CN114854829A - Target gene detection method, device and computer - Google Patents
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Abstract
The invention belongs to the technical field of personalized service algorithms, and particularly relates to a target gene detection method, a device and a computer, which comprise the following steps: fixing a target gene probe for detecting a sample to be detected on an IDE biosensor; the sample to be detected reacts with the target gene probe after being combined to obtain a real part and an imaginary part of the electric signal data; and judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data. The present application does not require typical sensor measurements and detection of the target gene, nor does it require comparison of the change in signal with its initial condition. The present application allows for clear differentiation of target samples immediately after target hybridization measurements.
Description
Technical Field
The invention belongs to the technical field of gene detection, and particularly relates to a target gene detection method, a target gene detection device and a target gene detection computer.
Background
The detection of target genes by biosensors usually involves the conversion of biological signals into electrical signals by means of redox labels, and such biosensors are based on the immobilization of probes for detecting target genes on metal electrodes (usually gold electrodes), and the measurement of the resulting redox signals by voltammetry after the probes have been bound to the target genes. Interdigital electrodes (IDE) are commonly used in microwave filters, surface acoustic wave devices, electro-optical shutters, and can also be applied to, for example, affinity biosensors, including label-free (direct/redox-free) and labeled (redox) biosensors, due to their ability to change dielectric properties.
Inter-digital electrode (IDE) planar sensors have been widely used in prior art research and prior art work for electrochemical measurements, in combination with biological detection. But the way of measurement is usually different types of voltammetry, or the change in electrical impedance is measured by selecting a fixed frequency point. Many methods have been developed for detecting pathogens, viruses, isothermal amplification and PCR amplification products. Among them, the application of biosensors has been widely reported. However, most biosensors require a specific redox reagent or label to generate a signal, and are limited to a specific electrode design, are complicated to manufacture, and require high-end instruments for signal measurement. Meanwhile, the detection method of the biosensor also requires an initial reading to perform baseline comparison.
Disclosure of Invention
In order to solve the problems that most biosensors in the prior art need a specific redox reagent or a label to generate a signal, are limited to a specific electrode design, are complex to manufacture, and require a high-end instrument for signal measurement, the embodiments of the present invention provide the following technical solutions:
in a first aspect, the present invention provides a target gene detection method, comprising:
fixing a target gene probe for detecting a sample to be detected on an IDE biosensor;
the sample to be detected reacts with the target gene probe after being combined to obtain a real part and an imaginary part of the electric signal data;
and judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data.
Further, the determining whether the sample to be tested contains a target gene according to the real part and the imaginary part of the electrical signal data includes:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is greater than the preset threshold value, the sample to be detected does not contain a target gene.
Further, the determining whether the sample to be tested contains a target gene according to the real part and the imaginary part of the electrical signal data includes:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is smaller than the preset threshold value, the sample to be detected contains a target gene.
Further, the electrical signal data is impedance data of the circuit.
Further, the electrical signal data comprises
And when the sample to be detected is combined with the target gene probe, the surface of the electrode or the electrode gap forms the change of the dielectric property, the charge distribution, the surface physical property, the size and the shape of a compound.
Further, after the sample to be tested is combined with the target gene probe and reacts to obtain electrical signal data, the method further comprises the following steps:
and generating a sine wave according to the electric signal data according to a preset frequency and amplitude.
Further, the IDE biosensor is a square IDE biosensor with 8 μm gaps.
In a second aspect, the present invention provides a target gene detecting apparatus comprising:
the fixing module is used for fixing a target gene probe for detecting a sample to be detected on the IDE biosensor;
the electric signal data module is used for reacting after the sample to be detected is combined with the target gene probe to obtain a real part and an imaginary part of electric signal data;
and the judging module is used for judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data.
In a third aspect, the present invention provides a computer comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the method of any of the first aspects.
