Micro-droplet characteristic data processing method, equipment, device and screening system

Abstract

The invention discloses a micro-droplet feature data processing method, equipment, a device and a screening system, and the method comprises the steps: obtaining the feature data of a plurality of micro-droplets; performing curve fitting on the feature data to obtain a feature data distribution curve indicating the distribution condition of the feature data; determining screening parameters according to the characteristic data distribution curve; configuring a first positive index parameter by using the screening parameter; the first positive index parameter is used as a screening condition of the micro-droplets after configuration. In a micro-droplet screening experiment, the change of experimental factors can cause the corresponding change of characteristic data of micro-droplets. Therefore, according to the scheme, the first positive index parameter serving as the screening condition is dynamically configured according to the feature data of the multiple micro-droplets, and the effects of dynamically adjusting the screening condition along with the change of experimental factors and improving the accuracy of the screening result can be achieved.

Description

Technical Field

[0001] The present application belongs to the field of micro-droplet screening technology, and in particular, relates to a method, equipment, device and screening system for processing micro-droplet characteristic data. Background Art

[0002] Microdroplet screening technology is a bioanalysis technology widely used in enzyme directed evolution, cell line breeding, monoclonal antibody development, etc. The implementation principle of this technology is as follows.

[0003] Multiple single cells that need to be analyzed are wrapped with a substrate to form multiple microdroplets, which are controlled to flow one by one through a specific flow channel. The cross-sectional area of ​​the flow channel just allows a single microdroplet to pass through. When the microdroplets flow through the flow channel, light signal data generated by each microdroplet after being irradiated by a light source is collected, and characteristic data of each microdroplet is obtained by analyzing the light signal data. Therefore, each microdroplet can be screened according to whether the characteristic data meets the preset screening conditions.

[0004] In the existing microdroplet screening technology, the positive indicator parameters that constitute the screening conditions are usually set before the screening experiment begins and remain unchanged throughout the screening process. However, in the actual screening experiment, as the experimental time increases, experimental factors such as light source intensity, microdroplet flow rate and ambient temperature will change, resulting in the positive indicator parameters set before the experiment beginning to no longer apply. Therefore, when conducting a screening experiment using the existing microdroplet screening technology, the longer the experiment lasts, the lower the accuracy of the screening results. Summary of the invention

[0005] To this end, this application discloses the following technical solutions:

[0006] The first aspect of the present application provides a method for processing micro-droplet characteristic data, comprising:

[0007] Obtaining characteristic data of multiple micro-droplets;

[0008] Performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data;

[0009] Determining screening parameters according to the characteristic data distribution curve;

[0010] The screening parameters are used to configure a first positive indicator parameter; wherein the first positive indicator parameter is used as a screening condition for microdroplets after configuration.

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

[0012] Using the screening parameter, the second positive indicator parameter of the micro-droplet screening system is updated to the first positive indicator parameter; wherein the second positive indicator parameter is used as a screening condition for micro-droplets before being updated;

[0013] or,

[0014] The screening parameter is set as the first positive indicator parameter.

[0015] Optionally, using the screening parameter to update the second positive indicator parameter of the micro-droplet screening system to the first positive indicator parameter includes:

[0016] Determining whether the second positive indicator parameter meets a preset update condition according to the screening parameter;

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

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

[0019] Optionally, the update condition includes:

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

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

[0022] Based on the normal distribution model, the characteristic data is curve-fitted using the least square method to obtain a characteristic data normal distribution curve indicating the distribution of the characteristic data.

[0023] Optionally, determining the screening parameters according to the characteristic data distribution curve includes:

[0024] Determining the mean and variance of the normal distribution curve of the characteristic data;

[0025] Obtaining a distribution interval corresponding to the probability ratio according to a preset probability ratio;

[0026] According to the distribution interval, the mean and the variance, characteristic data corresponding to the interval of the distribution interval in the normal distribution curve of the characteristic data is obtained as a screening parameter; wherein the interval boundary includes at least one of the upper bound and the lower bound of the distribution interval.

[0027] Optionally, obtaining characteristic data of a plurality of micro-droplets includes:

[0028] For each micro-droplet of the plurality of micro-droplets, when the micro-droplet passes through a detection position, collecting optical signal data of the micro-droplet in at least one channel;

[0029] For each of the plurality of micro-droplets, characteristic data of the micro-droplet is determined according to the optical signal data of the micro-droplet in the at least one channel.

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

[0031] The second aspect of the present application provides a device for processing micro-droplet characteristic data, comprising:

[0032] An acquisition unit, used for acquiring characteristic data of a plurality of micro-droplets;

[0033] A fitting unit, used for performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data;

[0034] A determination unit, used to determine a screening parameter according to the characteristic data distribution curve;

[0035] A configuration unit is used to configure a first positive indicator parameter using the screening parameter; wherein the first positive indicator parameter is used as a screening condition for micro-droplets after configuration.

[0036] A third aspect of the present application provides a device for processing micro-droplet characteristic 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 method for processing micro-droplet characteristic data provided in any one of the first aspects of the present application.

[0039] The fourth aspect of the present application also provides a micro-droplet 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 flow channel, and the chip flow channel is provided with a detection position and an electrode, and the micro droplets passing through the chip flow channel enter one of the multiple outlets of the chip flow channel under the action of the electrode;

[0041] The power supply module is connected to the electrodes and the control system respectively to energize the electrodes according to the control instructions 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 the micro-droplets when the micro-droplets pass through the detection position to obtain characteristic data of a plurality of micro-droplets, and transmit the characteristic data to the control system;

[0044] The control system is used to implement the method for processing micro-droplet characteristic data provided in any one of the first aspects of the present application according to the characteristic data of the plurality of micro-droplets.

[0045] The present application discloses a method, equipment, device and screening system for processing characteristic data of microdroplets, including obtaining characteristic data of 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 according to the characteristic data distribution curve; configuring a first positive indicator parameter using the screening parameters; and using the first positive indicator parameter as a screening condition for microdroplets after configuration. In a microdroplet screening experiment, changes in experimental factors will cause corresponding changes in the characteristic data of the microdroplets. Therefore, this scheme 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 as the experimental factors change, thereby improving the accuracy of the screening results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

[0048] Figure 2 is a schematic diagram of a micro-droplet signal provided in an embodiment of the present application;

[0049] Figure 3 It is a structural schematic diagram of a micro-droplet screening system provided in an embodiment of the present application;

[0050] Figure 4 is a control flow chart of a micro-droplet screening system provided in an embodiment of the present application;

[0051] Figure 5 It is a flow chart of a method for processing micro-droplet characteristic data provided in an embodiment of the present application;

[0052] Figure 61 is a schematic diagram of a normal distribution curve of the mean of channel 1 and channel 2 provided in an embodiment of the present application;

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

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

[0055] Fig. 9 is a normal distribution curve of the mean ratio of channel 1 to channel 2 provided in an embodiment of the present application;

[0056] Fig.10 is a histogram of the distribution ratio of a signal duration provided in an embodiment of the present application;

[0057] Fig.11 It is a structural schematic diagram of a micro-droplet characteristic data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] In order to facilitate understanding of the technical solution of the present application, the terms that may be involved in the embodiments of the present application are first briefly described.

