CN111751349A - Method and system for label-free analyte detection - Google Patents
Method and system for label-free analyte detection Download PDFInfo
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Abstract
The invention discloses a method and a system for label-free analyte detection, comprising the following steps: step 1, storing analysis results of various in-vitro body fluid samples and corresponding labels to form a CMAP database; step 2, obtaining an analysis result of a sample to be detected, wherein the sample to be detected comprises a non-labeled analyte; and (4) inquiring the analysis result of the sample to be detected in the CMAP database obtained in the step (1) to finish detection. The method and the system can be used in the field of cell detection, and can improve the accuracy of judgment.
Description
Technical Field
The present invention belongs to the technical field of Principal Component Analysis (PCA) and Deep Neural Networks (DNN), and particularly relates to a method and system for label-free analyte detection.
Background
Cells are ubiquitous in the biological world; each cell is an independent entity, like a factory that consumes raw materials and energy and then produces a product. Cells are the basic biological building blocks that make up higher order structures, such as organs within the body of a mammal.
The basic structure and function of a cell varies according to its type. The most common eukaryotic cells (and some prokaryotic cells) include cell membranes, cytoplasm, and nucleus. The cell membrane consists of a lipid bilayer. Inside the cell membrane is the cytoplasm, which is a liquid containing proteins, messenger RNA, ATP, biomolecules, etc.; the cell nucleus contains DNA. Cellular metabolism involves the expression of a portion or portion of DNA, i.e., the production of a protein. Proteins produced within a cell may ultimately be present in many places. Some proteins are retained in the intracellular fluid, while others are integrated into the outer cell membrane. Still other proteins may be transported through the cell membrane and then deposited into the extracellular space. These proteins are commonly referred to as the cellular proteome. Due to normal metabolism or cell bursting, these proteins, peptides, amino acids, nucleic acids and fragments thereof are present in body fluids and, in addition to being part of the intact cell, form the basis of biomarkers.
In raman spectroscopy, light from a light source (such as a laser) is directed at a test surface. Most of the photons scattered by the surface have exactly the same wavelength as the incident photons, a phenomenon known as rayleigh scattering. Unlike rayleigh scattering, a small number of photons are scattered with a slight wavelength shift. This effect of scattering photons with a wavelength shift relative to the incident wavelength is known as raman effect or raman scattering. The change in wavelength is due to the interaction of the incident photons with vibrational quanta of molecules or atoms on the surface, which are called phonons. This change in wavelength can be monitored to obtain a vibrational spectrum of the proteome present in the cell under test.
However, in biological cases, these applications are limited, mainly due to the weak sensitivity and high laser power, and Surface Enhanced Raman Spectroscopy (SERS) overcomes this drawback by incorporating surface plasmon resonance in the raman process. Currently existing SERS platforms for cell detection all rely on tags or biomarkers to detect specificity; all biomarker-based detection methods have a disadvantage in that the biomarkers may change due to cell mutation, and the accuracy of the judgment is affected.
In view of the above, there is a need for a new method and system for label-free analyte detection.
Disclosure of Invention
It is an object of the present invention to provide a method and system for label-free analyte detection that solves one or more of the above-identified problems. The method and the system can be used in the field of cell detection, and can improve the accuracy of judgment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of the invention for label-free analyte detection, comprising the steps of:
step 1, constructing a recombinant protein (CMAP) Database, comprising:
step 1.1, preparing and obtaining various in vitro body fluid samples; each ex vivo bodily fluid sample comprises: one or more label-free analytes;
step 1.2, for each ex vivo body fluid sample: preparing and obtaining a sample substrate; loading the isolated body fluid sample prepared in the step 1.1 on a sample substrate to obtain a substrate loaded with the isolated body sample;
step 1.3, for each ex vivo body fluid sample: carrying out spectrum test on the substrate loaded with the in-vitro sample obtained in the step 1.2 to obtain original spectrum data of the label-free analyte;
step 1.4, for each ex vivo body fluid sample: preprocessing and normalizing the original vibration spectrum data obtained in the step 1.3 to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing;
step 1.5, for each ex vivo body fluid sample: inputting the processed spectrum data obtained in the step 1.4 into a software analysis system for analysis, and gathering the spectrums of the same type of analytes together by using data nodes with the same color to obtain an analysis result; the software analysis system includes: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples; step 1.6, storing the analysis results of various in vitro body fluid samples and corresponding labels to form a CMAP database;
step 2, obtaining an analysis result of the sample to be detected, which comprises the unmarked analyte, according to the method from the step 1.3 to the step 1.5; and (4) inquiring the analysis result of the sample to be detected in the CMAP database obtained in the step (1) to finish detection.