The invention has the following beneficial effects:
the invention provides a target gene detection method, which comprises the following steps: fixing a target gene probe for detecting a sample to be detected on an IDE biosensor; the sample to be detected reacts with the target gene probe after being combined to obtain a real part and an imaginary part of electric signal data; and judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data. The present application does not require typical sensor measurements and detection of the target gene, nor does it require comparison of the change in signal with its initial condition. The present application allows for clear differentiation of target samples immediately after target hybridization measurements.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a target gene detection method according to an embodiment of the present invention.
FIG. 2(a) is a schematic diagram of an IDE biosensor microsystem in one embodiment of the present invention.
Fig. 2(b) is a simplified equivalent circuit diagram of an impedance measurement circuit in one embodiment of the present invention.
FIG. 3 is a Nyquist plot of the impedance response for detection of HLA-B15: 02 (exon 2) in one embodiment of the present invention.
FIG. 4 is a schematic representation of each sample of LAMP amplification product of HLA-B15: 02 exon 2 according to one embodiment of the present invention.
FIG. 5 shows the electrochemical impedance spectrum of one positive and one negative LAMP amplification product of HLA-B15: 02 bound to the surface of an IDE biosensor according to one embodiment of the present invention.
FIG. 6 is a graph showing the correlation of baseline and post-hybridization impedance measurements, Δ Z, in one embodiment of the invention R Table (2).
FIG. 7 is a graph of Δ Z displayed in the baseline measurement bar for one embodiment of the present invention R Values and target samples displayed clearly.
FIG. 8 is a statistical t-test result between positive and negative target detection measurements in one embodiment of the invention.
FIG. 9 is a statistical t-test result of baseline measurements between biosensors for positive and negative sample detection in one embodiment of the invention.
FIG. 10 is a Nyquist representation of the impedance response of different types of IDE sensors in one embodiment of the invention.
FIG. 11 shows an embodiment of the invention with a surface area of 1mm 2 Nyquist representation of impedance response of square biosensors with different gap sizes
FIG. 12 is a Nyquist plot of the impedance response of square IDE sensors with different surface areas and different gap sizes in one embodiment of the invention.
FIG. 13 is a schematic diagram showing a structure of an apparatus for detecting a target gene in one embodiment of the present invention.
FIG. 14 is a computer architecture diagram in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Most biosensors in the prior art require a specific redox reagent or label to generate a signal, and are limited to a specific electrode design, are complicated to manufacture, and require high-end instruments for signal measurement. Meanwhile, the detection method of the biosensor also requires an initial reading to compare the reference lines, and the present invention provides a target gene detection method, device and computer, please refer to fig. 1, fig. 1 is a schematic flow chart of a target gene detection method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps: the method comprises the following steps:
s101, fixing a target gene probe for detecting a sample to be detected on an IDE biosensor;
interdigitated electrodes (IDE) refer to microelectrodes deposited on a substrate, the working and counter electrodes presenting the appearance of a small pitch of interdigitated fingers or a periodic repetition of a pattern. Interdigital electrodes are commonly used in microwave filters, surface acoustic wave devices, electro-optical shutters, and also in materials having electrical and dielectric properties, such as affinity biosensors, including label-free (direct/redox-free) and labeled (redox) biosensors.
S102, reacting after the sample to be detected is combined with the target gene probe to obtain a real part and an imaginary part of electric signal data; the electrical signal data is impedance data of the circuit.
Impedance spectroscopy is a powerful method that can be used to determine the response of a biosensor system. The changes caused by the target material may be related to changes in the dielectric constant, physical structure and ionic properties of the solution. These changes in the initial system will be reflected in the impedance spectrum as passive electrical component characteristic changes (resistance, capacitance and inductance) due to subsequently introduced objectives. Referring to fig. 2(a), fig. 2(a) is a schematic diagram of an IDE biosensor microsystem for detection of DNA hybridization incubation between electrodes and indicating all passive circuit components. Each component is explained as follows.