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

[0061] One-dimensional normal distribution is recorded as: X~N(μ,σ 2 ), the probability density function is .

[0062]

[0063] The normal curve is Figure 1 As shown in the figure, with X = μ as the symmetry axis, it is similar to a bell shape, with a high middle and low sides, and completely symmetrical left and right sides, and the value is 0 at positive (negative) infinity. The size of σ reflects the steepness of the curve. The smaller the σ value, the steeper the curve change trend, and the more the curve shape tends to be tall and thin; the larger the σ value, the gentler the curve change trend, and the more the curve shape tends to be flat.

[0064] The total area of ​​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 frequency percentage that occurs in this interval. σ is the inflection point of the normal curve. The commonly used interval areas are, for example, 68.27% of the area in the horizontal axis interval [μ-σ,μ+σ], 95.45% of the area in the horizontal axis interval [μ-2σ,μ+2σ], and 99.73% of the area in the horizontal axis interval [μ-3σ,μ+3σ]. The area in the horizontal axis interval (-∞,μ] is 50%, and the area in the horizontal axis interval [μ,+∞) is 50%. The area in the horizontal axis interval (-∞,μ+σ] is 84.13%, and the area in the horizontal axis interval [μ+σ,+∞) is 15.87%.

[0065] When μ=0,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 through the variable transformation formula. The variable transformation formula is as follows:

[0066]

[0067] The least squares method is a mathematical optimization technique. It finds the best function matching data by minimizing the sum of squares of errors. The least squares method can be used to easily obtain unknown data and minimize the sum of squares of errors between the obtained data and the actual data.

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

[0069] The principles of this solution are as follows:

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

[0071] If we treat the microdroplets as a random event and count the microdroplets detected in a continuous time period, when the number of microdroplets is large enough, the statistics of each characteristic data obey the normal distribution. With the help of the high-speed computing power of computers, through the method of big data statistical frequency, using the law of normal distribution, we can obtain the reasonable conditions of each positive indicator parameter according to the actual environment of the screening system and the reaction state of the screening target. In actual operation, the cumbersome indicator selection and setting process can be simplified; the screening conditions can be optimized in real time and online to improve the screening performance of the system and the enrichment effect of the target product.

[0072] The present application embodiment provides a micro-droplet screening system, see Figure 3 , which is a schematic diagram of the structure of the system. The system may include 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-computer interaction unit, a module control unit, and a data statistics and analysis unit.

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

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

[0077] The working principle of the micro-droplet screening system is as follows.

[0078] The microfluidic chip is provided with a flow channel having multiple inlets, such as Figure 3 The mid-flow channel has three inlets, which are marked as inlet 1, inlet 2 and inlet 3.

[0079] In the microdroplet screening experiment, the microdroplets enter the flow channel of the microfluidic chip through multiple inlets. The microdroplets can be composed of a substrate and a substance wrapped by the substrate. The substance wrapped by the substrate can be a single cell or other substances that need to be analyzed.

[0080] After the microdroplets enter the flow channel, they move forward in the flow channel at a certain speed driven by the pump force. When the microdroplets flow through the detection position, the screening platform continuously collects the optical signal during the movement of the microdroplets through the signal acquisition unit at a fixed sampling frequency to obtain continuous sampling data representing the optical signal intensity of the microdroplets, that is, the optical signal data of the microdroplets.

[0081] The detection position can be understood as a specific pre-specified position in the flow channel, or a small section of the flow channel.

[0082] The signal acquisition unit may be a photoelectric sensor or other sensor or element capable of collecting optical signals. The signal acquisition unit is used to collect optical signal data of a micro-droplet in real time at a preset sampling interval when the micro-droplet passes through the detection position.

[0083] The screening platform can work in single-channel mode or multi-channel mode according to the user's settings. When working in multi-channel mode, the signal acquisition unit can simultaneously collect optical signals of multiple target channels, thereby obtaining optical signal data of microdroplets in each target channel.

[0084] The target channel is a number of channels specified according to the user's settings among the multiple channels preset by the screening platform. There is no limit on the number of channels preset by the screening platform. As an example, Figure 3 The system has 8 preset channels, which are recorded as channel 1 to channel 8.

[0085] The channels preset in the screening platform in this embodiment can be understood as different wavelengths (or wavelength ranges).

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

[0087] Correspondingly, collecting optical signals from multiple target channels is actually collecting signals of multiple different wavelengths from microdroplets.

[0088] In combination with the above example, in the multi-channel mode, the user specifies channel 1 and channel 2 as target channels. Then, when the micro-droplet passes through the detection position, the signal acquisition unit collects the optical signal of the wavelength A1 of the micro-droplet, and the optical signal of the wavelength B1 of the micro-droplet, or collects the optical signal of the micro-droplet with a wavelength between A1 and A2, and collects the optical signal of the micro-droplet with a wavelength between B1 and B2.

[0089] As an example, the target channel can be set by the command: chn_index_set = [1, 2], the value in the brackets indicates the number of the selected target channel, for example, this example indicates that channel 1 and channel 2 are set as the target channels. If the command sets two or more target channels, it is determined that the current mode is multi-channel, and if only one target channel is set, it is determined to be single-channel mode.

[0090] It can be understood that for a single microdroplet, in the multi-channel mode, each target channel can collect a set of optical signal data of the microdroplet, that is, in the 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, and the data processing and decision-making unit will determine the characteristic data of the micro-droplets based on the optical signal data of the micro-droplets.

[0092] There may be multiple types of characteristic data of microdroplets, 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 signal duration, mean value, peak value, peak-to-average ratio of optical signal data of a single channel, and mean-to-average ratio of optical signal data between channels.

[0093] See also Figure 2 The signal duration refers to the time taken for the light signal of a single micro-droplet to travel from the starting point to the end point. The starting point refers to the moment when the signal acquisition unit first recognizes that the signal intensity is greater than the preset signal threshold. The end point refers to the moment when the signal acquisition unit first recognizes that the signal intensity is less than or equal to the signal threshold after the starting point. The light signal collected between the starting point and the end point is equivalent to the complete light signal of a micro-droplet.

[0094] The signal duration of a certain micro-droplet can be determined based on the number of light signal data collected from the micro-droplet and the sampling interval of the signal collection unit, and can be specifically equal to the product of the number of light signal data and the sampling interval. For example, if the signal collection unit collects a total of 20 light signal data for a certain micro-droplet and the sampling interval is 10 microseconds, then it can be determined that the signal duration of the micro-droplet is equal to 200 microseconds.

