peak is obtained. i , i = 1, 2, ..., n, N is an integer not less than 2;

[0030] If the peak and H1 is the first threshold, and the peak value and so on. Figure 4 As shown, the cell localization probe is controlled to move toward the cell at a first speed v1;

[0031] If the peak value and The region S is divided into a needle tip region and a needle arm region, and the difference in sharpness ΔR between the needle tip region and the needle arm region is obtained, such as... Figure 5 As shown;

[0032] If the difference ΔR < R1, where R1 is the second threshold, the cell localization probe is controlled to move toward the cell at a second speed v2.

[0033] If the difference ΔR ≥ R1, the cell localization probe is controlled to move toward the cell at a third speed v3, where v3 < v2 < v1.

[0034] To improve the accuracy of speed segmentation, the second threshold is further determined as follows:

[0035] In the trend of the difference, the magnitude of the starting point where the difference shows an upward trend is used as the second threshold.

[0036] To accurately determine the speed, the needle tip region and needle arm region are further divided as follows:

[0037] Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge;

[0038] If the number M ≥ Q1, where Q1 is the third threshold, calculate the middle column F of the same row X value at the top edge;

[0039] If F≥P·S Y P is the fourth threshold, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the needle tip region. The midpoint between the upper and lower edge points is used as the center of the needle tip region.

[0040] If F < P·S Y Run the process as described above, and set Q1 = Q1-1 to obtain the needle tip region.

[0041] To accurately determine whether the probe has contacted the cell, the control method for the cell localization probe further includes a contact determination process. If the cell localization probe moves towards the cell at a third velocity v3, a contact determination is initiated. The contact determination process is as follows:

[0042] Obtain an image of the cell localization probe and extract the region S including the probe tip;

[0043] Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge;

[0044] If the number M ≥ Q1, where Q1 is the third threshold, calculate the middle column F of the same row X value at the top edge;

[0045] If F≥P·S Y P is the fourth threshold, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the contact area, and the midpoint between the upper and lower edge points is used as the center of the contact area.

[0046] If F < P·S Y Install and run the above method, and set Q1 = Q1-1 to obtain the contact area;

[0047] Feature extraction is performed on the contact area, such as the number of pixels with gray values ​​below the fifth threshold, the maximum effective gray value, and the minimum effective gray value;

[0048] The extracted features are used as features for machine learning, and a Support Vector Machine (SVM) is trained to maximize the margin in the feature space, thereby establishing a needle tip contact detection model. This model is then used to predict whether the cell localization probe has contacted the cell.

[0049] Example 2:

[0050] An application example of the cell localization probe control method according to Embodiment 1 of the present invention.

[0051] like Figure 1 As shown, the control method for the cell localization probe includes speed control of the cell localization probe, and the speed control method is as follows:

[0052] Obtain an image of the cell localization probe and extract the region S including the probe tip, such as... Figure 2 The image shows the grayscale histogram of the statistical region S.

[0053] Perform a triple Gaussian fit on the grayscale histogram, such as... Figure 3 As shown, the peak value A corresponding to each peak is obtained. i i = 1, 2, ..., N, N = 3;

[0054] If the peak and H1 = 50, peak value and such Figure 4 As shown, the cell localization probe is controlled to move toward the cell at a first speed v1;

[0055] If the peak value and The region S is divided into a needle tip region and a needle arm region, and the division method is as follows:

[0056] Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge;

[0057] If the number M ≥ Q1 and Q1 = 3, calculate the middle column F of the row with the same X value at the top edge;

[0058] If F≥P·S Y P is the fourth threshold, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the needle tip region. The midpoint between the upper and lower edge points is used as the center of the needle tip region.

[0059] If F < P·S YRun the process as described above, and set Q1 = Q1 - 1 = 2 to obtain the needle tip region;

[0060] Obtain the difference in sharpness ΔR between the needle tip region and the needle arm region, such as Figure 5 As shown;

[0061] If the difference ΔR < R1, and R1 = 21 is the second threshold, the cell localization probe is controlled to move toward the cell at a second speed v2; the second threshold is determined by taking the magnitude of the starting point where the difference shows an upward trend in the changing trend of the difference as the second threshold.

[0062] If the difference ΔR ≥ R1, the cell localization probe is controlled to move towards the cell at a third speed v3, where v3 < v2 < v1, and a contact judgment is initiated. The contact judgment method is as follows:

[0063] Obtain an image of the cell localization probe and extract the region S including the probe tip;

[0064] Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge;

[0065] If the number M ≥ Q1 and Q1 = 3, calculate the middle column F of the row with the same X value at the top edge;

[0066] If F≥P·S Y P = 0.6, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the contact area, and the midpoint between the upper and lower edge points is used as the center of the contact area.