(1)R ct -intermetallic charge transfer resistance-measuring solution;
(2)R sol -measuring the resistance of the solution;
(3)R bio resistance of immobilized DNA molecules, organics, and polymers (biorecognition element layer) between electrodes;
(4)C sol -measuring the parasitic capacitance of the solution;
(5)C sub -a parasitic capacitance of the glass substrate;
(6)C dl intermetallic double layer capacitance-measuring solution.
Referring to fig. 2(b), fig. 2(b) is a simplified equivalent circuit diagram of the impedance measuring circuit, i.e. the corresponding electrical components can be simplified into an equivalent circuit model. As follows:
(1)C sol and C sub Is regarded as being in combination with C cell ＝C sol +C sub An equivalent parallel capacitor;
(2)R sol and R bio Is considered to be the same as R cell ＝((R sol ) -1 +(R bio ) -1 ) -1 An equivalent parallel capacitor;
(3) in practice, with a Warburg impedance Z W To represent R ct (Metal-measuring the Charge transfer resistance between solutions) and C dl (metal-measuring double layer capacitance between solutions) effect at low frequency.
It can be considered as one having a Z equivalent cell ＝Z real -jZ imaginary Electrochemical cell of complex impedance of value (cartesian form) where the real part of the impedance is Z real Imaginary part being Z imaginary . The magnitude and phase of the impedance can be derived by the following relationship:
(1) amplitude impedance, | Z cell |＝(Z cell Z * cell ) 1/2 ＝(Z 2 real +Z 2 imaginary ) 1/2 ；
(2) Impedance phase, -theta-tan -1 (-Z imaginary /Z real )。
In a typical electrochemical impedance measurement, a sine wave is generated at a preset frequency and amplitude from the electrical signal data using a Frequency Response Analyzer (FRA) module. This signal is superimposed on the dc potential or current applied to the battery. The alternating voltage and current components are analyzed by two frequency response analyzer channels, calculating the transfer function, total impedance, phase angle offset, and real and imaginary parts of the total impedance.
It should be noted that the calculation of the transfer function, the total impedance, the phase angle shift, and the real and imaginary parts of the total impedance is a function of the frequency response analyzer itself.
And S103, judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data.
In one embodiment, when the target gene to be detected is combined with the probe attached to the glass substrate, the impedance characteristics of the circuit are changed, and for example, the resistance value of the real part of the high frequency band (semicircular part) is reduced as seen from the EIS impedance spectrum. If the detected sample has no target DNA, no DNA can be combined with the probe connected before after cleaning, and the real part impedance value obtained by measurement is larger. Therefore, the impedance measurement after the hybridization of the measured sample and the modified IDE electrode can know the positivity and negativity of the sample.
As a further improvement of the above method, in an embodiment, the determining whether the sample to be tested contains a target gene according to the real part and the imaginary part of the electrical signal data includes:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is greater than the preset threshold value, the sample to be detected does not contain a target gene.
As a further improvement of the above method, in an embodiment, the determining whether the sample to be tested contains a target gene according to the real part and the imaginary part of the electrical signal data includes:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is smaller than the preset threshold value, the sample to be detected contains a target gene.
Referring to FIG. 3, FIG. 3 is a Nyquist plot of the impedance response when detecting HLA-B15: 02 (exon 2) according to one embodiment of the present invention, as shown in FIG. 2, showing positive LAMP amplification products from HLA-B15: 02 exon 2, negative LAMP amplification products from exon 2, a DNA template-free control group NTC, and baseline measurements for all samples.
Referring to fig. 4, fig. 4 is a schematic representation of each sample of the LAMP amplification product of HLA-B15: 02 exon 2 according to one embodiment of the present invention.
Referring to fig. 5, fig. 5 shows the electrochemical impedance spectrum of the LAMP amplification product of HLA-B15: 02 bound to the surface of the IDE biosensor according to an embodiment of the present invention.
The present invention uses the Nyquist representation and Δ Z of the impedance response of two sensors R ＝Z real (turning point) -Z real (100 kHz).