The invention has the further improvement that the CMAP database in the step 1 is a cloud database.
A further development of the invention is that in step 1.1 the ex vivo body fluid sample comprises: cells, cellular components, biological entities; wherein the isolated sample is taken from blood, sweat, urine, cerebrospinal fluid, saliva, semen or pleural fluid; alternatively, the ex vivo bodily fluid sample is whole blood;
the cell types include: a cell phenotype; circulating tumor cells, cell mutation types; cancer cell type, bacterial type, fungal type, extracellular vesicle type, exosome type;
the biological entity comprises an exosome.
The invention has the further improvement that in the step 1.2, the sample substrate is an SERS chip substrate, an SERS groove grid or a microfluidic device;
the SERS chip substrate is a plasma substrate; the plasma substrate includes: plasmonic nanostructures and van der waals material overlying the plasmonic nanostructures.
The invention is further improved in that in the step 1.3, a Raman spectrometer, a mass spectrometer, an FTIR spectrometer, nuclear magnetic resonance and an infrared spectrum are adopted during the spectrum test.
In a further development of the invention, in step 1.1, the label-free analyte is in the group consisting of one or more of a protein, a peptide, a nucleic acid, a carbohydrate, a lipid, a cell, a virus, a small molecule, a hapten.
In a further development of the invention, in step 1.3, the sample of body fluid ex vivo is in a dry or wet state when the spectroscopic test is performed.
The invention is further improved in that the step 4 specifically comprises the following steps: and (3) carrying out classification and identification on the spectrum of the body fluid sample by using PCA, and then training CNN (CNN) by using the classified spectrum data to establish a CMAP (China Mobile application processor) database.
The invention is further improved in that the database of step 1 comprises: CMAP data generated using SERS spectroscopy, mass spectroscopy, FTIR spectroscopy, nuclear magnetic resonance, infrared spectroscopy, and SERS spectra of known metabolic states of a single type of cell.
A system of the present invention for label-free analyte detection, comprising:
the acquisition module of the analysis result of the sample to be detected is used for carrying out spectrum test on the sample to be detected containing the label-free analyte to obtain the original spectrum data of the label-free analyte; preprocessing and normalizing the original spectrum data to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing; inputting the processed vibration wave spectrum data into a software analysis system for analysis, and gathering the wave spectrums of the same type of analytes together by using data nodes with the same color to obtain an analysis result of a sample to be detected; wherein the software analysis system comprises: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
the database module is used for inquiring in the database according to the analysis result of the sample to be detected, obtaining the inquiry result and completing detection; wherein the construction of the database comprises:
(1) preparing and obtaining a plurality of in vitro body fluid samples; each ex vivo bodily fluid sample comprises: one or more label-free analytes;
(2) for each ex vivo body fluid sample: preparing and obtaining a sample substrate; loading the isolated body fluid sample prepared in the step (1) on a sample substrate to obtain a substrate loaded with the isolated body sample;
(3) for each ex vivo body fluid sample: performing spectrum test on the substrate loaded with the in-vitro sample obtained in the step (2) to obtain original spectrum data of the label-free analyte;
(4) for each ex vivo body fluid sample: preprocessing and normalizing the original spectrum data obtained in the step (3) to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing;
(5) for each ex vivo body fluid sample: inputting the processed spectrum data obtained in the step (4) into a software analysis system for analysis, and gathering the spectra of the same type of analytes together by using data nodes with the same color to obtain an analysis result; the software analysis system includes: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
(6) and storing the analysis results of the various in vitro body fluid samples and the corresponding labels to form a database.
Compared with the prior art, the invention has the following beneficial effects:
the method of the invention uses a spectrometer to detect the cells; wherein, a spectrum testing technology is used for testing the label-free analyte to obtain spectrum information; establishing spectral databases of different types of analytes, inputting spectral data into a software analysis system, and combining a Principal Component Analysis (PCA) method and a Deep Neural Network (DNN) method to obtain scatter distribution maps of different types of analytes. The invention has the characteristics of rapidness, easy operation, good universality, low cost and the like, reduces the labor intensity of doctors for cell detection, and improves the judgment accuracy.