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

[0096] The threshold is a preset reference value for determining micro-droplet signals. Only when the signal intensity is greater than the threshold does the screening platform determine that the currently collected signal belongs to the optical signal of a certain micro-droplet.

[0097] In this embodiment, the signal strength can be represented by a value in millivolts.

[0098] For a single channel, the mean of its optical signal data refers to the average value of a group of optical signal data corresponding to the channel; the peak value refers to the maximum value in a group of optical signal data corresponding to the channel; and the peak-to-average ratio refers to the ratio of the peak value of a group of optical signal data corresponding to the channel divided by the mean value.

[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 the microdroplet. This characteristic data is only obtained in the multi-channel mode, and a microdroplet may have different mean-to-mean ratios depending on the channels used for comparison and the channels used as numerators and denominators.

[0100] Exemplarily, the channel 1 / channel 2 light signal data average ratio represents the ratio obtained by dividing the average value of the microdroplets in channel 1 by the average value of the microdroplets in channel 2.

[0101] After obtaining the characteristic data of a micro-droplet, the data processing and decision-making unit identifies whether the micro-droplet is a positive micro-droplet according to the characteristic data and preset screening conditions, and transmits the identification result to the driving control unit.

[0102] The screening conditions may include a plurality of positive indicator parameters. The data processing and decision-making unit compares the characteristic data with the positive indicator parameters and determines whether the microdroplet is a positive microdroplet according to the comparison result.

[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 positive indicator parameter, and the mean of a single channel corresponds to the mean positive indicator parameter. Furthermore, different channels may also be configured with different positive indicator parameters, for example, the channel 1 positive indicator parameter corresponding to channel 1, and the channel 2 positive indicator parameter corresponding to channel 2. The aforementioned comparison may specifically be to compare the feature data of a specific type of a specific channel with its corresponding positive indicator parameter.

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

[0105] Exemplarily, the corresponding relationship 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, then 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 instruction to the communication interaction unit, the communication interaction unit transmits the drive instruction to the control system through the instruction channel, and the drive control unit of the control system transmits the drive instruction to the power module through the instruction channel. Then, when the positive microdroplet reaches the electrode position, the power module starts and energizes 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 is deflected to outlet 1, leaves the flow channel from outlet 1 and enters an external collection container.

[0107] If the identification result indicates that the micro-droplet is a non-positive micro-droplet, the drive control unit does not output a drive instruction, so the micro-droplet will not be deflected when it reaches the electrode and will flow into the outlet 2 in the original direction.

[0108] The above working principle is only an example. In other optional examples, non-positive micro-droplets can also be set to flow out from outlet 1 and positive micro-droplets can flow out from outlet 2. At this time, the above working principle can be adjusted accordingly, so that non-positive micro-droplets are deflected and positive micro-droplets are not deflected. The specific principle can be modified accordingly based on the above text and will not be repeated.

[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 operations on the human-computer interaction interface to the screening platform, so as to realize the functions of setting the screening environment and positive indicator parameters required for the screening process, switch control of the screening action, and data display.

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

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

[0112] In this type of instruction, the selectable value range of each parameter can be:

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

[0114] Exemplarily, the channel parameter instruction can be specifically an instruction in the following form: chn_para_set=[1,TH1,a1,b1,c1] and chn_para_set=[2,TH2,a2,b2,c2]. The first item in the bracket indicates the number of the set channel, the second item indicates the threshold, and the third to fifth items respectively indicate the lower limit of the mean, the lower limit of the peak value, and the lower limit of the peak-to-average ratio. The third to fifth items can be set to non-zero values ​​or to 0. If set to non-zero values, the screening platform determines the value as a positive indicator parameter of the corresponding type of characteristic data, based on which positive microdroplets are screened. If set to 0, the screening platform ignores the judgment of the characteristic data corresponding to the 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, the microdroplets with a mean value of channel 1 higher than 600 can be identified as positive microdroplets; if a1 is set to 0, then when the screening platform is working.

[0116] Among them, the value 0 will be ignored in the screening platform.

[0117] Signal duration instructions: duration lower limit, duration upper limit.

[0118] Channel comparison instructions: ratio mode; numerator channel number, denominator channel number, ratio lower limit, ratio upper limit.

[0119] Channel comparison instructions can be divided into two types: inter-channel average value instructions and inter-channel peak-to-peak value instructions according to the different compared characteristic data.

[0120] The value ranges of the parameters in this type of instruction are as follows:

[0121] Numerator 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-to-mean ratio between channels can be set by the following instruction: chn_cmp_mean_set=[1, 2, a, b], where 1 represents the number of the channel as the numerator in the mean-to-mean ratio, and 2 represents the number of the channel as the denominator. Channel 1 and channel 2 here are only used as examples. In actual applications, they can be replaced with the numbers of other channels according to user operations. a and b represent the lower and upper limits, respectively. Similar to the aforementioned instructions, when a and b are both set to 0, the screening platform will ignore the mean-to-mean ratio between channels 1 and 2 of the microdroplet when identifying positive microdroplets. When a and b are both set to non-zero values, the screening platform identifies whether the corresponding microdroplet is a positive microdroplet based on whether the mean-to-mean ratio between channels 1 and 2 is within the range.

[0123] Working channel instruction: any combination of channel number N (N=1 to 8).

[0124] Control command: Start / Stop.

[0125] As an example, the instruction for controlling the screening platform to start screening may be device_start.

[0126] Among them, the threshold, mean lower limit, peak lower limit, and peak-to-average ratio lower limit 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, which is composed of a communication interaction unit, a drive control unit, a signal acquisition unit, and a data processing and decision-making unit.

[0128] The start and stop of the screening platform can be controlled by the control system.

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

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

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

[0132] The 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] According to the above-mentioned micro-droplet screening system, the present application embodiment provides a method for processing micro-droplet characteristic data. The method can be applied to Figure 3 The micro-droplet screening system shown can also be applied to other micro-droplet screening systems.

[0134] The working principle of the micro-droplet screening system of this embodiment can be found in Figure 4 , is the control flow chart of the system, and the workflow of the system may include the following steps.

[0135] S401, select a channel and a threshold.

[0136] S402, control the screening platform.

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

[0138] S404, collect data packets and count characteristic data.

[0139] S405, fitting a normal curve and optimizing screening conditions.

[0140] S406, storing the results and updating the positive indicator parameters online.

[0141] Steps S403 to S406 are equivalent to the process of setting or updating the positive index parameters according to the characteristic data of the micro-droplets. Figure 5 The corresponding embodiment describes the specific implementation process of steps S403 to S406.

[0142] See also Figure 5 , is a flow chart of the method for processing micro-droplet characteristic data of this embodiment, and the method may include the following steps.