As shown in the impedance-impedance curves of fig. 3-5, the nyquist plot has the abscissa representing the real part resistance value of the entire system and the ordinate representing the imaginary part resistance value of the entire system. The impedance can be seen as a graph of an arc with a straight line, the arc being the curve at high frequency and the straight line being the curve at low frequency. The real part impedance at the turn is the resistance of the resistor plus the impedance of the capacitor. The impedance of the capacitor changes after the LAMP positive product is bound to the electrode, resulting in a smaller arc, i.e., a smaller impedance in the real part. The LAMP negative product can not be combined on the electrode, and the change of the capacitance impedance is not large.
Thus, Δ Z can be measured R To determine whether the sample is positive or negative. From the absolute value, Z can be regarded as R Positive if less than a predetermined threshold, or judging Z by statistical methods R Whether it is in the positive range.
As a further improvement of the above method, in an embodiment, the determining whether the sample to be tested contains a target gene according to the real part and the imaginary part of the electrical signal data includes:
and if the real part impedance value is not greater than a preset impedance value, the sample to be detected contains a target gene.
In some embodiments, the electrical signal data includes changes in dielectric properties, charge distribution, surface physical properties, size and shape of an electrode or electrode gap surface forming complex upon binding of the test sample to the gene of interest probe.
The present application analyzes/compares the impedance measurement data detected by each target, as shown in fig. 3, which includes:
a baseline, which is a curve obtained by measuring the sensor before the sample is added, and is a line with a square mark;
the curve obtained after the test, in which the sample is negative, is marked by a line with triangles, wherein 2 lines with pentagons are doped is blank control, and the negative can also be calculated.
It is known that the curve obtained after the detection is positive in the sample is a line marked with a circle.
And obtaining a statistical result, and when an unknown sample is detected, if the real part impedance value at the turning point of the unknown sample is smaller than a preset threshold value, determining that the sample to be detected contains the target gene.
And if the real part impedance value at the turning point of the unknown sample is larger than a preset threshold value, the sample to be detected does not contain the target gene.
The preset threshold value can be obtained according to the statistical result in the actual measurement.
The nyquist plot for detecting the impedance response of HLA-B15: 02 (exon 2) clearly shows the difference between the LAMP-positive and LAMP-negative amplification products of HLA-B15: 02 exon 2, demonstrating the working principle of this new technology.
Next, a statistical analysis is performed on one of the features clearly shown in the nyquist plot for better classification. In this regard, as shown in FIG. 4, labels of specific samples are added on the basis of FIG. 3, and numbers N1 to N12 represent negative samples. The numbers P1 to P8 represent positive samples. Numbers B1 to B2 represent blank control samples.
The present invention calculates the difference between the real part of the impedance at the imaginary part minimum (turning point) and the real part of the impedance at 100kHz frequency for each sensor used. As shown in fig. 5, theoretically, such a difference would represent the change in Resistance (RDNA) of the immobilized DNA molecules, organic matter, and polymer (biorecognition element layer) between the electrodes.
ΔZ R ＝Z real (turning point) -Z real (100kHz)
Referring to FIG. 6, FIG. 6 is a graph showing the correlation between baseline and post-hybridization impedance measurements, Δ Z, according to one embodiment of the present invention R Table (2). Isothermal amplification of exon 2 was performed in LAMP experiments, and after the product was bound to a biosensor, the Δ Z associated with the target was measured R 。
Referring to FIG. 7, FIG. 7 is a graph of Δ Z shown in the baseline measurement bar R Values and clearly displayed target samples. The statistical t-test results between positive and negative showed significant statistically significant differences (p)<0.0001)。
FIGS. 6 and 7 show the Δ Z of each sensor used (after hybridization of baseline and target) R . Baseline measurements of the sensors and performance of the sensors after hybridization of each target were evaluated by performing a statistical t-test after performing the normality test, and performing data analysis using GraphPad Prism 8.0 software. A p value of 0.05 or less is considered statistically significant.