The system of the invention uses a spectrometer to detect the cells; wherein, a spectrum testing technology is used for testing the label-free analyte to obtain spectrum information; establishing spectral databases of different types of analytes, inputting spectral data into a software analysis system, and combining a Principal Component Analysis (PCA) method and a Deep Neural Network (DNN) method to obtain scatter distribution maps of different types of analytes. The invention has the characteristics of rapidness, easy operation, good universality, low cost and the like, reduces the labor intensity of doctors for cell detection, and improves the judgment accuracy.
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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 are briefly introduced below; it is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of a method for label-free analyte detection according to an embodiment of the present invention;
FIG. 2 is a OM diagram of a mapping test performed on cells in an embodiment of the present invention;
FIG. 3 is a corresponding Raman spectrum of FIG. 2;
FIG. 4 is a Raman spectrum of a cell after pretreatment and normalization in the example of the present invention.
Detailed Description
In order to make the purpose, technical effect and technical solution of the embodiments of the present invention clearer, the following clearly and completely describes the technical solution of the embodiments of the present invention with reference to the drawings in the embodiments of the present invention; it is to be understood that the described embodiments are only some of the embodiments of the present invention. Other embodiments, which can be derived by one of ordinary skill in the art from the disclosed embodiments without inventive faculty, are intended to be within the scope of the invention.
A method for label-free analyte detection according to embodiments of the present invention comprises the steps of:
step 1: preparing a sample; wherein the sample comprises cells, cell components (e.g., extracellular vesicles such as exosomes), or other biological entities.
Step 2: preparing a sample substrate; the sample substrate may be a chip, slide, plate, grooved grid or microfluidic device.
And step 3: performing spectrum test on the sample on the substrate prepared in the step 2 by using spectrometer equipment;
wherein, the spectrometer equipment comprises a Raman spectrometer, a mass spectrometer, an FTIR spectrometer, nuclear magnetic resonance, infrared spectroscopy and the like;
wherein the sample comprises one or more unlabeled analytes.
And 4, step 4: preprocessing the spectrum data stored in the step 3 by using Labspec6 software to carry out background matting, noise reduction and smoothing on the original spectrum;
and 5: inputting the spectral data processed in the step 4 into a software analysis system; the system employs two main computational analysis systems: deep Neural Network (DNN) and Principal Component Analysis (PCA), analyzing the spectral data processed in step 4, and gathering the spectra of the same type of analytes by data nodes with the same color;
step 6: and (5) inquiring the result obtained in the step (5) in a database of known sample CMAP data to finish detection.
In the embodiment of the present invention, the software analysis system in step 5 may analyze the spectrum data obtained locally by remote transmission and a computer, and specifically includes:
step 5-1: using PythonTMWriting custom DNN software and constructing the software in TensorFlow through GoogleTMOn the library, optimized for use
The library implements a GPU accelerated neural network. The graphical user interface may simplify data import, normalization, and result output. Typical evaluation times are about 30 seconds per iteration, with sensitivity and specificity averaging over 10 iterations to compensate for possible neural network over-modeling.
Step 5-2: writing user-defined PCA software by using R, constructing the software on the basis of the ggbiplot library, and using Python in the step 5-1TMAnd another custom program is written to carry out data analysis and data organization on the R. For ease of data interpretation, the principal component analysis plot is plotted along with the first two principal components. Sensitivity and specificity are calculated by the program based on the number of data nodes that fall into each different colored ellipse, which represents the data confidence of the classification label for each data set. Typical data analysis to PCA plots takes approximately 1 minute.
Step 5-3: and (3) inputting the Raman spectrum processed in the step (4) into the DNN model constructed in the step (5-1) and the PCA compiled in the step (5-2), and performing data analysis to obtain an analyte classification chart and a scatter distribution chart, wherein the spectrums of the same type of analytes are gathered together by data nodes with the same color.
In an embodiment of the invention, the database of step 6 contains known or "gold standard" data corresponding to cells, exosomes, body fluids or other biological entities of known type or state and which have been validated by different testing or analysis procedures;
wherein the database contains CMAP data generated using SERS spectrometer, mass spectrometer, FTIR spectrometer, nuclear magnetic resonance, infrared spectroscopy, etc.;
wherein the database may further comprise SERS spectra of known metabolic states of a single type of healthy cell, e.g., metabolic SERS spectra of healthy cells, unhealthy cells, diseased cells, or cells in a stressed cellular state;
wherein the data stored in the database may also be based on the results of the bodily fluid.
