Processing method, device, apparatus and screening system for microdroplet characteristic data

Abstract

The application discloses a microdroplet feature data processing method, device, apparatus and screening system, comprising: obtaining feature data of a plurality of microdroplets; performing curve fitting on the feature data to obtain a feature data distribution curve indicating distribution of the feature data; determining a screening parameter according to the feature data distribution curve; configuring a first positive index parameter by using the screening parameter; and using the first positive index parameter as a screening condition of the microdroplets after configuration. In a microdroplet screening experiment, changes in experimental factors will cause corresponding changes in feature data of the microdroplets. Therefore, the first positive index parameter configured as a screening condition according to the feature data of the plurality of microdroplets can dynamically adjust the screening condition according to changes in the experimental factors, thereby improving the accuracy of the screening result.

Description

Technical Field

[0001] This application belongs to the field of microdroplet screening technology, and in particular relates to a method, device, apparatus and screening system for processing microdroplet characteristic data. Background Technology

[0002] Microdroplet screening technology is a bioanalytical technique widely used in enzyme directed evolution, cell line selection, and monoclonal antibody development. The principle behind this technology is as follows.

[0003] Multiple single cells to be analyzed are encapsulated with a substrate to form multiple microdroplets. The microdroplets are controlled to flow one by one through a specific channel with a cross-sectional area that allows a single microdroplet to pass through. As the microdroplets flow through the channel, the light signal data generated by each microdroplet after being irradiated by a light source is collected. By analyzing the light signal data, the characteristic data of each microdroplet is obtained. Thus, each microdroplet can be screened based on whether the characteristic data meets the preset screening conditions.

[0004] In existing microdroplet screening technologies, the positive indicator parameters constituting the screening conditions are usually set before the screening experiment begins and remain fixed throughout the screening process. However, in actual screening experiments, as the experimental time increases, experimental factors such as light source intensity, microdroplet flow rate, and ambient temperature change, causing the positive indicator parameters set before the experiment to become inapplicable. Therefore, when conducting screening experiments using existing microdroplet screening technologies, the longer the experiment lasts, the lower the accuracy of the screening results. Summary of the Invention

[0005] Therefore, this application discloses the following technical solution:

[0006] The first aspect of this application provides a method for processing microdroplet feature data, including:

[0007] Obtain characteristic data of multiple microdroplets;

[0008] Curve fitting is performed on the feature data to obtain a feature data distribution curve that indicates the distribution of the feature data;

[0009] Based on the feature data distribution curve, determine the screening parameters;

[0010] The first positive indicator parameter is configured using the screening parameters; wherein, the first positive indicator parameter is used as the screening condition for microdroplets after configuration.

[0011] Optionally, configuring the first positive indicator parameter using the screening parameters includes:

[0012] Using the screening parameters, the second positive indicator parameter of the microdroplet screening system is updated to the first positive indicator parameter; wherein, the second positive indicator parameter was used as the screening condition for microdroplets before the update;

[0013] or,

[0014] Set the screening parameter to the first positive indicator parameter.

[0015] Optionally, updating the second positive indicator parameter of the microdroplet screening system to the first positive indicator parameter using the screening parameters includes:

[0016] Based on the screening parameters, determine whether the second positive indicator parameter meets the preset update conditions;

[0017] If the second positive indicator parameter meets the update condition, the screening parameter is determined as the first positive indicator parameter;

[0018] If the second positive indicator parameter does not meet the update conditions, the second positive indicator parameter is determined as the first positive indicator parameter.

[0019] Optionally, the update conditions include:

[0020] The difference between the second positive indicator parameter and the screening parameter is outside a preset deviation range, which is determined based on the second positive indicator parameter.

[0021] Optionally, the step of performing curve fitting on the feature data to obtain a feature data distribution curve indicating the distribution of the feature data includes:

[0022] Based on the normal distribution model, the least squares method is used to perform curve fitting on the feature data to obtain the feature data normal distribution curve indicating the distribution of the feature data.

[0023] Optionally, determining the screening parameters based on the feature data distribution curve includes:

[0024] Determine the mean and variance of the normal distribution curve of the feature data;

[0025] The distribution interval corresponding to the preset probability percentage is obtained;

[0026] Based on the distribution interval, the mean, and the variance, the feature data corresponding to the intervals in the normal distribution curve of the feature data and the distribution interval are obtained as screening parameters; wherein, the interval boundary includes at least one of the upper and lower bounds of the distribution interval.

[0027] Optionally, obtaining the feature data of multiple microdroplets includes:

[0028] For each of the multiple microdroplets, when the microdroplet passes through the detection position, the optical signal data of the microdroplet in at least one channel is collected;

[0029] For each of the multiple microdroplets, characteristic data of the microdroplet are determined based on the optical signal data of the microdroplet in at least one channel.

[0030] Optionally, the characteristic data of the microdroplet includes at least one of the following: signal duration, mean value, peak value, peak-to-average value ratio of optical signal data in a single channel, and mean-to-average value ratio of optical signal data between channels.

[0031] A second aspect of this application provides a device for processing microdroplet feature data, comprising:

[0032] The acquisition unit is used to acquire feature data of multiple microdroplets;

[0033] A fitting unit is used to perform curve fitting on the feature data to obtain a feature data distribution curve that indicates the distribution of the feature data.

[0034] The determining unit is used to determine the screening parameters based on the feature data distribution curve;

[0035] A configuration unit is used to configure a first positive indicator parameter using the screening parameters; wherein the first positive indicator parameter, after configuration, serves as a screening condition for microdroplets.

[0036] A third aspect of this application provides a device for processing microdroplet feature data, including a memory and a processor;

[0037] The memory is used to store computer programs;

[0038] The processor is used to execute the computer program, specifically to implement the microdroplet feature data processing method provided in any of the first aspects of this application.

[0039] The fourth aspect of this application also provides a microdroplet screening system, including a microfluidic chip, a power module, a light source module, a screening platform, and a control system;

[0040] The microfluidic chip is provided with a chip channel, and the chip channel is provided with a detection position and an electrode. Microdroplets passing through the chip channel enter one of the multiple outlets of the chip channel under the action of the electrode.

[0041] The power module is connected to the electrode and the control system respectively, so as to energize the electrode according to the control command of the control system;

[0042] The light source module is used to illuminate the chip flow channel;

[0043] The screening platform is used to collect optical signal data of microdroplets when microdroplets pass through the detection position, so as to obtain feature data of multiple microdroplets, and transmit the feature data to the control system.

[0044] The control system is used to implement the microdroplet feature data processing method provided in any one of the first aspects of this application based on the feature data of the plurality of microdroplets.

[0045] This application discloses a method, device, apparatus, and screening system for processing microdroplet characteristic data. The method includes: obtaining characteristic data from multiple microdroplets; performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data; determining screening parameters based on the characteristic data distribution curve; configuring a first positive indicator parameter using the screening parameters; and using the configured first positive indicator parameter as a screening condition for the microdroplets. In microdroplet screening experiments, changes in experimental factors lead to corresponding changes in the characteristic data of the microdroplets. Therefore, this method dynamically configures the first positive indicator parameter as a screening condition based on the characteristic data of multiple microdroplets, which can dynamically adjust the screening conditions according to changes in experimental factors, thereby improving the accuracy of the screening results. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of a normal distribution curve provided in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of a microdroplet signal provided in an embodiment of this application;

[0049] Figure 3 This is a schematic diagram of the structure of a microdroplet screening system provided in an embodiment of this application;

[0050] Figure 4 This is a control flowchart of a microdroplet screening system provided in an embodiment of this application;

[0051] Figure 5 This is a flowchart of a method for processing microdroplet feature data provided in an embodiment of this application;

[0052] Figure 6This is a schematic diagram of the mean normal distribution curves of channel 1 and channel 2 provided in an embodiment of this application;

[0053] Figure 7 This is a schematic diagram of the peak normal distribution curves of channel 1 and channel 2 provided in an embodiment of this application;

[0054] Figure 8 This is a schematic diagram of the normal distribution curves of the peak-to-average ratio of channel 1 and channel 2 provided in an embodiment of this application;

[0055] Figure 9 This application provides a normal distribution curve of the ratio of the mean values ​​of channel 1 and channel 2.

[0056] Figure 10 This is a histogram showing the distribution of signal duration percentages provided in an embodiment of this application;

[0057] Figure 11 This is a schematic diagram of the structure of a microdroplet feature data processing device provided in an embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] To facilitate understanding of the technical solutions of this application, the terminology that may be involved in the embodiments of this application will be briefly explained first.

[0060] Normal distribution. The normal distribution is a probability distribution widely used in mathematics, physics, engineering, and other fields, and has a significant influence in statistics.

[0061] A one-dimensional normal distribution is denoted as: X ~ N(μ,σ) 2 ), with the probability density function being .