[0143] Each step in the method provided in this embodiment can be Figure 5 The data statistics and analysis unit shown can also be performed by Figure 5 The modules and / or units in the system shown are executed in coordination, that is, the modules and / or units respectively execute one or more steps of the method, so that the micro-droplet screening system has the function of dynamically configuring positive indicator parameters.

[0144] S501, obtaining characteristic data of a plurality of micro-droplets.

[0145] The multiple micro-droplets in S501 may be micro-droplets of a preset statistical number or micro-droplets detected within a preset statistical time range. Both the statistical number and the statistical time range may be set as required without limitation. For example, the statistical number may be 500,000 or 5,000, and the statistical time range may be 30 minutes or 10 minutes.

[0146] The implementation of S501 may include:

[0147] For each micro-droplet of the plurality of micro-droplets, when the micro-droplet passes through a detection position, light signal data of the micro-droplet in at least one channel is collected;

[0148] For each of the plurality of micro-droplets, characteristic data of the micro-droplet is determined according to optical signal data of the micro-droplet in at least one channel.

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

[0150] The calculation method of the characteristic data of the micro-droplets can refer to the description of different types of characteristic data in the previous article, which will not be repeated here.

[0151] Taking the acquisition of characteristic data of a preset statistical number of micro-droplets as an example, when executing S501, the data processing and decision-making unit generates characteristic data of the micro-droplets passing through the detection position in real time according to the optical signal data, and sends the generated characteristic data to the control system, and the data statistics and analysis unit counts how many characteristic data of the micro-droplets are received. Whenever the characteristic data of a statistical number of micro-droplets are accumulated, the data statistics and analysis unit starts to execute S502 and subsequent steps based on the characteristic data of the most recent batch of statistical number of micro-droplets, such as the characteristic data of the most recent 5000 micro-droplets.

[0152] Optionally, before executing S502 , the characteristic data of the plurality of micro-droplets may be screened, and only the characteristic data satisfying a preset filtering condition is used for the fitting in S502 , and the characteristic data not satisfying the filtering condition is deleted.

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

[0154] Based on this example, if the signal duration in the characteristic data of a micro-droplet is not within the valid range, the characteristic data of the micro-droplet is deleted. If it is within the valid range, the characteristic data of the micro-droplet is retained for subsequent curve fitting.

[0155] The valid range of signal duration can be set by the instruction signal_time_set=[a,b], where a and b represent the lower and upper limits of the valid range. When both are 0, the screening platform will ignore the valid range parameter.

[0156] The benefit of setting the above filtering conditions is to avoid 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 subsequently determined based on the curve.

[0157] Exemplarily, when the screening platform is set to multi-channel mode, channel 1 and channel 2 are designated as target channels, and the number of statistics is set to 50,000, the screening platform can obtain the following characteristic data packets for a 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 channels 1 and 2, Y represents positive microdroplets, and X represents non-positive microdroplets; T n is the duration of the microdroplet signal, in microseconds; M 1,n , A 1,n , R 1,n They are the peak value, mean value and peak-to-average ratio of channel 1 respectively; M 2,n , A 2,n , R 2,n They are the peak value, average value and peak-to-average ratio of channel 2; AR 12,n is the average ratio of channel 1 to channel 2.

[0160] S502, performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data.

[0161] When curve fitting is performed, the fitting can be performed based on any existing probability distribution model and fitting algorithm. This embodiment does not limit the selected model and fitting.

[0162] As an example, based on a normal distribution model, the least square method may be used to perform curve fitting on the characteristic data to obtain a characteristic data normal distribution curve indicating the distribution of the characteristic data.

[0163] When executing S502, the data statistics and analysis unit can first perform quantitative statistics on the characteristic data of multiple micro-droplets according to the numerical values, so as to determine the quantitative distribution of various types of characteristic data, and then perform curve fitting for each type of characteristic data according to the quantitative distribution of the characteristic data of that type, so as to obtain the characteristic data distribution curve corresponding to the characteristic data of that type.

[0164] Illustratively, the data statistics and analysis unit can obtain the mean of 50,000 micro-droplets through S501, and then perform quantitative statistics on the 50,000 means according to the numerical values, so as to determine the quantity distribution of the 50,000 means, determine the distribution ratio based on the statistical quantity distribution, perform curve fitting based on the distribution ratio, and obtain a mean distribution curve indicating the mean distribution of the micro-droplets.

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

[0166] Peak value, according to the millivolt value of the light signal intensity, the number of microdroplets is counted. Generally, the light signal intensity of microdroplets is not greater than 5000 millivolts. The range of peak value and mean value can be set to 0-5000 millivolts. Therefore, the number distribution of the peak value of channel 1 can be expressed as a one-dimensional array MS 1,i , i∈[0,5000] means, where MS 1,0 It indicates the number of microdroplets with a peak value of 0 mV in channel 1 among the 50,000 microdroplets counted, MS 1,10 The number of droplets with a peak value of 10 mV in channel 1 is shown in Figure 2. The distribution of the peak value of channel 2 can be expressed by MS 2,i , i∈[0,5000] represents.

[0167] Mean, according to the above range of mean, the number distribution of the mean of channel 1 and channel 2 can also be represented by a one-dimensional array of 5001 elements, where the i-th element of a particular channel represents the number of microdroplets with mean i in that channel. The number distribution of the mean of channel 1 is recorded as AS 1,i , i∈[0,5000]; the number distribution of the mean of channel 2 is recorded as AS 2,i , i∈[0,5000].

[0168] Peak-to-average ratio. In this embodiment, in order to facilitate subsequent processing, the peak-to-average ratio and other ratios involved in the subsequent process can be converted into integer variables by multiplying the original ratio by 1000. Furthermore, according to the aforementioned range of peak values ​​and mean values, it is not difficult to find that any ratio obtained by comparing the peak value and / or mean value has a range of 0 to 5, and the range after conversion to an integer variable is 0 to 5000. Therefore, the number distribution of the peak-to-average ratio in this embodiment can also be counted using a one-dimensional array of 5001 elements, where the i-th element of the array of a certain channel represents the number of microdroplets with a peak-to-average ratio of i in the channel. For example, the peak-to-average ratio number distribution of channel 1 is recorded as RS 1,i , i∈[0,5000]; the number distribution of the peak-to-average ratio of channel 2 is recorded as RS 2,i , i∈[0,5000].

[0169] The average-to-average ratio between channels is converted into an integer variable in the same manner as the peak-to-average ratio. For example, the number distribution of the average-to-average ratio between channel 1 and channel 2 can be expressed using the array ARS 12,i , i∈[0,5000] represents, where the i-th element represents the number of microdroplets with the average ratio of channel 1 to channel 2 being i, for example, ARS 12,100 The number of droplets whose average ratio of channel 1 to channel 2 is 100.