In an embodiment of the invention, the biological entity comprises exosomes.
In embodiments of the present invention, the body fluid includes blood, sweat, urine, cerebrospinal fluid, saliva, semen, pleural fluid, and the like.
In embodiments of the invention, the one or more analytes are selected from the group consisting of proteins, peptides, nucleic acids, carbohydrates, lipids, cells, viruses, small molecules, or haptens.
In embodiments of the invention, the cell type comprises a cell phenotype.
In an embodiment of the invention, the biological sample is dried on the plasma substrate prior to collecting the vibration spectrum.
In embodiments of the invention, the biological sample is in a wet state when the spectra are collected.
In embodiments of the invention, the cell type comprises a cancer cell type, a bacterial type, a fungal type, an Extracellular Vesicle (EV) type, or an exosome type.
In embodiments of the invention, the cell type comprises a Circulating Tumor Cell (CTC) or a mutated cell type.
In an embodiment of the invention, the biological sample comprises whole blood.
In embodiments of the invention, the sample substrate may be a chip, a slide, a plate, a grooved grid, or a microfluidic device.
In an embodiment of the invention, the SERS chip substrate is a plasma substrate, and the plasma substrate includes plasma nanostructure features and van der waals (vdW) material covering the nanostructures;
in embodiments of the invention, the grooved grid allows for the placement of biological samples in different wells, wherein the bottom surface of the grooved grid may contain plasmonic nanostructures in addition to the vdW material.
In embodiments of the invention, the spectroscopy comprises one of SERS, conventional raman spectroscopy, mass spectroscopy, FTIR spectroscopy, or other spectroscopy methods.
In embodiments of the invention, the pre-stored relative abundance data comprises data for a plurality of different types of spectroscopic analysis.
The method for identifying the cell type in the biological sample comprises the following steps:
providing a plasma nano-substrate having a plurality of plasma nano-features, wherein a Van Der Waals (VDW) material is disposed on and covers the plasma substrate;
loading a biological sample onto a plasma substrate; wherein the biological sample comprises one or more unlabeled cells;
placing the plasma substrate containing the biological sample in a raman spectrum and collecting a vibrational spectrum of one or more unlabeled cells located on or adjacent to the plasma substrate;
inputting the collected vibration spectra into a software analysis system, the software analysis system operable to compare the collected vibration spectra with previously stored vibration spectra in a database operatively accessible by the software analysis system; wherein the software analysis system automatically identifies cell types of one or more unlabeled cells in the biological sample based on a comparison of the collected vibration spectrum to previously stored vibration spectra in the database.
In embodiments of the invention, the software analysis system outputs cellular proteomic information for one or more cells.
In embodiments of the invention, the cellular proteomic information includes cellular health.
In an embodiment of the invention, the comparison performed by the software analysis system comprises a multivariate analysis system.
In an embodiment of the invention, the comparison performed by the software analysis system comprises a machine learning analysis system.
In embodiments of the invention, loading a biological sample onto a plasma substrate comprises depositing a volume of the biological sample onto the plasma substrate.
In embodiments of the invention, loading the biological sample onto the plasma substrate comprises flowing a volume of the biological sample over the plasma substrate.
In an embodiment of the present invention, a previously stored vibration spectra database comprises a plurality of records, each record comprising a cell type tag.
A system for label-free analyte detection of an embodiment of the invention includes:
the acquisition module of the analysis result of the sample to be detected is used for carrying out spectrum test on the sample to be detected containing the label-free analyte to obtain the original spectrum data of the label-free analyte; preprocessing and normalizing the original spectrum data to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing; inputting the processed vibration wave spectrum data into a software analysis system for analysis, and gathering the wave spectrums of the same type of analytes together by using data nodes with the same color to obtain an analysis result of a sample to be detected; wherein the software analysis system comprises: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
the database module is used for inquiring in the database according to the analysis result of the sample to be detected, obtaining the inquiry result and completing detection; wherein the construction of the database comprises:
(1) preparing and obtaining a plurality of in vitro body fluid samples; each ex vivo bodily fluid sample comprises: one or more label-free analytes;
(2) for each ex vivo body fluid sample: preparing and obtaining a sample substrate; loading the isolated body fluid sample prepared in the step (1) on a sample substrate to obtain a substrate loaded with the isolated body sample;
(3) for each ex vivo body fluid sample: performing spectrum test on the substrate loaded with the in-vitro sample obtained in the step (2) to obtain original spectrum data of the label-free analyte;
(4) for each ex vivo body fluid sample: preprocessing and normalizing the original spectrum data obtained in the step (3) to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing;
(5) for each ex vivo body fluid sample: inputting the processed spectrum data obtained in the step (4) into a software analysis system for analysis, and gathering the spectra of the same type of analytes together by using data nodes with the same color to obtain an analysis result; the software analysis system includes: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
(6) and storing the analysis results of the various in vitro body fluid samples and the corresponding labels to form a database.