[0062]

[0063] Normal curve as Figure 1 As shown, with X = μ as the axis of symmetry, it resembles a bell shape, exhibiting a high center and low sides, with perfect symmetry on both sides, and takes a value of 0 at positive (negative) infinity. The magnitude of σ reflects the steepness of the curve; the smaller the value of σ, the steeper the curve's trend, and the more slender the curve's shape; the larger the value of σ, the gentler the curve's trend, and the more flattened the curve's shape.

[0064] The total area formed by the normal curve and the horizontal axis is 1, or 100%, representing the overall frequency. The area of ​​any interval on the horizontal axis reflects the proportion of frequencies occurring within that interval. σ is the inflection point of the normal curve. Commonly used interval areas include: 68.27% for the interval [μ-σ, μ+σ], 95.45% for [μ-2σ, μ+2σ], and 99.73% for [μ-3σ, μ+3σ]. The area within the interval (-∞, μ) and [μ, +∞) is 50%. The area within the interval (-∞, μ+σ) is 84.13%, and the area within [μ+σ, +∞) is 15.87%.

[0065] When μ = 0 and σ = 1, the normal distribution is called the standard normal distribution. In practical applications, the general normal distribution can be transformed into the standard normal distribution using the variable transformation formula. The variable transformation formula is as follows:

[0066]

[0067] Least squares is a mathematical optimization technique. It finds the best function match for data by minimizing the sum of squared errors. Least squares can be used to easily obtain unknown data while minimizing the sum of squared errors between the obtained data and the actual data.

[0068] Least squares is a mathematical tool that is widely used in many disciplines of data processing, such as error estimation, uncertainty, system identification and prediction, and forecasting.

[0069] The principle behind this solution is as follows:

[0070] In microdroplet screening technology, the microdroplet screening system encapsulates single cells and substrates into independent microdroplets. Using a photoelectric sensor, the light intensity of any microdroplet passing through a detection location is sampled at a certain frequency and converted into an electrical signal. Characteristic data of the microdroplets are extracted from the sequence of sampled values ​​of the microdroplet electrical signal, including the duration, mean, peak value, ratio of peak value to mean, and the ratio of mean to peak value between channels in multi-channel mode.

[0071] If we treat microdroplets as a random event and statistically analyze the detected microdroplets over a continuous time period, when the number of microdroplets is sufficiently large, the statistical distribution of various characteristic data follows a normal distribution. Leveraging the high-speed computing power of computers and employing big data statistical frequency analysis, we can utilize the normal distribution to obtain reasonable conditions for each positive indicator parameter, tailored to the actual environment of the screening system and the reaction state of the target. In practice, this simplifies the cumbersome process of indicator selection and setting; it allows for real-time, online optimization of screening conditions, facilitating improvements in system screening performance and the enrichment effect of the target product.

[0072] This application provides a microdroplet screening system. Please refer to [link to relevant documentation]. Figure 3 This is a schematic diagram of the system structure. The system may include several parts: a microfluidic chip, a power module, a light source module, a screening platform, and a control system.

[0073] The screening platform includes a signal acquisition unit, a data processing and decision-making unit, a drive control unit, and a communication interaction unit.

[0074] The control system includes a human-machine interaction unit, a module control unit, and a data statistics and analysis unit.

[0075] The light source module is used to illuminate the microdroplets flowing through the detection position under the control of the control system, so that the microdroplets generate light signals.

[0076] The light intensity of the light source module and the signal gain parameters of the power supply module can both be controlled by the control system through the command channel.

[0077] The working principle of this microdroplet screening system is as follows.

[0078] Microfluidic chips have flow channels with multiple inlets, for example... Figure 3 The central channel has three inlets, which are designated as inlet 1, inlet 2, and inlet 3.

[0079] In microdroplet screening experiments, microdroplets enter the flow channels of the microfluidic chip through multiple inlets. Microdroplets can consist of a substrate and a substance encapsulated by the substrate; the encapsulated substance can be a single cell or other substances to be analyzed.

[0080] After entering the flow channel, the microdroplet moves forward at a certain speed under the drive of the pump. When the microdroplet flows past the detection position, the screening platform continuously collects the light signal during the movement of the microdroplet through the signal acquisition unit at a fixed sampling frequency, and obtains continuous sampling data that characterizes the light signal intensity of the microdroplet, which is the light signal data of the microdroplet.

[0081] A detection position can be understood as a specific location or a small section of the flow channel that is pre-designated in the flow channel.

[0082] The signal acquisition unit can be a photoelectric sensor, or other sensors or components capable of acquiring optical signals. The signal acquisition unit is used to acquire the optical signal data of a microdroplet in real time at preset sampling intervals as the microdroplet passes through the detection position.

[0083] The screening platform can operate in single-channel or multi-channel mode according to user settings. In multi-channel mode, the signal acquisition unit can simultaneously acquire optical signals from multiple target channels, thereby obtaining optical signal data of microdroplets in each target channel.

[0084] The target channels are several channels specified by the user from among the preset channels of the filtering platform. There is no limit to the number of preset channels in the filtering platform. As an example, Figure 3 The system has 8 preset channels, which are numbered Channel 1 to Channel 8.

[0085] In this embodiment, the preset channels of the filtering platform can be understood as different wavelengths (or wavelength ranges).

[0086] Continuing with the previous example, channel 1 can correspond to wavelength A1, channel 2 can correspond to wavelength B1, or channel 1 can correspond to wavelength range A1 to A2, channel 2 can correspond to wavelength range B1 to B2, and so on.

[0087] Correspondingly, collecting optical signals from multiple target channels essentially means collecting signals of various different wavelengths from microdroplets.

[0088] Referring to the aforementioned example, in multi-channel mode, if the user specifies channel 1 and channel 2 as target channels, the signal acquisition unit will acquire the optical signal of the microdroplet at wavelength A1 and the optical signal of the microdroplet at wavelength B1 when the microdroplet passes through the detection position, or acquire the optical signal of the microdroplet at wavelengths between A1 and A2 and the optical signal of the microdroplet at wavelengths between B1 and B2.

[0089] As an example, the target channel can be set using the command: chn_index_set = [1, 2], where the value in parentheses represents the number of the selected target channel. For example, this example sets channel 1 and channel 2 as the target channels. If the command sets two or more target channels, the current mode is multi-channel; if only one target channel is set, the current mode is single-channel.

[0090] It is understandable that for a single microdroplet, in multi-channel mode, each target channel can acquire a set of optical signal data of the microdroplet. That is, in multi-channel mode, a microdroplet has multiple sets of optical signal data, and each set of optical signal data corresponds to a target channel.

[0091] The optical signal data obtained by the signal acquisition unit will be transmitted to the data processing and decision-making unit, which will determine the characteristic data of the microdroplet based on the optical signal data of the microdroplet.

[0092] The characteristic data of microdroplets can be of various types, and this embodiment does not limit the specific types. As an example, the characteristic data of microdroplets in this embodiment may include at least one of the following: signal duration, mean value, peak value, peak-to-average power ratio of optical signal data in a single channel, and mean-to-average power ratio of optical signal data between channels.

[0093] Please see Figure 2 The signal duration refers to the time it takes for the optical signal of a single microdroplet to travel from the starting point to the ending point. The starting point is the moment when the signal acquisition unit first detects that the signal strength is greater than the preset signal threshold, and the ending point is the moment when the signal acquisition unit first detects that the signal strength is less than or equal to the signal threshold after the starting point. The optical signal acquired between the starting point and the ending point is equivalent to the complete optical signal of a microdroplet.

[0094] The signal duration of a microdroplet can be determined based on the number of optical signal data points collected from that microdroplet and the sampling interval of the signal acquisition unit. Specifically, it can be equal to the product of the number of optical signal data points and the sampling interval. For example, if the signal acquisition unit collects 20 optical signal data points from a microdroplet at a sampling interval of 10 microseconds, then the signal duration of that microdroplet can be determined to be 200 microseconds.

[0095] The signal duration is related to the liquid flow rate and the quality of the microdroplet coating, and adhesion may occur. Generally, the sampling interval of the screening platform is greater than 10 microseconds.

[0096] The threshold is a preset benchmark value for judging microdroplet signals. Only when the signal intensity is greater than the threshold will the screening platform determine that the currently collected signal belongs to the optical signal of a certain microdroplet.

[0097] In this embodiment, the signal strength can be expressed in millivolts.

[0098] For a single channel, the mean of its optical signal data refers to the average value of a set of optical signal data corresponding to that channel; the peak value refers to the maximum value in a set of optical signal data corresponding to that channel; and the peak-to-average ratio (PAR) refers to the ratio obtained by dividing the peak value by the mean value in a set of optical signal data corresponding to that channel.

[0099] The mean-to-mean ratio of optical signal data between channels refers to the ratio between the two means corresponding to the two channels of a microdroplet. This characteristic data is only obtained in multi-channel mode, and a microdroplet can have different mean-to-mean ratios depending on the channels used for comparison and the channels used as the numerator and denominator.