[0170] Signal duration. Since the sampling frequency is greater than 10 microseconds, the duration is divided by 10 and the integer is taken. Since the duration of the micro-droplet signal of the control system does not exceed 1000 microseconds, the corresponding statistical data range is 0 to 100, so the number distribution of the signal duration can be represented by a one-dimensional array of 101 elements, where the i-th element represents the number of micro-droplets with a signal duration of i. The array can be recorded as TS i , i∈[0,100].

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

[0172] Peak distribution ratio. Channel 1 peak distribution ratio: i∈[0,5000]; Channel 2 peak distribution ratio: 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 of a channel represents the proportion of micro-droplets with a peak value of i millivolts in the N micro-droplets counted in the channel.

[0173] N represents the number of micro-droplets used for statistics. When S501 obtains characteristic data of multiple micro-droplets 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 characteristic data of multiple micro-droplets according to a statistical time range, N is equal to the total number of micro-droplets identified by the screening platform within the time range. For example, if the statistical time range is 10 minutes, N is equal to the total number of micro-droplets identified by the screening platform within these 10 minutes.

[0174] Distribution ratio of the mean. Distribution ratio of microdroplets in channel 1: i∈[0,5000]; Channel 2 droplet distribution ratio: i∈[0,5000]. Where i∈[0,5000] represents the voltage signal range, in millivolts. In the mean distribution ratio array, the i-th element of the array of a channel represents the proportion of micro-droplets with a mean of i millivolts in the N micro-droplets counted in the channel.

[0175] Distribution ratio of peak-to-average ratio. Distribution ratio of microdroplets in channel 1: i∈[0,5000]; Channel 2 droplet distribution ratio: i∈[0,5000]. i∈[0,5000] indicates that the ratio is multiplied by 1000 and the integer is obtained. In the distribution percentage array of the peak-to-average ratio, the i-th element of the array of a channel indicates the proportion of the microdroplets with the value i after the peak-to-average ratio of the channel is multiplied by 1000 and rounded to the integer among the N microdroplets counted.

[0176] The distribution ratio of the mean value between channels. The distribution ratio of the droplet of the ratio of the mean value of channel 1 to the mean value of channel 2: i∈[0,5000]. i∈[0,5000] means the ratio is multiplied by 1000 and rounded to an integer. In the distribution ratio array of the average ratio between channels, the i-th element represents the proportion of the micro-droplet with the value i among the N micro-droplets counted after the average ratio between channels is multiplied by 1000 and rounded to an integer. For example, ARP 12,10 It indicates the proportion of micro-droplets with the average ratio of channel 1 to channel 2 equal to 10 among the N micro-droplets.

[0177] Distribution percentage of signal duration. Distribution percentage of droplets by signal duration: i∈[0,100] i∈[0,100] represents the signal duration divided by 10 and rounded to an integer. In the distribution ratio array of signal duration, the i-th element represents the proportion of the micro-droplet with the value i after the signal duration is divided by 10 and rounded to an integer among the N micro-droplets counted.

[0178] After obtaining the distribution ratio of the above-mentioned different characteristic data, we can use the characteristic data as the horizontal axis and the distribution ratio as the vertical axis to draw a scatter plot of the corresponding characteristic data. Based on the scatter plot, we use the normal distribution as the mathematical model and adopt the principle of least squares method for fitting to obtain the corresponding normal distribution curve of the characteristic data.

[0179] Optionally, the data statistics and analysis unit may also perform statistics on the detection result of each micro-droplet to determine the number of positive micro-droplets and the number of non-positive micro-droplets in the plurality of micro-droplets.

[0180] S503, determining screening parameters according to the characteristic data distribution curve.

[0181] Specific implementations of S503 may include:

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

[0183] A2, obtaining the distribution interval corresponding to the probability proportion according to the preset probability proportion;

[0184] A3, according to the distribution interval, mean and variance, obtain the characteristic data corresponding to the interval of the distribution interval in the normal distribution curve of the characteristic data as the screening parameter.

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

[0186] The mean of the normal distribution curve of 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 characteristic data can be fitted for different channels and different types of characteristic 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, see Figures 6 to 9 , which is a schematic diagram of the normal distribution curve of characteristic data fitted in this embodiment, from Figures 6 to 9 They are, respectively, the mean normal distribution curve of channel 1 and channel 2, the peak normal distribution curve of channel 1 and channel 2, the peak-to-average ratio normal distribution curve of channel 1 and channel 2, and the average-to-average ratio normal distribution curve of channel 1 and channel 2.

[0190] Figure 6 In the figure, the mean of channel 1 and channel 2 is the horizontal axis, in millivolts; the distribution ratio is the vertical axis.

[0191] Figure 7 In the figure, the peak values ​​of channel 1 and channel 2 are the horizontal coordinates, in millivolts; the distribution ratio is the vertical coordinate.

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

[0193] Fig. 9 In the figure, the mean ratio of channel 1 to channel 2 (converted into the ratio of integer variables) is the horizontal axis, and the distribution proportion is the vertical axis.

[0194] Fig.10 The histogram of the distribution ratio of the signal duration is shown in Figure 1. The signal duration divided by 10 is used as the horizontal axis, and the distribution ratio is used as the vertical axis. The signal duration is mainly used to control the size of the microdroplets and solve the problem of microdroplet adhesion. At the same time, the signal duration is affected by the liquid flow rate and the signal sampling time. In practical applications, it is only necessary to select the interval time that adapts to the liquid system.

[0195] In step A2, according to the signal characteristics of the micro-droplet screening system, the distribution interval determined can be any one of the right interval and the symmetric interval.

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

[0197] P{y|y≥K}=1-P{y|y≤K}

[0198] Wherein K represents the characteristic data value corresponding to the lower limit of the right interval, y represents the characteristic data, and the above probability value represents the probability value that the characteristic 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 expressed as [μ-x, μ+x], x∈[0.8σ~2σ], which is used for the signal duration and the mean-to-mean ratio and the peak-to-peak ratio in the multi-channel mode in this embodiment. 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 interval can be set in two ways.

[0202] The first setting method is to directly set the lower limit of the horizontal axis 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 of the right interval can be 1% to 50%. When x is set to 2.3σ, the probability of the right interval is 1%, and the probability of the feature data falling in the right interval is 1%. When x is set to 0, the probability of the right interval is 50%, and the probability of the feature data falling in the right interval is 50%. In this setting method, x can be set in steps of 0.01σ, that is, the minimum interval between different values ​​of x is 0.01σ.

[0203] The second setting method is the setting method of step A2, that is, determining the lower limit of the right interval according to the preset probability ratio. Refer to the first setting method. If the preset probability ratio is 50%, x can be set equal to 0, and the horizontal coordinate of the corresponding right interval is [μ, +∞). In this setting method, the probability ratio is set in 0.1% steps.

[0204] Similarly, there are two ways to set the symmetric interval. One is to directly set the upper and lower limits of the horizontal axis, that is, directly set the value of x in [μ-x,μ+x]. At this time, 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 interval on the right mentioned above, which will not be repeated here.