In view of the foregoing, the present invention discloses a method and system for label-free analyte detection, the method or system comprising: the unlabeled analyte is tested using spectroscopic testing techniques to obtain spectroscopic information. And establishing a spectral database of different types of analytes, and combining a Principal Component Analysis (PCA) method and a Deep Neural Network (DNN) method to obtain scatter distribution maps of different types of analytes, thereby realizing the detection of the label-free analytes. The invention has the characteristics of rapidness, easy operation, good universality, low cost and the like, reduces the labor intensity of doctors for cell detection, improves the diagnosis accuracy, and shortens the detection time of the unmarked analyte by over 90 percent. The method may also be applied to biological entities or cell structures, such as exosomes, or even to proteins or nucleic acid fragments.
Referring to fig. 1, a method for detecting an unlabeled analyte according to an embodiment of the present invention includes the following steps:
step 1, obtaining a cerebrospinal fluid in-vitro sample;
step 2, etching an inverted pyramid structure with the bottom side length of 250nm on the Si sheet of 4' by using a microelectronic technology, wherein each nano structure is separated by 250 nm;
step 3, plating a layer of gold film on the nano structure prepared in the step 2 by magnetron sputtering;
step 4, the gold film with the pyramid nano structure prepared in the step 3 is taken off from the Si sheet and is adhered to a glass slide;
step 5, transferring a layer of single-layer graphene on the gold film with the pyramid structure prepared in the step 4 by a paraffin method to obtain an SERS (surface enhanced Raman scattering) mixed platform;
and 6, mounting the substrate obtained in the step 5 on a centrifugal precipitator, and performing centrifugal sedimentation on cells, wherein the centrifugal parameters are as follows: 800r/min, 8 min; wherein the cerebrospinal fluid sample is not subjected to any staining and fixing treatment;
and 7, adjusting the parameters of the micro confocal Raman spectrometer as follows: the excitation wavelength of the diode is 633nm, the power is 25%, the exposure time is 10s, and the integration frequency is 2 times.
Step 8, find cells under 50X objective, as shown in figure 2. Using the parameters adjusted in step 7, at 2400cm-1-3300cm-1Carrying out single-point test on the wave band;
step 9, observing the Raman spectrum measured in the step 8, if the Raman spectrum is 2900cm-1The peak appears at the same position and is 600cm-1-1800cm-1Single-point testing of wave bands;
step 10, observing the peak intensity of the spectrum measured in the step 9, if the intensity is appropriate, performing 3 × 3 imaging test on the cell according to the diameter of the cell being 9 μm, and storing the measured spectrum data in a TXT format as shown in fig. 3;
step 11, substrate subtraction is carried out on the spectrum data saved in the step 10 by using LabSpec6 software, and then normalization processing is carried out on 9 spectra of the same cell, as shown in FIG. 4;
step 12, compiling user-defined PCA software by using R, constructing the software on the basis of the ggbiplot library, and using PythonTMAnd another custom program is written to carry out data analysis and data organization on the R. For ease of data interpretation, the principal component analysis plot is plotted along with the first two principal components. Sensitivity and specificity are calculated by the program based on the number of data nodes that fall into each different colored ellipse, which represents the data confidence of the classification label for each data set. Typical data analysis to PCA plots takes approximately 1 minute.
Step 13, inputting the Raman spectrum processed in the step 10 into the constructed CNN model and the compiled PCA, and performing data analysis to obtain a white blood cell classification chart and a scattered point distribution chart, wherein the Raman spectra of cerebrospinal fluid cells of the same type are gathered together by data nodes with the same color;
and step 14, inquiring the result obtained in the step 13 in a database of known sample CMAP data to finish detection.