[0100] For example, the average ratio of optical signal data in channel 1 to channel 2 represents the ratio obtained by dividing the average value of microdroplets in channel 1 by the average value of microdroplets in channel 2.

[0101] After obtaining the characteristic data of a microdroplet, the data processing and decision-making unit identifies whether the microdroplet is a positive microdroplet based on the characteristic data and preset screening conditions, and transmits the identification result to the drive control unit.

[0102] The screening criteria can include multiple positive indicator parameters. The data processing and decision-making unit compares the feature data with these positive indicator parameters and determines whether the microdroplet belongs to the positive microdroplet category based on the comparison results.

[0103] Different types of feature data correspond to different positive indicator parameters. For example, the peak value of a single channel corresponds to the peak value positive indicator parameter, and the mean value of a single channel corresponds to the mean value positive indicator parameter. Furthermore, different channels can also be configured with different positive indicator parameters. For example, there can be channel 1 positive indicator parameters corresponding to channel 1 and channel 2 positive indicator parameters corresponding to channel 2. The aforementioned comparison can specifically involve comparing a specific type of feature data for a specific channel with its corresponding positive indicator parameters.

[0104] The correspondence between the comparison result and whether it is positive or negative varies depending on factors such as the substance being analyzed, the type of selected characteristic data, and experimental requirements. This embodiment does not limit this.

[0105] For example, the correspondence can be that if the peak value of at least one channel is greater than the corresponding positive indicator parameter, and the mean value of at least one channel is greater than the corresponding positive indicator parameter, then the microdroplet is a positive microdroplet; if this condition is not met, it is a non-positive microdroplet.

[0106] If the identification result indicates that the microdroplet is a positive microdroplet, the drive control unit outputs a drive command to the communication interaction unit. The communication interaction unit transmits the drive command to the control system through the command channel. The drive control unit of the control system transmits the drive command to the power module through the command channel. Then, when the positive microdroplet reaches the electrode position, the power module starts and powers the electrode, forming a high-voltage electric field at the position corresponding to the electrode in the flow channel. Under the action of the high-voltage electric field, the positive microdroplet deflects to outlet 1 and leaves the flow channel from outlet 1 into the external collection container.

[0107] If the identification result indicates that the microdroplet is a non-positive microdroplet, the drive control unit will not output a drive command. As a result, the microdroplet will not deflect when it reaches the electrode and will flow into outlet 2 in its original direction.

[0108] The above working principle is only an example. In some alternative examples, non-positive microdroplets can be set to flow out from outlet 1 and positive microdroplets from outlet 2. In this case, the above working principle can be adjusted accordingly so that non-positive microdroplets are deflected while positive ones are not. The specific principle can be modified based on the above and will not be repeated here.

[0109] The human-computer interaction unit is used to display the human-computer interaction interface and send the control instructions corresponding to the user's operation on the human-computer interaction interface to the screening platform, thereby realizing functions such as setting the screening environment and positive indicator parameters required for the screening process, controlling the on / off of screening actions, and displaying data.

[0110] As examples, the control instructions sent by the control system to the screening platform may include at least one of the following:

[0111] Channel parameter instructions: Channel number, threshold, lower limit of mean, lower limit of peak value, lower limit of peak-to-average ratio.

[0112] In this type of instruction, the selectable values ​​for each parameter can be:

[0113] Channel number, 1 to 8; threshold, 500 to 1500, in millivolts; lower limit of mean, 500 to 3000, in millivolts; lower limit of peak value, 500 to 3000, in millivolts; lower limit of peak-to-average ratio, 0.5 to 5.

[0114] For example, the channel parameter instructions can specifically be in the form of: chn_para_set = [1, TH1, a1, b1, c1] and chn_para_set = [2, TH2, a2, b2, c2]. The first item in parentheses represents the channel number being set, the second item represents the threshold, and the third to fifth items represent the lower limit of the mean, the lower limit of the peak value, and the lower limit of the peak-to-average ratio, respectively. The third to fifth items can be set to non-zero values ​​or to 0. If set to a non-zero value, the screening platform determines this value as a positive indicator parameter for the corresponding type of feature data, and screens positive microdroplets based on this. If set to 0, the screening platform ignores the judgment of the feature data corresponding to this item when screening positive and non-positive microdroplets.

[0115] For example, if a1 is set to 600 in the above example, then when the screening platform is working, it can identify microdroplets with a mean value of channel 1 higher than 600 as positive microdroplets. If a1 is set to 0, then when the screening platform is working...

[0116] The value 0 will be ignored in the filtering platform.

[0117] Signal duration command: lower limit of duration, upper limit of duration.

[0118] Channel comparison command: Ratio mode; Numerator channel number, Denominator channel number, Lower limit of ratio, Upper limit of ratio.

[0119] Channel comparison commands can be divided into two types based on the different characteristic data being compared: inter-channel average value command and inter-channel peak-to-peak value command.

[0120] The value ranges of each parameter in this type of instruction are as follows:

[0121] Molecular channel number, 1 to 8; denominator channel number, 1 to 8; lower limit of ratio, 0.5 to 5; upper limit of ratio, 0.5 to 5.

[0122] The upper and lower limits of the mean-mean ratio between channels can be set using the following command: chn_cmp_mean_set = [1, 2, a, b], where 1 represents the channel number as the numerator of the mean-mean ratio, 2 represents the channel number as the denominator, and channels 1 and 2 here are only examples. In actual applications, they can be replaced with other channel numbers according to user operation. a and b represent the lower and upper limits, respectively. Similar to the aforementioned command, when both a and b are set to 0, the screening platform will ignore the mean-mean ratio between channels 1 and 2 of the microdroplet when identifying positive microdroplets. When both a and b are set to non-zero values, the screening platform will identify whether the corresponding microdroplet is a positive microdroplet based on whether the mean-mean ratio between channels 1 and 2 is within this range.

[0123] Working channel instructions: Any combination of channel numbers N (N = 1 to 8).

[0124] Control commands: Start / Stop.

[0125] As an example, the command to control the filtering platform to start filtering could be device_start.

[0126] Among them, the threshold, lower limit of mean, lower limit of peak value, and lower limit of peak-to-mean ratio can be understood as different types of positive indicator parameters.

[0127] In this embodiment, the screening platform can be an independent embedded terminal with complete screening functions, consisting of a communication interaction unit, a drive control unit, a signal acquisition unit, and a data processing and decision-making unit.

[0128] The screening platform can be started and stopped by the control system.

[0129] The data statistics and analysis unit is used to collect and analyze the feature data generated by the data processing and decision-making unit through the data channel.

[0130] The data processing and decision-making unit can send characteristic data to the control system in the form of characteristic data packet instructions. The characteristic data packet instructions can specifically include the following information:

[0131] Test result (positive / non-positive); duration; channel number, mean, peak value, peak-to-mean ratio; channel number / channel number, mean-to-mean ratio; channel number / channel number, peak-to-peak ratio.

[0132] Peak-to-peak ratio is the ratio of the peak values ​​of two channels. For example, the peak-to-peak ratio of channel 1 / channel 2 is the ratio obtained by dividing the peak value of channel 1 by the peak value of channel 2.

[0133] Based on the aforementioned microdroplet screening system, this application provides a method for processing microdroplet feature data, which can be applied to, for example... Figure 3 The microdroplet screening system shown can also be applied to other microdroplet screening systems.

[0134] The working principle of the microdroplet screening system in this embodiment can be found in [reference needed]. Figure 4 Here is the control flow diagram for the system. The workflow of the system may include the following steps.

[0135] S401, Select channel and threshold.

[0136] S402, Control Screening Platform.

[0137] S403, obtain the characteristic data of microdroplets from the screening platform.

[0138] S404, collect data packets and statistical feature data.

[0139] S405, fit a normal curve and optimize the screening criteria.

[0140] S406, Store results and update positive indicator parameters online.

[0141] Steps S403 to S406 are equivalent to the process of setting or updating positive indicator parameters based on the characteristic data of microdroplets. The following will combine... Figure 5 The corresponding embodiments will describe the specific implementation process of steps S403 to S406.

[0142] Please see Figure 5 Here is a flowchart of the microdroplet feature data processing method of this embodiment, which may include the following steps.

[0143] Each step in the method provided in this embodiment can be performed by... Figure 5 The data statistics and analysis unit shown can be executed by [the relevant authority / organization]. Figure 5 In the system shown, each module and / or unit works in concert, that is, each module and / or unit executes one or more steps of the method, so that the microdroplet screening system has the function of dynamically configuring positive index parameters.

[0144] S501, obtains characteristic data of multiple microdroplets.

[0145] The multiple microdroplets in S501 can be a preset statistical number of microdroplets or microdroplets detected within a preset statistical time range. Both the statistical number and the statistical time range can be set as needed and are not limited. For example, the statistical number can be 500,000 or 5,000, and the statistical time range can be 30 minutes or 10 minutes.