[0067] If F < P·S Y Install and run the above method, and set Q1 = Q1-1 to obtain the contact area;

[0068] Feature extraction is performed on the contact area. The extracted features include the number of pixels with gray values ​​lower than the fifth threshold (60), the effective maximum gray value (determined based on the frequency of the maximum gray value in the area, and when the frequency of the maximum gray value is ≥3, it is determined as the effective maximum gray value) and the effective minimum gray value (determined based on the frequency of the minimum gray value in the area, and when the frequency of the minimum gray value is ≥3, it is determined as the effective minimum gray value).

[0069] The extracted features are used as features for machine learning, and a Support Vector Machine (SVM) is trained to maximize the margin in the feature space, thereby establishing a needle tip contact detection model. This model is then used to predict whether the cell localization probe has contacted the cell.

Claims

1. A method for controlling a cell localization probe, wherein the method includes speed control of the cell localization probe, and the speed control method is as follows: Obtain an image of the cell localization probe, extract the region S including the probe tip, and statistically analyze the grayscale histogram of region S; Perform multi-Gaussian fitting on the gray-level histogram to obtain the peak value A corresponding to each peak. i , i = 1, 2, ..., n, N is an integer not less than 2; If the peak and H1 is the first threshold, which controls the cell localization probe to move toward the cell at a first speed v1; If the peak value and The region S is divided into a needle tip region and a needle arm region, and the difference in sharpness ΔR between the needle tip region and the needle arm region is obtained. If the difference ΔR < R1, where R1 is the second threshold, the cell localization probe is controlled to move toward the cell at a second speed v2. If the difference ΔR ≥ R1, the cell localization probe is controlled to move toward the cell at a third speed v3, where v3 < v2 < v1; The second threshold is determined as follows: in the trend of the difference, the magnitude of the starting point where the difference shows an upward trend is used as the second threshold.

2. The method for controlling the cell localization probe according to claim 1, characterized in that, The method for dividing the needle tip area and the needle arm area is as follows: Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge; If the number M ≥ Q1, where Q1 is the third threshold, calculate the middle column F of the same row X value at the top edge; If F≥P·S Y P is the fourth threshold, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the needle tip region. The midpoint between the upper and lower edge points is used as the center of the needle tip region. If F < P·S Y Run the process as described above, and set Q1 = Q1-1 to obtain the needle tip region.

3. The method for controlling the cell localization probe according to claim 2, characterized in that, The third threshold Q1 = 3.

4. The method for controlling the cell localization probe according to claim 1, characterized in that, The control method for the cell localization probe also includes contact detection; if the cell localization probe moves towards the cell at a third speed v3, a contact detection is initiated. The method for determining contact is as follows: Obtain an image of the cell localization probe and extract the region S including the probe tip; Extract the edges of the region S, and count the number M of consecutive columns with the same row number X value from right to left at the top edge; If the number M ≥ Q1, where Q1 is the third threshold, calculate the middle column F of the same row X value at the top edge; If F≥P·S Y P is the fourth threshold, S Y Let S be the length of region S. Calculate the distance between the upper and lower edge points of column F. This distance is used as the diameter of the contact area, and the midpoint between the upper and lower edge points is used as the center of the contact area. If F < P·S Y Operate in the manner described above, and set Q1 = Q1 - 1 to obtain the contact area; Feature extraction is performed on the contact area; The extracted features are used as features for machine learning, and a support vector machine (SVM) is trained to maximize the margin in the feature space, thereby establishing a needle tip contact detection model. This model is then used to predict whether the cell localization probe has contacted the cell.

5. The method for controlling the cell localization probe according to claim 4, characterized in that, The extracted features include the number of pixels with gray values ​​below the fifth threshold, the maximum effective gray value, and the minimum effective gray value.

6. The method for controlling the cell localization probe according to claim 5, characterized in that, The fifth threshold is the highest gray value after the cell localization probe comes into contact with the cell.

7. The method for controlling the cell localization probe according to claim 2 or 4, characterized in that, Q1＝3。 8. The method for controlling the cell localization probe according to claim 1, characterized in that, N＝3。 9. The method for controlling the cell localization probe according to claim 1, characterized in that, P＝0.6。

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