The method for detecting the label-free analyte comprises the following steps
Step 1, obtaining cerebrospinal fluid;
step 2, etching an inverted pyramid structure with the bottom side length of 250nm on the Si sheet of 4' by using a microelectronic technology, wherein each nano structure is separated by 250 nm;
step 3, plating a layer of gold film on the nano structure prepared in the step 2 by magnetron sputtering;
step 4, the gold film with the pyramid nano structure prepared in the step 3 is taken off from the Si wafer and is adhered to the silicon wafer with the thickness of 40 multiplied by 40mm by using glue;
step 5, transferring a layer of single-layer graphene on the gold film with the pyramid structure prepared in the step 4 by a paraffin method to obtain an SERS (surface enhanced Raman scattering) mixed platform;
step 6, plating a layer of photoresist with the thickness of 13 mu m on the mixing platform prepared in the step 5 by using a spin coater;
step 7, using a photoetching machine to obtain 36 groove grids of 5 multiplied by 5mm on the platform prepared in the step 6;
step 8, extracting 0.5ml of untreated cerebrospinal fluid obtained in the step 1, and dripping the cerebrospinal fluid into a groove grid;
step 9, covering a quartz cover glass with the thickness of 0.1-0.2mm on the groove gate processed in the step 8;
step 10, adjusting the parameters of the micro confocal Raman spectrometer as follows: the excitation wavelength of the diode is 633nm, the power is 25%, the exposure time is 10s, and the integration frequency is 2 times.
Step 11, find the cell under 50X objective. Adjusted using step 10At 50cm-1-3300cm-1Carrying out single-point test on the wave band;
step 12, observing the peak intensity of the spectrum measured in the step 11, if the intensity is proper, carrying out 3X 3 imaging test on the cell according to the diameter of the cell being 9 μm, and storing the measured spectrum data in a TXT format;
step 13, using LabSpec6 software to perform background removal, noise reduction and smoothing on the spectrum data stored in the step 12, and then performing normalization processing on 9 spectra of the same cell;
step 14, writing user-defined PCA software by using R, constructing the user-defined PCA software on the basis of the ggbiplot library, and using PythonTMAnd another custom program is written to carry out data analysis and data organization on the R. For ease of data interpretation, the principal component analysis plot is plotted along with the first two principal components. Sensitivity and specificity are calculated by the program based on the number of data nodes that fall into each different colored ellipse, which represents the data confidence of the classification label for each data set. Typical data analysis to PCA plots takes approximately 1 minute.
Step 15, inputting the Raman spectrum processed in the step 13 into the constructed CNN model and the compiled PCA, performing data analysis to obtain a white blood cell classification chart and a scattered point distribution chart, and gathering the Raman spectra of the cerebrospinal fluid cells of the same type together by using data nodes with the same color;
step 16, the result obtained in step 15 is queried in a database of known sample CMAP data, and the detection is completed.
In summary, the present invention pertains to a method for detecting cells in a body fluid by using a spectrometer, comprising the steps of: the unlabeled analyte is tested using spectroscopic testing techniques to obtain spectroscopic information. Establishing spectral databases of different types of analytes, inputting spectral data into a software analysis system, combining a Principal Component Analysis (PCA) method and a deep neural network (CNN) to obtain scatter distribution maps of different types of analytes, and inquiring the health or disease state of a subject in the database according to an output result, thereby realizing the detection of the unmarked analytes and the prediction of diseases. The invention has the characteristics of rapidness, easy operation, good universality, low cost and the like, reduces the labor intensity of doctors for cell detection and improves the diagnosis accuracy.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (10)
1. A method for label-free analyte detection, comprising the steps of:
step 1, constructing a CMAP database, comprising:
step 1.1, preparing and obtaining various in vitro body fluid samples; each ex vivo bodily fluid sample comprises: one or more label-free analytes;
step 1.2, for each ex vivo body fluid sample: preparing and obtaining a sample substrate; loading the isolated body fluid sample prepared in the step 1.1 on a sample substrate to obtain a substrate loaded with the isolated body sample;
step 1.3, for each ex vivo body fluid sample: carrying out spectrum test on the substrate loaded with the in-vitro sample obtained in the step 1.2 to obtain original spectrum data of the label-free analyte;
step 1.4, for each ex vivo body fluid sample: preprocessing and normalizing the original vibration spectrum data obtained in the step 1.3 to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing;
step 1.5, for each ex vivo body fluid sample: inputting the processed spectrum data obtained in the step 1.4 into a software analysis system for analysis, and gathering the spectrums of the same type of analytes together by using data nodes with the same color to obtain an analysis result; the software analysis system includes: the principal component analysis is used for the classification and identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
step 1.6, storing the analysis results of various in vitro body fluid samples and corresponding labels to form a CMAP database;
step 2, obtaining an analysis result of the sample to be detected, which comprises the unmarked analyte, according to the method from the step 1.3 to the step 1.5; and (4) inquiring the analysis result of the sample to be detected in the CMAP database obtained in the step (1) to finish detection.