[0146] Implementations of S501 may include:

[0147] For each of the multiple microdroplets, optical signal data of the microdroplet in at least one channel is collected when the microdroplet passes through the detection position;

[0148] For each of the multiple microdroplets, the characteristic data of the microdroplet are determined based on the optical signal data of the microdroplet in at least one channel.

[0149] Optical signal data is equivalent to the optical signal intensity of microdroplets.

[0150] The calculation methods for the characteristic data of microdroplets can be found in the previous explanations of different types of characteristic data, and will not be repeated here.

[0151] Taking the acquisition of a preset number of microdroplet feature data as an example, when executing S501, the data processing and decision unit generates feature data of microdroplets passing through the detection position in real time based on the optical signal data, and sends the generated feature data to the control system. The data statistics and analysis unit then counts how many microdroplet feature data have been received. Whenever the cumulative number of microdroplet feature data received is reached, the data statistics and analysis unit starts executing S502 and subsequent steps based on the feature data of the most recent batch of microdroplets, such as the feature data of the most recent 5000 microdroplets.

[0152] Optionally, before executing S502, feature data of multiple microdroplets can be filtered. Only feature data that meets the preset filtering conditions will be used for fitting in S502, and feature data that does not meet the conditions will be deleted.

[0153] The filtering conditions can be set according to the actual situation and are not limited. As an example, the filtering condition can be that the signal duration is within a preset effective range, which can be any value between 10 and 200 microseconds, for example, it can be set to greater than or equal to 80 microseconds.

[0154] Based on this example, if the signal duration in the feature data of a microdroplet is outside the valid range, the feature data of that microdroplet is deleted; if it is within the valid range, the feature data of that microdroplet is retained for subsequent curve fitting.

[0155] The effective range of signal duration can be set using the command signal_time_set = [a,b], where a and b represent the lower and upper limits of the effective range. When both are 0, the filtering platform will ignore this parameter.

[0156] The advantage of setting the above filtering conditions is that it avoids using abnormal feature data that does not meet the filtering conditions for fitting, so that the fitted feature data distribution curve can reflect the distribution of normal feature data as much as possible, thereby improving the accuracy of the first positive indicator parameter determined based on the curve.

[0157] For example, when the screening platform is set to multi-channel mode, with channel 1 and channel 2 designated as target channels, and the statistical count set to 50,000, the screening platform can obtain the following feature data package for a single microdroplet:

[0158] Y / X; T n C1, M 1,n R 1,n C2, M 2,n A 2,n R 2,n A1 / 2, AR 12,n .

[0159] Where n represents the nth microdroplet identified by the screening platform after the start of the experiment, C1 and C2 represent channel 1 and channel 2, Y represents a positive microdroplet, and X represents a non-positive microdroplet; T n M is the duration of the microdroplet signal, measured in microseconds. 1,n A 1,n R 1,n The values ​​are, in order, the peak value, mean value, and peak-to-means ratio (PMR) of channel 1; M 2,n A 2,n R 2,n The values ​​are, in order, the peak value, mean value, and peak-to-average ratio (PAR) of channel 2; AR 12,n It is the ratio of the average of channel 1 to that of channel 2.

[0160] S502, Perform curve fitting on the feature data to obtain the feature data distribution curve indicating the distribution of the feature data.

[0161] When fitting curves, any existing probability distribution model and fitting algorithm can be used. This embodiment does not limit the selected model and fitting method.

[0162] As an example, based on the normal distribution model, the least squares method can be used to fit the feature data to obtain the feature data normal distribution curve that indicates the distribution of the feature data.

[0163] When executing S502, the data statistics and analysis unit can first perform quantity statistics on the characteristic data of multiple microdroplets according to their numerical values, thereby determining the quantity distribution of various types of characteristic data. Then, for each type of characteristic data, curve fitting is performed based on the quantity distribution of that type of characteristic data to obtain the characteristic data distribution curve corresponding to that type of characteristic data.

[0164] For example, the data statistics and analysis unit can obtain the mean of 50,000 microdroplets through S501, and then perform quantity statistics on the 50,000 mean values ​​according to their numerical values ​​to determine the quantity distribution of these 50,000 mean values. Based on the statistical quantity distribution, the distribution ratio is determined, and curve fitting is performed based on the distribution ratio to obtain the mean distribution curve indicating the mean distribution of microdroplets.

[0165] As a specific example, based on the characteristic data of 50,000 microdroplets obtained in S501, the data statistics and analysis unit can statistically obtain the following distribution of the number of characteristic data:

[0166] The peak value is determined by counting the number of microdroplets based on the millivolt value of the optical signal intensity. Generally, the optical signal intensity of a microdroplet is no greater than 5000 millivolts, and the peak and mean values ​​can both be set to the range of 0–5000 millivolts. Therefore, the distribution of the number of peak values ​​in channel 1 can be represented by a one-dimensional array MS. 1,i , i∈[0,5000] means, where MS 1,0 This represents the number of microdroplets with a peak value of 0 mV in channel 1 out of a statistically analyzed sample of 50,000 microdroplets. 1,10 This indicates the number of microdroplets with a peak value of 10 mV in channel 1, and so on; the number distribution of peak values ​​in channel 2 can be represented by MS. 2,i , i∈[0,5000] represents.

[0167] Based on the aforementioned range of mean values, the distribution of the mean values ​​for channels 1 and 2 can also be represented by a one-dimensional array of 5001 elements, where the i-th element of a specific channel represents the number of microdroplets with a mean value of i in that channel. The distribution of the mean values ​​for channel 1 is denoted as AS. 1,i , i∈[0,5000]; the distribution of the mean of channel 2 is denoted as AS 2,i , i∈[0,5000].

[0168] In this embodiment, for ease of subsequent processing, the peak-to-average ratio (PAR), as well as other ratios involved later, can be converted into integer variables by multiplying the original ratio by 1000. Furthermore, based on the aforementioned ranges of peak and mean values, it is easy to see that any ratio obtained by comparing the peak and / or mean values ​​falls within the range of 0 to 5, which translates to a range of 0 to 5000 after conversion to integers. Therefore, in this embodiment, the distribution of PAR values ​​can also be statistically analyzed using a one-dimensional array of 5001 elements, where the i-th element of the array for a certain channel represents the number of microdroplets with a PAR value of i in that channel. For example, the PAR distribution for channel 1 is denoted as RS. 1,i , i∈[0,5000]; the number distribution of the peak-to-average ratio of channel 2 is denoted as RS 2,i , i∈[0,5000].

[0169] The average-to-average ratio between channels is converted into an integer variable in the same way as the peak-to-average ratio described above. For example, the distribution of the average-to-average ratios of channel 1 and channel 2 can be represented by the array ARS. 12,i , i∈[0,5000] means that the i-th element represents the number of microdroplets with a mean-to-mean ratio of i between channel 1 and channel 2, for example, ARS 12,100 This indicates the number of microdroplets with a mean-to-mean ratio of 100 between channels 1 and 2.

[0170] The signal duration is determined by dividing the sampling frequency by 10 and rounding down to the nearest integer, since the sampling frequency is greater than 10 microseconds. Because the duration of the microdroplet signal in the control system does not exceed 1000 microseconds, corresponding to a statistical data range of 0 to 100, the distribution of signal durations can be represented by a one-dimensional array of 101 elements, where the i-th element represents the number of microdroplets with signal duration i. This array can be denoted as TS. i , i∈[0,100].

[0171] Based on the quantity distribution of the above feature data, the following distribution proportions can be obtained:

[0172] Peak distribution percentage. Peak distribution percentage for Channel 1: i∈[0,5000]; Peak distribution percentage of channel 2: i∈[0,5000]. Where i∈[0,5000] represents the voltage signal range, in millivolts. In the peak distribution ratio array, the i-th element of the array for a certain channel represents the proportion of microdroplets with a peak value of i millivolts in that channel among the N microdroplets being counted.

[0173] N represents the number of microdroplets used for statistics. When S501 obtains feature data of multiple microdroplets according to a preset statistical number, N is equal to the statistical number. For example, when the statistical number is 50,000, N is equal to 50,000. When S501 obtains feature data of multiple microdroplets according to a statistical time range, N is equal to the total number of microdroplets identified by the screening platform within that time range. For example, if the statistical time range is 10 minutes, then N is equal to the total number of microdroplets identified by the screening platform within those 10 minutes.

[0174] Distribution percentage of the mean. Distribution percentage of microdroplets in Channel 1: i∈[0,5000]; Distribution percentage of microdroplets in channel 2: i∈[0,5000]. Where i∈[0,5000] represents the voltage signal range, in millivolts. In the distribution proportion array of the mean, the i-th element of the array for a certain channel represents the proportion of microdroplets with a mean of i millivolts in that channel among the N microdroplets being counted.