[0205] In step A3, combine Figures 6 to 10 After the distribution interval is determined, the characteristic data corresponding to the upper limit and / or lower limit of the distribution interval can be found in the corresponding characteristic data normal distribution curve, and the found characteristic data can be determined as the screening parameter.

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

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

[0208] Based on the above distribution interval settings, according to Figure 6 The peak normal distribution curve of channel 1 shown in (a) can determine the mean μ = 1078.5 and the standard deviation σ = 37.0. The parameters set in the right interval are [μ+σ,+∞), and the corresponding lower limit of the mean is: μ+σ = 1115.5, which is an integer of 1115 millivolts.

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

[0210] according to Figure 7 The peak normal distribution curve of channel 1 shown in (a) can determine the mean μ = 1298.1 and the standard deviation σ = 60.9. The parameters set in the right interval are [μ+σ, +∞), and the corresponding peak lower limit is: μ+σ = 1359, which is an integer of 1359 millivolts.

[0211] according to Figure 7 The peak normal distribution curve of channel 2 shown in (b) can determine the mean μ = 1540.9 and the standard deviation σ = 65.4. Combined with the setting of the right interval above, the corresponding peak lower limit is: μ + σ = 1606.3, which is an integer of 1606 mV.

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

[0213] according to Figure 8 (b) shows the normal distribution curve of the peak-to-average ratio of channel 2, and the mean μ = 1176.5 and the standard deviation σ = 28.0 can be determined. According to the above right interval, the corresponding lower limit of the peak-to-average ratio is: μ + σ = 1204.5, taking the integer 1204, that is, the lowest ratio is 1.204.

[0214] according to Fig. 9 The normal distribution curve of the mean-to-mean ratio of channel 1 and channel 2 shown in the figure can determine the mean μ = 822.8 and the standard deviation σ = 13.6. The parameters of the symmetric interval setting are [μ-σ, μ+σ], and the corresponding mean-to-mean ratio interval is [u-σ = 809.2, μ+σ = 836.4], and the integer interval is [809, 836], that is, 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 around the maximum percentage value, such as Fig.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 parameter using the screening parameter.

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

[0218] When executing S504, the control system may:

[0219] Using the screening parameter, the second positive indicator parameter of the micro-droplet screening system is updated to the first positive indicator parameter; wherein the second positive indicator parameter is used as the screening condition of the micro-droplet before the update;

[0220] You can also:

[0221] The screening parameter is set as the first positive indicator parameter.

[0222] If before executing S504, the screening platform has been screening the micro-droplets based on the second positive indicator parameter, the control system executes S504 in the previous manner. This situation 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 obtained characteristic data of multiple micro-droplets, wherein the second positive indicator parameter is equivalent to the old screening condition, and the first positive indicator parameter is equivalent to the new screening condition.

[0223] If the screening platform has not been configured with the second positive indicator parameter and the screening of microdroplets has not started before executing S504, the control system executes S504 in the latter manner. This situation is equivalent to the control system collecting characteristic data of multiple microdroplets before the screening experiment starts, and determining the screening conditions (i.e., the first positive indicator parameter) 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 may be performed under the premise of satisfying certain update conditions, that is,

[0225] Using the screening parameters, updating the second positive indicator parameter of the micro-droplet screening system to the first positive indicator parameter includes:

[0226] Determining whether the second positive indicator parameter meets a preset update condition according to the screening parameter;

[0227] If the second positive indicator parameter meets the update condition, the screening parameter is determined as the first positive indicator parameter;

[0228] If the second positive indicator parameter does not meet the update condition, the second positive indicator parameter is determined as the first positive indicator parameter.

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

[0230] The update conditions can be set according to the actual situation without any limitation.

[0231] Exemplarily, the update conditions may include:

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

[0233] The deviation interval here can be set to 0.5 thousandths of the second positive indicator parameter.

[0234] In the 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 5 / 1000 of the second positive indicator parameter, it is determined that the second positive indicator parameter does not meet the update condition and the second positive indicator parameter is not updated; if the absolute value of the difference between the second positive indicator parameter and the screening parameter is greater than 5 / 1000 of the second positive indicator parameter, it is determined that the second positive indicator parameter meets the update condition and the second positive indicator parameter is updated.

[0235] The second positive indicator parameter is of the same type as the screening parameter, that is, if the screening parameters include the lower limit of the mean, the lower limit of the peak and the lower limit of the peak-to-average ratio of channels 1 and 2, the upper and lower limits of the mean-to-average ratio of channels 1 and 2, and the upper and lower limits of the signal duration, then the second positive indicator parameter also includes the lower limit of the mean, the lower limit of the peak and the lower limit of the peak-to-average ratio of channels 1 and 2, the upper and lower limits of the mean-to-average ratio of 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 condition, the difference calculation between the second positive indicator parameter and the screening parameter, and the calculation of the deviation interval are based on the difference between the second positive indicator parameter and the screening parameter of the same type.

[0237] For example, the difference between the mean lower limit of channel 1 in the screening parameter and the mean lower limit of channel 1 in the second positive indicator parameter is compared with 5 thousandths of the mean lower limit of channel 1 in the second positive indicator parameter;

[0238] The difference between the lower limit of the peak-to-average ratio of channel 2 in the screening parameter and the lower limit of the peak-to-average ratio of channel 2 in the second positive indicator parameter is compared with 5 / 1000 of the lower limit of the peak-to-average ratio of channel 2 in the second positive indicator parameter, and so on.

[0239] Continuing with the above example, when it is found that the second positive indicator meets 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 in the previous text. Through these instructions, the second positive indicator parameters used as screening conditions by the screening platform can be replaced by 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 parameter is the first positive indicator parameter in S504.

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

[0246] As an example, assuming that the statistical number 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 according to the characteristic data of these 50,000 microdroplets, thereby updating or maintaining the positive indicator parameters of the screening platform. Subsequently, the counter is reset to zero and the counting of the cumulative number of detected microdroplets starts again. When 50,000 microdroplets are detected cumulatively again, the control system repeats the above process, and so on.

[0247] Therefore, the screening system of this embodiment can regularly statistically analyze the characteristic data of the most recently identified 50,000 (or other number) microdroplets 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:

[0249] By real-time statistics of the distribution of each characteristic value of the microdroplet signal, establishing a distribution graph of each characteristic value of the microdroplet signal, analyzing the aggregation of the signal and adjusting the screening parameters in real time, it is beneficial to reduce the missed judgment and false positive of positive microdroplets, improve the enrichment effect of dominant proteins, and enhance the screening performance of the system.

[0250] For ease of understanding, the process of performing micro-droplet screening based on the method of this embodiment is described below with reference to examples.

[0251] The micro-droplet screening process is divided into two stages, the first stage is to debug parameters, and the second stage is formal screening. The following takes two-channel screening as an example to describe the implementation process in combination with the method of this embodiment.