Referring to FIG. 8, FIG. 8 is a statistical t-test result between the measured values of positive and negative target detection in one embodiment of the present invention.
Referring to fig. 9, fig. 9 is a statistical t-test result of baseline measurement between biosensors for positive and negative sample detection in one embodiment of the present invention.
As shown in fig. 8 and 9, the t-tests performed between the read groups after hybridization of the sensors with positive and negative amplified samples showed significant statistical differences (p <0.0001), demonstrating that the biosensor was not affected when used as a biosensor.
Furthermore, to confirm and demonstrate that the detection/difference is not caused by a change in the initial conditions of the sensor (prior to incubation hybridization of the target product with the sensor), a t-test was performed on the baseline measurements of the sensors (positive and negative sensor sets) used in the experiment. the t-test is a common statistical test. As shown in fig. 9, no statistically significant difference was found between the sensor baseline phases (p-0.3047). This confirms that the final results (i.e., negative, positive tests in fig. 8) are not affected by variations between sensors.
Similarly, different features of the impedance spectrum data extracted from these sensors may be analyzed to determine detection. The different characteristics mean that the data in the graph can be analyzed by an algorithm not only by judging from the line type in the graph. Such as the slope of the data change, the shape characteristics of the first half arc, the difference in the ordinate of the arc, etc. In addition, developed machine learning or algorithms may also be used for this purpose.
In some embodiments, the IDE biosensor is a square IDE biosensor with 8 μm gaps.
For different types of sensor impedance measurements, as shown by the experiment, the concept of detecting a positive or negative sample with a sensor can be generalized as a "total impedance change, where a positive target sample results in a large drop in impedance compared to a baseline or negative sample. In this regard, a higher impedance (or stretched nyquist plot) is advantageous for the detection system. In view of this phenomenon, we evaluated the impedance spectra of different types of IDE biosensors and IDE biosensors with different gap sizes. In this regard, we used baseline measurements to compare each class to check which IDE biosensors had higher total impedance and were significantly more effective in the detection system.
Referring to FIG. 10, FIG. 10 is a Nyquist representation of the impedance response of different types of IDE sensors, where type1 is a square IDE sensor Nyquist representation, type2 is a semicircular IDE sensor Nyquist representation, type3 is a labyrinth 1 IDE sensor Nyquist representation, type4 is a labyrinth 2 IDE sensor Nyquist representation, and type5 is a circular IDE sensor Nyquist representationThe stewart notation. The surface area of the sensor is fixed to 1mm 2 The gap size was 10 μm.
First, the sensor surface area was fixed at 1mm 2 Gap size 10 μm, to evaluate different types of IDE biosensors. As shown in fig. 9, the square IDE sensor shows higher total impedance in the nyquist plot than other types. Therefore, the present application has selected a square IDE sensor to further study the effect of gap size.
Referring to FIG. 11, FIG. 11 shows a surface area of 1mm 2 Nyquist representation of the square biosensor impedance response of (1). The gap sizes were 10 μm and 8 μm, respectively. The 8 μm gap size (or increasing the number of fingers N) shows better characteristics.
The sensor type is fixed to be square, and the area is fixed to be 1mm 2 The gap size of the IDE sensor was studied. As shown in fig. 11, the gap size is reduced from 10 μm to 8 μm (or the number of fingers in the electrode is increased accordingly). The results show that the data extracted in the higher frequency range is improved, which is beneficial for the overall detection system. However, gap size variations of the order of a few microns (10 μm range) can be considered to be the smallest impact introduced on the sensor detection system. After gap size change experiments, we evaluated the effect of the sensor surface area. In this connection, a surface area of 4mm was used in the experiments 2 A square IDE sensor (2mm x 2mm) with a gap size of 5 μm (corresponding electrode index N-200).
Referring to fig. 12, fig. 12 is a nyquist plot of the impedance response of a square IDE sensor with different surface areas and different gap sizes in accordance with one embodiment of the present invention.