[0175] Peak-to-average ratio distribution percentage. Distribution percentage of microdroplets in Channel 1: i∈[0,5000]; Distribution percentage of microdroplets in channel 2: i∈[0,5000]. i∈[0,5000] means the ratio is multiplied by 1000 and rounded down to the nearest integer. In the distribution ratio distribution array of peak-to-average ratios, the i-th element of the array for a certain channel represents the proportion of microdroplets whose peak-to-average ratio of that channel is multiplied by 1000 and rounded down to the nearest integer among the N microdroplets being counted.

[0176] Distribution of the mean-to-mean ratio between channels. Distribution of microdroplets based on the ratio of the mean of channel 1 to the mean of channel 2: i∈[0,5000]. i∈[0,5000] represents the ratio multiplied by 1000 and rounded down to the nearest integer. In the distribution ratio distribution array between channels, the i-th element represents the proportion of microdroplets whose average ratio between channels is multiplied by 1000 and rounded down to the nearest integer among the N microdroplets being counted. For example, ARP 12,10 This represents the proportion of microdroplets in N microdroplets whose mean-to-mean ratio of channel 1 and channel 2 is equal to 10.

[0177] Distribution percentage of signal duration. Distribution percentage of microdroplet segments based on signal duration: i∈[0,100] i∈[0,100] represents the signal duration divided by 10 and rounded down to the nearest integer. In the distribution ratio array of signal duration, the i-th element represents the proportion of microdroplets whose signal duration is divided by 10 and rounded down to the nearest integer among the N microdroplets being counted.

[0178] After obtaining the distribution proportions of the different feature data, we can use the feature data as the x-axis and the distribution proportions as the y-axis to draw a scatter plot of the corresponding feature data. Based on the scatter plot, using the normal distribution as the mathematical model, and fitting it with the least squares method, we can obtain the normal distribution curve of the corresponding feature data.

[0179] Optionally, the data statistics and analysis unit can also perform statistical analysis on the detection results of each microdroplet to determine the number of positive microdroplets and the number of non-positive microdroplets among multiple microdroplets.

[0180] S503, determine the screening parameters based on the feature data distribution curve.

[0181] Specific implementations of S503 may include:

[0182] A1, determine the mean and variance of the normal distribution curve of the feature data;

[0183] A2, obtain the distribution interval corresponding to the preset probability percentage;

[0184] A3, based on the distribution interval, mean, and variance, obtain the feature data corresponding to the intervals of the normal distribution curve and the distribution interval as the screening parameters.

[0185] The interval boundary includes at least one of the upper and lower bounds of the distribution interval.

[0186] The mean of the normal distribution curve of the characteristic data is denoted as μ, and the variance is denoted as σ. 2 .

[0187] It should be noted that in step A1, different normal distribution curves of feature data can be fitted for different channels and different types of feature data, and correspondingly, different means and variances can be determined.

[0188] Continuing with the previous example, for the peak value of channel 1 and the peak value of channel 2, a normal distribution curve of the peak value of channel 1 and a normal distribution curve of the peak value of channel 2 can be fitted respectively. For the mean value of channel 1 and the mean value of channel 2, a normal distribution curve of the mean value of channel 1 and a normal distribution curve of the mean value of channel 2 can be fitted respectively.

[0189] For example, please see Figures 6 to 9 This is a schematic diagram of the normal distribution curve of the feature data obtained by fitting in this embodiment. Figures 6 to 9 The curves are, in order: the mean normal distribution curves of Channel 1 and Channel 2, the peak normal distribution curves of Channel 1 and Channel 2, the peak-to-mean ratio normal distribution curves of Channel 1 and Channel 2, and the mean-to-mean ratio normal distribution curves of Channel 1 and Channel 2.

[0190] Figure 6 In the graph, the mean values ​​of Channel 1 and Channel 2 are represented on the x-axis in millivolts; the percentage distribution is represented on the y-axis.

[0191] Figure 7 In the graph, the peak values ​​of Channel 1 and Channel 2 are represented on the x-axis in millivolts; the distribution percentage is represented on the y-axis.

[0192] Figure 8 In the graph, the peak-to-average ratio (converted to an integer variable) of channel 1 and channel 2 is represented by the horizontal axis; the distribution percentage is represented by the vertical axis.

[0193] Figure 9 In the graph, the ratio of the mean values ​​of channel 1 and channel 2 (converted to integer values) is the horizontal axis; the distribution percentage is the vertical axis.

[0194] Figure 10 This is a histogram showing the distribution percentage of signal duration, with the signal duration divided by 10 as the x-axis and the distribution percentage as the y-axis. Signal duration primarily controls the size of microdroplets and addresses the issue of microdroplet adhesion. Furthermore, signal duration is affected by the liquid flow rate and the signal sampling time; in practical applications, it is only necessary to select an interval time suitable for the liquid path system.

[0195] In step A2, based on the signal characteristics of the microdroplet screening system, the determined distribution interval can be either the right-side interval or the symmetrical interval.

[0196] The x-coordinate of the right-hand interval is represented as [μ+x,+∞), x∈[0~2.3σ]. In this embodiment, the right-hand interval can be used for statistical analysis of the mean, peak value, and peak-to-mean ratio. The cumulative distribution function of the right-hand interval can be expressed by the following equation.

[0197] P{y|y≥K}=1-P{y|y≤K}

[0198] Where K represents the feature data value corresponding to the lower limit of the right interval, y represents the feature data, and the above probability value represents the probability that the feature data of the microdroplet is greater than the K value. Conversely, the K value can be selected when the probability value is known.

[0199] The abscissa of the symmetric interval is represented as [μ-x, μ+x], x∈[0.8σ~2σ]. In this embodiment, it is used for the mean-to-means ratio and peak-to-peak ratio in signal duration and multi-channel mode. The cumulative probability distribution function of the symmetric interval can be expressed by the following equation.

[0200] P{y|-K≤y≤K}=2×P{y|y≤K}-1

[0201] In this embodiment, the right-side interval can be set in two ways.

[0202] The first setting method is to directly set the lower limit of the x-coordinate of the right interval, that is, to set the value of x in the interval [μ+x,+∞). In this case, x can be selected between 0 and 2.3σ. Depending on the selected value, the probability percentage corresponding to the right interval can be 1% to 50%. Specifically, when x is set to 2.3σ, the probability percentage corresponding to the right interval is 1%, and the probability of the feature data falling within the right interval is 1%. When x is set to 0, the probability percentage corresponding to the right interval is 50%, and the probability of the feature data falling within the right interval is 50%. In this setting method, x can be set with a step size of 0.01σ, that is, the minimum interval between different values ​​of x is 0.01σ.

[0203] The second setting method is the method in step A2, which determines the lower limit of the right interval based on the preset probability percentage. Refer to the first setting method. If the preset probability percentage is 50%, then x can be set to 0, and the corresponding horizontal coordinate of the right interval is [μ, +∞). In this setting method, the probability percentage is set with a step size of 0.1%.

[0204] Similarly, there are two ways to set the symmetrical interval. One is to directly set the upper and lower limits of the horizontal coordinate, that is, to directly set the value of x in [μ-x,μ+x]. In this case, the value range of x is 0.8σ~2σ, the step size is 0.01σ, and the corresponding probability ratio is 57.63%~95.45%. The other is to set it according to the preset probability ratio. For details, please refer to the second setting method of the right interval mentioned above, which will not be repeated here.

[0205] In step A3, combined Figures 6 to 10 Once the distribution interval is determined, the characteristic data corresponding to the upper and / or lower limits of the distribution interval can be found in the normal distribution curve of the corresponding characteristic data, and the found characteristic data can be used as the screening parameters.

[0206] For example, in the right interval, x can take the value 1σ, with the horizontal coordinate interval [μ+σ,+∞), and the corresponding distribution ratio is 15.87%; in the symmetrical interval, x can take the value 1σ, with the horizontal coordinate interval [μ-σ,μ+σ], and the corresponding distribution ratio is 68.27%.

[0207] Here are some examples of screening parameters determined based on this distribution range.

[0208] Based on the above distribution range settings, according to Figure 6 The peak normal distribution curve of channel 1 shown in (a) indicates a mean μ = 1078.5 and a standard deviation σ = 37.0. The parameters for the right-hand interval are set to [μ + σ, +∞), resulting in a lower limit for the mean of μ + σ = 1115.5, rounded to the nearest integer 1115 millivolts.

[0209] according to Figure 6 The peak normal distribution curve of channel 2 shown in (b) can be used to determine the mean μ = 1312.2 and the standard deviation σ = 38.9. Combined with the setting of the right interval above, the corresponding lower limit of the mean can be determined as: μ + σ = 1351.1, rounded to the integer 1351 millivolts.

[0210] according to Figure 7 The peak normal distribution curve of channel 1 shown in (a) indicates that the mean μ = 1298.1 and the standard deviation σ = 60.9. The parameters set for the right-hand interval are [μ+σ, +∞), and the corresponding lower limit of the peak value is: μ+σ = 1359, rounded to the nearest integer 1359 millivolts.