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

[0253] Input parameters.

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

[0255] Set up channel 1 and channel 2.

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

[0257] Set the statistical method: Select the micro-droplet number statistical method, and the statistical data volume is 50,000 micro-droplets.

[0258] The valid range for setting the signal duration is 80 microseconds.

[0259] The right interval of the normal curve takes the value of 1σ, the horizontal axis interval is [μ+σ,+∞), and the corresponding distribution accounts for 15.87%; it is used for data analysis of mean, peak value and peak-to-average ratio.

[0260] The symmetric interval of the normal curve takes the value of 1σ, the horizontal axis interval is [μ-σ,μ+σ], and the corresponding distribution accounts for 68.27%; it is used for data analysis of the mean-to-mean ratio between channels.

[0261] Start filtering.

[0262] In this link, the control system creates instruction packets and sends them to the screening platform one by one. Among them, the value 0 will be ignored by the screening platform. The instruction packets sent in this example specifically include:

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

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

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

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

[0267] The command for setting the mean ratio range of channel 1 and channel 2 is: chn_cmp_mean_set = [1, 2, 0, 0].

[0268] Filter the startup command: device_start.

[0269] After the screening is started, the control system begins to receive, unpack and count the micro-droplet characteristic data packets returned from the screening platform.

[0270] Data statistical analysis.

[0271] In this process, the control system first counts the characteristic data packets of each microdroplet one by one, and then quantifies the distribution ratio of each category. According to the principle of least squares method, the normal curve is fitted, the μ value and σ value of the curve are obtained, and each screening parameter is calculated. The calculation results are sent to the screening platform as the positive indicator parameters constituting the screening conditions. The calculation process includes the following steps:

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

[0273] 2. The number of microdroplets increases by 1 step to determine whether it is equal to the previously set 50,000 microdroplets. If it is less than 50,000, repeat the data classification and statistics step, otherwise, proceed to the next step, the classification and quantification step;

[0274] 3. Classification and quantification step: calculate the signal duration, mean, peak and peak-to-average ratio of channel 1, mean, peak and peak-to-average ratio of channel 2, and the distribution ratio of the mean-to-average ratio of channel 1 to channel 2;

[0275] 4. Normal curve fitting step, sequentially fitting the normal curves of 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 mean-to-average ratio of channel 1 to channel 2; obtaining the corresponding μ value and σ value of the normal curve;

[0276] 5. Calculate the screening parameters step, including:

[0277] Mean parameter, channel 1 fits the normal curve, and obtains μ = 1078.5 and σ = 37.0, μ + σ = 1115.5. Take the lower limit of the mean: 1115 mV;

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

[0279] The lower limit of the average value is 1351 mV;

[0280] Peak parameters, channel 1 fits the normal curve, and obtains μ = 1298.1 and σ = 60.9, μ + σ = 1359. Take the peak lower limit: 1359 mV;

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

[0282] Take the peak lower limit: 1606 mV;

[0283] Peak-to-average ratio parameters, channel 1 fits the normal curve, and obtains μ = 1206.8 and σ = 28.5, μ + σ = 1235.3. The lower limit of the peak-to-average ratio is 1.235;

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

[0285] The mean-to-mean ratio parameter between channels, the mean-to-mean ratio between channel 1 and channel 2 is fitted with a normal curve, and μ = 822.8 and σ = 13.6, [u-σ = 809.2, μ + σ = 836.4]. The mean-to-mean ratio interval is: [0.809, 0.836];

[0286] Signal duration parameter, the interval of signal duration setting is 80 microseconds, and the signal duration interval is: [130,200] microseconds.

[0287] 6. Update the screening platform step. Combine the conditional parameters obtained from the previous step into the following instructions and send them to the screening 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 in sequence, the positive screening rate of the screening platform is 1.67%. At this time, the screening rate can be increased or decreased by adjusting the values ​​of the right interval and the symmetric interval of the normal curve.

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

[0294] Adjust the right interval of the normal curve to 0.65σ, and the horizontal axis interval is [μ+0.65σ,+∞), corresponding to a distribution ratio of 25.78%; used for data analysis of mean, peak, and peak-to-average ratio. Perform steps 1 to 4 in sequence to calculate new parameters:

[0295] Mean parameters, channel 1 fits the normal curve, and obtains μ = 1079.5 and σ = 34.7, μ + 0.65σ = 1102.055. Take the lower limit of the mean: 1102 mV;

[0296] Channel 2 is fitted with a normal curve, and μ = 1314.6 and σ = 37.5, μ + 0.65σ = 1338.975. The lower limit of the mean is 1338 mV;

[0297] Peak parameters, channel 1 fits the normal curve, and obtains μ = 1307.4 and σ = 56.5, μ + 0.65σ = 1344.125. Take the lower limit of the peak value: 1344 mV;

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

[0299] Peak-to-average ratio parameters, channel 1 fits the normal curve, and obtains μ = 1212.5 and σ = 26.6, μ + 0.65σ = 1229.79. Take the lower limit of the peak-to-average ratio: 1.229;

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

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

[0302] Signal duration parameter, the interval of signal duration setting is 80 microseconds, and the signal duration interval is: [130,200] microseconds;

[0303] Since the signal duration and the average ratio interval of channel 1 and channel 2 are less than 0.5% compared with the current parameters, no update is performed to reduce fluctuations. It is necessary to modify the parameters of channel 1 and channel 2, and send the following command to the screening 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 conditions, the positive screening rate increased to 3.39%.

[0307] According to the screening sample situation, by adjusting the two parameters of the right interval and the symmetric interval of the normal curve, multiple screening parameters can be optimized and updated online and automatically. Then, the power supply of the deflection electrode is turned on to enter the formal screening process.

[0308] Formal screening process. During the screening process, according to the two parameters of the right interval and the symmetric interval of the normal curve, steps 1 to 7 of the data statistical analysis link are repeatedly executed to track the changes in the screening environment online and optimize the screening parameters. At the same time, the data generated and the update records during the screening process are statistically stored in the document to facilitate the tracking and analysis of the screening samples and screening conditions.

[0309] According to the method for processing micro-droplet characteristic data provided in the embodiment of the present application, the embodiment of the present application also provides a device for processing micro-droplet characteristic data, see Fig.11 , is a schematic diagram of the structure of the device, and the device may include the following units.

[0310] The obtaining unit 1101 is used to obtain characteristic data of a plurality of micro-droplets.

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

[0312] The determination unit 1103 is used to determine the screening parameters according to the characteristic data distribution curve.

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

[0314] Optionally, when the configuration unit 1104 configures the first positive indicator parameter using the screening parameter, it is specifically used to:

[0315] Using the screening parameter, the second positive indicator parameter of the micro-droplet screening system is updated to the first positive indicator parameter; wherein the second positive indicator parameter is used as the screening condition of the micro-droplet before the update;

[0316] or,

[0317] The screening parameter is set as the first positive indicator parameter.