As shown in FIG. 12, when the sensor surface area is 4mm 2 (2 mm. times.2 mm) and a gap size of 5 μm (corresponding to an electrode index N of 200), the total impedance is greatly reduced, 1mm 2 The total impedance of (a) is reduced by about 0.5. This demonstrates that the sensor surface area has a significant effect on the total impedance and will have a significant effect on the overall detection system. In the present invention, it is proposed to use a smaller sensor surface area (< 1 mm) 2 )。
In some embodiments, referring to FIG. 13, FIG. 13 is a structural diagram of a target gene detecting device according to the present application, as shown in FIG. 13, including:
the fixing module 131 is used for fixing a target gene probe for detecting a sample to be detected on the IDE biosensor;
the electric signal data module 132 is used for reacting the sample to be detected after being combined with the target gene probe to obtain a real part and an imaginary part of electric signal data;
and a judging module 133, configured to judge whether the sample to be detected contains the target gene according to a real part and an imaginary part of the electrical signal data.
The structure diagram of the target gene detection device provided by the invention fixes and detects a target gene probe of a sample to be detected on an IDE biosensor through a fixing module; the sample to be detected reacts with the target gene probe after being combined, and a real part and an imaginary part of electric signal data are obtained through an electric signal data module; and judging whether the sample to be detected contains the target gene or not by a judging module according to the real part and the imaginary part of the electric signal data. In the prior art, typical sensor measurements and detections are made of the target in order to compare the change in signal with its initial condition. In the present invention, such initial measurements are irrelevant and the target sample can be clearly distinguished at one stage of reading (i.e. after target hybridization measurement). Furthermore, in this innovation, the type of IDE sensor designed and manufactured can be easily optimized and used according to the detection target and the user's electronic requirements.
Based on the computer disclosed in the above embodiment of the present invention, fig. 14 is a computer structure diagram provided in an embodiment of the present invention.
As shown in fig. 14, includes: a memory 141 on which an executable program is stored;
a processor 142 for executing the executable program in the memory 142 to implement the steps of the method as described in any one of the above.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (9)
1. A method for detecting a target gene, comprising:
fixing a target gene probe for detecting a sample to be detected on an IDE biosensor;
the sample to be detected reacts with the target gene probe after being combined to obtain a real part and an imaginary part of the electric signal data;
and judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data.
2. The method of claim 1, wherein the determining whether the sample contains the target gene according to the real part and the imaginary part of the electrical signal data comprises:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is greater than the preset threshold value, the sample to be detected does not contain a target gene.
3. The method of claim 1, wherein the determining whether the sample contains the target gene according to the real part and the imaginary part of the electrical signal data comprises:
and calculating the difference value between the real part impedance value at the turning point and a preset threshold value, wherein if the real part impedance value at the turning point is smaller than the preset threshold value, the sample to be detected does not contain the target gene.
4. The method of claim 1, wherein the electrical signal data is impedance data of an electrical circuit.
5. The method of claim 4, wherein the electrical signal data comprises
And when the sample to be detected is combined with the target gene probe, the surface of the electrode or the electrode gap forms the change of the dielectric property, the charge distribution, the surface physical property, the size and the shape of a compound.
6. The method of claim 1, further comprising, after the step of obtaining electrical signal data by reacting the sample to be tested after binding to the target gene probe:
and generating a sine wave according to the electric signal data according to a preset frequency and amplitude.
7. The method of claim 4, wherein the IDE biosensor is a square IDE biosensor with 8 μm gaps.
8. A target gene detection device, comprising:
the fixing module is used for fixing a target gene probe for detecting a sample to be detected on the IDE biosensor;
the electric signal data module is used for reacting after the sample to be detected is combined with the target gene probe to obtain a real part and an imaginary part of electric signal data;
and the judging module is used for judging whether the sample to be detected contains the target gene or not according to the real part and the imaginary part of the electric signal data.
9. A computer, comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the method of any one of claims 1-7.
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