[0211] according to Figure 7 The peak normal distribution curve of channel 2 shown in (b) indicates that the mean μ = 1540.9 and the standard deviation σ = 65.4. Combining this with the settings of the aforementioned right-hand interval, the corresponding lower limit of the peak value is: μ + σ = 1606.3, rounded to the nearest integer 1606 millivolts.

[0212] according to Figure 8 The normal distribution curve of the peak-to-mean ratio (PMR) of channel 1 shown in (a) indicates that the mean μ = 1206.8 and the standard deviation σ = 28.5. The parameters set for the right-hand interval are [μ + σ, +∞), and the corresponding lower limit of the PMR is μ + σ = 1235.3. Taking the integer 1235, the lowest PMR that meets the positive condition is 1.235.

[0213] according to Figure 8 The normal distribution curve of the peak-to-mean ratio (PMR) of channel 2 shown in (b) indicates that the mean μ = 1176.5 and the standard deviation σ = 28.0. Based on the aforementioned right-hand interval, the corresponding lower limit of the PMR is μ + σ = 1204.5. Taking the integer 1204, the lowest PMR is 1.204.

[0214] according to Figure 9 The normal distribution curves of the mean-to-mean ratio of Channel 1 and Channel 2 shown indicate a mean μ = 822.8 and a standard deviation σ = 13.6. With the parameters set for the symmetric interval as [μ-σ, μ+σ], the corresponding mean-to-mean ratio interval is [u-σ = 809.2, μ+σ = 836.4]. Taking the integer interval [809, 836], the lower limit of the mean-to-mean ratio is 0.809, and the upper limit is 0.836.

[0215] The signal duration is set to an interval of 80 microseconds, centered on the maximum percentage value, such as... Figure 10 As shown, the 80-microsecond interval [13,20] with the highest probability is selected, and the corresponding signal duration interval is [130,200] microseconds.

[0216] S504, configure the first positive indicator parameters using screening parameters.

[0217] Among them, the first positive indicator parameter is used as a screening condition for microdroplets after configuration.

[0218] When S504 is executed, the control system can:

[0219] Using screening parameters, the second positive index parameter of the microdroplet screening system is updated to the first positive index parameter; wherein, the second positive index parameter was used as the screening condition for microdroplets before the update.

[0220] Alternatively:

[0221] Set the screening parameter to the first positive indicator parameter.

[0222] If the screening platform is already screening microdroplets based on the second positive indicator parameter before executing S504, the control system will execute S504 in the former manner. This is equivalent to the control system updating the old screening conditions currently being used by the screening platform to new screening conditions based on the characteristic data of multiple microdroplets obtained. The second positive indicator parameter is equivalent to the old screening conditions, and the first positive indicator parameter is equivalent to the new screening conditions.

[0223] If the screening platform has not yet configured the second positive indicator parameter and the screening of microdroplets has not started before executing S504, the control system executes S504 in the latter way. This is equivalent to the control system collecting the characteristic data of multiple microdroplets before the screening experiment starts, and determining the screening conditions (i.e., the first positive indicator parameter) to be used when starting the experiment based on these characteristic data.

[0224] In the first implementation of S504, the update of the second positive indicator parameter can be performed under certain update conditions, that is:

[0225] Using the screening parameters, the second positive indicator parameter of the microdroplet screening system is updated to the first positive indicator parameter, including:

[0226] Determine whether the second positive indicator parameter meets the preset update conditions based on the screening parameters;

[0227] If the second positive indicator parameter meets the update conditions, the screening parameter will be determined as the first positive indicator parameter;

[0228] If the second positive indicator parameter does not meet the update conditions, the second positive indicator parameter will be determined as the first positive indicator parameter.

[0229] The above process is equivalent to updating the second positive indicator parameter with the screening parameter if the second positive indicator parameter meets the update conditions, and not updating the second positive indicator parameter if the second positive indicator parameter does not meet the update conditions, keeping the original parameter unchanged.

[0230] The update conditions can be set according to the actual situation and are not limited.

[0231] For example, update conditions may include:

[0232] The difference between the second positive indicator parameter and the screening parameter is outside the preset deviation range, which is determined based on the second positive indicator parameter.

[0233] The deviation range here can be set to five per thousand of the second positive indicator parameter.

[0234] In this update condition, if the absolute value of the difference between the second positive indicator parameter and the screening parameter is less than or equal to 0.5% of the second positive indicator parameter, then the second positive indicator parameter is determined not to meet the update condition and is not updated. If the absolute value of the difference between the second positive indicator parameter and the screening parameter is greater than 0.5% of the second positive indicator parameter, then the second positive indicator parameter is determined to meet the update condition and is updated.

[0235] The second positive indicator parameters are of the same type as the screening parameters. That is, if the screening parameters include the lower limit of the mean, the lower limit of the peak value, and the lower limit of the peak-to-average ratio for channels 1 and 2, the upper and lower limits of the average-to-average ratio for channels 1 and 2, and the upper and lower limits of the signal duration, then the second positive indicator parameters also include the lower limit of the mean, the lower limit of the peak value, and the lower limit of the peak-to-average ratio for channels 1 and 2, the upper and lower limits of the average-to-average ratio for channels 1 and 2, and the upper and lower limits of the signal duration.

[0236] Furthermore, when determining whether the second positive indicator parameter meets the update conditions, the calculation of the difference between the second positive indicator parameter and the screening parameter, as well as the calculation of the deviation interval, are all based on the difference between the second positive indicator parameter and the screening parameter of the same type.

[0237] For example, compare the difference between the lower limit of the mean of channel 1 in the screening parameters and the lower limit of the mean of channel 1 in the second positive indicator parameters, and 0.5% of the lower limit of the mean of channel 1 in the second positive indicator parameters.

[0238] The difference between the lower limit of the peak-to-average ratio of channel 2 in the screening parameters and the lower limit of the peak-to-average ratio of channel 2 in the second positive indicator parameters is compared with 0.5% of the lower limit of the peak-to-average ratio of channel 2 in the second positive indicator parameters, and so on.

[0239] Continuing with the previous example, when the second positive indicator is found to meet the update conditions, the control system can send the following instructions to the screening platform. The parameters carried in the instructions are all the screening parameters calculated above. Through these instructions, the second positive indicator parameters used by the screening platform as screening conditions can be completely replaced with the screening parameters calculated according to the method of this embodiment, thereby completing the update.

[0240] chn_para_set=[1,TH1,1115,1359,1.235].

[0241] chn_para_set=[2,TH2,1351,1606,1.204].

[0242] signal_time_set=[130,200].

[0243] chn_cmp_mean_set=[1,2,0.809,0.836].

[0244] The updated positive indicator parameters are the first positive indicator parameters in S504.

[0245] It is understood that the method provided in this embodiment can be continuously executed throughout the screening experiment.

[0246] As an example, assuming the statistical count is set to 50,000, after the screening experiment begins, the screening platform detects the characteristic data of each microdroplet in real time and sends it to the control system. The control system counts the number of detected microdroplets. Whenever 50,000 microdroplets are detected cumulatively, the control system executes the method of this embodiment based on the characteristic data of these 50,000 microdroplets to update or maintain the positive index parameters of the screening platform. Subsequently, the counter is reset to zero, and the cumulative number of detected microdroplets is counted again. When 50,000 microdroplets are detected cumulatively again, the control system repeats the aforementioned process, and so on.

[0247] Therefore, the screening system of this embodiment can periodically analyze the characteristic data of the 50,000 (or other numbers) microdroplets recently identified during the entire screening process, and selectively update the positive indicator parameters used as screening conditions of the screening platform based on these characteristic data.

[0248] The beneficial effects of this embodiment are as follows:

[0249] By statistically analyzing the distribution of various characteristic values ​​of microdroplet signals in real time, a distribution graph of each characteristic value of microdroplet signals can be established. The signal aggregation can be analyzed and the screening parameters can be adjusted in real time. This helps to reduce the missed detection and false positives of positive microdroplets, improve the enrichment effect of dominant proteins, and enhance the screening performance of the system.

[0250] To facilitate understanding, the process of microdroplet screening based on the method of this embodiment is illustrated below with examples.

[0251] The microdroplet screening process consists of two stages: the first stage is parameter adjustment, and the second stage is formal screening. The following description uses a two-channel screening method as an example to illustrate the implementation process.

[0252] The parameter debugging phase includes the following steps:

[0253] Input parameters.

[0254] In this step, the user inputs the following parameters through the human-computer interaction unit of the control system:

[0255] Configure channel 1 and channel 2.

[0256] Set channel thresholds: the threshold for channel 1 is 800 millivolts, and the threshold for channel 2 is 800 millivolts.

[0257] Set the statistical method: Select the microdroplet number statistics method, and the data volume is 50,000 microdroplets.

[0258] Set the effective range for signal duration: 80 microseconds.