[0318] Optionally, when the configuration unit 1104 uses the screening parameters to update the second positive indicator parameter of the micro-droplet screening system to the first positive indicator parameter, it is specifically used to:

[0319] Determining whether the second positive indicator parameter meets a preset update condition according to the screening parameter;

[0320] If the second positive indicator parameter meets the update condition, the screening parameter is determined as the first positive indicator parameter;

[0321] If the second positive indicator parameter does not meet the update condition, the second positive indicator parameter is determined as the first positive indicator parameter.

[0322] Optionally, update conditions include:

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

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

[0325] Based on the normal distribution model, the least square method is used to perform curve fitting on the characteristic data to obtain a characteristic data normal distribution curve indicating the distribution of the characteristic data.

[0326] Optionally, when the determining unit 1103 determines the screening parameters according to the characteristic data distribution curve, it is specifically used to:

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

[0328] Obtain the distribution interval corresponding to the probability ratio according to the preset probability ratio;

[0329] According to the distribution interval, mean and variance, characteristic data corresponding to the interval of the distribution interval in the normal distribution curve of the characteristic data is obtained as a screening parameter; wherein the interval boundary includes at least one of the upper bound and the lower bound of the distribution interval.

[0330] Optionally, when the obtaining unit 1101 obtains characteristic data of a plurality of micro-droplets, it is specifically used to:

[0331] For each micro-droplet of the plurality of micro-droplets, when the micro-droplet passes through a detection position, light signal data of the micro-droplet in at least one channel is collected;

[0332] For each of the plurality of micro-droplets, characteristic data of the micro-droplet is determined according to optical signal data of the micro-droplet in at least one channel.

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

[0334] The specific working principle of the device for processing micro-droplet characteristic data provided in this embodiment can be found in the relevant steps of the method for processing micro-droplet characteristic data in the embodiment of the present application, and will not be repeated here.

[0335] The embodiment of the present application also provides a micro-droplet 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 a chip flow channel, on which detection positions and electrodes are provided, and microdroplets passing through the chip flow channel enter one of the multiple outlets of the chip flow channel under the action of the electrodes;

[0337] The power module is connected to the electrode and the control system respectively to energize the electrode according to the control instruction of the control system;

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

[0339] The screening platform is used to collect optical signal data of micro-droplets when the micro-droplets pass through the detection position to obtain characteristic data of multiple micro-droplets, and transmit the characteristic data to the control system;

[0340] Control systems are used to:

[0341] Performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data;

[0342] Determine the screening parameters according to the characteristic data distribution curve;

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

[0344] The structure of the system can be seen in Figure 3 The working principle of the system can be found in the relevant steps of the method for processing micro-droplet characteristic data provided in this embodiment, and will not be described in detail.

[0345] The embodiment of the present application also provides a device for processing micro-droplet characteristic data, including a memory and a processor;

[0346] The memory is used to store computer programs;

[0347] The processor is used to execute a computer program, specifically to implement the method for processing micro-droplet characteristic data provided in any embodiment of the present application.

[0348] Optionally, the device can be considered Figure 3 In the control system shown, the processor 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, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0350] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0351] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0352] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0353] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing micro-droplet characteristic data, characterized in that: include: Obtaining characteristic data of multiple micro-droplets; Performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data; Determining screening parameters according to the characteristic data distribution curve; The screening parameters are used to configure a first positive indicator parameter; wherein the first positive indicator parameter is used as a screening condition for microdroplets after configuration.

2. The method according to claim 1, characterized in that The step of configuring the first positive indicator parameter using the screening parameter includes: Using the screening parameter, the second positive indicator parameter of the micro-droplet screening system is updated to the first positive indicator parameter; wherein the second positive indicator parameter is used as a screening condition for micro-droplets before being updated; or, The screening parameter is set as the first positive indicator parameter.

3. The method according to claim 2, characterized in that The method of updating the second positive indicator parameter of the micro-droplet screening system to the first positive indicator parameter by using the screening parameter comprises: Determining whether the second positive indicator parameter meets a preset update condition according to the screening parameter; If the second positive indicator parameter meets the update condition, determining the screening parameter as the first positive indicator parameter; If the second positive indicator parameter does not meet the update condition, the second positive indicator parameter is determined as the first positive indicator parameter.

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

5. The method according to claim 1, characterized in that The performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data includes: Based on the normal distribution model, the characteristic data is curve-fitted using the least square method to obtain a characteristic data normal distribution curve indicating the distribution of the characteristic data.

6. The method according to claim 5, characterized in that Determining the screening parameters according to the characteristic data distribution curve includes: Determining the mean and variance of the normal distribution curve of the characteristic data; Obtaining a distribution interval corresponding to the probability ratio according to a preset probability ratio; According to the distribution interval, the mean and the variance, characteristic data corresponding to the interval boundary of the distribution interval in the normal distribution curve of the characteristic data is obtained as a screening parameter; wherein the interval boundary includes at least one of the upper bound and the lower bound of the distribution interval.

7. The method according to claim 1, characterized in that The step of obtaining characteristic data of a plurality of micro-droplets comprises: For each micro-droplet of the plurality of micro-droplets, when the micro-droplet passes through a detection position, collecting optical signal data of the micro-droplet in at least one channel; For each of the plurality of micro-droplets, characteristic data of the micro-droplet is determined according to the optical signal data of the micro-droplet in the at least one channel.

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

9. A device for processing micro-droplet characteristic data, characterized in that: include: An acquisition unit, used for acquiring characteristic data of a plurality of micro-droplets; A fitting unit, used for performing curve fitting on the characteristic data to obtain a characteristic data distribution curve indicating the distribution of the characteristic data; A determination unit, used to determine a screening parameter according to the characteristic data distribution curve; A configuration unit is used to configure a first positive indicator parameter using the screening parameter; wherein the first positive indicator parameter is used as a screening condition for micro-droplets after configuration.

10. A device for processing micro-droplet characteristic 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 method for processing micro-droplet characteristic data as described in any one of claims 1 to 8.

11. A micro-droplet screening system, characterized in that: Including microfluidic chip, power module, light source module, screening platform and control system; The microfluidic chip is provided with a chip flow channel, and the chip flow channel is provided with a detection position and an electrode, and the micro droplets passing through the chip flow channel enter one of the multiple outlets of the chip flow channel under the action of the electrode; The power supply module is connected to the electrodes and the control system respectively to energize the electrodes according to the control instructions 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 the micro-droplets when the micro-droplets pass through the detection position to obtain characteristic data of a plurality of micro-droplets, and transmit the characteristic data to the control system; The control system is used to implement the method for processing micro-droplet characteristic data as claimed in any one of claims 1 to 8 based on the characteristic data of the plurality of micro-droplets.

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