2. The method of claim 1, wherein the CMAP database in step 1 is a cloud database.
3. A method for label-free analyte detection according to claim 1, wherein in step 1.1 the ex vivo bodily fluid sample comprises: cells, cellular components, biological entities; wherein the isolated sample is taken from blood, sweat, urine, cerebrospinal fluid, saliva, semen or pleural fluid; alternatively, the ex vivo bodily fluid sample is whole blood;
the cell types include: a cell phenotype; circulating tumor cells, cell mutation types; cancer cell type, bacterial type, fungal type, extracellular vesicle type, exosome type;
the biological entity comprises an exosome.
4. The method for label-free analyte detection according to claim 1, wherein in step 1.2, the sample substrate is a SERS chip substrate, a SERS channel grid or a microfluidic device;
the SERS chip substrate is a plasma substrate; the plasma substrate includes: plasmonic nanostructures and van der waals material overlying the plasmonic nanostructures.
5. A method for label-free analyte detection according to claim 1, wherein in step 1.3, spectroscopic measurements are performed using raman spectroscopy, mass spectroscopy, FTIR spectroscopy, nuclear magnetic resonance or infrared spectroscopy.
6. The method according to claim 1, wherein in step 1.1, the label-free analyte is selected from the group consisting of one or more of proteins, peptides, nucleic acids, carbohydrates, lipids, cells, viruses, small molecules, and haptens.
7. The method of claim 1, wherein the spectroscopic testing is performed in step 1.3 with the ex vivo bodily fluid sample in a dry or wet state.
8. The method for label-free analyte detection according to claim 1, wherein step 4 comprises the following steps: classifying and identifying the wave spectrum of the body fluid sample by using principal component analysis; and training the deep neural network by using the classified spectral data to establish a CMAP database.
9. The method of claim 1, wherein the database of step 1 comprises: CMAP data generated using SERS spectroscopy, mass spectroscopy, FTIR spectroscopy, nuclear magnetic resonance, infrared spectroscopy, and SERS spectra of known metabolic states of a single type of cell.
10. A system for label-free analyte detection, comprising:
the acquisition module of the analysis result of the sample to be detected is used for carrying out spectrum test on the sample to be detected containing the label-free analyte to obtain the original spectrum data of the label-free analyte; preprocessing and normalizing the original spectrum data to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing; inputting the processed vibration wave spectrum data into a software analysis system for analysis, and gathering the wave spectrums of the same type of analytes together by using data nodes with the same color to obtain an analysis result of a sample to be detected; wherein the software analysis system comprises: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
the database module is used for inquiring in the database according to the analysis result of the sample to be detected, obtaining the inquiry result and completing detection; wherein the construction of the database comprises:
(1) preparing and obtaining a plurality of in vitro body fluid samples; each ex vivo bodily fluid sample comprises: one or more label-free analytes;
(2) for each ex vivo body fluid sample: preparing and obtaining a sample substrate; loading the isolated body fluid sample prepared in the step (1) on a sample substrate to obtain a substrate loaded with the isolated body sample;
(3) for each ex vivo body fluid sample: performing spectrum test on the substrate loaded with the in-vitro sample obtained in the step (2) to obtain original spectrum data of the label-free analyte;
(4) for each ex vivo body fluid sample: preprocessing and normalizing the original spectrum data obtained in the step (3) to obtain processed spectrum data; the pretreatment comprises the following steps: background scratching, noise reduction and smoothing;
(5) for each ex vivo body fluid sample: inputting the processed spectrum data obtained in the step (4) into a software analysis system for analysis, and gathering the spectra of the same type of analytes together by using data nodes with the same color to obtain an analysis result; the software analysis system includes: the principal component analysis system is used for the classification identification of different types of samples and the subclass identification of the same type of samples; the deep neural network is used for classifying, identifying and assisting in proving the accuracy of the principal component analysis result of different types of samples;
(6) and storing the analysis results of the various in vitro body fluid samples and the corresponding labels to form a database.
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