[0259] The right-hand interval of the normal curve, taking the value 1σ, with the horizontal axis interval [μ+σ,+∞), corresponds to a distribution percentage of 15.87%; used for data analysis of mean, peak value, and peak-to-mean ratio.

[0260] The symmetrical interval of the normal curve, taking the value 1σ, with the horizontal axis interval [μ-σ,μ+σ], corresponds to a distribution ratio of 68.27%; used for data analysis of the average-to-average ratio between channels.

[0261] Start the filter.

[0262] In this step, the control system generates instruction packets and sends them one by one to the filtering platform. Values ​​of 0 will be ignored by the filtering platform. The instruction packets sent in this example specifically include:

[0263] Channel selection command: chn_index_set = [1, 2].

[0264] Channel 1 parameter setting command: chn_para_set = [1,800,0,0,0].

[0265] Channel 2 parameter setting command: chn_para_set = [2,800,0,0,0].

[0266] Signal duration setting command: signal_time_set = [0,0].

[0267] Command to set the range of the mean-mean ratio between Channel 1 and Channel 2: chn_cmp_mean_set = [1,2,0,0].

[0268] Select the startup command: device_start.

[0269] After the screening is initiated, the control system begins to receive, unpack, and statistically analyze the microdroplet characteristic data packets returned from the screening platform.

[0270] Data statistical analysis.

[0271] In this stage, the control system first statistically analyzes the characteristic data packets of each microdroplet, then quantifies the distribution ratio of each category. A normal curve is fitted using the least squares method to obtain the μ and σ values ​​of the curve, and various screening parameters are calculated. The calculation results are then sent to the screening platform as positive indicator parameters constituting the screening criteria. The calculation process includes the following steps:

[0272] 1. Quantity classification and statistics steps: The control system obtains a feature data packet of a microdroplet from the screening platform, and sequentially counts the signal duration, mean, peak value and peak-to-average ratio of channel 1, mean, peak value and peak-to-average ratio of channel 2, and mean-to-average ratio of channel 1 and channel 2.

[0273] Step 2, incremental microdroplet count: Determine if the count equals the previously set target of 50,000 microdroplets. If less than 50,000, proceed to the duplicate data classification and statistics step; otherwise, proceed to the next step, classification and quantification.

[0274] 3. Classification and quantization steps: Calculate the signal duration, mean, peak value and peak-to-average ratio of channel 1, mean, peak value and peak-to-average ratio of channel 2, and the distribution ratio of the mean-to-average ratio of channel 1 and channel 2 in sequence.

[0275] 4. Normal curve fitting steps: Fit the mean, peak value, and peak-to-average ratio of channel 1, the mean, peak value, and peak-to-average ratio of channel 2, and the normal curve of the mean-to-average ratio of channel 1 and channel 2 in sequence; obtain the μ value and σ value of the corresponding normal curve;

[0276] 5. The steps for calculating the screening parameters include:

[0277] The mean parameters, when fitted to a normal curve using channel 1, yield μ = 1078.5 and σ = 37.0, with μ + σ = 1115.5. The lower limit of the mean is set to 1115 millivolts.

[0278] Channel 2 is fitted with a normal curve, yielding μ = 1312.2 and σ = 38.9, μ + σ = 1351.1.

[0279] Lower limit of the average: 1351 millivolts;

[0280] Peak parameters, fitted with a normal curve for channel 1, yielded μ = 1298.1 and σ = 60.9, μ + σ = 1359. The lower limit of the peak value was set at 1359 millivolts.

[0281] Channel 2 is fitted with a normal curve, yielding μ = 1540.9 and σ = 65.4, μ + σ = 1606.3.

[0282] Lower limit of peak value: 1606 millivolts;

[0283] For the peak-to-average power ratio (PAPR) parameter, fitting a normal curve to channel 1 yields μ = 1206.8 and σ = 28.5, with μ + σ = 1235.3. The lower limit for the PAPR is set at 1.235.

[0284] Channel 2 was fitted with a normal curve, yielding μ = 1176.5 and σ = 28.0, with μ + σ = 1204.5. The lower limit of the peak-to-average power ratio was set at 1.204.

[0285] The average ratio parameter between channels, the average ratio of channel 1 and channel 2 fitted to a normal curve, yields μ = 822.8 and σ = 13.6, [u-σ = 809.2, μ+σ = 836.4]. The average ratio interval is taken as [0.809, 0.836].

[0286] The signal duration parameter is set to an interval of 80 microseconds, with a signal duration range of [130, 200] microseconds.

[0287] 6. Update the filtering platform steps: Combine the condition parameters obtained from the previous steps into the following commands, and send them to the filtering platform one by one to complete the update:

[0288] chn_para_set=[1,800,1115,1359,1.235].

[0289] chn_para_set=[2,800,1351,1606,1.204].

[0290] signal_time_set=[130,200].

[0291] chn_cmp_mean_set=[1,2,0.809,0.836].

[0292] After executing steps 1 to 7 sequentially, the positive screening rate of the screening platform is 1.67%. At this point, the screening rate can be increased or decreased by adjusting the values ​​of the right-hand interval and the symmetrical interval of the normal curve.

[0293] The process to improve the positive rate of screening is as follows:

[0294] Adjust the right-hand side of the normal curve to a value of 0.65σ, with the x-axis ranging from [μ+0.65σ,+∞), corresponding to a distribution percentage of 25.78%; this is used for data analysis of the mean, peak value, and peak-to-mean ratio. Perform steps 1 to 4 sequentially to calculate the new parameters:

[0295] The mean parameters, when fitted to a normal curve using channel 1, yield μ = 1079.5 and σ = 34.7, with μ + 0.65σ = 1102.055. The lower limit of the mean is set to 1102 millivolts.

[0296] Channel 2 was fitted with a normal curve, yielding μ = 1314.6 and σ = 37.5, with μ + 0.65σ = 1338.975. The lower limit of the mean was set to 1338 millivolts.

[0297] Peak parameters, fitted with a normal curve for channel 1, yielded μ = 1307.4 and σ = 56.5, μ + 0.65σ = 1344.125. The lower limit of the peak value was set at 1344 millivolts.

[0298] Channel 2 was fitted with a normal curve, yielding μ = 1550.7 and σ = 62.9, with μ + 0.65σ = 1591.585. The lower limit of the peak value was set at 1591 mV.

[0299] For the peak-to-average power ratio (PAPR) parameter, fitting a normal curve to channel 1 yields μ = 1212.5 and σ = 26.6, with μ + 0.65σ = 1229.79. The lower limit for the PAPR is set at 1.229.

[0300] Channel 2 was fitted with a normal curve, yielding μ = 1180.7 and σ = 27.0, with μ + 0.65σ = 1198.25. The lower limit of the peak-to-average power ratio was set at 1.198.

[0301] The average ratio parameter between channels, the average ratio of channel 1 and channel 2 fitted to a normal curve, yields μ = 822.0 and σ = 13.5, [u-σ = 808.5, μ+σ = 835.5]. The average ratio interval is taken as [0.808, 0.836], with the upper limit rounded to the nearest integer.

[0302] The signal duration parameter is set to an interval of 80 microseconds, and the signal duration range is [130, 200] microseconds.

[0303] Since the signal duration and the mean-to-mean ratio range of Channel 1 and Channel 2 differ from the current parameters by less than 0.5%, no update will be performed to reduce fluctuations. To modify the parameters of Channel 1 and Channel 2, send the following combined command to the filtering platform to complete the update:

[0304] chn_para_set=[1,800,1102,1344,1.229].

[0305] chn_para_set=[2,800,1338,1591,1.198].

[0306] After updating the screening criteria, the positive screening rate increased to 3.39%.

[0307] Based on the sample selection, multiple selection parameters can be optimized and updated online and automatically by adjusting two parameters: the right-hand interval and the symmetrical interval of the normal curve. Then, the power supply to the deflection electrode is turned on, and the formal selection process begins.

[0308] Formal screening process. During the screening process, based on the two parameters of the right-hand interval and the symmetrical interval of the normal curve, steps 1 to 7 of the data statistical analysis are repeatedly executed. Changes in the screening environment are tracked online, and the screening parameters are optimized. At the same time, the data and update records generated during the screening process are stored in a document for easy tracking and analysis of the screened samples and screening conditions.

[0309] According to the microdroplet feature data processing method provided in the embodiments of this application, the embodiments of this application also provide a microdroplet feature data processing apparatus. Please refer to [link to relevant documentation]. Figure 11 This is a schematic diagram of the structure of the device, which may include the following units.

[0310] The acquisition unit 1101 is used to acquire characteristic data of multiple microdroplets.

[0311] The fitting unit 1102 is used to perform curve fitting on the feature data to obtain the feature data distribution curve indicating the distribution of the feature data.

[0312] The determination unit 1103 is used to determine the screening parameters based on the feature data distribution curve.

[0313] The configuration unit 1104 is used to configure the first positive indicator parameter using the screening parameters; wherein the first positive indicator parameter is used as the screening condition for microdroplets after configuration.

[0314] Optionally, when configuring the first positive indicator parameter using the screening parameters, the configuration unit 1104 is specifically used for:

[0315] Using screening parameters, the second positive index parameter of the microdroplet screening system is updated to the first positive index parameter; wherein, the second positive index parameter was used as the screening condition for microdroplets before the update.

[0316] or,

[0317] Set the screening parameter to the first positive indicator parameter.

[0318] Optionally, when the configuration unit 1104 updates the second positive indicator parameter of the microdroplet screening system to the first positive indicator parameter using the screening parameters, it is specifically used for:

[0319] Determine whether the second positive indicator parameter meets the preset update conditions based on the screening parameters;

[0320] If the second positive indicator parameter meets the update conditions, the screening parameter will be determined as the first positive indicator parameter;

[0321] If the second positive indicator parameter does not meet the update conditions, the second positive indicator parameter will be determined as the first positive indicator parameter.

[0322] Optional update conditions include:

[0323] The difference between the second positive indicator parameter and the screening parameter is outside the preset deviation range, which is determined based on the second positive indicator parameter.

[0324] Optionally, when the fitting unit 1102 performs curve fitting on the feature data to obtain a feature data distribution curve indicating the distribution of the feature data, it is specifically used for:

[0325] Based on the normal distribution model, the least squares method is used to fit the feature data to obtain the normal distribution curve of the feature data indicating the distribution of the feature data.

[0326] Optionally, when determining the screening parameters based on the feature data distribution curve, the determining unit 1103 is specifically used for:

[0327] Determine the mean and variance of the normal distribution curve for the feature data;

[0328] The distribution interval corresponding to the preset probability percentage is obtained;

[0329] Based on the distribution interval, mean, and variance, the characteristic data corresponding to the intervals in the normal distribution curve and the distribution interval are obtained as screening parameters; wherein, the interval boundary includes at least one of the upper and lower bounds of the distribution interval.

[0330] Optionally, when obtaining feature data of multiple microdroplets, the obtaining unit 1101 is specifically used for:

[0331] For each of the multiple microdroplets, optical signal data of the microdroplet in at least one channel is collected when the microdroplet passes through the detection position;

[0332] For each of the multiple microdroplets, the characteristic data of the microdroplet are determined based on the optical signal data of the microdroplet in at least one channel.

[0333] Optionally, the characteristic data of the microdroplet includes at least one of the mean, peak, peak-to-average ratio of optical signal data for a single channel, and the mean-to-average ratio of optical signal data between channels.

[0334] The specific working principle of the microdroplet feature data processing device provided in this embodiment can be found in the relevant steps of the microdroplet feature data processing method in the embodiments of this application, and will not be repeated here.

[0335] This application also provides a microdroplet screening system, including a microfluidic chip, a power module, a light source module, a screening platform, and a control system;

[0336] The microfluidic chip is provided with chip channels, and detection positions and electrodes are provided on the chip channels. Microdroplets passing through the chip channels enter one of the multiple outlets of the chip channels under the action of the electrodes.

[0337] The power module is connected to both the electrodes and the control system to energize the electrodes according to the control commands from the control system.

[0338] The light source module is used to illuminate the chip flow channels;

[0339] The screening platform is used to collect optical signal data of microdroplets when they pass through the detection site, so as to obtain the feature data of multiple microdroplets and transmit the feature data to the control system.

[0340] The control system is used for:

[0341] Curve fitting is performed on the feature data to obtain the feature data distribution curve that indicates the distribution of the feature data;

[0342] Determine the screening parameters based on the feature data distribution curve;

[0343] The first positive indicator parameter is configured using the screening parameters; the first positive indicator parameter is used as the screening condition for microdroplets after configuration.

[0344] The structure of the system can be found in [reference]. Figure 3 The working principle of this system can be found in the relevant steps of the microdroplet feature data processing method provided in this embodiment, and will not be repeated here.

[0345] This application embodiment also provides a device for processing microdroplet feature data, including a memory and a processor;

[0346] Memory is used to store computer programs;

[0347] The processor is used to execute computer programs, specifically to implement the microdroplet feature data processing method provided in any embodiment of this application.

[0348] Optionally, the device can be considered as Figure 3 The processor in the control system shown is equivalent to the data statistics and analysis unit of the control system.

[0349] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0350] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0351] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0352] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0353] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for processing microdroplet feature data, characterized in that, The method, applied to a microdroplet screening system, includes: Characteristic data of multiple microdroplets are obtained, including positive microdroplets and negative microdroplets; Without performing positive or negative pre-classification, curve fitting is performed on the feature data of the multiple microdroplets to obtain a feature data distribution curve indicating the distribution of the feature data; The screening parameters are determined based on the mean and variance corresponding to the feature data distribution curve; Using the screening parameters, the second positive indicator parameter of the microdroplet screening system is updated to the first positive indicator parameter; wherein, the second positive indicator parameter was used as the screening condition for microdroplets before the update; or, The screening parameters are set as the first positive indicator parameters; wherein, the first positive indicator parameters are used as screening conditions for microdroplets after configuration.

2. The method according to claim 1, characterized in that, The step of updating the second positive indicator parameter of the microdroplet screening system to the first positive indicator parameter using the screening parameters includes: Based on the screening parameters, determine whether the second positive indicator parameter meets the preset update conditions; If the second positive indicator parameter meets the update condition, the screening parameter is determined as the first positive indicator parameter; If the second positive indicator parameter does not meet the update conditions, the second positive indicator parameter is determined as the first positive indicator parameter.

3. The method according to claim 2, characterized in that, The update conditions include: The difference between the second positive indicator parameter and the screening parameter is outside a preset deviation range, which is determined based on the second positive indicator parameter.

4. The method according to claim 1, characterized in that, The step of curve fitting the feature data of the plurality of microdroplets to obtain a feature data distribution curve indicating the distribution of the feature data includes: Based on the normal distribution model, the least squares method is used to perform curve fitting on the feature data to obtain the feature data normal distribution curve indicating the distribution of the feature data.

5. The method according to claim 4, characterized in that, The step of determining the screening parameters based on the mean and variance corresponding to the feature data distribution curve includes: Determine the mean and variance of the normal distribution curve of the feature data; The distribution interval corresponding to the preset probability percentage is obtained; Based on the distribution interval, the mean, and the variance, the feature data corresponding to the interval boundary of the normal distribution curve of the feature data and the distribution interval are obtained as screening parameters; wherein, the interval boundary includes at least one of the upper and lower bounds of the distribution interval.

6. The method according to claim 1, characterized in that, The acquisition of feature data for multiple microdroplets includes: For each of the multiple microdroplets, when the microdroplet passes through the detection position, the optical signal data of the microdroplet in at least one channel is collected; For each of the multiple microdroplets, characteristic data of the microdroplet are determined based on the optical signal data of the microdroplet in at least one channel.

7. The method according to claim 6, characterized in that, The characteristic data of the microdroplet includes at least one of the following: signal duration, mean value, peak value, peak-to-average value ratio of optical signal data in a single channel, and mean-to-average value ratio of optical signal data between channels.

8. A device for processing microdroplet feature data, characterized in that, The device, used in a microdroplet screening system, comprises: The acquisition unit is used to acquire feature data of multiple microdroplets, including positive microdroplets and negative microdroplets; The fitting unit is used to perform curve fitting on the feature data of the multiple microdroplets without pre-classification by positive or negative, to obtain a feature data distribution curve indicating the distribution of the feature data. The determining unit is used to determine the screening parameters based on the mean and variance corresponding to the feature data distribution curve; A configuration unit is used to update the second positive indicator parameter of the microdroplet screening system to the first positive indicator parameter using the screening parameters; wherein the second positive indicator parameter was used as the screening condition for microdroplets before the update; or, The screening parameters are set as the first positive indicator parameters; wherein, the first positive indicator parameters are used as screening conditions for microdroplets after configuration.

9. A device for processing microdroplet feature data, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program, specifically to implement the microdroplet feature data processing method as described in any one of claims 1 to 7.

10. A microdroplet screening system, characterized in that, This includes microfluidic chips, power modules, light source modules, screening platforms, and control systems. The microfluidic chip is provided with a chip channel, and the chip channel is provided with a detection position and an electrode. Microdroplets passing through the chip channel enter one of the multiple outlets of the chip channel under the action of the electrode. The power module is connected to the electrode and the control system respectively, so as to energize the electrode according to the control command of the control system; The light source module is used to illuminate the chip flow channel; The screening platform is used to collect optical signal data of microdroplets when microdroplets pass through the detection position, so as to obtain feature data of multiple microdroplets, and transmit the feature data to the control system. The control system is used to implement the microdroplet feature data processing method as described in any one of claims 1 to 7 based on the feature data of the plurality of microdroplets.

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